From 43df7012f2e269023f6d2d3c61fe829b59b6b144 Mon Sep 17 00:00:00 2001
From: SwarnenduJUIT <163632954+SwarnenduJUIT@users.noreply.github.com>
Date: Tue, 14 May 2024 19:01:12 +0530
Subject: [PATCH 1/3] Add files via upload

---
 Stackoverflow_Survey_Analysis.ipynb | 36596 +++++++++++++-------------
 1 file changed, 18298 insertions(+), 18298 deletions(-)

diff --git a/Stackoverflow_Survey_Analysis.ipynb b/Stackoverflow_Survey_Analysis.ipynb
index 2a5b6e3..85b1642 100644
--- a/Stackoverflow_Survey_Analysis.ipynb
+++ b/Stackoverflow_Survey_Analysis.ipynb
@@ -1,18298 +1,18298 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stackoverflow_Survey_Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction\n",
-    "Stack overflow is a professional community for developers. They conduct developer surveys every year since 2011. The collected data is available open-source on the web. The Dataset would help us to answer real-world questions with the help of proper analysis. The most popular language that developers use can be found through the analysis. We also can find the developer role which pays the highest salary. The aim of our project is to analyze the 2018,2019 and 2020 developer surveys datasets from where we collect valuable insights from them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import seaborn as sns\n",
-    "import warnings; \n",
-    "warnings.simplefilter('ignore')\n",
-    "import pycountry\n",
-    "import plotly.express as px\n",
-    "import matplotlib.patches as mpatches\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn import preprocessing\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
-    "from sklearn.metrics import r2_score\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "from sklearn.naive_bayes import MultinomialNB\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from sklearn.svm import LinearSVC\n",
-    "import time\n",
-    "from sklearn.metrics import hamming_loss\n",
-    "from sklearn.metrics import jaccard_score\n",
-    "from sklearn.linear_model import SGDClassifier\n",
-    "from sklearn.multiclass import OneVsRestClassifier\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "from sklearn.metrics import accuracy_score\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stackoverflow 2018 Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Respondent</th>\n",
-       "      <th>Hobby</th>\n",
-       "      <th>OpenSource</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Student</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>FormalEducation</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>CompanySize</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Exercise</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>SexualOrientation</th>\n",
-       "      <th>EducationParents</th>\n",
-       "      <th>RaceEthnicity</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Dependents</th>\n",
-       "      <th>MilitaryUS</th>\n",
-       "      <th>SurveyTooLong</th>\n",
-       "      <th>SurveyEasy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Kenya</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Employed part-time</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Mathematics or statistics</td>\n",
-       "      <td>20 to 99 employees</td>\n",
-       "      <td>Full-stack developer</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3 - 4 times per week</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Straight or heterosexual</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Black or of African descent</td>\n",
-       "      <td>25 - 34 years old</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>The survey was an appropriate length</td>\n",
-       "      <td>Very easy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Employed full-time</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
-       "      <td>10,000 or more employees</td>\n",
-       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Daily or almost every day</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Straight or heterosexual</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>The survey was an appropriate length</td>\n",
-       "      <td>Somewhat easy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Employed full-time</td>\n",
-       "      <td>Associate degree</td>\n",
-       "      <td>Computer science, computer engineering, or sof...</td>\n",
-       "      <td>20 to 99 employees</td>\n",
-       "      <td>Engineering manager;Full-stack developer</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3 rows × 129 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Respondent Hobby OpenSource         Country Student          Employment  \\\n",
-       "0           1   Yes         No           Kenya      No  Employed part-time   \n",
-       "1           3   Yes        Yes  United Kingdom      No  Employed full-time   \n",
-       "2           4   Yes        Yes   United States      No  Employed full-time   \n",
-       "\n",
-       "                            FormalEducation  \\\n",
-       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
-       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
-       "2                          Associate degree   \n",
-       "\n",
-       "                                      UndergradMajor  \\\n",
-       "0                          Mathematics or statistics   \n",
-       "1  A natural science (ex. biology, chemistry, phy...   \n",
-       "2  Computer science, computer engineering, or sof...   \n",
-       "\n",
-       "                CompanySize  \\\n",
-       "0        20 to 99 employees   \n",
-       "1  10,000 or more employees   \n",
-       "2        20 to 99 employees   \n",
-       "\n",
-       "                                             DevType  ...  \\\n",
-       "0                               Full-stack developer  ...   \n",
-       "1  Database administrator;DevOps specialist;Full-...  ...   \n",
-       "2           Engineering manager;Full-stack developer  ...   \n",
-       "\n",
-       "                    Exercise Gender         SexualOrientation  \\\n",
-       "0       3 - 4 times per week   Male  Straight or heterosexual   \n",
-       "1  Daily or almost every day   Male  Straight or heterosexual   \n",
-       "2                        NaN    NaN                       NaN   \n",
-       "\n",
-       "                           EducationParents                 RaceEthnicity  \\\n",
-       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   Black or of African descent   \n",
-       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)  White or of European descent   \n",
-       "2                                       NaN                           NaN   \n",
-       "\n",
-       "                 Age Dependents  MilitaryUS  \\\n",
-       "0  25 - 34 years old        Yes         NaN   \n",
-       "1  35 - 44 years old        Yes         NaN   \n",
-       "2                NaN        NaN         NaN   \n",
-       "\n",
-       "                          SurveyTooLong     SurveyEasy  \n",
-       "0  The survey was an appropriate length      Very easy  \n",
-       "1  The survey was an appropriate length  Somewhat easy  \n",
-       "2                                   NaN            NaN  \n",
-       "\n",
-       "[3 rows x 129 columns]"
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2018 = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2018.csv')\n",
-    "df2018.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(98855, 129)"
-      ]
-     },
-     "execution_count": 190,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2018.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#print(df2018.columns.tolist()      !--> Listing coloumsn in table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#dropping the columns\n",
-    "#drop_cols = ['Respondent', 'OpenSource', 'Student', 'FormalEducation', 'CompanySize', 'CareerSatisfaction', 'HopeFiveYears', 'LastNewJob', 'AssessJob1', 'AssessJob2', 'AssessJob3', 'AssessJob4', 'AssessJob5', 'AssessJob6', 'AssessJob7', 'AssessJob8', 'AssessJob9', 'AssessJob10', 'AssessBenefits1', 'AssessBenefits2', 'AssessBenefits3', 'AssessBenefits4', 'AssessBenefits5', 'AssessBenefits6', 'AssessBenefits7', 'AssessBenefits8', 'AssessBenefits9', 'AssessBenefits10', 'AssessBenefits11', 'JobContactPriorities1', 'JobContactPriorities2', 'JobContactPriorities3', 'JobContactPriorities4', 'JobContactPriorities5', 'JobEmailPriorities1', 'JobEmailPriorities2', 'JobEmailPriorities3', 'JobEmailPriorities4', 'JobEmailPriorities5', 'JobEmailPriorities6', 'JobEmailPriorities7', 'UpdateCV', 'CommunicationTools', 'TimeFullyProductive', 'EducationTypes', 'SelfTaughtTypes', 'TimeAfterBootcamp', 'HackathonReasons', 'AgreeDisagree1', 'AgreeDisagree2', 'AgreeDisagree3', 'DatabaseWorkedWith', 'DatabaseDesireNextYear', 'PlatformDesireNextYear', 'FrameworkWorkedWith', 'FrameworkDesireNextYear', 'IDE', 'NumberMonitors', 'Methodology', 'VersionControl', 'CheckInCode', 'AdBlocker', 'AdBlockerDisable', 'AdBlockerReasons', 'AdsAgreeDisagree1', 'AdsAgreeDisagree2', 'AdsAgreeDisagree3', 'AdsActions', 'AdsPriorities1', 'AdsPriorities2', 'AdsPriorities3', 'AdsPriorities4', 'AdsPriorities5', 'AdsPriorities6', 'AdsPriorities7', 'AIDangerous', 'AIInteresting', 'AIResponsible', 'AIFuture', 'EthicsChoice', 'EthicsReport', 'EthicsResponsible', 'EthicalImplications', 'StackOverflowRecommend', 'StackOverflowVisit', 'StackOverflowHasAccount', 'StackOverflowParticipate', 'StackOverflowJobs', 'StackOverflowDevStory', 'StackOverflowJobsRecommend', 'StackOverflowConsiderMember', 'HypotheticalTools1', 'HypotheticalTools2', 'HypotheticalTools3', 'HypotheticalTools4', 'HypotheticalTools5', 'WakeTime', 'HoursComputer', 'HoursOutside', 'SkipMeals', 'ErgonomicDevices', 'Exercise', 'SexualOrientation', 'EducationParents', 'Dependents', 'MilitaryUS', 'SurveyTooLong', 'SurveyEasy']\n",
-    "#df2018.drop(drop_cols, axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df2018.shape #checking rows and col after dropping the table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Filtering - Sorting & Renaming\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col=['Age','ConvertedSalary','Country','Currency','DevType','Employment','RaceEthnicity','Gender','SalaryType','Hobby','JobSatisfaction','JobSearchStatus','OperatingSystem','UndergradMajor','YearsCoding','YearsCodingProf','LanguageDesireNextYear','LanguageWorkedWith','FormalEducation']\n",
-    "df=df2018[col]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#renaming the colo\n",
-    "# 'ConvertedSalary': 'SalaryUSD'\n",
-    "df.rename(columns={'ConvertedSalary': 'SalaryUSD' }, inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Currency</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>FormalEducation</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Hobby</th>\n",
-       "      <th>JobSatisfaction</th>\n",
-       "      <th>JobSearchStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>OperatingSystem</th>\n",
-       "      <th>RaceEthnicity</th>\n",
-       "      <th>SalaryType</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCoding</th>\n",
-       "      <th>YearsCodingProf</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>25 - 34 years old</td>\n",
-       "      <td>Kenya</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Full-stack developer</td>\n",
-       "      <td>Employed part-time</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Extremely satisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>JavaScript;Python;HTML;CSS</td>\n",
-       "      <td>JavaScript;Python;HTML;CSS</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Black or of African descent</td>\n",
-       "      <td>Monthly</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Mathematics or statistics</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>3-5 years</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>British pounds sterling (£)</td>\n",
-       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
-       "      <td>Employed full-time</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately dissatisfied</td>\n",
-       "      <td>I am actively looking for a job</td>\n",
-       "      <td>Go;Python</td>\n",
-       "      <td>JavaScript;Python;Bash/Shell</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>70841.0</td>\n",
-       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
-       "      <td>30 or more years</td>\n",
-       "      <td>18-20 years</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Age         Country                     Currency  \\\n",
-       "0  25 - 34 years old           Kenya                          NaN   \n",
-       "1  35 - 44 years old  United Kingdom  British pounds sterling (£)   \n",
-       "\n",
-       "                                             DevType          Employment  \\\n",
-       "0                               Full-stack developer  Employed part-time   \n",
-       "1  Database administrator;DevOps specialist;Full-...  Employed full-time   \n",
-       "\n",
-       "                            FormalEducation Gender Hobby  \\\n",
-       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   Male   Yes   \n",
-       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)   Male   Yes   \n",
-       "\n",
-       "           JobSatisfaction                                    JobSearchStatus  \\\n",
-       "0      Extremely satisfied  I’m not actively looking, but I am open to new...   \n",
-       "1  Moderately dissatisfied                    I am actively looking for a job   \n",
-       "\n",
-       "       LanguageDesireNextYear            LanguageWorkedWith OperatingSystem  \\\n",
-       "0  JavaScript;Python;HTML;CSS    JavaScript;Python;HTML;CSS     Linux-based   \n",
-       "1                   Go;Python  JavaScript;Python;Bash/Shell     Linux-based   \n",
-       "\n",
-       "                  RaceEthnicity SalaryType  SalaryUSD  \\\n",
-       "0   Black or of African descent    Monthly        NaN   \n",
-       "1  White or of European descent     Yearly    70841.0   \n",
-       "\n",
-       "                                      UndergradMajor       YearsCoding  \\\n",
-       "0                          Mathematics or statistics         3-5 years   \n",
-       "1  A natural science (ex. biology, chemistry, phy...  30 or more years   \n",
-       "\n",
-       "  YearsCodingProf  \n",
-       "0       3-5 years  \n",
-       "1     18-20 years  "
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.sort_index(axis=1).head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(98855, 19)"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#21 col has been selected rfom 129, compared the shape\n",
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                       34281\n",
-      "SalaryUSD                 51153\n",
-      "Country                     412\n",
-      "Currency                  36847\n",
-      "DevType                    6757\n",
-      "Employment                 3534\n",
-      "RaceEthnicity             41382\n",
-      "Gender                    34386\n",
-      "SalaryType                47785\n",
-      "Hobby                         0\n",
-      "JobSatisfaction           29579\n",
-      "JobSearchStatus           19367\n",
-      "OperatingSystem           22676\n",
-      "UndergradMajor            19819\n",
-      "YearsCoding                5020\n",
-      "YearsCodingProf           20952\n",
-      "LanguageDesireNextYear    25611\n",
-      "LanguageWorkedWith        20521\n",
-      "FormalEducation            4152\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df.isnull().sum()) #Finding Null Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                        object\n",
-       "SalaryUSD                 float64\n",
-       "Country                    object\n",
-       "Currency                   object\n",
-       "DevType                    object\n",
-       "Employment                 object\n",
-       "RaceEthnicity              object\n",
-       "Gender                     object\n",
-       "SalaryType                 object\n",
-       "Hobby                      object\n",
-       "JobSatisfaction            object\n",
-       "JobSearchStatus            object\n",
-       "OperatingSystem            object\n",
-       "UndergradMajor             object\n",
-       "YearsCoding                object\n",
-       "YearsCodingProf            object\n",
-       "LanguageDesireNextYear     object\n",
-       "LanguageWorkedWith         object\n",
-       "FormalEducation            object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes #data_types"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Validation - Total Cells vs Missing %"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  1878245\n",
-      "Total missing :  424234\n",
-      "Missing Percentage:  22.58672324430519 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = df.isnull().sum() #number of missing\n",
-    "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Missing Percentage column-wise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def missing(df,column,n):\n",
-    "    empty_cells=df[column].isnull().sum()\n",
-    "    return (empty_cells*100.0)/n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age : 34.68 %\n",
-      "SalaryUSD : 51.75 %\n",
-      "Country : 0.42 %\n",
-      "Currency : 37.27 %\n",
-      "DevType : 6.84 %\n",
-      "Employment : 3.57 %\n",
-      "RaceEthnicity : 41.86 %\n",
-      "Gender : 34.78 %\n",
-      "SalaryType : 48.34 %\n",
-      "Hobby : 0.00 %\n",
-      "JobSatisfaction : 29.92 %\n",
-      "JobSearchStatus : 19.59 %\n",
-      "OperatingSystem : 22.94 %\n",
-      "UndergradMajor : 20.05 %\n",
-      "YearsCoding : 5.08 %\n",
-      "YearsCodingProf : 21.19 %\n",
-      "LanguageDesireNextYear : 25.91 %\n",
-      "LanguageWorkedWith : 20.76 %\n",
-      "FormalEducation : 4.20 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "total_cells=df.shape[0]\n",
-    "for column in df.columns:\n",
-    "    res=missing(df,column,total_cells)\n",
-    "    print(column,\":\",\"{:.2f}\".format(res),\"%\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gender Filtering          \n",
-    "### Data Cleaning Starts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gender\n",
-       "Female                                                                        4025\n",
-       "Female;Male                                                                     98\n",
-       "Female;Male;Non-binary, genderqueer, or gender non-conforming                    3\n",
-       "Female;Male;Transgender                                                         14\n",
-       "Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming       50\n",
-       "Female;Non-binary, genderqueer, or gender non-conforming                        50\n",
-       "Female;Transgender                                                             145\n",
-       "Female;Transgender;Non-binary, genderqueer, or gender non-conforming            24\n",
-       "Male                                                                         59458\n",
-       "Male;Non-binary, genderqueer, or gender non-conforming                         128\n",
-       "Male;Transgender                                                                29\n",
-       "Male;Transgender;Non-binary, genderqueer, or gender non-conforming               5\n",
-       "Non-binary, genderqueer, or gender non-conforming                              284\n",
-       "Transgender                                                                    105\n",
-       "Transgender;Non-binary, genderqueer, or gender non-conforming                   51\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Gender: null = 13312 (21.6%)\n",
-    "df['Gender'].unique()\n",
-    "#count number of each gender\n",
-    "df.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#replace\n",
-    "df['Gender'] = df['Gender'].fillna('Non-binary, genderqueer, or gender non-conforming')\n",
-    "df['Gender'].replace('Female;Male;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Male;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
-    "df['Gender'].replace('Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
-    "df['Gender'].replace('Transgender;Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) ##not sure\n",
-    "df['Gender'].replace('Female;Male', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Male;Transgender', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Transgender', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Male;Transgender', 'Female', inplace =True) \n",
-    "df['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) #\n",
-    "df['Gender'].replace('Transgender', 'Male', inplace =True) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "lst=df.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ciJ85cya7d+9m06ZNpm0uXrxIcHAwJ06coFmzZpY9uMwzsG8FHFwFV1Ms21Zd4N0U7p4ELR8BpUruagQbIdvDpT4+PvTv35/ly5cjSRL9+/fHx8fHbJ3ExERmzJjBX3/9RUZGhqmHdf78eaKiosrsc+/evWzbtg1XV9cyryUmJloutM5sh98XGHsHiN5BjblyCtY9A7/Ng66TIPpRUInnoes6WX8DRo0axfjx4wH46KOPyrw+cOBAgoOD+fTTTwkMDMRgMBAVFUVJSUm5+zMYDAwcOJB58+aVeS0gIKBmiwdI3g+/zjKGlmA5mWeMp46/vwVdJ0KroaBSy12VIBNZQ6tPnz6mAOrdu7fZa1euXCEhIYFPPvmErl27AvDHH39UuL+YmBi+++47QkJCcHCw4KFlnIats413AUXPynqykuCH5+D3+XDXS9BmuAivOkjWUR5UKhUJCQkkJCSgUplfs/Dy8sLb25slS5Zw+vRptm7dyksvvVTh/saNG0dmZiZDhw7l77//5syZM2zevJlRo0ah11fh83e3kpsCP0yAjzvAsf8hAksm2efhxxfg/Tbwz2egK7/nLdROsg9N4+7ujru7e5nlSqWS1atXs3fvXqKionjxxReZP39+hfsKDAxk586d6PV6evfuTVRUFM8//zweHh4olXdwqIVZ8MtM4z+SfcuNH0IW5JdzAX6aCO+3NobXLe4qC7WLXX+Mx+JKCmD3Itj5nvEpdsG2NWgP938IvhFyVyJYkAitWzn2P/h5inh0wd6oHOHuyXDXi+J6Vy0lQutm+Vdgw0Q4uk7uSoQ74dcSHvgAAtvcfl3BrojQutGx/xmvkeSny12JUBMUKuj8HHSfCmqN3NUINUSEFkBBpjGsjq69/bqC/fFuYrzW1aiT3JUINUCE1tkdsPZpuJosdyWCRSmg/WjoNQucyn5iQrAfdTe09DrY/gb88TZI4lZ5neERDA99Dg07yF2JUE11M7SyzsF3o+Hi33JXIshB5QQD34PWQ+WuRKiGuhdaJ36Gtc9AsXjuqs7r8jzcMwvu5MFjwerqVmj98zlsmFy1KbWE2i28PwxeIq5z2ZG6E1pb5sCOBXJXIdgivygYuho8g+WuRKiE2h9aeh2snwAHVspdiWDLXHzhsZXGWYMEm1a7Q6s4D759Ak7/Knclgj1QOcH9H0CrR+WuRKhA7Q2tvMuw8hHjzMyCUBV3vQT3zASFQu5KhHLUztC6kghfDTYOGicI1RE5EB76Ahwc5a5EuEntC62Le+DrIVBwRe5KBHsXMQAeWS7GpbcxtesBlZObYPlAEVhCzTj+I3w3yngzR7AZtaenlfQHrBgM+mK5KxFqmxaD4aHPxDRmNqJ29LQuJ8Dq/xOBJVjG0bWwbowYztlG2H9o5SbDVw+L4ZAFyzr8X/ip4olVBOuw79AqyjU+1pB7Ue5KhLpg71LY9obcVdR59hta+lL4ZhikHZG7EqEu+e1N42dYBdnYZ2hJEnw/Fs7+LnclQl20YdK/814KcrDP0Pp1lvEagyDIQTLAd0/BuV1yV1In2V9o/f0p7HxX7iqEuk5fDGtGQn6G3JXUOfYVWgnr4eeX5a5CEIyupsC6Z4yXKwSrsZ/QSj9h7JKL8dwFW3L6V+MM5ILV2Edo6Uth7VOgK5S7EkEoa+scuCDmG7AW+wit7W9CykG5qxCE8hl0sOZJKMySu5I6wfZD68Lf8Mc7clchCBXLOQ//Gy93FXWCbYdWSb5xIlUxEYVgD47/CLuXyF1FrWfbobXpVcg6K3cVglB5m6eLSxkWZruhdXIT7F0mdxWCUDX6Yvh2JBRflbuSWss2Qyv/irg+INivzET4aaLcVdRathla6ydA/mVZS7iUa2D42kK837qK8+u5tF6cx97k69fWJEli1vYiAhdeRft6Lt2X5XP0cuWvva0+UooiPpcHVxeYLV95qJTgd65Sb14ukzcXmb2WlG2g2Qd55BaLhxlt3qFv4OwOuauolWwvtPavNF7QlFFWoUSXL/JRq+DnYc4cG+fKwvs0eGquz87y1s4S3t5Vwof9NPzzlAv+rgruXVHA1UoEyrlsA5M2F9G1oflImBkFBkavL2TBvRo2DXdh+cFSfjpZanr92Z8KebOXE+5OYpYYu7Bpqhg40AJsK7RyU2DjK3JXwbydxQR7KFn6gJbYIBUhnkruaexAWD3j2yVJEu/uLmFaVycGR6qJ8lWx/EEtBaUSXx8urXDfeoPEsLWFxHd3orGX+dt/JkvCw0nBo1Fq2gep6BGq4li68Zf+68OlOKoUDI5UW+aghZqXehj2fyl3FbWObYXW1jlQnCt3FfxwQke7ABWPfFuA7/yrtPkkj0/3lpheP5stkZoncV/Y9VlanBwUdAtx4M+LFZ8izv6tmPouCp6MKTs1VdN6SgpKJfan6MkslPjnkp5oPxWZhRIztxXxYV9NzR2kYB1b5xoHqxRqjO2EVuphOLhK7ioAOJNlYNGeEprWU7JpuDNj2joyYWMRXx40BldqnrH34+dqfprm56IwvVaened1fL6/lE8Hlh8+XloFyx/U8vj3hcR+msfjrdT0buLApM1FPBfryNlsA20+ySPq4zzWHKu4RyfYiPx0+H2+3FXUKrYzodvm6TbzYWiDBO0CVfznHmO4tAlQcTTdwKI9pTze6noP6eYrS5JUdtk1V4slhq8r5NOBGnycb/23YlCkmkE3nAJuT9Jx+LKeD/tpaPJ+Hqse0uLvqiD2s3zubqTC18V2/u4It7B7MbQbCfUay11JrWAboXVyM5zZLncVJgFuCprXNw+DSB8l3yUYezf+rsbXUvMkAtyur3O5QMLPtfwQScwykJQtMXBVIWD84Lfh32v2DrNzOTHe1XTN7JpincTYn4r4arCW05kGdAboFmL8kTXzVrL7op6B4SK0bJ6+BDbPgMdWyl1JrSB/aBn08MtMuasw0yVYxYkr5r2+k1cMNPIwBkSopwJ/VwW/nNHRJsB4B7BEL/Fbko55vco/9YvwUXL4WRezZdO3FnO1ROK9PhqCPcr20eb8XkzfJg7EBKjYn6JHZ7h+Z7JUD3rx5IP9OP6jcXjw0LvlrsTuyR9aB1dDeoLcVZh5saMTnb/I5z87ihnSQs3fl/Qs2VfCkgFaABQKBS90cOQ/O4ppWk9JU28l/9lRjLNawf+1vH5q9/i6QoLcFLzRS4PGQUGUr/kjDtceobh5OcDRy3q+OarjwDPGoIvwUaJUKPh8Xwn+rgqOZxhoHygmD7UrG1+FZ34Hpegd3wl5Q0uvg9/fkrWE8rQPUrHuUS1TtxQz+7diQr2UvNtbw7Do64H0chdHCnUSYzcUkVUo0aGBis0jnHG74Rmq8zkGlIqq/4JKksTTPxbxTm8nXByN+9OqFSx7UMO4DUUU6+DDfhqC3MUvv11JOwz7lhuvbwnVppAkGceK3fcl/PCcbM0LgtW51IfnD4Gjs9yV2C35/lTrS8WtYKHuyU83fsRHqDb5Qmv/V5B9XrbmBUE2f38qdwV2TZ7QMuhhx9uyNC0Isrt8FJL+kLsKuyVPaJ36xTg8rSDUVbs/kbsCuyVPaO1fIUuzgmAzTmyAnEtyV2GXrB9aeZfh5EarNysINsWggz2fy12FXbJ+aB1cZfyBCUJdt3c56IrlrsLuWD+09olTQ0EAoCADjqyVuwq7Y93QOv8XXDll1SYFwab9LaYcqyrrhpboZQmCueR9cHGP3FXYFeuFVvFVOLrOas0Jgt0Qva0qsV5oHfkOSvOt1pwg2I2j30NxntxV2A3rhZY4NRSE8umL4fSvcldhN6wTWpcT4JI4bxeEWzqxQe4K7IZ1QuvwGqs0Iwh26+Qm4/hywm1ZJ7TO/maVZgTBbhVlw7mdcldhFywfWsV5kLzf4s0Igt07/pPcFdgFyw+3fO5P8bEdQbgFSeVITv12/O3Qjk3n27BQ7oLsgOVDS5waCoIZvWsgZ706sakkmmUpIaQnXZ97YHxGPqE+LhVsLVg+tJJ2WLwJQbBlkkJFXv027HNqz+rsSH5O94GM8tf9/WS6CK3bsGxoFWYZp7sXhDrGoPXhgndnftW3YmlKYy6ed6rUdjtOpfNE5xDLFmfnLBtaSX/YzFT3gmBJEgoKfKI5pI3lu9xIvrvsh5RVdgLe29l3Prvmi6tlLBtaZ8WpoVB7SU4eJPt04jepDUvTmnDqovaO95mZX8K5K/k08haniLdi4dD63aK7FwRrK6oXwTHXjqzLa8Hq1EBKc6rem7qdAxeya01ohYSE8MILL/DCCy/U2D4tF1p56TY33b0gVJXk6MJln47sVMSwLL0ph5JdLd7m/vPZPNA6qMrbxcXFsXz58jLLT506RZMmTWqiNJtgudBKEr0swT6VeDbmhFsnfiqMYkVKMPm51h12bv+F7Gpv26dPH5YuXWq2rH79+ndYkW2x3E/jwt8W27Ug1CTJQcOVgLvZ0OAFhmoX0Sx1LgNP9WfxxUbk660/InlCci7FOn21tnVycsLf39/sS6VSsX79etq2bYtGo6Fx48bEx8ej011/6FuhUPDJJ58wYMAAnJ2diYyMZNeuXZw+fZru3bvj4uJCp06dSExMNG2TmJjIAw88gJ+fH66urrRv355ff614tIqcnByefvppfH19cXd3p2fPnhw8eLBKx2i5n8iVxNuvIwgy0bk14ETwEN7znUtM8RLanh3D2NOx7MrykLs0SvQGjiXn1tj+Nm3axPDhw5kwYQLHjh3jk08+YdmyZbz++utm682ZM4fHH3+cAwcOEBERwf/93//xzDPPMHXqVPbsMY7SMn78eNP6eXl59OvXj19//ZX9+/fTu3dvBg4cyPnz5c9pKkkS/fv3JzU1lQ0bNrB3715iYmK45557yMzMrPTxKCRJkqrxPtzeB+3EePCCzZCUanLrx7DHsT2rMiP49Uo9uUuq0BuDWzI0tmGVtomLi+Orr75Co9GYlvXt25e0tDT69u3L1KlTTcu/+uorXn75ZZKTkwFjT2v69OnMmTMHgL/++otOnTrx+eefM2rUKABWr17NyJEjKSwsvGUNLVq04NlnnzWF240X4rdu3cqgQYO4fPkyTk7Xn1tr0qQJL7/8Mk8//XSljtMy17QMBsgWM0gL8tK7+HHOqzO/lLZiaWoIqecc5S6p0hIvV28k0x49erBo0SLT9y4uLjRp0oR//vnHrGel1+spKiqioKAAZ2dnAKKjo02v+/n5AdCyZUuzZUVFReTm5uLu7k5+fj7x8fH8+OOPJCcno9PpKCwsvGVPa+/eveTl5eHt7W22vLCw0Oy083YsE1pXk42jMQqCFUkKJfk+rTmgac9/cyJZn14f6UrNP5JgDWcyqjc0+bWQupHBYCA+Pp7BgweXWf/GXplaff0zkAqF4pbLDAbjA+OTJ09m06ZNLFiwgCZNmqDVann44YcpKSkptzaDwUBAQADbt28v85qnp2flDhBLhVZWkkV2Kwg3M2jrccm7E9v0rfkiNYykC5rbb2QHzqTX3JjxMTExnDhxosYfe9ixYwdxcXEMGjQIMF7jSkpKqrCO1NRUHBwcCAkJqXa7IrQEuyKhoMi7BYedO7A2rzlL1v1O1m8rcGubT71eLcrdpuj8IdJWvVpmeeDoRai9gwEoPLufzF8Woc/PxrlZR7z7PIdCZexlGIrzSVn+In6PzcXB3ddyB3eDS9mF6A0SKuWd9xRnzpzJgAEDCA4O5pFHHkGpVHLo0CEOHz7M3Llzq73fJk2asHbtWgYOHIhCoWDGjBmmXlh5evXqRadOnXjwwQeZN28e4eHhJCcns2HDBh588EHatWtXqXZFaAk2T3JyI9WnEzukNiy93JSES8ZrMMUpJ8k9sBl1/ZBK7SfwqU9QOjqbvlc6uxv3LxnI+HEBHh0eRhMaQ/r3b5B3cBNuMQMAyNq+FLfWfa0WWACleomUnEIaeDnffuXb6N27Nz/++COzZ8/mrbfeQq1WExERwejRo+9ov++88w6jRo2ic+fO+Pj4MGXKFHJzb33XU6FQsGHDBqZNm8aoUaNIT0/H39+fu+++23QNrTIsc/fwu9Fw+Nsa361QdxR7NeO4awf+VxDFqtRACvUqs9cNJYWkLHueeveNJefP1Tj6NqZer/LvPl3raQU/vxqlpuwT7fr8bC5+OJyGE9eicHAka/tSDCVFeN/3LEUXj5G1ZQn+IxaiUKrK2bvlrHqqI53CvG+/Yh0jelqCTZDUzmTU78CfihiWZzRjX4pbhetn/rIIbVh7tCGtyflzdaXaSF72POhKUPsE49HpMTSNjHfLlM4eqFzrUXh2P5qQ1hRfOIpLy3uQ9KVkbv4Y777PWz2wAJKzb/1oQV0mQkuQTalHCKfcO/FTUUu+TGnA1TOV+3XMP/YbJamJBDzxTqXWV7nUo17v8Tj6NwF9KXlHt5G2ehp+//cGmuAoFAoFPg9MIWvLZ2RuWYK2cTtcW95Lzl/fomnUCoWDI6lfTUZfmItbzADc2w68k8OutKyC8u/C1XU1H1rFeZCfXuO7FeyfpHIku357/nZoy9eZ4fyW5gVpVduHLjedzC2f4vfobBQOlXvuSu3dALV3A9P3TkGR6HPTyf17LZrgKAA0DVqYhWBp5iXyj24lIO590r5+Bbd296MNbUvyF+PQBEfh6BtatcKrIaew1OJt2KOaDy3xUKlwA71rIGe8OrOpOJrlqY3MxkOvjpLU0xgKsklZ9sL1hZKB4gtHubrvRxpOWlepUzmnwAjyjm4r9zVJkriy8QO8eowGSaIkLRHn8C4o1Ro0wVEUXThildDKLhChVZ6aD62i7BrfpWA/JKUDV32M46F/kx1R4Xjo1aFp1IqAUR+aLbuy4T3U3g1w7/BQpa89laQlonIt/6M8eYc2o9K649y0A/qif5+XMhg/wCwZ9MZPfFhBtuhplavmQ0tXVOO7FGybwdmH8/U6s0Xfii9SGnOpkuOhV4fSyRnHmx5xUKidUGrcTMuzfluG/uoVfAZMBCD3n//h4OGL2qchkl5H/rFtFJz8k/oPln12S5+fTc6f3+A//C0AVBpX1N7B5P7zP7ShbSg6dxCPjo9Y7PhulC2uaZWr5kOrVIRWbXd9PPQOfJsbybrLvkiZtvNxGX1eFrrc69dVJUMpWdu+QJ93BYWDI2qfhvg+/BrasPZlts3csgT32EE4uPmYlnn3e4GMn97h6t71uMcOxikw3CrHIU4Py1fzz2kdWQtrRtboLgX5GTSepHh34jdDa5ZebsKp/DsfD12oWMN6zvz+cg+5y7A54vRQuKWiepEcdenA9/n/joeebTu9qbqgRCdmsiqPBU4PxQNx9urG8dCXXm7KYSuMhy7cms5gmaHu7F3Nh5aY59CulHiGccKtI+sLo1gpw3jowq0ZLDQ+p72z7BRigk0yKJR82upBTir9uVz67wdyPa8QE3BF3sIEMxoHDXCv3GXYnJoPLYX4S23rlkT34aOcPXKXIdyGl5MXMFnuMmxOzSeMQlystWV7GrVl8VUxH6U9UIoOQLksEFrijbZV2c71mOIsoZeqNz2VYF0qhfVHlrAHFkgY0dOyVdPD23O5qAY/UyNYlKPKfibisKaaDy2H2jFGd23zZcs+/JYtTgvtibuTu9wl2KSaDy03/xrfpXBnjga15N0CMQelvfFwlH/iWFtU86HlHljjuxSqL0/jziRPLaUG8Tk2e+PhJEKrPBboaQXU+C6F6otv3oWLBalylyFUgwit8tV8aDm5gjgXtwlrWtzLxqyjcpchVJO7o/h3VB7LPJ8geluyO+UXzrziJLnLEO6A6GmVzzKh5S5CS06Fjs5M9vWmSF8sdynCHfDSeMldgk0SPa1a6M2oHiTmXZS7DOEONXBtcPuV6iARWrXMhogerM06LHcZQg1o6N5Q7hJskoVOD8VjD3I47xPKbH2K3GUINUDroMVH63P7Fesg0dOqJUpVjkwKDCZfVyB3KUINCHYLlrsEmyUuxNcSC6PvI+FqktxlCDWkoZs4NbwVC4VWkEV2K5Rva9OurMw+JHcZQg0SPa1bs9DpoT+4+Fpk14K5VM8GzFRmyV2GUMPERfhbs9zgV8GxFtu1YKRTOvBySDNySnLlLkWoYZHekXKXYLNEaNmxj6N7sz/ntNxlCDXMUelIM69mcpdhsywYWh0stmsBdoXG8nmu+FxhbRRRLwK1Ui13GTbLcqEV2AZUThbbfV2W4erLVKdiDGK6tlopyidK7hJsmuVCy8EJAlpZbPd1lYSCV5u24kqxuPheW4nQqphlZ6EQ17Vq3Oet+rIr+4TcZQgWJEKrYhYOLXFdqyYdCG7DR1ePy12GYEFujm6EuIfIXYZNE6FlJ3K0nrzspkQn6eQuRbCgWP9YFGLu0ApZNrTc/MCzkUWbqCtmRHYkpTBd7jIEC7sr6C65S7B5lp9ZVfS27tjKlr3ZlnVM7jIEKxChdXuWD62GIrTuREJAc94uSJS7DMEKmng2wd9FTMF3O5YPrab3IWadrp4CJ1cme7tTYiiRuxTBCroEdpG7BLtg+dDybAgNO1q8mdpodou7OZefLHcZgpXc1UCcGlaG5UMLoOUjVmmmNlnXvBc/ZR2RuwzBSpwdnGnr21buMuyCdUKrxSAQn6WqtDO+TXmj5LzcZQhW1KNhD9Qq8W+kMqwTWs71oEkvqzRl74odNEzy86NQXyR3KYIVDWg8QO4S7IZ1QgsgWpwiVsa86Hs4lSd6WXWJt8abTgGd5C7DblgvtML7gaOb1ZqzR5vCu/GtmP6rzukb2heVUiV3GXbDeqGl1kLkQKs1Z28u1mtIvCSeeK+LxKlh1VgvtECcIt5CqVLN5OBQrpbmyV2KYGUh7iG08Gkhdxl2xbqhFdoNXP2s2qQ9eK9Vb47knpW7DEEGA8PE2UdVWTe0lCqIesiqTdq638M682W2uI5VFzkqHXmoqfj3UFXWDS2A6CFWb9JWpXkEMt3hKhKS3KUIMujXuB/eWm+5y7A71g+twDYQLD7Wo1eomBIaSVZJjtylCDIZHjlc7hLskvVDC6DrS7I0a0sWt+rD3pxTcpchyCTWP5bweuFyl2GX5AmtZr3Br+6Og/13SHuWiOm/6rQRzUfIXYLdkie0ALq8IFvTcsp08eEVbamY/qsOa+jWkG4Nusldht2SL7SiBoNXiGzNy0FCwavNYkgvypS7FEFGT7R4QowDfwfkCy2lqs71tpZF92FntphNpy4LdgtmUNNBcpdh1+QLLYA2w+tMb+tQg1a8ny/mK6zrnm31rJjy/g7JG1oqNXSfKmsJ1pCr9eBlD0d0BjH9V13WxLMJ/Rv3l7sMuydvaAG0HAL1I+SuwqJmRXbmUkGa3GUIMhvfZjxKhfz/5Oydg9wFoFRCj2nw39p5C/ibqPv4Jcv+Hm+4svUKmVszKc0oBcApyAnfB3xxizYOL3Tx04tk78w220bbWEvYzLBb7rPoUhGX116mMKmQ0iul+A/1x6e3j9k62X9mk7omFalYwqurF/6PXZ+dpiS9hKQFSYTNCkOlta+hXFr6tOSehvfIXUatIH9oATS/HwJaQ8oBuSupUSf8mzO/yD4/CK32UuP/iD+Ofo4AZP+Rzfn3zhM2OwxNkAYA15auBD0ZZNpG4VDxHTFDsQHH+o64t3cndVVqmdd1V3VcWnqJBqMboK6v5tw753CJcMGttTEok79Mxu8RP7sLLIAJMRPkLqHWsJ2+at+3oBZ1nQscXZjk40GxvljuUqrFvY07bq3ccPJ3wsnfCb+H/VBqlBScLjCto3BQoPZUm74cXCv+G+jc2Bn/x/zx7OhZbsCVpJeg0qrw6OCBc2NnXCJdKEo2DjudvSsbhYMCj3YeNXugVtAzuCcdA8RH12qK7aREww4Q+7TcVdSY16O6k5R/Se4yaoRkkMj+KxtDsQHnJs6m5fnH80l4LoGTU05y6YtL6HLv7EaDk58ThhIDhecK0eXpKDxbiCZYgy5Px+V1lwkYHnCnh2J1GpWGKbFT5C6jVrGN08Nr7pkJJ36G7HNyV3JH1kf25IdaMGxy0YUizsw9g6HUgNJJScPnGppODd2i3fBo74HaR01JegmX117m7LyzhM0KQ6mu3t9ClYuKBk814OKnF5FKJDw7e+LW0o2Ln1+kXq96lGaUcv6980h6Cd8HffFob/u9rtEtRxPoGih3GbWKbYWWowvc/wF8eb/clVRbUv0w5upqxwSrjgGOhM0Ow1BgIGdPDhc/u0joK6FogjR4dLgeGJoGGrShWk5OPMnVg1fv6BTOva077m3dTd/nJeRRfLGYwOGBnJxykuAxwTh4OJA4OxGXcBcc3G3rV/hGjT0aMypqlNxl1Dq2c3p4TeNuEPOE3FVUS4nKiUkBgRToCm6/sh1QOihx8nNCG6rF/xF/NMEarvxypdx11Z5qY68rraTG2jeUGkhZkULgE4GUXC5B0ku4RLjgFGC8zlaQaLvvswIFMzvNFHMZWoDthRbAfXPBPej269mY+a3u5cRV+z61rZAEUmn5Axbq8nSUXinFwbPmej7pP6Tj2tIVbYgWySDBDZ8xl3Tm39uawU0H09ZPzBhtCbYZWhp3GPCO3FVUya/NurI665DcZdSY1DWp5J/IpyS9hKILRaStSSP/eD6enTzRF+lJWZ1CwekCStJLyEvI49y751C5qXCPuX5qd3HJRVK/vf5og0FnvMheeK4QSS+hy9JReK6Q4rSyd1iLLhWR83cOfoONcwo4BTiBAjJ/y+TqgasUpxSjbay1/BtRDUGuQUxqN0nuMmot270g0Ky38Wn5w/+Vu5LbSvZqyEzKP22yV7ocHReXXESXo0OpVaIJ1hAyMQTXKFcMJQaKLxZzbuc5DAUGHDwdcIlwIfjZYLNnqEqulMANTzbosnQkvpZo+j5jYwYZGzNwDnem8dTGpuWSJJG8NBn/of4onYx/V5WOSoJGB5GyIgWpVCJgRABqL9s79VIpVLzZ9U1cHV3lLqXWUkiSZLsDlBdkwkexkG+78wHqlA7EtezKwdzE268s1HpjW43l2dbPyl1GrWabp4fXONeDfvPlrqJCH7TqLQJLAKCNbxuejq49zxraKtsOLYAWg2x22rGdjTuyNPuI3GUINsBN7cYbXd8Q09tbge2HFsD9H0JAK7mrMJPu7s+rjoVi+i8BgGkdpxHkan93vO2RfYSWozMMXQ1utvExDoNCydTGUWQWZ8ldimADhkYMFeNkWZF9hBaAeyA89jU4yH+b+9PoPuzOOSl3GYIN6BjQkSntxWcLrcl+QgsgKAYGLcLsPrqV7W3YlkVXE2RrX7AdjdwbsaDbAnEdy8rsK7TAeGG+x6uyNJ3tXI8prhJ6SS9L+4LtcFO78X7P9/Fwsv0Pbdc29hdaAN1ehpaPWL3ZGRHtSSvMsHq7gm1RKVS81e0tGns0vv3KQo2zz9AC4x3FBu2t1tyXLfuwPUucFgowqd0k7gq6S+4y6iz7DS21xnhh3iPY4k0dDWrJuwWnLN6OYPvGtBrD8ObD5S6jTrPf0AJw9TU+CmHBz3nladyZ5Kml1FBqsTYE+zCi+QjGtR4ndxl1nn2HFoB/lEWDK755Fy4WlJ2EQahbHmr6EC+3f1nuMgRqQ2gBhHaFx38ArVeN7nZNi3vZaIfTfwk1q29oX2Z2mil3GcK/bHuUh6pKOworBkHenU+MesovnP9zM1Bkp7PpCDWje3B33un+Dg5K2x3Fqa6pHT2ta/xawMifwaPhHe2m0NGZyb7eIrDquH6h/Xi7+9sisGxM7QotAO8wGLURvJtWexdvRvUgMe9iDRYl2Jv/i/g/3uz6Jmql7Q00WNfVrtPDG+VnGE8VU6s2BPKGiB5MKRbjY9VlY1uP5dlWYiA/W1V7QwugKAdWDoELf1Vq9fM+oQzxciK/lsymI1SNUqHk1dhXeTTiUblLESpQ+04Pb6TxgBHrIKznbVctVTkyOTBYBFYd5ah0ZF7XeSKw7EDtDi34dyyub6D5gxWu9nar+zh2NckqJQm2xdfZl2V9ltEntI/cpQiVULtPD2+28z3YMhsMOrPF25rexQTdeZmKEuQU4xvDwu4L8dH6yF2KUEl1K7QAknbCmpGmZ7lSPRvwsJ8nOSW5MhcmWNuj4Y8yJXaKuENoZ+peaAFcTYM1o9Cf38WoVt3Yl3Na7ooEK3JUOjK943QGNR0kdylCNdTN0AIw6Plz/2eMOfKRmJyiDglxD+HNrm/SwqeF3KUI1VR3Q+tff6X8xbQd07hceFnuUgQLUqDg0fBHeandS2htYJ4BofrqfGgBZBVlEb8rni3nt8hdimABvlpf5nSZQ+egznKXItQAEVo3+OXcL7yx+w3SC9PlLkWoIX1C+jC943QxlnstIkLrJrklubyz9x2+O/mduNZlx+pr6/Ny+5fFs1e1kAitW9iTuof4XfEk5SbJXYpQBQ4KB4ZFDuPZ1s/ionaRuxzBAkRoVaBEX8Jnhz9j2dFlFOoK5S5HuI0O/h2Y2mEqYZ5hcpciWJAIrUq4XHCZjw98zPenvxdzHtogfxd/JrabSJ8QcSpYF4jQqoLE7ETe3fcu2y9sl7sUAXBzdGNki5EMixyGs9pZ7nIEKxGhVQ370vaxcO9CDqVXbawuoWY4OzgzvPlwnmjxBO6O7nKXI1iZCK07sPPSTpYeXcrulN1yl1InuKndGBo5lBGRI/DUeMpdjiATEVo14NiVYyw7uozNSZvFNS8L8HX2ZUizIQyNHCp6VoIIrZqUnJfMimMr+O7Ud+JuYw1o59eOoRFD6dmwp5hcQjCp/YMAWlGgayBTYqfw6yO/Mr3DdFr6tJS7JLujddAypNkQ1t6/lqV9lnJfyH0isG7y/fff06RJE1QqFS+88ILV2w8JCeHdd9+1ervXiJ6WhSVmJ/K/0/9j/Zn1ZBRmyF2OTVIqlLT1a0ufkD70De2Lm6NbtfcVFxfH8uXLeeONN3jllVdMy7///nsGDRpEbfh19/PzY+TIkUyYMAE3Nzfc3Kr/flVHeno6Li4uODvLc8dWhJaV6A16dibvZH3iev649Ad5pXlylyQrBQpa1W9Fn9A+3NfoPuo716+R/cbFxfHNN9+g0Wg4c+YMXl7GWcdrS2jl5eXh5ubG1q1b6dGjR7X3U1JSgqOjYw1WZj3i9NBKVEoVdze4m/nd5vP7Y7+z5N4lDI8cTgPXBnKXZjUOSgdifGOY2HYimx7axIp+KxgWOazGAuuaXr164e/vzxtvvHHLdb777jtatGiBk5MTISEhLFy40Oz1kJAQ/vOf/zBq1Cjc3Nxo2LAhS5YsuW3bR48epX///ri7u+Pm5kbXrl1JTDROSWcwGJg9ezYNGjTAycmJ1q1bs3HjRtO2SUlJKBQK1q5dS48ePXB2dqZVq1bs2rULgO3bt5t6VT179kShULB9+/ZKH8/cuXOJi4vDw8ODp556imXLluHp6cmPP/5IeHg4zs7OPPzww+Tn57N8+XJCQkLw8vLiueeeQ6/Xm+3rxtNDhULBZ599xqBBg3B2dqZp06b88MMPZu3/8MMPNG3aFK1WS48ePVi+fDkKhYLs7Ozbvqc3E6ElA7VSTafATkyJncLPD/3M9w98zwsxL9DOrx0alUbu8mpUE88mDI8czkf3fMTOx3ayvO9y4qLiCHANsFibKpWK//znP3zwwQdcvFh20t29e/cyZMgQHnvsMQ4fPsysWbOYMWMGy5YtM1tv4cKFtGvXjv379zN27FieffZZjh8/fst2L126xN13341Go2Hr1q3s3buXUaNGodMZ5yR47733WLhwIQsWLODQoUP07t2b+++/n1OnTpntZ9q0aUyaNIkDBw7QrFkzhg4dik6no3Pnzpw4cQIwhlRKSgqdO3eu9PHMnz+fqKgo9u7dy4wZMwAoKCjg/fffZ/Xq1WzcuJHt27czePBgNmzYwIYNG1ixYgVLlixhzZo1Fb7n8fHxDBkyhEOHDtGvXz+GDRtGZmYmYAzjhx9+mAcffJADBw7wzDPPMG3atAr3VxFxemhjSg2lnMg8wYHLBziQfoADlw+QVpAmd1mV4qRyoqlnUyK9I2nj24ZOgZ2sPmFEXFwc2dnZfP/993Tq1InmzZvz+eefm50eDhs2jPT0dDZv3mza7uWXX+ann37i6NGjgLE30bVrV1asWAGAJEn4+/sTHx/PmDFjym371VdfZfXq1Zw4cQK1uuy480FBQYwbN45XX33VtCw2Npb27dvz0UcfkZSURGhoKJ999hlPPvkkAMeOHaNFixYkJCQQERFBdnY2Xl5ebNu2je7duwNU+njatGnDunXrTOssW7aMkSNHcvr0acLCjJ/XHDNmDCtWrCAtLQ1XV1cA+vTpQ0hICIsXLzbt64UXXjDdBFAoFEyfPp05c+YAkJ+fj5ubGxs2bKBPnz688sor/PTTTxw+fNjU9vTp03n99dfJysrC09Ozoh9pGeK2jI1RK9VE+UQR5RPFcIYDkJqfyqH0QyTmJHI25yxJOUkk5SbJ+liFu6M7TTybEOkdSWS9SCK9I2ns0dim7vTNmzePnj17MnHiRLPlCQkJPPDAA2bLunTpwrvvvoter0elUgEQHR1tel2hUODv78/ly8YRbvv27cuOHTsAaNSoEUePHuXAgQN07dq13MDKzc0lOTmZLl26lGn34MGDZstubDcgwNgjvXz5MhEREeUeZ2WPp127dmW2dXZ2NgUWGC/yh4SEmALr2rJrx30rN9bs4uKCm5ubaZsTJ07Qvn17s/VjY2Mr3F9FbOc3TLglfxd//F38zZZJkkRaQRpncs6QlJNEWkEaGYUZZBRmcKXwCtnF2eQU51CkL6pSW1oHLa5qV1zULrg5uuHv4k+ASwCBroEEuQYR4BJAkGsQro6ut9+ZzO6++2569+7Nq6++SlxcnGm5JEkoFAqzdcs74bg5fBQKBQaDAYDPPvuMwsJCs/W02tsP41xeuzcvu7Hda69da7c8lT0eF5eyQ/WUd4wVHfetVLRNZeurLBFadkqhUJjCrHPgrYcRLtIVUagrRC/p0Rl06CU9eoPe9F8AF7ULLo4uuDi4oFKqrHUIVvHmm2/SunVrmjVrZlrWvHlz/vjjD7P1/vzzT5o1a2bqldxOUFBQmWXR0dEsX76c0tLSMv+I3d3dCQwM5I8//uDuu+82a/dOeh1QM8djSREREWzYsMFs2Z49e6q9PxFatZzGQYPGoXZd3K+Kli1bMmzYMD744APTsokTJ9K+fXvmzJnDo48+yq5du/jwww/5+OOP76it8ePH88EHH/DYY48xdepUPDw8+Ouvv4iNjSU8PJzJkyfz2muvERYWRuvWrVm6dCkHDhxg5cqVd9SupY6npjzzzDO8/fbbTJkyhSeffJIDBw6YbhLc3AOrDHH3UKj15syZY3Y6EhMTw3//+19Wr15NVFQUM2fOZPbs2WankNXh7e3N1q1bycvLo1u3brRt25ZPP/3U1OuaMGECEydOZOLEibRs2ZKNGzeaHgW4E5Y6npoSGhrKmjVrWLt2LdHR0SxatMh099DJyanK+xN3DwVBsLrXX3+dxYsXc+HChSpvK04PBUGwuI8//pj27dvj7e3Nzp07mT9/PuPHj6/WvkRoCYJgcadOnWLu3LlkZmbSsGFDJk6cyNSpU6u1L3F6KAiCXREX4gVBsCsitARBsCsitARBsCsitARBsCsitARBsCsitARBsCsitARBsCsitARBsCsitARBsCsitARBsCsitARBsCsitARBsCsitARBsCv/D0/ulNt/2ltrAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 300x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(3,3))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Gender Distribution')  # Add a title\n",
-    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(98855, 19)"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isnull().sum()['Gender']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Country"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Country\n",
-       "Afghanistan                              64\n",
-       "Albania                                 109\n",
-       "Algeria                                 130\n",
-       "Andorra                                  15\n",
-       "Angola                                   11\n",
-       "                                       ... \n",
-       "Venezuela, Bolivarian Republic of...    123\n",
-       "Viet Nam                                331\n",
-       "Yemen                                    13\n",
-       "Zambia                                    9\n",
-       "Zimbabwe                                 39\n",
-       "Name: Country, Length: 183, dtype: int64"
-      ]
-     },
-     "execution_count": 209,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('Country')['Country'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "412"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Country'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 212,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 311,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst=df.groupby('Country')['Country'].count()\n",
-    "lst = lst.sort_values(ascending=False)\n",
-    "lst=lst[:50]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15,4))\n",
-    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
-    "\n",
-    "# Adding labels and title\n",
-    "plt.xlabel('Country')  # Label for x-axis\n",
-    "plt.ylabel('Count')      # Label for y-axis\n",
-    "plt.title('Top 50 Countries according to participation')  # Title of the plot\n",
-    "plt.xticks(rotation=90)  # Rotate labels by 90 degrees\n",
-    "\n",
-    "# Display the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hobby"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 215,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Hobby'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hobby\n",
-       "No     18958\n",
-       "Yes    79897\n",
-       "Name: Hobby, dtype: int64"
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('Hobby')['Hobby'].count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## UndergradMajor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19819"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "UndergradMajor\n",
-       "Computer science, computer engineering, or software engineering          50336\n",
-       "Another engineering discipline (ex. civil, electrical, mechanical)        6945\n",
-       "Information systems, information technology, or system administration     6507\n",
-       "A natural science (ex. biology, chemistry, physics)                       3050\n",
-       "Mathematics or statistics                                                 2818\n",
-       "Web development or web design                                             2418\n",
-       "A business discipline (ex. accounting, finance, marketing)                1921\n",
-       "A humanities discipline (ex. literature, history, philosophy)             1590\n",
-       "A social science (ex. anthropology, psychology, political science)        1377\n",
-       "Fine arts or performing arts (ex. graphic design, music, studio art)      1135\n",
-       "I never declared a major                                                   693\n",
-       "A health science (ex. nursing, pharmacy, radiology)                        246\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 218,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['UndergradMajor'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def refactor_major(df):\n",
-    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n",
-    "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
-    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n",
-    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
-    "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)') \n",
-    "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'), \n",
-    "                        (df['UndergradMajor'] == 'Web development or web design'), \n",
-    "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'), \n",
-    "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
-    "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
-    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)'),\n",
-    "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
-    "    \n",
-    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
-    "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
-    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
-    "    return df\n",
-    "\n",
-    "df = refactor_major(df)\n",
-    "df['UndergradMajor'].replace('nan', 'No major', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst=df['UndergradMajor'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(5,5))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Undergrad Major')  # Add a title\n",
-    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['UndergradMajor'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 224,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Job Status"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobSearchStatus\n",
-       "I’m not actively looking, but I am open to new opportunities    47556\n",
-       "I am not interested in new job opportunities                    19296\n",
-       "I am actively looking for a job                                 12636\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 225,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSearchStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['JobSearchStatus'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# refactoring JobStatus\n",
-    "# changing the jobstatus to seeking and non seeking\n",
-    "def refactor_job(df):\n",
-    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
-    "    \n",
-    "    conditions_job = [(df['JobSearchStatus'] == 'I am actively looking for a job'),\n",
-    "                    (df['JobSearchStatus'] == 'I am not interested in new job opportunities')\n",
-    "                    | (df['JobSearchStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
-    "    \n",
-    "    choices_job = ['Seeking', 'Not seeking']\n",
-    "    \n",
-    "    df['JobSearchStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "df = refactor_job(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobSearchStatus\n",
-       "Not seeking    66852\n",
-       "Seeking        12636\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 228,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSearchStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 229,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSearchStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Employment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employment\n",
-       "Employed full-time                                      58551\n",
-       "Independent contractor, freelancer, or self-employed     7797\n",
-       "Not employed, but looking for work                       4604\n",
-       "Employed part-time                                       4170\n",
-       "Not employed, and not looking for work                   3210\n",
-       "Retired                                                   138\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 230,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1018"
-      ]
-     },
-     "execution_count": 231,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Employment'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#im not considering the retired person here\n",
-    "#Refactoring the employment\n",
-    "def refactor_emp(df):\n",
-    "    \n",
-    "    conditions_emp = [(df['Employment'] == 'Employed full-time'),\n",
-    "                    (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n",
-    "                    (df['Employment'] == 'Not employed, but looking for work'),\n",
-    "                    (df['Employment'] == 'Employed part-time')]\n",
-    "    \n",
-    "    choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n",
-    "    \n",
-    "    df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "df = refactor_emp(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst=df['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(4,4))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Employment Type')  # Add a title\n",
-    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JobSatisfaction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobSatisfaction\n",
-       "Moderately satisfied                  25908\n",
-       "Extremely satisfied                   12395\n",
-       "Slightly satisfied                     9973\n",
-       "Slightly dissatisfied                  7037\n",
-       "Moderately dissatisfied                6286\n",
-       "Neither satisfied nor dissatisfied     4935\n",
-       "Extremely dissatisfied                 2472\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 237,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSatisfaction'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10482"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSatisfaction'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['JobSatisfaction'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSatisfaction'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 313,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lst=df['JobSatisfaction'].value_counts()\n",
-    "plt.figure(figsize=(12,4))\n",
-    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
-    "\n",
-    "# Adding labels and title\n",
-    "plt.xlabel('Satisfaction Level')  # Label for x-axis\n",
-    "plt.ylabel('Counts')      # Label for y-axis\n",
-    "plt.title('Job Satisfaction')  # Title of the plot\n",
-    "plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
-    "\n",
-    "# Display the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ethnicity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 242,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23578"
-      ]
-     },
-     "execution_count": 242,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['RaceEthnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RaceEthnicity\n",
-       "Black or of African descent                                                                                                                                 1204\n",
-       "Black or of African descent;East Asian                                                                                                                         7\n",
-       "Black or of African descent;East Asian;Hispanic or Latino/Latina                                                                                               2\n",
-       "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian                    1\n",
-       "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian        1\n",
-       "                                                                                                                                                           ...  \n",
-       "Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent                                                           2\n",
-       "Native American, Pacific Islander, or Indigenous Australian;White or of European descent                                                                     160\n",
-       "South Asian                                                                                                                                                 6112\n",
-       "South Asian;White or of European descent                                                                                                                      88\n",
-       "White or of European descent                                                                                                                               39320\n",
-       "Name: RaceEthnicity, Length: 71, dtype: int64"
-      ]
-     },
-     "execution_count": 243,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#count number of each Ethnicity\n",
-    "df.groupby('RaceEthnicity')['RaceEthnicity'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
-    "df.loc[df['RaceEthnicity'].str.match('Biracial') == True, 'RaceEthnicity'] = 'Biracial'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Black or of African descent') == True, 'RaceEthnicity'] = 'Black or African descent'\n",
-    "df.loc[df['RaceEthnicity'].str.match('East Asian') == True, 'RaceEthnicity'] = 'East Asian'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Hispanic or Latino') == True, 'RaceEthnicity'] = 'Hispanic or Latino'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Indigenous') == True, 'RaceEthnicity'] = 'Indigenous'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Middle Eastern') == True, 'RaceEthnicity'] = 'Middle Eastern'\n",
-    "df.loc[df['RaceEthnicity'].str.match('South') == True, 'RaceEthnicity'] = 'South Asian'\n",
-    "df.loc[df['RaceEthnicity'].str.match('White or of European descent') == True, 'RaceEthnicity'] = 'White or European descent'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Multiracial') == True, 'RaceEthnicity'] = 'Multiracial'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Native American') == True, 'RaceEthnicity'] = 'Native American'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RaceEthnicity\n",
-       "Black or African descent      1549\n",
-       "East Asian                    2787\n",
-       "Hispanic or Latino            3592\n",
-       "Middle Eastern                2176\n",
-       "Native American                286\n",
-       "South Asian                   6200\n",
-       "White or European descent    39320\n",
-       "Name: RaceEthnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 245,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('RaceEthnicity')['RaceEthnicity'].count() #11 groups of Ethnicity after combining"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23578"
-      ]
-     },
-     "execution_count": 246,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['RaceEthnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['RaceEthnicity']=df.groupby(['Country'])['RaceEthnicity'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 248,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['RaceEthnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lst=df.groupby('RaceEthnicity')['RaceEthnicity'].count()\n",
-    "plt.figure(figsize=(6,6))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Race/Ethnicity')  # Add a title\n",
-    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Developer Roles"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "728"
-      ]
-     },
-     "execution_count": 250,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['DevType'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DevType\n",
-       "Back-end developer                                                                                                                                                                                           5372\n",
-       "Back-end developer;C-suite executive (CEO, CTO, etc.)                                                                                                                                                          59\n",
-       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst                                                                                                                                  5\n",
-       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Data scientist or machine learning specialist                                                                                    1\n",
-       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Data scientist or machine learning specialist;Database administrator;Designer;Desktop or enterprise applications developer       1\n",
-       "                                                                                                                                                                                                             ... \n",
-       "QA or test developer;Student;System administrator                                                                                                                                                               5\n",
-       "QA or test developer;System administrator                                                                                                                                                                      10\n",
-       "Student                                                                                                                                                                                                      2523\n",
-       "Student;System administrator                                                                                                                                                                                   63\n",
-       "System administrator                                                                                                                                                                                          247\n",
-       "Name: DevType, Length: 8820, dtype: int64"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('DevType')['DevType'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
-    "df.loc[df['DevType'].str.match('Back-end developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Student') == True, 'DevType'] = 'Student'\n",
-    "df.loc[df['DevType'].str.match('QA or test developer') == True, 'DevType'] = 'Non developer'\n",
-    "df.loc[df['DevType'].str.match('Product manager') == True, 'DevType'] = 'Manager'\n",
-    "df.loc[df['DevType'].str.match('Mobile developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Marketing or sales professional') == True, 'DevType'] = 'Non developer'\n",
-    "\n",
-    "df.loc[df['DevType'].str.match('System administrator') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Game or graphics developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Full-stack developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Front-end developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Engineering manager') == True, 'DevType'] = 'Manager'\n",
-    "df.loc[df['DevType'].str.match('Embedded applications or devices developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Educator or academic researcher') == True, 'DevType'] = 'Student'\n",
-    "df.loc[df['DevType'].str.match('DevOps specialist') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Desktop or enterprise applications developer') == True, 'DevType'] = 'Developer'\n",
-    "\n",
-    "df.loc[df['DevType'].str.match('Designer') == True, 'DevType'] = 'Non developer'\n",
-    "df.loc[df['DevType'].str.match('Database administrator') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Data scientist or machine learning specialist') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Data or business analyst') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('C-suite executive') == True, 'DevType'] = 'Developer'\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DevType\n",
-       "Developer        73032\n",
-       "Manager            665\n",
-       "Non developer     2791\n",
-       "Student           3000\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 254,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('DevType')['DevType'].count() #11 groups of Ethnicity after combining"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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RnE4vwpaz2dh1IQcXskvFjkM3wU2lwMAITwxt443eYR6wl8vEjkREDYiFiKiJnEkvxqYzmdh8JgtpBRVix6E7oFLI0C/cE0PaeGFAhCfnQyKyAixERI3obEYxfj+Thc1nspCSXy52HGoECpkUfcKa4Z6OfhjYygsKOS/tJ7JELEREDSynpBK/xKXjl+PpuJKnFTsONSGNox1GtPXBPdH+6BjoKnYcIroFLEREDUBvMGLnhRysOZ6GXRdzeWk8obmHCuM6+mFsR3/4aRzEjkNEN8BCRHQHEnPL8POxNKyNy0BeWZXYccgMSSRA75bNMLlbEAZEeELKK9WIzBILEdEtMhoFbD9/FYsPJOFIUoHYcciCBLg54MGuQXigcwA0jpzjiMicsBAR3aSyKj1WH0vDkoPJSC3gAGm6fUo7KUa188XDPYIR6ecidhwiAgsR0Q2lFZRj8YFkrDmexkkTqcFFB7ni8T7NMbi1FyQSnk4jEgsLEdF1nEorwte7L2PH+avgGGlqbGFeajzZrwVGtfOFXMZL94maGgsR0b/EpRbi8z8SsOdSrthRyAYFuDngsT4tcF+0P5R2nA2bqKmwEBH95XhyAT7fmYB9CXliRyFCMyd7PNIrBA92C4LaXi52HCKrx0JENu/wlXx8/kcCDl3JFzsKUR1uKgWe6Nsck7sH84gRUSNiISKbdTajGHM3x+NgIosQmT9vZyWeGRCK8Z0DOMaIqBGwEJHNSS8sx/xtF/HrqUzwq58sTYiHCrOHhGNEOx+xoxBZFRYishnFFTp8vesyFh9MRrXeKHYcojvSPkCDV+6KQLfm7mJHIbIKLERk9XQGI5YeSsGXfyagsFwndhyiBjWirQ/+b0Qr+HK9NKI7wkJEVm3PpVy8+etZJOdzZmmyXg52MjzdvwWm92kOezkHXhPdDhYiskrZxZV4Z9M5bD6TLXYUoiYT5O6I10e0xqDWXmJHIbI4LERkVfQGI2IOJuPTHZegrTaIHYdIFP3Dm+GNUW0Q4qESOwqRxWAhIqsRm1KA/1t/FheyS8WOQiQ6hVyKZ/uH4sl+LXiZPtFNYCEii1dcocMHm+Ox+ngaL6Mn+pfWPs746N52iPRzETsKkVljISKL9ueFq3hl3RlcLakSOwqR2ZJLJXi8b3M8N7AlB10TXQcLEVmk4god3v7tHNbFZYgdhchihHqqMe+edogOchU7CpHZYSEii7P3Ui5e/OU0sksqxY5CZHGkEmBKjxC8OCyca6MR/QMLEVmM8mo93v89HsuPpIodhcjihXs54X8TohDu7SR2FCKzwEJEFuFsRjGeXXkCSXlasaMQWQ17uRSvDm+Fh3sEix2FSHQsRGT2Fh9IwgebL6DawPXHiBrDoFae+Oje9nBTKcSOQiQaFiIyW8UVOrz4yylsO3dV7ChEVs/TyR6f3N8BvVp6iB2FSBQsRGSWTqQW4tmVJ5BeWCF2FCKbIZEAj/dpgTlDwyGTSsSOQ9SkWIjIrAiCgO/3XcHH2y5CZ+CXJpEYerRwx5cTO/IUGtkUFiIyG2VVesxcfRI7zvMUGZHY/DQOWPhgNNr6c4Zrsg0sRGQWUvK1eHTJcSTklIkdhYj+Yi+X4t27I3F/pwCxoxA1OhYiEt3+hDw8szIOReU6saMQUT0mdQ3Em6PaQCHnIrFkvViISFQ/7k/C3M3xMBj5ZUhkzjoGarDwoWh4OinFjkLUKFiISBRVegP+b/1Z/BKbLnYUIrpJfhoHxEztjJZenN2arA8LETW5/LIqTF96HHGpRWJHIaJb5KSUY+GD0egZyvmKyLqwEFGTSs0vx8OLj3IJDiILZieTYO7YtriPg63JirAQUZM5k16MqTFHkVdWLXYUImoAzw4Ixawh4WLHIGoQLETUJPZcysVTy2KhrTaIHYWIGtDYKD/Mu6cdr0Aji8dCRI1ubWw6Xl53mjNPE1mpXqEe+G5yNBwVcrGjEN02FiJqVF/tuoyPt10UOwYRNbJOQa5YNLUznJV2Ykchui0sRNRoPtgSj2/3XBE7BhE1kUg/Zyyd1pVroJFFYiGiRvHOb+ex6ECS2DGIqIm19FRj2aNd4eXMCRzJsrAQUYMSBAFvbjyHpYdSxI5CRCIJdHPE8ke7IsDNUewoRDeNhYgajCAIeHX9Waw8mip2FCISmY+LEsse7YoWzdRiRyG6KSxE1CCMRgEvrT2NNVyKg4j+4uVsj58f744gd5XYUYhuiBNH0B0zGgXMXnOKZYiIarlaUoWJ3x9BRlGF2FGIboiFiO7Yq+vPYN2JDLFjEJEZyiiqwMTvD+NqSaXYUYj+EwsR3ZG5m+Ox6lia2DGIyIyl5Jdj4veHkVdWJXYUoutiIaLb9tWuy/huL+cZIqIbS8zV4sEfjqConGsZknliIaLb8tOhZM5ATUS35EJ2KSYvOorSSp3YUYjqYCGiW7bhRAbe2HhO7BhEZIFOpxfjsaWxqNYbxY5CVAsLEd2SnfFXMXvNKXCyBiK6XYeu5OOFn0+Cs76QOWEhopt2Kq0IT6+Ig97Ib2JEdGc2nc7Ce7/Hix2DyISFiG5KRlEFHl16HJU6HuYmoobx4/4k/Lifax6SeWAhohsqq9LjkZhjyC3lJbNE1LDe//08tp3LFjsGEQsR/TeDUcAzK+JwIbtU7ChEZIWMAjBj1UmcSisSOwrZOBYi+k9vbTyH3RdzxY5BRFasQmfAo0uPI7uYs1mTeFiI6LoW7U/CT4dTxI5BRDYgt7QKTyyLRZXeIHYUslEsRFSvvZdy8d7v58WOQUQ25GRaEd7YwDnOSBwsRFRHemE5nl91Ary6noia2urjafjpULLYMcgGsRBRLVV6A55aHofCck6tT0TieGfTeRxNKhA7BtkYFiKq5a2N53A6vVjsGERkw3QGAU8tj0VWcYXYUciGsBCRyc/H07DyaJrYMYiIkFdWjSd+4ppn1HRYiAgAcDajGK9vOCt2DCIik1PpxfhwywWxY5CNYCEiFJfr8OTyWFTxNzEiMjOLDiRhZ/xVsWOQDWAhIry87jTSCniunojM05xfTuNqCSdtpMbFQmTjVh5NxZazXEeIiMxXgba6ZioQzgVCjYiFyIYl5pbhnd84+SIRmb/DVwrw1a7LYscgK8ZCZKN0BiOeX3UCFTpOk09EluGznQk4lsz5iahxsBDZqM/+uISzGSVixyAiumkGo4AZq06itJITx1LDYyGyQceTC7BwzxWxYxAR3bKMogrM3RwvdgyyQixENkZbpcfMn0/CwMGJRGShVh5Nw76EXLFjkJVhIbIxH2+7yEvsicjivbz2DMqq9GLHICvCQmRD4lILsZSrSBORFeCpM2poLEQ2olpvxMtrT4NnyojIWqw8mooDl/PEjkFWgoXIRny9+zIuXS0TOwYRUYMRBOCltaeh5akzagAsRDbgck4pvt6VKHYMIqIGl15YgY+3XRQ7BlkBFiIrZzQKeGntGVQbuHArEVmnnw6n4Hwm51WjO8NCZOWWH0lBbEqh2DGIiBqNwSjgzY1nIQgcJEm3j4XIihVoq3komYhswrHkQqyNyxA7BlkwFiIrNn/7RZRUcrAhEdmGD7fEo4TLetBtYiGyUuczS7DqaKrYMYiImkxeWTUW8Kg43SYWIiv19m/nOOcQEdmcZUdScS6zWOwYZIFYiKzQ76ezcCSpQOwYRERNzmAU8NbGc2LHIAvEQmRlKnUGTmdPRDbtWHIhtp/LFjsGWRgWIivz3d4ryCji4q1EZNs+2nYRBo4boFvAQmRF8suq8O0ezkhNRHQ5pwxrjqeJHYMsCAuRFfl6dyK01QaxYxARmYVP/7iESh2/J9LNYSGyElnFFVh2OEXsGEREZuNqSRV+3J8kdgyyECxEVuJ/Oy+jSs/1yoiI/mnhnkQUaqvFjkEWgIXICqTka3munIioHqWVeny167LYMcgCsBBZgU93XIKeV1MQEdVr2ZEU5JVViR2DzBwLkYW7mF2KjacyxY5BRGS2KnVGfL/vitgxyMyxEFm4z3de4hIdREQ3sOxQCorKOZaIro+FyIJdyS3D1rOcjZWI6Ea01QYs4hVn9B9YiCzY9/uu8OgQEdFNijmYjNJKndgxyEyxEFmonNJKrI3LEDsGEZHFKKnUY+khztdG9WMhslCL9iejmvMOERHdkh/3J6GCM/pTPViILFBppQ7Lj/C3HCKiW1WgrcbqY6lixyAzxEJkgZYfSUVppV7sGEREFmnJoRQIAgdgUm0sRBamWm/klRJERHcgKU+L3RdzxY5BZoaFyMJsPpOFnFLOuEpEdCcWH0wWOwKZGRYiC/MTV7QnIrpj+xJykZhbJnYMMiMsRBYkPqsEsSmFYscgIrJ4ggAs4VEi+gcWIgvCo0NERA1nbWw6J2okExYiC1FWpcevJzgRIxFRQ9FWG7DmeLrYMchMsBBZiHVx6dByMjEioga14ijnJKIaLEQWYhlPlxERNbjLOWUcm0kAWIgswvHkAly6yqshiIgaw8/H0sSOQGaAhcgCrI3jOW4iosay6XQmtFWc/d/WsRCZuSq9Ab+fzhI7BhGR1dJWG7DlbLbYMUhkLERmbteFHJRw3TIioka1jkfibR4LkZlbz0vtiYga3eEr+cgsqhA7BomIhciMFZfrsOsCFyAkImpsRoG/gNo6FiIztulMJqoNRrFjEBHZBI7XtG0sRGZsA39bISJqMuezSpCSrxU7BomEhchMZRZV4DgnCyMialK82sx2WWwh6tevH2bMmCF2jEaz4/xVCILYKYiIbMuWMzxtZqtuqRBNmTIFEokEEokEdnZ28PLywuDBg7Fo0SIYjRzr0pB2nL8qdgQiIptzKr0YGbzazCbd8hGiYcOGISsrC8nJydiyZQv69++P559/HiNHjoReb3vz5eh0ugbfZ3GFDkeS8ht8v0REdGM8SmSbbrkQ2dvbw9vbG35+fujYsSNeffVV/Prrr9iyZQtiYmIAAMXFxXjsscfg6ekJZ2dnDBgwAKdOnQIAXLx4ERKJBBcuXKi1308++QTBwcEQ/jpPdP78eQwfPhxqtRpeXl546KGHkJeXd91chYWFmDx5MlxdXeHo6Ii77roLCQkJpvtjYmKg0WiwYcMGhIWFQalUYvDgwUhLq72GzW+//Ybo6GgolUo0b94cb7/9dq2iJ5FIsHDhQowZMwYqlQrvvfferb6FN7T7Yg50Bp4vIyISA8cR2aYGGUM0YMAAtG/fHuvWrYMgCBgxYgSys7OxefNmxMbGomPHjhg4cCAKCgoQHh6O6OhoLF++vNY+VqxYgYkTJ0IikSArKwt9+/ZFhw4dcPz4cWzduhVXr17F/ffff90MU6ZMwfHjx7Fx40YcOnQIgiBg+PDhtY7glJeX4/3338eSJUtw4MABlJSUYPz48ab7t23bhgcffBDPPfcczp8/j2+//RYxMTF4//33az3Xm2++iTFjxuDMmTOYNm1aQ7yFtWzn6TIiItHEpRYip6RS7BjUxBpsUHVERASSk5Oxa9cunDlzBmvWrEGnTp3QsmVLzJ8/HxqNBr/88gsAYNKkSVixYoXpsZcuXUJsbCwefPBBAMA333yDjh07Yu7cuYiIiEBUVBQWLVqEXbt24dKlS3WeOyEhARs3bsQPP/yA3r17o3379li+fDkyMjKwYcMG03Y6nQ5ffvklunfvjujoaCxZsgQHDx7E0aNHAQDvv/8+Xn75ZTz88MNo3rw5Bg8ejHfffRfffvttreebOHEipk2bhubNmyMoKKih3kIAQLXeiD0XORkjEZFYBAHYc4nfh21NgxUiQRAgkUgQGxuLsrIyuLu7Q61Wmz6SkpKQmJgIABg/fjxSUlJw+PBhAMDy5cvRoUMHtG7dGgAQGxuLXbt21Xp8REQEAJj28U/x8fGQy+Xo2rWr6TZ3d3eEh4cjPj7edJtcLkenTp1Mn0dERECj0Zi2iY2NxTvvvFPreadPn46srCyUl5ebHvfPfTS0g4l5KOOqy0REotqbcP0hGmSd5A21o/j4eISEhMBoNMLHxwe7d++us41GowEA+Pj4oH///lixYgW6deuGlStX4vHHHzdtZzQaMWrUKMybN6/OPnx8fOrcJlzn+vRrJe2f/v35P28zGo14++23MW7cuDrbKJVK099VKlW9z9cQ/ryQ02j7JiKim7M/IRdGowCptO7PDLJODVKI/vzzT5w5cwYzZ86Ev78/srOzIZfLERwcfN3HTJo0CS+99BImTJiAxMTEWmN5OnbsiLVr1yI4OBhy+Y0jtm7dGnq9HkeOHEGPHj0AAPn5+bh06RJatWpl2k6v1+P48ePo0qULgJoB3kVFRaajTx07dsTFixcRGhp6O29Dg9jH30qIiERXWK7DmYxitA/QiB2FmsgtnzKrqqpCdnY2MjIyEBcXh7lz52LMmDEYOXIkJk+ejEGDBqF79+64++67sW3bNiQnJ+PgwYN47bXXcPz4cdN+xo0bh5KSEjz55JPo378//Pz8TPc9/fTTKCgowIQJE3D06FFcuXIF27dvx7Rp02AwGOpkatmyJcaMGYPp06dj//79OHXqFB588EH4+flhzJgxpu3s7Ozw7LPP4siRI4iLi8PUqVPRrVs3U0F64403sHTpUrz11ls4d+4c4uPjsXr1arz22mu3+jbdlvTCciTlcdp4IiJzsJfjiGzKLReirVu3wsfHB8HBwRg2bBh27dqF//3vf/j1118hk8kgkUiwefNm9OnTB9OmTUNYWBjGjx+P5ORkeHl5mfbj7OyMUaNG4dSpU5g0aVKt5/D19cWBAwdgMBgwdOhQREZG4vnnn4eLiwuk0vojL168GNHR0Rg5ciS6d+8OQRCwefNm2NnZmbZxdHTESy+9hIkTJ6J79+5wcHDAqlWrTPcPHToUmzZtwo4dO9C5c2d069YNn3zySYMPnL6eA5d5dIiIyFzsTWAhsiUS4XoDcKxMTEwMZsyYgaKiIrGjXNezK0/gt1OZYscgIiIAcqkEJ94YDCel3Y03JotnsWuZWaPDVzg7NRGRudAbBRy4zO/LtoKFyExczilFbmmV2DGIiOgfjiUXiB2BmojNFKIpU6aY9emyQ4n8LYSIyNzEphSKHYGaiM0UInN3JIm/hRARmZtzmcWo1NW9upmsDwuRmTiRWiR2BCIi+hedQcCptCKxY1ATYCEyA7mlVcgoqhA7BhER1eM4T5vZBBYiM8DfPoiIzBfHEdkGFiIzcJKFiIjIbMWlFl53zUyyHixEZoCFiIjIfBWV65CYWyZ2DGpkLEQiEwQBp9KLxI5BRET/4WxGidgRqJGxEIksMVeL0kq92DGIiOg/xGexEFk7FiKRnebRISIisxefXSp2BGpkcrED2LqLVvyfzFhVjqJ9y1CecAjG8mIoPJvDddBjsPcJAwCUXzyI0pNbUH01EcaKEvhM+R8UXs3/c5+CQY/iw2ugPbsT+tJ82Ln5wbXfVDg0jzZtU3ZuF4r2LIGgq4S63RC49p9muk9ffBVXV78On4c/g9TesXFeOBFZHR4hsn48QiSyS1ettxDlb/0Clckn4TFyFnymfQllSBSurnoN+tI8AIBRVwl7/9bQ9H34pvdZtO8nlJ3cArdBj8P30W/gFDUcuevfR/XVRACAobwYBVu/gGv/afC8/x2Und2J8sRjf2fa9jVc+05hGSKiW5JbWoW8Mq43ac1YiESWkGOdVy4YdVUov3gAmv5ToQyIhJ2rLzS9JkGu8ULpiS0AAHXkAGh6ToBDcIeb3q/23C64dL8fDi06w07jDaeo4VCGdETJ0fUAAH1RNiT2jlC16gN7nzAoA9tBl5da89jzuyGRyeEY3qPBXy8RWb8LWdb7CyyxEImqvFpvvTNUGw2AYIREZlfrZolcgar0c7e9W0GvA2SKOvusTD8PAJC7+UHQVaH6aiIMFaWozroERbNgGCpKUbRvOdwGP3Hbz01Eto2nzawbxxCJ6HJOGax1ri+pvSPsfSNQfHAV7NwDIFNpoI3fi+rMS5C7+d72fpUhHVF6bAOUAW0gd/VBZfIpVCQcgSDULL4oU6rhMWIm8jZ9AkFfDVXkADg0j0be5s/gFD0S+uKryFn7LmDUw6XnRKgiejXUSyYiK8dCZN1YiER06ap1ni67xn3kLORv+RwZXz8MSKRQeLeAqnVf03if2+E26DHkb/0CmT88CQCQu/pA1XYQtGf+MG3jGNYDjmF/nxarTD0NXW4K3AY/gczvHoPHqDmQqVyRtfQFKAMiIVNpbjsPEdkOTs5o3ViIRJSQY93no+1cfeA98UMYqythrC6HXO2G3F/nQe7iddv7lDm6wHPcaxD01TBUlECmdkfRnpjr7lPQ61Cw/Ru4j5wFfWEWBKMBysC2Nfnc/FCVdRGOoV1vOw8R2Y7UgnKxI1Aj4hgiEV228iNE10gVSsjVbjBUlqEiKQ4OLbvd8T4lcgXkTh6A0YDyiwfh0LL+UlN0cBWUzaNh7x0KCMaasU1/EYx6wGi84yxEZBsKy3UoqdSJHYMaCY8QiSjFyn/bqLgSC6BmoLO+MAuFuxfBzs0P6raDAACGilIYSnJhKMsHAOgK0gEAMpUrZGpXAEDepgWQObnDte8UAEBV5kUYSvNh59UchtI8FB9YAQhGuHS9p87zV+emoPzCXvhM+eKvHP6ARIrSU9shU7tCl58OhU/LRn0PiMi6pOaXI9LPRewY1AhYiESUUWilV5j9xVhVjqK9S6AvzYNM6QTH8B7Q9JkMiazmy67i8hHkb/7MtH3exo8AAC49J0DTaxIAQF+SC0j+PpAp6KtRtO8n6IqyIVU4wKF5NNxHzIJUqa713IIgoGDbl3AdMB1ShRIAILWzh/vwGSjY8Q0Egw5ug5+oOcpERHSTUliIrJZEEKz1OifzVqCtRsd3d4gdg4iIbsGLw8LxVL9QsWNQI+AYIpFkWuv8Q0REViw137qHOtgyFiKRpFv56TIiImuUwkJktViIRGK1M1QTEVkxfu+2XixEIuEpMyIiy8MFXq0XC5FIrP0KMyIia1RebUB5tV7sGNQIWIhEklNaKXYEIiK6DbmlPEpkjViIRFJYztlOiYgsEU+bWScWIpEUlleLHYGIiG5Dbim/f1sjFiIRGI0CSip4hIiIyBLxCJF1YiESQXGFDkbOD05EZJFYiKwTC5EIeLqMiMhy5Zfxe7g1YiESAQsREZHl0lbxsntrxEIkgkItxw8REVkqLechskosRCIoqWQhIiKyVOXVBrEjUCNgIRJBpc4odgQiIrpNLETWiYVIBFV6/mciIrJUHENknViIRFCl5xEiIiJLVaHjL7XWiIVIBNUsREREFktbxUJkjViIRMBTZkRElquCV5lZJRYiEVRxUDURkcXSGbjUgDViIRJBtYGFiIjIUhkFFiJrxEIkAo4hIiKyXCxE1omFSAQSidgJiIjodnFxbuskFzuALZJJ2YjIunXUlOABhzPwSsuAZ0IeJJdTAAMvJiArIZEAH44QOwU1MBYiEcilPDBH1i2uyBlxRT0xPDwbir6HcTq/EINK/BGd5QC/y0WQX0yGUM1FjslCyWRiJ6BGwEIkAh4hIluxOdEbdklj8GR4K+SGnMbLrqegj9BDPUKBQWUt0PWqEwKvlEFxPglCRYXYcYluioTjHqwSC5EI5CxEZEN0Rgn+Fx8Ob/sQ/F/LQJxxTcJvxeewwSkBG5wAhAKKwTL0Lw9Dj1wNQpIq4HA+BUJJidjRierHI0RWiYVIBDxCRLYou0qB2We7oqNLG3wV4IydrkVYX3wB1cZqVEsM2Ka6gm0qAMGApB/QsyoEfXI90DKlGk7xaTDmFYian8iEwx6sEguRCHiEiGxZXLEaE4qH417vq1ih0WG9qxy/lF5ClaHKtI0gAfYr07A/IA0IANALiK4OQL98L0SkGqG5kAkhM1u8F0E2TcIjRFaJhUgEMv52QYRfsr3wS/bDeDX4EjZIsrDSOwxrShNQYaisd/tYRRZifbIAHwBdgQidNwYV+qJNugQeF65CSElv2hdANkuqVosdgRoBC5EIHBQsRETXzE0Ow+eyF/Cp7CimVFzAT4FtsbosEeX68v983AW7PFzwzAM8AXQEAg0eGFLkj/YZdn9f6m+0rUlQVxUWYlVRETL0OgBAqEKBJ9090Oc/foD/VlKMRQUFSKmuhloqRS+VGi96ekLz11GQg1ot3r2ajXyDAQPVarzt7QPFX4OKSw0G3J+SjB8DAuFrZ9f4L9BMyJycxI5AjUAiCJxys6mtPJqKV9adETsGkdkJdKjEN/5/wLfoTyxtEY2V2kRob1CMrsfToMbg0gBEZyrhe7kI0otJgN66F+XcVVYKKSQIUigAABuKi7GoIB9rg0PQ0t6+zvax5eV4OC0VL3l6or9Kjat6Pd6+mo0ghQJf+PnDKAjok3gZj7q5o6dKhZmZGZioccVEV1cAwFvZ2QhWKDDFza1JX6fYHKKjEbx8mdgxqIHxCJEInJR824nqk1qhxIiEkejl1hsLctZiStFV/BTWAyvKk1CqK7ulfeXIyrBcE4/lGgCtASejPYZow9E5W4XAxFLYXUiCUFH/6TlL1V9d+8jFjGbNsKqoEKcrKuotRKcqK+BnZ4eHXGsKjb9Cgfs1GiwqqBnAXmgwoMBgwASNBvZSKfqr1UisrhnrFVdejnOVlXjdy6uRX5X5kfGUmVXiT2YROCtt59Ay0e3YX+CCrgXT8LDvULyUvhQPl2VgWXhPLKtMQUl16W3ts1RahbVOF7HWCUBLwH6oDAPKw9AjxwXBVyqgjE+GUHprpcucGQQB20pLUSEIaO/gUO82UQ4O+DwvD3vKytBHpUK+wYDtpaXoo6r5ge8mk6GZTI4D5Vr0cFQhtrwCY1ycUS0IeOfqVbzn4w2ZDc7JI+UpM6vEU2YiOJFaiLFfHxQ7BpFFkEgEvBdyDuNLYlCpK8WKiF5YWpmOouriBn0eGSToVRGA3rnuCE2phup8KoSCwgZ9jqZwqaoSE1JSUC0IcJRK8ZGPL/r+xxGNbaUl+L+sbFQLRugB9Fer8ZmvH+z+Kjqx5eWYl5uDQoMBfVQqvOzphe/z81FiNOAeFw3eys5GocGASa6umPTXqTRrp5kwHj5vvil2DGpgLEQiSMwtw8AFe8SOQWRRXOz0+DL4IHpd/QkVEglWhvfG0uoMFFQVNdpzdq70Rb98T0SkGuB8IQNCdk6jPVdDqRYEZOl0KDXWHO1ZW1yMJQGBCK3nlNnlqio8kpaGyW6u6OWoQq5Bj/k5uYh0UOI9b596959cXY0n0tOwNjgEk1NTMNnVDb1UKoxJTsKP/gEIVyob+yWKzn36dHjOekHsGNTAeMpMBBxDRHTrinVyPJTQB2GqzvjadzOmndmAiXb2+DmiDxZXZyG/quGP5hxTZuKYXybgB6A70LraB4MKfdAmXQK3C9kQUjMa/DnvlELy96DqSKUDzlZW4qfCQrzt7V1n2+8L8hHl4IBH3NwBAOEAHLykeCgtFc97NEMzee3vVYIg4M3sLLzo6QlBEBBfVYUhTk5wkErRycERxyrKbaIQSZ15yswa8SezCDiGiOj2XdI6YFDCPRji0R8fqlfh4dNb8YCdA36J6IPF+hzkVOY32nOfV+TivFcu4AUgGgjW11zq3y7TDp6XcoHEVLO71F8AoBPqz1RpNNYZA3TtcwF1Tx6sLS6GRibDALUTig0GAID+r5MMeggw2sj5Bl52b51YiESgtJNBaSdFpc68vnESWZLteW7YnvcUngkYjmf1S/DgmW24X2aPta36YpExH9kVuY2eIVlehO88igAPAO0AT6MThhUHISrLHj4JhZBeSm7SS/0/zc1Fb5UKPnZyaI1GbC4pxbHycnznHwAA+CQ3Bzl6PT708QUA9FOr8WZ2NlYVFqKnSoVcvR4f5uagrVIJT3ntX9zy9XoszM/D8sAgAICLTIbmCgWW/vXYw9pyPPbXkSZrJ1WzEFkjjiESSe+P/kRaAVf3JmoIdlIB80JO4u7CGEjLc6GTKbA+oi9+FAqRWSHeuB8XoxJDyoLQOVsF/8QS2F1IhlDZeJf6v5adhcNaLXINBjhJpQizt8ejbu7ooVIBAF7NykSGToclf5UaAFhWWIDVRUXI0OngJJWhq6MjZjVrBq9/TbQ4OzMDUQ6OtQZOn66owKvZWcjX6/GQqxue8vBotNdmTgK+/w7q3r3FjkENjIVIJPd+cxDHUyzvChYic+Zpr8PXQXsQnbkCEn0ldFI7bIzoix8kJUgvF3/tM6UgxyBtELpddUFwkhb28ckQyrRix6JbFPLrr1CGh4kd47b069cPHTp0wGeffSZ2FLPDNSRE4ulc94oPIrozOVV2uPfSIIyV/g+p/qMgN+pxz/k/8Ft8HN5VtkSQylfUfJUSPTapE/Faizg8OOgiJjyjw8LnQ3F2YmdU9GwHiatG1Hx0c+y8G3YyypycHDz++OMIDAyEvb09vL29MXToUBw6dAgAIJFIsGHDhgZ9zoY0ZcoU3H333WLHuGMcQyQSTyfrvxKDSCwnS9ToUzIB47wG4i375XDOOYa743dilESGzeF98L28Ekla8a8Q00uM+NMxGX8GJQNBgKQ30LUqCH3zmyE8xQCnC+kQrjb+WCi6eRJHR8hcXBp0n/fccw90Oh2WLFmC5s2b4+rVq9i5cycK/poxnJoGjxCJxMeFhYiosa276ol2qTOx0Ost6FxCIBMMGHVhFzacO4J59s3RQu0vdsRaBAlwWJmBeX4nMa3HGdw3rRDvzfLFwanRKBocDUmAuEe4CLDz9GzQ/RUVFWH//v2YN28e+vfvj6CgIHTp0gWvvPIKRowYgeDgYADA2LFjIZFITJ/Xd1RmxowZ6Nevn+lzrVaLyZMnQ61Ww8fHBwsWLKjz/NXV1XjxxRfh5+cHlUqFrl27Yvfu3ab7Y2JioNFosG3bNrRq1QpqtRrDhg1DVlYWAOCtt97CkiVL8Ouvv0IikUAikdR6vCXhESKR+Grqn0qfiBrehylh+Fz2Dj4LOY7BeUsgrSzC8Au7cRck2B7eG98pjLhUlip2zHqdVuTgtHcO4A2gE9BC74nBhX5omyFDs4u5QFIqwKGgTUbuU3c+pzuhVquhVquxYcMGdOvWDfb/mkDz2LFj8PT0xOLFizFs2DDIZLKb3vecOXOwa9curF+/Ht7e3nj11VcRGxuLDh06mLaZOnUqkpOTsWrVKvj6+mL9+vUYNmwYzpw5g5YtWwIAysvLMX/+fPz000+QSqV48MEHMXv2bCxfvhyzZ89GfHw8SkpKsHjxYgCAm4Uu9stCJBJfDY8QETWlCoMMj1/uCn9lB3wT8AciM36GxKjD0It7MQQS/NmyF751AOJLU8SO+p8S5QVIbFYANAPQAfAxaDC0JBBRGQp4Xy6AJCGlSS/1tzV2vg17lE4ulyMmJgbTp0/HwoUL0bFjR/Tt2xfjx49Hu3bt0KxZMwCARqOBdz2Ta15PWVkZfvzxRyxduhSDBw8GACxZsgT+/n8fFU1MTMTKlSuRnp4O379e1+zZs7F161YsXrwYc+fOBQDodDosXLgQLVq0AAA888wzeOeddwDUFDoHBwdUVVXdUj5zxEIkEh4hIhJHeqU9RiWMQE/XXljgtg7eGTsggYCBCfswEMDu0J74ViXH2ZIksaPelCxZKWJczyHGFUAk4Gp0wJCSIHTOdoRfYjHkF5IgVFeLHdNqKPwb/jTrPffcgxEjRmDfvn04dOgQtm7dio8++gg//PADpkyZclv7TExMRHV1Nbp37266zc3NDeHh4abP4+LiIAgCwsJqXzFXVVUFd/e/55RydHQ0lSEA8PHxQU6O+S9jc6tYiETi7azk5IxEIjpQ6IJuhVMx2XcoXpYuhWPeaQBAv8sH0A/AvhbdsVCtxOmSRFFz3qpCaQVWay5gtQZABOB4lx0GaVuhW44Tgq5ooTifBKG8XOyYFsuuEQoRACiVSgwePBiDBw/GG2+8gUcffRRvvvnmdQuRVCrFv2fN0el0pr/fzIw6RqMRMpkMsbGxdU7Fqf+xILDdv+akkkgkN7V/S8NCJBKJRIJgdxUuZJeKHYXIpi3N9MVPkpfwTvB5TCiLgby05uqz3omH0BvAwZCuWOiiwoniy+IGvU3lUh02OiVgoxOAFoB8kBT9y1uiZ64GzZMr4XA+BUJxidgxLYadX9MMxG/durXpUns7OzsY/loq5ZpmzZrh7NmztW47efKkqbyEhobCzs4Ohw8fRmBgIACgsLAQly5dQt++fQEAUVFRMBgMyMnJQe87mGhSoVDUyWeJeJWZiFo0U994IyJqdIIgwetJbRBV+AH2BjwJQfH3/80eSUew9OSf+EHwRCeXliKmbBh6iRE7VEl4K/gEJveLx/1PluPzmSE48VBnaPt0gNTDNpbfuF12/n4Nur/8/HwMGDAAy5Ytw+nTp5GUlIQ1a9bgo48+wpgxYwAAwcHB2LlzJ7Kzs1FYWDOh74ABA3D8+HEsXboUCQkJePPNN2sVJLVajUceeQRz5szBzp07cfbsWUyZMgVS6d8/9sPCwjBp0iRMnjwZ69atQ1JSEo4dO4Z58+Zh8+bNN/0agoODcfr0aVy8eBF5eXm1jlRZEh4hElHzZiqxIxDRP5Tq5Zic0Bthqk74KmArQtPXQSLU/ObbNfk4ugI4HhSNhW6uOFJ0SdywDUSQAAeUaTjgnwb4A+gJdKj2x4B8L7RKE6C5kAUhI0vsmGZB4ugI+V+DnBuKWq1G165d8emnnyIxMRE6nQ4BAQGYPn06Xn31VQDAggUL8MILL+D777+Hn58fkpOTMXToULz++ut48cUXUVlZiWnTpmHy5Mk4c+aMad8ff/wxysrKMHr0aDg5OWHWrFkoLi6u9fyLFy/Ge++9h1mzZiEjIwPu7u7o3r07hg8fftOvYfr06di9ezc6deqEsrIy7Nq1q9bl/5aCS3eIaP2JdMxcfUrsGER0HYM9CvCh089wz9pb574TAVFY6OGBg0UXRUjWtFrq3TG4wA9tM6Rwv3gVSE63yUv9le3bIWT1arFjUCNhIRLRybQi3P3VAbFjENENPBmQjBmGJbAvqFt+Tvu3x8JmXthXdEGEZOLw17tgSLE/OmQq4JWQD8nlFMAKxpDciOa+++Dz7jtix6BGwkIkopJKHdq9tV3sGER0E+ykAuaFnMTdhTGQltddTuOcbyQWevlhd1G8COnE5W50xNCSIERnOcA3sQiyC0mAhY4j+S9er70GtwcniR2DGgkLkcg6vfcH8sqqxI5BRDepmUKHr4L2onPWCkj0FXXuv+DTGt/6BGJnYTwE2Oa3V7WgwODSIHTJcULglbKaS/0r6r5Xlibop6Vw7NxZ7BjUSFiIRPbQj0ewLyFP7BhEdIvaOZfhC89NCEz/DZJ6ik+CVzi+9WuOHUXxMAoNO99Y/p/5KPizALq8mqMw9n728BzjCad2TvVuXxZfhuR5yXVubzm3Jex9a5aKKDtbhsyfMqEv0cO5ozN8p/pCKq+5IslQbkDi24kIfjEYCnfFLedVCDL0Lw9CjxwNmidXQHkuGUKp5U05Enb0CGTOzmLHoEbCQiSyeVsv4JvdljXxGxH97W6vHLyjXAnnq0fqvT/RMwzf+odiW9H5BitGJSdKIJFKoPCqKSdF+4uQtyUPLd5pAaVf3WWBrhWilh+2hFT592XXcmc5JFIJBKOAC89fQLMRzaBuq0bal2lwG+gG90E1l+BnxGTA3tseHsM8GiS/DBL0rAhA7zx3hKZUwyk+DcY8817ZXe7jg5a7/hQ7BjUiXnYvsrZ+LmJHIKI7sOGqJzbgecwJSsBjlUtgV3yl1v0tci7ho5xLeLJZC3wf0Bqbi+JhEO5sALJzVO2jFF73eqFgVwHKL5fXW4iukTvJIVPVXRzUUGaAodQAtwFukCqkcIpyQlVmzal8bYIWFckV8J3ccGt4GSBgr0Mq9gakAgEAegHRVQHoX+CFiFQjXOIzIGRdbbDnawjKfy1vQdaHhUhkLERE1uHjlJb4UvY2Pgk5jqF5SyGtLKx1f0huIubmJuJJ92B8F9QWm4rioRfufBFWwSig+GgxjFVGOIY6/ue2l9+8DEEnwN7XHs1GN4O6Vc0ElDInGeQaOcrOlUHdRg3tJS1ce7rCqDcic0km/B/xh0QqueOs/yXWPguxPlmAD4CuQGudDwYW+qBNuhTuF7IhpKQ36vPfiP0/1gAj68RTZmagwzvbUVRufVdkENkqf2UVvgnciciMnyEx1L+warpbIH4Ibotfi+OhN956MapMq8SV967AqDNCai9FwBMBcGpf/xiiqqwqaC9q4RDsAEEvoOhgEQp2FSDk5RCowmsmiNVe0iJ7ZTb0pXo4tXOCz0Qf5P6eC4PWANe+rshcnAl9mR7ug9xNp9KaUpBegyFF/miXaQevS3lAYgpgbLq1IH0XzIfLiBFN9nzU9FiIzMCDPxzB/sscWE1kbbq7FuMTt/Xwybj+9BpZrgH4IaQd1hdfgM54878YGfVG6PJ1MJYbUXy8GIV7CxHycsh/njL7p5RPUwAJEDQjqN77q7KrkPJpClq83QJJHyTBfYg7nNo6IeH/EhDyYgiUATf3PI3F06DG4NIARGcq4Xu5CNKLSYD+zo+4XU/zTb/BPjS00fZP4uMpMzMQ6efCQkRkhQ4VuqB74RQ85DsYL8uWQ5V7ss42PoVpeL0wDdM1flgU0gHrSi+hynDjqTikcinsvWquEHMIcUBFUgXyd+TDb8rNrbXl0MIBxYeK671PEARkLM6A93hvQAAqUyrh0skFUnspVOEqaC9oRS9EObIyLNfEY7kGQGvARVBiUGkQumSrEHClFHbxSRAqKxvkuaSOjlCEhDTIvsh8sRCZAY4jIrJuP2X6YZlkDt4OjsfEssWQl2bU2ca7KAOvnsjAdGdvLGoRjV9KL6HyJoqRiQAIups/4F+ZWgm5pv4fAYV7CyFXy+Ec5QyDtmYAuGAQTH8KRvM7sVAsqcRa54tY6wwgDLAfKsPA8nB0z3FG8JUKKOOTIZSW3da+HTq0h0RWdzA6WRcWIjMQFagROwIRNTJBkOCNpNb4WP4Bvgg5jL45P0FSVXcunmYl2XjpxO94RO2JJS07Y3XZZVT8awLI7F+y4dTWCXZudjBWGlF8pBjaC1oEzwquuX9NNvSFevg/5g8AyNuWB4WHAvZ+9hAMNWOISo6XIOCZgDrPry/RI3djLpq/1hwAIFPJYO9rj/zt+VBHqqGN16LZqIZd4LQxVEkM2KxKxOYQACGAbKAEvStaoHeuG1okV0N1PgVCYdFN7cshqmOjZiXzwDFEZqL3R38ircDyZ3IlopvTUlWBr3y3omX6Okj+4zL8ApUHlrTsilXaRJTrywEA6T+mQ3teC32xHlIHKZQBSjQb3gzqyJqrxtK/T0d1XjWav1JTanI356JwdyF0hTpIFVLY+9mj2Yhm9Q7CTvsmDY4tHWsNnC6/Uo6M7zOgL9HDfYg7PMd4NuRbIZouVX7om9cMEakGOMdnQLiaU+92gYt+hKpHjyZOR02NhchMzPr5FNbGiXtZKRE1vYHuBfjIeQ3cs/b853ZFjm5YGtYdK8uvoEynbaJ0tiWy2hMDC3zQOh1wu5gNITUDkMkQfvQIpCqV2PGokbEQmYmfj6fhxV9Oix2DiETyZEAKnjcshbLgvxeHLXFwwbKwnlhWkYxS3e2NiaGbE6zX4AFpVzw4db7YUagJSG+8CTWFbiFNP68HEZmPb9KC0Cbr//CL70swqK5/Ssq5ohhPndqM7anpeMY5Ei4Krq3VWJLlRciK9BI7BjURFiIzEejuCB8XcS9jJSJxGQQpZl9pj26lH+NIwKMQ7K4/87S6sgSPn9qMbckpeN65DVwVvFq1MXT25ur2toKnzMzIjFUnsOFkptgxiMhMtHXS4guv3/D7+l8w/2AVskoFtPGU4rOhSvQOqn2RcLlChdURvRFTnYUru5KQtyUPVVerIHOQQd1WDe/x3pCrax7TmCvbWxOZRIZ94/fBSVH/DOBkXXiEyIx0bc7TZkT0tzOlKnT+zRfPbdPhhRGtcOJxFXoHynHX8nKkFtdetsKxWoupp7fijZ1nkfFDBgIG+KHl+y0R8HQAKpIqkLGoZu4jwSgg7ds0uPV3Q/PXmqPiSgUKd/+97lr2z9lw6+9m82UIACLcIliGbAgLkRnp2cJD7AhEZGZKjm2AY9shWOD/Ef5oMxcfjW+NABcpvjlW/xppJ1LKEeICnAktxRsh7RHSLghu/dxQkVwzrcc/V7ZX+inrXdnefQh/OQOArj5dxY5ATYiFyIwEujuiRTNe2klENQSDDtXZl+EQEgUAWJDaAu1z30ZY+044mFn/6vM9AmRILxGwM74UE09vRUzcGWjidPDqWDNQ+58r2xurjdBeqlmG49rK9n4P+zX6yvaWoq9/X7EjUBPiTNVmZmArLyTmXhE7BhGZAUN5CSAYIXV0Nd1WYZBhry4ClSXpOBUwFu0yV0Ni+PtoUY8AOZaPc8ADv1SgUg/ojcDocDlW9nDGZsdw/IAilD9VjuyV2changWndk5w7e2K3N9zoW6thkQhwZX3roi6sr050Nhr0L5Ze7FjUBNiITIz/cM98d1eFiIi+pvk3wdsBAF6QYoxCXehq6YnPvXeAN+MrQCA87kGPLelEm/0scfQUDmySgXM2VGJZzeW4kfJDtwtleO39v3wfYcSpJVnA6hZ2b7oYFG9K9urwlWiL+Qqht5+vSGTcv0yW8JTZmamc7ArnJTsqUQEyBydAYkUBm1hrdsN5UWQqTQAgCNFzuiROBmvui6AtlkHfLC/Gj0DZZjT0x7tvGQYGirH1yOUWHRSh6xSI+RGPcae/wO/nY/F+8pQBDn61LuyvdxZblrZ3hb1DeDpMlvDQmRm5DIp+oSZ/8KJRNT4JDI7KLxDUZF8stbtlcknYe8XUeu2FVk+iEyfg3hZOCT/mr9I9tcRpn/OsSITDBgd/ydG/3QA7V1c0KF3a+CvC9fMfWX7xiaXytHTt6fYMaiJsRCZoQHh1rFwIhHdOefOd6Ps1HaUnd4OXV4aCnZ+D31JLpw6DAcAFO6JQd6mBQAAQZAg2WcQ1pzRYsbl7kjUOuBAqh7Pba1EFz8pfJ1qf8vP0Roxd28F1vQsxfqzh/GJayic/FXI356P8svl0MZr4djy+pNDWqtOXp2gVqjFjkFNjOdmzFC/8GaQSgAb/MWMiP5F1aoPjBWlKDqwCgZtARQeQfC87y3IXWp+cTKUFUJfkmvaXt12EIzVFfh66xZ8UVIId0c5hgTJMW9g3XmFnt9aidk97OHnLAUEI4Zd3IMdw4D7NmuR/kchPO7ygGNz2ytE/QL6iR2BRMCZqs3U/QsP4WhygdgxiMgKDHAvxEcua+CRufumHyNAgl0te+JbBynOlyY3WjZztGXcFvg7+Ysdg5oYT5mZqZHtfcSOQERW4s98V3S68hjmenyISvfWN/UYCQQMSNiP1af34iupP9o6N2/klOahhUsLliEbxUJkpoa39YGMk6MRUQP6Lj0QbTJfxc++L8GguvlV3PskHsSKU7uxUOKL9s4tGjGh+AYGDRQ7AomEp8zM2IM/HMH+y3lixyAiK+Su0OGroH3omr0CEl35LT32UEgXLHRRI674ciOlE8+vd/+K5i62cTSMauMRIjM2iqfNiKiR5FfbYXzCAIzG50jyvxuC5OZ/HHRPOoolJ//EIqMnuriENWLKphXhFsEyZMNYiMzYsDY+UMj4T0REjedMqQr9L9+PZ9Wfoci7+y09tnPKcfx48g8sMXiguya8kRI2neEhw8WOQCLiKTMz90jMMey8kCN2DCKyEbOCEvF4VQwURYm3/NiTAR2w0MMTB4ouNEKyxiWBBNvv3Q5vlbfYUUgkPPxg5ka19xU7AhHZkAUpLdA2521s8p8Jo8OtLezaIe0kFp7YjpXVLuiradVICRtHF+8uVl2G+vXrhxkzZjTpc06ZMgV33313kz7nnWAhMnND23jDyZ7zZxJR06kySvHM5c7oWbEAJwMmQ5DZ39LjIzPO4MsT2/BzpRoDXFtDAvO/YnZ06OgG29eUKVMgkUjw4Ycf1rp9w4YNkNRZqZfMBQuRmXNQyDC6A48SEVHTy6pU4O6EYXjA7nNk+g275ce3yjqPz+O2Yk2FIwa7tjHbYuQod8SgwEENuk+lUol58+ahsLDwxhtTo6qurr6p7ViILMCELoFiRyAiG3a0yBk9EifjZdcFKGsWdcuPD8+OxydxW7CuXIlhrm0gvYUr2prC4KDBcLRr2CVKBg0aBG9vb3zwwQf/ud3atWvRpk0b2NvbIzg4GAsWLKh1f3BwMObOnYtp06bByckJgYGB+O677/5zn1qtFpMnT4ZarYaPj0+dfQI1JeHFF1+En58fVCoVunbtit27dwMAiouL4eDggK1bt9Z6zLp166BSqVBWVgYAyMjIwAMPPABXV1e4u7tjzJgxSE5Ovm6uqqoqPPfcc/D09IRSqUSvXr1w7Ngx0/27d++GRCLB77//jvbt20OpVKJr1644c+ZMrf0cPHgQffr0gYODAwICAvDcc89Bq9XWes/ee+89TJkyBS4uLpg+ffp/vl/XmNdXJdUr0s8FkX7OYscgIhu3KssHkWlzsMjndeidA2758aFXL+LjuC1YXybHCNdIyCSyRkh568aEjmnwfcpkMsydOxdffPEF0tPT690mNjYW999/P8aPH48zZ87grbfewuuvv46YmJha2y1YsACdOnXCiRMn8NRTT+HJJ5/EhQvXH7g+Z84c7Nq1C+vXr8f27duxe/duxMbG1tpm6tSpOHDgAFatWoXTp0/jvvvuw7Bhw5CQkAAXFxeMGDECy5cvr/WYFStWYMyYMVCr1SgvL0f//v2hVquxd+9e7N+/H2q1GsOGDbvuEZkXX3wRa9euxZIlSxAXF4fQ0FAMHToUBQW1l6maM2cO5s+fj2PHjsHT0xOjR4+GTqcDAJw5cwZDhw7FuHHjcPr0aaxevRr79+/HM888U2sfH3/8MSIjIxEbG4vXX3/9uu/VP/EqMwux7HAKXttwVuwYREQAAJXcgC9CjqB/zlJIqkpuax8pHs3xXWAENhddgF7QN3DCmxOqCcX6MesbdJ9TpkxBUVERNmzYgO7du6N169b48ccfsWHDBowdOxbXfuxOmjQJubm52L59u+mxL774In7//XecO3cOQM3Rjt69e+Onn34CAAiCAG9vb7z99tt44okn6jx3WVkZ3N3dsXTpUjzwwAMAgIKCAvj7++Oxxx7DZ599hsTERLRs2RLp6enw9f17SMagQYPQpUsXzJ07F+vXr8fkyZNx9epVODo6oqSkBF5eXli7di2GDx+ORYsW4aOPPkJ8fLxpXFR1dTU0Gg02bNiAIUOG1HoftFotXF1dERMTg4kTJwIAdDodgoODMWPGDMyZMwe7d+9G//79sWrVqjrZY2JicP/992Py5MlwcHDAt99+a8q9f/9+9O3bF1qtFkqlEsHBwYiKisL69bf278ojRBZiTAdfOCrM47cpIiKtXoZpCT0wUPcZLgQ8AEF66xd/BOVdwftxm7Gx2IBxrm0hv4193KkJERMadf/z5s3DkiVLcP78+Tr3xcfHo2fPnrVu69mzJxISEmAwGEy3tWvXzvR3iUQCb29v5OTUPx1LYmIiqqur0b3733NKubm5ITz873mi4uLiIAgCwsLCoFarTR979uxBYmLNdAsjRoyAXC7Hxo0bAdSc2nNycsKQIUMA1Bzdunz5MpycnEyPd3NzQ2VlpWkf/86l0+lqvV47Ozt06dIF8fHxtbatL/u1bWJjYxETE1Mr99ChQ2E0GpGUlGR6XKdOnep9f/4LL1+yEE5KO4xo64M1sfUfeiUiEsOVciWGJYxBP7c++FizFs0y/7zlfQTkp+Dt/BQ85haIH4Lb4tfiC9AZdY2QtjYnhRNGNh/ZqM/Rp08fDB06FK+++iqmTJlS6z5BEOpcdVbfSRs7O7tan0skEhiNxnqf72ZO+hiNRshkMsTGxkImq/2LtlqtBgAoFArce++9WLFiBcaPH48VK1bggQcegFwuN+0jOjq6zmk1AGjWrNl1c9X3em/myrtr2xiNRjz++ON47rnn6mwTGPj3eFuVSnXDff4bjxBZkAldObiaiMzT7gJXdL7yKN7zmIcK9za3tQ+/glS8Gfc7NudX4QHXtlBIFQ2csra7Q+9u8MHU9fnwww/x22+/4eDBg7Vub926Nfbv31/rtoMHDyIsLKxOUblZoaGhsLOzw+HDh023FRYW4tKlS6bPo6KiYDAYkJOTg9DQ0Fof3t5/z8U0adIkbN26FefOncOuXbswadIk030dO3ZEQkICPD096+zDxcWl3lwKhaLW69XpdDh+/Dhatao9Z1V92SMiIkzPe+7cuTrPeW3/d4KFyIJ0DHRF+wCN2DGIiK7rh/QARGa+gtW+L8NwmxMdehel47W437ElT4tJmrawv8V5kG6GVCLFhPDGPV12Tdu2bTFp0iR88cUXtW6fNWsWdu7ciXfffReXLl3CkiVL8OWXX2L27Nm3/VxqtRqPPPII5syZg507d+Ls2bOYMmUKpNK/f9yHhYVh0qRJmDx5MtatW4ekpCQcO3YM8+bNw+bNm03b9e3bF15eXpg0aRKCg4PRrVs3032TJk2Ch4cHxowZg3379iEpKQl79uzB888/X+8gcpVKhSeffBJz5szB1q1bcf78eUyfPh3l5eV45JFHam37zjvv1Mru4eFhmuDxpZdewqFDh/D000/j5MmTSEhIwMaNG/Hss8/e9nt2DQuRhZneO0TsCERE/8kgSPHSlXboUjoPhwIeg2B366cvAMCzOAsvn/gdW6+WYLKmLRxkygbL2MuvFwJu40q52/Xuu+/WOZ3VsWNH/Pzzz1i1ahUiIyPxxhtv4J133qlzau1Wffzxx+jTpw9Gjx6NQYMGoVevXoiOjq61zeLFizF58mTMmjUL4eHhGD16NI4cOYKAgL/fE4lEggkTJuDUqVO1jg4BgKOjI/bu3YvAwECMGzcOrVq1wrRp01BRUQFn5/qviv7www9xzz334KGHHkLHjh1x+fJlbNu2Da6urnW2e/755xEdHY2srCxs3LjRdPSnXbt22LNnDxISEtC7d29ERUXh9ddfh4/PnS+GzqvMLIzBKKDPR7uQUVQhdhQiopvSxkmLL71+R3DGRkiE+se+3IwClQdiwrpidVkiyvXld5Rp4aCF6OnX88YbUpO5dpVZYWEhNBpNkz8/jxBZGJlUgqk9g8WOQUR0086VqtD/8v14Wv0Zirx73PZ+3LR5eOHE79iWkYNHXdpCJb+98T/BzsHo4Xv7Ocg6sRBZoPFdAuGk5AWCRGRZNud6oEPyM/jc8z1Ua0Jvez+a8gI8f/J3bEvPxuMubeFkp76lx4+PGM81xagOnjKzUO//fh7f70u68YZERGbIXmrE/OYnMCJ/CaQVeXe0r1KlC5aF98SyyhSUVJf+57Yu9i7Yfs/2Jrm6jCwLjxBZqKk9QyCX8jccIrJMVUYpnr0cjZ4V83Ei8GEId3AlmVNlMZ48tRnbUtLwrHMkNIq6l31fMyliEssQ1YtHiCzYjFUnsOFkptgxiIjuWCeXUnzW7Ff4p2++8cY3UG6vxsrw3lhanYGCqiLT7So7Fbbdsw0u9tcvTGS7WIgs2OWcMgz5dA+M/BckIivxgE82Xpcvgzo37o73VaFwxM8RfbC4Ogv5VYWYFjkNM6NnNkBKskYsRBbumRVx2HQ6S+wYREQN6rXgi5hSvhjyktQ73lelnQPWthmEYUM/h7uDewOkI2vEMUQW7rmBLcGLJYjI2ryXHI72Be9jZ8CzEO7wFJdSV4FJykCWIfpPLEQWLszLCcPb3vkMnURE5karl+GRhO7oX/0p4gPGQ5De5nQjcgeg5/MNG46sDguRFZg5KAwyXnFGRFYquUKJuxJGY4rD58jxHXjrO+j8CODk1fDByKqwEFmBUE81xrT3FTsGEVGj2pPvii5XHsG77vNQ4R55cw9SOAG9XmjcYGQVWIisxPODWnJeIiKyCT9mBCAy82Ws9H0FBvUNhgx0fxpQcewQ3RgLkZUIcldhUtdAsWMQETUJgyDFK1faolPxPBwMeAyCnaruRo4eQI9nmj4cWSQWIisyY1AYnLnGGRHZkEKdHBMT+mG48DmuBIyDIPnHj7W+LwL2TuKFI4vCQmRFXFUKPDPg9hdMJCKyVPFljhiQcC+eUn+GQu+egFsLoNO0Bn2OKVOmQCKR4Iknnqhz31NPPQWJRIIpU6Y06HNS02EhsjJTeoQg0I3r9BCRbdqS64Go5KdxYshqQGbX4PsPCAjAqlWrUFFRYbqtsrISK1euRGCgdQxbMBgMMBqNYsdocixEVkYhl+LluyLEjkFEJJpuzd0QFdGyUfbdsWNHBAYGYt26dabb1q1bh4CAAERFRZlu27p1K3r16gWNRgN3d3eMHDkSiYmJpvuTk5MhkUiwbt069O/fH46Ojmjfvj0OHTpk2iY/Px8TJkyAv78/HB0d0bZtW6xcubJWntLSUkyaNAkqlQo+Pj749NNP0a9fP8yYMcO0TXV1NV588UX4+flBpVKha9eu2L17t+n+mJgYaDQabNq0Ca1bt4a9vT1SUlIa8F2zDCxEVmh4Wx90DnYVOwYRUZOTSoDXR7Zu1OeYOnUqFi9ebPp80aJFmDat9uk5rVaLF154AceOHcPOnTshlUoxduzYOkde/u///g+zZ8/GyZMnERYWhgkTJkCv1wOoOfIUHR2NTZs24ezZs3jsscfw0EMP4ciRI6bHv/DCCzhw4AA2btyIHTt2YN++fYiLq70O3NSpU3HgwAGsWrUKp0+fxn333Ydhw4YhISHBtE15eTk++OAD/PDDDzh37hw8PT0b7P2yFFzLzEqdSivC3V8fAP91iciWPNApAPPubdco+54yZQqKiorwww8/wN/fHxcuXIBEIkFERATS0tLw6KOPQqPRICYmps5jc3Nz4enpiTNnziAyMhLJyckICQnBDz/8gEceeQQAcP78ebRp0wbx8fGIiKj/SP+IESPQqlUrzJ8/H6WlpXB3d8eKFStw7733AgCKi4vh6+uL6dOn47PPPkNiYiJatmyJ9PR0+Pr+PV/doEGD0KVLF8ydOxcxMTGYOnUqTp48ifbt2zf8G2cheEmSlWofoMG9Hf2xJjZd7ChERE1C42iHF4eFN/rzeHh4YMSIEViyZAkEQcCIESPg4eFRa5vExES8/vrrOHz4MPLy8kxHhlJTUxEZ+fekku3a/V3efHxq5lTKyclBREQEDAYDPvzwQ6xevRoZGRmoqqpCVVUVVKqaKQauXLkCnU6HLl26mPbh4uKC8PC/34O4uDgIgoCwsLBa+aqqquDu/vf8TAqFolYWW8RCZMVeHd4KOy/koEBbLXYUIqJG98pdEXBX2zfJc02bNg3PPFMzx9FXX31V5/5Ro0YhICAA33//PXx9fWE0GhEZGYnq6trfj+3s/h74Lflrpe5r5WnBggX49NNP8dlnn6Ft27ZQqVSYMWOGaR/XTvBI/rXC9z9P/BiNRshkMsTGxkImk9XaTq1Wm/7u4OBQZz+2hoXIirmqFHh1eCvMXnNK7ChERI2qc7Ar7u8U0GTPN2zYMFMxGTp0aK378vPzER8fj2+//Ra9e/cGAOzfv/+Wn2Pfvn0YM2YMHnzwQQA15SYhIQGtWrUCALRo0QJ2dnY4evQoAgJqXntJSQkSEhLQt29fAEBUVBQMBgNycnJMWah+LERW7t5of/wSm4bDVwrEjkJE1CjsZBLMHdu2SY9wyGQyxMfHm/7+T66urnB3d8d3330HHx8fpKam4uWXX77l5wgNDcXatWtx8OBBuLq64pNPPkF2drapEDk5OeHhhx/GnDlz4ObmBk9PT7z55puQSqWm9yIsLAyTJk3C5MmTsWDBAkRFRSEvLw9//vkn2rZti+HDh9/hO2E9eJWZDXh/bFsoZPynJiLrNL13c7T0avoZqZ2dneHs7FzndqlUilWrViE2NhaRkZGYOXMmPv7441ve/+uvv46OHTti6NCh6NevH7y9vXH33XfX2uaTTz5B9+7dMXLkSAwaNAg9e/ZEq1atoFQqTdssXrwYkydPxqxZsxAeHo7Ro0fjyJEjpqNKVINXmdmIT7ZfxP/+vCx2DCKiBhXo5ojtM/tAaSe78cY2QKvVws/PDwsWLDBdvUY3h4cNbMTTA0IR4lHP4odERBbsnTFtbLoMnThxAitXrkRiYiLi4uIwadIkAMCYMWNETmZ5WIhshL1chg/GtYWNX0RARFZkXEc/9Au3vQkE/23+/Plo3749Bg0aBK1Wi3379tWZBoBujKfMbMy7m87jx/1JYscgIrojvi5KbJ3ZB87Khl+vjGwTjxDZmDlDw9HSU33jDYmIzJREAnx8X3uWIWpQLEQ2Rmknwyf3d4BcynNnRGSZJncLQs9QnhKihsVCZIPa+rvgmQGhYscgIrplzT1UePmuVmLHICvEQmSjnukfivb+LmLHICK6aTKpBAvubw8Hhe1eVUaNh4XIRsllUiy4vwPs5fwSICLL8ETf5ogKdBU7Blkp/jS0YaGearwxqrXYMYiIbig6yBUzB4XdeEOi28RCZOMmdQ3CmA6+YscgIrouV0c7fDkxCnIuQUSNiF9dhLlj26J5M85iTUTmRyIBPnmgA3xcHMSOQlaOhYigspfjm0nRUNrxy4GIzMsTfVugP2ejpibAn4AEAAj3dsI7YyLFjkFEZNIl2A2zh4SLHYNsBAsRmdzfKQD3RvuLHYOICO4qBb6YGAUZJ5GlJsJCRLW8OyYSEd5OYscgIhsmk0rw2fgO8HJWih2FbAgLEdXioJDh+8md4KZSiB2FiGzUK3dFoHfLZmLHIBvDQkR1BLg54utJHWEn46FqImpa93fyx6O9m4sdg2wQCxHVq1tzdw6yJqIm1SnIFe/d3VbsGGSjWIjouiZ0CcSUHsFixyAiG+CnccDCh6Kh4HJCJBJ+5dF/en1ka/Ru6SF2DCKyYo5/jV30UNuLHYVsGAsR/SeZVIIvJ3ZEcw/OZE1EDU8iAT65vwNa+zqLHYVsHAsR3ZCLgx0WT+0MDzWvPCOihvXaiNYYFuktdgwiFiK6OUHuKsRM7QK1vVzsKERkJR7v2xyP9AoROwYRABYiugWRfi749qFoKLjiNBHdoXuj/fHKXa3EjkFkwp9sdEt6hnrg0wc6gLPpE9HtGhDhiQ/H8fJ6Mi8sRHTLRrTzwZuj2ogdg4gsUMdADb6a2BFyHmkmM8OvSLotD/cIxrMDQsWOQUQWJNRTjUVTOsNBIRM7ClEdLER022YNCcfk7kFixyAiCxDk7ohlj3SFxpFXq5J5YiGiO/L26DZ4sFug2DGIyIwFujli5fRu8Hbh6vVkvliI6I5IJBK8OyYSk7qyFBFRXQFuDlj5WDf4ahzEjkL0n1iI6I5JJBK8d3ckJnRhKSKiv/lpHLByejf4sQyRBWAhogYhkUgwd2wkxncOEDsKEZkBP40DVj3WDf6ujmJHIbopLETUYCQSCT4Y1xYPdGIpIrJlvi5KrHqsGwLcWIbIcrAQUYOSSCT48J62HFNEZKNCPFRY/Xh3liGyOBJBEASxQ5B1mrf1Ar7ZnSh2DCJqIm18nbFkWhd4qO3FjkJ0y1iIqFEt3JOID7dcEDsGETWyriFu+OHhTnBS2okdhei2sBBRo/v5WBpeWX8GBiO/1Iis0aBWXvhyYhSUdpyBmiwXCxE1iT/OX8UzK+NQqTOKHYWIGtA9Hf3x0b3tIOOKz2ThWIioyRxPLsAjS46juEIndhQiagDTe4fg1eGtIJGwDJHlYyGiJnUltwyPLDmOpDyt2FGI6DbJpBK8Nao1HuoeLHYUogbDQkRNrrhchyeXx+JgYr7YUYjoFqnt5fhiYhT6h3uKHYWoQbEQkSj0BiNe//UcVh5NFTsKEd0kP40Dfni4E1r5OIsdhajBsRCRqH7cn4S5m+N5BRqRmesU5IpvH4qGO+cYIivFQkSi23UhB8+tPIHSKr3YUYioHvdF++P9sW2hkHNxA7JeLERkFhKuluKJZbFIzOVgayJzoZBJ8drIVpjMwdNkA1iIyGxoq/R4ed0Z/HYqU+woRDbPT+OAryZ1RIcAjdhRiJoECxGZnaWHkvHepnhUGziJI5EY+oU3w2cPdIDGUSF2FKImw0JEZulkWhGeXh6HjKIKsaMQ2QypBJg5KAzPDAjlZItkc1iIyGwVlVdj5uqT2HUxV+woRFbPXaXA5+Oj0Kulh9hRiETBQkRmTRAEfLf3ChZsv8RTaESNpHdLD8y/rz28nJViRyESDQsRWYTzmSWYufokLl4tFTsKkdVQ2knx8rAIPNwjmKfIyOaxEJHFqNIb8PHWi/jxQBL4VUt0ZyL9nPHZAx0Q6ukkdhQis8BCRBbnYGIe5qw5zQHXRLdBKgGe7NcCMwaFwU7GiRaJrmEhIotUUqnDm7+ew/oTGWJHIbIYAW4O+PT+DugU7CZ2FCKzw0JEFu2P81fxxq9nkVlcKXYUIrMlk0owrWcwZg4Og6NCLnYcIrPEQkQWT1ulx8fbLmLpoWRwjVii2iL9nPHhuHaI9HMROwqRWWMhIqtxKq0Ir6w7g/NZJWJHIRKdo0KGmYPCMK1XCGRSXkFGdCMsRGRV9AYjftifhM//SECFziB2HCJR9AtvhnfHRCLAzVHsKEQWg4WIrFJaQTne/u0c/ojPETsKUZPx0zjg5bsiMKq9r9hRiCwOCxFZtf0JeXh303lO6EhWzVEhwxN9W+CxPs2htJOJHYfIIrEQkdUzGAWsPJqKT3dcQr62Wuw4RA1GIgHGdvDDi8Mi4O3CZTeI7gQLEdmMkkodvtiZgCUHU7guGlm86CBXvDGyNdoHaMSOQmQVWIjI5iTnaTFv6wVsPZfNJUDI4gS7O+KFIeEYzXFCRA2KhYhs1tmMYny64xJ2XuDAazJ/fhoHPDcwFPd09IecS24QNTgWIrJ5J1IL8cmOS9iXkCd2FKI6vJzt8Uz/UDzQORAKOYsQUWNhISL6y7HkAizYfhGHrxSIHYUIHmoFnujbAg92C+KVY0RNgIWI6F8OXs7DN3sSecSIROHrosS0XiGY2DWQ644RNSEWIqLrOJdZjO/3XsGm01nQc5E0amQR3k54vG9zjGznCzuOESJqcixERDeQWVSBRfuTsOpYGsqq9GLHISvTK9QDj/Vpjj5hzcSOQmTTWIiIblJJpQ7LD6diycFkZJdUih2HLJidTIK7In3wWJ/mXIWeyEywEBHdIr3BiB3nr2LZkRQcTMznXEZ00wLdHDG+SwDu7xQAD7W92HGI6B9YiIjuwJXcMqw4kop1JzJQwGVBqB5yqQQDW3liUtcg9G7pAYlEInYkIqoHCxFRA6jWG7HtXDZWHUvlUSMCUDOR4gOdAzC+cwA8nbnOGJG5YyEiamCZRRXYeCoTv57MRHxWidhxqAk5KeUYHumDu6P80K25G48GEVkQFiKiRpRwtRQbT2Vi46lMpOSXix2HGoG9XIr+4Z4Y3cEXAyI8OYkikYViISJqIidSC/HryUxsO5eNrGJepWbJlHZS9GzhgRHtfDCkjTfU9pxAkcjSsRARieBcZjH+OJ+DnReu4kxGMcccWQAPtT0GRnhiUGsv9Ar1gIOCR4KIrAkLEZHIckoqsfNCDnbGX8X+y3mo1BnFjkR/ifB2wqBWXhjYyhMdAjQcE0RkxViIiMxIpc6AY8kFOJSYj8NX8nE6vZjLhjQhf1cHdG/ujm7N3dG9hTt8NQ5iRyKiJsJCRGTGtFV6HEsuwOErBTh0JR9nM4phYEFqMD4uypoC1MId3Zu7I8DNUexIRCQSFiIiC1JaqcOptGKczijC6bRinMkoRkZRhdixLIJKIUMbPxe093dBO38N2vtrEOjOAkRENViIiCxcXlkVzqQX41R6Ec6kF+N8VonNX8Wmtpcj1FONdqby44IWzdSQSjkGiIjqx0JEZIXKqvRIzCnD5ZwyJOb+/WdKfrlVjUnyUNsj1FOFUE81QpupEerphFBPNbxdODM0Ed0aFiIiG6IzGJFWUI7MokpkFlcgq6gS2SUVyCyqRNZfn5dW6cWOCaBmDTAvZyX8NA7w0Sjh4+IA37/+9HFRIsDVES6OdmLHJCIrwUJERLWUVelRqK1GcYXO9FFUrqv1eXm1HlU6I6oNRlTraz70RiMMAmAwGiEIgFwmhUImgZ1MavpQyGs+V8ikUCvl0DgooHG0g4uDHVz++lPjUPOnq6OCp7iIqMmwEBEREZHNk4odgIiIiEhsLERERERk81iIiIiIyOaxEBEREZHNYyEiIiIim8dCRERERDaPhYiIiIhsHgsRERER2TwWIiIiIrJ5LERERERk81iIiIiIyOaxEBEREZHNYyEiIiIim8dCRERERDaPhYiIiIhsHgsRERER2TwWIiIiIrJ5LERERERk81iIiIiIyOaxEBEREZHNYyEiIiIim8dCRERERDaPhYiIiIhsHgsRERER2TwWIiIiIrJ5LERERERk81iIiIiIyOaxEBEREZHNYyEiIiIim8dCRERERDaPhYiIiIhsHgsRERER2TwWIiIiIrJ5LERERERk81iIiIiIyOaxEBEREZHNYyEiIiIim8dCRERERDaPhYiIiIhsHgsRERER2TwWIiIiIrJ5LERERERk81iIiIiIyOaxEBEREZHNYyEiIiIim8dCRERERDaPhYiIiIhsHgsRERER2TwWIiIiIrJ5LERERERk81iIiIiIyOaxEBEREZHNYyEiIiIim8dCRERERDaPhYiIiIhs3v8DO1M7ZrCwJ/cAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lst=df.groupby('DevType')['DevType'].count()\n",
-    "plt.figure(figsize=(6,6))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Developer Type')  # Add a title\n",
-    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Language to worked with"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LanguageWorkedWith\n",
-       "C#;JavaScript;SQL;HTML;CSS     1235\n",
-       "JavaScript;PHP;SQL;HTML;CSS    1095\n",
-       "Java                            855\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageWorkedWith'].value_counts().nlargest(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9985"
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LanguageWorkedWith\n",
-       "C#;JavaScript;SQL;HTML;CSS     1383\n",
-       "JavaScript;PHP;SQL;HTML;CSS    1226\n",
-       "Java                            989\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageWorkedWith'].value_counts().nlargest(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageDesireNextYear"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LanguageDesireNextYear\n",
-       "Python                                   718\n",
-       "C#;JavaScript;SQL;TypeScript;HTML;CSS    557\n",
-       "C#                                       522\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14147"
-      ]
-     },
-     "execution_count": 262,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 264,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LanguageDesireNextYear\n",
-       "Python                                   878\n",
-       "C#;JavaScript;SQL;TypeScript;HTML;CSS    690\n",
-       "C#                                       629\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 265,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Years of coding (Exp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCodingProf\n",
-       "0-2 years           22612\n",
-       "3-5 years           20883\n",
-       "6-8 years           11177\n",
-       "9-11 years           7456\n",
-       "12-14 years          4220\n",
-       "15-17 years          2987\n",
-       "18-20 years          2810\n",
-       "21-23 years          1352\n",
-       "30 or more years     1289\n",
-       "24-26 years           853\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCodingProf'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3349"
-      ]
-     },
-     "execution_count": 267,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCodingProf'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['YearsCodingProf'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 269,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCodingProf'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 270,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCodingProf\n",
-       "3-5 years           23773\n",
-       "0-2 years           22781\n",
-       "6-8 years           11274\n",
-       "9-11 years           7527\n",
-       "12-14 years          4267\n",
-       "15-17 years          3007\n",
-       "18-20 years          2841\n",
-       "21-23 years          1365\n",
-       "30 or more years     1294\n",
-       "24-26 years           856\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 270,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCodingProf'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Years of coding (Non Exp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCoding\n",
-       "3-5 years           19100\n",
-       "6-8 years           16537\n",
-       "9-11 years          10578\n",
-       "0-2 years            8022\n",
-       "12-14 years          7069\n",
-       "15-17 years          5459\n",
-       "18-20 years          4472\n",
-       "30 or more years     3136\n",
-       "21-23 years          2377\n",
-       "24-26 years          1671\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 271,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCoding'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 272,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "105"
-      ]
-     },
-     "execution_count": 272,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCoding'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['YearsCoding'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 274,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCoding'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCoding\n",
-       "3-5 years           19135\n",
-       "6-8 years           16554\n",
-       "9-11 years          10585\n",
-       "0-2 years            8043\n",
-       "12-14 years          7077\n",
-       "15-17 years          5462\n",
-       "18-20 years          4476\n",
-       "30 or more years     3144\n",
-       "21-23 years          2378\n",
-       "24-26 years          1671\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 275,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCoding'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Operating System"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OperatingSystem\n",
-       "Windows        34268\n",
-       "MacOS          18638\n",
-       "Linux-based    16069\n",
-       "BSD/Unix         139\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 276,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['OperatingSystem'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10374"
-      ]
-     },
-     "execution_count": 277,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['OperatingSystem'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['OperatingSystem'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 279,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['OperatingSystem'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst=df['OperatingSystem'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(4,4))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Oeratining System Used')  # Add a title\n",
-    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Salary Type"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SalaryType\n",
-       "Monthly    26201\n",
-       "Yearly     22541\n",
-       "Weekly      2248\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 282,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28498"
-      ]
-     },
-     "execution_count": 283,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['SalaryType'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 285,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SalaryType\n",
-       "Monthly    40953\n",
-       "Yearly     34333\n",
-       "Weekly      4202\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 286,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Currency"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Currency\n",
-       "U.S. dollars ($)               20599\n",
-       "Euros (€)                      15201\n",
-       "Indian rupees (₹)               7908\n",
-       "British pounds sterling (£)     4856\n",
-       "Canadian dollars (C$)           2535\n",
-       "Russian rubles (₽)              1768\n",
-       "Brazilian reais (R$)            1663\n",
-       "Australian dollars (A$)         1571\n",
-       "Polish złoty (zł)               1434\n",
-       "Swedish kroner (SEK)             864\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Currency'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17483"
-      ]
-     },
-     "execution_count": 288,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Currency'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Currency'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 290,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Currency'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['Currency'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Currency\n",
-       "U.S. dollars ($)               26356\n",
-       "Euros (€)                      19465\n",
-       "Indian rupees (₹)              10152\n",
-       "British pounds sterling (£)     6194\n",
-       "Canadian dollars (C$)           3289\n",
-       "Russian rubles (₽)              2340\n",
-       "Brazilian reais (R$)            2122\n",
-       "Australian dollars (A$)         1970\n",
-       "Polish złoty (zł)               1856\n",
-       "Swedish kroner (SEK)            1101\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 292,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Currency'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Salary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SalaryUSD\n",
-       "0.0          842\n",
-       "120000.0     524\n",
-       "100000.0     497\n",
-       "80000.0      396\n",
-       "1000000.0    382\n",
-       "110000.0     371\n",
-       "90000.0      364\n",
-       "150000.0     357\n",
-       "60000.0      351\n",
-       "75000.0      337\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 293,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryUSD'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31786"
-      ]
-     },
-     "execution_count": 294,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryUSD'].isnull().sum() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DevType        Country      \n",
-       "Student        Saudi Arabia     1500000.0\n",
-       "Developer      Andorra           525089.5\n",
-       "Manager        Hungary           516000.0\n",
-       "               Netherlands       507175.0\n",
-       "Non developer  Algeria           360000.0\n",
-       "               Cyprus            293736.0\n",
-       "Developer      Liechtenstein     284028.0\n",
-       "Student        Finland           272212.0\n",
-       "Manager        Denmark           262920.6\n",
-       "Student        Israel            256522.4\n",
-       "Name: SalaryUSD, dtype: float64"
-      ]
-     },
-     "execution_count": 295,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean_salary = df.groupby(['DevType','Country'])['SalaryUSD'].mean() \n",
-    "mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "means = df.groupby(['YearsCodingProf','DevType', 'Country'])['SalaryUSD'].transform('mean')\n",
-    "df['SalaryUSD'] = df['SalaryUSD'].fillna(means)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCodingProf  DevType        Country                             \n",
-       "9-11 years       Student        Saudi Arabia                            1500000.0\n",
-       "12-14 years      Non developer  Norway                                  1000000.0\n",
-       "                 Student        Switzerland                             1000000.0\n",
-       "15-17 years      Non developer  Australia                               1000000.0\n",
-       "                                New Zealand                             1000000.0\n",
-       "21-23 years      Developer      Japan                                   1000000.0\n",
-       "                                Venezuela, Bolivarian Republic of...    1000000.0\n",
-       "                 Non developer  Sweden                                  1000000.0\n",
-       "                 Student        Finland                                 1000000.0\n",
-       "24-26 years      Manager        Canada                                  1000000.0\n",
-       "Name: SalaryUSD, dtype: float64"
-      ]
-     },
-     "execution_count": 297,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean_salary = df.groupby(['YearsCodingProf','DevType','Country'])['SalaryUSD'].mean()\n",
-    "mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['SalaryUSD'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age\n",
-       "25 - 34 years old     30969\n",
-       "18 - 24 years old     14847\n",
-       "35 - 44 years old     10980\n",
-       "45 - 54 years old      3072\n",
-       "Under 18 years old     1549\n",
-       "55 - 64 years old       865\n",
-       "65 years or older       144\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 299,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Age'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16374"
-      ]
-     },
-     "execution_count": 300,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Age'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Age'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Age'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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qPfyfzhhjXdZ/usOHDyszM1PBwcGSpMjISHl6emrx4sVWTVZWlrZt26a2bdtKkqKiouRwOLRu3TqrZu3atXI4HE4127ZtU1ZWllWzaNEi2Ww2RUZGlvs+AgAAAABwMVXqZf9PPfWUunXrptDQUB09elQpKSlavny5UlNTdezYMY0aNUp33HGHgoOD9cMPP+ipp56Sv7+/br/9dkmS3W5X//79NXz4cNWuXVu1atXSiBEj1KRJE3Xu3FmS1LhxY3Xt2lVJSUmaOnWqJGnAgAGKjY1VWFiYJCk6Olrh4eFKSEjQuHHj9Msvv2jEiBFKSkribD4AAAAA4JJXqeH/4MGDSkhIUFZWlux2u5o2barU1FR16dJFJ06c0NatW/XOO+/oyJEjCg4OVseOHfXee+/J19fXWsekSZPk4eGhPn366MSJE+rUqZNmzpwpd3d3q2bOnDkaOnSo9asAcXFxmjx5srXc3d1d8+fP16BBg9SuXTt5e3srPj5e48ePv3gvBgAAAAAAFcTNGGMquxGu4mx/X7EqGLvpUGU3AX96soV/ZTcBAAAAwCXqbHNolbvnHwAAAAAAlC/CPwAAAAAALo7wDwAAAACAiyP8AwAAAADg4gj/AAAAAAC4OMI/AAAAAAAujvAPAAAAAICLI/wDAAAAAODiCP8AAAAAALg4wj8AAAAAAC6O8A8AAAAAgIsj/AMAAAAA4OII/wAAAAAAuDjCPwAAAAAALo7wDwAAAACAiyP8AwAAAADg4gj/AAAAAAC4OMI/AAAAAAAujvAPAAAAAICLI/wDAAAAAODiCP8AAAAAALg4wj8AAAAAAC6O8A8AAAAAgIsj/AMAAAAA4OII/wAAAAAAuDjCPwAAAAAALo7wDwAAAACAi6vU8D9lyhQ1bdpUfn5+8vPzU1RUlBYuXGgtN8Zo1KhRCgkJkbe3tzp06KBvvvnGaR35+fkaMmSI/P395ePjo7i4OO3fv9+pJjc3VwkJCbLb7bLb7UpISNCRI0ecavbt26cePXrIx8dH/v7+Gjp0qAoKCips3wEAAAAAuFgqNfxfffXVGjt2rDZs2KANGzbo1ltvVc+ePa2A//LLL2vixImaPHmy1q9fr6CgIHXp0kVHjx611pGcnKx58+YpJSVFq1at0rFjxxQbG6uioiKrJj4+XhkZGUpNTVVqaqoyMjKUkJBgLS8qKlL37t11/PhxrVq1SikpKfroo480fPjwi/diAAAAAABQQdyMMaayG3GqWrVqady4cXrggQcUEhKi5ORkPfHEE5L+OMsfGBiol156SQMHDpTD4VCdOnU0a9Ys9e3bV5J04MABhYaGasGCBYqJidH27dsVHh6utLQ0tW7dWpKUlpamqKgo7dixQ2FhYVq4cKFiY2OVmZmpkJAQSVJKSooSExOVk5MjPz+/s2p7Xl6e7Ha7HA7HWT+nsozddKiym4A/PdnCv7KbAAAAAOASdbY5tMrc819UVKSUlBQdP35cUVFR2rt3r7KzsxUdHW3V2Gw2tW/fXqtXr5Ykpaenq7Cw0KkmJCREERERVs2aNWtkt9ut4C9Jbdq0kd1ud6qJiIiwgr8kxcTEKD8/X+np6WW2OT8/X3l5eU4TAAAAAABVTaWH/61bt6pGjRqy2Wx66KGHNG/ePIWHhys7O1uSFBgY6FQfGBhoLcvOzpaXl5dq1qx5xpqAgIAS2w0ICHCqOX07NWvWlJeXl1VTmjFjxljjCNjtdoWGhp7j3gMAAAAAUPEqPfyHhYUpIyNDaWlpevjhh9WvXz99++231nI3NzenemNMiXmnO72mtPrzqTndyJEj5XA4rCkzM/OM7QIAAAAAoDJUevj38vJSgwYN1KpVK40ZM0bNmjXTv/71LwUFBUlSiTPvOTk51ln6oKAgFRQUKDc394w1Bw8eLLHdn3/+2anm9O3k5uaqsLCwxBUBp7LZbNYvFZycAAAAAACoaio9/J/OGKP8/HzVr19fQUFBWrx4sbWsoKBAK1asUNu2bSVJkZGR8vT0dKrJysrStm3brJqoqCg5HA6tW7fOqlm7dq0cDodTzbZt25SVlWXVLFq0SDabTZGRkRW6vwAAAAAAVDSPytz4U089pW7duik0NFRHjx5VSkqKli9frtTUVLm5uSk5OVmjR49Ww4YN1bBhQ40ePVrVq1dXfHy8JMlut6t///4aPny4ateurVq1amnEiBFq0qSJOnfuLElq3LixunbtqqSkJE2dOlWSNGDAAMXGxiosLEySFB0drfDwcCUkJGjcuHH65ZdfNGLECCUlJXE2HwAAAABwyavU8H/w4EElJCQoKytLdrtdTZs2VWpqqrp06SJJevzxx3XixAkNGjRIubm5at26tRYtWiRfX19rHZMmTZKHh4f69OmjEydOqFOnTpo5c6bc3d2tmjlz5mjo0KHWrwLExcVp8uTJ1nJ3d3fNnz9fgwYNUrt27eTt7a34+HiNHz/+Ir0SAAAAAABUHDdjjKnsRriKs/19xapg7KZDld0E/OnJFv6V3QQAAAAAl6izzaFV7p5/AAAAAABQvgj/AAAAAAC4OMI/AAAAAAAujvAPAAAAAICLI/wDAAAAAODiCP8AAAAAALg4wj8AAAAAAC6O8A8AAAAAgIsj/AMAAAAA4OII/wAAAAAAuDjCPwAAAAAALo7wDwAAAACAiyP8AwAAAADg4gj/AAAAAAC4OMI/AAAAAAAujvAPAAAAAICLI/wDAAAAAODiCP8AAAAAALg4wj8AAAAAAC6O8A8AAAAAgIsj/AMAAAAA4OII/wAAAAAAuDjCPwAAAAAALo7wDwAAAACAiyP8AwAAAADg4gj/AAAAAAC4OMI/AAAAAAAurlLD/5gxY3TjjTfK19dXAQEB6tWrl3bu3OlUk5iYKDc3N6epTZs2TjX5+fkaMmSI/P395ePjo7i4OO3fv9+pJjc3VwkJCbLb7bLb7UpISNCRI0ecavbt26cePXrIx8dH/v7+Gjp0qAoKCipk3wEAAAAAuFgqNfyvWLFCjzzyiNLS0rR48WL9/vvvio6O1vHjx53qunbtqqysLGtasGCB0/Lk5GTNmzdPKSkpWrVqlY4dO6bY2FgVFRVZNfHx8crIyFBqaqpSU1OVkZGhhIQEa3lRUZG6d++u48ePa9WqVUpJSdFHH32k4cOHV+yLAAAAAABABXMzxpjKbsRJP//8swICArRixQrdcsstkv4483/kyBF9/PHHpT7H4XCoTp06mjVrlvr27StJOnDggEJDQ7VgwQLFxMRo+/btCg8PV1pamlq3bi1JSktLU1RUlHbs2KGwsDAtXLhQsbGxyszMVEhIiCQpJSVFiYmJysnJkZ+f31+2Py8vT3a7XQ6H46zqK9PYTYcquwn405Mt/Cu7CQAAAAAuUWebQ6vUPf8Oh0OSVKtWLaf5y5cvV0BAgBo1aqSkpCTl5ORYy9LT01VYWKjo6GhrXkhIiCIiIrR69WpJ0po1a2S3263gL0lt2rSR3W53qomIiLCCvyTFxMQoPz9f6enppbY3Pz9feXl5ThMAAAAAAFVNlQn/xhgNGzZMN910kyIiIqz53bp105w5c7R06VJNmDBB69ev16233qr8/HxJUnZ2try8vFSzZk2n9QUGBio7O9uqCQgIKLHNgIAAp5rAwECn5TVr1pSXl5dVc7oxY8ZYYwjY7XaFhoae/wsAAAAAAEAF8ajsBpw0ePBgbdmyRatWrXKaf/JSfkmKiIhQq1atVLduXc2fP1+9e/cuc33GGLm5uVmPT/33hdScauTIkRo2bJj1OC8vjy8AAAAAAABVTpU48z9kyBB9+umnWrZsma6++uoz1gYHB6tu3bravXu3JCkoKEgFBQXKzc11qsvJybHO5AcFBengwYMl1vXzzz871Zx+hj83N1eFhYUlrgg4yWazyc/Pz2kCAAAAAKCqqdTwb4zR4MGD9d///ldLly5V/fr1//I5hw8fVmZmpoKDgyVJkZGR8vT01OLFi62arKwsbdu2TW3btpUkRUVFyeFwaN26dVbN2rVr5XA4nGq2bdumrKwsq2bRokWy2WyKjIwsl/0FAAAAAKAyVOpl/4888ojeffddffLJJ/L19bXOvNvtdnl7e+vYsWMaNWqU7rjjDgUHB+uHH37QU089JX9/f91+++1Wbf/+/TV8+HDVrl1btWrV0ogRI9SkSRN17txZktS4cWN17dpVSUlJmjp1qiRpwIABio2NVVhYmCQpOjpa4eHhSkhI0Lhx4/TLL79oxIgRSkpK4ow+AAAAAOCSVqln/qdMmSKHw6EOHTooODjYmt577z1Jkru7u7Zu3aqePXuqUaNG6tevnxo1aqQ1a9bI19fXWs+kSZPUq1cv9enTR+3atVP16tX12Wefyd3d3aqZM2eOmjRpoujoaEVHR6tp06aaNWuWtdzd3V3z589XtWrV1K5dO/Xp00e9evXS+PHjL94LAgAAAABABXAzxpjKboSrONvfV6wKxm46VNlNwJ+ebOFf2U0AAAAAcIk62xxaJQb8AwAAAAAAFYfwDwAAAACAiyP8AwAAAADg4gj/AAAAAAC4OMI/AAAAAAAujvAPAAAAAICLI/wDAAAAAODiziv8b9y4UVu3brUef/LJJ+rVq5eeeuopFRQUlFvjAAAAAADAhTuv8D9w4EDt2rVLkvT999/r7rvvVvXq1fXBBx/o8ccfL9cGAgAAAACAC3Ne4X/Xrl1q3ry5JOmDDz7QLbfconfffVczZ87URx99VJ7tAwAAAAAAF+i8wr8xRsXFxZKkJUuW6LbbbpMkhYaG6tChQ+XXOgAAAAAAcMHOK/y3atVKL7zwgmbNmqUVK1aoe/fukqS9e/cqMDCwXBsIAAAAAAAuzHmF/0mTJmnjxo0aPHiwnn76aTVo0ECS9OGHH6pt27bl2kAAAAAAAHBhPM7nSc2aNXMa7f+kcePGycPjvFYJAAAAAAAqyHmd+b/22mt1+PDhEvN/++03NWrU6IIbBQAAAAAAys95hf8ffvhBRUVFJebn5+dr//79F9woAAAAAABQfs7pGv1PP/3U+vcXX3whu91uPS4qKtKXX36p+vXrl1/rAAAAAADABTun8N+rVy9Jkpubm/r16+e0zNPTU/Xq1dOECRPKrXEAAAAAAODCnVP4Ly4uliTVr19f69evl7+/f4U0CgAAAAAAlJ/zGpp/79695d0OAAAAAABQQc77d/m+/PJLffnll8rJybGuCDhp+vTpF9wwAAAAAABQPs4r/D/33HN6/vnn1apVKwUHB8vNza282wUAAAAAAMrJeYX/N954QzNnzlRCQkJ5twcAAAAAAJSzK87nSQUFBWrbtm15twUAAAAAAFSA8wr/Dz74oN59993ybgsAAAAAAKgA53XZ/2+//aY333xTS5YsUdOmTeXp6em0fOLEieXSOAAAAAAAcOHOK/xv2bJFzZs3lyRt27bNaRmD/wEAAAAAULWc12X/y5YtK3NaunTpWa9nzJgxuvHGG+Xr66uAgAD16tVLO3fudKoxxmjUqFEKCQmRt7e3OnTooG+++capJj8/X0OGDJG/v798fHwUFxen/fv3O9Xk5uYqISFBdrtddrtdCQkJOnLkiFPNvn371KNHD/n4+Mjf319Dhw5VQUHBub04AAAAAABUMecV/svLihUr9MgjjygtLU2LFy/W77//rujoaB0/ftyqefnllzVx4kRNnjxZ69evV1BQkLp06aKjR49aNcnJyZo3b55SUlK0atUqHTt2TLGxsSoqKrJq4uPjlZGRodTUVKWmpiojI8Pp1wqKiorUvXt3HT9+XKtWrVJKSoo++ugjDR8+/OK8GAAAAAAAVBA3Y4w51yd17NjxjJf3n8vZ/1P9/PPPCggI0IoVK3TLLbfIGKOQkBAlJyfriSeekPTHWf7AwEC99NJLGjhwoBwOh+rUqaNZs2apb9++kqQDBw4oNDRUCxYsUExMjLZv367w8HClpaWpdevWkqS0tDRFRUVpx44dCgsL08KFCxUbG6vMzEyFhIRIklJSUpSYmKicnBz5+fn9Zfvz8vJkt9vlcDjOqr4yjd10qLKbgD892cK/spsAAAAA4BJ1tjn0vM78N2/eXM2aNbOm8PBwFRQUaOPGjWrSpMl5N9rhcEiSatWqJUnau3evsrOzFR0dbdXYbDa1b99eq1evliSlp6ersLDQqSYkJEQRERFWzZo1a2S3263gL0lt2rSR3W53qomIiLCCvyTFxMQoPz9f6enppbY3Pz9feXl5ThMAAAAAAFXNeQ34N2nSpFLnjxo1SseOHTuvhhhjNGzYMN10002KiIiQJGVnZ0uSAgMDnWoDAwP1448/WjVeXl6qWbNmiZqTz8/OzlZAQECJbQYEBDjVnL6dmjVrysvLy6o53ZgxY/Tcc8+d664CAAAAAHBRles9//fdd5+mT59+Xs8dPHiwtmzZorlz55ZYdvotBsaYv/xVgdNrSqs/n5pTjRw5Ug6Hw5oyMzPP2CYAAAAAACpDuYb/NWvWqFq1auf8vCFDhujTTz/VsmXLdPXVV1vzg4KCJKnEmfecnBzrLH1QUJAKCgqUm5t7xpqDBw+W2O7PP//sVHP6dnJzc1VYWFjiioCTbDab/Pz8nCYAAAAAAKqa8wr/vXv3dppuv/12tWnTRn//+981cODAs16PMUaDBw/Wf//7Xy1dulT169d3Wl6/fn0FBQVp8eLF1ryCggKtWLFCbdu2lSRFRkbK09PTqSYrK0vbtm2zaqKiouRwOLRu3TqrZu3atXI4HE4127ZtU1ZWllWzaNEi2Ww2RUZGnsOrAwAAAABA1XJe9/zb7Xanx1dccYXCwsL0/PPPOw2891ceeeQRvfvuu/rkk0/k6+trnXm32+3y9vaWm5ubkpOTNXr0aDVs2FANGzbU6NGjVb16dcXHx1u1/fv31/Dhw1W7dm3VqlVLI0aMUJMmTdS5c2dJUuPGjdW1a1clJSVp6tSpkqQBAwYoNjZWYWFhkqTo6GiFh4crISFB48aN0y+//KIRI0YoKSmJM/oAAAAAgEvaeYX/GTNmlMvGp0yZIknq0KFDifUnJiZKkh5//HGdOHFCgwYNUm5urlq3bq1FixbJ19fXqp80aZI8PDzUp08fnThxQp06ddLMmTPl7u5u1cyZM0dDhw61vpyIi4vT5MmTreXu7u6aP3++Bg0apHbt2snb21vx8fEaP358uewrAAAAAACVxc0YY873yenp6dq+fbvc3NwUHh6uFi1alGfbLjln+/uKVcHYTYcquwn405Mt/Cu7CQAAAAAuUWebQ8/rzH9OTo7uvvtuLV++XFdeeaWMMXI4HOrYsaNSUlJUp06d8244gPLHlz1VC1/4AAAA4GI7rwH/hgwZory8PH3zzTf65ZdflJubq23btikvL09Dhw4t7zYCAAAAAIALcF5n/lNTU7VkyRI1btzYmhceHq5///vf5zTgHwAAAAAAqHjndea/uLhYnp6eJeZ7enqquLj4ghsFAAAAAADKz3mF/1tvvVWPPvqoDhw4YM376aef9Nhjj6lTp07l1jgAAAAAAHDhziv8T548WUePHlW9evV03XXXqUGDBqpfv76OHj2q1157rbzbCAAAAAAALsB53fMfGhqqjRs3avHixdqxY4eMMQoPD1fnzp3Lu30AAAAAAOACndOZ/6VLlyo8PFx5eXmSpC5dumjIkCEaOnSobrzxRt1www366quvKqShAAAAAADg/JxT+H/llVeUlJQkPz+/EsvsdrsGDhyoiRMnllvjAAAAAADAhTun8L9582Z17dq1zOXR0dFKT0+/4EYBAAAAAIDyc07h/+DBg6X+xN9JHh4e+vnnny+4UQAAAAAAoPycU/i/6qqrtHXr1jKXb9myRcHBwRfcKAAAAAAAUH7OKfzfdttt+n//7//pt99+K7HsxIkTevbZZxUbG1tujQMAAAAAABfunH7q75lnntF///tfNWrUSIMHD1ZYWJjc3Ny0fft2/fvf/1ZRUZGefvrpimorAAAAAAA4D+cU/gMDA7V69Wo9/PDDGjlypIwxkiQ3NzfFxMTo9ddfV2BgYIU0FAAAAAAAnJ9zCv+SVLduXS1YsEC5ubn67rvvZIxRw4YNVbNmzYpoHwAAAAAAuEDnHP5Pqlmzpm688cbybAsAAAAAAKgA5zTgHwAAAAAAuPQQ/gEAAAAAcHGEfwAAAAAAXBzhHwAAAAAAF0f4BwAAAADAxRH+AQAAAABwcYR/AAAAAABcHOEfAAAAAAAXR/gHAAAAAMDFVWr4X7lypXr06KGQkBC5ubnp448/dlqemJgoNzc3p6lNmzZONfn5+RoyZIj8/f3l4+OjuLg47d+/36kmNzdXCQkJstvtstvtSkhI0JEjR5xq9u3bpx49esjHx0f+/v4aOnSoCgoKKmK3AQAAAAC4qCo1/B8/flzNmjXT5MmTy6zp2rWrsrKyrGnBggVOy5OTkzVv3jylpKRo1apVOnbsmGJjY1VUVGTVxMfHKyMjQ6mpqUpNTVVGRoYSEhKs5UVFRerevbuOHz+uVatWKSUlRR999JGGDx9e/jsNAAAAAMBF5lGZG+/WrZu6det2xhqbzaagoKBSlzkcDk2bNk2zZs1S586dJUmzZ89WaGiolixZopiYGG3fvl2pqalKS0tT69atJUlvvfWWoqKitHPnToWFhWnRokX69ttvlZmZqZCQEEnShAkTlJiYqBdffFF+fn7luNcAAAAAAFxcVf6e/+XLlysgIECNGjVSUlKScnJyrGXp6ekqLCxUdHS0NS8kJEQRERFavXq1JGnNmjWy2+1W8JekNm3ayG63O9VERERYwV+SYmJilJ+fr/T09DLblp+fr7y8PKcJAAAAAICqpkqH/27dumnOnDlaunSpJkyYoPXr1+vWW29Vfn6+JCk7O1teXl6qWbOm0/MCAwOVnZ1t1QQEBJRYd0BAgFNNYGCg0/KaNWvKy8vLqinNmDFjrHEE7Ha7QkNDL2h/AQAAAACoCJV62f9f6du3r/XviIgItWrVSnXr1tX8+fPVu3fvMp9njJGbm5v1+NR/X0jN6UaOHKlhw4ZZj/Py8vgCAAAAAABQ5VTpM/+nCw4OVt26dbV7925JUlBQkAoKCpSbm+tUl5OTY53JDwoK0sGDB0us6+eff3aqOf0Mf25urgoLC0tcEXAqm80mPz8/pwkAAAAAgKrmkgr/hw8fVmZmpoKDgyVJkZGR8vT01OLFi62arKwsbdu2TW3btpUkRUVFyeFwaN26dVbN2rVr5XA4nGq2bdumrKwsq2bRokWy2WyKjIy8GLsGAAAAAECFqdTL/o8dO6bvvvvOerx3715lZGSoVq1aqlWrlkaNGqU77rhDwcHB+uGHH/TUU0/J399ft99+uyTJbrerf//+Gj58uGrXrq1atWppxIgRatKkiTX6f+PGjdW1a1clJSVp6tSpkqQBAwYoNjZWYWFhkqTo6GiFh4crISFB48aN0y+//KIRI0YoKSmJs/kAAAAAgEtepYb/DRs2qGPHjtbjk/fP9+vXT1OmTNHWrVv1zjvv6MiRIwoODlbHjh313nvvydfX13rOpEmT5OHhoT59+ujEiRPq1KmTZs6cKXd3d6tmzpw5Gjp0qPWrAHFxcZo8ebK13N3dXfPnz9egQYPUrl07eXt7Kz4+XuPHj6/olwAAAAAAgArnZowxld0IV5GXlye73S6Hw1HlrxgYu+lQZTcBf3qyhX+Fb4P+rlouRp8DAADg8nC2OfSSuucfAAAAAACcO8I/AAAAAAAujvAPAAAAAICLq9QB/wAAFYNxHqoOxngAAABVAWf+AQAAAABwcYR/AAAAAABcHOEfAAAAAAAXR/gHAAAAAMDFEf4BAAAAAHBxhH8AAAAAAFwc4R8AAAAAABdH+AcAAAAAwMUR/gEAAAAAcHGEfwAAAAAAXBzhHwAAAAAAF0f4BwAAAADAxRH+AQAAAABwcYR/AAAAAABcHOEfAAAAAAAXR/gHAAAAAMDFEf4BAAAAAHBxhH8AAAAAAFwc4R8AAAAAABdH+AcAAAAAwMUR/gEAAAAAcHGEfwAAAAAAXBzhHwAAAAAAF1ep4X/lypXq0aOHQkJC5Obmpo8//thpuTFGo0aNUkhIiLy9vdWhQwd98803TjX5+fkaMmSI/P395ePjo7i4OO3fv9+pJjc3VwkJCbLb7bLb7UpISNCRI0ecavbt26cePXrIx8dH/v7+Gjp0qAoKCipitwEAAAAAuKgqNfwfP35czZo10+TJk0td/vLLL2vixImaPHmy1q9fr6CgIHXp0kVHjx61apKTkzVv3jylpKRo1apVOnbsmGJjY1VUVGTVxMfHKyMjQ6mpqUpNTVVGRoYSEhKs5UVFRerevbuOHz+uVatWKSUlRR999JGGDx9ecTsPAAAAAMBF4lGZG+/WrZu6detW6jJjjF555RU9/fTT6t27tyTp7bffVmBgoN59910NHDhQDodD06ZN06xZs9S5c2dJ0uzZsxUaGqolS5YoJiZG27dvV2pqqtLS0tS6dWtJ0ltvvaWoqCjt3LlTYWFhWrRokb799ltlZmYqJCREkjRhwgQlJibqxRdflJ+f30V4NQAAAAAAqBhV9p7/vXv3Kjs7W9HR0dY8m82m9u3ba/Xq1ZKk9PR0FRYWOtWEhIQoIiLCqlmzZo3sdrsV/CWpTZs2stvtTjURERFW8JekmJgY5efnKz09vcw25ufnKy8vz2kCAAAAAKCqqbLhPzs7W5IUGBjoND8wMNBalp2dLS8vL9WsWfOMNQEBASXWHxAQ4FRz+nZq1qwpLy8vq6Y0Y8aMscYRsNvtCg0NPce9BAAAAACg4lXZ8H+Sm5ub02NjTIl5pzu9prT686k53ciRI+VwOKwpMzPzjO0CAAAAAKAyVNnwHxQUJEklzrzn5ORYZ+mDgoJUUFCg3NzcM9YcPHiwxPp//vlnp5rTt5Obm6vCwsISVwScymazyc/Pz2kCAAAAAKCqqbLhv379+goKCtLixYuteQUFBVqxYoXatm0rSYqMjJSnp6dTTVZWlrZt22bVREVFyeFwaN26dVbN2rVr5XA4nGq2bdumrKwsq2bRokWy2WyKjIys0P0EAAAAAKCiVepo/8eOHdN3331nPd67d68yMjJUq1YtXXPNNUpOTtbo0aPVsGFDNWzYUKNHj1b16tUVHx8vSbLb7erfv7+GDx+u2rVrq1atWhoxYoSaNGlijf7fuHFjde3aVUlJSZo6daokacCAAYqNjVVYWJgkKTo6WuHh4UpISNC4ceP0yy+/aMSIEUpKSuJsPgAAAADgklep4X/Dhg3q2LGj9XjYsGGSpH79+mnmzJl6/PHHdeLECQ0aNEi5ublq3bq1Fi1aJF9fX+s5kyZNkoeHh/r06aMTJ06oU6dOmjlzptzd3a2aOXPmaOjQodavAsTFxWny5MnWcnd3d82fP1+DBg1Su3bt5O3trfj4eI0fP76iXwIAAAAAACqcmzHGVHYjXEVeXp7sdrscDkeVv2Jg7KZDld0E/OnJFv4Vvg36u2qhzy8vF6O/AQDA5etsc2iVvecfAAAAAACUD8I/AAAAAAAujvAPAAAAAICLI/wDAAAAAODiCP8AAAAAALg4wj8AAAAAAC6O8A8AAAAAgIsj/AMAAAAA4OII/wAAAAAAuDjCPwAAAAAALo7wDwAAAACAiyP8AwAAAADg4gj/AAAAAAC4OMI/AAAAAAAujvAPAAAAAICLI/wDAAAAAODiCP8AAAAAALg4wj8AAAAAAC6O8A8AAAAAgIsj/AMAAAAA4OII/wAAAAAAuDjCPwAAAAAALo7wDwAAAACAiyP8AwAAAADg4gj/AAAAAAC4OMI/AAAAAAAujvAPAAAAAICLq9Lhf9SoUXJzc3OagoKCrOXGGI0aNUohISHy9vZWhw4d9M033zitIz8/X0OGDJG/v798fHwUFxen/fv3O9Xk5uYqISFBdrtddrtdCQkJOnLkyMXYRQAAAAAAKlyVDv+SdMMNNygrK8uatm7dai17+eWXNXHiRE2ePFnr169XUFCQunTpoqNHj1o1ycnJmjdvnlJSUrRq1SodO3ZMsbGxKioqsmri4+OVkZGh1NRUpaamKiMjQwkJCRd1PwEAAAAAqCgeld2Av+Lh4eF0tv8kY4xeeeUVPf300+rdu7ck6e2331ZgYKDeffddDRw4UA6HQ9OmTdOsWbPUuXNnSdLs2bMVGhqqJUuWKCYmRtu3b1dqaqrS0tLUunVrSdJbb72lqKgo7dy5U2FhYRdvZwEAAAAAqABV/sz/7t27FRISovr16+vuu+/W999/L0nau3evsrOzFR0dbdXabDa1b99eq1evliSlp6ersLDQqSYkJEQRERFWzZo1a2S3263gL0lt2rSR3W63asqSn5+vvLw8pwkAAAAAgKqmSof/1q1b65133tEXX3yht956S9nZ2Wrbtq0OHz6s7OxsSVJgYKDTcwIDA61l2dnZ8vLyUs2aNc9YExAQUGLbAQEBVk1ZxowZY40TYLfbFRoaet77CgAAAABARanS4b9bt26644471KRJE3Xu3Fnz58+X9Mfl/Se5ubk5PccYU2Le6U6vKa3+bNYzcuRIORwOa8rMzPzLfQIAAAAA4GKr0uH/dD4+PmrSpIl2795tjQNw+tn5nJwc62qAoKAgFRQUKDc394w1Bw8eLLGtn3/+ucRVBaez2Wzy8/NzmgAAAAAAqGouqfCfn5+v7du3Kzg4WPXr11dQUJAWL15sLS8oKNCKFSvUtm1bSVJkZKQ8PT2darKysrRt2zarJioqSg6HQ+vWrbNq1q5dK4fDYdUAAAAAAHApq9Kj/Y8YMUI9evTQNddco5ycHL3wwgvKy8tTv3795ObmpuTkZI0ePVoNGzZUw4YNNXr0aFWvXl3x8fGSJLvdrv79+2v48OGqXbu2atWqpREjRli3EUhS48aN1bVrVyUlJWnq1KmSpAEDBig2NpaR/gEAAAAALqFKh//9+/frnnvu0aFDh1SnTh21adNGaWlpqlu3riTp8ccf14kTJzRo0CDl5uaqdevWWrRokXx9fa11TJo0SR4eHurTp49OnDihTp06aebMmXJ3d7dq5syZo6FDh1q/ChAXF6fJkydf3J0FAAAAAKCCuBljTGU3wlXk5eXJbrfL4XBU+fv/x246VNlNwJ+ebOFf4dugv6sW+vzycjH6GwAAXL7ONodeUvf8AwAAAACAc0f4BwAAAADAxVXpe/4BAMBf4zaPqoPbPAAAVRVn/gEAAAAAcHGEfwAAAAAAXBzhHwAAAAAAF0f4BwAAAADAxRH+AQAAAABwcYR/AAAAAABcHOEfAAAAAAAXR/gHAAAAAMDFEf4BAAAAAHBxhH8AAAAAAFwc4R8AAAAAABfnUdkNAAAAwNkbu+lQZTcBp3iyhX9lNwEAzgpn/gEAAAAAcHGEfwAAAAAAXBzhHwAAAAAAF0f4BwAAAADAxRH+AQAAAABwcYR/AAAAAABcHOEfAAAAAAAX51HZDQAAAABQtrGbDlV2E/CnJ1v4V3YTgPPGmX8AAAAAAFwc4R8AAAAAABdH+AcAAAAAwMUR/gEAAAAAcHGE/9O8/vrrql+/vqpVq6bIyEh99dVXld0kAAAAAAAuCOH/FO+9956Sk5P19NNPa9OmTbr55pvVrVs37du3r7KbBgAAAADAeeOn/k4xceJE9e/fXw8++KAk6ZVXXtEXX3yhKVOmaMyYMZXcOgAAAACujp92rDpc7acdCf9/KigoUHp6up588kmn+dHR0Vq9enWpz8nPz1d+fr712OFwSJLy8vIqrqHl5LdjRyu7CfhTXp5XhW+D/q5a6PPLC/19eaG/Lz/0+eWF/r68XIz+Lg8n86cx5ox1hP8/HTp0SEVFRQoMDHSaHxgYqOzs7FKfM2bMGD333HMl5oeGhlZIG+GaSr6D4Oro88sL/X15ob8vP/T55YX+vrxcav199OhR2e32MpcT/k/j5ubm9NgYU2LeSSNHjtSwYcOsx8XFxfrll19Uu3btMp+D8pOXl6fQ0FBlZmbKz8+vspuDCkZ/X17o78sPfX55ob8vL/T35Yc+v7iMMTp69KhCQkLOWEf4/5O/v7/c3d1LnOXPyckpcTXASTabTTabzWnelVdeWVFNRBn8/Pz4ULmM0N+XF/r78kOfX17o78sL/X35oc8vnjOd8T+J0f7/5OXlpcjISC1evNhp/uLFi9W2bdtKahUAAAAAABeOM/+nGDZsmBISEtSqVStFRUXpzTff1L59+/TQQw9VdtMAAAAAADhvhP9T9O3bV4cPH9bzzz+vrKwsRUREaMGCBapbt25lNw2lsNlsevbZZ0vcegHXRH9fXujvyw99fnmhvy8v9Pflhz6vmtzMX/0eAAAAAAAAuKRxzz8AAAAAAC6O8A8AAAAAgIsj/AMAAAAA4OII/0Al+OGHH+Tm5qaMjIwya5YvXy43NzcdOXLkorULFYP+vvzQ55eXqtjfHTp0UHJy8kXZFkpyc3PTxx9/XObys3nPoOqoisc4zk69evX0yiuvnLHmr45XV0L4d1FjxozRjTfeKF9fXwUEBKhXr17auXOnU01iYqLc3NycpjZt2lzwtnfu3KmOHTsqMDBQ1apV07XXXqtnnnlGhYWFpdZ//fXX8vDwUPPmzS9425erlStXqkePHgoJCSnzA+zYsWMaPHiwrr76anl7e6tx48aaMmXKBW97+fLl6tmzp4KDg+Xj46PmzZtrzpw5ZdbT3+VjypQpatq0qfz8/OTn56eoqCgtXLjQqaaijvFTfffdd/L19dWVV15ZZg19Xr7GjBkjNze3EsGuovr75H96T59SU1NLrae/z05Z4fzjjz+Wm5vbxW/QOXj00UcVGRkpm81WZj9/8cUXatOmjXx9fVWnTh3dcccd2rt378Vt6CVk1KhRJY6xoKAgp5qK/Ew3xmj8+PFq1KiRbDabQkNDNXr06FJrOcYv3E8//aT77rtPtWvXVvXq1dW8eXOlp6dbyy/G329cnvipPxe1YsUKPfLII7rxxhv1+++/6+mnn1Z0dLS+/fZb+fj4WHVdu3bVjBkzrMdeXl4XvG1PT0/df//9atmypa688kpt3rxZSUlJKi4uLvGHxOFw6P7771enTp108ODBC952RSgoKCiX16UiHT9+XM2aNdPf//533XHHHaXWPPbYY1q2bJlmz56tevXqadGiRRo0aJBCQkLUs2fP89726tWr1bRpUz3xxBMKDAzU/Pnzdf/998vPz089evRwqqW/y8/VV1+tsWPHqkGDBpKkt99+Wz179tSmTZt0ww03WHUVcYyfVFhYqHvuuUc333yzVq9eXWoNfV6+1q9frzfffFNNmzYtdXlF9veSJUuc3lu1atUqUUN/u46ioiK5ubnpiitKnicyxuiBBx7Q2rVrtWXLlhLLv//+e/Xs2VPDhg3TnDlz5HA49Nhjj6l3797atGnTxWj+WatK74cbbrhBS5YssR67u7uXqKmoY/zRRx/VokWLNH78eDVp0kQOh0OHDh0qUccxfuFyc3PVrl07dezYUQsXLlRAQID27NlT4kv0ivw8Ly/GGBUVFcnDo2IjZWFhoTw9PSt0Gxeiqr/nTsWZfxeVmpqqxMRE3XDDDWrWrJlmzJihffv2OX2rKP3xG5xBQUHWVNp/5s7Vtddeq7///e9q1qyZ6tatq7i4ON1777366quvStQOHDhQ8fHxioqKOuM6jTFq0KCBxo8f7zR/27ZtuuKKK7Rnzx5Jf/xRGjBggAICAuTn56dbb71Vmzdvtur37Nmjnj17KjAwUDVq1NCNN97o9IdW+uPyoBdeeEGJiYmy2+1KSkpSQUGBBg8erODgYFWrVk316tXTmDFjymxvcXGxnn/+eV199dXWmZGyzpKdtGDBAjVq1Eje3t7q2LGjfvjhhzPWn6pbt2564YUX1Lt37zJr1qxZo379+qlDhw6qV6+eBgwYoGbNmmnDhg1nvZ3SPPXUU/q///s/tW3bVtddd52GDh2qrl27at68eSVq6e//uZD+lqQePXrotttuU6NGjdSoUSO9+OKLqlGjhtLS0pzqKuIYP+mZZ57R9ddfrz59+pRZQ5//z4X2+bFjx3TvvffqrbfeUs2aNUutqcj+rl27ttO6S/uPDv39Pxfa3yeNGjVKzZs316xZs1SvXj3Z7XbdfffdOnr0qFVz/Phx3X///apRo4aCg4M1YcKEEuspKCjQ448/rquuuko+Pj5q3bq1li9fbi2fOXOmrrzySn3++ecKDw+XzWbTjz/+WGqbXn31VT3yyCO69tprS12+ceNGFRUV6YUXXtB1112nli1basSIEdq8eXOZVwHeeuutGjx4sNO8w4cPy2azaenSpWe1D4cPH9Y999yjq6++WtWrV1eTJk00d+5cp3V26NBBgwcP1rBhw+Tv768uXbpYr/M111wjm82mkJAQDR06tNR2njRlyhRdd9118vLyUlhYmGbNmnXG+nXr1qlFixaqVq2aWrVqVeqXIB4eHk7HWJ06dUrUVMQxvn37dk2ZMkWffPKJ4uLiVL9+fTVv3lydO3cuUcsx/j/ne4y/9NJLCg0N1YwZM/S3v/1N9erVU6dOnXTdddc51Z1LX69cuVKenp7Kzs52mj98+HDdcsst1uPVq1frlltukbe3t0JDQzV06FAdP37cWj579my1atVKvr6+CgoKUnx8vHJycqzlJ29t+OKLL9SqVSvZbDZ99dVX2rx5szp27ChfX1/5+fkpMjLyjP+/3Ldvn3r27KkaNWrIz89Pffr0cfoy6eTn3vTp03XttdfKZrOprF+n/+ijj3TDDTfIZrOpXr16pX7+nWr37t265ZZbVK1aNYWHh2vx4sUlan766Sf17dtXNWvWVO3atdWzZ0+n/k1MTFSvXr00ZswYhYSEqFGjRmfcZpVicFnYvXu3kWS2bt1qzevXr5+x2+2mTp06pmHDhubBBx80Bw8erJBtN27c2Dz99NNO86dPn25atWplCgsLzbPPPmuaNWt2xvW8+OKLJjw83GneY489Zm655RZjjDHFxcWmXbt2pkePHmb9+vVm165dZvjw4aZ27drm8OHDxhhjMjIyzBtvvGG2bNlidu3aZZ5++mlTrVo18+OPP1rrrFu3rvHz8zPjxo0zu3fvNrt37zbjxo0zoaGhZuXKleaHH34wX331lXn33XfLbOvEiRONn5+fmTt3rtmxY4d5/PHHjaenp9m1a5cxxpi9e/caSWbTpk3GGGP27dtnbDabefTRR82OHTvM7NmzTWBgoJFkcnNzz+Zltkgy8+bNKzF/4MCBplWrVmb//v2muLjYLF261NSoUcN89dVX57T+s9GuXTszfPhwp3n0d8X0tzHG/P7772bu3LnGy8vLfPPNN9b8ijzGv/zyS1O/fn3jcDjMjBkzjN1uL1FDn5dvn99///0mOTnZGGNM+/btzaOPPuq0vKL6++S+hIaGmjp16pi2bduaDz74oEQd/X1u/V1aHxpjzLx588yp/z179tlnTY0aNUzv3r3N1q1bzcqVK01QUJB56qmnrJqHH37YXH311WbRokVmy5YtJjY21tSoUcNp/fHx8aZt27Zm5cqV5rvvvjPjxo0zNpvN2ocZM2YYT09P07ZtW/P111+bHTt2mGPHjp2xD8vq57179xqbzWb+85//mN9//90cOXLE3HXXXSYmJqbMdc2ZM8fUrFnT/Pbbb9a8f/3rX6ZevXqmuLj4rPZh//79Zty4cWbTpk1mz5495tVXXzXu7u4mLS3NWmf79u1NjRo1zD/+8Q+zY8cOs337dvPBBx8YPz8/s2DBAvPjjz+atWvXmjfffLPMtv73v/81np6e5t///rfZuXOnmTBhgnF3dzdLly61ak79W3zs2DFTp04d07dvX7Nt2zbz2WefmWuvvdbpPfPss8+a6tWrm+DgYFOvXj3Tt29fs2fPHqftVtQx/tJLL5lGjRqZ8ePHm3r16pm6deua/v37W8fZSRzj5fOZ3rhxY5OcnGzuvPNOU6dOHdO8efMS77fz6etGjRqZl19+2XpcWFhoAgICzPTp040xxmzZssXUqFHDTJo0yezatct8/fXXpkWLFiYxMdF6zrRp08yCBQvMnj17zJo1a0ybNm1Mt27drOXLli0zkkzTpk3NokWLzHfffWcOHTpkbrjhBnPfffeZ7du3m127dpn333/fZGRklNrO4uJi06JFC3PTTTeZDRs2mLS0NNOyZUvTvn17q+bZZ581Pj4+JiYmxmzcuNFs3rzZ+hw41YYNG8wVV1xhnn/+ebNz504zY8YM4+3tbWbMmGHV1K1b10yaNMkYY0xRUZGJiIgwHTp0MJs2bTIrVqwwLVq0cDpejx8/bho2bGgeeOABs2XLFvPtt9+a+Ph4ExYWZvLz863+qVGjhklISDDbtm1zyldVHeH/MlBcXGx69OhhbrrpJqf5KSkp5vPPPzdbt241n376qWnWrJm54YYbnP7wXoioqChjs9mMJDNgwABTVFRkLdu1a5cJCAgwO3fuNMaU/R+IUx04cMC4u7ubtWvXGmOMKSgoMHXq1DEzZ840xvwRRvz8/Eq0/7rrrjNTp04tc73h4eHmtddesx7XrVvX9OrVy6lmyJAh5tZbby31g6c0ISEh5sUXX3Sad+ONN5pBgwYZY0r+ERk5cqRp3Lix0/qfeOKJcg3/+fn55v777zeSjIeHh/Hy8jLvvPPOOa37bHzwwQfGy8vLbNu2zZpHf1dMf2/ZssX4+PgYd3d3Y7fbzfz5852WV9QxfujQIRMaGmpWrFhhjDGlhn/6vHz7fO7cuSYiIsKcOHHCGFN6cKyo/v7555/NxIkTzdq1a8369evNP//5T3PFFVeYWbNmWTX097n397mE/+rVq5u8vDxr3j/+8Q/TunVrY4wxR48eNV5eXiYlJcVafvjwYePt7W2t/7vvvjNubm7mp59+ctpWp06dzMiRI40xfxzHksr8D3tpztTPK1asMAEBAcbd3d1IMlFRUWd8r//222+mVq1a5r333rPmNW/e3IwaNeqs96E0t912m9OX0e3btzfNmzd3qpkwYYJp1KiRKSgoKHM9p2rbtq1JSkpymnfXXXeZ2267zXp86t/iqVOnmlq1apnjx49by6dMmeL0nlmwYIH58MMPzZYtW8zixYtN+/btTWBgoDl06JD1nIo6xgcOHGhsNptp3bq1WblypVm2bJlp3ry56dixo1XDMV5+n+k2m83YbDYzcuRIs3HjRvPGG2+YatWqmbffftuqOZ++fumll0zjxo2txx9//LGpUaOG9SVeQkKCGTBggNNzvvrqK3PFFVdYf1tOt27dOiPJHD161Bjzv/D/8ccfO9X5+vpa/fpXFi1aZNzd3c2+ffused98842RZNatW2eM+eP95enpaXJycs64rvj4eNOlSxenef/4xz+cvnQ6Nfx/8cUXxt3d3WRmZlrLFy5c6HS8Tps2zYSFhTn1bX5+vvH29jZffPGFMeaP8B8YGGh9GXAp4bL/y8DgwYO1ZcuWEpe+9e3bV927d1dERIR69OihhQsXateuXZo/f36p6/nqq69Uo0YNazrToG6S9N5772njxo169913NX/+fOvSr6KiIsXHx+u55547p8tkgoOD1b17d02fPl2S9Pnnn+u3337TXXfdJUlKT0/XsWPHVLt2bad27t2717q87Pjx43r88ccVHh6uK6+8UjVq1NCOHTu0b98+p221atXK6XFiYqIyMjIUFhamoUOHatGiRWW2My8vTwcOHFC7du2c5rdr107bt28v9Tnbt29XmzZtnAZ5+qtL6s7Vq6++qrS0NH366adKT0/XhAkTNGjQoBKXz510rv0t/XE5WGJiot566y3r3mD6u6Ty6u+wsDBlZGQoLS1NDz/8sPr166dvv/3WWl5Rx3hSUpLi4+OdLiU8FX1e0oX0eWZmph599FHNnj1b1apVK7Ouovrb399fjz32mP72t7+pVatWev755zVo0CC9/PLLkujv0pT3Z3q9evXk6+trPQ4ODrYuxd2zZ48KCgqc1l+rVi2FhYVZjzdu3ChjjBo1auT0Wq5YscJ6LaU/7ikuazyJc5Gdna0HH3xQ/fr10/r167VixQp5eXnpzjvvLPPSXZvNpvvuu896P2RkZGjz5s1KTEw8630oKirSiy++qKZNm1rvm0WLFv3l++Guu+7SiRMndO211yopKUnz5s3T77//Xub+bd++/ZzfD82aNVP16tWteae/H7p166Y77rhDTZo0UefOna3j9u2337ZqKuoYLy4uVn5+vt555x3dfPPN6tChg6ZNm6Zly5Zp586dHOOluJBjvLi4WC1bttTo0aPVokULDRw4UElJSU6DMJ9rX5/c5++++866/W/69Onq06ePNdZXenq6Zs6c6fRax8TEqLi42BqMc9OmTerZs6fq1q0rX19fdejQQZL+8vUeNmyYHnzwQXXu3Fljx451+lw53fbt2xUaGqrQ0FBr3sn+PfX1rlu3bqm3vpy+rtL6bffu3SoqKiq1/pprrtHVV19tzTu939LT063BjE++TrVq1dJvv/3mtF9NmjS5ZO7zPxUD/rm4IUOG6NNPP9XKlSud3uilCQ4OVt26dbV79+5Sl7dq1crpJ04CAwPPuL6TB3V4eLiKioo0YMAADR8+XEePHtWGDRu0adMm6/6+4uJiGWPk4eGhRYsW6dZbby11nQ8++KASEhI0adIkzZgxQ3379rX+mBYXFys4ONjp/r+TTg6i8o9//ENffPGFxo8frwYNGsjb21t33nmnCgoKnOpPHRRRklq2bKm9e/dq4cKFWrJkifr06aPOnTvrww8/LHP/Tx+t2RhT5gjOZf1nqLycOHFCTz31lObNm6fu3btLkpo2baqMjAyNHz++1Pv6zrW/V6xYoR49emjixIm6//77rfn0d0nl1d9eXl7WgH+tWrXS+vXr9a9//UtTp04ttb68jvGlS5fq008/tb7QM8aouLhYHh4eevPNN9W7d2/6/DQX0ufp6enKyclRZGSkNa+oqEgrV67U5MmTlZ+fX+rAYOX9mX6qNm3a6D//+Y8kjvHSnE1/+/n5yeFwlJh/5MgR+fn5Oc07faArNzc3FRcXn/W2iouL5e7urvT09BLvlRo1alj/9vb2LpdfGvj3v/8tPz8/6wsi6Y97iUNDQ7V27doyRy1/8MEH1bx5c+3fv1/Tp09Xp06dVLdu3bPehwkTJmjSpEl65ZVX1KRJE/n4+Cg5Ofkv3w+hoaHauXOnFi9erCVLlmjQoEEaN26cVqxYUeYgYxV9/Pv4+KhJkyZlHr9S+R3jwcHB8vDwcAr2jRs3lvRH6AsMDOQYP82FfKYHBwcrPDzcaV7jxo310UcfnfE5Z+prSQoICFCPHj00Y8YMXXvttVqwYIHTa1tcXKyBAweWOp7FNddco+PHjys6OlrR0dGaPXu26tSpo3379ikmJuYvX+9Ro0YpPj5e8+fP18KFC/Xss88qJSVFt99+e4ltlfW6nj7/9G2UprR1nalvSlt2+vOLi4sVGRlZ6pdlp34ZcTbtq4oI/y7KGKMhQ4Zo3rx5Wr58uerXr/+Xzzl8+LAyMzMVHBxc6nJvb28raJxPewoLC2WMkZ+fn7Zu3eq0/PXXX9fSpUv14YcfnrGtt912m3x8fDRlyhQtXLhQK1eutJa1bNlS2dnZ8vDwUL169Up9/ldffaXExETrw+jYsWNnPUCLn5+f+vbtq759++rOO+9U165d9csvv5QYgMXPz08hISFatWpViUFW/va3v5W67vDw8BI/z3f6wG0XorCwUIWFhSVGbXZ3d7f+A3m6c+nv5cuXKzY2Vi+99JIGDBjgtIz+Lqmi+tsYo/z8/DKXl9cxvmbNGqdv1D/55BO99NJLWr16ta666ir6vBQX0uedOnUq8Xr+/e9/1/XXX68nnnii1OAvVexn+qZNm6z10t8lnU1/X3/99SV+nlP64xcdTj1r/1caNGggT09PpaWl6ZprrpH0x2jiu3btUvv27SVJLVq0UFFRkXJycnTzzTef9brP16+//lrifXnycVl/c6Q/zqS1atVKb731lt5991299tpr1rKz2YevvvpKPXv21H333Wdta/fu3VaQPRNvb2/FxcUpLi5OjzzyiK6//npt3bpVLVu2LFHbuHFjrVq1yumL7tWrV5e5nfDwcM2aNUsnTpyQt7e3pL8+/vPz87V9+/Yz9ld5HePt2rXT77//rj179liDzu3atUvSH2dfOcZLupDP9Hbt2pX4+e1du3ZZX3SV5q/6+qQHH3xQd999t66++mpdd911TmfFW7ZsqW+++abM98TWrVt16NAhjR071jqBdy6DQp8cgPixxx7TPffcoxkzZpQa/sPDw7Vv3z5lZmZa2/n222/lcDjO6lg9fV2rVq1ymrd69Wo1atSo1L+NJ7d94MABhYSESPrj/zSnatmypd577z1rEEqXcxFuLUAlePjhh43dbjfLly83WVlZ1vTrr78aY/64R3D48OFm9erVZu/evWbZsmUmKirKXHXVVU73FZ6P2bNnm/fee898++23Zs+ePeb99983V111lbn33nvLfM7Z3Dt20lNPPWW8vLzM9ddf7zS/uLjY3HTTTaZZs2YmNTXV7N2713z99dfm6aefNuvXrzfGGNOrVy/TvHlzs2nTJpORkWF69OhhfH19ne67PPXeoJMmTpxo5s6da7Zv32527txp+vfvb4KCgpzGMTjVpEmTjJ+fn0lJSTE7duwwTzzxxBkHjvnxxx+Nl5eXeeyxx8yOHTvMnDlzTFBQ0FnfD3z06FGzadMms2nTJiPJTJw40WzatMlpQJz27dubG264wSxbtsx8//33ZsaMGaZatWrm9ddf/8v1n8myZctM9erVzciRI53ea6cPFHQq+vvC+tuYP+43XLlypdm7d6/ZsmWLeeqpp8wVV1xhFi1aZIyp2GP8dGUN+Hcq+vzC+/xUp98vXpH9PXPmTDNnzhzz7bffmh07dphx48YZT09PM3HixDKfQ3//dX/v3bvXeHt7m0GDBpmMjAyzc+dOM3nyZGOz2cz7779/xtdy0qRJpm7dutbjhx56yFxzzTVmyZIlZuvWrSYuLq7EgH/33nuvqVevnvnoo4/M999/b9atW2fGjh1rjRVyNsfxSbt37zabNm0yAwcONI0aNbL+/py8//XLL780bm5u5rnnnjO7du0y6enpJiYmxtStW9f6f0hZ3nzzTePl5WWuvPLKEvch/9U+JCcnm9DQUPP111+bb7/91jz44IPGz8/P9OzZ01pHaWMtzJgxw/znP/8xW7duNXv27DFPP/208fb2drrf/lTz5s0znp6eZsqUKWbXrl3WgH/Lli2zanTKPcRHjx41/v7+5p577jHffPONmT9/vmnQoIHTe2b48OFm+fLl5vvvvzdpaWkmNjbW+Pr6mh9++MFaR0Ud40VFRaZly5bmlltuMRs3bjQbNmwwrVu3LnEv9ak4xs//M33dunXGw8PDvPjii2b37t1mzpw5pnr16mb27NnGmAvr66KiIhMaGmq8vLzM2LFjnZZt3rzZ+szZtGmT2bVrl/nkk0/M4MGDjTHG5OTkGC8vL/OPf/zD7Nmzx3zyySemUaNGTvt98p7/U/fx119/NY888ohZtmyZ+eGHH8yqVavMddddZx5//PFS23hywL+bb77ZpKenm7Vr15rIyMgSA/6dzfsrPT3dacC/mTNn/uWAf+Hh4aZTp04mIyPDrFy50kRGRpY64F+HDh3MypUrzffff2+WL19uhg4dao0V0K9fP6fPlUsJ4d9FSSp1Onkw/PrrryY6OtrUqVPHeHp6mmuuucb069fPafCN85WSkmJatmxpatSoYXx8fEx4eLgZPXp0mYOJGHNuf0T27NljJDmNaHpSXl6eGTJkiAkJCTGenp4mNDTU3HvvvdZ+7d2713Ts2NF4e3ub0NBQM3ny5BL/ESjtj8ibb75pmjdvbnx8fIyfn5/p1KmT2bhxY5ltLCoqMs8995y56qqrjKenp2nWrJlZuHChtfz0PyLGGPPZZ5+ZBg0aGJvNZm6++WYzffr0sw4GJz+MT5/69etn1WRlZZnExEQTEhJiqlWrZsLCwsyECRPOejCcsvTr16/UbZ/6IX46+vvC+tsYYx544AFTt25d4+XlZerUqWM6depkBX9jKvYYP115h3/6/K+dvk8V2d8zZ840jRs3NtWrVze+vr4mMjLSabC/0tDfZ9ffGzZsMDExMSYgIMD4+fmZVq1amblz5zpt+2zC/9GjR819991nqlevbgIDA83LL79c4nUoKCgw/+///T9Tr1494+npaYKCgsztt99utmzZYow5t/Dfvn37Uj/39+7da9XMnTvXtGjRwvj4+Jg6deqYuLg4s3379r9c99GjR0316tWtgdZO9Vf7cPjwYdOzZ09To0YNExAQYJ555hlz//33/2X4nzdvnmndurXx8/MzPj4+pk2bNmbJkiVnbOfrr79urr32WuPp6WkaNWpUYgDdU8OEMcasWbPGNGvWzHh5eZnmzZubjz76yOk907dvXxMcHGw8PT1NSEiI6d27t9Ovt1T0Z/pPP/1kevfubWrUqGECAwNNYmJiuX2Jfzkf42X57LPPTEREhLHZbOb66693Gu3/Qvv6n//8p3F3dzcHDhwosWzdunWmS5cu1v/RmzZt6jTQ4bvvvmvq1atnbDabiYqKMp9++ulfhv/8/Hxz9913W186hISEmMGDB5/x//0//vijiYuLMz4+PsbX19fcddddJjs721p+Lu+vDz/80ISHh1uv1bhx45yWn/7+2Llzp7npppuMl5eXadSokUlNTS1xvGZlZZn777/f+Pv7G5vNZq699lqTlJRkHA6HMebSDv9uxlTwzcZAOfv666/VoUMH7d+//5zuUcWlif6+/NDnlxf6G6fKzMxUvXr1tH79+lIvucelh2P84kpKStLBgwf16aefVnZTUAUR/nHJyM/PV2ZmpgYMGKDg4OCzGn0ely76+/JDn19e6G+cqrCwUFlZWXryySf1448/6uuvv67sJuECcYxfXA6HQ+vXr1dcXJw++eQTdenSpbKbhCqIn/rDJWPu3LkKCwuTw+FwGkEYron+vvzQ55cX+hun+vrrr1W3bl2lp6frjTfeqOzmoBxwjF9cPXv2VFxcnAYOHEjwR5k48w8AAAAAgIvjzD8AAAAAAC6O8A8AAAAAgIsj/AMAAAAA4OII/wAAAAAAuDjCPwAAAAAALo7wDwAAAACAiyP8AwBwmVu9erXc3d3VtWvXi7rdgoICjRs3Ti1btpSPj4/sdruaNWumZ555RgcOHLiobQEAwNW5GWNMZTcCAABUngcffFA1atTQf/7zH3377be65pprKnyb+fn5io6O1pYtW/Tcc8+pXbt2stvt2rNnjz7++GNdeeWVGjNmTKnPLSgokJeXV4W3EQAAV8KZfwAALmPHjx/X+++/r4cfflixsbGaOXNmiZpPP/1UDRs2lLe3tzp27Ki3335bbm5uOnLkiFWzevVq3XLLLfL29lZoaKiGDh2q48ePl7ndSZMmadWqVVq6dKmGDh2qyMhINWjQQDExMZoyZYpGjx5t1Xbo0EGDBw/WsGHD5O/vry5dukiSVqxYob/97W+y2WwKDg7Wk08+qd9//916Xr169fTKK684bbd58+YaNWqU9djNzU1TpkxRt27d5O3trfr16+uDDz44txcRAIBLAOEfAIDL2HvvvaewsDCFhYXpvvvu04wZM3TqRYE//PCD7rzzTvXq1UsZGRkaOHCgnn76aad1bN26VTExMerdu7e2bNmi9957T6tWrdLgwYPL3O7cuXPVpUsXtWjRotTlbm5uTo/ffvtteXh46Ouvv9bUqVP1008/6bbbbtONN96ozZs3a8qUKZo2bZpeeOGFc34N/vnPf+qOO+7Q5s2bdd999+mee+7R9u3bz3k9AABUZYR/AAAuY9OmTdN9990nSeratauOHTumL7/80lr+xhtvKCwsTOPGjVNYWJjuvvtuJSYmOq1j3Lhxio+PV3Jysho2bKi2bdvq1Vdf1TvvvKPffvut1O3u2rVLYWFhTvNuv/121ahRQzVq1FDbtm2dljVo0EAvv/yywsLCdP311+v1119XaGioJk+erOuvv169evXSc889pwkTJqi4uPicXoO77rpLDz74oBo1aqT/+7//U6tWrfTaa6+d0zoAAKjqCP8AAFymdu7cqXXr1unuu++WJHl4eKhv376aPn26U82NN97o9Ly//e1vTo/T09M1c+ZMK7jXqFFDMTExKi4u1t69e8vc/uln919//XVlZGTogQce0K+//uq0rFWrVk6Pt2/frqioKKd1tGvXTseOHdP+/fvPYu//JyoqqsRjzvwDAFyNR2U3AAAAVI5p06bp999/11VXXWXNM8bI09NTubm5qlmzpowxJUL66WMFFxcXa+DAgRo6dGiJbZQ1eGDDhg21Y8cOp3nBwcGSpFq1apWo9/HxKdGGstp1cv4VV1xRoq2FhYWltud0p68bAIBLHWf+AQC4DP3+++965513NGHCBGVkZFjT5s2bVbduXc2ZM0eSdP3112v9+vVOz92wYYPT45YtW+qbb75RgwYNSkxljcp/zz33aPHixdq0adN5tT88PFyrV692CverV6+Wr6+v9WVGnTp1lJWVZS3Py8sr9UqEtLS0Eo+vv/7682oXAABVFeEfAIDL0Oeff67c3Fz1799fERERTtOdd96padOmSZIGDhyoHTt26IknntCuXbv0/vvvW78IcPLs+BNPPKE1a9bokUceUUZGhnbv3q1PP/1UQ4YMKXP7jz32mKKionTrrbfqX//6lzZu3Ki9e/fqiy++0MKFC+Xu7n7G9g8aNEiZmZkaMmSIduzYoU8++UTPPvushg0bpiuu+OO/N7feeqtmzZqlr776Stu2bVO/fv1KXe8HH3yg6dOna9euXXr22We1bt26Mw5WCADApYjwDwDAZWjatGnq3Lmz7HZ7iWV33HGHMjIytHHjRtWvX18ffvih/vvf/6pp06aaMmWKNdq/zWaTJDVt2lQrVqzQ7t27dfPNN6tFixb65z//aV3GX5pq1arpyy+/1JNPPqkZM2bopptuUuPGjZWcnKx27drp448/PmP7r7rqKi1YsEDr1q1Ts2bN9NBDD6l///565plnrJqRI0fqlltuUWxsrG677Tb16tVL1113XYl1Pffcc0pJSVHTpk319ttva86cOQoPDz+blxEAgEuGmzn9ZjgAAIAzePHFF/XGG28oMzOzsptywdzc3DRv3jz16tWrspsCAECFYsA/AABwRq+//rpuvPFG1a5dW19//bXGjRvHZfEAAFxiCP8AAOCMdu/erRdeeEG//PKLrrnmGg0fPlwjR46s7GYBAIBzwGX/AAAAAAC4OAb8AwAAAADAxRH+AQAAAABwcYR/AAAAAABcHOEfAAAAAAAXR/gHAAAAAMDFEf4BAAAAAHBxhH8AAAAAAFwc4R8AAAAAABf3/wFm5zJ/y4R2PwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lst=df['Age'].value_counts().nlargest(10)\n",
-    "plt.figure(figsize=(12,4))\n",
-    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
-    "\n",
-    "# Adding labels and title\n",
-    "plt.xlabel('Age Group')  # Label for x-axis\n",
-    "plt.ylabel('Counts')      # Label for y-axis\n",
-    "plt.title('Age')  # Title of the plot\n",
-    "#plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
-    "\n",
-    "# Display the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 304,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                          0\n",
-      "SalaryUSD                    0\n",
-      "Country                      0\n",
-      "Currency                     0\n",
-      "DevType                      0\n",
-      "Employment                   0\n",
-      "RaceEthnicity                0\n",
-      "Gender                       0\n",
-      "SalaryType                   0\n",
-      "Hobby                        0\n",
-      "JobSatisfaction              0\n",
-      "JobSearchStatus              0\n",
-      "OperatingSystem              0\n",
-      "UndergradMajor               0\n",
-      "YearsCoding                  0\n",
-      "YearsCodingProf              0\n",
-      "LanguageDesireNextYear       0\n",
-      "LanguageWorkedWith           0\n",
-      "FormalEducation           1549\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 305,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1549"
-      ]
-     },
-     "execution_count": 305,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['FormalEducation'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "FormalEducation\n",
-       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                              36010\n",
-       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                           17529\n",
-       "Some college/university study without earning a degree                                 9737\n",
-       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     7088\n",
-       "Associate degree                                                                       2407\n",
-       "Other doctoral degree (Ph.D, Ed.D., etc.)                                              1754\n",
-       "Primary/elementary school                                                              1217\n",
-       "Professional degree (JD, MD, etc.)                                                     1073\n",
-       "I never completed any formal education                                                  436\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['FormalEducation'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 307,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "EdLevel\n",
-       "Bachelors       37559\n",
-       "No Degree       18478\n",
-       "Masters         17529\n",
-       "Associate        2407\n",
-       "Doctorate        1754\n",
-       "Professional     1073\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 307,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Changing column's name\n",
-    "df.rename(columns={'FormalEducation':'EdLevel'}, inplace =True)\n",
-    "#Refactoring EdLevel\n",
-    "def refactor_ed(df):\n",
-    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
-    "    conditions_ed = [(df['EdLevel'] == 'Associate degree'),\n",
-    "                    (df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
-    "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
-    "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
-    "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
-    "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
-    "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
-    "    \n",
-    "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
-    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
-    "    return df\n",
-    "\n",
-    "# applying function to subsets\n",
-    "df = refactor_ed(df)\n",
-    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
-    "df['EdLevel'].replace('nan', 'Bachelors', inplace=True)\n",
-    "\n",
-    "df['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleaned Dataset : 2018_Survey"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 308,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(78800, 19)"
-      ]
-     },
-     "execution_count": 308,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cleaned_2018 = df[df.notnull()]\n",
-    "cleaned_2018.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Currency</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>RaceEthnicity</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>SalaryType</th>\n",
-       "      <th>Hobby</th>\n",
-       "      <th>JobSatisfaction</th>\n",
-       "      <th>JobSearchStatus</th>\n",
-       "      <th>OperatingSystem</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCoding</th>\n",
-       "      <th>YearsCodingProf</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>EdLevel</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>70841.000000</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>British pounds sterling (£)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately dissatisfied</td>\n",
-       "      <td>Seeking</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Other Science</td>\n",
-       "      <td>30 or more years</td>\n",
-       "      <td>18-20 years</td>\n",
-       "      <td>Go;Python</td>\n",
-       "      <td>JavaScript;Python;Bash/Shell</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>153030.333333</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>British pounds sterling (£)</td>\n",
-       "      <td>Manager</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Non-conforming</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>24-26 years</td>\n",
-       "      <td>6-8 years</td>\n",
-       "      <td>Go;Python</td>\n",
-       "      <td>JavaScript;Python;Bash/Shell</td>\n",
-       "      <td>Associate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>165809.207657</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Neither satisfied nor dissatisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Windows</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>18-20 years</td>\n",
-       "      <td>12-14 years</td>\n",
-       "      <td>C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell</td>\n",
-       "      <td>C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>18 - 24 years old</td>\n",
-       "      <td>21426.000000</td>\n",
-       "      <td>South Africa</td>\n",
-       "      <td>South African rands (R)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Windows</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>6-8 years</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>Assembly;C;C++;Matlab;SQL;Bash/Shell</td>\n",
-       "      <td>C;C++;Java;Matlab;R;SQL;Bash/Shell</td>\n",
-       "      <td>No Degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>18 - 24 years old</td>\n",
-       "      <td>41671.000000</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>British pounds sterling (£)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately satisfied</td>\n",
-       "      <td>Seeking</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>6-8 years</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...</td>\n",
-       "      <td>Java;JavaScript;Python;TypeScript;HTML;CSS</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>18 - 24 years old</td>\n",
-       "      <td>120000.000000</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>MacOS</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>9-11 years</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>C;Go;JavaScript;Python;HTML;CSS</td>\n",
-       "      <td>JavaScript;HTML;CSS</td>\n",
-       "      <td>No Degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>25 - 34 years old</td>\n",
-       "      <td>93336.000000</td>\n",
-       "      <td>Nigeria</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Non developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Black or African descent</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Windows</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>Matlab;SQL;Kotlin;Bash/Shell</td>\n",
-       "      <td>JavaScript;TypeScript;HTML;CSS</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>250000.000000</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>MacOS</td>\n",
-       "      <td>Arts and Science</td>\n",
-       "      <td>30 or more years</td>\n",
-       "      <td>21-23 years</td>\n",
-       "      <td>Erlang;Go;Python;Rust;SQL</td>\n",
-       "      <td>Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua...</td>\n",
-       "      <td>No Degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>26023.003365</td>\n",
-       "      <td>India</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>South Asian</td>\n",
-       "      <td>Non-conforming</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Extremely satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Engineering</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>Java;Python</td>\n",
-       "      <td>Java</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>18 - 24 years old</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>Netherlands</td>\n",
-       "      <td>Euros (€)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Monthly</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Neither satisfied nor dissatisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Windows</td>\n",
-       "      <td>No major</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>Java;Python</td>\n",
-       "      <td>Java;JavaScript;PHP;VB.NET;HTML;CSS</td>\n",
-       "      <td>No Degree</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Age      SalaryUSD         Country  \\\n",
-       "1   35 - 44 years old   70841.000000  United Kingdom   \n",
-       "2   35 - 44 years old  153030.333333   United States   \n",
-       "3   35 - 44 years old  165809.207657   United States   \n",
-       "4   18 - 24 years old   21426.000000    South Africa   \n",
-       "5   18 - 24 years old   41671.000000  United Kingdom   \n",
-       "6   18 - 24 years old  120000.000000   United States   \n",
-       "7   25 - 34 years old   93336.000000         Nigeria   \n",
-       "8   35 - 44 years old  250000.000000   United States   \n",
-       "13  35 - 44 years old   26023.003365           India   \n",
-       "14  18 - 24 years old       0.000000     Netherlands   \n",
-       "\n",
-       "                       Currency        DevType Employment  \\\n",
-       "1   British pounds sterling (£)      Developer  Full-time   \n",
-       "2   British pounds sterling (£)        Manager  Full-time   \n",
-       "3              U.S. dollars ($)      Developer  Full-time   \n",
-       "4       South African rands (R)      Developer  Full-time   \n",
-       "5   British pounds sterling (£)      Developer  Full-time   \n",
-       "6              U.S. dollars ($)      Developer  Full-time   \n",
-       "7              U.S. dollars ($)  Non developer  Full-time   \n",
-       "8              U.S. dollars ($)      Developer  Full-time   \n",
-       "13             U.S. dollars ($)      Developer  Full-time   \n",
-       "14                    Euros (€)      Developer  Full-time   \n",
-       "\n",
-       "                RaceEthnicity          Gender SalaryType Hobby  \\\n",
-       "1   White or European descent            Male     Yearly   Yes   \n",
-       "2   White or European descent  Non-conforming     Yearly   Yes   \n",
-       "3   White or European descent            Male     Yearly    No   \n",
-       "4   White or European descent            Male     Yearly   Yes   \n",
-       "5   White or European descent            Male     Yearly   Yes   \n",
-       "6   White or European descent            Male     Yearly   Yes   \n",
-       "7    Black or African descent          Female     Yearly   Yes   \n",
-       "8   White or European descent            Male     Yearly   Yes   \n",
-       "13                South Asian  Non-conforming     Yearly    No   \n",
-       "14  White or European descent            Male    Monthly    No   \n",
-       "\n",
-       "                       JobSatisfaction JobSearchStatus OperatingSystem  \\\n",
-       "1              Moderately dissatisfied         Seeking     Linux-based   \n",
-       "2                 Moderately satisfied     Not seeking     Linux-based   \n",
-       "3   Neither satisfied nor dissatisfied     Not seeking         Windows   \n",
-       "4                   Slightly satisfied     Not seeking         Windows   \n",
-       "5                 Moderately satisfied         Seeking     Linux-based   \n",
-       "6                   Slightly satisfied     Not seeking           MacOS   \n",
-       "7                   Slightly satisfied     Not seeking         Windows   \n",
-       "8                 Moderately satisfied     Not seeking           MacOS   \n",
-       "13                 Extremely satisfied     Not seeking     Linux-based   \n",
-       "14  Neither satisfied nor dissatisfied     Not seeking         Windows   \n",
-       "\n",
-       "      UndergradMajor       YearsCoding YearsCodingProf  \\\n",
-       "1      Other Science  30 or more years     18-20 years   \n",
-       "2   Computer Science       24-26 years       6-8 years   \n",
-       "3   Computer Science       18-20 years     12-14 years   \n",
-       "4   Computer Science         6-8 years       0-2 years   \n",
-       "5   Computer Science         6-8 years       3-5 years   \n",
-       "6   Computer Science        9-11 years       0-2 years   \n",
-       "7   Computer Science         0-2 years       3-5 years   \n",
-       "8   Arts and Science  30 or more years     21-23 years   \n",
-       "13       Engineering         3-5 years       3-5 years   \n",
-       "14          No major         0-2 years       0-2 years   \n",
-       "\n",
-       "                               LanguageDesireNextYear  \\\n",
-       "1                                           Go;Python   \n",
-       "2                                           Go;Python   \n",
-       "3    C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell   \n",
-       "4                Assembly;C;C++;Matlab;SQL;Bash/Shell   \n",
-       "5   C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...   \n",
-       "6                     C;Go;JavaScript;Python;HTML;CSS   \n",
-       "7                        Matlab;SQL;Kotlin;Bash/Shell   \n",
-       "8                           Erlang;Go;Python;Rust;SQL   \n",
-       "13                                        Java;Python   \n",
-       "14                                        Java;Python   \n",
-       "\n",
-       "                                   LanguageWorkedWith    EdLevel  \n",
-       "1                        JavaScript;Python;Bash/Shell  Bachelors  \n",
-       "2                        JavaScript;Python;Bash/Shell  Associate  \n",
-       "3    C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell  Bachelors  \n",
-       "4                  C;C++;Java;Matlab;R;SQL;Bash/Shell  No Degree  \n",
-       "5          Java;JavaScript;Python;TypeScript;HTML;CSS  Bachelors  \n",
-       "6                                 JavaScript;HTML;CSS  No Degree  \n",
-       "7                      JavaScript;TypeScript;HTML;CSS  Bachelors  \n",
-       "8   Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua...  No Degree  \n",
-       "13                                               Java  Bachelors  \n",
-       "14                Java;JavaScript;PHP;VB.NET;HTML;CSS  No Degree  "
-      ]
-     },
-     "execution_count": 309,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cleaned_2018.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## After Cleaning Dataset 2018"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  1497200\n",
-      "Total missing :  0\n",
-      "Missing Percentage:  0.0 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = df.isnull().sum() #number of missing\n",
-    "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stackoverflow 2019 Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "na_vals = ['NA', 'Missing']\n",
-    "survey_main_df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2019.csv', na_values=na_vals)\n",
-    "schema_df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2019.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Cleaning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Selecting only the required columns for analysis\n",
-    "survey_df_2019 = survey_main_df[['Age', 'CareerSat', 'ConvertedComp', 'Country', 'Dependents', 'EdLevel', 'Employment', 'Ethnicity', 'Gender', 'Hobbyist', 'ImpSyn', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n",
-    " 'UndergradMajor', 'YearsCodePro', 'DevType']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#changing the name of columns for easier understanding\n",
-    "# 'MainBranch': 'Profession'\n",
-    "# 'ConvertedComp': 'SalaryUSD'\n",
-    "# 'CareerSat': 'JobSatisfaction'\n",
-    "# 'ImpSyn' : 'CompetenceLevel'\n",
-    "# 'JobSat' : 'CurrentJobSatis'\n",
-    "# 'JobSeek' : 'JobStatus'\n",
-    "\n",
-    "\n",
-    "survey_df_2019.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD',  'CareerSat': 'JobSatisfaction', 'ImpSyn' : 'CompetenceLevel', 'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>CompetenceLevel</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "      <th>Dependents</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>JobSatisfaction</th>\n",
-       "      <th>JobStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>Profession</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>14.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>No</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Primary/elementary school</td>\n",
-       "      <td>Not employed, and not looking for work</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL</td>\n",
-       "      <td>HTML/CSS;Java;JavaScript;Python</td>\n",
-       "      <td>I am a student who is learning to code</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>19.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Bosnia and Herzegovina</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Developer, desktop or enterprise applications;...</td>\n",
-       "      <td>Secondary school (e.g. American high school, G...</td>\n",
-       "      <td>Not employed, but looking for work</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>No</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>I am actively looking for a job</td>\n",
-       "      <td>C++;HTML/CSS;JavaScript;SQL</td>\n",
-       "      <td>C++;HTML/CSS;Python</td>\n",
-       "      <td>I am a student who is learning to code</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>Thailand</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Employed full-time</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>Elixir;HTML/CSS</td>\n",
-       "      <td>HTML/CSS</td>\n",
-       "      <td>I am not primarily a developer, but I write co...</td>\n",
-       "      <td>8820.0</td>\n",
-       "      <td>Web development or web design</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Age CompetenceLevel                 Country     CurrentJobSatis  \\\n",
-       "0  14.0             NaN          United Kingdom                 NaN   \n",
-       "1  19.0             NaN  Bosnia and Herzegovina                 NaN   \n",
-       "2  28.0         Average                Thailand  Slightly satisfied   \n",
-       "\n",
-       "  Dependents                                            DevType  \\\n",
-       "0         No                                                NaN   \n",
-       "1         No  Developer, desktop or enterprise applications;...   \n",
-       "2        Yes  Designer;Developer, back-end;Developer, front-...   \n",
-       "\n",
-       "                                             EdLevel  \\\n",
-       "0                          Primary/elementary school   \n",
-       "1  Secondary school (e.g. American high school, G...   \n",
-       "2           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
-       "\n",
-       "                               Employment Ethnicity Gender Hobbyist  \\\n",
-       "0  Not employed, and not looking for work       NaN    Man      Yes   \n",
-       "1      Not employed, but looking for work       NaN    Man       No   \n",
-       "2                      Employed full-time       NaN    Man      Yes   \n",
-       "\n",
-       "      JobSatisfaction                                          JobStatus  \\\n",
-       "0                 NaN                                                NaN   \n",
-       "1                 NaN                    I am actively looking for a job   \n",
-       "2  Slightly satisfied  I’m not actively looking, but I am open to new...   \n",
-       "\n",
-       "                            LanguageDesireNextYear  \\\n",
-       "0  C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL   \n",
-       "1                      C++;HTML/CSS;JavaScript;SQL   \n",
-       "2                                  Elixir;HTML/CSS   \n",
-       "\n",
-       "                LanguageWorkedWith  \\\n",
-       "0  HTML/CSS;Java;JavaScript;Python   \n",
-       "1              C++;HTML/CSS;Python   \n",
-       "2                         HTML/CSS   \n",
-       "\n",
-       "                                          Profession  SalaryUSD  \\\n",
-       "0             I am a student who is learning to code        NaN   \n",
-       "1             I am a student who is learning to code        NaN   \n",
-       "2  I am not primarily a developer, but I write co...     8820.0   \n",
-       "\n",
-       "                  UndergradMajor YearsCodePro  \n",
-       "0                            NaN          NaN  \n",
-       "1                            NaN          NaN  \n",
-       "2  Web development or web design            1  "
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#sorting the columns alphabetically\n",
-    "survey_df_2019.sort_index(axis=1).head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                       float64\n",
-       "JobSatisfaction            object\n",
-       "SalaryUSD                 float64\n",
-       "Country                    object\n",
-       "Dependents                 object\n",
-       "EdLevel                    object\n",
-       "Employment                 object\n",
-       "Ethnicity                  object\n",
-       "Gender                     object\n",
-       "Hobbyist                   object\n",
-       "CompetenceLevel            object\n",
-       "CurrentJobSatis            object\n",
-       "JobStatus                  object\n",
-       "LanguageDesireNextYear     object\n",
-       "LanguageWorkedWith         object\n",
-       "Profession                 object\n",
-       "UndergradMajor             object\n",
-       "YearsCodePro               object\n",
-       "DevType                    object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#datatype of survey data\n",
-    "survey_df_2019.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Validation - Total Cells vs Missing %"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  1688777\n",
-      "Total missing :  169969\n",
-      "Missing Percentage:  10.064620728491684 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = survey_df_2019.isnull().sum() #number of missing\n",
-    "total_cells = np.product(survey_df_2019.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleaning and Refactoring column values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Gender"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Man                                                            77919\n",
-       "Woman                                                           6344\n",
-       "Non-binary, genderqueer, or gender non-conforming                597\n",
-       "Man;Non-binary, genderqueer, or gender non-conforming            181\n",
-       "Woman;Non-binary, genderqueer, or gender non-conforming          163\n",
-       "Woman;Man                                                        132\n",
-       "Woman;Man;Non-binary, genderqueer, or gender non-conforming       70\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Gender'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#lets refactor Gender values to Male, female and Non binary\n",
-    "#For the purpose of our data analysis we are considering three gender category. This not to defame any gender.\n",
-    "#refactoring Gender\n",
-    "\n",
-    "def refactor_gender(df):\n",
-    "    '''function to change gender category to Male, Female, Non binary'''\n",
-    "    conditions = [(df['Gender'] == 'Man') | (df['Gender'] == 'Man;Non-binary, genderqueer, or gender non-conforming'),\n",
-    "                  (df['Gender'] == 'Woman') | (df['Gender'] == 'Woman;Non-binary, genderqueer, or gender non-conforming'),\n",
-    "                  (df['Gender'] == 'Non-binary, genderqueer, or gender non-conforming') \n",
-    "                  | (df['Gender'] == 'Woman;Man') \n",
-    "                  | (df['Gender'] == 'Woman;Man;Non-binary, genderqueer, or gender non-conforming')]\n",
-    "\n",
-    "    values = ['Man', 'Woman', 'Non-binary']\n",
-    "\n",
-    "    df['Gender'] = np.select(conditions, values, default = np.NaN)\n",
-    "    \n",
-    "    return df\n",
-    "            \n",
-    "survey_df_2019 = refactor_gender(survey_df_2019)\n",
-    "survey_df_2019['Gender'].replace('nan', 'Non-binary', inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Gender'] = survey_df_2019['Gender'].fillna('Non-binary')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n",
-    "survey_df_2019.isnull().sum()['Gender']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gender\n",
-       "Man           78100\n",
-       "Non-binary     4276\n",
-       "Woman          6507\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n",
-    "plt.title(\"Before cleaning Age's outliers from genders\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#We are considering developes of age 15 to 60\n",
-    "filt = (survey_df_2019['Age'] >= 15) & (survey_df_2019['Age'] <= 60)\n",
-    "survey_df_2019 = survey_df_2019[filt]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n",
-    "plt.title(\"After cleaning Age's outliers from genders\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Age'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Profession column (Mainbranch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I am a developer by profession                                                   59247\n",
-       "I am a student who is learning to code                                            8382\n",
-       "I am not primarily a developer, but I write code sometimes as part of my work     6531\n",
-       "I code primarily as a hobby                                                       2370\n",
-       "I used to be a developer by profession, but no longer am                          1210\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Profession'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "255"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Profession'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Profession'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I am a developer by profession                                                   59502\n",
-       "I am a student who is learning to code                                            8382\n",
-       "I am not primarily a developer, but I write code sometimes as part of my work     6531\n",
-       "I code primarily as a hobby                                                       2370\n",
-       "I used to be a developer by profession, but no longer am                          1210\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Profession'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#Lets refactor column values of Profession column\n",
-    "#refactoring profession column\n",
-    "\n",
-    "def refactor_prof(df):\n",
-    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
-    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
-    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
-    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
-    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
-    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
-    "    \n",
-    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
-    "    \n",
-    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "survey_df_2019 = refactor_prof(survey_df_2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer        59502\n",
-       "Student           8382\n",
-       "Non developer     6531\n",
-       "Novoice           2370\n",
-       "Ex-Developer      1210\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Profession'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## EdLevel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                              34926\n",
-       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                           17305\n",
-       "Some college/university study without earning a degree                                 9571\n",
-       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     7638\n",
-       "Associate degree                                                                       2585\n",
-       "Other doctoral degree (Ph.D, Ed.D., etc.)                                              2032\n",
-       "Professional degree (JD, MD, etc.)                                                     1037\n",
-       "Primary/elementary school                                                               981\n",
-       "I never completed any formal education                                                  352\n",
-       "Name: EdLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1568"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['EdLevel'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#  Refactoring EdLevel\n",
-    "def refactor_ed(df):\n",
-    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
-    "    conditions_ed = [(df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
-    "                     (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
-    "                     (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
-    "                     (df['EdLevel'] == 'Associate degree'),\n",
-    "                     (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
-    "                     (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
-    "                     | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
-    "                     | (df['EdLevel'] == 'Primary/elementary school')\n",
-    "                     | (df['EdLevel'] == 'I never completed any formal education')]\n",
-    "\n",
-    "    choices_ed = ['Bachelors', 'Masters', 'Professional', 'Associate', 'Doctorate', 'No Degree']\n",
-    "\n",
-    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
-    "    \n",
-    "    return df\n",
-    "\n",
-    "# applying function to subsets\n",
-    "survey_df_2019 = refactor_ed(survey_df_2019)\n",
-    "survey_df_2019['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Bachelors       36494\n",
-       "No Degree       18542\n",
-       "Masters         17305\n",
-       "Associate        2585\n",
-       "Doctorate        2032\n",
-       "Professional     1037\n",
-       "Name: EdLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019.isnull().sum()['EdLevel']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Undergrad Major"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Computer science, computer engineering, or software engineering          42211\n",
-       "Another engineering discipline (ex. civil, electrical, mechanical)        5472\n",
-       "Information systems, information technology, or system administration     4646\n",
-       "Web development or web design                                             2975\n",
-       "A natural science (ex. biology, chemistry, physics)                       2866\n",
-       "Mathematics or statistics                                                 2557\n",
-       "A business discipline (ex. accounting, finance, marketing)                1633\n",
-       "A humanities discipline (ex. literature, history, philosophy)             1408\n",
-       "A social science (ex. anthropology, psychology, political science)        1246\n",
-       "Fine arts or performing arts (ex. graphic design, music, studio art)      1124\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10787"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['UndergradMajor'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Computer science, computer engineering, or software engineering          49010\n",
-       "Another engineering discipline (ex. civil, electrical, mechanical)        6368\n",
-       "Information systems, information technology, or system administration     5392\n",
-       "Web development or web design                                             3424\n",
-       "A natural science (ex. biology, chemistry, physics)                       3285\n",
-       "Mathematics or statistics                                                 2984\n",
-       "A business discipline (ex. accounting, finance, marketing)                1908\n",
-       "A humanities discipline (ex. literature, history, philosophy)             1627\n",
-       "A social science (ex. anthropology, psychology, political science)        1431\n",
-       "Fine arts or performing arts (ex. graphic design, music, studio art)      1327\n",
-       "I never declared a major                                                   922\n",
-       "A health science (ex. nursing, pharmacy, radiology)                        316\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].value_counts().nlargest(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019.dropna(subset=['UndergradMajor'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Refactoring UndergradMajor\n",
-    "def refactor_major(df):\n",
-    "    '''function to change undergrad major category to Computer Science, Engineering, Info Systems, Math/Stat, \n",
-    "        Other Science, Web Design/Dev, Business, Arts and Science'''\n",
-    "    \n",
-    "    \n",
-    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'),\n",
-    "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
-    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'),\n",
-    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
-    "                        (df['UndergradMajor'] == 'I never declared a major'),\n",
-    "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)')\n",
-    "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'),\n",
-    "                        (df['UndergradMajor'] == 'Web development or web design'),\n",
-    "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'),\n",
-    "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
-    "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
-    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)')]\n",
-    "\n",
-    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'No Major', 'Other Science',\n",
-    "                     'Web Design/Dev', 'Business', 'Arts and Science']\n",
-    "\n",
-    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
-    "    \n",
-    "    return df\n",
-    "\n",
-    "# applying function to subsets\n",
-    "survey_df_2019 = refactor_major(survey_df_2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Computer Science    49010\n",
-       "Engineering          6368\n",
-       "Info Systems         5392\n",
-       "Arts and Science     4385\n",
-       "Other Science        3601\n",
-       "Web Design/Dev       3424\n",
-       "Math/Stat            2984\n",
-       "Business             1908\n",
-       "No Major              922\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Job Status"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I’m not actively looking, but I am open to new opportunities    42258\n",
-       "I am not interested in new job opportunities                    19161\n",
-       "I am actively looking for a job                                 10491\n",
-       "Name: JobStatus, dtype: int64"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6084"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['JobStatus'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I’m not actively looking, but I am open to new opportunities    45917\n",
-       "I am not interested in new job opportunities                    20712\n",
-       "I am actively looking for a job                                 11365\n",
-       "Name: JobStatus, dtype: int64"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019.dropna(subset=['JobStatus'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# refactoring JobStatus\n",
-    "# changing the jobstatus to seeking and non seeking\n",
-    "def refactor_job(df):\n",
-    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
-    "    \n",
-    "    conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n",
-    "                    (df['JobStatus'] == 'I am not interested in new job opportunities')\n",
-    "                    | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
-    "    \n",
-    "    choices_job = ['Seeking', 'Not seeking']\n",
-    "    \n",
-    "    df['JobStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "survey_df_2019 = refactor_job(survey_df_2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Not seeking    66629\n",
-       "Seeking        11365\n",
-       "Name: JobStatus, dtype: int64"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JobSatisfaction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        26584\n",
-       "Slightly satisfied                    22739\n",
-       "Slightly dissatisfied                  6843\n",
-       "Neither satisfied nor dissatisfied     6158\n",
-       "Very dissatisfied                      3203\n",
-       "Name: JobSatisfaction, dtype: int64"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12467"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        31507\n",
-       "Slightly satisfied                    26970\n",
-       "Slightly dissatisfied                  8343\n",
-       "Neither satisfied nor dissatisfied     7313\n",
-       "Very dissatisfied                      3861\n",
-       "Name: JobSatisfaction, dtype: int64"
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Employment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employed full-time                                      58069\n",
-       "Independent contractor, freelancer, or self-employed     7305\n",
-       "Not employed, but looking for work                       4703\n",
-       "Employed part-time                                       3958\n",
-       "Not employed, and not looking for work                   2914\n",
-       "Retired                                                    76\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "969"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Employment'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 164,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employed full-time                                      58771\n",
-       "Independent contractor, freelancer, or self-employed     7397\n",
-       "Not employed, but looking for work                       4770\n",
-       "Employed part-time                                       4017\n",
-       "Not employed, and not looking for work                   2960\n",
-       "Retired                                                    79\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 165,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Refactoring the employment\n",
-    "def refactor_emp(df):\n",
-    "    '''function to change Employment category to Full-time, Self-employed, Not employed, Part-time '''\n",
-    "    conditions_emp = [(df['Employment'] == 'Employed full-time'),\n",
-    "                    (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n",
-    "                    (df['Employment'] == 'Not employed, but looking for work')\n",
-    "                    | (df['Employment'] == 'Not employed, and not looking for work')\n",
-    "                    | (df['Employment'] == 'Retired'),\n",
-    "                    (df['Employment'] == 'Employed part-time')]\n",
-    "    \n",
-    "    choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n",
-    "    \n",
-    "    df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "\n",
-    "survey_df_2019 = refactor_emp(survey_df_2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Full-time        58771\n",
-       "Not employed      7809\n",
-       "Self-employed     7397\n",
-       "Part-time         4017\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ethnicity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ethnicity_list = survey_df_2019['Ethnicity'].unique().tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[nan,\n",
-       " 'White or of European descent',\n",
-       " 'White or of European descent;Multiracial',\n",
-       " 'East Asian',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent',\n",
-       " 'Hispanic or Latino/Latina;Multiracial',\n",
-       " 'Hispanic or Latino/Latina',\n",
-       " 'Middle Eastern',\n",
-       " 'South Asian',\n",
-       " 'Multiracial',\n",
-       " 'East Asian;South Asian',\n",
-       " 'Biracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Black or of African descent;White or of European descent;Biracial',\n",
-       " 'Middle Eastern;White or of European descent',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Black or of African descent;White or of European descent',\n",
-       " 'White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;White or of European descent',\n",
-       " 'East Asian;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'East Asian;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;White or of European descent;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
-       " 'South Asian;White or of European descent;Multiracial',\n",
-       " 'South Asian;Biracial',\n",
-       " 'Middle Eastern;South Asian',\n",
-       " 'East Asian;South Asian;Multiracial',\n",
-       " 'White or of European descent;Biracial',\n",
-       " 'East Asian;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina',\n",
-       " 'East Asian;Hispanic or Latino/Latina;White or of European descent',\n",
-       " 'East Asian;White or of European descent;Multiracial',\n",
-       " 'South Asian;White or of European descent;Biracial',\n",
-       " 'East Asian;South Asian;White or of European descent;Multiracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;Biracial',\n",
-       " 'Hispanic or Latino/Latina;South Asian;Multiracial',\n",
-       " 'Black or of African descent;East Asian;South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'East Asian;Middle Eastern',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;Multiracial',\n",
-       " 'Middle Eastern;White or of European descent;Biracial',\n",
-       " 'East Asian;Middle Eastern;South Asian',\n",
-       " 'East Asian;Biracial',\n",
-       " 'Middle Eastern;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;Biracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
-       " 'Middle Eastern;Multiracial',\n",
-       " 'Black or of African descent;Middle Eastern',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Black or of African descent;South Asian;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina',\n",
-       " 'South Asian;Multiracial',\n",
-       " 'East Asian;South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Multiracial',\n",
-       " 'South Asian;White or of European descent',\n",
-       " 'Black or of African descent;East Asian',\n",
-       " 'Black or of African descent;Middle Eastern;Multiracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
-       " 'Black or of African descent;South Asian;White or of European descent;Multiracial',\n",
-       " 'East Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Middle Eastern;Biracial',\n",
-       " 'East Asian;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial',\n",
-       " 'Hispanic or Latino/Latina;Biracial;Multiracial',\n",
-       " 'Black or of African descent;South Asian',\n",
-       " 'Black or of African descent;East Asian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;White or of European descent;Biracial;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;South Asian',\n",
-       " 'East Asian;Middle Eastern;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
-       " 'Middle Eastern;South Asian;Multiracial',\n",
-       " 'Black or of African descent;Middle Eastern;White or of European descent',\n",
-       " 'Black or of African descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern',\n",
-       " 'Black or of African descent;Middle Eastern;Biracial',\n",
-       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;Biracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Middle Eastern;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'East Asian;Middle Eastern;South Asian;White or of European descent',\n",
-       " 'East Asian;Middle Eastern;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Biracial',\n",
-       " 'East Asian;Middle Eastern;White or of European descent',\n",
-       " 'East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern;White or of European descent',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;South Asian;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian;Biracial',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'East Asian;South Asian;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'East Asian;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
-       " 'East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Hispanic or Latino/Latina;South Asian;White or of European descent',\n",
-       " 'Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Black or of African descent;East Asian;Biracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Biracial',\n",
-       " 'East Asian;South Asian;White or of European descent',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Middle Eastern;Biracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;South Asian',\n",
-       " 'East Asian;South Asian;Biracial;Multiracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;South Asian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;South Asian;Biracial',\n",
-       " 'East Asian;Middle Eastern;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Middle Eastern;South Asian;White or of European descent',\n",
-       " 'Middle Eastern;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'East Asian;Middle Eastern;South Asian;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;Biracial;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Middle Eastern;White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;Middle Eastern;South Asian',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;Biracial;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'South Asian;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;South Asian',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern;South Asian',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Multiracial',\n",
-       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'East Asian;Middle Eastern;South Asian;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;East Asian;South Asian',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'Black or of African descent;South Asian;Biracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
-       " 'East Asian;South Asian;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;East Asian;Middle Eastern;South Asian;Multiracial']"
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#here, you can see that we have long list of values. lets refactor them\n",
-    "ethnicity_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "173"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(ethnicity_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#refactoring long list of values into categories.\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Biracial', na=False), 'Ethnicity'] = 'Biracial'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Black or of African descent', na=False), 'Ethnicity'] = 'Black or of African descent'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('East Asian', na=False), 'Ethnicity'] = 'East Asian'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Hispanic or Latino', na=False), 'Ethnicity'] = 'Hispanic or Latino'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Indigenous', na=False), 'Ethnicity'] = 'Indigenous'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Middle Eastern', na=False), 'Ethnicity'] = 'Middle Eastern'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('South Asian', na=False), 'Ethnicity'] = 'South Asian'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('White or of European descent', na=False), 'Ethnicity'] = 'White or of European descent'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Multiracial', na=False), 'Ethnicity'] = 'Multiracial'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Native American', na=False), 'Ethnicity'] = 'Native American'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7804"
-      ]
-     },
-     "execution_count": 172,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Ethnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent    47587\n",
-       "South Asian                      7417\n",
-       "Hispanic or Latino               4901\n",
-       "East Asian                       3698\n",
-       "Middle Eastern                   3057\n",
-       "Black or of African descent      2360\n",
-       "Multiracial                       572\n",
-       "Native American                   322\n",
-       "Biracial                          276\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 173,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Ethnicity'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Ethnicity']=survey_df_2019.groupby(['Country'])['Ethnicity'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 175,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Ethnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent    50883\n",
-       "South Asian                     10061\n",
-       "Hispanic or Latino               5204\n",
-       "East Asian                       4391\n",
-       "Middle Eastern                   3596\n",
-       "Black or of African descent      2570\n",
-       "Multiracial                       632\n",
-       "Native American                   355\n",
-       "Biracial                          302\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Ethnicity'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dependents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "No     46457\n",
-       "Yes    28918\n",
-       "Name: Dependents, dtype: int64"
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019[\"Dependents\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2619"
-      ]
-     },
-     "execution_count": 178,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019[\"Dependents\"].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Lets consider that people who didnt respond has no dependents for the purpose of analysis\n",
-    "survey_df_2019[\"Dependents\"].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 180,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019[\"Dependents\"].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "No     48085\n",
-       "Yes    29909\n",
-       "Name: Dependents, dtype: int64"
-      ]
-     },
-     "execution_count": 181,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019[\"Dependents\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DevType"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5025"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer, full-stack                                                                                           7636\n",
-       "Developer, back-end                                                                                             4387\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  2216\n",
-       "Developer, front-end                                                                                            1985\n",
-       "Developer, mobile                                                                                               1934\n",
-       "Developer, back-end;Developer, full-stack                                                                       1886\n",
-       "Student                                                                                                         1289\n",
-       "Developer, front-end;Developer, full-stack                                                                       940\n",
-       "Developer, desktop or enterprise applications                                                                    900\n",
-       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     815\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 183,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['DevType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['DevType'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer, full-stack                                             8147\n",
-       "Developer, back-end                                               4680\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack    2365\n",
-       "Developer, front-end                                              2129\n",
-       "Developer, mobile                                                 2086\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['DevType'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageWorkedWith"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "656"
-      ]
-     },
-     "execution_count": 187,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HTML/CSS;JavaScript;PHP;SQL              1345\n",
-       "C#;HTML/CSS;JavaScript;SQL               1282\n",
-       "HTML/CSS;JavaScript                      1098\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript     908\n",
-       "HTML/CSS;JavaScript;PHP                   821\n",
-       "Java                                      757\n",
-       "HTML/CSS;JavaScript;TypeScript            644\n",
-       "Python                                    634\n",
-       "HTML/CSS;Java;JavaScript;SQL              596\n",
-       "C#                                        484\n",
-       "Name: LanguageWorkedWith, dtype: int64"
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 190,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HTML/CSS;JavaScript;PHP;SQL              1366\n",
-       "C#;HTML/CSS;JavaScript;SQL               1288\n",
-       "HTML/CSS;JavaScript                      1108\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript     914\n",
-       "HTML/CSS;JavaScript;PHP                   831\n",
-       "Java                                      765\n",
-       "HTML/CSS;JavaScript;TypeScript            650\n",
-       "Python                                    640\n",
-       "HTML/CSS;Java;JavaScript;SQL              600\n",
-       "C#                                        489\n",
-       "Name: LanguageWorkedWith, dtype: int64"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## CompetenceLevel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "A little above average    29693\n",
-       "Average                   15532\n",
-       "Far above average         13840\n",
-       "A little below average     4837\n",
-       "Far below average          1322\n",
-       "Name: CompetenceLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 192,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CompetenceLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12770"
-      ]
-     },
-     "execution_count": 193,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CompetenceLevel'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign the null values based on forward fill.\n",
-    "survey_df_2019['CompetenceLevel'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 195,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CompetenceLevel'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "A little above average    35394\n",
-       "Average                   18436\n",
-       "Far above average         16821\n",
-       "A little below average     5739\n",
-       "Far below average          1604\n",
-       "Name: CompetenceLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CompetenceLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Current Job Satisfaction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Slightly satisfied                    22123\n",
-       "Very satisfied                        20452\n",
-       "Slightly dissatisfied                  9751\n",
-       "Neither satisfied nor dissatisfied     7547\n",
-       "Very dissatisfied                      4283\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13838"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CurrentJobSatis'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign the null values based on forward fill.\n",
-    "survey_df_2019['CurrentJobSatis'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CurrentJobSatis'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Slightly satisfied                    26780\n",
-       "Very satisfied                        24873\n",
-       "Slightly dissatisfied                 12043\n",
-       "Neither satisfied nor dissatisfied     9111\n",
-       "Very dissatisfied                      5187\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 201,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageDesireNextYear"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Python                                   1003\n",
-       "HTML/CSS;JavaScript                       624\n",
-       "HTML/CSS;JavaScript;TypeScript            569\n",
-       "C#                                        533\n",
-       "C#;HTML/CSS;JavaScript;SQL                525\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript     515\n",
-       "HTML/CSS;JavaScript;PHP;SQL               472\n",
-       "Java                                      457\n",
-       "Go                                        373\n",
-       "HTML/CSS;JavaScript;Python                354\n",
-       "Swift                                     348\n",
-       "Kotlin                                    335\n",
-       "HTML/CSS;JavaScript;PHP                   326\n",
-       "C++;Python                                324\n",
-       "C#;SQL                                    309\n",
-       "JavaScript                                307\n",
-       "C++                                       306\n",
-       "C#;HTML/CSS;JavaScript;TypeScript         297\n",
-       "Java;Kotlin                               280\n",
-       "JavaScript;Python                         275\n",
-       "Name: LanguageDesireNextYear, dtype: int64"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3424"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign the null values based on forward fill.\n",
-    "survey_df_2019['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 205,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Python                                   1054\n",
-       "HTML/CSS;JavaScript                       656\n",
-       "HTML/CSS;JavaScript;TypeScript            597\n",
-       "C#                                        557\n",
-       "C#;HTML/CSS;JavaScript;SQL                553\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript     533\n",
-       "HTML/CSS;JavaScript;PHP;SQL               493\n",
-       "Java                                      484\n",
-       "Go                                        397\n",
-       "HTML/CSS;JavaScript;Python                370\n",
-       "Swift                                     360\n",
-       "Kotlin                                    360\n",
-       "HTML/CSS;JavaScript;PHP                   347\n",
-       "C++;Python                                336\n",
-       "C#;SQL                                    320\n",
-       "C++                                       319\n",
-       "JavaScript                                312\n",
-       "C#;HTML/CSS;JavaScript;TypeScript         305\n",
-       "Java;Kotlin                               298\n",
-       "JavaScript;Python                         289\n",
-       "Name: LanguageDesireNextYear, dtype: int64"
-      ]
-     },
-     "execution_count": 206,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## YearsCodePro"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<bound method IndexOpsMixin.value_counts of 2                       1\n",
-       "3        Less than 1 year\n",
-       "4                       9\n",
-       "5                       3\n",
-       "6                       4\n",
-       "               ...       \n",
-       "88870                 NaN\n",
-       "88871                   5\n",
-       "88872                   3\n",
-       "88876                  15\n",
-       "88882                   3\n",
-       "Name: YearsCodePro, Length: 77994, dtype: object>"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].value_counts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#changing the dtype to float\n",
-    "survey_df_2019['YearsCodePro'] = survey_df_2019['YearsCodePro'].apply(pd.to_numeric, errors='coerce')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0     7243\n",
-       "3.0     7164\n",
-       "5.0     5855\n",
-       "4.0     5764\n",
-       "6.0     4133\n",
-       "1.0     3995\n",
-       "10.0    3934\n",
-       "7.0     3374\n",
-       "8.0     3166\n",
-       "12.0    2008\n",
-       "Name: YearsCodePro, dtype: int64"
-      ]
-     },
-     "execution_count": 209,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].value_counts().head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14639"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['YearsCodePro'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 212,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019.dropna(subset=['YearsCodePro'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 214,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0     8853\n",
-       "3.0     8843\n",
-       "5.0     7186\n",
-       "4.0     7124\n",
-       "6.0     5103\n",
-       "1.0     4925\n",
-       "10.0    4830\n",
-       "7.0     4146\n",
-       "8.0     3910\n",
-       "12.0    2487\n",
-       "Name: YearsCodePro, dtype: int64"
-      ]
-     },
-     "execution_count": 215,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].value_counts().head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Country"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "United States         18335\n",
-       "India                  7276\n",
-       "Germany                5316\n",
-       "United Kingdom         5130\n",
-       "Canada                 2976\n",
-       "France                 2225\n",
-       "Brazil                 1860\n",
-       "Poland                 1773\n",
-       "Netherlands            1687\n",
-       "Australia              1657\n",
-       "Russian Federation     1551\n",
-       "Spain                  1477\n",
-       "Italy                  1451\n",
-       "Sweden                 1165\n",
-       "Switzerland             884\n",
-       "Name: Country, dtype: int64"
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Country'].value_counts().nlargest(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Country'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 219,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "United States         18335\n",
-       "India                  7276\n",
-       "Germany                5316\n",
-       "United Kingdom         5130\n",
-       "Canada                 2976\n",
-       "France                 2225\n",
-       "Brazil                 1860\n",
-       "Poland                 1773\n",
-       "Netherlands            1687\n",
-       "Australia              1657\n",
-       "Russian Federation     1551\n",
-       "Spain                  1477\n",
-       "Italy                  1451\n",
-       "Sweden                 1165\n",
-       "Switzerland             884\n",
-       "Name: Country, dtype: int64"
-      ]
-     },
-     "execution_count": 220,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Country'].value_counts().nlargest(15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SalaryUSD"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000000.0    667\n",
-       "1000000.0    529\n",
-       "120000.0     475\n",
-       "100000.0     450\n",
-       "150000.0     399\n",
-       "Name: SalaryUSD, dtype: int64"
-      ]
-     },
-     "execution_count": 221,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['SalaryUSD'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24805"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['SalaryUSD'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['SalaryUSD'] = survey_df_2019.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3537"
-      ]
-     },
-     "execution_count": 224,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['SalaryUSD'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000000.0    669\n",
-       "1000000.0    547\n",
-       "150000.0     494\n",
-       "120000.0     476\n",
-       "100000.0     450\n",
-       "Name: SalaryUSD, dtype: int64"
-      ]
-     },
-     "execution_count": 225,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "survey_df_2019['SalaryUSD'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "country_mean_salary = survey_df_2019.groupby('Country')['SalaryUSD'].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Country\n",
-       "Liechtenstein        811188.000000\n",
-       "San Marino           301788.000000\n",
-       "Ireland              247051.427005\n",
-       "Swaziland            242607.500000\n",
-       "United States        240269.159270\n",
-       "Timor-Leste          229500.000000\n",
-       "Qatar                203892.571429\n",
-       "Republic of Korea    174593.739130\n",
-       "Norway               173173.193026\n",
-       "Andorra              171862.000000\n",
-       "Name: SalaryUSD, dtype: float64"
-      ]
-     },
-     "execution_count": 227,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "country_mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019.dropna(subset=['SalaryUSD'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleaned Dataset:2019_Survey"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                       0\n",
-       "JobSatisfaction           0\n",
-       "SalaryUSD                 0\n",
-       "Country                   0\n",
-       "Dependents                0\n",
-       "EdLevel                   0\n",
-       "Employment                0\n",
-       "Ethnicity                 0\n",
-       "Gender                    0\n",
-       "Hobbyist                  0\n",
-       "CompetenceLevel           0\n",
-       "CurrentJobSatis           0\n",
-       "JobStatus                 0\n",
-       "LanguageDesireNextYear    0\n",
-       "LanguageWorkedWith        0\n",
-       "Profession                0\n",
-       "UndergradMajor            0\n",
-       "YearsCodePro              0\n",
-       "DevType                   0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 229,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#handle all the null value\n",
-    "survey_df_2019.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#resetting the index values\n",
-    "survey_df_2019 = survey_df_2019.reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of rows before cleaning the data is 88883\n",
-      "Number of rows after cleaning the data is 74457\n"
-     ]
-    }
-   ],
-   "source": [
-    "cleaned_df_2019 = survey_df_2019[survey_df_2019.notnull()]\n",
-    "\n",
-    "print(f\"Number of rows before cleaning the data is {survey_main_df.shape[0]}\")\n",
-    "print(f\"Number of rows after cleaning the data is {cleaned_df_2019.shape[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cleaned_df_2019['Age']=cleaned_df_2019['Age'].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>JobSatisfaction</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Dependents</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>CompetenceLevel</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "      <th>JobStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>Profession</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>DevType</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>8820.0</td>\n",
-       "      <td>Thailand</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>East Asian</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Elixir;HTML/CSS</td>\n",
-       "      <td>HTML/CSS</td>\n",
-       "      <td>Non developer</td>\n",
-       "      <td>Web Design/Dev</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>22</td>\n",
-       "      <td>Very satisfied</td>\n",
-       "      <td>61000.0</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>No</td>\n",
-       "      <td>A little below average</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>C;C#;JavaScript;SQL</td>\n",
-       "      <td>C;C++;C#;Python;SQL</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Developer, full-stack</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>30</td>\n",
-       "      <td>Very dissatisfied</td>\n",
-       "      <td>33184.8</td>\n",
-       "      <td>Ukraine</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>A little above average</td>\n",
-       "      <td>Slightly dissatisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>HTML/CSS;Java;JavaScript;SQL;WebAssembly</td>\n",
-       "      <td>C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>Academic researcher;Developer, desktop or ente...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Very satisfied</td>\n",
-       "      <td>366420.0</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>East Asian</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>A little above average</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Python;Scala;SQL</td>\n",
-       "      <td>Java;R;SQL</td>\n",
-       "      <td>Non developer</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>Data or business analyst;Data scientist or mac...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>42</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>36000.0</td>\n",
-       "      <td>Ukraine</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Self-employed</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>Neither satisfied nor dissatisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>HTML/CSS;JavaScript</td>\n",
-       "      <td>HTML/CSS;JavaScript</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Engineering</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Designer;Developer, front-end</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age     JobSatisfaction  SalaryUSD        Country Dependents    EdLevel  \\\n",
-       "0   28  Slightly satisfied     8820.0       Thailand        Yes  Bachelors   \n",
-       "1   22      Very satisfied    61000.0  United States         No  Bachelors   \n",
-       "2   30   Very dissatisfied    33184.8        Ukraine         No  Bachelors   \n",
-       "3   28      Very satisfied   366420.0         Canada         No  Bachelors   \n",
-       "4   42  Slightly satisfied    36000.0        Ukraine        Yes  Bachelors   \n",
-       "\n",
-       "      Employment                     Ethnicity Gender Hobbyist  \\\n",
-       "0      Full-time                    East Asian    Man      Yes   \n",
-       "1      Full-time  White or of European descent    Man       No   \n",
-       "2      Full-time  White or of European descent    Man      Yes   \n",
-       "3      Full-time                    East Asian    Man      Yes   \n",
-       "4  Self-employed  White or of European descent    Man       No   \n",
-       "\n",
-       "          CompetenceLevel                     CurrentJobSatis    JobStatus  \\\n",
-       "0                 Average                  Slightly satisfied  Not seeking   \n",
-       "1  A little below average                  Slightly satisfied  Not seeking   \n",
-       "2  A little above average               Slightly dissatisfied  Not seeking   \n",
-       "3  A little above average                  Slightly satisfied  Not seeking   \n",
-       "4                 Average  Neither satisfied nor dissatisfied  Not seeking   \n",
-       "\n",
-       "                     LanguageDesireNextYear  \\\n",
-       "0                           Elixir;HTML/CSS   \n",
-       "1                       C;C#;JavaScript;SQL   \n",
-       "2  HTML/CSS;Java;JavaScript;SQL;WebAssembly   \n",
-       "3                          Python;Scala;SQL   \n",
-       "4                       HTML/CSS;JavaScript   \n",
-       "\n",
-       "                            LanguageWorkedWith     Profession  \\\n",
-       "0                                     HTML/CSS  Non developer   \n",
-       "1                          C;C++;C#;Python;SQL      Developer   \n",
-       "2  C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA      Developer   \n",
-       "3                                   Java;R;SQL  Non developer   \n",
-       "4                          HTML/CSS;JavaScript      Developer   \n",
-       "\n",
-       "     UndergradMajor  YearsCodePro  \\\n",
-       "0    Web Design/Dev           1.0   \n",
-       "1  Computer Science           1.0   \n",
-       "2  Computer Science           9.0   \n",
-       "3         Math/Stat           3.0   \n",
-       "4       Engineering           4.0   \n",
-       "\n",
-       "                                             DevType  \n",
-       "0  Designer;Developer, back-end;Developer, front-...  \n",
-       "1                              Developer, full-stack  \n",
-       "2  Academic researcher;Developer, desktop or ente...  \n",
-       "3  Data or business analyst;Data scientist or mac...  \n",
-       "4                      Designer;Developer, front-end  "
-      ]
-     },
-     "execution_count": 233,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "cleaned_df_2019.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## After Cleaning  Dataset 2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  1414683\n",
-      "Total missing :  0\n",
-      "Missing Percentage:  0.0 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = survey_df_2019.isnull().sum() #number of missing\n",
-    "total_cells = np.product(survey_df_2019.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stackoverflow Survey Analysis 2020"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2020.csv')\n",
-    "#df2020.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#drop unnecessary columns\n",
-    "drop_cols = [  'Age1stCode', 'CompFreq', 'CompTotal', 'CurrencyDesc', 'CurrencySymbol', 'NEWJobHunt','NEWJobHuntResearch', 'NEWLearn', \n",
-    "             'NEWOffTopic', 'NEWOnboardGood', 'NEWOtherComms', 'NEWOvertime', 'NEWPurchaseResearch', \n",
-    "             'NEWPurpleLink', 'NEWSOSites', 'NEWStuck', 'OpSys', 'OrgSize', 'PlatformDesireNextYear', 'PlatformWorkedWith',\n",
-    "             'PurchaseWhat', 'Respondent', 'SOAccount', 'SOComm', 'SOPartFreq', 'SOVisitFreq', 'Sexuality', 'SurveyEase', \n",
-    "             'SurveyLength', 'Trans', 'WebframeDesireNextYear', 'WebframeWorkedWith', 'WelcomeChange', 'WorkWeekHrs', 'YearsCode']\n",
-    "df.drop(drop_cols, axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Selecting only the required columns for analysis\n",
-    "cols =['Age','Gender', 'ConvertedComp', 'Country', 'DevType', 'Hobbyist', 'EdLevel', 'Employment', \n",
-    "       'Ethnicity', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n",
-    "        'UndergradMajor', 'YearsCodePro']\n",
-    "df2020 = df[cols]\n",
-    "#df2020.head()\n",
-    "#df2020.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#changing the name of columns for easier understanding\n",
-    "# 'MainBranch': 'Profession'\n",
-    "# 'ConvertedComp': 'SalaryUSD'\n",
-    "# 'JobSat' : 'CurrentJobSatis'\n",
-    "# 'JobSeek' : 'JobStatus'\n",
-    "\n",
-    "df2020.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD', \n",
-    "                                'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, \n",
-    "                      inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                       19015\n",
-      "Gender                    13904\n",
-      "SalaryUSD                 29705\n",
-      "Country                     389\n",
-      "DevType                   15091\n",
-      "Hobbyist                     45\n",
-      "EdLevel                    7030\n",
-      "Employment                  607\n",
-      "Ethnicity                 18513\n",
-      "CurrentJobSatis           19267\n",
-      "JobStatus                 12734\n",
-      "LanguageDesireNextYear    10348\n",
-      "LanguageWorkedWith         7083\n",
-      "Profession                  299\n",
-      "UndergradMajor            13466\n",
-      "YearsCodePro              18112\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df2020.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Validation - Total Cells vs Missing %"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total cell:  1031376\n",
-      "Total missing values:  371516\n",
-      "Missing:  36.02139278013062 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Finding % of missing data\n",
-    "missing_count = df.isnull().sum() #number of missing\n",
-    "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total cell: ', total_cells)\n",
-    "print('Total missing values: ', total_missing)\n",
-    "print('Missing: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gender"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 241,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13904"
-      ]
-     },
-     "execution_count": 241,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Gender'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 242,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gender\n",
-       "Man                                                            46013\n",
-       "Man;Non-binary, genderqueer, or gender non-conforming            121\n",
-       "Non-binary, genderqueer, or gender non-conforming                385\n",
-       "Woman                                                           3844\n",
-       "Woman;Man                                                         76\n",
-       "Woman;Man;Non-binary, genderqueer, or gender non-conforming       26\n",
-       "Woman;Non-binary, genderqueer, or gender non-conforming           92\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 242,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Counting number of each gender\n",
-    "df2020.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n",
-    "df2020['Gender'] = df['Gender'].fillna('Non-binary') \n",
-    "\n",
-    "#Grouping genders into 3 groups Man, Womanand Non-binary\n",
-    "df2020['Gender'].replace('Man;Non-binary, genderqueer, or gender non-conforming', 'Man', inplace =True)\n",
-    "df2020['Gender'].replace('Woman;Non-binary, genderqueer, or gender non-conforming', 'Woman', inplace =True)\n",
-    "df2020['Gender'].replace('Woman;Man;Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)\n",
-    "df2020['Gender'].replace('Woman;Man', 'Non-binary', inplace =True)\n",
-    "df2020['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gender\n",
-       "Man           46134\n",
-       "Non-binary    14391\n",
-       "Woman          3936\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 244,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Counting number of each gender after\n",
-    "df2020.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "df shape after clean Gender:  (64461, 16)\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "print('df shape after clean Gender: ', df2020.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19015"
-      ]
-     },
-     "execution_count": 246,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Age'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Plottig boxplot to check outliers\n",
-    "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n",
-    "plt.title(\"Before cleaning Age's outliers from genders\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Cleaning Age's outliers from each gender)\n",
-    "df2020 = df2020[(df['Age'] >= 15) & (df2020['Age'] <= 60)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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llpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnl1qBJStI4Sb8aiWDMzMyyBk1SEbEBeF7SdiMQT93q6Oigo6Oj1mGYWYafl7VX7l3Q1wL3SFoAPFdYGBH+8ZsKGanfVjKz8vl5WXvlJqmfpQ8zM7MRU1aSioh5kiYAe0TEfVWOyczMDCjz6j5J7wDuBn6ezh8g6adVjMvMzKzsS9DbganAUwARcTfwyqpEZGZmlio3Sb0YEU/3WhaVDsbMzCyr3Asn/iLpBGCcpMlAG3BH9cIyMzMrvyd1BvBaYB1wFfAMcGaVYjIzMwPKv7rveeA/0oeZmdmIGDBJSbqRAT57ioh3VjwiMzOz1GDDfd8ELgAeANYAl6aPbuAv1Q3NzCyfZs6cSWtrKyeccEKfspNOOonW1lY+/OEP9yk7/fTTaW1t5cwzz+xTdvbZZ9Pa2srs2bOLlp977rm0trZy/vnn99lmoLILL7yQ1tZWLr744j5lixYtYtq0aSxZsqTsbYYax3ANmKQi4taIuBV4Q0TMjIgb08cJwGEVj6YXSf9H0pmZ+V9I+m5m/gJJn6l2HGZmWStXrgSgq6urT9ny5cuB0rdUWrp0KQB33nlnn7I77kiuRbvtttuKlt9yyy0ALFiwoM82A5Vdf/31AFx99dV9ytrb29m4cSPnnHNO2dsMNY7hKvfCiR0l7VWYkfRKYMeKR9PXHcAh6T4bgIkkF3AUHAL8bgTiMDMDkl5UVrY3ddJJJxWVZXtTp59+elFZtjd19tlnF5UVelPnnntu0fJsT2WgsgsvvLCoLNszWrRoEd3d3QB0d3f39KYG2maocVSCIgb/upOktwOXAP9MFzUDH4+IX1Q0mr773RVYFBFNkl4HfA7YBZgJPA88lk5/neTztT8Bp0XEOknLgSuBw4EtgVOBrwGTgP8dEd+W1Aj8BNghXWd2RPxEUjNwM3A7SSJ8GHhXRKwZKN6WlpZYvHjxkI51xowZrFmzhsmTJw9p+9Gms7OTrdY/w0Wtz1R9X3MWNwIwu6W76vsaqjNueykvbPnSuvn/jxadnZ1MmDChp4cB0Nra2me9Qu+n0mXV2NfRRx/dk6QAGhsbmT9/flX2VS5JSyKipVRZWT2piPg5MBn4dPrYp9oJKt3vI8CLkvYgSRa/B/4IHAy0APcD3wVmRsTrSBLVaZkqVkTEwcBvgbnAccCbgPPS8rXAeyLiQJJkdoEkpWWTgf8bEa8ludPGe0vFKOlUSYslLV61alVFjtvMrFqyCarUfN6U+2VegCkkPagtgNdLIiIur0pUxX5HkqAOAf4L2C2dfpqkh7M2Iu5P150HfAq4MJ0v3F/wHqAxIp4FnpW0VtL2JD878lVJrcDGtO6d0m0eSG//BLCE5Nj7iIhLSHqZtLS0DPkuHE1NTQB189s1bW1trF3+p1qHkRs7bb2R8c2T6+b/P1q0tY29XyNqbGzs05PKs3JvMHsFyZV+hwFvTB8lu2ZVUPhc6nUkVxT+gaQndQjQ99PHYuvSvxsz04X5LYAPkny2NiUiDiAZPhzfa1uADWxeQjezMWqXXXYpmi+8wQRobm4uKps0aVLP9P77719UduCBB/ZMH3LIIUVlhSG0ww8/vGj5kUce2TM9UNmMGTOKyt7//vf3TLe3txeVFT5DGmibocZRCeVeONECHBoRn4yIM9LHSL3F+B1wLPBkRGyIiCeB7UkS1feAZkmFM+FE4NbNqHs74PGIWC/pcGDPyoVtZmPRj370o6L5K6+8smf68suLB5cuu+yynunel3VnL1T4+te/XlQ2Z84cAL785S8XLc9ejTdQWe9L3LMXbUydOrWn99TY2MiUKVMG3WaocVRCuUnqL8DOFd1z+e4huarvD72WPR0RXcApwDWS7iHpIX17M+r+AdAiaTFJr+rvlQnZzMayQm8q24sqKPSmsr2ogkJvKtuLKij0pnpfiFDoqZTqoQxUVugZZXtEBe3t7TQ0NPS5Em+gbYYax3CVe3XfLcABwCIyw2C+40Sx4VzdVxj7rpfPJAqfSY3EFXej4eq+OYsbGd/8xrr5/48W9fa8rJWBru4r93OW9sqFY2ZmVp5ybzB7q6Q9gckR8StJWwPjqhuamZnVu3Kv7vsYcC3wnXTRbsANVYrJzMwMKP/CiU8Bh5L8jhQR0Qm8olpBmZmZQflJal1EvFCYkbQF/vl4MzOrsnKT1K2SvghMkHQkcA1wY/XCMjMzKz9JnQ2sIvl+0qnAzyLCv9JrZmZVNWCSkvQuSZ+KiI0RcSnJHRlagC9KOm5EIjQzs7o1WE/q82y6SSvAViQ3mp1G8d3GzczMKm6w70ltFRErMvO3p/fOe1LSNlWMq+6UuoWKmdWWn5e1N1iS2iE7ExHZn5YciV/mrRtj8ScBzEY7Py9rb7Dhvj+mX+QtIunjJPfxMzMzq5rBelJnATdIOoFNv900BXgJ8O4qxmVmZjZwkoqIx4FDJL0VeG26+GcR8ZuqR2ZmZnWv3BvM/gZwYjIzsxFV7pd5zczMRpyTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5VZZ35Myq4aHuscxZ3Fj1ffz4LPjAEZkX0P1UPc49q51EGY55CRlNTGSd5fepqsLgPFNTSO2z821N77jtlkpTlJWE767tJmVw59JmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbvkGs1ZVHR0dLFu2rKYxdKV3QW/K8V3QBzNp0iTflNfqkpOUVdWyZcu46693wfY1DOLp5M8qraphEMPwVK0DMKsdJymrvu1h47SNNdt9w8JkVLuWMQxHIX6zeuSz38zMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJKic6Ojro6OiodRhmViN+DSjNN5jNiVr/nIWZ1ZZfA0pzT8rMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHKraklKUki6IDP/OUntFap7rqTjSixvkeSbX5nZmLFo0SKmTZvGkiVL+pRdccUVtLa2ctVVV/UpW716NWeccQZPPPFE2XXef//9TJ8+veQtmoZaNlzV7EmtA2ZImljFfRSJiMUR0VaJuiSNq0Q9ZmbD0d7ezsaNGznnnHP6lF166aUAfOtb3+pTNm/ePJYuXcq8efPKrnPOnDk899xznHfeeX22GWrZcFUzSb0IXAKc1btA0p6Sfi1pafp3j3T5XEkdku6Q9M9SvaWMIyT9VtL9ko5Nt58m6aZ0ul3SZZIWpnX1JC9JN0haIumvkk7NLO+WdJ6kPwKzJf04U3akpOuH2yhmZuVatGgR3d3dAHR3dxf1fK644oqidbO9qdWrV3PzzTcTEdx8881Fvan+6rz//vtZvnw5AMuXLy/qFQ21rBIUERWtsKdiqRvYFVgKvB74GNAYEe2SbgSujYh5kj4MvDMi3i1pLrANMBN4NfDTiJhUou65wM7A0cCrgFuAScCbgM9FxLHp0OJRwOHAtsB9wM4RsV7SyyLiSUkTgD8Bb4mIJyQFMDMirpYk4G/AmyNilaQrgasi4sb+jrmlpSUWL148pPaaMWMGa9asYfLkyUPaPq86Ozvp3tjNxmM31iyGhoXJe7GN02oXw3A03NRAY0PjmDs3rFhnZycTJkzg+us3vRc++uijexIKQGNjI/PnzwegtbW1Tx233XYbABdccAHz589n/fr1bLnllhxzzDF85jOfGbDOk046qSfZADQ3N3P55ZcDDLmsXJKWRERLqbKqXjgREc8AlwO9h+AOBq5Mp68ADsuU3RARGyPiXmCnAaq/Ol2vE/gnSVLr7WcRsS4iVgOPZ+prk/Rn4A/A7kDh2b8BuC6NPdLYPiRp+zTmm3vvQNKpkhZLWrxq1aoBwjUz2zzZZFJqvj8LFixg/fr1AKxfv55f/vKXg9aZTTS954daVgkj8XtSFwJ3At8bYJ1sd25dZloAkr4CHAMQEQeU2KbUfO+6NgBbSJoGHAEcHBHPS1oIjE/XWRsRGzLbfA+4EVgLXBMRL/YJPOISkmFNWlpahtwtbWpqAhhzP3rW1tbGXQ/fVeswRrdGmLzb5DF3blixtra+H6c3Njb26fWU48gjjyzqSR111FGD1tnc3NynR5SdHkpZJVT9EvSIeBK4GvhIZvEdwAfS6Q8Ctw9Sx39ExAGZBAXwPkkNkl4F7EUynFeO7YB/pQnq1SRDhP3t9xHgEWA2MLfM+s3MKqK9vb1o/vzzz++Z/tjHPlZUdtppp/VMz5o1i+QTC2hoaGDWrFmD1jl79uyi5V/60pd6podaVgkj9T2pC4DsVX5twCmSlgInAp8eQp33AbeSDMF9IiLWlrndz0l6VEuB80mG/AbyA2BFOvxoZjZipk6d2tPTaWxsZMqUKT1lJ554YtG6xx9/fM/0xIkTmT59OpKYPn06L3/5ywetc++99+7pBTU3NzNp0qbLAYZaVglVS1IR0ZiZfiwito6I9nR+eUS8NSL2j4i3RcRD6fKTI+LaUnX0qvvkiDgrIt4cEXtHxE3p8oURcWw63R4R38xss1+633URMT3d9/siYlpELBxgf4cBlw63PczMhqK9vZ2GhoaiXlRBoTeV7UUVzJo1i/3337+oFzVYnbNnz2abbbYp2RsaatlwjcRnUqOWpCXAc8Bnax2LmdWnqVOnsnDhwpJlJ554Yp8eVcHEiRO56KKLNqvOvffem5tv7nN92LDKhstJagARMWXwtczMrFp87z4zM8stJykzM8stJykzM8stJykzM8stJykzM8stJykzM8stJykzM8stf08qJyp9KxEzG138GlCak1ROlLoDspnVD78GlObhPjMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy3fYNaq7yloWFjD90NPJX9qGsNwPAXsVusgzGrDScqqKg8/P9AVXQA07dZU40iGaLd8tKNZLThJWVX55wfMbDhG6fiHmZnVAycpMzPLLScpMzPLLScpMzPLLScpMzPLLUVErWMYMyStAh6sdRzDNBFYXesgcsTtUcztsYnbothw2mPPiNixVIGTlBWRtDgiWmodR164PYq5PTZxWxSrVnt4uM/MzHLLScrMzHLLScp6u6TWAeSM26OY22MTt0WxqrSHP5MyM7Pcck/KzMxyy0nKzMxyy0mqjknaXdItkv4m6a+SPp0uf5mkBZI607871DrWapM0XtIiSX9O2+LL6fK6a4ssSeMk3SXppnS+bttD0nJJ90i6W9LidFldtoek7SVdK+nv6evHwdVqCyep+vYi8NmIeA3wJuBTkvYFzgZ+HRGTgV+n82PdOuCtEfF64ADg7ZLeRH22Rdangb9l5uu9PQ6PiAMy3weq1/b4b+DnEfFq4PUk50hV2sJJqo5FxMqIuDOdfpbkRNsNeBcwL11tHvDumgQ4giLRnc5umT6COmyLAklNwDHAdzOL67Y9+lF37SHppUAr8P8AIuKFiHiKKrWFk5QBIKkZeAPwR2CniFgJSSIDXlHD0EZMOrR1N/A4sCAi6rYtUhcCnwc2ZpbVc3sE8EtJSySdmi6rx/bYC1gFfC8dCv6upG2oUls4SRmSGoHrgDMj4plax1MrEbEhIg4AmoCpkvarcUg1I+lY4PGIWFLrWHLk0Ig4EJhOMjTeWuuAamQL4EDgWxHxBuA5qjjM6SRV5yRtSZKgfhAR16eLH5O0S1q+C0nPom6kQxcLgbdTv21xKPBOScuBHwJvlfR96rc9iIhH0r+PAz8GplKf7dEFdKUjDQDXkiStqrSFk1QdkySSceW/RcR/ZYp+CsxKp2cBPxnp2EaapB0lbZ9OTwCOAP5OHbYFQER8ISKaIqIZ+ADwm4j4EHXaHpK2kbRtYRo4CvgLddgeEfEosELSPumitwH3UqW28B0n6pikw4DfAvew6XOHL5J8LnU1sAfwEPC+iHiyJkGOEEn7k3zYO47kzdvVEXGepJdTZ23Rm6RpwOci4th6bQ9Je5H0niAZ7royIr5Sx+1xAMkFNVsB/wROIX3eUOG2cJIyM7Pc8nCfmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOU2Rgh6T2SQtKrax2LWaU4SZmNHccDt5N8+dZsTHCSMhsD0vsvHgp8hDRJSWqQ9D/p72PdJGm+pOPSsimSbk1vlvqLwu1szPLGScpsbHg3ye/73A88KelAYAbQDLwO+ChwMPTcr/Ei4LiImAJcBnylBjGbDWqLWgdgZhVxPMlPa0ByQ9jjSX4T65qI2Ag8KumWtHwfYD9gQXL7RsYBK0c0WrMyOUmZjXLp/ePeCuwnKUiSTrDpXnN9NgH+GhEHj1CIZkPm4T6z0e844PKI2DMimiNid+ABYDXw3vSzqZ2Aaen69wE7SuoZ/pP02loEbjYYJymz0e94+vaargN2Jfntn78A3yG5u/3TEfECSWL7hqQ/A3cDh4xYtGabwXdBNxvDJDVGRHc6JLiI5NdlH611XGbl8mdSZmPbTemPOW4FnO8EZaONe1JmZpZb/kzKzMxyy0nKzMxyy0nKzMxyy0nKzMxyy0nKzMxy6/8D+DkkLlWihPUAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Plottig boxplot to check outliers after cleaning some outliers\n",
-    "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n",
-    "plt.title(\"After cleaning Age's outliers from genders\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#fill Age's null values with mean of each gender\n",
-    "means = df2020.groupby('Gender')['Age'].transform('mean')\n",
-    "df2020['Age'] = df2020['Age'].fillna(means)\n",
-    "\n",
-    "#convert from float to int\n",
-    "df2020['Age'] = df2020['Age'].apply(str).str[:2]\n",
-    "df2020['Age'] = df2020['Age'].apply(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "df shape after clean Age:  (44709, 16)\n"
-     ]
-    }
-   ],
-   "source": [
-    "#df before 64461\n",
-    "print('df shape after clean Age: ', df2020.shape) #no. of Ages' outliners = 64461-44709=19752 (30.6%)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## EdLevel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "933"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['EdLevel'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Bachelor’s degree (B.A., B.S., B.Eng., etc.)                                          20290\n",
-       "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)                                       10000\n",
-       "Some college/university study without earning a degree                                 5699\n",
-       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     3676\n",
-       "Associate degree (A.A., A.S., etc.)                                                    1455\n",
-       "Other doctoral degree (Ph.D., Ed.D., etc.)                                             1256\n",
-       "Primary/elementary school                                                               590\n",
-       "Professional degree (JD, MD, etc.)                                                      578\n",
-       "I never completed any formal education                                                  232\n",
-       "Name: EdLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 253,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Refactoring EdLevel\n",
-    "def refactor_ed(df):\n",
-    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
-    "    conditions_ed = [(df['EdLevel'] == 'Associate degree (A.A., A.S., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Bachelor’s degree (B.A., B.S., B.Eng., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)'),\n",
-    "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
-    "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D., Ed.D., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
-    "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
-    "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
-    "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
-    "    \n",
-    "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
-    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
-    "    return df\n",
-    "\n",
-    "# applying function to subsets\n",
-    "df2020 = refactor_ed(df2020)\n",
-    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
-    "df2020['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Bachelors       21223\n",
-       "No Degree       10197\n",
-       "Masters         10000\n",
-       "Associate        1455\n",
-       "Doctorate        1256\n",
-       "Professional      578\n",
-       "Name: EdLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JobSat (CurrentJobSatis)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8690"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        11751\n",
-       "Slightly satisfied                    11198\n",
-       "Slightly dissatisfied                  5790\n",
-       "Neither satisfied nor dissatisfied     4373\n",
-       "Very dissatisfied                      2907\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020['CurrentJobSatis'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        14628\n",
-       "Slightly satisfied                    13834\n",
-       "Slightly dissatisfied                  7192\n",
-       "Neither satisfied nor dissatisfied     5446\n",
-       "Very dissatisfied                      3609\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JobSeek (JobStatus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2153"
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['JobStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobStatus\n",
-       "I am actively looking for a job                                  6980\n",
-       "I am not interested in new job opportunities                    10919\n",
-       "I’m not actively looking, but I am open to new opportunities    24657\n",
-       "Name: JobStatus, dtype: int64"
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('JobStatus')['JobStatus'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020['JobStatus'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Refactoring JobStatus\n",
-    "#Changing the jobstatus to seeking and non seeking\n",
-    "def refactor_job(df):\n",
-    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
-    "    \n",
-    "    conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n",
-    "                    (df['JobStatus'] == 'I am not interested in new job opportunities')\n",
-    "                    | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
-    "    \n",
-    "    choices_job = ['Seeking', 'Not seeking']\n",
-    "    df['JobSeek'] = np.select(conditions_job, choices_job, default=np.nan)                      \n",
-    "    return df\n",
-    "\n",
-    "df2020 = refactor_job(df2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobSeek\n",
-       "Not seeking    37369\n",
-       "Seeking         7340\n",
-       "Name: JobSeek, dtype: int64"
-      ]
-     },
-     "execution_count": 264,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('JobSeek')['JobSeek'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 265,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['JobStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DevType"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5954"
-      ]
-     },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer, full-stack                                                                                           3399\n",
-       "Developer, back-end                                                                                             2374\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  1838\n",
-       "Developer, back-end;Developer, full-stack                                                                       1216\n",
-       "Developer, front-end                                                                                            1071\n",
-       "Developer, mobile                                                                                                953\n",
-       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     668\n",
-       "Developer, front-end;Developer, full-stack                                                                       667\n",
-       "Developer, back-end;Developer, desktop or enterprise applications                                                528\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack;Developer, mobile                                 475\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 267,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['DevType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020['DevType'] = df2020['DevType'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer, full-stack                                                                                           3940\n",
-       "Developer, back-end                                                                                             2721\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  2146\n",
-       "Developer, back-end;Developer, full-stack                                                                       1411\n",
-       "Developer, front-end                                                                                            1229\n",
-       "Developer, mobile                                                                                               1074\n",
-       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     779\n",
-       "Developer, front-end;Developer, full-stack                                                                       758\n",
-       "Developer, back-end;Developer, desktop or enterprise applications                                                617\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack;Developer, mobile                                 532\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 269,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['DevType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 270,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(64461, 26)"
-      ]
-     },
-     "execution_count": 270,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 271,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 272,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "      <th>JobStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>Profession</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>JobSeek</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [Age, Gender, SalaryUSD, Country, DevType, Hobbyist, EdLevel, Employment, Ethnicity, CurrentJobSatis, JobStatus, LanguageDesireNextYear, LanguageWorkedWith, Profession, UndergradMajor, YearsCodePro, JobSeek]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 272,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020[df2020['DevType'].isnull()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ethnicity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4051"
-      ]
-     },
-     "execution_count": 273,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['Ethnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent                           26552\n",
-       "South Asian                                             3707\n",
-       "Hispanic or Latino/a/x                                  2078\n",
-       "Middle Eastern                                          1417\n",
-       "Southeast Asian                                         1371\n",
-       "East Asian                                              1342\n",
-       "Black or of African descent                             1327\n",
-       "Hispanic or Latino/a/x;White or of European descent      720\n",
-       "Middle Eastern;White or of European descent              344\n",
-       "Multiracial                                              245\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 274,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#count number of each Ethnicity\n",
-    "df2020.groupby('Ethnicity')['Ethnicity'].count()\n",
-    "df2020['Ethnicity'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
-    "df2020.loc[df['Ethnicity'].str.match('Biracial') == True, 'Ethnicity'] = 'Biracial'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Black or of African descent') == True, 'Ethnicity'] = 'Black or of African descent'\n",
-    "df2020.loc[df['Ethnicity'].str.match('East Asian') == True, 'Ethnicity'] = 'East Asian'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Hispanic or Latino') == True, 'Ethnicity'] = 'Hispanic or Latino'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Indigenous') == True, 'Ethnicity'] = 'Indigenous'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Middle Eastern') == True, 'Ethnicity'] = 'Middle Eastern'\n",
-    "df2020.loc[df['Ethnicity'].str.match('South Asian') == True, 'Ethnicity'] = 'South Asian'\n",
-    "df2020.loc[df['Ethnicity'].str.match('White or of European descent') == True, 'Ethnicity'] = 'White or of European descent'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Multiracial') == True, 'Ethnicity'] = 'Multiracial'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent    26848\n",
-       "South Asian                      3783\n",
-       "Hispanic or Latino               3072\n",
-       "Middle Eastern                   1840\n",
-       "East Asian                       1661\n",
-       "Black or of African descent      1633\n",
-       "Southeast Asian                  1371\n",
-       "Multiracial                       249\n",
-       "Biracial                          138\n",
-       "Indigenous                         63\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 276,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020.groupby('Ethnicity')['Ethnicity'].count() #11 groups of Ethnicity after combining \n",
-    "df2020['Ethnicity'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "df2020['Ethnicity']=df2020.groupby(['Country'])['Ethnicity'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent    28466\n",
-       "South Asian                      5101\n",
-       "Hispanic or Latino               3270\n",
-       "Middle Eastern                   2104\n",
-       "East Asian                       1907\n",
-       "Black or of African descent      1762\n",
-       "Southeast Asian                  1614\n",
-       "Multiracial                       263\n",
-       "Biracial                          151\n",
-       "Indigenous                         71\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 278,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#count number of each Ethnicity\n",
-    "df2020.groupby('Ethnicity')['Ethnicity'].count()\n",
-    "df2020['Ethnicity'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 279,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Ethnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                           0\n",
-      "Gender                        0\n",
-      "SalaryUSD                 14358\n",
-      "Country                       0\n",
-      "DevType                       0\n",
-      "Hobbyist                      0\n",
-      "EdLevel                       0\n",
-      "Employment                  118\n",
-      "Ethnicity                     0\n",
-      "CurrentJobSatis               0\n",
-      "JobStatus                     0\n",
-      "LanguageDesireNextYear     2394\n",
-      "LanguageWorkedWith          396\n",
-      "Profession                   77\n",
-      "UndergradMajor             5522\n",
-      "YearsCodePro               8212\n",
-      "JobSeek                       0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "print(df2020.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageDesireNextYear"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2394"
-      ]
-     },
-     "execution_count": 281,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Python                                   773\n",
-       "Rust                                     417\n",
-       "HTML/CSS;JavaScript;TypeScript           405\n",
-       "C#                                       342\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript    339\n",
-       "HTML/CSS;JavaScript                      307\n",
-       "Go                                       300\n",
-       "HTML/CSS;JavaScript;PHP;SQL              229\n",
-       "TypeScript                               227\n",
-       "Java                                     224\n",
-       "Name: LanguageDesireNextYear, dtype: int64"
-      ]
-     },
-     "execution_count": 282,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df2020['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)\n",
-    "df2020['LanguageDesireNextYear']=df2020['LanguageDesireNextYear'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Python                                   802\n",
-       "Rust                                     432\n",
-       "HTML/CSS;JavaScript;TypeScript           425\n",
-       "C#                                       377\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript    372\n",
-       "HTML/CSS;JavaScript                      323\n",
-       "Go                                       310\n",
-       "HTML/CSS;JavaScript;PHP;SQL              245\n",
-       "Java                                     238\n",
-       "C#;HTML/CSS;JavaScript;SQL               236\n",
-       "Name: LanguageDesireNextYear, dtype: int64"
-      ]
-     },
-     "execution_count": 284,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 285,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageWorkedWith"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "396"
-      ]
-     },
-     "execution_count": 286,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HTML/CSS;JavaScript;PHP;SQL              819\n",
-       "C#;HTML/CSS;JavaScript;SQL               669\n",
-       "HTML/CSS;JavaScript                      655\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript    624\n",
-       "HTML/CSS;JavaScript;TypeScript           568\n",
-       "Python                                   449\n",
-       "Java                                     392\n",
-       "HTML/CSS;JavaScript;PHP                  382\n",
-       "HTML/CSS;Java;JavaScript;SQL             301\n",
-       "C#                                       296\n",
-       "Name: LanguageWorkedWith, dtype: int64"
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageWorkedWith'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df2020['LanguageWorkedWith'].fillna(method='ffill', inplace=True)\n",
-    "df2020['LanguageWorkedWith']=df2020['LanguageWorkedWith'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HTML/CSS;JavaScript;PHP;SQL              822\n",
-       "C#;HTML/CSS;JavaScript;SQL               670\n",
-       "HTML/CSS;JavaScript                      658\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript    631\n",
-       "HTML/CSS;JavaScript;TypeScript           572\n",
-       "Python                                   450\n",
-       "Java                                     394\n",
-       "HTML/CSS;JavaScript;PHP                  385\n",
-       "HTML/CSS;Java;JavaScript;SQL             306\n",
-       "C#                                       298\n",
-       "Name: LanguageWorkedWith, dtype: int64"
-      ]
-     },
-     "execution_count": 289,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageWorkedWith'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 290,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## MainBranch (Profession)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "77"
-      ]
-     },
-     "execution_count": 291,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Profession'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Profession\n",
-       "I am a developer by profession                                                   34037\n",
-       "I am a student who is learning to code                                            4900\n",
-       "I am not primarily a developer, but I write code sometimes as part of my work     3718\n",
-       "I code primarily as a hobby                                                       1301\n",
-       "I used to be a developer by profession, but no longer am                           676\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 292,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('Profession')['Profession'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020.dropna(subset=['Profession'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Lets refactor column values of Profession column\n",
-    "#refactoring profession column\n",
-    "\n",
-    "def refactor_prof(df):\n",
-    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
-    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
-    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
-    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
-    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
-    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
-    "    \n",
-    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
-    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)          \n",
-    "    return df\n",
-    "\n",
-    "df2020 = refactor_prof(df2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer        34037\n",
-       "Student           4900\n",
-       "Non developer     3718\n",
-       "Novoice           1301\n",
-       "Ex-Developer       676\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 295,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Profession'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 296,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Profession'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## UndergradMajor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5501"
-      ]
-     },
-     "execution_count": 297,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "UndergradMajor\n",
-       "A business discipline (such as accounting, finance, marketing, etc.)               1033\n",
-       "A health science (such as nursing, pharmacy, radiology, etc.)                       190\n",
-       "A humanities discipline (such as literature, history, philosophy, etc.)             815\n",
-       "A natural science (such as biology, chemistry, physics, etc.)                      1754\n",
-       "A social science (such as anthropology, psychology, political science, etc.)        733\n",
-       "Another engineering discipline (such as civil, electrical, mechanical, etc.)       3542\n",
-       "Computer science, computer engineering, or software engineering                   24429\n",
-       "Fine arts or performing arts (such as graphic design, music, studio art, etc.)      581\n",
-       "I never declared a major                                                            331\n",
-       "Information systems, information technology, or system administration              3074\n",
-       "Mathematics or statistics                                                          1419\n",
-       "Web development or web design                                                      1230\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 298,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('UndergradMajor')['UndergradMajor'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def refactor_major(df):\n",
-    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n",
-    "                        (df['UndergradMajor'] == 'Another engineering discipline (such as civil, electrical, mechanical, etc.)'),\n",
-    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n",
-    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
-    "                        (df['UndergradMajor'] == 'A natural science (such as biology, chemistry, physics, etc.)') \n",
-    "                        |(df['UndergradMajor'] == 'A health science (such as nursing, pharmacy, radiology, etc.)'), \n",
-    "                        (df['UndergradMajor'] == 'Web development or web design'), \n",
-    "                        (df['UndergradMajor'] == 'A business discipline (such as accounting, finance, marketing, etc.)'), \n",
-    "                        (df['UndergradMajor'] == 'A humanities discipline (such as literature, history, philosophy, etc.)')\n",
-    "                        | (df['UndergradMajor'] == 'A social science (such as anthropology, psychology, political science, etc.)')\n",
-    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (such as graphic design, music, studio art, etc.)'),\n",
-    "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
-    "    \n",
-    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
-    "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
-    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
-    "    return df\n",
-    "\n",
-    "df2020 = refactor_major(df2020)\n",
-    "df2020['UndergradMajor'].replace('nan', 'No major', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "UndergradMajor\n",
-       "Arts and Science     2129\n",
-       "Business             1033\n",
-       "Computer Science    24429\n",
-       "Engineering          3542\n",
-       "Info Systems         3074\n",
-       "Math/Stat            1419\n",
-       "No major             5832\n",
-       "Other Science        1944\n",
-       "Web Design/Dev       1230\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 300,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('UndergradMajor')['UndergradMajor'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 301,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Employment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "111"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employment\n",
-       "Employed full-time                                      32474\n",
-       "Employed part-time                                       1489\n",
-       "Independent contractor, freelancer, or self-employed     3859\n",
-       "Not employed, and not looking for work                    181\n",
-       "Not employed, but looking for work                       1500\n",
-       "Retired                                                    32\n",
-       "Student                                                  4986\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 303,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020.groupby('Employment')['Employment'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 304,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020.dropna(subset=['Employment'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 305,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Refactoring Employment\n",
-    "df2020['Employment'].replace('Employed full-time', 'Full-time', inplace =True)\n",
-    "df2020['Employment'].replace('Employed part-time', 'Part-time',inplace =True)\n",
-    "df2020['Employment'].replace('Independent contractor, freelancer, or self-employed', 'Self-employed', inplace =True)\n",
-    "df2020['Employment'].replace('Not employed, but looking for work', 'Not employed', inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employment\n",
-       "Full-time                                 32474\n",
-       "Not employed                               1500\n",
-       "Not employed, and not looking for work      181\n",
-       "Part-time                                  1489\n",
-       "Retired                                      32\n",
-       "Self-employed                              3859\n",
-       "Student                                    4986\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('Employment')['Employment'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 307,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 307,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Country"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 308,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 308,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Country\n",
-       "Afghanistan                              22\n",
-       "Albania                                  29\n",
-       "Algeria                                  47\n",
-       "Andorra                                   3\n",
-       "Angola                                    2\n",
-       "                                       ... \n",
-       "Venezuela, Bolivarian Republic of...     53\n",
-       "Viet Nam                                159\n",
-       "Yemen                                     2\n",
-       "Zambia                                   10\n",
-       "Zimbabwe                                 19\n",
-       "Name: Country, Length: 170, dtype: int64"
-      ]
-     },
-     "execution_count": 309,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020.groupby('Country')['Country'].count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## YearsCodePro"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8123"
-      ]
-     },
-     "execution_count": 310,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 311,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                         int64\n",
-       "Gender                     object\n",
-       "SalaryUSD                 float64\n",
-       "Country                    object\n",
-       "DevType                    object\n",
-       "Hobbyist                   object\n",
-       "EdLevel                    object\n",
-       "Employment                 object\n",
-       "Ethnicity                  object\n",
-       "CurrentJobSatis            object\n",
-       "JobStatus                  object\n",
-       "LanguageDesireNextYear     object\n",
-       "LanguageWorkedWith         object\n",
-       "Profession                 object\n",
-       "UndergradMajor             object\n",
-       "YearsCodePro               object\n",
-       "JobSeek                    object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 311,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#convert YearsCodePro data type from obj to int\n",
-    "df2020[\"YearsCodePro\"]=pd.to_numeric(df2020[\"YearsCodePro\"],errors='coerce')\n",
-    "\n",
-    "#fill YearsCodePro's null values with mean\n",
-    "means = df2020['YearsCodePro'].mean() #means 8.673142457693764\n",
-    "df2020['YearsCodePro'] = df2020['YearsCodePro'].fillna(means)\n",
-    "df2020['YearsCodePro'] = df2020['YearsCodePro'].round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 313,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 313,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hobbyist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 314,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 314,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Hobbyist'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 315,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hobbyist\n",
-       "No      9583\n",
-       "Yes    34938\n",
-       "Name: Hobbyist, dtype: int64"
-      ]
-     },
-     "execution_count": 315,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('Hobbyist')['Hobbyist'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                           0\n",
-      "Gender                        0\n",
-      "SalaryUSD                 14202\n",
-      "Country                       0\n",
-      "DevType                       0\n",
-      "Hobbyist                      0\n",
-      "EdLevel                       0\n",
-      "Employment                    0\n",
-      "Ethnicity                     0\n",
-      "CurrentJobSatis               0\n",
-      "JobStatus                     0\n",
-      "LanguageDesireNextYear        0\n",
-      "LanguageWorkedWith            0\n",
-      "Profession                    0\n",
-      "UndergradMajor                0\n",
-      "YearsCodePro                  0\n",
-      "JobSeek                       0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df2020.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ConvertedComp (SalaryUSD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14202"
-      ]
-     },
-     "execution_count": 317,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['SalaryUSD'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "120000.0     284\n",
-       "100000.0     254\n",
-       "64859.0      224\n",
-       "150000.0     221\n",
-       "2000000.0    216\n",
-       "Name: SalaryUSD, dtype: int64"
-      ]
-     },
-     "execution_count": 318,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['SalaryUSD'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
-    "mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df2020['SalaryUSD'] = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))\n",
-    "\n",
-    "means = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform('mean')\n",
-    "df2020['SalaryUSD'] = df2020['SalaryUSD'].fillna(means)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age  EdLevel       Country      \n",
-       "40   Professional  United States    2000000.0\n",
-       "37   Masters       Nomadic          1320000.0\n",
-       "41   Masters       Israel           1200000.0\n",
-       "47   Professional  United States    1047500.0\n",
-       "33   Doctorate     Italy            1018376.5\n",
-       "15   Bachelors     Germany          1000000.0\n",
-       "20   Associate     Australia        1000000.0\n",
-       "25   Bachelors     Paraguay         1000000.0\n",
-       "28   Doctorate     Singapore        1000000.0\n",
-       "32   No Degree     Ireland          1000000.0\n",
-       "Name: SalaryUSD, dtype: float64"
-      ]
-     },
-     "execution_count": 320,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
-    "mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "120000.0     286\n",
-       "100000.0     255\n",
-       "64859.0      239\n",
-       "150000.0     227\n",
-       "1000000.0    219\n",
-       "Name: SalaryUSD, dtype: int64"
-      ]
-     },
-     "execution_count": 321,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['SalaryUSD'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2952"
-      ]
-     },
-     "execution_count": 322,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['SalaryUSD'].isnull().sum() #2952 out of 64461 -> 4.6%"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020.dropna(subset=['SalaryUSD'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 324,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['SalaryUSD'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleaned Dataset:2020_Survey"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                       0\n",
-      "Gender                    0\n",
-      "SalaryUSD                 0\n",
-      "Country                   0\n",
-      "DevType                   0\n",
-      "Hobbyist                  0\n",
-      "EdLevel                   0\n",
-      "Employment                0\n",
-      "Ethnicity                 0\n",
-      "CurrentJobSatis           0\n",
-      "JobStatus                 0\n",
-      "LanguageDesireNextYear    0\n",
-      "LanguageWorkedWith        0\n",
-      "Profession                0\n",
-      "UndergradMajor            0\n",
-      "YearsCodePro              0\n",
-      "JobSeek                   0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df2020.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#resetting the index values\n",
-    "df2020 = df2020.reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "      <th>JobStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>Profession</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>JobSeek</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>31</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>214247.736842</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Slightly dissatisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>Java;Ruby;Scala</td>\n",
-       "      <td>HTML/CSS;Ruby;SQL</td>\n",
-       "      <td>Ex-Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>Not seeking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>36</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>116000.000000</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Slightly dissatisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>JavaScript</td>\n",
-       "      <td>Python;SQL</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>Not seeking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>22</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>32315.000000</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Database administrator;Developer, full-stack;D...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Very satisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>HTML/CSS;Java;JavaScript;Python;R;SQL</td>\n",
-       "      <td>HTML/CSS;Java;JavaScript;Python;SQL</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Not seeking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>23</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>40070.000000</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Slightly dissatisfied</td>\n",
-       "      <td>I am actively looking for a job</td>\n",
-       "      <td>Go;JavaScript;Swift;TypeScript</td>\n",
-       "      <td>C#;JavaScript;Swift</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Seeking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>49</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>14268.000000</td>\n",
-       "      <td>Spain</td>\n",
-       "      <td>Designer;Developer, front-end</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No Degree</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Very dissatisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>HTML/CSS;JavaScript</td>\n",
-       "      <td>HTML/CSS;JavaScript</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Not seeking</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age Gender      SalaryUSD         Country  \\\n",
-       "0   31    Man  214247.736842   United States   \n",
-       "1   36    Man  116000.000000   United States   \n",
-       "2   22    Man   32315.000000  United Kingdom   \n",
-       "3   23    Man   40070.000000  United Kingdom   \n",
-       "4   49    Man   14268.000000           Spain   \n",
-       "\n",
-       "                                             DevType Hobbyist    EdLevel  \\\n",
-       "0  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
-       "1  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
-       "2  Database administrator;Developer, full-stack;D...      Yes    Masters   \n",
-       "3  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
-       "4                      Designer;Developer, front-end       No  No Degree   \n",
-       "\n",
-       "  Employment                     Ethnicity        CurrentJobSatis  \\\n",
-       "0  Full-time  White or of European descent  Slightly dissatisfied   \n",
-       "1  Full-time  White or of European descent  Slightly dissatisfied   \n",
-       "2  Full-time  White or of European descent         Very satisfied   \n",
-       "3  Full-time  White or of European descent  Slightly dissatisfied   \n",
-       "4  Full-time  White or of European descent      Very dissatisfied   \n",
-       "\n",
-       "                                           JobStatus  \\\n",
-       "0  I’m not actively looking, but I am open to new...   \n",
-       "1  I’m not actively looking, but I am open to new...   \n",
-       "2  I’m not actively looking, but I am open to new...   \n",
-       "3                    I am actively looking for a job   \n",
-       "4  I’m not actively looking, but I am open to new...   \n",
-       "\n",
-       "                  LanguageDesireNextYear                   LanguageWorkedWith  \\\n",
-       "0                        Java;Ruby;Scala                    HTML/CSS;Ruby;SQL   \n",
-       "1                             JavaScript                           Python;SQL   \n",
-       "2  HTML/CSS;Java;JavaScript;Python;R;SQL  HTML/CSS;Java;JavaScript;Python;SQL   \n",
-       "3         Go;JavaScript;Swift;TypeScript                  C#;JavaScript;Swift   \n",
-       "4                    HTML/CSS;JavaScript                  HTML/CSS;JavaScript   \n",
-       "\n",
-       "     Profession    UndergradMajor  YearsCodePro      JobSeek  \n",
-       "0  Ex-Developer  Computer Science           8.0  Not seeking  \n",
-       "1     Developer  Computer Science          13.0  Not seeking  \n",
-       "2     Developer         Math/Stat           4.0  Not seeking  \n",
-       "3     Developer  Computer Science           2.0      Seeking  \n",
-       "4     Developer         Math/Stat           7.0  Not seeking  "
-      ]
-     },
-     "execution_count": 327,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 41569 entries, 0 to 41568\n",
-      "Data columns (total 17 columns):\n",
-      " #   Column                  Non-Null Count  Dtype  \n",
-      "---  ------                  --------------  -----  \n",
-      " 0   Age                     41569 non-null  int64  \n",
-      " 1   Gender                  41569 non-null  object \n",
-      " 2   SalaryUSD               41569 non-null  float64\n",
-      " 3   Country                 41569 non-null  object \n",
-      " 4   DevType                 41569 non-null  object \n",
-      " 5   Hobbyist                41569 non-null  object \n",
-      " 6   EdLevel                 41569 non-null  object \n",
-      " 7   Employment              41569 non-null  object \n",
-      " 8   Ethnicity               41569 non-null  object \n",
-      " 9   CurrentJobSatis         41569 non-null  object \n",
-      " 10  JobStatus               41569 non-null  object \n",
-      " 11  LanguageDesireNextYear  41569 non-null  object \n",
-      " 12  LanguageWorkedWith      41569 non-null  object \n",
-      " 13  Profession              41569 non-null  object \n",
-      " 14  UndergradMajor          41569 non-null  object \n",
-      " 15  YearsCodePro            41569 non-null  float64\n",
-      " 16  JobSeek                 41569 non-null  object \n",
-      "dtypes: float64(2), int64(1), object(14)\n",
-      "memory usage: 5.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df2020.info()#after cleaning the dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### After Cleaning Dataset 2020"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 329,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  706673\n",
-      "Total missing :  0\n",
-      "Missing Percentage:  0.0 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = df2020.isnull().sum() #number of missing\n",
-    "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization\n",
-    "After cleaning the datasets, we started visualizations to analyze the datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## To find whether there is any difference between men and women's income from latest stack overflow survey (2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 330,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.style.use('seaborn-darkgrid')\n",
-    "plt.rcParams[\"figure.figsize\"] = (20,10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#sns.boxplot('SalaryUSD', data=df2020, width=0.3) \n",
-    "#Cleaning SalaryUSD's outliers\n",
-    "df2020 = df2020[(df2020['SalaryUSD'] < 200000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Income vs Gender')"
-      ]
-     },
-     "execution_count": 332,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(x ='Gender', y='SalaryUSD', data=df2020)\n",
-    "plt.title('Income vs Gender', fontsize = 14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Analysis**<br>\n",
-    "There is a little bit of difference between Gender and income they received respectively. Men tend to receive more salary than women from the above analysis."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Impact on participation rate due to different ethnicity based on country."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 333,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['White or of European descent', 'South Asian', 'Hispanic or Latino', 'Middle Eastern', 'East Asian', 'Southeast Asian', 'Black or of African descent', 'Multiracial', 'Biracial', 'Indigenous']\n",
-      "[24573, 4585, 2877, 1757, 1539, 1348, 1336, 226, 133, 62]\n"
-     ]
-    }
-   ],
-   "source": [
-    "participation_rate = df2020['Ethnicity'].value_counts().keys().tolist()\n",
-    "print(participation_rate)\n",
-    "count = df2020['Ethnicity'].value_counts().tolist()\n",
-    "print(count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 334,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()    \n",
-    " \n",
-    "ax.bar(participation_rate,count)\n",
-    "plt.title('Ethnicity VS Participation',size=20)\n",
-    "plt.xlabel('Total Count',size = 20)\n",
-    "plt.ylabel('# Of Developer Did Survey',size = 20) \n",
-    "for i, v in enumerate(count):\n",
-    "    ax.text(i-.15, \n",
-    "              v+3,\n",
-    "              count[i],\n",
-    "              style = 'italic',\n",
-    "              fontsize=14,\n",
-    "              color = 'magenta')\n",
-    "ax.grid(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 335,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1080x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(15, 5))\n",
-    "sns.barplot(x = count, y = participation_rate, palette = 'Set1')\n",
-    "plt.xlabel('Ethnicity', size = 16)\n",
-    "for i, v in enumerate(count):\n",
-    "    ax.text(  v+3,\n",
-    "              i-.15,\n",
-    "              f'{count[i]*100/sum(count):.2f}%',\n",
-    "              style = 'italic',\n",
-    "              fontsize=14,\n",
-    "              )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**From the Survey Analysis, more particpation has been happened from White or of European Ethnicity which is 24573 participation which is very high comparing to others.<br>\n",
-    "The least has been recorded as only 0.16% from Indigenous.<br>\n",
-    "The second top survey contributors are from South Asians which is 11.93% of the respondents.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Geographical plot to show number of respondents in each country in 2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 336,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#geoplot_2019=cleaned_df_2019.groupby('Country').agg('count')\n",
-    "geoplot_2019=cleaned_df_2019.groupby('Country').size()\n",
-    "geoplot_2019=geoplot_2019.to_frame('Respondents')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 337,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_country_code(name):\n",
-    "    try:\n",
-    "        return pycountry.countries.lookup(name).alpha_3\n",
-    "    except:\n",
-    "        return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 338,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geoplot_2019['Country'] = geoplot_2019.index\n",
-    "geoplot_2019['Country_code'] = geoplot_2019['Country'].apply(get_country_code)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 339,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "        require.undef(\"plotly\");\n",
-       "        define('plotly', function(require, exports, module) {\n",
-       "            /**\n",
-       "* plotly.js v1.58.4\n",
-       "* Copyright 2012-2020, Plotly, Inc.\n",
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-       "* Licensed under the MIT license\n",
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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,M=0|o[5],A=8191&M,S=M>>>13,E=0|o[6],C=8191&E,L=E>>>13,I=0|o[7],P=8191&I,z=I>>>13,O=0|o[8],D=8191&O,R=O>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(g,U)|0,a=Math.imul(g,V);var vt=(c+(n=n+Math.imul(f,H)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,H)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,H)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,H)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,H)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(g,J)|0,a=a+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(A,U),i=(i=Math.imul(A,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,V),n=n+Math.imul(A,H)|0,i=(i=i+Math.imul(A,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,U),i=(i=Math.imul(P,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(A,W)|0,i=(i=i+Math.imul(A,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,X)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,X)|0,n=n+Math.imul(A,J)|0,i=(i=i+Math.imul(A,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,W)|0,i=(i=i+Math.imul(P,X)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,X)|0,n=n+Math.imul(C,J)|0,i=(i=i+Math.imul(C,K)|0)+Math.imul(L,J)|0,a=a+Math.imul(L,K)|0,n=n+Math.imul(A,$)|0,i=(i=i+Math.imul(A,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(g,ft)|0,a=a+Math.imul(g,ht)|0;var Mt=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,gt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(P,J)|0,i=(i=i+Math.imul(P,K)|0)+Math.imul(z,J)|0,a=a+Math.imul(z,K)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(A,rt)|0,i=(i=i+Math.imul(A,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var At=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(P,$)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(A,at)|0,i=(i=i+Math.imul(A,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(A,lt)|0,i=(i=i+Math.imul(A,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(A,ft)|0,i=(i=i+Math.imul(A,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Ct=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ft)|0,i=(i=i+Math.imul(C,ht)|0)+Math.imul(L,ft)|0,a=a+Math.imul(L,ht)|0;var Lt=(c+(n=n+Math.imul(A,dt)|0)|0)+((8191&(i=(i=i+Math.imul(A,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(P,ft)|0,i=(i=i+Math.imul(P,ht)|0)+Math.imul(z,ft)|0,a=a+Math.imul(z,ht)|0;var It=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,gt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var Ot=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=Mt,l[10]=At,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=It,l[16]=Pt,l[17]=zt,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=h),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?h(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=f*m-h*v;v=f*v+h*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number 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numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var m={k256:null,p224:null,p192:null,p25519:null};function v(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){v.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff 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r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},T.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(\"undefined\"==typeof e||e,this)},{buffer:108}],100:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],101:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function 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-       "/*!\n",
-       " * The buffer module from node.js, for the browser.\n",
-       " *\n",
-       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
-       " * @license  MIT\n",
-       " */\n",
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a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function g(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],248:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function a(t){if(t<0)return Number(\"0/0\");for(var e=i[0],r=i.length-1;r>0;--r)e+=i[r]/(t+r);var n=t+607/128+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(a(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var o=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(o,e+.5)*Math.exp(-o)*r},e.exports.log=a},{}],249:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e||{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf(\"webgl\")&&a.push(\"experimental-\"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],250:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],m={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=f[M],this.lastCubeProps.axis[M]=h[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,f,h);e=this.gl;var S,E=g;for(M=0;M<3;++M)this.backgroundEnable[M]?E[M]=h[M]:E[M]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(M=0;M<3;++M){var C=[0,0,0];h[M]>0?C[M]=a[1][M]:C[M]=a[0][M];for(var L=0;L<2;++L){var I=(M+1+L)%3,P=(M+1+(1^L))%3;this.gridEnable[I]&&this._lines.drawGrid(I,P,this.bounds,C,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(L=0;L<2;++L){I=(M+1+L)%3,P=(M+1+(1^L))%3;this.zeroEnable[P]&&Math.min(a[0][P],a[1][P])<=0&&Math.max(a[0][P],a[1][P])>=0&&this._lines.drawZero(I,P,this.bounds,C,this.zeroLineColor[P],this.zeroLineWidth[P]*this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio);var z=c(v,A[M].primalMinor),O=c(y,A[M].mirrorMinor),D=this.lineTickLength;for(L=0;L<3;++L){var R=k/r[5*L];z[L]*=D[L]*R,O[L]*=D[L]*R}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,z,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,O,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(M=0;M<3;++M){var U=A[M].primalMinor,V=A[M].mirrorMinor,q=c(x,A[M].primalOffset);for(L=0;L<3;++L)this.lineTickEnable[M]&&(q[L]+=k*U[L]*Math.max(this.lineTickLength[L],0)/r[5*L]);var H=[0,0,0];if(H[M]=1,this.tickEnable[M]){-3600===this.tickAngle[M]?(this.tickAngle[M]=0,this.tickAlign[M]=\"auto\"):this.tickAlign[M]=-1,F=1,\"auto\"===(S=[this.tickAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(M,U,V);for(L=0;L<3;++L)q[L]+=k*U[L]*this.tickPad[L]/r[5*L];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],q,this.tickColor[M],H,B,S)}if(this.labelEnable[M]){F=0,B=[0,0,0],this.labels[M].length>4&&(N(M),F=1),\"auto\"===(S=[this.labelAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(L=0;L<3;++L)q[L]+=k*U[L]*this.labelPad[L]/r[5*L];q[M]+=.5*(a[0][M]+a[1][M]),this._text.drawLabel(M,this.labelSize[M],this.labelAngle[M],q,this.labelColor[M],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":251,\"./lib/cube.js\":252,\"./lib/lines.js\":253,\"./lib/text.js\":255,\"./lib/ticks.js\":256}],251:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var g=-1;g<=1;g+=2)f[u]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var m=c;c=u,u=m}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":254,\"gl-buffer\":259,\"gl-vao\":358}],252:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,S=(M+1)%3,E=-1,C=-1,L=0;L<2;++L){var I=(z=L<<M)+(L<<A)+(1-L<<S),P=z+(1-L<<A)+(L<<S);o(c[z],c[I],c[P],f)<0||(L?E=1:C=1)}if(E<0||C<0)C>E&&(w|=1<<M);else{for(L=0;L<2;++L){I=(z=L<<M)+(L<<A)+(1-L<<S),P=z+(1-L<<A)+(L<<S);var z,O=d([l[z],l[I],l[P],l[z+(1<<A)+(1<<S)]]);L?E=O:C=O}C>E&&(w|=1<<M)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=g;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=m,q=w;for(M=0;M<3;++M)V[M]=q&1<<M?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{\"bit-twiddle\":97,\"gl-mat4/multiply\":295,\"robust-orientation\":548,\"split-polygon\":566}],253:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;s[f]=h,l[f]=m-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){g=+r[f][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}m=o.length/3|0;c[f]=h,u[f]=m-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":254,\"gl-buffer\":259,\"gl-vao\":358}],254:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":335,glslify:257}],255:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var 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n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],m=[0,0,0]},{\"./lib/cube.js\":252,\"extract-frustum-planes\":240,\"gl-mat4/multiply\":295,\"gl-mat4/transpose\":306,\"gl-vec4/transformMat4\":429,\"split-polygon\":566}],259:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&\"undefined\"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:495,\"ndarray-ops\":490,\"typedarray-pool\":595}],260:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,g=[],m=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(m=Math.min(m,_),v=!1):v=!0}v||(p=x,d=b),g.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(m)||(m=1),a.vectorScale=m;var M=t.coneSize||.5;t.absoluteConeSize&&(M=t.absoluteConeSize*k),a.coneScale=M;y=0;for(var A=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],C=x[2],L=g[y],I=n.length(L)*k,P=0;P<8;P++){a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vertexIntensity.push(I,I,I),a.vertexIntensity.push(I,I,I);var z=a.positions.length;a.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":261,\"./lib/shaders\":262,\"gl-vec3\":377}],261:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function g(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var m=0;m<p.length;++m){var v=p[m];d=Math.min(d,v),g=Math.max(g,v)}else for(m=0;m<n.length;++m){v=n[m][2];d=Math.min(d,v),g=Math.max(g,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(m=0;m<n.length;++m)for(var y=n[m],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(m=0;m<r.length;++m){var _=r[m];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var M,A=h;3===A.length?s.push(A[0],A[1],A[2],1):s.push(A[0],A[1],A[2],A[3]),M=p?[(p[T]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(M[0],M[1]),f.push(m)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=g(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,m,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-shader\":335,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495}],262:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:263}],263:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],264:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\"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p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":335,glslify:267}],269:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new 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t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var g=d.prototype;function m(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,m(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":353}],270:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));h[g]=d}}var m=o(e).split(\"\\n\");for(p=0;p<m.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=m[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":66,\"gl-constants/lookup\":265,\"glsl-shader-name\":431,\"sprintf-js\":567}],271:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],g=t.colorValues||[0,0,0,1],m=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),M=a.mallocUint8(2*w),A=a.mallocUint32(w),S=0,E=l?b-1:b,C=l?_-1:_,L=0;L<C;++L){var I,P;l?(I=x*(o[L]-u),P=x*(o[L+1]-u)):(I=L<_-1?x*(o[L]-(o[L+1]-o[L])/2-u):x*(o[L]-(o[L]-o[L-1])/2-u),P=L<_-1?x*(o[L]+(o[L+1]-o[L])/2-u):x*(o[L]+(o[L]-o[L-1])/2-u));for(var z=0;z<E;++z){var O,D;l?(O=y*(r[z]-c),D=y*(r[z+1]-c)):(O=z<b-1?y*(r[z]-(r[z+1]-r[z])/2-c):y*(r[z]-(r[z]-r[z-1])/2-c),D=z<b-1?y*(r[z]+(r[z+1]-r[z])/2-c):y*(r[z]+(r[z]-r[z-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],q=s[l?(L+V)*b+(z+U):L*b+z],H=n.le(d,q);if(H<0)F=g[0],B=g[1],N=g[2],j=g[3];else if(H===m-1)F=g[4*m-4],B=g[4*m-3],N=g[4*m-2],j=g[4*m-1];else{var G=(q-d[H])/(d[H+1]-d[H]),Y=1-G,W=4*H,X=4*(H+1);F=Y*g[W]+G*g[X],B=Y*g[W+1]+G*g[X+1],N=Y*g[W+2]+G*g[X+2],j=Y*g[W+3]+G*g[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*O+.5*D,k[2*S+1]=.5*I+.5*P,M[2*S]=U,M[2*S+1]=V,A[S]=L*b+z,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(M),this.colorBuffer.update(T),this.idBuffer.update(A),a.free(k),a.free(T),a.free(M),a.free(A)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":272,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335,\"iota-array\":463,\"typedarray-pool\":595}],272:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:273}],273:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],274:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":335,glslify:276}],275:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var g=new v(e,r,o,s,l,d);return g.update(t),g};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function g(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function m(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:g(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:g(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,m=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,m=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var 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t[0]=1-(p+g),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=h+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],285:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],286:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],287:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],288:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],289:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],290:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),c=1/(a-o);return t[0]=2*a*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*a*l,t[6]=0,t[7]=0,t[8]=(r+e)*s,t[9]=(i+n)*l,t[10]=(o+a)*c,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*a*2*c,t[15]=0,t}},{}],291:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],292:[function(t,e,r){e.exports={create:t(\"./create\"),clone:t(\"./clone\"),copy:t(\"./copy\"),identity:t(\"./identity\"),transpose:t(\"./transpose\"),invert:t(\"./invert\"),adjoint:t(\"./adjoint\"),determinant:t(\"./determinant\"),multiply:t(\"./multiply\"),translate:t(\"./translate\"),scale:t(\"./scale\"),rotate:t(\"./rotate\"),rotateX:t(\"./rotateX\"),rotateY:t(\"./rotateY\"),rotateZ:t(\"./rotateZ\"),fromRotation:t(\"./fromRotation\"),fromRotationTranslation:t(\"./fromRotationTranslation\"),fromScaling:t(\"./fromScaling\"),fromTranslation:t(\"./fromTranslation\"),fromXRotation:t(\"./fromXRotation\"),fromYRotation:t(\"./fromYRotation\"),fromZRotation:t(\"./fromZRotation\"),fromQuat:t(\"./fromQuat\"),frustum:t(\"./frustum\"),perspective:t(\"./perspective\"),perspectiveFromFieldOfView:t(\"./perspectiveFromFieldOfView\"),ortho:t(\"./ortho\"),lookAt:t(\"./lookAt\"),str:t(\"./str\")}},{\"./adjoint\":277,\"./clone\":278,\"./copy\":279,\"./create\":280,\"./determinant\":281,\"./fromQuat\":282,\"./fromRotation\":283,\"./fromRotationTranslation\":284,\"./fromScaling\":285,\"./fromTranslation\":286,\"./fromXRotation\":287,\"./fromYRotation\":288,\"./fromZRotation\":289,\"./frustum\":290,\"./identity\":291,\"./invert\":293,\"./lookAt\":294,\"./multiply\":295,\"./ortho\":296,\"./perspective\":297,\"./perspectiveFromFieldOfView\":298,\"./rotate\":299,\"./rotateX\":300,\"./rotateY\":301,\"./rotateZ\":302,\"./scale\":303,\"./str\":304,\"./translate\":305,\"./transpose\":306}],293:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*g-f*d,M=u*m-h*d,A=u*v-p*d,S=f*m-h*g,E=f*v-p*g,C=h*v-p*m,L=y*C-x*E+b*S+_*A-w*M+T*k;if(!L)return null;return L=1/L,t[0]=(s*C-l*E+c*S)*L,t[1]=(i*E-n*C-a*S)*L,t[2]=(g*T-m*w+v*_)*L,t[3]=(h*w-f*T-p*_)*L,t[4]=(l*A-o*C-c*M)*L,t[5]=(r*C-i*A+a*M)*L,t[6]=(m*b-d*T-v*x)*L,t[7]=(u*T-h*b+p*x)*L,t[8]=(o*E-s*A+c*k)*L,t[9]=(n*A-r*E-a*k)*L,t[10]=(d*w-g*b+v*y)*L,t[11]=(f*b-u*w-p*y)*L,t[12]=(s*M-o*S-l*k)*L,t[13]=(r*S-n*M+i*k)*L,t[14]=(g*x-d*_-m*y)*L,t[15]=(u*_-f*x+h*y)*L,t}},{}],294:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,g=e[0],m=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=g-_,h=m-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{\"./identity\":291}],295:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*g,t[1]=x*i+b*l+_*h+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*g,t[5]=x*i+b*l+_*h+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*g,t[9]=x*i+b*l+_*h+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],296:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],297:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],298:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],299:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S,E=n[0],C=n[1],L=n[2],I=Math.sqrt(E*E+C*C+L*L);if(Math.abs(I)<1e-6)return null;E*=I=1/I,C*=I,L*=I,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=C*E*o+L*i,_=L*E*o-C*i,w=E*C*o-L*i,T=C*C*o+a,k=L*C*o+E*i,M=E*L*o+C*i,A=C*L*o-E*i,S=L*L*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*M+f*A+g*S,t[9]=l*M+h*A+m*S,t[10]=c*M+p*A+v*S,t[11]=u*M+d*A+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],300:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],301:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],302:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],303:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],304:[function(t,e,r){e.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},{}],305:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],306:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],307:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,g=0;g<2;++g)d+=Math.pow(u[f][g]-e[g],2);d<p&&(p=d,h=f)}var m=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(m[f]<-.001||m[f]>1.0001)return null;v+=m[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,m),m]}},{barycentric:78,\"polytope-closest-point/lib/closest_point_2d.js\":525}],308:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:310}],309:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),m=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,T,k,M,A,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=M,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function M(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function A(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function C(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function L(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function I(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],m=i[f],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*M(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,A=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,A)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,C=t.meshColor||[1,1,1,1],L=t.vertexUVs,I=t.vertexIntensity,P=t.cellUVs,z=t.cellIntensity,O=1/0,D=-1/0;if(!L&&!P)if(I)if(t.vertexIntensityBounds)O=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];O=Math.min(O,F),D=Math.max(D,F)}else if(z)if(t.cellIntensityBounds)O=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<z.length;++R){F=z[R];O=Math.min(O,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];O=Math.min(O,F),D=Math.max(D,F)}this.intensity=I||(z||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(z||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:C,this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:C,this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],g.push(Z[0],Z[1]),m.push(R)}q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:C)?this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=g(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=A(t),s=S(t),l=E(t),c=C(t),f=L(t),h=I(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),P=i(t),z=i(t),O=i(t),D=i(t),R=i(t),F=a(t,[{buffer:P,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:O,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,g,m,v,x,b,k,_,w,M,P,R,z,O,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":307,\"./lib/shaders\":308,colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-shader\":335,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495,normals:498,\"simplicial-complex-contour\":556,\"typedarray-pool\":595}],310:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],311:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"gl-buffer\":259,\"gl-shader\":335}],312:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=h[g];var _=6*n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,M=.5*(d[w+2]+d[w]),A=d[w+2]-d[w];e[w]=2*k/A,t[w]=2*(T-M)/A}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,C=s.tickMarkWidth,L=s.tickMarkColor,I=6*f[0].length,P=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),z=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),O=0+6*P,D=6*Math.max(0,z-P),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=I+6*R,N=6*Math.max(0,F-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=C[1]*m/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=C[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,O,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=C[3]*m/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=C[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,O,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":314,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335}],313:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"gl-buffer\":259,\"gl-shader\":335}],314:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:316}],315:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,m=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),M=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var A=2/h[2+(1^t)]-h[1^t];u[1^t]=A*s[1^t]-1,d[t]&&(u[1^t]-=A*p*g[t],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t]&&T&&(u[1^t]-=A*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=A*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=A*p*g[t+2],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t+2]&&T&&(u[1^t]+=A*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],g=a[o+2]-f,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,h[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,g=p.text,m=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(m,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335,\"text-cache\":575}],316:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],317:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":311,\"./lib/grid\":312,\"./lib/line\":313,\"./lib/text\":315,\"gl-select-static\":334}],318:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,g=!!a.shift,m=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,M=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&m&&!h&&!p&&!g||m&&!h&&!p&&g)&&c.rotate(M,w*k*b,-T*k*_,0),(l&&m&&!h&&!p&&!g||v||m&&h&&!p&&!g)&&c.pan(M,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&m&&!h&&!p&&!g||y||m&&!h&&p&&!g)){var A=-d.zoomSpeed*_/window.innerHeight*(M-c.lastT())*100;c.pan(M,0,0,f*(Math.exp(A)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":54,\"has-passive-events\":441,\"mouse-change\":483,\"mouse-event-offset\":484,\"mouse-wheel\":486,\"right-now\":542}],319:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":335,glslify:320}],320:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],321:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function m(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new g,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},M=i(r,k);M.enable=!k.disable;var A=t.spikes||{},S=o(r,A),E=[],C=[],L=[],I=[],P=!0,z=!0,O=new Array(16),D=new Array(16),R={view:null,projection:O,model:D,_ortho:!1},F=(z=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:M,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,z=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",P=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=I.length,n=0;n<e;++n)L[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(L[o]+a<255){C[n]=o,i.setPickBase(L[o]+1),L[o]+=a;continue t}var l=s(r,F);C[n]=e,I.push(l),L.push(a),i.setPickBase(1),e+=1}else C[n]=-1}for(;e>0&&0===L[e-1];)L.pop(),I.pop().dispose()}function q(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},P=!0,z=!0)},N.add=function(t){N._stopped||(t.axes=M,E.push(t),C.push(-1),P=!0,z=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),C.pop(),P=!0,z=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",q),N.mouseListener.enabled=!1,!N.contextLost)){M.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<I.length;++t)I[t].dispose();_.dispose(),r=null,M=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=I.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(z=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=I[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(C[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),P=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),P=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",q);var H=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[H[0].slice(),H[1].slice()];function Y(){if(!q()){U();var t=N.camera.tick();R.view=N.camera.matrix,P=P||t,z=z||t,M.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=H[0],i=H[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(L=E[o]).pixelRatio=N.pixelRatio,L.axes=N.axes,P=P||!!L.dirty,z=z||!!L.dirty;var s=L.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var g=.05*(i[p]-n[p]);n[p]=n[p]-g,i[p]=i[p]+g}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(z=z||v,P=P||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=m((d[1][o]-d[0][o])/10);M.autoTicks?M.update({bounds:d,tickSpacing:y}):M.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var g=0;for(d=0;d<3;++d)g=Math.max(g,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/g,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(L=E[o]).axesBounds=d,N.clipToBounds&&(L.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),z&&(z=!1,function(){if(!q()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=I.length,n=0;n<e;++n){var i=I[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(C[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var A=!1;M.enable&&(A=A||M.isTransparent(),M.draw(R)),S.axes=M,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(L=E[o]).axes=M,L.pixelRatio=N.pixelRatio,L.isOpaque&&L.isOpaque()&&L.draw(R),L.isTransparent&&L.isTransparent()&&(A=!0)}if(A){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),M.enable&&M.isTransparent()&&M.drawTransparent(R);for(o=0;o<e;++o){(L=E[o]).isOpaque&&L.isOpaque()&&L.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),M.isTransparent()&&M.drawTransparent(R);for(o=0;o<e;++o){var L;(L=E[o]).isTransparent&&L.isTransparent()&&L.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}P=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(P=!0,Y())},N},createCamera:n}},{\"./camera.js\":318,\"./lib/shader\":319,\"a-big-triangle\":64,\"gl-axes3d\":250,\"gl-axes3d/properties\":258,\"gl-fbo\":269,\"gl-mat4/ortho\":296,\"gl-mat4/perspective\":297,\"gl-select-static\":334,\"gl-spikes3d\":345,\"is-mobile\":467,\"mouse-change\":483}],322:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:323}],323:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],324:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":322,\"gl-buffer\":259,\"gl-shader\":335,\"typedarray-pool\":595}],325:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+f*g+h*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*g,t[3]=s*h+l*m,t}},{}],326:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],327:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":600}],328:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{\"gl-shader\":335,glslify:329}],329:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],330:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t||t>1?1:t}function m(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),g=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new m(e,r,n,o,f,h,p,d,g,s,c,u);return v.update(t),v};var v=m.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],M=[[0,0,0],[0,0,0]];function A(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function C(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,g=function(t){for(var e=M,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var v=T,C=0;C<16;++C)v[C]=0;for(C=0;C<4;++C)v[5*C]=1;v[5*m]=0,i[m]<0?v[12+m]=d[0][m]:v[12+m]=d[1][m],s(v,c,v),l.model=v;var L=(m+1)%3,I=(m+2)%3,P=A(x),z=A(b);P[L]=1,z[I]=1;var O=p(0,0,0,S(_,P)),D=p(0,0,0,S(w,z));if(Math.abs(O[1])>Math.abs(D[1])){var R=O;O=D,D=R,R=P,P=z,z=R;var F=L;L=I,I=F}O[0]<0&&(P[L]=-1),D[1]>0&&(z[I]=-1);var B=0,N=0;for(C=0;C<4;++C)B+=Math.pow(c[4*L+C],2),N+=Math.pow(c[4*I+C],2);P[L]/=Math.sqrt(B),z[I]/=Math.sqrt(N),l.axes[0]=P,l.axes[1]=z,l.fragClipBounds[0]=E(k,g[0],m,-1e8),l.fragClipBounds[1]=E(k,g[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function I(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&C(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=L,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){I(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,m=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=P(h,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;x+=3*k.cells.length,b+=2*M.edges.length}}var S=x+b,E=o.mallocFloat(3*S),C=o.mallocFloat(4*S),L=o.mallocFloat(2*S),I=o.mallocUint32(S);if(S>0){var z=0,O=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var q=0;Array.isArray(m)?q=n<m.length?+m[n]:0:m&&(q=+m);var H=Math.cos(q),G=Math.sin(q);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-A[0][0]:Y<0?1+A[1][0]:1,W*=W>0?1-A[0][1]:W<0?1+A[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)C[4*z+$]=D[$];I[z]=y;var tt=J[K[Q]];L[2*z]=V*(H*tt[0]-G*tt[1]+X[0]),L[2*z+1]=V*(G*tt[0]+H*tt[1]+X[1]),z+=1}for(Z=M.edges,J=M.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)C[4*O+$]=R[$];I[O]=y;tt=J[K[Q]];L[2*O]=V*(H*tt[0]-G*tt[1]+X[0]),L[2*O+1]=V*(G*tt[0]+H*tt[1]+X[1]),O+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(C),this.glyphBuffer.update(L),this.idBuffer.update(I),o.free(E),o.free(C),o.free(L),o.free(I)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":326,\"./lib/glyphs\":327,\"./lib/shaders\":328,\"gl-buffer\":259,\"gl-mat4/multiply\":295,\"gl-vao\":358,\"is-string-blank\":470,\"typedarray-pool\":595}],331:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:332}],332:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],333:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var g=s[2]-s[0],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,h,g,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":331,\"gl-buffer\":259,\"gl-shader\":335}],334:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function 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a=[t.id,e.id,r.join(\":\"),i.join(\":\")].join(\"@\"),o=this.programs[a];return o&&this.gl.isProgram(o)||(this.programs[a]=o=function(t,e,r,i,a){var o=t.createProgram();t.attachShader(o,e),t.attachShader(o,r);for(var s=0;s<i.length;++s)t.bindAttribLocation(o,a[s],i[s]);if(t.linkProgram(o),!t.getProgramParameter(o,t.LINK_STATUS)){var l=t.getProgramInfoLog(o);throw new n(l,\"Error linking program: \"+l)}return o}(this.gl,t.shader,e.shader,r,i),t.programs.push(a),e.programs.push(a)),o}},{\"./GLError\":336,\"gl-format-compiler-error\":270,\"weakmap-shim\":605}],342:[function(t,e,r){\"use strict\";function n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}e.exports=function(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r};var i=n.prototype;i.update=function(t){t=t||{},this.enable=(t.enable||[!0,!0,!1,!1]).slice(),this.width=(t.width||[1,1,1,1]).slice(),this.color=(t.color||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map((function(t){return t.slice()})),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},i.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],343:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],344:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta = weight * clipOffset * screenShape;\\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":335,glslify:343}],345:[function(t,e,r){\"use strict\";var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":344,\"gl-buffer\":259,\"gl-vao\":358}],346:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:347}],347:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],348:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],g=0,m=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),m=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(g,m,m,m,g,g);var M=c.length;u.push([M-6,M-5,M-4],[M-3,M-2,M-1])}var A=h;h=p,p=A;var S=y;y=v,v=S;var E=g;g=m,m=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var g=u[s],m=f.length;f=f.concat(g.positions),p=p.concat(g.vectors),d=d.concat(g.vertexIntensity);for(c=0;c<g.cells.length;c++){var v=g.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+m)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),g=s(a[1],c),m=s(a[2],u),v=d+1,y=g+1,x=m+1;if(d=l(d,0,f-1),v=l(v,0,f-1),g=l(g,0,h-1),y=l(y,0,h-1),m=l(m,0,p-1),x=l(x,0,p-1),d<0||g<0||m<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,M,A=a[0][d],S=a[0][v],E=a[1][g],C=a[1][y],L=a[2][m],I=(o-A)/(S-A),P=(c-E)/(C-E),z=(u-L)/(a[2][x]-L);switch(isFinite(I)||(I=.5),isFinite(P)||(P=.5),isFinite(z)||(z=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(g=h-1-g,y=h-1-y),r.reversedZ&&(m=p-1-m,x=p-1-x),r.filled){case 5:k=m,M=x,w=g*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=m,M=x,b=d*p,_=v*p,w=g*p*f,T=y*p*f;break;case 3:w=g,T=y,k=m*h,M=x*h,b=d*h*p,_=v*h*p;break;case 2:w=g,T=y,b=d*h,_=v*h,k=m*h*f,M=x*h*f;break;case 1:b=d,_=v,k=m*f,M=x*f,w=g*f*p,T=y*f*p;break;default:b=d,_=v,w=g*f,T=y*f,k=m*f*h,M=x*f*h}var O=i[b+w+k],D=i[b+w+M],R=i[b+T+k],F=i[b+T+M],B=i[_+w+k],N=i[_+w+M],j=i[_+T+k],U=i[_+T+M],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,O,B,I),n.lerp(q,D,N,I),n.lerp(H,R,j,I),n.lerp(G,F,U,I);var Y=n.create(),W=n.create();n.lerp(Y,V,H,P),n.lerp(W,q,G,P);var X=n.create();return n.lerp(X,Y,W,z),X}(e,t,p)},g=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},m=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,M=k*k,A=1,S=0,E=r.length;E>1&&(A=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),g=c(r),m=c(n),v=Math.min(d,g,m);return isFinite(v)?v:1}(r));for(var C=0;C<E;C++){var L=n.create();n.copy(L,r[C]);var I=[L],P=[],z=d(L),O=L;P.push(z);var D=[],R=g(L,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),m.push({points:I,velocities:P,divergences:D});for(var B=0;B<100*i&&I.length<i&&T(L);){B++;var N=n.clone(z),j=n.squaredLength(N);if(0===j)break;if(j>M&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,L),z=d(N),n.squaredDistance(O,N)-M>-1e-4*M){I.push(N),O=N,P.push(z);R=g(N,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}L=N}}var U=o(m,t.colormap,S,A);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*A/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":346,\"gl-cone3d\":260,\"gl-vec3\":377,\"gl-vec4\":413}],349:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":335,glslify:350}],350:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],351:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new A(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",m.update(v),m};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),m=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function A(t,e,r,n,i,a,o,l,c,u,h,p,d,g,m){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=m,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=A.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],C={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function L(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=C.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=C.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return C.showSurface=o,C.showContour=s,C}var I={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},P=w.slice(),z=[1,0,0,0,1,0,0,0,1];function O(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=P;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=L(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return O.call(this,t,!1)},S.drawTransparent=function(t){return O.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=z;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=L(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,m=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var g=[0,0];g[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],g,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)m(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),M=x.get(1,o,v,1),A=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-M*A,C=A*w-S*_,L=_*M-w*k,I=Math.sqrt(E*E+C*C+L*L);I<1e-8?(I=Math.max(Math.abs(E),Math.abs(C),Math.abs(L)))<1e-8?(L=1,C=E=0,I=1):I=1/I:I=1/Math.sqrt(I),b.set(o,v,0,E*I),b.set(o,v,1,C*I),b.set(o,v,2,L*I)}s.free(x.data);var P=[1/0,1/0,1/0],z=[-1/0,-1/0,-1/0],O=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+v+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var Y=o+T[H][0],W=v+T[H][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),C=b.get(Y+1,W+1,1),L=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=C,N[j++]=L,P[0]=Math.min(P[0],X+this.objectOffset[0]),P[1]=Math.min(P[1],Z+this.objectOffset[1]),P[2]=Math.min(P[2],G+this.objectOffset[2]),O=Math.min(O,J),z[0]=Math.max(z[0],X+this.objectOffset[0]),z[1]=Math.max(z[1],Z+this.objectOffset[1]),z[2]=Math.max(z[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(O=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-O)/(D-O);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[P,z],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===O&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[O,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var gt=($+dt+1)%3;for(V=0;V<2;++V){var mt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var vt=q?ht:1-ht;if(W=0|Math.min(Math.max(ft+q,0),a[1]),G=dt<2?this._field[gt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=mt*vt;nt[dt]+=yt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,M=+x[1],A=0|M,S=0|Math.min(A+1,i[1]),E=M-A,C=1-E,L=k*C,I=k*E,P=T*C,z=T*E,O=L*f.get(_,A)+I*f.get(_,S)+P*f.get(w,A)+z*f.get(w,S),D=L*p.get(_,A)+I*p.get(_,S)+P*p.get(w,A)+z*p.get(w,S);if(isNaN(O)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=O,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":349,\"binary-search-bounds\":96,\"bit-twiddle\":97,colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495,\"ndarray-gradient\":488,\"ndarray-ops\":490,\"ndarray-pack\":491,\"surface-nets\":570,\"typedarray-pool\":595}],352:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),g=t(\"detect-kerning\"),m=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\t\"+(T.normalViewport?\"\":\"vec2 positionOffset = vec2(positionOffset.x,- positionOffset.y);\")+\"\\n\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ positionOffset))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\t\"+(T.normalViewport?\"position.y = 1. - position.y;\":\"\")+\"\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),T.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);m(t.kerning,g(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),M=0,A=0;M<this.counts.length;M++){var S=this.counts[M];if(w)for(var E=0;E<S;E++)k[A++]=t.position[2*M],k[A++]=t.position[2*M+1];else for(var C=0;C<S;C++)k[A++]=t.position[M][0],k[A++]=t.position[M][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var L=u.mallocUint8(this.count),I=u.mallocFloat(2*this.count);this.textWidth=[];for(var P=0,z=0;P<this.counts.length;P++){for(var O=this.counts[P],D=this.font[P]||this.font[0],R=this.fontAtlas[P]||this.fontAtlas[0],F=0;F<O;F++){var B=this.text.charAt(z),N=this.text.charAt(z-1);if(L[z]=R.ids[B],I[2*z]=D.width[B],F){var j=I[2*z-2],U=I[2*z],V=I[2*z-1]+.5*j+.5*U;if(this.kerning){var q=D.kerning[N+B];q&&(V+=.001*q)}I[2*z+1]=V}else I[2*z+1]=.5*I[2*z];z++}this.textWidth.push(I.length?.5*I[2*z-2]+I[2*z-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:L,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:I,type:\"float\",usage:\"stream\"}),u.freeUint8(L),u.freeFloat(I),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],T.normalViewport||(i*=-1),i}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)H.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;H=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.normalViewport=!1,T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{\"bit-twiddle\":97,\"color-normalize\":125,\"css-font\":144,\"detect-kerning\":172,\"es6-weak-map\":233,\"flatten-vertex-data\":244,\"font-atlas\":245,\"font-measure\":246,\"gl-util/context\":354,\"is-plain-obj\":469,\"object-assign\":499,\"parse-rect\":504,\"parse-unit\":506,\"pick-by-alias\":511,regl:540,\"to-px\":578,\"typedarray-pool\":595}],353:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function g(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function m(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=g(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var 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strict\";e.exports=function(t,e){var r=t.length;if(0===r)throw new Error(\"Must have at least d+1 points\");var i=t[0].length;if(r<=i)throw new Error(\"Must input at least d+1 points\");var o=t.slice(0,i+1),s=n.apply(void 0,o);if(0===s)throw new Error(\"Input not in general position\");for(var l=new Array(i+1),u=0;u<=i;++u)l[u]=u;s<0&&(l[0]=1,l[1]=0);var f=new a(l,new Array(i+1),!1),h=f.adjacent,p=new Array(i+2);for(u=0;u<=i;++u){for(var d=l.slice(),g=0;g<=i;++g)g===u&&(d[g]=-1);var m=d[0];d[0]=d[1],d[1]=m;var v=new a(d,new Array(i+1),!0);h[u]=v,p[u]=v}p[i+1]=f;for(u=0;u<=i;++u){d=h[u].vertices;var y=h[u].adjacent;for(g=0;g<=i;++g){var x=d[g];if(x<0)y[g]=f;else for(var b=0;b<=i;++b)h[b].vertices.indexOf(x)<0&&(y[g]=h[b])}}var _=new c(i,o,p),w=!!e;for(u=i+1;u<r;++u)_.insert(t[u],w);return _.boundary()};var n=t(\"robust-orientation\"),i=t(\"simplicial-complex\").compareCells;function a(t,e,r){this.vertices=t,this.adjacent=e,this.boundary=r,this.lastVisited=-1}function 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s=t.adjacent,l=0;l<=r;++l){var c=s[l];if(c.boundary&&!(c.lastVisited<=-n)){for(var u=c.vertices,f=0;f<=r;++f){var h=u[f];i[f]=h<0?e:a[h]}var p=this.orient();if(p>0)return c;c.lastVisited=-n,0===p&&o.push(c)}}}return null},u.walk=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,a=this.tuple,o=e?this.interior.length*Math.random()|0:this.interior.length-1,s=this.interior[o];t:for(;!s.boundary;){for(var l=s.vertices,c=s.adjacent,u=0;u<=n;++u)a[u]=i[l[u]];s.lastVisited=r;for(u=0;u<=n;++u){var f=c[u];if(!(f.lastVisited>=r)){var h=a[u];a[u]=t;var p=this.orient();if(a[u]=h,p<0){s=f;continue t}f.boundary?f.lastVisited=-r:f.lastVisited=r}}return}return s},u.addPeaks=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,l=this.tuple,c=this.interior,u=this.simplices,f=[e];e.lastVisited=r,e.vertices[e.vertices.indexOf(-1)]=r,e.boundary=!1,c.push(e);for(var h=[];f.length>0;){var p=(e=f.pop()).vertices,d=e.adjacent,g=p.indexOf(r);if(!(g<0))for(var m=0;m<=n;++m)if(m!==g){var v=d[m];if(v.boundary&&!(v.lastVisited>=r)){var y=v.vertices;if(v.lastVisited!==-r){for(var x=0,b=0;b<=n;++b)y[b]<0?(x=b,l[b]=t):l[b]=i[y[b]];if(this.orient()>0){y[x]=r,v.boundary=!1,c.push(v),f.push(v),v.lastVisited=r;continue}v.lastVisited=-r}var _=v.adjacent,w=p.slice(),T=d.slice(),k=new a(w,T,!0);u.push(k);var M=_.indexOf(e);if(!(M<0)){_[M]=k,T[g]=v,w[m]=-1,T[m]=e,d[m]=k,k.flip();for(b=0;b<=n;++b){var A=w[b];if(!(A<0||A===r)){for(var S=new Array(n-1),E=0,C=0;C<=n;++C){var L=w[C];L<0||C===b||(S[E++]=L)}h.push(new o(S,k,b))}}}}}}h.sort(s);for(m=0;m+1<h.length;m+=2){var I=h[m],P=h[m+1],z=I.index,O=P.index;z<0||O<0||(I.cell.adjacent[I.index]=P.cell,P.cell.adjacent[P.index]=I.cell)}},u.insert=function(t,e){var r=this.vertices;r.push(t);var n=this.walk(t,e);if(n){for(var i=this.dimension,a=this.tuple,o=0;o<=i;++o){var s=n.vertices[o];a[o]=s<0?t:r[s]}var l=this.orient(a);l<0||(0!==l||(n=this.handleBoundaryDegeneracy(n,t)))&&this.addPeaks(t,n)}},u.boundary=function(){for(var t=this.dimension,e=[],r=this.simplices,n=r.length,i=0;i<n;++i){var a=r[i];if(a.boundary){for(var o=new Array(t),s=a.vertices,l=0,c=0,u=0;u<=t;++u)s[u]>=0?o[l++]=s[u]:c=1&u;if(c===(1&t)){var f=o[0];o[0]=o[1],o[1]=f}e.push(o)}}return e}},{\"robust-orientation\":548,\"simplicial-complex\":558}],460:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\");function i(t,e,r,n,i){this.mid=t,this.left=e,this.right=r,this.leftPoints=n,this.rightPoints=i,this.count=(e?e.count:0)+(r?r.count:0)+n.length}e.exports=function(t){if(!t||0===t.length)return new v(null);return new v(m(t))};var a=i.prototype;function o(t,e){t.mid=e.mid,t.left=e.left,t.right=e.right,t.leftPoints=e.leftPoints,t.rightPoints=e.rightPoints,t.count=e.count}function s(t,e){var r=m(e);t.mid=r.mid,t.left=r.left,t.right=r.right,t.leftPoints=r.leftPoints,t.rightPoints=r.rightPoints,t.count=r.count}function l(t,e){var r=t.intervals([]);r.push(e),s(t,r)}function c(t,e){var r=t.intervals([]),n=r.indexOf(e);return n<0?0:(r.splice(n,1),s(t,r),1)}function u(t,e,r){for(var n=0;n<t.length&&t[n][0]<=e;++n){var i=r(t[n]);if(i)return i}}function f(t,e,r){for(var n=t.length-1;n>=0&&t[n][1]>=e;--n){var i=r(t[n]);if(i)return i}}function h(t,e){for(var r=0;r<t.length;++r){var n=e(t[r]);if(n)return n}}function p(t,e){return t-e}function d(t,e){var r=t[0]-e[0];return r||t[1]-e[1]}function g(t,e){var r=t[1]-e[1];return r||t[0]-e[0]}function m(t){if(0===t.length)return null;for(var e=[],r=0;r<t.length;++r)e.push(t[r][0],t[r][1]);e.sort(p);var n=e[e.length>>1],a=[],o=[],s=[];for(r=0;r<t.length;++r){var l=t[r];l[1]<n?a.push(l):n<l[0]?o.push(l):s.push(l)}var c=s,u=s.slice();return c.sort(d),u.sort(g),new i(n,m(a),m(o),c,u)}function v(t){this.root=t}a.intervals=function(t){return 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this.leftPoints[0]===t?2:0;if(1===this.leftPoints.length&&this.leftPoints[0]===t){if(this.left&&this.right){for(var r=this,i=this.left;i.right;)r=i,i=i.right;if(r===this)i.right=this.right;else{var a=this.left,s=this.right;r.count-=i.count,r.right=i.left,i.left=a,i.right=s}o(this,i),this.count=(this.left?this.left.count:0)+(this.right?this.right.count:0)+this.leftPoints.length}else this.left?o(this,this.left):o(this,this.right);return 1}for(a=n.ge(this.leftPoints,t,d);a<this.leftPoints.length&&this.leftPoints[a][0]===t[0];++a)if(this.leftPoints[a]===t){this.count-=1,this.leftPoints.splice(a,1);for(s=n.ge(this.rightPoints,t,g);s<this.rightPoints.length&&this.rightPoints[s][1]===t[1];++s)if(this.rightPoints[s]===t)return this.rightPoints.splice(s,1),1}return 0},a.queryPoint=function(t,e){if(t<this.mid){if(this.left)if(r=this.left.queryPoint(t,e))return r;return u(this.leftPoints,t,e)}if(t>this.mid){var r;if(this.right)if(r=this.right.queryPoint(t,e))return r;return f(this.rightPoints,t,e)}return h(this.leftPoints,e)},a.queryInterval=function(t,e,r){var n;if(t<this.mid&&this.left&&(n=this.left.queryInterval(t,e,r)))return n;if(e>this.mid&&this.right&&(n=this.right.queryInterval(t,e,r)))return n;return e<this.mid?u(this.leftPoints,e,r):t>this.mid?f(this.rightPoints,t,r):h(this.leftPoints,r)};var y=v.prototype;y.insert=function(t){this.root?this.root.insert(t):this.root=new i(t[0],null,null,[t],[t])},y.remove=function(t){if(this.root){var e=this.root.remove(t);return 2===e&&(this.root=null),0!==e}return!1},y.queryPoint=function(t,e){if(this.root)return this.root.queryPoint(t,e)},y.queryInterval=function(t,e,r){if(t<=e&&this.root)return this.root.queryInterval(t,e,r)},Object.defineProperty(y,\"count\",{get:function(){return this.root?this.root.count:0}}),Object.defineProperty(y,\"intervals\",{get:function(){return this.root?this.root.intervals([]):[]}})},{\"binary-search-bounds\":461}],461:[function(t,e,r){arguments[4][243][0].apply(r,arguments)},{dup:243}],462:[function(t,e,r){\"use strict\";e.exports=function(t,e){e=e||new Array(t.length);for(var r=0;r<t.length;++r)e[t[r]]=r;return e}},{}],463:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=r;return e}},{}],464:[function(t,e,r){e.exports=!0},{}],465:[function(t,e,r){function n(t){return!!t.constructor&&\"function\"==typeof t.constructor.isBuffer&&t.constructor.isBuffer(t)}\n",
-       "/*!\n",
-       " * Determine if an object is a Buffer\n",
-       " *\n",
-       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
-       " * @license  MIT\n",
-       " */\n",
-       "e.exports=function(t){return null!=t&&(n(t)||function(t){return\"function\"==typeof t.readFloatLE&&\"function\"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],466:[function(t,e,r){\"use strict\";e.exports=\"undefined\"!=typeof navigator&&(/MSIE/.test(navigator.userAgent)||/Trident\\//.test(navigator.appVersion))},{}],467:[function(t,e,r){\"use strict\";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||\"undefined\"==typeof navigator||(e=navigator.userAgent),e&&e.headers&&\"string\"==typeof e.headers[\"user-agent\"]&&(e=e.headers[\"user-agent\"]),\"string\"!=typeof e)return!1;var r=t.tablet?i.test(e):n.test(e);return!r&&t.tablet&&t.featureDetect&&navigator&&navigator.maxTouchPoints>1&&-1!==e.indexOf(\"Macintosh\")&&-1!==e.indexOf(\"Safari\")&&(r=!0),r}},{}],468:[function(t,e,r){\"use strict\";e.exports=function(t){var e=typeof t;return null!==t&&(\"object\"===e||\"function\"===e)}},{}],469:[function(t,e,r){\"use strict\";var n=Object.prototype.toString;e.exports=function(t){var e;return\"[object Object]\"===n.call(t)&&(null===(e=Object.getPrototypeOf(t))||e===Object.getPrototypeOf({}))}},{}],470:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e,r=t.length,n=0;n<r;n++)if(((e=t.charCodeAt(n))<9||e>13)&&32!==e&&133!==e&&160!==e&&5760!==e&&6158!==e&&(e<8192||e>8205)&&8232!==e&&8233!==e&&8239!==e&&8287!==e&&8288!==e&&12288!==e&&65279!==e)return!1;return!0}},{}],471:[function(t,e,r){\"use strict\";e.exports=function(t){return\"string\"==typeof t&&(t=t.trim(),!!(/^[mzlhvcsqta]\\s*[-+.0-9][^mlhvzcsqta]+/i.test(t)&&/[\\dz]$/i.test(t)&&t.length>4))}},{}],472:[function(t,e,r){e.exports=function(t,e,r){return t*(1-r)+e*r}},{}],473:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():(t=t||self).mapboxgl=n()}(this,(function(){\"use strict\";var t,e,r;function n(n,i){if(t)if(e){var a=\"var sharedChunk = {}; (\"+t+\")(sharedChunk); (\"+e+\")(sharedChunk);\",o={};t(o),(r=i(o)).workerUrl=window.URL.createObjectURL(new Blob([a],{type:\"text/javascript\"}))}else e=i;else t=i}return n(0,(function(t){function e(t,e){return t(e={exports:{}},e.exports),e.exports}var r=n;function n(t,e,r,n){this.cx=3*t,this.bx=3*(r-t)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*e,this.by=3*(n-e)-this.cy,this.ay=1-this.cy-this.by,this.p1x=t,this.p1y=n,this.p2x=r,this.p2y=n}n.prototype.sampleCurveX=function(t){return((this.ax*t+this.bx)*t+this.cx)*t},n.prototype.sampleCurveY=function(t){return((this.ay*t+this.by)*t+this.cy)*t},n.prototype.sampleCurveDerivativeX=function(t){return(3*this.ax*t+2*this.bx)*t+this.cx},n.prototype.solveCurveX=function(t,e){var r,n,i,a,o;for(void 0===e&&(e=1e-6),i=t,o=0;o<8;o++){if(a=this.sampleCurveX(i)-t,Math.abs(a)<e)return i;var s=this.sampleCurveDerivativeX(i);if(Math.abs(s)<1e-6)break;i-=a/s}if((i=t)<(r=0))return r;if(i>(n=1))return n;for(;r<n;){if(a=this.sampleCurveX(i),Math.abs(a-t)<e)return i;t>a?r=i:n=i,i=.5*(n-r)+r}return i},n.prototype.solve=function(t,e){return this.sampleCurveY(this.solveCurveX(t,e))};var i=a;function a(t,e){this.x=t,this.y=e}function o(t,e,n,i){var a=new r(t,e,n,i);return function(t){return 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r={transparent:[0,0,0,0],aliceblue:[240,248,255,1],antiquewhite:[250,235,215,1],aqua:[0,255,255,1],aquamarine:[127,255,212,1],azure:[240,255,255,1],beige:[245,245,220,1],bisque:[255,228,196,1],black:[0,0,0,1],blanchedalmond:[255,235,205,1],blue:[0,0,255,1],blueviolet:[138,43,226,1],brown:[165,42,42,1],burlywood:[222,184,135,1],cadetblue:[95,158,160,1],chartreuse:[127,255,0,1],chocolate:[210,105,30,1],coral:[255,127,80,1],cornflowerblue:[100,149,237,1],cornsilk:[255,248,220,1],crimson:[220,20,60,1],cyan:[0,255,255,1],darkblue:[0,0,139,1],darkcyan:[0,139,139,1],darkgoldenrod:[184,134,11,1],darkgray:[169,169,169,1],darkgreen:[0,100,0,1],darkgrey:[169,169,169,1],darkkhaki:[189,183,107,1],darkmagenta:[139,0,139,1],darkolivegreen:[85,107,47,1],darkorange:[255,140,0,1],darkorchid:[153,50,204,1],darkred:[139,0,0,1],darksalmon:[233,150,122,1],darkseagreen:[143,188,143,1],darkslateblue:[72,61,139,1],darkslategray:[47,79,79,1],darkslategrey:[47,79,79,1],darkturquoise:[0,206,209,1],darkviolet:[148,0,211,1],deeppink:[255,20,147,1],deepskyblue:[0,191,255,1],dimgray:[105,105,105,1],dimgrey:[105,105,105,1],dodgerblue:[30,144,255,1],firebrick:[178,34,34,1],floralwhite:[255,250,240,1],forestgreen:[34,139,34,1],fuchsia:[255,0,255,1],gainsboro:[220,220,220,1],ghostwhite:[248,248,255,1],gold:[255,215,0,1],goldenrod:[218,165,32,1],gray:[128,128,128,1],green:[0,128,0,1],greenyellow:[173,255,47,1],grey:[128,128,128,1],honeydew:[240,255,240,1],hotpink:[255,105,180,1],indianred:[205,92,92,1],indigo:[75,0,130,1],ivory:[255,255,240,1],khaki:[240,230,140,1],lavender:[230,230,250,1],lavenderblush:[255,240,245,1],lawngreen:[124,252,0,1],lemonchiffon:[255,250,205,1],lightblue:[173,216,230,1],lightcoral:[240,128,128,1],lightcyan:[224,255,255,1],lightgoldenrodyellow:[250,250,210,1],lightgray:[211,211,211,1],lightgreen:[144,238,144,1],lightgrey:[211,211,211,1],lightpink:[255,182,193,1],lightsalmon:[255,160,122,1],lightseagreen:[32,178,170,1],lightskyblue:[135,206,250,1],lightslategray:[119,136,153,1],lightslategrey:[119,136,153,1],lightsteelblue:[176,196,222,1],lightyellow:[255,255,224,1],lime:[0,255,0,1],limegreen:[50,205,50,1],linen:[250,240,230,1],magenta:[255,0,255,1],maroon:[128,0,0,1],mediumaquamarine:[102,205,170,1],mediumblue:[0,0,205,1],mediumorchid:[186,85,211,1],mediumpurple:[147,112,219,1],mediumseagreen:[60,179,113,1],mediumslateblue:[123,104,238,1],mediumspringgreen:[0,250,154,1],mediumturquoise:[72,209,204,1],mediumvioletred:[199,21,133,1],midnightblue:[25,25,112,1],mintcream:[245,255,250,1],mistyrose:[255,228,225,1],moccasin:[255,228,181,1],navajowhite:[255,222,173,1],navy:[0,0,128,1],oldlace:[253,245,230,1],olive:[128,128,0,1],olivedrab:[107,142,35,1],orange:[255,165,0,1],orangered:[255,69,0,1],orchid:[218,112,214,1],palegoldenrod:[238,232,170,1],palegreen:[152,251,152,1],paleturquoise:[175,238,238,1],palevioletred:[219,112,147,1],papayawhip:[255,239,213,1],peachpuff:[255,218,185,1],peru:[205,133,63,1],pink:[255,192,203,1],plum:[221,160,221,1],powderblue:[176,224,230,1],purple:[128,0,128,1],rebeccapurple:[102,51,153,1],red:[255,0,0,1],rosybrown:[188,143,143,1],royalblue:[65,105,225,1],saddlebrown:[139,69,19,1],salmon:[250,128,114,1],sandybrown:[244,164,96,1],seagreen:[46,139,87,1],seashell:[255,245,238,1],sienna:[160,82,45,1],silver:[192,192,192,1],skyblue:[135,206,235,1],slateblue:[106,90,205,1],slategray:[112,128,144,1],slategrey:[112,128,144,1],snow:[255,250,250,1],springgreen:[0,255,127,1],steelblue:[70,130,180,1],tan:[210,180,140,1],teal:[0,128,128,1],thistle:[216,191,216,1],tomato:[255,99,71,1],turquoise:[64,224,208,1],violet:[238,130,238,1],wheat:[245,222,179,1],white:[255,255,255,1],whitesmoke:[245,245,245,1],yellow:[255,255,0,1],yellowgreen:[154,205,50,1]};function n(t){return(t=Math.round(t))<0?0:t>255?255:t}function i(t){return n(\"%\"===t[t.length-1]?parseFloat(t)/100*255:parseInt(t))}function a(t){return(e=\"%\"===t[t.length-1]?parseFloat(t)/100:parseFloat(t))<0?0:e>1?1:e;var e}function o(t,e,r){return r<0?r+=1:r>1&&(r-=1),6*r<1?t+(e-t)*r*6:2*r<1?e:3*r<2?t+(e-t)*(2/3-r)*6:t}try{e.parseCSSColor=function(t){var e,s=t.replace(/ /g,\"\").toLowerCase();if(s in r)return r[s].slice();if(\"#\"===s[0])return 4===s.length?(e=parseInt(s.substr(1),16))>=0&&e<=4095?[(3840&e)>>4|(3840&e)>>8,240&e|(240&e)>>4,15&e|(15&e)<<4,1]:null:7===s.length&&(e=parseInt(s.substr(1),16))>=0&&e<=16777215?[(16711680&e)>>16,(65280&e)>>8,255&e,1]:null;var l=s.indexOf(\"(\"),c=s.indexOf(\")\");if(-1!==l&&c+1===s.length){var u=s.substr(0,l),f=s.substr(l+1,c-(l+1)).split(\",\"),h=1;switch(u){case\"rgba\":if(4!==f.length)return null;h=a(f.pop());case\"rgb\":return 3!==f.length?null:[i(f[0]),i(f[1]),i(f[2]),h];case\"hsla\":if(4!==f.length)return 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Qt=function(t,e,r){this.sensitivity=t?e?\"variant\":\"case\":e?\"accent\":\"base\",this.locale=r,this.collator=new Intl.Collator(this.locale?this.locale:[],{sensitivity:this.sensitivity,usage:\"search\"})};Qt.prototype.compare=function(t,e){return this.collator.compare(t,e)},Qt.prototype.resolvedLocale=function(){return new Intl.Collator(this.locale?this.locale:[]).resolvedOptions().locale};var $t=function(t,e,r,n,i){this.text=t,this.image=e,this.scale=r,this.fontStack=n,this.textColor=i},te=function(t){this.sections=t};te.fromString=function(t){return new te([new $t(t,null,null,null,null)])},te.prototype.isEmpty=function(){return 0===this.sections.length||!this.sections.some((function(t){return 0!==t.text.length||t.image&&0!==t.image.name.length}))},te.factory=function(t){return t instanceof te?t:te.fromString(t)},te.prototype.toString=function(){return 0===this.sections.length?\"\":this.sections.map((function(t){return t.text})).join(\"\")},te.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];if(n.image)t.push([\"image\",n.image.name]);else{t.push(n.text);var i={};n.fontStack&&(i[\"text-font\"]=[\"literal\",n.fontStack.split(\",\")]),n.scale&&(i[\"font-scale\"]=n.scale),n.textColor&&(i[\"text-color\"]=[\"rgba\"].concat(n.textColor.toArray())),t.push(i)}}return t};var ee=function(t){this.name=t.name,this.available=t.available};function re(t,e,r,n){return\"number\"==typeof t&&t>=0&&t<=255&&\"number\"==typeof e&&e>=0&&e<=255&&\"number\"==typeof r&&r>=0&&r<=255?void 0===n||\"number\"==typeof n&&n>=0&&n<=1?null:\"Invalid rgba value [\"+[t,e,r,n].join(\", \")+\"]: 'a' must be between 0 and 1.\":\"Invalid rgba value [\"+(\"number\"==typeof n?[t,e,r,n]:[t,e,r]).join(\", \")+\"]: 'r', 'g', and 'b' must be between 0 and 255.\"}function ne(t){if(null===t)return!0;if(\"string\"==typeof t)return!0;if(\"boolean\"==typeof t)return!0;if(\"number\"==typeof t)return!0;if(t instanceof Kt)return!0;if(t instanceof Qt)return!0;if(t instanceof te)return!0;if(t instanceof ee)return!0;if(Array.isArray(t)){for(var e=0,r=t;e<r.length;e+=1)if(!ne(r[e]))return!1;return!0}if(\"object\"==typeof t){for(var n in t)if(!ne(t[n]))return!1;return!0}return!1}function ie(t){if(null===t)return Ot;if(\"string\"==typeof t)return Rt;if(\"boolean\"==typeof t)return Ft;if(\"number\"==typeof t)return Dt;if(t instanceof Kt)return Bt;if(t instanceof Qt)return Ut;if(t instanceof te)return Vt;if(t instanceof ee)return qt;if(Array.isArray(t)){for(var e,r=t.length,n=0,i=t;n<i.length;n+=1){var a=ie(i[n]);if(e){if(e===a)continue;e=jt;break}e=a}return Ht(e||jt,r)}return Nt}function ae(t){var e=typeof t;return null===t?\"\":\"string\"===e||\"number\"===e||\"boolean\"===e?String(t):t instanceof Kt||t instanceof te||t instanceof ee?t.toString():JSON.stringify(t)}ee.prototype.toString=function(){return this.name},ee.fromString=function(t){return t?new ee({name:t,available:!1}):null},ee.prototype.serialize=function(){return[\"image\",this.name]};var oe=function(t,e){this.type=t,this.value=e};oe.parse=function(t,e){if(2!==t.length)return e.error(\"'literal' expression requires exactly one argument, but found \"+(t.length-1)+\" instead.\");if(!ne(t[1]))return e.error(\"invalid value\");var r=t[1],n=ie(r),i=e.expectedType;return\"array\"!==n.kind||0!==n.N||!i||\"array\"!==i.kind||\"number\"==typeof i.N&&0!==i.N||(n=i),new oe(n,r)},oe.prototype.evaluate=function(){return this.value},oe.prototype.eachChild=function(){},oe.prototype.outputDefined=function(){return!0},oe.prototype.serialize=function(){return\"array\"===this.type.kind||\"object\"===this.type.kind?[\"literal\",this.value]:this.value instanceof Kt?[\"rgba\"].concat(this.value.toArray()):this.value instanceof te?this.value.serialize():this.value};var se=function(t){this.name=\"ExpressionEvaluationError\",this.message=t};se.prototype.toJSON=function(){return this.message};var le={string:Rt,number:Dt,boolean:Ft,object:Nt},ce=function(t,e){this.type=t,this.args=e};ce.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r,n=1,i=t[0];if(\"array\"===i){var a,o;if(t.length>2){var s=t[1];if(\"string\"!=typeof s||!(s in le)||\"object\"===s)return e.error('The item type argument of \"array\" must be one of string, number, boolean',1);a=le[s],n++}else a=jt;if(t.length>3){if(null!==t[2]&&(\"number\"!=typeof t[2]||t[2]<0||t[2]!==Math.floor(t[2])))return e.error('The length argument to \"array\" must be a positive integer literal',2);o=t[2],n++}r=Ht(a,o)}else r=le[i];for(var l=[];n<t.length;n++){var c=e.parse(t[n],n,jt);if(!c)return null;l.push(c)}return new ce(r,l)},ce.prototype.evaluate=function(t){for(var e=0;e<this.args.length;e++){var r=this.args[e].evaluate(t);if(!Wt(this.type,ie(r)))return r;if(e===this.args.length-1)throw new se(\"Expected value to be of type \"+Gt(this.type)+\", but found \"+Gt(ie(r))+\" instead.\")}return null},ce.prototype.eachChild=function(t){this.args.forEach(t)},ce.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},ce.prototype.serialize=function(){var t=this.type,e=[t.kind];if(\"array\"===t.kind){var r=t.itemType;if(\"string\"===r.kind||\"number\"===r.kind||\"boolean\"===r.kind){e.push(r.kind);var n=t.N;(\"number\"==typeof n||this.args.length>1)&&e.push(n)}}return e.concat(this.args.map((function(t){return t.serialize()})))};var ue=function(t){this.type=Vt,this.sections=t};ue.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[1];if(!Array.isArray(r)&&\"object\"==typeof r)return e.error(\"First argument must be an image or text section.\");for(var n=[],i=!1,a=1;a<=t.length-1;++a){var o=t[a];if(i&&\"object\"==typeof o&&!Array.isArray(o)){i=!1;var s=null;if(o[\"font-scale\"]&&!(s=e.parse(o[\"font-scale\"],1,Dt)))return null;var l=null;if(o[\"text-font\"]&&!(l=e.parse(o[\"text-font\"],1,Ht(Rt))))return null;var c=null;if(o[\"text-color\"]&&!(c=e.parse(o[\"text-color\"],1,Bt)))return null;var u=n[n.length-1];u.scale=s,u.font=l,u.textColor=c}else{var f=e.parse(t[a],1,jt);if(!f)return null;var h=f.type.kind;if(\"string\"!==h&&\"value\"!==h&&\"null\"!==h&&\"resolvedImage\"!==h)return e.error(\"Formatted text type must be 'string', 'value', 'image' or 'null'.\");i=!0,n.push({content:f,scale:null,font:null,textColor:null})}}return new ue(n)},ue.prototype.evaluate=function(t){return new te(this.sections.map((function(e){var r=e.content.evaluate(t);return ie(r)===qt?new $t(\"\",r,null,null,null):new $t(ae(r),null,e.scale?e.scale.evaluate(t):null,e.font?e.font.evaluate(t).join(\",\"):null,e.textColor?e.textColor.evaluate(t):null)})))},ue.prototype.eachChild=function(t){for(var e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t(n.content),n.scale&&t(n.scale),n.font&&t(n.font),n.textColor&&t(n.textColor)}},ue.prototype.outputDefined=function(){return!1},ue.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t.push(n.content.serialize());var i={};n.scale&&(i[\"font-scale\"]=n.scale.serialize()),n.font&&(i[\"text-font\"]=n.font.serialize()),n.textColor&&(i[\"text-color\"]=n.textColor.serialize()),t.push(i)}return t};var fe=function(t){this.type=qt,this.input=t};fe.parse=function(t,e){if(2!==t.length)return e.error(\"Expected two arguments.\");var r=e.parse(t[1],1,Rt);return r?new fe(r):e.error(\"No image name provided.\")},fe.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=ee.fromString(e);return r&&t.availableImages&&(r.available=t.availableImages.indexOf(e)>-1),r},fe.prototype.eachChild=function(t){t(this.input)},fe.prototype.outputDefined=function(){return!1},fe.prototype.serialize=function(){return[\"image\",this.input.serialize()]};var he={\"to-boolean\":Ft,\"to-color\":Bt,\"to-number\":Dt,\"to-string\":Rt},pe=function(t,e){this.type=t,this.args=e};pe.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[0];if((\"to-boolean\"===r||\"to-string\"===r)&&2!==t.length)return e.error(\"Expected one argument.\");for(var n=he[r],i=[],a=1;a<t.length;a++){var o=e.parse(t[a],a,jt);if(!o)return null;i.push(o)}return new pe(n,i)},pe.prototype.evaluate=function(t){if(\"boolean\"===this.type.kind)return Boolean(this.args[0].evaluate(t));if(\"color\"===this.type.kind){for(var e,r,n=0,i=this.args;n<i.length;n+=1){if(r=null,(e=i[n].evaluate(t))instanceof Kt)return e;if(\"string\"==typeof e){var a=t.parseColor(e);if(a)return a}else if(Array.isArray(e)&&!(r=e.length<3||e.length>4?\"Invalid rbga value \"+JSON.stringify(e)+\": expected an array containing either three or four numeric values.\":re(e[0],e[1],e[2],e[3])))return new Kt(e[0]/255,e[1]/255,e[2]/255,e[3])}throw new se(r||\"Could not parse color from value '\"+(\"string\"==typeof e?e:String(JSON.stringify(e)))+\"'\")}if(\"number\"===this.type.kind){for(var o=null,s=0,l=this.args;s<l.length;s+=1){if(null===(o=l[s].evaluate(t)))return 0;var c=Number(o);if(!isNaN(c))return c}throw new se(\"Could not convert \"+JSON.stringify(o)+\" to number.\")}return\"formatted\"===this.type.kind?te.fromString(ae(this.args[0].evaluate(t))):\"resolvedImage\"===this.type.kind?ee.fromString(ae(this.args[0].evaluate(t))):ae(this.args[0].evaluate(t))},pe.prototype.eachChild=function(t){this.args.forEach(t)},pe.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},pe.prototype.serialize=function(){if(\"formatted\"===this.type.kind)return new ue([{content:this.args[0],scale:null,font:null,textColor:null}]).serialize();if(\"resolvedImage\"===this.type.kind)return new fe(this.args[0]).serialize();var t=[\"to-\"+this.type.kind];return this.eachChild((function(e){t.push(e.serialize())})),t};var de=[\"Unknown\",\"Point\",\"LineString\",\"Polygon\"],ge=function(){this.globals=null,this.feature=null,this.featureState=null,this.formattedSection=null,this._parseColorCache={},this.availableImages=null,this.canonical=null};ge.prototype.id=function(){return this.feature&&\"id\"in this.feature?this.feature.id:null},ge.prototype.geometryType=function(){return this.feature?\"number\"==typeof this.feature.type?de[this.feature.type]:this.feature.type:null},ge.prototype.geometry=function(){return this.feature&&\"geometry\"in this.feature?this.feature.geometry:null},ge.prototype.canonicalID=function(){return this.canonical},ge.prototype.properties=function(){return this.feature&&this.feature.properties||{}},ge.prototype.parseColor=function(t){var e=this._parseColorCache[t];return e||(e=this._parseColorCache[t]=Kt.parse(t)),e};var me=function(t,e,r,n){this.name=t,this.type=e,this._evaluate=r,this.args=n};me.prototype.evaluate=function(t){return 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a=Ne(e,n),o=rr.interpolationFactor(this.interpolation,n,e[a],e[a+1]),s=r[a].evaluate(t),l=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ve[this.type.kind.toLowerCase()](s,l,o):\"interpolate-hcl\"===this.operator?tr.reverse(tr.interpolate(tr.forward(s),tr.forward(l),o)):$e.reverse($e.interpolate($e.forward(s),$e.forward(l),o))},rr.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1)t(r[e])},rr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},rr.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var ir=function(t,e){this.type=t,this.args=e};ir.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=e.parse(o[a],1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!s)return null;r=r||s.type,i.push(s)}var l=n&&i.some((function(t){return Wt(n,t.type)}));return new ir(l?jt:r,i)},ir.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length&&(n++,(r=a[i].evaluate(t))&&r instanceof ee&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null===r);i+=1);return r},ir.prototype.eachChild=function(t){this.args.forEach(t)},ir.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},ir.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var ar=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};ar.prototype.evaluate=function(t){return this.result.evaluate(t)},ar.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1)t(r[e][1]);t(this.result)},ar.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new ar(r,o):null},ar.prototype.outputDefined=function(){return this.result.outputDefined()},ar.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e];t.push(n[0],n[1].serialize())}return t.push(this.result.serialize()),t};var or=function(t,e,r){this.type=t,this.index=e,this.input=r};or.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Dt),n=e.parse(t[2],2,Ht(e.expectedType||jt));return r&&n?new or(n.type.itemType,r,n):null},or.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new se(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new se(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new se(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},or.prototype.eachChild=function(t){t(this.index),t(this.input)},or.prototype.outputDefined=function(){return!1},or.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var sr=function(t,e){this.type=Ft,this.needle=t,this.haystack=e};sr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,jt),n=e.parse(t[2],2,jt);return r&&n?Xt(r.type,[Ft,Rt,Dt,Ot,jt])?new sr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(r.type)+\" instead\"):null},sr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Zt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new se(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(ie(e))+\" instead.\");if(!Zt(r,[\"string\",\"array\"]))throw new se(\"Expected second argument to be of type array or string, but found \"+Gt(ie(r))+\" instead.\");return r.indexOf(e)>=0},sr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},sr.prototype.outputDefined=function(){return!0},sr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var lr=function(t,e,r){this.type=Dt,this.needle=t,this.haystack=e,this.fromIndex=r};lr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,jt),n=e.parse(t[2],2,jt);if(!r||!n)return null;if(!Xt(r.type,[Ft,Rt,Dt,Ot,jt]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Dt);return i?new lr(r,n,i):null}return new lr(r,n)},lr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Zt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new se(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(ie(e))+\" instead.\");if(!Zt(r,[\"string\",\"array\"]))throw new se(\"Expected second argument to be of type array or string, but found \"+Gt(ie(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},lr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},lr.prototype.outputDefined=function(){return!1},lr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var cr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};cr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of arguments.\");var r,n;e.expectedType&&\"value\"!==e.expectedType.kind&&(n=e.expectedType);for(var i={},a=[],o=2;o<t.length-1;o+=2){var s=t[o],l=t[o+1];Array.isArray(s)||(s=[s]);var 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e=this.input.evaluate(t);return(ie(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},cr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},cr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},cr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];e.push(1===h.length?s(h[0]):h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var ur=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};ur.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 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fr=function(t,e,r,n){this.type=t,this.input=e,this.beginIndex=r,this.endIndex=n};function hr(t,e){return\"==\"===t||\"!=\"===t?\"boolean\"===e.kind||\"string\"===e.kind||\"number\"===e.kind||\"null\"===e.kind||\"value\"===e.kind:\"string\"===e.kind||\"number\"===e.kind||\"value\"===e.kind}function pr(t,e,r,n){return 0===n.compare(e,r)}function dr(t,e,r){var n=\"==\"!==t&&\"!=\"!==t;return function(){function i(t,e,r){this.type=Ft,this.lhs=t,this.rhs=e,this.collator=r,this.hasUntypedArgument=\"value\"===t.type.kind||\"value\"===e.type.kind}return i.parse=function(t,e){if(3!==t.length&&4!==t.length)return e.error(\"Expected two or three arguments.\");var r=t[0],a=e.parse(t[1],1,jt);if(!a)return null;if(!hr(r,a.type))return e.concat(1).error('\"'+r+\"\\\" comparisons are not supported for type '\"+Gt(a.type)+\"'.\");var o=e.parse(t[2],2,jt);if(!o)return null;if(!hr(r,o.type))return e.concat(2).error('\"'+r+\"\\\" comparisons are not supported for type 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0,o={}),e=e.concat(Xr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:Jr,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Vr(It(a[1]))?e.concat([new St(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(bn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=zr(t.value),l=Lt(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new St(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new St(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return Lr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new St(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new St(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new St(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new St(t.key,c,\"integer expected, found \"+l)]}}function Qr(t){var e=(\"property\"===t.expressionContext?Yr:qr)(It(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new St(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new St(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". 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en={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function rn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};tn(t)||(t=an(t));var e=qr(t,en);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function nn(t,e){return t<e?-1:t>e?1:0}function an(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?on(t[1],t[2],\"==\"):\"!=\"===r?cn(on(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?on(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(an))):\"all\"===r?[\"all\"].concat(t.slice(1).map(an)):\"none\"===r?[\"all\"].concat(t.slice(1).map(an).map(cn)):\"in\"===r?sn(t[1],t.slice(2)):\"!in\"===r?cn(sn(t[1],t.slice(2))):\"has\"===r?ln(t[1]):\"!has\"===r?cn(ln(t[1])):\"within\"!==r||t}function on(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function sn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(nn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function ln(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function cn(t){return[\"!\",t]}function un(t){return tn(It(t.value))?Qr(Ct({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==zr(r))return[new St(n,r,\"array expected, \"+zr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new St(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat($r({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Lt(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Lt(r[1])&&o.push(new St(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new St(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=zr(r[1]))&&o.push(new St(n+\"[1]\",r[1],\"string expected, \"+i+\" found\"));for(var s=2;s<r.length;s++)i=zr(r[s]),\"$type\"===Lt(r[1])?o=o.concat($r({key:n+\"[\"+s+\"]\",value:r[s],valueSpec:a.geometry_type,style:e.style,styleSpec:e.styleSpec})):\"string\"!==i&&\"number\"!==i&&\"boolean\"!==i&&o.push(new St(n+\"[\"+s+\"]\",r[s],\"string, number, or boolean expected, \"+i+\" found\"));break;case\"any\":case\"all\":case\"none\":for(var l=1;l<r.length;l++)o=o.concat(t({key:n+\"[\"+l+\"]\",value:r[l],style:e.style,styleSpec:e.styleSpec}));break;case\"has\":case\"!has\":i=zr(r[1]),2!==r.length?o.push(new St(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"string\"!==i&&o.push(new St(n+\"[1]\",r[1],\"string expected, \"+i+\" found\"));break;case\"within\":i=zr(r[1]),2!==r.length?o.push(new St(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"object\"!==i&&o.push(new St(n+\"[1]\",r[1],\"object expected, \"+i+\" found\"))}return o}(t)}function fn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return bn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new St(r,a,'unknown property \"'+o+'\"')];if(\"string\"===zr(a)&&Lr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new St(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new St(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Or(It(a))&&\"identity\"===Lt(a.type)&&f.push(new St(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Lt(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new St(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new St(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new St(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new St(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new St(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new St(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new St(n,r,'missing required property \"source\"'));return e=e.concat(Xr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(){return[]},type:function(){return bn({key:n+\".type\",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:\"type\"})},filter:un,layout:function(t){return Xr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return pn(Ct({layerType:s},t))}}})},paint:function(t){return Xr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return hn(Ct({layerType:s},t))}}})}}}))}function gn(t){var e=t.value,r=t.key,n=zr(e);return\"string\"!==n?[new St(r,e,\"string expected, \"+n+\" found\")]:[]}var mn={promoteId:function(t){var e=t.key,r=t.value;if(\"string\"===zr(r))return gn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,gn({key:e+\".\"+i,value:r[i]}));return n}};function vn(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new St(r,e,'\"type\" is required')];var a,o=Lt(e.type);switch(o){case\"vector\":case\"raster\":case\"raster-dem\":return Xr({key:r,value:e,valueSpec:n[\"source_\"+o.replace(\"-\",\"_\")],style:t.style,styleSpec:n,objectElementValidators:mn});case\"geojson\":if(a=Xr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:mn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=\"string\"==typeof c?[c,[\"accumulated\"],[\"get\",s]]:c;a.push.apply(a,Qr({key:r+\".\"+s+\".map\",value:l[1],expressionContext:\"cluster-map\"})),a.push.apply(a,Qr({key:r+\".\"+s+\".reduce\",value:u,expressionContext:\"cluster-reduce\"}))}return a;case\"video\":return Xr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case\"image\":return Xr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case\"canvas\":return[new St(r,null,\"Please use runtime APIs to add canvas sources, rather than including them in stylesheets.\",\"source.canvas\")];default:return $r({key:r+\".type\",value:e.type,valueSpec:{values:[\"vector\",\"raster\",\"raster-dem\",\"geojson\",\"video\",\"image\"]},style:i,styleSpec:n})}}function yn(t){var e=t.value,r=t.styleSpec,n=r.light,i=t.style,a=[],o=zr(e);if(void 0===e)return a;if(\"object\"!==o)return a.concat([new St(\"light\",e,\"object expected, \"+o+\" found\")]);for(var s in e){var l=s.match(/^(.*)-transition$/);a=a.concat(l&&n[l[1]]&&n[l[1]].transition?bn({key:s,value:e[s],valueSpec:r.transition,style:i,styleSpec:r}):n[s]?bn({key:s,value:e[s],valueSpec:n[s],style:i,styleSpec:r}):[new St(s,e[s],'unknown property \"'+s+'\"')])}return a}var xn={\"*\":function(){return[]},array:Zr,boolean:function(t){var e=t.value,r=t.key,n=zr(e);return\"boolean\"!==n?[new St(r,e,\"boolean expected, \"+n+\" found\")]:[]},number:Jr,color:function(t){var e=t.key,r=t.value,n=zr(r);return\"string\"!==n?[new St(e,r,\"color expected, \"+n+\" 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r=r.concat(bn({key:\"\",value:t,valueSpec:e.$root,styleSpec:e,style:t,objectElementValidators:{glyphs:_n,\"*\":function(){return[]}}})),t.constants&&(r=r.concat(Et({key:\"constants\",value:t.constants,style:t,styleSpec:e}))),Tn(r)}function Tn(t){return[].concat(t).sort((function(t,e){return t.line-e.line}))}function kn(t){return function(){for(var e=[],r=arguments.length;r--;)e[r]=arguments[r];return Tn(t.apply(this,e))}}wn.source=kn(vn),wn.light=kn(yn),wn.layer=kn(dn),wn.filter=kn(un),wn.paintProperty=kn(hn),wn.layoutProperty=kn(pn);var Mn=wn,An=Mn.light,Sn=Mn.paintProperty,En=Mn.layoutProperty;function Cn(t,e){var r=!1;if(e&&e.length)for(var n=0,i=e;n<i.length;n+=1)t.fire(new kt(new Error(i[n].message))),r=!0;return r}var Ln=In;function In(t,e,r){var n=this.cells=[];if(t instanceof ArrayBuffer){this.arrayBuffer=t;var i=new Int32Array(this.arrayBuffer);t=i[0],this.d=(e=i[1])+2*(r=i[2]);for(var a=0;a<this.d*this.d;a++){var o=i[3+a],s=i[3+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[3+n.length+1];this.keys=i.subarray(i[3+n.length],l),this.bboxes=i.subarray(l),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var c=0;c<this.d*this.d;c++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var u=r/e*t;this.min=-u,this.max=t+u}In.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},In.prototype._insertReadonly=function(){throw\"Cannot insert into a GridIndex created from an ArrayBuffer.\"},In.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},In.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},In.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var c=this.keys,u=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(u[p+0],u[p+1],u[p+2],u[p+3]):t<=u[p+2]&&e<=u[p+3]&&r>=u[p+0]&&n>=u[p+1])?(o[h]=!0,a.push(c[h])):o[h]=!1}}},In.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},In.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},In.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},In.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=3+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[3+o]=a,i.set(s,a),a+=s.length}return i[3+t.length]=a,i.set(this.keys,a),i[3+t.length+1]=a+=this.keys.length,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var Pn=self.ImageData,zn=self.ImageBitmap,On={};function Dn(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),On[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var Rn in Dn(\"Object\",Object),Ln.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},Ln.deserialize=function(t){return new Ln(t.buffer)},Dn(\"Grid\",Ln),Dn(\"Color\",Kt),Dn(\"Error\",Error),Dn(\"ResolvedImage\",ee),Dn(\"StylePropertyFunction\",Wr),Dn(\"StyleExpression\",Ur,{omit:[\"_evaluator\"]}),Dn(\"ZoomDependentExpression\",Gr),Dn(\"ZoomConstantExpression\",Hr),Dn(\"CompoundExpression\",me,{omit:[\"_evaluate\"]}),Tr)Tr[Rn]._classRegistryKey||Dn(\"Expression_\"+Rn,Tr[Rn]);function Fn(t){return t&&\"undefined\"!=typeof ArrayBuffer&&(t instanceof ArrayBuffer||t.constructor&&\"ArrayBuffer\"===t.constructor.name)}function Bn(t){return zn&&t instanceof zn}function Nn(t,e){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp)return t;if(Fn(t)||Bn(t))return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof Pn)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1)n.push(Nn(a[i],e));return n}if(\"object\"==typeof t){var o=t.constructor,s=o._classRegistryKey;if(!s)throw new Error(\"can't serialize object of unregistered class\");var l=o.serialize?o.serialize(t,e):{};if(!o.serialize){for(var c in t)if(t.hasOwnProperty(c)&&!(On[s].omit.indexOf(c)>=0)){var u=t[c];l[c]=On[s].shallow.indexOf(c)>=0?u:Nn(u,e)}t instanceof Error&&(l.message=t.message)}if(l.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==s&&(l.$name=s),l}throw new Error(\"can't serialize object of type \"+typeof t)}function jn(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||Fn(t)||Bn(t)||ArrayBuffer.isView(t)||t instanceof Pn)return t;if(Array.isArray(t))return t.map(jn);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=On[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class \"+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i<a.length;i+=1){var o=a[i];if(\"$name\"!==o){var s=t[o];n[o]=On[e].shallow.indexOf(o)>=0?s:jn(s)}}return n}throw new Error(\"can't deserialize object of type \"+typeof t)}var Un=function(){this.first=!0};Un.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))};var Vn={\"Latin-1 Supplement\":function(t){return t>=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic Supplement\":function(t){return t>=1872&&t<=1919},\"Arabic Extended-A\":function(t){return t>=2208&&t<=2303},\"Hangul Jamo\":function(t){return t>=4352&&t<=4607},\"Unified Canadian Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram Symbols\":function(t){return t>=19904&&t<=19967},\"CJK Unified Ideographs\":function(t){return t>=19968&&t<=40959},\"Yi Syllables\":function(t){return t>=40960&&t<=42127},\"Yi Radicals\":function(t){return t>=42128&&t<=42191},\"Hangul Jamo Extended-A\":function(t){return t>=43360&&t<=43391},\"Hangul Syllables\":function(t){return t>=44032&&t<=55215},\"Hangul Jamo Extended-B\":function(t){return t>=55216&&t<=55295},\"Private Use Area\":function(t){return t>=57344&&t<=63743},\"CJK Compatibility Ideographs\":function(t){return t>=63744&&t<=64255},\"Arabic Presentation Forms-A\":function(t){return t>=64336&&t<=65023},\"Vertical Forms\":function(t){return t>=65040&&t<=65055},\"CJK Compatibility Forms\":function(t){return t>=65072&&t<=65103},\"Small Form Variants\":function(t){return t>=65104&&t<=65135},\"Arabic Presentation Forms-B\":function(t){return t>=65136&&t<=65279},\"Halfwidth and Fullwidth Forms\":function(t){return t>=65280&&t<=65519}};function qn(t){for(var e=0,r=t;e<r.length;e+=1)if(Hn(r[e].charCodeAt(0)))return!0;return!1}function Hn(t){return!(746!==t&&747!==t&&(t<4352||!(Vn[\"Bopomofo Extended\"](t)||Vn.Bopomofo(t)||Vn[\"CJK Compatibility 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0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},ii.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var ai=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Or(t))return new Wr(t,e);if(Vr(t)){var r=Yr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=Kt.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};ai.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},ai.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var oi=function(t){this.property=t,this.value=new ai(t,void 0)};oi.prototype.transitioned=function(t,e){return new li(this.property,this.value,e,u({},t.transition,this.transition),t.now)},oi.prototype.untransitioned=function(){return new li(this.property,this.value,null,{},0)};var si=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};si.prototype.getValue=function(t){return x(this._values[t].value.value)},si.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new oi(this._values[t].property)),this._values[t].value=new ai(this._values[t].property,null===e?void 0:x(e))},si.prototype.getTransition=function(t){return x(this._values[t].transition)},si.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new oi(this._values[t].property)),this._values[t].transition=x(e)||void 0},si.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},si.prototype.transitioned=function(t,e){for(var r=new ci(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},si.prototype.untransitioned=function(){for(var t=new ci(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var li=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};li.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var ci=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};ci.prototype.possiblyEvaluate=function(t,e,r){for(var n=new hi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},ci.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1)if(this._values[e[t]].prior)return!0;return!1};var ui=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};ui.prototype.getValue=function(t){return x(this._values[t].value)},ui.prototype.setValue=function(t,e){this._values[t]=new ai(this._values[t].property,null===e?void 0:x(e))},ui.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},ui.prototype.possiblyEvaluate=function(t,e,r){for(var n=new hi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var fi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};fi.prototype.isConstant=function(){return\"constant\"===this.value.kind},fi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},fi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var hi=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};hi.prototype.get=function(t){return this._values[t]};var pi=function(t){this.specification=t};pi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},pi.prototype.interpolate=function(t,e,r){var n=Ve[this.specification.type];return n?n(t,e,r):t};var di=function(t,e){this.specification=t,this.overrides=e};di.prototype.possiblyEvaluate=function(t,e,r,n){return new fi(this,\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)}:t.expression,e)},di.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new fi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ve[this.specification.type];return n?new fi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},di.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new fi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new fi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new fi(this,{kind:\"constant\",value:s},e)}return new fi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(di),mi=function(t){this.specification=t};mi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new ii(Math.floor(e.zoom-1),e)),t.expression.evaluate(new ii(Math.floor(e.zoom),e)),t.expression.evaluate(new ii(Math.floor(e.zoom+1),e)),e)}},mi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},mi.prototype.interpolate=function(t){return t};var vi=function(t){this.specification=t};vi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},vi.prototype.interpolate=function(){return!1};var yi=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new ai(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new oi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Dn(\"DataDrivenProperty\",di),Dn(\"DataConstantProperty\",pi),Dn(\"CrossFadedDataDrivenProperty\",gi),Dn(\"CrossFadedProperty\",mi),Dn(\"ColorRampProperty\",vi);var xi=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(this.metadata=(e=e).metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new ui(r.layout)),r.paint)){for(var n in this._transitionablePaint=new si(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new hi(r.paint)}}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){void 0===r&&(r={}),null!=e&&this._validate(En,\"layers.\"+this.id+\".layout.\"+t,t,e,r)||(\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e)},e.prototype.getPaintProperty=function(t){return m(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e&&this._validate(Sn,\"layers.\"+this.id+\".paint.\"+t,t,e,r))return!1;if(m(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var n=this._transitionablePaint._values[t],i=\"cross-faded-data-driven\"===n.property.specification[\"property-type\"],a=n.value.isDataDriven(),o=n.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var s=this._transitionablePaint._values[t].value;return s.isDataDriven()||a||i||this._handleOverridablePaintPropertyUpdate(t,o,s)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Cn(this,t.call(Mn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:At,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof fi&&Lr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(Mt),bi={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},_i=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},wi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ti(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i=bi[t.type].BYTES_PER_ELEMENT,a=r=ki(r,Math.max(e,i)),o=t.components||1;return n=Math.max(n,i),r+=i*o,{name:t.name,type:t.type,components:o,offset:a}})),size:ki(r,Math.max(n,e)),alignment:e}}function ki(t,e){return Math.ceil(t/e)*e}wi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},wi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},wi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},wi.prototype.clear=function(){this.length=0},wi.prototype.resize=function(t){this.reserve(t),this.length=t},wi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},wi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var Mi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(wi);Mi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout2i4\",Mi);var Ai=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(wi);Ai.prototype.bytesPerElement=8,Dn(\"StructArrayLayout4i8\",Ai);var Si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(wi);Si.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i4i12\",Si);var Ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(wi);Ei.prototype.bytesPerElement=8,Dn(\"StructArrayLayout2i4ub8\",Ei);var Ci=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(wi);Ci.prototype.bytesPerElement=18,Dn(\"StructArrayLayout8ui2ub18\",Ci);var Li=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(wi);Li.prototype.bytesPerElement=24,Dn(\"StructArrayLayout4i4ui4i24\",Li);var Ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(wi);Ii.prototype.bytesPerElement=12,Dn(\"StructArrayLayout3f12\",Ii);var Pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.uint32[1*t+0]=e,t},e}(wi);Pi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1ul4\",Pi);var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(wi);zi.prototype.bytesPerElement=20,Dn(\"StructArrayLayout6i1ul2ui20\",zi);var Oi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(wi);Oi.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i2i2i12\",Oi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(wi);Di.prototype.bytesPerElement=16,Dn(\"StructArrayLayout2f1f2i16\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(wi);Ri.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2ub2f12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(wi);Fi.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3ui6\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=g,this.uint32[x+10]=m,this.int16[y+22]=v,t},e}(wi);Bi.prototype.bytesPerElement=48,Dn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S,E){var C=34*t,L=17*t;return this.int16[C+0]=e,this.int16[C+1]=r,this.int16[C+2]=n,this.int16[C+3]=i,this.int16[C+4]=a,this.int16[C+5]=o,this.int16[C+6]=s,this.int16[C+7]=l,this.uint16[C+8]=c,this.uint16[C+9]=u,this.uint16[C+10]=f,this.uint16[C+11]=h,this.uint16[C+12]=p,this.uint16[C+13]=d,this.uint16[C+14]=g,this.uint16[C+15]=m,this.uint16[C+16]=v,this.uint16[C+17]=y,this.uint16[C+18]=x,this.uint16[C+19]=b,this.uint16[C+20]=_,this.uint16[C+21]=w,this.uint16[C+22]=T,this.uint32[L+12]=k,this.float32[L+13]=M,this.float32[L+14]=A,this.float32[L+15]=S,this.float32[L+16]=E,t},e}(wi);Ni.prototype.bytesPerElement=68,Dn(\"StructArrayLayout8i15ui1ul4f68\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.float32[1*t+0]=e,t},e}(wi);ji.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1f4\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(wi);Ui.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3i6\",Ui);var 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a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},Sa.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Sa.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var Ea=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return\"u_\"+t+\"_t\"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:\"a_\"+t,type:\"Float32\",components:\"color\"===r?4:2,offset:0}})),this.paintVertexArray=new a};Ea.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new ii(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new ii(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},Ea.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},Ea.prototype._setPaintValue=function(t,e,r,n){if(\"color\"===this.type)for(var i=ka(r),a=ka(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},Ea.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Ea.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ea.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=l(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},Ea.prototype.getBinding=function(t,e,r){return new va(t,e)};var Ca=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};Ca.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},Ca.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},Ca.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=n[r.min],a=n[r.mid],o=n[r.max];if(i&&a&&o)for(var s=t;s<e;s++)this.zoomInPaintVertexArray.emplace(s,a.tl[0],a.tl[1],a.br[0],a.br[1],i.tl[0],i.tl[1],i.br[0],i.br[1],a.pixelRatio,i.pixelRatio),this.zoomOutPaintVertexArray.emplace(s,a.tl[0],a.tl[1],a.br[0],a.br[1],o.tl[0],o.tl[1],o.br[0],o.br[1],a.pixelRatio,o.pixelRatio)}},Ca.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,oa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,oa.members,this.expression.isStateDependent))},Ca.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var La=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof fi&&Lr(o.property.specification)){var s=Pa(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new Aa(l.value,s):new Ma(l.value,s,c),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=za(a,c,\"source\");this.binders[a]=h?new Ca(l,c,u,e,p,t.id):new Sa(l,s,c,p),i.push(\"/a_\"+a)}else{var d=za(a,c,\"composite\");this.binders[a]=new Ea(l,s,c,u,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};La.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Sa||e instanceof Ea?e.maxValue:0},La.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Sa||o instanceof Ea||o instanceof Ca)&&o.populatePaintArray(t,e,r,n,i)}},La.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof Aa&&n.setConstantPatternPositions(t,e)}},La.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof Sa||h instanceof Ea||h instanceof Ca)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},La.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof Ma||r instanceof Aa)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},La.prototype.getPaintVertexBuffers=function(){return this._buffers},La.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof Ma||i instanceof Aa||i instanceof Ea)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},La.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property;this.binders[l].setUniform(o.binding,n,r.get(l),s)}},La.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Ca){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Sa||r instanceof Ea)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},La.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Sa||r instanceof Ea||r instanceof Ca)&&r.upload(t)}this.updatePaintBuffers()},La.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Sa||e instanceof Ea||e instanceof Ca)&&e.destroy()}};var Ia=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new La(o,r,n,t)}this.needsUpload=!1,this._featureMap=new fa,this._bufferOffset=0};function Pa(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function za(t,e,r){var n={color:{source:Gi,composite:Yi},number:{source:ji,composite:Gi}},i=function(t){return{\"line-pattern\":{source:Ci,composite:Ci},\"fill-pattern\":{source:Ci,composite:Ci},\"fill-extrusion-pattern\":{source:Ci,composite:Ci}}[t]}(t);return i&&i[r]||n[e][r]}Ia.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},Ia.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},Ia.prototype.get=function(t){return this.programConfigurations[t]},Ia.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},Ia.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},Dn(\"ConstantBinder\",Ma),Dn(\"CrossFadedConstantBinder\",Aa),Dn(\"SourceExpressionBinder\",Sa),Dn(\"CrossFadedCompositeBinder\",Ca),Dn(\"CompositeExpressionBinder\",Ea),Dn(\"ProgramConfiguration\",La,{omit:[\"_buffers\"]}),Dn(\"ProgramConfigurationSet\",Ia);var Oa={min:-1*Math.pow(2,14),max:Math.pow(2,14)-1};function Da(t){for(var e=8192/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a];o.x=Math.round(o.x*e),o.y=Math.round(o.y*e),(o.x<Oa.min||o.x>Oa.max||o.y<Oa.min||o.y>Oa.max)&&(_(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\"),o.x=l(o.x,Oa.min,Oa.max),o.y=l(o.y,Oa.min,Oa.max))}return r}function Ra(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var Fa=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Mi,this.indexArray=new Fi,this.segments=new ia,this.programConfigurations=new Ia(na,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function Ba(t,e){for(var r=0;r<t.length;r++)if(Wa(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Wa(t,e[n]))return!0;return!!Va(t,e)}function Na(t,e,r){return!!Wa(t,e)||!!Ha(e,t,r)}function ja(t,e){if(1===t.length)return Ya(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Wa(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Ya(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(Va(t,e[o]))return!0;return!1}function Ua(t,e,r){if(t.length>1){if(Va(t,e))return!0;for(var n=0;n<e.length;n++)if(Ha(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(Ha(t[i],e,r))return!0;return!1}function Va(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(qa(n,i,e[a],e[a+1]))return!0;return!1}function qa(t,e,r,n){return w(t,r,n)!==w(e,r,n)&&w(t,e,r)!==w(t,e,n)}function Ha(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Ga(t,e[i-1],e[i])<n)return!0;return!1}function Ga(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return t.distSqr(i<0?e:i>1?r:r.sub(e)._mult(i)._add(e))}function Ya(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)(n=r[s]).y>e.y!=(i=r[l]).y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Wa(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function Xa(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=w(t,e,r[0]);return a!==w(t,e,r[1])||a!==w(t,e,r[2])||a!==w(t,e,r[3])}function Za(t,e,r){var n=e.paint.get(t).value;return\"constant\"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function Ja(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function Ka(t,e,r,n,a){if(!e[0]&&!e[1])return t;var o=i.convert(e)._mult(a);\"viewport\"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++)s.push(t[l].sub(o));return s}Fa.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;\"circle\"===n.type&&(a=n.layout.get(\"circle-sort-key\"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],c=l.feature,u=l.id,f=l.index,h=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d={type:c.type,id:u,properties:c.properties,geometry:p?Da(c):[]};if(this.layers[0]._featureFilter.filter(new ii(this.zoom),d,r)){p||(d.geometry=Da(c));var g=a?a.evaluate(d,{},r):void 0;i.push({id:u,properties:c.properties,type:c.type,sourceLayerIndex:h,index:f,geometry:d.geometry,patterns:{},sortKey:g})}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var m=0,v=i;m<v.length;m+=1){var y=v[m],x=y.geometry,b=y.index,_=y.sourceLayerIndex,w=t[b].feature;this.addFeature(y,x,b,r),e.featureIndex.insert(w,x,b,_,this.index)}},Fa.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Fa.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Fa.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Fa.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,na),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Fa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Fa.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],c=l.x,u=l.y;if(!(c<0||c>=8192||u<0||u>=8192)){var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),h=f.vertexLength;Ra(this.layoutVertexArray,c,u,-1,-1),Ra(this.layoutVertexArray,c,u,1,-1),Ra(this.layoutVertexArray,c,u,1,1),Ra(this.layoutVertexArray,c,u,-1,1),this.indexArray.emplaceBack(h,h+1,h+2),this.indexArray.emplaceBack(h,h+3,h+2),f.vertexLength+=4,f.primitiveLength+=2}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{},n)},Dn(\"CircleBucket\",Fa,{omit:[\"layers\"]});var Qa=new yi({\"circle-sort-key\":new di(At.layout_circle[\"circle-sort-key\"])}),$a={paint:new yi({\"circle-radius\":new di(At.paint_circle[\"circle-radius\"]),\"circle-color\":new di(At.paint_circle[\"circle-color\"]),\"circle-blur\":new di(At.paint_circle[\"circle-blur\"]),\"circle-opacity\":new di(At.paint_circle[\"circle-opacity\"]),\"circle-translate\":new pi(At.paint_circle[\"circle-translate\"]),\"circle-translate-anchor\":new 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2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=us(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var fs=hs;function hs(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(ps,this,e),this.length=this._features.length}function ps(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function ds(t,e,r){if(3===t){var n=new fs(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}hs.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new ss(this._pbf,e,this.extent,this._keys,this._values)};var gs={VectorTile:function(t,e){this.layers=t.readFields(ds,{},e)},VectorTileFeature:ss,VectorTileLayer:fs},ms=gs.VectorTileFeature.types,vs=Math.pow(2,13);function ys(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*vs)+o,i*vs*2,a*vs*2,Math.round(s))}var xs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Si,this.indexArray=new Fi,this.programConfigurations=new Ia(os,t.layers,t.zoom),this.segments=new ia,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function bs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}xs.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ts(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Da(o):[]};if(this.layers[0]._featureFilter.filter(new ii(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Da(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(es(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},xs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},xs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},xs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},xs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},xs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,os),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},xs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},xs.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Qo(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1)l+=u[c].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((P=d).every((function(t){return t.x<0}))||P.every((function(t){return t.x>8192}))||P.every((function(t){return t.y<0}))||P.every((function(t){return t.y>8192}))))for(var g=0,m=0;m<d.length;m++){var v=d[m];if(m>=1){var y=d[m-1];if(!bs(v,y)){f.vertexLength+4>ia.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);g+b>32768&&(g=0),ys(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,g),ys(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,g),ys(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,g+=b),ys(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,g);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>ia.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===ms[t.type]){for(var w=[],T=[],k=f.vertexLength,M=0,A=s;M<A.length;M+=1){var S=A[M];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var C=S[E];ys(this.layoutVertexArray,C.x,C.y,0,0,1,1,0),w.push(C.x),w.push(C.y)}}}for(var 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a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Ts(s,s),f=Ts(s,l),h=Ts(l,l),p=Ts(c,s),d=Ts(c,l),g=u*h-f*f,m=(h*p-f*d)/g,v=(u*d-f*p)/g,y=i.z*(1-m-v)+r.z*m+a.z*v;if(isFinite(y))return y}return 1/0}for(var x=1/0,b=0,_=e;b<_.length;b+=1)x=Math.min(x,_[b].z);return x}var Ms=Ti([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,As=gs.VectorTileFeature.types,Ss=Math.cos(Math.PI/180*37.5),Es=Math.pow(2,14)/.5,Cs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Ei,this.indexArray=new Fi,this.programConfigurations=new Ia(Ms,t.layers,t.zoom),this.segments=new ia,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return 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a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1)this.addLine(f[u],t,o,s,l,c);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Cs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===As[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 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o=e.y*n-e.x,s=-e.y-e.x*n;this.addHalfVertex(t,e.x+e.y*r,e.y-e.x*r,a,!1,r,i),this.addHalfVertex(t,o,s,a,!0,-n,i),this.distance>Es/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},Cs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((t.x<<1)+(n?1:0),(t.y<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&s)<<2,s>>6);var l=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,l),o.primitiveLength++),i?this.e2=l:this.e1=l},Cs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Es-1):this.distance},Cs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Dn(\"LineBucket\",Cs,{omit:[\"layers\",\"patternFeatures\"]});var Ls=new yi({\"line-cap\":new 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n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&Zs(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),qs(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),qs(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&Zs(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,Gs.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,Js,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Ks,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,tl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Qs,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,$s,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,el,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,rl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,nl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,il,e)},writeBytesField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var hl=function(t,e){var r=e.pixelRatio,n=e.version,i=e.stretchX,a=e.stretchY,o=e.content;this.paddedRect=t,this.pixelRatio=r,this.stretchX=i,this.stretchY=a,this.content=o,this.version=n},pl={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};pl.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},pl.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},pl.tlbr.get=function(){return this.tl.concat(this.br)},pl.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(hl.prototype,pl);var dl=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var i=[];this.addImages(t,r,i),this.addImages(e,n,i);var a=fl(i),o=new po({width:a.w||1,height:a.h||1});for(var s in t){var l=t[s],c=r[s].paddedRect;po.copy(l.data,o,{x:0,y:0},{x:c.x+1,y:c.y+1},l.data)}for(var u in e){var f=e[u],h=n[u].paddedRect,p=h.x+1,d=h.y+1,g=f.data.width,m=f.data.height;po.copy(f.data,o,{x:0,y:0},{x:p,y:d},f.data),po.copy(f.data,o,{x:0,y:m-1},{x:p,y:d-1},{width:g,height:1}),po.copy(f.data,o,{x:0,y:0},{x:p,y:d+m},{width:g,height:1}),po.copy(f.data,o,{x:g-1,y:0},{x:p-1,y:d},{width:1,height:m}),po.copy(f.data,o,{x:0,y:0},{x:p+g,y:d},{width:1,height:m})}this.image=o,this.iconPositions=r,this.patternPositions=n};dl.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new hl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},dl.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},dl.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl;r.update(e.data,void 0,{x:n[0],y:n[1]})}},Dn(\"ImagePosition\",hl),Dn(\"ImageAtlas\",dl);var gl={horizontal:1,vertical:2,horizontalOnly:3},ml=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};ml.forText=function(t,e){var r=new ml;return r.scale=t||1,r.fontStack=e,r},ml.forImage=function(t){var e=new ml;return e.imageName=t,e};var vl=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function yl(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=vl.fromFeature(t,i);f===gl.vertical&&v.verticalizePunctuation();var y=ni.processBidirectionalText,x=ni.processStyledBidirectionalText;if(y&&1===v.sections.length){m=[];for(var b=0,_=y(v.toString(),Ml(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new vl;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);m.push(T)}}else if(x){m=[];for(var M=0,A=x(v.text,v.sectionIndex,Ml(v,c,a,e,n,p,d));M<A.length;M+=1){var S=A[M],E=new vl;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,m.push(E)}}else m=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,Ml(v,c,a,e,n,p,d));var C=[],L={positionedLines:C,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,g=0,m=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,M=0;if(b.length()){for(var A=0;A<b.length();A++){var S=b.getSection(A),E=b.getSectionIndex(A),C=b.getCharCode(A),L=0,I=null,P=null,z=null,O=24,D=!(l===gl.horizontal||!u&&!Hn(C)||u&&(xl[C]||(H=C,Vn.Arabic(H)||Vn[\"Arabic Supplement\"](H)||Vn[\"Arabic Extended-A\"](H)||Vn[\"Arabic Presentation Forms-A\"](H)||Vn[\"Arabic Presentation Forms-B\"](H))));if(S.imageName){var R=n[S.imageName];if(!R)continue;z=S.imageName,t.iconsInText=t.iconsInText||!0,P=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,L=w+(24-F[1]*S.scale),O=(I={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]}).advance;var B=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;B>0&&B>M&&(M=B)}else{var N=r[S.fontStack],j=N&&N[C];if(j&&j.rect)P=j.rect,I=j.metrics;else{var U=e[S.fontStack],V=U&&U[C];if(!V)continue;I=V.metrics}L=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:C,imageName:z,x:h,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),h+=O*S.scale+c):(k.push({glyph:C,imageName:z,x:h,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),h+=I.advance*S.scale+c)}0!==k.length&&(d=Math.max(h-c,d),Sl(k,0,k.length-1,m,M)),h=0;var q=a*_+M;T.lineOffset=Math.max(M,w),p+=q,g=Math.max(q,g),++v}else p+=a,++v}var H,G=p- -17,Y=Al(o),W=Y.horizontalAlign,X=Y.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c,u=(e-r)*i;c=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var g=d[p];g.x+=u,g.y+=c}})(t.positionedLines,m,W,X,d,g,a,G,i.length),t.top+=-X*G,t.bottom=t.top+G,t.left+=-W*d,t.right=t.left+d}(L,e,r,n,m,o,s,l,f,c,h,g),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(C)&&L}vl.fromFeature=function(t,e){for(var r=new vl,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},vl.prototype.length=function(){return this.text.length},vl.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},vl.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},vl.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},vl.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;e+=n&&Gn(n)&&!Us[t[r+1]]||i&&Gn(i)&&!Us[t[r-1]]||!Us[t[r]]?t[r]:Us[t[r]]}return e}(this.text)},vl.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&xl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&xl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},vl.prototype.substring=function(t,e){var r=new vl;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},vl.prototype.toString=function(){return this.text},vl.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},vl.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(ml.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},vl.prototype.addImageSection=function(t){var e=t.image?t.image.name:\"\";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(ml.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_(\"Reached maximum number of images 6401\")}else _(\"Can't add FormattedSection with an empty image.\")},vl.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var xl={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},bl={};function _l(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*24/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function wl(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function Tl(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function kl(t,e,r,n,i,a){for(var o=null,s=wl(e,r,i,a),l=0,c=n;l<c.length;l+=1){var u=c[l],f=wl(e-u.x,r,i,a)+u.badness;f<=s&&(o=u,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function Ml(t,e,r,n,i,a,o){if(\"point\"!==a)return[];if(!t)return[];for(var s,l=[],c=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=_l(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf(\"\\u200b\")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(xl[d]||(f+=_l(d,p,n,i,e,o)),h<t.length()-1){var g=!((s=d)<11904||!(Vn[\"Bopomofo Extended\"](s)||Vn.Bopomofo(s)||Vn[\"CJK Compatibility Forms\"](s)||Vn[\"CJK Compatibility Ideographs\"](s)||Vn[\"CJK Compatibility\"](s)||Vn[\"CJK Radicals Supplement\"](s)||Vn[\"CJK Strokes\"](s)||Vn[\"CJK Symbols and Punctuation\"](s)||Vn[\"CJK Unified Ideographs Extension A\"](s)||Vn[\"CJK Unified 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o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,g=e.right*a;\"width\"===r||\"both\"===r?(p=i[0]+d-n[3],f=i[0]+g+n[1]):f=(p=i[0]+(d+g-s.displaySize[0])/2)+s.displaySize[0];var m=e.top*a,v=e.bottom*a;return\"height\"===r||\"both\"===r?(u=i[1]+m-n[0],h=i[1]+v+n[2]):h=(u=i[1]+(m+v-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:u,right:f,bottom:h,left:p,collisionPadding:o}}bl[10]=!0,bl[32]=!0,bl[38]=!0,bl[40]=!0,bl[41]=!0,bl[43]=!0,bl[45]=!0,bl[47]=!0,bl[173]=!0,bl[183]=!0,bl[8203]=!0,bl[8208]=!0,bl[8211]=!0,bl[8231]=!0;var Cl=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(i);function Ll(t,e){var r=e.expression;if(\"constant\"===r.kind)return{kind:\"constant\",layoutSize:r.evaluate(new ii(t+1))};if(\"source\"===r.kind)return{kind:\"source\"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return\"composite\"===r.kind?{kind:\"composite\",minZoom:s,maxZoom:l,interpolationType:i}:{kind:\"camera\",minZoom:s,maxZoom:l,minSize:r.evaluate(new ii(s)),maxSize:r.evaluate(new ii(l)),interpolationType:i}}function Il(t,e,r){var n=e.uSize,i=r.lowerSize;return\"source\"===t.kind?i/128:\"composite\"===t.kind?Ue(i/128,r.upperSize/128,e.uSizeT):n}function Pl(t,e){var r=0,n=0;if(\"constant\"===t.kind)n=t.layoutSize;else if(\"source\"!==t.kind){var i=t.interpolationType,a=i?l(rr.interpolationFactor(i,e,t.minZoom,t.maxZoom),0,1):0;\"camera\"===t.kind?n=Ue(t.minSize,t.maxSize,a):r=a}return{uSizeT:r,uSize:n}}Dn(\"Anchor\",Cl);var 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h=Hl(n.stretch-x,b,u,t.left),p=Gl(n.fixed-T,k,n.stretch,g),d=Hl(a.stretch-_,w,f,t.top),v=Gl(a.fixed-M,A,a.stretch,m),y=Hl(l.stretch-x,b,u,t.left),S=Gl(l.fixed-T,k,l.stretch,g),E=Hl(c.stretch-_,w,f,t.top),C=Gl(c.fixed-M,A,c.stretch,m),L=new i(h,d),I=new i(y,d),P=new i(y,E),z=new i(h,E),O=new i(p/s,v/s),D=new i(S/s,C/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];L._matMult(N),I._matMult(N),z._matMult(N),P._matMult(N)}var j=n.stretch+n.fixed,U=a.stretch+a.fixed;return{tl:L,tr:I,bl:z,br:P,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+U,w:l.stretch+l.fixed-j,h:c.stretch+c.fixed-U},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:O,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:A/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var C=ql(h,v,g),L=ql(p,y,m),I=0;I<C.length-1;I++)for(var P=C[I],z=C[I+1],O=0;O<L.length-1;O++)a.push(E(P,L[O],z,L[O+1]));else 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k=s[m.imageName];x=k.sdf,y=1/(b=k.pixelRatio)}var M=a?[m.x+T,m.y]:[0,0],A=a?[0,0]:[m.x+T+r[0],m.y+r[1]-_],S=[0,0];w&&(S=A,A=[0,0]);var E=(m.metrics.left-y)*m.scale-T+A[0],C=(-m.metrics.top-y)*m.scale+A[1],L=E+v.w*m.scale/b,I=C+v.h*m.scale/b,P=new i(E,C),z=new i(L,C),O=new i(E,I),D=new i(L,I);if(w){var R=new i(-T,T- -17),F=-Math.PI/2,B=12-T,N=new i(22-B,-(m.imageName?B:0)),j=new(Function.prototype.bind.apply(i,[null].concat(S)));P._rotateAround(F,R)._add(N)._add(j),z._rotateAround(F,R)._add(N)._add(j),O._rotateAround(F,R)._add(N)._add(j),D._rotateAround(F,R)._add(N)._add(j)}if(c){var U=Math.sin(c),V=Math.cos(c),q=[V,-U,U,V];P._matMult(q),z._matMult(q),O._matMult(q),D._matMult(q)}var H=new i(0,0),G=new i(0,0);u.push({tl:P,tr:z,bl:O,br:D,tex:v,writingMode:e.writingMode,glyphOffset:M,sectionIndex:m.sectionIndex,isSDF:x,pixelOffsetTL:H,pixelOffsetBR:G,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. 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Fi,this.programConfigurations=t,this.segments=new ia,this.dynamicLayoutVertexArray=new Ii,this.opacityVertexArray=new Pi,this.placedSymbolArray=new Ji};cc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},cc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ds.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Rs.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,ac,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},cc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Dn(\"SymbolBuffers\",cc);var uc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new ia,this.collisionVertexArray=new Ri};uc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Fs.members,!0)},uc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Dn(\"CollisionBuffers\",uc);var fc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=eo([]),this.placementViewportMatrix=eo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Ll(this.zoom,e[\"text-size\"]),this.iconSizeData=Ll(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1),this.sortFeaturesByY=(\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey)&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return gl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};fc.prototype.createArrays=function(){this.text=new cc(new Ia(Ds.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new cc(new Ia(Ds.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new $i,this.lineVertexArray=new ta,this.symbolInstances=new Qi},fc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=Us[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},fc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof te&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new ii(this.zoom),g=0,m=t;g<m.length;g+=1){var v=m[g],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Da(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Da(y));var k=void 0;if(l){var M=n.getValueAndResolveTokens(\"text-field\",T,r,p),A=te.factory(M);lc(A)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ei()||this.hasRTLText&&ni.isParsed())&&(k=js(A,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ee?E:ee.fromString(E)}if(k||S){var C=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,L={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Da(y),properties:y.properties,type:ic[y.type],sortKey:C};if(this.features.push(L),S&&(f[S.name]=!0),k){var I=a.evaluate(T,{},r).join(\",\"),P=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(gl.vertical)>=0;for(var z=0,O=k.sections;z<O.length;z+=1){var D=O[z];if(D.image)f[D.image.name]=!0;else{var R=qn(k.toString()),F=D.fontStack||I,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,P,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,f),m=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[m].geometry,!0)]=m,n[g].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},fc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},fc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},fc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},fc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},fc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},fc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},fc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},fc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),g=this.glyphOffsetArray.length,m=d.vertexLength,v=this.allowVerticalPlacement&&o===gl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,M=b.tex,A=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,C=b.minFontScaleY,L=b.glyphOffset,I=b.isSDF,P=b.sectionIndex,z=d.vertexLength,O=L[1];oc(p,s.x,s.y,_.x,O+_.y,M.x,M.y,r,I,A.x,A.y,E,C),oc(p,s.x,s.y,w.x,O+w.y,M.x+M.w,M.y,r,I,S.x,A.y,E,C),oc(p,s.x,s.y,T.x,O+T.y,M.x,M.y+M.h,r,I,A.x,S.y,E,C),oc(p,s.x,s.y,k.x,O+k.y,M.x+M.w,M.y+M.h,r,I,S.x,S.y,E,C),sc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(z,z+1,z+2),h.emplaceBack(z+1,z+2,z+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(L[0]),x!==e.length-1&&P===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[P])}t.placedSymbolArray.emplaceBack(s.x,s.y,g,this.glyphOffsetArray.length-g,m,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},fc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},fc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},fc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i);this.addCollisionDebugVertices(a.x1,a.y1,a.x2,a.y2,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},fc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new uc(Oi,Bs.members,qi),this.iconCollisionBox=new uc(Oi,Bs.members,qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},fc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var g=t.get(d);c.iconBox={x1:g.x1,y1:g.y1,x2:g.x2,y2:g.y2,anchorPointX:g.anchorPointX,anchorPointY:g.anchorPointY},c.iconFeatureIndex=g.featureIndex;break}for(var m=s;m<l;m++){var v=t.get(m);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},fc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},fc.prototype.hasTextData=function(){return this.text.segments.get().length>0},fc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},fc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},fc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},fc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},fc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},fc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},fc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},fc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=this.symbolInstances.get(n[r]);this.featureSortOrder.push(i.featureIndex),[i.rightJustifiedTextSymbolIndex,i.centerJustifiedTextSymbolIndex,i.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),i.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,i.verticalPlacedTextSymbolIndex),i.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.placedIconSymbolIndex),i.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Dn(\"SymbolBucket\",fc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),fc.MAX_GLYPHS=65535,fc.addDynamicAttributes=sc;var hc=new yi({\"symbol-placement\":new pi(At.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new pi(At.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new pi(At.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new di(At.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new pi(At.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new pi(At.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new pi(At.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new pi(At.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new pi(At.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new di(At.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new pi(At.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new pi(At.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new di(At.layout_symbol[\"icon-image\"]),\"icon-rotate\":new di(At.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new pi(At.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new pi(At.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new di(At.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new di(At.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new pi(At.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new pi(At.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new pi(At.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new di(At.layout_symbol[\"text-field\"]),\"text-font\":new di(At.layout_symbol[\"text-font\"]),\"text-size\":new di(At.layout_symbol[\"text-size\"]),\"text-max-width\":new di(At.layout_symbol[\"text-max-width\"]),\"text-line-height\":new pi(At.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new di(At.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new di(At.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new di(At.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new pi(At.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new di(At.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new pi(At.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new pi(At.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new di(At.layout_symbol[\"text-rotate\"]),\"text-padding\":new pi(At.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new pi(At.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new di(At.layout_symbol[\"text-transform\"]),\"text-offset\":new di(At.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new pi(At.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new pi(At.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new pi(At.layout_symbol[\"text-optional\"])}),pc={paint:new yi({\"icon-opacity\":new di(At.paint_symbol[\"icon-opacity\"]),\"icon-color\":new di(At.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new di(At.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new di(At.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new di(At.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new pi(At.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new pi(At.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new di(At.paint_symbol[\"text-opacity\"]),\"text-color\":new di(At.paint_symbol[\"text-color\"],{runtimeType:Bt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new di(At.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new di(At.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new di(At.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new pi(At.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new pi(At.paint_symbol[\"text-translate-anchor\"])}),layout:hc},dc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ot,this.defaultValue=t};dc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},dc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},dc.prototype.outputDefined=function(){return!1},dc.prototype.serialize=function(){return null},Dn(\"FormatSectionOverride\",dc,{omit:[\"defaultValue\"]});var gc=function(t){function e(e){t.call(this,e,pc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(this.layout._values[\"icon-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(this.layout._values[\"text-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Vr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new fc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=pc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new dc(a),s=new Ur(o,a.property.specification);i=\"constant\"===a.value.kind||\"source\"===a.value.kind?new Hr(\"source\",s):new Gr(\"composite\",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new fi(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=pc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1)if(n.overrides&&n.overrides.hasOverride(r[e]))return void(i=!0)};if(\"constant\"===r.value.kind&&r.value.value instanceof te)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){i||(t instanceof oe&&ie(t.value)===Vt?a(t.value.sections):t instanceof ue?a(t.sections):t.eachChild(o))},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(xi),mc={paint:new yi({\"background-color\":new pi(At.paint_background[\"background-color\"]),\"background-pattern\":new mi(At.paint_background[\"background-pattern\"]),\"background-opacity\":new pi(At.paint_background[\"background-opacity\"])})},vc=function(t){function e(e){t.call(this,e,mc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(xi),yc={paint:new yi({\"raster-opacity\":new pi(At.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new pi(At.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new pi(At.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new pi(At.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new pi(At.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new pi(At.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new pi(At.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new pi(At.paint_raster[\"raster-fade-duration\"])})},xc=function(t){function e(e){t.call(this,e,yc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(xi),bc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(xi),_c={circle:oo,heatmap:vo,hillshade:xo,fill:as,\"fill-extrusion\":ws,line:zs,symbol:gc,background:vc,raster:xc},wc=self.HTMLImageElement,Tc=self.HTMLCanvasElement,kc=self.HTMLVideoElement,Mc=self.ImageData,Ac=self.ImageBitmap,Sc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Sc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof wc||t instanceof Tc||t instanceof kc||t instanceof Mc||Ac&&t instanceof Ac?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof wc||t instanceof Tc||t instanceof kc||t instanceof Mc||Ac&&t instanceof Ac?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Sc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Sc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Sc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Ec=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Ec.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Ec.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Cc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g([\"receive\",\"process\"],this),this.invoker=new Ec(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Lc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Cc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Nn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(jn(e.error)):n(null,jn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Nn(e):null,data:Nn(n,a)},a)}:function(t){i=!0},s=null,l=jn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Ic=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Ic.prototype.setNorthEast=function(t){return this._ne=t instanceof Pc?new Pc(t.lng,t.lat):Pc.convert(t),this},Ic.prototype.setSouthWest=function(t){return this._sw=t instanceof Pc?new Pc(t.lng,t.lat):Pc.convert(t),this},Ic.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Pc)e=t,r=t;else{if(!(t instanceof Ic))return Array.isArray(t)?4===t.length||t.every(Array.isArray)?this.extend(Ic.convert(t)):this.extend(Pc.convert(t)):this;if(r=t._ne,!(e=t._sw)||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Pc(e.lng,e.lat),this._ne=new Pc(r.lng,r.lat)),this},Ic.prototype.getCenter=function(){return new Pc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Ic.prototype.getSouthWest=function(){return this._sw},Ic.prototype.getNorthEast=function(){return this._ne},Ic.prototype.getNorthWest=function(){return new Pc(this.getWest(),this.getNorth())},Ic.prototype.getSouthEast=function(){return new Pc(this.getEast(),this.getSouth())},Ic.prototype.getWest=function(){return this._sw.lng},Ic.prototype.getSouth=function(){return this._sw.lat},Ic.prototype.getEast=function(){return this._ne.lng},Ic.prototype.getNorth=function(){return this._ne.lat},Ic.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Ic.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Ic.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Ic.prototype.contains=function(t){var e=Pc.convert(t),r=e.lng,n=e.lat,i=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(i=this._sw.lng>=r&&r>=this._ne.lng),this._sw.lat<=n&&n<=this._ne.lat&&i},Ic.convert=function(t){return!t||t instanceof Ic?t:new Ic(t)};var Pc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Pc.prototype.wrap=function(){return new Pc(c(this.lng,-180,180),this.lat)},Pc.prototype.toArray=function(){return[this.lng,this.lat]},Pc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Pc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Pc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Ic(new Pc(this.lng-r,this.lat-e),new Pc(this.lng+r,this.lat+e))},Pc.convert=function(t){if(t instanceof Pc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Pc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Pc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var zc=2*Math.PI*6371008.8;function Oc(t){return zc*Math.cos(t*Math.PI/180)}function Dc(t){return(180+t)/360}function Rc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Fc(t,e){return t/Oc(e)}function Bc(t){return 360/Math.PI*Math.atan(Math.exp((180-360*t)*Math.PI/180))-90}var Nc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Nc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Pc.convert(t);return new Nc(Dc(r.lng),Rc(r.lat),Fc(e,r.lat))},Nc.prototype.toLngLat=function(){return new Pc(360*this.x-180,Bc(this.y))},Nc.prototype.toAltitude=function(){return this.z*Oc(Bc(this.y))},Nc.prototype.meterInMercatorCoordinateUnits=function(){return 1/zc*(t=Bc(this.y),1/Math.cos(t*Math.PI/180));var t};var jc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=qc(0,t,t,e,r)};jc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},jc.prototype.url=function(t,e){var r,n,i,a,o,s=(n=this.y,i=this.z,a=Lc(256*(r=this.x),256*(n=Math.pow(2,i)-n-1),i),o=Lc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},jc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},jc.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var Uc=function(t,e){this.wrap=t,this.canonical=e,this.key=qc(t,e.z,e.z,e.x,e.y)},Vc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new jc(r,+n,+i),this.key=qc(e,t,r,n,i)};function qc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Vc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Vc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Vc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Vc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Vc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?qc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):qc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Vc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Vc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Vc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Vc(e,this.wrap,e,r,n),new Vc(e,this.wrap,e,r+1,n),new Vc(e,this.wrap,e,r,n+1),new Vc(e,this.wrap,e,r+1,n+1)]},Vc.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Vc.prototype.wrapped=function(){return new Vc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Vc.prototype.unwrapTo=function(t){return new Vc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Vc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Vc.prototype.toUnwrapped=function(){return new Uc(this.wrap,this.canonical)},Vc.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Vc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Nc(t.x-this.wrap,t.y))},Dn(\"CanonicalTileID\",jc),Dn(\"OverscaledTileID\",Vc,{omit:[\"posMatrix\"]});var Hc=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Hc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Hc.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Hc.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Hc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Hc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Hc.prototype.getPixels=function(){return new po({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Hc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Dn(\"DEMData\",Hc);var Gc=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Gc.prototype.encode=function(t){return this._stringToNumber[t]},Gc.prototype.decode=function(t){return this._numberToString[t]};var Yc=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},Wc={geometry:{configurable:!0}};Wc.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Wc.geometry.set=function(t){this._geometry=t},Yc.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Yc.prototype,Wc);var Xc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Xc.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Xc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Xc.prototype.getState=function(t,e){var r=String(e),n=u({},(this.state[t]||{})[r],(this.stateChanges[t]||{})[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var i=this.deletedStates[t][e];if(null===i)return{};for(var a in i)delete n[a]}return n},Xc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Xc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1)delete this.state[o][c][h[f]];s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var p in t)t[p].setFeatureState(r,e)};var Zc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new Ln(8192,16,0),this.grid3D=new Ln(8192,16,0),this.featureIndexArray=new ra,this.promoteId=e};function Jc(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof hi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Kc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function Qc(t,e){return e-t}Zc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},Zc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new gs.VectorTile(new Hs(this.rawTileData)).layers,this.sourceLayerCoder=new Gc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Zc.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=rn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=Kc(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=Kc(t.cameraQueryGeometry),d=0,g=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1)if(Wa(t,f[u]))return!0;for(var h=0;h<t.length-1;h++)if(Xa(t[h],t[h+1],c))return!0;return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)}));d<g.length;d+=1)h.push(g[d]);h.sort(Qc);for(var m,v={},y=function(i){var u=h[i];if(u!==m){m=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(v,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Da(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},x=0;x<h.length;x++)y(x);return v},Zc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new ii(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),g=0;g<f.length;g++){var m=f[g];if(!(a&&a.indexOf(m)<0)){var v=s[m];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[m];x.paint=Jc(x.paint,v.paint,p,y,o),x.layout=Jc(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new Yc(p,this.z,this.x,this.y,d);_.layer=x;var w=t[m];void 0===w&&(w=t[m]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},Zc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=rn(i),u=0,f=t;u<f.length;u+=1)this.loadMatchingFeature(l,r,n,f[u],c,a,o,s,e);return l},Zc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Zc.prototype.getId=function(t,e){var r=t.id;return this.promoteId&&\"boolean\"==typeof(r=t.properties[\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e]])&&(r=Number(r)),r},Dn(\"FeatureIndex\",Zc,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var $c=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};$c.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},$c.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},$c.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1)r[s[o].id]=t}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof fc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof fc&&o.hasRTLText){this.hasRTLText=!0,ni.isLoading()||ni.isLoaded()||\"deferred\"!==ei()||ri();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new Xi},$c.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},$c.prototype.getBucket=function(t){return this.buckets[t.id]},$c.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Sc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Sc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},$c.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},$c.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},$c.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=rn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new ii(this.tileID.overscaledZ),p)){var d=r.getId(p,i),g=new Yc(p,l,c,u,d);g.tile=f,t.push(g)}}}},$c.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},$c.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},$c.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=M(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},$c.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},$c.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},$c.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},$c.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},$c.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},$c.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},$c.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},$c.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=this.dependencies[n[r]];if(i)for(var a=0,o=e;a<o.length;a+=1)if(i[o[a]])return!0}return!1};var tu=self.performance,eu=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},tu.mark(this._marks.start)};eu.prototype.finish=function(){tu.mark(this._marks.end);var t=tu.getEntriesByName(this._marks.measure);return 0===t.length&&(tu.measure(this._marks.measure,this._marks.start,this._marks.end),t=tu.getEntriesByName(this._marks.measure),tu.clearMarks(this._marks.start),tu.clearMarks(this._marks.end),tu.clearMeasures(this._marks.measure)),t},t.Actor=Cc,t.AlphaImage=ho,t.CanonicalTileID=jc,t.CollisionBoxArray=Xi,t.Color=Kt,t.DEMData=Hc,t.DataConstantProperty=pi,t.DictionaryCoder=Gc,t.EXTENT=8192,t.ErrorEvent=kt,t.EvaluationParameters=ii,t.Event=Tt,t.Evented=Mt,t.FeatureIndex=Zc,t.FillBucket=rs,t.FillExtrusionBucket=xs,t.ImageAtlas=dl,t.ImagePosition=hl,t.LineBucket=Cs,t.LngLat=Pc,t.LngLatBounds=Ic,t.MercatorCoordinate=Nc,t.ONE_EM=24,t.OverscaledTileID=Vc,t.Point=i,t.Point$1=i,t.Properties=yi,t.Protobuf=Hs,t.RGBAImage=po,t.RequestManager=q,t.RequestPerformance=eu,t.ResourceType=ht,t.SegmentVector=ia,t.SourceFeatureState=Xc,t.StructArrayLayout1ui2=Hi,t.StructArrayLayout2f1f2i16=Di,t.StructArrayLayout2i4=Mi,t.StructArrayLayout3ui6=Fi,t.StructArrayLayout4i8=Ai,t.SymbolBucket=fc,t.Texture=Sc,t.Tile=$c,t.Transitionable=si,t.Uniform1f=va,t.Uniform1i=ma,t.Uniform2f=ya,t.Uniform3f=xa,t.Uniform4f=ba,t.UniformColor=_a,t.UniformMatrix4f=Ta,t.UnwrappedTileID=Uc,t.ValidationError=St,t.WritingMode=gl,t.ZoomHistory=Un,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=sc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=g,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ut>ot&&(t.getActor().send(\"enforceCacheSizeLimit\",at),ut=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete(\"mapbox-tiles\");t&&e.catch(t).then((function(){return t()}))},t.clipLine=jl,t.clone=function(t){var e=new to(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new to(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ns,t.config=F,t.create=function(){var t=new to(16);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new to(9);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new to(4);return to!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=qr,t.createLayout=Ti,t.createStyleLayer=function(t){return\"custom\"===t.type?new bc(t):new _c[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if(\"object\"==typeof e&&null!==e&&null!==r){if(\"object\"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=s,t.emitValidationErrors=Cn,t.endsWith=m,t.enforceCacheSizeLimit=function(t){st(),Q&&Q.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Il,t.evaluateSizeForZoom=Pl,t.evaluateVariableOffset=$l,t.evented=ti,t.extend=u,t.featureFilter=rn,t.filterObject=y,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=Al,t.getAnchorJustification=tc,t.getArrayBuffer=yt,t.getImage=bt,t.getJSON=function(t,e){return vt(u(t,{type:\"json\"}),e)},t.getRTLTextPluginStatus=ei,t.getReferrer=mt,t.getVideo=function(t,e){var r,n,i=self.document.createElement(\"video\");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=self.document.createElement(\"source\");r=t[a],n=void 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r}function ft(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function ht(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(pt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(pt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function pt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function dt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,gt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function gt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a||\"MultiPoint\"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if(\"LineString\"===a)mt(o,i,t,r,!1,!1);else if(\"MultiLineString\"===a||\"Polygon\"===a)for(s=0;s<i.length;s++)mt(o,i[s],t,r,\"Polygon\"===a,0===s);else if(\"MultiPolygon\"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)mt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if(\"LineString\"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function mt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function vt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)J(r,t.features[n],e,n);else J(r,\"Feature\"===t.type?t:{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=rt(t,1,-1-r,r,0,-1,2,e),a=rt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=rt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=ut(i,1).concat(n)),a&&(n=n.concat(ut(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function yt(t,e,r){return 32*((1<<t)*r+e)+t}function xt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=b(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}F.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(N(t[l],l));this.trees[i+1]=new D(s,G,Y,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new D(s,G,Y,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},F.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(V(r),q(a),V(i),q(n));u<f.length;u+=1){var h=l.points[f[u]];c.push(h.numPoints?j(h):this.points[h.index])}return c},F.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=i.points[c[l]];u.parentId===t&&s.push(u.numPoints?j(u):this.points[u.index])}if(0===s.length)throw new Error(n);return s},F.prototype.getLeaves=function(t,e,r){var n=[];return this._appendLeaves(n,t,e=e||10,r=r||0,0),n},F.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.radius/a.extent,s=(r-o)/i,l=(r+1+o)/i,c={features:[]};return this._addTileFeatures(n.range((e-o)/i,s,(e+1+o)/i,l),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-o/i,s,1,l),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,s,o/i,l),n.points,-1,r,i,c),c.features.length?c:null},F.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},F.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},F.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?U(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},F.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},F.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.reduce,a=n.radius/(n.extent*Math.pow(2,e)),o=0;o<t.length;o++){var s=t[o];if(!(s.zoom<=e)){s.zoom=e;for(var l=this.trees[e+1],c=l.within(s.x,s.y,a),u=s.numPoints||1,f=s.x*u,h=s.y*u,p=i&&u>1?this._map(s,!0):null,d=(o<<5)+(e+1)+this.points.length,g=0,m=c;g<m.length;g+=1){var v=l.points[m[g]];if(!(v.zoom<=e)){v.zoom=e;var y=v.numPoints||1;f+=v.x*y,h+=v.y*y,u+=y,v.parentId=d,i&&(p||(p=this._map(s,!0)),i(p,this._map(v)))}}1===u?r.push(s):(s.parentId=d,r.push(B(f/u,h/u,d,u,p)))}}return r},F.prototype._getOriginId=function(t){return t-this.points.length>>5},F.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},F.prototype._map=function(t,e){if(t.numPoints)return e?H({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?H({},n):n},vt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},vt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=yt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=dt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;g=m=v=y=null,x=rt(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=rt(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(g=rt(x,u,n-_,n+T,1,h.minY,h.maxY,l),m=rt(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=rt(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=rt(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},vt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=yt(t,e=(e%o+o)%o,r);if(this.tiles[s])return ht(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[yt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?ht(this.tiles[s],i):null):null};var bt=function(e){function r(t,r,n,i){e.call(this,t,r,n,xt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));!function t(e,r){var n,i=e&&e.type;if(\"FeatureCollection\"===i)for(n=0;n<e.features.length;n++)t(e.features[n],r);else if(\"GeometryCollection\"===i)for(n=0;n<e.geometries.length;n++)t(e.geometries[n],r);else if(\"Feature\"===i)t(e.geometry,r);else if(\"Polygon\"===i)f(e.coordinates,r);else if(\"MultiPolygon\"===i)for(n=0;n<e.coordinates.length;n++)f(e.coordinates[n],r);return e}(o,!0);try{e._geoJSONIndex=n.cluster?new F(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=t.createExpression(h[1]),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=d.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new vt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded;return n&&n[t.uid]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l),_t=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:bt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return _t.prototype.setReferrer=function(t,e){this.referrer=e},_t.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in i)i[a].availableImages=e}r()},_t.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},_t.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},_t.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},_t.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},_t.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},_t.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},_t.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},_t.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},_t.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},_t.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},_t.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error(\"RTL Text Plugin failed to import scripts from \"+i),a)}}catch(t){n(t.toString())}},_t.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},_t.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},_t.prototype.getWorkerSource=function(t,e,r){var n=this;return this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),this.workerSources[t][e][r]||(this.workerSources[t][e][r]=new this.workerSourceTypes[e]({send:function(e,r,i){n.actor.send(e,r,i,t)}},this.getLayerIndex(t),this.getAvailableImages(t))),this.workerSources[t][e][r]},_t.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new u),this.demWorkerSources[t][e]},_t.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},\"undefined\"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new _t(t.window)),_t})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return\"undefined\"==typeof window||\"undefined\"==typeof document?\"not a browser\":Array.prototype&&Array.prototype.every&&Array.prototype.filter&&Array.prototype.forEach&&Array.prototype.indexOf&&Array.prototype.lastIndexOf&&Array.prototype.map&&Array.prototype.some&&Array.prototype.reduce&&Array.prototype.reduceRight&&Array.isArray?Function.prototype&&Function.prototype.bind?Object.keys&&Object.create&&Object.getPrototypeOf&&Object.getOwnPropertyNames&&Object.isSealed&&Object.isFrozen&&Object.isExtensible&&Object.getOwnPropertyDescriptor&&Object.defineProperty&&Object.defineProperties&&Object.seal&&Object.freeze&&Object.preventExtensions?\"JSON\"in window&&\"parse\"in JSON&&\"stringify\"in JSON?function(){if(!(\"Worker\"in window&&\"Blob\"in window&&\"URL\"in window))return!1;var t,e,r=new Blob([\"\"],{type:\"text/javascript\"}),n=URL.createObjectURL(r);try{e=new Worker(n),t=!0}catch(e){t=!1}return e&&e.terminate(),URL.revokeObjectURL(n),t}()?\"Uint8ClampedArray\"in window?ArrayBuffer.isView?function(){var t=document.createElement(\"canvas\");t.width=t.height=1;var e=t.getContext(\"2d\");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?(void 0===n[r=t&&t.failIfMajorPerformanceCaveat]&&(n[r]=function(t){var r=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)||r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)||r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)||r.getContext(\"experimental-webgl\",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);return!(!n||r.isContextLost())&&(r.shaderSource(n,\"void main() {}\"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS))}(r)),n[r]?void 0:\"insufficient WebGL support\"):\"insufficient Canvas/getImageData support\":\"insufficient ArrayBuffer support\":\"insufficient Uint8ClampedArray support\":\"insufficient worker support\":\"insufficient JSON support\":\"insufficient Object support\":\"insufficient Function support\":\"insufficent Array support\";var r}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]=\"none\")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",c,c),t.window.removeEventListener(\"test\",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),t.addEventListener(e,r,\"passive\"in n&&l?n:n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),t.removeEventListener(e,r,\"passive\"in n&&l?n:n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",u,!0)};function f(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",u,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e];this._notify(n.ids,n.callback)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!(t&&(4!==t.length||t[0]<0||e.data.width<t[0]||t[1]<0||e.data.height<t[1]||t[2]<0||e.data.width<t[2]||t[3]<0||e.data.height<t[3]||t[2]<t[0]||t[3]<t[1]))},r.prototype.updateImage=function(t,e){e.version=this.images[t].version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1)this.images[i[n]]||(r=!1);this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. 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o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented),p=m,d=m,g=1e20;function m(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-g,n[1]=+g;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+g}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}m.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?g:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?g:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++)r[n]=Math.max(0,Math.min(255,Math.round(255-255*((this.gridOuter[n]-this.gridInner[n])/this.radius+this.cutoff))));return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1)i.push({stack:a,id:s[o]});t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){var c=t[l];n.push({left:i=s*r,right:a=(s+=c)*r,isDash:o=!o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.width*(this.nextRow+r+i),o=0,s=t[o],l=0;l<this.width;l++){l/s.right>1&&(s=t[++o]);var c=Math.abs(l-s.left),u=Math.abs(l-s.right),f=Math.min(c,u),h=void 0,p=i/r*(n+1);if(s.isDash){var d=n-Math.abs(p);h=Math.sqrt(f*f+d*d)}else h=n-Math.sqrt(f*f+p*p);this.data[a+l]=Math.max(0,Math.min(255,h+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f);this.data[o+c]=Math.max(0,Math.min(255,(l.isDash?h:-h)+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=new e.Actor(i[a],n,this.id);o.name=\"Worker \"+a,this.actors.push(o)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n=n||function(){})},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var A=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};A.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},A.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new A(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new A(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor,e.request=this.actor.send(i,{type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId},(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),I=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),P=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=Math.max(i-r,a-n),u=Math.max(0,Math.floor(-Math.log(c)/Math.LN2)),f=Math.pow(2,u);return new t.CanonicalTileID(u,Math.floor((r+i)/2*f),Math.floor((n+a)/2*f))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented),z=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1)this.urls.push(this.map._requestManager.transformRequest(i[n],t.ResourceType.Source).url);t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(P),O=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(P),D={vector:S,raster:E,\"raster-dem\":C,geojson:L,video:z,image:P,canvas:O};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var g=d[p];f[g.featureIndex]||(f[g.featureIndex]=!0,h.push(g))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=e.attributes[this.attributes[r].name];void 0!==n&&t.enableVertexAttribArray(n)}},V.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[U[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},V.prototype.destroy=function(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)};var q=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};q.prototype.get=function(){return this.current},q.prototype.set=function(t){},q.prototype.getDefault=function(){return this.default},q.prototype.setDefault=function(){this.set(this.default)};var H=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var 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e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(q),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(q),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(q),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var 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e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(q),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),_t=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e}(q),wt=function(t){function 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t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var At=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};At.disabled=new At({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Ct=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new H(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new mt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Ct.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Ct.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Ct.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Ct.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Ct.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Ct.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Ct.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Ct.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Ct.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Ct.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Ct.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Ct.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Lt=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=Math.ceil(t.width/this._source.tileSize)+1,r=Math.ceil(t.height/this._source.tileSize)+1,n=Math.floor(e*r*5),i=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,n):n;this._cache.setMaxSize(i)},r.prototype.handleWrapJump=function(t){var e=Math.round((t-(void 0===this._prevLng?t:this._prevLng))/360);if(this._prevLng=t,e){var r={};for(var n in this._tiles){var i=this._tiles[n];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+e),r[i.tileID.key]=i}for(var a in this._tiles=r,this._timers)clearTimeout(this._timers[a]),delete this._timers[a];for(var o in this._tiles)this._setTileReloadTimer(o,this._tiles[o])}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Pt(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],g=this._tiles[p];if(g&&!(g.fadeEndTime&&g.fadeEndTime<=t.browser.now())){var m=this.findLoadedParent(d,o);m&&(this._addTile(m.tileID),c[m.tileID.key]=m.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],g=this._tiles[d.key];if(!g.hasData()){if(e+1>this._source.maxzoom){var m=d.children(this._source.maxzoom)[0],v=this.getTile(m);if(v&&v.hasData()){n[m.key]=m;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=g.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1)this._loadedParentTiles[o[a]]=r}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,g=0,m=c;g<m.length;g+=1){var v=m[g];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,g=Math.pow(2,o.zoom-n.tileID.overscaledZ),m=r*n.queryPadding*t.EXTENT/n.tileSize/g,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-m<t.EXTENT&&v[0].y-m<t.EXTENT&&v[1].x+m>=0&&v[1].y+m>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:g})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Pt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){this._state.updateState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.removeFeatureState=function(t,e,r){this._state.removeFeatureState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.getFeatureState=function(t,e){return this._state.getState(t=t||\"_geojsonTileLayer\",e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Pt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(Yi.workerUrl)}Lt.maxOverzooming=10,Lt.maxUnderzooming=3;var Ot=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new zt);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[Ot]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function qt(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function Ht(t,e,r,n){qt(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}var Zt=function(t,e){this.reset(t,e)};Zt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Zt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Jt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Kt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function Qt(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function $t(e,r){var n=[e.x,e.y,0,1];ue(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function te(t,e){return.5+t/e*.5}function ee(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function re(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!m)ce(y.numGlyphs,h);else{m=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),ee(x,f)){var b=te(n.transform.cameraToCenterDistance,x[3]),_=t.evaluateSizeForFeature(c,u,y),w=s?_/b:_*b,T=new t.Point(y.anchorX,y.anchorY),k=$t(T,a).point,M={},A=ae(y,w,!1,l,r,a,o,e.glyphOffsetArray,p,h,k,T,M,g);m=A.useVertical,(A.notEnoughRoom||m||A.needsFlipping&&ae(y,w,!0,l,r,a,o,e.glyphOffsetArray,p,h,k,T,M,g).notEnoughRoom)&&ce(y.numGlyphs,h)}else ce(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ne(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),g=e.getoffsetX(f-1),m=se(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!m)return null;var v=se(t*g,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:m,last:v}:null}function ie(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function ae(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=r/24,v=e.lineOffsetX*m,y=e.lineOffsetY*m;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ne(m,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=$t(w.first.point,s).point,k=$t(w.last.point,s).point;if(i&&!n){var M=ie(e.writingMode,T,k,d);if(M)return M}g=[w.first];for(var A=e.glyphStartIndex+1;A<x-1;A++)g.push(se(m*l.getoffsetX(A),v,y,n,f,h,e.segment,b,_,c,o,p));g.push(w.last)}else{if(i&&!n){var S=$t(h,a).point,E=e.lineStartIndex+e.segment+1,C=new t.Point(c.getx(E),c.gety(E)),L=$t(C,a),I=L.signedDistanceFromCamera>0?L.point:oe(h,C,S,1,a),P=ie(e.writingMode,S,I,d);if(P)return P}var z=se(m*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!z)return{notEnoughRoom:!0};g=[z]}for(var O=0,D=g;O<D.length;O+=1){var R=D[O];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function oe(t,e,r,n,i){var a=$t(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function se(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,g=0;i&&(d*=-1,g=Math.PI),d<0&&(g+=Math.PI);for(var m=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((m+=d)<l||m>=c)return null;if(y=v,w.push(v),void 0===(v=h[m])){var T=new t.Point(u.getx(m),u.gety(m)),k=$t(T,f);if(k.signedDistanceFromCamera>0)v=h[m]=k.point;else{var M=m-d;v=oe(0===x?o:new t.Point(u.getx(M),u.gety(M)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var A=(_-x)/b,S=v.sub(y),E=S.mult(A)._add(y);E._add(S._unit()._perp()._mult(n*d));var C=g+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:C,path:w}}Jt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Jt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Jt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Jt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Jt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Jt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}return this._forEachCell(t,e,r,n,this._queryCell,o,{hitTest:i,seenUids:{box:{},circle:{}}},a),i?o.length>0:o},Jt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[];return this._forEachCell(a,s,o,l,this._queryCellCircle,c,{hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}},i),n?c.length>0:c},Jt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Jt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Jt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Jt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(m[b],m[b+1],m[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=m[b],w=m[b+1],T=m[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Jt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var g=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[g+0],f[g+1],f[g+2],f[g+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var m=this.circleCells[i];if(null!==m)for(var v=this.circles,y=0,x=m;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Jt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++)if(i.call(this,t,e,r,n,this.xCellCount*p+h,a,o,s))return},Jt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Jt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Jt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Jt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var le=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ce(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(le,3*n)}}function ue(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var fe=function(t,e,r){void 0===e&&(e=new Jt(t.width+200,t.height+200,25)),void 0===r&&(r=new Jt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function he(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}fe.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},fe.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=new t.Point(r.anchorX,r.anchorY),m=$t(g,o),v=te(this.transform.cameraToCenterDistance,m.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=$t(g,s).point,b=ne(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,g,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,M=new t.Point(-100,-100),A=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Zt,E=b.first,C=b.last,L=[],I=E.path.length-1;I>=1;I--)L.push(E.path[I]);for(var P=1;P<C.path.length;P++)L.push(C.path[P]);var z=2.5*k;if(l){var O=L.map((function(t){return $t(t,l)}));L=O.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:O.map((function(t){return t.point}))}var D=[];if(L.length>0){for(var R=L[0].clone(),F=L[0].clone(),B=1;B<L.length;B++)R.x=Math.min(R.x,L[B].x),R.y=Math.min(R.y,L[B].y),F.x=Math.max(F.x,L[B].x),F.y=Math.max(F.y,L[B].y);D=R.x>=M.x&&F.x<=A.x&&R.y>=M.y&&F.y<=A.y?[L]:F.x<M.x||R.x>A.x||F.y<M.y||R.y>A.y?[]:t.clipLine([L],M.x,M.y,A.x,A.y)}for(var N=0,j=D;N<j.length;N+=1){var U;S.reset(j[N],.25*k),U=S.length<=.5*k?1:Math.ceil(S.paddedLength/z)+1;for(var V=0;V<U;V++){var q=V/Math.max(U-1,1),H=S.lerp(q),G=H.x+100,Y=H.y+100;d.push(G,Y,k,0);var W=G-k,X=Y-k,Z=G+k,J=Y+k;if(T=T&&this.isOffscreen(W,X,Z,J),w=w||this.isInsideGrid(W,X,Z,J),!e&&this.grid.hitTestCircle(G,Y,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},fe.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],m=g.key;if(void 0===f[m.bucketInstanceId]&&(f[m.bucketInstanceId]={}),!f[m.bucketInstanceId][m.featureIndex]){var v=[new t.Point(g.x1,g.y1),new t.Point(g.x2,g.y1),new t.Point(g.x2,g.y2),new t.Point(g.x1,g.y2)];t.polygonIntersectsPolygon(r,v)&&(f[m.bucketInstanceId][m.featureIndex]=!0,void 0===h[m.bucketInstanceId]&&(h[m.bucketInstanceId]=[]),h[m.bucketInstanceId].push(m.featureIndex))}}return h},fe.prototype.insertCollisionBox=function(t,e,r,n,i){(e?this.ignoredGrid:this.grid).insert({bucketInstanceId:r,featureIndex:n,collisionGroupID:i},t[0],t[1],t[2],t[3])},fe.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},fe.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return ue(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},fe.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},fe.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},fe.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var pe=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};pe.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var de=function(t,e,r,n,i){this.text=new pe(t?t.text:null,e,r,i),this.icon=new pe(t?t.icon:null,e,n,i)};de.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},me=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ve=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},ye=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function xe(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function be(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}ye.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var _e=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new fe(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new ye(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function we(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}_e.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=he(n,1,this.transform.zoom),g=Kt(f,h,p,this.transform,d),m=null;if(h){var v=Qt(f,h,p,this.transform,d);m=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ve(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:g,labelToScreenMatrix:m,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x];e.push({sortKey:_.sortKey,symbolInstanceStart:_.symbolInstanceStart,symbolInstanceEnd:_.symbolInstanceEnd,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},_e.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=[f.textOffset0,f.textOffset1],v=xe(t,r,n,m,i),y=this.collisionIndex.placeCollisionBox(be(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(be(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return y.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(g=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:m,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:g},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}):void 0},_e.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,g=a.collisionGroup,m=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),M=x&&(y||!o.hasTextData()||m);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var A=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,A=!1,S=!0,E=null,C={box:null,offscreen:null},L={box:null,offscreen:null},I=null,P=null,z=0,O=0,D=0;a.textFeatureIndex?z=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(z=e.featureIndex),a.verticalTextFeatureIndex&&(O=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,i.markUsedOrientation(o,n=a,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(C=n(),L=C):C=r(),!(C&&C.box&&C.box.length));i+=1);else C=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var m=i.attemptAnchorPlacement(N[d%N.length],t,a,s,c,b,_,f,l,g,d>=N.length,e,o,n,u);if(m&&(h=m.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=m.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&!(C&&C.box&&C.box.length)&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),C&&(T=C.box,S=C.offscreen);var V=F(C&&C.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,g.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(C&&C.box&&C.box.length)}}if(T=(p=C)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\");I=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,g.predicate,e.collisionCircleDiameter,W),T=y||I.circles.length>0&&!I.collisionDetected,S=S&&I.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?be(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,g.predicate)};A=L&&L.box&&L.box.length&&a.verticalIconBox?(P=X(a.verticalIconBox)).box.length>0:(P=X(a.iconBox)).box.length>0,S=S&&P.offscreen}var Z=m||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=v||0===e.numIconVertices;if(Z||J?J?Z||(A=A&&T):T=A&&T:A=T=A&&T,T&&p&&p.box&&i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,L&&L.box&&O?O:z,g.ID),A&&P&&i.collisionIndex.insertCollisionBox(P.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,g.ID),I&&(T&&i.collisionIndex.insertCollisionCircles(I.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,g.ID),n)){var K=o.bucketInstanceId,Q=i.collisionCircleArrays[K];void 0===Q&&(Q=i.collisionCircleArrays[K]=new me);for(var $=0;$<I.circles.length;$+=4)Q.circles.push(I.circles[$+0]),Q.circles.push(I.circles[$+1]),Q.circles.push(I.circles[$+2]),Q.circles.push(I.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,A||M,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var C=S[E];A(o.symbolInstances.get(C),o.collisionArrays[C])}else for(var L=e.symbolInstanceStart;L<e.symbolInstanceEnd;L++)A(o.symbolInstances.get(L),o.collisionArrays[L]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var I=this.collisionCircleArrays[o.bucketInstanceId];t.invert(I.invProjMatrix,l),I.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},_e.prototype.markUsedJustification=function(e,r,n,i){var a;a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:{left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex}[t.getAnchorJustification(r)];for(var o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];o<s.length;o+=1){var l=s[o];l>=0&&(e.text.placedSymbolArray.get(l).crossTileID=a>=0&&l!==a?0:n.crossTileID)}},_e.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1)e.text.placedSymbolArray.get(s[o]).placedOrientation=i;n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},_e.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new de(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new de(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new de(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},_e.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},_e.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new de(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new de(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},g=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,g=a.crossTileID,m=i.opacities[g];r[g]?m=o:m||(i.opacities[g]=m=p),r[g]=!0;var v=a.numIconVertices>0,y=i.placedOrientations[a.crossTileID],x=y===t.WritingMode.vertical,b=y===t.WritingMode.horizontal||y===t.WritingMode.horizontalOnly;if(s>0||l>0){var _=Le(m.text);d(e.text,s,x?Ie:_),d(e.text,l,b?Ie:_);var w=m.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=w||x?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=w||b?1:0);var T=i.variableOffsets[a.crossTileID];T&&i.markUsedJustification(e,T.anchor,a,y);var k=i.placedOrientations[a.crossTileID];k&&(i.markUsedJustification(e,\"left\",a,k),i.markUsedOrientation(e,k,a))}if(v){var M=Le(m.icon),A=!(h&&a.verticalPlacedIconSymbolIndex&&x);a.placedIconSymbolIndex>=0&&(d(e.icon,a.numIconVertices,A?M:Ie),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=m.icon.isHidden()),a.verticalPlacedIconSymbolIndex>=0&&(d(e.icon,a.numVerticalIconVertices,A?Ie:M),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=m.icon.isHidden())}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var S=e.collisionArrays[n];if(S){var E=new t.Point(0,0);if(S.textBox||S.verticalTextBox){var C=!0;if(c){var L=i.variableOffsets[g];L?(E=xe(L.anchor,L.width,L.height,L.textOffset,L.textBoxScale),u&&E._rotate(f?i.transform.angle:-i.transform.angle)):C=!1}S.textBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||x,E.x,E.y),S.verticalTextBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||b,E.x,E.y)}var I=Boolean(!b&&S.verticalIconBox);S.iconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,I,h?E.x:0,h?E.y:0),S.verticalIconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,!I,h?E.x:0,h?E.y:0)}}},m=0;m<e.symbolInstances.length;m++)g(m);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},_e.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},_e.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},_e.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},_e.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},_e.prototype.setStale=function(){this.stale=!0};var Te=Math.pow(2,25),ke=Math.pow(2,24),Me=Math.pow(2,17),Ae=Math.pow(2,16),Se=Math.pow(2,9),Ee=Math.pow(2,8),Ce=Math.pow(2,1);function Le(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Te+e*ke+r*Me+e*Ae+r*Se+e*Ee+r*Ce+e}var Ie=0,Pe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Pe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;)if(e.getBucketParts(a,n,t[this._currentTileIndex],this._sortAcrossTiles),this._currentTileIndex++,i())return!0;for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;)if(e.placeLayerBucketPart(a[this._currentPartIndex],this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0;return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new _e(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Pe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Oe=512/t.EXTENT/2,De=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};De.prototype.getScaledCoordinates=function(e,r){var n=Oe/Math.pow(2,r.canonical.z-this.tileID.canonical.z);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*n),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*n)}},De.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Re=function(){this.maxCrossTileID=0};Re.prototype.generate=function(){return++this.maxCrossTileID};var Fe=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Fe.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Fe.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new De(t,e.symbolInstances,e.bucketInstanceId),!0},Fe.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1)delete this.usedCrossTileIDs[t][i[n].crossTileID]},Fe.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Be=function(){this.layerIndexes={},this.crossTileIDs=new Re,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Be.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Fe);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Be.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var Ne=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},je=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ue=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Ve=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i,a=t.styleSpec.$root[n];a.required&&null!=(i=\"version\"===n?r:\"array\"===a.type?[]:{})&&(e[n]=i)}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Be,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){o.dispatcher.broadcast(\"syncRTLPluginState\",{pluginStatus:e.pluginStatus,pluginURL:e.pluginURL},(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(Ve,!1)},r.prototype._load=function(e,r){if(!r||!Ne(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,g=l.stretchX,m=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:g,stretchY:m,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=this._layers[n[r]];\"custom\"!==i.type&&e.push(i.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=this._layers[u[c]];f.recalculate(e,this._availableImages),!f.isHidden(e.zoom)&&f.source&&(this.sourceCaches[f.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),Ne(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=function(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||qt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):Ht(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),g=f.slice(),m=Object.create(null);for(i=0,a=0;i<f.length;i++)d.hasOwnProperty(o=f[i])?a++:(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)g[g.length-1-i]!==(o=h[h.length-1-i])&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,n.push({command:Ut.addLayer,args:[d[o],c=g[g.length-i]]}),g.splice(g.length-i,0,o),m[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!m[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}(this.serialize(),e).filter((function(t){return!(t.command in Ue)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in je)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Lt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(Ne(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1)i.push(f[u])}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var h=[],p=this._order.length-1;p>=0;p--){var d=this._order[p];if(r(d))for(var g=i.length-1;g>=0;g--){var m=i[g].feature;if(n[m.layer.id]<p)break;h.push(m),i.pop()}else for(var v=0,y=t;v<y.length;v+=1){var x=y[v][d];if(x)for(var b=0,_=x;b<_.length;b+=1)h.push(_[b].feature)}}return h},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1)c.push(o[f[u]]);c.sort(B);for(var h=function(){var r=d[p],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1)o.push(f[u])}},p=0,d=c;p<d.length;p+=1)h();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=r[t[e].source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=a}))};for(var m in s)g(m);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&Ne(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=this._layers[u[c]];if(\"symbol\"===f.type){if(!l[f.source]){var h=this.sourceCaches[f.source];l[f.source]=h.getRenderableIds(!0).map((function(t){return h.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var p=this.crossTileSymbolIndex.addLayer(f,l[f.source],e.center.lng);o=o||p}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var d=0,g=this._order;d<g.length;d+=1){var m=this._layers[g[d]];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var He=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ge=vr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),Ye=vr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),We=vr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Xe=vr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Ze=vr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Je=vr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Ke=vr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),Qe=vr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),$e=vr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),tr=vr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),er=vr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),rr=vr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),nr=vr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=vr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ar=vr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),or=vr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),sr=vr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),lr=vr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),cr=vr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),ur=vr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=vr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),hr=vr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),pr=vr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),dr=vr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=vr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),mr=vr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function vr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var yr=Object.freeze({__proto__:null,prelude:Ge,background:Ye,backgroundPattern:We,circle:Xe,clippingMask:Ze,heatmap:Je,heatmapTexture:Ke,collisionBox:Qe,collisionCircle:$e,debug:tr,fill:er,fillOutline:rr,fillOutlinePattern:nr,fillPattern:ir,fillExtrusion:ar,fillExtrusionPattern:or,hillshadePrepare:sr,hillshade:lr,line:cr,lineGradient:ur,linePattern:fr,lineSDF:hr,raster:pr,symbolIcon:dr,symbolSDF:gr,symbolTextAndIcon:mr}),xr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};xr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);t.extVertexArrayObject&&this.vao&&this.boundProgram===e&&this.boundLayoutVertexBuffer===r&&!l&&this.boundIndexBuffer===i&&this.boundVertexOffset===a&&this.boundDynamicVertexBuffer===o&&this.boundDynamicVertexBuffer2===s?(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind()):this.freshBind(e,r,n,i,a,o,s)},xr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(u,t);a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var m=g[d];m.bind(),m.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},xr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var br=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ge.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ge.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var g=a.getActiveAttrib(this.program,d);g&&(this.attributes[g.name]=a.getAttribLocation(this.program,g.name))}for(var m=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<m;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function _r(t,e,r){var n=1/he(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}br.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(m={},m[v.LINES]=2,m[v.TRIANGLES]=3,m[v.LINE_STRIP]=1,m)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new xr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var wr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},Tr=function(e,r,n,i,a,o,s){return t.extend(wr(e,r,n,i),_r(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},kr=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(kr(e),_r(n,r,i))},Ar=function(t,e){return{u_matrix:t,u_world:e}},Sr=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Er=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=he(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=he(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Lr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Pr=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:he(e,1,r),u_intensity:n}},Or=function(e,r,n){var i=e.transform;return{u_matrix:Nr(e,r,n),u_ratio:1/he(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Dr=function(e,r,n){return t.extend(Or(e,r,n),{u_image:0})},Rr=function(e,r,n,i){var a=e.transform,o=Br(r,a);return{u_matrix:Nr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/he(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Fr=function(e,r,n,i,a){var o=e.lineAtlas,s=Br(r,e.transform),l=\"round\"===n.layout.get(\"line-cap\"),c=o.getDash(i.from,l),u=o.getDash(i.to,l),f=c.width*a.fromScale,h=u.width*a.toScale;return t.extend(Or(e,r,n),{u_patternscale_a:[s/f,-c.height/2],u_patternscale_b:[s/h,-u.height/2],u_sdfgamma:o.width/(256*Math.min(f,h)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:u.y,u_mix:a.t})};function Br(t,e){return 1/he(t,1,e.tileZoom)}function Nr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var jr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Ur(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Ur(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Vr,qr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Hr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(qr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Gr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Hr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Yr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Wr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/he(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Xr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Zr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var g=i[d],m=r.getTile(g),v=m.getBucket(n);if(v){var y=g.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(g.posMatrix,m,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,m),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var M=0,A=0,S=f;A<S.length;A+=1)for(var E=S[A],C=0;C<E.circleArray.length/4;C++){var L=4*C,I=E.circleArray[L+0],P=E.circleArray[L+1],z=E.circleArray[L+2],O=E.circleArray[L+3];k.emplace(M++,I,P,z,O,0),k.emplace(M++,I,P,z,O,1),k.emplace(M++,I,P,z,O,2),k.emplace(M++,I,P,z,O,3)}(!Vr||Vr.length<2*h)&&(Vr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(Vr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Lr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Jr=t.identity(new Float32Array(16));function Kr(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function Qr(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,g={};p.clear();for(var m=0;m<h.length;m++){var v=h.get(m),y=v.hidden||!v.crossTileID||e.allowVerticalPlacement&&!v.placedOrientation?null:i[v.crossTileID];if(y){var x=new t.Point(v.anchorX,v.anchorY),b=$t(x,n?l:s),_=te(o.cameraToCenterDistance,b.signedDistanceFromCamera),w=a.evaluateSizeForFeature(e.textSizeData,u,v)*_/t.ONE_EM;n&&(w*=e.tilePixelRatio/c);for(var T=Kr(y.anchor,y.width,y.height,y.textOffset,y.textBoxScale,w),k=n?$t(x.add(T),s).point:b.point.add(r?T.rotate(-o.angle):T),M=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,A=0;A<v.numGlyphs;A++)t.addDynamicAttributes(p,k,M);f&&v.associatedIconIndex>=0&&(g[v.associatedIconIndex]={shiftedAnchor:k,angle:M})}else ce(v.numGlyphs,p)}if(f){d.clear();for(var S=e.icon.placedSymbolArray,E=0;E<S.length;E++){var C=S.get(E);if(C.hidden)ce(C.numGlyphs,d);else{var L=g[E];if(L)for(var I=0;I<C.numGlyphs;I++)t.addDynamicAttributes(d,L.shiftedAnchor,L.angle);else ce(C.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function $r(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function tn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,g=e.transform,m=\"map\"===l,v=\"map\"===c,y=m&&\"point\"!==n.layout.get(\"symbol-placement\"),x=m&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,Mt.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,M=i;k<M.length;k+=1){var A=M[k],S=r.getTile(A),E=S.getBucket(n);if(E){var C=a?E.text:E.icon;if(C&&C.segments.get().length){var L=C.programConfigurations.get(n.id),I=a||E.sdfIcons,P=a?E.textSizeData:E.iconSizeData,z=v||0!==g.pitch,O=e.useProgram($r(I,a,E),L),D=t.evaluateSizeForZoom(P,g.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a)B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText&&(F=S.imageAtlasTexture.size,j=S.imageAtlasTexture,U=z||e.options.rotating||e.options.zooming||\"composite\"===P.kind||\"camera\"===P.kind?d.LINEAR:d.NEAREST);else{var V=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=I||e.options.rotating||e.options.zooming||V||z?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=he(S,1,e.transform.zoom),H=Kt(A.posMatrix,v,m,e.transform,q),G=Qt(A.posMatrix,v,m,e.transform,q),Y=w&&E.hasTextData(),W=\"none\"!==n.layout.get(\"icon-text-fit\")&&Y&&E.hasIconData();y&&re(E,A.posMatrix,e,a,H,G,v,u);var X=e.translatePosMatrix(A.posMatrix,S,o,s),Z=y||a&&w||W?Jr:H,J=e.translatePosMatrix(G,S,o,s,!0),K=I&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),Q={program:O,buffers:C,uniformValues:I?E.iconsInText?Gr(P.kind,D,x,v,e,X,Z,J,R,F):Hr(P.kind,D,x,v,e,X,Z,J,a,R,!0):qr(P.kind,D,x,v,e,X,Z,J,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:I,hasHalo:K};if(b)for(var $=0,tt=C.segments.get();$<tt.length;$+=1){var et=tt[$];T.push({segments:new t.SegmentVector([et]),sortKey:et.sortKey,state:Q})}else T.push({segments:C.segments,sortKey:0,state:Q})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var rt=0,nt=T;rt<nt.length;rt+=1){var it=nt[rt],at=it.state;if(p.activeTexture.set(d.TEXTURE0),at.atlasTexture.bind(at.atlasInterpolation,d.CLAMP_TO_EDGE),at.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),at.atlasTextureIcon&&at.atlasTextureIcon.bind(at.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),at.isSDF){var ot=at.uniformValues;at.hasHalo&&(ot.u_is_halo=1,en(at.buffers,it.segments,n,e,at.program,_,f,h,ot)),ot.u_is_halo=0}en(at.buffers,it.segments,n,e,at.program,_,f,h,at.uniformValues)}}function en(t,e,r,n,i,a,o,s,l){var c=n.context;i.draw(c,c.gl.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function rn(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),g=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var m=0,v=n;m<v.length;m+=1){var y=v[m],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(g));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,M=k.patternPositions[T.to.toString()],A=k.patternPositions[T.from.toString()];M&&A&&_.setConstantPatternPositions(M,A)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Sr(S,t,g,x,E):Ar(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,g,x):kr(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function nn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var g=d[p],m=e.getTile(g),v=m.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),m.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&m.imageAtlas){var _=m.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(g.posMatrix,m,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),M=r.paint.get(\"fill-extrusion-vertical-gradient\"),A=u?Tr(k,t,M,h,g,f,m):wr(k,t,M,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,A,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function an(e,r,n,i,a,o){var s=e.context,l=s.gl,c=r.fbo;if(c){var u=e.useProgram(\"hillshade\");s.activeTexture.set(l.TEXTURE0),l.bindTexture(l.TEXTURE_2D,c.colorAttachment.get());var f=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,c,u,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,c=Math.pow(2,l.canonical.z),u=l.canonical.y,[new t.MercatorCoordinate(0,u/c).toLngLat().lat,new t.MercatorCoordinate(0,(u+1)/c).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}}(e,r,n);u.draw(s,l.TRIANGLES,i,a,o,Et.disabled,f,n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments)}}function on(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var g=r.fbo;if(!g){var m=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);m.bind(c.LINEAR,c.CLAMP_TO_EDGE),(g=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(m.texture)}l.bindFramebuffer.set(g.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function sn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var ln=new t.Color(1,0,0,1),cn=new t.Color(0,1,0,1),un=new t.Color(0,0,1,1),fn=new t.Color(1,0,1,1),hn=new t.Color(0,1,1,1);function pn(t,e,r,n){gn(t,0,e+r/2,t.transform.width,r,n)}function dn(t,e,r,n){gn(t,e-r/2,0,r,t.transform.height,n)}function gn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function mn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,c=At.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=Math.floor((f&&f.byteLength||0)/1024),p=r.getTile(n).tileSize,d=512/Math.min(p,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+h+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,d),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var vn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=At.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),g=d.getBucket(n);if(g&&g.text&&g.text.segments.get().length){var m=t.evaluateSizeForZoom(g.textSizeData,l.zoom),v=he(d,1,r.transform.zoom),y=Kt(p.posMatrix,u,c,r.transform,v),x=\"none\"!==n.layout.get(\"icon-text-fit\")&&g.hasIconData();if(m){var b=Math.pow(2,l.zoom-d.tileID.overscaledZ);Qr(g,c,u,s,t.symbolSize,l,y,p.posMatrix,b,m,x)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&tn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&tn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Zr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Zr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=At.disabled,p=e.colorModeForRenderPass(),d=[],g=0;g<i.length;g++){var m=i[g],v=r.getTile(m),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Er(e,m,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,M=d;k<M.length;k+=1){var A=M[k],S=A.state;S.program.draw(c,u.TRIANGLES,f,h,p,Et.disabled,S.uniformValues,n.id,S.layoutVertexBuffer,S.indexBuffer,A.segments,n.paint,e.transform.zoom,S.programConfiguration)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=At.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id);e.useProgram(\"heatmap\",p).draw(a,o.TRIANGLES,Mt.disabled,s,l,Et.disabled,zr(u.posMatrix,f,e.transform.zoom,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:0,u_color_ramp:1,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",g=e.context,m=g.gl,v=!0;if(h){g.activeTexture.set(m.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(g,n.gradient,m.RGBA)),y.bind(m.LINEAR,m.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),M=e.context.program.get(),A=e.useProgram(d,k),S=v||A.program!==M,E=u.constantOr(null);if(E&&w.imageAtlas){var C=w.imageAtlas,L=C.patternPositions[E.to.toString()],I=C.patternPositions[E.from.toString()];L&&I&&k.setConstantPatternPositions(L,I)}var P=f?Rr(e,w,n,p):c?Fr(e,w,n,c,p):h?Dr(e,w,n):Or(e,w,n);f?(g.activeTexture.set(m.TEXTURE0),w.imageAtlasTexture.bind(m.LINEAR,m.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(g.activeTexture.set(m.TEXTURE0),e.lineAtlas.bind(g)),A.draw(g,m.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,P,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);rn(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,Mt.ReadOnly);rn(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))nn(t,e,r,n,a,At.disabled,St.disabled),nn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();nn(t,e,r,n,a,At.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,Mt.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?on(t,p,r,a,o,At.disabled,s):\"translucent\"===t.renderPass&&an(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof P?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,g=f;d<g.length;d+=1){var m=g[d],v=t.depthModeForSublayer(m.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),y=e.getTile(m),x=t.transform.calculatePosMatrix(m.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(m,0),_=sn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var M=jr(x,T||[0,0],w||1,_,r);o instanceof P?s.draw(i,a.TRIANGLES,v,At.disabled,l,Et.disabled,M,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[m.overscaledZ],l,Et.disabled,M,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=At.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),g=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var m=r.getCrossfadeParameters(),v=0,y=g;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Wr(b,i,t,c,{tileID:x,tileSize:l},m):Yr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)mn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(At.disabled);var o=\"3d\"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},yn=function(t,e){this.context=new Ct(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Lt.maxUnderzooming+Lt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Be,this.gpuTimers={}};yn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1)this.style._layers[i[n]].resize()},yn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,He.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,He.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,I.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,He.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new At({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},yn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,St.disabled,Et.disabled,Pr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},yn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new At({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Pr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},yn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new At({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},yn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new At({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},yn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new At({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=At.disabled,e),n]},yn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;return this._showOverdrawInspector?new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0]):\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},yn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},yn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},yn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var g=0;g<i.length;g++)if(this.style._layers[i[g]].is3D()){this.opaquePassCutoff=g;break}this.renderPass=\"offscreen\";for(var m=0,v=i;m<v.length;m+=1){var y=this.style._layers[v[m]];if(y.hasOffscreenPass()&&!y.isHidden(this.transform.zoom)){var x=f[y.source];(\"custom\"===y.type||x.length)&&this.renderLayer(this,a[y.source],y,x)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var b=this.style._layers[i[this.currentLayer]],_=a[b.source],w=u[b.source];this._renderTileClippingMasks(b,w),this.renderLayer(this,_,b,w)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var T=this.style._layers[i[this.currentLayer]],k=a[T.source],M=(\"symbol\"===T.type?h:f)[T.source];this._renderTileClippingMasks(T,u[T.source]),this.renderLayer(this,k,T,M)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&vn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&function(t){var e=t.transform.padding;pn(t,t.transform.height-(e.top||0),3,ln),pn(t,e.bottom||0,3,cn),dn(t,e.left||0,3,un),dn(t,t.transform.width-(e.right||0),3,fn);var r=t.transform.centerPoint;!function(t,e,r,n){gn(t,e-1,r-10,2,20,n),gn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,hn)}(this),this.context.setDefault()},yn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),vn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},yn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},yn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},yn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},yn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},yn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:he(r,n[0],this.transform.zoom),a?n[1]:he(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},yn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},yn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},yn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},yn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new br(this.context,yr[t],e,Xr[t],this._showOverdrawInspector)),this.cache[r]},yn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},yn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},yn.prototype.initDebugOverlayCanvas=function(){null==this.debugOverlayCanvas&&(this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))},yn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var xn=function(t,e){this.points=t,this.planes=e};xn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new xn(a,o)};var bn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};bn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new bn(n,i)},bn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},bn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},bn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var _n=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};_n.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},_n.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},_n.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},_n.prototype.clone=function(){return new _n(this.top,this.bottom,this.left,this.right)},_n.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var wn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new _n,this._posMatrixCache={},this._alignedPosMatrixCache={}},Tn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};wn.prototype.clone=function(){var t=new wn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Tn.minZoom.get=function(){return this._minZoom},Tn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Tn.maxZoom.get=function(){return this._maxZoom},Tn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Tn.minPitch.get=function(){return this._minPitch},Tn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Tn.maxPitch.get=function(){return this._maxPitch},Tn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Tn.renderWorldCopies.get=function(){return this._renderWorldCopies},Tn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Tn.worldSize.get=function(){return this.tileSize*this.scale},Tn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Tn.size.get=function(){return new t.Point(this.width,this.height)},Tn.bearing.get=function(){return-this.angle/Math.PI*180},Tn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Tn.pitch.get=function(){return this._pitch/Math.PI*180},Tn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Tn.fov.get=function(){return this._fov/Math.PI*180},Tn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Tn.zoom.get=function(){return this._zoom},Tn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Tn.center.get=function(){return this._center},Tn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Tn.padding.get=function(){return this._edgeInsets.toJSON()},Tn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Tn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},wn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},wn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},wn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},wn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},wn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=xn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new bn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var g=u.pop(),m=g.x,v=g.y,y=g.fullyVisible;if(!y){var x=g.aabb.intersects(s);if(0===x)continue;y=2===x}var b=g.aabb.distanceX(o),_=g.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_));if(g.zoom===h||w>3+(1<<h-g.zoom)-2&&g.zoom>=l)f.push({tileID:new t.OverscaledTileID(g.zoom===h?p:g.zoom,g.wrap,g.zoom,m,v),distanceSq:t.sqrLen([o[0]-.5-m,o[1]-.5-v])});else for(var T=0;T<4;T++){var k=(m<<1)+T%2,M=(v<<1)+(T>>1);u.push({aabb:g.aabb.quadrant(T),zoom:g.zoom+1,x:k,y:M,wrap:g.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},wn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Tn.unmodified.get=function(){return this._unmodified},wn.prototype.zoomScale=function(t){return Math.pow(2,t)},wn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},wn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},wn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Tn.point.get=function(){return this.project(this.center)},wn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},wn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},wn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},wn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},wn.prototype.coordinateLocation=function(t){return t.toLngLat()},wn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[1]/i,s=n[1]/a,l=r[2]/i,c=n[2]/a,u=l===c?0:(0-l)/(c-l);return new t.MercatorCoordinate(t.number(r[0]/i,n[0]/a,u)/this.worldSize,t.number(o,s,u)/this.worldSize)},wn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},wn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},wn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},wn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},wn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},wn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},wn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var g=p.y,m=c.y/2;g-m<a&&(i=a+m),g+m>o&&(i=o-m)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},wn.prototype._calcMatrices=function(){if(this.height){var e=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height;var r=Math.PI/2+this._pitch,n=this._fov*(.5+e.y/this.height),i=Math.sin(n)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-r-n,.01,Math.PI-.01)),a=this.point,o=a.x,s=a.y,l=1.01*(Math.cos(Math.PI/2-this._pitch)*i+this.cameraToCenterDistance),c=this.height/50,u=new Float64Array(16);t.perspective(u,this._fov,this.width/this.height,c,l),u[8]=2*-e.x/this.width,u[9]=2*e.y/this.height,t.scale(u,u,[1,-1,1]),t.translate(u,u,[0,0,-this.cameraToCenterDistance]),t.rotateX(u,u,this._pitch),t.rotateZ(u,u,this.angle),t.translate(u,u,[-o,-s,0]),this.mercatorMatrix=t.scale([],u,[this.worldSize,this.worldSize,this.worldSize]),t.scale(u,u,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=u,this.invProjMatrix=t.invert([],this.projMatrix);var f=this.width%2/2,h=this.height%2/2,p=Math.cos(this.angle),d=Math.sin(this.angle),g=o-Math.round(o)+p*f+d*h,m=s-Math.round(s)+p*h+d*f,v=new Float64Array(u);if(t.translate(v,v,[g>.5?g-1:g,m>.5?m-1:m,0]),this.alignedProjMatrix=v,u=t.create(),t.scale(u,u,[this.width/2,-this.height/2,1]),t.translate(u,u,[1,-1,0]),this.labelPlaneMatrix=u,u=t.create(),t.scale(u,u,[1,-1,1]),t.translate(u,u,[-1,-1,0]),t.scale(u,u,[2/this.width,2/this.height,1]),this.glCoordMatrix=u,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(u=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=u,this._posMatrixCache={},this._alignedPosMatrixCache={}}},wn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},wn.prototype.getCameraPoint=function(){var e=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,e))},wn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(wn.prototype,Tn);var kn=function(e){var r,n,i,a;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=!1,i=null,a=function(){i=null,n&&(r(),i=setTimeout(a,300),n=!1)},function(){return n=!0,i||a(),i})};kn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},kn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},kn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},kn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},kn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},kn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var Mn={linearity:.3,easing:t.bezier(0,0,.3,1)},An=t.extend({deceleration:2500,maxSpeed:1400},Mn),Sn=t.extend({deceleration:20,maxSpeed:1400},Mn),En=t.extend({deceleration:1e3,maxSpeed:360},Mn),Cn=t.extend({deceleration:1e3,maxSpeed:90},Mn),Ln=function(t){this._map=t,this.clear()};function In(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Pn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Ln.prototype.clear=function(){this._inertiaBuffer=[]},Ln.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Ln.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Ln.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Pn(r.pan.mag(),o,t.extend({},An,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,In(s,l)}if(r.zoom){var c=Pn(r.zoom,o,Sn);s.zoom=this._map.transform.zoom+c.amount,In(s,c)}if(r.bearing){var u=Pn(r.bearing,o,En);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),In(s,u)}if(r.pitch){var f=Pn(r.pitch,o,Cn);s.pitch=this._map.transform.pitch+f.amount,In(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var zn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),On=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Dn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Rn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Rn.prototype.reset=function(){delete this._mousedownPos},Rn.prototype.wheel=function(t){return this._firePreventable(new Dn(t.type,this._map,t))},Rn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseup=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.dblclick=function(t){return this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseover=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.mouseout=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.touchstart=function(t){return this._firePreventable(new On(t.type,this._map,t))},Rn.prototype.touchmove=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchend=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchcancel=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Rn.prototype.isEnabled=function(){return!0},Rn.prototype.isActive=function(){return!1},Rn.prototype.enable=function(){},Rn.prototype.disable=function(){};var Fn=function(t){this._map=t};Fn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Fn.prototype.mousemove=function(t){this._map.fire(new zn(t.type,this._map,t))},Fn.prototype.mousedown=function(){this._delayContextMenu=!0},Fn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new zn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Fn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new zn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Fn.prototype.isEnabled=function(){return!0},Fn.prototype.isActive=function(){return!1},Fn.prototype.enable=function(){},Fn.prototype.disable=function(){};var Bn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Nn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Bn.prototype.isEnabled=function(){return!!this._enabled},Bn.prototype.isActive=function(){return!!this._active},Bn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Bn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Bn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Bn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Bn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Bn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Bn.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Bn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var jn=function(t){this.reset(),this.numTouches=t.numTouches};jn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},jn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1)r._add(i[n]);return r.div(e.length)}(r),this.touches=Nn(n,r)))},jn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Nn(r,e);for(var i in this.touches){var a=n[i];(!a||a.dist(this.touches[i])>30)&&(this.aborted=!0)}}},jn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Un=function(t){this.singleTap=new jn(t),this.numTaps=t.numTaps,this.reset()};Un.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Un.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Un.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Un.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Vn=function(){this._zoomIn=new Un({numTouches:1,numTaps:2}),this._zoomOut=new Un({numTouches:2,numTaps:1}),this.reset()};Vn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Vn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Vn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Vn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Vn.prototype.touchcancel=function(){this.reset()},Vn.prototype.enable=function(){this._enabled=!0},Vn.prototype.disable=function(){this._enabled=!1,this.reset()},Vn.prototype.isEnabled=function(){return this._enabled},Vn.prototype.isActive=function(){return this._active};var qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},qn.prototype._correctButton=function(t,e){return!1},qn.prototype._move=function(t,e){return{}},qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},qn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},qn.prototype.enable=function(){this._enabled=!0},qn.prototype.disable=function(){this._enabled=!1,this.reset()},qn.prototype.isEnabled=function(){return this._enabled},qn.prototype.isActive=function(){return this._active};var Hn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(qn),Gn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(qn),Yn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(qn),Wn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Wn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Wn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Wn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Wn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Wn.prototype.touchcancel=function(){this.reset()},Wn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Nn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Wn.prototype.enable=function(){this._enabled=!0},Wn.prototype.disable=function(){this._enabled=!1,this.reset()},Wn.prototype.isEnabled=function(){return this._enabled},Wn.prototype.isActive=function(){return this._active};var Xn=function(){this.reset()};function Zn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}function Jn(t,e){return Math.log(t/e)/Math.LN2}Xn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Xn.prototype._start=function(t){},Xn.prototype._move=function(t,e,r){return{}},Xn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Xn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[1],a=Zn(r,e,n[0]),o=Zn(r,e,i);if(a&&o){var s=this._aroundCenter?null:a.add(o).div(2);return this._move([a,o],s,t)}}},Xn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[1],o=Zn(n,e,i[0]),s=Zn(n,e,a);o&&s||(this._active&&r.suppressClick(),this.reset())}},Xn.prototype.touchcancel=function(){this.reset()},Xn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Xn.prototype.disable=function(){this._enabled=!1,this.reset()},Xn.prototype.isEnabled=function(){return this._enabled},Xn.prototype.isActive=function(){return this._active};var Kn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(Jn(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:Jn(this._distance,r),pinchAround:e}},e}(Xn);function Qn(t,e){return 180*t.angleWith(e)/Math.PI}var $n=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:Qn(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=Qn(t,this._startVector);return Math.abs(r)<e},e}(Xn);function ti(t){return Math.abs(t.y)>Math.abs(t.x)}var ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ti(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ti(t)&&ti(e)&&a}},e}(Xn),ri={panStep:100,bearingStep:15,pitchStep:10},ni=function(){var t=ri;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function ii(t){return t*(2-t)}ni.prototype.reset=function(){this._active=!1},ni.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:ii,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ai=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};ai.prototype.setZoomRate=function(t){this._defaultZoomRate=t},ai.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},ai.prototype.isEnabled=function(){return!!this._enabled},ai.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},ai.prototype.isZooming=function(){return!!this._zooming},ai.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},ai.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},ai.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},ai.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},ai.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},ai.prototype.renderFrame=function(){return this._onScrollFrame()},ai.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},ai.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},ai.prototype.reset=function(){this._active=!1};var oi=function(t,e){this._clickZoom=t,this._tapZoom=e};oi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},oi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},oi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},oi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var si=function(){this.reset()};si.prototype.reset=function(){this._active=!1},si.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},si.prototype.enable=function(){this._enabled=!0},si.prototype.disable=function(){this._enabled=!1,this.reset()},si.prototype.isEnabled=function(){return this._enabled},si.prototype.isActive=function(){return this._active};var li=function(){this._tap=new Un({numTouches:1,numTaps:1}),this.reset()};li.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},li.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},li.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},li.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},li.prototype.touchcancel=function(){this.reset()},li.prototype.enable=function(){this._enabled=!0},li.prototype.disable=function(){this._enabled=!1,this.reset()},li.prototype.isEnabled=function(){return this._enabled},li.prototype.isActive=function(){return this._active};var ci=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};ci.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},ci.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},ci.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},ci.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var ui=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};ui.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},ui.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},ui.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},ui.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var fi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};fi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},fi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},fi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},fi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},fi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},fi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var hi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(t.Event);function di(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var gi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Ln(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0];r.addEventListener(l,s[1],l===t.window.document?this.handleWindowEvent:this.handleEvent,s[2])}};gi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0];r.removeEventListener(a,i[1],a===t.window.document?this.handleWindowEvent:this.handleEvent,i[2])}},gi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Rn(e,t));var n=e.boxZoom=new Bn(e,t);this._add(\"boxZoom\",n);var i=new Vn,a=new si;e.doubleClickZoom=new oi(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new li;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ei;this._add(\"touchPitch\",s);var l=new Gn(t),c=new Yn(t);e.dragRotate=new ui(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Hn(t),f=new Wn(t);e.dragPan=new ci(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new $n,p=new Kn;e.touchZoomRotate=new fi(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new ai(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var g=e.keyboard=new ni;this._add(\"keyboard\",g),this._add(\"blockableMapEvent\",new Fn(e));for(var m=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];m<v.length;m+=1){var y=v[m];t.interactive&&t[y]&&e[y].enable(t[y])}},gi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},gi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1)e[t].handler.reset();this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},gi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},gi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},gi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},gi.prototype.isMoving=function(){return Boolean(hi(this._eventsInProgress))||this.isZooming()},gi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},gi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},gi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r];this._el.contains(i.target)&&e.push(i)}return e},gi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var g=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(g=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,g,h,n),g&&g.needsRenderFrame&&this._triggerRenderFrame()),(g||p.isActive())&&(o[h]=p)}}var m={};for(var v in this._previousActiveHandlers)o[v]||(m[v]=n);this._previousActiveHandlers=o,(Object.keys(m).length||di(i))&&(this._changes.push([i,a,m]),this._triggerRenderFrame()),(Object.keys(o).length||di(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},gi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},gi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},gi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!di(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},gi.prototype._fireEvents=function(e,r){var n=this,i=hi(this._eventsInProgress),a=hi(e),o={};for(var s in e)this._eventsInProgress[s]||(o[s+\"start\"]=e[s].originalEvent),this._eventsInProgress[s]=e[s];for(var l in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(l,o[l]);for(var c in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e)this._fireEvent(c,e[c].originalEvent);var u,f={};for(var h in this._eventsInProgress){var p=this._eventsInProgress[h],d=p.handlerName,g=p.originalEvent;this._handlersById[d].isActive()||(delete this._eventsInProgress[h],f[h+\"end\"]=u=r[d]||g)}for(var m in f)this._fireEvent(m,f[m]);var v=hi(this._eventsInProgress);if((i||a)&&!v){this._updatingCamera=!0;var y=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),x=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};y?(x(y.bearing||this._map.getBearing())&&(y.bearing=0),this._map.easeTo(y,{originalEvent:u})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:u})),x(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},gi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},gi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new pi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var mi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),g=p.sub(d),m=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/g.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/g.y;if(!(v<0||m<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(m,v)),i.maxZoom),x=t.Point.convert(i.offset),b=new t.Point(x.x+(i.padding.left-i.padding.right)/2,x.y+(i.padding.top-i.padding.bottom)/2).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(b)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),g=i.pointLocation(d),m=t.LngLat.convert(e.center||g);this._normalizeCenter(m);var v,y,x=i.project(g),b=i.project(m).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var g=i.zoomScale(i.zoom-a),m=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(m,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(g));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),g=t.Point.convert(e.offset),m=a.centerPoint.add(g),v=a.pointLocation(m),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var M=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),A=w/a.zoomScale(M-o);_=Math.sqrt(A/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function C(t){return(Math.exp(t)-Math.exp(-t))/2}function L(t){return(Math.exp(t)+Math.exp(-t))/2}var I=E(0),P=function(t){return L(I)/L(I+_*t)},z=function(t){return w*((L(I)*(C(e=I+_*t)/L(e))-C(I))/S)/k;var e},O=(E(1)-I)/_;if(Math.abs(k)<1e-6||!isFinite(O)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;O=Math.abs(Math.log(T/w))/_,z=function(){return 0},P=function(t){return Math.exp(D*_*t)}}return e.duration=\"duration\"in e?+e.duration:1e3*O/(\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed),e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*O,d=1/P(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),m=a.centerPoint.add(g));var v=1===e?y:a.unproject(x.add(b.mult(z(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,m),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),vi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};vi.prototype.getDefaultPosition=function(){return\"bottom-right\"},vi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},vi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},vi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},vi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},vi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},vi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var yi=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};yi.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},yi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},yi.prototype.getDefaultPosition=function(){return\"bottom-left\"},yi.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},yi.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},yi.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var xi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};xi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},xi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},xi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},xi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var bi={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},_i=t.window.HTMLImageElement,wi=t.window.HTMLElement,Ti=t.window.ImageBitmap,ki={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Mi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},ki,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new wn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new xi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},bi,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof wi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new gi(this,e),this._hash=e.hash&&new kn(\"string\"==typeof e.hash&&e.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new vi({customAttribution:e.customAttribution})),this.addControl(new yi,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),(i.prototype=Object.create(n&&n.prototype)).constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new zn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new zn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new zn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;return void 0===r?n.prototype.off.call(this,t,e):(this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this)},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;if(this._lazyInitEmptyStyle(),r instanceof _i||Ti&&r instanceof Ti){var c=t.browser.getImageData(r);this.style.addImage(e,{data:new t.RGBAImage({width:c.width,height:c.height},c.data),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var u=r;this.style.addImage(e,{data:new t.RGBAImage({width:r.width,height:r.height},new Uint8Array(r.data)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:u}),u.onAdd&&u.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof _i||Ti&&r instanceof Ti?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;return void 0===a||void 0===o?this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\"))):a!==n.data.width||o!==n.data.height?this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\"))):(n.data.replace(s,!(r instanceof _i||Ti&&r instanceof Ti)),void this.style.updateImage(e,n))},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new yn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Ai(this._canvasContainer),Ai(this._controlContainer),Ai(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(mi);function Ai(t){t.parentNode&&t.parentNode.removeChild(t)}var Si={showCompass:!0,showZoom:!0,visualizePitch:!1},Ei=function(e){var n=this;this.options=t.extend({},Si,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Ei.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Ei.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Ei.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ci(this._map,this._compass,this.options.visualizePitch)),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Ei.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Ei.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ci=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Gn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Yn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Li(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ci.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ci.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ci.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ci.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ci.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ci.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ci.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ci.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ci.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ci.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ci.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var Ii={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Pi(t,e,r){var n=t.classList;for(var i in Ii)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var zi,Oi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");g.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(g);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"opacity\",\"0.25\"),m.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),m.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(m),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Pi(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Li(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,Ii[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Di={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},Ri=0,Fi=!1,Bi=function(e){function n(r){e.call(this),this.options=t.extend({},Di,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==zi?n(zi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){n(zi=\"denied\"!==t.state)})):n(zi=!!t.window.navigator.geolocation),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ri=0,Fi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Fi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Oi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Oi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||e.originalEvent&&\"resize\"===e.originalEvent.type||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ri--,Fi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ri>1?(e={maximumAge:6e5,timeout:0},Fi=!0):(e=this.options.positionOptions,Fi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ni={maxWidth:100,unit:\"metric\"},ji=function(e){this.options=t.extend({},Ni,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Ui(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?Vi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):Vi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?Vi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?Vi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Vi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Vi(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*(o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o)));t.style.width=e*(s/r)+\"px\",t.innerHTML=s+\"&nbsp;\"+n}ji.prototype.getDefaultPosition=function(){return\"bottom-left\"},ji.prototype._onMove=function(){Ui(this._map,this._container,this.options)},ji.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},ji.prototype.setUnit=function(t){this.options.unit=t,Ui(this._map,this._container,this.options)};var qi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};qi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},qi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},qi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},qi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},qi.prototype._isFullscreen=function(){return this._fullscreen},qi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},qi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Hi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Gi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Hi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this;if(this._map&&(this._lngLat||this._trackPointer)&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Li(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var i=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),a=this.options.anchor,o=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!a){var s,l=this._container.offsetWidth,c=this._container.offsetHeight;s=i.y+o.bottom.y<c?[\"top\"]:i.y>this._map.transform.height-c?[\"bottom\"]:[],i.x<l/2?s.push(\"left\"):i.x>this._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var u=i.add(o[a]).round();r.setTransform(this._container,Ii[a]+\" translate(\"+u.x+\"px,\"+u.y+\"px)\"),Pi(this._container,a,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented),Yi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Mi,NavigationControl:Ei,GeolocateControl:Bi,AttributionControl:vi,ScaleControl:ji,FullscreenControl:qi,Popup:Gi,Marker:Oi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(Ot)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Ot),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Yi})),r}))},{}],474:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=1<<t+1,r=new Array(e),n=0;n<e;++n)r[n]=a(t,n);return r};var n=t(\"convex-hull\");function i(t,e,r){for(var n=new Array(t),i=0;i<t;++i)n[i]=0,i===e&&(n[i]+=.5),i===r&&(n[i]+=.5);return n}function a(t,e){if(0===e||e===(1<<t+1)-1)return[];for(var r=[],a=[],o=0;o<=t;++o)if(e&1<<o){r.push(i(t,o-1,o-1)),a.push(null);for(var s=0;s<=t;++s)~e&1<<s&&(r.push(i(t,o-1,s-1)),a.push([o,s]))}var l=n(r),c=[];t:for(o=0;o<l.length;++o){var u=l[o],f=[];for(s=0;s<u.length;++s){if(!a[u[s]])continue t;f.push(a[u[s]].slice())}c.push(f)}return c}},{\"convex-hull\":135}],475:[function(t,e,r){var n=t(\"./normalize\"),i=t(\"gl-mat4/create\"),a=t(\"gl-mat4/clone\"),o=t(\"gl-mat4/determinant\"),s=t(\"gl-mat4/invert\"),l=t(\"gl-mat4/transpose\"),c={length:t(\"gl-vec3/length\"),normalize:t(\"gl-vec3/normalize\"),dot:t(\"gl-vec3/dot\"),cross:t(\"gl-vec3/cross\")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function g(t,e,r,n,i){t[0]=e[0]*n+r[0]*i,t[1]=e[1]*n+r[1]*i,t[2]=e[2]*n+r[2]*i}e.exports=function(t,e,r,i,m,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),m||(m=[0,0,0,1]),v||(v=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,T,k,M=u[3],A=u[7],S=u[11],E=u[12],C=u[13],L=u[14],I=u[15];if(0!==M||0!==A||0!==S){if(h[0]=M,h[1]=A,h[2]=S,h[3]=I,!s(f,f))return!1;l(f,f),y=m,b=f,_=(x=h)[0],w=x[1],T=x[2],k=x[3],y[0]=b[0]*_+b[4]*w+b[8]*T+b[12]*k,y[1]=b[1]*_+b[5]*w+b[9]*T+b[13]*k,y[2]=b[2]*_+b[6]*w+b[10]*T+b[14]*k,y[3]=b[3]*_+b[7]*w+b[11]*T+b[15]*k}else m[0]=m[1]=m[2]=0,m[3]=1;if(e[0]=E,e[1]=C,e[2]=L,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),g(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),g(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),g(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var P=0;P<3;P++)r[P]*=-1,p[P][0]*=-1,p[P][1]*=-1,p[P][2]*=-1;return v[0]=.5*Math.sqrt(Math.max(1+p[0][0]-p[1][1]-p[2][2],0)),v[1]=.5*Math.sqrt(Math.max(1-p[0][0]+p[1][1]-p[2][2],0)),v[2]=.5*Math.sqrt(Math.max(1-p[0][0]-p[1][1]+p[2][2],0)),v[3]=.5*Math.sqrt(Math.max(1+p[0][0]+p[1][1]+p[2][2],0)),p[2][1]>p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{\"./normalize\":476,\"gl-mat4/clone\":278,\"gl-mat4/create\":280,\"gl-mat4/determinant\":281,\"gl-mat4/invert\":293,\"gl-mat4/transpose\":306,\"gl-vec3/cross\":365,\"gl-vec3/dot\":370,\"gl-vec3/length\":380,\"gl-vec3/normalize\":387}],476:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],477:[function(t,e,r){var n=t(\"gl-vec3/lerp\"),i=t(\"mat4-recompose\"),a=t(\"mat4-decompose\"),o=t(\"gl-mat4/determinant\"),s=t(\"quat-slerp\"),l=f(),c=f(),u=f();function f(){return{translate:h(),scale:h(1),skew:h(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function h(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,f){if(0===o(e)||0===o(r))return!1;var h=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!h||!p)&&(n(u.translate,l.translate,c.translate,f),n(u.skew,l.skew,c.skew,f),n(u.scale,l.scale,c.scale,f),n(u.perspective,l.perspective,c.perspective,f),s(u.quaternion,l.quaternion,c.quaternion,f),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),!0)}},{\"gl-mat4/determinant\":281,\"gl-vec3/lerp\":381,\"mat4-decompose\":475,\"mat4-recompose\":478,\"quat-slerp\":527}],478:[function(t,e,r){var n={identity:t(\"gl-mat4/identity\"),translate:t(\"gl-mat4/translate\"),multiply:t(\"gl-mat4/multiply\"),create:t(\"gl-mat4/create\"),scale:t(\"gl-mat4/scale\"),fromRotationTranslation:t(\"gl-mat4/fromRotationTranslation\")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{\"gl-mat4/create\":280,\"gl-mat4/fromRotationTranslation\":284,\"gl-mat4/identity\":291,\"gl-mat4/multiply\":295,\"gl-mat4/scale\":303,\"gl-mat4/translate\":305}],479:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],480:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\"),i=t(\"mat4-interpolate\"),a=t(\"gl-mat4/invert\"),o=t(\"gl-mat4/rotateX\"),s=t(\"gl-mat4/rotateY\"),l=t(\"gl-mat4/rotateZ\"),c=t(\"gl-mat4/lookAt\"),u=t(\"gl-mat4/translate\"),f=(t(\"gl-mat4/scale\"),t(\"gl-vec3/normalize\")),h=[0,0,0];function p(t){this._components=t.slice(),this._time=[0],this.prevMatrix=t.slice(),this.nextMatrix=t.slice(),this.computedMatrix=t.slice(),this.computedInverse=t.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}e.exports=function(t){return new p((t=t||{}).matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])};var d=p.prototype;d.recalcMatrix=function(t){var e=this._time,r=n.le(e,t),o=this.computedMatrix;if(!(r<0)){var s=this._components;if(r===e.length-1)for(var l=16*r,c=0;c<16;++c)o[c]=s[l++];else{var u=e[r+1]-e[r],h=(l=16*r,this.prevMatrix),p=!0;for(c=0;c<16;++c)h[c]=s[l++];var d=this.nextMatrix;for(c=0;c<16;++c)d[c]=s[l++],p=p&&h[c]===d[c];if(u<1e-6||p)for(c=0;c<16;++c)o[c]=h[c];else i(o,h,d,(t-e[r])/u)}var g=this.computedUp;g[0]=o[1],g[1]=o[5],g[2]=o[9],f(g,g);var m=this.computedInverse;a(m,o);var v=this.computedEye,y=m[15];v[0]=m[12]/y,v[1]=m[13]/y,v[2]=m[14]/y;var 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-       "/*\n",
-       "object-assign\n",
-       "(c) Sindre Sorhus\n",
-       "@license MIT\n",
-       "*/\n",
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-       "/*\n",
-       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
-       " * @license MIT\n",
-       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
-       " */\n",
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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":70,\"color-normalize\":125,\"flatten-vertex-data\":244,\"object-assign\":499,\"pick-by-alias\":511,\"to-float32\":577,\"update-diff\":599}],535:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),g=t(\"parse-rect\");function m(t,e){if(!(this instanceof m))return new m(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=m.shaders.has(t)?m.shaders.get(t):m.shaders.set(t,m.createShaders(t)).get(t),this.update(e)}e.exports=m,m.dashMult=2,m.maxPatternLength=256,m.precisionThreshold=3e6,m.maxPoints=1e4,m.maxLines=2048,m.shaders=new d,m.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashPattern:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashSize:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\n\\nuniform float dashSize, pixelRatio, thickness, opacity, id;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},m.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},m.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},m.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>m.precisionThreshold||e.scale[1]*e.viewport.height>m.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=m.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},m.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},m.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<m.maxLines&&(d.depth=2*(m.maxLines-1-f%m.maxLines)/m.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=g(t.viewport)),d.viewport||(d.viewport=g([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var v,y;if(t.positions.x&&t.positions.y){var x=t.positions.x,b=t.positions.y;y=d.count=Math.max(x.length,b.length),v=new Float64Array(2*y);for(var _=0;_<y;_++)v[2*_]=x[_],v[2*_+1]=b[_]}else v=l(t.positions,\"float64\"),y=d.count=Math.floor(v.length/2);var w=d.bounds=i(v,2);if(d.fill){for(var T=[],k={},M=0,A=0,S=0,E=d.count;A<E;A++){var C=v[2*A],L=v[2*A+1];isNaN(C)||isNaN(L)||null==C||null==L?(C=v[2*M],L=v[2*M+1],k[A]=M):M=A,T[S++]=C,T[S++]=L}for(var I=c(T,d.hole||[]),P=0,z=I.length;P<z;P++)null!=k[I[P]]&&(I[P]=k[I[P]]);d.triangles=I}var O=new Float64Array(v);u(O,2,w);var D=new Float64Array(2*y+6);d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[0]=O[2*y-4],D[1]=O[2*y-3]):(D[0]=O[2*y-2],D[1]=O[2*y-1]):(D[0]=O[0],D[1]=O[1]),D.set(O,2),d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[2*y+2]=O[2],D[2*y+3]=O[3],d.count-=1):(D[2*y+2]=O[0],D[2*y+3]=O[1],D[2*y+4]=O[2],D[2*y+5]=O[3]):(D[2*y+2]=O[2*y-2],D[2*y+3]=O[2*y-1],D[2*y+4]=O[2*y-2],D[2*y+5]=O[2*y-1]),d.positionBuffer(h(D)),d.positionFractBuffer(p(D))}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var R=d.bounds,F=R[2]-R[0],B=R[3]-R[1],N=d.range[2]-d.range[0],j=d.range[3]-d.range[1];d.scale=[F/N,B/j],d.translate=[-d.range[0]/N+R[0]/N||0,-d.range[1]/j+R[1]/j||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var U,V=0;if(!t.dashes||t.dashes.length<2)V=1,U=new Uint8Array([255,255,255,255,255,255,255,255]);else{V=0;for(var q=0;q<t.dashes.length;++q)V+=t.dashes[q];U=new Uint8Array(V*m.dashMult);for(var H=0,G=255,Y=0;Y<2;Y++)for(var W=0;W<t.dashes.length;++W){for(var X=0,Z=t.dashes[W]*m.dashMult*.5;X<Z;++X)U[H++]=G;G^=255}}d.dashLength=V,d.dashTexture({channels:1,data:U,width:U.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var J=d.count,K=t.color;K||(K=\"transparent\");var Q=new Uint8Array(4*J+4);if(Array.isArray(K)&&\"number\"!=typeof K[0]){for(var $=0;$<J;$++){var tt=n(K[$],\"uint8\");Q.set(tt,4*$)}Q.set(n(K[0],\"uint8\"),4*J)}else for(var et=n(K,\"uint8\"),rt=0;rt<J+1;rt++)Q.set(et,4*rt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:Q})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var v=[],y=0;y<this.passes.length;y++)null!==this.passes[y]&&v.push(this.passes[y]);return this.passes=v,this}},m.prototype.destroy=function(){return this.passes.forEach((function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()})),this.passes.length=0,this}},{\"array-bounds\":70,\"array-normalize\":71,\"color-normalize\":125,earcut:177,\"es6-weak-map\":233,\"flatten-vertex-data\":244,glslify:536,\"object-assign\":499,\"parse-rect\":504,\"pick-by-alias\":511,\"to-float32\":577}],536:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],537:[function(t,e,r){\"use strict\";function n(t,e){return function(t){if(Array.isArray(t))return t}(t)||function(t,e){if(\"undefined\"==typeof Symbol||!(Symbol.iterator in Object(t)))return;var r=[],n=!0,i=!1,a=void 0;try{for(var o,s=t[Symbol.iterator]();!(n=(o=s.next()).done)&&(r.push(o.value),!e||r.length!==e);n=!0);}catch(t){i=!0,a=t}finally{try{n||null==s.return||s.return()}finally{if(i)throw a}}return r}(t,e)||a(t,e)||function(){throw new TypeError(\"Invalid attempt to destructure non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function i(t){return function(t){if(Array.isArray(t))return o(t)}(t)||function(t){if(\"undefined\"!=typeof Symbol&&Symbol.iterator in Object(t))return Array.from(t)}(t)||a(t)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function a(t,e){if(t){if(\"string\"==typeof t)return o(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return\"Object\"===r&&t.constructor&&(r=t.constructor.name),\"Map\"===r||\"Set\"===r?Array.from(t):\"Arguments\"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?o(t,e):void 0}}function o(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var s=t(\"color-normalize\"),l=t(\"array-bounds\"),c=t(\"color-id\"),u=t(\"@plotly/point-cluster\"),f=t(\"object-assign\"),h=t(\"glslify\"),p=t(\"pick-by-alias\"),d=t(\"update-diff\"),g=t(\"flatten-vertex-data\"),m=t(\"is-iexplorer\"),v=t(\"to-float32\"),y=t(\"parse-rect\"),x=b;function b(t,e){var r=this;if(!(this instanceof b))return new b(t,e);\"function\"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=m,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),f(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,pixelRatio:t.context(\"pixelRatio\"),palette:n,paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),opacity:t.prop(\"opacity\"),marker:t.prop(\"markerTexture\")},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop(\"elements\"),count:t.prop(\"count\"),offset:t.prop(\"offset\"),primitive:\"points\"},s=f({},o);s.frag=h([\"precision 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fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform float pixelRatio;\\nuniform bool constPointSize;\\nuniform sampler2D palette;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\n\\nuniform float opacity;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio;\\nuniform bool constPointSize;\\nuniform sampler2D palette;\\nuniform vec2 paletteSize;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),m&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),g=0;g<r.length;g++){var m=r[g];d[m]=p?p[m]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new 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t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=g(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}return i({data:v.float(t),usage:\"dynamic\"}),a({data:v.fract(t),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,M=_.sizeBuffer,A=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)A[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),A[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);M({data:A,usage:\"dynamic\"})}if(x){var E,C=n,L=C.count,I=C.color,P=C.borderColor,z=C.colorBuffer;if(t.tooManyColors){if(I.length||P.length){E=new Uint8Array(8*L);for(var O=0;O<L;O++){var D=I[O];E[8*O]=h[4*D],E[8*O+1]=h[4*D+1],E[8*O+2]=h[4*D+2],E[8*O+3]=h[4*D+3];var R=P[O];E[8*O+4]=h[4*R],E[8*O+5]=h[4*R+1],E[8*O+6]=h[4*R+2],E[8*O+7]=h[4*R+3]}}}else if(I.length||P.length){E=new Uint8Array(4*L+2);for(var F=0;F<L;F++)null!=I[F]&&(E[4*F]=I[F]%c,E[4*F+1]=Math.floor(I[F]/c)),null!=P[F]&&(E[4*F+2]=P[F]%c,E[4*F+3]=Math.floor(P[F]/c))}z({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":57,\"array-bounds\":70,\"color-id\":123,\"color-normalize\":125,\"flatten-vertex-data\":244,glslify:538,\"is-iexplorer\":466,\"object-assign\":499,\"parse-rect\":504,\"pick-by-alias\":511,\"to-float32\":577,\"update-diff\":599}],538:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],539:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var 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u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var g=s.columns,m=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/g,w=y/g;s.passes=[];for(var T=0;T<g;T++)for(var k=0;k<g;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var M=f(s.id,T,k),A=this.passes[M]||(this.passes[M]={});if(o.data&&(o.transpose?A.positions={x:{buffer:s.buffer,offset:k,count:m,stride:g},y:{buffer:s.buffer,offset:T,count:m,stride:g}}:A.positions={x:{buffer:s.buffer,offset:k*m,count:m},y:{buffer:s.buffer,offset:T*m,count:m}},A.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),C=E[0],L=E[1],I=E[2],P=E[3];A.viewport=[x+C*v+S[0],b+L*y+S[1],x+I*v-S[2],b+P*y-S[3]]}else A.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(A.color=s.color),o.size&&(A.size=s.size),o.marker&&(A.marker=s.marker),o.borderSize&&(A.borderSize=s.borderSize),o.borderColor&&(A.borderColor=s.borderColor),o.opacity&&(A.opacity=s.opacity),o.range&&(A.range=n?h(s.range,T,k):s.range||A.bounds),s.passes.push(M)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return this.traces.forEach((function(t){t.buffer&&t.buffer.destroy&&t.buffer.destroy()})),this.traces=null,this.passes=null,this.scatter.destroy(),this}},{\"array-bounds\":70,\"array-range\":72,\"flatten-vertex-data\":244,\"parse-rect\":504,\"pick-by-alias\":511,raf:528,\"regl-scatter2d\":537}],540:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():t.createREGL=n()}(this,(function(){function t(t,e){this.id=V++,this.type=t,this.data=e}function e(t){return\"[\"+function t(e){if(0===e.length)return[];var r=e.charAt(0),n=e.charAt(e.length-1);if(1<e.length&&r===n&&('\"'===r||\"'\"===r))return['\"'+e.substr(1,e.length-2).replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];if(r=/\\[(false|true|null|\\d+|'[^']*'|\"[^\"]*\")\\]/.exec(e))return 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r,n=parseInt(e);if(isNaN(n))\"\\u95f0\"===e[0]&&(r=!0,e=e.substring(1)),\"\\u6708\"===e[e.length-1]&&(e=e.substring(0,e.length-1)),n=1+[\"\\u4e00\",\"\\u4e8c\",\"\\u4e09\",\"\\u56db\",\"\\u4e94\",\"\\u516d\",\"\\u4e03\",\"\\u516b\",\"\\u4e5d\",\"\\u5341\",\"\\u5341\\u4e00\",\"\\u5341\\u4e8c\"].indexOf(e);else{var i=e[e.length-1];r=\"i\"===i||\"I\"===i}return this.toMonthIndex(t,n,r)},dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},_validateYear:function(t,e){if(t.year&&(t=t.year()),\"number\"!=typeof t||t<1888||t>2111)throw e.replace(/\\{0\\}/,this.local.name);return t},toMonthIndex:function(t,e,r){var i=this.intercalaryMonth(t);if(r&&e!==i||e<1||e>12)throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return i?!r&&e<=i?e-1:e:e-1},toChineseMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);if(e<0||e>(r?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r?e<r?e+1:e:e+1},intercalaryMonth:function(t){return t=this._validateYear(t),f[t-f[0]]>>13},isIntercalaryMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);return!!r&&r===e},leapYear:function(t){return 0!==this.intercalaryMonth(t)},weekOfYear:function(t,e,r){var i,o=this._validateYear(t,n.local.invalidyear),s=h[o-h[0]],l=s>>9&4095,c=s>>5&15,u=31&s;(i=a.newDate(l,c,u)).add(4-(i.dayOfWeek()||7),\"d\");var f=this.toJD(t,e,r)-i.toJD();return 1+Math.floor(f/7)},monthsInYear:function(t){return this.leapYear(t)?13:12},daysInMonth:function(t,e){t.year&&(e=t.month(),t=t.year()),t=this._validateYear(t);var r=f[t-f[0]];if(e>(r>>13?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r&1<<12-e?30:29},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,s,r,n.local.invalidDate);t=this._validateYear(i.year()),e=i.month(),r=i.day();var o=this.isIntercalaryMonth(t,e),s=this.toChineseMonth(t,e),l=function(t,e,r,n,i){var a,o,s;if(\"object\"==typeof t)o=t,a=e||{};else{var l;if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Lunar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Lunar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=30))throw new Error(\"Lunar day outside range 1 - 30\");\"object\"==typeof n?(l=!1,a=n):(l=!!n,a=i||{}),o={year:t,month:e,day:r,isIntercalary:l}}s=o.day-1;var c,u=f[o.year-f[0]],p=u>>13;c=p&&(o.month>p||o.isIntercalary)?o.month:o.month-1;for(var d=0;d<c;d++){s+=u&1<<12-d?30:29}var g=h[o.year-h[0]],m=new Date(g>>9&4095,(g>>5&15)-1,(31&g)+s);return a.year=m.getFullYear(),a.month=1+m.getMonth(),a.day=m.getDate(),a}(t,s,r,o);return a.toJD(l.year,l.month,l.day)},fromJD:function(t){var e=a.fromJD(t),r=function(t,e,r,n){var i,a;if(\"object\"==typeof t)i=t,a=e||{};else{if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Solar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Solar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=31))throw new Error(\"Solar day outside range 1 - 31\");i={year:t,month:e,day:r},a=n||{}}var o=h[i.year-h[0]],s=i.year<<9|i.month<<5|i.day;a.year=s>=o?i.year:i.year-1,o=h[a.year-h[0]];var l,c=new Date(o>>9&4095,(o>>5&15)-1,31&o),u=new Date(i.year,i.month-1,i.day);l=Math.round((u-c)/864e5);var p,d=f[a.year-f[0]];for(p=0;p<13;p++){var g=d&1<<12-p?30:29;if(l<g)break;l-=g}var m=d>>13;!m||p<m?(a.isIntercalary=!1,a.month=1+p):p===m?(a.isIntercalary=!0,a.month=p):(a.isIntercalary=!1,a.month=p);return 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Coptic\",jdEpoch:1825029.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Coptic\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Thout\",\"Paopi\",\"Hathor\",\"Koiak\",\"Tobi\",\"Meshir\",\"Paremhat\",\"Paremoude\",\"Pashons\",\"Paoni\",\"Epip\",\"Mesori\",\"Pi Kogi Enavot\"],monthNamesShort:[\"Tho\",\"Pao\",\"Hath\",\"Koi\",\"Tob\",\"Mesh\",\"Pat\",\"Pad\",\"Pash\",\"Pao\",\"Epi\",\"Meso\",\"PiK\"],dayNames:[\"Tkyriaka\",\"Pesnau\",\"Pshoment\",\"Peftoou\",\"Ptiou\",\"Psoou\",\"Psabbaton\"],dayNamesShort:[\"Tky\",\"Pes\",\"Psh\",\"Pef\",\"Pti\",\"Pso\",\"Psa\"],dayNamesMin:[\"Tk\",\"Pes\",\"Psh\",\"Pef\",\"Pt\",\"Pso\",\"Psa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.coptic=a},{\"../main\":621,\"object-assign\":499}],609:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Discworld\",jdEpoch:1721425.5,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":621,\"object-assign\":499}],610:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{\"../main\":621,\"object-assign\":499}],611:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{\"../main\":621,\"object-assign\":499}],612:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Islamic\",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012bs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":621,\"object-assign\":499}],613:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":621,\"object-assign\":499}],614:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":621,\"object-assign\":499}],615:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{\"../main\":621,\"object-assign\":499}],616:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return 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c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)\"undefined\"==typeof 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":621,\"object-assign\":499}],619:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":621,\"object-assign\":499}],620:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":778,\"../../plot_api/plot_template\":817,\"../../registry\":911}],629:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),g=r.hoverlabel||{};if(d){var m=a(\"hoverlabel.bgcolor\",g.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",g.bordercolor||i.contrast(m));n.coerceFont(a,\"hoverlabel.font\",{family:g.font.family,size:g.font.size,color:g.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":778,\"../color\":643}],630:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":805,\"fast-isnumeric\":241}],631:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var g=f[d],m=i.coerceRef(t,e,p,g,\"\",\"paper\");if(\"paper\"!==m)i.getFromId(p,m)._annIndices.push(e._index);if(i.coercePosition(e,p,a,m,g,.5),u){var v=\"a\"+g,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==m&&(y=e[v]=\"pixel\");var x=\"pixel\"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(g+\"anchor\"),a(g+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),u&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),c){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:\"annotations\",handleItemDefaults:l})}},{\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"./attributes\":626,\"./common_defaults\":629}],632:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../registry\"),a=t(\"../../plots/plots\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../plots/cartesian/axes\"),c=t(\"../color\"),u=t(\"../drawing\"),f=t(\"../fx\"),h=t(\"../../lib/svg_text_utils\"),p=t(\"../../lib/setcursor\"),d=t(\"../dragelement\"),g=t(\"../../plot_api/plot_template\").arrayEditor,m=t(\"./draw_arrow_head\");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var M=g(t.layout,_,e),A=M.modifyBase,S=M.modifyItem,E=M.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var C=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var L={x:{},y:{}},I=+e.textangle||0,P=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),z=P.append(\"g\").classed(\"annotation-text-g\",!0),O=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||O,R=z.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+C).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",C).append(\"rect\"),V.exit().remove();var q=e.font,H=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(H);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&A(v._name+\".autorange\",!0),x&&x.autorange&&A(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+C).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,q).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),g=u.bBox((f?n:G).node()),b=g.width,_=g.height,M=e.width||b,D=e.height||_,B=Math.round(M+2*N),q=Math.round(D+2*N);function H(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(I+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=q*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=L[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var gt=e[\"a\"+et];if(tt=ot*H(.5,e.xanchor)-st*H(.5,e.yanchor),nt===rt){var mt=l.getRefType(nt);\"domain\"===mt?(\"y\"===et&&(gt=1-gt),ft.tail=it._offset+it._length*gt):\"paper\"===mt?\"y\"===et?(gt=1-gt,ft.tail=T.t+T.h*gt):ft.tail=T.l+T.w*gt:ft.tail=it._offset+it.r2p(gt),K=tt}else ft.tail=J+gt,K=tt+gt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*H($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(M-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?C:null,t);else{var wt=N+_t-g.top,Tt=N+bt-g.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?C:null,t)}V.select(\"rect\").call(u.setRect,N,N,M,D),j.call(u.setRect,F/2,F/2,B-F,q-F),R.call(u.setTranslate,Math.round(L.x.text-B/2),Math.round(L.y.text-q/2)),z.attr({transform:\"rotate(\"+I+\",\"+L.x.text+\",\"+L.y.text+\")\"});var kt,Mt=function(r,n){P.selectAll(\".annotation-arrow-g\").remove();var l=L.x.head,f=L.y.head,h=L.x.tail+r,p=L.y.tail+n,g=L.x.text+r,b=L.y.text+n,_=o.rotationXYMatrix(I,g,b),w=o.apply2DTransform(_),M=o.apply2DTransform2(_),C=+j.attr(\"width\"),O=+j.attr(\"height\"),D=g-.5*C,F=D+C,B=b-.5*O,N=B+O,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(M);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,q=e.arrowcolor,H=e.arrowside,G=P.append(\"g\").style({opacity:c.opacity(q)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(q));if(m(Y,H,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var 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n=t(\"../annotations/attributes\"),i=t(\"../../plot_api/edit_types\").overrideAll,a=t(\"../../plot_api/plot_template\").templatedArray;e.exports=i(a(\"annotation\",{visible:n.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),\"calc\",\"from-root\")},{\"../../plot_api/edit_types\":810,\"../../plot_api/plot_template\":817,\"../annotations/attributes\":626}],636:[function(t,e,r){\"use 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n.coerce(t,e,s,r,i)}function c(t){var n=t+\"axis\",a={_fullLayout:{}};return a._fullLayout[n]=r[n],i.coercePosition(e,a,l,t,t,.5)}l(\"visible\")&&(o(t,e,a.fullLayout,l),c(\"x\"),c(\"y\"),c(\"z\"),n.noneOrAll(t,e,[\"x\",\"y\",\"z\"]),e.xref=\"x\",e.yref=\"y\",e.zref=\"z\",l(\"xanchor\"),l(\"yanchor\"),l(\"xshift\"),l(\"yshift\"),e.showarrow&&(e.axref=\"pixel\",e.ayref=\"pixel\",l(\"ax\",-10),l(\"ay\",-30),n.noneOrAll(t,e,[\"ax\",\"ay\"])))}e.exports=function(t,e,r){a(t,e,{name:\"annotations\",handleItemDefaults:l,fullLayout:r.fullLayout})}},{\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"../annotations/common_defaults\":629,\"./attributes\":635}],638:[function(t,e,r){\"use strict\";var n=t(\"../annotations/draw\").drawRaw,i=t(\"../../plots/gl3d/project\"),a=[\"x\",\"y\",\"z\"];e.exports=function(t){for(var e=t.fullSceneLayout,r=t.dataScale,o=e.annotations,s=0;s<o.length;s++){for(var l=o[s],c=!1,u=0;u<3;u++){var 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If `target` is a string (eg *x*) we use the\",\"corresponding trace attribute (eg `xcalendar`) if it exists,\",\"even if `targetcalendar` is provided.\"].join(\" \"))}}},layoutAttributes:l,handleDefaults:c,handleTraceDefaults:function(t,e,r,n){for(var i=0;i<r.length;i++)c(t,e,r[i]+\"calendar\",n.calendar)},CANONICAL_SUNDAY:{chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},CANONICAL_TICK:{chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},DFLTRANGE:{chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},getCal:h,worldCalFmt:function(t,e,r){for(var 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T={y:e.y,b:u.height*g[w],t:u.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*g[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*g[b]),i.autoMargin(t,m(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var g=c.lineCount(l),m=(a.currentValueMaxLines+1-g)*e.currentvalue.font.size*p;return 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r=t[e[n]],s=(r._extremes||{})[o];if(!0===r.visible&&s){for(i=0;i<s.min.length;i++)a=s.min[i],d(u,a.val,a.pad,{extrapad:a.extrapad});for(i=0;i<s.max.length;i++)a=s.max[i],g(f,a.val,a.pad,{extrapad:a.extrapad})}}}if(h(s,e._traceIndices),h(c.annotations||[],e._annIndices||[]),h(c.shapes||[],e._shapeIndices||[]),e._matchGroup&&!r)for(var m in e._matchGroup)if(m!==e._id){var v=l(t,m),y=p(t,v,!0),x=e._length/v._length;for(i=0;i<y.min.length;i++)a=y.min[i],d(u,a.val,a.pad*x,{extrapad:a.extrapad});for(i=0;i<y.max.length;i++)a=y.max[i],g(f,a.val,a.pad*x,{extrapad:a.extrapad})}return{min:u,max:f}}function d(t,e,r,n){m(t,e,r,n,y)}function g(t,e,r,n){m(t,e,r,n,x)}function m(t,e,r,n,i){for(var a=n.tozero,o=n.extrapad,s=!0,l=0;l<t.length&&s;l++){var c=t[l];if(i(c.val,e)&&c.pad>=r&&(c.extrapad||!o)){s=!1;break}i(e,c.val)&&c.pad<=r&&(o||!c.extrapad)&&(t.splice(l,1),l--)}if(s){var u=a&&0===e;t.push({val:e,pad:u?0:r,extrapad:!u&&o})}}function v(t){return n(t)&&Math.abs(t)<a}function y(t,e){return 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n,a=t.nticks;a||(\"category\"===t.type||\"multicategory\"===t.type?(n=t.tickfont?1.2*(t.tickfont.size||12):15,a=t._length/n):(n=\"y\"===t._id.charAt(0)?40:80,a=s.constrain(t._length/n,4,9)+1),\"radialaxis\"===t._name&&(a*=2)),\"array\"===t.tickmode&&(a*=100),t._roughDTick=Math.abs(r[1]-r[0])/a,B.autoTicks(t,t._roughDTick),t._minDtick>0&&t.dtick<2*t._minDtick&&(t.dtick=t._minDtick,t.tick0=t.l2r(t._forceTick0))}\"period\"===t.ticklabelmode&&function(t){var e;function r(){return!(i(t.dtick)||\"M\"!==t.dtick.charAt(0))}var n=r(),a=B.getTickFormat(t);if(a){var 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c=\"category\"===t.type?t.d2l_noadd:t.d2l;\"log\"===t.type&&\"L\"!==String(t.dtick).charAt(0)&&(t.dtick=\"L\"+Math.pow(10,Math.floor(Math.min(t.range[0],t.range[1]))-1));for(var u=0;u<e.length;u++){var f=c(e[u]);f>a&&f<o&&(void 0===r[u]?n[l]=B.tickText(t,f):n[l]=it(t,f,String(r[u])),l++)}l<e.length&&n.splice(l,e.length-l);t.rangebreaks&&(n=n.filter((function(e){return t.maskBreaks(e.x)!==P})));return n}(t);var n=H(r),a=n[0],o=n[1],l=r[1]<r[0],c=Math.min(r[0],r[1]),u=Math.max(r[0],r[1]),f=\"log\"===t.type&&!(i(t.dtick)||\"L\"===t.dtick.charAt(0)),h=\"period\"===t.ticklabelmode;if(t._tmin=B.tickFirst(t,e),t._tmin<a!==l)return[];\"category\"!==t.type&&\"multicategory\"!==t.type||(o=l?Math.max(-.5,o):Math.min(t._categories.length-.5,o));var p=t._tmin;t.rangebreaks&&t._tick0Init!==t.tick0&&(p=wt(p,t),l||(p=B.tickIncrement(p,t.dtick,!l,t.calendar))),h&&(p=B.tickIncrement(p,t.dtick,!l,t.calendar));for(var d,g=Math.max(1e3,t._length||0),C=[],L=null;l?p>=o:p<=o;p=B.tickIncrement(p,t.dtick,l,t.calendar)){if(t.rangebreaks&&!l){if(p<a)continue;if(t.maskBreaks(p)===P&&wt(p,t)>=u)break}if(C.length>g||p===L)break;L=p;var I=!1;f&&p!==(0|p)&&(I=!0),C.push({minor:I,value:p})}if(h&&function(t,e,r){for(var n=0;n<t.length;n++){var i=t[n].value,a=n,o=n+1;n<t.length-1?(a=n,o=n+1):n>0?(a=n-1,o=n):(a=n,o=n);var s,l=t[a].value,c=t[o].value,u=Math.abs(c-l),f=r||u,h=0;f>=y?h=u>=y&&u<=m?u:v:r===b&&f>=_?h=u>=_&&u<=x?u:b:f>=k?h=u>=k&&u<=w?u:T:r===M&&f>=M?h=M:f>=A?h=A:r===S&&f>=S?h=S:r===E&&f>=E&&(h=E),h>=u&&(h=u,s=!0);var p=i+h;if(e.rangebreaks&&h>0){for(var d=0,g=0;g<84;g++){var C=(g+.5)/84;e.maskBreaks(i*(1-C)+C*p)!==P&&d++}(h*=d/84)||(t[n].drop=!0),s&&u>M&&(h=u)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var z=\"y\"===t._id.charAt(0),O=1;\"auto\"===t.tickmode&&(O=t.tickfont?t.tickfont.size:12);var 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w=e._anchorAxis;w&&w.autorange&&-1!==(e.ticklabelposition||\"\").indexOf(\"inside\")&&!V(a,e._id)&&(a._insideTickLabelsAutorange||(a._insideTickLabelsAutorange={}),a._insideTickLabelsAutorange[w._name+\".autorange\"]=w.autorange,_.push((function(){v.each((function(t,r){var n=dt(this);e._vals[r].bb=h.bBox(n.node())}))})));var T=s.syncOrAsync(_);return T&&T.then&&t._promises.push(T),T},B.getPxPosition=function(t,e){var r,n=t._fullLayout._size,i=e._id.charAt(0),a=e.side;return\"free\"!==e.anchor?r=e._anchorAxis:\"x\"===i?r={_offset:n.t+(1-(e.position||0))*n.h,_length:0}:\"y\"===i&&(r={_offset:n.l+(e.position||0)*n.w,_length:0}),\"top\"===a||\"left\"===a?r._offset:\"bottom\"===a||\"right\"===a?r._offset+r._length:void 0},B.shouldShowZeroLine=function(t,e,r){var n=s.simpleMap(e.range,e.r2l);return n[0]*n[1]<=0&&e.zeroline&&(\"linear\"===e.type||\"-\"===e.type)&&!(e.rangebreaks&&e.maskBreaks(0)===P)&&(pt(e,0)||!function(t,e,r,n){var i=r._mainAxis;if(!i)return;var 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r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":834,\"has-passive-events\":441,\"mouse-change\":483,\"mouse-event-offset\":484,\"mouse-wheel\":486}],867:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":802,\"../cartesian/axes\":828}],868:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":684,\"../../constants/xmlns_namespaces\":754,\"../../plot_api/edit_types\":810,\"../cartesian\":841,\"../cartesian/attributes\":826,\"../cartesian/constants\":834,\"../get_data\":865,\"../layout_attributes\":882,\"./scene2d\":869}],869:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"gl-plot2d\"),c=t(\"gl-spikes2d\"),u=t(\"gl-select-box\"),f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),g=t(\"../cartesian/constraints\"),m=g.enforce,v=g.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var M=k.prototype;M.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},M.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},M.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},M.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},M.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},M.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},M.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},M.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},M.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},M.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();m(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},M.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},M.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},M.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},M.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},M.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},M.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var m=this.fullData[g.trace.index]||{},v=g.pointIndex,y=s.castHoverinfo(m,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(m,v,\"bgcolor\")||g.color,borderColor:s.castHoverOption(m,v,\"bordercolor\"),fontFamily:s.castHoverOption(m,v,\"font.family\"),fontSize:s.castHoverOption(m,v,\"font.size\"),fontColor:s.castHoverOption(m,v,\"font.color\"),nameLength:s.castHoverOption(m,v,\"namelength\"),textAlign:s.castHoverOption(m,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},M.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},M.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/dragelement/helpers\":661,\"../../components/fx\":683,\"../../lib/show_no_webgl_msg\":800,\"../../plots/cartesian/axes\":828,\"../../registry\":911,\"../cartesian/autorange\":827,\"../cartesian/constants\":834,\"../cartesian/constraints\":835,\"./camera\":866,\"./convert\":867,\"gl-plot2d\":317,\"gl-select-box\":333,\"gl-spikes2d\":342,\"webgl-context\":606}],870:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":684,\"../../constants/xmlns_namespaces\":754,\"../../lib\":778,\"../../plot_api/edit_types\":810,\"../get_data\":865,\"./layout/attributes\":871,\"./layout/defaults\":875,\"./layout/layout_attributes\":876,\"./scene\":880}],871:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],872:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":643,\"../../../lib/extend\":768,\"../../../plot_api/edit_types\":810,\"../../cartesian/layout_attributes\":842}],873:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":778,\"../../../plot_api/plot_template\":817,\"../../cartesian/axis_defaults\":830,\"../../cartesian/type_defaults\":853,\"./axis_attributes\":872,tinycolor2:576}],874:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":778,\"../../../lib/str2rgbarray\":802}],875:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var m=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(m&&v&&y?y/Math.sqrt(m*m+v*v+y*y)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":643,\"../../../lib\":778,\"../../../registry\":911,\"../../get_data\":865,\"../../subplot_defaults\":905,\"./axis_defaults\":873,\"./layout_attributes\":876}],876:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":778,\"../../../lib/extend\":768,\"../../domain\":855,\"./axis_attributes\":872}],877:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":802}],878:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":778,\"../../cartesian/axes\":828}],879:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],880:[function(t,e,r){\"use strict\";var n,i,a=t(\"gl-plot3d\"),o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),g=t(\"../../lib/str2rgbarray\"),m=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return m(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c,u=Object.keys(e.traces),h=null,g=e.glplot.selection,m=0;m<u.length;++m)\"skip\"!==(t=e.traces[u[m]]).data.hoverinfo&&t.handlePick(g)&&(h=t),t.setContourLevels&&t.setContourLevels();function y(t,r){var n=e.fullSceneLayout[t];return p.tickText(n,n.d2l(r),\"hover\").text}if(null!==h){var x=v(e.glplot.cameraParams,g.dataCoordinate);t=h.data;var _,w=r._fullData[t.index],T=g.index,k={xLabel:y(\"xaxis\",g.traceCoordinate[0]),yLabel:y(\"yaxis\",g.traceCoordinate[1]),zLabel:y(\"zaxis\",g.traceCoordinate[2])},M=d.castHoverinfo(w,e.fullLayout,T),A=(M||\"\").split(\"+\"),S=M&&\"all\"===M;w.hovertemplate||S||(-1===A.indexOf(\"x\")&&(k.xLabel=void 0),-1===A.indexOf(\"y\")&&(k.yLabel=void 0),-1===A.indexOf(\"z\")&&(k.zLabel=void 0),-1===A.indexOf(\"text\")&&(g.textLabel=void 0),-1===A.indexOf(\"name\")&&(h.name=void 0));var E=[];\"cone\"===t.type||\"streamtube\"===t.type?(k.uLabel=y(\"xaxis\",g.traceCoordinate[3]),(S||-1!==A.indexOf(\"u\"))&&E.push(\"u: \"+k.uLabel),k.vLabel=y(\"yaxis\",g.traceCoordinate[4]),(S||-1!==A.indexOf(\"v\"))&&E.push(\"v: \"+k.vLabel),k.wLabel=y(\"zaxis\",g.traceCoordinate[5]),(S||-1!==A.indexOf(\"w\"))&&E.push(\"w: \"+k.wLabel),k.normLabel=g.traceCoordinate[6].toPrecision(3),(S||-1!==A.indexOf(\"norm\"))&&E.push(\"norm: \"+k.normLabel),\"streamtube\"===t.type&&(k.divergenceLabel=g.traceCoordinate[7].toPrecision(3),(S||-1!==A.indexOf(\"divergence\"))&&E.push(\"divergence: \"+k.divergenceLabel)),g.textLabel&&E.push(g.textLabel),_=E.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.tickText(e._mockAxis,e._mockAxis.d2l(g.traceCoordinate[3]),\"hover\").text,E.push(\"value: \"+k.valueLabel),g.textLabel&&E.push(g.textLabel),_=E.join(\"<br>\")):_=g.textLabel;var C={x:g.traceCoordinate[0],y:g.traceCoordinate[1],z:g.traceCoordinate[2],data:w._input,fullData:w,curveNumber:w.index,pointNumber:T};d.appendArrayPointValue(C,w,T),t._module.eventData&&(C=w._module.eventData(C,g,w,{},T));var L={points:[C]};e.fullSceneLayout.hovermode&&d.loneHover({trace:w,x:(.5+.5*x[0]/x[3])*s,y:(.5-.5*x[1]/x[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:_,name:h.name,color:d.castHoverOption(w,T,\"bgcolor\")||h.color,borderColor:d.castHoverOption(w,T,\"bordercolor\"),fontFamily:d.castHoverOption(w,T,\"font.family\"),fontSize:d.castHoverOption(w,T,\"font.size\"),fontColor:d.castHoverOption(w,T,\"font.color\"),nameLength:d.castHoverOption(w,T,\"namelength\"),textAlign:d.castHoverOption(w,T,\"align\"),hovertemplate:f.castOption(w,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},C,k),eventData:[C]},{container:n,gd:r}),g.buttons&&g.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),c=L}else d.loneUnhover(n),r.emit(\"plotly_unhover\",c);e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function M(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(g(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&M(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,m=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){m[0][a]=1/0,m[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,A=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?m[0][a]=Math.min(m[0][a],s._lowerLogErrorBound):m[0][a]=Math.min(m[0][a],T[0][a]/h[a]-A),m[1][a]=Math.max(m[1][a],T[1][a]/h[a]+A)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);m[0][a]=Math.min(m[0][a],E),m[1][a]=Math.max(m[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var C=m[1][a]-m[0][a];m[0][a]-=C/32,m[1][a]+=C/32}if(\"reversed\"===s.autorange){var L=m[0][a];m[0][a]=m[1][a],m[1][a]=L}}else{var I=s.range;m[0][a]=s.r2l(I[0]),m[1][a]=s.r2l(I[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.setBounds(a,{min:m[0][a]*h[a],max:m[1][a]*h[a]})}var P=c.aspectmode;if(\"cube\"===P)d=[1,1,1];else if(\"manual\"===P){var z=c.aspectratio;d=[z.x,z.y,z.z]}else{if(\"auto\"!==P&&\"data\"!==P)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var O=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];O[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===P||Math.max.apply(null,O)/Math.min.apply(null,O)<=4?O:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var 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r=t.visible;return\"undefined\"==typeof r||!0===r})),d=!1,g=p.map((function(t,e){return d=d||\"undefined\"!=typeof t.groupId,t}));if(d){var m=n.nest().key((function(t,e){return\"undefined\"!=typeof t.groupId?t.groupId:\"unstacked\"})).entries(g),v=[],y=m.map((function(t,e){if(\"unstacked\"===t.key)return t.values;var r=t.values[0].r.map((function(t,e){return 0}));return t.values.forEach((function(t,e,n){t.yStack=[r],v.push(r),r=o.util.sumArrays(t.r,r)})),t.values}));p=n.merge(y)}p.forEach((function(t,e){t.t=Array.isArray(t.t[0])?t.t:[t.t],t.r=Array.isArray(t.r[0])?t.r:[t.r]}));var x=Math.min(h.width-h.margin.left-h.margin.right,h.height-h.margin.top-h.margin.bottom)/2;x=Math.max(10,x);var b,_=[h.margin.left+x,h.margin.top+x];d?b=[0,n.max(o.util.sumArrays(o.util.arrayLast(p).r[0],o.util.arrayLast(v)))]:b=n.extent(o.util.flattenArray(p.map((function(t,e){return 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P=n.range.apply(this,C);if(P=P.map((function(t,e){return parseFloat(t.toPrecision(12))})),s=n.scale.linear().domain(C.slice(0,2)).range(\"clockwise\"===h.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=s.domain(),u.layout.angularAxis.endPadding=A?E:0,\"undefined\"==typeof(t=n.select(this).select(\"svg.chart-root\"))||t.empty()){var z=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g class='legend-group'></g>' + '<g class='tooltips-group'></g>' + '<g class='title-group'><text></text></g>' + '</g>' + 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o=n.hsl(a.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",f=i||\"\";e.style({fill:u,\"font-size\":a.fontSize+\"px\"}).text(f);var h=a.padding,p=e.node().getBBox(),d={fill:a.color,stroke:s,\"stroke-width\":\"2px\"},g=p.width+2*h+l,m=p.height+2*h;return r.attr({d:\"M\"+[[l,-m/2],[l,-m/4],[a.hasTick?0:l,0],[l,m/4],[l,m/2],[g,m/2],[g,-m/2]].join(\"L\")+\"Z\"}).style(d),t.attr({transform:\"translate(\"+[l,-m/2+2*h]+\")\"}),t.style({display:\"block\"}),c},c.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),c},c.hide=function(){if(t)return t.style({display:\"none\"}),c},c.show=function(){if(t)return t.style({display:\"block\"}),c},c.config=function(t){return i(a,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map((function(t,r){var 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s=i({},t.layout);if([[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach((function(t,r){o.util.translator.apply(null,t.concat(e))})),e?(\"undefined\"!=typeof 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n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strRotate,l=o.strTranslate,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../plots\"),h=t(\"../../plots/cartesian/axes\"),p=t(\"../cartesian/set_convert\"),d=t(\"./set_convert\"),g=t(\"../cartesian/autorange\").doAutoRange,m=t(\"../cartesian/dragbox\"),v=t(\"../../components/dragelement\"),y=t(\"../../components/fx\"),x=t(\"../../components/titles\"),b=t(\"../cartesian/select\").prepSelect,_=t(\"../cartesian/select\").selectOnClick,w=t(\"../cartesian/select\").clearSelect,T=t(\"../../lib/setcursor\"),k=t(\"../../lib/clear_gl_canvases\"),M=t(\"../../plot_api/subroutines\").redrawReglTraces,A=t(\"../../constants/alignment\").MID_SHIFT,S=t(\"./constants\"),E=t(\"./helpers\"),C=o._,L=o.mod,I=o.deg2rad,P=o.rad2deg;function 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e,r,n,i,a=t[0],o=t[1]-a,s=L(a,360),l=s+o,c=Math.cos(I(s)),u=Math.sin(I(s)),f=Math.cos(I(l)),h=Math.sin(I(l));i=s<=90&&l>=90||s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(p),b=x[2]-x[0],_=x[3]-x[1],w=h/f,T=Math.abs(_/b);w>T?(d=f,y=(h-(g=f*T))/n.h/2,m=[o[0],o[1]],v=[s[0]+y,s[1]-y]):(g=h,y=(f-(d=h/T))/n.w/2,m=[o[0]+y,o[1]-y],v=[s[0],s[1]]),this.xLength2=d,this.yLength2=g,this.xDomain2=m,this.yDomain2=v;var 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e=m(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[f+u*r,p-u*n]+\"L\"+[f+a*r,p-a*n]},transFn:o.noop,crisp:!1}),h.drawLabels(n,g,{vals:w,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:function(t){return v(m(t))},labelFns:x})}F(i[\"angular-line\"].select(\"path\"),d.showline,{d:r.pathSubplot(),transform:l(f,p)}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},O.updateFx=function(t,e){this.gd._context.staticPlot||(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1),this.updateMainDrag(t))},O.updateMainDrag=function(t){var e,r,s=this,c=s.gd,u=s.layers,f=t._zoomlayer,h=S.MINZOOM,p=S.OFFEDGE,d=s.radius,g=s.innerRadius,x=s.cx,T=s.cy,k=s.cxx,M=s.cyy,A=s.sectorInRad,C=s.vangles,L=s.radialAxis,I=E.clampTiny,P=E.findXYatLength,z=E.findEnclosingVertexAngles,O=S.cornerHalfWidth,D=S.cornerLen/2,R=m.makeDragger(u,\"path\",\"maindrag\",\"crosshair\");n.select(R).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(x,T));var F,B,N,j,U,V,q,H,G,Y={element:R,gd:c,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function W(t,e){return Math.sqrt(t*t+e*e)}function X(t,e){return W(t-k,e-M)}function Z(t,e){return Math.atan2(M-e,t-k)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function K(t,e){if(0===t)return s.pathSector(2*O);var r=D/t,n=e-r,i=e+r,a=Math.max(0,Math.min(t,d)),o=a-O,l=a+O;return\"M\"+J(o,n)+\"A\"+[o,o]+\" 0,0,0 \"+J(o,i)+\"L\"+J(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+J(l,n)+\"Z\"}function Q(t,e,r){if(0===t)return s.pathSector(2*O);var n,i,a=J(t,e),o=J(t,r),l=I((a[0]+o[0])/2),c=I((a[1]+o[1])/2);if(l&&c){var u=c/l,f=-1/u,h=P(O,u,l,c);n=P(D,f,h[0][0],h[0][1]),i=P(D,f,h[1][0],h[1][1])}else{var p,d;c?(p=D,d=O):(p=O,d=D),n=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+n.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function $(t,e){return 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e=L._rl,r=(e[1]-e[0])/(1-g/d)/d,n=[e[0]+(N-g)*r,e[0]+(j-g)*r];t[s.id+\".radialaxis.range\"]=n}function ot(t,e){var r=c._fullLayout.clickmode;if(m.removeZoombox(c),2===t){var n={};for(var i in s.viewInitial)n[s.id+\".\"+i]=s.viewInitial[i];c.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",c,n)}r.indexOf(\"select\")>-1&&1===t&&_(e,c,[s.xaxis],[s.yaxis],s.id,Y),r.indexOf(\"event\")>-1&&y.click(c,e,s.id)}Y.prepFn=function(t,n,a){var l=c._fullLayout.dragmode,u=R.getBoundingClientRect();c._fullLayout._calcInverseTransform(c);var h=c._fullLayout._invTransform;e=c._fullLayout._invScaleX,r=c._fullLayout._invScaleY;var p=o.apply3DTransform(h)(n-u.left,a-u.top);if(F=p[0],B=p[1],C){var g=E.findPolygonOffset(d,A[0],A[1],C);F+=k+g[0],B+=M+g[1]}switch(l){case\"zoom\":Y.moveFn=C?nt:et,Y.clickFn=ot,Y.doneFn=it,function(){N=null,j=null,U=s.pathSubplot(),V=!1;var t=c._fullLayout[s.id];q=i(t.bgcolor).getLuminance(),(H=m.makeZoombox(f,q,x,T,U)).attr(\"fill-rule\",\"evenodd\"),G=m.makeCorners(f,x,T),w(c)}();break;case\"select\":case\"lasso\":b(t,n,a,Y,l)}},R.onmousemove=function(t){y.hover(c,t,s.id),c._fullLayout._lasthover=R,c._fullLayout._hoversubplot=s.id},R.onmouseout=function(t){c._dragging||v.unhover(c,t)},v.init(Y)},O.updateRadialDrag=function(t,e,r){var i=this,c=i.gd,u=i.layers,f=i.radius,h=i.innerRadius,p=i.cx,d=i.cy,g=i.radialAxis,y=S.radialDragBoxSize,x=y/2;if(g.visible){var b,_,T,A=I(i.radialAxisAngle),E=g._rl,C=E[0],L=E[1],z=E[r],O=.75*(E[1]-E[0])/(1-e.hole)/f;r?(b=p+(f+x)*Math.cos(A),_=d-(f+x)*Math.sin(A),T=\"radialdrag\"):(b=p+(h-x)*Math.cos(A),_=d-(h-x)*Math.sin(A),T=\"radialdrag-inner\");var D,B,N,j=m.makeRectDragger(u,T,\"crosshair\",-x,-x,y,y),U={element:j,gd:c};F(n.select(j),g.visible&&h<f,{transform:l(b,_)}),U.prepFn=function(){D=null,B=null,N=null,U.moveFn=V,U.doneFn=q,w(c)},U.clampFn=function(t,e){return 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v=e.gd._fullLayout,y=v[e.id],x=_(A+c*t._invScaleX,E+m*t._invScaleY),b=P(x-z);if(L=C+b,i.frontplot.attr(\"transform\",l(e.xOffset2,e.yOffset2)+s([-b,d,g])),e.vangles){I=e.radialAxisAngle+b;var w=l(h,p)+s(-b),T=l(h,p)+s(-I);i.bg.attr(\"transform\",w),i[\"radial-grid\"].attr(\"transform\",w),i[\"radial-axis\"].attr(\"transform\",T),i[\"radial-line\"].select(\"line\").attr(\"transform\",T),e.updateRadialAxisTitle(v,y,I)}else e.clipPaths.forTraces.select(\"path\").attr(\"transform\",l(d,g)+s(b));D.each((function(){var t=n.select(this),e=u.getTranslate(t);t.attr(\"transform\",l(e.x,e.y)+s([b]))})),R.each((function(){var t=n.select(this),e=t.select(\"text\"),r=u.getTranslate(t);t.attr(\"transform\",s([b,e.attr(\"x\"),e.attr(\"y\")])+l(r.x,r.y))})),f.rotation=o.modHalf(L,360),e.updateAngularAxis(v,y),e._hasClipOnAxisFalse&&!o.isFullCircle(e.sectorInRad)&&O.call(u.hideOutsideRangePoints,e);var S=!1;for(var F in e.traceHash)if(a.traceIs(F,\"gl\")){var 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x={};x[h]=[v/d,(v+1)/d],a(l,e,m,x),o.id=y,f(s,l,m,o)}}},{\"../lib\":778,\"../plot_api/plot_template\":817,\"./domain\":855}],906:[function(t,e,r){\"use strict\";['Variables are inserted using %{variable}, for example \"y: %{y}\".','Numbers are formatted using d3-format\\'s syntax %{variable:d3-format}, for example \"Price: %{y:$.2f}\".',t(\"../constants/docs\").FORMAT_LINK,\"for details on the formatting syntax.\",'Dates are formatted using d3-time-format\\'s syntax %{variable|d3-time-format}, for example \"Day: %{2019-01-01|%A}\".',t(\"../constants/docs\").DATE_FORMAT_LINK,\"for details on the date formatting syntax.\"].join(\" \");function n(t){var e=t.description?\" \"+t.description:\"\",r=t.keys||[];if(r.length>0){for(var n=[],i=0;i<r.length;i++)n[i]=\"`\"+r[i]+\"`\";e+=\"Finally, the template string has access to \",e=1===r.length?\"variable \"+n[0]:\"variables \"+n.slice(0,-1).join(\", \")+\" and \"+n.slice(-1)+\".\"}return e}r.hovertemplateAttrs=function(t,e){t=t||{};n(e=e||{});var r={valType:\"string\",dflt:\"\",editType:t.editType||\"none\"};return!1!==t.arrayOk&&(r.arrayOk=!0),r},r.texttemplateAttrs=function(t,e){t=t||{};n(e=e||{});var r={valType:\"string\",dflt:\"\",editType:t.editType||\"calc\"};return!1!==t.arrayOk&&(r.arrayOk=!0),r}},{\"../constants/docs\":748}],907:[function(t,e,r){\"use strict\";var n=t(\"./ternary\"),i=t(\"../../plots/get_data\").getSubplotCalcData,a=t(\"../../lib\").counterRegex;r.name=\"ternary\";var o=r.attr=\"subplot\";r.idRoot=\"ternary\",r.idRegex=r.attrRegex=a(\"ternary\"),(r.attributes={})[o]={valType:\"subplotid\",dflt:\"ternary\",editType:\"calc\"},r.layoutAttributes=t(\"./layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout_defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t.calcdata,a=e._subplots.ternary,o=0;o<a.length;o++){var s=a[o],l=i(r,\"ternary\",s),c=e[s]._subplot;c||(c=new 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n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../cartesian/layout_attributes\"),o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../lib/extend\").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:\"number\",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:\"ternary\"}),bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},\"plot\",\"from-root\");c.uirevision={valType:\"any\",editType:\"none\"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":642,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../cartesian/layout_attributes\":842,\"../domain\":855}],909:[function(t,e,r){\"use 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n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strTranslate,l=o._,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,p=t(\"../plots\"),d=t(\"../cartesian/axes\"),g=t(\"../../components/dragelement\"),m=t(\"../../components/fx\"),v=t(\"../../components/dragelement/helpers\"),y=v.freeMode,x=v.rectMode,b=t(\"../../components/titles\"),_=t(\"../cartesian/select\").prepSelect,w=t(\"../cartesian/select\").selectOnClick,T=t(\"../cartesian/select\").clearSelect,k=t(\"../cartesian/select\").clearSelectionsCache,M=t(\"../cartesian/constants\");function A(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=A;var S=A.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select(\"path\").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below 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E=Math.sqrt(4/3);S.adjustLayout=function(t,e){var r,n,i,a,o,l,p=this,d=t.domain,g=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*g-i/2,n=e.t+e.h*(1-m)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[g-o/2,g+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[m-l/2,m+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var M=p.yaxis.domain[0],A=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[M,M+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(A,p.graphDiv._fullLayout),A.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var C=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[M,M+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(C,p.graphDiv._fullLayout),C.setScale();var L=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",L),p.layers.plotbg.select(\"path\").attr(\"d\",L);var I=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",I);var P=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",P),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var z=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",z),p.layers.bgrid.attr(\"transform\",z);var O=s(r+i/2,n)+\"rotate(30)\"+s(0,-A._offset);p.layers.aaxis.attr(\"transform\",O),p.layers.agrid.attr(\"transform\",O);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-C._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",A.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,A.linecolor||\"#000\").style(\"stroke-width\",(A.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",C.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,C.linecolor||\"#000\").style(\"stroke-width\",(C.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var 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n=t(\"../pie/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../lib/extend\").extendFlat;e.exports={labels:n.labels,label0:n.label0,dlabel:n.dlabel,values:n.values,marker:{colors:n.marker.colors,line:{color:l({},n.marker.line.color,{dflt:null}),width:l({},n.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},text:n.text,hovertext:n.hovertext,scalegroup:l({},n.scalegroup,{}),textinfo:l({},n.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:s({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hoverinfo:l({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:o({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),textposition:l({},n.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:n.textfont,insidetextfont:n.insidetextfont,title:{text:n.title.text,font:n.title.font,position:l({},n.title.position,{values:[\"top 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText,s=t(\"../pie/defaults\").handleLabelsAndValues;e.exports=function(t,e,r,l){function c(r,a){return n.coerce(t,e,i,r,a)}var u=c(\"labels\"),f=c(\"values\"),h=s(u,f),p=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),p){e._length=p,c(\"marker.line.width\")&&c(\"marker.line.color\",l.paper_bgcolor),c(\"marker.colors\"),c(\"scalegroup\");var d,g=c(\"text\"),m=c(\"texttemplate\");if(m||(d=c(\"textinfo\",Array.isArray(g)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),m||d&&\"none\"!==d){var v=c(\"textposition\");o(t,e,l,c,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}a(e,l,c),c(\"title.text\")&&(c(\"title.position\"),n.coerceFont(c,\"title.font\",l.font)),c(\"aspectratio\"),c(\"baseratio\")}else 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n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":1168}],1062:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":778,\"./layout_attributes\":1061}],1063:[function(t,e,r){\"use strict\";var 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0===d._deltaLastValue&&(d._deltaLastValue=a(r[0]));var l=C.select(\"text.delta\");function p(){l.text(o(a(r[0]),i)).call(m.fill,s(r[0])).call(f.convertToTspans,t)}return l.call(c.font,d.delta.font).call(m.fill,s({delta:d._deltaLastValue})),b(S)?l.transition().duration(S.duration).ease(S.easing).tween(\"text\",(function(){var t=n.select(this),e=a(r[0]),l=d._deltaLastValue,c=P(d.delta.valueformat,i,l,e),u=n.interpolateNumber(l,e);return d._deltaLastValue=e,function(e){t.text(o(u(e),c)),t.call(m.fill,s({delta:u(e)}))}})).each(\"end\",(function(){p(),E&&E()})).each(\"interrupt\",(function(){p(),E&&E()})):p(),u=k(o(a(r[0]),i),d.delta.font,A,t),l}(),O+=d.delta.position+d.delta.font.size+d.delta.font.family+d.delta.valueformat,O+=d.delta.increasing.symbol+d.delta.decreasing.symbol,p=u);d._hasNumber&&(!function(){var e=w(t,{tickformat:d.number.valueformat},d._range);e.setScale(),h.prepTicks(e);var i=function(t){return h.tickText(e,t).text},a=d.number.suffix,o=d.number.prefix,s=C.select(\"text.number\");function u(){var e=\"number\"==typeof r[0].y?o+i(r[0].y)+a:\"-\";s.text(e).call(c.font,d.number.font).call(f.convertToTspans,t)}b(S)?s.transition().duration(S.duration).ease(S.easing).each(\"end\",(function(){u(),E&&E()})).each(\"interrupt\",(function(){u(),E&&E()})).attrTween(\"text\",(function(){var t=n.select(this),e=n.interpolateNumber(r[0].lastY,r[0].y);d._lastValue=r[0].y;var s=P(d.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(o+s(e(r))+a)}})):u(),l=k(o+i(r[0].y)+a,d.number.font,A,t)}(),O+=d.number.font.size+d.number.font.family+d.number.valueformat+d.number.suffix+d.number.prefix,p=l);if(d._hasDelta&&d._hasNumber){var D,R,F=[(l.left+l.right)/2,(l.top+l.bottom)/2],B=[(u.left+u.right)/2,(u.top+u.bottom)/2],N=.75*d.delta.font.size;\"left\"===d.delta.position&&(D=M(d,\"deltaPos\",0,-1*(l.width*y[d.align]+u.width*(1-y[d.align])+N),O,Math.min),R=F[1]-B[1],p={width:l.width+u.width+N,height:Math.max(l.height,u.height),left:u.left+D,right:l.right,top:Math.min(l.top,u.top+R),bottom:Math.max(l.bottom,u.bottom+R)}),\"right\"===d.delta.position&&(D=M(d,\"deltaPos\",0,l.width*(1-y[d.align])+u.width*y[d.align]+N,O,Math.max),R=F[1]-B[1],p={width:l.width+u.width+N,height:Math.max(l.height,u.height),left:l.left,right:u.right+D,top:Math.min(l.top,u.top+R),bottom:Math.max(l.bottom,u.bottom+R)}),\"bottom\"===d.delta.position&&(D=null,R=u.height,p={width:Math.max(l.width,u.width),height:l.height+u.height,left:Math.min(l.left,u.left),right:Math.max(l.right,u.right),top:l.bottom-l.height,bottom:l.bottom+u.height}),\"top\"===d.delta.position&&(D=null,R=l.top,p={width:Math.max(l.width,u.width),height:l.height+u.height,left:Math.min(l.left,u.left),right:Math.max(l.right,u.right),top:l.bottom-l.height-u.height,bottom:l.bottom}),z.attr({dx:D,dy:R})}(d._hasNumber||d._hasDelta)&&C.attr(\"transform\",(function(){var t=s.numbersScaler(p);O+=t[2];var e,r=M(d,\"numbersScale\",1,t[0],O,Math.min);d._scaleNumbers||(r=1),e=d._isAngular?_-r*p.bottom:_-r*(p.top+p.bottom)/2,d._numbersTop=r*p.top+e;var n=p[T];\"center\"===T&&(n=(p.left+p.right)/2);var i=g-r*n;return i=M(d,\"numbersTranslate\",0,i,O,Math.max),o(i,e)+a(r)}))}(t,I,e,{numbersX:p,numbersY:A,numbersScaler:S,transitionOpts:r,onComplete:d}),P&&(E={range:L.gauge.axis.range,color:L.gauge.bgcolor,line:{color:L.gauge.bordercolor,width:0},thickness:1},C={range:L.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:L.gauge.bordercolor,width:L.gauge.borderwidth},thickness:1});var H=I.selectAll(\"g.angular\").data(z?e:[]);H.exit().remove();var G=I.selectAll(\"g.angularaxis\").data(z?e:[]);G.exit().remove(),z&&function(t,e,r,i){var a,c,u,f,p=r[0].trace,d=i.size,g=i.radius,m=i.innerRadius,v=i.gaugeBg,y=i.gaugeOutline,x=[d.l+d.w/2,d.t+d.h/2+g/2],T=i.gauge,k=i.layer,M=i.transitionOpts,A=i.onComplete,S=Math.PI/2;function E(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-S;return r<-S?-S:r>S?S:r}function C(t){return n.svg.arc().innerRadius((m+g)/2-t/2*(g-m)).outerRadius((m+g)/2+t/2*(g-m)).startAngle(-S)}function L(t){t.attr(\"d\",(function(t){return C(t.thickness).startAngle(E(t.range[0])).endAngle(E(t.range[1]))()}))}T.enter().append(\"g\").classed(\"angular\",!0),T.attr(\"transform\",o(x[0],x[1])),k.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),k.selectAll(\"g.xangularaxistick,path,text\").remove(),(a=w(t,p.gauge.axis)).type=\"linear\",a.range=p.gauge.axis.range,a._id=\"xangularaxis\",a.setScale();var I=function(t){return(a.range[0]-t.x)/(a.range[1]-a.range[0])*Math.PI+Math.PI},P={},z=h.makeLabelFns(a,0).labelStandoff;P.xFn=function(t){var e=I(t);return Math.cos(e)*z},P.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*l)},P.anchorFn=function(t){var e=I(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},P.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var O=function(t){return o(x[0]+g*Math.cos(t),x[1]-g*Math.sin(t))};u=function(t){return O(I(t))};if(c=h.calcTicks(a),f=h.getTickSigns(a)[2],a.visible){f=\"inside\"===a.ticks?-1:1;var D=(a.linewidth||1)/2;h.drawTicks(t,a,{vals:c,layer:k,path:\"M\"+f*D+\",0h\"+f*a.ticklen,transFn:function(t){var e=I(t);return O(e)+\"rotate(\"+-s(e)+\")\"}}),h.drawLabels(t,a,{vals:c,layer:k,transFn:u,labelFns:P})}var R=[v].concat(p.gauge.steps),F=T.selectAll(\"g.bg-arc\").data(R);F.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),F.select(\"path\").call(L).call(_),F.exit().remove();var B=C(p.gauge.bar.thickness),N=T.selectAll(\"g.value-arc\").data([p.gauge.bar]);N.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var j=N.select(\"path\");b(M)?(j.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){A&&A()})).each(\"interrupt\",(function(){A&&A()})).attrTween(\"d\",(U=B,V=E(r[0].lastY),q=E(r[0].y),function(){var t=n.interpolate(V,q);return function(e){return U.endAngle(t(e))()}})),p._lastValue=r[0].y):j.attr(\"d\",\"number\"==typeof r[0].y?B.endAngle(E(r[0].y)):\"M0,0Z\");var U,V,q;j.call(_),N.exit().remove(),R=[];var H=p.gauge.threshold.value;H&&R.push({range:[H,H],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var G=T.selectAll(\"g.threshold-arc\").data(R);G.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),G.select(\"path\").call(L).call(_),G.exit().remove();var Y=T.selectAll(\"g.gauge-outline\").data([y]);Y.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),Y.select(\"path\").call(L).call(_),Y.exit().remove()}(t,0,e,{radius:N,innerRadius:j,gauge:H,layer:G,size:R,gaugeBg:E,gaugeOutline:C,transitionOpts:r,onComplete:d});var Y=I.selectAll(\"g.bullet\").data(O?e:[]);Y.exit().remove();var W=I.selectAll(\"g.bulletaxis\").data(O?e:[]);W.exit().remove(),O&&function(t,e,r,n){var i,a,s,l,c,f=r[0].trace,p=n.gauge,d=n.layer,g=n.gaugeBg,v=n.gaugeOutline,y=n.size,x=f.domain,T=n.transitionOpts,k=n.onComplete;p.enter().append(\"g\").classed(\"bullet\",!0),p.attr(\"transform\",o(y.l,y.t)),d.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),d.selectAll(\"g.xbulletaxistick,path,text\").remove();var M=y.h,A=f.gauge.bar.thickness*M,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(f._hasNumber||f._hasDelta?1-u.bulletNumberDomainSize:1);(i=w(t,f.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=h.calcTicks(i),s=h.makeTransTickFn(i),l=h.getTickSigns(i)[2],c=y.t+y.h,i.visible&&(h.drawTicks(t,i,{vals:\"inside\"===i.ticks?h.clipEnds(i,a):a,layer:d,path:h.makeTickPath(i,c,l),transFn:s}),h.drawLabels(t,i,{vals:a,layer:d,transFn:s,labelFns:h.makeLabelFns(i,c)}));function C(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*M})).attr(\"height\",(function(t){return t.thickness*M}))}var L=[g].concat(f.gauge.steps),I=p.selectAll(\"g.bg-bullet\").data(L);I.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),I.select(\"rect\").call(C).call(_),I.exit().remove();var P=p.selectAll(\"g.value-bullet\").data([f.gauge.bar]);P.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),P.select(\"rect\").attr(\"height\",A).attr(\"y\",(M-A)/2).call(_),b(T)?P.select(\"rect\").transition().duration(T.duration).ease(T.easing).each(\"end\",(function(){k&&k()})).each(\"interrupt\",(function(){k&&k()})).attr(\"width\",Math.max(0,i.c2p(Math.min(f.gauge.axis.range[1],r[0].y)))):P.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(f.gauge.axis.range[1],r[0].y))):0);P.exit().remove();var z=r.filter((function(){return f.gauge.threshold.value})),O=p.selectAll(\"g.threshold-bullet\").data(z);O.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),O.select(\"line\").attr(\"x1\",i.c2p(f.gauge.threshold.value)).attr(\"x2\",i.c2p(f.gauge.threshold.value)).attr(\"y1\",(1-f.gauge.threshold.thickness)/2*M).attr(\"y2\",(1-(1-f.gauge.threshold.thickness)/2)*M).call(m.stroke,f.gauge.threshold.line.color).style(\"stroke-width\",f.gauge.threshold.line.width),O.exit().remove();var D=p.selectAll(\"g.gauge-outline\").data([v]);D.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),D.select(\"rect\").call(C).call(_),D.exit().remove()}(t,0,e,{gauge:Y,layer:W,size:R,gaugeBg:E,gaugeOutline:C,transitionOpts:r,onComplete:d});var X=I.selectAll(\"text.title\").data(e);X.exit().remove(),X.enter().append(\"text\").classed(\"title\",!0),X.attr(\"text-anchor\",(function(){return O?v.right:v[L.title.align]})).text(L.title.text).call(c.font,L.title.font).call(f.convertToTspans,t),X.attr(\"transform\",(function(){var t,e=R.l+R.w*y[L.title.align],r=u.titlePadding,n=c.bBox(X.node());if(P){if(z)if(L.gauge.axis.visible)t=c.bBox(G.node()).top-r-n.bottom;else t=R.t+R.h/2-N/2-n.bottom-r;O&&(t=A-(n.top+n.bottom)/2,e=R.l-u.bulletPadding*R.w)}else t=L._numbersTop-r-n.bottom;return o(e,t)}))}))}},{\"../../components/color\":643,\"../../components/drawing\":665,\"../../constants/alignment\":745,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plots/cartesian/axes\":828,\"../../plots/cartesian/axis_defaults\":830,\"../../plots/cartesian/layout_attributes\":842,\"../../plots/cartesian/position_defaults\":845,\"./constants\":1119,d3:169}],1123:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../mesh3d/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;var c=e.exports=l(s({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:i(),showlegend:s({},o.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:a.opacity,lightposition:a.lightposition,lighting:a.lighting,flatshading:a.flatshading,contour:a.contour,hoverinfo:s({},o.hoverinfo)}),\"calc\",\"nested\");c.flatshading.dflt=!0,c.lighting.facenormalsepsilon.dflt=0,c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 0},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/template_attributes\":906,\"../mesh3d/attributes\":1128}],1124:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\"),i=t(\"../streamtube/calc\").processGrid,a=t(\"../streamtube/calc\").filter;e.exports=function(t,e){e._len=Math.min(e.x.length,e.y.length,e.z.length,e.value.length),e._x=a(e.x,e._len),e._y=a(e.y,e._len),e._z=a(e.z,e._len),e._value=a(e.value,e._len);var r=i(e);e._gridFill=r.fill,e._Xs=r.Xs,e._Ys=r.Ys,e._Zs=r.Zs,e._len=r.len;for(var o=1/0,s=-1/0,l=0;l<e._len;l++){var c=e._value[l];o=Math.min(o,c),s=Math.max(s,c)}e._minValues=o,e._maxValues=s,e._vMin=void 0===e.isomin||null===e.isomin?o:e.isomin,e._vMax=void 0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":651,\"../streamtube/calc\":1295}],1125:[function(t,e,r){\"use strict\";var n=t(\"gl-mesh3d\"),i=t(\"../../lib/gl_format_color\").parseColorScale,a=t(\"../../lib/str2rgbarray\"),o=t(\"../../components/colorscale\").extractOpts,s=t(\"../../plots/gl3d/zip3\"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var 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0),\"category descending\"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,m[e])}));var v,y=e.line;y?(i(e,\"line\")&&a(t,e,{vals:e.line.color,containerStr:\"line\",cLetter:\"c\"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,M={},A=m.map((function(t){return t.inds}));for(g=0,x=0;x<k;x++){var S=[];for(b=0;b<A.length;b++)S.push(A[b][x]);d=p[x%p.length],g+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),C=S+\"-\"+E.rawColor;void 0===M[C]&&(M[C]=f(S,E.color,E.rawColor)),h(M[C],x,d)}var L,I=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,g)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<I.length;b++){var P=I[b].containerInd,z=m[b].inds[x],O=I[b].categories;if(void 0===O[z]){var D=e.dimensions[P]._categoryarray[z],R=e.dimensions[P]._ticktext[z];O[z]={dimensionInd:b,categoryInd:L=z,categoryValue:D,displayInd:L,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(O[z],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(I,M,g))}},{\"../../components/colorscale/calc\":651,\"../../components/colorscale/helpers\":654,\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/filter_unique.js\":769,\"../../lib/gup\":775,\"fast-isnumeric\":241}],1145:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"./attributes\"),c=t(\"../parcoords/merge_length\");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r(\"values\"),a=r(\"visible\");if(i&&i.length||(a=e.visible=!1),a){r(\"label\"),r(\"displayindex\",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o=\"array\");var u=r(\"categoryorder\",o);\"array\"===u?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==u||(e.categoryorder=\"trace\")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:\"dimensions\",handleItemDefaults:u}),d=function(t,e,r,o,s){s(\"line.shape\"),s(\"line.hovertemplate\");var l=s(\"line.color\",o.colorway[0]);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,\"values\",d),h(\"hoveron\"),h(\"hovertemplate\"),h(\"arrangement\"),h(\"bundlecolors\"),h(\"sortpaths\"),h(\"counts\");var g={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",g);var m={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",m)}},{\"../../components/colorscale/defaults\":653,\"../../components/colorscale/helpers\":654,\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/domain\":855,\"../parcoords/merge_length\":1158,\"./attributes\":1142}],1146:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":1142,\"./base_plot\":1143,\"./calc\":1144,\"./defaults\":1145,\"./plot\":1148}],1147:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plot_api/plot_api\"),a=t(\"../../components/fx\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../components/drawing\"),c=t(\"tinycolor2\"),u=t(\"../../lib/svg_text_utils\");function f(t,e,r,i){var a=t.map(R.bind(0,e,r)),c=i.selectAll(\"g.parcatslayer\").data([null]);c.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var f=c.selectAll(\"g.trace.parcats\").data(a,h),v=f.enter().append(\"g\").attr(\"class\",\"trace parcats\");f.attr(\"transform\",(function(t){return s(t.x,t.y)})),v.append(\"g\").attr(\"class\",\"paths\");var y=f.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),h);y.attr(\"fill\",(function(t){return t.model.color}));var _=y.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);b(_),y.attr(\"d\",(function(t){return t.svgD})),_.empty()||y.sort(d),y.exit().remove(),y.on(\"mouseover\",g).on(\"mouseout\",m).on(\"click\",x),v.append(\"g\").attr(\"class\",\"dimensions\");var k=f.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),h);k.enter().append(\"g\").attr(\"class\",\"dimension\"),k.attr(\"transform\",(function(t){return s(t.x,0)})),k.exit().remove();var M=k.selectAll(\"g.category\").data((function(t){return t.categories}),h),A=M.enter().append(\"g\").attr(\"class\",\"category\");M.attr(\"transform\",(function(t){return s(0,t.y)})),A.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),M.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),w(A);var S=M.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),h);S.each((function(){o.raiseToTop(this)})),S.attr(\"fill\",(function(t){return t.color}));var z=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),T(z),S.exit().remove(),A.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var O=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return p(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",O+\" -1px  1px 2px, \"+O+\" 1px  1px 2px, \"+O+\"  1px -1px 2px, \"+O+\" -1px -1px 2px\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return p(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){l.font(n.select(this),t.parcatsViewModel.categorylabelfont),u.convertToTspans(n.select(this),e)})),A.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){l.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",E).on(\"mouseout\",C),M.exit().remove(),k.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",L).on(\"drag\",I).on(\"dragend\",P)),f.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),f.exit().remove()}function h(t){return t.key}function p(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function d(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){o.raiseToTop(this),_(n.select(this));var e=v(t),r=y(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,s,l,u=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),g=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=u&&u<=t.leftXs[l+1]+2){var m=t.parcatsViewModel.dimensions[l],x=t.parcatsViewModel.dimensions[l+1];i=(m.x+m.width+x.x)/2,s=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,w=t.parcatsViewModel.y+s,T=c.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,M=k/t.parcatsViewModel.model.count,A={countLabel:k,probabilityLabel:M.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",A.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",A.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),C=n.mouse(f)[0];a.loneHover({trace:h,x:b-d.left+g.left,y:w-d.top+g.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:C<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:A,eventData:[{data:h._input,fullData:h,count:k,probability:M}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function m(t){if(!t.parcatsViewModel.dragDimension&&(b(n.select(this)),a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(d),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=v(t),r=y(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function v(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function y(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function x(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=v(t),r=y(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function b(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function _(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return c.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function w(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function T(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function k(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){k(t).each((function(t){Array.prototype.push.apply(l,v(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function A(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=k(i),l=[];s.each((function(t){Array.prototype.push.apply(l,v(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var g=u.model.count,m=u.model.categoryLabel,v=g/u.parcatsViewModel.model.count,y={countLabel:g,categoryLabel:m,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", 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e=k(t);_(e),e.each((function(){o.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=S(r,s,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,g=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var m=f.model.categoryLabel,v=u.parcatsViewModel.model.count,y=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:m,probabilityLabel:_.toFixed(3)},M=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&M.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(M.push(\"P(color \\u2229 \"+m+\"): \"+k.probabilityLabel),M.push(\"P(\"+m+\" | color): \"+w.toFixed(3)),M.push(\"P(color | \"+m+\"): \"+T.toFixed(3)));var A=M.join(\"<br>\"),S=c.mostReadable(u.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(g-e.top),text:A,color:u.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:m,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,s,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(S(t,e,this))})),i}(r,s,this)),e&&a.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(b(e.pathSelection),w(e.dimensionSelection.selectAll(\"g.category\")),T(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),a.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(d),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?A(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function L(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,o.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}N(t.parcatsViewModel),B(t.parcatsViewModel),D(t.parcatsViewModel),O(t.parcatsViewModel)}}function P(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),a=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==a[e]}));o&&a.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?A(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,N(t.parcatsViewModel),B(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){D(t.parcatsViewModel,!0),O(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&i.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function O(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),h),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function D(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),h);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),h);r(t.dimensionSelection).attr(\"transform\",(function(t){return s(t.x,0)})),r(i).attr(\"transform\",(function(t){return s(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return p(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return p(t)?t.width+5:-5})).each((function(t){var e,r;p(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),h),l=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),T(l),a.each((function(){o.raiseToTop(this)})),a.exit().remove()}function R(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var g={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(N(g),B(g)),g}function F(t,e,r,i,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=n.interpolateNumber(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+i+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+i)+\" \"+l[s]+\",\"+(e[s]+i)+\" \"+(t[s]+r[s])+\",\"+(e[s]+i),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function B(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),g=0;g<c.length;g++){var m,v=c[g];m=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var _=v.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=m;var k=t.dimensions[T].categories[w],M=k.bands.length,A=k.bands[M-1];if(void 0===A||v.rawColor!==A.rawColor){var S=void 0===A?0:A.y+A.height;k.bands.push({key:S,color:v.color,rawColor:v.rawColor,height:m,width:k.width,count:v.count,y:S,categoryViewModel:k,parcatsViewModel:t})}else{var E=k.bands[M-1];E.height+=m,E.count+=v.count}}y=\"hspline\"===t.pathShape?F(s,x,l,m,.5):F(s,x,l,m,0),h[g]={key:v.valueInds[0],model:v,height:m,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=h}function N(t){var e=t.model.dimensions.map((function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}}));e.sort((function(t,e){return t.displayInd-e.displayInd}));var r=[];for(var n in e){var i=e[n].dimensionInd,a=t.model.dimensions[i];r.push(j(t,a))}t.dimensions=r}function j(t,e){var r,n=t.model.dimensions.length,i=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],f=t.model.maxCats,h=e.categories.length,p=e.count,d=t.height-8*(f-1),g=8*(f-h)/2,m=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(m.sort((function(t,e){return t.displayInd-e.displayInd})),c=0;c<h;c++)l=m[c].categoryInd,o=e.categories[l],a=p>0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:a,y:null!==o.dragY?o.dragY:g,bands:[],parcatsViewModel:t},g=g+a+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}e.exports=function(t,e,r,n){f(r,t,n,e)}},{\"../../components/drawing\":665,\"../../components/fx\":683,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plot_api/plot_api\":814,d3:169,tinycolor2:576}],1148:[function(t,e,r){\"use strict\";var n=t(\"./parcats\");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{\"./parcats\":1147}],1149:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/layout_attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/plot_template\":817,\"../../plots/cartesian/layout_attributes\":842,\"../../plots/domain\":855,\"../../plots/font_attributes\":856}],1150:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"d3\"),a=t(\"../../lib/gup\").keyFun,o=t(\"../../lib/gup\").repeat,s=t(\"../../lib\").sorterAsc,l=t(\"../../lib\").strTranslate,c=n.bar.snapRatio;function u(t,e){return t*(1-c)+e*c}var f=n.bar.snapClose;function h(t,e){return t*(1-f)+e*f}function p(t,e,r,n){if(function(t,e){for(var r=0;r<e.length;r++)if(t>=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],c=l,f=a;i*f<i*o;f+=i){var p=f+i,d=e[p];if(i*r<i*h(l,d))return u(l,c);if(i*r<i*d||p===o)return u(d,l);c=l,l=d}}function d(t){t.attr(\"x\",-n.bar.captureWidth/2).attr(\"width\",n.bar.captureWidth)}function g(t){t.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function m(t){if(!t.brush.filterSpecified)return\"0,\"+t.height;for(var 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k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||y()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&A(r)):A(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||A(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function M(t,e){return t[0]-e[0]}function A(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(M)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t){var e=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);e.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t){var e=t.selectAll(\".background\").data(o);e.enter().append(\"rect\").classed(\"background\",!0).call(d).call(g).style(\"pointer-events\",\"auto\").attr(\"transform\",l(0,n.verticalPadding)),e.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var r=t.selectAll(\".highlight-shadow\").data(o);r.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",n.bar.strokeColor).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),r.attr(\"y1\",(function(t){return t.height})).call(x);var i=t.selectAll(\".highlight\").data(o);i.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",(function(t){return t.height})).call(x)}(e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(M)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":778,\"../../lib/gup\":775,\"./constants\":1153,d3:169}],1151:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plots/get_data\").getModuleCalcData,a=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.width,height:this.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":754,\"../../plots/get_data\":865,\"./plot\":1160,d3:169}],1152:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,i=t(\"../../components/colorscale\"),a=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{\"../../components/colorscale\":655,\"../../lib\":778,\"../../lib/gup\":775}],1153:[function(t,e,r){\"use strict\";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:[\"contextLineLayer\",\"focusLineLayer\",\"pickLineLayer\"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:\"#777\",bar:{width:4,captureWidth:10,fillColor:\"magenta\",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeColor:\"white\",strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:\"axis-extent-text\",parcoordsLineLayers:\"parcoords-line-layers\",parcoordsLineLayer:\"parcoords-lines\",parcoords:\"parcoords\",parcoordsControlView:\"parcoords-control-view\",yAxis:\"y-axis\",axisOverlays:\"axis-overlays\",axis:\"axis\",axisHeading:\"axis-heading\",axisTitle:\"axis-title\",axisExtent:\"axis-extent\",axisExtentTop:\"axis-extent-top\",axisExtentTopText:\"axis-extent-top-text\",axisExtentBottom:\"axis-extent-bottom\",axisExtentBottomText:\"axis-extent-bottom-text\",axisBrush:\"axis-brush\"},id:{filterBarPattern:\"filter-bar-pattern\"}}},{}],1154:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"../../plots/cartesian/axes\"),c=t(\"./attributes\"),u=t(\"./axisbrush\"),f=t(\"./constants\").maxDimensionCount,h=t(\"./merge_length\");function p(t,e,r,i){function a(r,i){return n.coerce(t,e,c.dimensions,r,i)}var o=a(\"values\"),s=a(\"visible\");if(o&&o.length||(s=e.visible=!1),s){a(\"label\"),a(\"tickvals\"),a(\"ticktext\"),a(\"tickformat\");var f=a(\"range\");e._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:f},l.setConvert(e._ax,i.layout),a(\"multiselect\");var h=a(\"constraintrange\");h&&(e.constraintrange=u.cleanRanges(h,e))}}e.exports=function(t,e,r,l){function u(r,i){return n.coerce(t,e,c,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log(\"parcoords traces support up to \"+f+\" dimensions at the moment\"),d.splice(f));var g=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),m=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(g)&&g.length||(e.visible=!1),h(e,g,\"values\",m);var v={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(u,\"labelfont\",v),n.coerceFont(u,\"tickfont\",v),n.coerceFont(u,\"rangefont\",v),u(\"labelangle\"),u(\"labelside\")}},{\"../../components/colorscale/defaults\":653,\"../../components/colorscale/helpers\":654,\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"../../plots/domain\":855,\"./attributes\":1149,\"./axisbrush\":1150,\"./constants\":1153,\"./merge_length\":1158}],1155:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":778}],1156:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":1149,\"./base_plot\":1151,\"./calc\":1152,\"./defaults\":1154,\"./plot\":1160}],1157:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform sampler2D mask, palette;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(mask,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function g(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,c=r[64*a+l];63===l&&0===o&&(c*=-1),n[i++]=c}return n}function m(t){var e=\"0\"+t;return e.substr(e.length-2)}function v(t){return t<o?\"p\"+m(t+1)+\"_\"+m(t+4):\"colors\"}function y(t,e,r,n,i,a,o,l,c,u,f,h,p){for(var d=[[],[]],g=0;g<64;g++)d[0][g]=g===i?1:0,d[1][g]=g===a?1:0;var m=t.lines.canvasOverdrag,v=t.domain,y=t.canvasWidth,x=t.canvasHeight,b=t.deselectedLines.color;return s.extendFlat({key:f,resolution:[y,x],viewBoxPos:[o+m,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:d[0].slice(0,16),dim0B:d[0].slice(16,32),dim0C:d[0].slice(32,48),dim0D:d[0].slice(48,64),dim1A:d[1].slice(0,16),dim1B:d[1].slice(16,32),dim1C:d[1].slice(32,48),dim1D:d[1].slice(48,64),drwLayer:h,contextColor:[b[0]/255,b[1]/255,b[2]/255,b[3]<1?b[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+m)+(t.pad.l-m)+t.layoutWidth*v.x[0],scissorWidth:(n===r?y-o+m:c+.5)+(n===e?o+m:0),scissorY:l+t.pad.b+t.layoutHeight*v.y[0],scissorHeight:u,viewportX:t.pad.l-m+t.layoutWidth*v.x[0],viewportY:t.pad.b+t.layoutHeight*v.y[0],viewportWidth:y,viewportHeight:x},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,m,b,_=e.context,w=e.pick,T=e.regl,k={currentRafs:{},drawCompleted:!0,clearOnly:!1},M=function(t){for(var e={},r=0;r<=o;r+=4)e[v(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(T),A=T.texture(u),S=[];C(e);var E=T({profile:!1,blend:{enable:_,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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r=t._fullLayout;if(i(t)){var s={},l={},c={},u={},f=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=u[r]=n._fullInput.index;s[r]=t.data[i].dimensions,l[r]=t.data[i].dimensions.slice()}));n(t,e,{width:f.w,height:f.h,margin:{t:f.t,r:f.r,b:f.b,l:f.l}},{filterChanged:function(e,n,i){var a=l[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",f=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===f[s]){var h=a.constraintrange;f[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[u[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"plot\"},editType:\"plot\"},domain:i({name:\"pie\",trace:!0,editType:\"calc\"}),hole:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},sort:{valType:\"boolean\",dflt:!0,editType:\"calc\"},direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"],dflt:\"counterclockwise\",editType:\"calc\"},rotation:{valType:\"number\",min:-360,max:360,dflt:0,editType:\"calc\"},pull:{valType:\"number\",min:0,max:1,dflt:0,arrayOk:!0,editType:\"calc\"},_deprecated:{title:{valType:\"string\",dflt:\"\",editType:\"calc\"},titlefont:c({},u,{}),titleposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":642,\"../../lib/extend\":768,\"../../plots/attributes\":824,\"../../plots/domain\":855,\"../../plots/font_attributes\":856,\"../../plots/template_attributes\":906}],1162:[function(t,e,r){\"use strict\";var n=t(\"../../plots/plots\");r.name=\"pie\",r.plot=function(t,e,i,a){n.plotBasePlot(r.name,t,e,i,a)},r.clean=function(t,e,i,a){n.cleanBasePlot(r.name,t,e,i,a)}},{\"../../plots/plots\":891}],1163:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"tinycolor2\"),a=t(\"../../components/color\"),o={};function s(t){return function(e,r){return!!e&&(!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e))}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}e.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},g=s(o[\"_\"+e.type+\"colormap\"]),m=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;if((y=+y)<0)continue}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(m+=y),a.push({v:y,label:x,color:g(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(m+=y),!1===i.color&&u[r]&&(i.color=g(u[r],x)))}return(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=m),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":643,\"fast-isnumeric\":241,tinycolor2:576}],1164:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var g=c(\"textposition\");s(t,e,n,c,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&c(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&c(\"insidetextorientation\")}o(e,n,c);var m=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",m?\"middle center\":\"top center\");m||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":778,\"../../plots/domain\":855,\"../bar/defaults\":925,\"./attributes\":1161,\"fast-isnumeric\":241}],1165:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":679}],1166:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":778}],1167:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":1161,\"./base_plot\":1162,\"./calc\":1163,\"./defaults\":1164,\"./layout_attributes\":1168,\"./layout_defaults\":1169,\"./plot\":1170,\"./style\":1171,\"./style_one\":1172}],1168:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],1169:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":778,\"./layout_attributes\":1168}],1170:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,g=t(\"../bar/constants\").TEXTPAD,m=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.trace,s=i.cx,c=i.cy;\"_hasHoverLabel\"in o||(o._hasHoverLabel=!1),\"_hasHoverEvent\"in o||(o._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,u=e._fullData[o.index];if(!e._dragging&&!1!==r.hovermode){var f=u.hoverinfo;if(Array.isArray(f)&&(f=a.castHoverinfo({hoverinfo:[m.castOption(f,t.pts)],_module:o._module},r,0)),\"all\"===f&&(f=\"label+text+value+percent+name\"),u.hovertemplate||\"none\"!==f&&\"skip\"!==f&&f){var h=t.rInscribed||0,p=s+t.pxmid[0]*(1-h),d=c+t.pxmid[1]*(1-h),g=r.separators,y=[];if(f&&-1!==f.indexOf(\"label\")&&y.push(t.label),t.text=m.castOption(u.hovertext||u.text,t.pts),f&&-1!==f.indexOf(\"text\")){var x=t.text;l.isValidTextValue(x)&&y.push(x)}t.value=t.v,t.valueLabel=m.formatPieValue(t.v,g),f&&-1!==f.indexOf(\"value\")&&y.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=m.formatPiePercent(t.percent,g),f&&-1!==f.indexOf(\"percent\")&&y.push(t.percentLabel);var b=u.hoverlabel,_=b.font;a.loneHover({trace:o,x0:p-h*i.r,x1:p+h*i.r,y:d,text:y.join(\"<br>\"),name:u.hovertemplate||-1!==f.indexOf(\"name\")?u.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:m.castOption(b.bgcolor,t.pts)||t.color,borderColor:m.castOption(b.bordercolor,t.pts),fontFamily:m.castOption(_.family,t.pts),fontSize:m.castOption(_.size,t.pts),fontColor:m.castOption(_.color,t.pts),nameLength:m.castOption(b.namelength,t.pts),textAlign:m.castOption(b.align,t.pts),hovertemplate:m.castOption(u.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,u)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e}),o._hasHoverLabel=!0}o._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,u)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[o.index],s=n.select(this).datum();o._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(s,i)],event:n.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),o._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[o.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=m.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=m.castOption(t._input.textfont.color,e.pts));var i=m.castOption(t.insidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.insidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,g=[];if(!d){var m,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,g.push(a)}};if(f||h){for(m=4;m>=-4;m-=2)v(Math.PI*m,\"tan\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+1),\"tan\")}if(f||p){for(m=4;m>=-4;m-=2)v(Math.PI*(m+1.5),\"rad\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;g.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a));for(var x=0,b=0,_=0;_<g.length;_++){var w=g[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return g[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:M(a,o/e),rotate:A(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:M(a,o/e),rotate:A(i+Math.PI/2)}}function M(t,e){return Math.cos(e)-t*e}function A(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function C(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function L(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=P(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=I(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function I(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function P(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function z(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=I(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+P(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=I(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function O(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),g=n.separators;if(s=f?[e.label]:[],h){var v=m.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(m.formatPieValue(e.v,g)),d&&s.push(m.formatPiePercent(e.v/r.vTotal,g)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:m.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:m.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=m.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),z(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],g=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=m.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=O(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=O(a,s),r.midangle=s,s+=l*r.v/2,n=O(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=g.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),g.pull){var v=+m.castOption(g.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=g.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+L(i.px0,i.pxmid,!0,1)+L(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+L(i.px0,i.pxmid,!1,T)+L(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var M=L(i.px0,i.px1,!0,1);if(T){var A=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+L(i.px1,i.px0,!1,T)+\"l\"+A*i.px0[0]+\",\"+A*i.px0[1]+M+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}D(t,i,d);var S=m.castOption(g.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=m.castOption(t.outsidetextfont.color,e.pts)||m.castOption(t.textfont.color,e.pts)||r.color,i=m.castOption(t.outsidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.outsidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(g,i,r.font):b(g,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=C(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,g.outsidetextfont);u.call(s.font,x),v=C(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:O(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var M=v.targetY;i.yLabelMin=M-y.height/2,i.yLabelMid=M,i.yLabelMax=M+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(g.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function L(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(g.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=g.title.text;g._meta&&(i=l.templateString(i,g._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,g.title.font).call(f.convertToTspans,t),e=\"middle center\"===g.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):L(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function g(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,g=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(m.castOption(e.pull,t.pts)||0)>=(m.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(g+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?g:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,g),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr(\"transform\",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+c):s+=\"l\"+t.labelExtraX+\",\"+u+\"v\"+(f-u)+\"h\"+c}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+c;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(v,g),_&&g.automargin){var k=s.bBox(h.node()),M=g.domain,A=a.w*(M.x[1]-M.x[0]),S=a.h*(M.y[1]-M.y[0]),E=(.5*A-d.r)/a.w,I=(.5*S-d.r)/a.h;i.autoMargin(t,\"pie.\"+g.uid+\".automargin\",{xl:M.x[0]-E,xr:M.x[1]+E,yb:M.y[0]-I,yt:M.y[1]+I,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:L,prerenderTitles:_,layoutAreas:z,attachFxHandlers:x,computeTransform:R}},{\"../../components/color\":643,\"../../components/drawing\":665,\"../../components/fx\":683,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plots/plots\":891,\"../bar/constants\":923,\"../bar/uniform_text\":937,\"./event_data\":1165,\"./helpers\":1166,d3:169}],1171:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"./style_one\"),a=t(\"../bar/uniform_text\").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each((function(t){n.select(this).call(i,t,e)}))}))}},{\"../bar/uniform_text\":937,\"./style_one\":1172,d3:169}],1172:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"./helpers\").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},{\"../../components/color\":643,\"./helpers\":1166}],1173:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},{\"../scatter/attributes\":1187}],1174:[function(t,e,r){\"use strict\";var n=t(\"gl-pointcloud2d\"),i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new Int32Array(e),l=0;l<e;l++)o=c[l],s=u[l],r[l]=l,n[2*l]=o,n[2*l+1]=s,o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var g=i(t.marker.color),m=i(t.marker.border.color),v=t.opacity*t.marker.opacity;g[3]*=v,this.pointcloudOptions.color=g;var y=t.marker.blend;if(null===y){y=c.length<100||u.length<100}this.pointcloudOptions.blend=y,m[3]*=v,this.pointcloudOptions.borderColor=m;var x=t.marker.sizemin,b=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=b,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var _=this.scene.xaxis,w=this.scene.yaxis,T=b/2||.5;t._extremes[_._id]=a(_,[d[0],d[2]],{ppad:T}),t._extremes[w._id]=a(w,[d[1],d[3]],{ppad:T})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{\"../../lib/str2rgbarray\":802,\"../../plots/cartesian/autorange\":827,\"../scatter/get_trace_color\":1197,\"gl-pointcloud2d\":324}],1175:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},{\"../../lib\":778,\"./attributes\":1173}],1176:[function(t,e,r){\"use 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../plots/template_attributes\").hovertemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../plot_api/plot_template\").templatedArray,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../plot_api/edit_types\").overrideAll;t(\"../../constants/docs\").FORMAT_LINK;(e.exports=h({hoverinfo:f({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),customdata:void 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o=n[t=i[w]],s=0,l={};for(T=0;T<o.length;T++)l[(e=o[T]).label]||(l[e.label]=0),l[e.label]+=e.value,s+=e.value;for(T=0;T<o.length;T++)(e=o[T]).flow={value:s,labelConcentration:l[e.label]/s,concentration:e.value/s,links:o},e.concentrationscale&&(e.color=a(e.concentrationscale(e.flow.labelConcentration)))}var c=0;for(w=0;w<r.sourceLinks.length;w++)c+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/c;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),s.node.x.length&&s.node.y.length){for(_=0;_<Math.min(s.node.x.length,s.node.y.length,k.nodes.length);_++)if(s.node.x[_]&&s.node.y[_]){var C=[s.node.x[_]*g,s.node.y[_]*v];k.nodes[_].x0=C[0]-d/2,k.nodes[_].x1=C[0]+d/2;var L=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=C[1]-L/2,k.nodes[_].y1=C[1]+L/2}if(\"snap\"===s.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(y=k.nodes));i.update(k)}return{circular:b,key:r,trace:s,guid:f.randstr(),horizontal:h,width:g,height:v,nodePad:s.node.pad,nodeLineColor:s.node.line.color,nodeLineWidth:s.node.line.width,linkLineColor:s.link.line.color,linkLineWidth:s.link.line.width,valueFormat:s.valueformat,valueSuffix:s.valuesuffix,textFont:s.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:h?v:g,dragPerpendicular:h?g:v,arrangement:s.arrangement,sankey:i,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function b(t,e,r){var n=a(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:o.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:_,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function _(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,i=t.link.source.x1,a=t.link.target.x0,o=v(i,a),s=o(.5),l=o(.5),c=t.link.y0-t.link.width/2,u=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2;return\"M\"+i+\",\"+c+\"C\"+s+\",\"+c+\" \"+l+\",\"+f+\" \"+a+\",\"+f+\"L\"+a+\",\"+h+\"C\"+l+\",\"+h+\" \"+s+\",\"+u+\" \"+i+\",\"+u+\"Z\"}}function w(t,e){var r=a(e.color),i=n.nodePadAcross,s=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var l=e.dx,c=Math.max(.5,e.dy),u=\"node_\"+e.pointNumber;return e.group&&(u=f.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:u,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(l),visibleHeight:c,zoneX:-i,zoneY:-s,zoneWidth:l+2*i,zoneHeight:c+2*s,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:o.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,u].join(\"_\"),interactionState:t.interactionState,figure:t}}function T(t){t.attr(\"transform\",(function(t){return h(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function k(t){t.call(T)}function M(t,e){t.call(k),e.attr(\"d\",_())}function A(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function S(t){return t.link.width>1||t.linkLineWidth>0}function E(t){return h(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function C(t){return h(t.horizontal?0:t.labelY,t.horizontal?t.labelY:0)}function L(t){return i.svg.line()([[t.horizontal?t.left?-t.sizeAcross:t.visibleWidth+n.nodeTextOffsetHorizontal:n.nodeTextOffsetHorizontal,0],[t.horizontal?t.left?-n.nodeTextOffsetHorizontal:t.sizeAcross:t.visibleHeight-n.nodeTextOffsetHorizontal,0]])}function I(t){return t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\"}function P(t){return t.horizontal?\"scale(1 1)\":\"scale(-1 1)\"}function z(t){return t.darkBackground&&!t.horizontal?\"rgb(255,255,255)\":\"rgb(0,0,0)\"}function O(t){return t.horizontal&&t.left?\"100%\":\"0%\"}function D(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function R(t,e,r,a){var o=i.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(i){if(\"fixed\"!==i.arrangement&&(f.ensureSingle(a._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){a._fullLayout._dragCover=t})),f.raiseToTop(this),i.interactionState.dragInProgress=i.node,B(i.node),i.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,i.interactionState.hovered),i.interactionState.hovered=!1),\"snap\"===i.arrangement)){var o=i.traceId+\"|\"+i.key;i.forceLayouts[o]?i.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=u.forceSimulation(a).alphaDecay(0).force(\"collide\",u.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(n.forceIterations)).force(\"constrain\",function(t,e,r,i){return function(){for(var t=0,a=0;a<r.length;a++){var o=r[a];o===i.interactionState.dragInProgress?(o.x=o.lastDraggedX,o.y=o.lastDraggedY):(o.vx=(o.originalX-o.x)/n.forceTicksPerFrame,o.y=Math.min(i.size-o.dy/2,Math.max(o.dy/2,o.y))),t=Math.max(t,Math.abs(o.vx),Math.abs(o.vy))}!i.interactionState.dragInProgress&&t<.1&&i.forceLayouts[e].alpha()>0&&i.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,i),function(t,e,r,i,a){window.requestAnimationFrame((function o(){var s;for(s=0;s<n.forceTicksPerFrame;s++)r.forceLayouts[i].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),M(t.filter(N(r)),e),r.forceLayouts[i].alpha()>0)window.requestAnimationFrame(o);else{var l=r.node.originalX;r.node.x0=l-r.visibleWidth/2,r.node.x1=l+r.visibleWidth/2,F(r,a)}}))}(t,e,i,o,a)}})).on(\"drag\",(function(r){if(\"fixed\"!==r.arrangement){var n=i.event.x,a=i.event.y;\"snap\"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=a-r.visibleHeight/2,r.node.y1=a+r.visibleHeight/2):(\"freeform\"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),a=Math.max(0,Math.min(r.size-r.visibleHeight/2,a)),r.node.y0=a-r.visibleHeight/2,r.node.y1=a+r.visibleHeight/2),B(r.node),\"snap\"!==r.arrangement&&(r.sankey.update(r.graph),M(t.filter(N(r)),e))}})).on(\"dragend\",(function(t){if(\"fixed\"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;\"snap\"!==t.arrangement&&F(t,a)}}));t.on(\".drag\",null).call(o)}function F(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}y.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function B(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function N(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,l,c){var u=!1;f.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){u=!0}));var h=t._fullLayout._dragCover,p=r.filter((function(t){return m(t).trace.visible})).map(x.bind(null,l)),v=e.selectAll(\".\"+n.cn.sankey).data(p,d);v.exit().remove(),v.enter().append(\"g\").classed(n.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").attr(\"transform\",E),v.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;f.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=i.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),v.transition().ease(n.ease).duration(n.duration).attr(\"transform\",E);var y=v.selectAll(\".\"+n.cn.sankeyLinks).data(g,d);y.enter().append(\"g\").classed(n.cn.sankeyLinks,!0).style(\"fill\",\"none\");var k=y.selectAll(\".\"+n.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(b.bind(null,t))}),d);k.enter().append(\"path\").classed(n.cn.sankeyLink,!0).call(D,v,c.linkEvents),k.style(\"stroke\",(function(t){return S(t)?o.tinyRGB(a(t.linkLineColor)):t.tinyColorHue})).style(\"stroke-opacity\",(function(t){return S(t)?o.opacity(t.linkLineColor):t.tinyColorAlpha})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})).style(\"stroke-width\",(function(t){return S(t)?t.linkLineWidth:1})).attr(\"d\",_()),k.style(\"opacity\",(function(){return t._context.staticPlot||u||h?1:0})).transition().ease(n.ease).duration(n.duration).style(\"opacity\",1),k.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var M=v.selectAll(\".\"+n.cn.sankeyNodeSet).data(g,d);M.enter().append(\"g\").classed(n.cn.sankeyNodeSet,!0),M.style(\"cursor\",(function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}}));var F=M.selectAll(\".\"+n.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return t-e})),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.map(w.bind(null,t))}),d);F.enter().append(\"g\").classed(n.cn.sankeyNode,!0).call(T).style(\"opacity\",(function(e){return!t._context.staticPlot&&!u||e.partOfGroup?0:1})),F.call(D,v,c.nodeEvents).call(R,k,c,t),F.transition().ease(n.ease).duration(n.duration).call(T).style(\"opacity\",(function(t){return t.partOfGroup?0:1})),F.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var B=F.selectAll(\".\"+n.cn.nodeRect).data(g);B.enter().append(\"rect\").classed(n.cn.nodeRect,!0).call(A),B.style(\"stroke-width\",(function(t){return t.nodeLineWidth})).style(\"stroke\",(function(t){return o.tinyRGB(a(t.nodeLineColor))})).style(\"stroke-opacity\",(function(t){return o.opacity(t.nodeLineColor)})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),B.transition().ease(n.ease).duration(n.duration).call(A);var N=F.selectAll(\".\"+n.cn.nodeCapture).data(g);N.enter().append(\"rect\").classed(n.cn.nodeCapture,!0).style(\"fill-opacity\",0),N.attr(\"x\",(function(t){return t.zoneX})).attr(\"y\",(function(t){return t.zoneY})).attr(\"width\",(function(t){return t.zoneWidth})).attr(\"height\",(function(t){return t.zoneHeight}));var 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),g=p(\"b\"),m=Math.min(d.length,g.length);if(m){e._length=m,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",m<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":778,\"../scatter/constants\":1191,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/line_shape_defaults\":1202,\"../scatter/marker_defaults\":1206,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"./attributes\":1221}],1224:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],1225:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],1226:[function(t,e,r){\"use 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n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r,o){var s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":665,\"../../plots/cartesian/axes\":828,\"../scatter/plot\":1208}],1229:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,g=f(\"geojson\");(\"string\"==typeof g&&\"\"!==g||n.isPlainObject(g))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var m=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(m.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":778,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/marker_defaults\":1206,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"./attributes\":1229}],1232:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],1233:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},{\"../../plots/cartesian/axes\":828}],1234:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/get_trace_color\"),o=t(\"../../lib\").fillText,s=t(\"./attributes\");e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=h.projection.isLonLatOverEdges,d=h.project;if(n.getClosest(l,(function(t){var n=t.lonlat;if(n[0]===i)return 1/0;if(p(n))return 1/0;var a=d(n),o=d([e,r]),s=Math.abs(a[0]-o[0]),l=Math.abs(a[1]-o[1]),c=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-c,1-3/c)}),t),!1!==t.index){var g=l[t.index],m=g.lonlat,v=[u.c2p(m),f.c2p(m)],y=g.mrc||1;t.x0=v[0]-y,t.x1=v[0]+y,t.y0=v[1]-y,t.y1=v[1]+y,t.loc=g.loc,t.lon=m[0],t.lat=m[1];var x={};x[c.geo]={_subplot:h};var b=c._module.formatLabels(g,c,x);return 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T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),g=Math.max(g,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,g]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,g=m[p],v=m[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[g*y/h,x/h]}}return o}}},{\"../../components/drawing\":665,\"../../components/fx/helpers\":679,\"../../constants/interactions\":752,\"../../lib\":778,\"../../lib/gl_format_color\":774,\"../../plots/cartesian/axis_ids\":831,\"../../registry\":911,\"../scatter/make_bubble_size_func\":1204,\"../scatter/subtypes\":1212,\"./constants\":1241,\"./helpers\":1246,\"color-normalize\":125,\"fast-isnumeric\":241,\"svg-path-sdf\":574}],1243:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,g){function m(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,g,m);if(x){u(t,e,g,m);var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";m(\"text\"),m(\"hovertext\"),m(\"hovertemplate\"),m(\"mode\",b),l.hasLines(e)&&(m(\"connectgaps\"),h(t,e,r,g,m),m(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,g,m),m(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(m(\"texttemplate\"),d(t,e,g,m));var _=(e.line||{}).color,w=(e.marker||{}).color;m(\"fill\"),\"none\"!==e.fill&&p(t,e,r,m);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,m)}else e.visible=!1}},{\"../../lib\":778,\"../../registry\":911,\"../scatter/constants\":1191,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/marker_defaults\":1206,\"../scatter/period_defaults\":1207,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"../scatter/xy_defaults\":1214,\"./attributes\":1239,\"./helpers\":1246}],1244:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},g=c.textUnselectedOptions[u]||{},m=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=g.color,x=p.color,b=Array.isArray(x);for(m.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],m.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;m.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(m)}}},{\"../../components/color\":643,\"../../constants/interactions\":752,\"../../lib\":778}],1245:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":1196}],1246:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":1241}],1247:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var g=d&&d.line;g&&(h.mlc=Array.isArray(g.color)?g.color[f]:g.color,h.mlw=i.isArrayOrTypedArray(g.width)?g.width[f]:g.width);var m=d&&d.gradient;m&&\"none\"!==m.type&&(h.mgt=Array.isArray(m.type)?m.type[f]:m.type,h.mgc=Array.isArray(m.color)?m.color[f]:m.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,M=o._origY,A=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:M?M[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?A.text=h.htx:h.tx?A.text=h.tx:o.text&&(A.text=o.text),i.fillText(h,o,A),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,A),A}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d=t.cd,g=d[0].t,m=d[0].trace,v=t.xa,y=t.ya,x=g.x,b=g.y,_=v.c2p(e),w=y.c2p(r),T=t.distance;if(g.tree){var k=v.p2c(_-T),M=v.p2c(_+T),A=y.p2c(w-T),S=y.p2c(w+T);i=\"x\"===n?g.tree.range(Math.min(k,M),Math.min(y._rl[0],y._rl[1]),Math.max(k,M),Math.max(y._rl[0],y._rl[1])):g.tree.range(Math.min(k,M),Math.min(A,S),Math.max(k,M),Math.max(A,S))}else i=g.ids;var E=T;if(\"x\"===n)for(c=0;c<i.length;c++)s=x[i[c]],(u=Math.abs(v.c2p(s)-_))<E&&(E=u,f=y.c2p(b[i[c]])-w,p=Math.sqrt(u*u+f*f),a=i[c]);else for(c=i.length-1;c>-1;c--)s=x[i[c]],l=b[i[c]],u=v.c2p(s)-_,f=y.c2p(l)-w,(h=Math.sqrt(u*u+f*f))<E&&(E=p=h,a=i[c]);return t.index=a,t.distance=E,t.dxy=p,void 0===a?[t]:[o(t,x,b,m)]},calcHover:o}},{\"../../lib\":778,\"../../registry\":911,\"../scatter/get_trace_color\":1197}],1248:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":841,\"../scatter/cross_trace_defaults\":1193,\"../scatter/marker_colorbar\":1205,\"./attributes\":1239,\"./calc\":1240,\"./defaults\":1243,\"./format_labels\":1245,\"./hover\":1247,\"./plot\":1249,\"./select\":1251}],1249:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r){var n=t._size,i=t.width,a=t.height;return[n.l+e.domain[0]*n.w,n.b+r.domain[0]*n.h,i-n.r-(1-e.domain[1])*n.w,a-n.t-(1-r.domain[1])*n.h]}e.exports=function(t,e,r){if(r.length){var d,g,m=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=m._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_,{constPointSize:!0})),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var g=s._nexttrace;if(g){var m=v.lineOptions[e+1];if(m){var y=m.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=g.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var M=m.dragmode,A=l(M),S=m.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],C=E.trace,L=E.t,I=L.index,P=C._length,z=L.x,O=L.y;if(C.selectedpoints||A||S){if(A||(A=!0),C.selectedpoints){var D=v.selectBatch[I]=s.selIndices2selPoints(C),R={};for(g=0;g<D.length;g++)R[D[g]]=1;var F=[];for(g=0;g<P;g++)R[g]||F.push(g);v.unselectBatch[I]=F}var B=L.xpx=new Array(P),N=L.ypx=new Array(P);for(g=0;g<P;g++)B[g]=y.c2p(z[g]),N[g]=x.c2p(O[g])}else L.xpx=L.ypx=null}if(A){if(v.select2d||(v.select2d=n(m._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(m,y,x),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":661,\"../../lib\":778,\"../../lib/prepare_regl\":791,\"../scatter/link_traces\":1203,\"../scatter/subtypes\":1212,\"./edit_style\":1244,\"gl-text\":352,\"regl-error2d\":534,\"regl-line2d\":535,\"regl-scatter2d\":537}],1250:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else 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n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/mapbox/layout_attributes\":887,\"../../plots/template_attributes\":906,\"../scatter/attributes\":1187,\"../scattergeo/attributes\":1229}],1253:[function(t,e,r){\"use 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e,r,i,a,s,l,c,u,f,h,p,d,g=t._x,m=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,M=\"\";for(y&&(c=g[0],f=m[0],p=v[0]),y>1&&(u=g[y-1],h=m[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,g[e]),b=Math.min(b,g[e]),_=Math.max(_,m[e]),w=Math.min(w,m[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||g[e]===c||(a=!0,M+=\"x\"),s||m[e]===f||(s=!0,M+=\"y\"),l||v[e]===p||(l=!0,M+=\"z\");a||(M+=\"x\"),s||(M+=\"y\"),l||(M+=\"z\");var A=o(t._x),S=o(t._y),E=o(t._z);M=(M=(M=M.replace(\"x\",(c>u?\"-\":\"+\")+\"x\")).replace(\"y\",(f>h?\"-\":\"+\")+\"y\")).replace(\"z\",(p>d?\"-\":\"+\")+\"z\");var C=function(){y=0,A=[],S=[],E=[]};(!y||y<A.length*S.length*E.length)&&C();var L=function(t){return\"x\"===t?g:\"y\"===t?m:v},I=function(t){return\"x\"===t?A:\"y\"===t?S:E},P=function(t){return t[y-1]<t[0]?-1:1},z=L(M[1]),O=L(M[3]),D=L(M[5]),R=I(M[1]).length,F=I(M[3]).length,B=I(M[5]).length,N=!1,j=function(t,e,r){return 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M={};u.label&&(M.label=u.label),u.hasOwnProperty(\"v\")&&(M.value=u.v,M.valueLabel=x.formatValue(u.v,l)),M.currentPath=x.getPath(t.data),h||(M.percentParent=d/x.getValue(p),M.percentParentLabel=x.formatPercent(M.percentParent,l),M.parent=x.getPtLabel(p)),M.percentEntry=d/x.getValue(e),M.percentEntryLabel=x.formatPercent(M.percentEntry,l),M.entry=x.getPtLabel(e),M.percentRoot=d/x.getValue(f),M.percentRootLabel=x.formatPercent(M.percentRoot,l),M.root=x.getPtLabel(f),u.hasOwnProperty(\"color\")&&(M.color=u.color);var A=o.castOption(r,u.i,\"text\");return(o.isValidTextValue(A)||\"\"===A)&&(M.text=A),M.customdata=o.castOption(r,u.i,\"customdata\"),o.texttemplateString(k,M,i._d3locale,M,r._meta||{})}},{\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../bar/style\":935,\"../bar/uniform_text\":937,\"../pie/helpers\":1166,\"../pie/plot\":1170,\"./constants\":1302,\"./fx\":1304,\"./helpers\":1305,\"./style\":1310,d3:169,\"d3-hierarchy\":161}],1310:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":643,\"../../lib\":778,\"../bar/uniform_text\":937,d3:169}],1311:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;function c(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var u=e.exports=l(s({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:a(),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:c(),y:c(),z:c()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:s({},i.zauto,{}),zmin:s({},i.zmin,{}),zmax:s({},i.zmax,{})},hoverinfo:s({},o.hoverinfo),showlegend:s({},o.showlegend,{dflt:!1})}),\"calc\",\"nested\");u.x.editType=u.y.editType=u.z.editType=\"calc+clearAxisTypes\",u.transforms=void 0},{\"../../components/color\":643,\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/template_attributes\":906}],1312:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":651}],1313:[function(t,e,r){\"use strict\";var n=t(\"gl-surface3d\"),i=t(\"ndarray\"),a=t(\"ndarray-linear-interpolate\").d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function g(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function m(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(g(t,n))}return e}function v(t){for(var e=m(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=m(t),n=m(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),g=l.dataScale,m=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<m;r++)x[e][r]=[];for(r=0;r<m;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<m;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=g[e];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v])];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(m*v),[m,v]);for(r=0;r<m;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=g[2],w.intensityBounds[1]*=g[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var M=[!0,!0,!0],A=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[A[e]];M[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=M,p.enableHighlight=M,p.snapToData=!0,\"lighting\"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),\"lightposition\"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{\"../../components/colorscale\":655,\"../../lib\":778,\"../../lib/gl_format_color\":774,\"../../lib/str2rgbarray\":802,\"../heatmap/find_empties\":1071,\"../heatmap/interp2d\":1074,\"gl-surface3d\":351,ndarray:495,\"ndarray-linear-interpolate\":489}],1314:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var 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d=i.ensureSingle(h,\"g\",\"slicetext\"),T=i.ensureSingle(d,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),E=i.ensureUniformFontSize(t,u.determineTextFont(C,s,S.font,{onPathbar:!0}));T.text(s._text||\" \").classed(\"slicetext\",!0).attr(\"text-anchor\",\"start\").call(a.font,E).call(o.convertToTspans,t),s.textBB=a.bBox(T.node()),s.transform=b(s,{fontSize:E.size,onPathbar:!0}),s.transform.fontSize=E.size,w?T.transition().attrTween(\"transform\",(function(t){var e=M(t,!0,A,[g,m]);return function(t){return _(e(t))}})):T.attr(\"transform\",_(s))}))}},{\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../sunburst/fx\":1304,\"../sunburst/helpers\":1305,\"./constants\":1328,\"./partition\":1335,\"./style\":1337,d3:169}],1331:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../components/drawing\"),o=t(\"../../lib/svg_text_utils\"),s=t(\"./partition\"),l=t(\"./style\").styleOne,c=t(\"./constants\"),u=t(\"../sunburst/helpers\"),f=t(\"../sunburst/fx\"),h=t(\"../sunburst/plot\").formatSliceLabel;e.exports=function(t,e,r,p,d){var 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strict\";e.exports={attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../box/defaults\").crossTraceDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),crossTraceCalc:t(\"./cross_trace_calc\"),plot:t(\"./plot\"),style:t(\"./style\"),styleOnSelect:t(\"../scatter/style\").styleOnSelect,hoverPoints:t(\"./hover\"),selectPoints:t(\"../box/select\"),moduleType:\"trace\",name:\"violin\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"violinLayout\",\"zoomScale\"],meta:{}}},{\"../../plots/cartesian\":841,\"../box/defaults\":949,\"../box/select\":956,\"../scatter/style\":1211,\"./attributes\":1338,\"./calc\":1339,\"./cross_trace_calc\":1340,\"./defaults\":1341,\"./hover\":1343,\"./layout_attributes\":1345,\"./layout_defaults\":1346,\"./plot\":1347,\"./style\":1348}],1345:[function(t,e,r){\"use strict\";var 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f={\"stroke-width\":u+\"px\",\"stroke-dasharray\":2*u+\"px,\"+u+\"px\"};o.selectAll(\"path.mean\").style(f).call(i.stroke,c.color),o.selectAll(\"path.meanline\").style(f).call(i.stroke,c.color),a(o,r,t)}))}},{\"../../components/color\":643,\"../scatter/style\":1211,d3:169}],1349:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../isosurface/attributes\"),a=t(\"../surface/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,c=e.exports=l(s({x:i.x,y:i.y,z:i.z,value:i.value,isomin:i.isomin,isomax:i.isomax,surface:i.surface,spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:1}},slices:i.slices,caps:i.caps,text:i.text,hovertext:i.hovertext,hovertemplate:i.hovertemplate},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{colorbar:i.colorbar,opacity:i.opacity,opacityscale:a.opacityscale,lightposition:i.lightposition,lighting:i.lighting,flatshading:i.flatshading,contour:i.contour,hoverinfo:s({},o.hoverinfo),showlegend:s({},o.showlegend,{dflt:!1})}),\"calc\",\"nested\");c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 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-   "source": [
-    "fig = px.choropleth(geoplot_2019,                        \n",
-    "                     locations=\"Country_code\",           \n",
-    "                     color=\"Respondents\",               \n",
-    "                     hover_name=\"Country\",              \n",
-    "                     projection=\"natural earth\",        \n",
-    "                     color_continuous_scale = 'Peach',  \n",
-    "                     range_color=[0,10000]     \n",
-    "                     )        \n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Analysing salary distribution among top ten countries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 340,
-   "metadata": {},
-   "outputs": [
-    {
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",
-      "text/plain": [
-       "<Figure size 1440x720 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize = (20, 10))\n",
-    "\n",
-    "countries = cleaned_df_2019['Country'].value_counts().sort_values(ascending = False)[:10].index.tolist()\n",
-    "\n",
-    "for i, country in enumerate(countries):\n",
-    "    plt.subplot(4, 3, i + 1)\n",
-    "    temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['Country'] == country, 'SalaryUSD']\n",
-    "\n",
-    "    ax = temp_salaries.plot(kind = 'kde')\n",
-    "    ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n",
-    "    ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n",
-    "    ax.set_xlabel('Annual Salary in USD')\n",
-    "    ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n",
-    "    ax.set_title('Annual Salary Distribution in {}'.format(country))\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overall, the country which has the highest mean annual salary is the United States of America(240,000) Dollars. The second highest country which provides the highest mean salary is Australia(164,926) Dollars. Though India has a higher number of respondents, it has the lowest mean salary of $25,213.We can understand that the mean salary of a developed country is much higher than that of a developing country."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Analysing impact of education level on salary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 341,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#removing outliers from Associate group\n",
-    "salary_edu = cleaned_df_2019.groupby(['EdLevel'])\n",
-    "associate_mean = salary_edu.get_group('Associate').mean()['SalaryUSD']\n",
-    "filt = (salary_edu.get_group('Associate')['SalaryUSD'] > associate_mean).to_frame()\n",
-    "filt = filt[filt['SalaryUSD'] == False]\n",
-    "cleaned_df_2019.drop(index=filt.index, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 342,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize = (20, 10))\n",
-    "\n",
-    "education_2019 = cleaned_df_2019['EdLevel'].value_counts().sort_values(ascending = False).index.tolist()\n",
-    "\n",
-    "for i, edu in enumerate(education_2019):\n",
-    "    plt.subplot(3, 2, i + 1)\n",
-    "    temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['EdLevel'] == edu, 'SalaryUSD']\n",
-    "\n",
-    "    ax = temp_salaries.plot(kind = 'kde')\n",
-    "    ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n",
-    "    ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n",
-    "    ax.set_xlabel('Annual Salary in USD')\n",
-    "    ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n",
-    "    ax.set_title('Annual Salary Distribution in {}'.format(edu))\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see, the respondents who have done Doctorate have the highest mean salary among all other education levels. Secondly, the respondents who have done Bachelors degree have more salary than that of Masters degree holders. This may be due to years of professional coding experience and due to the higher number of respondents in that category than that of Masters degree(No of respondents in Bachelor degree is 35659 and number of respondents in masters degree is 16940)\n",
-    "\n",
-    "The most interesting is that the respondents who do not have any degree have a mean salary of $90k. This shows the improvement in online learning and advancement of technology that is shifting the company from relying on University degrees."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Distribution of respondents based on age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 343,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col =['Age', 'Country']\n",
-    "df_2020= cleaned_df_2019[col]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 344,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "df_2020['Age_range'] = 0\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=15) & (df_2020['Age']<=19), '15 - 19 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=20) & (df_2020['Age']<=24), '20 - 24 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=25) & (df_2020['Age']<=29), '25 - 29 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=30) & (df_2020['Age']<=34), '30 - 34 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=35) & (df_2020['Age']<=39), '35 - 39 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=40) & (df_2020['Age']<=45), '40 - 45 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=46), '46 and above years', df_2020.Age_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 345,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_2020_age = df_2020.groupby(['Age_range']).size().reset_index(name='Count')\n",
-    "df_2020_age.sort_values(by=['Count'], ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 346,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (10, 6))\n",
-    "sns.set_style('white')\n",
-    "sns.set_context('paper', font_scale=1.5)\n",
-    "sns.barplot(x=\"Count\",  y=\"Age_range\", palette='inferno', data=df_2020_age).set(xlabel=\"Age\", ylabel = \"Count\")\n",
-    "plt.title('Distribution of respondents based on age')\n",
-    "\n",
-    "for y, x in enumerate(df_2020_age['Count']):\n",
-    "    label = \"{:,}\".format(int(x))\n",
-    "    plt.annotate(label, xy=(x, y), va='center')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Impact on the increase in popularity of a language in the current-year due to developer’s interest in the previous year.(Based on 2019 and 2020 dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 347,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#seperate language for getting individual one\n",
-    "cols = ['LanguageWorkedWith']\n",
-    "df_19 = survey_df_2019[cols]\n",
-    "df_20 = df2020[cols]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 348,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#splitting 'LanguageWorkedWith' and sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
-    "language_2019['Language'] = language_2019.index\n",
-    "language_2019.reset_index(drop=True, inplace=True)\n",
-    "language_2019 = language_2019[['Language', '2019']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 349,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
-    "language_2020['Language'] = language_2020.index\n",
-    "language_2020.reset_index(drop=True, inplace=True)\n",
-    "language_2020 = language_2020[['Language', '2020']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 350,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "language_all= pd.merge(language_2019, language_2020,on = ['Language'], how = 'outer')\n",
-    "language_all.fillna(0, inplace=True)\n",
-    "language_all['2019'] = language_all['2019']. astype(int)\n",
-    "language_all['2020'] = language_all['2020']. astype(int)\n",
-    "language_all.set_index('Language', inplace=True)\n",
-    "#language_all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 351,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Language</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>JavaScript</th>\n",
-       "      <td>0.136468</td>\n",
-       "      <td>0.137808</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>HTML/CSS</th>\n",
-       "      <td>0.126595</td>\n",
-       "      <td>0.126495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SQL</th>\n",
-       "      <td>0.109882</td>\n",
-       "      <td>0.112110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Python</th>\n",
-       "      <td>0.081963</td>\n",
-       "      <td>0.086418</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Java</th>\n",
-       "      <td>0.080446</td>\n",
-       "      <td>0.078374</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                2019      2020\n",
-       "Language                      \n",
-       "JavaScript  0.136468  0.137808\n",
-       "HTML/CSS    0.126595  0.126495\n",
-       "SQL         0.109882  0.112110\n",
-       "Python      0.081963  0.086418\n",
-       "Java        0.080446  0.078374"
-      ]
-     },
-     "execution_count": 351,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "language19_20=(language_all/language_all.sum())\n",
-    "language19_20.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 352,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "language19_20.plot(kind='bar', figsize=(12,8))\n",
-    "plt.title('Programming Language worked by Respondents in 2019 and 2020', fontsize = 18)\n",
-    "plt.xlabel('Languages', fontsize = 14)\n",
-    "plt.ylabel('Percentages', fontsize = 14)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The most language that worked in 2019 and 2020 is JavaScript.In 2020, people worked slightly in javascript compare to 2019. The 2nd highest working language is HTML/CSS. For HTML/CSS the percentage is slightly low in 2020. There are some language people worked in only one year. Elixir, Clojure, F#, Web assembly are those languages that people used in 2019. Respondent started to use Perl, Haskell, Julia in 2020 on a small scale."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Programming language desired to work"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 353,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#language desire net year\n",
-    "cols_1 = ['LanguageDesireNextYear']\n",
-    "df_19 = survey_df_2019[cols_1]\n",
-    "df_20 = df2020[cols_1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 354,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "languagedesire_2019= df_19['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
-    "languagedesire_2019['Language'] = languagedesire_2019.index\n",
-    "languagedesire_2019.reset_index(drop=True, inplace=True)\n",
-    "languagedesire_2019 = languagedesire_2019[['Language', '2019']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 355,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "languagedesire_2020= df_20['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
-    "languagedesire_2020['Language'] = languagedesire_2020.index\n",
-    "languagedesire_2020.reset_index(drop=True, inplace=True)\n",
-    "languagedesire_2020= languagedesire_2020[['Language','2020']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 356,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "languagedesire_all = pd.merge(languagedesire_2019, languagedesire_2020,on = ['Language'], how = 'outer')\n",
-    "languagedesire_all.fillna(0, inplace=True)\n",
-    "languagedesire_all['2019'] = languagedesire_all['2019']. astype(int)\n",
-    "languagedesire_all['2020'] = languagedesire_all['2020']. astype(int)\n",
-    "languagedesire_all.set_index('Language', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 357,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Language</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>JavaScript</th>\n",
-       "      <td>0.113559</td>\n",
-       "      <td>0.111381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Python</th>\n",
-       "      <td>0.099827</td>\n",
-       "      <td>0.110728</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>HTML/CSS</th>\n",
-       "      <td>0.092490</td>\n",
-       "      <td>0.088983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SQL</th>\n",
-       "      <td>0.085326</td>\n",
-       "      <td>0.085879</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TypeScript</th>\n",
-       "      <td>0.062380</td>\n",
-       "      <td>0.076744</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                2019      2020\n",
-       "Language                      \n",
-       "JavaScript  0.113559  0.111381\n",
-       "Python      0.099827  0.110728\n",
-       "HTML/CSS    0.092490  0.088983\n",
-       "SQL         0.085326  0.085879\n",
-       "TypeScript  0.062380  0.076744"
-      ]
-     },
-     "execution_count": 357,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "languagedesire19_20=(languagedesire_all/languagedesire_all.sum())\n",
-    "languagedesire19_20.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 358,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "languagedesire19_20.plot(kind='bar', figsize=(12,8))\n",
-    "plt.title('Programming Language desire to work', fontsize = 18)\n",
-    "plt.xlabel('Languages', fontsize = 14)\n",
-    "plt.ylabel('Percentages', fontsize = 14)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In 2019, respondents said that they wanted to work in javascript is around more than 10 % and the fewer respond have a desire to work on VBA next year. People started to work in Haskell, Julia, and pearl in 2019 though the amount was less around 5% of people have the desire to work in those languages in 2021. Here, phyton is the 2nd one in which people have the desire to work in both 2019 and 2020."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distribution of surveyors based on their developer role."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 359,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col = ['DevType']\n",
-    "dev_18=df[col]\n",
-    "dev_19 = survey_df_2019[col]\n",
-    "dev_20= df2020[col]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 360,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "dev_2018= dev_18['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n",
-    "dev_2018['Developer'] = dev_2018.index\n",
-    "dev_2018.reset_index(drop=True, inplace=True)\n",
-    "dev_2018 = dev_2018[['Developer', '2018']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 361,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dev_2019= dev_19['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
-    "dev_2019['Developer'] = dev_2019.index\n",
-    "dev_2019.reset_index(drop=True, inplace=True)\n",
-    "dev_2019 = dev_2019[['Developer', '2019']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 362,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dev_2020= dev_20['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
-    "dev_2020['Developer'] = dev_2020.index\n",
-    "dev_2020.reset_index(drop=True, inplace=True)\n",
-    "dev_2020 = dev_2020[['Developer', '2020']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 363,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Developer, back-end</th>\n",
-       "      <td>27228</td>\n",
-       "      <td>37700</td>\n",
-       "      <td>21683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, full-stack</th>\n",
-       "      <td>27125</td>\n",
-       "      <td>39479</td>\n",
-       "      <td>21683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, front-end</th>\n",
-       "      <td>18296</td>\n",
-       "      <td>24527</td>\n",
-       "      <td>14356</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, desktop or enterprise applications</th>\n",
-       "      <td>11784</td>\n",
-       "      <td>15748</td>\n",
-       "      <td>9172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, mobile</th>\n",
-       "      <td>9482</td>\n",
-       "      <td>13298</td>\n",
-       "      <td>7254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DevOps specialist</th>\n",
-       "      <td>5969</td>\n",
-       "      <td>8463</td>\n",
-       "      <td>4895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Database administrator</th>\n",
-       "      <td>5722</td>\n",
-       "      <td>8616</td>\n",
-       "      <td>4452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Designer</th>\n",
-       "      <td>5321</td>\n",
-       "      <td>7883</td>\n",
-       "      <td>3791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>System administrator</th>\n",
-       "      <td>5242</td>\n",
-       "      <td>8094</td>\n",
-       "      <td>4094</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, embedded applications or devices</th>\n",
-       "      <td>4750</td>\n",
-       "      <td>6494</td>\n",
-       "      <td>3536</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Data or business analyst</th>\n",
-       "      <td>4024</td>\n",
-       "      <td>5526</td>\n",
-       "      <td>2939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Data scientist or machine learning specialist</th>\n",
-       "      <td>3988</td>\n",
-       "      <td>5788</td>\n",
-       "      <td>2939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, QA or test</th>\n",
-       "      <td>3947</td>\n",
-       "      <td>5927</td>\n",
-       "      <td>3161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Engineer, data</th>\n",
-       "      <td>3738</td>\n",
-       "      <td>5240</td>\n",
-       "      <td>2776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Academic researcher</th>\n",
-       "      <td>3552</td>\n",
-       "      <td>5070</td>\n",
-       "      <td>2610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Educator</th>\n",
-       "      <td>2928</td>\n",
-       "      <td>3930</td>\n",
-       "      <td>2213</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, game or graphics</th>\n",
-       "      <td>2789</td>\n",
-       "      <td>3902</td>\n",
-       "      <td>2118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Engineering manager</th>\n",
-       "      <td>2724</td>\n",
-       "      <td>3803</td>\n",
-       "      <td>1979</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Product manager</th>\n",
-       "      <td>2497</td>\n",
-       "      <td>3630</td>\n",
-       "      <td>1891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Scientist</th>\n",
-       "      <td>2086</td>\n",
-       "      <td>3149</td>\n",
-       "      <td>1573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Engineer, site reliability</th>\n",
-       "      <td>1940</td>\n",
-       "      <td>2684</td>\n",
-       "      <td>1491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Senior executive/VP</th>\n",
-       "      <td>1320</td>\n",
-       "      <td>1795</td>\n",
-       "      <td>850</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Marketing or sales professional</th>\n",
-       "      <td>642</td>\n",
-       "      <td>789</td>\n",
-       "      <td>401</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Student</th>\n",
-       "      <td>0</td>\n",
-       "      <td>10113</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                2018   2019   2020\n",
-       "Developer                                                         \n",
-       "Developer, back-end                            27228  37700  21683\n",
-       "Developer, full-stack                          27125  39479  21683\n",
-       "Developer, front-end                           18296  24527  14356\n",
-       "Developer, desktop or enterprise applications  11784  15748   9172\n",
-       "Developer, mobile                               9482  13298   7254\n",
-       "DevOps specialist                               5969   8463   4895\n",
-       "Database administrator                          5722   8616   4452\n",
-       "Designer                                        5321   7883   3791\n",
-       "System administrator                            5242   8094   4094\n",
-       "Developer, embedded applications or devices     4750   6494   3536\n",
-       "Data or business analyst                        4024   5526   2939\n",
-       "Data scientist or machine learning specialist   3988   5788   2939\n",
-       "Developer, QA or test                           3947   5927   3161\n",
-       "Engineer, data                                  3738   5240   2776\n",
-       "Academic researcher                             3552   5070   2610\n",
-       "Educator                                        2928   3930   2213\n",
-       "Developer, game or graphics                     2789   3902   2118\n",
-       "Engineering manager                             2724   3803   1979\n",
-       "Product manager                                 2497   3630   1891\n",
-       "Scientist                                       2086   3149   1573\n",
-       "Engineer, site reliability                      1940   2684   1491\n",
-       "Senior executive/VP                             1320   1795    850\n",
-       "Marketing or sales professional                  642    789    401\n",
-       "Student                                            0  10113      0"
-      ]
-     },
-     "execution_count": 363,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df18_19 = pd.merge(dev_2018, dev_2019,on = ['Developer'], how = 'outer')\n",
-    "devtype_all = pd.merge(df18_19,dev_2020, on=[\"Developer\"], how=\"outer\")\n",
-    "devtype_all.fillna(0, inplace=True)\n",
-    "devtype_all['2018'] = devtype_all['2018']. astype(int)\n",
-    "devtype_all['2019'] = devtype_all['2019']. astype(int)\n",
-    "devtype_all['2020'] =devtype_all['2020'].astype(int)\n",
-    "devtype_all.set_index('Developer', inplace=True)\n",
-    "devtype_all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 364,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>24.000000</td>\n",
-       "      <td>24.000000</td>\n",
-       "      <td>24.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>6545.583333</td>\n",
-       "      <td>9652.000000</td>\n",
-       "      <td>5077.375000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>7441.476051</td>\n",
-       "      <td>10257.524072</td>\n",
-       "      <td>5956.696034</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>789.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2667.250000</td>\n",
-       "      <td>3877.250000</td>\n",
-       "      <td>1957.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3967.500000</td>\n",
-       "      <td>5857.500000</td>\n",
-       "      <td>2939.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>5783.750000</td>\n",
-       "      <td>8990.250000</td>\n",
-       "      <td>4562.750000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>27228.000000</td>\n",
-       "      <td>39479.000000</td>\n",
-       "      <td>21683.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               2018          2019          2020\n",
-       "count     24.000000     24.000000     24.000000\n",
-       "mean    6545.583333   9652.000000   5077.375000\n",
-       "std     7441.476051  10257.524072   5956.696034\n",
-       "min        0.000000    789.000000      0.000000\n",
-       "25%     2667.250000   3877.250000   1957.000000\n",
-       "50%     3967.500000   5857.500000   2939.000000\n",
-       "75%     5783.750000   8990.250000   4562.750000\n",
-       "max    27228.000000  39479.000000  21683.000000"
-      ]
-     },
-     "execution_count": 364,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "devtype_all.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 365,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Developer, back-end</th>\n",
-       "      <td>0.173323</td>\n",
-       "      <td>0.162747</td>\n",
-       "      <td>0.177938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, full-stack</th>\n",
-       "      <td>0.172667</td>\n",
-       "      <td>0.170427</td>\n",
-       "      <td>0.177938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, front-end</th>\n",
-       "      <td>0.116465</td>\n",
-       "      <td>0.105880</td>\n",
-       "      <td>0.117810</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, desktop or enterprise applications</th>\n",
-       "      <td>0.075012</td>\n",
-       "      <td>0.067982</td>\n",
-       "      <td>0.075269</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, mobile</th>\n",
-       "      <td>0.060359</td>\n",
-       "      <td>0.057406</td>\n",
-       "      <td>0.059529</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   2018      2019      2020\n",
-       "Developer                                                                  \n",
-       "Developer, back-end                            0.173323  0.162747  0.177938\n",
-       "Developer, full-stack                          0.172667  0.170427  0.177938\n",
-       "Developer, front-end                           0.116465  0.105880  0.117810\n",
-       "Developer, desktop or enterprise applications  0.075012  0.067982  0.075269\n",
-       "Developer, mobile                              0.060359  0.057406  0.059529"
-      ]
-     },
-     "execution_count": 365,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dt_all=devtype_all/devtype_all.sum()\n",
-    "dt_all.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 366,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(devtype_all/devtype_all.sum()).plot(kind='bar', figsize=(12,8))\n",
-    "plt.title('Developer Types pertcentages', fontsize = 18)\n",
-    "plt.xlabel('Developer Types', fontsize = 14)\n",
-    "plt.ylabel('Percentages', fontsize = 14)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In developer types, developers who are full stack and working backends are the most in the three years. There is a presence of student developers only in 2019 the percentage is 7.5%. Those who are working back end and full stack their percentages increased throughout the three years. For those who are working as marketing and sales professionals, their percentage is lowest compare to others."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  Impact of education/experience/responsibilities on gender inequalities.(Based on 2019 dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 367,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['Gender','EdLevel', 'Dependents', 'SalaryUSD', 'YearsCodePro', 'Age', 'Country']\n",
-    "df2019 = survey_df_2019[cols]\n",
-    "df2019 = df2019[df2019.Gender != \"Non-binary\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 368,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2019['exp_range'] = 0\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro >= 0) & (df2019.YearsCodePro <= 5), '0 - 5 years', df2019.exp_range)\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 5) & (df2019.YearsCodePro <= 10), '6 - 10 years', df2019.exp_range)\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 10) & (df2019.YearsCodePro <= 15), '11 - 15 years', df2019.exp_range)\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 15) & (df2019.YearsCodePro <= 20), '16 - 20 years', df2019.exp_range)\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 20), 'more that 20 years', df2019.exp_range)\n",
-    "#df2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 369,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(2,2, figsize = (12, 8))\n",
-    "g1 = sns.countplot('Gender', data=df2019, ax=axes[0][0]).set(title = 'A. Gender')\n",
-    "g2 = sns.countplot('Dependents', data=df2019, ax=axes[0][1]).set( title = 'B. Dependents')\n",
-    "g3 = sns.countplot('EdLevel', data=df2019, ax=axes[1][0]).set(title = 'C. EdLevel')\n",
-    "g4 = sns.countplot('exp_range', data=df2019, ax=axes[1][1]).set(title = 'D. exp_range')\n",
-    "\n",
-    "axes[1][0].tick_params(axis='x', rotation=45)\n",
-    "axes[1][1].tick_params(axis='x', rotation=45)\n",
-    "    \n",
-    "fig.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 370,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='EdLevel', ylabel='count'>"
-      ]
-     },
-     "execution_count": 370,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.countplot('EdLevel', hue='Gender', data=df2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 371,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='Dependents', ylabel='count'>"
-      ]
-     },
-     "execution_count": 371,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "sns.countplot('Dependents', hue='Gender', data=df2019)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Analysis**<br>\n",
-    "\n",
-    "\n",
-    "After exploring the 2019 dataset, we have found that we cannot answer this question since male and female observations are significantly unbalanced."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What is the gender distribution among top 5 countries of respondents in 2019?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 372,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Total</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>252</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>15895</td>\n",
-       "      <td>17837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>253</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>1942</td>\n",
-       "      <td>17837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>India</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>6632</td>\n",
-       "      <td>7046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>India</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>414</td>\n",
-       "      <td>7046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>4847</td>\n",
-       "      <td>5130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>283</td>\n",
-       "      <td>5130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>395</td>\n",
-       "      <td>4933</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>249</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>4538</td>\n",
-       "      <td>4933</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>275</td>\n",
-       "      <td>2857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>2582</td>\n",
-       "      <td>2857</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Country Gender  Count  Total\n",
-       "252   United States    Man  15895  17837\n",
-       "253   United States  Woman   1942  17837\n",
-       "101           India    Man   6632   7046\n",
-       "102           India  Woman    414   7046\n",
-       "84          Germany    Man   4847   5130\n",
-       "85          Germany  Woman    283   5130\n",
-       "250  United Kingdom  Woman    395   4933\n",
-       "249  United Kingdom    Man   4538   4933\n",
-       "39           Canada  Woman    275   2857\n",
-       "38           Canada    Man   2582   2857"
-      ]
-     },
-     "execution_count": 372,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all = df2019.groupby(['Country','Gender']).size().reset_index(name ='Count')\n",
-    "all['Total'] = all.groupby(['Country'])['Count'].transform('sum')\n",
-    "all = all.sort_values(by=['Total'], ascending=False)\n",
-    "#all.set_index('Total')\n",
-    "Top = all[:10].sort_values(by=['Total'], ascending=False)\n",
-    "Top"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 373,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# from raw value to percentage\n",
-    "total = Top.groupby(['Country'])['Count'].sum().reset_index()\n",
-    "total['Percentage'] = [i / j * 100 for i,j in zip(total['Count'], total['Count'])]\n",
-    "\n",
-    "woman = Top[Top.Gender=='Woman'].groupby(['Country'])['Count'].sum().reset_index()\n",
-    "woman['Percentage'] = [i / j * 100 for i,j in zip(woman['Count'], total['Count'])]\n",
-    "woman.sort_values(by=['Percentage'], ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 374,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>1942</td>\n",
-       "      <td>10.887481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>275</td>\n",
-       "      <td>9.625481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>395</td>\n",
-       "      <td>8.007298</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>India</td>\n",
-       "      <td>414</td>\n",
-       "      <td>5.875674</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>283</td>\n",
-       "      <td>5.516569</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Country  Count  Percentage\n",
-       "4   United States   1942   10.887481\n",
-       "0          Canada    275    9.625481\n",
-       "3  United Kingdom    395    8.007298\n",
-       "2           India    414    5.875674\n",
-       "1         Germany    283    5.516569"
-      ]
-     },
-     "execution_count": 374,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "woman"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 375,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>2857</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>5130</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>India</td>\n",
-       "      <td>7046</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>4933</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>17837</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Country  Count  Percentage\n",
-       "0          Canada   2857       100.0\n",
-       "1         Germany   5130       100.0\n",
-       "2           India   7046       100.0\n",
-       "3  United Kingdom   4933       100.0\n",
-       "4   United States  17837       100.0"
-      ]
-     },
-     "execution_count": 375,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 376,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize = (10, 6))\n",
-    "\n",
-    "# bar chart 1 -> top bars (group of 'Man')\n",
-    "bar1 = sns.barplot(x=\"Country\",  y=\"Percentage\", data=total, color='darkblue')\n",
-    "# bar chart 2 -> bottom bars (group of 'Woman')\n",
-    "bar2 = sns.barplot(x=\"Country\", y=\"Percentage\", data=woman, color='#5E96E9')\n",
-    "\n",
-    "# add legend\n",
-    "top_bar = mpatches.Patch(color='darkblue', label='Man')\n",
-    "bottom_bar = mpatches.Patch(color='#5E96E9', label='Woman')\n",
-    "plt.legend(handles=[top_bar, bottom_bar])\n",
-    "\n",
-    "# Fix the legend so it's not on top of the bars.\n",
-    "legend = ax.get_legend()\n",
-    "legend.set_bbox_to_anchor((1, 1))\n",
-    "\n",
-    "ax.set_ylabel('Percentage', fontsize = 12)\n",
-    "ax.set_xlabel('Top 5 Countries', fontsize = 12)\n",
-    "plt.title('Gender vs Top 5 Countries in 2019', fontsize = 14)\n",
-    "\n",
-    "def add_value_labels(bar2, spacing=5):\n",
-    "    \"\"\"Add labels to the end of each bar in a bar chart.\n",
-    "\n",
-    "    Arguments:\n",
-    "        ax (matplotlib.axes.Axes): The matplotlib object containing the axes\n",
-    "            of the plot to annotate.\n",
-    "        spacing (int): The distance between the labels and the bars.\n",
-    "    \"\"\"\n",
-    "    # For each bar: Place a label\n",
-    "    for rect in bar2.patches:\n",
-    "        # Get X and Y placement of label from rect.\n",
-    "        y_value = rect.get_height()\n",
-    "        x_value = rect.get_x() + rect.get_width() / 2\n",
-    "\n",
-    "        space = spacing  # Number of points between bar and label. Change to your liking.\n",
-    "        va = 'bottom' # Vertical alignment for positive values\n",
-    "        label = \"{:.1f}%\".format(y_value) # Use Y value as label and format number with one decimal place\n",
-    "\n",
-    "        # Create annotation\n",
-    "        bar2.annotate(\n",
-    "            label,                      # Use `label` as label\n",
-    "            (x_value, y_value),         # Place label at end of the bar\n",
-    "            xytext=(0, space),          # Vertically shift label by `space`\n",
-    "            textcoords=\"offset points\", # Interpret `xytext` as offset in points\n",
-    "            ha='center',                # Horizontally center label\n",
-    "            va=va,                      # Vertically align label differently for\n",
-    "            color='white', fontsize=12, style='italic')       \n",
-    "\n",
-    "#Add value bar\n",
-    "add_value_labels(bar2)\n",
-    "\n",
-    "plt.tight_layout(pad=0., w_pad=-16.5, h_pad=0.0)  \n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Analysis**<br>\n",
-    "\n",
-    "\n",
-    "In terms of male and female statistics, it can be seen that the US has the relatively largest female percentage at about 10.9%. Follow by Canada, the UK at 9.6% and 8.0% respectively. India and Germany have the fewest female respondents among the top 5 at around 5%."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Where are the most data scientist come from in 2019?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 377,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5788"
-      ]
-     },
-     "execution_count": 377,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#creating data scientist scientist df\n",
-    "ds = survey_df_2019[survey_df_2019['DevType'].str.contains('Data scientist') == True ]\n",
-    "ds = ds.reset_index(drop=True)\n",
-    "len(ds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 378,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>113</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>1550</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>India</td>\n",
-       "      <td>543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>111</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>France</td>\n",
-       "      <td>169</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>Netherlands</td>\n",
-       "      <td>148</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Brazil</td>\n",
-       "      <td>143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>Russian Federation</td>\n",
-       "      <td>123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Australia</td>\n",
-       "      <td>119</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Country  Count\n",
-       "113       United States   1550\n",
-       "49                India    543\n",
-       "41              Germany    427\n",
-       "111      United Kingdom    339\n",
-       "18               Canada    195\n",
-       "39               France    169\n",
-       "74          Netherlands    148\n",
-       "14               Brazil    143\n",
-       "88   Russian Federation    123\n",
-       "5             Australia    119"
-      ]
-     },
-     "execution_count": 378,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_country = ds.groupby(['Country']).size().reset_index(name ='Count')\n",
-    "ds_country.sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "top_ds_country = ds_country[:10]\n",
-    "top_ds_country"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 379,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize = (10, 6))\n",
-    "ax=sns.barplot(x=\"Count\",  y=\"Country\", data=top_ds_country )\n",
-    "ax.set_ylabel('Countries', fontsize = 12)\n",
-    "ax.set_xlabel('The Number of Data Scientists', fontsize = 12)\n",
-    "plt.title('Top 10 Countries of Data Scientists in 2019', fontsize = 14)\n",
-    "\n",
-    "for y, x in enumerate(top_ds_country['Count']):\n",
-    "    label = \"{:,}\".format(int(x))\n",
-    "    plt.annotate(label, xy=(x, y), va='center')\n",
-    "\n",
-    "plt.tight_layout()  \n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "\n",
-    "\n",
-    "There are 5,788 data scientists who responded to the Stackoverflow survey in 2019. Most data scientists are from the US with 1,550 people and it is 3 times higher than data scientists from India. Followed by Germany and the UK with 427 and 339 people respectively. The rest are Canada, France, Netherlands, Brazil, Russia, and Australia which have less than 200 data scientists."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#  Which countries pay the most to Data Scientists in 2019?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 380,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>Qatar</td>\n",
-       "      <td>1000000.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>Myanmar</td>\n",
-       "      <td>333757.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>275851.466092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>272796.133333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>113</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>265211.014843</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>Syrian Arab Republic</td>\n",
-       "      <td>2916.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>1800.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>116</th>\n",
-       "      <td>Venezuela, Bolivarian Republic of...</td>\n",
-       "      <td>1500.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Cambodia</td>\n",
-       "      <td>816.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>Zambia</td>\n",
-       "      <td>400.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>120 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Country            Mean\n",
-       "85                                  Qatar  1000000.000000\n",
-       "72                                Myanmar   333757.333333\n",
-       "52                                Ireland   275851.466092\n",
-       "63                             Luxembourg   272796.133333\n",
-       "113                         United States   265211.014843\n",
-       "..                                    ...             ...\n",
-       "101                  Syrian Arab Republic     2916.000000\n",
-       "64                             Madagascar     1800.000000\n",
-       "116  Venezuela, Bolivarian Republic of...     1500.000000\n",
-       "16                               Cambodia      816.000000\n",
-       "118                                Zambia      400.000000\n",
-       "\n",
-       "[120 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 380,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_mean_salary = ds.groupby('Country')['SalaryUSD'].mean().reset_index(name ='Mean')\n",
-    "ds_mean_salary.sort_values(by=['Mean'], ascending=False, inplace=True)\n",
-    "ds_mean_salary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 381,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Plottig boxplot to check outliers after cleaning some outliers\n",
-    "sns.boxplot('Mean', data=ds_mean_salary)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 382,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Cleaning Age's outliers from each gender)\n",
-    "ds_mean_salary = ds_mean_salary[(ds_mean_salary['Mean'] <= 280000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 383,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Plottig boxplot to check outliers after cleaning some outliers\n",
-    "sns.boxplot('Mean', data=ds_mean_salary)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 384,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>275851.466092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>272796.133333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>113</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>265211.014843</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>111</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>169366.692664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>Switzerland</td>\n",
-       "      <td>165462.430196</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Cyprus</td>\n",
-       "      <td>150936.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Australia</td>\n",
-       "      <td>146803.174460</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>Norway</td>\n",
-       "      <td>145948.523273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>125228.788666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>Japan</td>\n",
-       "      <td>118969.194525</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Country           Mean\n",
-       "52          Ireland  275851.466092\n",
-       "63       Luxembourg  272796.133333\n",
-       "113   United States  265211.014843\n",
-       "111  United Kingdom  169366.692664\n",
-       "100     Switzerland  165462.430196\n",
-       "25           Cyprus  150936.000000\n",
-       "5         Australia  146803.174460\n",
-       "78           Norway  145948.523273\n",
-       "18           Canada  125228.788666\n",
-       "56            Japan  118969.194525"
-      ]
-     },
-     "execution_count": 384,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Top_mean_salary = ds_mean_salary[:10]\n",
-    "Top_mean_salary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 385,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize = (10, 6))\n",
-    "ax=sns.barplot(x=\"Mean\",  y=\"Country\", data=Top_mean_salary )\n",
-    "ax.set_ylabel('Country', fontsize = 12)\n",
-    "ax.set_xlabel('Average Salary in US$', fontsize = 12)\n",
-    "plt.title('The Top 10 highest paying data scientist country in 2019', fontsize = 14)\n",
-    "\n",
-    "for y, x in enumerate(Top_mean_salary['Mean']):\n",
-    "    label = \"${:,}\".format(int(x))\n",
-    "    plt.annotate(label, xy=(x, y), va='center')\n",
-    "\n",
-    "plt.tight_layout()  \n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Analysis**<br>\n",
-    "\n",
-    "\n",
-    "In 2019, the top three countries which have a highest mean annual salary of a data scientist are Ireland (275,851), Luxembourg (272,769), and the USA (265,211). Apart from that, the mean salary of the rest of the countries is less than (200,000) per year. Japan provides the highest mean annual salary among Asian countries (118,969)<br>\n",
-    "*Figures in Dollars* **$**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mydf2018 = pd.read_csv(r\"C:\\Users\\Aneesh Angane\\Downloads\\stack-overflow-developer-survey-2018\\survey_results_public_2018.csv\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Most Popular IDE's in 2018"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Split IDEs and explode into separate rows\n",
-    "individual_ides = mydf2018['IDE'].str.split(';').explode()\n",
-    "\n",
-    "# Count occurrences of each IDE and sort by value\n",
-    "ide_counts_value_sorted = individual_ides.value_counts().sort_values(ascending=False)\n",
-    "\n",
-    "# Plotting - Sorted by value\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.bar(ide_counts_value_sorted.index, ide_counts_value_sorted.values, color='skyblue')\n",
-    "plt.title('IDE Usage')\n",
-    "plt.xlabel('IDE')\n",
-    "plt.ylabel('Count')\n",
-    "plt.xticks(rotation=45, ha='right')\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis of IDE Usage\n",
-    "\n",
-    "1. **Popular IDEs**: Visual Studio Code, Visual Studio, and Notepad++ are among the most widely used IDEs, with high user counts ranging from 25,870 to 26,280.\n",
-    "\n",
-    "2. **Text Editors**: Sublime Text, Vim, and IntelliJ are also popular choices, with user counts ranging from 19,477 to 21,810.\n",
-    "\n",
-    "3. **General-purpose Editors**: TextMate, Coda, and Light Table are also used, although they have lower user counts compared to other IDEs.\n",
-    "\n",
-    "4. **Emerging Trends**: IPython / Jupyter, Atom, and Emacs show significant adoption, indicating a growing interest in interactive computing environments, lightweight editors, and customizable text editors, respectively.\n",
-    "\n",
-    "5. **Industry Standard**: Xcode, primarily used for macOS and iOS development, maintains a substantial user base due to its integration with Apple's development ecosystem.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Coders perception about AI in 2018"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0avjw4SpXrlxq2LBhauzYsUqn0ylnZ2dtfdeuXZWVlZVatWqVNlxPiP9U0mFgM2bMUI0bN1ZVqlRRzs7O6ubNm0oppU6fPq1++OEH1aJFC3XixIlPXiM0NFQ1bNhQnT59OsXiTqsSh2gEBQWpwoULqzNnziillDp06JAyNjZWq1ev1rYNDw9XQ4YMUfXq1ZOhHd8BDw8PlS9fPjVhwgRtKE90dLQqUaKEsrOzU6dPn9Y+z9u3b1dt2rRRLi4u2vfDX/8vhPg2fe58/fbtWzVjxgyl0+nU9OnTteUBAQGqXLlyqmnTpio2NlbO9SJFfK6d3b9/X7m6uqqyZcuqadOmacsXLVqkChQooIYMGSLfP8KgkrbbxJ+jo6PV4MGDlampqQoICFBKKRUVFaW2bt2qMmfOrNq1a2eQWD9HklIixR09elTZ2tqqo0ePKqWU2rx5szI3N1fe3t7JtmvatGmyca9C/Lfc3d2VtbW18vT0VC4uLqpOnToqS5Ys6uLFi0oppc6cOaOKFy+uatSooa5evZps34SEBBUVFWWIsNOEOXPmqODg4GTLduzYoSpXrqyUUmrdunXK0tJSLVq0SCmlVEREhPr9999VQkKCevXqlfbFKzcrqc/gwYPVnj17tN+nTp2qbGxs1IQJE7RaMn9NTCXWP4iMjNT2S1oTQdqBEN++/fv3q1WrViVb9ubNGzVz5kyl0+mUp6entnzr1q3a+UCIlHLy5Em1cuXKZMvu37+vhgwZouzs7NTs2bO15cuWLUtWc0oIQzlw4ID2QPdziaktW7YopT5eQ+3YsUPduXPHYLH+lSSlRIrbsGGD+vHHH5VSSgUGBqoMGTKoJUuWKKU+Pi3bsWOHtu3fFTwW4p8KCQlRJUuWVNu2bdOW3bt3T7Vu3VrlyZNH3b9/Xyml1KlTp1T79u2lzaWgU6dOqQIFCqhOnTol64n222+/qUqVKqk9e/aojBkzqgULFmjrtm3bpvr06aMeP36sLZNEROoTEhKievXq9UmRzSlTpnw2MVWyZElVpkwZdfTo0c8+DRRCfHuSfj4Tv1tjYmLU4MGDlU6nU2vXrk22fXh4uOrUqdMnPaaE+Fo+9x0SGxur2rVrp+zt7ZP10FZKqYcPH6oaNWooGxsbNXHixJQKU4hkkrbbxOuoDx8+qLZt2yqdTqfOnTuXbLuIiAjVpEkTZW1trTZt2pTyAf8DUnhDpBj1/+v0ZMyYkaxZs+Lj48Mvv/zCrFmz6NWrFwBnzpxh48aN3Lt3D0CrISXEP6X+UqQvPDycu3fvYm1trS0rWLAgY8eOJUeOHBw+fBilFJUqVcLf31/aXAqqVKkS8+bN486dO8ybN48TJ04A0K5dO16+fEmjRo3w8vLCxcUFgOjoaBYvXszbt2/JlSuX9jpS1Dp1UUpRsGBBlixZgrGxMWvXrmX16tUAuLu707dvX5YtW8bKlSu5f/8+pqamnD9/nkePHrFkyZJkx1uOvRDfJvX/Jw0JCwsjJCQEvV7P5s2bOX78OEOHDmX48OH07t2bNWvWaPtYWVlRvHhxChQowMyZM3n58qVBC++K759Op+Ply5eEh4cDsHXrVs6cOcOECROwt7fH29sbPz8/bXsbGxuqVq2KqakpR48e5cWLF4YKXaRRSc+tERERGBsbs3fvXs6cOcOcOXNo3749tWvX5ty5c9o1UoYMGfjhhx+Ii4ujX79+REREGPhdfEqqBoqvJmlRc/jXzUPOnDm5evUqu3btYvr06fTu3RuAqKgoZs2aRdasWSlUqJC2nxQtFv/Uo0ePsLGxAcDX15cePXpQokQJypYty549eyhXrhxmZmbodDqKFy9OTEwM9+7d++TGVtrc1xcbG4uJiQnNmjXj1atXeHt7M3/+fExMTKhQoQJz5sxhwIAB+Pn5kTt3bl6+fMmKFSt48uQJW7du1QrSS1Ii9Uk8ZkopXr58ydy5c7G0tMTMzIzWrVvj7u4OgLe3NwBdu3Ylf/78PH36VI63EKmETqcjPDycdu3aUbp0aYoWLUr//v1Zs2YNNWvWpH///sTHx9OvXz/0ej0dOnQAPk5IMnz4cDp16kTGjBkN/C7E90wpxevXrylatChubm5kz56d7t27s3btWqpUqcKwYcPw8PBg6dKlAHTp0gVAu7F3cnJK9sBTiJSQmEjt0qULRYsWpWLFinTt2pWtW7eSO3du5syZQ1xcHHXq1OHgwYOUL18eACMjI1atWkXVqlWxtLQ08Lv4DEN10RLft6TdCn19fdW4ceOUm5ubunXrllLqY80YnU6nevfurfz8/NT27dtV3bp1VenSpbVuiDIsQ/wnDh48qMqVK6eOHDmiDQ0ICQlR8fHxauDAgapSpUrKz89P2/79+/eqcuXKysvLy4BRp01JP9sTJkxQffr0Ufnz51c6nU61bNlSXbhwQSn1sf5cpUqVVP78+VWlSpVU+/bttUKicXFxhghd/I8+VwPs8uXLqmHDhqpevXpq/fr12vJp06ap/Pnzq6FDh6qnT59qy+XYC5F6LFq0SNna2iqdTqdmzZqVbN2DBw/U8OHDxPvCrwAAe9BJREFUlV6vV02bNlVNmzZVmTNn1q4VhUgJK1asUKampsrIyEirX5n4HXX58mX1yy+/qJIlS6pmzZopJycnZWVlJTWkhEFFR0crDw8P9cMPPygTExO1ePFipdS/hkmHhoaqDh06KAsLC9WjRw/VqlUrZW1trW7fvm3IsP8tnVLSL1Z8WUl7SA0bNgxfX1/KlCnD8+fPefDgAdOnT6dv375s3ryZ+fPnc+nSJUqUKEGOHDnw9/fHxMSE+Ph4jIyMDPxORGry9u1bGjVqxIMHD4iIiODw4cOULVsWgJiYGDp37sydO3fInz8/5cuX58CBA7x8+ZKLFy/KVNMGMnfuXMaNG8eWLVvInj07p06dYsaMGZQpU4aRI0dSpkwZAP78808yZ85MxowZ0el0xMXFyTFLhWJiYrSph58+fUqmTJlISEggQ4YMXLlyhaFDhwLQs2dP2rZtC3wcznfjxg0CAwOll5QQqUjidVxISAhVq1bFxMSEdu3a0adPH2xtbbXt3r59y969e/Hz8yNr1qy4urpSqlQpA0Yu0orE+5X79+9TsGBBAGbOnEn37t3JnDmztl1ISAj79u1j/fr1ZM2alV9//ZXSpUsbKmyRxiW223PnzlG/fn0yZsxIq1atGDduHJaWltoogtjYWGbOnMmRI0cwNzdnwoQJ33S7laSU+GrCwsLo378/o0aNonTp0hgZGTFs2DBWrlzJ3Llz6dSpE69eveLDhw9YWFiQKVMmueEU/5XENjNlyhQmTZpEsWLF8PT0pHr16piYmAAfh4stXryYI0eO8Pr1awoUKMCiRYskCWogSilatWqFtbU1y5Yt05Zv3LiR/v37U61aNUaMGEGlSpU+2U+SE6mLj48PrVu3xsrKCoBx48YRGBgIQLZs2Zg9ezZly5bl2rVruLq6otPp6NmzJ23atAH+dczl2AuRuiQOqb937x579uzBx8eH6tWrM2jQoGSJKfiYtNbr9XL9J1LUixcvyJo1K/fv32f//v306tWLiRMn4uLikiwxlejDhw+YmZkZIFIh/uXly5dYWlpy7tw5goODCQwMpGLFikydOhVLS8tk9zXx8fEkJCRo90PfKimcIr6KZcuWUa5cOe7fv0/27Nm1G4lZs2bRpk0bhg0bxrt378iSJQu5c+fGyspKu+mQCxLxn0psM9WrV+fo0aNYWVkxZswY9u7dS3x8PAAmJiYMGDCAjRs3smXLFpYtW4aJiQlxcXGSkEphickFCwsLIiMjAbTj1KZNG3r27MmBAweYMGECV69eTbavJCVSl40bN+Lh4cHYsWOJj49n3bp1LFiwgKFDh9K1a1csLS2pVq0au3fvpmTJksyaNQu9Xo+HhweHDh0CkISUEKnQjRs3aNiwIZ6entja2uLi4kKXLl0IDg5m/vz52oQ2Hh4e7N+/H1NTU7n+Eynq7NmzNG3alA0bNpAvXz569uzJvHnzGDt2LIsXL9aKn8+ePZtdu3YBSEJKGNy1a9coVaoUu3btokqVKgwcOJAmTZpw5swZfv31V969e4eRkRHe3t4cO3YMIyOjbz4hBVLoXHwFCQkJWFtbkydPHu7evYuJiQl6vZ7IyEgsLCwYOXIkmzZt4vTp09SuXTvZvnLTIf6ppMNE4+Pj0el01KhRA4CAgAAcHR2ZOnUqRkZGNGrUCIBJkybx66+/kj59egBJgqaQvyYUEn8uX7487u7unD17lgoVKmjrs2TJQsmSJSlUqBAlSpRI8XjFl9OsWTPu3LnD9u3bcXV1RSnFjBkzcHJyAqB///64urrSunVrrly5QunSpfHw8OC3336jVq1a2uvId4MQqYupqSlVqlRh3bp1AAwZMoQhQ4ag0+nw9/fn5s2bZM+endWrV3P+/HkDRyvSojx58qDX61m6dClKKdq2bcuAAQOAj+317t27xMXF4e/vz5kzZwwcrRD/4uDgwNChQ1FK0aJFC9zc3ADYs2cPbdu2pUiRIsyfP59r164ZONJ/Tobvif/ZX2fZg48z6QUFBdGvXz9y5crF8ePHtXW3b9/GwcEBf39/LYkgxH8iaZvz8vLi4sWL3L17F1dXV8qXL0++fPl49eoVjo6OxMbG0rBhQ86cOcPRo0d5+fKl9IxKQUmP1aNHj4iKiiJv3rza08ZWrVpx9OhR1q1bR7FixbCysqJDhw40bdqUHj16oNPpPnuOEd+2zp0707t3b6pXr05sbCweHh4cPHiQa9euMW/ePDp27EhCQgI6nY7Xr1/TqFEjateuzaRJk5J9PuXYC5E6fK434x9//MGcOXM4evQonTt3ZsiQIQD89ttvnDx5kkePHuHh4YGdnZ0hQhZpQOJt7t+VBwkNDaVLly7ExMTQr18/2rZti06nY/ny5QQGBpKQkMC0adOwt7c3RPhCfPbcev36debMmcPevXuZN28eLVq0IDo6mhUrVnDo0CGeP3/OvHnzvukaUn8lSSnxP0l6w3Du3DmioqLInDkzJUuWBGDv3r307t2bbNmyMX78eIyMjJg/fz5Pnz7lzJkzkhwQ/5G/nphHjRqFr68vffr04cmTJxw5coRmzZrRq1cvfvjhB8LDw3FxceHVq1cYGxuzefNmTExM5EY3hST9O48dO5Y9e/Zw5coVmjdvTv369enevTuRkZE4Ozuzbds2cuTIgU6nw8jIiKtXr2JsbCzDtlKh69ev4+fnx8SJE7Uu49HR0Xh6ejJ37lxKlSpFYGAgGTNm1G4YGjVqRP78+VmyZIkhQxdC/EOJ5/eXL19ibW0NwOnTpwkJCaF9+/badiEhIcybN49Dhw7Ru3dv+vfvD3ysBRkfH0+6dOkMEr/4/iW20cS6UQDnz58nIiKCmjVratuFhobyyy+/8ObNG4YPH06rVq3Q6XS8ffsWU1NTGbInUlRiu339+jWmpqZYWFhonTt++uknbbvEh3y7d+/G29ubpk2bavsmjk5KVVJolj/xHUo6pbebm5uysbFRefPmVcbGxsrZ2Vmb1n3Pnj3qhx9+UDqdTvXq1UuNGTNGvX//XiklU3uL/1zidKerV69WBQsWVGfPnlVKKXX06FGl0+lUkSJFVP/+/dXdu3eVUh+nTX39+rXWXmNjYw0TeBo2btw4lT17drVlyxZ18eJFVbt2bVW8eHE1b948bZtt27YpPz8/tXTpUu0Yyfkh9Vu8eLHavHmzUkqpmJgY5eHhocqUKaOcnZ1VTEyMUurjcf7xxx+Vq6urASMVQvxTid/D58+fV0ZGRur48ePq7du3qk2bNqps2bJq/fr1ybYPCQlRP/30k8qfP7+aMWOGIUIWaUxiG71w4YKysLBQR44cUe/evVMlSpRQNWrUUEeOHEm2/YsXL1SBAgVU1apV1W+//WaIkIXQ2u25c+dU/vz51aVLl1R4eLhydHRUhQsXVidOnEi2/cWLF9WPP/6o8ubNq11rpVaSlBL/lcQPjVJKLVy4UGXLlk39/vvv6sGDB2rLli2qdOnSqn379urWrVsqPj5ebd++XVWqVEnVrFlT2y8yMtIAkYvUqEuXLqpfv37a7zExMWrTpk1q+vTpSimltmzZoqysrNTy5cvVjBkzlLm5uRo4cKC6fv16stdJmkgVKePYsWPK3t5eBQUFKaWU+v3335WZmZmqUaOGKlmypPLy8vrsfpKQSp2Sfjc8fvxYNW3aVBUtWlTt2rVLKfUxSTxp0iRVuHBhVaJECdW5c2fVtm1bVbRoUUkYC5EKJH7GL168qCwtLdWwYcO0dSdPnlQdO3ZUVatWVf7+/sn2GzZsmCpYsKCqV6+eevnyZYrGLNKWpG3UwsJCjRo1Slt3/fp1VbZsWdWgQQP1+++/J9uve/fuKkOGDKpFixbqzZs3KRqzEEnbbYYMGdSQIUO0dfv371etW7dWZcqUUcePH0+2X/fu3VXWrFlVqVKl1Nu3b1PtvY4kpcR/5MCBA9rPiTeNXbp0Uc7Ozp9sZ2Njo8aPH6+UUioqKkrt2LFDFStWTDVo0CDlAhap3rt379TUqVOVtbW1cnd315Y/efJEPXv2TIWGhqpKlSqpWbNmKaWUioiIUDY2NipXrlxqzpw5BopaJAoLC1NeXl7qw4cPav/+/Spbtmxq+fLl6tWrV6pYsWKqcOHCasKECYYOU3wBSRNS0dHRSimlTp06pbp06aLs7OzUzp07tXUzZsxQhQoVUsWKFVP+/v7a94kkpoT4diXe7Fy+fFmZm5ursWPHJlv/4cMHFRISotq2batq1Kih1q1bp60bNmyYmj17tnrx4kWKxizSlsTvob9ro0op9eeff6pSpUqpRo0aaQ/MlFJq+PDhat26derBgwcpFq8QSv3r3Hrp0qVPEqmJ6y9duqRatGihypUrp06dOqWtGzJkiPL29k7151apKSX+sVmzZuHj48Pw4cPp0aMH8LEmQJs2bbCysmLFihXExsZiZGSkTem9YMECrl27RqZMmYiNjeXgwYM4OTlRuXJltmzZYtg3JFKN8PBw1qxZw9ixY+nduzfTpk3T1l25coUWLVqwYsUKqlevzq1bt5g+fTqVK1emR48eUrcsBf1dra7379+TLl06OnXqROHChZkwYQLGxsa0a9eOGzdu4ODgwNy5c6V2VCqW9NjPmDGDly9fMnz4cLJmzcqpU6fw8vLi8uXLTJ8+ncaNGxMTE8OYMWOIjY3F09NTCtoLkUqEhoZSpUoV8ubNy5EjR7TlM2bM4NSpUwQEBHD+/Hk8PT25ePEiJUqUwNLSks2bN3P+/HkKFChguOBFmvDo0SOKFStGgwYNCAgI0JbPmDGDJ0+eMHfuXG7fvk379u2xsrKiUKFCpEuXjjVr1nD9+nVy585twOhFWhUaGkrZsmWpUKEC27dv15ZPnTqVGzdu4Ofnx++//86CBQs4duwYnTp14sWLF+zdu5cTJ06QP39+A0b/v5OrP/GPderUidKlS7Nq1Sp8fX0BMDY2pkaNGqxevZrLly9rRW3h47TuBQoUwNzcHAATExNq166Nn58fs2fPNsh7EKlLfHw8AJkzZ6ZTp06MHz+eJUuWMGrUKG2bt2/fYmJiwrFjxzhy5AjDhg3j7du39OrVCyMjI+01xNeVNKFw/Phxtm7dypkzZwgLCyN9+vTo9Xr+/PNPPnz4gLGxMXFxcRgZGTF69GgtISXPSFKvxGPv5ubG3LlzyZ8/PzExMQD8+OOPDBo0iFKlSjFixAh2796NqakpU6ZMkYSUEKnMhw8fKF++PHq9nmXLlgEfH1pOmTKFPn36AFCuXDnGjh1Lr169CA0NJTw8nKCgIElIiRSRPn16ChYsyNu3b9m7dy8As2fPZvz48TRt2hSAH374gYCAAEqUKMG9e/e4efMmR44ckYSUMJjQ0FAqV67M48eP2bVrFwAeHh7MmjWLzp07A1CzZk3GjRtHz5492b9/P8+ePWPXrl2pPiEFMvue+Ifi4+MxMjIiLCwMFxcXbaYKZ2dnlFK0atWK4OBgtm7dSvHixTExMaFVq1ZkyJCBTZs2SQ8I8T9JnN3n1atXrF69mvHjxyfrMeXq6sr27duJiYnBxsaG33//HRMTE5m5zQBGjhzJ+vXryZQpE9HR0RQuXJjhw4fz008/0a9fP+7cuUOJEiW4efMmr1694ty5c+j1eklKfAfWrl2Lq6sr+/bt06bPfv/+PTExMWTOnJkbN24wZcoU9u3bx9atW6lSpQrw+emOhRDfrjt37jB16lTu3btH5syZOX78OBs2bMDBweGzn+eoqCjtAaUQX1PS+5XmzZuTIUMG8ubNy9atWwkICKBWrVrAvx6kxcTEYGpqyvv370mfPr1hgxdp3rlz55g3bx6XLl2idOnS7Nu3Dz8/P+rXr//JuTUiIgJjY+Pv5twqdwDiH0nscZI9e3YWLFhAjhw5WLlyJb6+vuh0Ory8vKhfvz4ODg5UrlyZH3/8kdDQUNatWyc9IMT/xN/fn7x583L9+nWyZMlC586dtR5Tbm5uAMyZM4eAgAB27tzJ0aNHMTExIS4uTm50U9jixYtZuXIlfn5+XLx4kZYtWxIUFKT1jho0aBBFihTh5s2bZM2aldOnT0tC6jvy5MkTatSogb29PdevX2fOnDmUK1cOBwcHxo8fT7Fixejbty8uLi5UqlRJ208+p0KkLkWKFGHUqFHY2tpy9OhROnTogIODA0Cy673En7+Xmybx7Ut6v7J161Y+fPjAb7/9xtChQ7WElFJKu+ZIHOEhCSlhSInnyvLlyzNw4EDs7e0JCAjAxcWF+vXrf3YfS0vL7+rcKj2lxL/115vFuLg4jI2NCQ0NZcCAATx+/JhevXrh5OQEwM6dO3n58qVWL8bIyEjbR4j/xrNnz+jQoQMhISHs3r2b4sWLaz2mJkyYgLOzMx4eHsn2SXxSJlJG4tObnj17kjNnTiZPnsyWLVvo2rUrM2bMoHfv3rx//x4TExOMjY3R6/XaPnJ+SJ0+1xvCy8uLwYMH4+rqyo4dO7C3t+fHH3/k0aNHbNu2jaNHj5IrVy5te/mcCpG63bt3j8mTJ3P79m26du1Kr169gL+vLyhESkn8fnn58iXNmjXDxMSEkSNH0qBBA+1huTwQEYaWtB0m/fns2bN4eXlx4cIFPDw8aNKkySfbfG8kKSX+VtKLCl9fX65cuUJoaCht27alZcuWvHz5kr59+/LkyROcnJzo2bPnJ68hNx3iP/HXC9nE358/f06nTp24fv06+/fv1xJTa9euZeDAgSxatEirZSFSXuKXpKOjI05OTlhbW9O4cWNmzZpF7969iYuLY8WKFWTOnJnmzZtrTya/5y/X71nSz+mzZ89Ily4dVlZWAIwfP56zZ8/StGlT6tWrh62trVZQds2aNRQvXtyAkQsh/onEc3NERASmpqakS5fub7e9ffs206ZN49atW3Tv3v2z14JCfGmJbTQyMhILC4vPXk/8dSifiYkJo0ePpn79+nLtIQwi8frp3bt3mJqaYmpq+n8mps6fP8/MmTNp1KiRIUP/6uQxhvhbSQvXjh8/ng8fPlCgQAFat27NlClTsLa2ZsGCBeTOnZvVq1czb968T15DElLiP5HY5nx8fHjz5o3WoyZbtmzaDW2DBg24efMmWbJkoX379mzYsEEugg0s8Qu0UKFCdOrUiQYNGrBkyRJ69+4NfBz3vm7dOu7du5dsMgS5KEydEj+n48ePp1atWtSvX1/rITF+/Hj8/f3p06cPtra2REdHM2jQILJly0bRokUNGbYQ4j8QFhZG8eLFWbVqFR8+fPjb7X744QdGjRpFiRIl8PT0ZOXKlSkYpUirdDodYWFh2NrasmXLls+WCvnrUD6lFMOHD+fQoUMGilqkdXq9nsePH9O0aVPWrVtHTExMsrab9OcKFSowcOBAKlasSI8ePdi/f78hQ//qJCkl/q0DBw6wfv16AgMDWbx4Mc2aNQPQqvxnz56d+fPno9fruXXrltSOEv+VpO3m5cuXTJgwgZ9++om3b99qJ+hs2bKxdOlS9Ho9HTp04OrVq2TNmpXWrVtrs7mJr+/169fAx6c9iRKP36hRo3BwcCBDhgzUrVuXN2/e8OTJEzp27Mi7d+8YOnSoIUIWX0jSY75q1SoWLlzIkCFDqFu3LocPH6ZmzZrAxzoHb9++xcvLi6ZNmxIaGsqOHTu0+mFCiG+bTqcje/bs1K1bl6FDh7Jx48b/MzE1dOhQHBwctPOAEF+bmZkZ9evXp1OnTuzatev/TExt3LiRnDlzUrhwYQNFLATkzJmThIQE5s+fz+bNm4mOjv63ianevXvTvHlzbG1tDRn2VyfD90Qyf+3+un79elavXs327dvZsGEDPXr00IbkvH79mqdPn2pDqaysrJLVihHiPxUdHU26dOm4du0aXbp0IT4+nuDgYDJmzAhAZGQkLVq0ICgoiIYNG7J161YDR5y2nDhxglq1anH8+HHKly//yXBLpRRHjx7F3d2dy5cvkytXLiwtLTEyMiI4OBgTExMZ0vsd2L59Oy9evMDCwoJ27doRFxdHcHAwXbt2xdbWlsOHDwMwb948bt26hZeXl5Y4lvphQnz7kp7b+/fvj6+vL0uXLqVNmzaYmZl9sn3idV9sbGyy3rBCfG1v3rxh9OjRLF26lC1bttC4ceN/O5RP6p0JQ0psh/Hx8Tg6OvLw4UPc3d1p3rw56dKl+9uhfB8+fPjsufd7Ikkp8VmJHxpfX18WL17M4MGD6devHx4eHvTt2xf4mLBav349ixcvJnv27IAUtxT/maTtZerUqTx//pzRo0eTNWtWrl+/Tvv27dHpdBw5coRMmTIRFxdH9+7dGTp0KKVKlZK2lsKePn2Ks7Mzp0+fZu/evZQtW1Y7hkm/PBMSEti4cSPv378nW7ZsNG7cWCY9+E5cv36dn376iXfv3rF69Wrat28PfPzOOHLkCN26daNw4cIcOHAg2X6SjBTi2/W5a7ek5+t/kpgS4mv6u4mX4GMP7jFjxvyfiSmQWpYiZSVtt4k/JybvExIScHR05MGDB4waNQpHR8dPElNpidzRiU/4+vrSsGFDEhISqFq1KunSpaN79+6MGjVKS0hFRUWxdu1aMmXKRLZs2bR9JUkg/qmkJ+qQkBBCQkKYN28e3t7ehIeHU6JECdatW4eRkRF2dnYMGDCA6tWrc+PGDezs7NDr9cTHxxv4XaQtuXLlwtfXlxo1alC7dm0uXLigDclK+gX6+vVratSoQffu3fn555+1p0KSkEr9bGxsWLBgATY2Nqxbt05bbmRkRI0aNfjtt984cuQI/fv3T7afJKSE+DYppdDr9dy9e5fdu3dry42NjYmOjgZgwYIF9OjRg169ev2fQ/mE+NIS2+jDhw+1Bx6J1xNKKaysrJg8eTK9evXC0dHxb4fygdSyFCknsd2GhIRw/fp1YmNjATAxMSEmJga9Xs+WLVvIly8f06ZNY8uWLZ8M5UtL5A5BJJOQkEB0dDSvX7/m8ePHFCtWjKZNm/Lq1SuuX7/OkSNHeP36Nd7e3jx58oSAgACZWlX8VxITUkOHDmXXrl3Url2bqlWrMm7cOCIjIxkxYgQlSpTg8OHDDBkyhMePH1OkSBF8fX21Lthyo5vycuTIwYIFC+jfvz+1a9fm0KFDlC1bVjsHhIWFUadOHdKnT8/Jkye15KMcq9Tnc7NhZsyYkVatWmFkZMSgQYPo3Lkzq1evBj4mnqpXr86pU6coXbq0ocIWQvwHdDodr169okSJEsTFxdGnTx/y5s3L8OHDk826t2DBApRS9OrVC71eT8uWLTE3Nzdg5CKt0Ol0vHz5kjJlyhATE0OtWrXo2bMnlStXJkeOHABYWVkxYcIElFI4Ojqybds2GjZsKPcnwmB0Oh2hoaEULlyYPHny0K5dOwoVKkS/fv20Yc6JianmzZszbdo09Ho9P//8c5rsjSrD99K4z52sX716RZkyZWjZsiVz584FYM6cOezdu5dDhw5RoUIFcuTIwYYNG6RGjPif7N69mw4dOrB//34qVqwIwJIlS+jbty8jR47E1dVV64mXtFaFDAMzvNDQUPr378/Bgwc5fPgwZcqU4enTp7Rr147Xr19z7tw5qS2SiiVNSC1evJhr167x559/4uTkRI0aNciWLRv+/v4MGzaM2rVr4+fn98lryHeDEKnHoEGDSJ8+PVmyZGHnzp08fPiQvn37UqtWLcqXL69t5+Ligre3N/7+/rRr186AEYu05MmTJ/Tt25dffvmFM2fOcO3aNa5evcq0adMoUaKE9iAkIiKCUaNGsWjRIvbu3Uu9evUMHLlI637++WcyZMjATz/9xMyZM7Gzs6NmzZr06dOHTJkyAR+vl1q2bMmFCxeYN28eLVq0MHDUKU/GWqVxn3t6kCVLFqZPn86+ffs4efIkAK6uruzatYurV6+yZ88eAgMDMTExIS4uTm46xH8tMjKSnDlzYmtrq83K1bt3b+bMmcOMGTNYunQpz549A9ASHEopSUilgP9rlrScOXOyYMEC6tSpQ+3atTlw4ABOTk68ePFCS0jJjIipV2JCys3NjV9//ZW3b98C4OzszLhx47hz5w7t27dn1qxZHDlyhCZNmnzyGvLdIETqoJTC2tqaO3fuMGzYMA4fPkz//v25ffs2tWrVYurUqWzbtg2AhQsXMmrUKOkNKVJU7ty5KViwIHPmzMHDw4MVK1YwaNAgfHx8aNOmDePHj+fOnTtYWlri5eWFq6srNjY2hg5bpGEJCQkopahRowY5cuRgwIABnD17lqpVq3Lq1ClKlSrF3LlzCQoKwsjIiM2bN+Pg4IC9vb2hQzcMJdK8yZMnqy5duqjdu3dry27cuKHs7e3VggULlFJKxcfHf7Lf55YJ8XcSEhKS/V8ppXbs2KH0er26du2aUkqp6OhopZRSt2/fVpkyZVImJibKw8Pjk/1Eyrlw4YKKjo7+279/aGio6tChg9LpdKpYsWIqJiZGKaVUbGxsSoYpvoLg4GCVN29edfr0aW2Zn5+fKl26tBoyZIiKi4tTb9++VT4+Pqp58+bynSBEKhYdHa0KFy6sJk2apC1r2rSpypIli3JwcFAlSpRQ9vb26sSJEwaMUqRFidcf7969U3Xq1FGrV6/W1tWuXVsVLFhQ5c+fX/3000+qRo0aKiQkxFChCvGJK1euqIwZMyo/Pz9tWfPmzZWlpaVq2LChypEjh2rdurW6dOmSAaM0POkplQapv4zYLFGiBDdv3sTd3Z2aNWty+vRpihUrxoABAxgzZgyPHz/+bAFzKWou/qmkhbCjo6O1XjhNmjShYcOGdOrUibt372JqagqAmZkZzs7OeHh4MHr0aE6fPi01AQxgx44dNGrUSDt+fz13wMcaUx4eHsyaNYsrV65oPaSkN1vqFxsbi06nw8rKSvvMdu7cmUGDBuHt7c2tW7ewtLSkc+fObNmyRSt6L4RIXeLj4zE1NcXFxYUHDx4A0LVrV86ePcvZs2e1mZazZs1KlixZDBytSGsSr/+MjY2xsbEhKCgI+NhGr127xuHDh7ly5QrdunXD1NRUKygthKEppbCzs8PNzY3Dhw8D0K1bN86cOcPZs2fx8fFhwYIF3L17lwwZMhg4WsOSmlJpzF8L10ZGRmJhYcH79++5ePEiEydO5P79++TLl4/WrVuzatUqHB0dcXV1laEY4r+StM15eXlx5MgRIiMjKViwIPPmzePy5cu4u7tz9+5dZsyYgbGxMYsWLUIpRWBgIKVLl6ZPnz4MHz7cwO8k7YmPj6do0aJ07NiRiRMn/qN9JCGVOqn/X19QJakzeODAAVq2bElwcDD29vZ8+PABMzMzEhISKFCgABMnTsTJycmwgQshvpjTp0/TuHFj8uTJw+vXrwkMDExWT0oIQ7t16xY1a9YkW7ZsvHjxgl27dlG2bFltvVyDiG/Rzp07GTlyJFZWVjx48IBNmzZptXRB2i1ITak0JWlyYPbs2XTo0IFy5coxc+ZM7ty5Q9WqVdm7dy/Tpk2jePHi9O/fn+PHj3Pr1i1JSIn/WmKbGzlyJFOmTKFatWo0b96ctWvX0qhRI+zt7Zk0aRI1a9akW7duuLm5ERUVxc6dO7GwsMDS0lKbXUV8PX99PhETE4NOp6NLly5cvHiRiIiIf/Q6af1LNTVK2pMxPj5eW163bl2qVauGo6MjL1++1GaDCQsLw9zcHGtra4PEK4T4OipVqkSfPn0IDQ1l5cqVkpAS35SEhAQKFy5M8+bNeffu3ScJKZBrEPFtatKkCfb29pw9e5aAgIBkCSmQGpwgSak0JTE54O7uzsyZM6lUqRJDhgxh6tSpTJkyhSdPngDQokUL5s2bx7Fjx5g0aRLe3t6GDFt8B65evcrOnTvZtGkTgwcPJleuXCQkJNC8eXOMjIyoWLEivr6+XLhwgePHjxMUFISxsTHu7u5ERERQs2ZNQ7+F71rS3jGXL18GwNTUFL1eT/Pmzdm/fz9bt241ZIjiK1FKJevJ2LFjR3r16sW8efOAjzPv5cyZkxIlSuDr68uKFSvo0aMH6dOnp3HjxoYMXQjxDyQdUps4rOnfDW+qWrUq2bNn14bTJ01UC/E1JH0o9u+GgOv1eoyMjKhfvz5hYWHaBDgybFwYwufOrZ8bgJa4XadOnShXrhxRUVGf7C8lSiQpleacO3eOzZs3s3nzZlxdXSlXrhwRERE0a9aM3Llzax8QpRQVK1Zk9OjRGBsbyyxa4r+S2J7CwsKIjIykevXqbNu2jU6dOjFjxgz69+9PRESENp18oUKFyJIlC2fPnsXFxYWlS5eyadMm8ufPb8i38V1L2ksmICCAjh070rx5c06dOsWrV68oU6YMgwcPZvXq1dpMiOL7kDQZOXnyZEaPHo21tTWPHj1i9uzZ/Pzzz+TLl4/du3fTtGlTpk+fzoIFCzAxMeHUqVMYGRnJDasQ3zi9Xs/Dhw958uQJJiYmbNu2jZUrV/5tYqpRo0bY2Njg6uoKyBN88fXpdDqePXvGvXv30Ov1BAYGsmfPnr/dvlWrVjRo0IARI0bw/v17qXErDEKv1/P48WPg4wzhu3fvZsWKFZ8kSRPbZ9WqVYmKimLx4sXJlouP5K+RxsTFxWFhYUGVKlXYsGEDDg4OzJ8/ny5duvDu3TuCgoKIjIz8JGMr3WHFP/XkyRMePXpEWFiYdsLNmjUrhQsXxtPTk06dOjF79mx69+4NwPXr19m5cydXrlzRXiNr1qyULVuWkydPftI1W3xZicdo4sSJBAQE4OnpSXh4OP3796dRo0bs27ePvHnzEhYWxosXLwB5Kvm9SDzPnz17litXrrBlyxa8vb3ZsmULy5cv58KFC7Rp0wYrKyt8fX0JDg4mKCiIzZs3awXt5YZViG9bREQELi4utGnThiVLluDo6EjGjBm1XiZJJSaZe/bsSXR0NGFhYSkdrkiDwsPDad++PXPnzmXhwoW0bt2aly9ffnbbxJ4oZcqUISwsTIqaC4OJiIjg559/pmnTpmzevJkmTZqQOXPmzyabEhISyJgxI4MHDyY4OJjw8PDP9qpK01J8vj+RYp4+faouX76s/Pz81JUrV9SrV6/UpUuXVLZs2dTSpUtVpkyZ1MKFC7XtDxw4oFq2bKlu3bplwKhFarZmzRpVoUIFVbBgQWVjY6N+//13pZRST548UWXLllU6nU5NnDhR2z4yMlI1atRItWrV6pPp5GV6+a8r6d93x44dytbWVp0+fVpbdujQITVw4EBVoEAB1b59e6XT6VSbNm3kuHxnVq9erapUqaKKFy+u7t27py2PiYlRgYGBqkiRIurgwYNKKaXi4uK09dIOhEg9du/erYoXL66MjY3V3LlzlVJKxcbG/u32L1++VE+ePEmp8IRQS5YsUba2tkqn06mZM2cqpZRKSEj42+3j4uLU/fv3Uyo8IT4RFRWl9u7dq7Jnz67SpUun1qxZo5T6eP30d+7fv68eP36cUiGmKtJT6jsVGBhIjx49qF+/Pi4uLlSqVAknJyciIyNp164dvXv3xtXVlX79+gEQHR3N3Llz0el0FC5c2MDRi9RoyZIldO/ene7duzN69GgKFixIkyZNuHPnDrly5WLNmjVYWVlx/Phxpk6dyvLly2nSpAmPHj1i3bp1n0wnL91av67Ev+/u3bvZvn07Xbp0oWLFikRHRwPg4ODAvHnzWL16NXXq1NEKNCb2aFPyhCdV+msvt4IFC2JsbMzt27fZt2+fttzExITy5cvz+vVrrd5g0l5R8vkU4tumlNI+70WLFiUqKor8+fOza9cuHj16hLGx8WeH3yYkJJAlSxZy5cqV0iGLNCixDdarV4/IyEhsbGx48uQJt2/f1maE/dw+RkZG5MuXL6XDFQKlFEopzMzMyJs3LxEREZiZmbFlyxYArSf5X/cByJcvH7lz507pkFMFuar8Di1btoyePXtSu3ZtVq9ezf379xk1ahS3b9/GycmJLFmy0K5dO1asWMGqVauYO3cuzZo1448//sDf3/+T5IAQ/xc/Pz/69u3L7t276du3Lz169MDR0ZGYmBju3LkDQPHixTl8+DBWVlasXLmS1atXU6BAAc6dO6fVLZMb3a9LKZXsJiQsLIzhw4ezcuVKnj59CkC6dOlISEjQvkCrVq1Kz549CQ4OxtTUlDVr1gBSlDE1SjoD6969ewkPD+enn37C29ubGjVq4O/vT2BgoLZ95syZsba2lpqCQqRCOp0OvV7PgwcPKFiwIPv372fOnDlER0fTtWtXHj16lKwu3IcPHwBJOIuUZWRkxMOHDylYsCDHjh1j9OjRBAUFMW/ePO7cufPZaw0ZNi4MSafTodPpePHiBUWLFuXkyZP4+/tz+vRpWrZsCZCsHnN8fLxcM/8ThuukJb6GpUuXKlNTUxUQEPDJunXr1qkKFSqoGjVqqHXr1ql+/fqpvHnzKgcHB9WjRw+tK/e/69ItxF89e/ZMVahQQdna2qrw8HBtedOmTZVOp1MDBw5Uvr6+6sSJE9q6yMhIFR0drf0ubS5lPHv2TPt5xYoV6vnz5+r06dOqatWqqmjRomrnzp3a+qTd5hO7Is+ePVs5ODio9+/fp1zQ4otIejzd3d1V4cKF1dy5c7Vje/78eeXg4KBKlCihhg0bppYsWaKaNWumihYtmmzYnhAi9QgJCUk2HEoppdavX69q1aql6tatqx49eqSUUmrOnDnKx8dHhuWKFHfjxg1VsmRJNWPGDG3Z3LlzVdmyZdWAAQO0kiJTp05Ve/bsMVSYQiRz/fp1ZWJiorZs2aKUUur9+/dq06ZNqkCBAqply5badkuWLFErVqwwUJSpiySlviOHDx9WOp1OTZgwQSn18SYkISEh2Q3/vHnzVKZMmbSkVVhYWLLXkOSA+E/s2bNHvXv3Tu3atUs5ODioevXqqTdv3qiOHTuqggULqjlz5qiRI0cqR0dHZWpqqurVq6c6duyYrN39u5oB4ss5efKk0ul06urVq2rYsGEqR44c6o8//lBKKXX8+HFVtWpV1bx5c7V//35tn78em9atW6tq1aqpqKiolAxdfEG//vqrypo1qzp+/Lh69epVsnWXLl1StWrVUjqdTjVp0kRNmzZNWyeJKSFSn9evX6tx48YpExMT5enpqS3fsGGDqlOnjipUqJDq3r270ul06vLlywaMVKRVISEhqlevXqpixYrJElPz5s1TFStWVPXq1VOdO3dWOp1OXbhwwXCBCpFEVFSU6tatm8qQIYPavn27UurjA/fExFSFChVUv379lE6nU9euXTNwtKmDTKn2HcmTJw/VqlXj/PnzBAcHU716deBjF8LEYRsDBw5k2bJlHDhwgJYtW2JlZaXtr5SSWfbEf2Ts2LGULFmS5cuXExMTw5w5cyhQoADW1tZcvXoVCwsLbduDBw9y+vRpgoKCyJIli7ZcurSmjCJFitC9e3cqV66MkZERly5dIn/+/CQkJFClShWmTZvGqFGjWLhwITqdjjp16iQ7Nm/fviUsLIzZs2djZmZmwHci/lsPHjxg3759+Pn5UaVKFcLCwjh37hz+/v7UqVOHRo0asWDBAgYMGIClpSUlS5bU9pUhPUJ8+5RSyc7bmTJlYsiQIaRLl46hQ4cC4OrqSps2bciaNSvbtm3j4cOHXL58GTs7O0OFLdKQv7bRggULMmrUKObOnYu/vz8Aw4cPZ+DAgVhZWXHy5Elpo8LgkrZb9f/rSXl7e2Nubk7r1q3ZtGkTTZs2pWnTpuTIkQNPT0+ePHnCxYsXKVGihIGjTyUMmhITX9zt27dVw4YNVYMGDVRwcLC2PLHHw5s3b1ThwoWTzYAmxH9r9+7dqkWLFur69etKKaU2btyoatasqapUqaL1hvq7WShkmEDKSNrbaebMmUqn0ylzc3N1/vx5pdTHHjCJ2xw5ckTVqFFDVatWTZ05c+aT1/p3M4qIb9+jR49U5syZ1dKlS9W5c+dU165dVenSpZWdnZ3S6/Vq8+bNSimlTp8+rRwcHFSTJk3Upk2bDBu0EOI/cuDAAbVu3bpky16/fq2mTZumdDqdmj9/frJ1SYfSC5ESTp06pVavXp1sWUhIiBo8eLCys7NT8+bN05bHxsZKGxXfhIMHD6pLly4ppf51bR0VFaVcXFyUmZlZshIYSn3sOSX+OXn0+Z0pUqQIXl5e6HQ6Jk+ezLFjx5KtDwkJwcbGhsqVKwMyg5b431SoUIFXr16xbNkyAFq3bk3//v1Jly4d7du35/Hjx5+dhQKk50VKSEhI0J7svHnzho4dO3L+/Hk6depEtWrVOHr0KEZGRsTGxgJQvXp1PDw8sLOzo1y5cp+8nomJSYrGL/57n5usIk+ePPTq1Qs3NzeqVatG5syZmTJlCleuXKF27doEBQUBULFiRWbPns2TJ09Yv3497969S+HohRD/jejoaLZu3UqHDh3YtGmTtjxTpkz06tWLdu3aMXDgQObNm6etMzU1NUSoIo169+4ds2fPZtasWaxbt05bXrBgQQYPHoyVlRWzZs1i+vTpwMfRHtJGhaHFxMQwb948ypcvz5UrV7SZIc3MzJg6dSo1a9akW7du7NixQ9vH3NzcgBGnPjolWYnv0p07dxg4cCBKKUaPHk316tWJi4ujefPm6PV6tm7dKkkB8UWcPn0aR0dHfHx8aNy4MQABAQEsXLgQY2NjfHx8ZNpeA0g605qHhwdhYWF0794dOzs7bda9TZs2ceDAAapUqQLAtGnT6NGjB9mzZ//kNUTqkfS4bdq0iT/++IOoqCg6deqEra0tly9fJiEhgTJlygAfZ4apXbs2zZs3Z8iQIVo39UuXLmFlZUX+/PkN+G6EEP+Je/fusXDhQnx8fPDx8aFt27bauokTJ7J8+XLevXvH7du3yZw5swyhFylCJRn+dPr0aebPn88ff/xBv3796Nixo7bdiBEj2LhxI0WKFGHt2rVYW1sbKmQhknn06BGDBg3i8OHDBAUFUbp0aW3doEGDWL58ORkyZODOnTtkyJDBgJGmTnK38Z1K2mPKw8ODY8eO0a5dO/78808CAwPR6/WffZIuxN+5c+eO9vPixYt5/vw5SinKlStHp06dCAoKIjIyEoBWrVoxYMAAnj59yuzZsw0VcpqWmJRwc3PD09OT8uXLkzVrVgCyZ8/O3LlzadmyJbVq1WLWrFnUqlULf3//ZBeAkpBKnRKP2/Dhw3FzcyMoKIiLFy9SpEgRtm3bRunSpSlTpgyRkZFcuHCB5s2b8+bNGwYOHKi9hlIKe3t7SUgJ8Q1LfK789u1bXrx4AYCtrS3u7u507doVZ2fnZD2mIiMjGTVqFCEhIWTJkkUSUuKrS2yj796948OHDwBUqlQJNzc38ubNi7e3N2vXrk22vYuLiySkhEElttvIyEjCw8MBsLGxwcfHh6pVq+Lg4MDly5e17U1MTPD39+fKlSuSkPpvGWTQoEgxt2/fVk2aNFEmJiaqaNGiWk0YmWVP/CdOnDihypUrp3777Tc1aNAgpdPp1N27d7X1gYGBqkCBAurKlSvJ9gsKCpJZuwxow4YNKnfu3MlmVQoPD1e3b9/Wfh82bJiqUKGCatWqlXZ+kHpfqd+GDRtUrly5tNpg27ZtUzqdTqs1k5CQoAIDA1Xjxo2Vg4ODduzl8ypE6pBY02Tr1q2qSpUqytbWVlWqVEktXLhQvXnzRr1+/Vr7vnZ0dFTNmjVTWbJkUbdu3TJw5CKtSGyj27dvVzVr1lTlypVTlSpVUrt27VJKKXXjxg3VuXNnVbJkSdWyZUvVrVs3ZWVlpc0MLIQhJLbbbdu2qfr166tChQqp9u3bqyVLliilPtZndnR0VBkyZFC9e/dWbdq0UdbW1urOnTuGDDvVk+F7acDNmzdZtGgRnp6eGBsbExcXJ7Psif9IREQE/fr149ChQ0RERBAUFES5cuWIjY3V6gz17t2bW7dusXPnTtKnT59s//j4eIyMjAwRepqi/jKrjY+PD/7+/hw4cIDbt2+zdetWFi9ejIWFBaVLl2bNmjXodDpevHiBtbU1Op1Ozg+pXGIbmDNnDrdv38bb25tNmzbRrVs3Zs+eTa9evXj79q02xO/s2bPUrFkTIyMjOfZCpDJ79+6lWbNmuLu7U7JkSbZs2cLNmzepXr06EydOJF26dGzatIm1a9eSNWtWhg4dSqlSpQwdtkhDdu/ejaOjIyNHjiRr1qwcO3aMffv2MX78eAYOHMi9e/fYs2cPGzduxNramnHjxiUbFiWEIezatYsWLVowbNgwsmfPzuHDh3nw4AENGjRg2rRpAIwbN44TJ05gbm7OpEmTpN3+jyQplcbITYf4TyUmlBYtWsSoUaMoUKAAgwcPplOnTpiamhITE4OpqSmXLl1i/Pjx9OzZkyZNmkg9IgNKTEz4+PgwceJEqlWrxqlTp6hSpQr29vakT5+e2bNns2nTJsqWLfvJfiJ1iY+PJzY2FjMzM23ZqFGjuHfvHh07duSXX35h+vTp9O3bFwBfX1+uXLmCh4eHto98XoVIPZRSxMbG0qVLF3LkyIGXl5e2bsaMGfj7+zNw4EC6desGQGxsLDqdTq7/RIpJbKNt2rShQIECyYrrjx49moULF7J582YcHBy05R8+fEj2PSZESlNKaTU4S5QowZQpUwAIDw9nyZIlbNiwgUGDBtG1a1fg4+QSer1eJgL6AuQKNI2RCxLxTyXWHEvs4VSlShWOHDlCmTJlWLZsGT4+PsTFxWmzopQsWRJra2uWLFkCSD0iQ/H09KRp06YA9OzZkwEDBpA+fXrGjh3L1KlTGT58OA4ODlhaWn4yo40kpFKfbdu24eTkRK1atZgxYwZRUVEAVK5cmVu3btGxY0cmTZqkJaQiIiLYsmULQLKLf/m8CpF66HQ6TE1N+fDhg/aZj4+PBz7WESxUqBCrVq3StjcxMZHrP5GidDoder2ely9farWhoqOjAZgyZQoODg5MnjwZ+FfblYSUMDSdToeFhQVhYWFERERoyzNnzkyfPn3ImjUrwcHB2vJ06dJJQuoLkatQIcQnkvaauHTpEg8ePCB37tzY29vj6elJgQIFWL16Nb6+vto+CxcuZOjQoVy4cIHAwEBDhZ7m2dnZceTIETp06AB8LHa9cOFCunbtSt68eXn37h3Dhw/H2tqa4sWLGzha8b9YunQp3bp1w8LCgkKFCjFy5EimTp0KQNOmTSlbtixZs2YlPj6eu3fvcubMGdq2bcujR4+YNWsW8K9inkKI1CPxoVGWLFm4cOECHz58wMjISLu5r1evHhEREdrkI0IYgrGxMXny5GHr1q3Axxv4xMSUvb29tp2UdxCGlngtlJCQQFRUFAULFuTRo0e8fv1aO99aWVlRtWpVLl26pD0MEF+ODN8TQvytUaNGsXr1agBKlSrFiBEjqFmzJq9evWLAgAHcu3ePUqVK8fjxY44fP054eDiHDx8mW7ZsUrciBfxdra7ff/+dFi1aUL9+fdauXYter+f9+/csXLiQw4cP8+zZM06dOoWJiYkM20qlli1bxoABA1i7di0tW7bk/fv3NG3alKtXr3Lu3Dny5ctHXFwczs7OXLx4kcuXL1OhQgUsLS3ZvXs3JiYmUutNiFQgISEBpRRGRkY8e/YMCwsLoqOjyZo1K6GhoZQpU4YaNWrw22+/YW5ujk6nw9nZmUePHrFlyxbSpUtn6Lcg0oDE75PEmcosLS0xNjbm/PnzdO7cGXt7e/z9/bXtnZ2def78OevXr8fU1FR6aosUFxsbi7GxMTqdjlevXmFpaUlcXBzm5uYcO3YMBwcHBg8ezKhRo8icOTMATk5OREZGsmbNGukh9YVJUkoIoUlaUygoKIhffvmF3377jTt37hAUFMTp06dZvHgx9erVIzw8nOnTp3P79m30ej3+/v6YmJjw7t07mQ71K/trMuHQoUPUrl072TZBQUG0bNmSxo0bs3LlSoyMjJg/fz5//vkn06dPl0kPUrHLly9TpkwZBg0axJw5c7TlFStWJCQkhOPHjxMdHa0V3Xz37h3nz58nT548FCxYEL1eL8deiG/cxo0byZ8/P5UqVQIgICCA8ePHExUVRdasWXFycqJPnz4EBwfTunVrcuXKha2tLSYmJuzcuZNjx45J4V3xVe3btw8bGxtKlCgBQGBgILNmzeLRo0dUr16dWrVq4ezszLp165gwYQLGxsbUrl2bZ8+esXPnTo4fPy4PMEWK27BhA3Xq1NGGlW7dupUJEyYAYG5uzvjx46lXrx47duygZcuW1K9fn8yZM2NiYsLGjRvl3PqVSFJKCPEJPz8/rl27hrW1NcOHDwfgwoULzJ49m2PHjrFs2TLq1q1LTEwM8LFehczcljK6du1K06ZNcXR0xMTEhCtXrmBvb8/gwYPx9PRMtu327dtp0aIFffv2Zf78+cnWSS+Z1OvOnTvMnj2bgIAAVq5cSePGjWnVqhVHjx6lfPnypE+fnn379lGzZk3KlStHs2bNKFWqlPZUT3rHCfFtu3nzJl26dCF79uzMmjULCwsL7O3tGT16NBkyZODBgwfMmDGDMWPGMGHCBF68eMGkSZN48+YN5ubmDBgwQEsUCPGlKaW4ceMG5cuXx8nJiVGjRvHixQvq1KnD4MGDSZ8+PVevXuXQoUM4Ozvz66+/cu3aNWbPns3Lly+xsrLCzc2NkiVLGvqtiDTm2rVrtGnThnz58rF582aePHlCiRIlGD16NObm5ly+fBl/f3+8vLzo168fJ0+exM/Pj/v375MjRw5cXV2xs7Mz9Nv4LklSSgiRTEhICM7Ozpw6dYrhw4czbtw4bd2FCxfw9PTkxIkTzJ8/n0aNGmnrZOa2lOHg4MDVq1dZsWIFDRs2xNjYmJUrV9KvXz/69evHzJkztW3v379PrVq1uH//PqNHj2bSpEkGjFx8Sffv32fGjBmsWbMGW1tbjIyMWL16NT/88AMfPnzg5s2brFixgm3btlG8eHF27twpn08hUpENGzbg4+ODpaUlJUuW5M2bN8lmMFuzZg1dunRh6dKl9OzZU1suDxxESlm7di3u7u60bt0aKysr3r9/z7Rp0wB4/vw5/v7+eHp6Mn78eJycnLT95MGIMJT4+Hj8/f1ZsmQJVlZWNGzYkCdPnmiz7AFMnjyZcePGcfDgQWrVqkVsbCwmJiba/8XXIUkpIdK4zyWT9uzZw+zZs7l+/To7d+6kTJky2rqLFy9qTxQ2bdqUwtGmXUkv4tq1a8f+/ftZuXIlDRs2xMTEhNWrV9OjRw8GDBigFbEODw9n9OjRdO3alQoVKsiNynfmzz//xMvLi4ULFzJ37lxthr3ECyellFa8U24AhEgdkp7rN27cyIoVK7h27Rq1atVi5cqVwL8ST4MHD+bKlSsEBgaSIUMGjIyM5AGR+OqStjF/f39GjBhBdHQ0rVu3ZuHChdp2z58/p3///lhaWrJs2TJAZvkVhpPYbuPj41m3bh2+vr5cuXKFdu3asWDBgmRJp7Zt2/L69Wu2b9+OsbGxnFtTgIyzESINS3rx+/r1a96+fUu+fPlo2LAhmTJlYsKECfTu3ZulS5dqM6WUKVOGuXPnYmtra8jQ07SVK1dSs2ZN3NzcAGjcuDGdO3cGPhYPffDgAbVq1WLLli3ExcVRqVIlGV75HSpQoAAuLi7ExsYyatQocubMSYsWLTA2NiYhIUGbkhuk94QQqYVer9c+r23atEGv1+Ph4cGePXs4c+YMFStW1D7LuXLl4vDhw5ibm2vL5KZJfG06nU67Qe/QoQPm5ub06NGD06dPc+nSJe16MVu2bBQrVoyAgAAiIyNJnz69gSMXaVliQsrIyIj27dujlGLWrFns2bOH8PBwMmfOrCWmihQpwtGjR5NNFCHn1q9LHp0KkUYppbQb1kmTJvHzzz9TsWJFHB0d2bBhA1WqVMHNzY3s2bPTq1cvLl++rO1bpEgR9Hq9Nk2q+PoSj5WrqystWrQgY8aMPH/+nJ49e7Jr1y7i4+Pp3Lkzu3fv5urVq6xatQqAvXv3aheQkpD6/tja2jJ06FC6dOlCt27d2Lx5s5aMSnoBJQkpIb59iT0bk35eW7Vqhbu7O0WLFmXs2LGcPXtWW/fo0SMyZMhAdHR0iscq0rak3y+Ojo4sXryYx48f4+3tzcWLF7V1jx8/Jk+ePNJbV3wTEs+tiYmpkSNHkiFDBq1nVGJPqefPn6PX64mMjEQGlaUMGb4nRBo3YcIEvL29mTdvHhUrVqRJkyaYmZkRGBhIwYIFOXDgAF5eXly5coUDBw5IDykDWrVqFQMHDuTw4cPY2Nig0+no3r07x44d47fffqN+/fqkS5eOyMhIYmJiyJQpk/SQSsUSn0T/ky7j9+/fx9PTk/nz53P48GFq1qyZQlEKIb6ExM/56dOnOXnyJEZGRtjZ2Wmf5fXr17NgwQKuX79OrVq1sLa2ZvPmzezbt4+yZcsaOHqRFiS20bNnz3Lnzh0iIiJo3LgxefLkQafTsWbNGoYPH07WrFmpUKEC6dKlIzAwkL179yYrAyFESkpstxcuXODcuXOYm5tTrFgxypcvT1xcHOvWrWPGjBm8ffuWatWqYW1tja+vL8eOHdN6/YmvT+5ShEijlFI8ffqUXbt2sWzZMn7++WeCg4N58OAB8+bNo2DBggDUrVuX6OhogoKCKFCggGGDTuNCQ0MpVaoUdnZ2GBsbo9Pp2LZtG40aNaJ///4sXLiQ2rVrY2FhgYWFBfBxiKYkpFKfpENrP3z4gLm5ebJ6CH/t+ZQ/f34GDhxIgQIFqFq1qiFCFkL8lxI/24GBgTg7O1OhQgWeP3+Oubk5bdq0YfDgwbRr1w5TU1NmzZrF8ePHGTp0KBcvXiRPnjyGDl+kAX9to6VLl+batWts2rSJzp0706VLFzp16kS6dOno378/u3fvxs3NjZMnT2rXk0KktKTt1sXFhXz58mFsbEx4eDgzZsygadOmtG/fHmNjY+bMmcOWLVvw9vZm2LBh5M2b19Dhpy1KCJFmPXr0SJUoUUIlJCSorVu3qgwZMihvb2+llFLv379Xq1evVs+fP0+2T1xcnCFCTdMSEhKUUkpNmDBB2djYaMsjIyOVUkrt3r1b6XQ6lS5dOnX06FGDxCi+nPj4eO1nT09P5ejoqOrWratcXV1VWFiYUur//hzGxsZ+1RiFEF9WcHCwyp07t1q8eLFSSqljx46pTJkyKRsbGzVp0iRtu5UrV6q2bduqx48fGypUkUYFBQWp7Nmzq2XLlimllLp06ZIyNjZWP/74o1q6dKl2reLn56fs7OzUixcvDBmuEEoppX7//XeVLVs27f7m4MGDyszMTGXIkEH5+/srpT5eMy1dulQ1a9ZMPXz40JDhplkyfE+INEJ9ZghQeHg4pUuXpn79+gQEBODh4UGfPn0AuH79OgMGDGDkyJHUq1fPECGnWX83XfLjx4/56aefqFu3Lr6+vtry4OBgtm/fjpGREZMmTZKeUd+JUaNGsWzZMlxdXbl79y43b97kyZMnHD9+nDx58kjxciG+E0oppk6dyv3791m6dCn379/HwcGBypUrY21tzYYNGxg1ahSDBw8G4O3bt2TMmNGwQYs0JS4ujpkzZxIWFsacOXMICQmhXr16VK5cmRcvXhASEoK7uztdu3ZFr9fz5s0bMmXKZOiwRRoXExPD2LFjUUoxffp0Hj16RLVq1ahWrRomJiZs3LiRdevW0bRpU+Li4nj//r20WwORpJQQaUDSJEdYWBjW1tZaIeRZs2YxefJkmjRpwpo1a1BK8eHDB9q0aUNcXBw7d+6UG98UlDR5uHLlSi5dukSpUqWoVq0aRYoUYfny5cycORN7e3umTZvG27dvGTlyJIUKFdKmYpZkReqT+BlNPP63b9+mWbNmzJ07l4YNGwJw48YNBg4cyMOHDzl58iRWVlaGDVoI8cW8e/eOq1evUqZMGerWrcsPP/zA8uXLuXr1KjVq1CAqKopff/0Vd3d3Q4cq0qgbN24AkC9fPho0aEDRokXx9fXl3r17lC9fnrx58+Lq6kr37t3/US1EIVLC7du3efbsGWXLlqVu3bqULl2apUuXcvjwYerVq0dCQgLr16+nTZs2hg41TZPH6UKkAYkJqfHjx7N3716io6NxdXWlSZMmdOnShT/++IPVq1fj5OSEmZkZt2/f5vnz55w/fx4jI6O/7bkjvqykF3G//vor8+fPp2LFivj5+VGjRg2GDBlC9+7dyZAhA7/++iv29vZkypSJnDlzsm3bNu11JCGV+oSGhpI7d26tDbx584YHDx6QO3dubZuiRYsyZcoUnJ2dOXDgAK1btzZgxEKI/8bnbtaVUmTIkIHKlStz/vx5IiIiGDZsGACmpqZUq1aNKlWq0L59e0OELNKgz7XTokWLotfrOXbsGG/fvtXa6KtXr6hQoQK5cuWibt26AJKQEt+MH374gR9++IFTp06hlMLNzQ2AbNmy8fPPP1O6dGlKly5t4CiF3GUKkUasXLmSpUuX0r17d/Lnz4+HhwfTp0/HxMSE6dOn4+3tzf3794mMjKRq1apcuHABExMT4uLiJCGVApJeAJ4/f567d++ye/du9u/fz/r163n79i1TpkzhyJEjtG3bllu3brF9+3Y2b97MyZMntWMlUp+LFy9iY2NDQECA9lmztbXlhx9+YM+ePcTHxwMfk8t2dna8f/+ekJAQQ4YshPgP+fr6EhwcrM2omdRfb+CfPHnCiRMngI/f3UZGRvTp00cKRouvasuWLQQHBwN8tp0mfj9FRkYSGRnJvXv3UEqxc+dOChUqhLe3N/ny5UvxuIVIlNhmPzcQLCIigjNnzvDo0SMA/P39ARg6dChFixZNuSDFZ0lPKSG+U3/t3RQdHc3YsWNxdnbG2dmZadOmsWHDBhISEnB1daVjx460b98+2T7x8fFSnyiFJN6UrFq1irVr1xIfH4+dnR0AtWvXRqfTMXXqVGbMmEFkZCQNGzbUpgoHOVapWa5cuejVqxcdO3Zkw4YNNG/eHAsLC8qWLcv27duxtbWlVatWwMcLLWtrazJnzmzgqIUQ/9TDhw/x9/fn2bNn+Pj48OOPP/7t8KY8efLg6OjImDFjmD17Nk+fPuXw4cPymRdf1d27d3Fzc6NMmTKYmpry448/aompv7bTUqVKkTNnToYOHcrIkSN58uQJBw8eJEOGDAaKXqRFifc5SUtWhIaGkitXrs+eW+3t7enQoQNNmjShVKlSXLt2jaNHj0p9vm+E1JQS4juU9CJizZo1PHv2jAsXLtCgQQM6d+6sbTdt2jQ2btxI3bp16dOnD4UKFfpkf/F1HTx4kBMnTjBmzBgAVqxYgYeHB69fv2bLli1UqVJF2/bw4cNMnz6dN2/e4OXlRcWKFQ0VtvjCnj17xtSpU5k/fz4BAQG0aNGCly9f0qlTJ169ekWRIkWoWLEiW7du5cWLF1y4cEGSkEKkIocPH2bBggX88ccfLFiwgJ9++ulvv2vv3bvH1atXefjwIY0aNcLW1tYAEYu0ZuvWrXh4eFCwYEFcXFyoWrUqkPyaMDER8ODBA3bt2sWHDx9o0qQJRYoUMWToIo1KHDUwbNgwNm7ciIuLC+fPn8fGxuZvtz9+/DihoaG0bt1a2u03RK5ohfjOJO0h5ebmxpIlS8iXLx83btzg7t27VKtWjQIFCgAfZ/fS6/XMnz+f/Pnz4+LiAkgtgJQSHR3Nhg0bOHHiBKampri5udGtWzcyZ87MuHHjmD9/PqamppQvXx4ABwcHoqOj2bNnj7ZMpE6PHj3C3Nwca2trAHLkyMGoUaNISEigVatWbNiwgdatW7N27VoWL15MUFAQW7ZsIV++fOzbtw9jY2MpaC9EKpD4OXVwcODDhw+sWrWK/v374+vrS9myZT+bmLK1tZVElEgx8fHx6PV6mjdvTnR0NF5eXixcuJB06dJRoUKFZD2m9Ho9CQkJ5MuXT5utWQhDOXr0KG5ubpw/f54NGzbg6+v72YRUYvstWrSoDNX7RklPKSG+U7dv38bDwwMXFxfKlCmDj48Pq1atomDBgkyZMoX8+fNr2/r5+dGxY0e5wTWAJ0+eMGPGDE6ePEmzZs20mZX8/f3x9PSkePHiDB48mHLlyn2yrxSgT50CAgLo2bMnuXPnxtnZmRw5ctChQwfg4/TFw4cPZ/78+dpsMInHOTIyEgsLC+Dj9NzSU0qIb1/izdDu3btZtWoVDx484MSJE5QpUwZvb+9/O5RPiJSQ2P527NjB0aNH2bp1K3fu3OHnn39m1KhRVKpUKdl2QnxLnJ2dWb58OY6OjmzatEnaaColdzNCfIfWr19Po0aNuH37NoUKFcLIyIjevXvzyy+/cP/+fUaPHs2DBw+07bt06YKRkZFWUFmknNy5czNy5EgqVqzItm3bmDp1KgAdOnRgyJAh3Lx5k/nz53Py5MlP9pWEVOoTExPDoUOHiIuL48WLF2zevJmRI0dSqlQpWrVqxZEjR3B0dGTUqFG0b9+effv2acc5MSGllJKElBCphE6n48iRIzRt2pRq1arh5eXF4sWLyZQpE3379uX06dOfLSotRErR6XQcPnyY5s2bky9fPubNm8eCBQu4fPkynp6enD59WttO2qn4VsTGxgIfr4k6dOjA5s2bmTx5Mq9fv/7s9tJ2v21yVSvEdyg+Pp68efNy+fJloqKitAKpvXv3RqfTsXbtWvr06cOKFSvIkSOHtp/0lDKMnDlzMnr0aKZMmcK2bdsAcHd3p0OHDuh0OkaOHEmhQoWoXLmygSMV/ytTU1PGjh2LiYkJf/zxByVLlmTTpk1s3ryZPXv20KtXL6Kjo7G1tUWn09GwYUPOnDmTbLimPAUUInVI7Fly8OBB6tWrpw2RL1++PAULFmTixIn06dOH5cuXU6ZMGemJIlJcYpvbsmUL9erVo1+/ftq67Nmz4+rqyrRp0xgzZgzly5eX9ikM7q/nSR8fHwB+/PFHBg0ahFKKQYMGkSlTJgDu3LlDkSJFpO1+4yQpJUQq97khXB07diR9+vRMnDiRdu3asWbNGm2a3l69evH+/Xvu3LlDtmzZDBGy+Iy/JqZ0Op3WW8ba2pratWsbOkTxheTIkQM3NzemTp3K/v37yZMnDy4uLvTq1YubN28SGhrKb7/9RmxsLC9evMDe3t7QIQsh/guJN0FmZmaEhIQQHh6uPSSqV68eN27cYPDgwbRo0YL169drw6SESGkWFhZ8+PCB2NhYjIyM0Ov1tGzZkgcPHjB69Gji4uIYM2YMP/74o6FDFWlY0kR/QEAAFhYW/Pzzz9SoUYMBAwYAMGjQIHQ6HR07dmTdunV4e3tz/fp1LC0tJTH1DZOklBCpWNKE1I4dO4iNjSV9+vTUr1+f5s2bk5CQwPz58+natSurVq0ib968ALi6umondqlL9O1ITExNnTqVHTt28PbtW6ZNm0a9evUApLD1dyR37tzasV61ahVv3rzB3d2dYsWKUaxYMWrWrJmsuKzUkBIi9SpVqhRKKfbt20ezZs0wNzfXllerVo2SJUvKQyJhEIk36T/88IM2VK9q1arad0+uXLkoUKAACQkJ2jWkEIai0+nYv38/jRo1ok2bNmzdupWTJ0/i6OjIkCFDGDBgAEZGRgwfPpzNmzfz4MED9uzZQ8aMGQ0duvg/SKFzIVKppN1Xhw8fztKlS8mZMyd//PEHI0aMYNKkSQBs3ryZBQsWoNPp8PHx0Wbe++triG9HaGgobm5umJmZsWTJEjlG37HQ0FCmTJnCmTNnaNGiBSNGjACSFzKXxLEQqUPid+rFixd5+vSpNu24paUl3bt35+DBg0ydOpW6deuSI0cO3N3dCQ0NxdPTEysrK0OHL9KAxDZ65coVwsLCeP78Oe3atUOn0+Hk5MTOnTsJCAigXLlyZMiQAXd3d8zNzenfv7/Wy08IQ3n06BFz587F1taWvn37Eh4ejpubG9evX6dZs2YMHz4cvV5PcHAwb968wc7OLtl9j/h2SVJKiFTu4cOHODo6snz5ciwtLTl69CjOzs7069ePOXPmALBlyxbGjh1LzZo1mT9/voEjFv/Eq1evsLKyQq/XS/LwOxcaGsrUqVM5d+4cDg4OTJ482dAhCSH+SwEBAbi4uFCyZElu375Nrly5GDRoEJ06daJTp05cuHCBt2/fki9fPi5evMipU6coVaqUocMWaUDitURAQABDhw7F2tqayMhIYmJiWL58OcWLF2fYsGFs2LABOzs7TExMuHjxIqdPn5Y2Kgzu/PnzjB49mqdPnzJ37lxq1aoFwMuXL3F3d+fKlSu0aNGCoUOHyoO8VEjGAgiRyiRNUEybNo2rV69SsWJF7OzsMDIyolChQpiZmdGlSxd0Oh2enp44OjqSOXNmqlWrZuDoxT+VJUsWQHrJpAU5c+bE3d0dNzc3wsLCJAkpRCp19uxZXFxcmDZtGt26deP27dsUK1aMp0+fArBmzRqCgoK4evUqsbGxrFy5kiJFihg4apFW6HQ6Tp48ibOzM7Nnz6Zbt26EhIRQuHBhrl27Rs2aNVm1ahVNmjTh/v37REZG8ttvv1G0aFFDhy4EWbJkISEhgdu3b3Ps2DEtKWVtba0V41+xYgVmZmZafSmRekhPKSFSkaQ3q2fOnOHMmTMMHjyYihUrcuzYsWTbbtiwgW7dutG+fXt8fX215VKXSIhvk/SOEyJ1W716NX5+fuzdu5dbt27RuHFjateuzbJly4CPT/Stra0NHKVIK65evYqdnV2yZb/99hv79+9nzZo13Llzh/r161O/fn2WLFlioCiF+LzPXQc9evSIgQMH8uTJE1xcXOjSpYu27uXLl0yZMoWBAwfKkL1USB6/C5FKJD05u7u706tXL2rXrs2cOXM4efIks2fPTrZ927ZtWbRoEffu3SMhIUFbLgkpIb5NWbJkQa/Xk5CQIAkpIb5RSb9PE3+OiIgA4I8//iBjxowopahbty5169bVbva3bt3K8uXLiYyMTPmgRZqzadMmateuzdu3b5Mtv3DhAu/evePt27fUqVOH+vXr4+3tDXxMWI0fP94A0QqRXOI9z4kTJ/Dy8mLMmDEEBwdjY2PDggULyJEjB76+vvj5+Wn7WFtbM2vWLElIpVKSlBIilUjaQ+rChQt4e3tTrFgxevbsyYwZMxg+fDhz585Ntk/Xrl0JCgrSbnSFEN8+Ga4pxLdLr9fz559/smvXLvR6PRs3btRu5Bs2bMi+fftInz49rVq1YsmSJdrn+cCBA5w+fVq+i0WKaN26NefPnydjxoyEhYVpy1u2bEloaCj58uWjUaNGyXpIXbp0iVu3bvHu3TtDhCyEJrH2WfPmzdm1axd37tyhZs2aTJ06ldy5c7NgwQIyZcrEypUr8fHx0faT66fUS46cEKmIn58fU6ZMQafTUaZMGZRSpEuXDhcXF2bOnMmwYcPw8vL67L5yohZCCCH+N+/evWPmzJn06dOHUaNG0a5dO0qXLg1A8eLF6du3L5kyZdLq8Dx8+BB3d3fWrl3LhAkTyJAhgyHDF2lAYmUWGxsb7ty5Q548edi0aRMAhQsXJm/evGTNmpUff/wRgLCwMMaMGcOaNWsYO3astFFhcNevX2fw4MFMmTKFPXv2aGVIoqKiiI+PJ2/evMyfP5+4uDi2bt36SY9AkfpITSkhUpEpU6awaNEi4uPjCQ4OTlYg9cOHD3h7ezN06FDWrVtH27ZtDRipEEII8f1YtWoVv/zyCwB37tyhZ8+eBAcHM3jwYDw9PbXtrl+/zooVK1iwYAG5cuUiY8aMREZGsn79esqWLWuo8EUakjj06dSpU+TOnZtp06axZs0aVq5ciaOjIzdu3ODXX3/l/PnzxMTEkDdvXkJDQwkMDJQ2Kr4JR48eZfz48Rw4cIC7d+9Sq1YtmjRpovXse/ToETY2Njx8+BCAvHnzGjJc8QVIUkqIb9Tfzbq2dOlSpk+fjoODA6NGjcLW1lZbFxUVxdatW2ndujXGxjK5phBCCPG/unDhAh06dGD37t0ULFiQ9+/f07x5c969e0dkZCSTJk2iefPm2vZRUVHcvXuXU6dOkS9fPkqWLEmePHkM+A5EWrN37146derEqlWraNy4MS4uLixfvpy1a9fSokULQkNDefToEUFBQdjZ2VGyZEm5sRcGk5hIPXDgADlz5uTPP/9kyJAhBAQE0KxZM632mV6v58CBA3h7e7Nw4UJy5sxp6NDFFyJJKSG+QUkTUvv37ycyMpI3b95oT2kXLlyIj48PNWrUYPDgwRQsWPCT14iLi5PElBBCCPE/ioqKIjo6GisrKy5duoS9vT2xsbFcv36dOXPmcObMGaZOnZosMRUbG4uJiYkBoxZp1bNnzxg/fjw//PADrq6u2vLExJS/vz+Ojo6GC1CIzzhy5AgNGzbEx8eHqlWr0qNHD06ePEmLFi3w8/PT7o1GjRrFuXPn8Pf3l9lMvyNyxyrENygxITVixAg2btxIjhw5CA0NxcvLi5UrV+Li4kJcXByrVq1Cr9fTr1+/ZEP5AElICSGEEP8jpRTm5uaYm5sTGhpKgwYNqFy5Mlu2bMHe3p6+ffsCMGbMGJRSODo6MmnSJN69e8fEiRNJly6dgd+B+N4lnZ35zJkzDBo0iMjISC3xlHgzv3DhQuDjJDg+Pj60adPGUCELkcyDBw/YvXs348ePp2PHjgDUrl2b+/fvkyFDBh48eMDr169Zu3YtS5YsITg4WBJS3xm5axXiG7V48WJ+++039uzZQ9myZfH396dTp048efKEkiVLMmjQIJRSzJo1i/z58zN48GBDhyyEEEJ8lxISErQpx8eOHUu7du1Yv369Vizax8eHrl27UqlSJX7//XdOnDghCSmRok6dOkXWrFkxMTHh8uXLnDlzhvr162szMCcmpt69e8fgwYNp1KiRFDUXBnft2jX69OnDkydPGDt2rLbc3d2duLg49uzZQ6FChbCzs0MpxeHDh7GzszNgxOJrkOF7Qnyjhg8fjqWlJWPHjmX9+vX07t0bDw8P+vTpQ0REBJaWlgCsW7eONm3aYGRkZOCIhRBCiO9HYg+U4OBgbt++TePGjcmaNSsBAQG4ublRpUoV1q9fD8CtW7c4deoUFy9epHfv3trse0KkhEOHDlG3bl2Cg4Oxs7Ojbdu2PH36lIkTJ/Lzzz9jZGSUrDREaGio1OMR34SYmBj69u3Lhg0baNasGcuXL0+W0H/16hWXLl0ib968ZMqUiWzZshkwWvG1SFJKiG9A0q7X8LEeVO3atalTpw716tWjQYMG2hTUCQkJjB07ljx58mjDBgDi4+MlMSWEEEJ8QQEBATg5OTFs2DBat25NyZIliYyMZNu2bZ8kpoQwhD///JOzZ8/y8OFDrYZUREQEzZo1IyoqCnd3d5o0aYKRkdEn15tCpLTPtcHY2FiGDh3KwYMHcXJywsXFBQsLCwNFKAzh06m9hBApLvHk7OnpyY4dOzA2NuaXX34hMDAQBwcH5s6dS58+fQB4//49Fy5cIDQ0NNlrSEJKCCGE+HLOnz9Pv379mDdvHr/++islS5YEwMLCgmbNmjFz5kzOnDlD48aNDRypSKv++OMPypQpQ69evbTeJTExMVhaWrJt2zbMzc2ZMWMGgYGBxMfHS0JKGFRiQurIkSOMHj2a3r174+vri4mJCZ6entSoUYONGzeyaNEioqKigI9Dp8X3T5JSQnwjXr9+zaFDh9i2bRsA5cqVI3PmzNjZ2ZErVy4A7t69S/v27Xn+/Dm//vqrIcMVQgghvmuXL18mX758tGzZUhv2lHiDZGFhQatWrZgwYQIPHjzg8ePHhgxVpFHm5uYMGzYMvV7PrVu3ADA1NU2WmIqMjGTp0qXaTb4QhqLT6QgMDMTR0ZG7d+9iaWmJs7Mzzs7O6HQ6vLy8KFeuHIGBgcyaNYuoqCjt3Cu+b3KUhfhGWFlZ0aBBA7Zv387Dhw8pV64cY8eOJVeuXDg7O2NjY0ObNm14/fo1x44dw9jYmPj4eEOHLYQQQnwX/lrRIiQkhKioKKysrICPw+QTb5DOnj3Lixcv6NChAydOnCBPnjwpHa5Ig/7aRnPmzEmvXr1wdXVl0aJFTJ8+HUiemAoODsbHx0eKmosU5+XlxZEjR7Tf//jjD0aMGMHkyZNZv349EyZMIH369GTKlAkAExMTvLy8yJ8/P7///rskUtMQqSklhAEkLTb5VzVq1CBv3rysXr0anU7Hw4cPCQ0N5erVq9ja2lK1alWMjIyIi4vD2Fgm0BRCCCG+lN27dwPQqFEjjhw5Qt26dVm2bBldu3bVtomOjmbEiBFUrFiRTp06GSpUkcYkDn06ffo0N2/e5MmTJ7Rr1468efMSGxuLp6cnM2bMYPTo0bi5uQEfh/KZmpoaOHKRFv36668sWbKEU6dOUbBgQQBu3LhB9+7dOXHiBH/88QfVq1enSZMmLFmyBIBLly5hb29PTEwML1++1EaKiO+f9JQSwgASE1Le3t6cO3eO58+fAx+TVZ06deLu3bvcu3cPgDx58lCxYkW6detGjRo1MDIyIj4+XhJSQgghxBcUExPDvHnzOHnyJAB2dnb07t2b8ePH4+vrC3yctWzKlCmsXbuWH3/80ZDhijQkMSG1adMm6tWrx8KFC1mwYAE//fSTNsxp8ODBjBgxghkzZjB+/HgASUiJFKeU4vXr1wQFBTF8+HAKFizIzZs3efbsGe/fv+fJkyfs37+fOnXq0KRJExYtWgR87H3q5ubG9evXMTU1lYRUGiNJKSEM5N27d/j6+tKhQwc6duzI1q1b0ev1dOnShfDwcO0k/bkeVVLUXAghhPiyTE1NyZQpExcuXAAgS5YsuLi40L59e/r160fhwoWpW7cuK1asYO/evRQuXNjAEYvvVWLtsg8fPgAfa/Fcv36dQYMGMW/ePA4dOsSjR4/o3r07/v7+LF++HAsLC3r27Enfvn1ZsWIFL1++/GS4nxBf24MHD7CyssLIyIi7d+/i5eXFTz/9RFhYGLa2tpQrV44WLVpQoUIFlixZot3TbN68maioKLJmzWrgdyAMQYbvCZFC/m7I3tatWzlw4ABLlizB0dGRZs2aYWZmxqRJk1i1ahX29vYGiFYIIYT4/iSdjjxxGHx4eDhmZmaYm5vj5eXFwYMH2bp1K/Hx8RgZGfH+/Xtu377NqVOnyJYtGxUrViRfvnwGfifie/f48WPq1avH3r17yZs3L4cPH6ZHjx7s27cPW1tbrR2PGDGCVatWcfHiRXLkyMGLFy/Q6XRYW1sb+B2ItGb48OHcvHmT7du3c+7cORwcHIiOjmbcuHG4u7sDsHLlSmbMmEHx4sUZMGAAer2eLVu24Ovry5EjRyhdurSB34UwBBn/I0QKSJqQ2r9/P+/fv+fDhw+0b9+e5s2b07x5c1q3bs3GjRsZM2YM4eHhREREcO3aNUlKCSGEEF9A4ndxaGgoOXPmxNjYmPPnz1OtWjV++OEHihUrxtOnT7l9+zZBQUEULVqUXLlykT59esqWLUvZsmUN/RZEGqLT6YiJicHNzY1Vq1aRkJDA27dvSZcuHTqdjsjISCwsLJg+fTrLly9n69at9OrVS3qaCIPYtGkTCxYs4Pz58wCkS5eOd+/eYW5uTlhYGHfu3KFIkSJ07dqVuLg4tmzZQr169ShRogTm5ub8/vvvkpBKw6SnlBApaNiwYaxduxZLS0uePn2KnZ0dU6ZMoVq1apiYmBAZGcm7d+9wd3fnxYsXbNq0SWpHCSGEEP+jxITUxYsXcXR0xMfHh7p16xIWFkZwcDBxcXEcOHCAuLg4Vq5cScaMGcmTJw/GxsZky5aNzp0707VrV613ihBfWtJefEoplFLMnTtX61lSr149SpcuTc6cOTlw4IC234sXL6hduzaTJ0+mWbNmhgpfpHErV67Ex8eHoKAg9u7dy7Nnz7CzsyMiIoIWLVrQqVMnXF1dKVKkCACRkZHcv38fa2trTE1NtVlORdokSSkhUsiKFSsYMWIE+/btI1euXCilaN68OQkJCSxZsoRy5colm1Ev8eJEZtkTQggh/nuJCalLly5RuXJlXF1dmTp16me3DQ8Pp2XLlnTu3JmSJUsSFBTE8+fPcXJyolSpUikcuUgrEtvoq1evyJIli7b8zZs3ODg4kCVLFg4cOMDx48dp27YtRYoUYeHChcTGxhIYGMiSJUs4efIkBQoUMNybEGnawYMHadeuHQ4ODgQEBBAQEECLFi2Aj6VKunbtSseOHRk6dCi2trYGjlZ8a6TQuRAp5M6dO1SpUoUyZcpgbW1Nzpw5+f3334mMjGTixIkAGBsba0UpdTodSilJSAkhhBD/paQJqSpVqnySkLpx40ay7c3Nzbl//z5v3ryhcuXKjBw5ktmzZ0tCSnxVer2ekJAQihQpQrNmzQgNDeX9+/dkypQJHx8fjh07xuzZs/npp5/YuHEjr1+/xsHBgRYtWrB27Vp27dolCSmR4lavXs27d+8AqFOnDo6OjmzZsoVGjRpRt25dbbvmzZuzatUq1q5dy9y5c7l7966hQhbfKElKCfEVBAcH4+npiaenJ6GhoQA8e/aMFy9eAB+TT1FRUZiZmTFr1ixOnjzJH3/8kazrNiDDBIQQQoj/gV6v5+7du1SuXJmhQ4cydepU7eHPlClTGDp0KGFhYcDHBJaZmRnVqlXj4cOHhgxbpEHx8fEkJCSwY8cOunbtyuLFi7l8+TLlypVjwIAB+Pn5cfr0aapUqcKFCxcIDAwkMDCQ4OBgypUrZ+jwRRoTEhKCm5sbL1++BD7OFHn//n3atWvH0aNHmThxYrLzaLNmzfDz82PhwoUsXryYuLg4Q4UuvkGSlBLiC/Pz86Nnz57cv3+f9OnTkzNnTgCcnZ25ceMGs2fPBj4+jQWIjY0la9aspE+fXpJQQgghxBeUkJDA8uXLsbS01GYj0+l0TJs2jZkzZzJo0CCyZ88OoE1IYmVlxfHjx0lISECqXIivKSEhAfg4E2SRIkWYMGECgwcPplKlSty9excnJyd27NhB69atiYuLY+fOndrNfNWqVSlTpox2nSlESklISKBQoULcvn2b/Pnzc+nSJXQ6Hdu2bWP16tV4eXmxZMkSvLy8ePTokbbfzz//zM6dO3F2dpaRICIZaQ1CfEF+fn707t2bJUuW0KJFCzJkyADAnDlzsLKyYtiwYSxbtowPHz7Qv39/wsPDWbZsGTY2NmTLls3A0QshhBDfF71eT//+/YmMjGTdunWYmZnx9u1bZs+ezfr162nQoMEn+7Rp00abqlyIryGxZ3xkZCQZMmTQbtDt7e3ZvXs3AwcOxMHBAV9fX7p06cLo0aOxsbHBy8uLli1byszMwmDmzJlDvnz5aNasGRkyZODVq1f89NNPNGjQgBUrVmBubk7Xrl0BGDBgAACDBw8mT548ADRq1MhgsYtvlxQ6F+ILuXHjBu3ataN///706tVLW96mTRsCAgJwdHTEwcGBhIQEJk2ahE6nw8rKisyZM3Ps2DFMTEy02hdCCCGE+HJCQ0OZMmUK+/fv5969e+zdu5fatWsnm0xk7NixhIeHM3/+fANHK9KC0NBQKlWqRJcuXejduzf58uUDYPLkycybN4+LFy+SJ08ejh07xsqVK3ny5Am7du2iUaNGbNu2Db1eLz3sRYqrV68ep0+fZu3atdSvXx8TExMOHz5MmzZtaNiwIYsWLSJjxowArFq1CldXV9q1a8eYMWPInTu3gaMX3yq5+xXiC3n48CERERHUqFFD647t4uLCxYsX2b59OxEREfz+++/kzZuX27dvs2LFCpYtW8aJEycwMTEhLi5OElJCCCHEV5AzZ07GjBlDgwYNKFGiBBcuXADQElLjxo1j5syZODk5GTBKkZaYmZnRs2dPFi1aRPfu3Zk7dy4AY8aMoUmTJowePZo3b95QtWpVJk+ejJubG02aNGHatGkYGRlJQkqkqMR+LPv376dRo0Z06dKFPXv2EBUVhYODA5s3b2b79u307duXN2/eAPDLL7/g4eHBjh07ZLie+Lekp5QQX8iUKVOYM2eOVswc4OnTp8THx2NjY8P169fp1asXsbGx7Nq1S6ttAR+LWxoZGRkibCGEECLNSOwxdebMGVq0aMGIESOYMmUKkydP5ujRo5QvX97QIYo05vr164wbN46LFy9iY2OjFTjfuXMnnTt3TjaL2V8nxBEiJcXExGBqagp87DH14MEDZs2aRf369UmXLh3BwcE0bdqUpk2b4u3trfWYevv2rfazEJ8j3TKE+EIKFy5MVFQU+/fv15blypULGxsbEhISKFGiBM2aNSNr1qyYmZkl21cSUkIIIcTXlzNnTkaPHk3FihXZuXMnP/74oySkhEGVKFGCJUuWMGfOHN68eUPjxo05f/48V69eZePGjcm2lYSUMBSlFKampqxbt44mTZqQPn16/vzzT3r16sW+ffuIiYmhevXq7Nixg71799K5c2fevn0LgKWlpYGjF986SUoJ8YVUrFgRY2NjlixZwoMHD5Kt0+v1REREEBwcTNGiRUmfPr2BohRCCCHStsTEVOHChXn16hUnTpyQhJQwqCxZstC0aVPOnz9Ps2bNuHjxIqGhoSxbtgwfHx9DhycEOp2O06dP07NnT1q3bs3s2bO5fv06FSpUwMnJib179xIdHU316tXZsGEDly5d4t27d9q+Qvw7MnxPiC/I39+fbt260bp1a4YNG0aZMmUAuH//Ps7OzoSFhXH27FmMjY2lC7YQQghhQM+fPychIYEcOXIYOhQhkl0XBgUFsWfPHhYtWsTp06cpVqyYgaMT4uN9zqRJkzh27BiZM2fWlicmVJcuXUqdOnUwNzcnKioKc3NzA0YrUhNJSgnxBcXHx7NixQr69etHjhw5sLOzIy4ujoiICACCg4MxMTGRGlJCCCGEECKZvz6wlFo84luybNkyRo4cSVhYGEZGRlri6dKlS1SoUIEMGTLg7+9Pw4YNDR2qSGVk+J4QX5CRkRE9e/bk9OnTNG/enPj4ePLnz88vv/zCsWPHtFn2JCElhBBCCCGS+msPeklICUP5XL+Vtm3bYmlpSffu3QG0nlDGxsZ06NCBevXqUbBgwRSNU3wfpKeUEClIekgJIYQQQgghvlWJPfZOnTrFsWPHsLW1pUyZMuTPn59Vq1Yxbdo0KlSogLe3NxERESxatEgrzG9sbGzo8EUqJK1GiK/kczWjJCElhBBCCCGE+FbpdDq2bdtGu3btsLOz4/r16/z8888MGDCAX375Bb1ez9ixY8mTJw/ZsmXj9evX7Nu3TxJS4r8mPaWEEEIIIYQQQog0LPGB+qNHj3B3d6d69eo4OzuzZ88eZs2ahampKaNGjaJ69epER0ezefNmMmbMSIkSJShQoIChwxepmCSlhBBCCCGEEEKINCQhIQG9PnmJ6VOnTrF48WIeP37M0qVLtWTToUOHmDZtGiYmJgwePJj69esbIGLxvZJC50IIIYQQQgghRBqi1+t58OABXl5e2rLbt2/z+++/c+rUKf744w9tee3atXF3d0cpxaRJkzh8+LAhQhbfKRn4KYQQQgghhBBCpCHx8fEsWrSIzZs3Ex0dzfDhw+nSpQsZM2bk119/ZdGiRWTMmJHy5csD4ODgQGxsLIsXL6Zw4cIGjl58T2T4nhBCCCGEEEIIkcY8fvyYmTNncvLkSZo1a4a7uzsA69atY/bs2RQvXpzBgwdTrlw5bZ/IyEgsLCwMFbL4DklSSgghhBBCCCGESINCQ0OZMmUKZ86cSZaY8vf3x9PTk1KlStGnTx8qVapk4EjF90qG7wkhhBBCCCGEEGlQzpw5GT16NFOmTGHbtm0AuLu706FDB/R6PaNHjyZdunTY29uTLl06A0crvkfSU0oIIYQQQgghhEjD/q7HVEBAAOXKlaNgwYIGjlB8r2T2PSFEquXk5IROp0On02FsbEy+fPno27cv4eHhhg5NCCGEEEKIVCOxx1TFihXZtWsXY8aMAaBVq1aSkBJflSSlhBCpWsOGDXn69Cl//vknPj4+bN++nX79+hk6LCGEEEIIIVKVxMRUkSJFOHXqFC9fvjR0SCINkKSUECJVS5cuHTlz5sTGxob69evTrl079u3bp61fsWIFxYsXx8zMjGLFirFo0SJtXUxMDP379ydXrlyYmZlRoEABpk2bpq3X6XR4e3vTqFEjzM3NKViwIBs3bkz271+5coXatWtjbm6OtbU1vXr14t27d9p6JycnHB0dmTVrFrly5cLa2hoXFxdiY2O1bRYtWkSRIkUwMzMjR44ctG7dWlunlGLGjBkUKlQIc3Nz7O3t2bRp0xf9GwohhBBCCAEfE1MeHh6sXr0aa2trQ4cj0gApdC6E+G6EhISwZ88eTExMAFi2bBnjxo1jwYIFlC1blgsXLuDs7Ez69Onp2rUrXl5ebNu2jQ0bNpAvXz4ePnzIw4cPk73mr7/+ioeHB/PmzcPPz48OHTpgZ2dH8eLFiYyMpGHDhlSuXJkzZ84QFhZGz5496d+/P7/99pv2GocPHyZXrlwcPnyYu3fv0q5dO8qUKYOzszNnz55l4MCB+Pn58dNPP/Hq1SuCg4O1fceMGUNgYCDe3t4UKVKEI0eO0LlzZ7Jly0bNmjVT5O8qhBBCCCHSjhw5chg6BJGGSKFzIUSq5eTkxOrVqzEzMyM+Pp4PHz4A4OnpiaurK/ny5WP69Ol06NBB22fy5Mns2rWL48ePM3DgQK5du8aBAwfQ6XSfvL5Op6NPnz54e3tryypXrky5cuVYtGgRy5YtY8SIETx8+JD06dMDsGvXLn7++WeePHlCjhw5cHJyIigoiHv37mFkZARA27Zt0ev1rFu3jsDAQLp168ajR4+wtLRM9u+/f/+erFmzcujQIapUqaIt79mzJ5GRkaxdu/bL/TGFEEIIIYQQIoVJTykhRKrm4OCAt7c3kZGR+Pj4cPv2bQYMGMDz5895+PAhPXr0wNnZWds+Li6OTJkyAR+TWvXq1aNo0aI0bNiQpk2bUr9+/WSvnzQZlPj7xYsXAbhx4wb29vZaQgqgatWqJCQkcOvWLe0pU8mSJbWEFECuXLm4cuUKAPXq1SN//vwUKlSIhg0b0rBhQ1q0aIGFhQXXr1/nw4cP1KtXL1kMMTExlC1b9n/8ywkhhBBCCCGEYUlSSgiRqqVPn57ChQsD4OXlhYODAxMmTKB///7AxyF8P/74Y7J9EhNE5cqV448//mD37t0cOHCAtm3bUrdu3f+zZlNiryql1Gd7WCXdBtCGEyZdl5CQAIClpSXnz58nKCiIffv2MXbsWMaPH8+ZM2e0bXbu3EmePHmSvUa6dOn+bYxCCCGEEEII8a2TQudCiO/KuHHjmDVrFvHx8eTJk4eQkBAKFy6c7L+k09pmzJiRdu3asWzZMtavX09AQACvXr3S1p88eTLZ6588eZJixYoBUKJECS5evMj79++19ceOHUOv1/PDDz/845iNjY2pW7cuM2bM4PLly/z5558cOnSIEiVKkC5dOh48ePDJe8ibN+9/+ycSQgghhBBCiG+C9JQSQnxXatWqRcmSJZk6dSrjx49n4MCBZMyYkUaNGhEdHc3Zs2cJDw9nyJAhzJkzh1y5clGmTBn0ej0bN24kZ86cWFlZaa+3ceNGKlSoQLVq1VizZg2nT5/G19cXgE6dOjFu3Di6du3K+PHjef78OQMGDKBLly7/uEDkjh07CAkJoUaNGmTOnJldu3aRkJBA0aJFsbS0ZNiwYbi6upKQkEC1atV4+/Ytx48fJ0OGDHTt2vVr/AmFEEIIIYQQIkVIUkoI8d0ZMmQI3bp14+7du/j4+DBz5kzc3NxInz49pUqVYvDgwQBkyJCB6dOnc+fOHYyMjKhYsSK7du1Cr/9XJ9IJEyawbt06+vXrR86cOVmzZg0lSpQAwMLCgr179zJo0CAqVqyIhYUFrVq1wtPT8x/HamVlRWBgIOPHj+fDhw8UKVIEf39/SpYsCcCkSZPInj0706ZNIyQkBCsrK8qVK4e7u/uX+4MJIYQQQgghhAHI7HtCCPE3dDodmzdvxtHR0dChCCGEEEIIIcR3R2pKCSGEEEIIIYQQQogUJ0kpIYQQQgghhEgjnJyc0Ol06HQ6jI2NyZcvH3379iU8PNzQoQkh0iCpKSWEEH9DRjcLIYQQ4nvUsGFDVqxYQVxcHNevX6d79+68fv0af39/Q4cmhEhjpKeUEEIIIYQQQqQh6dKlI2fOnNjY2FC/fn3atWvHvn37tPUrVqygePHimJmZUaxYMRYtWqSti4mJoX///uTKlQszMzMKFCjAtGnTtPU6nQ5vb28aNWqEubk5BQsWZOPGjcn+/StXrlC7dm3Mzc2xtramV69evHv3Tlvv5OSEo6Mjs2bNIleuXFhbW+Pi4kJsbKy2zaJFiyhSpAhmZmbkyJGD1q1ba+uUUsyYMYNChQphbm6Ovb09mzZt+qJ/QyHElyE9pYQQQgghhBAijQoJCWHPnj2YmJgAsGzZMsaNG8eCBQsoW7YsFy5cwNnZmfTp09O1a1e8vLzYtm0bGzZsIF++fDx8+JCHDx8me81ff/0VDw8P5s2bh5+fHx06dMDOzo7ixYsTGRlJw4YNqVy5MmfOnCEsLIyePXvSv39/fvvtN+01Dh8+TK5cuTh8+DB3796lXbt2lClTBmdnZ86ePcvAgQPx8/Pjp59+4tWrVwQHB2v7jhkzhsDAQLy9vSlSpAhHjhyhc+fOZMuWjZo1a6bI31UI8c/I7HtCCCGEEEIIkUY4OTmxevVqzMzMiI+P58OHDwB4enri6upKvnz5mD59Oh06dND2mTx5Mrt27eL48eMMHDiQa9euceDAAXQ63Sevr9Pp6NOnD97e3tqyypUrU65cORYtWsSyZcsYMWIEDx8+JH369ADs2rWLn3/+mSdPnpAjRw6cnJwICgri3r17GBkZAdC2bVv0ej3r1q0jMDCQbt268ejRIywtLZP9++/fvydr1qwcOnSIKlWqaMt79uxJZGQka9eu/XJ/TCHE/0x6SgkhhBBCCCFEGuLg4PD/2rubkCi3OI7j38ZAGF+aNuYwRBiTygxiSdIiiQInJmhRCLWIkIg24VsSLVrkiGD0QkGbFtlSLKTalGFEtEmkIiShSGMSXQ4kBIVJTS2iucq93Lpdm8vF7weexXDOc/g/Z/njnP9w+fJlPnz4QF9fHxMTE7S2tpLJZJiZmeHw4cMcOXIkN//Tp0+sWrUK+BZqJRIJqqqqSCaT7N69m507dy5af2EY9P332NgYAC9fvqS2tjYXSAFs3bqVbDbLq1evWLNmDQDxeDwXSAGEw2HGx8cBSCQSrFu3jvXr15NMJkkmk+zdu5dgMMiLFy+Ym5sjkUgsqmF+fp5Nmzb9y52TtNQMpSRJkiRpGSkqKiIajQJw6dIlduzYQXd3Ny0tLcC3K3xbtmxZ9M73gKiuro43b95w9+5d7t+/z759+2hsbPxhz6bvp6q+fPnylyesFs4BctcJF45ls1kASkpKePbsGQ8fPuTevXucOnWKVCrFkydPcnPu3LlDJBJZtEZhYeHf1igp/2x0LkmSJEnLWFdXF+fPn+fz589EIhHS6TTRaHTRU1FRkZtfWlrK/v37uXLlCtevX+fGjRu8ffs2Nz46Orpo/dHRUaqrqwGIxWKMjY3x/v373PijR48IBAJUVlb+dM0rV66ksbGRs2fP8vz5c6ampnjw4AGxWIzCwkKmp6f/9A1r16791S2S9Jt4UkqSJEmSlrHt27cTj8fp7e0llUrR1tZGaWkpu3bt4uPHjzx9+pTZ2Vk6Ozu5ePEi4XCYjRs3EggEGBwcpLy8nFAolFtvcHCQzZs309DQQH9/P48fP+bq1asAHDhwgK6uLpqbm0mlUmQyGVpbWzl48GDu6t6P3L59m3Q6zbZt21i9ejVDQ0Nks1mqqqooKSnh+PHjHDt2jGw2S0NDA+/evWNkZITi4mKam5t/xxZK+kWGUpIkSZK0zHV2dnLo0CFev35NX18f586d48SJExQVFVFTU0NHRwcAxcXFnDlzhsnJSQoKCqivr2doaIhA4I9LON3d3Vy7do2jR49SXl5Of38/sVgMgGAwyPDwMO3t7dTX1xMMBmlqauLChQs/XWsoFOLmzZukUinm5ubYsGEDAwMDxONxAHp6eigrK+P06dOk02lCoRB1dXWcPHly6TZM0pLw3/ckSZIkSUtixYoV3Lp1iz179vzXpUj6H7CnlCRJkiRJkvLOUEqSJEmSJEl5Z08pSZIkSdKSsDuMpH/Ck1KSJEmSJEnKO0MpSZIkSZIk5Z2hlCRJkiRJkvLOUEqSJEmSJEl5ZyglSZIkSZKkvDOUkiRJkiRJUt4ZSkmSJEmSJCnvDKUkSZIkSZKUd4ZSkiRJkiRJyruv6im6QUY8OxUAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import random\n",
-    "# Assuming df2018 is your DataFrame\n",
-    "df = df2018[['AIDangerous','AIInteresting','AIResponsible','AIFuture']]\n",
-    "\n",
-    "# Strip leading and trailing whitespace from all columns\n",
-    "df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
-    "\n",
-    "# Mapping for shorter versions\n",
-    "short_mapping = {\n",
-    "    'Algorithms making important decisions': 'Algorithms',\n",
-    "    'Artificial intelligence surpassing human intelligence (\"the singularity\")': 'AI Singularity',\n",
-    "    'Evolving definitions of \"fairness\" in algorithmic versus human decisions': 'Fairness Evolution',\n",
-    "    \"Increasing automation of jobs\": 'Automation',\n",
-    "    \"The developers or the people creating the AI\": 'Developers',\n",
-    "    \"A governmental or other regulatory body\": 'Government/Regulatory',\n",
-    "    \"Prominent industry leaders\": 'Industry Leaders',\n",
-    "    \"Nobody\": 'No Responsibility',\n",
-    "    \"I'm excited about the possibilities more than worried about the dangers.\": 'Excited about AI Future',\n",
-    "    \"I'm worried about the dangers more than I'm excited about the possibilities.\": 'Worried about AI Future',\n",
-    "    \"I don't care about it, or I haven't thought about it.\": 'Indifferent about AI Future'\n",
-    "}\n",
-    "\n",
-    "# Replace responses with shorter versions\n",
-    "df.replace(short_mapping, inplace=True)\n",
-    "\n",
-    "# Function to create value count plots for each column\n",
-    "def plot_value_counts(column_name, ax):\n",
-    "    colors = ['skyblue','yellow']\n",
-    "    df[column_name].value_counts().plot(kind='bar', color=random.choice(colors), ax=ax)\n",
-    "    ax.set_title(f'Value Counts for {column_name}')\n",
-    "    ax.set_xlabel('Response')\n",
-    "    ax.set_ylabel('Count')\n",
-    "    ax.tick_params(axis='x', rotation=45)\n",
-    "\n",
-    "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
-    "\n",
-    "# Plot value counts for each column\n",
-    "for i, column in enumerate(df.columns):\n",
-    "    plot_value_counts(column, axes[i//2, i%2])\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analysis\n",
-    "\n",
-    "### AIDangerous:\n",
-    "- The most commonly cited concern is \"Algorithms making important decisions,\" followed closely by \"Artificial intelligence surpassing human intelligence\" and \"Evolving definitions of fairness.\"\n",
-    "- \"Increasing automation of jobs\" is also a significant concern but appears to be less frequently mentioned compared to the other categories.\n",
-    "\n",
-    "### AIInteresting:\n",
-    "- The most interesting aspect for respondents seems to be \"Increasing automation of jobs,\" followed by \"Algorithms making important decisions\" and \"Artificial intelligence surpassing human intelligence.\"\n",
-    "- \"Evolving definitions of fairness\" appears to be less intriguing to respondents compared to other categories.\n",
-    "\n",
-    "### AIResponsible:\n",
-    "- The majority of respondents believe that responsibility lies with \"The developers or the people creating the AI.\"\n",
-    "- Fewer respondents attribute responsibility to \"A governmental or other regulatory body,\" \"Prominent industry leaders,\" or \"Nobody.\"\n",
-    "\n",
-    "### AIFuture:\n",
-    "- A significant proportion of respondents express excitement about the future of AI, indicating that they are \"Excited about the possibilities more than worried about the dangers.\"\n",
-    "- However, there is also a notable percentage of respondents who are \"Worried about the dangers more than excited about the possibilities.\"\n",
-    "- A smaller portion of respondents either \"Don't care about it\" or \"Haven't thought about it.\"\n",
-    "\n",
-    "Overall, these results suggest a complex and varied perspective on AI technology. While many see great potential in AI, there are also concerns about its implications, particularly regarding decision-making, automation of jobs, and the ethical considerations surrounding its development and regulation.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 386,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['LanguageWorkedWith']\n",
-    "df_18 = df[cols]\n",
-    "df_19 = survey_df_2019[cols]\n",
-    "df_20 = df2020[cols]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 387,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#splitting 'LanguageWorkedWith' on ';' \n",
-    "language_2018= df_18['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n",
-    "language_2018['Language'] = language_2018.index\n",
-    "language_2018.reset_index(drop=True, inplace=True)\n",
-    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "language_2018 = language_2018[['Language', '2018']]\n",
-    "#language_2018"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 388,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#splitting 'LanguageWorkedWith' on ';' \n",
-    "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
-    "language_2019['Language'] = language_2019.index\n",
-    "language_2019.reset_index(drop=True, inplace=True)\n",
-    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "language_2019 = language_2019[['Language', '2019']]\n",
-    "#language_2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 389,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#splitting 'LanguageWorkedWith' on ';' \n",
-    "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
-    "language_2020['Language'] = language_2020.index\n",
-    "language_2020.reset_index(drop=True, inplace=True)\n",
-    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "language_2020 = language_2020[['Language', '2020']]\n",
-    "#language_2020"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 390,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compare_df = pd.merge(language_2018, language_2019,on = ['Language'], how = 'outer')\n",
-    "language_all = pd.merge(compare_df, language_2020,on = ['Language'], how = 'outer')\n",
-    "language_all.fillna(0, inplace=True)\n",
-    "language_all['2018'] = language_all['2018']. astype(int)\n",
-    "language_all['2019'] = language_all['2019']. astype(int)\n",
-    "language_all['2020'] = language_all['2020']. astype(int)\n",
-    "language_all.set_index('Language', inplace=True)\n",
-    "#language_all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 391,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(language_all/language_all.sum()).plot(kind='bar', figsize=(12,8))\n",
-    "plt.title('Programming Language Use by Respondents', fontsize = 14)\n",
-    "plt.xlabel('Languages', fontsize = 12)\n",
-    "plt.ylabel('Percentages', fontsize = 12)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  **Analysing the growth of languages from 2018 to 2020 before predicting part**\n",
-    "\n",
-    "The most language the developers use between 2018 to 2020 is JavaScript(14%). The second and third highest working language is HTML/CSS(13%) and SQL(11%). JavaScript and SQL had the same steady increasing trend over the three years. The percentage of HTML/CSS was slightly increased from 2018 to 2019. However, it dropped to the same level as 2018 in 2020. Python was responsible for about 9% in 2018. After then, it decreased to 8% in 2019 and it rose 1% in 2020.\n",
-    "\n",
-    "There are some languages that were in only 2019; Elixir, Clojure, F#, Web assembly, and Erlang. Perl, Haskell, Julia was in the 2019 and 2020 surveys with small percentages."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 392,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Preparing data for ML\n",
-    "df_language_2018 = language_2018[['Language', '2018']]\n",
-    "df_language_2018 = df_language_2018.rename(columns={'2018': 'Number'})\n",
-    "df_language_2018['Year'] = '2018'\n",
-    "df_language_2018['Year_Total'] = df_language_2018['Number'].sum()\n",
-    "df_language_2018['Fraction'] = df_language_2018['Number']/df_language_2018['Number'].sum()\n",
-    "df_language_2018 = df_language_2018[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
-    "df_language_2018.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
-    "#df_language_2018\n",
-    "df_language_2019 = language_2019[['Language', '2019']]\n",
-    "df_language_2019 = df_language_2019.rename(columns={'2019': 'Number'})\n",
-    "df_language_2019['Year'] = '2019'\n",
-    "df_language_2019['Year_Total'] = df_language_2019['Number'].sum()\n",
-    "df_language_2019['Fraction'] = df_language_2019['Number']/df_language_2019['Number'].sum()\n",
-    "df_language_2019 = df_language_2019[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
-    "df_language_2019.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
-    "#df_language_2019\n",
-    "df_language_2020 = language_2020[['Language', '2020']]\n",
-    "df_language_2020 = df_language_2020.rename(columns={'2020': 'Number'})\n",
-    "df_language_2020['Year'] = '2020'\n",
-    "df_language_2020['Year_Total'] = df_language_2020['Number'].sum()\n",
-    "df_language_2020['Fraction'] = df_language_2020['Number']/df_language_2020['Number'].sum()\n",
-    "df_language_2020 = df_language_2020[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
-    "df_language_2020.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
-    "#df_language_2020\n",
-    "\n",
-    "#Append Dataset 2018 x 2019 x 2020\n",
-    "df_language = pd.concat([df_language_2018[:10], df_language_2019[:10], df_language_2020[:10]] , axis=0)\n",
-    "#resetting the index values\n",
-    "df_language = df_language.reset_index(drop=True)\n",
-    "#df_language"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 393,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['Language', 'Fraction']\n",
-    "df_language_2018_ = df_language_2018[cols][:10]\n",
-    "#df_language_2018_\n",
-    "cols = ['Language', 'Fraction']\n",
-    "df_language_2019_ = df_language_2019[cols][:10]\n",
-    "#df_language_2019_\n",
-    "cols = ['Language', 'Fraction']\n",
-    "df_language_2020_ = df_language_2020[cols][:10]\n",
-    "#df_language_2020_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 394,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Language</th>\n",
-       "      <th>JavaScript</th>\n",
-       "      <th>HTML/CSS</th>\n",
-       "      <th>SQL</th>\n",
-       "      <th>Python</th>\n",
-       "      <th>Java</th>\n",
-       "      <th>Bash/Shell/PowerShell</th>\n",
-       "      <th>C#</th>\n",
-       "      <th>PHP</th>\n",
-       "      <th>TypeScript</th>\n",
-       "      <th>C++</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01</th>\n",
-       "      <td>0.134797</td>\n",
-       "      <td>0.125627</td>\n",
-       "      <td>0.109071</td>\n",
-       "      <td>0.087801</td>\n",
-       "      <td>0.080117</td>\n",
-       "      <td>0.065902</td>\n",
-       "      <td>0.062641</td>\n",
-       "      <td>0.052107</td>\n",
-       "      <td>0.050617</td>\n",
-       "      <td>0.047593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-01-01</th>\n",
-       "      <td>0.136468</td>\n",
-       "      <td>0.126595</td>\n",
-       "      <td>0.109882</td>\n",
-       "      <td>0.081963</td>\n",
-       "      <td>0.080446</td>\n",
-       "      <td>0.074697</td>\n",
-       "      <td>0.062234</td>\n",
-       "      <td>0.051494</td>\n",
-       "      <td>0.044158</td>\n",
-       "      <td>0.044193</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-01</th>\n",
-       "      <td>0.137808</td>\n",
-       "      <td>0.126495</td>\n",
-       "      <td>0.112110</td>\n",
-       "      <td>0.086418</td>\n",
-       "      <td>0.078374</td>\n",
-       "      <td>0.069555</td>\n",
-       "      <td>0.063638</td>\n",
-       "      <td>0.051339</td>\n",
-       "      <td>0.054607</td>\n",
-       "      <td>0.043372</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Language    JavaScript  HTML/CSS       SQL    Python      Java  \\\n",
-       "Year                                                             \n",
-       "2018-01-01    0.134797  0.125627  0.109071  0.087801  0.080117   \n",
-       "2019-01-01    0.136468  0.126595  0.109882  0.081963  0.080446   \n",
-       "2020-01-01    0.137808  0.126495  0.112110  0.086418  0.078374   \n",
-       "\n",
-       "Language    Bash/Shell/PowerShell        C#       PHP  TypeScript       C++  \n",
-       "Year                                                                         \n",
-       "2018-01-01               0.065902  0.062641  0.052107    0.050617  0.047593  \n",
-       "2019-01-01               0.074697  0.062234  0.051494    0.044158  0.044193  \n",
-       "2020-01-01               0.069555  0.063638  0.051339    0.054607  0.043372  "
-      ]
-     },
-     "execution_count": 394,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_language_2018_.set_index('Language', inplace = True)\n",
-    "df_language_2018_t = df_language_2018_.T\n",
-    "df_language_2018_t['Year'] = '2018'\n",
-    "df_language_2018_t.Year = pd.to_datetime(df_language_2018_t.Year)\n",
-    "df_language_2018_t = df_language_2018_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
-    "#df_language_2018_t\n",
-    "df_language_2019_.set_index('Language', inplace = True)\n",
-    "df_language_2019_t = df_language_2019_.T\n",
-    "df_language_2019_t['Year'] = '2019'\n",
-    "df_language_2019_t.Year = pd.to_datetime(df_language_2019_t.Year)\n",
-    "df_language_2019_t = df_language_2019_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
-    "#df_language_2019_t\n",
-    "df_language_2020_.set_index('Language', inplace = True)\n",
-    "df_language_2020_t = df_language_2020_.T\n",
-    "df_language_2020_t['Year'] = '2020'\n",
-    "df_language_2020_t.Year = pd.to_datetime(df_language_2020_t.Year)\n",
-    "df_language_2020_t = df_language_2020_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
-    "#df_language_2020_t\n",
-    "\n",
-    "#Append Dataset 2018 x 2019 x 2020\n",
-    "all_language = pd.concat([df_language_2018_t, df_language_2019_t, df_language_2020_t] , axis=0)\n",
-    "#resetting the index values\n",
-    "all_language = all_language.reset_index(drop=True)\n",
-    "all_language.set_index('Year', inplace = True)\n",
-    "all_language"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 395,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java',\n",
-       "       'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++'],\n",
-       "      dtype='object', name='Language')"
-      ]
-     },
-     "execution_count": 395,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_language.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 396,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Fraction of total queries in the year (%)')"
-      ]
-     },
-     "execution_count": 396,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1008x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = all_language.plot(grid=True, lw=0.5, figsize=(14,6), marker='o')\n",
-    "\n",
-    "#Show the legend outside of the plot.\n",
-    "legend = ax.get_legend()\n",
-    "legend.set_bbox_to_anchor((1, 1))\n",
-    "plt.title('Fraction of total queries in the year for top programming languages', fontsize = 14)\n",
-    "plt.xlabel('Languages', fontsize = 12)\n",
-    "plt.ylabel('Fraction of total queries in the year (%)', fontsize = 12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are trying to answer the question \"Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020).\"\n",
-    "\n",
-    "Since we have only 3 years of datasets, there is not enough data to use the time series forecasting method to predict the future popularity of programming languages. With the very small number of observations, there is insufficient data to split the observations into training and testing. We need more observations to build the predictive model, this question we leave for further exploration in future projects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Can we predict the salary of Data Scientists?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 397,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Rename columns\n",
-    "cleaned_2018.rename(columns={'JobSatisfaction': 'CurrentJobSatis', 'JobSearchStatus': 'JobStatus', 'YearsCodingProf':'YearsCodePro'}, inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 398,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sal_df = ['Age', 'Country', 'EdLevel', 'DevType', 'YearsCodePro', 'SalaryUSD']\n",
-    "df1 = cleaned_2018\n",
-    "df2 = survey_df_2019\n",
-    "df3 = df2020"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 399,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(191693, 6)"
-      ]
-     },
-     "execution_count": 399,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Append Dataset 2018 x 2019 x 2020\n",
-    "df_sal = pd.concat([df1[sal_df], df2[sal_df], df3[sal_df]], axis=0)\n",
-    "#resetting the index values\n",
-    "df_sal = df_sal.reset_index(drop=True)\n",
-    "df_sal.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 400,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8727"
-      ]
-     },
-     "execution_count": 400,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#creating data scientist scientist df\n",
-    "all_ds = df_sal[df_sal['DevType'].str.contains('Data scientist') == True ]\n",
-    "all_ds = all_ds.reset_index(drop=True)\n",
-    "len(all_ds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 401,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>3</td>\n",
-       "      <td>366420.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>21</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>No Degree</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>4</td>\n",
-       "      <td>170292.187500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>25</td>\n",
-       "      <td>Argentina</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8400.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>19</td>\n",
-       "      <td>Netherlands</td>\n",
-       "      <td>Associate</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>1</td>\n",
-       "      <td>87994.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>25</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>6</td>\n",
-       "      <td>66750.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8722</th>\n",
-       "      <td>23</td>\n",
-       "      <td>Russian Federation</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>3</td>\n",
-       "      <td>33972.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8723</th>\n",
-       "      <td>27</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>2</td>\n",
-       "      <td>97284.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8724</th>\n",
-       "      <td>47</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>22</td>\n",
-       "      <td>148951.282051</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8725</th>\n",
-       "      <td>33</td>\n",
-       "      <td>Panama</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>2</td>\n",
-       "      <td>72000.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8726</th>\n",
-       "      <td>28</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>5</td>\n",
-       "      <td>180000.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8727 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Age             Country    EdLevel         DevType YearsCodePro  \\\n",
-       "0     28              Canada  Bachelors  Data scientist            3   \n",
-       "1     21              Canada  No Degree  Data scientist            4   \n",
-       "2     25           Argentina    Masters  Data scientist            3   \n",
-       "3     19         Netherlands  Associate  Data scientist            1   \n",
-       "4     25       United States  Bachelors  Data scientist            6   \n",
-       "...   ..                 ...        ...             ...          ...   \n",
-       "8722  23  Russian Federation  Bachelors  Data scientist            3   \n",
-       "8723  27             Germany    Masters  Data scientist            2   \n",
-       "8724  47       United States  Bachelors  Data scientist           22   \n",
-       "8725  33              Panama    Masters  Data scientist            2   \n",
-       "8726  28       United States    Masters  Data scientist            5   \n",
-       "\n",
-       "          SalaryUSD  \n",
-       "0     366420.000000  \n",
-       "1     170292.187500  \n",
-       "2       8400.000000  \n",
-       "3      87994.000000  \n",
-       "4      66750.000000  \n",
-       "...             ...  \n",
-       "8722   33972.000000  \n",
-       "8723   97284.000000  \n",
-       "8724  148951.282051  \n",
-       "8725   72000.000000  \n",
-       "8726  180000.000000  \n",
-       "\n",
-       "[8727 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 401,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_ds['DevType'] = 'Data scientist'\n",
-    "all_ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 402,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "54049.0"
-      ]
-     },
-     "execution_count": 402,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Divide SalaryUSD into 2 groups; SalaryUSD >= median and SalaryUSD < median \n",
-    "all_ds['greater than median'] = all_ds['SalaryUSD'] >= all_ds['SalaryUSD'].median()\n",
-    "all_ds['SalaryUSD'].median() #56616.0 USD"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 403,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{False: 0, True: 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "#Encoding the target\n",
-    "labelencoder = preprocessing.LabelEncoder()\n",
-    "all_ds['gt_median'] = labelencoder.fit_transform(all_ds['greater than median'])\n",
-    "\n",
-    "le_name_mapping = dict(zip(labelencoder.classes_, labelencoder.transform(labelencoder.classes_)))\n",
-    "print(le_name_mapping)\n",
-    "#{False: 0 (SalaryUSD < median), True: 1 (SalaryUSD >= median}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 404,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(8727, 4)"
-      ]
-     },
-     "execution_count": 404,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X = all_ds.drop(['SalaryUSD', 'greater than median', 'gt_median', 'DevType'], axis = 1)\n",
-    "y = all_ds['gt_median']\n",
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 405,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(8727, 225)"
-      ]
-     },
-     "execution_count": 405,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cats_lst = X.select_dtypes(include = ['object']).columns.tolist()\n",
-    "for col in cats_lst:\n",
-    "    X = pd.concat([X.drop(col, axis=1), pd.get_dummies(X[col], prefix=col, prefix_sep='_', drop_first=True)], axis=1)\n",
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 406,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Splitting data\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.30, random_state=142)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 407,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_metrics = {}\n",
-    "\n",
-    "def metrics_data(title, labels, predictions):\n",
-    "    \"\"\"\n",
-    "    INPUT:\n",
-    "        title - Display title for classification algorithm\n",
-    "        labels - Actual values for target variable\n",
-    "        predictions - Predicted values for target variable\n",
-    "        \n",
-    "    OUTPUT:\n",
-    "        metrics - Dictionary of classification metrics for given title\n",
-    "    \"\"\"\n",
-    "    metrics = {\n",
-    "        title: {\n",
-    "            \"model\": title,\n",
-    "            \"accuracy\": accuracy_score(labels, predictions),\n",
-    "            \"precision\": precision_score(labels, predictions),\n",
-    "            \"recall\": recall_score(labels, predictions),\n",
-    "            \"f1-score\": f1_score(labels, predictions),\n",
-    "            \"r2\":  r2_score(labels, predictions)\n",
-    "        }\n",
-    "    }\n",
-    "    print(metrics)\n",
-    "    return metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 408,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  0.11200189590454102\n",
-      "{'Decision Trees': {'model': 'Decision Trees', 'accuracy': 0.8300878197785414, 'precision': 0.8640611724723875, 'recall': 0.7811059907834101, 'f1-score': 0.8204921339249698, 'r2': 0.32032898397069154}}\n",
-      "Accuracy on train set: 0.823510150622135\n"
-     ]
-    }
-   ],
-   "source": [
-    "#DecisionTreeClassifier\n",
-    "start = time.time()\n",
-    "modelDC = DecisionTreeClassifier(max_depth = 12, min_samples_leaf = 10)\n",
-    "modelDC.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeDC = end - start\n",
-    "print('Time: ', TimeDC)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = modelDC.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Decision Trees\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = modelDC.predict(X_train)\n",
-    "accuracyDC2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyDC2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 409,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'Multinomial Naive Bayes': {'model': 'Multinomial Naive Bayes', 'accuracy': 0.8335242458953799, 'precision': 0.8315467075038285, 'recall': 0.8341013824884793, 'f1-score': 0.8328220858895706, 'r2': 0.3340751393510596}}\n",
-      "Accuracy on train set: 0.8366077275703995\n"
-     ]
-    }
-   ],
-   "source": [
-    "#MultinomialNB\n",
-    "start = time.time()\n",
-    "modelNB = MultinomialNB(alpha=0.005)\n",
-    "modelNB.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeNB = end - start\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = modelNB.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Multinomial Naive Bayes\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = modelNB.predict(X_train)\n",
-    "accuracyNB2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyNB2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 410,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  0.040006399154663086\n",
-      "{'Gaussian Naive Bayes': {'model': 'Gaussian Naive Bayes', 'accuracy': 0.6380297823596792, 'precision': 0.5806745670009116, 'recall': 0.978494623655914, 'f1-score': 0.72883295194508, 'r2': -0.4479283667320999}}\n",
-      "Accuracy on train set: 0.64603798297315\n"
-     ]
-    }
-   ],
-   "source": [
-    "#GaussianNB\n",
-    "start = time.time()\n",
-    "modelGNB = GaussianNB()\n",
-    "modelGNB.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeGNB = end - start\n",
-    "print('Time: ', TimeGNB)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = modelGNB.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Gaussian Naive Bayes\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = modelGNB.predict(X_train)\n",
-    "accuracyGNB2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyGNB2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 411,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  0.27199530601501465\n",
-      "{'Logistic Regression': {'model': 'Logistic Regression', 'accuracy': 0.8518518518518519, 'precision': 0.8520801232665639, 'recall': 0.8494623655913979, 'f1-score': 0.8507692307692308, 'r2': 0.4073879680463558}}\n",
-      "Accuracy on train set: 0.8542894564505567\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Logistic Regression\n",
-    "start = time.time()\n",
-    "modelLR = LogisticRegression()\n",
-    "modelLR.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeLR = end - start\n",
-    "print('Time: ', TimeLR)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = modelLR.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Logistic Regression\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = modelLR.predict(X_train)\n",
-    "accuracyLR2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyLR2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 412,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  2.9950499534606934\n",
-      "{'Random Forest': {'model': 'Random Forest', 'accuracy': 0.8369606720122184, 'precision': 0.8480509148766905, 'recall': 0.8187403993855606, 'f1-score': 0.8331379445095739, 'r2': 0.34782129473142764}}\n",
-      "Accuracy on train set: 0.9616895874263262\n"
-     ]
-    }
-   ],
-   "source": [
-    "#RandomForestClassifier\n",
-    "start = time.time()\n",
-    "rfc = RandomForestClassifier()\n",
-    "rfc.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeRFC = end - start\n",
-    "print('Time: ', TimeRFC)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = rfc.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Random Forest\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = rfc.predict(X_train)\n",
-    "accuracyRFC2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyRFC2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 413,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  0.06399965286254883\n",
-      "{'LinearSVC': {'model': 'LinearSVC', 'accuracy': 0.8518518518518519, 'precision': 0.8395245170876672, 'recall': 0.8678955453149002, 'f1-score': 0.8534743202416918, 'r2': 0.4073879680463558}}\n",
-      "Accuracy on train set: 0.8551080550098232\n"
-     ]
-    }
-   ],
-   "source": [
-    "#LinearSVC\n",
-    "start = time.time()\n",
-    "svc = LinearSVC()\n",
-    "svc.fit(X_train, y_train) \n",
-    "end = time.time()\n",
-    "TimeSVC = end - start\n",
-    "print('Time: ', TimeSVC)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = svc.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"LinearSVC\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = svc.predict(X_train)\n",
-    "accuracySVC2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracySVC2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model performance comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 414,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1-score</th>\n",
-       "      <th>r2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Decision Trees</td>\n",
-       "      <td>0.830088</td>\n",
-       "      <td>0.864061</td>\n",
-       "      <td>0.781106</td>\n",
-       "      <td>0.820492</td>\n",
-       "      <td>0.320329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Multinomial Naive Bayes</td>\n",
-       "      <td>0.833524</td>\n",
-       "      <td>0.831547</td>\n",
-       "      <td>0.834101</td>\n",
-       "      <td>0.832822</td>\n",
-       "      <td>0.334075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Gaussian Naive Bayes</td>\n",
-       "      <td>0.63803</td>\n",
-       "      <td>0.580675</td>\n",
-       "      <td>0.978495</td>\n",
-       "      <td>0.728833</td>\n",
-       "      <td>-0.447928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Logistic Regression</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.85208</td>\n",
-       "      <td>0.849462</td>\n",
-       "      <td>0.850769</td>\n",
-       "      <td>0.407388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Random Forest</td>\n",
-       "      <td>0.836961</td>\n",
-       "      <td>0.848051</td>\n",
-       "      <td>0.81874</td>\n",
-       "      <td>0.833138</td>\n",
-       "      <td>0.347821</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>LinearSVC</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.839525</td>\n",
-       "      <td>0.867896</td>\n",
-       "      <td>0.853474</td>\n",
-       "      <td>0.407388</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     model  accuracy precision    recall  f1-score        r2\n",
-       "0           Decision Trees  0.830088  0.864061  0.781106  0.820492  0.320329\n",
-       "1  Multinomial Naive Bayes  0.833524  0.831547  0.834101  0.832822  0.334075\n",
-       "2     Gaussian Naive Bayes   0.63803  0.580675  0.978495  0.728833 -0.447928\n",
-       "3      Logistic Regression  0.851852   0.85208  0.849462  0.850769  0.407388\n",
-       "4            Random Forest  0.836961  0.848051   0.81874  0.833138  0.347821\n",
-       "5                LinearSVC  0.851852  0.839525  0.867896  0.853474  0.407388"
-      ]
-     },
-     "execution_count": 414,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_metrics = pd.DataFrame(all_metrics).T\n",
-    "all_metrics = all_metrics.reset_index(drop=True)\n",
-    "all_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 415,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Model</th>\n",
-       "      <th>Accuracy_train</th>\n",
-       "      <th>Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Decision Trees</td>\n",
-       "      <td>0.823510</td>\n",
-       "      <td>0.112002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Multinomial Naive Bayes</td>\n",
-       "      <td>0.836608</td>\n",
-       "      <td>0.031969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Gaussian Naive Bayes</td>\n",
-       "      <td>0.646038</td>\n",
-       "      <td>0.040006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Logistic Regression</td>\n",
-       "      <td>0.854289</td>\n",
-       "      <td>0.271995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Random Forest</td>\n",
-       "      <td>0.961690</td>\n",
-       "      <td>2.995050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>LinearSVC</td>\n",
-       "      <td>0.855108</td>\n",
-       "      <td>0.064000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     Model  Accuracy_train      Time\n",
-       "0           Decision Trees        0.823510  0.112002\n",
-       "1  Multinomial Naive Bayes        0.836608  0.031969\n",
-       "2     Gaussian Naive Bayes        0.646038  0.040006\n",
-       "3      Logistic Regression        0.854289  0.271995\n",
-       "4            Random Forest        0.961690  2.995050\n",
-       "5                LinearSVC        0.855108  0.064000"
-      ]
-     },
-     "execution_count": 415,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Creating new df to store model performances\n",
-    "Model = ['Decision Trees', 'Multinomial Naive Bayes', 'Gaussian Naive Bayes', 'Logistic Regression', 'Random Forest', 'LinearSVC']\n",
-    "Accuracy_train = [accuracyDC2, accuracyNB2, accuracyGNB2, accuracyLR2, accuracyRFC2, accuracySVC2]\n",
-    "Time = [TimeDC, TimeNB, TimeGNB, TimeLR, TimeRFC, TimeSVC]\n",
-    "\n",
-    "#Create df from lists\n",
-    "cols = ['Model', 'Accuracy_train', 'Time']\n",
-    "data = list(zip(Model, Accuracy_train, Time))\n",
-    "\n",
-    "performance = pd.DataFrame(data, columns=cols)\n",
-    "performance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 416,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1-score</th>\n",
-       "      <th>r2</th>\n",
-       "      <th>Accuracy_train</th>\n",
-       "      <th>Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Decision Trees</td>\n",
-       "      <td>0.830088</td>\n",
-       "      <td>0.864061</td>\n",
-       "      <td>0.781106</td>\n",
-       "      <td>0.820492</td>\n",
-       "      <td>0.320329</td>\n",
-       "      <td>0.823510</td>\n",
-       "      <td>0.112002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Multinomial Naive Bayes</td>\n",
-       "      <td>0.833524</td>\n",
-       "      <td>0.831547</td>\n",
-       "      <td>0.834101</td>\n",
-       "      <td>0.832822</td>\n",
-       "      <td>0.334075</td>\n",
-       "      <td>0.836608</td>\n",
-       "      <td>0.031969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Gaussian Naive Bayes</td>\n",
-       "      <td>0.63803</td>\n",
-       "      <td>0.580675</td>\n",
-       "      <td>0.978495</td>\n",
-       "      <td>0.728833</td>\n",
-       "      <td>-0.447928</td>\n",
-       "      <td>0.646038</td>\n",
-       "      <td>0.040006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Logistic Regression</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.85208</td>\n",
-       "      <td>0.849462</td>\n",
-       "      <td>0.850769</td>\n",
-       "      <td>0.407388</td>\n",
-       "      <td>0.854289</td>\n",
-       "      <td>0.271995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Random Forest</td>\n",
-       "      <td>0.836961</td>\n",
-       "      <td>0.848051</td>\n",
-       "      <td>0.81874</td>\n",
-       "      <td>0.833138</td>\n",
-       "      <td>0.347821</td>\n",
-       "      <td>0.961690</td>\n",
-       "      <td>2.995050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>LinearSVC</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.839525</td>\n",
-       "      <td>0.867896</td>\n",
-       "      <td>0.853474</td>\n",
-       "      <td>0.407388</td>\n",
-       "      <td>0.855108</td>\n",
-       "      <td>0.064000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     model  accuracy precision    recall  f1-score        r2  \\\n",
-       "0           Decision Trees  0.830088  0.864061  0.781106  0.820492  0.320329   \n",
-       "1  Multinomial Naive Bayes  0.833524  0.831547  0.834101  0.832822  0.334075   \n",
-       "2     Gaussian Naive Bayes   0.63803  0.580675  0.978495  0.728833 -0.447928   \n",
-       "3      Logistic Regression  0.851852   0.85208  0.849462  0.850769  0.407388   \n",
-       "4            Random Forest  0.836961  0.848051   0.81874  0.833138  0.347821   \n",
-       "5                LinearSVC  0.851852  0.839525  0.867896  0.853474  0.407388   \n",
-       "\n",
-       "   Accuracy_train      Time  \n",
-       "0        0.823510  0.112002  \n",
-       "1        0.836608  0.031969  \n",
-       "2        0.646038  0.040006  \n",
-       "3        0.854289  0.271995  \n",
-       "4        0.961690  2.995050  \n",
-       "5        0.855108  0.064000  "
-      ]
-     },
-     "execution_count": 416,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Join result2018 with weather2018 to get the Maximum temperature (Degree C)\n",
-    "all_performance = pd.merge(left = all_metrics , right = performance ,\n",
-    "                 left_on = ['model'], right_on = ['Model'], how = 'left')\n",
-    "drop_cols = ['Model']\n",
-    "all_performance.drop(drop_cols, axis=1, inplace=True)\n",
-    "\n",
-    "all_performance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Unfortunately, none of the models has good enough r2 values. The best model is Logistic Regression with $R^2$ just approximately 0.4. We cannot confidently say that Logistic Regression is a good fit to predict the salary of Data Scientists.\n",
-    "\n",
-    "**This question we leave for further exploration in future projects.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hamming Loss (HL) and Jacard Score On Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Hamming loss is the fraction of labels that are incorrectly predicted ( evaluation metrics for a classifier model.) \n",
-    "- The Jaccard Index, also known as the Jaccard similarity coefficient, is a statistic used in understanding the similarities between sample sets. (To measure Similarity)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 417,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clf:  RandomForestClassifier\n",
-      "Jacard score: 0.7062750333778371\n",
-      "Hamming loss: 0.16800305460099274\n",
-      "---\n"
-     ]
-    }
-   ],
-   "source": [
-    "def avg_jacard(y_true,y_pred):\n",
-    "\n",
-    "    jacard = np.minimum(y_true,y_pred).sum(axis=0) / np.maximum(y_true,y_pred).sum(axis=0)\n",
-    "    \n",
-    "    return jacard.mean()\n",
-    "\n",
-    "def print_score(y_pred, clf):\n",
-    "    print(\"Clf: \", clf.__class__.__name__)\n",
-    "    print(\"Jacard score: {}\".format(avg_jacard(y_test, y_pred)))\n",
-    "    print(\"Hamming loss: {}\".format(hamming_loss(y_pred, y_test)))\n",
-    "    print(\"---\")    \n",
-    "\n",
-    "rfc = RandomForestClassifier()\n",
-    "rfc.fit(X_train, y_train)\n",
-    "\n",
-    "y_pred = rfc.predict(X_test)\n",
-    "\n",
-    "print_score(y_pred, rfc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 418,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clf:  MLPClassifier\n",
-      "Jacard score: 0.6990861618798956\n",
-      "Hamming loss: 0.1760213822069492\n",
-      "---\n"
-     ]
-    }
-   ],
-   "source": [
-    "mlpc = MLPClassifier()\n",
-    "mlpc.fit(X_train, y_train)\n",
-    "\n",
-    "y_pred = mlpc.predict(X_test)\n",
-    "\n",
-    "print_score(y_pred, mlpc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 419,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clf:  SGDClassifier\n",
-      "Jacard score: 0.7312042581503659\n",
-      "Hamming loss: 0.15425735013363878\n",
-      "---\n",
-      "Clf:  LogisticRegression\n",
-      "Jacard score: 0.7402945113788487\n",
-      "Hamming loss: 0.14814814814814814\n",
-      "---\n",
-      "Clf:  MultinomialNB\n",
-      "Jacard score: 0.7124183006535948\n",
-      "Hamming loss: 0.16800305460099274\n",
-      "---\n",
-      "Clf:  LinearSVC\n",
-      "Jacard score: 0.7444005270092227\n",
-      "Hamming loss: 0.14814814814814814\n",
-      "---\n"
-     ]
-    }
-   ],
-   "source": [
-    "sgd = SGDClassifier()\n",
-    "lr = LogisticRegression()\n",
-    "mn = MultinomialNB()\n",
-    "svc = LinearSVC()\n",
-    "\n",
-    "for classifier in [sgd, lr, mn, svc,]:\n",
-    "    clf = OneVsRestClassifier(classifier)\n",
-    "    clf.fit(X_train, y_train)\n",
-    "    y_pred = clf.predict(X_test)\n",
-    "    print_score(y_pred, classifier)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Findings: It has been found that better Hamming loss has been found in Logistic Regression and Linear SVC **which is 0.14815**<br>\n",
-    "Jaccard similarity scores give us the distribution of label sets when using the models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predicting what causing Job satisfaction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An examination of work satisfaction variables based on Stack Over Flow survey data from 2020. Job satisfaction can be defined by factors such as compensation, benefits, work environment, team members, work-life balance, education level, place, and so on. By analyzing the Stack Over Flow survey data from 2020, I will try to find some features that are negatively and positively affecting job satisfaction in various countries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 420,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        12439\n",
-       "Slightly satisfied                    11953\n",
-       "Slightly dissatisfied                  6269\n",
-       "Neither satisfied nor dissatisfied     4669\n",
-       "Very dissatisfied                      3106\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 420,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 421,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Very satisfied', 'Slightly satisfied', 'Slightly dissatisfied', 'Neither satisfied nor dissatisfied', 'Very dissatisfied']\n",
-      "[12439, 11953, 6269, 4669, 3106]\n"
-     ]
-    }
-   ],
-   "source": [
-    "participation_rate = df2020['CurrentJobSatis'].value_counts().keys().tolist()\n",
-    "print(participation_rate)\n",
-    "count = df2020['CurrentJobSatis'].value_counts().tolist()\n",
-    "print(count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 422,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pie(count, explode = (0.01,0.01,0.1,0.1,0.2), labels = participation_rate, shadow=True, autopct=lambda p : f'{p:.2f}%  ({p * sum(count)/100:,.0f})', textprops={'fontsize':18, 'weight':'bold'})\n",
-    "plt.title(\"Propotion of Job Satisfaction from the Dataset\",fontsize = 20)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**In further Analysis, we will split into two categories like Satisfied or Not Satisfied**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 423,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Applying one hot encoding\n",
-    "df_indicator = df.isnull().astype(int).add_suffix('_nan')\n",
-    "df = pd.concat([df, df_indicator], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 424,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "best mean cross-validation score: -0.262\n",
-      "best parameters: {'max_depth': 40, 'min_samples_leaf': 10}\n",
-      "test-set score: -0.258\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Grid search for good parameters, I used the mean absolute error as the main measure of quality\n",
-    "param_grid = {'min_samples_leaf': [10,15,20],'max_depth': [20,30,40]}\n",
-    "grid = GridSearchCV(RandomForestRegressor(n_estimators=100,n_jobs=-1, oob_score=True), param_grid=param_grid,\n",
-    "                    scoring='neg_mean_absolute_error',cv=5, return_train_score=True)\n",
-    "X_train_grit = X_train.sample(frac=0.5, random_state=42)\n",
-    "grid.fit(X_train_grit, y_train.loc[X_train_grit.index])\n",
-    "print(\"best mean cross-validation score: {:.3f}\".format(grid.best_score_))\n",
-    "print(\"best parameters: {}\".format(grid.best_params_))\n",
-    "print(\"test-set score: {:.3f}\".format(grid.score(X_test, y_test)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Here Random Forest is used to Predicting Job satisfaction, model did not yield much better output and turned out to be very complex to get insights.** Random forest Regressor, Logistic Regression which may yield good results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Trying with Logistic Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Used Sklearn library to create a Logistic Regression model.\n",
-    "\n",
-    "Before creating a model, need to create data, Using model coefficients, features that have negative and positive effects on job satisfaction to be calculated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 425,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numericals = [\"Age\",\"SalaryUSD\",\"YearsCodePro\"]\n",
-    "categoricals = [\"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 426,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('display.max_columns', None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 427,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        12439\n",
-       "Slightly satisfied                    11953\n",
-       "Slightly dissatisfied                  6269\n",
-       "Neither satisfied nor dissatisfied     4669\n",
-       "Very dissatisfied                      3106\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 427,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Performing further Spliting of CurrentJobSatis Coloumn**\n",
-    "- Delete \"Neither satisfied nor dissatisfied\"\n",
-    "- Combine \"Very satisfied\" and \"Slightly satisfied\", label as \"Satisfied\" -->1\n",
-    "- Combine \"Very dissatisfied\" and \"Slightly dissatisfied\", label as \"Dissatisfied\"-->0\n",
-    "- Delete rows \"Neither satisfied nor dissatisfied\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 428,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "df = df2020.drop(df2020[df2020.CurrentJobSatis == \"Neither satisfied nor dissatisfied\"].index)\n",
-    "\n",
-    "df.CurrentJobSatis = [1 if each == \"Very satisfied\" else \n",
-    "             1 if each == \"Slightly satisfied\" else \n",
-    "             0 if each == \"Very dissatisfied\"else \n",
-    "             0 if each == \"Slightly dissatisfied\" else\n",
-    "             each for each in df.CurrentJobSatis]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 429,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dropping nan in Converted Salary if any\n",
-    "df = df.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 430,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>36</td>\n",
-       "      <td>116000.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>22</td>\n",
-       "      <td>32315.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>23</td>\n",
-       "      <td>40070.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>49</td>\n",
-       "      <td>14268.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Spain</td>\n",
-       "      <td>No Degree</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>53</td>\n",
-       "      <td>38916.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>Netherlands</td>\n",
-       "      <td>No Degree</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No major</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age  SalaryUSD  YearsCodePro         Country    EdLevel Employment  \\\n",
-       "1   36   116000.0          13.0   United States  Bachelors  Full-time   \n",
-       "2   22    32315.0           4.0  United Kingdom    Masters  Full-time   \n",
-       "3   23    40070.0           2.0  United Kingdom  Bachelors  Full-time   \n",
-       "4   49    14268.0           7.0           Spain  No Degree  Full-time   \n",
-       "5   53    38916.0          20.0     Netherlands  No Degree  Full-time   \n",
-       "\n",
-       "  Hobbyist    UndergradMajor  CurrentJobSatis  \n",
-       "1      Yes  Computer Science                0  \n",
-       "2      Yes         Math/Stat                1  \n",
-       "3      Yes  Computer Science                0  \n",
-       "4       No         Math/Stat                0  \n",
-       "5      Yes          No major                1  "
-      ]
-     },
-     "execution_count": 430,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cols= [\"Age\",\"SalaryUSD\",\"YearsCodePro\", \"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\", \"CurrentJobSatis\"]\n",
-    "df = df[cols]\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 431,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# one hot encoding\n",
-    "df = pd.get_dummies(df,  columns = categoricals )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 432,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Normalization of numerical features\n",
-    "for each in numericals:\n",
-    "    df[each] = (df[each] - df[each].min()) / (df[each].max() - df[each].min())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 433,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 434,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Split data into X and y\n",
-    "X = df.drop(\"CurrentJobSatis\", axis = 1)\n",
-    "y = df.CurrentJobSatis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 435,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>Age</th>\n",
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-       "      <th>Country_Ireland</th>\n",
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-       "      <th>Country_Italy</th>\n",
-       "      <th>Country_Jamaica</th>\n",
-       "      <th>Country_Japan</th>\n",
-       "      <th>Country_Jordan</th>\n",
-       "      <th>Country_Kazakhstan</th>\n",
-       "      <th>Country_Kenya</th>\n",
-       "      <th>Country_Kosovo</th>\n",
-       "      <th>Country_Kuwait</th>\n",
-       "      <th>Country_Kyrgyzstan</th>\n",
-       "      <th>Country_Lao People's Democratic Republic</th>\n",
-       "      <th>Country_Latvia</th>\n",
-       "      <th>Country_Lebanon</th>\n",
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-       "      <th>Country_Mongolia</th>\n",
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-       "      <th>Country_Myanmar</th>\n",
-       "      <th>Country_Namibia</th>\n",
-       "      <th>Country_Nepal</th>\n",
-       "      <th>Country_Netherlands</th>\n",
-       "      <th>Country_New Zealand</th>\n",
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-       "      <th>Country_Niger</th>\n",
-       "      <th>Country_Nigeria</th>\n",
-       "      <th>Country_Nomadic</th>\n",
-       "      <th>Country_Norway</th>\n",
-       "      <th>Country_Oman</th>\n",
-       "      <th>Country_Pakistan</th>\n",
-       "      <th>Country_Panama</th>\n",
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-       "      <th>Country_Portugal</th>\n",
-       "      <th>Country_Qatar</th>\n",
-       "      <th>Country_Republic of Korea</th>\n",
-       "      <th>Country_Republic of Moldova</th>\n",
-       "      <th>Country_Romania</th>\n",
-       "      <th>Country_Russian Federation</th>\n",
-       "      <th>Country_Rwanda</th>\n",
-       "      <th>Country_San Marino</th>\n",
-       "      <th>Country_Saudi Arabia</th>\n",
-       "      <th>Country_Senegal</th>\n",
-       "      <th>Country_Serbia</th>\n",
-       "      <th>Country_Singapore</th>\n",
-       "      <th>Country_Slovakia</th>\n",
-       "      <th>Country_Slovenia</th>\n",
-       "      <th>Country_Somalia</th>\n",
-       "      <th>Country_South Africa</th>\n",
-       "      <th>Country_South Korea</th>\n",
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-       "      <th>Country_Sweden</th>\n",
-       "      <th>Country_Switzerland</th>\n",
-       "      <th>Country_Syrian Arab Republic</th>\n",
-       "      <th>Country_Taiwan</th>\n",
-       "      <th>Country_Thailand</th>\n",
-       "      <th>Country_The former Yugoslav Republic of Macedonia</th>\n",
-       "      <th>Country_Togo</th>\n",
-       "      <th>Country_Trinidad and Tobago</th>\n",
-       "      <th>Country_Tunisia</th>\n",
-       "      <th>Country_Turkey</th>\n",
-       "      <th>Country_Turkmenistan</th>\n",
-       "      <th>Country_Uganda</th>\n",
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-       "      <th>Country_United Arab Emirates</th>\n",
-       "      <th>Country_United Kingdom</th>\n",
-       "      <th>Country_United Republic of Tanzania</th>\n",
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-       "      <th>Country_Uruguay</th>\n",
-       "      <th>Country_Uzbekistan</th>\n",
-       "      <th>Country_Venezuela, Bolivarian Republic of...</th>\n",
-       "      <th>Country_Viet Nam</th>\n",
-       "      <th>Country_Yemen</th>\n",
-       "      <th>Country_Zambia</th>\n",
-       "      <th>Country_Zimbabwe</th>\n",
-       "      <th>EdLevel_Associate</th>\n",
-       "      <th>EdLevel_Bachelors</th>\n",
-       "      <th>EdLevel_Doctorate</th>\n",
-       "      <th>EdLevel_Masters</th>\n",
-       "      <th>EdLevel_No Degree</th>\n",
-       "      <th>EdLevel_Professional</th>\n",
-       "      <th>Employment_Full-time</th>\n",
-       "      <th>Employment_Not employed</th>\n",
-       "      <th>Employment_Not employed, and not looking for work</th>\n",
-       "      <th>Employment_Part-time</th>\n",
-       "      <th>Employment_Retired</th>\n",
-       "      <th>Employment_Self-employed</th>\n",
-       "      <th>Employment_Student</th>\n",
-       "      <th>Hobbyist_No</th>\n",
-       "      <th>Hobbyist_Yes</th>\n",
-       "      <th>UndergradMajor_Arts and Science</th>\n",
-       "      <th>UndergradMajor_Business</th>\n",
-       "      <th>UndergradMajor_Computer Science</th>\n",
-       "      <th>UndergradMajor_Engineering</th>\n",
-       "      <th>UndergradMajor_Info Systems</th>\n",
-       "      <th>UndergradMajor_Math/Stat</th>\n",
-       "      <th>UndergradMajor_No major</th>\n",
-       "      <th>UndergradMajor_Other Science</th>\n",
-       "      <th>UndergradMajor_Web Design/Dev</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.466667</td>\n",
-       "      <td>0.580055</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.155556</td>\n",
-       "      <td>0.161590</td>\n",
-       "      <td>0.065217</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.177778</td>\n",
-       "      <td>0.200369</td>\n",
-       "      <td>0.021739</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.755556</td>\n",
-       "      <td>0.071347</td>\n",
-       "      <td>0.130435</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.844444</td>\n",
-       "      <td>0.194598</td>\n",
-       "      <td>0.413043</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>33767 rows × 180 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Age  SalaryUSD  YearsCodePro  Country_Afghanistan  \\\n",
-       "1      0.466667   0.580055      0.260870                    0   \n",
-       "2      0.155556   0.161590      0.065217                    0   \n",
-       "3      0.177778   0.200369      0.021739                    0   \n",
-       "4      0.755556   0.071347      0.130435                    0   \n",
-       "5      0.844444   0.194598      0.413043                    0   \n",
-       "...         ...        ...           ...                  ...   \n",
-       "41564  0.133333   0.041597      0.168261                    0   \n",
-       "41565  0.133333   0.041597      0.168261                    0   \n",
-       "41566  0.133333   0.026343      0.168261                    0   \n",
-       "41567  0.155556   0.048065      0.043478                    0   \n",
-       "41568  0.577778   0.001000      0.413043                    0   \n",
-       "\n",
-       "       Country_Albania  Country_Algeria  Country_Andorra  Country_Angola  \\\n",
-       "1                    0                0                0               0   \n",
-       "2                    0                0                0               0   \n",
-       "3                    0                0                0               0   \n",
-       "4                    0                0                0               0   \n",
-       "5                    0                0                0               0   \n",
-       "...                ...              ...              ...             ...   \n",
-       "41564                0                0                0               0   \n",
-       "41565                0                0                0               0   \n",
-       "41566                0                0                0               0   \n",
-       "41567                0                0                0               0   \n",
-       "41568                0                0                0               0   \n",
-       "\n",
-       "       Country_Argentina  Country_Armenia  Country_Australia  Country_Austria  \\\n",
-       "1                      0                0                  0                0   \n",
-       "2                      0                0                  0                0   \n",
-       "3                      0                0                  0                0   \n",
-       "4                      0                0                  0                0   \n",
-       "5                      0                0                  0                0   \n",
-       "...                  ...              ...                ...              ...   \n",
-       "41564                  0                0                  0                0   \n",
-       "41565                  0                0                  0                0   \n",
-       "41566                  0                0                  0                0   \n",
-       "41567                  0                0                  0                0   \n",
-       "41568                  0                0                  0                0   \n",
-       "\n",
-       "       Country_Azerbaijan  Country_Bahamas  Country_Bahrain  \\\n",
-       "1                       0                0                0   \n",
-       "2                       0                0                0   \n",
-       "3                       0                0                0   \n",
-       "4                       0                0                0   \n",
-       "5                       0                0                0   \n",
-       "...                   ...              ...              ...   \n",
-       "41564                   0                0                0   \n",
-       "41565                   0                0                0   \n",
-       "41566                   0                0                0   \n",
-       "41567                   0                0                0   \n",
-       "41568                   0                0                0   \n",
-       "\n",
-       "       Country_Bangladesh  Country_Barbados  Country_Belarus  Country_Belgium  \\\n",
-       "1                       0                 0                0                0   \n",
-       "2                       0                 0                0                0   \n",
-       "3                       0                 0                0                0   \n",
-       "4                       0                 0                0                0   \n",
-       "5                       0                 0                0                0   \n",
-       "...                   ...               ...              ...              ...   \n",
-       "41564                   0                 0                0                0   \n",
-       "41565                   0                 0                0                0   \n",
-       "41566                   0                 0                0                0   \n",
-       "41567                   0                 0                0                0   \n",
-       "41568                   0                 0                0                0   \n",
-       "\n",
-       "       Country_Benin  Country_Bhutan  Country_Bolivia  \\\n",
-       "1                  0               0                0   \n",
-       "2                  0               0                0   \n",
-       "3                  0               0                0   \n",
-       "4                  0               0                0   \n",
-       "5                  0               0                0   \n",
-       "...              ...             ...              ...   \n",
-       "41564              0               0                0   \n",
-       "41565              0               0                0   \n",
-       "41566              0               0                0   \n",
-       "41567              0               0                0   \n",
-       "41568              0               0                0   \n",
-       "\n",
-       "       Country_Bosnia and Herzegovina  Country_Botswana  Country_Brazil  \\\n",
-       "1                                   0                 0               0   \n",
-       "2                                   0                 0               0   \n",
-       "3                                   0                 0               0   \n",
-       "4                                   0                 0               0   \n",
-       "5                                   0                 0               0   \n",
-       "...                               ...               ...             ...   \n",
-       "41564                               0                 0               0   \n",
-       "41565                               0                 0               0   \n",
-       "41566                               0                 0               0   \n",
-       "41567                               0                 0               0   \n",
-       "41568                               0                 0               0   \n",
-       "\n",
-       "       Country_Brunei Darussalam  Country_Bulgaria  Country_Burkina Faso  \\\n",
-       "1                              0                 0                     0   \n",
-       "2                              0                 0                     0   \n",
-       "3                              0                 0                     0   \n",
-       "4                              0                 0                     0   \n",
-       "5                              0                 0                     0   \n",
-       "...                          ...               ...                   ...   \n",
-       "41564                          0                 0                     0   \n",
-       "41565                          0                 0                     0   \n",
-       "41566                          0                 0                     0   \n",
-       "41567                          0                 0                     0   \n",
-       "41568                          0                 0                     0   \n",
-       "\n",
-       "       Country_Cambodia  Country_Cameroon  Country_Canada  Country_Chile  \\\n",
-       "1                     0                 0               0              0   \n",
-       "2                     0                 0               0              0   \n",
-       "3                     0                 0               0              0   \n",
-       "4                     0                 0               0              0   \n",
-       "5                     0                 0               0              0   \n",
-       "...                 ...               ...             ...            ...   \n",
-       "41564                 0                 0               0              0   \n",
-       "41565                 0                 0               0              0   \n",
-       "41566                 0                 0               0              0   \n",
-       "41567                 0                 0               0              0   \n",
-       "41568                 0                 0               0              0   \n",
-       "\n",
-       "       Country_China  Country_Colombia  Country_Costa Rica  Country_Croatia  \\\n",
-       "1                  0                 0                   0                0   \n",
-       "2                  0                 0                   0                0   \n",
-       "3                  0                 0                   0                0   \n",
-       "4                  0                 0                   0                0   \n",
-       "5                  0                 0                   0                0   \n",
-       "...              ...               ...                 ...              ...   \n",
-       "41564              0                 0                   0                0   \n",
-       "41565              0                 0                   0                0   \n",
-       "41566              0                 0                   0                0   \n",
-       "41567              0                 0                   0                0   \n",
-       "41568              0                 0                   0                0   \n",
-       "\n",
-       "       Country_Cuba  Country_Cyprus  Country_Czech Republic  \\\n",
-       "1                 0               0                       0   \n",
-       "2                 0               0                       0   \n",
-       "3                 0               0                       0   \n",
-       "4                 0               0                       0   \n",
-       "5                 0               0                       0   \n",
-       "...             ...             ...                     ...   \n",
-       "41564             0               0                       0   \n",
-       "41565             0               0                       0   \n",
-       "41566             0               0                       0   \n",
-       "41567             0               0                       0   \n",
-       "41568             0               0                       0   \n",
-       "\n",
-       "       Country_Côte d'Ivoire  Country_Democratic Republic of the Congo  \\\n",
-       "1                          0                                         0   \n",
-       "2                          0                                         0   \n",
-       "3                          0                                         0   \n",
-       "4                          0                                         0   \n",
-       "5                          0                                         0   \n",
-       "...                      ...                                       ...   \n",
-       "41564                      0                                         0   \n",
-       "41565                      0                                         0   \n",
-       "41566                      0                                         0   \n",
-       "41567                      0                                         0   \n",
-       "41568                      0                                         0   \n",
-       "\n",
-       "       Country_Denmark  Country_Dominican Republic  Country_Ecuador  \\\n",
-       "1                    0                           0                0   \n",
-       "2                    0                           0                0   \n",
-       "3                    0                           0                0   \n",
-       "4                    0                           0                0   \n",
-       "5                    0                           0                0   \n",
-       "...                ...                         ...              ...   \n",
-       "41564                0                           0                0   \n",
-       "41565                0                           0                0   \n",
-       "41566                0                           0                0   \n",
-       "41567                0                           0                0   \n",
-       "41568                0                           0                0   \n",
-       "\n",
-       "       Country_Egypt  Country_El Salvador  Country_Estonia  Country_Ethiopia  \\\n",
-       "1                  0                    0                0                 0   \n",
-       "2                  0                    0                0                 0   \n",
-       "3                  0                    0                0                 0   \n",
-       "4                  0                    0                0                 0   \n",
-       "5                  0                    0                0                 0   \n",
-       "...              ...                  ...              ...               ...   \n",
-       "41564              0                    0                0                 0   \n",
-       "41565              0                    0                0                 0   \n",
-       "41566              0                    0                0                 0   \n",
-       "41567              0                    0                0                 0   \n",
-       "41568              0                    0                0                 0   \n",
-       "\n",
-       "       Country_Finland  Country_France  Country_Georgia  Country_Germany  \\\n",
-       "1                    0               0                0                0   \n",
-       "2                    0               0                0                0   \n",
-       "3                    0               0                0                0   \n",
-       "4                    0               0                0                0   \n",
-       "5                    0               0                0                0   \n",
-       "...                ...             ...              ...              ...   \n",
-       "41564                0               0                0                0   \n",
-       "41565                0               0                0                0   \n",
-       "41566                0               0                0                0   \n",
-       "41567                0               0                0                0   \n",
-       "41568                0               0                0                0   \n",
-       "\n",
-       "       Country_Ghana  Country_Greece  Country_Guatemala  Country_Guinea  \\\n",
-       "1                  0               0                  0               0   \n",
-       "2                  0               0                  0               0   \n",
-       "3                  0               0                  0               0   \n",
-       "4                  0               0                  0               0   \n",
-       "5                  0               0                  0               0   \n",
-       "...              ...             ...                ...             ...   \n",
-       "41564              0               0                  0               0   \n",
-       "41565              0               0                  0               0   \n",
-       "41566              0               0                  0               0   \n",
-       "41567              0               0                  0               0   \n",
-       "41568              0               0                  0               0   \n",
-       "\n",
-       "       Country_Guyana  Country_Haiti  Country_Honduras  \\\n",
-       "1                   0              0                 0   \n",
-       "2                   0              0                 0   \n",
-       "3                   0              0                 0   \n",
-       "4                   0              0                 0   \n",
-       "5                   0              0                 0   \n",
-       "...               ...            ...               ...   \n",
-       "41564               0              0                 0   \n",
-       "41565               0              0                 0   \n",
-       "41566               0              0                 0   \n",
-       "41567               0              0                 0   \n",
-       "41568               0              0                 0   \n",
-       "\n",
-       "       Country_Hong Kong (S.A.R.)  Country_Hungary  Country_Iceland  \\\n",
-       "1                               0                0                0   \n",
-       "2                               0                0                0   \n",
-       "3                               0                0                0   \n",
-       "4                               0                0                0   \n",
-       "5                               0                0                0   \n",
-       "...                           ...              ...              ...   \n",
-       "41564                           0                0                0   \n",
-       "41565                           0                0                0   \n",
-       "41566                           0                0                0   \n",
-       "41567                           0                0                0   \n",
-       "41568                           0                0                0   \n",
-       "\n",
-       "       Country_India  Country_Indonesia  Country_Iran  Country_Iraq  \\\n",
-       "1                  0                  0             0             0   \n",
-       "2                  0                  0             0             0   \n",
-       "3                  0                  0             0             0   \n",
-       "4                  0                  0             0             0   \n",
-       "5                  0                  0             0             0   \n",
-       "...              ...                ...           ...           ...   \n",
-       "41564              1                  0             0             0   \n",
-       "41565              1                  0             0             0   \n",
-       "41566              0                  1             0             0   \n",
-       "41567              0                  0             0             0   \n",
-       "41568              0                  0             0             0   \n",
-       "\n",
-       "       Country_Ireland  Country_Isle of Man  Country_Israel  Country_Italy  \\\n",
-       "1                    0                    0               0              0   \n",
-       "2                    0                    0               0              0   \n",
-       "3                    0                    0               0              0   \n",
-       "4                    0                    0               0              0   \n",
-       "5                    0                    0               0              0   \n",
-       "...                ...                  ...             ...            ...   \n",
-       "41564                0                    0               0              0   \n",
-       "41565                0                    0               0              0   \n",
-       "41566                0                    0               0              0   \n",
-       "41567                0                    0               0              0   \n",
-       "41568                0                    0               0              0   \n",
-       "\n",
-       "       Country_Jamaica  Country_Japan  Country_Jordan  Country_Kazakhstan  \\\n",
-       "1                    0              0               0                   0   \n",
-       "2                    0              0               0                   0   \n",
-       "3                    0              0               0                   0   \n",
-       "4                    0              0               0                   0   \n",
-       "5                    0              0               0                   0   \n",
-       "...                ...            ...             ...                 ...   \n",
-       "41564                0              0               0                   0   \n",
-       "41565                0              0               0                   0   \n",
-       "41566                0              0               0                   0   \n",
-       "41567                0              0               0                   0   \n",
-       "41568                0              0               0                   0   \n",
-       "\n",
-       "       Country_Kenya  Country_Kosovo  Country_Kuwait  Country_Kyrgyzstan  \\\n",
-       "1                  0               0               0                   0   \n",
-       "2                  0               0               0                   0   \n",
-       "3                  0               0               0                   0   \n",
-       "4                  0               0               0                   0   \n",
-       "5                  0               0               0                   0   \n",
-       "...              ...             ...             ...                 ...   \n",
-       "41564              0               0               0                   0   \n",
-       "41565              0               0               0                   0   \n",
-       "41566              0               0               0                   0   \n",
-       "41567              0               0               0                   0   \n",
-       "41568              0               0               0                   0   \n",
-       "\n",
-       "       Country_Lao People's Democratic Republic  Country_Latvia  \\\n",
-       "1                                             0               0   \n",
-       "2                                             0               0   \n",
-       "3                                             0               0   \n",
-       "4                                             0               0   \n",
-       "5                                             0               0   \n",
-       "...                                         ...             ...   \n",
-       "41564                                         0               0   \n",
-       "41565                                         0               0   \n",
-       "41566                                         0               0   \n",
-       "41567                                         0               0   \n",
-       "41568                                         0               0   \n",
-       "\n",
-       "       Country_Lebanon  Country_Lithuania  Country_Luxembourg  \\\n",
-       "1                    0                  0                   0   \n",
-       "2                    0                  0                   0   \n",
-       "3                    0                  0                   0   \n",
-       "4                    0                  0                   0   \n",
-       "5                    0                  0                   0   \n",
-       "...                ...                ...                 ...   \n",
-       "41564                0                  0                   0   \n",
-       "41565                0                  0                   0   \n",
-       "41566                0                  0                   0   \n",
-       "41567                0                  0                   0   \n",
-       "41568                0                  0                   0   \n",
-       "\n",
-       "       Country_Madagascar  Country_Malawi  Country_Malaysia  Country_Maldives  \\\n",
-       "1                       0               0                 0                 0   \n",
-       "2                       0               0                 0                 0   \n",
-       "3                       0               0                 0                 0   \n",
-       "4                       0               0                 0                 0   \n",
-       "5                       0               0                 0                 0   \n",
-       "...                   ...             ...               ...               ...   \n",
-       "41564                   0               0                 0                 0   \n",
-       "41565                   0               0                 0                 0   \n",
-       "41566                   0               0                 0                 0   \n",
-       "41567                   0               0                 0                 0   \n",
-       "41568                   0               0                 0                 0   \n",
-       "\n",
-       "       Country_Malta  Country_Mauritius  Country_Mexico  Country_Monaco  \\\n",
-       "1                  0                  0               0               0   \n",
-       "2                  0                  0               0               0   \n",
-       "3                  0                  0               0               0   \n",
-       "4                  0                  0               0               0   \n",
-       "5                  0                  0               0               0   \n",
-       "...              ...                ...             ...             ...   \n",
-       "41564              0                  0               0               0   \n",
-       "41565              0                  0               0               0   \n",
-       "41566              0                  0               0               0   \n",
-       "41567              0                  1               0               0   \n",
-       "41568              0                  0               0               0   \n",
-       "\n",
-       "       Country_Mongolia  Country_Montenegro  Country_Morocco  Country_Myanmar  \\\n",
-       "1                     0                   0                0                0   \n",
-       "2                     0                   0                0                0   \n",
-       "3                     0                   0                0                0   \n",
-       "4                     0                   0                0                0   \n",
-       "5                     0                   0                0                0   \n",
-       "...                 ...                 ...              ...              ...   \n",
-       "41564                 0                   0                0                0   \n",
-       "41565                 0                   0                0                0   \n",
-       "41566                 0                   0                0                0   \n",
-       "41567                 0                   0                0                0   \n",
-       "41568                 0                   0                0                0   \n",
-       "\n",
-       "       Country_Namibia  Country_Nepal  Country_Netherlands  \\\n",
-       "1                    0              0                    0   \n",
-       "2                    0              0                    0   \n",
-       "3                    0              0                    0   \n",
-       "4                    0              0                    0   \n",
-       "5                    0              0                    1   \n",
-       "...                ...            ...                  ...   \n",
-       "41564                0              0                    0   \n",
-       "41565                0              0                    0   \n",
-       "41566                0              0                    0   \n",
-       "41567                0              0                    0   \n",
-       "41568                0              0                    0   \n",
-       "\n",
-       "       Country_New Zealand  Country_Nicaragua  Country_Niger  Country_Nigeria  \\\n",
-       "1                        0                  0              0                0   \n",
-       "2                        0                  0              0                0   \n",
-       "3                        0                  0              0                0   \n",
-       "4                        0                  0              0                0   \n",
-       "5                        0                  0              0                0   \n",
-       "...                    ...                ...            ...              ...   \n",
-       "41564                    0                  0              0                0   \n",
-       "41565                    0                  0              0                0   \n",
-       "41566                    0                  0              0                0   \n",
-       "41567                    0                  0              0                0   \n",
-       "41568                    0                  0              0                0   \n",
-       "\n",
-       "       Country_Nomadic  Country_Norway  Country_Oman  Country_Pakistan  \\\n",
-       "1                    0               0             0                 0   \n",
-       "2                    0               0             0                 0   \n",
-       "3                    0               0             0                 0   \n",
-       "4                    0               0             0                 0   \n",
-       "5                    0               0             0                 0   \n",
-       "...                ...             ...           ...               ...   \n",
-       "41564                0               0             0                 0   \n",
-       "41565                0               0             0                 0   \n",
-       "41566                0               0             0                 0   \n",
-       "41567                0               0             0                 0   \n",
-       "41568                0               0             0                 0   \n",
-       "\n",
-       "       Country_Panama  Country_Paraguay  Country_Peru  Country_Philippines  \\\n",
-       "1                   0                 0             0                    0   \n",
-       "2                   0                 0             0                    0   \n",
-       "3                   0                 0             0                    0   \n",
-       "4                   0                 0             0                    0   \n",
-       "5                   0                 0             0                    0   \n",
-       "...               ...               ...           ...                  ...   \n",
-       "41564               0                 0             0                    0   \n",
-       "41565               0                 0             0                    0   \n",
-       "41566               0                 0             0                    0   \n",
-       "41567               0                 0             0                    0   \n",
-       "41568               0                 0             0                    0   \n",
-       "\n",
-       "       Country_Poland  Country_Portugal  Country_Qatar  \\\n",
-       "1                   0                 0              0   \n",
-       "2                   0                 0              0   \n",
-       "3                   0                 0              0   \n",
-       "4                   0                 0              0   \n",
-       "5                   0                 0              0   \n",
-       "...               ...               ...            ...   \n",
-       "41564               0                 0              0   \n",
-       "41565               0                 0              0   \n",
-       "41566               0                 0              0   \n",
-       "41567               0                 0              0   \n",
-       "41568               0                 0              0   \n",
-       "\n",
-       "       Country_Republic of Korea  Country_Republic of Moldova  \\\n",
-       "1                              0                            0   \n",
-       "2                              0                            0   \n",
-       "3                              0                            0   \n",
-       "4                              0                            0   \n",
-       "5                              0                            0   \n",
-       "...                          ...                          ...   \n",
-       "41564                          0                            0   \n",
-       "41565                          0                            0   \n",
-       "41566                          0                            0   \n",
-       "41567                          0                            0   \n",
-       "41568                          0                            0   \n",
-       "\n",
-       "       Country_Romania  Country_Russian Federation  Country_Rwanda  \\\n",
-       "1                    0                           0               0   \n",
-       "2                    0                           0               0   \n",
-       "3                    0                           0               0   \n",
-       "4                    0                           0               0   \n",
-       "5                    0                           0               0   \n",
-       "...                ...                         ...             ...   \n",
-       "41564                0                           0               0   \n",
-       "41565                0                           0               0   \n",
-       "41566                0                           0               0   \n",
-       "41567                0                           0               0   \n",
-       "41568                0                           0               0   \n",
-       "\n",
-       "       Country_San Marino  Country_Saudi Arabia  Country_Senegal  \\\n",
-       "1                       0                     0                0   \n",
-       "2                       0                     0                0   \n",
-       "3                       0                     0                0   \n",
-       "4                       0                     0                0   \n",
-       "5                       0                     0                0   \n",
-       "...                   ...                   ...              ...   \n",
-       "41564                   0                     0                0   \n",
-       "41565                   0                     0                0   \n",
-       "41566                   0                     0                0   \n",
-       "41567                   0                     0                0   \n",
-       "41568                   0                     0                0   \n",
-       "\n",
-       "       Country_Serbia  Country_Singapore  Country_Slovakia  Country_Slovenia  \\\n",
-       "1                   0                  0                 0                 0   \n",
-       "2                   0                  0                 0                 0   \n",
-       "3                   0                  0                 0                 0   \n",
-       "4                   0                  0                 0                 0   \n",
-       "5                   0                  0                 0                 0   \n",
-       "...               ...                ...               ...               ...   \n",
-       "41564               0                  0                 0                 0   \n",
-       "41565               0                  0                 0                 0   \n",
-       "41566               0                  0                 0                 0   \n",
-       "41567               0                  0                 0                 0   \n",
-       "41568               0                  0                 0                 0   \n",
-       "\n",
-       "       Country_Somalia  Country_South Africa  Country_South Korea  \\\n",
-       "1                    0                     0                    0   \n",
-       "2                    0                     0                    0   \n",
-       "3                    0                     0                    0   \n",
-       "4                    0                     0                    0   \n",
-       "5                    0                     0                    0   \n",
-       "...                ...                   ...                  ...   \n",
-       "41564                0                     0                    0   \n",
-       "41565                0                     0                    0   \n",
-       "41566                0                     0                    0   \n",
-       "41567                0                     0                    0   \n",
-       "41568                0                     0                    0   \n",
-       "\n",
-       "       Country_Spain  Country_Sri Lanka  Country_Sudan  Country_Swaziland  \\\n",
-       "1                  0                  0              0                  0   \n",
-       "2                  0                  0              0                  0   \n",
-       "3                  0                  0              0                  0   \n",
-       "4                  1                  0              0                  0   \n",
-       "5                  0                  0              0                  0   \n",
-       "...              ...                ...            ...                ...   \n",
-       "41564              0                  0              0                  0   \n",
-       "41565              0                  0              0                  0   \n",
-       "41566              0                  0              0                  0   \n",
-       "41567              0                  0              0                  0   \n",
-       "41568              0                  0              0                  0   \n",
-       "\n",
-       "       Country_Sweden  Country_Switzerland  Country_Syrian Arab Republic  \\\n",
-       "1                   0                    0                             0   \n",
-       "2                   0                    0                             0   \n",
-       "3                   0                    0                             0   \n",
-       "4                   0                    0                             0   \n",
-       "5                   0                    0                             0   \n",
-       "...               ...                  ...                           ...   \n",
-       "41564               0                    0                             0   \n",
-       "41565               0                    0                             0   \n",
-       "41566               0                    0                             0   \n",
-       "41567               0                    0                             0   \n",
-       "41568               0                    0                             0   \n",
-       "\n",
-       "       Country_Taiwan  Country_Thailand  \\\n",
-       "1                   0                 0   \n",
-       "2                   0                 0   \n",
-       "3                   0                 0   \n",
-       "4                   0                 0   \n",
-       "5                   0                 0   \n",
-       "...               ...               ...   \n",
-       "41564               0                 0   \n",
-       "41565               0                 0   \n",
-       "41566               0                 0   \n",
-       "41567               0                 0   \n",
-       "41568               0                 0   \n",
-       "\n",
-       "       Country_The former Yugoslav Republic of Macedonia  Country_Togo  \\\n",
-       "1                                                      0             0   \n",
-       "2                                                      0             0   \n",
-       "3                                                      0             0   \n",
-       "4                                                      0             0   \n",
-       "5                                                      0             0   \n",
-       "...                                                  ...           ...   \n",
-       "41564                                                  0             0   \n",
-       "41565                                                  0             0   \n",
-       "41566                                                  0             0   \n",
-       "41567                                                  0             0   \n",
-       "41568                                                  0             0   \n",
-       "\n",
-       "       Country_Trinidad and Tobago  Country_Tunisia  Country_Turkey  \\\n",
-       "1                                0                0               0   \n",
-       "2                                0                0               0   \n",
-       "3                                0                0               0   \n",
-       "4                                0                0               0   \n",
-       "5                                0                0               0   \n",
-       "...                            ...              ...             ...   \n",
-       "41564                            0                0               0   \n",
-       "41565                            0                0               0   \n",
-       "41566                            0                0               0   \n",
-       "41567                            0                0               0   \n",
-       "41568                            0                0               0   \n",
-       "\n",
-       "       Country_Turkmenistan  Country_Uganda  Country_Ukraine  \\\n",
-       "1                         0               0                0   \n",
-       "2                         0               0                0   \n",
-       "3                         0               0                0   \n",
-       "4                         0               0                0   \n",
-       "5                         0               0                0   \n",
-       "...                     ...             ...              ...   \n",
-       "41564                     0               0                0   \n",
-       "41565                     0               0                0   \n",
-       "41566                     0               0                0   \n",
-       "41567                     0               0                0   \n",
-       "41568                     0               0                0   \n",
-       "\n",
-       "       Country_United Arab Emirates  Country_United Kingdom  \\\n",
-       "1                                 0                       0   \n",
-       "2                                 0                       1   \n",
-       "3                                 0                       1   \n",
-       "4                                 0                       0   \n",
-       "5                                 0                       0   \n",
-       "...                             ...                     ...   \n",
-       "41564                             0                       0   \n",
-       "41565                             0                       0   \n",
-       "41566                             0                       0   \n",
-       "41567                             0                       0   \n",
-       "41568                             0                       0   \n",
-       "\n",
-       "       Country_United Republic of Tanzania  Country_United States  \\\n",
-       "1                                        0                      1   \n",
-       "2                                        0                      0   \n",
-       "3                                        0                      0   \n",
-       "4                                        0                      0   \n",
-       "5                                        0                      0   \n",
-       "...                                    ...                    ...   \n",
-       "41564                                    0                      0   \n",
-       "41565                                    0                      0   \n",
-       "41566                                    0                      0   \n",
-       "41567                                    0                      0   \n",
-       "41568                                    0                      1   \n",
-       "\n",
-       "       Country_Uruguay  Country_Uzbekistan  \\\n",
-       "1                    0                   0   \n",
-       "2                    0                   0   \n",
-       "3                    0                   0   \n",
-       "4                    0                   0   \n",
-       "5                    0                   0   \n",
-       "...                ...                 ...   \n",
-       "41564                0                   0   \n",
-       "41565                0                   0   \n",
-       "41566                0                   0   \n",
-       "41567                0                   0   \n",
-       "41568                0                   0   \n",
-       "\n",
-       "       Country_Venezuela, Bolivarian Republic of...  Country_Viet Nam  \\\n",
-       "1                                                 0                 0   \n",
-       "2                                                 0                 0   \n",
-       "3                                                 0                 0   \n",
-       "4                                                 0                 0   \n",
-       "5                                                 0                 0   \n",
-       "...                                             ...               ...   \n",
-       "41564                                             0                 0   \n",
-       "41565                                             0                 0   \n",
-       "41566                                             0                 0   \n",
-       "41567                                             0                 0   \n",
-       "41568                                             0                 0   \n",
-       "\n",
-       "       Country_Yemen  Country_Zambia  Country_Zimbabwe  EdLevel_Associate  \\\n",
-       "1                  0               0                 0                  0   \n",
-       "2                  0               0                 0                  0   \n",
-       "3                  0               0                 0                  0   \n",
-       "4                  0               0                 0                  0   \n",
-       "5                  0               0                 0                  0   \n",
-       "...              ...             ...               ...                ...   \n",
-       "41564              0               0                 0                  0   \n",
-       "41565              0               0                 0                  0   \n",
-       "41566              0               0                 0                  0   \n",
-       "41567              0               0                 0                  0   \n",
-       "41568              0               0                 0                  0   \n",
-       "\n",
-       "       EdLevel_Bachelors  EdLevel_Doctorate  EdLevel_Masters  \\\n",
-       "1                      1                  0                0   \n",
-       "2                      0                  0                1   \n",
-       "3                      1                  0                0   \n",
-       "4                      0                  0                0   \n",
-       "5                      0                  0                0   \n",
-       "...                  ...                ...              ...   \n",
-       "41564                  1                  0                0   \n",
-       "41565                  1                  0                0   \n",
-       "41566                  0                  0                0   \n",
-       "41567                  0                  0                1   \n",
-       "41568                  0                  0                0   \n",
-       "\n",
-       "       EdLevel_No Degree  EdLevel_Professional  Employment_Full-time  \\\n",
-       "1                      0                     0                     1   \n",
-       "2                      0                     0                     1   \n",
-       "3                      0                     0                     1   \n",
-       "4                      1                     0                     1   \n",
-       "5                      1                     0                     1   \n",
-       "...                  ...                   ...                   ...   \n",
-       "41564                  0                     0                     0   \n",
-       "41565                  0                     0                     0   \n",
-       "41566                  1                     0                     0   \n",
-       "41567                  0                     0                     1   \n",
-       "41568                  1                     0                     1   \n",
-       "\n",
-       "       Employment_Not employed  \\\n",
-       "1                            0   \n",
-       "2                            0   \n",
-       "3                            0   \n",
-       "4                            0   \n",
-       "5                            0   \n",
-       "...                        ...   \n",
-       "41564                        0   \n",
-       "41565                        0   \n",
-       "41566                        0   \n",
-       "41567                        0   \n",
-       "41568                        0   \n",
-       "\n",
-       "       Employment_Not employed, and not looking for work  \\\n",
-       "1                                                      0   \n",
-       "2                                                      0   \n",
-       "3                                                      0   \n",
-       "4                                                      0   \n",
-       "5                                                      0   \n",
-       "...                                                  ...   \n",
-       "41564                                                  0   \n",
-       "41565                                                  0   \n",
-       "41566                                                  0   \n",
-       "41567                                                  0   \n",
-       "41568                                                  0   \n",
-       "\n",
-       "       Employment_Part-time  Employment_Retired  Employment_Self-employed  \\\n",
-       "1                         0                   0                         0   \n",
-       "2                         0                   0                         0   \n",
-       "3                         0                   0                         0   \n",
-       "4                         0                   0                         0   \n",
-       "5                         0                   0                         0   \n",
-       "...                     ...                 ...                       ...   \n",
-       "41564                     0                   0                         0   \n",
-       "41565                     0                   0                         0   \n",
-       "41566                     0                   0                         0   \n",
-       "41567                     0                   0                         0   \n",
-       "41568                     0                   0                         0   \n",
-       "\n",
-       "       Employment_Student  Hobbyist_No  Hobbyist_Yes  \\\n",
-       "1                       0            0             1   \n",
-       "2                       0            0             1   \n",
-       "3                       0            0             1   \n",
-       "4                       0            1             0   \n",
-       "5                       0            0             1   \n",
-       "...                   ...          ...           ...   \n",
-       "41564                   1            0             1   \n",
-       "41565                   1            0             1   \n",
-       "41566                   1            0             1   \n",
-       "41567                   0            0             1   \n",
-       "41568                   0            0             1   \n",
-       "\n",
-       "       UndergradMajor_Arts and Science  UndergradMajor_Business  \\\n",
-       "1                                    0                        0   \n",
-       "2                                    0                        0   \n",
-       "3                                    0                        0   \n",
-       "4                                    0                        0   \n",
-       "5                                    0                        0   \n",
-       "...                                ...                      ...   \n",
-       "41564                                0                        0   \n",
-       "41565                                0                        0   \n",
-       "41566                                0                        0   \n",
-       "41567                                0                        0   \n",
-       "41568                                0                        0   \n",
-       "\n",
-       "       UndergradMajor_Computer Science  UndergradMajor_Engineering  \\\n",
-       "1                                    1                           0   \n",
-       "2                                    0                           0   \n",
-       "3                                    1                           0   \n",
-       "4                                    0                           0   \n",
-       "5                                    0                           0   \n",
-       "...                                ...                         ...   \n",
-       "41564                                1                           0   \n",
-       "41565                                1                           0   \n",
-       "41566                                0                           0   \n",
-       "41567                                1                           0   \n",
-       "41568                                1                           0   \n",
-       "\n",
-       "       UndergradMajor_Info Systems  UndergradMajor_Math/Stat  \\\n",
-       "1                                0                         0   \n",
-       "2                                0                         1   \n",
-       "3                                0                         0   \n",
-       "4                                0                         1   \n",
-       "5                                0                         0   \n",
-       "...                            ...                       ...   \n",
-       "41564                            0                         0   \n",
-       "41565                            0                         0   \n",
-       "41566                            0                         0   \n",
-       "41567                            0                         0   \n",
-       "41568                            0                         0   \n",
-       "\n",
-       "       UndergradMajor_No major  UndergradMajor_Other Science  \\\n",
-       "1                            0                             0   \n",
-       "2                            0                             0   \n",
-       "3                            0                             0   \n",
-       "4                            0                             0   \n",
-       "5                            1                             0   \n",
-       "...                        ...                           ...   \n",
-       "41564                        0                             0   \n",
-       "41565                        0                             0   \n",
-       "41566                        1                             0   \n",
-       "41567                        0                             0   \n",
-       "41568                        0                             0   \n",
-       "\n",
-       "       UndergradMajor_Web Design/Dev  \n",
-       "1                                  0  \n",
-       "2                                  0  \n",
-       "3                                  0  \n",
-       "4                                  0  \n",
-       "5                                  0  \n",
-       "...                              ...  \n",
-       "41564                              0  \n",
-       "41565                              0  \n",
-       "41566                              0  \n",
-       "41567                              0  \n",
-       "41568                              0  \n",
-       "\n",
-       "[33767 rows x 180 columns]"
-      ]
-     },
-     "execution_count": 435,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 436,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1        0\n",
-       "2        1\n",
-       "3        0\n",
-       "4        0\n",
-       "5        1\n",
-       "        ..\n",
-       "41564    1\n",
-       "41565    1\n",
-       "41566    1\n",
-       "41567    1\n",
-       "41568    1\n",
-       "Name: CurrentJobSatis, Length: 33767, dtype: int64"
-      ]
-     },
-     "execution_count": 436,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 437,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# split data into train and test sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Checking Model Coefficent**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 438,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 72.11%\n"
-     ]
-    }
-   ],
-   "source": [
-    "# define the model\n",
-    "model = LogisticRegression()\n",
-    "# fit the model\n",
-    "model.fit(X, y)\n",
-    "\n",
-    "# get importance\n",
-    "importance = model.coef_[0]\n",
-    "\n",
-    "# make predictions for test data and evaluate\n",
-    "y_pred = model.predict(X_test)\n",
-    "predictions = [round(value) for value in y_pred]\n",
-    "accuracy = accuracy_score(y_test, predictions)\n",
-    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have recieved **72% Accuracy** which is good enough to move ahead with predictions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting Features affecting Job Satisfaction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 439,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x2160 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results_df = pd.DataFrame()\n",
-    "results_df[\"Rates\"] = importance.tolist()\n",
-    "results_df[\"Columns\"] = X.columns\n",
-    "\n",
-    "new_index = results_df.Rates.sort_values(ascending = False).index\n",
-    "sorted_results = results_df.reindex(new_index)\n",
-    "filtered_results = sorted_results[np.abs(sorted_results.Rates) > 0.1]\n",
-    "\n",
-    "plt.figure(figsize =(20,30))\n",
-    "plt.barh(filtered_results.Columns, filtered_results.Rates)\n",
-    "plt.xlabel(\"Negative and Postive Features\", fontsize = 25)\n",
-    "plt.title(\"Features Affecting Job Satistaction\",fontsize = 25)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The top 2 features negatively affecting Job Satisfaction are age, country. So, in the elderly ages, job satisfaction may decrease because of the personal expectation increases. In the same way, as the professional coding years are increasing, satisfaction may decrease.\n",
-    "\n",
-    "- Among the countries; most dissatisfied countries are Angolia, Rwanda, Krygyzstan, Sudan.\n",
-    "- UndergradMajor and other Science, are mostly satisfied.\n",
-    "- Most satisfied countries Malta, Ghana, Cyprus."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Conclusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overall, we performed various analyses on the Stack overflow developer survey and derived insights from it. We found which country has the highest no of respondents, which is the most popular language, education level of respondents, different roles of developers, and so on.<br>\n",
-    "Additionally, we performed machine learning models to predict the growth of languages, the salary of data scientists, what is causing job satisfaction. "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow_Survey_Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "Stack overflow is a professional community for developers. They conduct developer surveys every year since 2011. The collected data is available open-source on the web. The Dataset would help us to answer real-world questions with the help of proper analysis. The most popular language that developers use can be found through the analysis. We also can find the developer role which pays the highest salary. The aim of our project is to analyze the 2018,2019 and 2020 developer surveys datasets from where we collect valuable insights from them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "import warnings; \n",
+    "warnings.simplefilter('ignore')\n",
+    "import pycountry\n",
+    "import plotly.express as px\n",
+    "import matplotlib.patches as mpatches\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.svm import LinearSVC\n",
+    "import time\n",
+    "from sklearn.metrics import hamming_loss\n",
+    "from sklearn.metrics import jaccard_score\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "from sklearn.multiclass import OneVsRestClassifier\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow 2018 Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Respondent</th>\n",
+       "      <th>Hobby</th>\n",
+       "      <th>OpenSource</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Student</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>FormalEducation</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>CompanySize</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Exercise</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SexualOrientation</th>\n",
+       "      <th>EducationParents</th>\n",
+       "      <th>RaceEthnicity</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>MilitaryUS</th>\n",
+       "      <th>SurveyTooLong</th>\n",
+       "      <th>SurveyEasy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Kenya</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed part-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Mathematics or statistics</td>\n",
+       "      <td>20 to 99 employees</td>\n",
+       "      <td>Full-stack developer</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3 - 4 times per week</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Black or of African descent</td>\n",
+       "      <td>25 - 34 years old</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Very easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>10,000 or more employees</td>\n",
+       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Daily or almost every day</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Somewhat easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Associate degree</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>20 to 99 employees</td>\n",
+       "      <td>Engineering manager;Full-stack developer</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 129 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Respondent Hobby OpenSource         Country Student          Employment  \\\n",
+       "0           1   Yes         No           Kenya      No  Employed part-time   \n",
+       "1           3   Yes        Yes  United Kingdom      No  Employed full-time   \n",
+       "2           4   Yes        Yes   United States      No  Employed full-time   \n",
+       "\n",
+       "                            FormalEducation  \\\n",
+       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "2                          Associate degree   \n",
+       "\n",
+       "                                      UndergradMajor  \\\n",
+       "0                          Mathematics or statistics   \n",
+       "1  A natural science (ex. biology, chemistry, phy...   \n",
+       "2  Computer science, computer engineering, or sof...   \n",
+       "\n",
+       "                CompanySize  \\\n",
+       "0        20 to 99 employees   \n",
+       "1  10,000 or more employees   \n",
+       "2        20 to 99 employees   \n",
+       "\n",
+       "                                             DevType  ...  \\\n",
+       "0                               Full-stack developer  ...   \n",
+       "1  Database administrator;DevOps specialist;Full-...  ...   \n",
+       "2           Engineering manager;Full-stack developer  ...   \n",
+       "\n",
+       "                    Exercise Gender         SexualOrientation  \\\n",
+       "0       3 - 4 times per week   Male  Straight or heterosexual   \n",
+       "1  Daily or almost every day   Male  Straight or heterosexual   \n",
+       "2                        NaN    NaN                       NaN   \n",
+       "\n",
+       "                           EducationParents                 RaceEthnicity  \\\n",
+       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   Black or of African descent   \n",
+       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)  White or of European descent   \n",
+       "2                                       NaN                           NaN   \n",
+       "\n",
+       "                 Age Dependents  MilitaryUS  \\\n",
+       "0  25 - 34 years old        Yes         NaN   \n",
+       "1  35 - 44 years old        Yes         NaN   \n",
+       "2                NaN        NaN         NaN   \n",
+       "\n",
+       "                          SurveyTooLong     SurveyEasy  \n",
+       "0  The survey was an appropriate length      Very easy  \n",
+       "1  The survey was an appropriate length  Somewhat easy  \n",
+       "2                                   NaN            NaN  \n",
+       "\n",
+       "[3 rows x 129 columns]"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2018 = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2018.csv')\n",
+    "df2018.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(98855, 129)"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2018.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(df2018.columns.tolist()      !--> Listing coloumsn in table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dropping the columns\n",
+    "#drop_cols = ['Respondent', 'OpenSource', 'Student', 'FormalEducation', 'CompanySize', 'CareerSatisfaction', 'HopeFiveYears', 'LastNewJob', 'AssessJob1', 'AssessJob2', 'AssessJob3', 'AssessJob4', 'AssessJob5', 'AssessJob6', 'AssessJob7', 'AssessJob8', 'AssessJob9', 'AssessJob10', 'AssessBenefits1', 'AssessBenefits2', 'AssessBenefits3', 'AssessBenefits4', 'AssessBenefits5', 'AssessBenefits6', 'AssessBenefits7', 'AssessBenefits8', 'AssessBenefits9', 'AssessBenefits10', 'AssessBenefits11', 'JobContactPriorities1', 'JobContactPriorities2', 'JobContactPriorities3', 'JobContactPriorities4', 'JobContactPriorities5', 'JobEmailPriorities1', 'JobEmailPriorities2', 'JobEmailPriorities3', 'JobEmailPriorities4', 'JobEmailPriorities5', 'JobEmailPriorities6', 'JobEmailPriorities7', 'UpdateCV', 'CommunicationTools', 'TimeFullyProductive', 'EducationTypes', 'SelfTaughtTypes', 'TimeAfterBootcamp', 'HackathonReasons', 'AgreeDisagree1', 'AgreeDisagree2', 'AgreeDisagree3', 'DatabaseWorkedWith', 'DatabaseDesireNextYear', 'PlatformDesireNextYear', 'FrameworkWorkedWith', 'FrameworkDesireNextYear', 'IDE', 'NumberMonitors', 'Methodology', 'VersionControl', 'CheckInCode', 'AdBlocker', 'AdBlockerDisable', 'AdBlockerReasons', 'AdsAgreeDisagree1', 'AdsAgreeDisagree2', 'AdsAgreeDisagree3', 'AdsActions', 'AdsPriorities1', 'AdsPriorities2', 'AdsPriorities3', 'AdsPriorities4', 'AdsPriorities5', 'AdsPriorities6', 'AdsPriorities7', 'AIDangerous', 'AIInteresting', 'AIResponsible', 'AIFuture', 'EthicsChoice', 'EthicsReport', 'EthicsResponsible', 'EthicalImplications', 'StackOverflowRecommend', 'StackOverflowVisit', 'StackOverflowHasAccount', 'StackOverflowParticipate', 'StackOverflowJobs', 'StackOverflowDevStory', 'StackOverflowJobsRecommend', 'StackOverflowConsiderMember', 'HypotheticalTools1', 'HypotheticalTools2', 'HypotheticalTools3', 'HypotheticalTools4', 'HypotheticalTools5', 'WakeTime', 'HoursComputer', 'HoursOutside', 'SkipMeals', 'ErgonomicDevices', 'Exercise', 'SexualOrientation', 'EducationParents', 'Dependents', 'MilitaryUS', 'SurveyTooLong', 'SurveyEasy']\n",
+    "#df2018.drop(drop_cols, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2018.shape #checking rows and col after dropping the table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Filtering - Sorting & Renaming\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col=['Age','ConvertedSalary','Country','Currency','DevType','Employment','RaceEthnicity','Gender','SalaryType','Hobby','JobSatisfaction','JobSearchStatus','OperatingSystem','UndergradMajor','YearsCoding','YearsCodingProf','LanguageDesireNextYear','LanguageWorkedWith','FormalEducation']\n",
+    "df=df2018[col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#renaming the colo\n",
+    "# 'ConvertedSalary': 'SalaryUSD'\n",
+    "df.rename(columns={'ConvertedSalary': 'SalaryUSD' }, inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Currency</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>FormalEducation</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Hobby</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>JobSearchStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>OperatingSystem</th>\n",
+       "      <th>RaceEthnicity</th>\n",
+       "      <th>SalaryType</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCoding</th>\n",
+       "      <th>YearsCodingProf</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25 - 34 years old</td>\n",
+       "      <td>Kenya</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Full-stack developer</td>\n",
+       "      <td>Employed part-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extremely satisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to new...</td>\n",
+       "      <td>JavaScript;Python;HTML;CSS</td>\n",
+       "      <td>JavaScript;Python;HTML;CSS</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Black or of African descent</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mathematics or statistics</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>British pounds sterling (£)</td>\n",
+       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately dissatisfied</td>\n",
+       "      <td>I am actively looking for a job</td>\n",
+       "      <td>Go;Python</td>\n",
+       "      <td>JavaScript;Python;Bash/Shell</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>70841.0</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>30 or more years</td>\n",
+       "      <td>18-20 years</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Age         Country                     Currency  \\\n",
+       "0  25 - 34 years old           Kenya                          NaN   \n",
+       "1  35 - 44 years old  United Kingdom  British pounds sterling (£)   \n",
+       "\n",
+       "                                             DevType          Employment  \\\n",
+       "0                               Full-stack developer  Employed part-time   \n",
+       "1  Database administrator;DevOps specialist;Full-...  Employed full-time   \n",
+       "\n",
+       "                            FormalEducation Gender Hobby  \\\n",
+       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   Male   Yes   \n",
+       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)   Male   Yes   \n",
+       "\n",
+       "           JobSatisfaction                                    JobSearchStatus  \\\n",
+       "0      Extremely satisfied  I’m not actively looking, but I am open to new...   \n",
+       "1  Moderately dissatisfied                    I am actively looking for a job   \n",
+       "\n",
+       "       LanguageDesireNextYear            LanguageWorkedWith OperatingSystem  \\\n",
+       "0  JavaScript;Python;HTML;CSS    JavaScript;Python;HTML;CSS     Linux-based   \n",
+       "1                   Go;Python  JavaScript;Python;Bash/Shell     Linux-based   \n",
+       "\n",
+       "                  RaceEthnicity SalaryType  SalaryUSD  \\\n",
+       "0   Black or of African descent    Monthly        NaN   \n",
+       "1  White or of European descent     Yearly    70841.0   \n",
+       "\n",
+       "                                      UndergradMajor       YearsCoding  \\\n",
+       "0                          Mathematics or statistics         3-5 years   \n",
+       "1  A natural science (ex. biology, chemistry, phy...  30 or more years   \n",
+       "\n",
+       "  YearsCodingProf  \n",
+       "0       3-5 years  \n",
+       "1     18-20 years  "
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_index(axis=1).head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(98855, 19)"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#21 col has been selected rfom 129, compared the shape\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                       34281\n",
+      "SalaryUSD                 51153\n",
+      "Country                     412\n",
+      "Currency                  36847\n",
+      "DevType                    6757\n",
+      "Employment                 3534\n",
+      "RaceEthnicity             41382\n",
+      "Gender                    34386\n",
+      "SalaryType                47785\n",
+      "Hobby                         0\n",
+      "JobSatisfaction           29579\n",
+      "JobSearchStatus           19367\n",
+      "OperatingSystem           22676\n",
+      "UndergradMajor            19819\n",
+      "YearsCoding                5020\n",
+      "YearsCodingProf           20952\n",
+      "LanguageDesireNextYear    25611\n",
+      "LanguageWorkedWith        20521\n",
+      "FormalEducation            4152\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum()) #Finding Null Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                        object\n",
+       "SalaryUSD                 float64\n",
+       "Country                    object\n",
+       "Currency                   object\n",
+       "DevType                    object\n",
+       "Employment                 object\n",
+       "RaceEthnicity              object\n",
+       "Gender                     object\n",
+       "SalaryType                 object\n",
+       "Hobby                      object\n",
+       "JobSatisfaction            object\n",
+       "JobSearchStatus            object\n",
+       "OperatingSystem            object\n",
+       "UndergradMajor             object\n",
+       "YearsCoding                object\n",
+       "YearsCodingProf            object\n",
+       "LanguageDesireNextYear     object\n",
+       "LanguageWorkedWith         object\n",
+       "FormalEducation            object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes #data_types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Validation - Total Cells vs Missing %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  1878245\n",
+      "Total missing :  424234\n",
+      "Missing Percentage:  22.58672324430519 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = df.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing Percentage column-wise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def missing(df,column,n):\n",
+    "    empty_cells=df[column].isnull().sum()\n",
+    "    return (empty_cells*100.0)/n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age : 34.68 %\n",
+      "SalaryUSD : 51.75 %\n",
+      "Country : 0.42 %\n",
+      "Currency : 37.27 %\n",
+      "DevType : 6.84 %\n",
+      "Employment : 3.57 %\n",
+      "RaceEthnicity : 41.86 %\n",
+      "Gender : 34.78 %\n",
+      "SalaryType : 48.34 %\n",
+      "Hobby : 0.00 %\n",
+      "JobSatisfaction : 29.92 %\n",
+      "JobSearchStatus : 19.59 %\n",
+      "OperatingSystem : 22.94 %\n",
+      "UndergradMajor : 20.05 %\n",
+      "YearsCoding : 5.08 %\n",
+      "YearsCodingProf : 21.19 %\n",
+      "LanguageDesireNextYear : 25.91 %\n",
+      "LanguageWorkedWith : 20.76 %\n",
+      "FormalEducation : 4.20 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_cells=df.shape[0]\n",
+    "for column in df.columns:\n",
+    "    res=missing(df,column,total_cells)\n",
+    "    print(column,\":\",\"{:.2f}\".format(res),\"%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gender Filtering          \n",
+    "### Data Cleaning Starts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Female                                                                        4025\n",
+       "Female;Male                                                                     98\n",
+       "Female;Male;Non-binary, genderqueer, or gender non-conforming                    3\n",
+       "Female;Male;Transgender                                                         14\n",
+       "Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming       50\n",
+       "Female;Non-binary, genderqueer, or gender non-conforming                        50\n",
+       "Female;Transgender                                                             145\n",
+       "Female;Transgender;Non-binary, genderqueer, or gender non-conforming            24\n",
+       "Male                                                                         59458\n",
+       "Male;Non-binary, genderqueer, or gender non-conforming                         128\n",
+       "Male;Transgender                                                                29\n",
+       "Male;Transgender;Non-binary, genderqueer, or gender non-conforming               5\n",
+       "Non-binary, genderqueer, or gender non-conforming                              284\n",
+       "Transgender                                                                    105\n",
+       "Transgender;Non-binary, genderqueer, or gender non-conforming                   51\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Gender: null = 13312 (21.6%)\n",
+    "df['Gender'].unique()\n",
+    "#count number of each gender\n",
+    "df.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#replace\n",
+    "df['Gender'] = df['Gender'].fillna('Non-binary, genderqueer, or gender non-conforming')\n",
+    "df['Gender'].replace('Female;Male;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Male;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
+    "df['Gender'].replace('Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
+    "df['Gender'].replace('Transgender;Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) ##not sure\n",
+    "df['Gender'].replace('Female;Male', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Male;Transgender', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Transgender', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Male;Transgender', 'Female', inplace =True) \n",
+    "df['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) #\n",
+    "df['Gender'].replace('Transgender', 'Male', inplace =True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "lst=df.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3,3))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Gender Distribution')  # Add a title\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(98855, 19)"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()['Gender']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "Afghanistan                              64\n",
+       "Albania                                 109\n",
+       "Algeria                                 130\n",
+       "Andorra                                  15\n",
+       "Angola                                   11\n",
+       "                                       ... \n",
+       "Venezuela, Bolivarian Republic of...    123\n",
+       "Viet Nam                                331\n",
+       "Yemen                                    13\n",
+       "Zambia                                    9\n",
+       "Zimbabwe                                 39\n",
+       "Name: Country, Length: 183, dtype: int64"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Country')['Country'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "412"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Country'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst=df.groupby('Country')['Country'].count()\n",
+    "lst = lst.sort_values(ascending=False)\n",
+    "lst=lst[:50]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,4))\n",
+    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('Country')  # Label for x-axis\n",
+    "plt.ylabel('Count')      # Label for y-axis\n",
+    "plt.title('Top 50 Countries according to participation')  # Title of the plot\n",
+    "plt.xticks(rotation=90)  # Rotate labels by 90 degrees\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hobby"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Hobby'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Hobby\n",
+       "No     18958\n",
+       "Yes    79897\n",
+       "Name: Hobby, dtype: int64"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Hobby')['Hobby'].count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## UndergradMajor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19819"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "Computer science, computer engineering, or software engineering          50336\n",
+       "Another engineering discipline (ex. civil, electrical, mechanical)        6945\n",
+       "Information systems, information technology, or system administration     6507\n",
+       "A natural science (ex. biology, chemistry, physics)                       3050\n",
+       "Mathematics or statistics                                                 2818\n",
+       "Web development or web design                                             2418\n",
+       "A business discipline (ex. accounting, finance, marketing)                1921\n",
+       "A humanities discipline (ex. literature, history, philosophy)             1590\n",
+       "A social science (ex. anthropology, psychology, political science)        1377\n",
+       "Fine arts or performing arts (ex. graphic design, music, studio art)      1135\n",
+       "I never declared a major                                                   693\n",
+       "A health science (ex. nursing, pharmacy, radiology)                        246\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 218,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def refactor_major(df):\n",
+    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n",
+    "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
+    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n",
+    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
+    "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)') \n",
+    "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'), \n",
+    "                        (df['UndergradMajor'] == 'Web development or web design'), \n",
+    "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'), \n",
+    "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
+    "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
+    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)'),\n",
+    "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
+    "    \n",
+    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
+    "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
+    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
+    "    return df\n",
+    "\n",
+    "df = refactor_major(df)\n",
+    "df['UndergradMajor'].replace('nan', 'No major', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst=df['UndergradMajor'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Undergrad Major')  # Add a title\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['UndergradMajor'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 224,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Job Status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSearchStatus\n",
+       "I’m not actively looking, but I am open to new opportunities    47556\n",
+       "I am not interested in new job opportunities                    19296\n",
+       "I am actively looking for a job                                 12636\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSearchStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['JobSearchStatus'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# refactoring JobStatus\n",
+    "# changing the jobstatus to seeking and non seeking\n",
+    "def refactor_job(df):\n",
+    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
+    "    \n",
+    "    conditions_job = [(df['JobSearchStatus'] == 'I am actively looking for a job'),\n",
+    "                    (df['JobSearchStatus'] == 'I am not interested in new job opportunities')\n",
+    "                    | (df['JobSearchStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
+    "    \n",
+    "    choices_job = ['Seeking', 'Not seeking']\n",
+    "    \n",
+    "    df['JobSearchStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "df = refactor_job(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSearchStatus\n",
+       "Not seeking    66852\n",
+       "Seeking        12636\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 228,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSearchStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSearchStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Employment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Employed full-time                                      58551\n",
+       "Independent contractor, freelancer, or self-employed     7797\n",
+       "Not employed, but looking for work                       4604\n",
+       "Employed part-time                                       4170\n",
+       "Not employed, and not looking for work                   3210\n",
+       "Retired                                                   138\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1018"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Employment'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#im not considering the retired person here\n",
+    "#Refactoring the employment\n",
+    "def refactor_emp(df):\n",
+    "    \n",
+    "    conditions_emp = [(df['Employment'] == 'Employed full-time'),\n",
+    "                    (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n",
+    "                    (df['Employment'] == 'Not employed, but looking for work'),\n",
+    "                    (df['Employment'] == 'Employed part-time')]\n",
+    "    \n",
+    "    choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n",
+    "    \n",
+    "    df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "df = refactor_emp(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst=df['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(4,4))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Employment Type')  # Add a title\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JobSatisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSatisfaction\n",
+       "Moderately satisfied                  25908\n",
+       "Extremely satisfied                   12395\n",
+       "Slightly satisfied                     9973\n",
+       "Slightly dissatisfied                  7037\n",
+       "Moderately dissatisfied                6286\n",
+       "Neither satisfied nor dissatisfied     4935\n",
+       "Extremely dissatisfied                 2472\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 237,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSatisfaction'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10482"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSatisfaction'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['JobSatisfaction'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSatisfaction'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df['JobSatisfaction'].value_counts()\n",
+    "plt.figure(figsize=(12,4))\n",
+    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('Satisfaction Level')  # Label for x-axis\n",
+    "plt.ylabel('Counts')      # Label for y-axis\n",
+    "plt.title('Job Satisfaction')  # Title of the plot\n",
+    "plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ethnicity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23578"
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['RaceEthnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RaceEthnicity\n",
+       "Black or of African descent                                                                                                                                 1204\n",
+       "Black or of African descent;East Asian                                                                                                                         7\n",
+       "Black or of African descent;East Asian;Hispanic or Latino/Latina                                                                                               2\n",
+       "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian                    1\n",
+       "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian        1\n",
+       "                                                                                                                                                           ...  \n",
+       "Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent                                                           2\n",
+       "Native American, Pacific Islander, or Indigenous Australian;White or of European descent                                                                     160\n",
+       "South Asian                                                                                                                                                 6112\n",
+       "South Asian;White or of European descent                                                                                                                      88\n",
+       "White or of European descent                                                                                                                               39320\n",
+       "Name: RaceEthnicity, Length: 71, dtype: int64"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#count number of each Ethnicity\n",
+    "df.groupby('RaceEthnicity')['RaceEthnicity'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
+    "df.loc[df['RaceEthnicity'].str.match('Biracial') == True, 'RaceEthnicity'] = 'Biracial'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Black or of African descent') == True, 'RaceEthnicity'] = 'Black or African descent'\n",
+    "df.loc[df['RaceEthnicity'].str.match('East Asian') == True, 'RaceEthnicity'] = 'East Asian'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Hispanic or Latino') == True, 'RaceEthnicity'] = 'Hispanic or Latino'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Indigenous') == True, 'RaceEthnicity'] = 'Indigenous'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Middle Eastern') == True, 'RaceEthnicity'] = 'Middle Eastern'\n",
+    "df.loc[df['RaceEthnicity'].str.match('South') == True, 'RaceEthnicity'] = 'South Asian'\n",
+    "df.loc[df['RaceEthnicity'].str.match('White or of European descent') == True, 'RaceEthnicity'] = 'White or European descent'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Multiracial') == True, 'RaceEthnicity'] = 'Multiracial'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Native American') == True, 'RaceEthnicity'] = 'Native American'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RaceEthnicity\n",
+       "Black or African descent      1549\n",
+       "East Asian                    2787\n",
+       "Hispanic or Latino            3592\n",
+       "Middle Eastern                2176\n",
+       "Native American                286\n",
+       "South Asian                   6200\n",
+       "White or European descent    39320\n",
+       "Name: RaceEthnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 245,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('RaceEthnicity')['RaceEthnicity'].count() #11 groups of Ethnicity after combining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23578"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['RaceEthnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['RaceEthnicity']=df.groupby(['Country'])['RaceEthnicity'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 248,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['RaceEthnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df.groupby('RaceEthnicity')['RaceEthnicity'].count()\n",
+    "plt.figure(figsize=(6,6))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Race/Ethnicity')  # Add a title\n",
+    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Developer Roles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "728"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['DevType'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Back-end developer                                                                                                                                                                                           5372\n",
+       "Back-end developer;C-suite executive (CEO, CTO, etc.)                                                                                                                                                          59\n",
+       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst                                                                                                                                  5\n",
+       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Data scientist or machine learning specialist                                                                                    1\n",
+       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Data scientist or machine learning specialist;Database administrator;Designer;Desktop or enterprise applications developer       1\n",
+       "                                                                                                                                                                                                             ... \n",
+       "QA or test developer;Student;System administrator                                                                                                                                                               5\n",
+       "QA or test developer;System administrator                                                                                                                                                                      10\n",
+       "Student                                                                                                                                                                                                      2523\n",
+       "Student;System administrator                                                                                                                                                                                   63\n",
+       "System administrator                                                                                                                                                                                          247\n",
+       "Name: DevType, Length: 8820, dtype: int64"
+      ]
+     },
+     "execution_count": 252,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('DevType')['DevType'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
+    "df.loc[df['DevType'].str.match('Back-end developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Student') == True, 'DevType'] = 'Student'\n",
+    "df.loc[df['DevType'].str.match('QA or test developer') == True, 'DevType'] = 'Non developer'\n",
+    "df.loc[df['DevType'].str.match('Product manager') == True, 'DevType'] = 'Manager'\n",
+    "df.loc[df['DevType'].str.match('Mobile developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Marketing or sales professional') == True, 'DevType'] = 'Non developer'\n",
+    "\n",
+    "df.loc[df['DevType'].str.match('System administrator') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Game or graphics developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Full-stack developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Front-end developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Engineering manager') == True, 'DevType'] = 'Manager'\n",
+    "df.loc[df['DevType'].str.match('Embedded applications or devices developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Educator or academic researcher') == True, 'DevType'] = 'Student'\n",
+    "df.loc[df['DevType'].str.match('DevOps specialist') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Desktop or enterprise applications developer') == True, 'DevType'] = 'Developer'\n",
+    "\n",
+    "df.loc[df['DevType'].str.match('Designer') == True, 'DevType'] = 'Non developer'\n",
+    "df.loc[df['DevType'].str.match('Database administrator') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Data scientist or machine learning specialist') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Data or business analyst') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('C-suite executive') == True, 'DevType'] = 'Developer'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Developer        73032\n",
+       "Manager            665\n",
+       "Non developer     2791\n",
+       "Student           3000\n",
+       "Name: DevType, dtype: int64"
+      ]
+     },
+     "execution_count": 254,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('DevType')['DevType'].count() #11 groups of Ethnicity after combining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df.groupby('DevType')['DevType'].count()\n",
+    "plt.figure(figsize=(6,6))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Developer Type')  # Add a title\n",
+    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Language to worked with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWorkedWith\n",
+       "C#;JavaScript;SQL;HTML;CSS     1235\n",
+       "JavaScript;PHP;SQL;HTML;CSS    1095\n",
+       "Java                            855\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageWorkedWith'].value_counts().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9985"
+      ]
+     },
+     "execution_count": 257,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWorkedWith\n",
+       "C#;JavaScript;SQL;HTML;CSS     1383\n",
+       "JavaScript;PHP;SQL;HTML;CSS    1226\n",
+       "Java                            989\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageWorkedWith'].value_counts().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageDesireNextYear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageDesireNextYear\n",
+       "Python                                   718\n",
+       "C#;JavaScript;SQL;TypeScript;HTML;CSS    557\n",
+       "C#                                       522\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 261,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14147"
+      ]
+     },
+     "execution_count": 262,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 264,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageDesireNextYear\n",
+       "Python                                   878\n",
+       "C#;JavaScript;SQL;TypeScript;HTML;CSS    690\n",
+       "C#                                       629\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 265,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Years of coding (Exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodingProf\n",
+       "0-2 years           22612\n",
+       "3-5 years           20883\n",
+       "6-8 years           11177\n",
+       "9-11 years           7456\n",
+       "12-14 years          4220\n",
+       "15-17 years          2987\n",
+       "18-20 years          2810\n",
+       "21-23 years          1352\n",
+       "30 or more years     1289\n",
+       "24-26 years           853\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3349"
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['YearsCodingProf'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodingProf\n",
+       "3-5 years           23773\n",
+       "0-2 years           22781\n",
+       "6-8 years           11274\n",
+       "9-11 years           7527\n",
+       "12-14 years          4267\n",
+       "15-17 years          3007\n",
+       "18-20 years          2841\n",
+       "21-23 years          1365\n",
+       "30 or more years     1294\n",
+       "24-26 years           856\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 270,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Years of coding (Non Exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCoding\n",
+       "3-5 years           19100\n",
+       "6-8 years           16537\n",
+       "9-11 years          10578\n",
+       "0-2 years            8022\n",
+       "12-14 years          7069\n",
+       "15-17 years          5459\n",
+       "18-20 years          4472\n",
+       "30 or more years     3136\n",
+       "21-23 years          2377\n",
+       "24-26 years          1671\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['YearsCoding'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCoding\n",
+       "3-5 years           19135\n",
+       "6-8 years           16554\n",
+       "9-11 years          10585\n",
+       "0-2 years            8043\n",
+       "12-14 years          7077\n",
+       "15-17 years          5462\n",
+       "18-20 years          4476\n",
+       "30 or more years     3144\n",
+       "21-23 years          2378\n",
+       "24-26 years          1671\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operating System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OperatingSystem\n",
+       "Windows        34268\n",
+       "MacOS          18638\n",
+       "Linux-based    16069\n",
+       "BSD/Unix         139\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 276,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['OperatingSystem'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10374"
+      ]
+     },
+     "execution_count": 277,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['OperatingSystem'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['OperatingSystem'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['OperatingSystem'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst=df['OperatingSystem'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(4,4))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Oeratining System Used')  # Add a title\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Salary Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryType\n",
+       "Monthly    26201\n",
+       "Yearly     22541\n",
+       "Weekly      2248\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28498"
+      ]
+     },
+     "execution_count": 283,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['SalaryType'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 285,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryType\n",
+       "Monthly    40953\n",
+       "Yearly     34333\n",
+       "Weekly      4202\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Currency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Currency\n",
+       "U.S. dollars ($)               20599\n",
+       "Euros (€)                      15201\n",
+       "Indian rupees (₹)               7908\n",
+       "British pounds sterling (£)     4856\n",
+       "Canadian dollars (C$)           2535\n",
+       "Russian rubles (₽)              1768\n",
+       "Brazilian reais (R$)            1663\n",
+       "Australian dollars (A$)         1571\n",
+       "Polish złoty (zł)               1434\n",
+       "Swedish kroner (SEK)             864\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 287,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Currency'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17483"
+      ]
+     },
+     "execution_count": 288,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Currency'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Currency'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 290,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Currency'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['Currency'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Currency\n",
+       "U.S. dollars ($)               26356\n",
+       "Euros (€)                      19465\n",
+       "Indian rupees (₹)              10152\n",
+       "British pounds sterling (£)     6194\n",
+       "Canadian dollars (C$)           3289\n",
+       "Russian rubles (₽)              2340\n",
+       "Brazilian reais (R$)            2122\n",
+       "Australian dollars (A$)         1970\n",
+       "Polish złoty (zł)               1856\n",
+       "Swedish kroner (SEK)            1101\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 292,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Currency'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryUSD\n",
+       "0.0          842\n",
+       "120000.0     524\n",
+       "100000.0     497\n",
+       "80000.0      396\n",
+       "1000000.0    382\n",
+       "110000.0     371\n",
+       "90000.0      364\n",
+       "150000.0     357\n",
+       "60000.0      351\n",
+       "75000.0      337\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryUSD'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31786"
+      ]
+     },
+     "execution_count": 294,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryUSD'].isnull().sum() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType        Country      \n",
+       "Student        Saudi Arabia     1500000.0\n",
+       "Developer      Andorra           525089.5\n",
+       "Manager        Hungary           516000.0\n",
+       "               Netherlands       507175.0\n",
+       "Non developer  Algeria           360000.0\n",
+       "               Cyprus            293736.0\n",
+       "Developer      Liechtenstein     284028.0\n",
+       "Student        Finland           272212.0\n",
+       "Manager        Denmark           262920.6\n",
+       "Student        Israel            256522.4\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 295,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_salary = df.groupby(['DevType','Country'])['SalaryUSD'].mean() \n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "means = df.groupby(['YearsCodingProf','DevType', 'Country'])['SalaryUSD'].transform('mean')\n",
+    "df['SalaryUSD'] = df['SalaryUSD'].fillna(means)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodingProf  DevType        Country                             \n",
+       "9-11 years       Student        Saudi Arabia                            1500000.0\n",
+       "12-14 years      Non developer  Norway                                  1000000.0\n",
+       "                 Student        Switzerland                             1000000.0\n",
+       "15-17 years      Non developer  Australia                               1000000.0\n",
+       "                                New Zealand                             1000000.0\n",
+       "21-23 years      Developer      Japan                                   1000000.0\n",
+       "                                Venezuela, Bolivarian Republic of...    1000000.0\n",
+       "                 Non developer  Sweden                                  1000000.0\n",
+       "                 Student        Finland                                 1000000.0\n",
+       "24-26 years      Manager        Canada                                  1000000.0\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 297,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_salary = df.groupby(['YearsCodingProf','DevType','Country'])['SalaryUSD'].mean()\n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['SalaryUSD'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age\n",
+       "25 - 34 years old     30969\n",
+       "18 - 24 years old     14847\n",
+       "35 - 44 years old     10980\n",
+       "45 - 54 years old      3072\n",
+       "Under 18 years old     1549\n",
+       "55 - 64 years old       865\n",
+       "65 years or older       144\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 299,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Age'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16374"
+      ]
+     },
+     "execution_count": 300,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Age'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df['Age'].value_counts().nlargest(10)\n",
+    "plt.figure(figsize=(12,4))\n",
+    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('Age Group')  # Label for x-axis\n",
+    "plt.ylabel('Counts')      # Label for y-axis\n",
+    "plt.title('Age')  # Title of the plot\n",
+    "#plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                          0\n",
+      "SalaryUSD                    0\n",
+      "Country                      0\n",
+      "Currency                     0\n",
+      "DevType                      0\n",
+      "Employment                   0\n",
+      "RaceEthnicity                0\n",
+      "Gender                       0\n",
+      "SalaryType                   0\n",
+      "Hobby                        0\n",
+      "JobSatisfaction              0\n",
+      "JobSearchStatus              0\n",
+      "OperatingSystem              0\n",
+      "UndergradMajor               0\n",
+      "YearsCoding                  0\n",
+      "YearsCodingProf              0\n",
+      "LanguageDesireNextYear       0\n",
+      "LanguageWorkedWith           0\n",
+      "FormalEducation           1549\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1549"
+      ]
+     },
+     "execution_count": 305,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['FormalEducation'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FormalEducation\n",
+       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                              36010\n",
+       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                           17529\n",
+       "Some college/university study without earning a degree                                 9737\n",
+       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     7088\n",
+       "Associate degree                                                                       2407\n",
+       "Other doctoral degree (Ph.D, Ed.D., etc.)                                              1754\n",
+       "Primary/elementary school                                                              1217\n",
+       "Professional degree (JD, MD, etc.)                                                     1073\n",
+       "I never completed any formal education                                                  436\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 306,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['FormalEducation'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EdLevel\n",
+       "Bachelors       37559\n",
+       "No Degree       18478\n",
+       "Masters         17529\n",
+       "Associate        2407\n",
+       "Doctorate        1754\n",
+       "Professional     1073\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 307,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Changing column's name\n",
+    "df.rename(columns={'FormalEducation':'EdLevel'}, inplace =True)\n",
+    "#Refactoring EdLevel\n",
+    "def refactor_ed(df):\n",
+    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
+    "    conditions_ed = [(df['EdLevel'] == 'Associate degree'),\n",
+    "                    (df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
+    "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
+    "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
+    "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
+    "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
+    "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
+    "    \n",
+    "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
+    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
+    "    return df\n",
+    "\n",
+    "# applying function to subsets\n",
+    "df = refactor_ed(df)\n",
+    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
+    "df['EdLevel'].replace('nan', 'Bachelors', inplace=True)\n",
+    "\n",
+    "df['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaned Dataset : 2018_Survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(78800, 19)"
+      ]
+     },
+     "execution_count": 308,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_2018 = df[df.notnull()]\n",
+    "cleaned_2018.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Currency</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>RaceEthnicity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SalaryType</th>\n",
+       "      <th>Hobby</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>JobSearchStatus</th>\n",
+       "      <th>OperatingSystem</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCoding</th>\n",
+       "      <th>YearsCodingProf</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>EdLevel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>70841.000000</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>British pounds sterling (£)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately dissatisfied</td>\n",
+       "      <td>Seeking</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Other Science</td>\n",
+       "      <td>30 or more years</td>\n",
+       "      <td>18-20 years</td>\n",
+       "      <td>Go;Python</td>\n",
+       "      <td>JavaScript;Python;Bash/Shell</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>153030.333333</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>British pounds sterling (£)</td>\n",
+       "      <td>Manager</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Non-conforming</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>24-26 years</td>\n",
+       "      <td>6-8 years</td>\n",
+       "      <td>Go;Python</td>\n",
+       "      <td>JavaScript;Python;Bash/Shell</td>\n",
+       "      <td>Associate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>165809.207657</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>U.S. dollars ($)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Neither satisfied nor dissatisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Windows</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>18-20 years</td>\n",
+       "      <td>12-14 years</td>\n",
+       "      <td>C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell</td>\n",
+       "      <td>C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>21426.000000</td>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>South African rands (R)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Windows</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>6-8 years</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>Assembly;C;C++;Matlab;SQL;Bash/Shell</td>\n",
+       "      <td>C;C++;Java;Matlab;R;SQL;Bash/Shell</td>\n",
+       "      <td>No Degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>41671.000000</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>British pounds sterling (£)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately satisfied</td>\n",
+       "      <td>Seeking</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>6-8 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...</td>\n",
+       "      <td>Java;JavaScript;Python;TypeScript;HTML;CSS</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>120000.000000</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>U.S. dollars ($)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>MacOS</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>9-11 years</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>C;Go;JavaScript;Python;HTML;CSS</td>\n",
+       "      <td>JavaScript;HTML;CSS</td>\n",
+       "      <td>No Degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>25 - 34 years old</td>\n",
+       "      <td>93336.000000</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>U.S. dollars ($)</td>\n",
+       "      <td>Non developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Black or African descent</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Windows</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>Matlab;SQL;Kotlin;Bash/Shell</td>\n",
+       "      <td>JavaScript;TypeScript;HTML;CSS</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>250000.000000</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>U.S. dollars ($)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>MacOS</td>\n",
+       "      <td>Arts and Science</td>\n",
+       "      <td>30 or more years</td>\n",
+       "      <td>21-23 years</td>\n",
+       "      <td>Erlang;Go;Python;Rust;SQL</td>\n",
+       "      <td>Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua...</td>\n",
+       "      <td>No Degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>26023.003365</td>\n",
+       "      <td>India</td>\n",
+       "      <td>U.S. dollars ($)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>South Asian</td>\n",
+       "      <td>Non-conforming</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extremely satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>Java;Python</td>\n",
+       "      <td>Java</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>Euros (€)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Neither satisfied nor dissatisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Windows</td>\n",
+       "      <td>No major</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>Java;Python</td>\n",
+       "      <td>Java;JavaScript;PHP;VB.NET;HTML;CSS</td>\n",
+       "      <td>No Degree</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Age      SalaryUSD         Country  \\\n",
+       "1   35 - 44 years old   70841.000000  United Kingdom   \n",
+       "2   35 - 44 years old  153030.333333   United States   \n",
+       "3   35 - 44 years old  165809.207657   United States   \n",
+       "4   18 - 24 years old   21426.000000    South Africa   \n",
+       "5   18 - 24 years old   41671.000000  United Kingdom   \n",
+       "6   18 - 24 years old  120000.000000   United States   \n",
+       "7   25 - 34 years old   93336.000000         Nigeria   \n",
+       "8   35 - 44 years old  250000.000000   United States   \n",
+       "13  35 - 44 years old   26023.003365           India   \n",
+       "14  18 - 24 years old       0.000000     Netherlands   \n",
+       "\n",
+       "                       Currency        DevType Employment  \\\n",
+       "1   British pounds sterling (£)      Developer  Full-time   \n",
+       "2   British pounds sterling (£)        Manager  Full-time   \n",
+       "3              U.S. dollars ($)      Developer  Full-time   \n",
+       "4       South African rands (R)      Developer  Full-time   \n",
+       "5   British pounds sterling (£)      Developer  Full-time   \n",
+       "6              U.S. dollars ($)      Developer  Full-time   \n",
+       "7              U.S. dollars ($)  Non developer  Full-time   \n",
+       "8              U.S. dollars ($)      Developer  Full-time   \n",
+       "13             U.S. dollars ($)      Developer  Full-time   \n",
+       "14                    Euros (€)      Developer  Full-time   \n",
+       "\n",
+       "                RaceEthnicity          Gender SalaryType Hobby  \\\n",
+       "1   White or European descent            Male     Yearly   Yes   \n",
+       "2   White or European descent  Non-conforming     Yearly   Yes   \n",
+       "3   White or European descent            Male     Yearly    No   \n",
+       "4   White or European descent            Male     Yearly   Yes   \n",
+       "5   White or European descent            Male     Yearly   Yes   \n",
+       "6   White or European descent            Male     Yearly   Yes   \n",
+       "7    Black or African descent          Female     Yearly   Yes   \n",
+       "8   White or European descent            Male     Yearly   Yes   \n",
+       "13                South Asian  Non-conforming     Yearly    No   \n",
+       "14  White or European descent            Male    Monthly    No   \n",
+       "\n",
+       "                       JobSatisfaction JobSearchStatus OperatingSystem  \\\n",
+       "1              Moderately dissatisfied         Seeking     Linux-based   \n",
+       "2                 Moderately satisfied     Not seeking     Linux-based   \n",
+       "3   Neither satisfied nor dissatisfied     Not seeking         Windows   \n",
+       "4                   Slightly satisfied     Not seeking         Windows   \n",
+       "5                 Moderately satisfied         Seeking     Linux-based   \n",
+       "6                   Slightly satisfied     Not seeking           MacOS   \n",
+       "7                   Slightly satisfied     Not seeking         Windows   \n",
+       "8                 Moderately satisfied     Not seeking           MacOS   \n",
+       "13                 Extremely satisfied     Not seeking     Linux-based   \n",
+       "14  Neither satisfied nor dissatisfied     Not seeking         Windows   \n",
+       "\n",
+       "      UndergradMajor       YearsCoding YearsCodingProf  \\\n",
+       "1      Other Science  30 or more years     18-20 years   \n",
+       "2   Computer Science       24-26 years       6-8 years   \n",
+       "3   Computer Science       18-20 years     12-14 years   \n",
+       "4   Computer Science         6-8 years       0-2 years   \n",
+       "5   Computer Science         6-8 years       3-5 years   \n",
+       "6   Computer Science        9-11 years       0-2 years   \n",
+       "7   Computer Science         0-2 years       3-5 years   \n",
+       "8   Arts and Science  30 or more years     21-23 years   \n",
+       "13       Engineering         3-5 years       3-5 years   \n",
+       "14          No major         0-2 years       0-2 years   \n",
+       "\n",
+       "                               LanguageDesireNextYear  \\\n",
+       "1                                           Go;Python   \n",
+       "2                                           Go;Python   \n",
+       "3    C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell   \n",
+       "4                Assembly;C;C++;Matlab;SQL;Bash/Shell   \n",
+       "5   C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...   \n",
+       "6                     C;Go;JavaScript;Python;HTML;CSS   \n",
+       "7                        Matlab;SQL;Kotlin;Bash/Shell   \n",
+       "8                           Erlang;Go;Python;Rust;SQL   \n",
+       "13                                        Java;Python   \n",
+       "14                                        Java;Python   \n",
+       "\n",
+       "                                   LanguageWorkedWith    EdLevel  \n",
+       "1                        JavaScript;Python;Bash/Shell  Bachelors  \n",
+       "2                        JavaScript;Python;Bash/Shell  Associate  \n",
+       "3    C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell  Bachelors  \n",
+       "4                  C;C++;Java;Matlab;R;SQL;Bash/Shell  No Degree  \n",
+       "5          Java;JavaScript;Python;TypeScript;HTML;CSS  Bachelors  \n",
+       "6                                 JavaScript;HTML;CSS  No Degree  \n",
+       "7                      JavaScript;TypeScript;HTML;CSS  Bachelors  \n",
+       "8   Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua...  No Degree  \n",
+       "13                                               Java  Bachelors  \n",
+       "14                Java;JavaScript;PHP;VB.NET;HTML;CSS  No Degree  "
+      ]
+     },
+     "execution_count": 309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_2018.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## After Cleaning Dataset 2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  1497200\n",
+      "Total missing :  0\n",
+      "Missing Percentage:  0.0 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = df.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow 2019 Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "na_vals = ['NA', 'Missing']\n",
+    "survey_main_df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2019.csv', na_values=na_vals)\n",
+    "schema_df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2019.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Selecting only the required columns for analysis\n",
+    "survey_df_2019 = survey_main_df[['Age', 'CareerSat', 'ConvertedComp', 'Country', 'Dependents', 'EdLevel', 'Employment', 'Ethnicity', 'Gender', 'Hobbyist', 'ImpSyn', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n",
+    " 'UndergradMajor', 'YearsCodePro', 'DevType']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#changing the name of columns for easier understanding\n",
+    "# 'MainBranch': 'Profession'\n",
+    "# 'ConvertedComp': 'SalaryUSD'\n",
+    "# 'CareerSat': 'JobSatisfaction'\n",
+    "# 'ImpSyn' : 'CompetenceLevel'\n",
+    "# 'JobSat' : 'CurrentJobSatis'\n",
+    "# 'JobSeek' : 'JobStatus'\n",
+    "\n",
+    "\n",
+    "survey_df_2019.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD',  'CareerSat': 'JobSatisfaction', 'ImpSyn' : 'CompetenceLevel', 'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>CompetenceLevel</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>JobStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Primary/elementary school</td>\n",
+       "      <td>Not employed, and not looking for work</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL</td>\n",
+       "      <td>HTML/CSS;Java;JavaScript;Python</td>\n",
+       "      <td>I am a student who is learning to code</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bosnia and Herzegovina</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Developer, desktop or enterprise applications;...</td>\n",
+       "      <td>Secondary school (e.g. American high school, G...</td>\n",
+       "      <td>Not employed, but looking for work</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>I am actively looking for a job</td>\n",
+       "      <td>C++;HTML/CSS;JavaScript;SQL</td>\n",
+       "      <td>C++;HTML/CSS;Python</td>\n",
+       "      <td>I am a student who is learning to code</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>Thailand</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to new...</td>\n",
+       "      <td>Elixir;HTML/CSS</td>\n",
+       "      <td>HTML/CSS</td>\n",
+       "      <td>I am not primarily a developer, but I write co...</td>\n",
+       "      <td>8820.0</td>\n",
+       "      <td>Web development or web design</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Age CompetenceLevel                 Country     CurrentJobSatis  \\\n",
+       "0  14.0             NaN          United Kingdom                 NaN   \n",
+       "1  19.0             NaN  Bosnia and Herzegovina                 NaN   \n",
+       "2  28.0         Average                Thailand  Slightly satisfied   \n",
+       "\n",
+       "  Dependents                                            DevType  \\\n",
+       "0         No                                                NaN   \n",
+       "1         No  Developer, desktop or enterprise applications;...   \n",
+       "2        Yes  Designer;Developer, back-end;Developer, front-...   \n",
+       "\n",
+       "                                             EdLevel  \\\n",
+       "0                          Primary/elementary school   \n",
+       "1  Secondary school (e.g. American high school, G...   \n",
+       "2           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "\n",
+       "                               Employment Ethnicity Gender Hobbyist  \\\n",
+       "0  Not employed, and not looking for work       NaN    Man      Yes   \n",
+       "1      Not employed, but looking for work       NaN    Man       No   \n",
+       "2                      Employed full-time       NaN    Man      Yes   \n",
+       "\n",
+       "      JobSatisfaction                                          JobStatus  \\\n",
+       "0                 NaN                                                NaN   \n",
+       "1                 NaN                    I am actively looking for a job   \n",
+       "2  Slightly satisfied  I’m not actively looking, but I am open to new...   \n",
+       "\n",
+       "                            LanguageDesireNextYear  \\\n",
+       "0  C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL   \n",
+       "1                      C++;HTML/CSS;JavaScript;SQL   \n",
+       "2                                  Elixir;HTML/CSS   \n",
+       "\n",
+       "                LanguageWorkedWith  \\\n",
+       "0  HTML/CSS;Java;JavaScript;Python   \n",
+       "1              C++;HTML/CSS;Python   \n",
+       "2                         HTML/CSS   \n",
+       "\n",
+       "                                          Profession  SalaryUSD  \\\n",
+       "0             I am a student who is learning to code        NaN   \n",
+       "1             I am a student who is learning to code        NaN   \n",
+       "2  I am not primarily a developer, but I write co...     8820.0   \n",
+       "\n",
+       "                  UndergradMajor YearsCodePro  \n",
+       "0                            NaN          NaN  \n",
+       "1                            NaN          NaN  \n",
+       "2  Web development or web design            1  "
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sorting the columns alphabetically\n",
+    "survey_df_2019.sort_index(axis=1).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                       float64\n",
+       "JobSatisfaction            object\n",
+       "SalaryUSD                 float64\n",
+       "Country                    object\n",
+       "Dependents                 object\n",
+       "EdLevel                    object\n",
+       "Employment                 object\n",
+       "Ethnicity                  object\n",
+       "Gender                     object\n",
+       "Hobbyist                   object\n",
+       "CompetenceLevel            object\n",
+       "CurrentJobSatis            object\n",
+       "JobStatus                  object\n",
+       "LanguageDesireNextYear     object\n",
+       "LanguageWorkedWith         object\n",
+       "Profession                 object\n",
+       "UndergradMajor             object\n",
+       "YearsCodePro               object\n",
+       "DevType                    object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#datatype of survey data\n",
+    "survey_df_2019.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Validation - Total Cells vs Missing %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  1688777\n",
+      "Total missing :  169969\n",
+      "Missing Percentage:  10.064620728491684 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = survey_df_2019.isnull().sum() #number of missing\n",
+    "total_cells = np.product(survey_df_2019.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning and Refactoring column values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Man                                                            77919\n",
+       "Woman                                                           6344\n",
+       "Non-binary, genderqueer, or gender non-conforming                597\n",
+       "Man;Non-binary, genderqueer, or gender non-conforming            181\n",
+       "Woman;Non-binary, genderqueer, or gender non-conforming          163\n",
+       "Woman;Man                                                        132\n",
+       "Woman;Man;Non-binary, genderqueer, or gender non-conforming       70\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#lets refactor Gender values to Male, female and Non binary\n",
+    "#For the purpose of our data analysis we are considering three gender category. This not to defame any gender.\n",
+    "#refactoring Gender\n",
+    "\n",
+    "def refactor_gender(df):\n",
+    "    '''function to change gender category to Male, Female, Non binary'''\n",
+    "    conditions = [(df['Gender'] == 'Man') | (df['Gender'] == 'Man;Non-binary, genderqueer, or gender non-conforming'),\n",
+    "                  (df['Gender'] == 'Woman') | (df['Gender'] == 'Woman;Non-binary, genderqueer, or gender non-conforming'),\n",
+    "                  (df['Gender'] == 'Non-binary, genderqueer, or gender non-conforming') \n",
+    "                  | (df['Gender'] == 'Woman;Man') \n",
+    "                  | (df['Gender'] == 'Woman;Man;Non-binary, genderqueer, or gender non-conforming')]\n",
+    "\n",
+    "    values = ['Man', 'Woman', 'Non-binary']\n",
+    "\n",
+    "    df['Gender'] = np.select(conditions, values, default = np.NaN)\n",
+    "    \n",
+    "    return df\n",
+    "            \n",
+    "survey_df_2019 = refactor_gender(survey_df_2019)\n",
+    "survey_df_2019['Gender'].replace('nan', 'Non-binary', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Gender'] = survey_df_2019['Gender'].fillna('Non-binary')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n",
+    "survey_df_2019.isnull().sum()['Gender']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man           78100\n",
+       "Non-binary     4276\n",
+       "Woman          6507\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n",
+    "plt.title(\"Before cleaning Age's outliers from genders\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#We are considering developes of age 15 to 60\n",
+    "filt = (survey_df_2019['Age'] >= 15) & (survey_df_2019['Age'] <= 60)\n",
+    "survey_df_2019 = survey_df_2019[filt]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcmvQJCVptKRb6hGMmZlZ1qBJKiJeB16WtGUd4mlanZ2ddHZ2NjoMs2HLr6GRqdK7oK8G7pN0M/BScWFE+IdxqqRev61kNlL5NTQyVZqkfpo+zMzM6qaiJBURV0oaC+wSEQ/UOCYzMzOgwqv7JB0N3AP8LJ3fW9JPahiXmZlZxZegdwD7A88BRMQ9wJtrEpGZmVmq0iT1WkQ8X7Ysqh2MmZlZVqUXTvxJ0onAaEnjganAnbULy8zMrPKe1L8CewJrgGuBF4BzahSTmZkZUPnVfS8D/54+zMzM6mLAJCVpHgOce4qID1Q9IjMzs9Rgw31fAb4KPASsAq5IHyuBP9U2NDOz6jj00EMpFAocdthhvcoKhULPo9zhhx9OoVDgiCOOqGg5wDHHHEOhUOBDH/pQr7Kjjz6aQqHAMccc06vsxBNPpFAocMopp5Qs//SnP02hUOBzn/tcr20mT55MoVDgxBNP7FU2bdo0CoUC06dP71U2a9YsCoUCF198ccnySy65hEKhwNe//vVe28yZM4dCocC1117bq2zhwoVMnDiRxYsX9yrbUAMmqYi4LSJuA/aJiMkRMS99nAgcUvVoykj6T0nnZOZ/LumbmfmvSvp0reMws+Ht1VdfBeCVV14Z0nZr1qwBYPXq1RUtB3j22WcBePrpp3uVPf/88yXrZHV3dwOwdOnSkuWLFi0C4K677uq1zbJly0q2zbrzzuTatttvv71X2U033QTAvHnzSpZfd911AMydO7fXNldccQUAl156aa+yjo4O1q5dywUXXNCrbENVeuHEtpJ2K85IejOwbdWj6e1OYEJa5yhgG5ILOIomAL+pQxxmNkwdeuihJfPZ3lR57yk7f/jhh5eUFXtN/S0HevWQsr2po48+uqQsu255T6jYm/r0p0s/g2d7U5MnTy4py+5j2rRpJWXZ3tSsWbNKyoq9qUsuuaRkebY3NWfOnJKybG9q4cKFrFy5EoCVK1dWvTdV6SXo5wILJP0tnW8DzqxqJH37DfCf6fSeJEOMO0jaGngZeBuwlaS7SY7l98BZEbFG0lLgGuC9wMbAGcAsYBxwcUR8Q1ILcAOwdbrO9Ii4QVIbcBNwB0kifAw4JiJW1epAu7u7WbVqFVOnNsc9e7u6utjk1eb6ObMnXx7FK11dTfM/rreuri7Gjh3ba3mxF1VUaW+q2FsqKvaa+lsOvXtI2d5UsRfV17rlPaFib6rYiyrK9qaKvai+9lHsRRVle1PFXlTRvHnzOO+883p6UUVz587l7LPPBtb1ooouvfRSTjjhBCDpRWVdcMEF3HjjjVRLRe8SEfEzYDzwqfSxR0T8vGpR9F/v48BrknYhSRa/BX4HHAS0Aw8C3wQmR8TfkySqszK7eDQiDgJ+DcwGjgMOBC5My1cDH4qIfUmS2VclKS0bD/y/iNiT5E4bH+4rRklnSFokadHy5curctxmZsNFsRfV3/yGqrQnBbAfSQ9qI+CdkoiIq6oaTd9+Q5KgJgBfA3ZKp58n6eGsjogH03WvBD4JXJLOF+8veB/QEhEvAi9KWi1pK5KfHfmSpAKwNt33duk2D6W3fwJYTHLsvUTE5cDlAO3t7et9F47W1laApvk9nKlTp7J66e8bHUZdbbfZWsa0jW+a/3G9uYfaGC0tLSWJqaWlpar7r/QGs3NIrvQ7BHhX+mivaiT9K56X+nuS4b67SHpSE4A/DLJtsV++NjNdnN8IOInk3Np+EbE38CQwpmxbgNcZWkI3s5zYeOONS+Y32WSTirbbdNNNS+bHjBkz4HKArbfeuqTsjW98Y8/0lluW/m5sdt3ih9SitrY2ANrbS99mDzzwwJ7pHXbYoaQsu48JEyaUlGXPtU2aNKmkrHiu7Nhjjy1Zfvzxx/dMn3766SVlZ521bsCqfLhvxowZVFOlJwXagYMj4l8i4l/TR70+tvwGOAp4JiJej4hngK1IEtV3gDZJ49J1TwZuG8K+twSeiohXJb0X2LV6YZtZHtx6660l87fcckvPdPmVb9n5m2++uaTsF7/4xYDLAW644YaSsh//+Mc90+VX0mXXveaaa0rKrroqGaT62te+VrL8y1/+cs/097///ZKy7D4uuuiikrKZM2f2TJ9//vklZeeddx4A55xzTsny4vkogJNPPrmkrHg+CmD//ffv6T21tLSw3377UU2VJqk/AdtXtebK3UdyVd9dZcuej4hu4DTgB5LuI+khfWMI+74aaJe0iKRX9dfqhGxmeVLsTVXaiyoq9pqyvaWBlsO6HlK2F1VU7E2V97hgXU+o2IsqKvamsr2oomJvqrwnBut6U319/6vYmyq/4rDYm8r2ooqKvalsL6qoo6ODUaNGVb0XBaCIwU+jSPoVsDewkMwwmO84Uaq9vT3Kr8apVHE8vVnOVxTPSU1vr+5J1r7MXJR8yqtHXYPFMabtXU3zP663ZnsNjSSSFkdEn6eQKj3P0lG9cMzMzCpT6Q1mb5O0KzA+Im6RtBkwurahmZlZs6v06r7TgR8Cl6WLdgKur1FMZmZmQOUXTnwSOJjkd6SIiC7gTbUKyszMDCpPUmsioudeIpI2wj8fb2ZmNVZpkrpN0ueBsZIOB34AzBtkGzMzsw1SaZKaBiwn+X7SGcBPI8K/0mtmZjU1YJKSdIykT0bE2oi4guSODO3A5yUdV5cIzcysaQ3Wk/oc627SCrAJyY1mJ1J6t3EzM7OqG+x7UptExKOZ+TvSe+c9I2nzGsbVdMaNGzf4SmbWL7+GRqbBklTJDaYi4uzMbD1+mbdp+GcGzDaMX0Mj02DDfb9Lv8hbQtKZJPfxMzMzq5nBelLnAtdLOpF1v920H7Ap8MEaxmVmZjZwkoqIp4AJkt4H7Jku/mlE/LLmkZmZWdOr9AazvwScmMzMrK4q/TKvmZlZ3TlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZblX0PSmzWnhk5WhmLmqpeT0PvzgaoC51DeSRlaPZvaERmA0/TlLWEPW8Y/Xm3d0AjGltrVudfdkd36nbbKicpKwhfMdqM6uEz0mZmVluOUmZmVluOUmZmVluOUmZmVluOUmZmVluOUmZmVluOUmZmVluOUmZmVluOUmZmVluOUmZmVluOUmZmVluOUmZmVlu+QazVlOdnZ0sWbKkLnV1p3c7b23w3c4HMm7cON9c12wInKSsppYsWcLdf74btqpDZc8nf5ZreR0qWw/PNToAs+HHScpqbytYO3FtzasZtSAZva5HXeujGJ+ZVc6vGjMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nqZzo7Oyks7Oz0WGYNR2/9vLNN5jNiXr9nIWZlfJrL9/ckzIzs9xykjIzs9xykjIzs9xykjIzs9xykjIzs9xykjIzs9xykjIzs9xykjIzs9xykjIzs9xykjIzs9yqWZKSFJK+mpn/rKSOKu17tqTj+ljeLsk34TKzqnjwwQeZNGlSr1snTZs2jUKhwPTp03ttM2vWLAqFAhdffPGQyhYuXMjEiRNZvHhxz7IVK1Zw/PHHUygUuPbaa3ttc9lll1EoFPjWt77Vq2zOnDlD3q6/4x2sbKC6NlQte1JrgGMlbVPDOkpExKKImFqNfUkaXY39mNnwNXPmTF566SUuvPDCkuV33nknALfffnuvbW666SYA5s2bN6Syjo4O1q5dywUXXNCz7Morr+SJJ54A4NJLL+21zdVXX92zXrkrrrhiyNv1d7yDlQ1U14aqZZJ6DbgcOLe8QNKukm6VdG/6d5d0+WxJnZLulPS3vnpLGYdJ+rWkByUdlW4/UdL8dLpD0rclLUj31ZO8JF0vabGkP0s6I7N8paQLJf0OmC7px5mywyVdt6GNYmbDw4MPPsjSpUsBWLp0aU8PYtq0aSXrZXtTs2bNKinL9pgGKlu4cCErV64EYOXKlSxevJgVK1Ywf/78km2yPZXLLruspCzbK5ozZ86Qt+vveAcrG6iualBEVHWHPTuWVgI7AvcC7wROB1oiokPSPOCHEXGlpH8GPhARH5Q0G9gcmAy8FfhJRIzrY9+zge2BI4G3AL8CxgEHAp+NiKPSocUjgPcCWwAPANtHxKuS3hARz0gaC/weeE9EPC0pgMkRMVeSgL8A746I5ZKuAa6NiN4fgVLt7e2xaNGi9WqvY489llWrVjF+/Pj12j6vurq6WLl2JWuPWlvzukYtSD5zrZ1Y+7rWx6j5o2gZ1TLi/sfDXVdXF2PHjuW660o/g55yyik9b8wAbW1tXHXVVRQKhV77KPao1rfsyCOP7ElSAC0tLRx66KHccMMNVa+rv7L+jhf6b4vB6qqUpMUR0d5XWU0vnIiIF4CrgPIhuIOAa9LpOcAhmbLrI2JtRNwPbDfA7uem63UBfyNJauV+GhFrImIF8FRmf1Ml/RG4C9gZKL5rvA78KI090tj+SdJWacw3lVcg6QxJiyQtWr58+QDhmtlwkn1T7mu+mrIJqjh/880316y+vgx0vPVsi3L1+D2pS4A/AN8ZYJ1sd25NZloAkr4IvB8gIvbuY5u+5sv39TqwkaSJwGHAQRHxsqQFwJh0ndUR8Xpmm+8A84DVwA8i4rVegUdcTjKsSXt7+3p3S1tbWwFG3I+vTZ06lbsfu7vRYeRDC4zfafyI+x8Pd1On9n0au62trVfvoVZaWloq7knVykDHW8+2KFfzS9Aj4hlgLvCxzOI7gY+m0ycBdwyyj3+PiL0zCQrgI5JGSXoLsBvJcF4ltgSeTRPUW0mGCPur93HgcWA6MLvC/ZvZCFB+5d4XvvAFACZMmFCyPDvcNWnSpJKyo48+uqKyjo6OkrIZM2YwZcoURo8uvX7rrLPO6pk+6aSTSsqmTJnSM3366acPebv+jnewsoHqqoZ6fU/qq0D2Kr+pwGmS7gVOBj61Hvt8ALiNZAjuExGxusLtfkbSo7oXmEEy5DeQq4FH0+FHM2sSu+++e0+Poa2tjXHjktPjF110Ucl6M2fO7Jk+//zzS8rOO++8isr2339/WlpagKQXtd9++7HNNttw1FFHlWxzwgkn9EyfeeaZJWUf+9i6fsDJJ5885O36O97BygaqqxpqlqQioiUz/WREbBYRHen80oh4X0S8IyIOjYhH0uWnRsQP+9pH2b5PjYhzI+LdEbF7RMxPly+IiKPS6Y6I+Epmm73SetdExKS07o9ExMSIWDBAfYcAV2xoe5jZ8DN9+nQ233zzkp4DrOtN9XXRQLHHlO0pVVLW0dHBqFGjmDFjRs+yKVOmsP322wN991CKvaJsL6qo2MMZynb9He9gZQPVtaFqdnXfSCBpMfAScHhErBls/Q25uq84Lj7SzlcUz0nV44q73F/dt2AU++y0z4j7Hw93I/W1N5wMdHVfPS6cGLYiYr9Gx2Bm1sx87z4zM8stJykzM8stJykzM8stJykzM8stJykzM8stJykzM8stJykzM8stf08qJ7K3GTGz+vFrL9+cpHKivzsxm1lt+bWXbx7uMzOz3HKSMjOz3HKSMjOz3HKSMjOz3HKSMjOz3HKSMjOz3HKSMjOz3HKSMjOz3HKSMjOz3HKSMjOz3HKSMjOz3HKSMjOz3PINZq32noNRC+rweei55E9d6lofzwE7NToIs+HFScpqqp4/g9Ad3QC07tRatzqHZCf/LITZUDlJWU35ZxDMbEPkdFzEzMzMScrMzHLMScrMzHLLScrMzHLLScrMzHJLEdHoGEYMScuBhxsdxwbaBljR6CByxO1Ryu2xjtui1Ia0x64RsW1fBU5SVkLSoohob3QceeH2KOX2WMdtUapW7eHhPjMzyy0nKTMzyy0nKSt3eaMDyBm3Rym3xzpui1I1aQ+fkzIzs9xyT8rMzHLLScrMzHLLSaqJSdpZ0q8k/UXSnyV9Kl3+Bkk3S+pK/27d6FhrTdIYSQsl/TFti/+bLm+6tsiSNFrS3ZLmp/NN2x6Slkq6T9I9khaly5qyPSRtJemHkv6avn8cVKu2cJJqbq8Bn4mItwEHAp+U9HZgGnBrRIwHbk3nR7o1wPsi4p3A3sA/SjqQ5myLrE8Bf8nMN3t7vDci9s58H6hZ2+O/gJ9FxFuBd5I8R2rSFk5STSwilkXEH9LpF0meaDsBxwBXpqtdCXywIQHWUSRWprMbp4+gCduiSFIr8H7gm5nFTdse/Wi69pD0d0AB+BZARLwSEc9Ro7ZwkjIAJLUB+wC/A7aLiGWQJDLgTQ0MrW7Soa17gKeAmyOiadsidQnwOWBtZlkzt0cAv5C0WNIZ6bJmbI/dgOXAd9Kh4G9K2pwatYWTlCGpBfgRcE5EvNDoeBolIl6PiL2BVmB/SXs1OKSGkXQU8FRELG50LDlycETsC0wiGRovNDqgBtkI2Be4NCL2AV6ihsOcTlJNTtLGJAnq6oi4Ll38pKQd0vIdSHoWTSMdulgA/CPN2xYHAx+QtBT4HvA+Sd+leduDiHg8/fsU8GNgf5qzPbqB7nSkAeCHJEmrJm3hJNXEJIlkXPkvEfG1TNFPgCnp9BTghnrHVm+StpW0VTo9FjgM+CtN2BYAEXF+RLRGRBvwUeCXEfFPNGl7SNpc0hbFaeAI4E80YXtExBPAo5L2SBcdCtxPjdrCd5xoYpIOAX4N3Me68w6fJzkvNRfYBXgE+EhEPNOQIOtE0jtITvaOJvnwNjciLpT0RpqsLcpJmgh8NiKOatb2kLQbSe8JkuGuayLii03cHnuTXFCzCfA34DTS1w1VbgsnKTMzyy0P95mZWW45SZmZWW45SZmZWW45SZmZWW45SZmZWW45SZmNEJI+JCkkvbXRsZhVi5OU2chxAnAHyZdvzUYEJymzESC9/+LBwMdIk5SkUZL+J/19rPmSbpR0XFq2n6Tb0pul/rx4OxuzvHGSMhsZPkjy+z4PAs9I2hc4FmgD/h74OHAQ9Nyv8b+B4yJiP+DbwBcbELPZoDZqdABmVhUnkPy0BiQ3hD2B5DexfhARa4EnJP0qLd8D2Au4Obl9I6OBZXWN1qxCTlJmw1x6/7j3AXtJCpKkE6y711yvTYA/R8RBdQrRbL15uM9s+DsOuCoido2ItojYGXgIWAF8OD03tR0wMV3/AWBbST3Df5L2bETgZoNxkjIb/k6gd6/pR8COJL/98yfgMpK72z8fEa+QJLb/kPRH4B5gQt2iNRsC3wXdbAST1BIRK9MhwYUkvy77RKPjMquUz0mZjWzz0x9z3ASY4QRlw417UmZmlls+J2VmZrnlJGVmZrnlJGVmZrnlJGVmZrnlJGVmZrn1/wFZSJnFYvQKMQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n",
+    "plt.title(\"After cleaning Age's outliers from genders\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Profession column (Mainbranch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "I am a developer by profession                                                   59247\n",
+       "I am a student who is learning to code                                            8382\n",
+       "I am not primarily a developer, but I write code sometimes as part of my work     6531\n",
+       "I code primarily as a hobby                                                       2370\n",
+       "I used to be a developer by profession, but no longer am                          1210\n",
+       "Name: Profession, dtype: int64"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "255"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Profession'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Profession'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "I am a developer by profession                                                   59502\n",
+       "I am a student who is learning to code                                            8382\n",
+       "I am not primarily a developer, but I write code sometimes as part of my work     6531\n",
+       "I code primarily as a hobby                                                       2370\n",
+       "I used to be a developer by profession, but no longer am                          1210\n",
+       "Name: Profession, dtype: int64"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Lets refactor column values of Profession column\n",
+    "#refactoring profession column\n",
+    "\n",
+    "def refactor_prof(df):\n",
+    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
+    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
+    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
+    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
+    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
+    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
+    "    \n",
+    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
+    "    \n",
+    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "survey_df_2019 = refactor_prof(survey_df_2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Developer        59502\n",
+       "Student           8382\n",
+       "Non developer     6531\n",
+       "Novoice           2370\n",
+       "Ex-Developer      1210\n",
+       "Name: Profession, dtype: int64"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EdLevel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                              34926\n",
+       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                           17305\n",
+       "Some college/university study without earning a degree                                 9571\n",
+       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     7638\n",
+       "Associate degree                                                                       2585\n",
+       "Other doctoral degree (Ph.D, Ed.D., etc.)                                              2032\n",
+       "Professional degree (JD, MD, etc.)                                                     1037\n",
+       "Primary/elementary school                                                               981\n",
+       "I never completed any formal education                                                  352\n",
+       "Name: EdLevel, dtype: int64"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1568"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['EdLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  Refactoring EdLevel\n",
+    "def refactor_ed(df):\n",
+    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
+    "    conditions_ed = [(df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
+    "                     (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
+    "                     (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
+    "                     (df['EdLevel'] == 'Associate degree'),\n",
+    "                     (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
+    "                     (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
+    "                     | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
+    "                     | (df['EdLevel'] == 'Primary/elementary school')\n",
+    "                     | (df['EdLevel'] == 'I never completed any formal education')]\n",
+    "\n",
+    "    choices_ed = ['Bachelors', 'Masters', 'Professional', 'Associate', 'Doctorate', 'No Degree']\n",
+    "\n",
+    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# applying function to subsets\n",
+    "survey_df_2019 = refactor_ed(survey_df_2019)\n",
+    "survey_df_2019['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelors       36494\n",
+       "No Degree       18542\n",
+       "Masters         17305\n",
+       "Associate        2585\n",
+       "Doctorate        2032\n",
+       "Professional     1037\n",
+       "Name: EdLevel, dtype: int64"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019.isnull().sum()['EdLevel']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Undergrad Major"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Computer science, computer engineering, or software engineering          42211\n",
+       "Another engineering discipline (ex. civil, electrical, mechanical)        5472\n",
+       "Information systems, information technology, or system administration     4646\n",
+       "Web development or web design                                             2975\n",
+       "A natural science (ex. biology, chemistry, physics)                       2866\n",
+       "Mathematics or statistics                                                 2557\n",
+       "A business discipline (ex. accounting, finance, marketing)                1633\n",
+       "A humanities discipline (ex. literature, history, philosophy)             1408\n",
+       "A social science (ex. anthropology, psychology, political science)        1246\n",
+       "Fine arts or performing arts (ex. graphic design, music, studio art)      1124\n",
+       "Name: UndergradMajor, dtype: int64"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10787"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['UndergradMajor'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Computer science, computer engineering, or software engineering          49010\n",
+       "Another engineering discipline (ex. civil, electrical, mechanical)        6368\n",
+       "Information systems, information technology, or system administration     5392\n",
+       "Web development or web design                                             3424\n",
+       "A natural science (ex. biology, chemistry, physics)                       3285\n",
+       "Mathematics or statistics                                                 2984\n",
+       "A business discipline (ex. accounting, finance, marketing)                1908\n",
+       "A humanities discipline (ex. literature, history, philosophy)             1627\n",
+       "A social science (ex. anthropology, psychology, political science)        1431\n",
+       "Fine arts or performing arts (ex. graphic design, music, studio art)      1327\n",
+       "I never declared a major                                                   922\n",
+       "A health science (ex. nursing, pharmacy, radiology)                        316\n",
+       "Name: UndergradMajor, dtype: int64"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].value_counts().nlargest(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019.dropna(subset=['UndergradMajor'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Refactoring UndergradMajor\n",
+    "def refactor_major(df):\n",
+    "    '''function to change undergrad major category to Computer Science, Engineering, Info Systems, Math/Stat, \n",
+    "        Other Science, Web Design/Dev, Business, Arts and Science'''\n",
+    "    \n",
+    "    \n",
+    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'),\n",
+    "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
+    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'),\n",
+    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
+    "                        (df['UndergradMajor'] == 'I never declared a major'),\n",
+    "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)')\n",
+    "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'),\n",
+    "                        (df['UndergradMajor'] == 'Web development or web design'),\n",
+    "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'),\n",
+    "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
+    "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
+    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)')]\n",
+    "\n",
+    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'No Major', 'Other Science',\n",
+    "                     'Web Design/Dev', 'Business', 'Arts and Science']\n",
+    "\n",
+    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# applying function to subsets\n",
+    "survey_df_2019 = refactor_major(survey_df_2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Computer Science    49010\n",
+       "Engineering          6368\n",
+       "Info Systems         5392\n",
+       "Arts and Science     4385\n",
+       "Other Science        3601\n",
+       "Web Design/Dev       3424\n",
+       "Math/Stat            2984\n",
+       "Business             1908\n",
+       "No Major              922\n",
+       "Name: UndergradMajor, dtype: int64"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Job Status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "I’m not actively looking, but I am open to new opportunities    42258\n",
+       "I am not interested in new job opportunities                    19161\n",
+       "I am actively looking for a job                                 10491\n",
+       "Name: JobStatus, dtype: int64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6084"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['JobStatus'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "I’m not actively looking, but I am open to new opportunities    45917\n",
+       "I am not interested in new job opportunities                    20712\n",
+       "I am actively looking for a job                                 11365\n",
+       "Name: JobStatus, dtype: int64"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019.dropna(subset=['JobStatus'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# refactoring JobStatus\n",
+    "# changing the jobstatus to seeking and non seeking\n",
+    "def refactor_job(df):\n",
+    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
+    "    \n",
+    "    conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n",
+    "                    (df['JobStatus'] == 'I am not interested in new job opportunities')\n",
+    "                    | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
+    "    \n",
+    "    choices_job = ['Seeking', 'Not seeking']\n",
+    "    \n",
+    "    df['JobStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "survey_df_2019 = refactor_job(survey_df_2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Not seeking    66629\n",
+       "Seeking        11365\n",
+       "Name: JobStatus, dtype: int64"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JobSatisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Very satisfied                        26584\n",
+       "Slightly satisfied                    22739\n",
+       "Slightly dissatisfied                  6843\n",
+       "Neither satisfied nor dissatisfied     6158\n",
+       "Very dissatisfied                      3203\n",
+       "Name: JobSatisfaction, dtype: int64"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12467"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Very satisfied                        31507\n",
+       "Slightly satisfied                    26970\n",
+       "Slightly dissatisfied                  8343\n",
+       "Neither satisfied nor dissatisfied     7313\n",
+       "Very dissatisfied                      3861\n",
+       "Name: JobSatisfaction, dtype: int64"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Employment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employed full-time                                      58069\n",
+       "Independent contractor, freelancer, or self-employed     7305\n",
+       "Not employed, but looking for work                       4703\n",
+       "Employed part-time                                       3958\n",
+       "Not employed, and not looking for work                   2914\n",
+       "Retired                                                    76\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "969"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Employment'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employed full-time                                      58771\n",
+       "Independent contractor, freelancer, or self-employed     7397\n",
+       "Not employed, but looking for work                       4770\n",
+       "Employed part-time                                       4017\n",
+       "Not employed, and not looking for work                   2960\n",
+       "Retired                                                    79\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring the employment\n",
+    "def refactor_emp(df):\n",
+    "    '''function to change Employment category to Full-time, Self-employed, Not employed, Part-time '''\n",
+    "    conditions_emp = [(df['Employment'] == 'Employed full-time'),\n",
+    "                    (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n",
+    "                    (df['Employment'] == 'Not employed, but looking for work')\n",
+    "                    | (df['Employment'] == 'Not employed, and not looking for work')\n",
+    "                    | (df['Employment'] == 'Retired'),\n",
+    "                    (df['Employment'] == 'Employed part-time')]\n",
+    "    \n",
+    "    choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n",
+    "    \n",
+    "    df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "survey_df_2019 = refactor_emp(survey_df_2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Full-time        58771\n",
+       "Not employed      7809\n",
+       "Self-employed     7397\n",
+       "Part-time         4017\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ethnicity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ethnicity_list = survey_df_2019['Ethnicity'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[nan,\n",
+       " 'White or of European descent',\n",
+       " 'White or of European descent;Multiracial',\n",
+       " 'East Asian',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Black or of African descent',\n",
+       " 'Hispanic or Latino/Latina;Multiracial',\n",
+       " 'Hispanic or Latino/Latina',\n",
+       " 'Middle Eastern',\n",
+       " 'South Asian',\n",
+       " 'Multiracial',\n",
+       " 'East Asian;South Asian',\n",
+       " 'Biracial',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
+       " 'Black or of African descent;White or of European descent;Biracial',\n",
+       " 'Middle Eastern;White or of European descent',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian',\n",
+       " 'Black or of African descent;White or of European descent',\n",
+       " 'White or of European descent;Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;White or of European descent',\n",
+       " 'East Asian;White or of European descent;Biracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'East Asian;White or of European descent',\n",
+       " 'Hispanic or Latino/Latina;White or of European descent;Biracial',\n",
+       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
+       " 'South Asian;White or of European descent;Multiracial',\n",
+       " 'South Asian;Biracial',\n",
+       " 'Middle Eastern;South Asian',\n",
+       " 'East Asian;South Asian;Multiracial',\n",
+       " 'White or of European descent;Biracial',\n",
+       " 'East Asian;Biracial;Multiracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina',\n",
+       " 'East Asian;Hispanic or Latino/Latina;White or of European descent',\n",
+       " 'East Asian;White or of European descent;Multiracial',\n",
+       " 'South Asian;White or of European descent;Biracial',\n",
+       " 'East Asian;South Asian;White or of European descent;Multiracial',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'South Asian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Black or of African descent;White or of European descent;Multiracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent',\n",
+       " 'Hispanic or Latino/Latina;Biracial',\n",
+       " 'Hispanic or Latino/Latina;South Asian;Multiracial',\n",
+       " 'Black or of African descent;East Asian;South Asian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
+       " 'East Asian;Middle Eastern',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial',\n",
+       " 'Black or of African descent;Multiracial',\n",
+       " 'Middle Eastern;White or of European descent;Biracial',\n",
+       " 'East Asian;Middle Eastern;South Asian',\n",
+       " 'East Asian;Biracial',\n",
+       " 'Middle Eastern;White or of European descent;Multiracial',\n",
+       " 'Black or of African descent;Biracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
+       " 'Middle Eastern;Multiracial',\n",
+       " 'Black or of African descent;Middle Eastern',\n",
+       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
+       " 'Black or of African descent;South Asian;Multiracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina',\n",
+       " 'South Asian;Multiracial',\n",
+       " 'East Asian;South Asian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;Multiracial',\n",
+       " 'South Asian;White or of European descent',\n",
+       " 'Black or of African descent;East Asian',\n",
+       " 'Black or of African descent;Middle Eastern;Multiracial',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
+       " 'Black or of African descent;South Asian;White or of European descent;Multiracial',\n",
+       " 'East Asian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
+       " 'Middle Eastern;Biracial',\n",
+       " 'East Asian;Multiracial',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial',\n",
+       " 'Hispanic or Latino/Latina;Biracial;Multiracial',\n",
+       " 'Black or of African descent;South Asian',\n",
+       " 'Black or of African descent;East Asian;White or of European descent;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;White or of European descent;Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'Black or of African descent;White or of European descent;Biracial;Multiracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina;South Asian',\n",
+       " 'East Asian;Middle Eastern;White or of European descent;Biracial',\n",
+       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian',\n",
+       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Biracial',\n",
+       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;Biracial',\n",
+       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
+       " 'Middle Eastern;South Asian;Multiracial',\n",
+       " 'Black or of African descent;Middle Eastern;White or of European descent',\n",
+       " 'Black or of African descent;Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern',\n",
+       " 'Black or of African descent;Middle Eastern;Biracial',\n",
+       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;Biracial',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;Middle Eastern;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
+       " 'East Asian;Middle Eastern;South Asian;White or of European descent',\n",
+       " 'East Asian;Middle Eastern;White or of European descent;Multiracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
+       " 'Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern;Biracial',\n",
+       " 'East Asian;Middle Eastern;White or of European descent',\n",
+       " 'East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
+       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
+       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern;White or of European descent',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'Black or of African descent;South Asian;White or of European descent',\n",
+       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian;Biracial',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
+       " 'East Asian;South Asian;Biracial',\n",
+       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
+       " 'East Asian;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
+       " 'East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian',\n",
+       " 'Hispanic or Latino/Latina;South Asian;White or of European descent',\n",
+       " 'Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
+       " 'Black or of African descent;East Asian;Biracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina;Biracial',\n",
+       " 'East Asian;South Asian;White or of European descent',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;Middle Eastern;Biracial',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina;Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;South Asian',\n",
+       " 'East Asian;South Asian;Biracial;Multiracial',\n",
+       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;Biracial;Multiracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent;Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;South Asian;White or of European descent;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;South Asian;Biracial',\n",
+       " 'East Asian;Middle Eastern;Multiracial',\n",
+       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Multiracial',\n",
+       " 'Black or of African descent;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'Middle Eastern;South Asian;White or of European descent',\n",
+       " 'Middle Eastern;White or of European descent;Biracial;Multiracial',\n",
+       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
+       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
+       " 'East Asian;Middle Eastern;South Asian;Multiracial',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;Biracial;Multiracial',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;South Asian;White or of European descent;Biracial;Multiracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina;Multiracial',\n",
+       " 'Black or of African descent;East Asian;Middle Eastern;White or of European descent;Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent;Biracial',\n",
+       " 'Black or of African descent;Middle Eastern;South Asian',\n",
+       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;Biracial;Multiracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
+       " 'South Asian;Biracial;Multiracial',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;South Asian',\n",
+       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Multiracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern;South Asian',\n",
+       " 'Hispanic or Latino/Latina;Middle Eastern;Multiracial',\n",
+       " 'Black or of African descent;East Asian;Multiracial',\n",
+       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
+       " 'East Asian;Middle Eastern;South Asian;White or of European descent;Multiracial',\n",
+       " 'Black or of African descent;East Asian;South Asian',\n",
+       " 'Black or of African descent;Hispanic or Latino/Latina;Biracial;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Biracial',\n",
+       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Black or of African descent;East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
+       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
+       " 'Black or of African descent;South Asian;Biracial',\n",
+       " 'East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
+       " 'East Asian;South Asian;White or of European descent;Biracial',\n",
+       " 'Black or of African descent;East Asian;Middle Eastern;South Asian;Multiracial']"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#here, you can see that we have long list of values. lets refactor them\n",
+    "ethnicity_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "173"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(ethnicity_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#refactoring long list of values into categories.\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Biracial', na=False), 'Ethnicity'] = 'Biracial'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Black or of African descent', na=False), 'Ethnicity'] = 'Black or of African descent'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('East Asian', na=False), 'Ethnicity'] = 'East Asian'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Hispanic or Latino', na=False), 'Ethnicity'] = 'Hispanic or Latino'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Indigenous', na=False), 'Ethnicity'] = 'Indigenous'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Middle Eastern', na=False), 'Ethnicity'] = 'Middle Eastern'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('South Asian', na=False), 'Ethnicity'] = 'South Asian'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('White or of European descent', na=False), 'Ethnicity'] = 'White or of European descent'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Multiracial', na=False), 'Ethnicity'] = 'Multiracial'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Native American', na=False), 'Ethnicity'] = 'Native American'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7804"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "White or of European descent    47587\n",
+       "South Asian                      7417\n",
+       "Hispanic or Latino               4901\n",
+       "East Asian                       3698\n",
+       "Middle Eastern                   3057\n",
+       "Black or of African descent      2360\n",
+       "Multiracial                       572\n",
+       "Native American                   322\n",
+       "Biracial                          276\n",
+       "Name: Ethnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Ethnicity']=survey_df_2019.groupby(['Country'])['Ethnicity'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "White or of European descent    50883\n",
+       "South Asian                     10061\n",
+       "Hispanic or Latino               5204\n",
+       "East Asian                       4391\n",
+       "Middle Eastern                   3596\n",
+       "Black or of African descent      2570\n",
+       "Multiracial                       632\n",
+       "Native American                   355\n",
+       "Biracial                          302\n",
+       "Name: Ethnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dependents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "No     46457\n",
+       "Yes    28918\n",
+       "Name: Dependents, dtype: int64"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019[\"Dependents\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2619"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019[\"Dependents\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Lets consider that people who didnt respond has no dependents for the purpose of analysis\n",
+    "survey_df_2019[\"Dependents\"].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019[\"Dependents\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "No     48085\n",
+       "Yes    29909\n",
+       "Name: Dependents, dtype: int64"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019[\"Dependents\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DevType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5025"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Developer, full-stack                                                                                           7636\n",
+       "Developer, back-end                                                                                             4387\n",
+       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  2216\n",
+       "Developer, front-end                                                                                            1985\n",
+       "Developer, mobile                                                                                               1934\n",
+       "Developer, back-end;Developer, full-stack                                                                       1886\n",
+       "Student                                                                                                         1289\n",
+       "Developer, front-end;Developer, full-stack                                                                       940\n",
+       "Developer, desktop or enterprise applications                                                                    900\n",
+       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     815\n",
+       "Name: DevType, dtype: int64"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['DevType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['DevType'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Developer, full-stack                                             8147\n",
+       "Developer, back-end                                               4680\n",
+       "Developer, back-end;Developer, front-end;Developer, full-stack    2365\n",
+       "Developer, front-end                                              2129\n",
+       "Developer, mobile                                                 2086\n",
+       "Name: DevType, dtype: int64"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['DevType'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageWorkedWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "656"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HTML/CSS;JavaScript;PHP;SQL              1345\n",
+       "C#;HTML/CSS;JavaScript;SQL               1282\n",
+       "HTML/CSS;JavaScript                      1098\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript     908\n",
+       "HTML/CSS;JavaScript;PHP                   821\n",
+       "Java                                      757\n",
+       "HTML/CSS;JavaScript;TypeScript            644\n",
+       "Python                                    634\n",
+       "HTML/CSS;Java;JavaScript;SQL              596\n",
+       "C#                                        484\n",
+       "Name: LanguageWorkedWith, dtype: int64"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HTML/CSS;JavaScript;PHP;SQL              1366\n",
+       "C#;HTML/CSS;JavaScript;SQL               1288\n",
+       "HTML/CSS;JavaScript                      1108\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript     914\n",
+       "HTML/CSS;JavaScript;PHP                   831\n",
+       "Java                                      765\n",
+       "HTML/CSS;JavaScript;TypeScript            650\n",
+       "Python                                    640\n",
+       "HTML/CSS;Java;JavaScript;SQL              600\n",
+       "C#                                        489\n",
+       "Name: LanguageWorkedWith, dtype: int64"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CompetenceLevel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A little above average    29693\n",
+       "Average                   15532\n",
+       "Far above average         13840\n",
+       "A little below average     4837\n",
+       "Far below average          1322\n",
+       "Name: CompetenceLevel, dtype: int64"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CompetenceLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12770"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CompetenceLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assign the null values based on forward fill.\n",
+    "survey_df_2019['CompetenceLevel'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CompetenceLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A little above average    35394\n",
+       "Average                   18436\n",
+       "Far above average         16821\n",
+       "A little below average     5739\n",
+       "Far below average          1604\n",
+       "Name: CompetenceLevel, dtype: int64"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CompetenceLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Current Job Satisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Slightly satisfied                    22123\n",
+       "Very satisfied                        20452\n",
+       "Slightly dissatisfied                  9751\n",
+       "Neither satisfied nor dissatisfied     7547\n",
+       "Very dissatisfied                      4283\n",
+       "Name: CurrentJobSatis, dtype: int64"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13838"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CurrentJobSatis'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assign the null values based on forward fill.\n",
+    "survey_df_2019['CurrentJobSatis'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CurrentJobSatis'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Slightly satisfied                    26780\n",
+       "Very satisfied                        24873\n",
+       "Slightly dissatisfied                 12043\n",
+       "Neither satisfied nor dissatisfied     9111\n",
+       "Very dissatisfied                      5187\n",
+       "Name: CurrentJobSatis, dtype: int64"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageDesireNextYear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Python                                   1003\n",
+       "HTML/CSS;JavaScript                       624\n",
+       "HTML/CSS;JavaScript;TypeScript            569\n",
+       "C#                                        533\n",
+       "C#;HTML/CSS;JavaScript;SQL                525\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript     515\n",
+       "HTML/CSS;JavaScript;PHP;SQL               472\n",
+       "Java                                      457\n",
+       "Go                                        373\n",
+       "HTML/CSS;JavaScript;Python                354\n",
+       "Swift                                     348\n",
+       "Kotlin                                    335\n",
+       "HTML/CSS;JavaScript;PHP                   326\n",
+       "C++;Python                                324\n",
+       "C#;SQL                                    309\n",
+       "JavaScript                                307\n",
+       "C++                                       306\n",
+       "C#;HTML/CSS;JavaScript;TypeScript         297\n",
+       "Java;Kotlin                               280\n",
+       "JavaScript;Python                         275\n",
+       "Name: LanguageDesireNextYear, dtype: int64"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3424"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assign the null values based on forward fill.\n",
+    "survey_df_2019['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Python                                   1054\n",
+       "HTML/CSS;JavaScript                       656\n",
+       "HTML/CSS;JavaScript;TypeScript            597\n",
+       "C#                                        557\n",
+       "C#;HTML/CSS;JavaScript;SQL                553\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript     533\n",
+       "HTML/CSS;JavaScript;PHP;SQL               493\n",
+       "Java                                      484\n",
+       "Go                                        397\n",
+       "HTML/CSS;JavaScript;Python                370\n",
+       "Swift                                     360\n",
+       "Kotlin                                    360\n",
+       "HTML/CSS;JavaScript;PHP                   347\n",
+       "C++;Python                                336\n",
+       "C#;SQL                                    320\n",
+       "C++                                       319\n",
+       "JavaScript                                312\n",
+       "C#;HTML/CSS;JavaScript;TypeScript         305\n",
+       "Java;Kotlin                               298\n",
+       "JavaScript;Python                         289\n",
+       "Name: LanguageDesireNextYear, dtype: int64"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## YearsCodePro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method IndexOpsMixin.value_counts of 2                       1\n",
+       "3        Less than 1 year\n",
+       "4                       9\n",
+       "5                       3\n",
+       "6                       4\n",
+       "               ...       \n",
+       "88870                 NaN\n",
+       "88871                   5\n",
+       "88872                   3\n",
+       "88876                  15\n",
+       "88882                   3\n",
+       "Name: YearsCodePro, Length: 77994, dtype: object>"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].value_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#changing the dtype to float\n",
+    "survey_df_2019['YearsCodePro'] = survey_df_2019['YearsCodePro'].apply(pd.to_numeric, errors='coerce')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0     7243\n",
+       "3.0     7164\n",
+       "5.0     5855\n",
+       "4.0     5764\n",
+       "6.0     4133\n",
+       "1.0     3995\n",
+       "10.0    3934\n",
+       "7.0     3374\n",
+       "8.0     3166\n",
+       "12.0    2008\n",
+       "Name: YearsCodePro, dtype: int64"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14639"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['YearsCodePro'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019.dropna(subset=['YearsCodePro'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0     8853\n",
+       "3.0     8843\n",
+       "5.0     7186\n",
+       "4.0     7124\n",
+       "6.0     5103\n",
+       "1.0     4925\n",
+       "10.0    4830\n",
+       "7.0     4146\n",
+       "8.0     3910\n",
+       "12.0    2487\n",
+       "Name: YearsCodePro, dtype: int64"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "United States         18335\n",
+       "India                  7276\n",
+       "Germany                5316\n",
+       "United Kingdom         5130\n",
+       "Canada                 2976\n",
+       "France                 2225\n",
+       "Brazil                 1860\n",
+       "Poland                 1773\n",
+       "Netherlands            1687\n",
+       "Australia              1657\n",
+       "Russian Federation     1551\n",
+       "Spain                  1477\n",
+       "Italy                  1451\n",
+       "Sweden                 1165\n",
+       "Switzerland             884\n",
+       "Name: Country, dtype: int64"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Country'].value_counts().nlargest(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Country'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "United States         18335\n",
+       "India                  7276\n",
+       "Germany                5316\n",
+       "United Kingdom         5130\n",
+       "Canada                 2976\n",
+       "France                 2225\n",
+       "Brazil                 1860\n",
+       "Poland                 1773\n",
+       "Netherlands            1687\n",
+       "Australia              1657\n",
+       "Russian Federation     1551\n",
+       "Spain                  1477\n",
+       "Italy                  1451\n",
+       "Sweden                 1165\n",
+       "Switzerland             884\n",
+       "Name: Country, dtype: int64"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Country'].value_counts().nlargest(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SalaryUSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2000000.0    667\n",
+       "1000000.0    529\n",
+       "120000.0     475\n",
+       "100000.0     450\n",
+       "150000.0     399\n",
+       "Name: SalaryUSD, dtype: int64"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24805"
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['SalaryUSD'] = survey_df_2019.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3537"
+      ]
+     },
+     "execution_count": 224,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2000000.0    669\n",
+       "1000000.0    547\n",
+       "150000.0     494\n",
+       "120000.0     476\n",
+       "100000.0     450\n",
+       "Name: SalaryUSD, dtype: int64"
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "survey_df_2019['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_mean_salary = survey_df_2019.groupby('Country')['SalaryUSD'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "Liechtenstein        811188.000000\n",
+       "San Marino           301788.000000\n",
+       "Ireland              247051.427005\n",
+       "Swaziland            242607.500000\n",
+       "United States        240269.159270\n",
+       "Timor-Leste          229500.000000\n",
+       "Qatar                203892.571429\n",
+       "Republic of Korea    174593.739130\n",
+       "Norway               173173.193026\n",
+       "Andorra              171862.000000\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "country_mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019.dropna(subset=['SalaryUSD'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaned Dataset:2019_Survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                       0\n",
+       "JobSatisfaction           0\n",
+       "SalaryUSD                 0\n",
+       "Country                   0\n",
+       "Dependents                0\n",
+       "EdLevel                   0\n",
+       "Employment                0\n",
+       "Ethnicity                 0\n",
+       "Gender                    0\n",
+       "Hobbyist                  0\n",
+       "CompetenceLevel           0\n",
+       "CurrentJobSatis           0\n",
+       "JobStatus                 0\n",
+       "LanguageDesireNextYear    0\n",
+       "LanguageWorkedWith        0\n",
+       "Profession                0\n",
+       "UndergradMajor            0\n",
+       "YearsCodePro              0\n",
+       "DevType                   0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#handle all the null value\n",
+    "survey_df_2019.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#resetting the index values\n",
+    "survey_df_2019 = survey_df_2019.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows before cleaning the data is 88883\n",
+      "Number of rows after cleaning the data is 74457\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_df_2019 = survey_df_2019[survey_df_2019.notnull()]\n",
+    "\n",
+    "print(f\"Number of rows before cleaning the data is {survey_main_df.shape[0]}\")\n",
+    "print(f\"Number of rows after cleaning the data is {cleaned_df_2019.shape[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_df_2019['Age']=cleaned_df_2019['Age'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>CompetenceLevel</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "      <th>JobStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>DevType</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>8820.0</td>\n",
+       "      <td>Thailand</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>East Asian</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Elixir;HTML/CSS</td>\n",
+       "      <td>HTML/CSS</td>\n",
+       "      <td>Non developer</td>\n",
+       "      <td>Web Design/Dev</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>61000.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>No</td>\n",
+       "      <td>A little below average</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>C;C#;JavaScript;SQL</td>\n",
+       "      <td>C;C++;C#;Python;SQL</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Developer, full-stack</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>30</td>\n",
+       "      <td>Very dissatisfied</td>\n",
+       "      <td>33184.8</td>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>A little above average</td>\n",
+       "      <td>Slightly dissatisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>HTML/CSS;Java;JavaScript;SQL;WebAssembly</td>\n",
+       "      <td>C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>Academic researcher;Developer, desktop or ente...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>366420.0</td>\n",
+       "      <td>Canada</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>East Asian</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>A little above average</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Python;Scala;SQL</td>\n",
+       "      <td>Java;R;SQL</td>\n",
+       "      <td>Non developer</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Data or business analyst;Data scientist or mac...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>42</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>36000.0</td>\n",
+       "      <td>Ukraine</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>Neither satisfied nor dissatisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>HTML/CSS;JavaScript</td>\n",
+       "      <td>HTML/CSS;JavaScript</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Designer;Developer, front-end</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age     JobSatisfaction  SalaryUSD        Country Dependents    EdLevel  \\\n",
+       "0   28  Slightly satisfied     8820.0       Thailand        Yes  Bachelors   \n",
+       "1   22      Very satisfied    61000.0  United States         No  Bachelors   \n",
+       "2   30   Very dissatisfied    33184.8        Ukraine         No  Bachelors   \n",
+       "3   28      Very satisfied   366420.0         Canada         No  Bachelors   \n",
+       "4   42  Slightly satisfied    36000.0        Ukraine        Yes  Bachelors   \n",
+       "\n",
+       "      Employment                     Ethnicity Gender Hobbyist  \\\n",
+       "0      Full-time                    East Asian    Man      Yes   \n",
+       "1      Full-time  White or of European descent    Man       No   \n",
+       "2      Full-time  White or of European descent    Man      Yes   \n",
+       "3      Full-time                    East Asian    Man      Yes   \n",
+       "4  Self-employed  White or of European descent    Man       No   \n",
+       "\n",
+       "          CompetenceLevel                     CurrentJobSatis    JobStatus  \\\n",
+       "0                 Average                  Slightly satisfied  Not seeking   \n",
+       "1  A little below average                  Slightly satisfied  Not seeking   \n",
+       "2  A little above average               Slightly dissatisfied  Not seeking   \n",
+       "3  A little above average                  Slightly satisfied  Not seeking   \n",
+       "4                 Average  Neither satisfied nor dissatisfied  Not seeking   \n",
+       "\n",
+       "                     LanguageDesireNextYear  \\\n",
+       "0                           Elixir;HTML/CSS   \n",
+       "1                       C;C#;JavaScript;SQL   \n",
+       "2  HTML/CSS;Java;JavaScript;SQL;WebAssembly   \n",
+       "3                          Python;Scala;SQL   \n",
+       "4                       HTML/CSS;JavaScript   \n",
+       "\n",
+       "                            LanguageWorkedWith     Profession  \\\n",
+       "0                                     HTML/CSS  Non developer   \n",
+       "1                          C;C++;C#;Python;SQL      Developer   \n",
+       "2  C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA      Developer   \n",
+       "3                                   Java;R;SQL  Non developer   \n",
+       "4                          HTML/CSS;JavaScript      Developer   \n",
+       "\n",
+       "     UndergradMajor  YearsCodePro  \\\n",
+       "0    Web Design/Dev           1.0   \n",
+       "1  Computer Science           1.0   \n",
+       "2  Computer Science           9.0   \n",
+       "3         Math/Stat           3.0   \n",
+       "4       Engineering           4.0   \n",
+       "\n",
+       "                                             DevType  \n",
+       "0  Designer;Developer, back-end;Developer, front-...  \n",
+       "1                              Developer, full-stack  \n",
+       "2  Academic researcher;Developer, desktop or ente...  \n",
+       "3  Data or business analyst;Data scientist or mac...  \n",
+       "4                      Designer;Developer, front-end  "
+      ]
+     },
+     "execution_count": 233,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "cleaned_df_2019.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## After Cleaning  Dataset 2019"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  1414683\n",
+      "Total missing :  0\n",
+      "Missing Percentage:  0.0 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = survey_df_2019.isnull().sum() #number of missing\n",
+    "total_cells = np.product(survey_df_2019.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow Survey Analysis 2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2020.csv')\n",
+    "#df2020.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#drop unnecessary columns\n",
+    "drop_cols = [  'Age1stCode', 'CompFreq', 'CompTotal', 'CurrencyDesc', 'CurrencySymbol', 'NEWJobHunt','NEWJobHuntResearch', 'NEWLearn', \n",
+    "             'NEWOffTopic', 'NEWOnboardGood', 'NEWOtherComms', 'NEWOvertime', 'NEWPurchaseResearch', \n",
+    "             'NEWPurpleLink', 'NEWSOSites', 'NEWStuck', 'OpSys', 'OrgSize', 'PlatformDesireNextYear', 'PlatformWorkedWith',\n",
+    "             'PurchaseWhat', 'Respondent', 'SOAccount', 'SOComm', 'SOPartFreq', 'SOVisitFreq', 'Sexuality', 'SurveyEase', \n",
+    "             'SurveyLength', 'Trans', 'WebframeDesireNextYear', 'WebframeWorkedWith', 'WelcomeChange', 'WorkWeekHrs', 'YearsCode']\n",
+    "df.drop(drop_cols, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Selecting only the required columns for analysis\n",
+    "cols =['Age','Gender', 'ConvertedComp', 'Country', 'DevType', 'Hobbyist', 'EdLevel', 'Employment', \n",
+    "       'Ethnicity', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n",
+    "        'UndergradMajor', 'YearsCodePro']\n",
+    "df2020 = df[cols]\n",
+    "#df2020.head()\n",
+    "#df2020.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#changing the name of columns for easier understanding\n",
+    "# 'MainBranch': 'Profession'\n",
+    "# 'ConvertedComp': 'SalaryUSD'\n",
+    "# 'JobSat' : 'CurrentJobSatis'\n",
+    "# 'JobSeek' : 'JobStatus'\n",
+    "\n",
+    "df2020.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD', \n",
+    "                                'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, \n",
+    "                      inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                       19015\n",
+      "Gender                    13904\n",
+      "SalaryUSD                 29705\n",
+      "Country                     389\n",
+      "DevType                   15091\n",
+      "Hobbyist                     45\n",
+      "EdLevel                    7030\n",
+      "Employment                  607\n",
+      "Ethnicity                 18513\n",
+      "CurrentJobSatis           19267\n",
+      "JobStatus                 12734\n",
+      "LanguageDesireNextYear    10348\n",
+      "LanguageWorkedWith         7083\n",
+      "Profession                  299\n",
+      "UndergradMajor            13466\n",
+      "YearsCodePro              18112\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2020.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Validation - Total Cells vs Missing %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total cell:  1031376\n",
+      "Total missing values:  371516\n",
+      "Missing:  36.02139278013062 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Finding % of missing data\n",
+    "missing_count = df.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total cell: ', total_cells)\n",
+    "print('Total missing values: ', total_missing)\n",
+    "print('Missing: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13904"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Gender'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man                                                            46013\n",
+       "Man;Non-binary, genderqueer, or gender non-conforming            121\n",
+       "Non-binary, genderqueer, or gender non-conforming                385\n",
+       "Woman                                                           3844\n",
+       "Woman;Man                                                         76\n",
+       "Woman;Man;Non-binary, genderqueer, or gender non-conforming       26\n",
+       "Woman;Non-binary, genderqueer, or gender non-conforming           92\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Counting number of each gender\n",
+    "df2020.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n",
+    "df2020['Gender'] = df['Gender'].fillna('Non-binary') \n",
+    "\n",
+    "#Grouping genders into 3 groups Man, Womanand Non-binary\n",
+    "df2020['Gender'].replace('Man;Non-binary, genderqueer, or gender non-conforming', 'Man', inplace =True)\n",
+    "df2020['Gender'].replace('Woman;Non-binary, genderqueer, or gender non-conforming', 'Woman', inplace =True)\n",
+    "df2020['Gender'].replace('Woman;Man;Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)\n",
+    "df2020['Gender'].replace('Woman;Man', 'Non-binary', inplace =True)\n",
+    "df2020['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man           46134\n",
+       "Non-binary    14391\n",
+       "Woman          3936\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 244,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Counting number of each gender after\n",
+    "df2020.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df shape after clean Gender:  (64461, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print('df shape after clean Gender: ', df2020.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19015"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plottig boxplot to check outliers\n",
+    "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n",
+    "plt.title(\"Before cleaning Age's outliers from genders\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Cleaning Age's outliers from each gender)\n",
+    "df2020 = df2020[(df['Age'] >= 15) & (df2020['Age'] <= 60)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plottig boxplot to check outliers after cleaning some outliers\n",
+    "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n",
+    "plt.title(\"After cleaning Age's outliers from genders\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fill Age's null values with mean of each gender\n",
+    "means = df2020.groupby('Gender')['Age'].transform('mean')\n",
+    "df2020['Age'] = df2020['Age'].fillna(means)\n",
+    "\n",
+    "#convert from float to int\n",
+    "df2020['Age'] = df2020['Age'].apply(str).str[:2]\n",
+    "df2020['Age'] = df2020['Age'].apply(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df shape after clean Age:  (44709, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#df before 64461\n",
+    "print('df shape after clean Age: ', df2020.shape) #no. of Ages' outliners = 64461-44709=19752 (30.6%)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EdLevel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "933"
+      ]
+     },
+     "execution_count": 252,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['EdLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelor’s degree (B.A., B.S., B.Eng., etc.)                                          20290\n",
+       "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)                                       10000\n",
+       "Some college/university study without earning a degree                                 5699\n",
+       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     3676\n",
+       "Associate degree (A.A., A.S., etc.)                                                    1455\n",
+       "Other doctoral degree (Ph.D., Ed.D., etc.)                                             1256\n",
+       "Primary/elementary school                                                               590\n",
+       "Professional degree (JD, MD, etc.)                                                      578\n",
+       "I never completed any formal education                                                  232\n",
+       "Name: EdLevel, dtype: int64"
+      ]
+     },
+     "execution_count": 253,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring EdLevel\n",
+    "def refactor_ed(df):\n",
+    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
+    "    conditions_ed = [(df['EdLevel'] == 'Associate degree (A.A., A.S., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Bachelor’s degree (B.A., B.S., B.Eng., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)'),\n",
+    "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
+    "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D., Ed.D., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
+    "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
+    "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
+    "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
+    "    \n",
+    "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
+    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
+    "    return df\n",
+    "\n",
+    "# applying function to subsets\n",
+    "df2020 = refactor_ed(df2020)\n",
+    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
+    "df2020['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelors       21223\n",
+       "No Degree       10197\n",
+       "Masters         10000\n",
+       "Associate        1455\n",
+       "Doctorate        1256\n",
+       "Professional      578\n",
+       "Name: EdLevel, dtype: int64"
+      ]
+     },
+     "execution_count": 255,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JobSat (CurrentJobSatis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8690"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Very satisfied                        11751\n",
+       "Slightly satisfied                    11198\n",
+       "Slightly dissatisfied                  5790\n",
+       "Neither satisfied nor dissatisfied     4373\n",
+       "Very dissatisfied                      2907\n",
+       "Name: CurrentJobSatis, dtype: int64"
+      ]
+     },
+     "execution_count": 257,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020['CurrentJobSatis'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Very satisfied                        14628\n",
+       "Slightly satisfied                    13834\n",
+       "Slightly dissatisfied                  7192\n",
+       "Neither satisfied nor dissatisfied     5446\n",
+       "Very dissatisfied                      3609\n",
+       "Name: CurrentJobSatis, dtype: int64"
+      ]
+     },
+     "execution_count": 259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JobSeek (JobStatus)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2153"
+      ]
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['JobStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobStatus\n",
+       "I am actively looking for a job                                  6980\n",
+       "I am not interested in new job opportunities                    10919\n",
+       "I’m not actively looking, but I am open to new opportunities    24657\n",
+       "Name: JobStatus, dtype: int64"
+      ]
+     },
+     "execution_count": 261,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('JobStatus')['JobStatus'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020['JobStatus'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring JobStatus\n",
+    "#Changing the jobstatus to seeking and non seeking\n",
+    "def refactor_job(df):\n",
+    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
+    "    \n",
+    "    conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n",
+    "                    (df['JobStatus'] == 'I am not interested in new job opportunities')\n",
+    "                    | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
+    "    \n",
+    "    choices_job = ['Seeking', 'Not seeking']\n",
+    "    df['JobSeek'] = np.select(conditions_job, choices_job, default=np.nan)                      \n",
+    "    return df\n",
+    "\n",
+    "df2020 = refactor_job(df2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSeek\n",
+       "Not seeking    37369\n",
+       "Seeking         7340\n",
+       "Name: JobSeek, dtype: int64"
+      ]
+     },
+     "execution_count": 264,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('JobSeek')['JobSeek'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 265,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['JobStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DevType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5954"
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Developer, full-stack                                                                                           3399\n",
+       "Developer, back-end                                                                                             2374\n",
+       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  1838\n",
+       "Developer, back-end;Developer, full-stack                                                                       1216\n",
+       "Developer, front-end                                                                                            1071\n",
+       "Developer, mobile                                                                                                953\n",
+       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     668\n",
+       "Developer, front-end;Developer, full-stack                                                                       667\n",
+       "Developer, back-end;Developer, desktop or enterprise applications                                                528\n",
+       "Developer, back-end;Developer, front-end;Developer, full-stack;Developer, mobile                                 475\n",
+       "Name: DevType, dtype: int64"
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['DevType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020['DevType'] = df2020['DevType'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Developer, full-stack                                                                                           3940\n",
+       "Developer, back-end                                                                                             2721\n",
+       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  2146\n",
+       "Developer, back-end;Developer, full-stack                                                                       1411\n",
+       "Developer, front-end                                                                                            1229\n",
+       "Developer, mobile                                                                                               1074\n",
+       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     779\n",
+       "Developer, front-end;Developer, full-stack                                                                       758\n",
+       "Developer, back-end;Developer, desktop or enterprise applications                                                617\n",
+       "Developer, back-end;Developer, front-end;Developer, full-stack;Developer, mobile                                 532\n",
+       "Name: DevType, dtype: int64"
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['DevType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(64461, 26)"
+      ]
+     },
+     "execution_count": 270,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "      <th>JobStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>JobSeek</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Age, Gender, SalaryUSD, Country, DevType, Hobbyist, EdLevel, Employment, Ethnicity, CurrentJobSatis, JobStatus, LanguageDesireNextYear, LanguageWorkedWith, Profession, UndergradMajor, YearsCodePro, JobSeek]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020[df2020['DevType'].isnull()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ethnicity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4051"
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "White or of European descent                           26552\n",
+       "South Asian                                             3707\n",
+       "Hispanic or Latino/a/x                                  2078\n",
+       "Middle Eastern                                          1417\n",
+       "Southeast Asian                                         1371\n",
+       "East Asian                                              1342\n",
+       "Black or of African descent                             1327\n",
+       "Hispanic or Latino/a/x;White or of European descent      720\n",
+       "Middle Eastern;White or of European descent              344\n",
+       "Multiracial                                              245\n",
+       "Name: Ethnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#count number of each Ethnicity\n",
+    "df2020.groupby('Ethnicity')['Ethnicity'].count()\n",
+    "df2020['Ethnicity'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
+    "df2020.loc[df['Ethnicity'].str.match('Biracial') == True, 'Ethnicity'] = 'Biracial'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Black or of African descent') == True, 'Ethnicity'] = 'Black or of African descent'\n",
+    "df2020.loc[df['Ethnicity'].str.match('East Asian') == True, 'Ethnicity'] = 'East Asian'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Hispanic or Latino') == True, 'Ethnicity'] = 'Hispanic or Latino'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Indigenous') == True, 'Ethnicity'] = 'Indigenous'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Middle Eastern') == True, 'Ethnicity'] = 'Middle Eastern'\n",
+    "df2020.loc[df['Ethnicity'].str.match('South Asian') == True, 'Ethnicity'] = 'South Asian'\n",
+    "df2020.loc[df['Ethnicity'].str.match('White or of European descent') == True, 'Ethnicity'] = 'White or of European descent'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Multiracial') == True, 'Ethnicity'] = 'Multiracial'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "White or of European descent    26848\n",
+       "South Asian                      3783\n",
+       "Hispanic or Latino               3072\n",
+       "Middle Eastern                   1840\n",
+       "East Asian                       1661\n",
+       "Black or of African descent      1633\n",
+       "Southeast Asian                  1371\n",
+       "Multiracial                       249\n",
+       "Biracial                          138\n",
+       "Indigenous                         63\n",
+       "Name: Ethnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 276,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020.groupby('Ethnicity')['Ethnicity'].count() #11 groups of Ethnicity after combining \n",
+    "df2020['Ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df2020['Ethnicity']=df2020.groupby(['Country'])['Ethnicity'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "White or of European descent    28466\n",
+       "South Asian                      5101\n",
+       "Hispanic or Latino               3270\n",
+       "Middle Eastern                   2104\n",
+       "East Asian                       1907\n",
+       "Black or of African descent      1762\n",
+       "Southeast Asian                  1614\n",
+       "Multiracial                       263\n",
+       "Biracial                          151\n",
+       "Indigenous                         71\n",
+       "Name: Ethnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 278,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#count number of each Ethnicity\n",
+    "df2020.groupby('Ethnicity')['Ethnicity'].count()\n",
+    "df2020['Ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                           0\n",
+      "Gender                        0\n",
+      "SalaryUSD                 14358\n",
+      "Country                       0\n",
+      "DevType                       0\n",
+      "Hobbyist                      0\n",
+      "EdLevel                       0\n",
+      "Employment                  118\n",
+      "Ethnicity                     0\n",
+      "CurrentJobSatis               0\n",
+      "JobStatus                     0\n",
+      "LanguageDesireNextYear     2394\n",
+      "LanguageWorkedWith          396\n",
+      "Profession                   77\n",
+      "UndergradMajor             5522\n",
+      "YearsCodePro               8212\n",
+      "JobSeek                       0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(df2020.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageDesireNextYear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2394"
+      ]
+     },
+     "execution_count": 281,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Python                                   773\n",
+       "Rust                                     417\n",
+       "HTML/CSS;JavaScript;TypeScript           405\n",
+       "C#                                       342\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript    339\n",
+       "HTML/CSS;JavaScript                      307\n",
+       "Go                                       300\n",
+       "HTML/CSS;JavaScript;PHP;SQL              229\n",
+       "TypeScript                               227\n",
+       "Java                                     224\n",
+       "Name: LanguageDesireNextYear, dtype: int64"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2020['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)\n",
+    "df2020['LanguageDesireNextYear']=df2020['LanguageDesireNextYear'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Python                                   802\n",
+       "Rust                                     432\n",
+       "HTML/CSS;JavaScript;TypeScript           425\n",
+       "C#                                       377\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript    372\n",
+       "HTML/CSS;JavaScript                      323\n",
+       "Go                                       310\n",
+       "HTML/CSS;JavaScript;PHP;SQL              245\n",
+       "Java                                     238\n",
+       "C#;HTML/CSS;JavaScript;SQL               236\n",
+       "Name: LanguageDesireNextYear, dtype: int64"
+      ]
+     },
+     "execution_count": 284,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 285,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageWorkedWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "396"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HTML/CSS;JavaScript;PHP;SQL              819\n",
+       "C#;HTML/CSS;JavaScript;SQL               669\n",
+       "HTML/CSS;JavaScript                      655\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript    624\n",
+       "HTML/CSS;JavaScript;TypeScript           568\n",
+       "Python                                   449\n",
+       "Java                                     392\n",
+       "HTML/CSS;JavaScript;PHP                  382\n",
+       "HTML/CSS;Java;JavaScript;SQL             301\n",
+       "C#                                       296\n",
+       "Name: LanguageWorkedWith, dtype: int64"
+      ]
+     },
+     "execution_count": 287,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2020['LanguageWorkedWith'].fillna(method='ffill', inplace=True)\n",
+    "df2020['LanguageWorkedWith']=df2020['LanguageWorkedWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HTML/CSS;JavaScript;PHP;SQL              822\n",
+       "C#;HTML/CSS;JavaScript;SQL               670\n",
+       "HTML/CSS;JavaScript                      658\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript    631\n",
+       "HTML/CSS;JavaScript;TypeScript           572\n",
+       "Python                                   450\n",
+       "Java                                     394\n",
+       "HTML/CSS;JavaScript;PHP                  385\n",
+       "HTML/CSS;Java;JavaScript;SQL             306\n",
+       "C#                                       298\n",
+       "Name: LanguageWorkedWith, dtype: int64"
+      ]
+     },
+     "execution_count": 289,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 290,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MainBranch (Profession)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "77"
+      ]
+     },
+     "execution_count": 291,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Profession'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Profession\n",
+       "I am a developer by profession                                                   34037\n",
+       "I am a student who is learning to code                                            4900\n",
+       "I am not primarily a developer, but I write code sometimes as part of my work     3718\n",
+       "I code primarily as a hobby                                                       1301\n",
+       "I used to be a developer by profession, but no longer am                           676\n",
+       "Name: Profession, dtype: int64"
+      ]
+     },
+     "execution_count": 292,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('Profession')['Profession'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020.dropna(subset=['Profession'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Lets refactor column values of Profession column\n",
+    "#refactoring profession column\n",
+    "\n",
+    "def refactor_prof(df):\n",
+    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
+    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
+    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
+    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
+    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
+    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
+    "    \n",
+    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
+    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)          \n",
+    "    return df\n",
+    "\n",
+    "df2020 = refactor_prof(df2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Developer        34037\n",
+       "Student           4900\n",
+       "Non developer     3718\n",
+       "Novoice           1301\n",
+       "Ex-Developer       676\n",
+       "Name: Profession, dtype: int64"
+      ]
+     },
+     "execution_count": 295,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 296,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Profession'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## UndergradMajor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5501"
+      ]
+     },
+     "execution_count": 297,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "A business discipline (such as accounting, finance, marketing, etc.)               1033\n",
+       "A health science (such as nursing, pharmacy, radiology, etc.)                       190\n",
+       "A humanities discipline (such as literature, history, philosophy, etc.)             815\n",
+       "A natural science (such as biology, chemistry, physics, etc.)                      1754\n",
+       "A social science (such as anthropology, psychology, political science, etc.)        733\n",
+       "Another engineering discipline (such as civil, electrical, mechanical, etc.)       3542\n",
+       "Computer science, computer engineering, or software engineering                   24429\n",
+       "Fine arts or performing arts (such as graphic design, music, studio art, etc.)      581\n",
+       "I never declared a major                                                            331\n",
+       "Information systems, information technology, or system administration              3074\n",
+       "Mathematics or statistics                                                          1419\n",
+       "Web development or web design                                                      1230\n",
+       "Name: UndergradMajor, dtype: int64"
+      ]
+     },
+     "execution_count": 298,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('UndergradMajor')['UndergradMajor'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def refactor_major(df):\n",
+    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n",
+    "                        (df['UndergradMajor'] == 'Another engineering discipline (such as civil, electrical, mechanical, etc.)'),\n",
+    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n",
+    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
+    "                        (df['UndergradMajor'] == 'A natural science (such as biology, chemistry, physics, etc.)') \n",
+    "                        |(df['UndergradMajor'] == 'A health science (such as nursing, pharmacy, radiology, etc.)'), \n",
+    "                        (df['UndergradMajor'] == 'Web development or web design'), \n",
+    "                        (df['UndergradMajor'] == 'A business discipline (such as accounting, finance, marketing, etc.)'), \n",
+    "                        (df['UndergradMajor'] == 'A humanities discipline (such as literature, history, philosophy, etc.)')\n",
+    "                        | (df['UndergradMajor'] == 'A social science (such as anthropology, psychology, political science, etc.)')\n",
+    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (such as graphic design, music, studio art, etc.)'),\n",
+    "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
+    "    \n",
+    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
+    "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
+    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
+    "    return df\n",
+    "\n",
+    "df2020 = refactor_major(df2020)\n",
+    "df2020['UndergradMajor'].replace('nan', 'No major', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "Arts and Science     2129\n",
+       "Business             1033\n",
+       "Computer Science    24429\n",
+       "Engineering          3542\n",
+       "Info Systems         3074\n",
+       "Math/Stat            1419\n",
+       "No major             5832\n",
+       "Other Science        1944\n",
+       "Web Design/Dev       1230\n",
+       "Name: UndergradMajor, dtype: int64"
+      ]
+     },
+     "execution_count": 300,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('UndergradMajor')['UndergradMajor'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Employment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "111"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Employed full-time                                      32474\n",
+       "Employed part-time                                       1489\n",
+       "Independent contractor, freelancer, or self-employed     3859\n",
+       "Not employed, and not looking for work                    181\n",
+       "Not employed, but looking for work                       1500\n",
+       "Retired                                                    32\n",
+       "Student                                                  4986\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020.groupby('Employment')['Employment'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020.dropna(subset=['Employment'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring Employment\n",
+    "df2020['Employment'].replace('Employed full-time', 'Full-time', inplace =True)\n",
+    "df2020['Employment'].replace('Employed part-time', 'Part-time',inplace =True)\n",
+    "df2020['Employment'].replace('Independent contractor, freelancer, or self-employed', 'Self-employed', inplace =True)\n",
+    "df2020['Employment'].replace('Not employed, but looking for work', 'Not employed', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Full-time                                 32474\n",
+       "Not employed                               1500\n",
+       "Not employed, and not looking for work      181\n",
+       "Part-time                                  1489\n",
+       "Retired                                      32\n",
+       "Self-employed                              3859\n",
+       "Student                                    4986\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 306,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('Employment')['Employment'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 307,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 308,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "Afghanistan                              22\n",
+       "Albania                                  29\n",
+       "Algeria                                  47\n",
+       "Andorra                                   3\n",
+       "Angola                                    2\n",
+       "                                       ... \n",
+       "Venezuela, Bolivarian Republic of...     53\n",
+       "Viet Nam                                159\n",
+       "Yemen                                     2\n",
+       "Zambia                                   10\n",
+       "Zimbabwe                                 19\n",
+       "Name: Country, Length: 170, dtype: int64"
+      ]
+     },
+     "execution_count": 309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020.groupby('Country')['Country'].count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## YearsCodePro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8123"
+      ]
+     },
+     "execution_count": 310,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                         int64\n",
+       "Gender                     object\n",
+       "SalaryUSD                 float64\n",
+       "Country                    object\n",
+       "DevType                    object\n",
+       "Hobbyist                   object\n",
+       "EdLevel                    object\n",
+       "Employment                 object\n",
+       "Ethnicity                  object\n",
+       "CurrentJobSatis            object\n",
+       "JobStatus                  object\n",
+       "LanguageDesireNextYear     object\n",
+       "LanguageWorkedWith         object\n",
+       "Profession                 object\n",
+       "UndergradMajor             object\n",
+       "YearsCodePro               object\n",
+       "JobSeek                    object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 311,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#convert YearsCodePro data type from obj to int\n",
+    "df2020[\"YearsCodePro\"]=pd.to_numeric(df2020[\"YearsCodePro\"],errors='coerce')\n",
+    "\n",
+    "#fill YearsCodePro's null values with mean\n",
+    "means = df2020['YearsCodePro'].mean() #means 8.673142457693764\n",
+    "df2020['YearsCodePro'] = df2020['YearsCodePro'].fillna(means)\n",
+    "df2020['YearsCodePro'] = df2020['YearsCodePro'].round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hobbyist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 314,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Hobbyist'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Hobbyist\n",
+       "No      9583\n",
+       "Yes    34938\n",
+       "Name: Hobbyist, dtype: int64"
+      ]
+     },
+     "execution_count": 315,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('Hobbyist')['Hobbyist'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                           0\n",
+      "Gender                        0\n",
+      "SalaryUSD                 14202\n",
+      "Country                       0\n",
+      "DevType                       0\n",
+      "Hobbyist                      0\n",
+      "EdLevel                       0\n",
+      "Employment                    0\n",
+      "Ethnicity                     0\n",
+      "CurrentJobSatis               0\n",
+      "JobStatus                     0\n",
+      "LanguageDesireNextYear        0\n",
+      "LanguageWorkedWith            0\n",
+      "Profession                    0\n",
+      "UndergradMajor                0\n",
+      "YearsCodePro                  0\n",
+      "JobSeek                       0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2020.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ConvertedComp (SalaryUSD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14202"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "120000.0     284\n",
+       "100000.0     254\n",
+       "64859.0      224\n",
+       "150000.0     221\n",
+       "2000000.0    216\n",
+       "Name: SalaryUSD, dtype: int64"
+      ]
+     },
+     "execution_count": 318,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2020['SalaryUSD'] = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))\n",
+    "\n",
+    "means = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform('mean')\n",
+    "df2020['SalaryUSD'] = df2020['SalaryUSD'].fillna(means)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age  EdLevel       Country      \n",
+       "40   Professional  United States    2000000.0\n",
+       "37   Masters       Nomadic          1320000.0\n",
+       "41   Masters       Israel           1200000.0\n",
+       "47   Professional  United States    1047500.0\n",
+       "33   Doctorate     Italy            1018376.5\n",
+       "15   Bachelors     Germany          1000000.0\n",
+       "20   Associate     Australia        1000000.0\n",
+       "25   Bachelors     Paraguay         1000000.0\n",
+       "28   Doctorate     Singapore        1000000.0\n",
+       "32   No Degree     Ireland          1000000.0\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 320,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "120000.0     286\n",
+       "100000.0     255\n",
+       "64859.0      239\n",
+       "150000.0     227\n",
+       "1000000.0    219\n",
+       "Name: SalaryUSD, dtype: int64"
+      ]
+     },
+     "execution_count": 321,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2952"
+      ]
+     },
+     "execution_count": 322,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['SalaryUSD'].isnull().sum() #2952 out of 64461 -> 4.6%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020.dropna(subset=['SalaryUSD'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 324,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaned Dataset:2020_Survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                       0\n",
+      "Gender                    0\n",
+      "SalaryUSD                 0\n",
+      "Country                   0\n",
+      "DevType                   0\n",
+      "Hobbyist                  0\n",
+      "EdLevel                   0\n",
+      "Employment                0\n",
+      "Ethnicity                 0\n",
+      "CurrentJobSatis           0\n",
+      "JobStatus                 0\n",
+      "LanguageDesireNextYear    0\n",
+      "LanguageWorkedWith        0\n",
+      "Profession                0\n",
+      "UndergradMajor            0\n",
+      "YearsCodePro              0\n",
+      "JobSeek                   0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2020.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#resetting the index values\n",
+    "df2020 = df2020.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "      <th>JobStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>JobSeek</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>214247.736842</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Slightly dissatisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to new...</td>\n",
+       "      <td>Java;Ruby;Scala</td>\n",
+       "      <td>HTML/CSS;Ruby;SQL</td>\n",
+       "      <td>Ex-Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>Not seeking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>36</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>116000.000000</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Slightly dissatisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to new...</td>\n",
+       "      <td>JavaScript</td>\n",
+       "      <td>Python;SQL</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>Not seeking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>32315.000000</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Database administrator;Developer, full-stack;D...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Masters</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to new...</td>\n",
+       "      <td>HTML/CSS;Java;JavaScript;Python;R;SQL</td>\n",
+       "      <td>HTML/CSS;Java;JavaScript;Python;SQL</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Not seeking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>40070.000000</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Slightly dissatisfied</td>\n",
+       "      <td>I am actively looking for a job</td>\n",
+       "      <td>Go;JavaScript;Swift;TypeScript</td>\n",
+       "      <td>C#;JavaScript;Swift</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Seeking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>14268.000000</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Designer;Developer, front-end</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Very dissatisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to new...</td>\n",
+       "      <td>HTML/CSS;JavaScript</td>\n",
+       "      <td>HTML/CSS;JavaScript</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>Not seeking</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age Gender      SalaryUSD         Country  \\\n",
+       "0   31    Man  214247.736842   United States   \n",
+       "1   36    Man  116000.000000   United States   \n",
+       "2   22    Man   32315.000000  United Kingdom   \n",
+       "3   23    Man   40070.000000  United Kingdom   \n",
+       "4   49    Man   14268.000000           Spain   \n",
+       "\n",
+       "                                             DevType Hobbyist    EdLevel  \\\n",
+       "0  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
+       "1  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
+       "2  Database administrator;Developer, full-stack;D...      Yes    Masters   \n",
+       "3  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
+       "4                      Designer;Developer, front-end       No  No Degree   \n",
+       "\n",
+       "  Employment                     Ethnicity        CurrentJobSatis  \\\n",
+       "0  Full-time  White or of European descent  Slightly dissatisfied   \n",
+       "1  Full-time  White or of European descent  Slightly dissatisfied   \n",
+       "2  Full-time  White or of European descent         Very satisfied   \n",
+       "3  Full-time  White or of European descent  Slightly dissatisfied   \n",
+       "4  Full-time  White or of European descent      Very dissatisfied   \n",
+       "\n",
+       "                                           JobStatus  \\\n",
+       "0  I’m not actively looking, but I am open to new...   \n",
+       "1  I’m not actively looking, but I am open to new...   \n",
+       "2  I’m not actively looking, but I am open to new...   \n",
+       "3                    I am actively looking for a job   \n",
+       "4  I’m not actively looking, but I am open to new...   \n",
+       "\n",
+       "                  LanguageDesireNextYear                   LanguageWorkedWith  \\\n",
+       "0                        Java;Ruby;Scala                    HTML/CSS;Ruby;SQL   \n",
+       "1                             JavaScript                           Python;SQL   \n",
+       "2  HTML/CSS;Java;JavaScript;Python;R;SQL  HTML/CSS;Java;JavaScript;Python;SQL   \n",
+       "3         Go;JavaScript;Swift;TypeScript                  C#;JavaScript;Swift   \n",
+       "4                    HTML/CSS;JavaScript                  HTML/CSS;JavaScript   \n",
+       "\n",
+       "     Profession    UndergradMajor  YearsCodePro      JobSeek  \n",
+       "0  Ex-Developer  Computer Science           8.0  Not seeking  \n",
+       "1     Developer  Computer Science          13.0  Not seeking  \n",
+       "2     Developer         Math/Stat           4.0  Not seeking  \n",
+       "3     Developer  Computer Science           2.0      Seeking  \n",
+       "4     Developer         Math/Stat           7.0  Not seeking  "
+      ]
+     },
+     "execution_count": 327,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 41569 entries, 0 to 41568\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                  Non-Null Count  Dtype  \n",
+      "---  ------                  --------------  -----  \n",
+      " 0   Age                     41569 non-null  int64  \n",
+      " 1   Gender                  41569 non-null  object \n",
+      " 2   SalaryUSD               41569 non-null  float64\n",
+      " 3   Country                 41569 non-null  object \n",
+      " 4   DevType                 41569 non-null  object \n",
+      " 5   Hobbyist                41569 non-null  object \n",
+      " 6   EdLevel                 41569 non-null  object \n",
+      " 7   Employment              41569 non-null  object \n",
+      " 8   Ethnicity               41569 non-null  object \n",
+      " 9   CurrentJobSatis         41569 non-null  object \n",
+      " 10  JobStatus               41569 non-null  object \n",
+      " 11  LanguageDesireNextYear  41569 non-null  object \n",
+      " 12  LanguageWorkedWith      41569 non-null  object \n",
+      " 13  Profession              41569 non-null  object \n",
+      " 14  UndergradMajor          41569 non-null  object \n",
+      " 15  YearsCodePro            41569 non-null  float64\n",
+      " 16  JobSeek                 41569 non-null  object \n",
+      "dtypes: float64(2), int64(1), object(14)\n",
+      "memory usage: 5.4+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df2020.info()#after cleaning the dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After Cleaning Dataset 2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  706673\n",
+      "Total missing :  0\n",
+      "Missing Percentage:  0.0 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = df2020.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization\n",
+    "After cleaning the datasets, we started visualizations to analyze the datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## To find whether there is any difference between men and women's income from latest stack overflow survey (2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 330,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('seaborn-darkgrid')\n",
+    "plt.rcParams[\"figure.figsize\"] = (20,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sns.boxplot('SalaryUSD', data=df2020, width=0.3) \n",
+    "#Cleaning SalaryUSD's outliers\n",
+    "df2020 = df2020[(df2020['SalaryUSD'] < 200000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 332,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Income vs Gender')"
+      ]
+     },
+     "execution_count": 332,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UVVWlsrIyy5cvT1NTU+bNm5f+/funb9++mTdvXhobG/PMM8+ksbFxk9VNAABtwaurm95sG4C2YdCgQ1NR8crajoqKigwadFiJJ4LSKHyF03/8x3/kpZdeyvTp05sf+P31r389F110US6//PL07NkzRx11VMrLyzN8+PAMGzYsTU1NGTVqVDp06JChQ4dm7NixGTp0aCorKzN16tQkycSJEzN69Og0NDSkpqYmffr0SZL0798/J554YhobGzN+/Pg3nQsAAABa2uDBQ3PvvXcnSdq1a5fBg08q8URQGmVNTU1NpR6ipa1atabUI0CL+dznjtlk3+zZd5ZgEgC2xtChn8369eubt9u3b5+ZM39cwokAeLu++93v5Je//K/8f//f0Rkx4kulHgdazOZuqWuRZzgBAPDWvDY2vdE2AG3H4MFDs2LFcqubeEcTnAAAAKBAO+/cJZMnX1rqMaCkCn9oOAAAb9373rfLZrcBANoSwQkAYBvwvve973XbghNAWzVv3tx87nPH5Le/vb/Uo0DJCE4AANuARYseft32wtIMAsBWmzbtlZ+2/u//flmJJ4HSEZwAAACgIPPmzc2GDRuSJBs2bLDKiXcswQkAAAAK8urqpldZ5cQ7leAEAAAABXl1ddObbcM7heAEAAAABamoqNjsNrxTCE4AAABQkJEjv7bR9le+MrpEk0BpCU4AANuAj3xkn42299573xJNAsDWqKkZ1LyqqaKiIh//+MASTwSlITgBAGwDvva1czfa/upXx5ZoEgC21qurnKxu4p3MzaQAAABQoJqaQampGVTqMaCkrHACANgG3HrrzJSXlydJysvLc+uts0o8EQDA2yc4AQBsA+bOnZOGhoYkSUNDQ+bOvbfEEwEAvH2CEwDANmDQoEM3esjsoEGHlXgiAIC3T3ACANgGDB48NE1NTUmSpqamDB58UoknAgB4+wQnAIBtwM47d0lZWVmSpKysLDvv3KXEEwEAvH2CEwDANuDhh3+fDRs2JEk2bNiQRYsWlnYgAICtIDgBAGwDpk69ZKPtyy67uESTAABsPcEJAGAbUFdXu9ltAIC2pKLUAwAA8EbKSj0AwCbmzLknv/71r0o9xjZv9eoXkiSdO+9c4knahsMPPzKHHnpEqcegYIITAMA2qanUAwDwNr3wguAEghMAAABb5NBDj7ASZQuMH39ukmTSpEv+wZGw/fIMJwCAbcDBBw943XZNiSYBANh6ghMAwDagrm7tRtv19S+XaBIAgK0nOAEAbAMWLfr9RtsPPbSgRJMAAGw9wQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwCAbUCnTu/a7DYAQFsiOAEAbAPGj79oo+0JEy56kyMBALZ9FaUeAADY/s2Zc09+/etflXqMNqNdu3b5/ve/W+oxtnmHH35kDj30iFKPAQC8ASucAAC2ETvssEOSZPfde5Z4EgCArWOFEwDQ4g499AgrUbbA+PHnJkkmTbqkxJMAAGwdK5wAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhapoqTd++OGHc9lll2XGjBkZNWpU/va3vyVJnn766fTp0ydXXHFFLrroojz00EPp2LFjkmT69OmprKzMmDFj8txzz6Vjx4659NJL06VLlyxcuDBTpkxJeXl5ampqctZZZyVJrrrqqsyZMycVFRU5//zz07t375Y6JQAAAAC2QIsEp2uvvTZ33HFHdtxxxyTJFVdckSR58cUXc+qpp+a8885LkixevDjf+9730qVLl+avvf7669OrV6+MHDkyd955Z6ZPn55x48ZlwoQJmTZtWrp165YRI0Zk8eLFSZIHH3wwt956a1auXJmRI0dm9uzZLXFKAAAAAGyhFrmlrnv37pk2bdom+6dNm5ZTTjkl733ve9PY2Jgnn3wy48ePz0knnZQf/ehHSZIFCxZk4MCBSZJBgwZl/vz5qa2tzfr169O9e/eUlZWlpqYm8+fPz4IFC1JTU5OysrLsuuuuaWhoyPPPP98SpwQAAADAFmqRFU5HHXVUnnrqqY32Pffcc5k/f37z6qa1a9fmlFNOyWmnnZaGhoaceuqp2XfffVNbW5vq6uokSceOHbNmzZrU1tamqqqq+b06duyYFStWpEOHDuncufNG+9esWbPRiqkkqarqkIqK8pY4Vdgmde68U6lHAOBtqKh45b8Fuo4DtG2u59CCz3B6vZ///Of51Kc+lfLyV8LPjjvumFNPPbX5truDDjooS5YsSVVVVerq6pIkdXV16dSp00b7Xru/srJyk/2vxqrXqq2tb8lTg23O6tVrSz0CAG/Dhg2NSVzHAdo613PeKbp23bTBvKrVfkrd/PnzM2jQoObtZcuWZdiwYWloaMjf//73PPTQQ9lnn33St2/f3HfffUmSuXPnpl+/fqmqqkplZWWWL1+epqamzJs3L/3790/fvn0zb968NDY25plnnkljY+Mmq5sAAAAAaF2ttsLpiSeeSLdu3Zq399hjjxx77LEZMmRIKisr85nPfCZ77rlnPvjBD2bs2LEZOnRoKisrM3Xq1CTJxIkTM3r06DQ0NKSmpiZ9+vRJkvTv3z8nnnhiGhsbM378+NY6HQAAAADeRFlTU1NTqYdoaatWrSn1CNBiPve5YzbZN3v2nSWYBICtNX78uUmSSZMuKfEkAGwN13PeKTZ3S12rrXCCt2POnHvy61//qtRjtDmv/gbHGzv88CNz6KFHlHoMAACA7VarPcMJAAAAgHcGK5zYph166BFWovwDQ4Z8Og0NDc3b5eXllu4CAABQUlY4QRt36aVXbrT9zW9e+YbHAQAAQGsRnKCN69GjZ/Ovy8vLs/vuPTdzNAAAALQ8wQm2Az17fijt2rWzugkAAIBtguAE24Edd9wxH/nIPlY3AQAAsE0QnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUC0WnB5++OEMHz48SbJ48eIMHDgww4cPz/Dhw3PXXXclSW655ZYcf/zxGTJkSO69994kybp16zJy5MgMGzYsX/jCF/L8888nSRYuXJjBgwfnpJNOylVXXdX8OVdddVVOOOGEnHTSSVm0aFFLnQ4AAAAAW6iiJd702muvzR133JEdd9wxSfKHP/whp512Wk4//fTmY1atWpUZM2Zk9uzZqa+vz7BhwzJgwIDMnDkzvXr1ysiRI3PnnXdm+vTpGTduXCZMmJBp06alW7duGTFiRBYvXpwkefDBB3Prrbdm5cqVGTlyZGbPnt0SpwQAAADAFmqRFU7du3fPtGnTmrcfffTRzJkzJyeffHLOP//81NbWZtGiRdl///3Tvn37VFdXp3v37lmyZEkWLFiQgQMHJkkGDRqU+fPnp7a2NuvXr0/37t1TVlaWmpqazJ8/PwsWLEhNTU3Kysqy6667pqGhoXlFFAAAAACl0SIrnI466qg89dRTzdu9e/fO4MGDs+++++bqq6/Od77zney1116prq5uPqZjx46pra1NbW1t8/6OHTtmzZo1qa2tTVVV1UbHrlixIh06dEjnzp032r9mzZp06dJlo3mqqjqkoqK8JU4VtgkVFa+0486ddyrxJABsDddzgO2D6zm0UHB6vSOPPDKdOnVq/vXkyZPTv3//1NXVNR9TV1eX6urqVFVVNe+vq6tLp06dNtr32v2VlZVv+B6vV1tb31KnBtuEDRsakySrV68t8SQAbA3Xc4Dtg+s57xRdu27aYF7VKj+l7owzzmh+oPf8+fOzzz77pHfv3lmwYEHq6+uzZs2aLF26NL169Urfvn1z3333JUnmzp2bfv36paqqKpWVlVm+fHmampoyb9689O/fP3379s28efPS2NiYZ555Jo2NjZusbgIAAACgdbXKCqcLL7wwkydPTmVlZd7znvdk8uTJqaqqyvDhwzNs2LA0NTVl1KhR6dChQ4YOHZqxY8dm6NChqayszNSpU5MkEydOzOjRo9PQ0JCampr06dMnSdK/f/+ceOKJaWxszPjx41vjdAAAAADYjLKmpqamUg/R0latWlPqEaBFjR9/bpJk0qRLSjwJAFvD9Rxg++B6zjtFyW+pAwAAAOCdQ3ACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAAClXRUm/88MMP57LLLsuMGTPyxz/+MZMnT055eXnat2+fSy+9NO95z3ty0UUX5aGHHkrHjh2TJNOnT09lZWXGjBmT5557Lh07dsyll16aLl26ZOHChZkyZUrKy8tTU1OTs846K0ly1VVXZc6cOamoqMj555+f3r17t9QpAQAAALAFWiQ4XXvttbnjjjuy4447JkmmTJmSCy64IB/5yEcya9asXHvttTnvvPOyePHifO9730uXLl2av/b6669Pr169MnLkyNx5552ZPn16xo0blwkTJmTatGnp1q1bRowYkcWLFydJHnzwwdx6661ZuXJlRo4cmdmzZ7fEKQEAAACwhVrklrru3btn2rRpzduXX355PvKRjyRJGhoa0qFDhzQ2NubJJ5/M+PHjc9JJJ+VHP/pRkmTBggUZOHBgkmTQoEGZP39+amtrs379+nTv3j1lZWWpqanJ/Pnzs2DBgtTU1KSsrCy77rprGhoa8vzzz7fEKQEAAACwhVpkhdNRRx2Vp556qnn7ve99b5LkoYceyo033pibbropa9euzSmnnJLTTjstDQ0NOfXUU7PvvvumtrY21dXVSZKOHTtmzZo1qa2tTVVVVfP7dezYMStWrEiHDh3SuXPnjfavWbNmoxVTSVJV1SEVFeUtcaqwTaioeKUdd+68U4knAWBruJ4DbB9cz6EFn+H0enfddVeuvvrqfPe7302XLl2aI9Ort90ddNBBWbJkSaqqqlJXV5ckqaurS6dOnTba99r9lZWVm+x/NVa9Vm1tfQufHZTWhg2NSZLVq9eWeBIAtobrOcD2wfWcd4quXTdtMK9qlZ9Sd/vtt+fGG2/MjBkz0q1btyTJsmXLMmzYsDQ0NOTvf/97Hnrooeyzzz7p27dv7rvvviTJ3Llz069fv1RVVaWysjLLly9PU1NT5s2bl/79+6dv376ZN29eGhsb88wzz6SxsXGT1U0AAAAAtK4WX+HU0NCQKVOm5P3vf39GjhyZJPnYxz6Ws88+O8cee2yGDBmSysrKfOYzn8mee+6ZD37wgxk7dmyGDh2aysrKTJ06NUkyceLEjB49Og0NDampqUmfPn2SJP3798+JJ56YxsbGjB8/vqVPBwAAAIB/oKypqamp1EO0tFWr1pR6BGhR48efmySZNOmSEk8CwNZwPQfYPrie805R8lvqAAAAAHjnEJwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEq/tEBN954Y+66666sXr06u+yyS44++uiccMIJrTEbAAAAAG3QZoPTtGnTsmrVqnzjG9/Ie97znjz99NP5/ve/n2effTZf+tKXWmtGAAAAANqQzd5SN2/evEyaNCm77757qqqq8uEPfzgXX3xxfvvb37bWfAAAAAC0MZsNTu3bt9/0C9q1S3l5eYsNBAAAAEDbttngVFZW9ob7m5qaWmQYAAAAANq+zT7D6aGHHkpNTc0m+1988cUWGwgAAACAtm2zwenRRx9trTkAAAAA2E5s9pa69evX5wc/+EGampry17/+NWeffXZGjx6dVatWtdZ8AAAAALQxmw1OkydPzjPPPJPGxsZceOGF2WuvvXLUUUflwgsvbKXxAAAAAGhrNhucnnnmmZx33nnZsGFDFixYkC984Qs58sgj8/zzz7fWfAAAAAC0MVv0U+oeeuihfPSjH01lZWWSpL6+vuUnAwAAAKBN2uxDw3faaaf88Ic/zC9+8Yt86lOfSmNjY2bPnp33v//9rTUfAAAAAG3MZlc4XXjhhVm+fHmOOOKIfPazn80DDzyQX//6157hBAAAAMCb2uwKpy5dumTMmDHN2wcffHAOPvjgFh8KAAAAgLZrs8Hp8MMPb36OU1lZWXbYYYd89KMfzejRo/Pud7+7VQYEAAAAoG3ZbHD6+c9/vtF2XV1d7rvvvowbNy5XX311iw4GAAAAQNu02Wc4tW/ffqO/dt555xx33HF58cUXW2s+AAAAANqYzQanN1NfX1/0HAAAAABsJzZ7S90TTzyx0fb69evzi1/8Irvvvvs/fOOHH344l112WWbMmJEnn3wy5557bsrKyrLnnntmwoQJadeuXW655ZbMmjUrFRUVOfPMM3PYYYdl3bp1GTNmTJ577rl07Ngxl156abp06ZKFCxdmypQpKS8vT01NTc4666wkyVVXXZU5c+akoqIi559/fnr37v32vxsAAAAAbLXNBqfx48dvtL3DDjtk7733zsSJEzf7ptdee23uuOOO7LjjjkmSiy++OOecc04OPPDAjB8/Pvfcc0/222+/zJgxI7Nnz059fX2GDRuWAQMGZObMmenVq1dGjhyZO++8M9OnT8+4ceMyYcKETJs2Ld26dcuIESOyePHiJMmDDz6YW2+9NStXrszIkSMze/bsrfl+AAAAALCVNhucZsyYkSR55JFH8tGPfnSL37R79+6ZNm1a/u3f/i1Jsnjx4hxwwAFJkkGDBuU3v/lN2rVrl/3337/5+VDdu3fPkiVLsmDBgnz+859vPnb69Ompra3N+vXr07179yRJTU1N5s+fn/bt26empiZlZWXZdddd09DQkOeffz5dunR5698JAAAAAAqx2eD0qu9///t5+umn8+lPfzqf/vSn06lTp80ef9RRR+Wpp55q3m5qakpZWVmSpGPHjlmzZk1qa2tTXV3dfEzHjh1TW1u70f7XHltVVbXRsStWrEiHDh3SuXPnjfavWbNmk+BUVdUhFRXlW3Kq0CZVVLzyOLbOnXcq8SQAbA3Xc4Dtg+s5bGFwuuKKK/Liiy/mZz/7Wb7yla+kS5cuGTJkSA488MAt+pB27f7v2eR1dXXp1KlTqqqqUldXt9H+6urqjfZv7thOnTqlsrLyDd/j9WprPeSc7duGDY1JktWr15Z4EgC2hus5wPbB9Zx3iq5dN20wr9rin1L3t7/9Lc8880xeeOGF7Lzzzvn5z3+e8847b4u+du+9984DDzyQJJk7d2769++f3r17Z8GCBamvr8+aNWuydOnS9OrVK3379s19993XfGy/fv1SVVWVysrKLF++PE1NTZk3b1769++fvn37Zt68eWlsbMwzzzyTxsZGt9MBAAAAlNgWrXAaPHhwdthhhwwZMiRf+cpX0r59+yTJGWecsUUfMnbs2FxwwQW5/PLL07Nnzxx11FEpLy/P8OHDM2zYsDQ1NWXUqFHp0KFDhg4dmrFjx2bo0KGprKzM1KlTkyQTJ07M6NGj09DQkJqamvTp0ydJ0r9//5x44olpbGzc5CHnAAAAALS+sqampqZ/dNBVV12Vs846qzXmaRGrVq0p9QjQosaPPzdJMmnSJSWeBICt4XoOsH1wPeedYqtvqfvv//7vwoYBAAAAYPu2RbfUrV+/Pscdd1x69OjR/ADwV291AwAAAIDX2qLgNHr06JaeAwAAAIDtxBbdUterV688++yzeeaZZ/L000/n97//fUvPBQAAAEAbtUUrnM4+++zsvvvu+dOf/pQOHTpkxx13bOm5AAAAAGijtmiFU5JMmjQpPXr0yPXXX58XX3yxJWcCAAAAoA3b4uBUX1+fl19+OWVlZVm7dm1LzgQAAABAG7ZFwenkk0/ODTfckAEDBuSQQw5Jz549W3ouAAAAANqoLXqG01FHHdX8609+8pOpqqpqsYEAAAAAaNs2G5xOPPHElJWVveFrs2bNapGBAAAAAGjbNhucLr/88taaAwAAAIDtxGaD0wc+8IEkyZNPPpmf//zn+fvf/54kefbZZzNp0qSWnw4AAACANmeLHho+duzYJMlDDz2Up556KqtXr27JmQAAAABow7YoOO2www7513/917zvfe/LJZdckr/97W8tPRcAAAAAbdQWBaempqasWrUqdXV1Wbt2bV588cWWngsAAACANmqLgtNZZ52Vu+++O4cffngOO+ywDBo0qKXnAgAAAKCN2mxwWrx4cY477rjst99+efe7351vfvOb6dy5c/r3799a8wEAAADQxmz2p9RdccUVueSSS1JZWZkrr7wy1157bXbbbbd8/vOfzxFHHNFaMwIAALSo73//u1m27PFSj8F24oknXvlnafz4c0s8CduT3XfvmdNPH1HqMbbYZoNTU1NT9tprr/z1r3/Nyy+/nH322SdJ0q7dFt2JBwAA0CYsW/Z4Hv3ToqRzqSdhu1D+yt8efXZRaedg+7G61AO8dZsNTo2NjUmS+++/PwcffHCSZP369amrq2v5yQAAAFpT56Tx0MZSTwGwiXZz2t7Cn80Gp4MPPjgnnXRS/vKXv+Tqq6/O8uXLc+GFF+boo49urfkAAAAAaGM2G5xGjBiRI444Il26dMnOO++c5cuXZ+jQoTnyyCNbaz4AAAAA2pjNBqck2WOPPZp/3b1793Tv3r1FBwIAAACgbWt7NwECAAAAsE0TnAAAAAAolOAEAAAAQKEEJwAAAAAK9Q8fGg4AvLnvf/+7Wbbs8VKPwXbiiSde+Wdp/PhzSzwJ25Pdd++Z008fUeoxAHiHEZwAYCssW/Z4nnjs4XSvaij1KGwH3pWyJEnD0w+VeBK2F8try0s9AgDvUIITAGyl7lUNGde/ttRjAGziot9VlXoEAN6hPMMJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUBWlHuCd6vvf/26WLXu81GOwnXjiiVf+WRo//twST8L2ZPfde+b000eUegwAAKANEpxKZNmyx/PoksfSuFOXUo/CdqCs8ZX/Ky9avqrEk7C9aLf2+VKPAAAAtGGCUwk17tQl6/b+VKnHANjEDn/4WalHAAAA2jDPcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhaporQ+67bbb8uMf/zhJUl9fnz/+8Y+ZNWtWvvjFL2b33XdPkgwdOjRHH310brnllsyaNSsVFRU588wzc9hhh2XdunUZM2ZMnnvuuXTs2DGXXnppunTpkoULF2bKlCkpLy9PTU1NzjrrrNY6JQAAAADeQKsFp+OPPz7HH398kmTixIn53Oc+lz/84Q857bTTcvrppzcft2rVqsyYMSOzZ89OfX19hg0blgEDBmTmzJnp1atXRo4cmTvvvDPTp0/PuHHjMmHChEybNi3dunXLiBEjsnjx4uyzzz6tdVoAAAAAvE6r31L3yCOP5H//939z4okn5tFHH82cOXNy8skn5/zzz09tbW0WLVqU/fffP+3bt091dXW6d++eJUuWZMGCBRk4cGCSZNCgQZk/f35qa2uzfv36dO/ePWVlZampqcn8+fNb+5QAAAAAeI1WW+H0qmuuuSZf/vKXkyS9e/fO4MGDs+++++bqq6/Od77zney1116prq5uPr5jx46pra1NbW1t8/6OHTtmzZo1qa2tTVVV1UbHrlixYpPPrKrqkIqK8hY+s7emosLjs4BtW0VFu3TuvFOpx9jmVVS0S0OphwDYDNfzLePP58C2rq1dz1s1OL300kt5/PHHc9BBByVJjjzyyHTq1Kn515MnT07//v1TV1fX/DV1dXWprq5OVVVV8/66urp06tRpo32v3f96tbX1LXlab8uGDY2lHgFgszZsaMzq1WtLPcY2z/Uc2Na5nm8Z13NgW7ctXs+7dq1+09daNeP/z//8Tz7+8Y83b59xxhlZtGhRkmT+/PnZZ5990rt37yxYsCD19fVZs2ZNli5dml69eqVv37657777kiRz585Nv379UlVVlcrKyixfvjxNTU2ZN29e+vfv35qnBAAAAMDrtOoKpyeeeCIf/OAHm7cvvPDCTJ48OZWVlXnPe96TyZMnp6qqKsOHD8+wYcPS1NSUUaNGpUOHDhk6dGjGjh2boUOHprKyMlOnTk3yygPIR48enYaGhtTU1KRPnz6teUoAAAAAvE6rBqfPf/7zG23vs88+mTVr1ibHDRkyJEOGDNlo34477phvf/vbmxy733775ZZbbil2UAAAAADeNk/GAwAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAAChURakHAAAAKLXVq19IVift5vhv8sA2aHWyuv0LpZ7iLXE1BQAAAKBQVjgBAADveJ0775yn1q9I46GNpR4FYBPt5rRL5847l3qMt6RVg9Nxxx2X6urqJMkHP/jBfPGLX8y5556bsrKy7LnnnpkwYULatWuXW265JbNmzUpFRUXOPPPMHHbYYVm3bl3GjBmT5557Lh07dsyll16aLl26ZOHChZkyZUrKy8tTU1OTs846qzVPCQAAAIDXabXgVF9fnySZMWNG874vfvGLOeecc3LggQdm/Pjxueeee7LffvtlxowZmT17durr6zNs2LAMGDAgM2fOTK9evTJy5MjceeedmT59esaNG5cJEyZk2rRp6datW0aMGJHFixdnn332aa3TAgAAAOB1Wu0ZTkuWLMnLL7+c008/PaeeemoWLlyYxYsX54ADDkiSDBo0KL/97W+zaNGi7L///mnfvn2qq6vTvXv3LFmyJAsWLMjAgQObj50/f35qa2uzfv36dO/ePWVlZampqcn8+fNb65QAAAAAeAOttsJphx12yBlnnJHBgwdn2bJl+cIXvpCmpqaUlZUlSTp27Jg1a9aktra2+ba7V/fX1tZutP+1x1ZVVW107IoVKzb57KqqDqmoKG/hM3xrKio8rx3YtlVUtEvnzjuVeoxt3ksvrc7f1pTnot9V/eODAVrZk2vK856XVruebwF/Pge2dW3tz+etFpx69OiR3XbbLWVlZenRo0c6d+6cxYsXN79eV1eXTp06paqqKnV1dRvtr66u3mj/5o7t1KnTJp9dW1vfgmf29mzY4GGEwLZtw4bGrF69ttRjbPMaG5tKPQLAZjU2NrmebwF/Pge2ddvin8+7dq1+09daLTj96Ec/yp/+9KdceOGF+etf/5ra2toMGDAgDzzwQA488MDMnTs3Bx10UHr37p0rr7wy9fX1Wb9+fZYuXZpevXqlb9++ue+++9K7d+/MnTs3/fr1S1VVVSorK7N8+fJ069Yt8+bN89BwAFpV5847p7ruiYzrX1vqUQA2cdHvqlLexn6qEQDbh1YLTieccELOO++8DB06NGVlZfnGN76RnXfeORdccEEuv/zy9OzZM0cddVTKy8szfPjwDBs2LE1NTRk1alQ6dOiQoUOHZuzYsRk6dGgqKyszderUJMnEiRMzevToNDQ0pKamJn369GmtUwIAAADgDbRacGrfvn1zJHqtG2+8cZN9Q4YMyZAhQzbat+OOO+bb3/72Jsfut99+ueWWW4obFAAAAICt4sl4AAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAoVKs9NJyNrV79QtqtfS47/OFnpR4FYBPt1j6X1av9FgEAALw9VjgBAAAAUCj/+bpEOnfeOctf2pB1e3+q1KMAbGKHP/wsnTvvXOoxAACANsoKJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAoVEVrfdDf//73nH/++Xn66aezfv36nHnmmdlll13yxS9+MbvvvnuSZOjQoTn66KNzyy23ZNasWamoqMiZZ56Zww47LOvWrcuYMWPy3HPPpWPHjrn00kvTpUuXLFy4MFOmTEl5eXlqampy1llntdYpAQAAAPAGWi043XHHHencuXO+9a1v5YUXXshnP/vZfPnLX85pp52W008/vfm4VatWZcaMGZk9e3bq6+szbNiwDBgwIDNnzkyvXr0ycuTI3HnnnZk+fXrGjRuXCRMmZNq0aenWrVtGjBiRxYsXZ5999mmt0wIAAADgdVotOP3TP/1TjjrqqObt8vLyPProo3niiSdyzz33ZLfddsv555+fRYsWZf/990/79u3Tvn37dO/ePUuWLMmCBQvy+c9/PkkyaNCgTJ8+PbW1tVm/fn26d++eJKmpqcn8+fMFJwAA4K1bnbSb46kjFGDd//v7DiWdgu3J6iTvLfUQb02rBaeOHTsmSWpra3P22WfnnHPOyfr16zN48ODsu+++ufrqq/Od73wne+21V6qrqzf6utra2tTW1jbv79ixY9asWZPa2tpUVVVtdOyKFSs2+eyqqg6pqChv4TN8ayoq/EYGbNsqKtqlc+edSj3GNq+iol0aSj0EwGa4nm+ZD3+4lz+jU5j//d//TZJ8aNcPlXgSthu7Jnvs8aE2dT1vteCUJCtXrsyXv/zlDBs2LMcee2xeeumldOrUKUly5JFHZvLkyenfv3/q6uqav6auri7V1dWpqqpq3l9XV5dOnTpttO+1+1+vtra+hc/srduwobHUIwBs1oYNjVm9em2px9jmuZ4D2zrX8y1z8smn/+ODYAuNH3/u//v7N0o8Cdubbe163rVr9Zu+1moJ/29/+1tOP/30jBkzJieccEKS5IwzzsiiRYuSpPlWuN69e2fBggWpr6/PmjVrsnTp0vTq1St9+/bNfffdlySZO3du+vXrl6qqqlRWVmb58uVpamrKvHnz0r9//9Y6JQAAAADeQKutcPqP//iPvPTSS5k+fXqmT5+eJDn33HPzjW98I5WVlXnPe96TyZMnp6qqKsOHD8+wYcPS1NSUUaNGpUOHDhk6dGjGjh2boUOHprKyMlOnTk2STJw4MaNHj05DQ0NqamrSp0+f1jolAAAAAN5AqwWncePGZdy4cZvsnzVr1ib7hgwZkiFDhmy0b8cdd8y3v/3tTY7db7/9cssttxQ3KAAAAABbxVPxAAAAAChUqz40HAC2R8try3PR76r+8YHwD7y4vixJ8q72TSWehO3F8try9Cj1EAC8IwlOALAVdt+9Z6lHYDvy4hOPJ0m6fMA/VxSjR1ynACgNwQkAtsLpp48o9QhsR179MdqTJl1S4kkAALaOZzgBAAAAUCjBCQAAAIBCuaWuhNqtfT47/OFnpR6D7UDZ319OkjRV7ljiSdhetFv7fJKupR4DAABoowSnEvHwRor0xP97yGyP7gIBRenqOgUAALxtglOJeMgsRfKQWQAAALYlnuEEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAolOAEAAAAQKEEJwAAAAAKJTgBAAAAUCjBCQAAAIBCCU4AAAAAFEpwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABSqotQDFKGxsTEXXnhhHnvssbRv3z4XXXRRdtttt1KPBQAAAPCOtF2scLr77ruzfv36/PCHP8zXvva1XHLJJaUeCQAAAOAdq6ypqamp1ENsrYsvvji9e/fOMccckyQZOHBg7r///ubXV61aU6rR2Epz5tyTX//6V6UeY5v3xBOPJ0l69OhZ4knahsMPPzKHHnpEqceAdxTX8y3jev7WuJ5D63M93zKu52+N63nb1bVr9Zu+tl3cUldbW5uqqqrm7fLy8mzYsCEVFa+cXlVVh1RUlJdqPLbCTju1T0XFdrEQr0W9+91dksT3agvttFP7dO68U6nHgHcU1/Mt43r+1rieQ+tzPd8yrudvjev59mm7WeHUp0+fHH300UmSQYMGZe7cuc2vW+EEAAAAUKzNrXDaLnJr3759mwPTwoUL06tXrxJPBAAAAPDOtV3cUnfkkUfmN7/5TU466aQ0NTXlG9/4RqlHAgAAAHjH2i5uqftH3FIHAAAAUKzt/pY6AAAAALYdghMAAAAAhRKcAAAAACiU4AQAAABAoQQnAAAAAAolOAEAAABQKMEJAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFAowQkAAACAQglOAAAAABRKcAIAAACgUIITAAAAAIUSnAAAAAAoVFlTU1NTqYcAAAAAYPthhRMAAAAAhRKcAAAAACiU4AQAAABAoQQnaAMeeOCBfPjDH85dd9210f5jjz025557bommAuD1Tj311CxatChJsn79+vTr1y/XXXdd8+unnHJKlixZUqrxAHidBx54IP3798/KlSub91122WW57bbb3vZ7Tps2LTNnztxk/1lnnfW23xPaIsEJ2oiePXvmZz/7WfP2Y489lpdffrmEEwHwejU1Nfnd736XJFmwYEFqamoyZ86cJEl9fX1WrlyZvfbaq4QTAvB6lZWVOe+889LSP0/rqquuatH3h21NRakHALbMXnvtlWXLluWll15Kp06dcscdd+TYY4/NypUrc+ONN+aXv/xlNmzYkOrq6kybNi0/+9nPct9992XdunVZvnx5vvCFL+T4448v9WkAbNc+/vGPZ/r06Tn99NNz3333ZfDgwbnsssuyZs2aLF68OAcccEDuuOOO/OAHP0j79u2z++67Z9KkSfnpT3+ae++9N+vWrcuqVaty6qmn5p577smf//zn/Nu//Vs+8YlPuNYDtJCDDjoojY2Nuemmm3LKKac07//+97+fO++8MxUVFenfv3/GjBmTadOm5amnnspzzz2XZ555Juedd14GDhy4yXvefffd+a//+q+sW7cu48aNS+/evTNgwID85je/yfDhw7PXXnvlz3/+c2pra/Pv//7v+cAHPpCpU6fm0UcfTV1dXfbYY49cfPHFmTZtWn7/+99n7dq1+eQnP5m//OUvGTt2bBoaGnLcccdl9uzZad++fWt+u2CLWeEEbciRRx6ZX/3qV2lqasqiRYuy//77p7GxMatXr84NN9yQm2++ORs2bMgjjzySJKmtrc0111yTq6++Ot/97ndLPD3A9m/vvffO448/nqampvzP//xPDjjggBx88MH57W9/mwcffDADBw7MtGnT8oMf/CAzZ85MdXV1fvjDHyZJ6urqcu211+YLX/hCZs6cmauuuiqTJk3Kbbfd5loP0MIuvPDC3HDDDVm2bFmSV67J//Vf/5VZs2Zl1qxZefLJJ3PvvfcmSdq3b5/vfe97+frXv54bbrjhDd/vAx/4QP7zP/8zU6ZMyYQJEzZ5vXfv3rnhhhsyYMCA3HnnnamtrU2nTp1y/fXXZ9asWVm4cGH++te/JnnlTodZs2blc5/7XO655540NDTk/vvvz4EHHig2sU2zwgnakGOPPTYXXnhhunXrlv79+ydJ2rVrl8rKynz1q1/NTjvtlL/85S/ZsGFDkjTftvH+978/69evL9ncAO8U7dq1y1577ZW5c+ema9euad++fQYNGpQ5c+ZkyZIlOeSQQ/KhD30oVVVVSZKPfexjmTdvXvr06ZOPfOQjSZLq6ursscceKSsry7ve9a7U19e71gO0sJ133jnnn39+zj333PTt2zf19fXp06dPKisrkyT9+/fPn//85yRpvl7vsssuWb9+fZ588smMGzcuSfLpT386ySvX9yTZc889s2rVqk0+b++9925+j7/97W/p0KFDnn/++ebr/Nq1a/P3v/89SdKjR48kSVVVVfPvG7fddlu+9KUvtdS3AwphhRO0Id26dcvatWszY8aM5t/Mamtrc/fdd+fKK6/MBRdckMbGxub7z8vKyko5LsA70oABA3LNNdc032LRr1+//OEPf0jyynV86dKlWbt2bZLkwQcfbP4Xic1ds5csWeJaD9DCDj/88PTo0SM//vGP06FDhyxatCgbNmxoXrX6Ztfr3XbbLTNmzMiMGTMyePDgJGn+ARKPPfZYdt1113/42XPnzs3KlStz+eWX56tf/WrWrVvXfJ1v1+7//rV9yJAhufXWW/Pcc895JiDbPCucoI05+uijc/vtt6dHjx5ZsWJFysvLs+OOO+b4449P+/bt07Vr1zz77LOlHhPgHevjH/94xo0bl29+85tJXrn1orq6OnvvvXe6dOmSkSNH5tRTT027du3SvXv3jB49Onfeeedm33O33XZzrQdoBV//+tfz3//93+nYsWM++clPZujQoWlsbEy/fv3yiU98Yot/0uhTTz2VU089NevXr8+kSZP+4fG9e/fO9OnTM2TIkLRv3z7dunV7w+t8nz598uSTT+bkk09+y+cGra2sqaUfxQ8AAABstcbGxgwdOjTXXXdd8+3ZsK1ySx0AAABs41asWJHPfvaz+cxnPiM20SZY4QQAAABAoaxwAgAAAKBQghMAAAAAhRKcAAAAACiU4AQAULAVK1bk7LPPzpAhQ3LqqadmxIgR+fOf//y232/p0qUZPnx4gRMCALSsilIPAACwPXn55Zdz5plnZvLkydl///2TJIsWLcqkSZMyY8aMEk8HANA6BCcAgALde++9Oeigg5pjU5L07t07//mf/5mVK1fmggsuSH19fTp06JDJkyenoaEhX/va17LLLrtkxYoV+ehHP5qJEyfm2WefzejRo9PU1JSuXbs2v9eDDz6YK664IuXl5enWrVsmTZqUn/70p5k9e3YaGxtz9tln5+CDDy7FqQMANBOcAAAK9NRTT6V79+7N22eeeWZqa2vz7LPPZpdddsnpp5+eQw45JPPnz89ll12WUaNGZdmyZbnuuuuy44475hOf+ERWrVqV66+/Pp/61KcyZMiQ3HXXXZk5c2aamppywQUX5Oabb8673/3uXHnllfnxj3+cioqKdOrUKVdffXUJzxwA4P8ITgAABdpll13y6KOPNm+/GoGGDBmShQsX5pprrsn3vve9NDU1pbKyMknSvXv3VFVVJUm6du2a+vr6/PnPf85nPvOZJEnfvn0zc+bMPP/883n22WdzzjnnJEnWrVuXAQMGpHv37unRo0crniUAwOYJTgAABTriiCNy7bXXZuHChdlvv/2SJE8++WT+8pe/pHfv3hk1alT69u2bpUuX5n/+53+SJGVlZZu8T8+ePfP73/8+e+21Vx555JEkyc4775xddtkl06dPT3V1de65557stNNOWblyZdq187NgAIBth+AEAFCgjh075uqrr87UqVNz2WWXZcOGDamoqMjkyZPTs2fPXHjhhamvr8+6devy9a9//U3f5ytf+UpGjRqVu+66Kx/84AeTJO3atcvXv/71jBgxIk1NTenYsWO++c1vZuXKla11egAAW6SsqampqdRDAAAAALD9sPYaAAAAgEIJTgAAAAAUSnACAAAAoFCCEwAAAACFEpwAAAAAKJTgBAAAAEChBCcAAAAACiU4AQAAAFCo/x9NKQb4tdhydQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x ='Gender', y='SalaryUSD', data=df2020)\n",
+    "plt.title('Income vs Gender', fontsize = 14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Analysis**<br>\n",
+    "There is a little bit of difference between Gender and income they received respectively. Men tend to receive more salary than women from the above analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Impact on participation rate due to different ethnicity based on country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['White or of European descent', 'South Asian', 'Hispanic or Latino', 'Middle Eastern', 'East Asian', 'Southeast Asian', 'Black or of African descent', 'Multiracial', 'Biracial', 'Indigenous']\n",
+      "[24573, 4585, 2877, 1757, 1539, 1348, 1336, 226, 133, 62]\n"
+     ]
+    }
+   ],
+   "source": [
+    "participation_rate = df2020['Ethnicity'].value_counts().keys().tolist()\n",
+    "print(participation_rate)\n",
+    "count = df2020['Ethnicity'].value_counts().tolist()\n",
+    "print(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 334,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()    \n",
+    " \n",
+    "ax.bar(participation_rate,count)\n",
+    "plt.title('Ethnicity VS Participation',size=20)\n",
+    "plt.xlabel('Total Count',size = 20)\n",
+    "plt.ylabel('# Of Developer Did Survey',size = 20) \n",
+    "for i, v in enumerate(count):\n",
+    "    ax.text(i-.15, \n",
+    "              v+3,\n",
+    "              count[i],\n",
+    "              style = 'italic',\n",
+    "              fontsize=14,\n",
+    "              color = 'magenta')\n",
+    "ax.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(15, 5))\n",
+    "sns.barplot(x = count, y = participation_rate, palette = 'Set1')\n",
+    "plt.xlabel('Ethnicity', size = 16)\n",
+    "for i, v in enumerate(count):\n",
+    "    ax.text(  v+3,\n",
+    "              i-.15,\n",
+    "              f'{count[i]*100/sum(count):.2f}%',\n",
+    "              style = 'italic',\n",
+    "              fontsize=14,\n",
+    "              )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**From the Survey Analysis, more particpation has been happened from White or of European Ethnicity which is 24573 participation which is very high comparing to others.<br>\n",
+    "The least has been recorded as only 0.16% from Indigenous.<br>\n",
+    "The second top survey contributors are from South Asians which is 11.93% of the respondents.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Geographical plot to show number of respondents in each country in 2019"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#geoplot_2019=cleaned_df_2019.groupby('Country').agg('count')\n",
+    "geoplot_2019=cleaned_df_2019.groupby('Country').size()\n",
+    "geoplot_2019=geoplot_2019.to_frame('Respondents')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_country_code(name):\n",
+    "    try:\n",
+    "        return pycountry.countries.lookup(name).alpha_3\n",
+    "    except:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 338,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geoplot_2019['Country'] = geoplot_2019.index\n",
+    "geoplot_2019['Country_code'] = geoplot_2019['Country'].apply(get_country_code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "        require.undef(\"plotly\");\n",
+       "        define('plotly', function(require, exports, module) {\n",
+       "            /**\n",
+       "* plotly.js v1.58.4\n",
+       "* Copyright 2012-2020, Plotly, Inc.\n",
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+       "* Licensed under the MIT license\n",
+       "*/\n",
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+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <feross@feross.org> <http://feross.org>\n",
+       " * @license  MIT\n",
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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,M=0|o[5],A=8191&M,S=M>>>13,E=0|o[6],C=8191&E,L=E>>>13,I=0|o[7],P=8191&I,z=I>>>13,O=0|o[8],D=8191&O,R=O>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(g,U)|0,a=Math.imul(g,V);var vt=(c+(n=n+Math.imul(f,H)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,H)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,H)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,H)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,H)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(g,J)|0,a=a+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(A,U),i=(i=Math.imul(A,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,V),n=n+Math.imul(A,H)|0,i=(i=i+Math.imul(A,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,U),i=(i=Math.imul(P,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(A,W)|0,i=(i=i+Math.imul(A,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,X)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,X)|0,n=n+Math.imul(A,J)|0,i=(i=i+Math.imul(A,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,W)|0,i=(i=i+Math.imul(P,X)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,X)|0,n=n+Math.imul(C,J)|0,i=(i=i+Math.imul(C,K)|0)+Math.imul(L,J)|0,a=a+Math.imul(L,K)|0,n=n+Math.imul(A,$)|0,i=(i=i+Math.imul(A,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(g,ft)|0,a=a+Math.imul(g,ht)|0;var Mt=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,gt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(P,J)|0,i=(i=i+Math.imul(P,K)|0)+Math.imul(z,J)|0,a=a+Math.imul(z,K)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(A,rt)|0,i=(i=i+Math.imul(A,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var At=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(P,$)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(A,at)|0,i=(i=i+Math.imul(A,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(A,lt)|0,i=(i=i+Math.imul(A,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(A,ft)|0,i=(i=i+Math.imul(A,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Ct=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ft)|0,i=(i=i+Math.imul(C,ht)|0)+Math.imul(L,ft)|0,a=a+Math.imul(L,ht)|0;var Lt=(c+(n=n+Math.imul(A,dt)|0)|0)+((8191&(i=(i=i+Math.imul(A,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(P,ft)|0,i=(i=i+Math.imul(P,ht)|0)+Math.imul(z,ft)|0,a=a+Math.imul(z,ht)|0;var It=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,gt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var Ot=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=Mt,l[10]=At,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=It,l[16]=Pt,l[17]=zt,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=h),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?h(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=f*m-h*v;v=f*v+h*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number 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numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var m={k256:null,p224:null,p192:null,p25519:null};function v(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){v.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff 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r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},T.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(\"undefined\"==typeof e||e,this)},{buffer:108}],100:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],101:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function 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m\".replace(\"_\",n.join()).replace(\"$\",t)),Function.apply(void 0,r)}},{}],106:[function(t,e,r){\"use strict\";e.exports=function(t,e){e<=128?n(0,e-1,t):function t(e,r,u){var f=(r-e+1)/6|0,h=e+f,p=r-f,d=e+r>>1,g=d-f,m=d+f,v=h,y=g,x=d,b=m,_=p,w=e+1,T=r-1,k=0;l(v,y,u)&&(k=v,v=y,y=k);l(b,_,u)&&(k=b,b=_,_=k);l(v,x,u)&&(k=v,v=x,x=k);l(y,x,u)&&(k=y,y=x,x=k);l(v,b,u)&&(k=v,v=b,b=k);l(x,b,u)&&(k=x,x=b,b=k);l(y,_,u)&&(k=y,y=_,_=k);l(y,x,u)&&(k=y,y=x,x=k);l(b,_,u)&&(k=b,b=_,_=k);for(var M=u[2*y],A=u[2*y+1],S=u[2*b],E=u[2*b+1],C=2*v,L=2*x,I=2*_,P=2*h,z=2*d,O=2*p,D=0;D<2;++D){var R=u[C+D],F=u[L+D],B=u[I+D];u[P+D]=R,u[z+D]=F,u[O+D]=B}a(g,e,u),a(m,r,u);for(var N=w;N<=T;++N)if(c(N,M,A,u))N!==w&&i(N,w,u),++w;else if(!c(N,S,E,u))for(;;){if(c(T,S,E,u)){c(T,M,A,u)?(o(N,w,T,u),++w,--T):(i(N,T,u),--T);break}if(--T<N)break}s(e,w-1,M,A,u),s(r,T+1,S,E,u),w-2-e<=32?n(e,w-2,u):t(e,w-2,u);r-(T+2)<=32?n(T+2,r,u):t(T+2,r,u);T-w<=32?n(w,T,u):t(w,T,u)}(0,e-1,t)};function n(t,e,r){for(var 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+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
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+       "/*!\n",
+       " * @overview es6-promise - a tiny implementation of Promises/A+.\n",
+       " * @copyright Copyright (c) 2014 Yehuda Katz, Tom Dale, Stefan Penner and contributors (Conversion to ES6 API by Jake Archibald)\n",
+       " * @license   Licensed under MIT license\n",
+       " *            See https://raw.githubusercontent.com/stefanpenner/es6-promise/master/LICENSE\n",
+       " * @version   v4.2.8+1e68dce6\n",
+       " */\n",
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a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function g(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],248:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function a(t){if(t<0)return Number(\"0/0\");for(var e=i[0],r=i.length-1;r>0;--r)e+=i[r]/(t+r);var n=t+607/128+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(a(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var o=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(o,e+.5)*Math.exp(-o)*r},e.exports.log=a},{}],249:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e||{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf(\"webgl\")&&a.push(\"experimental-\"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],250:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],m={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=f[M],this.lastCubeProps.axis[M]=h[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,f,h);e=this.gl;var S,E=g;for(M=0;M<3;++M)this.backgroundEnable[M]?E[M]=h[M]:E[M]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(M=0;M<3;++M){var C=[0,0,0];h[M]>0?C[M]=a[1][M]:C[M]=a[0][M];for(var L=0;L<2;++L){var I=(M+1+L)%3,P=(M+1+(1^L))%3;this.gridEnable[I]&&this._lines.drawGrid(I,P,this.bounds,C,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(L=0;L<2;++L){I=(M+1+L)%3,P=(M+1+(1^L))%3;this.zeroEnable[P]&&Math.min(a[0][P],a[1][P])<=0&&Math.max(a[0][P],a[1][P])>=0&&this._lines.drawZero(I,P,this.bounds,C,this.zeroLineColor[P],this.zeroLineWidth[P]*this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio);var z=c(v,A[M].primalMinor),O=c(y,A[M].mirrorMinor),D=this.lineTickLength;for(L=0;L<3;++L){var R=k/r[5*L];z[L]*=D[L]*R,O[L]*=D[L]*R}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,z,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,O,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(M=0;M<3;++M){var U=A[M].primalMinor,V=A[M].mirrorMinor,q=c(x,A[M].primalOffset);for(L=0;L<3;++L)this.lineTickEnable[M]&&(q[L]+=k*U[L]*Math.max(this.lineTickLength[L],0)/r[5*L]);var H=[0,0,0];if(H[M]=1,this.tickEnable[M]){-3600===this.tickAngle[M]?(this.tickAngle[M]=0,this.tickAlign[M]=\"auto\"):this.tickAlign[M]=-1,F=1,\"auto\"===(S=[this.tickAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(M,U,V);for(L=0;L<3;++L)q[L]+=k*U[L]*this.tickPad[L]/r[5*L];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],q,this.tickColor[M],H,B,S)}if(this.labelEnable[M]){F=0,B=[0,0,0],this.labels[M].length>4&&(N(M),F=1),\"auto\"===(S=[this.labelAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(L=0;L<3;++L)q[L]+=k*U[L]*this.labelPad[L]/r[5*L];q[M]+=.5*(a[0][M]+a[1][M]),this._text.drawLabel(M,this.labelSize[M],this.labelAngle[M],q,this.labelColor[M],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":251,\"./lib/cube.js\":252,\"./lib/lines.js\":253,\"./lib/text.js\":255,\"./lib/ticks.js\":256}],251:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var g=-1;g<=1;g+=2)f[u]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var m=c;c=u,u=m}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":254,\"gl-buffer\":259,\"gl-vao\":358}],252:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,S=(M+1)%3,E=-1,C=-1,L=0;L<2;++L){var I=(z=L<<M)+(L<<A)+(1-L<<S),P=z+(1-L<<A)+(L<<S);o(c[z],c[I],c[P],f)<0||(L?E=1:C=1)}if(E<0||C<0)C>E&&(w|=1<<M);else{for(L=0;L<2;++L){I=(z=L<<M)+(L<<A)+(1-L<<S),P=z+(1-L<<A)+(L<<S);var z,O=d([l[z],l[I],l[P],l[z+(1<<A)+(1<<S)]]);L?E=O:C=O}C>E&&(w|=1<<M)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=g;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=m,q=w;for(M=0;M<3;++M)V[M]=q&1<<M?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{\"bit-twiddle\":97,\"gl-mat4/multiply\":295,\"robust-orientation\":548,\"split-polygon\":566}],253:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;s[f]=h,l[f]=m-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){g=+r[f][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}m=o.length/3|0;c[f]=h,u[f]=m-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":254,\"gl-buffer\":259,\"gl-vao\":358}],254:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":335,glslify:257}],255:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var 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n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],m=[0,0,0]},{\"./lib/cube.js\":252,\"extract-frustum-planes\":240,\"gl-mat4/multiply\":295,\"gl-mat4/transpose\":306,\"gl-vec4/transformMat4\":429,\"split-polygon\":566}],259:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&\"undefined\"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:495,\"ndarray-ops\":490,\"typedarray-pool\":595}],260:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,g=[],m=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(m=Math.min(m,_),v=!1):v=!0}v||(p=x,d=b),g.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(m)||(m=1),a.vectorScale=m;var M=t.coneSize||.5;t.absoluteConeSize&&(M=t.absoluteConeSize*k),a.coneScale=M;y=0;for(var A=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],C=x[2],L=g[y],I=n.length(L)*k,P=0;P<8;P++){a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vertexIntensity.push(I,I,I),a.vertexIntensity.push(I,I,I);var z=a.positions.length;a.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":261,\"./lib/shaders\":262,\"gl-vec3\":377}],261:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function g(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var m=0;m<p.length;++m){var v=p[m];d=Math.min(d,v),g=Math.max(g,v)}else for(m=0;m<n.length;++m){v=n[m][2];d=Math.min(d,v),g=Math.max(g,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(m=0;m<n.length;++m)for(var y=n[m],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(m=0;m<r.length;++m){var _=r[m];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var M,A=h;3===A.length?s.push(A[0],A[1],A[2],1):s.push(A[0],A[1],A[2],A[3]),M=p?[(p[T]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(M[0],M[1]),f.push(m)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=g(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,m,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-shader\":335,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495}],262:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:263}],263:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],264:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\"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p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":335,glslify:267}],269:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new 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t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var g=d.prototype;function m(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,m(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":353}],270:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));h[g]=d}}var m=o(e).split(\"\\n\");for(p=0;p<m.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=m[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":66,\"gl-constants/lookup\":265,\"glsl-shader-name\":431,\"sprintf-js\":567}],271:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],g=t.colorValues||[0,0,0,1],m=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),M=a.mallocUint8(2*w),A=a.mallocUint32(w),S=0,E=l?b-1:b,C=l?_-1:_,L=0;L<C;++L){var I,P;l?(I=x*(o[L]-u),P=x*(o[L+1]-u)):(I=L<_-1?x*(o[L]-(o[L+1]-o[L])/2-u):x*(o[L]-(o[L]-o[L-1])/2-u),P=L<_-1?x*(o[L]+(o[L+1]-o[L])/2-u):x*(o[L]+(o[L]-o[L-1])/2-u));for(var z=0;z<E;++z){var O,D;l?(O=y*(r[z]-c),D=y*(r[z+1]-c)):(O=z<b-1?y*(r[z]-(r[z+1]-r[z])/2-c):y*(r[z]-(r[z]-r[z-1])/2-c),D=z<b-1?y*(r[z]+(r[z+1]-r[z])/2-c):y*(r[z]+(r[z]-r[z-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],q=s[l?(L+V)*b+(z+U):L*b+z],H=n.le(d,q);if(H<0)F=g[0],B=g[1],N=g[2],j=g[3];else if(H===m-1)F=g[4*m-4],B=g[4*m-3],N=g[4*m-2],j=g[4*m-1];else{var G=(q-d[H])/(d[H+1]-d[H]),Y=1-G,W=4*H,X=4*(H+1);F=Y*g[W]+G*g[X],B=Y*g[W+1]+G*g[X+1],N=Y*g[W+2]+G*g[X+2],j=Y*g[W+3]+G*g[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*O+.5*D,k[2*S+1]=.5*I+.5*P,M[2*S]=U,M[2*S+1]=V,A[S]=L*b+z,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(M),this.colorBuffer.update(T),this.idBuffer.update(A),a.free(k),a.free(T),a.free(M),a.free(A)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":272,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335,\"iota-array\":463,\"typedarray-pool\":595}],272:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:273}],273:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],274:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":335,glslify:276}],275:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var g=new v(e,r,o,s,l,d);return g.update(t),g};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function g(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function m(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:g(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:g(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,m=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,m=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var 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t[0]=1-(p+g),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=h+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],285:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],286:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],287:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],288:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],289:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],290:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),c=1/(a-o);return t[0]=2*a*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*a*l,t[6]=0,t[7]=0,t[8]=(r+e)*s,t[9]=(i+n)*l,t[10]=(o+a)*c,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*a*2*c,t[15]=0,t}},{}],291:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],292:[function(t,e,r){e.exports={create:t(\"./create\"),clone:t(\"./clone\"),copy:t(\"./copy\"),identity:t(\"./identity\"),transpose:t(\"./transpose\"),invert:t(\"./invert\"),adjoint:t(\"./adjoint\"),determinant:t(\"./determinant\"),multiply:t(\"./multiply\"),translate:t(\"./translate\"),scale:t(\"./scale\"),rotate:t(\"./rotate\"),rotateX:t(\"./rotateX\"),rotateY:t(\"./rotateY\"),rotateZ:t(\"./rotateZ\"),fromRotation:t(\"./fromRotation\"),fromRotationTranslation:t(\"./fromRotationTranslation\"),fromScaling:t(\"./fromScaling\"),fromTranslation:t(\"./fromTranslation\"),fromXRotation:t(\"./fromXRotation\"),fromYRotation:t(\"./fromYRotation\"),fromZRotation:t(\"./fromZRotation\"),fromQuat:t(\"./fromQuat\"),frustum:t(\"./frustum\"),perspective:t(\"./perspective\"),perspectiveFromFieldOfView:t(\"./perspectiveFromFieldOfView\"),ortho:t(\"./ortho\"),lookAt:t(\"./lookAt\"),str:t(\"./str\")}},{\"./adjoint\":277,\"./clone\":278,\"./copy\":279,\"./create\":280,\"./determinant\":281,\"./fromQuat\":282,\"./fromRotation\":283,\"./fromRotationTranslation\":284,\"./fromScaling\":285,\"./fromTranslation\":286,\"./fromXRotation\":287,\"./fromYRotation\":288,\"./fromZRotation\":289,\"./frustum\":290,\"./identity\":291,\"./invert\":293,\"./lookAt\":294,\"./multiply\":295,\"./ortho\":296,\"./perspective\":297,\"./perspectiveFromFieldOfView\":298,\"./rotate\":299,\"./rotateX\":300,\"./rotateY\":301,\"./rotateZ\":302,\"./scale\":303,\"./str\":304,\"./translate\":305,\"./transpose\":306}],293:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*g-f*d,M=u*m-h*d,A=u*v-p*d,S=f*m-h*g,E=f*v-p*g,C=h*v-p*m,L=y*C-x*E+b*S+_*A-w*M+T*k;if(!L)return null;return L=1/L,t[0]=(s*C-l*E+c*S)*L,t[1]=(i*E-n*C-a*S)*L,t[2]=(g*T-m*w+v*_)*L,t[3]=(h*w-f*T-p*_)*L,t[4]=(l*A-o*C-c*M)*L,t[5]=(r*C-i*A+a*M)*L,t[6]=(m*b-d*T-v*x)*L,t[7]=(u*T-h*b+p*x)*L,t[8]=(o*E-s*A+c*k)*L,t[9]=(n*A-r*E-a*k)*L,t[10]=(d*w-g*b+v*y)*L,t[11]=(f*b-u*w-p*y)*L,t[12]=(s*M-o*S-l*k)*L,t[13]=(r*S-n*M+i*k)*L,t[14]=(g*x-d*_-m*y)*L,t[15]=(u*_-f*x+h*y)*L,t}},{}],294:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,g=e[0],m=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=g-_,h=m-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{\"./identity\":291}],295:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*g,t[1]=x*i+b*l+_*h+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*g,t[5]=x*i+b*l+_*h+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*g,t[9]=x*i+b*l+_*h+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],296:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],297:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],298:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],299:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S,E=n[0],C=n[1],L=n[2],I=Math.sqrt(E*E+C*C+L*L);if(Math.abs(I)<1e-6)return null;E*=I=1/I,C*=I,L*=I,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=C*E*o+L*i,_=L*E*o-C*i,w=E*C*o-L*i,T=C*C*o+a,k=L*C*o+E*i,M=E*L*o+C*i,A=C*L*o-E*i,S=L*L*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*M+f*A+g*S,t[9]=l*M+h*A+m*S,t[10]=c*M+p*A+v*S,t[11]=u*M+d*A+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],300:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],301:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],302:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],303:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],304:[function(t,e,r){e.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},{}],305:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],306:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],307:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,g=0;g<2;++g)d+=Math.pow(u[f][g]-e[g],2);d<p&&(p=d,h=f)}var m=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(m[f]<-.001||m[f]>1.0001)return null;v+=m[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,m),m]}},{barycentric:78,\"polytope-closest-point/lib/closest_point_2d.js\":525}],308:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:310}],309:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),m=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,T,k,M,A,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=M,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function M(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function A(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function C(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function L(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function I(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],m=i[f],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*M(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,A=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,A)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,C=t.meshColor||[1,1,1,1],L=t.vertexUVs,I=t.vertexIntensity,P=t.cellUVs,z=t.cellIntensity,O=1/0,D=-1/0;if(!L&&!P)if(I)if(t.vertexIntensityBounds)O=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];O=Math.min(O,F),D=Math.max(D,F)}else if(z)if(t.cellIntensityBounds)O=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<z.length;++R){F=z[R];O=Math.min(O,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];O=Math.min(O,F),D=Math.max(D,F)}this.intensity=I||(z||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(z||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:C,this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:C,this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],g.push(Z[0],Z[1]),m.push(R)}q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:C)?this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=g(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=A(t),s=S(t),l=E(t),c=C(t),f=L(t),h=I(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),P=i(t),z=i(t),O=i(t),D=i(t),R=i(t),F=a(t,[{buffer:P,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:O,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,g,m,v,x,b,k,_,w,M,P,R,z,O,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":307,\"./lib/shaders\":308,colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-shader\":335,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495,normals:498,\"simplicial-complex-contour\":556,\"typedarray-pool\":595}],310:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],311:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"gl-buffer\":259,\"gl-shader\":335}],312:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=h[g];var _=6*n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,M=.5*(d[w+2]+d[w]),A=d[w+2]-d[w];e[w]=2*k/A,t[w]=2*(T-M)/A}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,C=s.tickMarkWidth,L=s.tickMarkColor,I=6*f[0].length,P=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),z=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),O=0+6*P,D=6*Math.max(0,z-P),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=I+6*R,N=6*Math.max(0,F-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=C[1]*m/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=C[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,O,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=C[3]*m/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=C[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,O,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":314,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335}],313:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"gl-buffer\":259,\"gl-shader\":335}],314:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:316}],315:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,m=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),M=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var A=2/h[2+(1^t)]-h[1^t];u[1^t]=A*s[1^t]-1,d[t]&&(u[1^t]-=A*p*g[t],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t]&&T&&(u[1^t]-=A*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=A*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=A*p*g[t+2],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t+2]&&T&&(u[1^t]+=A*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],g=a[o+2]-f,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,h[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,g=p.text,m=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(m,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335,\"text-cache\":575}],316:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],317:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":311,\"./lib/grid\":312,\"./lib/line\":313,\"./lib/text\":315,\"gl-select-static\":334}],318:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,g=!!a.shift,m=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,M=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&m&&!h&&!p&&!g||m&&!h&&!p&&g)&&c.rotate(M,w*k*b,-T*k*_,0),(l&&m&&!h&&!p&&!g||v||m&&h&&!p&&!g)&&c.pan(M,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&m&&!h&&!p&&!g||y||m&&!h&&p&&!g)){var A=-d.zoomSpeed*_/window.innerHeight*(M-c.lastT())*100;c.pan(M,0,0,f*(Math.exp(A)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":54,\"has-passive-events\":441,\"mouse-change\":483,\"mouse-event-offset\":484,\"mouse-wheel\":486,\"right-now\":542}],319:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":335,glslify:320}],320:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],321:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function m(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new g,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},M=i(r,k);M.enable=!k.disable;var A=t.spikes||{},S=o(r,A),E=[],C=[],L=[],I=[],P=!0,z=!0,O=new Array(16),D=new Array(16),R={view:null,projection:O,model:D,_ortho:!1},F=(z=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:M,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,z=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",P=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=I.length,n=0;n<e;++n)L[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(L[o]+a<255){C[n]=o,i.setPickBase(L[o]+1),L[o]+=a;continue t}var l=s(r,F);C[n]=e,I.push(l),L.push(a),i.setPickBase(1),e+=1}else C[n]=-1}for(;e>0&&0===L[e-1];)L.pop(),I.pop().dispose()}function q(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},P=!0,z=!0)},N.add=function(t){N._stopped||(t.axes=M,E.push(t),C.push(-1),P=!0,z=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),C.pop(),P=!0,z=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",q),N.mouseListener.enabled=!1,!N.contextLost)){M.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<I.length;++t)I[t].dispose();_.dispose(),r=null,M=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=I.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(z=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=I[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(C[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),P=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),P=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",q);var H=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[H[0].slice(),H[1].slice()];function Y(){if(!q()){U();var t=N.camera.tick();R.view=N.camera.matrix,P=P||t,z=z||t,M.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=H[0],i=H[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(L=E[o]).pixelRatio=N.pixelRatio,L.axes=N.axes,P=P||!!L.dirty,z=z||!!L.dirty;var s=L.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var g=.05*(i[p]-n[p]);n[p]=n[p]-g,i[p]=i[p]+g}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(z=z||v,P=P||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=m((d[1][o]-d[0][o])/10);M.autoTicks?M.update({bounds:d,tickSpacing:y}):M.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var g=0;for(d=0;d<3;++d)g=Math.max(g,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/g,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(L=E[o]).axesBounds=d,N.clipToBounds&&(L.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),z&&(z=!1,function(){if(!q()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=I.length,n=0;n<e;++n){var i=I[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(C[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var A=!1;M.enable&&(A=A||M.isTransparent(),M.draw(R)),S.axes=M,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(L=E[o]).axes=M,L.pixelRatio=N.pixelRatio,L.isOpaque&&L.isOpaque()&&L.draw(R),L.isTransparent&&L.isTransparent()&&(A=!0)}if(A){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),M.enable&&M.isTransparent()&&M.drawTransparent(R);for(o=0;o<e;++o){(L=E[o]).isOpaque&&L.isOpaque()&&L.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),M.isTransparent()&&M.drawTransparent(R);for(o=0;o<e;++o){var L;(L=E[o]).isTransparent&&L.isTransparent()&&L.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}P=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(P=!0,Y())},N},createCamera:n}},{\"./camera.js\":318,\"./lib/shader\":319,\"a-big-triangle\":64,\"gl-axes3d\":250,\"gl-axes3d/properties\":258,\"gl-fbo\":269,\"gl-mat4/ortho\":296,\"gl-mat4/perspective\":297,\"gl-select-static\":334,\"gl-spikes3d\":345,\"is-mobile\":467,\"mouse-change\":483}],322:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:323}],323:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],324:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":322,\"gl-buffer\":259,\"gl-shader\":335,\"typedarray-pool\":595}],325:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+f*g+h*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*g,t[3]=s*h+l*m,t}},{}],326:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],327:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":600}],328:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{\"gl-shader\":335,glslify:329}],329:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],330:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t||t>1?1:t}function m(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),g=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new m(e,r,n,o,f,h,p,d,g,s,c,u);return v.update(t),v};var v=m.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],M=[[0,0,0],[0,0,0]];function A(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function C(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,g=function(t){for(var e=M,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var v=T,C=0;C<16;++C)v[C]=0;for(C=0;C<4;++C)v[5*C]=1;v[5*m]=0,i[m]<0?v[12+m]=d[0][m]:v[12+m]=d[1][m],s(v,c,v),l.model=v;var L=(m+1)%3,I=(m+2)%3,P=A(x),z=A(b);P[L]=1,z[I]=1;var O=p(0,0,0,S(_,P)),D=p(0,0,0,S(w,z));if(Math.abs(O[1])>Math.abs(D[1])){var R=O;O=D,D=R,R=P,P=z,z=R;var F=L;L=I,I=F}O[0]<0&&(P[L]=-1),D[1]>0&&(z[I]=-1);var B=0,N=0;for(C=0;C<4;++C)B+=Math.pow(c[4*L+C],2),N+=Math.pow(c[4*I+C],2);P[L]/=Math.sqrt(B),z[I]/=Math.sqrt(N),l.axes[0]=P,l.axes[1]=z,l.fragClipBounds[0]=E(k,g[0],m,-1e8),l.fragClipBounds[1]=E(k,g[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function I(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&C(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=L,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){I(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,m=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=P(h,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;x+=3*k.cells.length,b+=2*M.edges.length}}var S=x+b,E=o.mallocFloat(3*S),C=o.mallocFloat(4*S),L=o.mallocFloat(2*S),I=o.mallocUint32(S);if(S>0){var z=0,O=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var q=0;Array.isArray(m)?q=n<m.length?+m[n]:0:m&&(q=+m);var H=Math.cos(q),G=Math.sin(q);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-A[0][0]:Y<0?1+A[1][0]:1,W*=W>0?1-A[0][1]:W<0?1+A[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)C[4*z+$]=D[$];I[z]=y;var tt=J[K[Q]];L[2*z]=V*(H*tt[0]-G*tt[1]+X[0]),L[2*z+1]=V*(G*tt[0]+H*tt[1]+X[1]),z+=1}for(Z=M.edges,J=M.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)C[4*O+$]=R[$];I[O]=y;tt=J[K[Q]];L[2*O]=V*(H*tt[0]-G*tt[1]+X[0]),L[2*O+1]=V*(G*tt[0]+H*tt[1]+X[1]),O+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(C),this.glyphBuffer.update(L),this.idBuffer.update(I),o.free(E),o.free(C),o.free(L),o.free(I)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":326,\"./lib/glyphs\":327,\"./lib/shaders\":328,\"gl-buffer\":259,\"gl-mat4/multiply\":295,\"gl-vao\":358,\"is-string-blank\":470,\"typedarray-pool\":595}],331:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:332}],332:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],333:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var g=s[2]-s[0],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,h,g,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":331,\"gl-buffer\":259,\"gl-shader\":335}],334:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function 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a=[t.id,e.id,r.join(\":\"),i.join(\":\")].join(\"@\"),o=this.programs[a];return o&&this.gl.isProgram(o)||(this.programs[a]=o=function(t,e,r,i,a){var o=t.createProgram();t.attachShader(o,e),t.attachShader(o,r);for(var s=0;s<i.length;++s)t.bindAttribLocation(o,a[s],i[s]);if(t.linkProgram(o),!t.getProgramParameter(o,t.LINK_STATUS)){var l=t.getProgramInfoLog(o);throw new n(l,\"Error linking program: \"+l)}return o}(this.gl,t.shader,e.shader,r,i),t.programs.push(a),e.programs.push(a)),o}},{\"./GLError\":336,\"gl-format-compiler-error\":270,\"weakmap-shim\":605}],342:[function(t,e,r){\"use strict\";function n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}e.exports=function(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r};var i=n.prototype;i.update=function(t){t=t||{},this.enable=(t.enable||[!0,!0,!1,!1]).slice(),this.width=(t.width||[1,1,1,1]).slice(),this.color=(t.color||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map((function(t){return t.slice()})),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},i.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],343:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],344:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta = weight * clipOffset * screenShape;\\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":335,glslify:343}],345:[function(t,e,r){\"use strict\";var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":344,\"gl-buffer\":259,\"gl-vao\":358}],346:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:347}],347:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],348:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],g=0,m=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),m=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(g,m,m,m,g,g);var M=c.length;u.push([M-6,M-5,M-4],[M-3,M-2,M-1])}var A=h;h=p,p=A;var S=y;y=v,v=S;var E=g;g=m,m=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var g=u[s],m=f.length;f=f.concat(g.positions),p=p.concat(g.vectors),d=d.concat(g.vertexIntensity);for(c=0;c<g.cells.length;c++){var v=g.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+m)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),g=s(a[1],c),m=s(a[2],u),v=d+1,y=g+1,x=m+1;if(d=l(d,0,f-1),v=l(v,0,f-1),g=l(g,0,h-1),y=l(y,0,h-1),m=l(m,0,p-1),x=l(x,0,p-1),d<0||g<0||m<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,M,A=a[0][d],S=a[0][v],E=a[1][g],C=a[1][y],L=a[2][m],I=(o-A)/(S-A),P=(c-E)/(C-E),z=(u-L)/(a[2][x]-L);switch(isFinite(I)||(I=.5),isFinite(P)||(P=.5),isFinite(z)||(z=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(g=h-1-g,y=h-1-y),r.reversedZ&&(m=p-1-m,x=p-1-x),r.filled){case 5:k=m,M=x,w=g*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=m,M=x,b=d*p,_=v*p,w=g*p*f,T=y*p*f;break;case 3:w=g,T=y,k=m*h,M=x*h,b=d*h*p,_=v*h*p;break;case 2:w=g,T=y,b=d*h,_=v*h,k=m*h*f,M=x*h*f;break;case 1:b=d,_=v,k=m*f,M=x*f,w=g*f*p,T=y*f*p;break;default:b=d,_=v,w=g*f,T=y*f,k=m*f*h,M=x*f*h}var O=i[b+w+k],D=i[b+w+M],R=i[b+T+k],F=i[b+T+M],B=i[_+w+k],N=i[_+w+M],j=i[_+T+k],U=i[_+T+M],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,O,B,I),n.lerp(q,D,N,I),n.lerp(H,R,j,I),n.lerp(G,F,U,I);var Y=n.create(),W=n.create();n.lerp(Y,V,H,P),n.lerp(W,q,G,P);var X=n.create();return n.lerp(X,Y,W,z),X}(e,t,p)},g=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},m=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,M=k*k,A=1,S=0,E=r.length;E>1&&(A=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),g=c(r),m=c(n),v=Math.min(d,g,m);return isFinite(v)?v:1}(r));for(var C=0;C<E;C++){var L=n.create();n.copy(L,r[C]);var I=[L],P=[],z=d(L),O=L;P.push(z);var D=[],R=g(L,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),m.push({points:I,velocities:P,divergences:D});for(var B=0;B<100*i&&I.length<i&&T(L);){B++;var N=n.clone(z),j=n.squaredLength(N);if(0===j)break;if(j>M&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,L),z=d(N),n.squaredDistance(O,N)-M>-1e-4*M){I.push(N),O=N,P.push(z);R=g(N,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}L=N}}var U=o(m,t.colormap,S,A);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*A/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":346,\"gl-cone3d\":260,\"gl-vec3\":377,\"gl-vec4\":413}],349:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":335,glslify:350}],350:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],351:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new A(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",m.update(v),m};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),m=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function A(t,e,r,n,i,a,o,l,c,u,h,p,d,g,m){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=m,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=A.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],C={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function L(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=C.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=C.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return C.showSurface=o,C.showContour=s,C}var I={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},P=w.slice(),z=[1,0,0,0,1,0,0,0,1];function O(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=P;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=L(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return O.call(this,t,!1)},S.drawTransparent=function(t){return O.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=z;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=L(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,m=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var g=[0,0];g[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],g,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)m(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),M=x.get(1,o,v,1),A=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-M*A,C=A*w-S*_,L=_*M-w*k,I=Math.sqrt(E*E+C*C+L*L);I<1e-8?(I=Math.max(Math.abs(E),Math.abs(C),Math.abs(L)))<1e-8?(L=1,C=E=0,I=1):I=1/I:I=1/Math.sqrt(I),b.set(o,v,0,E*I),b.set(o,v,1,C*I),b.set(o,v,2,L*I)}s.free(x.data);var P=[1/0,1/0,1/0],z=[-1/0,-1/0,-1/0],O=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+v+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var Y=o+T[H][0],W=v+T[H][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),C=b.get(Y+1,W+1,1),L=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=C,N[j++]=L,P[0]=Math.min(P[0],X+this.objectOffset[0]),P[1]=Math.min(P[1],Z+this.objectOffset[1]),P[2]=Math.min(P[2],G+this.objectOffset[2]),O=Math.min(O,J),z[0]=Math.max(z[0],X+this.objectOffset[0]),z[1]=Math.max(z[1],Z+this.objectOffset[1]),z[2]=Math.max(z[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(O=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-O)/(D-O);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[P,z],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===O&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[O,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var gt=($+dt+1)%3;for(V=0;V<2;++V){var mt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var vt=q?ht:1-ht;if(W=0|Math.min(Math.max(ft+q,0),a[1]),G=dt<2?this._field[gt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=mt*vt;nt[dt]+=yt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,M=+x[1],A=0|M,S=0|Math.min(A+1,i[1]),E=M-A,C=1-E,L=k*C,I=k*E,P=T*C,z=T*E,O=L*f.get(_,A)+I*f.get(_,S)+P*f.get(w,A)+z*f.get(w,S),D=L*p.get(_,A)+I*p.get(_,S)+P*p.get(w,A)+z*p.get(w,S);if(isNaN(O)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=O,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":349,\"binary-search-bounds\":96,\"bit-twiddle\":97,colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495,\"ndarray-gradient\":488,\"ndarray-ops\":490,\"ndarray-pack\":491,\"surface-nets\":570,\"typedarray-pool\":595}],352:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),g=t(\"detect-kerning\"),m=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\t\"+(T.normalViewport?\"\":\"vec2 positionOffset = vec2(positionOffset.x,- positionOffset.y);\")+\"\\n\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ positionOffset))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\t\"+(T.normalViewport?\"position.y = 1. - position.y;\":\"\")+\"\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),T.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);m(t.kerning,g(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),M=0,A=0;M<this.counts.length;M++){var S=this.counts[M];if(w)for(var E=0;E<S;E++)k[A++]=t.position[2*M],k[A++]=t.position[2*M+1];else for(var C=0;C<S;C++)k[A++]=t.position[M][0],k[A++]=t.position[M][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var L=u.mallocUint8(this.count),I=u.mallocFloat(2*this.count);this.textWidth=[];for(var P=0,z=0;P<this.counts.length;P++){for(var O=this.counts[P],D=this.font[P]||this.font[0],R=this.fontAtlas[P]||this.fontAtlas[0],F=0;F<O;F++){var B=this.text.charAt(z),N=this.text.charAt(z-1);if(L[z]=R.ids[B],I[2*z]=D.width[B],F){var j=I[2*z-2],U=I[2*z],V=I[2*z-1]+.5*j+.5*U;if(this.kerning){var q=D.kerning[N+B];q&&(V+=.001*q)}I[2*z+1]=V}else I[2*z+1]=.5*I[2*z];z++}this.textWidth.push(I.length?.5*I[2*z-2]+I[2*z-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:L,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:I,type:\"float\",usage:\"stream\"}),u.freeUint8(L),u.freeFloat(I),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],T.normalViewport||(i*=-1),i}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)H.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;H=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.normalViewport=!1,T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{\"bit-twiddle\":97,\"color-normalize\":125,\"css-font\":144,\"detect-kerning\":172,\"es6-weak-map\":233,\"flatten-vertex-data\":244,\"font-atlas\":245,\"font-measure\":246,\"gl-util/context\":354,\"is-plain-obj\":469,\"object-assign\":499,\"parse-rect\":504,\"parse-unit\":506,\"pick-by-alias\":511,regl:540,\"to-px\":578,\"typedarray-pool\":595}],353:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function g(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function m(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=g(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var 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strict\";e.exports=function(t,e){var r=t.length;if(0===r)throw new Error(\"Must have at least d+1 points\");var i=t[0].length;if(r<=i)throw new Error(\"Must input at least d+1 points\");var o=t.slice(0,i+1),s=n.apply(void 0,o);if(0===s)throw new Error(\"Input not in general position\");for(var l=new Array(i+1),u=0;u<=i;++u)l[u]=u;s<0&&(l[0]=1,l[1]=0);var f=new a(l,new Array(i+1),!1),h=f.adjacent,p=new Array(i+2);for(u=0;u<=i;++u){for(var d=l.slice(),g=0;g<=i;++g)g===u&&(d[g]=-1);var m=d[0];d[0]=d[1],d[1]=m;var v=new a(d,new Array(i+1),!0);h[u]=v,p[u]=v}p[i+1]=f;for(u=0;u<=i;++u){d=h[u].vertices;var y=h[u].adjacent;for(g=0;g<=i;++g){var x=d[g];if(x<0)y[g]=f;else for(var b=0;b<=i;++b)h[b].vertices.indexOf(x)<0&&(y[g]=h[b])}}var _=new c(i,o,p),w=!!e;for(u=i+1;u<r;++u)_.insert(t[u],w);return _.boundary()};var n=t(\"robust-orientation\"),i=t(\"simplicial-complex\").compareCells;function a(t,e,r){this.vertices=t,this.adjacent=e,this.boundary=r,this.lastVisited=-1}function o(t,e,r){this.vertices=t,this.cell=e,this.index=r}function s(t,e){return i(t.vertices,e.vertices)}a.prototype.flip=function(){var t=this.vertices[0];this.vertices[0]=this.vertices[1],this.vertices[1]=t;var e=this.adjacent[0];this.adjacent[0]=this.adjacent[1],this.adjacent[1]=e};var l=[];function c(t,e,r){this.dimension=t,this.vertices=e,this.simplices=r,this.interior=r.filter((function(t){return!t.boundary})),this.tuple=new Array(t+1);for(var i=0;i<=t;++i)this.tuple[i]=this.vertices[i];var a=l[t];a||(a=l[t]=function(t){for(var e=[\"function orient(){var tuple=this.tuple;return test(\"],r=0;r<=t;++r)r>0&&e.push(\",\"),e.push(\"tuple[\",r,\"]\");e.push(\")}return orient\");var i=new Function(\"test\",e.join(\"\")),a=n[t+1];return a||(a=n),i(a)}(t)),this.orient=a}var u=c.prototype;u.handleBoundaryDegeneracy=function(t,e){var r=this.dimension,n=this.vertices.length-1,i=this.tuple,a=this.vertices,o=[t];for(t.lastVisited=-n;o.length>0;){(t=o.pop()).vertices;for(var s=t.adjacent,l=0;l<=r;++l){var c=s[l];if(c.boundary&&!(c.lastVisited<=-n)){for(var u=c.vertices,f=0;f<=r;++f){var h=u[f];i[f]=h<0?e:a[h]}var p=this.orient();if(p>0)return c;c.lastVisited=-n,0===p&&o.push(c)}}}return null},u.walk=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,a=this.tuple,o=e?this.interior.length*Math.random()|0:this.interior.length-1,s=this.interior[o];t:for(;!s.boundary;){for(var l=s.vertices,c=s.adjacent,u=0;u<=n;++u)a[u]=i[l[u]];s.lastVisited=r;for(u=0;u<=n;++u){var f=c[u];if(!(f.lastVisited>=r)){var h=a[u];a[u]=t;var p=this.orient();if(a[u]=h,p<0){s=f;continue t}f.boundary?f.lastVisited=-r:f.lastVisited=r}}return}return s},u.addPeaks=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,l=this.tuple,c=this.interior,u=this.simplices,f=[e];e.lastVisited=r,e.vertices[e.vertices.indexOf(-1)]=r,e.boundary=!1,c.push(e);for(var h=[];f.length>0;){var p=(e=f.pop()).vertices,d=e.adjacent,g=p.indexOf(r);if(!(g<0))for(var m=0;m<=n;++m)if(m!==g){var v=d[m];if(v.boundary&&!(v.lastVisited>=r)){var y=v.vertices;if(v.lastVisited!==-r){for(var x=0,b=0;b<=n;++b)y[b]<0?(x=b,l[b]=t):l[b]=i[y[b]];if(this.orient()>0){y[x]=r,v.boundary=!1,c.push(v),f.push(v),v.lastVisited=r;continue}v.lastVisited=-r}var _=v.adjacent,w=p.slice(),T=d.slice(),k=new a(w,T,!0);u.push(k);var M=_.indexOf(e);if(!(M<0)){_[M]=k,T[g]=v,w[m]=-1,T[m]=e,d[m]=k,k.flip();for(b=0;b<=n;++b){var A=w[b];if(!(A<0||A===r)){for(var S=new Array(n-1),E=0,C=0;C<=n;++C){var L=w[C];L<0||C===b||(S[E++]=L)}h.push(new o(S,k,b))}}}}}}h.sort(s);for(m=0;m+1<h.length;m+=2){var I=h[m],P=h[m+1],z=I.index,O=P.index;z<0||O<0||(I.cell.adjacent[I.index]=P.cell,P.cell.adjacent[P.index]=I.cell)}},u.insert=function(t,e){var r=this.vertices;r.push(t);var n=this.walk(t,e);if(n){for(var i=this.dimension,a=this.tuple,o=0;o<=i;++o){var s=n.vertices[o];a[o]=s<0?t:r[s]}var l=this.orient(a);l<0||(0!==l||(n=this.handleBoundaryDegeneracy(n,t)))&&this.addPeaks(t,n)}},u.boundary=function(){for(var t=this.dimension,e=[],r=this.simplices,n=r.length,i=0;i<n;++i){var a=r[i];if(a.boundary){for(var o=new Array(t),s=a.vertices,l=0,c=0,u=0;u<=t;++u)s[u]>=0?o[l++]=s[u]:c=1&u;if(c===(1&t)){var f=o[0];o[0]=o[1],o[1]=f}e.push(o)}}return e}},{\"robust-orientation\":548,\"simplicial-complex\":558}],460:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\");function i(t,e,r,n,i){this.mid=t,this.left=e,this.right=r,this.leftPoints=n,this.rightPoints=i,this.count=(e?e.count:0)+(r?r.count:0)+n.length}e.exports=function(t){if(!t||0===t.length)return new v(null);return new v(m(t))};var a=i.prototype;function o(t,e){t.mid=e.mid,t.left=e.left,t.right=e.right,t.leftPoints=e.leftPoints,t.rightPoints=e.rightPoints,t.count=e.count}function s(t,e){var r=m(e);t.mid=r.mid,t.left=r.left,t.right=r.right,t.leftPoints=r.leftPoints,t.rightPoints=r.rightPoints,t.count=r.count}function l(t,e){var r=t.intervals([]);r.push(e),s(t,r)}function c(t,e){var r=t.intervals([]),n=r.indexOf(e);return n<0?0:(r.splice(n,1),s(t,r),1)}function u(t,e,r){for(var n=0;n<t.length&&t[n][0]<=e;++n){var i=r(t[n]);if(i)return i}}function f(t,e,r){for(var n=t.length-1;n>=0&&t[n][1]>=e;--n){var i=r(t[n]);if(i)return i}}function h(t,e){for(var r=0;r<t.length;++r){var n=e(t[r]);if(n)return n}}function p(t,e){return t-e}function d(t,e){var r=t[0]-e[0];return r||t[1]-e[1]}function g(t,e){var r=t[1]-e[1];return r||t[0]-e[0]}function m(t){if(0===t.length)return null;for(var e=[],r=0;r<t.length;++r)e.push(t[r][0],t[r][1]);e.sort(p);var n=e[e.length>>1],a=[],o=[],s=[];for(r=0;r<t.length;++r){var l=t[r];l[1]<n?a.push(l):n<l[0]?o.push(l):s.push(l)}var c=s,u=s.slice();return c.sort(d),u.sort(g),new i(n,m(a),m(o),c,u)}function v(t){this.root=t}a.intervals=function(t){return t.push.apply(t,this.leftPoints),this.left&&this.left.intervals(t),this.right&&this.right.intervals(t),t},a.insert=function(t){var e=this.count-this.leftPoints.length;if(this.count+=1,t[1]<this.mid)this.left?4*(this.left.count+1)>3*(e+1)?l(this,t):this.left.insert(t):this.left=m([t]);else if(t[0]>this.mid)this.right?4*(this.right.count+1)>3*(e+1)?l(this,t):this.right.insert(t):this.right=m([t]);else{var r=n.ge(this.leftPoints,t,d),i=n.ge(this.rightPoints,t,g);this.leftPoints.splice(r,0,t),this.rightPoints.splice(i,0,t)}},a.remove=function(t){var e=this.count-this.leftPoints;if(t[1]<this.mid)return this.left?4*(this.right?this.right.count:0)>3*(e-1)?c(this,t):2===(s=this.left.remove(t))?(this.left=null,this.count-=1,1):(1===s&&(this.count-=1),s):0;if(t[0]>this.mid)return this.right?4*(this.left?this.left.count:0)>3*(e-1)?c(this,t):2===(s=this.right.remove(t))?(this.right=null,this.count-=1,1):(1===s&&(this.count-=1),s):0;if(1===this.count)return this.leftPoints[0]===t?2:0;if(1===this.leftPoints.length&&this.leftPoints[0]===t){if(this.left&&this.right){for(var r=this,i=this.left;i.right;)r=i,i=i.right;if(r===this)i.right=this.right;else{var a=this.left,s=this.right;r.count-=i.count,r.right=i.left,i.left=a,i.right=s}o(this,i),this.count=(this.left?this.left.count:0)+(this.right?this.right.count:0)+this.leftPoints.length}else this.left?o(this,this.left):o(this,this.right);return 1}for(a=n.ge(this.leftPoints,t,d);a<this.leftPoints.length&&this.leftPoints[a][0]===t[0];++a)if(this.leftPoints[a]===t){this.count-=1,this.leftPoints.splice(a,1);for(s=n.ge(this.rightPoints,t,g);s<this.rightPoints.length&&this.rightPoints[s][1]===t[1];++s)if(this.rightPoints[s]===t)return this.rightPoints.splice(s,1),1}return 0},a.queryPoint=function(t,e){if(t<this.mid){if(this.left)if(r=this.left.queryPoint(t,e))return r;return u(this.leftPoints,t,e)}if(t>this.mid){var r;if(this.right)if(r=this.right.queryPoint(t,e))return r;return f(this.rightPoints,t,e)}return h(this.leftPoints,e)},a.queryInterval=function(t,e,r){var n;if(t<this.mid&&this.left&&(n=this.left.queryInterval(t,e,r)))return n;if(e>this.mid&&this.right&&(n=this.right.queryInterval(t,e,r)))return n;return e<this.mid?u(this.leftPoints,e,r):t>this.mid?f(this.rightPoints,t,r):h(this.leftPoints,r)};var y=v.prototype;y.insert=function(t){this.root?this.root.insert(t):this.root=new i(t[0],null,null,[t],[t])},y.remove=function(t){if(this.root){var e=this.root.remove(t);return 2===e&&(this.root=null),0!==e}return!1},y.queryPoint=function(t,e){if(this.root)return this.root.queryPoint(t,e)},y.queryInterval=function(t,e,r){if(t<=e&&this.root)return this.root.queryInterval(t,e,r)},Object.defineProperty(y,\"count\",{get:function(){return this.root?this.root.count:0}}),Object.defineProperty(y,\"intervals\",{get:function(){return this.root?this.root.intervals([]):[]}})},{\"binary-search-bounds\":461}],461:[function(t,e,r){arguments[4][243][0].apply(r,arguments)},{dup:243}],462:[function(t,e,r){\"use strict\";e.exports=function(t,e){e=e||new Array(t.length);for(var r=0;r<t.length;++r)e[t[r]]=r;return e}},{}],463:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=r;return e}},{}],464:[function(t,e,r){e.exports=!0},{}],465:[function(t,e,r){function n(t){return!!t.constructor&&\"function\"==typeof t.constructor.isBuffer&&t.constructor.isBuffer(t)}\n",
+       "/*!\n",
+       " * Determine if an object is a Buffer\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
+       "e.exports=function(t){return null!=t&&(n(t)||function(t){return\"function\"==typeof t.readFloatLE&&\"function\"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],466:[function(t,e,r){\"use strict\";e.exports=\"undefined\"!=typeof navigator&&(/MSIE/.test(navigator.userAgent)||/Trident\\//.test(navigator.appVersion))},{}],467:[function(t,e,r){\"use strict\";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||\"undefined\"==typeof navigator||(e=navigator.userAgent),e&&e.headers&&\"string\"==typeof e.headers[\"user-agent\"]&&(e=e.headers[\"user-agent\"]),\"string\"!=typeof e)return!1;var r=t.tablet?i.test(e):n.test(e);return!r&&t.tablet&&t.featureDetect&&navigator&&navigator.maxTouchPoints>1&&-1!==e.indexOf(\"Macintosh\")&&-1!==e.indexOf(\"Safari\")&&(r=!0),r}},{}],468:[function(t,e,r){\"use strict\";e.exports=function(t){var e=typeof t;return null!==t&&(\"object\"===e||\"function\"===e)}},{}],469:[function(t,e,r){\"use strict\";var n=Object.prototype.toString;e.exports=function(t){var e;return\"[object Object]\"===n.call(t)&&(null===(e=Object.getPrototypeOf(t))||e===Object.getPrototypeOf({}))}},{}],470:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e,r=t.length,n=0;n<r;n++)if(((e=t.charCodeAt(n))<9||e>13)&&32!==e&&133!==e&&160!==e&&5760!==e&&6158!==e&&(e<8192||e>8205)&&8232!==e&&8233!==e&&8239!==e&&8287!==e&&8288!==e&&12288!==e&&65279!==e)return!1;return!0}},{}],471:[function(t,e,r){\"use strict\";e.exports=function(t){return\"string\"==typeof t&&(t=t.trim(),!!(/^[mzlhvcsqta]\\s*[-+.0-9][^mlhvzcsqta]+/i.test(t)&&/[\\dz]$/i.test(t)&&t.length>4))}},{}],472:[function(t,e,r){e.exports=function(t,e,r){return t*(1-r)+e*r}},{}],473:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():(t=t||self).mapboxgl=n()}(this,(function(){\"use strict\";var t,e,r;function n(n,i){if(t)if(e){var a=\"var sharedChunk = {}; (\"+t+\")(sharedChunk); (\"+e+\")(sharedChunk);\",o={};t(o),(r=i(o)).workerUrl=window.URL.createObjectURL(new Blob([a],{type:\"text/javascript\"}))}else e=i;else t=i}return n(0,(function(t){function e(t,e){return t(e={exports:{}},e.exports),e.exports}var r=n;function n(t,e,r,n){this.cx=3*t,this.bx=3*(r-t)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*e,this.by=3*(n-e)-this.cy,this.ay=1-this.cy-this.by,this.p1x=t,this.p1y=n,this.p2x=r,this.p2y=n}n.prototype.sampleCurveX=function(t){return((this.ax*t+this.bx)*t+this.cx)*t},n.prototype.sampleCurveY=function(t){return((this.ay*t+this.by)*t+this.cy)*t},n.prototype.sampleCurveDerivativeX=function(t){return(3*this.ax*t+2*this.bx)*t+this.cx},n.prototype.solveCurveX=function(t,e){var r,n,i,a,o;for(void 0===e&&(e=1e-6),i=t,o=0;o<8;o++){if(a=this.sampleCurveX(i)-t,Math.abs(a)<e)return i;var s=this.sampleCurveDerivativeX(i);if(Math.abs(s)<1e-6)break;i-=a/s}if((i=t)<(r=0))return r;if(i>(n=1))return n;for(;r<n;){if(a=this.sampleCurveX(i),Math.abs(a-t)<e)return i;t>a?r=i:n=i,i=.5*(n-r)+r}return i},n.prototype.solve=function(t,e){return this.sampleCurveY(this.solveCurveX(t,e))};var i=a;function a(t,e){this.x=t,this.y=e}function o(t,e,n,i){var a=new r(t,e,n,i);return function(t){return 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r={transparent:[0,0,0,0],aliceblue:[240,248,255,1],antiquewhite:[250,235,215,1],aqua:[0,255,255,1],aquamarine:[127,255,212,1],azure:[240,255,255,1],beige:[245,245,220,1],bisque:[255,228,196,1],black:[0,0,0,1],blanchedalmond:[255,235,205,1],blue:[0,0,255,1],blueviolet:[138,43,226,1],brown:[165,42,42,1],burlywood:[222,184,135,1],cadetblue:[95,158,160,1],chartreuse:[127,255,0,1],chocolate:[210,105,30,1],coral:[255,127,80,1],cornflowerblue:[100,149,237,1],cornsilk:[255,248,220,1],crimson:[220,20,60,1],cyan:[0,255,255,1],darkblue:[0,0,139,1],darkcyan:[0,139,139,1],darkgoldenrod:[184,134,11,1],darkgray:[169,169,169,1],darkgreen:[0,100,0,1],darkgrey:[169,169,169,1],darkkhaki:[189,183,107,1],darkmagenta:[139,0,139,1],darkolivegreen:[85,107,47,1],darkorange:[255,140,0,1],darkorchid:[153,50,204,1],darkred:[139,0,0,1],darksalmon:[233,150,122,1],darkseagreen:[143,188,143,1],darkslateblue:[72,61,139,1],darkslategray:[47,79,79,1],darkslategrey:[47,79,79,1],darkturquoise:[0,206,209,1],darkviolet:[148,0,211,1],deeppink:[255,20,147,1],deepskyblue:[0,191,255,1],dimgray:[105,105,105,1],dimgrey:[105,105,105,1],dodgerblue:[30,144,255,1],firebrick:[178,34,34,1],floralwhite:[255,250,240,1],forestgreen:[34,139,34,1],fuchsia:[255,0,255,1],gainsboro:[220,220,220,1],ghostwhite:[248,248,255,1],gold:[255,215,0,1],goldenrod:[218,165,32,1],gray:[128,128,128,1],green:[0,128,0,1],greenyellow:[173,255,47,1],grey:[128,128,128,1],honeydew:[240,255,240,1],hotpink:[255,105,180,1],indianred:[205,92,92,1],indigo:[75,0,130,1],ivory:[255,255,240,1],khaki:[240,230,140,1],lavender:[230,230,250,1],lavenderblush:[255,240,245,1],lawngreen:[124,252,0,1],lemonchiffon:[255,250,205,1],lightblue:[173,216,230,1],lightcoral:[240,128,128,1],lightcyan:[224,255,255,1],lightgoldenrodyellow:[250,250,210,1],lightgray:[211,211,211,1],lightgreen:[144,238,144,1],lightgrey:[211,211,211,1],lightpink:[255,182,193,1],lightsalmon:[255,160,122,1],lightseagreen:[32,178,170,1],lightskyblue:[135,206,250,1],lightslategray:[119,136,153,1],lightslategrey:[119,136,153,1],lightsteelblue:[176,196,222,1],lightyellow:[255,255,224,1],lime:[0,255,0,1],limegreen:[50,205,50,1],linen:[250,240,230,1],magenta:[255,0,255,1],maroon:[128,0,0,1],mediumaquamarine:[102,205,170,1],mediumblue:[0,0,205,1],mediumorchid:[186,85,211,1],mediumpurple:[147,112,219,1],mediumseagreen:[60,179,113,1],mediumslateblue:[123,104,238,1],mediumspringgreen:[0,250,154,1],mediumturquoise:[72,209,204,1],mediumvioletred:[199,21,133,1],midnightblue:[25,25,112,1],mintcream:[245,255,250,1],mistyrose:[255,228,225,1],moccasin:[255,228,181,1],navajowhite:[255,222,173,1],navy:[0,0,128,1],oldlace:[253,245,230,1],olive:[128,128,0,1],olivedrab:[107,142,35,1],orange:[255,165,0,1],orangered:[255,69,0,1],orchid:[218,112,214,1],palegoldenrod:[238,232,170,1],palegreen:[152,251,152,1],paleturquoise:[175,238,238,1],palevioletred:[219,112,147,1],papayawhip:[255,239,213,1],peachpuff:[255,218,185,1],peru:[205,133,63,1],pink:[255,192,203,1],plum:[221,160,221,1],powderblue:[176,224,230,1],purple:[128,0,128,1],rebeccapurple:[102,51,153,1],red:[255,0,0,1],rosybrown:[188,143,143,1],royalblue:[65,105,225,1],saddlebrown:[139,69,19,1],salmon:[250,128,114,1],sandybrown:[244,164,96,1],seagreen:[46,139,87,1],seashell:[255,245,238,1],sienna:[160,82,45,1],silver:[192,192,192,1],skyblue:[135,206,235,1],slateblue:[106,90,205,1],slategray:[112,128,144,1],slategrey:[112,128,144,1],snow:[255,250,250,1],springgreen:[0,255,127,1],steelblue:[70,130,180,1],tan:[210,180,140,1],teal:[0,128,128,1],thistle:[216,191,216,1],tomato:[255,99,71,1],turquoise:[64,224,208,1],violet:[238,130,238,1],wheat:[245,222,179,1],white:[255,255,255,1],whitesmoke:[245,245,245,1],yellow:[255,255,0,1],yellowgreen:[154,205,50,1]};function n(t){return(t=Math.round(t))<0?0:t>255?255:t}function i(t){return n(\"%\"===t[t.length-1]?parseFloat(t)/100*255:parseInt(t))}function a(t){return(e=\"%\"===t[t.length-1]?parseFloat(t)/100:parseFloat(t))<0?0:e>1?1:e;var e}function o(t,e,r){return r<0?r+=1:r>1&&(r-=1),6*r<1?t+(e-t)*r*6:2*r<1?e:3*r<2?t+(e-t)*(2/3-r)*6:t}try{e.parseCSSColor=function(t){var e,s=t.replace(/ /g,\"\").toLowerCase();if(s in r)return r[s].slice();if(\"#\"===s[0])return 4===s.length?(e=parseInt(s.substr(1),16))>=0&&e<=4095?[(3840&e)>>4|(3840&e)>>8,240&e|(240&e)>>4,15&e|(15&e)<<4,1]:null:7===s.length&&(e=parseInt(s.substr(1),16))>=0&&e<=16777215?[(16711680&e)>>16,(65280&e)>>8,255&e,1]:null;var l=s.indexOf(\"(\"),c=s.indexOf(\")\");if(-1!==l&&c+1===s.length){var u=s.substr(0,l),f=s.substr(l+1,c-(l+1)).split(\",\"),h=1;switch(u){case\"rgba\":if(4!==f.length)return null;h=a(f.pop());case\"rgb\":return 3!==f.length?null:[i(f[0]),i(f[1]),i(f[2]),h];case\"hsla\":if(4!==f.length)return 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Qt=function(t,e,r){this.sensitivity=t?e?\"variant\":\"case\":e?\"accent\":\"base\",this.locale=r,this.collator=new Intl.Collator(this.locale?this.locale:[],{sensitivity:this.sensitivity,usage:\"search\"})};Qt.prototype.compare=function(t,e){return this.collator.compare(t,e)},Qt.prototype.resolvedLocale=function(){return new Intl.Collator(this.locale?this.locale:[]).resolvedOptions().locale};var $t=function(t,e,r,n,i){this.text=t,this.image=e,this.scale=r,this.fontStack=n,this.textColor=i},te=function(t){this.sections=t};te.fromString=function(t){return new te([new $t(t,null,null,null,null)])},te.prototype.isEmpty=function(){return 0===this.sections.length||!this.sections.some((function(t){return 0!==t.text.length||t.image&&0!==t.image.name.length}))},te.factory=function(t){return t instanceof te?t:te.fromString(t)},te.prototype.toString=function(){return 0===this.sections.length?\"\":this.sections.map((function(t){return t.text})).join(\"\")},te.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];if(n.image)t.push([\"image\",n.image.name]);else{t.push(n.text);var i={};n.fontStack&&(i[\"text-font\"]=[\"literal\",n.fontStack.split(\",\")]),n.scale&&(i[\"font-scale\"]=n.scale),n.textColor&&(i[\"text-color\"]=[\"rgba\"].concat(n.textColor.toArray())),t.push(i)}}return t};var ee=function(t){this.name=t.name,this.available=t.available};function re(t,e,r,n){return\"number\"==typeof t&&t>=0&&t<=255&&\"number\"==typeof e&&e>=0&&e<=255&&\"number\"==typeof r&&r>=0&&r<=255?void 0===n||\"number\"==typeof n&&n>=0&&n<=1?null:\"Invalid rgba value [\"+[t,e,r,n].join(\", \")+\"]: 'a' must be between 0 and 1.\":\"Invalid rgba value [\"+(\"number\"==typeof n?[t,e,r,n]:[t,e,r]).join(\", \")+\"]: 'r', 'g', and 'b' must be between 0 and 255.\"}function ne(t){if(null===t)return!0;if(\"string\"==typeof t)return!0;if(\"boolean\"==typeof t)return!0;if(\"number\"==typeof t)return!0;if(t instanceof Kt)return!0;if(t instanceof Qt)return!0;if(t instanceof te)return!0;if(t instanceof ee)return!0;if(Array.isArray(t)){for(var e=0,r=t;e<r.length;e+=1)if(!ne(r[e]))return!1;return!0}if(\"object\"==typeof t){for(var n in t)if(!ne(t[n]))return!1;return!0}return!1}function ie(t){if(null===t)return Ot;if(\"string\"==typeof t)return Rt;if(\"boolean\"==typeof t)return Ft;if(\"number\"==typeof t)return Dt;if(t instanceof Kt)return Bt;if(t instanceof Qt)return Ut;if(t instanceof te)return Vt;if(t instanceof ee)return qt;if(Array.isArray(t)){for(var e,r=t.length,n=0,i=t;n<i.length;n+=1){var a=ie(i[n]);if(e){if(e===a)continue;e=jt;break}e=a}return Ht(e||jt,r)}return Nt}function ae(t){var e=typeof t;return null===t?\"\":\"string\"===e||\"number\"===e||\"boolean\"===e?String(t):t instanceof Kt||t instanceof te||t instanceof ee?t.toString():JSON.stringify(t)}ee.prototype.toString=function(){return this.name},ee.fromString=function(t){return t?new ee({name:t,available:!1}):null},ee.prototype.serialize=function(){return[\"image\",this.name]};var oe=function(t,e){this.type=t,this.value=e};oe.parse=function(t,e){if(2!==t.length)return e.error(\"'literal' expression requires exactly one argument, but found \"+(t.length-1)+\" instead.\");if(!ne(t[1]))return e.error(\"invalid value\");var r=t[1],n=ie(r),i=e.expectedType;return\"array\"!==n.kind||0!==n.N||!i||\"array\"!==i.kind||\"number\"==typeof i.N&&0!==i.N||(n=i),new oe(n,r)},oe.prototype.evaluate=function(){return this.value},oe.prototype.eachChild=function(){},oe.prototype.outputDefined=function(){return!0},oe.prototype.serialize=function(){return\"array\"===this.type.kind||\"object\"===this.type.kind?[\"literal\",this.value]:this.value instanceof Kt?[\"rgba\"].concat(this.value.toArray()):this.value instanceof te?this.value.serialize():this.value};var se=function(t){this.name=\"ExpressionEvaluationError\",this.message=t};se.prototype.toJSON=function(){return this.message};var le={string:Rt,number:Dt,boolean:Ft,object:Nt},ce=function(t,e){this.type=t,this.args=e};ce.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r,n=1,i=t[0];if(\"array\"===i){var a,o;if(t.length>2){var s=t[1];if(\"string\"!=typeof s||!(s in le)||\"object\"===s)return e.error('The item type argument of \"array\" must be one of string, number, boolean',1);a=le[s],n++}else a=jt;if(t.length>3){if(null!==t[2]&&(\"number\"!=typeof t[2]||t[2]<0||t[2]!==Math.floor(t[2])))return e.error('The length argument to \"array\" must be a positive integer literal',2);o=t[2],n++}r=Ht(a,o)}else r=le[i];for(var l=[];n<t.length;n++){var c=e.parse(t[n],n,jt);if(!c)return null;l.push(c)}return new ce(r,l)},ce.prototype.evaluate=function(t){for(var e=0;e<this.args.length;e++){var r=this.args[e].evaluate(t);if(!Wt(this.type,ie(r)))return r;if(e===this.args.length-1)throw new se(\"Expected value to be of type \"+Gt(this.type)+\", but found \"+Gt(ie(r))+\" instead.\")}return null},ce.prototype.eachChild=function(t){this.args.forEach(t)},ce.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},ce.prototype.serialize=function(){var t=this.type,e=[t.kind];if(\"array\"===t.kind){var r=t.itemType;if(\"string\"===r.kind||\"number\"===r.kind||\"boolean\"===r.kind){e.push(r.kind);var n=t.N;(\"number\"==typeof n||this.args.length>1)&&e.push(n)}}return e.concat(this.args.map((function(t){return t.serialize()})))};var ue=function(t){this.type=Vt,this.sections=t};ue.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[1];if(!Array.isArray(r)&&\"object\"==typeof r)return e.error(\"First argument must be an image or text section.\");for(var n=[],i=!1,a=1;a<=t.length-1;++a){var o=t[a];if(i&&\"object\"==typeof o&&!Array.isArray(o)){i=!1;var s=null;if(o[\"font-scale\"]&&!(s=e.parse(o[\"font-scale\"],1,Dt)))return null;var l=null;if(o[\"text-font\"]&&!(l=e.parse(o[\"text-font\"],1,Ht(Rt))))return null;var c=null;if(o[\"text-color\"]&&!(c=e.parse(o[\"text-color\"],1,Bt)))return null;var u=n[n.length-1];u.scale=s,u.font=l,u.textColor=c}else{var f=e.parse(t[a],1,jt);if(!f)return null;var h=f.type.kind;if(\"string\"!==h&&\"value\"!==h&&\"null\"!==h&&\"resolvedImage\"!==h)return e.error(\"Formatted text type must be 'string', 'value', 'image' or 'null'.\");i=!0,n.push({content:f,scale:null,font:null,textColor:null})}}return new ue(n)},ue.prototype.evaluate=function(t){return new te(this.sections.map((function(e){var r=e.content.evaluate(t);return ie(r)===qt?new $t(\"\",r,null,null,null):new $t(ae(r),null,e.scale?e.scale.evaluate(t):null,e.font?e.font.evaluate(t).join(\",\"):null,e.textColor?e.textColor.evaluate(t):null)})))},ue.prototype.eachChild=function(t){for(var e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t(n.content),n.scale&&t(n.scale),n.font&&t(n.font),n.textColor&&t(n.textColor)}},ue.prototype.outputDefined=function(){return!1},ue.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t.push(n.content.serialize());var i={};n.scale&&(i[\"font-scale\"]=n.scale.serialize()),n.font&&(i[\"text-font\"]=n.font.serialize()),n.textColor&&(i[\"text-color\"]=n.textColor.serialize()),t.push(i)}return t};var fe=function(t){this.type=qt,this.input=t};fe.parse=function(t,e){if(2!==t.length)return e.error(\"Expected two arguments.\");var r=e.parse(t[1],1,Rt);return r?new fe(r):e.error(\"No image name provided.\")},fe.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=ee.fromString(e);return r&&t.availableImages&&(r.available=t.availableImages.indexOf(e)>-1),r},fe.prototype.eachChild=function(t){t(this.input)},fe.prototype.outputDefined=function(){return!1},fe.prototype.serialize=function(){return[\"image\",this.input.serialize()]};var he={\"to-boolean\":Ft,\"to-color\":Bt,\"to-number\":Dt,\"to-string\":Rt},pe=function(t,e){this.type=t,this.args=e};pe.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[0];if((\"to-boolean\"===r||\"to-string\"===r)&&2!==t.length)return e.error(\"Expected one argument.\");for(var n=he[r],i=[],a=1;a<t.length;a++){var o=e.parse(t[a],a,jt);if(!o)return null;i.push(o)}return new pe(n,i)},pe.prototype.evaluate=function(t){if(\"boolean\"===this.type.kind)return Boolean(this.args[0].evaluate(t));if(\"color\"===this.type.kind){for(var e,r,n=0,i=this.args;n<i.length;n+=1){if(r=null,(e=i[n].evaluate(t))instanceof Kt)return e;if(\"string\"==typeof e){var a=t.parseColor(e);if(a)return a}else if(Array.isArray(e)&&!(r=e.length<3||e.length>4?\"Invalid rbga value \"+JSON.stringify(e)+\": expected an array containing either three or four numeric values.\":re(e[0],e[1],e[2],e[3])))return new Kt(e[0]/255,e[1]/255,e[2]/255,e[3])}throw new se(r||\"Could not parse color from value '\"+(\"string\"==typeof e?e:String(JSON.stringify(e)))+\"'\")}if(\"number\"===this.type.kind){for(var o=null,s=0,l=this.args;s<l.length;s+=1){if(null===(o=l[s].evaluate(t)))return 0;var c=Number(o);if(!isNaN(c))return c}throw new se(\"Could not convert \"+JSON.stringify(o)+\" to number.\")}return\"formatted\"===this.type.kind?te.fromString(ae(this.args[0].evaluate(t))):\"resolvedImage\"===this.type.kind?ee.fromString(ae(this.args[0].evaluate(t))):ae(this.args[0].evaluate(t))},pe.prototype.eachChild=function(t){this.args.forEach(t)},pe.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},pe.prototype.serialize=function(){if(\"formatted\"===this.type.kind)return new ue([{content:this.args[0],scale:null,font:null,textColor:null}]).serialize();if(\"resolvedImage\"===this.type.kind)return new fe(this.args[0]).serialize();var t=[\"to-\"+this.type.kind];return this.eachChild((function(e){t.push(e.serialize())})),t};var de=[\"Unknown\",\"Point\",\"LineString\",\"Polygon\"],ge=function(){this.globals=null,this.feature=null,this.featureState=null,this.formattedSection=null,this._parseColorCache={},this.availableImages=null,this.canonical=null};ge.prototype.id=function(){return this.feature&&\"id\"in this.feature?this.feature.id:null},ge.prototype.geometryType=function(){return this.feature?\"number\"==typeof this.feature.type?de[this.feature.type]:this.feature.type:null},ge.prototype.geometry=function(){return this.feature&&\"geometry\"in this.feature?this.feature.geometry:null},ge.prototype.canonicalID=function(){return this.canonical},ge.prototype.properties=function(){return this.feature&&this.feature.properties||{}},ge.prototype.parseColor=function(t){var e=this._parseColorCache[t];return e||(e=this._parseColorCache[t]=Kt.parse(t)),e};var me=function(t,e,r,n){this.name=t,this.type=e,this._evaluate=r,this.args=n};me.prototype.evaluate=function(t){return 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a=Ne(e,n),o=rr.interpolationFactor(this.interpolation,n,e[a],e[a+1]),s=r[a].evaluate(t),l=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ve[this.type.kind.toLowerCase()](s,l,o):\"interpolate-hcl\"===this.operator?tr.reverse(tr.interpolate(tr.forward(s),tr.forward(l),o)):$e.reverse($e.interpolate($e.forward(s),$e.forward(l),o))},rr.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1)t(r[e])},rr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},rr.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var ir=function(t,e){this.type=t,this.args=e};ir.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=e.parse(o[a],1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!s)return null;r=r||s.type,i.push(s)}var l=n&&i.some((function(t){return Wt(n,t.type)}));return new ir(l?jt:r,i)},ir.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length&&(n++,(r=a[i].evaluate(t))&&r instanceof ee&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null===r);i+=1);return r},ir.prototype.eachChild=function(t){this.args.forEach(t)},ir.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},ir.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var ar=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};ar.prototype.evaluate=function(t){return this.result.evaluate(t)},ar.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1)t(r[e][1]);t(this.result)},ar.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new ar(r,o):null},ar.prototype.outputDefined=function(){return this.result.outputDefined()},ar.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e];t.push(n[0],n[1].serialize())}return t.push(this.result.serialize()),t};var or=function(t,e,r){this.type=t,this.index=e,this.input=r};or.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Dt),n=e.parse(t[2],2,Ht(e.expectedType||jt));return r&&n?new or(n.type.itemType,r,n):null},or.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new se(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new se(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new se(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},or.prototype.eachChild=function(t){t(this.index),t(this.input)},or.prototype.outputDefined=function(){return!1},or.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var sr=function(t,e){this.type=Ft,this.needle=t,this.haystack=e};sr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,jt),n=e.parse(t[2],2,jt);return r&&n?Xt(r.type,[Ft,Rt,Dt,Ot,jt])?new sr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(r.type)+\" instead\"):null},sr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Zt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new se(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(ie(e))+\" instead.\");if(!Zt(r,[\"string\",\"array\"]))throw new se(\"Expected second argument to be of type array or string, but found \"+Gt(ie(r))+\" instead.\");return r.indexOf(e)>=0},sr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},sr.prototype.outputDefined=function(){return!0},sr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var lr=function(t,e,r){this.type=Dt,this.needle=t,this.haystack=e,this.fromIndex=r};lr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,jt),n=e.parse(t[2],2,jt);if(!r||!n)return null;if(!Xt(r.type,[Ft,Rt,Dt,Ot,jt]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Dt);return i?new lr(r,n,i):null}return new lr(r,n)},lr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Zt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new se(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(ie(e))+\" instead.\");if(!Zt(r,[\"string\",\"array\"]))throw new se(\"Expected second argument to be of type array or string, but found \"+Gt(ie(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},lr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},lr.prototype.outputDefined=function(){return!1},lr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var cr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};cr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of arguments.\");var r,n;e.expectedType&&\"value\"!==e.expectedType.kind&&(n=e.expectedType);for(var i={},a=[],o=2;o<t.length-1;o+=2){var s=t[o],l=t[o+1];Array.isArray(s)||(s=[s]);var 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e=this.input.evaluate(t);return(ie(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},cr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},cr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},cr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];e.push(1===h.length?s(h[0]):h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var ur=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};ur.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 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fr=function(t,e,r,n){this.type=t,this.input=e,this.beginIndex=r,this.endIndex=n};function hr(t,e){return\"==\"===t||\"!=\"===t?\"boolean\"===e.kind||\"string\"===e.kind||\"number\"===e.kind||\"null\"===e.kind||\"value\"===e.kind:\"string\"===e.kind||\"number\"===e.kind||\"value\"===e.kind}function pr(t,e,r,n){return 0===n.compare(e,r)}function dr(t,e,r){var n=\"==\"!==t&&\"!=\"!==t;return function(){function i(t,e,r){this.type=Ft,this.lhs=t,this.rhs=e,this.collator=r,this.hasUntypedArgument=\"value\"===t.type.kind||\"value\"===e.type.kind}return i.parse=function(t,e){if(3!==t.length&&4!==t.length)return e.error(\"Expected two or three arguments.\");var r=t[0],a=e.parse(t[1],1,jt);if(!a)return null;if(!hr(r,a.type))return e.concat(1).error('\"'+r+\"\\\" comparisons are not supported for type '\"+Gt(a.type)+\"'.\");var o=e.parse(t[2],2,jt);if(!o)return null;if(!hr(r,o.type))return e.concat(2).error('\"'+r+\"\\\" comparisons are not supported for type 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0,o={}),e=e.concat(Xr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:Jr,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Vr(It(a[1]))?e.concat([new St(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(bn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=zr(t.value),l=Lt(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new St(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new St(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return Lr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new St(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new St(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new St(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new St(t.key,c,\"integer expected, found \"+l)]}}function Qr(t){var e=(\"property\"===t.expressionContext?Yr:qr)(It(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new St(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new St(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". 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en={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function rn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};tn(t)||(t=an(t));var e=qr(t,en);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function nn(t,e){return t<e?-1:t>e?1:0}function an(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?on(t[1],t[2],\"==\"):\"!=\"===r?cn(on(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?on(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(an))):\"all\"===r?[\"all\"].concat(t.slice(1).map(an)):\"none\"===r?[\"all\"].concat(t.slice(1).map(an).map(cn)):\"in\"===r?sn(t[1],t.slice(2)):\"!in\"===r?cn(sn(t[1],t.slice(2))):\"has\"===r?ln(t[1]):\"!has\"===r?cn(ln(t[1])):\"within\"!==r||t}function on(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function sn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(nn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function ln(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function cn(t){return[\"!\",t]}function un(t){return tn(It(t.value))?Qr(Ct({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==zr(r))return[new St(n,r,\"array expected, \"+zr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new St(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat($r({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Lt(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Lt(r[1])&&o.push(new St(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new St(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=zr(r[1]))&&o.push(new St(n+\"[1]\",r[1],\"string expected, \"+i+\" found\"));for(var s=2;s<r.length;s++)i=zr(r[s]),\"$type\"===Lt(r[1])?o=o.concat($r({key:n+\"[\"+s+\"]\",value:r[s],valueSpec:a.geometry_type,style:e.style,styleSpec:e.styleSpec})):\"string\"!==i&&\"number\"!==i&&\"boolean\"!==i&&o.push(new St(n+\"[\"+s+\"]\",r[s],\"string, number, or boolean expected, \"+i+\" found\"));break;case\"any\":case\"all\":case\"none\":for(var l=1;l<r.length;l++)o=o.concat(t({key:n+\"[\"+l+\"]\",value:r[l],style:e.style,styleSpec:e.styleSpec}));break;case\"has\":case\"!has\":i=zr(r[1]),2!==r.length?o.push(new St(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"string\"!==i&&o.push(new St(n+\"[1]\",r[1],\"string expected, \"+i+\" found\"));break;case\"within\":i=zr(r[1]),2!==r.length?o.push(new St(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"object\"!==i&&o.push(new St(n+\"[1]\",r[1],\"object expected, \"+i+\" found\"))}return o}(t)}function fn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return bn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new St(r,a,'unknown property \"'+o+'\"')];if(\"string\"===zr(a)&&Lr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new St(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new St(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Or(It(a))&&\"identity\"===Lt(a.type)&&f.push(new St(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Lt(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new St(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new St(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new St(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new St(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new St(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new St(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new St(n,r,'missing required property \"source\"'));return e=e.concat(Xr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(){return[]},type:function(){return bn({key:n+\".type\",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:\"type\"})},filter:un,layout:function(t){return Xr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return pn(Ct({layerType:s},t))}}})},paint:function(t){return Xr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return hn(Ct({layerType:s},t))}}})}}}))}function gn(t){var e=t.value,r=t.key,n=zr(e);return\"string\"!==n?[new St(r,e,\"string expected, \"+n+\" found\")]:[]}var mn={promoteId:function(t){var e=t.key,r=t.value;if(\"string\"===zr(r))return gn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,gn({key:e+\".\"+i,value:r[i]}));return n}};function vn(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new St(r,e,'\"type\" is required')];var a,o=Lt(e.type);switch(o){case\"vector\":case\"raster\":case\"raster-dem\":return Xr({key:r,value:e,valueSpec:n[\"source_\"+o.replace(\"-\",\"_\")],style:t.style,styleSpec:n,objectElementValidators:mn});case\"geojson\":if(a=Xr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:mn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=\"string\"==typeof c?[c,[\"accumulated\"],[\"get\",s]]:c;a.push.apply(a,Qr({key:r+\".\"+s+\".map\",value:l[1],expressionContext:\"cluster-map\"})),a.push.apply(a,Qr({key:r+\".\"+s+\".reduce\",value:u,expressionContext:\"cluster-reduce\"}))}return a;case\"video\":return Xr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case\"image\":return Xr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case\"canvas\":return[new St(r,null,\"Please use runtime APIs to add canvas sources, rather than including them in stylesheets.\",\"source.canvas\")];default:return $r({key:r+\".type\",value:e.type,valueSpec:{values:[\"vector\",\"raster\",\"raster-dem\",\"geojson\",\"video\",\"image\"]},style:i,styleSpec:n})}}function yn(t){var e=t.value,r=t.styleSpec,n=r.light,i=t.style,a=[],o=zr(e);if(void 0===e)return a;if(\"object\"!==o)return a.concat([new St(\"light\",e,\"object expected, \"+o+\" found\")]);for(var s in e){var l=s.match(/^(.*)-transition$/);a=a.concat(l&&n[l[1]]&&n[l[1]].transition?bn({key:s,value:e[s],valueSpec:r.transition,style:i,styleSpec:r}):n[s]?bn({key:s,value:e[s],valueSpec:n[s],style:i,styleSpec:r}):[new St(s,e[s],'unknown property \"'+s+'\"')])}return a}var xn={\"*\":function(){return[]},array:Zr,boolean:function(t){var e=t.value,r=t.key,n=zr(e);return\"boolean\"!==n?[new St(r,e,\"boolean expected, \"+n+\" found\")]:[]},number:Jr,color:function(t){var e=t.key,r=t.value,n=zr(r);return\"string\"!==n?[new St(e,r,\"color expected, \"+n+\" 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r=r.concat(bn({key:\"\",value:t,valueSpec:e.$root,styleSpec:e,style:t,objectElementValidators:{glyphs:_n,\"*\":function(){return[]}}})),t.constants&&(r=r.concat(Et({key:\"constants\",value:t.constants,style:t,styleSpec:e}))),Tn(r)}function Tn(t){return[].concat(t).sort((function(t,e){return t.line-e.line}))}function kn(t){return function(){for(var e=[],r=arguments.length;r--;)e[r]=arguments[r];return Tn(t.apply(this,e))}}wn.source=kn(vn),wn.light=kn(yn),wn.layer=kn(dn),wn.filter=kn(un),wn.paintProperty=kn(hn),wn.layoutProperty=kn(pn);var Mn=wn,An=Mn.light,Sn=Mn.paintProperty,En=Mn.layoutProperty;function Cn(t,e){var r=!1;if(e&&e.length)for(var n=0,i=e;n<i.length;n+=1)t.fire(new kt(new Error(i[n].message))),r=!0;return r}var Ln=In;function In(t,e,r){var n=this.cells=[];if(t instanceof ArrayBuffer){this.arrayBuffer=t;var i=new Int32Array(this.arrayBuffer);t=i[0],this.d=(e=i[1])+2*(r=i[2]);for(var a=0;a<this.d*this.d;a++){var o=i[3+a],s=i[3+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[3+n.length+1];this.keys=i.subarray(i[3+n.length],l),this.bboxes=i.subarray(l),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var c=0;c<this.d*this.d;c++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var u=r/e*t;this.min=-u,this.max=t+u}In.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},In.prototype._insertReadonly=function(){throw\"Cannot insert into a GridIndex created from an ArrayBuffer.\"},In.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},In.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},In.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var c=this.keys,u=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(u[p+0],u[p+1],u[p+2],u[p+3]):t<=u[p+2]&&e<=u[p+3]&&r>=u[p+0]&&n>=u[p+1])?(o[h]=!0,a.push(c[h])):o[h]=!1}}},In.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},In.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},In.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},In.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=3+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[3+o]=a,i.set(s,a),a+=s.length}return i[3+t.length]=a,i.set(this.keys,a),i[3+t.length+1]=a+=this.keys.length,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var Pn=self.ImageData,zn=self.ImageBitmap,On={};function Dn(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),On[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var Rn in Dn(\"Object\",Object),Ln.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},Ln.deserialize=function(t){return new Ln(t.buffer)},Dn(\"Grid\",Ln),Dn(\"Color\",Kt),Dn(\"Error\",Error),Dn(\"ResolvedImage\",ee),Dn(\"StylePropertyFunction\",Wr),Dn(\"StyleExpression\",Ur,{omit:[\"_evaluator\"]}),Dn(\"ZoomDependentExpression\",Gr),Dn(\"ZoomConstantExpression\",Hr),Dn(\"CompoundExpression\",me,{omit:[\"_evaluate\"]}),Tr)Tr[Rn]._classRegistryKey||Dn(\"Expression_\"+Rn,Tr[Rn]);function Fn(t){return t&&\"undefined\"!=typeof ArrayBuffer&&(t instanceof ArrayBuffer||t.constructor&&\"ArrayBuffer\"===t.constructor.name)}function Bn(t){return zn&&t instanceof zn}function Nn(t,e){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp)return t;if(Fn(t)||Bn(t))return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof Pn)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1)n.push(Nn(a[i],e));return n}if(\"object\"==typeof t){var o=t.constructor,s=o._classRegistryKey;if(!s)throw new Error(\"can't serialize object of unregistered class\");var l=o.serialize?o.serialize(t,e):{};if(!o.serialize){for(var c in t)if(t.hasOwnProperty(c)&&!(On[s].omit.indexOf(c)>=0)){var u=t[c];l[c]=On[s].shallow.indexOf(c)>=0?u:Nn(u,e)}t instanceof Error&&(l.message=t.message)}if(l.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==s&&(l.$name=s),l}throw new Error(\"can't serialize object of type \"+typeof t)}function jn(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||Fn(t)||Bn(t)||ArrayBuffer.isView(t)||t instanceof Pn)return t;if(Array.isArray(t))return t.map(jn);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=On[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class \"+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i<a.length;i+=1){var o=a[i];if(\"$name\"!==o){var s=t[o];n[o]=On[e].shallow.indexOf(o)>=0?s:jn(s)}}return n}throw new Error(\"can't deserialize object of type \"+typeof t)}var Un=function(){this.first=!0};Un.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))};var Vn={\"Latin-1 Supplement\":function(t){return t>=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic Supplement\":function(t){return t>=1872&&t<=1919},\"Arabic Extended-A\":function(t){return t>=2208&&t<=2303},\"Hangul Jamo\":function(t){return t>=4352&&t<=4607},\"Unified Canadian Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram Symbols\":function(t){return t>=19904&&t<=19967},\"CJK Unified Ideographs\":function(t){return t>=19968&&t<=40959},\"Yi Syllables\":function(t){return t>=40960&&t<=42127},\"Yi Radicals\":function(t){return t>=42128&&t<=42191},\"Hangul Jamo Extended-A\":function(t){return t>=43360&&t<=43391},\"Hangul Syllables\":function(t){return t>=44032&&t<=55215},\"Hangul Jamo Extended-B\":function(t){return t>=55216&&t<=55295},\"Private Use Area\":function(t){return t>=57344&&t<=63743},\"CJK Compatibility Ideographs\":function(t){return t>=63744&&t<=64255},\"Arabic Presentation Forms-A\":function(t){return t>=64336&&t<=65023},\"Vertical Forms\":function(t){return t>=65040&&t<=65055},\"CJK Compatibility Forms\":function(t){return t>=65072&&t<=65103},\"Small Form Variants\":function(t){return t>=65104&&t<=65135},\"Arabic Presentation Forms-B\":function(t){return t>=65136&&t<=65279},\"Halfwidth and Fullwidth Forms\":function(t){return t>=65280&&t<=65519}};function qn(t){for(var e=0,r=t;e<r.length;e+=1)if(Hn(r[e].charCodeAt(0)))return!0;return!1}function Hn(t){return!(746!==t&&747!==t&&(t<4352||!(Vn[\"Bopomofo Extended\"](t)||Vn.Bopomofo(t)||Vn[\"CJK Compatibility 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0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},ii.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var ai=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Or(t))return new Wr(t,e);if(Vr(t)){var r=Yr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=Kt.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};ai.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},ai.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var oi=function(t){this.property=t,this.value=new ai(t,void 0)};oi.prototype.transitioned=function(t,e){return new li(this.property,this.value,e,u({},t.transition,this.transition),t.now)},oi.prototype.untransitioned=function(){return new li(this.property,this.value,null,{},0)};var si=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};si.prototype.getValue=function(t){return x(this._values[t].value.value)},si.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new oi(this._values[t].property)),this._values[t].value=new ai(this._values[t].property,null===e?void 0:x(e))},si.prototype.getTransition=function(t){return x(this._values[t].transition)},si.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new oi(this._values[t].property)),this._values[t].transition=x(e)||void 0},si.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},si.prototype.transitioned=function(t,e){for(var r=new ci(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},si.prototype.untransitioned=function(){for(var t=new ci(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var li=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};li.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var ci=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};ci.prototype.possiblyEvaluate=function(t,e,r){for(var n=new hi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},ci.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1)if(this._values[e[t]].prior)return!0;return!1};var ui=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};ui.prototype.getValue=function(t){return x(this._values[t].value)},ui.prototype.setValue=function(t,e){this._values[t]=new ai(this._values[t].property,null===e?void 0:x(e))},ui.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},ui.prototype.possiblyEvaluate=function(t,e,r){for(var n=new hi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var fi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};fi.prototype.isConstant=function(){return\"constant\"===this.value.kind},fi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},fi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var hi=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};hi.prototype.get=function(t){return this._values[t]};var pi=function(t){this.specification=t};pi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},pi.prototype.interpolate=function(t,e,r){var n=Ve[this.specification.type];return n?n(t,e,r):t};var di=function(t,e){this.specification=t,this.overrides=e};di.prototype.possiblyEvaluate=function(t,e,r,n){return new fi(this,\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)}:t.expression,e)},di.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new fi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ve[this.specification.type];return n?new fi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},di.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new fi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new fi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new fi(this,{kind:\"constant\",value:s},e)}return new fi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(di),mi=function(t){this.specification=t};mi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new ii(Math.floor(e.zoom-1),e)),t.expression.evaluate(new ii(Math.floor(e.zoom),e)),t.expression.evaluate(new ii(Math.floor(e.zoom+1),e)),e)}},mi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},mi.prototype.interpolate=function(t){return t};var vi=function(t){this.specification=t};vi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},vi.prototype.interpolate=function(){return!1};var yi=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new ai(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new oi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Dn(\"DataDrivenProperty\",di),Dn(\"DataConstantProperty\",pi),Dn(\"CrossFadedDataDrivenProperty\",gi),Dn(\"CrossFadedProperty\",mi),Dn(\"ColorRampProperty\",vi);var xi=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(this.metadata=(e=e).metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new ui(r.layout)),r.paint)){for(var n in this._transitionablePaint=new si(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new hi(r.paint)}}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){void 0===r&&(r={}),null!=e&&this._validate(En,\"layers.\"+this.id+\".layout.\"+t,t,e,r)||(\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e)},e.prototype.getPaintProperty=function(t){return m(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e&&this._validate(Sn,\"layers.\"+this.id+\".paint.\"+t,t,e,r))return!1;if(m(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var n=this._transitionablePaint._values[t],i=\"cross-faded-data-driven\"===n.property.specification[\"property-type\"],a=n.value.isDataDriven(),o=n.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var s=this._transitionablePaint._values[t].value;return s.isDataDriven()||a||i||this._handleOverridablePaintPropertyUpdate(t,o,s)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Cn(this,t.call(Mn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:At,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof fi&&Lr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(Mt),bi={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},_i=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},wi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ti(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i=bi[t.type].BYTES_PER_ELEMENT,a=r=ki(r,Math.max(e,i)),o=t.components||1;return n=Math.max(n,i),r+=i*o,{name:t.name,type:t.type,components:o,offset:a}})),size:ki(r,Math.max(n,e)),alignment:e}}function ki(t,e){return Math.ceil(t/e)*e}wi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},wi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},wi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},wi.prototype.clear=function(){this.length=0},wi.prototype.resize=function(t){this.reserve(t),this.length=t},wi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},wi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var Mi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(wi);Mi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout2i4\",Mi);var Ai=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(wi);Ai.prototype.bytesPerElement=8,Dn(\"StructArrayLayout4i8\",Ai);var Si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(wi);Si.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i4i12\",Si);var Ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(wi);Ei.prototype.bytesPerElement=8,Dn(\"StructArrayLayout2i4ub8\",Ei);var Ci=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(wi);Ci.prototype.bytesPerElement=18,Dn(\"StructArrayLayout8ui2ub18\",Ci);var Li=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(wi);Li.prototype.bytesPerElement=24,Dn(\"StructArrayLayout4i4ui4i24\",Li);var Ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(wi);Ii.prototype.bytesPerElement=12,Dn(\"StructArrayLayout3f12\",Ii);var Pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.uint32[1*t+0]=e,t},e}(wi);Pi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1ul4\",Pi);var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(wi);zi.prototype.bytesPerElement=20,Dn(\"StructArrayLayout6i1ul2ui20\",zi);var Oi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(wi);Oi.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i2i2i12\",Oi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(wi);Di.prototype.bytesPerElement=16,Dn(\"StructArrayLayout2f1f2i16\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(wi);Ri.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2ub2f12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(wi);Fi.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3ui6\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=g,this.uint32[x+10]=m,this.int16[y+22]=v,t},e}(wi);Bi.prototype.bytesPerElement=48,Dn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S,E){var C=34*t,L=17*t;return this.int16[C+0]=e,this.int16[C+1]=r,this.int16[C+2]=n,this.int16[C+3]=i,this.int16[C+4]=a,this.int16[C+5]=o,this.int16[C+6]=s,this.int16[C+7]=l,this.uint16[C+8]=c,this.uint16[C+9]=u,this.uint16[C+10]=f,this.uint16[C+11]=h,this.uint16[C+12]=p,this.uint16[C+13]=d,this.uint16[C+14]=g,this.uint16[C+15]=m,this.uint16[C+16]=v,this.uint16[C+17]=y,this.uint16[C+18]=x,this.uint16[C+19]=b,this.uint16[C+20]=_,this.uint16[C+21]=w,this.uint16[C+22]=T,this.uint32[L+12]=k,this.float32[L+13]=M,this.float32[L+14]=A,this.float32[L+15]=S,this.float32[L+16]=E,t},e}(wi);Ni.prototype.bytesPerElement=68,Dn(\"StructArrayLayout8i15ui1ul4f68\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.float32[1*t+0]=e,t},e}(wi);ji.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1f4\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(wi);Ui.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3i6\",Ui);var 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a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},Sa.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Sa.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var Ea=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return\"u_\"+t+\"_t\"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:\"a_\"+t,type:\"Float32\",components:\"color\"===r?4:2,offset:0}})),this.paintVertexArray=new a};Ea.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new ii(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new ii(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},Ea.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},Ea.prototype._setPaintValue=function(t,e,r,n){if(\"color\"===this.type)for(var i=ka(r),a=ka(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},Ea.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Ea.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ea.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=l(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},Ea.prototype.getBinding=function(t,e,r){return new va(t,e)};var Ca=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};Ca.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},Ca.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},Ca.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=n[r.min],a=n[r.mid],o=n[r.max];if(i&&a&&o)for(var s=t;s<e;s++)this.zoomInPaintVertexArray.emplace(s,a.tl[0],a.tl[1],a.br[0],a.br[1],i.tl[0],i.tl[1],i.br[0],i.br[1],a.pixelRatio,i.pixelRatio),this.zoomOutPaintVertexArray.emplace(s,a.tl[0],a.tl[1],a.br[0],a.br[1],o.tl[0],o.tl[1],o.br[0],o.br[1],a.pixelRatio,o.pixelRatio)}},Ca.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,oa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,oa.members,this.expression.isStateDependent))},Ca.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var La=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof fi&&Lr(o.property.specification)){var s=Pa(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new Aa(l.value,s):new Ma(l.value,s,c),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=za(a,c,\"source\");this.binders[a]=h?new Ca(l,c,u,e,p,t.id):new Sa(l,s,c,p),i.push(\"/a_\"+a)}else{var d=za(a,c,\"composite\");this.binders[a]=new Ea(l,s,c,u,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};La.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Sa||e instanceof Ea?e.maxValue:0},La.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Sa||o instanceof Ea||o instanceof Ca)&&o.populatePaintArray(t,e,r,n,i)}},La.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof Aa&&n.setConstantPatternPositions(t,e)}},La.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof Sa||h instanceof Ea||h instanceof Ca)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},La.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof Ma||r instanceof Aa)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},La.prototype.getPaintVertexBuffers=function(){return this._buffers},La.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof Ma||i instanceof Aa||i instanceof Ea)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},La.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property;this.binders[l].setUniform(o.binding,n,r.get(l),s)}},La.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Ca){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Sa||r instanceof Ea)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},La.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Sa||r instanceof Ea||r instanceof Ca)&&r.upload(t)}this.updatePaintBuffers()},La.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Sa||e instanceof Ea||e instanceof Ca)&&e.destroy()}};var Ia=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new La(o,r,n,t)}this.needsUpload=!1,this._featureMap=new fa,this._bufferOffset=0};function Pa(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function za(t,e,r){var n={color:{source:Gi,composite:Yi},number:{source:ji,composite:Gi}},i=function(t){return{\"line-pattern\":{source:Ci,composite:Ci},\"fill-pattern\":{source:Ci,composite:Ci},\"fill-extrusion-pattern\":{source:Ci,composite:Ci}}[t]}(t);return i&&i[r]||n[e][r]}Ia.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},Ia.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},Ia.prototype.get=function(t){return this.programConfigurations[t]},Ia.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},Ia.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},Dn(\"ConstantBinder\",Ma),Dn(\"CrossFadedConstantBinder\",Aa),Dn(\"SourceExpressionBinder\",Sa),Dn(\"CrossFadedCompositeBinder\",Ca),Dn(\"CompositeExpressionBinder\",Ea),Dn(\"ProgramConfiguration\",La,{omit:[\"_buffers\"]}),Dn(\"ProgramConfigurationSet\",Ia);var Oa={min:-1*Math.pow(2,14),max:Math.pow(2,14)-1};function Da(t){for(var e=8192/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a];o.x=Math.round(o.x*e),o.y=Math.round(o.y*e),(o.x<Oa.min||o.x>Oa.max||o.y<Oa.min||o.y>Oa.max)&&(_(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\"),o.x=l(o.x,Oa.min,Oa.max),o.y=l(o.y,Oa.min,Oa.max))}return r}function Ra(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var Fa=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Mi,this.indexArray=new Fi,this.segments=new ia,this.programConfigurations=new Ia(na,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function Ba(t,e){for(var r=0;r<t.length;r++)if(Wa(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Wa(t,e[n]))return!0;return!!Va(t,e)}function Na(t,e,r){return!!Wa(t,e)||!!Ha(e,t,r)}function ja(t,e){if(1===t.length)return Ya(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Wa(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Ya(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(Va(t,e[o]))return!0;return!1}function Ua(t,e,r){if(t.length>1){if(Va(t,e))return!0;for(var n=0;n<e.length;n++)if(Ha(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(Ha(t[i],e,r))return!0;return!1}function Va(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(qa(n,i,e[a],e[a+1]))return!0;return!1}function qa(t,e,r,n){return w(t,r,n)!==w(e,r,n)&&w(t,e,r)!==w(t,e,n)}function Ha(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Ga(t,e[i-1],e[i])<n)return!0;return!1}function Ga(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return t.distSqr(i<0?e:i>1?r:r.sub(e)._mult(i)._add(e))}function Ya(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)(n=r[s]).y>e.y!=(i=r[l]).y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Wa(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function Xa(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=w(t,e,r[0]);return a!==w(t,e,r[1])||a!==w(t,e,r[2])||a!==w(t,e,r[3])}function Za(t,e,r){var n=e.paint.get(t).value;return\"constant\"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function Ja(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function Ka(t,e,r,n,a){if(!e[0]&&!e[1])return t;var o=i.convert(e)._mult(a);\"viewport\"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++)s.push(t[l].sub(o));return s}Fa.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;\"circle\"===n.type&&(a=n.layout.get(\"circle-sort-key\"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],c=l.feature,u=l.id,f=l.index,h=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d={type:c.type,id:u,properties:c.properties,geometry:p?Da(c):[]};if(this.layers[0]._featureFilter.filter(new ii(this.zoom),d,r)){p||(d.geometry=Da(c));var g=a?a.evaluate(d,{},r):void 0;i.push({id:u,properties:c.properties,type:c.type,sourceLayerIndex:h,index:f,geometry:d.geometry,patterns:{},sortKey:g})}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var m=0,v=i;m<v.length;m+=1){var y=v[m],x=y.geometry,b=y.index,_=y.sourceLayerIndex,w=t[b].feature;this.addFeature(y,x,b,r),e.featureIndex.insert(w,x,b,_,this.index)}},Fa.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Fa.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Fa.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Fa.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,na),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Fa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Fa.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],c=l.x,u=l.y;if(!(c<0||c>=8192||u<0||u>=8192)){var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),h=f.vertexLength;Ra(this.layoutVertexArray,c,u,-1,-1),Ra(this.layoutVertexArray,c,u,1,-1),Ra(this.layoutVertexArray,c,u,1,1),Ra(this.layoutVertexArray,c,u,-1,1),this.indexArray.emplaceBack(h,h+1,h+2),this.indexArray.emplaceBack(h,h+3,h+2),f.vertexLength+=4,f.primitiveLength+=2}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{},n)},Dn(\"CircleBucket\",Fa,{omit:[\"layers\"]});var Qa=new yi({\"circle-sort-key\":new di(At.layout_circle[\"circle-sort-key\"])}),$a={paint:new yi({\"circle-radius\":new di(At.paint_circle[\"circle-radius\"]),\"circle-color\":new di(At.paint_circle[\"circle-color\"]),\"circle-blur\":new di(At.paint_circle[\"circle-blur\"]),\"circle-opacity\":new di(At.paint_circle[\"circle-opacity\"]),\"circle-translate\":new pi(At.paint_circle[\"circle-translate\"]),\"circle-translate-anchor\":new 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2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=us(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var fs=hs;function hs(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(ps,this,e),this.length=this._features.length}function ps(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function ds(t,e,r){if(3===t){var n=new fs(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}hs.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new ss(this._pbf,e,this.extent,this._keys,this._values)};var gs={VectorTile:function(t,e){this.layers=t.readFields(ds,{},e)},VectorTileFeature:ss,VectorTileLayer:fs},ms=gs.VectorTileFeature.types,vs=Math.pow(2,13);function ys(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*vs)+o,i*vs*2,a*vs*2,Math.round(s))}var xs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Si,this.indexArray=new Fi,this.programConfigurations=new Ia(os,t.layers,t.zoom),this.segments=new ia,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function bs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}xs.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ts(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Da(o):[]};if(this.layers[0]._featureFilter.filter(new ii(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Da(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(es(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},xs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},xs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},xs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},xs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},xs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,os),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},xs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},xs.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Qo(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1)l+=u[c].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((P=d).every((function(t){return t.x<0}))||P.every((function(t){return t.x>8192}))||P.every((function(t){return t.y<0}))||P.every((function(t){return t.y>8192}))))for(var g=0,m=0;m<d.length;m++){var v=d[m];if(m>=1){var y=d[m-1];if(!bs(v,y)){f.vertexLength+4>ia.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);g+b>32768&&(g=0),ys(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,g),ys(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,g),ys(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,g+=b),ys(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,g);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>ia.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===ms[t.type]){for(var w=[],T=[],k=f.vertexLength,M=0,A=s;M<A.length;M+=1){var S=A[M];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var C=S[E];ys(this.layoutVertexArray,C.x,C.y,0,0,1,1,0),w.push(C.x),w.push(C.y)}}}for(var 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a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Ts(s,s),f=Ts(s,l),h=Ts(l,l),p=Ts(c,s),d=Ts(c,l),g=u*h-f*f,m=(h*p-f*d)/g,v=(u*d-f*p)/g,y=i.z*(1-m-v)+r.z*m+a.z*v;if(isFinite(y))return y}return 1/0}for(var x=1/0,b=0,_=e;b<_.length;b+=1)x=Math.min(x,_[b].z);return x}var Ms=Ti([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,As=gs.VectorTileFeature.types,Ss=Math.cos(Math.PI/180*37.5),Es=Math.pow(2,14)/.5,Cs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Ei,this.indexArray=new Fi,this.programConfigurations=new Ia(Ms,t.layers,t.zoom),this.segments=new ia,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return 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a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1)this.addLine(f[u],t,o,s,l,c);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Cs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===As[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 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o=e.y*n-e.x,s=-e.y-e.x*n;this.addHalfVertex(t,e.x+e.y*r,e.y-e.x*r,a,!1,r,i),this.addHalfVertex(t,o,s,a,!0,-n,i),this.distance>Es/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},Cs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((t.x<<1)+(n?1:0),(t.y<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&s)<<2,s>>6);var l=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,l),o.primitiveLength++),i?this.e2=l:this.e1=l},Cs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Es-1):this.distance},Cs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Dn(\"LineBucket\",Cs,{omit:[\"layers\",\"patternFeatures\"]});var Ls=new yi({\"line-cap\":new 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n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&Zs(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),qs(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),qs(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&Zs(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,Gs.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,Js,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Ks,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,tl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Qs,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,$s,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,el,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,rl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,nl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,il,e)},writeBytesField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var hl=function(t,e){var r=e.pixelRatio,n=e.version,i=e.stretchX,a=e.stretchY,o=e.content;this.paddedRect=t,this.pixelRatio=r,this.stretchX=i,this.stretchY=a,this.content=o,this.version=n},pl={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};pl.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},pl.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},pl.tlbr.get=function(){return this.tl.concat(this.br)},pl.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(hl.prototype,pl);var dl=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var i=[];this.addImages(t,r,i),this.addImages(e,n,i);var a=fl(i),o=new po({width:a.w||1,height:a.h||1});for(var s in t){var l=t[s],c=r[s].paddedRect;po.copy(l.data,o,{x:0,y:0},{x:c.x+1,y:c.y+1},l.data)}for(var u in e){var f=e[u],h=n[u].paddedRect,p=h.x+1,d=h.y+1,g=f.data.width,m=f.data.height;po.copy(f.data,o,{x:0,y:0},{x:p,y:d},f.data),po.copy(f.data,o,{x:0,y:m-1},{x:p,y:d-1},{width:g,height:1}),po.copy(f.data,o,{x:0,y:0},{x:p,y:d+m},{width:g,height:1}),po.copy(f.data,o,{x:g-1,y:0},{x:p-1,y:d},{width:1,height:m}),po.copy(f.data,o,{x:0,y:0},{x:p+g,y:d},{width:1,height:m})}this.image=o,this.iconPositions=r,this.patternPositions=n};dl.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new hl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},dl.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},dl.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl;r.update(e.data,void 0,{x:n[0],y:n[1]})}},Dn(\"ImagePosition\",hl),Dn(\"ImageAtlas\",dl);var gl={horizontal:1,vertical:2,horizontalOnly:3},ml=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};ml.forText=function(t,e){var r=new ml;return r.scale=t||1,r.fontStack=e,r},ml.forImage=function(t){var e=new ml;return e.imageName=t,e};var vl=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function yl(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=vl.fromFeature(t,i);f===gl.vertical&&v.verticalizePunctuation();var y=ni.processBidirectionalText,x=ni.processStyledBidirectionalText;if(y&&1===v.sections.length){m=[];for(var b=0,_=y(v.toString(),Ml(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new vl;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);m.push(T)}}else if(x){m=[];for(var M=0,A=x(v.text,v.sectionIndex,Ml(v,c,a,e,n,p,d));M<A.length;M+=1){var S=A[M],E=new vl;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,m.push(E)}}else m=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,Ml(v,c,a,e,n,p,d));var C=[],L={positionedLines:C,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,g=0,m=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,M=0;if(b.length()){for(var A=0;A<b.length();A++){var S=b.getSection(A),E=b.getSectionIndex(A),C=b.getCharCode(A),L=0,I=null,P=null,z=null,O=24,D=!(l===gl.horizontal||!u&&!Hn(C)||u&&(xl[C]||(H=C,Vn.Arabic(H)||Vn[\"Arabic Supplement\"](H)||Vn[\"Arabic Extended-A\"](H)||Vn[\"Arabic Presentation Forms-A\"](H)||Vn[\"Arabic Presentation Forms-B\"](H))));if(S.imageName){var R=n[S.imageName];if(!R)continue;z=S.imageName,t.iconsInText=t.iconsInText||!0,P=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,L=w+(24-F[1]*S.scale),O=(I={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]}).advance;var B=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;B>0&&B>M&&(M=B)}else{var N=r[S.fontStack],j=N&&N[C];if(j&&j.rect)P=j.rect,I=j.metrics;else{var U=e[S.fontStack],V=U&&U[C];if(!V)continue;I=V.metrics}L=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:C,imageName:z,x:h,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),h+=O*S.scale+c):(k.push({glyph:C,imageName:z,x:h,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),h+=I.advance*S.scale+c)}0!==k.length&&(d=Math.max(h-c,d),Sl(k,0,k.length-1,m,M)),h=0;var q=a*_+M;T.lineOffset=Math.max(M,w),p+=q,g=Math.max(q,g),++v}else p+=a,++v}var H,G=p- -17,Y=Al(o),W=Y.horizontalAlign,X=Y.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c,u=(e-r)*i;c=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var g=d[p];g.x+=u,g.y+=c}})(t.positionedLines,m,W,X,d,g,a,G,i.length),t.top+=-X*G,t.bottom=t.top+G,t.left+=-W*d,t.right=t.left+d}(L,e,r,n,m,o,s,l,f,c,h,g),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(C)&&L}vl.fromFeature=function(t,e){for(var r=new vl,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},vl.prototype.length=function(){return this.text.length},vl.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},vl.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},vl.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},vl.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;e+=n&&Gn(n)&&!Us[t[r+1]]||i&&Gn(i)&&!Us[t[r-1]]||!Us[t[r]]?t[r]:Us[t[r]]}return e}(this.text)},vl.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&xl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&xl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},vl.prototype.substring=function(t,e){var r=new vl;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},vl.prototype.toString=function(){return this.text},vl.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},vl.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(ml.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},vl.prototype.addImageSection=function(t){var e=t.image?t.image.name:\"\";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(ml.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_(\"Reached maximum number of images 6401\")}else _(\"Can't add FormattedSection with an empty image.\")},vl.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var xl={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},bl={};function _l(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*24/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function wl(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function Tl(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function kl(t,e,r,n,i,a){for(var o=null,s=wl(e,r,i,a),l=0,c=n;l<c.length;l+=1){var u=c[l],f=wl(e-u.x,r,i,a)+u.badness;f<=s&&(o=u,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function Ml(t,e,r,n,i,a,o){if(\"point\"!==a)return[];if(!t)return[];for(var s,l=[],c=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=_l(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf(\"\\u200b\")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(xl[d]||(f+=_l(d,p,n,i,e,o)),h<t.length()-1){var g=!((s=d)<11904||!(Vn[\"Bopomofo Extended\"](s)||Vn.Bopomofo(s)||Vn[\"CJK Compatibility Forms\"](s)||Vn[\"CJK Compatibility Ideographs\"](s)||Vn[\"CJK Compatibility\"](s)||Vn[\"CJK Radicals Supplement\"](s)||Vn[\"CJK Strokes\"](s)||Vn[\"CJK Symbols and Punctuation\"](s)||Vn[\"CJK Unified Ideographs Extension A\"](s)||Vn[\"CJK Unified 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o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,g=e.right*a;\"width\"===r||\"both\"===r?(p=i[0]+d-n[3],f=i[0]+g+n[1]):f=(p=i[0]+(d+g-s.displaySize[0])/2)+s.displaySize[0];var m=e.top*a,v=e.bottom*a;return\"height\"===r||\"both\"===r?(u=i[1]+m-n[0],h=i[1]+v+n[2]):h=(u=i[1]+(m+v-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:u,right:f,bottom:h,left:p,collisionPadding:o}}bl[10]=!0,bl[32]=!0,bl[38]=!0,bl[40]=!0,bl[41]=!0,bl[43]=!0,bl[45]=!0,bl[47]=!0,bl[173]=!0,bl[183]=!0,bl[8203]=!0,bl[8208]=!0,bl[8211]=!0,bl[8231]=!0;var Cl=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(i);function Ll(t,e){var r=e.expression;if(\"constant\"===r.kind)return{kind:\"constant\",layoutSize:r.evaluate(new ii(t+1))};if(\"source\"===r.kind)return{kind:\"source\"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return\"composite\"===r.kind?{kind:\"composite\",minZoom:s,maxZoom:l,interpolationType:i}:{kind:\"camera\",minZoom:s,maxZoom:l,minSize:r.evaluate(new ii(s)),maxSize:r.evaluate(new ii(l)),interpolationType:i}}function Il(t,e,r){var n=e.uSize,i=r.lowerSize;return\"source\"===t.kind?i/128:\"composite\"===t.kind?Ue(i/128,r.upperSize/128,e.uSizeT):n}function Pl(t,e){var r=0,n=0;if(\"constant\"===t.kind)n=t.layoutSize;else if(\"source\"!==t.kind){var i=t.interpolationType,a=i?l(rr.interpolationFactor(i,e,t.minZoom,t.maxZoom),0,1):0;\"camera\"===t.kind?n=Ue(t.minSize,t.maxSize,a):r=a}return{uSizeT:r,uSize:n}}Dn(\"Anchor\",Cl);var 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h=Hl(n.stretch-x,b,u,t.left),p=Gl(n.fixed-T,k,n.stretch,g),d=Hl(a.stretch-_,w,f,t.top),v=Gl(a.fixed-M,A,a.stretch,m),y=Hl(l.stretch-x,b,u,t.left),S=Gl(l.fixed-T,k,l.stretch,g),E=Hl(c.stretch-_,w,f,t.top),C=Gl(c.fixed-M,A,c.stretch,m),L=new i(h,d),I=new i(y,d),P=new i(y,E),z=new i(h,E),O=new i(p/s,v/s),D=new i(S/s,C/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];L._matMult(N),I._matMult(N),z._matMult(N),P._matMult(N)}var j=n.stretch+n.fixed,U=a.stretch+a.fixed;return{tl:L,tr:I,bl:z,br:P,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+U,w:l.stretch+l.fixed-j,h:c.stretch+c.fixed-U},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:O,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:A/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var C=ql(h,v,g),L=ql(p,y,m),I=0;I<C.length-1;I++)for(var P=C[I],z=C[I+1],O=0;O<L.length-1;O++)a.push(E(P,L[O],z,L[O+1]));else 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k=s[m.imageName];x=k.sdf,y=1/(b=k.pixelRatio)}var M=a?[m.x+T,m.y]:[0,0],A=a?[0,0]:[m.x+T+r[0],m.y+r[1]-_],S=[0,0];w&&(S=A,A=[0,0]);var E=(m.metrics.left-y)*m.scale-T+A[0],C=(-m.metrics.top-y)*m.scale+A[1],L=E+v.w*m.scale/b,I=C+v.h*m.scale/b,P=new i(E,C),z=new i(L,C),O=new i(E,I),D=new i(L,I);if(w){var R=new i(-T,T- -17),F=-Math.PI/2,B=12-T,N=new i(22-B,-(m.imageName?B:0)),j=new(Function.prototype.bind.apply(i,[null].concat(S)));P._rotateAround(F,R)._add(N)._add(j),z._rotateAround(F,R)._add(N)._add(j),O._rotateAround(F,R)._add(N)._add(j),D._rotateAround(F,R)._add(N)._add(j)}if(c){var U=Math.sin(c),V=Math.cos(c),q=[V,-U,U,V];P._matMult(q),z._matMult(q),O._matMult(q),D._matMult(q)}var H=new i(0,0),G=new i(0,0);u.push({tl:P,tr:z,bl:O,br:D,tex:v,writingMode:e.writingMode,glyphOffset:M,sectionIndex:m.sectionIndex,isSDF:x,pixelOffsetTL:H,pixelOffsetBR:G,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. 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Fi,this.programConfigurations=t,this.segments=new ia,this.dynamicLayoutVertexArray=new Ii,this.opacityVertexArray=new Pi,this.placedSymbolArray=new Ji};cc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},cc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ds.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Rs.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,ac,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},cc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Dn(\"SymbolBuffers\",cc);var uc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new ia,this.collisionVertexArray=new Ri};uc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Fs.members,!0)},uc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Dn(\"CollisionBuffers\",uc);var fc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=eo([]),this.placementViewportMatrix=eo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Ll(this.zoom,e[\"text-size\"]),this.iconSizeData=Ll(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1),this.sortFeaturesByY=(\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey)&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return gl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};fc.prototype.createArrays=function(){this.text=new cc(new Ia(Ds.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new cc(new Ia(Ds.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new $i,this.lineVertexArray=new ta,this.symbolInstances=new Qi},fc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=Us[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},fc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof te&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new ii(this.zoom),g=0,m=t;g<m.length;g+=1){var v=m[g],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Da(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Da(y));var k=void 0;if(l){var M=n.getValueAndResolveTokens(\"text-field\",T,r,p),A=te.factory(M);lc(A)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ei()||this.hasRTLText&&ni.isParsed())&&(k=js(A,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ee?E:ee.fromString(E)}if(k||S){var C=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,L={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Da(y),properties:y.properties,type:ic[y.type],sortKey:C};if(this.features.push(L),S&&(f[S.name]=!0),k){var I=a.evaluate(T,{},r).join(\",\"),P=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(gl.vertical)>=0;for(var z=0,O=k.sections;z<O.length;z+=1){var D=O[z];if(D.image)f[D.image.name]=!0;else{var R=qn(k.toString()),F=D.fontStack||I,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,P,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,f),m=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[m].geometry,!0)]=m,n[g].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},fc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},fc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},fc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},fc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},fc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},fc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},fc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},fc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),g=this.glyphOffsetArray.length,m=d.vertexLength,v=this.allowVerticalPlacement&&o===gl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,M=b.tex,A=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,C=b.minFontScaleY,L=b.glyphOffset,I=b.isSDF,P=b.sectionIndex,z=d.vertexLength,O=L[1];oc(p,s.x,s.y,_.x,O+_.y,M.x,M.y,r,I,A.x,A.y,E,C),oc(p,s.x,s.y,w.x,O+w.y,M.x+M.w,M.y,r,I,S.x,A.y,E,C),oc(p,s.x,s.y,T.x,O+T.y,M.x,M.y+M.h,r,I,A.x,S.y,E,C),oc(p,s.x,s.y,k.x,O+k.y,M.x+M.w,M.y+M.h,r,I,S.x,S.y,E,C),sc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(z,z+1,z+2),h.emplaceBack(z+1,z+2,z+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(L[0]),x!==e.length-1&&P===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[P])}t.placedSymbolArray.emplaceBack(s.x,s.y,g,this.glyphOffsetArray.length-g,m,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},fc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},fc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},fc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i);this.addCollisionDebugVertices(a.x1,a.y1,a.x2,a.y2,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},fc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new uc(Oi,Bs.members,qi),this.iconCollisionBox=new uc(Oi,Bs.members,qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},fc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var g=t.get(d);c.iconBox={x1:g.x1,y1:g.y1,x2:g.x2,y2:g.y2,anchorPointX:g.anchorPointX,anchorPointY:g.anchorPointY},c.iconFeatureIndex=g.featureIndex;break}for(var m=s;m<l;m++){var v=t.get(m);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},fc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},fc.prototype.hasTextData=function(){return this.text.segments.get().length>0},fc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},fc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},fc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},fc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},fc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},fc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},fc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},fc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=this.symbolInstances.get(n[r]);this.featureSortOrder.push(i.featureIndex),[i.rightJustifiedTextSymbolIndex,i.centerJustifiedTextSymbolIndex,i.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),i.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,i.verticalPlacedTextSymbolIndex),i.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.placedIconSymbolIndex),i.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Dn(\"SymbolBucket\",fc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),fc.MAX_GLYPHS=65535,fc.addDynamicAttributes=sc;var hc=new yi({\"symbol-placement\":new pi(At.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new pi(At.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new pi(At.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new di(At.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new pi(At.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new pi(At.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new pi(At.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new pi(At.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new pi(At.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new di(At.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new pi(At.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new pi(At.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new di(At.layout_symbol[\"icon-image\"]),\"icon-rotate\":new di(At.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new pi(At.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new pi(At.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new di(At.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new di(At.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new pi(At.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new pi(At.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new pi(At.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new di(At.layout_symbol[\"text-field\"]),\"text-font\":new di(At.layout_symbol[\"text-font\"]),\"text-size\":new di(At.layout_symbol[\"text-size\"]),\"text-max-width\":new di(At.layout_symbol[\"text-max-width\"]),\"text-line-height\":new pi(At.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new di(At.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new di(At.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new di(At.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new pi(At.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new di(At.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new pi(At.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new pi(At.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new di(At.layout_symbol[\"text-rotate\"]),\"text-padding\":new pi(At.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new pi(At.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new di(At.layout_symbol[\"text-transform\"]),\"text-offset\":new di(At.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new pi(At.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new pi(At.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new pi(At.layout_symbol[\"text-optional\"])}),pc={paint:new yi({\"icon-opacity\":new di(At.paint_symbol[\"icon-opacity\"]),\"icon-color\":new di(At.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new di(At.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new di(At.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new di(At.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new pi(At.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new pi(At.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new di(At.paint_symbol[\"text-opacity\"]),\"text-color\":new di(At.paint_symbol[\"text-color\"],{runtimeType:Bt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new di(At.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new di(At.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new di(At.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new pi(At.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new pi(At.paint_symbol[\"text-translate-anchor\"])}),layout:hc},dc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ot,this.defaultValue=t};dc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},dc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},dc.prototype.outputDefined=function(){return!1},dc.prototype.serialize=function(){return null},Dn(\"FormatSectionOverride\",dc,{omit:[\"defaultValue\"]});var gc=function(t){function e(e){t.call(this,e,pc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(this.layout._values[\"icon-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(this.layout._values[\"text-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Vr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new fc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=pc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new dc(a),s=new Ur(o,a.property.specification);i=\"constant\"===a.value.kind||\"source\"===a.value.kind?new Hr(\"source\",s):new Gr(\"composite\",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new fi(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=pc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1)if(n.overrides&&n.overrides.hasOverride(r[e]))return void(i=!0)};if(\"constant\"===r.value.kind&&r.value.value instanceof te)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){i||(t instanceof oe&&ie(t.value)===Vt?a(t.value.sections):t instanceof ue?a(t.sections):t.eachChild(o))},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(xi),mc={paint:new yi({\"background-color\":new pi(At.paint_background[\"background-color\"]),\"background-pattern\":new mi(At.paint_background[\"background-pattern\"]),\"background-opacity\":new pi(At.paint_background[\"background-opacity\"])})},vc=function(t){function e(e){t.call(this,e,mc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(xi),yc={paint:new yi({\"raster-opacity\":new pi(At.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new pi(At.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new pi(At.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new pi(At.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new pi(At.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new pi(At.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new pi(At.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new pi(At.paint_raster[\"raster-fade-duration\"])})},xc=function(t){function e(e){t.call(this,e,yc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(xi),bc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(xi),_c={circle:oo,heatmap:vo,hillshade:xo,fill:as,\"fill-extrusion\":ws,line:zs,symbol:gc,background:vc,raster:xc},wc=self.HTMLImageElement,Tc=self.HTMLCanvasElement,kc=self.HTMLVideoElement,Mc=self.ImageData,Ac=self.ImageBitmap,Sc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Sc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof wc||t instanceof Tc||t instanceof kc||t instanceof Mc||Ac&&t instanceof Ac?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof wc||t instanceof Tc||t instanceof kc||t instanceof Mc||Ac&&t instanceof Ac?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Sc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Sc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Sc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Ec=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Ec.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Ec.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Cc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g([\"receive\",\"process\"],this),this.invoker=new Ec(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Lc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Cc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Nn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(jn(e.error)):n(null,jn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Nn(e):null,data:Nn(n,a)},a)}:function(t){i=!0},s=null,l=jn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Ic=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Ic.prototype.setNorthEast=function(t){return this._ne=t instanceof Pc?new Pc(t.lng,t.lat):Pc.convert(t),this},Ic.prototype.setSouthWest=function(t){return this._sw=t instanceof Pc?new Pc(t.lng,t.lat):Pc.convert(t),this},Ic.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Pc)e=t,r=t;else{if(!(t instanceof Ic))return Array.isArray(t)?4===t.length||t.every(Array.isArray)?this.extend(Ic.convert(t)):this.extend(Pc.convert(t)):this;if(r=t._ne,!(e=t._sw)||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Pc(e.lng,e.lat),this._ne=new Pc(r.lng,r.lat)),this},Ic.prototype.getCenter=function(){return new Pc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Ic.prototype.getSouthWest=function(){return this._sw},Ic.prototype.getNorthEast=function(){return this._ne},Ic.prototype.getNorthWest=function(){return new Pc(this.getWest(),this.getNorth())},Ic.prototype.getSouthEast=function(){return new Pc(this.getEast(),this.getSouth())},Ic.prototype.getWest=function(){return this._sw.lng},Ic.prototype.getSouth=function(){return this._sw.lat},Ic.prototype.getEast=function(){return this._ne.lng},Ic.prototype.getNorth=function(){return this._ne.lat},Ic.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Ic.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Ic.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Ic.prototype.contains=function(t){var e=Pc.convert(t),r=e.lng,n=e.lat,i=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(i=this._sw.lng>=r&&r>=this._ne.lng),this._sw.lat<=n&&n<=this._ne.lat&&i},Ic.convert=function(t){return!t||t instanceof Ic?t:new Ic(t)};var Pc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Pc.prototype.wrap=function(){return new Pc(c(this.lng,-180,180),this.lat)},Pc.prototype.toArray=function(){return[this.lng,this.lat]},Pc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Pc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Pc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Ic(new Pc(this.lng-r,this.lat-e),new Pc(this.lng+r,this.lat+e))},Pc.convert=function(t){if(t instanceof Pc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Pc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Pc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var zc=2*Math.PI*6371008.8;function Oc(t){return zc*Math.cos(t*Math.PI/180)}function Dc(t){return(180+t)/360}function Rc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Fc(t,e){return t/Oc(e)}function Bc(t){return 360/Math.PI*Math.atan(Math.exp((180-360*t)*Math.PI/180))-90}var Nc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Nc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Pc.convert(t);return new Nc(Dc(r.lng),Rc(r.lat),Fc(e,r.lat))},Nc.prototype.toLngLat=function(){return new Pc(360*this.x-180,Bc(this.y))},Nc.prototype.toAltitude=function(){return this.z*Oc(Bc(this.y))},Nc.prototype.meterInMercatorCoordinateUnits=function(){return 1/zc*(t=Bc(this.y),1/Math.cos(t*Math.PI/180));var t};var jc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=qc(0,t,t,e,r)};jc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},jc.prototype.url=function(t,e){var r,n,i,a,o,s=(n=this.y,i=this.z,a=Lc(256*(r=this.x),256*(n=Math.pow(2,i)-n-1),i),o=Lc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},jc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},jc.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var Uc=function(t,e){this.wrap=t,this.canonical=e,this.key=qc(t,e.z,e.z,e.x,e.y)},Vc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new jc(r,+n,+i),this.key=qc(e,t,r,n,i)};function qc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Vc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Vc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Vc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Vc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Vc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?qc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):qc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Vc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Vc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Vc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Vc(e,this.wrap,e,r,n),new Vc(e,this.wrap,e,r+1,n),new Vc(e,this.wrap,e,r,n+1),new Vc(e,this.wrap,e,r+1,n+1)]},Vc.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Vc.prototype.wrapped=function(){return new Vc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Vc.prototype.unwrapTo=function(t){return new Vc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Vc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Vc.prototype.toUnwrapped=function(){return new Uc(this.wrap,this.canonical)},Vc.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Vc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Nc(t.x-this.wrap,t.y))},Dn(\"CanonicalTileID\",jc),Dn(\"OverscaledTileID\",Vc,{omit:[\"posMatrix\"]});var Hc=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Hc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Hc.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Hc.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Hc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Hc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Hc.prototype.getPixels=function(){return new po({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Hc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Dn(\"DEMData\",Hc);var Gc=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Gc.prototype.encode=function(t){return this._stringToNumber[t]},Gc.prototype.decode=function(t){return this._numberToString[t]};var Yc=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},Wc={geometry:{configurable:!0}};Wc.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Wc.geometry.set=function(t){this._geometry=t},Yc.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Yc.prototype,Wc);var Xc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Xc.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Xc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Xc.prototype.getState=function(t,e){var r=String(e),n=u({},(this.state[t]||{})[r],(this.stateChanges[t]||{})[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var i=this.deletedStates[t][e];if(null===i)return{};for(var a in i)delete n[a]}return n},Xc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Xc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1)delete this.state[o][c][h[f]];s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var p in t)t[p].setFeatureState(r,e)};var Zc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new Ln(8192,16,0),this.grid3D=new Ln(8192,16,0),this.featureIndexArray=new ra,this.promoteId=e};function Jc(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof hi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Kc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function Qc(t,e){return e-t}Zc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},Zc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new gs.VectorTile(new Hs(this.rawTileData)).layers,this.sourceLayerCoder=new Gc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Zc.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=rn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=Kc(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=Kc(t.cameraQueryGeometry),d=0,g=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1)if(Wa(t,f[u]))return!0;for(var h=0;h<t.length-1;h++)if(Xa(t[h],t[h+1],c))return!0;return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)}));d<g.length;d+=1)h.push(g[d]);h.sort(Qc);for(var m,v={},y=function(i){var u=h[i];if(u!==m){m=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(v,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Da(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},x=0;x<h.length;x++)y(x);return v},Zc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new ii(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),g=0;g<f.length;g++){var m=f[g];if(!(a&&a.indexOf(m)<0)){var v=s[m];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[m];x.paint=Jc(x.paint,v.paint,p,y,o),x.layout=Jc(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new Yc(p,this.z,this.x,this.y,d);_.layer=x;var w=t[m];void 0===w&&(w=t[m]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},Zc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=rn(i),u=0,f=t;u<f.length;u+=1)this.loadMatchingFeature(l,r,n,f[u],c,a,o,s,e);return l},Zc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Zc.prototype.getId=function(t,e){var r=t.id;return this.promoteId&&\"boolean\"==typeof(r=t.properties[\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e]])&&(r=Number(r)),r},Dn(\"FeatureIndex\",Zc,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var $c=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};$c.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},$c.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},$c.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1)r[s[o].id]=t}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof fc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof fc&&o.hasRTLText){this.hasRTLText=!0,ni.isLoading()||ni.isLoaded()||\"deferred\"!==ei()||ri();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new Xi},$c.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},$c.prototype.getBucket=function(t){return this.buckets[t.id]},$c.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Sc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Sc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},$c.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},$c.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},$c.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=rn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new ii(this.tileID.overscaledZ),p)){var d=r.getId(p,i),g=new Yc(p,l,c,u,d);g.tile=f,t.push(g)}}}},$c.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},$c.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},$c.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=M(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},$c.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},$c.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},$c.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},$c.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},$c.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},$c.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},$c.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},$c.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=this.dependencies[n[r]];if(i)for(var a=0,o=e;a<o.length;a+=1)if(i[o[a]])return!0}return!1};var tu=self.performance,eu=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},tu.mark(this._marks.start)};eu.prototype.finish=function(){tu.mark(this._marks.end);var t=tu.getEntriesByName(this._marks.measure);return 0===t.length&&(tu.measure(this._marks.measure,this._marks.start,this._marks.end),t=tu.getEntriesByName(this._marks.measure),tu.clearMarks(this._marks.start),tu.clearMarks(this._marks.end),tu.clearMeasures(this._marks.measure)),t},t.Actor=Cc,t.AlphaImage=ho,t.CanonicalTileID=jc,t.CollisionBoxArray=Xi,t.Color=Kt,t.DEMData=Hc,t.DataConstantProperty=pi,t.DictionaryCoder=Gc,t.EXTENT=8192,t.ErrorEvent=kt,t.EvaluationParameters=ii,t.Event=Tt,t.Evented=Mt,t.FeatureIndex=Zc,t.FillBucket=rs,t.FillExtrusionBucket=xs,t.ImageAtlas=dl,t.ImagePosition=hl,t.LineBucket=Cs,t.LngLat=Pc,t.LngLatBounds=Ic,t.MercatorCoordinate=Nc,t.ONE_EM=24,t.OverscaledTileID=Vc,t.Point=i,t.Point$1=i,t.Properties=yi,t.Protobuf=Hs,t.RGBAImage=po,t.RequestManager=q,t.RequestPerformance=eu,t.ResourceType=ht,t.SegmentVector=ia,t.SourceFeatureState=Xc,t.StructArrayLayout1ui2=Hi,t.StructArrayLayout2f1f2i16=Di,t.StructArrayLayout2i4=Mi,t.StructArrayLayout3ui6=Fi,t.StructArrayLayout4i8=Ai,t.SymbolBucket=fc,t.Texture=Sc,t.Tile=$c,t.Transitionable=si,t.Uniform1f=va,t.Uniform1i=ma,t.Uniform2f=ya,t.Uniform3f=xa,t.Uniform4f=ba,t.UniformColor=_a,t.UniformMatrix4f=Ta,t.UnwrappedTileID=Uc,t.ValidationError=St,t.WritingMode=gl,t.ZoomHistory=Un,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=sc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=g,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ut>ot&&(t.getActor().send(\"enforceCacheSizeLimit\",at),ut=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete(\"mapbox-tiles\");t&&e.catch(t).then((function(){return t()}))},t.clipLine=jl,t.clone=function(t){var e=new to(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new to(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ns,t.config=F,t.create=function(){var t=new to(16);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new to(9);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new to(4);return to!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=qr,t.createLayout=Ti,t.createStyleLayer=function(t){return\"custom\"===t.type?new bc(t):new _c[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if(\"object\"==typeof e&&null!==e&&null!==r){if(\"object\"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=s,t.emitValidationErrors=Cn,t.endsWith=m,t.enforceCacheSizeLimit=function(t){st(),Q&&Q.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Il,t.evaluateSizeForZoom=Pl,t.evaluateVariableOffset=$l,t.evented=ti,t.extend=u,t.featureFilter=rn,t.filterObject=y,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=Al,t.getAnchorJustification=tc,t.getArrayBuffer=yt,t.getImage=bt,t.getJSON=function(t,e){return vt(u(t,{type:\"json\"}),e)},t.getRTLTextPluginStatus=ei,t.getReferrer=mt,t.getVideo=function(t,e){var r,n,i=self.document.createElement(\"video\");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=self.document.createElement(\"source\");r=t[a],n=void 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r}function ft(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function ht(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(pt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(pt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function pt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function dt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,gt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function gt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a||\"MultiPoint\"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if(\"LineString\"===a)mt(o,i,t,r,!1,!1);else if(\"MultiLineString\"===a||\"Polygon\"===a)for(s=0;s<i.length;s++)mt(o,i[s],t,r,\"Polygon\"===a,0===s);else if(\"MultiPolygon\"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)mt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if(\"LineString\"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function mt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function vt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)J(r,t.features[n],e,n);else J(r,\"Feature\"===t.type?t:{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=rt(t,1,-1-r,r,0,-1,2,e),a=rt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=rt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=ut(i,1).concat(n)),a&&(n=n.concat(ut(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function yt(t,e,r){return 32*((1<<t)*r+e)+t}function xt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=b(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}F.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(N(t[l],l));this.trees[i+1]=new D(s,G,Y,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new D(s,G,Y,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},F.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(V(r),q(a),V(i),q(n));u<f.length;u+=1){var h=l.points[f[u]];c.push(h.numPoints?j(h):this.points[h.index])}return c},F.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=i.points[c[l]];u.parentId===t&&s.push(u.numPoints?j(u):this.points[u.index])}if(0===s.length)throw new Error(n);return s},F.prototype.getLeaves=function(t,e,r){var n=[];return this._appendLeaves(n,t,e=e||10,r=r||0,0),n},F.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.radius/a.extent,s=(r-o)/i,l=(r+1+o)/i,c={features:[]};return this._addTileFeatures(n.range((e-o)/i,s,(e+1+o)/i,l),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-o/i,s,1,l),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,s,o/i,l),n.points,-1,r,i,c),c.features.length?c:null},F.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},F.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},F.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?U(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},F.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},F.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.reduce,a=n.radius/(n.extent*Math.pow(2,e)),o=0;o<t.length;o++){var s=t[o];if(!(s.zoom<=e)){s.zoom=e;for(var l=this.trees[e+1],c=l.within(s.x,s.y,a),u=s.numPoints||1,f=s.x*u,h=s.y*u,p=i&&u>1?this._map(s,!0):null,d=(o<<5)+(e+1)+this.points.length,g=0,m=c;g<m.length;g+=1){var v=l.points[m[g]];if(!(v.zoom<=e)){v.zoom=e;var y=v.numPoints||1;f+=v.x*y,h+=v.y*y,u+=y,v.parentId=d,i&&(p||(p=this._map(s,!0)),i(p,this._map(v)))}}1===u?r.push(s):(s.parentId=d,r.push(B(f/u,h/u,d,u,p)))}}return r},F.prototype._getOriginId=function(t){return t-this.points.length>>5},F.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},F.prototype._map=function(t,e){if(t.numPoints)return e?H({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?H({},n):n},vt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},vt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=yt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=dt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;g=m=v=y=null,x=rt(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=rt(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(g=rt(x,u,n-_,n+T,1,h.minY,h.maxY,l),m=rt(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=rt(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=rt(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},vt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=yt(t,e=(e%o+o)%o,r);if(this.tiles[s])return ht(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[yt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?ht(this.tiles[s],i):null):null};var bt=function(e){function r(t,r,n,i){e.call(this,t,r,n,xt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));!function t(e,r){var n,i=e&&e.type;if(\"FeatureCollection\"===i)for(n=0;n<e.features.length;n++)t(e.features[n],r);else if(\"GeometryCollection\"===i)for(n=0;n<e.geometries.length;n++)t(e.geometries[n],r);else if(\"Feature\"===i)t(e.geometry,r);else if(\"Polygon\"===i)f(e.coordinates,r);else if(\"MultiPolygon\"===i)for(n=0;n<e.coordinates.length;n++)f(e.coordinates[n],r);return e}(o,!0);try{e._geoJSONIndex=n.cluster?new F(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=t.createExpression(h[1]),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=d.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new vt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded;return n&&n[t.uid]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l),_t=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:bt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return _t.prototype.setReferrer=function(t,e){this.referrer=e},_t.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in i)i[a].availableImages=e}r()},_t.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},_t.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},_t.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},_t.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},_t.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},_t.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},_t.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},_t.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},_t.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},_t.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},_t.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error(\"RTL Text Plugin failed to import scripts from \"+i),a)}}catch(t){n(t.toString())}},_t.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},_t.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},_t.prototype.getWorkerSource=function(t,e,r){var n=this;return this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),this.workerSources[t][e][r]||(this.workerSources[t][e][r]=new this.workerSourceTypes[e]({send:function(e,r,i){n.actor.send(e,r,i,t)}},this.getLayerIndex(t),this.getAvailableImages(t))),this.workerSources[t][e][r]},_t.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new u),this.demWorkerSources[t][e]},_t.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},\"undefined\"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new _t(t.window)),_t})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return\"undefined\"==typeof window||\"undefined\"==typeof document?\"not a browser\":Array.prototype&&Array.prototype.every&&Array.prototype.filter&&Array.prototype.forEach&&Array.prototype.indexOf&&Array.prototype.lastIndexOf&&Array.prototype.map&&Array.prototype.some&&Array.prototype.reduce&&Array.prototype.reduceRight&&Array.isArray?Function.prototype&&Function.prototype.bind?Object.keys&&Object.create&&Object.getPrototypeOf&&Object.getOwnPropertyNames&&Object.isSealed&&Object.isFrozen&&Object.isExtensible&&Object.getOwnPropertyDescriptor&&Object.defineProperty&&Object.defineProperties&&Object.seal&&Object.freeze&&Object.preventExtensions?\"JSON\"in window&&\"parse\"in JSON&&\"stringify\"in JSON?function(){if(!(\"Worker\"in window&&\"Blob\"in window&&\"URL\"in window))return!1;var t,e,r=new Blob([\"\"],{type:\"text/javascript\"}),n=URL.createObjectURL(r);try{e=new Worker(n),t=!0}catch(e){t=!1}return e&&e.terminate(),URL.revokeObjectURL(n),t}()?\"Uint8ClampedArray\"in window?ArrayBuffer.isView?function(){var t=document.createElement(\"canvas\");t.width=t.height=1;var e=t.getContext(\"2d\");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?(void 0===n[r=t&&t.failIfMajorPerformanceCaveat]&&(n[r]=function(t){var r=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)||r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)||r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)||r.getContext(\"experimental-webgl\",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);return!(!n||r.isContextLost())&&(r.shaderSource(n,\"void main() {}\"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS))}(r)),n[r]?void 0:\"insufficient WebGL support\"):\"insufficient Canvas/getImageData support\":\"insufficient ArrayBuffer support\":\"insufficient Uint8ClampedArray support\":\"insufficient worker support\":\"insufficient JSON support\":\"insufficient Object support\":\"insufficient Function support\":\"insufficent Array support\";var r}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]=\"none\")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",c,c),t.window.removeEventListener(\"test\",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),t.addEventListener(e,r,\"passive\"in n&&l?n:n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),t.removeEventListener(e,r,\"passive\"in n&&l?n:n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",u,!0)};function f(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",u,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e];this._notify(n.ids,n.callback)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!(t&&(4!==t.length||t[0]<0||e.data.width<t[0]||t[1]<0||e.data.height<t[1]||t[2]<0||e.data.width<t[2]||t[3]<0||e.data.height<t[3]||t[2]<t[0]||t[3]<t[1]))},r.prototype.updateImage=function(t,e){e.version=this.images[t].version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1)this.images[i[n]]||(r=!1);this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. 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o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented),p=m,d=m,g=1e20;function m(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-g,n[1]=+g;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+g}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}m.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?g:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?g:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++)r[n]=Math.max(0,Math.min(255,Math.round(255-255*((this.gridOuter[n]-this.gridInner[n])/this.radius+this.cutoff))));return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1)i.push({stack:a,id:s[o]});t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){var c=t[l];n.push({left:i=s*r,right:a=(s+=c)*r,isDash:o=!o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.width*(this.nextRow+r+i),o=0,s=t[o],l=0;l<this.width;l++){l/s.right>1&&(s=t[++o]);var c=Math.abs(l-s.left),u=Math.abs(l-s.right),f=Math.min(c,u),h=void 0,p=i/r*(n+1);if(s.isDash){var d=n-Math.abs(p);h=Math.sqrt(f*f+d*d)}else h=n-Math.sqrt(f*f+p*p);this.data[a+l]=Math.max(0,Math.min(255,h+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f);this.data[o+c]=Math.max(0,Math.min(255,(l.isDash?h:-h)+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=new e.Actor(i[a],n,this.id);o.name=\"Worker \"+a,this.actors.push(o)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n=n||function(){})},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var A=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};A.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},A.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new A(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new A(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor,e.request=this.actor.send(i,{type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId},(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),I=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),P=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=Math.max(i-r,a-n),u=Math.max(0,Math.floor(-Math.log(c)/Math.LN2)),f=Math.pow(2,u);return new t.CanonicalTileID(u,Math.floor((r+i)/2*f),Math.floor((n+a)/2*f))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented),z=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1)this.urls.push(this.map._requestManager.transformRequest(i[n],t.ResourceType.Source).url);t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(P),O=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(P),D={vector:S,raster:E,\"raster-dem\":C,geojson:L,video:z,image:P,canvas:O};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var g=d[p];f[g.featureIndex]||(f[g.featureIndex]=!0,h.push(g))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=e.attributes[this.attributes[r].name];void 0!==n&&t.enableVertexAttribArray(n)}},V.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[U[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},V.prototype.destroy=function(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)};var q=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};q.prototype.get=function(){return this.current},q.prototype.set=function(t){},q.prototype.getDefault=function(){return this.default},q.prototype.setDefault=function(){this.set(this.default)};var H=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var 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e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(q),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(q),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(q),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var 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e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(q),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),_t=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e}(q),wt=function(t){function 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t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var At=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};At.disabled=new At({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Ct=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new H(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new mt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Ct.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Ct.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Ct.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Ct.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Ct.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Ct.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Ct.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Ct.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Ct.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Ct.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Ct.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Ct.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Lt=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=Math.ceil(t.width/this._source.tileSize)+1,r=Math.ceil(t.height/this._source.tileSize)+1,n=Math.floor(e*r*5),i=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,n):n;this._cache.setMaxSize(i)},r.prototype.handleWrapJump=function(t){var e=Math.round((t-(void 0===this._prevLng?t:this._prevLng))/360);if(this._prevLng=t,e){var r={};for(var n in this._tiles){var i=this._tiles[n];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+e),r[i.tileID.key]=i}for(var a in this._tiles=r,this._timers)clearTimeout(this._timers[a]),delete this._timers[a];for(var o in this._tiles)this._setTileReloadTimer(o,this._tiles[o])}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Pt(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],g=this._tiles[p];if(g&&!(g.fadeEndTime&&g.fadeEndTime<=t.browser.now())){var m=this.findLoadedParent(d,o);m&&(this._addTile(m.tileID),c[m.tileID.key]=m.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],g=this._tiles[d.key];if(!g.hasData()){if(e+1>this._source.maxzoom){var m=d.children(this._source.maxzoom)[0],v=this.getTile(m);if(v&&v.hasData()){n[m.key]=m;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=g.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1)this._loadedParentTiles[o[a]]=r}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,g=0,m=c;g<m.length;g+=1){var v=m[g];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,g=Math.pow(2,o.zoom-n.tileID.overscaledZ),m=r*n.queryPadding*t.EXTENT/n.tileSize/g,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-m<t.EXTENT&&v[0].y-m<t.EXTENT&&v[1].x+m>=0&&v[1].y+m>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:g})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Pt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){this._state.updateState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.removeFeatureState=function(t,e,r){this._state.removeFeatureState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.getFeatureState=function(t,e){return this._state.getState(t=t||\"_geojsonTileLayer\",e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Pt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(Yi.workerUrl)}Lt.maxOverzooming=10,Lt.maxUnderzooming=3;var Ot=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new zt);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[Ot]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function qt(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function Ht(t,e,r,n){qt(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}var Zt=function(t,e){this.reset(t,e)};Zt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Zt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Jt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Kt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function Qt(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function $t(e,r){var n=[e.x,e.y,0,1];ue(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function te(t,e){return.5+t/e*.5}function ee(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function re(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!m)ce(y.numGlyphs,h);else{m=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),ee(x,f)){var b=te(n.transform.cameraToCenterDistance,x[3]),_=t.evaluateSizeForFeature(c,u,y),w=s?_/b:_*b,T=new t.Point(y.anchorX,y.anchorY),k=$t(T,a).point,M={},A=ae(y,w,!1,l,r,a,o,e.glyphOffsetArray,p,h,k,T,M,g);m=A.useVertical,(A.notEnoughRoom||m||A.needsFlipping&&ae(y,w,!0,l,r,a,o,e.glyphOffsetArray,p,h,k,T,M,g).notEnoughRoom)&&ce(y.numGlyphs,h)}else ce(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ne(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),g=e.getoffsetX(f-1),m=se(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!m)return null;var v=se(t*g,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:m,last:v}:null}function ie(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function ae(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=r/24,v=e.lineOffsetX*m,y=e.lineOffsetY*m;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ne(m,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=$t(w.first.point,s).point,k=$t(w.last.point,s).point;if(i&&!n){var M=ie(e.writingMode,T,k,d);if(M)return M}g=[w.first];for(var A=e.glyphStartIndex+1;A<x-1;A++)g.push(se(m*l.getoffsetX(A),v,y,n,f,h,e.segment,b,_,c,o,p));g.push(w.last)}else{if(i&&!n){var S=$t(h,a).point,E=e.lineStartIndex+e.segment+1,C=new t.Point(c.getx(E),c.gety(E)),L=$t(C,a),I=L.signedDistanceFromCamera>0?L.point:oe(h,C,S,1,a),P=ie(e.writingMode,S,I,d);if(P)return P}var z=se(m*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!z)return{notEnoughRoom:!0};g=[z]}for(var O=0,D=g;O<D.length;O+=1){var R=D[O];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function oe(t,e,r,n,i){var a=$t(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function se(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,g=0;i&&(d*=-1,g=Math.PI),d<0&&(g+=Math.PI);for(var m=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((m+=d)<l||m>=c)return null;if(y=v,w.push(v),void 0===(v=h[m])){var T=new t.Point(u.getx(m),u.gety(m)),k=$t(T,f);if(k.signedDistanceFromCamera>0)v=h[m]=k.point;else{var M=m-d;v=oe(0===x?o:new t.Point(u.getx(M),u.gety(M)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var A=(_-x)/b,S=v.sub(y),E=S.mult(A)._add(y);E._add(S._unit()._perp()._mult(n*d));var C=g+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:C,path:w}}Jt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Jt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Jt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Jt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Jt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Jt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}return this._forEachCell(t,e,r,n,this._queryCell,o,{hitTest:i,seenUids:{box:{},circle:{}}},a),i?o.length>0:o},Jt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[];return this._forEachCell(a,s,o,l,this._queryCellCircle,c,{hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}},i),n?c.length>0:c},Jt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Jt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Jt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Jt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(m[b],m[b+1],m[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=m[b],w=m[b+1],T=m[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Jt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var g=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[g+0],f[g+1],f[g+2],f[g+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var m=this.circleCells[i];if(null!==m)for(var v=this.circles,y=0,x=m;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Jt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++)if(i.call(this,t,e,r,n,this.xCellCount*p+h,a,o,s))return},Jt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Jt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Jt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Jt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var le=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ce(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(le,3*n)}}function ue(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var fe=function(t,e,r){void 0===e&&(e=new Jt(t.width+200,t.height+200,25)),void 0===r&&(r=new Jt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function he(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}fe.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},fe.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=new t.Point(r.anchorX,r.anchorY),m=$t(g,o),v=te(this.transform.cameraToCenterDistance,m.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=$t(g,s).point,b=ne(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,g,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,M=new t.Point(-100,-100),A=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Zt,E=b.first,C=b.last,L=[],I=E.path.length-1;I>=1;I--)L.push(E.path[I]);for(var P=1;P<C.path.length;P++)L.push(C.path[P]);var z=2.5*k;if(l){var O=L.map((function(t){return $t(t,l)}));L=O.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:O.map((function(t){return t.point}))}var D=[];if(L.length>0){for(var R=L[0].clone(),F=L[0].clone(),B=1;B<L.length;B++)R.x=Math.min(R.x,L[B].x),R.y=Math.min(R.y,L[B].y),F.x=Math.max(F.x,L[B].x),F.y=Math.max(F.y,L[B].y);D=R.x>=M.x&&F.x<=A.x&&R.y>=M.y&&F.y<=A.y?[L]:F.x<M.x||R.x>A.x||F.y<M.y||R.y>A.y?[]:t.clipLine([L],M.x,M.y,A.x,A.y)}for(var N=0,j=D;N<j.length;N+=1){var U;S.reset(j[N],.25*k),U=S.length<=.5*k?1:Math.ceil(S.paddedLength/z)+1;for(var V=0;V<U;V++){var q=V/Math.max(U-1,1),H=S.lerp(q),G=H.x+100,Y=H.y+100;d.push(G,Y,k,0);var W=G-k,X=Y-k,Z=G+k,J=Y+k;if(T=T&&this.isOffscreen(W,X,Z,J),w=w||this.isInsideGrid(W,X,Z,J),!e&&this.grid.hitTestCircle(G,Y,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},fe.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],m=g.key;if(void 0===f[m.bucketInstanceId]&&(f[m.bucketInstanceId]={}),!f[m.bucketInstanceId][m.featureIndex]){var v=[new t.Point(g.x1,g.y1),new t.Point(g.x2,g.y1),new t.Point(g.x2,g.y2),new t.Point(g.x1,g.y2)];t.polygonIntersectsPolygon(r,v)&&(f[m.bucketInstanceId][m.featureIndex]=!0,void 0===h[m.bucketInstanceId]&&(h[m.bucketInstanceId]=[]),h[m.bucketInstanceId].push(m.featureIndex))}}return h},fe.prototype.insertCollisionBox=function(t,e,r,n,i){(e?this.ignoredGrid:this.grid).insert({bucketInstanceId:r,featureIndex:n,collisionGroupID:i},t[0],t[1],t[2],t[3])},fe.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},fe.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return ue(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},fe.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},fe.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},fe.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var pe=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};pe.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var de=function(t,e,r,n,i){this.text=new pe(t?t.text:null,e,r,i),this.icon=new pe(t?t.icon:null,e,n,i)};de.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},me=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ve=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},ye=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function xe(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function be(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}ye.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var _e=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new fe(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new ye(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function we(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}_e.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=he(n,1,this.transform.zoom),g=Kt(f,h,p,this.transform,d),m=null;if(h){var v=Qt(f,h,p,this.transform,d);m=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ve(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:g,labelToScreenMatrix:m,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x];e.push({sortKey:_.sortKey,symbolInstanceStart:_.symbolInstanceStart,symbolInstanceEnd:_.symbolInstanceEnd,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},_e.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=[f.textOffset0,f.textOffset1],v=xe(t,r,n,m,i),y=this.collisionIndex.placeCollisionBox(be(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(be(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return y.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(g=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:m,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:g},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}):void 0},_e.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,g=a.collisionGroup,m=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),M=x&&(y||!o.hasTextData()||m);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var A=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,A=!1,S=!0,E=null,C={box:null,offscreen:null},L={box:null,offscreen:null},I=null,P=null,z=0,O=0,D=0;a.textFeatureIndex?z=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(z=e.featureIndex),a.verticalTextFeatureIndex&&(O=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,i.markUsedOrientation(o,n=a,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(C=n(),L=C):C=r(),!(C&&C.box&&C.box.length));i+=1);else C=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var m=i.attemptAnchorPlacement(N[d%N.length],t,a,s,c,b,_,f,l,g,d>=N.length,e,o,n,u);if(m&&(h=m.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=m.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&!(C&&C.box&&C.box.length)&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),C&&(T=C.box,S=C.offscreen);var V=F(C&&C.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,g.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(C&&C.box&&C.box.length)}}if(T=(p=C)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\");I=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,g.predicate,e.collisionCircleDiameter,W),T=y||I.circles.length>0&&!I.collisionDetected,S=S&&I.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?be(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,g.predicate)};A=L&&L.box&&L.box.length&&a.verticalIconBox?(P=X(a.verticalIconBox)).box.length>0:(P=X(a.iconBox)).box.length>0,S=S&&P.offscreen}var Z=m||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=v||0===e.numIconVertices;if(Z||J?J?Z||(A=A&&T):T=A&&T:A=T=A&&T,T&&p&&p.box&&i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,L&&L.box&&O?O:z,g.ID),A&&P&&i.collisionIndex.insertCollisionBox(P.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,g.ID),I&&(T&&i.collisionIndex.insertCollisionCircles(I.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,g.ID),n)){var K=o.bucketInstanceId,Q=i.collisionCircleArrays[K];void 0===Q&&(Q=i.collisionCircleArrays[K]=new me);for(var $=0;$<I.circles.length;$+=4)Q.circles.push(I.circles[$+0]),Q.circles.push(I.circles[$+1]),Q.circles.push(I.circles[$+2]),Q.circles.push(I.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,A||M,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var C=S[E];A(o.symbolInstances.get(C),o.collisionArrays[C])}else for(var L=e.symbolInstanceStart;L<e.symbolInstanceEnd;L++)A(o.symbolInstances.get(L),o.collisionArrays[L]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var I=this.collisionCircleArrays[o.bucketInstanceId];t.invert(I.invProjMatrix,l),I.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},_e.prototype.markUsedJustification=function(e,r,n,i){var a;a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:{left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex}[t.getAnchorJustification(r)];for(var o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];o<s.length;o+=1){var l=s[o];l>=0&&(e.text.placedSymbolArray.get(l).crossTileID=a>=0&&l!==a?0:n.crossTileID)}},_e.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1)e.text.placedSymbolArray.get(s[o]).placedOrientation=i;n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},_e.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new de(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new de(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new de(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},_e.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},_e.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new de(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new de(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},g=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,g=a.crossTileID,m=i.opacities[g];r[g]?m=o:m||(i.opacities[g]=m=p),r[g]=!0;var v=a.numIconVertices>0,y=i.placedOrientations[a.crossTileID],x=y===t.WritingMode.vertical,b=y===t.WritingMode.horizontal||y===t.WritingMode.horizontalOnly;if(s>0||l>0){var _=Le(m.text);d(e.text,s,x?Ie:_),d(e.text,l,b?Ie:_);var w=m.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=w||x?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=w||b?1:0);var T=i.variableOffsets[a.crossTileID];T&&i.markUsedJustification(e,T.anchor,a,y);var k=i.placedOrientations[a.crossTileID];k&&(i.markUsedJustification(e,\"left\",a,k),i.markUsedOrientation(e,k,a))}if(v){var M=Le(m.icon),A=!(h&&a.verticalPlacedIconSymbolIndex&&x);a.placedIconSymbolIndex>=0&&(d(e.icon,a.numIconVertices,A?M:Ie),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=m.icon.isHidden()),a.verticalPlacedIconSymbolIndex>=0&&(d(e.icon,a.numVerticalIconVertices,A?Ie:M),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=m.icon.isHidden())}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var S=e.collisionArrays[n];if(S){var E=new t.Point(0,0);if(S.textBox||S.verticalTextBox){var C=!0;if(c){var L=i.variableOffsets[g];L?(E=xe(L.anchor,L.width,L.height,L.textOffset,L.textBoxScale),u&&E._rotate(f?i.transform.angle:-i.transform.angle)):C=!1}S.textBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||x,E.x,E.y),S.verticalTextBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||b,E.x,E.y)}var I=Boolean(!b&&S.verticalIconBox);S.iconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,I,h?E.x:0,h?E.y:0),S.verticalIconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,!I,h?E.x:0,h?E.y:0)}}},m=0;m<e.symbolInstances.length;m++)g(m);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},_e.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},_e.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},_e.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},_e.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},_e.prototype.setStale=function(){this.stale=!0};var Te=Math.pow(2,25),ke=Math.pow(2,24),Me=Math.pow(2,17),Ae=Math.pow(2,16),Se=Math.pow(2,9),Ee=Math.pow(2,8),Ce=Math.pow(2,1);function Le(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Te+e*ke+r*Me+e*Ae+r*Se+e*Ee+r*Ce+e}var Ie=0,Pe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Pe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;)if(e.getBucketParts(a,n,t[this._currentTileIndex],this._sortAcrossTiles),this._currentTileIndex++,i())return!0;for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;)if(e.placeLayerBucketPart(a[this._currentPartIndex],this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0;return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new _e(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Pe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Oe=512/t.EXTENT/2,De=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};De.prototype.getScaledCoordinates=function(e,r){var n=Oe/Math.pow(2,r.canonical.z-this.tileID.canonical.z);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*n),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*n)}},De.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Re=function(){this.maxCrossTileID=0};Re.prototype.generate=function(){return++this.maxCrossTileID};var Fe=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Fe.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Fe.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new De(t,e.symbolInstances,e.bucketInstanceId),!0},Fe.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1)delete this.usedCrossTileIDs[t][i[n].crossTileID]},Fe.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Be=function(){this.layerIndexes={},this.crossTileIDs=new Re,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Be.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Fe);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Be.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var Ne=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},je=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ue=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Ve=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i,a=t.styleSpec.$root[n];a.required&&null!=(i=\"version\"===n?r:\"array\"===a.type?[]:{})&&(e[n]=i)}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Be,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){o.dispatcher.broadcast(\"syncRTLPluginState\",{pluginStatus:e.pluginStatus,pluginURL:e.pluginURL},(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(Ve,!1)},r.prototype._load=function(e,r){if(!r||!Ne(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,g=l.stretchX,m=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:g,stretchY:m,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=this._layers[n[r]];\"custom\"!==i.type&&e.push(i.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=this._layers[u[c]];f.recalculate(e,this._availableImages),!f.isHidden(e.zoom)&&f.source&&(this.sourceCaches[f.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),Ne(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=function(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||qt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):Ht(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),g=f.slice(),m=Object.create(null);for(i=0,a=0;i<f.length;i++)d.hasOwnProperty(o=f[i])?a++:(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)g[g.length-1-i]!==(o=h[h.length-1-i])&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,n.push({command:Ut.addLayer,args:[d[o],c=g[g.length-i]]}),g.splice(g.length-i,0,o),m[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!m[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}(this.serialize(),e).filter((function(t){return!(t.command in Ue)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in je)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Lt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(Ne(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1)i.push(f[u])}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var h=[],p=this._order.length-1;p>=0;p--){var d=this._order[p];if(r(d))for(var g=i.length-1;g>=0;g--){var m=i[g].feature;if(n[m.layer.id]<p)break;h.push(m),i.pop()}else for(var v=0,y=t;v<y.length;v+=1){var x=y[v][d];if(x)for(var b=0,_=x;b<_.length;b+=1)h.push(_[b].feature)}}return h},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1)c.push(o[f[u]]);c.sort(B);for(var h=function(){var r=d[p],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1)o.push(f[u])}},p=0,d=c;p<d.length;p+=1)h();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=r[t[e].source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=a}))};for(var m in s)g(m);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&Ne(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=this._layers[u[c]];if(\"symbol\"===f.type){if(!l[f.source]){var h=this.sourceCaches[f.source];l[f.source]=h.getRenderableIds(!0).map((function(t){return h.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var p=this.crossTileSymbolIndex.addLayer(f,l[f.source],e.center.lng);o=o||p}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var d=0,g=this._order;d<g.length;d+=1){var m=this._layers[g[d]];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var He=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ge=vr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),Ye=vr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),We=vr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Xe=vr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Ze=vr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Je=vr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Ke=vr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),Qe=vr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),$e=vr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),tr=vr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),er=vr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),rr=vr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),nr=vr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=vr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ar=vr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),or=vr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),sr=vr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),lr=vr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),cr=vr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),ur=vr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=vr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),hr=vr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),pr=vr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),dr=vr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=vr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),mr=vr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function vr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var yr=Object.freeze({__proto__:null,prelude:Ge,background:Ye,backgroundPattern:We,circle:Xe,clippingMask:Ze,heatmap:Je,heatmapTexture:Ke,collisionBox:Qe,collisionCircle:$e,debug:tr,fill:er,fillOutline:rr,fillOutlinePattern:nr,fillPattern:ir,fillExtrusion:ar,fillExtrusionPattern:or,hillshadePrepare:sr,hillshade:lr,line:cr,lineGradient:ur,linePattern:fr,lineSDF:hr,raster:pr,symbolIcon:dr,symbolSDF:gr,symbolTextAndIcon:mr}),xr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};xr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);t.extVertexArrayObject&&this.vao&&this.boundProgram===e&&this.boundLayoutVertexBuffer===r&&!l&&this.boundIndexBuffer===i&&this.boundVertexOffset===a&&this.boundDynamicVertexBuffer===o&&this.boundDynamicVertexBuffer2===s?(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind()):this.freshBind(e,r,n,i,a,o,s)},xr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(u,t);a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var m=g[d];m.bind(),m.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},xr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var br=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ge.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ge.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var g=a.getActiveAttrib(this.program,d);g&&(this.attributes[g.name]=a.getAttribLocation(this.program,g.name))}for(var m=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<m;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function _r(t,e,r){var n=1/he(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}br.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(m={},m[v.LINES]=2,m[v.TRIANGLES]=3,m[v.LINE_STRIP]=1,m)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new xr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var wr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},Tr=function(e,r,n,i,a,o,s){return t.extend(wr(e,r,n,i),_r(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},kr=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(kr(e),_r(n,r,i))},Ar=function(t,e){return{u_matrix:t,u_world:e}},Sr=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Er=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=he(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=he(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Lr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Pr=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:he(e,1,r),u_intensity:n}},Or=function(e,r,n){var i=e.transform;return{u_matrix:Nr(e,r,n),u_ratio:1/he(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Dr=function(e,r,n){return t.extend(Or(e,r,n),{u_image:0})},Rr=function(e,r,n,i){var a=e.transform,o=Br(r,a);return{u_matrix:Nr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/he(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Fr=function(e,r,n,i,a){var o=e.lineAtlas,s=Br(r,e.transform),l=\"round\"===n.layout.get(\"line-cap\"),c=o.getDash(i.from,l),u=o.getDash(i.to,l),f=c.width*a.fromScale,h=u.width*a.toScale;return t.extend(Or(e,r,n),{u_patternscale_a:[s/f,-c.height/2],u_patternscale_b:[s/h,-u.height/2],u_sdfgamma:o.width/(256*Math.min(f,h)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:u.y,u_mix:a.t})};function Br(t,e){return 1/he(t,1,e.tileZoom)}function Nr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var jr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Ur(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Ur(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Vr,qr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Hr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(qr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Gr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Hr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Yr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Wr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/he(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Xr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Zr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var g=i[d],m=r.getTile(g),v=m.getBucket(n);if(v){var y=g.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(g.posMatrix,m,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,m),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var M=0,A=0,S=f;A<S.length;A+=1)for(var E=S[A],C=0;C<E.circleArray.length/4;C++){var L=4*C,I=E.circleArray[L+0],P=E.circleArray[L+1],z=E.circleArray[L+2],O=E.circleArray[L+3];k.emplace(M++,I,P,z,O,0),k.emplace(M++,I,P,z,O,1),k.emplace(M++,I,P,z,O,2),k.emplace(M++,I,P,z,O,3)}(!Vr||Vr.length<2*h)&&(Vr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(Vr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Lr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Jr=t.identity(new Float32Array(16));function Kr(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function Qr(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,g={};p.clear();for(var m=0;m<h.length;m++){var v=h.get(m),y=v.hidden||!v.crossTileID||e.allowVerticalPlacement&&!v.placedOrientation?null:i[v.crossTileID];if(y){var x=new t.Point(v.anchorX,v.anchorY),b=$t(x,n?l:s),_=te(o.cameraToCenterDistance,b.signedDistanceFromCamera),w=a.evaluateSizeForFeature(e.textSizeData,u,v)*_/t.ONE_EM;n&&(w*=e.tilePixelRatio/c);for(var T=Kr(y.anchor,y.width,y.height,y.textOffset,y.textBoxScale,w),k=n?$t(x.add(T),s).point:b.point.add(r?T.rotate(-o.angle):T),M=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,A=0;A<v.numGlyphs;A++)t.addDynamicAttributes(p,k,M);f&&v.associatedIconIndex>=0&&(g[v.associatedIconIndex]={shiftedAnchor:k,angle:M})}else ce(v.numGlyphs,p)}if(f){d.clear();for(var S=e.icon.placedSymbolArray,E=0;E<S.length;E++){var C=S.get(E);if(C.hidden)ce(C.numGlyphs,d);else{var L=g[E];if(L)for(var I=0;I<C.numGlyphs;I++)t.addDynamicAttributes(d,L.shiftedAnchor,L.angle);else ce(C.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function $r(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function tn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,g=e.transform,m=\"map\"===l,v=\"map\"===c,y=m&&\"point\"!==n.layout.get(\"symbol-placement\"),x=m&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,Mt.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,M=i;k<M.length;k+=1){var A=M[k],S=r.getTile(A),E=S.getBucket(n);if(E){var C=a?E.text:E.icon;if(C&&C.segments.get().length){var L=C.programConfigurations.get(n.id),I=a||E.sdfIcons,P=a?E.textSizeData:E.iconSizeData,z=v||0!==g.pitch,O=e.useProgram($r(I,a,E),L),D=t.evaluateSizeForZoom(P,g.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a)B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText&&(F=S.imageAtlasTexture.size,j=S.imageAtlasTexture,U=z||e.options.rotating||e.options.zooming||\"composite\"===P.kind||\"camera\"===P.kind?d.LINEAR:d.NEAREST);else{var V=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=I||e.options.rotating||e.options.zooming||V||z?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=he(S,1,e.transform.zoom),H=Kt(A.posMatrix,v,m,e.transform,q),G=Qt(A.posMatrix,v,m,e.transform,q),Y=w&&E.hasTextData(),W=\"none\"!==n.layout.get(\"icon-text-fit\")&&Y&&E.hasIconData();y&&re(E,A.posMatrix,e,a,H,G,v,u);var X=e.translatePosMatrix(A.posMatrix,S,o,s),Z=y||a&&w||W?Jr:H,J=e.translatePosMatrix(G,S,o,s,!0),K=I&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),Q={program:O,buffers:C,uniformValues:I?E.iconsInText?Gr(P.kind,D,x,v,e,X,Z,J,R,F):Hr(P.kind,D,x,v,e,X,Z,J,a,R,!0):qr(P.kind,D,x,v,e,X,Z,J,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:I,hasHalo:K};if(b)for(var $=0,tt=C.segments.get();$<tt.length;$+=1){var et=tt[$];T.push({segments:new t.SegmentVector([et]),sortKey:et.sortKey,state:Q})}else T.push({segments:C.segments,sortKey:0,state:Q})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var rt=0,nt=T;rt<nt.length;rt+=1){var it=nt[rt],at=it.state;if(p.activeTexture.set(d.TEXTURE0),at.atlasTexture.bind(at.atlasInterpolation,d.CLAMP_TO_EDGE),at.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),at.atlasTextureIcon&&at.atlasTextureIcon.bind(at.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),at.isSDF){var ot=at.uniformValues;at.hasHalo&&(ot.u_is_halo=1,en(at.buffers,it.segments,n,e,at.program,_,f,h,ot)),ot.u_is_halo=0}en(at.buffers,it.segments,n,e,at.program,_,f,h,at.uniformValues)}}function en(t,e,r,n,i,a,o,s,l){var c=n.context;i.draw(c,c.gl.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function rn(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),g=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var m=0,v=n;m<v.length;m+=1){var y=v[m],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(g));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,M=k.patternPositions[T.to.toString()],A=k.patternPositions[T.from.toString()];M&&A&&_.setConstantPatternPositions(M,A)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Sr(S,t,g,x,E):Ar(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,g,x):kr(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function nn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var g=d[p],m=e.getTile(g),v=m.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),m.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&m.imageAtlas){var _=m.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(g.posMatrix,m,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),M=r.paint.get(\"fill-extrusion-vertical-gradient\"),A=u?Tr(k,t,M,h,g,f,m):wr(k,t,M,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,A,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function an(e,r,n,i,a,o){var s=e.context,l=s.gl,c=r.fbo;if(c){var u=e.useProgram(\"hillshade\");s.activeTexture.set(l.TEXTURE0),l.bindTexture(l.TEXTURE_2D,c.colorAttachment.get());var f=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,c,u,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,c=Math.pow(2,l.canonical.z),u=l.canonical.y,[new t.MercatorCoordinate(0,u/c).toLngLat().lat,new t.MercatorCoordinate(0,(u+1)/c).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}}(e,r,n);u.draw(s,l.TRIANGLES,i,a,o,Et.disabled,f,n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments)}}function on(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var g=r.fbo;if(!g){var m=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);m.bind(c.LINEAR,c.CLAMP_TO_EDGE),(g=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(m.texture)}l.bindFramebuffer.set(g.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function sn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var ln=new t.Color(1,0,0,1),cn=new t.Color(0,1,0,1),un=new t.Color(0,0,1,1),fn=new t.Color(1,0,1,1),hn=new t.Color(0,1,1,1);function pn(t,e,r,n){gn(t,0,e+r/2,t.transform.width,r,n)}function dn(t,e,r,n){gn(t,e-r/2,0,r,t.transform.height,n)}function gn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function mn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,c=At.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=Math.floor((f&&f.byteLength||0)/1024),p=r.getTile(n).tileSize,d=512/Math.min(p,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+h+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,d),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var vn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=At.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),g=d.getBucket(n);if(g&&g.text&&g.text.segments.get().length){var m=t.evaluateSizeForZoom(g.textSizeData,l.zoom),v=he(d,1,r.transform.zoom),y=Kt(p.posMatrix,u,c,r.transform,v),x=\"none\"!==n.layout.get(\"icon-text-fit\")&&g.hasIconData();if(m){var b=Math.pow(2,l.zoom-d.tileID.overscaledZ);Qr(g,c,u,s,t.symbolSize,l,y,p.posMatrix,b,m,x)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&tn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&tn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Zr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Zr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=At.disabled,p=e.colorModeForRenderPass(),d=[],g=0;g<i.length;g++){var m=i[g],v=r.getTile(m),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Er(e,m,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,M=d;k<M.length;k+=1){var A=M[k],S=A.state;S.program.draw(c,u.TRIANGLES,f,h,p,Et.disabled,S.uniformValues,n.id,S.layoutVertexBuffer,S.indexBuffer,A.segments,n.paint,e.transform.zoom,S.programConfiguration)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=At.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id);e.useProgram(\"heatmap\",p).draw(a,o.TRIANGLES,Mt.disabled,s,l,Et.disabled,zr(u.posMatrix,f,e.transform.zoom,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:0,u_color_ramp:1,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",g=e.context,m=g.gl,v=!0;if(h){g.activeTexture.set(m.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(g,n.gradient,m.RGBA)),y.bind(m.LINEAR,m.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),M=e.context.program.get(),A=e.useProgram(d,k),S=v||A.program!==M,E=u.constantOr(null);if(E&&w.imageAtlas){var C=w.imageAtlas,L=C.patternPositions[E.to.toString()],I=C.patternPositions[E.from.toString()];L&&I&&k.setConstantPatternPositions(L,I)}var P=f?Rr(e,w,n,p):c?Fr(e,w,n,c,p):h?Dr(e,w,n):Or(e,w,n);f?(g.activeTexture.set(m.TEXTURE0),w.imageAtlasTexture.bind(m.LINEAR,m.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(g.activeTexture.set(m.TEXTURE0),e.lineAtlas.bind(g)),A.draw(g,m.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,P,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);rn(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,Mt.ReadOnly);rn(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))nn(t,e,r,n,a,At.disabled,St.disabled),nn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();nn(t,e,r,n,a,At.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,Mt.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?on(t,p,r,a,o,At.disabled,s):\"translucent\"===t.renderPass&&an(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof P?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,g=f;d<g.length;d+=1){var m=g[d],v=t.depthModeForSublayer(m.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),y=e.getTile(m),x=t.transform.calculatePosMatrix(m.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(m,0),_=sn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var M=jr(x,T||[0,0],w||1,_,r);o instanceof P?s.draw(i,a.TRIANGLES,v,At.disabled,l,Et.disabled,M,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[m.overscaledZ],l,Et.disabled,M,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=At.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),g=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var m=r.getCrossfadeParameters(),v=0,y=g;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Wr(b,i,t,c,{tileID:x,tileSize:l},m):Yr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)mn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(At.disabled);var o=\"3d\"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},yn=function(t,e){this.context=new Ct(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Lt.maxUnderzooming+Lt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Be,this.gpuTimers={}};yn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1)this.style._layers[i[n]].resize()},yn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,He.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,He.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,I.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,He.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new At({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},yn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,St.disabled,Et.disabled,Pr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},yn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new At({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Pr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},yn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new At({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},yn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new At({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},yn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new At({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=At.disabled,e),n]},yn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;return this._showOverdrawInspector?new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0]):\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},yn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},yn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},yn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var g=0;g<i.length;g++)if(this.style._layers[i[g]].is3D()){this.opaquePassCutoff=g;break}this.renderPass=\"offscreen\";for(var m=0,v=i;m<v.length;m+=1){var y=this.style._layers[v[m]];if(y.hasOffscreenPass()&&!y.isHidden(this.transform.zoom)){var x=f[y.source];(\"custom\"===y.type||x.length)&&this.renderLayer(this,a[y.source],y,x)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var b=this.style._layers[i[this.currentLayer]],_=a[b.source],w=u[b.source];this._renderTileClippingMasks(b,w),this.renderLayer(this,_,b,w)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var T=this.style._layers[i[this.currentLayer]],k=a[T.source],M=(\"symbol\"===T.type?h:f)[T.source];this._renderTileClippingMasks(T,u[T.source]),this.renderLayer(this,k,T,M)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&vn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&function(t){var e=t.transform.padding;pn(t,t.transform.height-(e.top||0),3,ln),pn(t,e.bottom||0,3,cn),dn(t,e.left||0,3,un),dn(t,t.transform.width-(e.right||0),3,fn);var r=t.transform.centerPoint;!function(t,e,r,n){gn(t,e-1,r-10,2,20,n),gn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,hn)}(this),this.context.setDefault()},yn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),vn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},yn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},yn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},yn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},yn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},yn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:he(r,n[0],this.transform.zoom),a?n[1]:he(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},yn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},yn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},yn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},yn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new br(this.context,yr[t],e,Xr[t],this._showOverdrawInspector)),this.cache[r]},yn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},yn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},yn.prototype.initDebugOverlayCanvas=function(){null==this.debugOverlayCanvas&&(this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))},yn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var xn=function(t,e){this.points=t,this.planes=e};xn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new xn(a,o)};var bn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};bn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new bn(n,i)},bn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},bn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},bn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var _n=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};_n.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},_n.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},_n.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},_n.prototype.clone=function(){return new _n(this.top,this.bottom,this.left,this.right)},_n.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var wn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new _n,this._posMatrixCache={},this._alignedPosMatrixCache={}},Tn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};wn.prototype.clone=function(){var t=new wn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Tn.minZoom.get=function(){return this._minZoom},Tn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Tn.maxZoom.get=function(){return this._maxZoom},Tn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Tn.minPitch.get=function(){return this._minPitch},Tn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Tn.maxPitch.get=function(){return this._maxPitch},Tn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Tn.renderWorldCopies.get=function(){return this._renderWorldCopies},Tn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Tn.worldSize.get=function(){return this.tileSize*this.scale},Tn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Tn.size.get=function(){return new t.Point(this.width,this.height)},Tn.bearing.get=function(){return-this.angle/Math.PI*180},Tn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Tn.pitch.get=function(){return this._pitch/Math.PI*180},Tn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Tn.fov.get=function(){return this._fov/Math.PI*180},Tn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Tn.zoom.get=function(){return this._zoom},Tn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Tn.center.get=function(){return this._center},Tn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Tn.padding.get=function(){return this._edgeInsets.toJSON()},Tn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Tn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},wn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},wn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},wn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},wn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},wn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=xn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new bn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var g=u.pop(),m=g.x,v=g.y,y=g.fullyVisible;if(!y){var x=g.aabb.intersects(s);if(0===x)continue;y=2===x}var b=g.aabb.distanceX(o),_=g.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_));if(g.zoom===h||w>3+(1<<h-g.zoom)-2&&g.zoom>=l)f.push({tileID:new t.OverscaledTileID(g.zoom===h?p:g.zoom,g.wrap,g.zoom,m,v),distanceSq:t.sqrLen([o[0]-.5-m,o[1]-.5-v])});else for(var T=0;T<4;T++){var k=(m<<1)+T%2,M=(v<<1)+(T>>1);u.push({aabb:g.aabb.quadrant(T),zoom:g.zoom+1,x:k,y:M,wrap:g.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},wn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Tn.unmodified.get=function(){return this._unmodified},wn.prototype.zoomScale=function(t){return Math.pow(2,t)},wn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},wn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},wn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Tn.point.get=function(){return this.project(this.center)},wn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},wn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},wn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},wn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},wn.prototype.coordinateLocation=function(t){return t.toLngLat()},wn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[1]/i,s=n[1]/a,l=r[2]/i,c=n[2]/a,u=l===c?0:(0-l)/(c-l);return new t.MercatorCoordinate(t.number(r[0]/i,n[0]/a,u)/this.worldSize,t.number(o,s,u)/this.worldSize)},wn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},wn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},wn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},wn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},wn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},wn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},wn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var g=p.y,m=c.y/2;g-m<a&&(i=a+m),g+m>o&&(i=o-m)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},wn.prototype._calcMatrices=function(){if(this.height){var e=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height;var r=Math.PI/2+this._pitch,n=this._fov*(.5+e.y/this.height),i=Math.sin(n)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-r-n,.01,Math.PI-.01)),a=this.point,o=a.x,s=a.y,l=1.01*(Math.cos(Math.PI/2-this._pitch)*i+this.cameraToCenterDistance),c=this.height/50,u=new Float64Array(16);t.perspective(u,this._fov,this.width/this.height,c,l),u[8]=2*-e.x/this.width,u[9]=2*e.y/this.height,t.scale(u,u,[1,-1,1]),t.translate(u,u,[0,0,-this.cameraToCenterDistance]),t.rotateX(u,u,this._pitch),t.rotateZ(u,u,this.angle),t.translate(u,u,[-o,-s,0]),this.mercatorMatrix=t.scale([],u,[this.worldSize,this.worldSize,this.worldSize]),t.scale(u,u,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=u,this.invProjMatrix=t.invert([],this.projMatrix);var f=this.width%2/2,h=this.height%2/2,p=Math.cos(this.angle),d=Math.sin(this.angle),g=o-Math.round(o)+p*f+d*h,m=s-Math.round(s)+p*h+d*f,v=new Float64Array(u);if(t.translate(v,v,[g>.5?g-1:g,m>.5?m-1:m,0]),this.alignedProjMatrix=v,u=t.create(),t.scale(u,u,[this.width/2,-this.height/2,1]),t.translate(u,u,[1,-1,0]),this.labelPlaneMatrix=u,u=t.create(),t.scale(u,u,[1,-1,1]),t.translate(u,u,[-1,-1,0]),t.scale(u,u,[2/this.width,2/this.height,1]),this.glCoordMatrix=u,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(u=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=u,this._posMatrixCache={},this._alignedPosMatrixCache={}}},wn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},wn.prototype.getCameraPoint=function(){var e=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,e))},wn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(wn.prototype,Tn);var kn=function(e){var r,n,i,a;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=!1,i=null,a=function(){i=null,n&&(r(),i=setTimeout(a,300),n=!1)},function(){return n=!0,i||a(),i})};kn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},kn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},kn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},kn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},kn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},kn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var Mn={linearity:.3,easing:t.bezier(0,0,.3,1)},An=t.extend({deceleration:2500,maxSpeed:1400},Mn),Sn=t.extend({deceleration:20,maxSpeed:1400},Mn),En=t.extend({deceleration:1e3,maxSpeed:360},Mn),Cn=t.extend({deceleration:1e3,maxSpeed:90},Mn),Ln=function(t){this._map=t,this.clear()};function In(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Pn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Ln.prototype.clear=function(){this._inertiaBuffer=[]},Ln.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Ln.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Ln.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Pn(r.pan.mag(),o,t.extend({},An,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,In(s,l)}if(r.zoom){var c=Pn(r.zoom,o,Sn);s.zoom=this._map.transform.zoom+c.amount,In(s,c)}if(r.bearing){var u=Pn(r.bearing,o,En);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),In(s,u)}if(r.pitch){var f=Pn(r.pitch,o,Cn);s.pitch=this._map.transform.pitch+f.amount,In(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var zn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),On=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Dn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Rn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Rn.prototype.reset=function(){delete this._mousedownPos},Rn.prototype.wheel=function(t){return this._firePreventable(new Dn(t.type,this._map,t))},Rn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseup=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.dblclick=function(t){return this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseover=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.mouseout=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.touchstart=function(t){return this._firePreventable(new On(t.type,this._map,t))},Rn.prototype.touchmove=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchend=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchcancel=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Rn.prototype.isEnabled=function(){return!0},Rn.prototype.isActive=function(){return!1},Rn.prototype.enable=function(){},Rn.prototype.disable=function(){};var Fn=function(t){this._map=t};Fn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Fn.prototype.mousemove=function(t){this._map.fire(new zn(t.type,this._map,t))},Fn.prototype.mousedown=function(){this._delayContextMenu=!0},Fn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new zn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Fn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new zn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Fn.prototype.isEnabled=function(){return!0},Fn.prototype.isActive=function(){return!1},Fn.prototype.enable=function(){},Fn.prototype.disable=function(){};var Bn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Nn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Bn.prototype.isEnabled=function(){return!!this._enabled},Bn.prototype.isActive=function(){return!!this._active},Bn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Bn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Bn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Bn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Bn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Bn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Bn.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Bn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var jn=function(t){this.reset(),this.numTouches=t.numTouches};jn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},jn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1)r._add(i[n]);return r.div(e.length)}(r),this.touches=Nn(n,r)))},jn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Nn(r,e);for(var i in this.touches){var a=n[i];(!a||a.dist(this.touches[i])>30)&&(this.aborted=!0)}}},jn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Un=function(t){this.singleTap=new jn(t),this.numTaps=t.numTaps,this.reset()};Un.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Un.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Un.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Un.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Vn=function(){this._zoomIn=new Un({numTouches:1,numTaps:2}),this._zoomOut=new Un({numTouches:2,numTaps:1}),this.reset()};Vn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Vn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Vn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Vn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Vn.prototype.touchcancel=function(){this.reset()},Vn.prototype.enable=function(){this._enabled=!0},Vn.prototype.disable=function(){this._enabled=!1,this.reset()},Vn.prototype.isEnabled=function(){return this._enabled},Vn.prototype.isActive=function(){return this._active};var qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},qn.prototype._correctButton=function(t,e){return!1},qn.prototype._move=function(t,e){return{}},qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},qn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},qn.prototype.enable=function(){this._enabled=!0},qn.prototype.disable=function(){this._enabled=!1,this.reset()},qn.prototype.isEnabled=function(){return this._enabled},qn.prototype.isActive=function(){return this._active};var Hn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(qn),Gn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(qn),Yn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(qn),Wn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Wn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Wn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Wn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Wn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Wn.prototype.touchcancel=function(){this.reset()},Wn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Nn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Wn.prototype.enable=function(){this._enabled=!0},Wn.prototype.disable=function(){this._enabled=!1,this.reset()},Wn.prototype.isEnabled=function(){return this._enabled},Wn.prototype.isActive=function(){return this._active};var Xn=function(){this.reset()};function Zn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}function Jn(t,e){return Math.log(t/e)/Math.LN2}Xn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Xn.prototype._start=function(t){},Xn.prototype._move=function(t,e,r){return{}},Xn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Xn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[1],a=Zn(r,e,n[0]),o=Zn(r,e,i);if(a&&o){var s=this._aroundCenter?null:a.add(o).div(2);return this._move([a,o],s,t)}}},Xn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[1],o=Zn(n,e,i[0]),s=Zn(n,e,a);o&&s||(this._active&&r.suppressClick(),this.reset())}},Xn.prototype.touchcancel=function(){this.reset()},Xn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Xn.prototype.disable=function(){this._enabled=!1,this.reset()},Xn.prototype.isEnabled=function(){return this._enabled},Xn.prototype.isActive=function(){return this._active};var Kn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(Jn(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:Jn(this._distance,r),pinchAround:e}},e}(Xn);function Qn(t,e){return 180*t.angleWith(e)/Math.PI}var $n=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:Qn(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=Qn(t,this._startVector);return Math.abs(r)<e},e}(Xn);function ti(t){return Math.abs(t.y)>Math.abs(t.x)}var ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ti(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ti(t)&&ti(e)&&a}},e}(Xn),ri={panStep:100,bearingStep:15,pitchStep:10},ni=function(){var t=ri;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function ii(t){return t*(2-t)}ni.prototype.reset=function(){this._active=!1},ni.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:ii,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ai=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};ai.prototype.setZoomRate=function(t){this._defaultZoomRate=t},ai.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},ai.prototype.isEnabled=function(){return!!this._enabled},ai.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},ai.prototype.isZooming=function(){return!!this._zooming},ai.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},ai.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},ai.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},ai.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},ai.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},ai.prototype.renderFrame=function(){return this._onScrollFrame()},ai.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},ai.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},ai.prototype.reset=function(){this._active=!1};var oi=function(t,e){this._clickZoom=t,this._tapZoom=e};oi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},oi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},oi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},oi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var si=function(){this.reset()};si.prototype.reset=function(){this._active=!1},si.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},si.prototype.enable=function(){this._enabled=!0},si.prototype.disable=function(){this._enabled=!1,this.reset()},si.prototype.isEnabled=function(){return this._enabled},si.prototype.isActive=function(){return this._active};var li=function(){this._tap=new Un({numTouches:1,numTaps:1}),this.reset()};li.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},li.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},li.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},li.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},li.prototype.touchcancel=function(){this.reset()},li.prototype.enable=function(){this._enabled=!0},li.prototype.disable=function(){this._enabled=!1,this.reset()},li.prototype.isEnabled=function(){return this._enabled},li.prototype.isActive=function(){return this._active};var ci=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};ci.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},ci.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},ci.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},ci.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var ui=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};ui.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},ui.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},ui.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},ui.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var fi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};fi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},fi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},fi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},fi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},fi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},fi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var hi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(t.Event);function di(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var gi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Ln(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0];r.addEventListener(l,s[1],l===t.window.document?this.handleWindowEvent:this.handleEvent,s[2])}};gi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0];r.removeEventListener(a,i[1],a===t.window.document?this.handleWindowEvent:this.handleEvent,i[2])}},gi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Rn(e,t));var n=e.boxZoom=new Bn(e,t);this._add(\"boxZoom\",n);var i=new Vn,a=new si;e.doubleClickZoom=new oi(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new li;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ei;this._add(\"touchPitch\",s);var l=new Gn(t),c=new Yn(t);e.dragRotate=new ui(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Hn(t),f=new Wn(t);e.dragPan=new ci(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new $n,p=new Kn;e.touchZoomRotate=new fi(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new ai(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var g=e.keyboard=new ni;this._add(\"keyboard\",g),this._add(\"blockableMapEvent\",new Fn(e));for(var m=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];m<v.length;m+=1){var y=v[m];t.interactive&&t[y]&&e[y].enable(t[y])}},gi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},gi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1)e[t].handler.reset();this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},gi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},gi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},gi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},gi.prototype.isMoving=function(){return Boolean(hi(this._eventsInProgress))||this.isZooming()},gi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},gi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},gi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r];this._el.contains(i.target)&&e.push(i)}return e},gi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var g=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(g=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,g,h,n),g&&g.needsRenderFrame&&this._triggerRenderFrame()),(g||p.isActive())&&(o[h]=p)}}var m={};for(var v in this._previousActiveHandlers)o[v]||(m[v]=n);this._previousActiveHandlers=o,(Object.keys(m).length||di(i))&&(this._changes.push([i,a,m]),this._triggerRenderFrame()),(Object.keys(o).length||di(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},gi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},gi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},gi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!di(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},gi.prototype._fireEvents=function(e,r){var n=this,i=hi(this._eventsInProgress),a=hi(e),o={};for(var s in e)this._eventsInProgress[s]||(o[s+\"start\"]=e[s].originalEvent),this._eventsInProgress[s]=e[s];for(var l in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(l,o[l]);for(var c in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e)this._fireEvent(c,e[c].originalEvent);var u,f={};for(var h in this._eventsInProgress){var p=this._eventsInProgress[h],d=p.handlerName,g=p.originalEvent;this._handlersById[d].isActive()||(delete this._eventsInProgress[h],f[h+\"end\"]=u=r[d]||g)}for(var m in f)this._fireEvent(m,f[m]);var v=hi(this._eventsInProgress);if((i||a)&&!v){this._updatingCamera=!0;var y=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),x=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};y?(x(y.bearing||this._map.getBearing())&&(y.bearing=0),this._map.easeTo(y,{originalEvent:u})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:u})),x(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},gi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},gi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new pi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var mi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),g=p.sub(d),m=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/g.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/g.y;if(!(v<0||m<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(m,v)),i.maxZoom),x=t.Point.convert(i.offset),b=new t.Point(x.x+(i.padding.left-i.padding.right)/2,x.y+(i.padding.top-i.padding.bottom)/2).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(b)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),g=i.pointLocation(d),m=t.LngLat.convert(e.center||g);this._normalizeCenter(m);var v,y,x=i.project(g),b=i.project(m).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var g=i.zoomScale(i.zoom-a),m=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(m,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(g));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),g=t.Point.convert(e.offset),m=a.centerPoint.add(g),v=a.pointLocation(m),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var M=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),A=w/a.zoomScale(M-o);_=Math.sqrt(A/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function C(t){return(Math.exp(t)-Math.exp(-t))/2}function L(t){return(Math.exp(t)+Math.exp(-t))/2}var I=E(0),P=function(t){return L(I)/L(I+_*t)},z=function(t){return w*((L(I)*(C(e=I+_*t)/L(e))-C(I))/S)/k;var e},O=(E(1)-I)/_;if(Math.abs(k)<1e-6||!isFinite(O)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;O=Math.abs(Math.log(T/w))/_,z=function(){return 0},P=function(t){return Math.exp(D*_*t)}}return e.duration=\"duration\"in e?+e.duration:1e3*O/(\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed),e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*O,d=1/P(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),m=a.centerPoint.add(g));var v=1===e?y:a.unproject(x.add(b.mult(z(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,m),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),vi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};vi.prototype.getDefaultPosition=function(){return\"bottom-right\"},vi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},vi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},vi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},vi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},vi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},vi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var yi=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};yi.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},yi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},yi.prototype.getDefaultPosition=function(){return\"bottom-left\"},yi.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},yi.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},yi.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var xi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};xi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},xi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},xi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},xi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var bi={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},_i=t.window.HTMLImageElement,wi=t.window.HTMLElement,Ti=t.window.ImageBitmap,ki={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Mi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},ki,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new wn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new xi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},bi,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof wi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new gi(this,e),this._hash=e.hash&&new kn(\"string\"==typeof e.hash&&e.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new vi({customAttribution:e.customAttribution})),this.addControl(new yi,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),(i.prototype=Object.create(n&&n.prototype)).constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new zn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new zn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new zn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;return void 0===r?n.prototype.off.call(this,t,e):(this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this)},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;if(this._lazyInitEmptyStyle(),r instanceof _i||Ti&&r instanceof Ti){var c=t.browser.getImageData(r);this.style.addImage(e,{data:new t.RGBAImage({width:c.width,height:c.height},c.data),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var u=r;this.style.addImage(e,{data:new t.RGBAImage({width:r.width,height:r.height},new Uint8Array(r.data)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:u}),u.onAdd&&u.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof _i||Ti&&r instanceof Ti?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;return void 0===a||void 0===o?this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\"))):a!==n.data.width||o!==n.data.height?this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\"))):(n.data.replace(s,!(r instanceof _i||Ti&&r instanceof Ti)),void this.style.updateImage(e,n))},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new yn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Ai(this._canvasContainer),Ai(this._controlContainer),Ai(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(mi);function Ai(t){t.parentNode&&t.parentNode.removeChild(t)}var Si={showCompass:!0,showZoom:!0,visualizePitch:!1},Ei=function(e){var n=this;this.options=t.extend({},Si,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Ei.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Ei.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Ei.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ci(this._map,this._compass,this.options.visualizePitch)),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Ei.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Ei.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ci=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Gn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Yn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Li(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ci.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ci.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ci.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ci.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ci.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ci.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ci.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ci.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ci.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ci.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ci.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var Ii={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Pi(t,e,r){var n=t.classList;for(var i in Ii)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var zi,Oi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");g.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(g);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"opacity\",\"0.25\"),m.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),m.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(m),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Pi(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Li(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,Ii[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Di={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},Ri=0,Fi=!1,Bi=function(e){function n(r){e.call(this),this.options=t.extend({},Di,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==zi?n(zi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){n(zi=\"denied\"!==t.state)})):n(zi=!!t.window.navigator.geolocation),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ri=0,Fi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Fi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Oi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Oi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||e.originalEvent&&\"resize\"===e.originalEvent.type||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ri--,Fi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ri>1?(e={maximumAge:6e5,timeout:0},Fi=!0):(e=this.options.positionOptions,Fi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ni={maxWidth:100,unit:\"metric\"},ji=function(e){this.options=t.extend({},Ni,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Ui(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?Vi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):Vi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?Vi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?Vi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Vi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Vi(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*(o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o)));t.style.width=e*(s/r)+\"px\",t.innerHTML=s+\"&nbsp;\"+n}ji.prototype.getDefaultPosition=function(){return\"bottom-left\"},ji.prototype._onMove=function(){Ui(this._map,this._container,this.options)},ji.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},ji.prototype.setUnit=function(t){this.options.unit=t,Ui(this._map,this._container,this.options)};var qi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};qi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},qi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},qi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},qi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},qi.prototype._isFullscreen=function(){return this._fullscreen},qi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},qi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Hi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Gi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Hi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this;if(this._map&&(this._lngLat||this._trackPointer)&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Li(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var i=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),a=this.options.anchor,o=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!a){var s,l=this._container.offsetWidth,c=this._container.offsetHeight;s=i.y+o.bottom.y<c?[\"top\"]:i.y>this._map.transform.height-c?[\"bottom\"]:[],i.x<l/2?s.push(\"left\"):i.x>this._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var u=i.add(o[a]).round();r.setTransform(this._container,Ii[a]+\" translate(\"+u.x+\"px,\"+u.y+\"px)\"),Pi(this._container,a,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented),Yi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Mi,NavigationControl:Ei,GeolocateControl:Bi,AttributionControl:vi,ScaleControl:ji,FullscreenControl:qi,Popup:Gi,Marker:Oi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(Ot)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Ot),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Yi})),r}))},{}],474:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=1<<t+1,r=new Array(e),n=0;n<e;++n)r[n]=a(t,n);return r};var n=t(\"convex-hull\");function i(t,e,r){for(var n=new Array(t),i=0;i<t;++i)n[i]=0,i===e&&(n[i]+=.5),i===r&&(n[i]+=.5);return n}function a(t,e){if(0===e||e===(1<<t+1)-1)return[];for(var r=[],a=[],o=0;o<=t;++o)if(e&1<<o){r.push(i(t,o-1,o-1)),a.push(null);for(var s=0;s<=t;++s)~e&1<<s&&(r.push(i(t,o-1,s-1)),a.push([o,s]))}var l=n(r),c=[];t:for(o=0;o<l.length;++o){var u=l[o],f=[];for(s=0;s<u.length;++s){if(!a[u[s]])continue t;f.push(a[u[s]].slice())}c.push(f)}return c}},{\"convex-hull\":135}],475:[function(t,e,r){var n=t(\"./normalize\"),i=t(\"gl-mat4/create\"),a=t(\"gl-mat4/clone\"),o=t(\"gl-mat4/determinant\"),s=t(\"gl-mat4/invert\"),l=t(\"gl-mat4/transpose\"),c={length:t(\"gl-vec3/length\"),normalize:t(\"gl-vec3/normalize\"),dot:t(\"gl-vec3/dot\"),cross:t(\"gl-vec3/cross\")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function g(t,e,r,n,i){t[0]=e[0]*n+r[0]*i,t[1]=e[1]*n+r[1]*i,t[2]=e[2]*n+r[2]*i}e.exports=function(t,e,r,i,m,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),m||(m=[0,0,0,1]),v||(v=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,T,k,M=u[3],A=u[7],S=u[11],E=u[12],C=u[13],L=u[14],I=u[15];if(0!==M||0!==A||0!==S){if(h[0]=M,h[1]=A,h[2]=S,h[3]=I,!s(f,f))return!1;l(f,f),y=m,b=f,_=(x=h)[0],w=x[1],T=x[2],k=x[3],y[0]=b[0]*_+b[4]*w+b[8]*T+b[12]*k,y[1]=b[1]*_+b[5]*w+b[9]*T+b[13]*k,y[2]=b[2]*_+b[6]*w+b[10]*T+b[14]*k,y[3]=b[3]*_+b[7]*w+b[11]*T+b[15]*k}else m[0]=m[1]=m[2]=0,m[3]=1;if(e[0]=E,e[1]=C,e[2]=L,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),g(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),g(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),g(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var P=0;P<3;P++)r[P]*=-1,p[P][0]*=-1,p[P][1]*=-1,p[P][2]*=-1;return v[0]=.5*Math.sqrt(Math.max(1+p[0][0]-p[1][1]-p[2][2],0)),v[1]=.5*Math.sqrt(Math.max(1-p[0][0]+p[1][1]-p[2][2],0)),v[2]=.5*Math.sqrt(Math.max(1-p[0][0]-p[1][1]+p[2][2],0)),v[3]=.5*Math.sqrt(Math.max(1+p[0][0]+p[1][1]+p[2][2],0)),p[2][1]>p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{\"./normalize\":476,\"gl-mat4/clone\":278,\"gl-mat4/create\":280,\"gl-mat4/determinant\":281,\"gl-mat4/invert\":293,\"gl-mat4/transpose\":306,\"gl-vec3/cross\":365,\"gl-vec3/dot\":370,\"gl-vec3/length\":380,\"gl-vec3/normalize\":387}],476:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],477:[function(t,e,r){var n=t(\"gl-vec3/lerp\"),i=t(\"mat4-recompose\"),a=t(\"mat4-decompose\"),o=t(\"gl-mat4/determinant\"),s=t(\"quat-slerp\"),l=f(),c=f(),u=f();function f(){return{translate:h(),scale:h(1),skew:h(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function h(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,f){if(0===o(e)||0===o(r))return!1;var h=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!h||!p)&&(n(u.translate,l.translate,c.translate,f),n(u.skew,l.skew,c.skew,f),n(u.scale,l.scale,c.scale,f),n(u.perspective,l.perspective,c.perspective,f),s(u.quaternion,l.quaternion,c.quaternion,f),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),!0)}},{\"gl-mat4/determinant\":281,\"gl-vec3/lerp\":381,\"mat4-decompose\":475,\"mat4-recompose\":478,\"quat-slerp\":527}],478:[function(t,e,r){var n={identity:t(\"gl-mat4/identity\"),translate:t(\"gl-mat4/translate\"),multiply:t(\"gl-mat4/multiply\"),create:t(\"gl-mat4/create\"),scale:t(\"gl-mat4/scale\"),fromRotationTranslation:t(\"gl-mat4/fromRotationTranslation\")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{\"gl-mat4/create\":280,\"gl-mat4/fromRotationTranslation\":284,\"gl-mat4/identity\":291,\"gl-mat4/multiply\":295,\"gl-mat4/scale\":303,\"gl-mat4/translate\":305}],479:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],480:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\"),i=t(\"mat4-interpolate\"),a=t(\"gl-mat4/invert\"),o=t(\"gl-mat4/rotateX\"),s=t(\"gl-mat4/rotateY\"),l=t(\"gl-mat4/rotateZ\"),c=t(\"gl-mat4/lookAt\"),u=t(\"gl-mat4/translate\"),f=(t(\"gl-mat4/scale\"),t(\"gl-vec3/normalize\")),h=[0,0,0];function p(t){this._components=t.slice(),this._time=[0],this.prevMatrix=t.slice(),this.nextMatrix=t.slice(),this.computedMatrix=t.slice(),this.computedInverse=t.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}e.exports=function(t){return new p((t=t||{}).matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])};var d=p.prototype;d.recalcMatrix=function(t){var e=this._time,r=n.le(e,t),o=this.computedMatrix;if(!(r<0)){var s=this._components;if(r===e.length-1)for(var l=16*r,c=0;c<16;++c)o[c]=s[l++];else{var u=e[r+1]-e[r],h=(l=16*r,this.prevMatrix),p=!0;for(c=0;c<16;++c)h[c]=s[l++];var d=this.nextMatrix;for(c=0;c<16;++c)d[c]=s[l++],p=p&&h[c]===d[c];if(u<1e-6||p)for(c=0;c<16;++c)o[c]=h[c];else i(o,h,d,(t-e[r])/u)}var g=this.computedUp;g[0]=o[1],g[1]=o[5],g[2]=o[9],f(g,g);var m=this.computedInverse;a(m,o);var v=this.computedEye,y=m[15];v[0]=m[12]/y,v[1]=m[13]/y,v[2]=m[14]/y;var 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\"+r+\"(data,\"+o.map((function(t){return\"shape[\"+t+\"]\"})).join(\",\")+\",\"+o.map((function(t){return\"stride[\"+t+\"]\"})).join(\",\")+\",offset)}\"),new Function(\"CTOR_LIST\",\"ORDER\",a.join(\"\\n\"))(c[t],s)}var c={float32:[],float64:[],int8:[],int16:[],int32:[],uint8:[],uint16:[],uint32:[],array:[],uint8_clamped:[],bigint64:[],biguint64:[],buffer:[],generic:[]};e.exports=function(t,e,r,n){if(void 0===t)return(0,c.array[0])([]);\"number\"==typeof t&&(t=[t]),void 0===e&&(e=[t.length]);var o=e.length;if(void 0===r){r=new Array(o);for(var s=o-1,u=1;s>=0;--s)r[s]=u,u*=e[s]}if(void 0===n){n=0;for(s=0;s<o;++s)r[s]<0&&(n-=(e[s]-1)*r[s])}for(var f=function(t){if(i(t))return\"buffer\";if(a)switch(Object.prototype.toString.call(t)){case\"[object Float64Array]\":return\"float64\";case\"[object Float32Array]\":return\"float32\";case\"[object Int8Array]\":return\"int8\";case\"[object Int16Array]\":return\"int16\";case\"[object Int32Array]\":return\"int32\";case\"[object 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h=e[l],p=e[c],d=e[u],g=new Array(3),m=0,v=new Array(3),y=0,x=0;x<3;++x)g[x]=h[x]-p[x],m+=g[x]*g[x],v[x]=d[x]-p[x],y+=v[x]*v[x];if(m*y>a){var b=i[c],_=1/Math.sqrt(m*y);for(x=0;x<3;++x){var w=(x+1)%3,T=(x+2)%3;b[x]+=_*(v[w]*g[T]-v[T]*g[w])}}}for(o=0;o<n;++o){b=i[o];var k=0;for(x=0;x<3;++x)k+=b[x]*b[x];if(k>a)for(_=1/Math.sqrt(k),x=0;x<3;++x)b[x]*=_;else for(x=0;x<3;++x)b[x]=0}return i},r.faceNormals=function(t,e,r){for(var n=t.length,i=new Array(n),a=void 0===r?1e-6:r,o=0;o<n;++o){for(var s=t[o],l=new Array(3),c=0;c<3;++c)l[c]=e[s[c]];var u=new Array(3),f=new Array(3);for(c=0;c<3;++c)u[c]=l[1][c]-l[0][c],f[c]=l[2][c]-l[0][c];var h=new Array(3),p=0;for(c=0;c<3;++c){var d=(c+1)%3,g=(c+2)%3;h[c]=u[d]*f[g]-u[g]*f[d],p+=h[c]*h[c]}p=p>a?1/Math.sqrt(p):0;for(c=0;c<3;++c)h[c]*=p;i[o]=h}return i}},{}],499:[function(t,e,r){\n",
+       "/*\n",
+       "object-assign\n",
+       "(c) Sindre Sorhus\n",
+       "@license MIT\n",
+       "*/\n",
+       "\"use strict\";var n=Object.getOwnPropertySymbols,i=Object.prototype.hasOwnProperty,a=Object.prototype.propertyIsEnumerable;function o(t){if(null==t)throw new TypeError(\"Object.assign cannot be called with null or undefined\");return Object(t)}e.exports=function(){try{if(!Object.assign)return!1;var t=new String(\"abc\");if(t[5]=\"de\",\"5\"===Object.getOwnPropertyNames(t)[0])return!1;for(var e={},r=0;r<10;r++)e[\"_\"+String.fromCharCode(r)]=r;if(\"0123456789\"!==Object.getOwnPropertyNames(e).map((function(t){return e[t]})).join(\"\"))return!1;var n={};return\"abcdefghijklmnopqrst\".split(\"\").forEach((function(t){n[t]=t})),\"abcdefghijklmnopqrst\"===Object.keys(Object.assign({},n)).join(\"\")}catch(t){return!1}}()?Object.assign:function(t,e){for(var r,s,l=o(t),c=1;c<arguments.length;c++){for(var u in r=Object(arguments[c]))i.call(r,u)&&(l[u]=r[u]);if(n){s=n(r);for(var f=0;f<s.length;f++)a.call(r,s[f])&&(l[s[f]]=r[s[f]])}}return l}},{}],500:[function(t,e,r){\"use 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+       "/*!\n",
+       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT license.\n",
+       " */\n",
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+       "/*\n",
+       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
+       " * @license MIT\n",
+       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
+       " */\n",
+       "var n,i=t(\"./lib/build-log\"),a=t(\"./lib/epsilon\"),o=t(\"./lib/intersecter\"),s=t(\"./lib/segment-chainer\"),l=t(\"./lib/segment-selector\"),c=t(\"./lib/geojson\"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return 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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":70,\"color-normalize\":125,\"flatten-vertex-data\":244,\"object-assign\":499,\"pick-by-alias\":511,\"to-float32\":577,\"update-diff\":599}],535:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),g=t(\"parse-rect\");function m(t,e){if(!(this instanceof m))return new m(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=m.shaders.has(t)?m.shaders.get(t):m.shaders.set(t,m.createShaders(t)).get(t),this.update(e)}e.exports=m,m.dashMult=2,m.maxPatternLength=256,m.precisionThreshold=3e6,m.maxPoints=1e4,m.maxLines=2048,m.shaders=new d,m.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashPattern:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashSize:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\n\\nuniform float dashSize, pixelRatio, thickness, opacity, id;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},m.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},m.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},m.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>m.precisionThreshold||e.scale[1]*e.viewport.height>m.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=m.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},m.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},m.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<m.maxLines&&(d.depth=2*(m.maxLines-1-f%m.maxLines)/m.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=g(t.viewport)),d.viewport||(d.viewport=g([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var v,y;if(t.positions.x&&t.positions.y){var x=t.positions.x,b=t.positions.y;y=d.count=Math.max(x.length,b.length),v=new Float64Array(2*y);for(var _=0;_<y;_++)v[2*_]=x[_],v[2*_+1]=b[_]}else v=l(t.positions,\"float64\"),y=d.count=Math.floor(v.length/2);var w=d.bounds=i(v,2);if(d.fill){for(var T=[],k={},M=0,A=0,S=0,E=d.count;A<E;A++){var C=v[2*A],L=v[2*A+1];isNaN(C)||isNaN(L)||null==C||null==L?(C=v[2*M],L=v[2*M+1],k[A]=M):M=A,T[S++]=C,T[S++]=L}for(var I=c(T,d.hole||[]),P=0,z=I.length;P<z;P++)null!=k[I[P]]&&(I[P]=k[I[P]]);d.triangles=I}var O=new Float64Array(v);u(O,2,w);var D=new Float64Array(2*y+6);d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[0]=O[2*y-4],D[1]=O[2*y-3]):(D[0]=O[2*y-2],D[1]=O[2*y-1]):(D[0]=O[0],D[1]=O[1]),D.set(O,2),d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[2*y+2]=O[2],D[2*y+3]=O[3],d.count-=1):(D[2*y+2]=O[0],D[2*y+3]=O[1],D[2*y+4]=O[2],D[2*y+5]=O[3]):(D[2*y+2]=O[2*y-2],D[2*y+3]=O[2*y-1],D[2*y+4]=O[2*y-2],D[2*y+5]=O[2*y-1]),d.positionBuffer(h(D)),d.positionFractBuffer(p(D))}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var R=d.bounds,F=R[2]-R[0],B=R[3]-R[1],N=d.range[2]-d.range[0],j=d.range[3]-d.range[1];d.scale=[F/N,B/j],d.translate=[-d.range[0]/N+R[0]/N||0,-d.range[1]/j+R[1]/j||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var U,V=0;if(!t.dashes||t.dashes.length<2)V=1,U=new Uint8Array([255,255,255,255,255,255,255,255]);else{V=0;for(var q=0;q<t.dashes.length;++q)V+=t.dashes[q];U=new Uint8Array(V*m.dashMult);for(var H=0,G=255,Y=0;Y<2;Y++)for(var W=0;W<t.dashes.length;++W){for(var X=0,Z=t.dashes[W]*m.dashMult*.5;X<Z;++X)U[H++]=G;G^=255}}d.dashLength=V,d.dashTexture({channels:1,data:U,width:U.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var J=d.count,K=t.color;K||(K=\"transparent\");var Q=new Uint8Array(4*J+4);if(Array.isArray(K)&&\"number\"!=typeof K[0]){for(var $=0;$<J;$++){var tt=n(K[$],\"uint8\");Q.set(tt,4*$)}Q.set(n(K[0],\"uint8\"),4*J)}else for(var et=n(K,\"uint8\"),rt=0;rt<J+1;rt++)Q.set(et,4*rt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:Q})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var v=[],y=0;y<this.passes.length;y++)null!==this.passes[y]&&v.push(this.passes[y]);return this.passes=v,this}},m.prototype.destroy=function(){return this.passes.forEach((function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()})),this.passes.length=0,this}},{\"array-bounds\":70,\"array-normalize\":71,\"color-normalize\":125,earcut:177,\"es6-weak-map\":233,\"flatten-vertex-data\":244,glslify:536,\"object-assign\":499,\"parse-rect\":504,\"pick-by-alias\":511,\"to-float32\":577}],536:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],537:[function(t,e,r){\"use strict\";function n(t,e){return function(t){if(Array.isArray(t))return t}(t)||function(t,e){if(\"undefined\"==typeof Symbol||!(Symbol.iterator in Object(t)))return;var r=[],n=!0,i=!1,a=void 0;try{for(var o,s=t[Symbol.iterator]();!(n=(o=s.next()).done)&&(r.push(o.value),!e||r.length!==e);n=!0);}catch(t){i=!0,a=t}finally{try{n||null==s.return||s.return()}finally{if(i)throw a}}return r}(t,e)||a(t,e)||function(){throw new TypeError(\"Invalid attempt to destructure non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function i(t){return function(t){if(Array.isArray(t))return o(t)}(t)||function(t){if(\"undefined\"!=typeof Symbol&&Symbol.iterator in Object(t))return Array.from(t)}(t)||a(t)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function a(t,e){if(t){if(\"string\"==typeof t)return o(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return\"Object\"===r&&t.constructor&&(r=t.constructor.name),\"Map\"===r||\"Set\"===r?Array.from(t):\"Arguments\"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?o(t,e):void 0}}function o(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var s=t(\"color-normalize\"),l=t(\"array-bounds\"),c=t(\"color-id\"),u=t(\"@plotly/point-cluster\"),f=t(\"object-assign\"),h=t(\"glslify\"),p=t(\"pick-by-alias\"),d=t(\"update-diff\"),g=t(\"flatten-vertex-data\"),m=t(\"is-iexplorer\"),v=t(\"to-float32\"),y=t(\"parse-rect\"),x=b;function b(t,e){var r=this;if(!(this instanceof b))return new b(t,e);\"function\"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=m,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),f(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,pixelRatio:t.context(\"pixelRatio\"),palette:n,paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),opacity:t.prop(\"opacity\"),marker:t.prop(\"markerTexture\")},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop(\"elements\"),count:t.prop(\"count\"),offset:t.prop(\"offset\"),primitive:\"points\"},s=f({},o);s.frag=h([\"precision 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fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform float pixelRatio;\\nuniform bool constPointSize;\\nuniform sampler2D palette;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\n\\nuniform float opacity;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio;\\nuniform bool constPointSize;\\nuniform sampler2D palette;\\nuniform vec2 paletteSize;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),m&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),g=0;g<r.length;g++){var m=r[g];d[m]=p?p[m]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new 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t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=g(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}return i({data:v.float(t),usage:\"dynamic\"}),a({data:v.fract(t),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,M=_.sizeBuffer,A=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)A[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),A[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);M({data:A,usage:\"dynamic\"})}if(x){var E,C=n,L=C.count,I=C.color,P=C.borderColor,z=C.colorBuffer;if(t.tooManyColors){if(I.length||P.length){E=new Uint8Array(8*L);for(var O=0;O<L;O++){var D=I[O];E[8*O]=h[4*D],E[8*O+1]=h[4*D+1],E[8*O+2]=h[4*D+2],E[8*O+3]=h[4*D+3];var R=P[O];E[8*O+4]=h[4*R],E[8*O+5]=h[4*R+1],E[8*O+6]=h[4*R+2],E[8*O+7]=h[4*R+3]}}}else if(I.length||P.length){E=new Uint8Array(4*L+2);for(var F=0;F<L;F++)null!=I[F]&&(E[4*F]=I[F]%c,E[4*F+1]=Math.floor(I[F]/c)),null!=P[F]&&(E[4*F+2]=P[F]%c,E[4*F+3]=Math.floor(P[F]/c))}z({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":57,\"array-bounds\":70,\"color-id\":123,\"color-normalize\":125,\"flatten-vertex-data\":244,glslify:538,\"is-iexplorer\":466,\"object-assign\":499,\"parse-rect\":504,\"pick-by-alias\":511,\"to-float32\":577,\"update-diff\":599}],538:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],539:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var 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u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var g=s.columns,m=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/g,w=y/g;s.passes=[];for(var T=0;T<g;T++)for(var k=0;k<g;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var M=f(s.id,T,k),A=this.passes[M]||(this.passes[M]={});if(o.data&&(o.transpose?A.positions={x:{buffer:s.buffer,offset:k,count:m,stride:g},y:{buffer:s.buffer,offset:T,count:m,stride:g}}:A.positions={x:{buffer:s.buffer,offset:k*m,count:m},y:{buffer:s.buffer,offset:T*m,count:m}},A.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),C=E[0],L=E[1],I=E[2],P=E[3];A.viewport=[x+C*v+S[0],b+L*y+S[1],x+I*v-S[2],b+P*y-S[3]]}else A.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(A.color=s.color),o.size&&(A.size=s.size),o.marker&&(A.marker=s.marker),o.borderSize&&(A.borderSize=s.borderSize),o.borderColor&&(A.borderColor=s.borderColor),o.opacity&&(A.opacity=s.opacity),o.range&&(A.range=n?h(s.range,T,k):s.range||A.bounds),s.passes.push(M)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return this.traces.forEach((function(t){t.buffer&&t.buffer.destroy&&t.buffer.destroy()})),this.traces=null,this.passes=null,this.scatter.destroy(),this}},{\"array-bounds\":70,\"array-range\":72,\"flatten-vertex-data\":244,\"parse-rect\":504,\"pick-by-alias\":511,raf:528,\"regl-scatter2d\":537}],540:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():t.createREGL=n()}(this,(function(){function t(t,e){this.id=V++,this.type=t,this.data=e}function e(t){return\"[\"+function t(e){if(0===e.length)return[];var r=e.charAt(0),n=e.charAt(e.length-1);if(1<e.length&&r===n&&('\"'===r||\"'\"===r))return['\"'+e.substr(1,e.length-2).replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];if(r=/\\[(false|true|null|\\d+|'[^']*'|\"[^\"]*\")\\]/.exec(e))return 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+       "/*!\n",
+       " * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT License.\n",
+       " */\n",
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r,n=parseInt(e);if(isNaN(n))\"\\u95f0\"===e[0]&&(r=!0,e=e.substring(1)),\"\\u6708\"===e[e.length-1]&&(e=e.substring(0,e.length-1)),n=1+[\"\\u4e00\",\"\\u4e8c\",\"\\u4e09\",\"\\u56db\",\"\\u4e94\",\"\\u516d\",\"\\u4e03\",\"\\u516b\",\"\\u4e5d\",\"\\u5341\",\"\\u5341\\u4e00\",\"\\u5341\\u4e8c\"].indexOf(e);else{var i=e[e.length-1];r=\"i\"===i||\"I\"===i}return this.toMonthIndex(t,n,r)},dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},_validateYear:function(t,e){if(t.year&&(t=t.year()),\"number\"!=typeof t||t<1888||t>2111)throw e.replace(/\\{0\\}/,this.local.name);return t},toMonthIndex:function(t,e,r){var i=this.intercalaryMonth(t);if(r&&e!==i||e<1||e>12)throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return i?!r&&e<=i?e-1:e:e-1},toChineseMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);if(e<0||e>(r?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r?e<r?e+1:e:e+1},intercalaryMonth:function(t){return t=this._validateYear(t),f[t-f[0]]>>13},isIntercalaryMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);return!!r&&r===e},leapYear:function(t){return 0!==this.intercalaryMonth(t)},weekOfYear:function(t,e,r){var i,o=this._validateYear(t,n.local.invalidyear),s=h[o-h[0]],l=s>>9&4095,c=s>>5&15,u=31&s;(i=a.newDate(l,c,u)).add(4-(i.dayOfWeek()||7),\"d\");var f=this.toJD(t,e,r)-i.toJD();return 1+Math.floor(f/7)},monthsInYear:function(t){return this.leapYear(t)?13:12},daysInMonth:function(t,e){t.year&&(e=t.month(),t=t.year()),t=this._validateYear(t);var r=f[t-f[0]];if(e>(r>>13?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r&1<<12-e?30:29},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,s,r,n.local.invalidDate);t=this._validateYear(i.year()),e=i.month(),r=i.day();var o=this.isIntercalaryMonth(t,e),s=this.toChineseMonth(t,e),l=function(t,e,r,n,i){var a,o,s;if(\"object\"==typeof t)o=t,a=e||{};else{var l;if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Lunar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Lunar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=30))throw new Error(\"Lunar day outside range 1 - 30\");\"object\"==typeof n?(l=!1,a=n):(l=!!n,a=i||{}),o={year:t,month:e,day:r,isIntercalary:l}}s=o.day-1;var c,u=f[o.year-f[0]],p=u>>13;c=p&&(o.month>p||o.isIntercalary)?o.month:o.month-1;for(var d=0;d<c;d++){s+=u&1<<12-d?30:29}var g=h[o.year-h[0]],m=new Date(g>>9&4095,(g>>5&15)-1,(31&g)+s);return a.year=m.getFullYear(),a.month=1+m.getMonth(),a.day=m.getDate(),a}(t,s,r,o);return a.toJD(l.year,l.month,l.day)},fromJD:function(t){var e=a.fromJD(t),r=function(t,e,r,n){var i,a;if(\"object\"==typeof t)i=t,a=e||{};else{if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Solar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Solar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=31))throw new Error(\"Solar day outside range 1 - 31\");i={year:t,month:e,day:r},a=n||{}}var o=h[i.year-h[0]],s=i.year<<9|i.month<<5|i.day;a.year=s>=o?i.year:i.year-1,o=h[a.year-h[0]];var l,c=new Date(o>>9&4095,(o>>5&15)-1,31&o),u=new Date(i.year,i.month-1,i.day);l=Math.round((u-c)/864e5);var p,d=f[a.year-f[0]];for(p=0;p<13;p++){var g=d&1<<12-p?30:29;if(l<g)break;l-=g}var m=d>>13;!m||p<m?(a.isIntercalary=!1,a.month=1+p):p===m?(a.isIntercalary=!0,a.month=p):(a.isIntercalary=!1,a.month=p);return 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Coptic\",jdEpoch:1825029.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Coptic\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Thout\",\"Paopi\",\"Hathor\",\"Koiak\",\"Tobi\",\"Meshir\",\"Paremhat\",\"Paremoude\",\"Pashons\",\"Paoni\",\"Epip\",\"Mesori\",\"Pi Kogi Enavot\"],monthNamesShort:[\"Tho\",\"Pao\",\"Hath\",\"Koi\",\"Tob\",\"Mesh\",\"Pat\",\"Pad\",\"Pash\",\"Pao\",\"Epi\",\"Meso\",\"PiK\"],dayNames:[\"Tkyriaka\",\"Pesnau\",\"Pshoment\",\"Peftoou\",\"Ptiou\",\"Psoou\",\"Psabbaton\"],dayNamesShort:[\"Tky\",\"Pes\",\"Psh\",\"Pef\",\"Pti\",\"Pso\",\"Psa\"],dayNamesMin:[\"Tk\",\"Pes\",\"Psh\",\"Pef\",\"Pt\",\"Pso\",\"Psa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.coptic=a},{\"../main\":621,\"object-assign\":499}],609:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Discworld\",jdEpoch:1721425.5,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":621,\"object-assign\":499}],610:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{\"../main\":621,\"object-assign\":499}],611:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{\"../main\":621,\"object-assign\":499}],612:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Islamic\",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012bs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":621,\"object-assign\":499}],613:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":621,\"object-assign\":499}],614:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":621,\"object-assign\":499}],615:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{\"../main\":621,\"object-assign\":499}],616:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return 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c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)\"undefined\"==typeof 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":621,\"object-assign\":499}],619:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":621,\"object-assign\":499}],620:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":778,\"../../plot_api/plot_template\":817,\"../../registry\":911}],629:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),g=r.hoverlabel||{};if(d){var m=a(\"hoverlabel.bgcolor\",g.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",g.bordercolor||i.contrast(m));n.coerceFont(a,\"hoverlabel.font\",{family:g.font.family,size:g.font.size,color:g.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":778,\"../color\":643}],630:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":805,\"fast-isnumeric\":241}],631:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var g=f[d],m=i.coerceRef(t,e,p,g,\"\",\"paper\");if(\"paper\"!==m)i.getFromId(p,m)._annIndices.push(e._index);if(i.coercePosition(e,p,a,m,g,.5),u){var v=\"a\"+g,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==m&&(y=e[v]=\"pixel\");var x=\"pixel\"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(g+\"anchor\"),a(g+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),u&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),c){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:\"annotations\",handleItemDefaults:l})}},{\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"./attributes\":626,\"./common_defaults\":629}],632:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../registry\"),a=t(\"../../plots/plots\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../plots/cartesian/axes\"),c=t(\"../color\"),u=t(\"../drawing\"),f=t(\"../fx\"),h=t(\"../../lib/svg_text_utils\"),p=t(\"../../lib/setcursor\"),d=t(\"../dragelement\"),g=t(\"../../plot_api/plot_template\").arrayEditor,m=t(\"./draw_arrow_head\");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var M=g(t.layout,_,e),A=M.modifyBase,S=M.modifyItem,E=M.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var C=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var L={x:{},y:{}},I=+e.textangle||0,P=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),z=P.append(\"g\").classed(\"annotation-text-g\",!0),O=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||O,R=z.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+C).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",C).append(\"rect\"),V.exit().remove();var q=e.font,H=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(H);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&A(v._name+\".autorange\",!0),x&&x.autorange&&A(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+C).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,q).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),g=u.bBox((f?n:G).node()),b=g.width,_=g.height,M=e.width||b,D=e.height||_,B=Math.round(M+2*N),q=Math.round(D+2*N);function H(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(I+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=q*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=L[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var gt=e[\"a\"+et];if(tt=ot*H(.5,e.xanchor)-st*H(.5,e.yanchor),nt===rt){var mt=l.getRefType(nt);\"domain\"===mt?(\"y\"===et&&(gt=1-gt),ft.tail=it._offset+it._length*gt):\"paper\"===mt?\"y\"===et?(gt=1-gt,ft.tail=T.t+T.h*gt):ft.tail=T.l+T.w*gt:ft.tail=it._offset+it.r2p(gt),K=tt}else ft.tail=J+gt,K=tt+gt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*H($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(M-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?C:null,t);else{var wt=N+_t-g.top,Tt=N+bt-g.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?C:null,t)}V.select(\"rect\").call(u.setRect,N,N,M,D),j.call(u.setRect,F/2,F/2,B-F,q-F),R.call(u.setTranslate,Math.round(L.x.text-B/2),Math.round(L.y.text-q/2)),z.attr({transform:\"rotate(\"+I+\",\"+L.x.text+\",\"+L.y.text+\")\"});var kt,Mt=function(r,n){P.selectAll(\".annotation-arrow-g\").remove();var l=L.x.head,f=L.y.head,h=L.x.tail+r,p=L.y.tail+n,g=L.x.text+r,b=L.y.text+n,_=o.rotationXYMatrix(I,g,b),w=o.apply2DTransform(_),M=o.apply2DTransform2(_),C=+j.attr(\"width\"),O=+j.attr(\"height\"),D=g-.5*C,F=D+C,B=b-.5*O,N=B+O,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(M);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,q=e.arrowcolor,H=e.arrowside,G=P.append(\"g\").style({opacity:c.opacity(q)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(q));if(m(Y,H,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var 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n=t(\"../annotations/attributes\"),i=t(\"../../plot_api/edit_types\").overrideAll,a=t(\"../../plot_api/plot_template\").templatedArray;e.exports=i(a(\"annotation\",{visible:n.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),\"calc\",\"from-root\")},{\"../../plot_api/edit_types\":810,\"../../plot_api/plot_template\":817,\"../annotations/attributes\":626}],636:[function(t,e,r){\"use 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n.coerce(t,e,s,r,i)}function c(t){var n=t+\"axis\",a={_fullLayout:{}};return a._fullLayout[n]=r[n],i.coercePosition(e,a,l,t,t,.5)}l(\"visible\")&&(o(t,e,a.fullLayout,l),c(\"x\"),c(\"y\"),c(\"z\"),n.noneOrAll(t,e,[\"x\",\"y\",\"z\"]),e.xref=\"x\",e.yref=\"y\",e.zref=\"z\",l(\"xanchor\"),l(\"yanchor\"),l(\"xshift\"),l(\"yshift\"),e.showarrow&&(e.axref=\"pixel\",e.ayref=\"pixel\",l(\"ax\",-10),l(\"ay\",-30),n.noneOrAll(t,e,[\"ax\",\"ay\"])))}e.exports=function(t,e,r){a(t,e,{name:\"annotations\",handleItemDefaults:l,fullLayout:r.fullLayout})}},{\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"../annotations/common_defaults\":629,\"./attributes\":635}],638:[function(t,e,r){\"use strict\";var n=t(\"../annotations/draw\").drawRaw,i=t(\"../../plots/gl3d/project\"),a=[\"x\",\"y\",\"z\"];e.exports=function(t){for(var e=t.fullSceneLayout,r=t.dataScale,o=e.annotations,s=0;s<o.length;s++){for(var l=o[s],c=!1,u=0;u<3;u++){var 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If `target` is a string (eg *x*) we use the\",\"corresponding trace attribute (eg `xcalendar`) if it exists,\",\"even if `targetcalendar` is provided.\"].join(\" \"))}}},layoutAttributes:l,handleDefaults:c,handleTraceDefaults:function(t,e,r,n){for(var i=0;i<r.length;i++)c(t,e,r[i]+\"calendar\",n.calendar)},CANONICAL_SUNDAY:{chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},CANONICAL_TICK:{chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},DFLTRANGE:{chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},getCal:h,worldCalFmt:function(t,e,r){for(var 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T={y:e.y,b:u.height*g[w],t:u.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*g[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*g[b]),i.autoMargin(t,m(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var g=c.lineCount(l),m=(a.currentValueMaxLines+1-g)*e.currentvalue.font.size*p;return 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r=t[e[n]],s=(r._extremes||{})[o];if(!0===r.visible&&s){for(i=0;i<s.min.length;i++)a=s.min[i],d(u,a.val,a.pad,{extrapad:a.extrapad});for(i=0;i<s.max.length;i++)a=s.max[i],g(f,a.val,a.pad,{extrapad:a.extrapad})}}}if(h(s,e._traceIndices),h(c.annotations||[],e._annIndices||[]),h(c.shapes||[],e._shapeIndices||[]),e._matchGroup&&!r)for(var m in e._matchGroup)if(m!==e._id){var v=l(t,m),y=p(t,v,!0),x=e._length/v._length;for(i=0;i<y.min.length;i++)a=y.min[i],d(u,a.val,a.pad*x,{extrapad:a.extrapad});for(i=0;i<y.max.length;i++)a=y.max[i],g(f,a.val,a.pad*x,{extrapad:a.extrapad})}return{min:u,max:f}}function d(t,e,r,n){m(t,e,r,n,y)}function g(t,e,r,n){m(t,e,r,n,x)}function m(t,e,r,n,i){for(var a=n.tozero,o=n.extrapad,s=!0,l=0;l<t.length&&s;l++){var c=t[l];if(i(c.val,e)&&c.pad>=r&&(c.extrapad||!o)){s=!1;break}i(e,c.val)&&c.pad<=r&&(o||!c.extrapad)&&(t.splice(l,1),l--)}if(s){var u=a&&0===e;t.push({val:e,pad:u?0:r,extrapad:!u&&o})}}function v(t){return n(t)&&Math.abs(t)<a}function y(t,e){return 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n,a=t.nticks;a||(\"category\"===t.type||\"multicategory\"===t.type?(n=t.tickfont?1.2*(t.tickfont.size||12):15,a=t._length/n):(n=\"y\"===t._id.charAt(0)?40:80,a=s.constrain(t._length/n,4,9)+1),\"radialaxis\"===t._name&&(a*=2)),\"array\"===t.tickmode&&(a*=100),t._roughDTick=Math.abs(r[1]-r[0])/a,B.autoTicks(t,t._roughDTick),t._minDtick>0&&t.dtick<2*t._minDtick&&(t.dtick=t._minDtick,t.tick0=t.l2r(t._forceTick0))}\"period\"===t.ticklabelmode&&function(t){var e;function r(){return!(i(t.dtick)||\"M\"!==t.dtick.charAt(0))}var n=r(),a=B.getTickFormat(t);if(a){var 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c=\"category\"===t.type?t.d2l_noadd:t.d2l;\"log\"===t.type&&\"L\"!==String(t.dtick).charAt(0)&&(t.dtick=\"L\"+Math.pow(10,Math.floor(Math.min(t.range[0],t.range[1]))-1));for(var u=0;u<e.length;u++){var f=c(e[u]);f>a&&f<o&&(void 0===r[u]?n[l]=B.tickText(t,f):n[l]=it(t,f,String(r[u])),l++)}l<e.length&&n.splice(l,e.length-l);t.rangebreaks&&(n=n.filter((function(e){return t.maskBreaks(e.x)!==P})));return n}(t);var n=H(r),a=n[0],o=n[1],l=r[1]<r[0],c=Math.min(r[0],r[1]),u=Math.max(r[0],r[1]),f=\"log\"===t.type&&!(i(t.dtick)||\"L\"===t.dtick.charAt(0)),h=\"period\"===t.ticklabelmode;if(t._tmin=B.tickFirst(t,e),t._tmin<a!==l)return[];\"category\"!==t.type&&\"multicategory\"!==t.type||(o=l?Math.max(-.5,o):Math.min(t._categories.length-.5,o));var p=t._tmin;t.rangebreaks&&t._tick0Init!==t.tick0&&(p=wt(p,t),l||(p=B.tickIncrement(p,t.dtick,!l,t.calendar))),h&&(p=B.tickIncrement(p,t.dtick,!l,t.calendar));for(var d,g=Math.max(1e3,t._length||0),C=[],L=null;l?p>=o:p<=o;p=B.tickIncrement(p,t.dtick,l,t.calendar)){if(t.rangebreaks&&!l){if(p<a)continue;if(t.maskBreaks(p)===P&&wt(p,t)>=u)break}if(C.length>g||p===L)break;L=p;var I=!1;f&&p!==(0|p)&&(I=!0),C.push({minor:I,value:p})}if(h&&function(t,e,r){for(var n=0;n<t.length;n++){var i=t[n].value,a=n,o=n+1;n<t.length-1?(a=n,o=n+1):n>0?(a=n-1,o=n):(a=n,o=n);var s,l=t[a].value,c=t[o].value,u=Math.abs(c-l),f=r||u,h=0;f>=y?h=u>=y&&u<=m?u:v:r===b&&f>=_?h=u>=_&&u<=x?u:b:f>=k?h=u>=k&&u<=w?u:T:r===M&&f>=M?h=M:f>=A?h=A:r===S&&f>=S?h=S:r===E&&f>=E&&(h=E),h>=u&&(h=u,s=!0);var p=i+h;if(e.rangebreaks&&h>0){for(var d=0,g=0;g<84;g++){var C=(g+.5)/84;e.maskBreaks(i*(1-C)+C*p)!==P&&d++}(h*=d/84)||(t[n].drop=!0),s&&u>M&&(h=u)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var z=\"y\"===t._id.charAt(0),O=1;\"auto\"===t.tickmode&&(O=t.tickfont?t.tickfont.size:12);var 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w=e._anchorAxis;w&&w.autorange&&-1!==(e.ticklabelposition||\"\").indexOf(\"inside\")&&!V(a,e._id)&&(a._insideTickLabelsAutorange||(a._insideTickLabelsAutorange={}),a._insideTickLabelsAutorange[w._name+\".autorange\"]=w.autorange,_.push((function(){v.each((function(t,r){var n=dt(this);e._vals[r].bb=h.bBox(n.node())}))})));var T=s.syncOrAsync(_);return T&&T.then&&t._promises.push(T),T},B.getPxPosition=function(t,e){var r,n=t._fullLayout._size,i=e._id.charAt(0),a=e.side;return\"free\"!==e.anchor?r=e._anchorAxis:\"x\"===i?r={_offset:n.t+(1-(e.position||0))*n.h,_length:0}:\"y\"===i&&(r={_offset:n.l+(e.position||0)*n.w,_length:0}),\"top\"===a||\"left\"===a?r._offset:\"bottom\"===a||\"right\"===a?r._offset+r._length:void 0},B.shouldShowZeroLine=function(t,e,r){var n=s.simpleMap(e.range,e.r2l);return n[0]*n[1]<=0&&e.zeroline&&(\"linear\"===e.type||\"-\"===e.type)&&!(e.rangebreaks&&e.maskBreaks(0)===P)&&(pt(e,0)||!function(t,e,r,n){var i=r._mainAxis;if(!i)return;var 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r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":834,\"has-passive-events\":441,\"mouse-change\":483,\"mouse-event-offset\":484,\"mouse-wheel\":486}],867:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":802,\"../cartesian/axes\":828}],868:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":684,\"../../constants/xmlns_namespaces\":754,\"../../plot_api/edit_types\":810,\"../cartesian\":841,\"../cartesian/attributes\":826,\"../cartesian/constants\":834,\"../get_data\":865,\"../layout_attributes\":882,\"./scene2d\":869}],869:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"gl-plot2d\"),c=t(\"gl-spikes2d\"),u=t(\"gl-select-box\"),f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),g=t(\"../cartesian/constraints\"),m=g.enforce,v=g.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var M=k.prototype;M.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},M.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},M.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},M.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},M.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},M.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},M.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},M.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},M.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},M.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();m(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},M.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},M.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},M.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},M.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},M.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},M.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var m=this.fullData[g.trace.index]||{},v=g.pointIndex,y=s.castHoverinfo(m,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(m,v,\"bgcolor\")||g.color,borderColor:s.castHoverOption(m,v,\"bordercolor\"),fontFamily:s.castHoverOption(m,v,\"font.family\"),fontSize:s.castHoverOption(m,v,\"font.size\"),fontColor:s.castHoverOption(m,v,\"font.color\"),nameLength:s.castHoverOption(m,v,\"namelength\"),textAlign:s.castHoverOption(m,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},M.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},M.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/dragelement/helpers\":661,\"../../components/fx\":683,\"../../lib/show_no_webgl_msg\":800,\"../../plots/cartesian/axes\":828,\"../../registry\":911,\"../cartesian/autorange\":827,\"../cartesian/constants\":834,\"../cartesian/constraints\":835,\"./camera\":866,\"./convert\":867,\"gl-plot2d\":317,\"gl-select-box\":333,\"gl-spikes2d\":342,\"webgl-context\":606}],870:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":684,\"../../constants/xmlns_namespaces\":754,\"../../lib\":778,\"../../plot_api/edit_types\":810,\"../get_data\":865,\"./layout/attributes\":871,\"./layout/defaults\":875,\"./layout/layout_attributes\":876,\"./scene\":880}],871:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],872:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":643,\"../../../lib/extend\":768,\"../../../plot_api/edit_types\":810,\"../../cartesian/layout_attributes\":842}],873:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":778,\"../../../plot_api/plot_template\":817,\"../../cartesian/axis_defaults\":830,\"../../cartesian/type_defaults\":853,\"./axis_attributes\":872,tinycolor2:576}],874:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":778,\"../../../lib/str2rgbarray\":802}],875:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var m=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(m&&v&&y?y/Math.sqrt(m*m+v*v+y*y)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":643,\"../../../lib\":778,\"../../../registry\":911,\"../../get_data\":865,\"../../subplot_defaults\":905,\"./axis_defaults\":873,\"./layout_attributes\":876}],876:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":778,\"../../../lib/extend\":768,\"../../domain\":855,\"./axis_attributes\":872}],877:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":802}],878:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":778,\"../../cartesian/axes\":828}],879:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],880:[function(t,e,r){\"use strict\";var n,i,a=t(\"gl-plot3d\"),o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),g=t(\"../../lib/str2rgbarray\"),m=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return m(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c,u=Object.keys(e.traces),h=null,g=e.glplot.selection,m=0;m<u.length;++m)\"skip\"!==(t=e.traces[u[m]]).data.hoverinfo&&t.handlePick(g)&&(h=t),t.setContourLevels&&t.setContourLevels();function y(t,r){var n=e.fullSceneLayout[t];return p.tickText(n,n.d2l(r),\"hover\").text}if(null!==h){var x=v(e.glplot.cameraParams,g.dataCoordinate);t=h.data;var _,w=r._fullData[t.index],T=g.index,k={xLabel:y(\"xaxis\",g.traceCoordinate[0]),yLabel:y(\"yaxis\",g.traceCoordinate[1]),zLabel:y(\"zaxis\",g.traceCoordinate[2])},M=d.castHoverinfo(w,e.fullLayout,T),A=(M||\"\").split(\"+\"),S=M&&\"all\"===M;w.hovertemplate||S||(-1===A.indexOf(\"x\")&&(k.xLabel=void 0),-1===A.indexOf(\"y\")&&(k.yLabel=void 0),-1===A.indexOf(\"z\")&&(k.zLabel=void 0),-1===A.indexOf(\"text\")&&(g.textLabel=void 0),-1===A.indexOf(\"name\")&&(h.name=void 0));var E=[];\"cone\"===t.type||\"streamtube\"===t.type?(k.uLabel=y(\"xaxis\",g.traceCoordinate[3]),(S||-1!==A.indexOf(\"u\"))&&E.push(\"u: \"+k.uLabel),k.vLabel=y(\"yaxis\",g.traceCoordinate[4]),(S||-1!==A.indexOf(\"v\"))&&E.push(\"v: \"+k.vLabel),k.wLabel=y(\"zaxis\",g.traceCoordinate[5]),(S||-1!==A.indexOf(\"w\"))&&E.push(\"w: \"+k.wLabel),k.normLabel=g.traceCoordinate[6].toPrecision(3),(S||-1!==A.indexOf(\"norm\"))&&E.push(\"norm: \"+k.normLabel),\"streamtube\"===t.type&&(k.divergenceLabel=g.traceCoordinate[7].toPrecision(3),(S||-1!==A.indexOf(\"divergence\"))&&E.push(\"divergence: \"+k.divergenceLabel)),g.textLabel&&E.push(g.textLabel),_=E.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.tickText(e._mockAxis,e._mockAxis.d2l(g.traceCoordinate[3]),\"hover\").text,E.push(\"value: \"+k.valueLabel),g.textLabel&&E.push(g.textLabel),_=E.join(\"<br>\")):_=g.textLabel;var C={x:g.traceCoordinate[0],y:g.traceCoordinate[1],z:g.traceCoordinate[2],data:w._input,fullData:w,curveNumber:w.index,pointNumber:T};d.appendArrayPointValue(C,w,T),t._module.eventData&&(C=w._module.eventData(C,g,w,{},T));var L={points:[C]};e.fullSceneLayout.hovermode&&d.loneHover({trace:w,x:(.5+.5*x[0]/x[3])*s,y:(.5-.5*x[1]/x[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:_,name:h.name,color:d.castHoverOption(w,T,\"bgcolor\")||h.color,borderColor:d.castHoverOption(w,T,\"bordercolor\"),fontFamily:d.castHoverOption(w,T,\"font.family\"),fontSize:d.castHoverOption(w,T,\"font.size\"),fontColor:d.castHoverOption(w,T,\"font.color\"),nameLength:d.castHoverOption(w,T,\"namelength\"),textAlign:d.castHoverOption(w,T,\"align\"),hovertemplate:f.castOption(w,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},C,k),eventData:[C]},{container:n,gd:r}),g.buttons&&g.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),c=L}else d.loneUnhover(n),r.emit(\"plotly_unhover\",c);e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function M(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(g(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&M(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,m=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){m[0][a]=1/0,m[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,A=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?m[0][a]=Math.min(m[0][a],s._lowerLogErrorBound):m[0][a]=Math.min(m[0][a],T[0][a]/h[a]-A),m[1][a]=Math.max(m[1][a],T[1][a]/h[a]+A)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);m[0][a]=Math.min(m[0][a],E),m[1][a]=Math.max(m[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var C=m[1][a]-m[0][a];m[0][a]-=C/32,m[1][a]+=C/32}if(\"reversed\"===s.autorange){var L=m[0][a];m[0][a]=m[1][a],m[1][a]=L}}else{var I=s.range;m[0][a]=s.r2l(I[0]),m[1][a]=s.r2l(I[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.setBounds(a,{min:m[0][a]*h[a],max:m[1][a]*h[a]})}var P=c.aspectmode;if(\"cube\"===P)d=[1,1,1];else if(\"manual\"===P){var z=c.aspectratio;d=[z.x,z.y,z.z]}else{if(\"auto\"!==P&&\"data\"!==P)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var O=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];O[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===P||Math.max.apply(null,O)/Math.min.apply(null,O)<=4?O:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var 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r=t.visible;return\"undefined\"==typeof r||!0===r})),d=!1,g=p.map((function(t,e){return d=d||\"undefined\"!=typeof t.groupId,t}));if(d){var m=n.nest().key((function(t,e){return\"undefined\"!=typeof t.groupId?t.groupId:\"unstacked\"})).entries(g),v=[],y=m.map((function(t,e){if(\"unstacked\"===t.key)return t.values;var r=t.values[0].r.map((function(t,e){return 0}));return t.values.forEach((function(t,e,n){t.yStack=[r],v.push(r),r=o.util.sumArrays(t.r,r)})),t.values}));p=n.merge(y)}p.forEach((function(t,e){t.t=Array.isArray(t.t[0])?t.t:[t.t],t.r=Array.isArray(t.r[0])?t.r:[t.r]}));var x=Math.min(h.width-h.margin.left-h.margin.right,h.height-h.margin.top-h.margin.bottom)/2;x=Math.max(10,x);var b,_=[h.margin.left+x,h.margin.top+x];d?b=[0,n.max(o.util.sumArrays(o.util.arrayLast(p).r[0],o.util.arrayLast(v)))]:b=n.extent(o.util.flattenArray(p.map((function(t,e){return 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P=n.range.apply(this,C);if(P=P.map((function(t,e){return parseFloat(t.toPrecision(12))})),s=n.scale.linear().domain(C.slice(0,2)).range(\"clockwise\"===h.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=s.domain(),u.layout.angularAxis.endPadding=A?E:0,\"undefined\"==typeof(t=n.select(this).select(\"svg.chart-root\"))||t.empty()){var z=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g class='legend-group'></g>' + '<g class='tooltips-group'></g>' + '<g class='title-group'><text></text></g>' + '</g>' + 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o=n.hsl(a.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",f=i||\"\";e.style({fill:u,\"font-size\":a.fontSize+\"px\"}).text(f);var h=a.padding,p=e.node().getBBox(),d={fill:a.color,stroke:s,\"stroke-width\":\"2px\"},g=p.width+2*h+l,m=p.height+2*h;return r.attr({d:\"M\"+[[l,-m/2],[l,-m/4],[a.hasTick?0:l,0],[l,m/4],[l,m/2],[g,m/2],[g,-m/2]].join(\"L\")+\"Z\"}).style(d),t.attr({transform:\"translate(\"+[l,-m/2+2*h]+\")\"}),t.style({display:\"block\"}),c},c.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),c},c.hide=function(){if(t)return t.style({display:\"none\"}),c},c.show=function(){if(t)return t.style({display:\"block\"}),c},c.config=function(t){return i(a,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map((function(t,r){var 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s=i({},t.layout);if([[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach((function(t,r){o.util.translator.apply(null,t.concat(e))})),e?(\"undefined\"!=typeof 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n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strRotate,l=o.strTranslate,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../plots\"),h=t(\"../../plots/cartesian/axes\"),p=t(\"../cartesian/set_convert\"),d=t(\"./set_convert\"),g=t(\"../cartesian/autorange\").doAutoRange,m=t(\"../cartesian/dragbox\"),v=t(\"../../components/dragelement\"),y=t(\"../../components/fx\"),x=t(\"../../components/titles\"),b=t(\"../cartesian/select\").prepSelect,_=t(\"../cartesian/select\").selectOnClick,w=t(\"../cartesian/select\").clearSelect,T=t(\"../../lib/setcursor\"),k=t(\"../../lib/clear_gl_canvases\"),M=t(\"../../plot_api/subroutines\").redrawReglTraces,A=t(\"../../constants/alignment\").MID_SHIFT,S=t(\"./constants\"),E=t(\"./helpers\"),C=o._,L=o.mod,I=o.deg2rad,P=o.rad2deg;function 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e,r,n,i,a=t[0],o=t[1]-a,s=L(a,360),l=s+o,c=Math.cos(I(s)),u=Math.sin(I(s)),f=Math.cos(I(l)),h=Math.sin(I(l));i=s<=90&&l>=90||s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(p),b=x[2]-x[0],_=x[3]-x[1],w=h/f,T=Math.abs(_/b);w>T?(d=f,y=(h-(g=f*T))/n.h/2,m=[o[0],o[1]],v=[s[0]+y,s[1]-y]):(g=h,y=(f-(d=h/T))/n.w/2,m=[o[0]+y,o[1]-y],v=[s[0],s[1]]),this.xLength2=d,this.yLength2=g,this.xDomain2=m,this.yDomain2=v;var 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e=m(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[f+u*r,p-u*n]+\"L\"+[f+a*r,p-a*n]},transFn:o.noop,crisp:!1}),h.drawLabels(n,g,{vals:w,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:function(t){return v(m(t))},labelFns:x})}F(i[\"angular-line\"].select(\"path\"),d.showline,{d:r.pathSubplot(),transform:l(f,p)}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},O.updateFx=function(t,e){this.gd._context.staticPlot||(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1),this.updateMainDrag(t))},O.updateMainDrag=function(t){var e,r,s=this,c=s.gd,u=s.layers,f=t._zoomlayer,h=S.MINZOOM,p=S.OFFEDGE,d=s.radius,g=s.innerRadius,x=s.cx,T=s.cy,k=s.cxx,M=s.cyy,A=s.sectorInRad,C=s.vangles,L=s.radialAxis,I=E.clampTiny,P=E.findXYatLength,z=E.findEnclosingVertexAngles,O=S.cornerHalfWidth,D=S.cornerLen/2,R=m.makeDragger(u,\"path\",\"maindrag\",\"crosshair\");n.select(R).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(x,T));var F,B,N,j,U,V,q,H,G,Y={element:R,gd:c,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function W(t,e){return Math.sqrt(t*t+e*e)}function X(t,e){return W(t-k,e-M)}function Z(t,e){return Math.atan2(M-e,t-k)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function K(t,e){if(0===t)return s.pathSector(2*O);var r=D/t,n=e-r,i=e+r,a=Math.max(0,Math.min(t,d)),o=a-O,l=a+O;return\"M\"+J(o,n)+\"A\"+[o,o]+\" 0,0,0 \"+J(o,i)+\"L\"+J(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+J(l,n)+\"Z\"}function Q(t,e,r){if(0===t)return s.pathSector(2*O);var n,i,a=J(t,e),o=J(t,r),l=I((a[0]+o[0])/2),c=I((a[1]+o[1])/2);if(l&&c){var u=c/l,f=-1/u,h=P(O,u,l,c);n=P(D,f,h[0][0],h[0][1]),i=P(D,f,h[1][0],h[1][1])}else{var p,d;c?(p=D,d=O):(p=O,d=D),n=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+n.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function $(t,e){return 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e=L._rl,r=(e[1]-e[0])/(1-g/d)/d,n=[e[0]+(N-g)*r,e[0]+(j-g)*r];t[s.id+\".radialaxis.range\"]=n}function ot(t,e){var r=c._fullLayout.clickmode;if(m.removeZoombox(c),2===t){var n={};for(var i in s.viewInitial)n[s.id+\".\"+i]=s.viewInitial[i];c.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",c,n)}r.indexOf(\"select\")>-1&&1===t&&_(e,c,[s.xaxis],[s.yaxis],s.id,Y),r.indexOf(\"event\")>-1&&y.click(c,e,s.id)}Y.prepFn=function(t,n,a){var l=c._fullLayout.dragmode,u=R.getBoundingClientRect();c._fullLayout._calcInverseTransform(c);var h=c._fullLayout._invTransform;e=c._fullLayout._invScaleX,r=c._fullLayout._invScaleY;var p=o.apply3DTransform(h)(n-u.left,a-u.top);if(F=p[0],B=p[1],C){var g=E.findPolygonOffset(d,A[0],A[1],C);F+=k+g[0],B+=M+g[1]}switch(l){case\"zoom\":Y.moveFn=C?nt:et,Y.clickFn=ot,Y.doneFn=it,function(){N=null,j=null,U=s.pathSubplot(),V=!1;var t=c._fullLayout[s.id];q=i(t.bgcolor).getLuminance(),(H=m.makeZoombox(f,q,x,T,U)).attr(\"fill-rule\",\"evenodd\"),G=m.makeCorners(f,x,T),w(c)}();break;case\"select\":case\"lasso\":b(t,n,a,Y,l)}},R.onmousemove=function(t){y.hover(c,t,s.id),c._fullLayout._lasthover=R,c._fullLayout._hoversubplot=s.id},R.onmouseout=function(t){c._dragging||v.unhover(c,t)},v.init(Y)},O.updateRadialDrag=function(t,e,r){var i=this,c=i.gd,u=i.layers,f=i.radius,h=i.innerRadius,p=i.cx,d=i.cy,g=i.radialAxis,y=S.radialDragBoxSize,x=y/2;if(g.visible){var b,_,T,A=I(i.radialAxisAngle),E=g._rl,C=E[0],L=E[1],z=E[r],O=.75*(E[1]-E[0])/(1-e.hole)/f;r?(b=p+(f+x)*Math.cos(A),_=d-(f+x)*Math.sin(A),T=\"radialdrag\"):(b=p+(h-x)*Math.cos(A),_=d-(h-x)*Math.sin(A),T=\"radialdrag-inner\");var D,B,N,j=m.makeRectDragger(u,T,\"crosshair\",-x,-x,y,y),U={element:j,gd:c};F(n.select(j),g.visible&&h<f,{transform:l(b,_)}),U.prepFn=function(){D=null,B=null,N=null,U.moveFn=V,U.doneFn=q,w(c)},U.clampFn=function(t,e){return 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v=e.gd._fullLayout,y=v[e.id],x=_(A+c*t._invScaleX,E+m*t._invScaleY),b=P(x-z);if(L=C+b,i.frontplot.attr(\"transform\",l(e.xOffset2,e.yOffset2)+s([-b,d,g])),e.vangles){I=e.radialAxisAngle+b;var w=l(h,p)+s(-b),T=l(h,p)+s(-I);i.bg.attr(\"transform\",w),i[\"radial-grid\"].attr(\"transform\",w),i[\"radial-axis\"].attr(\"transform\",T),i[\"radial-line\"].select(\"line\").attr(\"transform\",T),e.updateRadialAxisTitle(v,y,I)}else e.clipPaths.forTraces.select(\"path\").attr(\"transform\",l(d,g)+s(b));D.each((function(){var t=n.select(this),e=u.getTranslate(t);t.attr(\"transform\",l(e.x,e.y)+s([b]))})),R.each((function(){var t=n.select(this),e=t.select(\"text\"),r=u.getTranslate(t);t.attr(\"transform\",s([b,e.attr(\"x\"),e.attr(\"y\")])+l(r.x,r.y))})),f.rotation=o.modHalf(L,360),e.updateAngularAxis(v,y),e._hasClipOnAxisFalse&&!o.isFullCircle(e.sectorInRad)&&O.call(u.hideOutsideRangePoints,e);var S=!1;for(var F in e.traceHash)if(a.traceIs(F,\"gl\")){var 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x={};x[h]=[v/d,(v+1)/d],a(l,e,m,x),o.id=y,f(s,l,m,o)}}},{\"../lib\":778,\"../plot_api/plot_template\":817,\"./domain\":855}],906:[function(t,e,r){\"use strict\";['Variables are inserted using %{variable}, for example \"y: %{y}\".','Numbers are formatted using d3-format\\'s syntax %{variable:d3-format}, for example \"Price: %{y:$.2f}\".',t(\"../constants/docs\").FORMAT_LINK,\"for details on the formatting syntax.\",'Dates are formatted using d3-time-format\\'s syntax %{variable|d3-time-format}, for example \"Day: %{2019-01-01|%A}\".',t(\"../constants/docs\").DATE_FORMAT_LINK,\"for details on the date formatting syntax.\"].join(\" \");function n(t){var e=t.description?\" \"+t.description:\"\",r=t.keys||[];if(r.length>0){for(var n=[],i=0;i<r.length;i++)n[i]=\"`\"+r[i]+\"`\";e+=\"Finally, the template string has access to \",e=1===r.length?\"variable \"+n[0]:\"variables \"+n.slice(0,-1).join(\", \")+\" and \"+n.slice(-1)+\".\"}return e}r.hovertemplateAttrs=function(t,e){t=t||{};n(e=e||{});var r={valType:\"string\",dflt:\"\",editType:t.editType||\"none\"};return!1!==t.arrayOk&&(r.arrayOk=!0),r},r.texttemplateAttrs=function(t,e){t=t||{};n(e=e||{});var r={valType:\"string\",dflt:\"\",editType:t.editType||\"calc\"};return!1!==t.arrayOk&&(r.arrayOk=!0),r}},{\"../constants/docs\":748}],907:[function(t,e,r){\"use strict\";var n=t(\"./ternary\"),i=t(\"../../plots/get_data\").getSubplotCalcData,a=t(\"../../lib\").counterRegex;r.name=\"ternary\";var o=r.attr=\"subplot\";r.idRoot=\"ternary\",r.idRegex=r.attrRegex=a(\"ternary\"),(r.attributes={})[o]={valType:\"subplotid\",dflt:\"ternary\",editType:\"calc\"},r.layoutAttributes=t(\"./layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout_defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t.calcdata,a=e._subplots.ternary,o=0;o<a.length;o++){var s=a[o],l=i(r,\"ternary\",s),c=e[s]._subplot;c||(c=new 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n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../cartesian/layout_attributes\"),o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../lib/extend\").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:\"number\",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:\"ternary\"}),bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},\"plot\",\"from-root\");c.uirevision={valType:\"any\",editType:\"none\"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":642,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../cartesian/layout_attributes\":842,\"../domain\":855}],909:[function(t,e,r){\"use 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n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strTranslate,l=o._,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,p=t(\"../plots\"),d=t(\"../cartesian/axes\"),g=t(\"../../components/dragelement\"),m=t(\"../../components/fx\"),v=t(\"../../components/dragelement/helpers\"),y=v.freeMode,x=v.rectMode,b=t(\"../../components/titles\"),_=t(\"../cartesian/select\").prepSelect,w=t(\"../cartesian/select\").selectOnClick,T=t(\"../cartesian/select\").clearSelect,k=t(\"../cartesian/select\").clearSelectionsCache,M=t(\"../cartesian/constants\");function A(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=A;var S=A.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select(\"path\").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below 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E=Math.sqrt(4/3);S.adjustLayout=function(t,e){var r,n,i,a,o,l,p=this,d=t.domain,g=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*g-i/2,n=e.t+e.h*(1-m)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[g-o/2,g+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[m-l/2,m+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var M=p.yaxis.domain[0],A=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[M,M+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(A,p.graphDiv._fullLayout),A.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var C=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[M,M+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(C,p.graphDiv._fullLayout),C.setScale();var L=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",L),p.layers.plotbg.select(\"path\").attr(\"d\",L);var I=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",I);var P=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",P),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var z=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",z),p.layers.bgrid.attr(\"transform\",z);var O=s(r+i/2,n)+\"rotate(30)\"+s(0,-A._offset);p.layers.aaxis.attr(\"transform\",O),p.layers.agrid.attr(\"transform\",O);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-C._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",A.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,A.linecolor||\"#000\").style(\"stroke-width\",(A.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",C.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,C.linecolor||\"#000\").style(\"stroke-width\",(C.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var 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n=t(\"../pie/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../lib/extend\").extendFlat;e.exports={labels:n.labels,label0:n.label0,dlabel:n.dlabel,values:n.values,marker:{colors:n.marker.colors,line:{color:l({},n.marker.line.color,{dflt:null}),width:l({},n.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},text:n.text,hovertext:n.hovertext,scalegroup:l({},n.scalegroup,{}),textinfo:l({},n.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:s({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hoverinfo:l({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:o({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),textposition:l({},n.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:n.textfont,insidetextfont:n.insidetextfont,title:{text:n.title.text,font:n.title.font,position:l({},n.title.position,{values:[\"top 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText,s=t(\"../pie/defaults\").handleLabelsAndValues;e.exports=function(t,e,r,l){function c(r,a){return n.coerce(t,e,i,r,a)}var u=c(\"labels\"),f=c(\"values\"),h=s(u,f),p=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),p){e._length=p,c(\"marker.line.width\")&&c(\"marker.line.color\",l.paper_bgcolor),c(\"marker.colors\"),c(\"scalegroup\");var d,g=c(\"text\"),m=c(\"texttemplate\");if(m||(d=c(\"textinfo\",Array.isArray(g)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),m||d&&\"none\"!==d){var v=c(\"textposition\");o(t,e,l,c,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}a(e,l,c),c(\"title.text\")&&(c(\"title.position\"),n.coerceFont(c,\"title.font\",l.font)),c(\"aspectratio\"),c(\"baseratio\")}else 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n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":1168}],1062:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":778,\"./layout_attributes\":1061}],1063:[function(t,e,r){\"use strict\";var 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0===d._deltaLastValue&&(d._deltaLastValue=a(r[0]));var l=C.select(\"text.delta\");function p(){l.text(o(a(r[0]),i)).call(m.fill,s(r[0])).call(f.convertToTspans,t)}return l.call(c.font,d.delta.font).call(m.fill,s({delta:d._deltaLastValue})),b(S)?l.transition().duration(S.duration).ease(S.easing).tween(\"text\",(function(){var t=n.select(this),e=a(r[0]),l=d._deltaLastValue,c=P(d.delta.valueformat,i,l,e),u=n.interpolateNumber(l,e);return d._deltaLastValue=e,function(e){t.text(o(u(e),c)),t.call(m.fill,s({delta:u(e)}))}})).each(\"end\",(function(){p(),E&&E()})).each(\"interrupt\",(function(){p(),E&&E()})):p(),u=k(o(a(r[0]),i),d.delta.font,A,t),l}(),O+=d.delta.position+d.delta.font.size+d.delta.font.family+d.delta.valueformat,O+=d.delta.increasing.symbol+d.delta.decreasing.symbol,p=u);d._hasNumber&&(!function(){var e=w(t,{tickformat:d.number.valueformat},d._range);e.setScale(),h.prepTicks(e);var i=function(t){return h.tickText(e,t).text},a=d.number.suffix,o=d.number.prefix,s=C.select(\"text.number\");function u(){var e=\"number\"==typeof r[0].y?o+i(r[0].y)+a:\"-\";s.text(e).call(c.font,d.number.font).call(f.convertToTspans,t)}b(S)?s.transition().duration(S.duration).ease(S.easing).each(\"end\",(function(){u(),E&&E()})).each(\"interrupt\",(function(){u(),E&&E()})).attrTween(\"text\",(function(){var t=n.select(this),e=n.interpolateNumber(r[0].lastY,r[0].y);d._lastValue=r[0].y;var s=P(d.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(o+s(e(r))+a)}})):u(),l=k(o+i(r[0].y)+a,d.number.font,A,t)}(),O+=d.number.font.size+d.number.font.family+d.number.valueformat+d.number.suffix+d.number.prefix,p=l);if(d._hasDelta&&d._hasNumber){var D,R,F=[(l.left+l.right)/2,(l.top+l.bottom)/2],B=[(u.left+u.right)/2,(u.top+u.bottom)/2],N=.75*d.delta.font.size;\"left\"===d.delta.position&&(D=M(d,\"deltaPos\",0,-1*(l.width*y[d.align]+u.width*(1-y[d.align])+N),O,Math.min),R=F[1]-B[1],p={width:l.width+u.width+N,height:Math.max(l.height,u.height),left:u.left+D,right:l.right,top:Math.min(l.top,u.top+R),bottom:Math.max(l.bottom,u.bottom+R)}),\"right\"===d.delta.position&&(D=M(d,\"deltaPos\",0,l.width*(1-y[d.align])+u.width*y[d.align]+N,O,Math.max),R=F[1]-B[1],p={width:l.width+u.width+N,height:Math.max(l.height,u.height),left:l.left,right:u.right+D,top:Math.min(l.top,u.top+R),bottom:Math.max(l.bottom,u.bottom+R)}),\"bottom\"===d.delta.position&&(D=null,R=u.height,p={width:Math.max(l.width,u.width),height:l.height+u.height,left:Math.min(l.left,u.left),right:Math.max(l.right,u.right),top:l.bottom-l.height,bottom:l.bottom+u.height}),\"top\"===d.delta.position&&(D=null,R=l.top,p={width:Math.max(l.width,u.width),height:l.height+u.height,left:Math.min(l.left,u.left),right:Math.max(l.right,u.right),top:l.bottom-l.height-u.height,bottom:l.bottom}),z.attr({dx:D,dy:R})}(d._hasNumber||d._hasDelta)&&C.attr(\"transform\",(function(){var t=s.numbersScaler(p);O+=t[2];var e,r=M(d,\"numbersScale\",1,t[0],O,Math.min);d._scaleNumbers||(r=1),e=d._isAngular?_-r*p.bottom:_-r*(p.top+p.bottom)/2,d._numbersTop=r*p.top+e;var n=p[T];\"center\"===T&&(n=(p.left+p.right)/2);var i=g-r*n;return i=M(d,\"numbersTranslate\",0,i,O,Math.max),o(i,e)+a(r)}))}(t,I,e,{numbersX:p,numbersY:A,numbersScaler:S,transitionOpts:r,onComplete:d}),P&&(E={range:L.gauge.axis.range,color:L.gauge.bgcolor,line:{color:L.gauge.bordercolor,width:0},thickness:1},C={range:L.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:L.gauge.bordercolor,width:L.gauge.borderwidth},thickness:1});var H=I.selectAll(\"g.angular\").data(z?e:[]);H.exit().remove();var G=I.selectAll(\"g.angularaxis\").data(z?e:[]);G.exit().remove(),z&&function(t,e,r,i){var a,c,u,f,p=r[0].trace,d=i.size,g=i.radius,m=i.innerRadius,v=i.gaugeBg,y=i.gaugeOutline,x=[d.l+d.w/2,d.t+d.h/2+g/2],T=i.gauge,k=i.layer,M=i.transitionOpts,A=i.onComplete,S=Math.PI/2;function E(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-S;return r<-S?-S:r>S?S:r}function C(t){return n.svg.arc().innerRadius((m+g)/2-t/2*(g-m)).outerRadius((m+g)/2+t/2*(g-m)).startAngle(-S)}function L(t){t.attr(\"d\",(function(t){return C(t.thickness).startAngle(E(t.range[0])).endAngle(E(t.range[1]))()}))}T.enter().append(\"g\").classed(\"angular\",!0),T.attr(\"transform\",o(x[0],x[1])),k.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),k.selectAll(\"g.xangularaxistick,path,text\").remove(),(a=w(t,p.gauge.axis)).type=\"linear\",a.range=p.gauge.axis.range,a._id=\"xangularaxis\",a.setScale();var I=function(t){return(a.range[0]-t.x)/(a.range[1]-a.range[0])*Math.PI+Math.PI},P={},z=h.makeLabelFns(a,0).labelStandoff;P.xFn=function(t){var e=I(t);return Math.cos(e)*z},P.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*l)},P.anchorFn=function(t){var e=I(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},P.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var O=function(t){return o(x[0]+g*Math.cos(t),x[1]-g*Math.sin(t))};u=function(t){return O(I(t))};if(c=h.calcTicks(a),f=h.getTickSigns(a)[2],a.visible){f=\"inside\"===a.ticks?-1:1;var D=(a.linewidth||1)/2;h.drawTicks(t,a,{vals:c,layer:k,path:\"M\"+f*D+\",0h\"+f*a.ticklen,transFn:function(t){var e=I(t);return O(e)+\"rotate(\"+-s(e)+\")\"}}),h.drawLabels(t,a,{vals:c,layer:k,transFn:u,labelFns:P})}var R=[v].concat(p.gauge.steps),F=T.selectAll(\"g.bg-arc\").data(R);F.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),F.select(\"path\").call(L).call(_),F.exit().remove();var B=C(p.gauge.bar.thickness),N=T.selectAll(\"g.value-arc\").data([p.gauge.bar]);N.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var j=N.select(\"path\");b(M)?(j.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){A&&A()})).each(\"interrupt\",(function(){A&&A()})).attrTween(\"d\",(U=B,V=E(r[0].lastY),q=E(r[0].y),function(){var t=n.interpolate(V,q);return function(e){return U.endAngle(t(e))()}})),p._lastValue=r[0].y):j.attr(\"d\",\"number\"==typeof r[0].y?B.endAngle(E(r[0].y)):\"M0,0Z\");var U,V,q;j.call(_),N.exit().remove(),R=[];var H=p.gauge.threshold.value;H&&R.push({range:[H,H],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var G=T.selectAll(\"g.threshold-arc\").data(R);G.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),G.select(\"path\").call(L).call(_),G.exit().remove();var Y=T.selectAll(\"g.gauge-outline\").data([y]);Y.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),Y.select(\"path\").call(L).call(_),Y.exit().remove()}(t,0,e,{radius:N,innerRadius:j,gauge:H,layer:G,size:R,gaugeBg:E,gaugeOutline:C,transitionOpts:r,onComplete:d});var Y=I.selectAll(\"g.bullet\").data(O?e:[]);Y.exit().remove();var W=I.selectAll(\"g.bulletaxis\").data(O?e:[]);W.exit().remove(),O&&function(t,e,r,n){var i,a,s,l,c,f=r[0].trace,p=n.gauge,d=n.layer,g=n.gaugeBg,v=n.gaugeOutline,y=n.size,x=f.domain,T=n.transitionOpts,k=n.onComplete;p.enter().append(\"g\").classed(\"bullet\",!0),p.attr(\"transform\",o(y.l,y.t)),d.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),d.selectAll(\"g.xbulletaxistick,path,text\").remove();var M=y.h,A=f.gauge.bar.thickness*M,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(f._hasNumber||f._hasDelta?1-u.bulletNumberDomainSize:1);(i=w(t,f.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=h.calcTicks(i),s=h.makeTransTickFn(i),l=h.getTickSigns(i)[2],c=y.t+y.h,i.visible&&(h.drawTicks(t,i,{vals:\"inside\"===i.ticks?h.clipEnds(i,a):a,layer:d,path:h.makeTickPath(i,c,l),transFn:s}),h.drawLabels(t,i,{vals:a,layer:d,transFn:s,labelFns:h.makeLabelFns(i,c)}));function C(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*M})).attr(\"height\",(function(t){return t.thickness*M}))}var L=[g].concat(f.gauge.steps),I=p.selectAll(\"g.bg-bullet\").data(L);I.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),I.select(\"rect\").call(C).call(_),I.exit().remove();var P=p.selectAll(\"g.value-bullet\").data([f.gauge.bar]);P.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),P.select(\"rect\").attr(\"height\",A).attr(\"y\",(M-A)/2).call(_),b(T)?P.select(\"rect\").transition().duration(T.duration).ease(T.easing).each(\"end\",(function(){k&&k()})).each(\"interrupt\",(function(){k&&k()})).attr(\"width\",Math.max(0,i.c2p(Math.min(f.gauge.axis.range[1],r[0].y)))):P.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(f.gauge.axis.range[1],r[0].y))):0);P.exit().remove();var z=r.filter((function(){return f.gauge.threshold.value})),O=p.selectAll(\"g.threshold-bullet\").data(z);O.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),O.select(\"line\").attr(\"x1\",i.c2p(f.gauge.threshold.value)).attr(\"x2\",i.c2p(f.gauge.threshold.value)).attr(\"y1\",(1-f.gauge.threshold.thickness)/2*M).attr(\"y2\",(1-(1-f.gauge.threshold.thickness)/2)*M).call(m.stroke,f.gauge.threshold.line.color).style(\"stroke-width\",f.gauge.threshold.line.width),O.exit().remove();var D=p.selectAll(\"g.gauge-outline\").data([v]);D.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),D.select(\"rect\").call(C).call(_),D.exit().remove()}(t,0,e,{gauge:Y,layer:W,size:R,gaugeBg:E,gaugeOutline:C,transitionOpts:r,onComplete:d});var X=I.selectAll(\"text.title\").data(e);X.exit().remove(),X.enter().append(\"text\").classed(\"title\",!0),X.attr(\"text-anchor\",(function(){return O?v.right:v[L.title.align]})).text(L.title.text).call(c.font,L.title.font).call(f.convertToTspans,t),X.attr(\"transform\",(function(){var t,e=R.l+R.w*y[L.title.align],r=u.titlePadding,n=c.bBox(X.node());if(P){if(z)if(L.gauge.axis.visible)t=c.bBox(G.node()).top-r-n.bottom;else t=R.t+R.h/2-N/2-n.bottom-r;O&&(t=A-(n.top+n.bottom)/2,e=R.l-u.bulletPadding*R.w)}else t=L._numbersTop-r-n.bottom;return o(e,t)}))}))}},{\"../../components/color\":643,\"../../components/drawing\":665,\"../../constants/alignment\":745,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plots/cartesian/axes\":828,\"../../plots/cartesian/axis_defaults\":830,\"../../plots/cartesian/layout_attributes\":842,\"../../plots/cartesian/position_defaults\":845,\"./constants\":1119,d3:169}],1123:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../mesh3d/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;var c=e.exports=l(s({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:i(),showlegend:s({},o.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:a.opacity,lightposition:a.lightposition,lighting:a.lighting,flatshading:a.flatshading,contour:a.contour,hoverinfo:s({},o.hoverinfo)}),\"calc\",\"nested\");c.flatshading.dflt=!0,c.lighting.facenormalsepsilon.dflt=0,c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 0},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/template_attributes\":906,\"../mesh3d/attributes\":1128}],1124:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\"),i=t(\"../streamtube/calc\").processGrid,a=t(\"../streamtube/calc\").filter;e.exports=function(t,e){e._len=Math.min(e.x.length,e.y.length,e.z.length,e.value.length),e._x=a(e.x,e._len),e._y=a(e.y,e._len),e._z=a(e.z,e._len),e._value=a(e.value,e._len);var r=i(e);e._gridFill=r.fill,e._Xs=r.Xs,e._Ys=r.Ys,e._Zs=r.Zs,e._len=r.len;for(var o=1/0,s=-1/0,l=0;l<e._len;l++){var c=e._value[l];o=Math.min(o,c),s=Math.max(s,c)}e._minValues=o,e._maxValues=s,e._vMin=void 0===e.isomin||null===e.isomin?o:e.isomin,e._vMax=void 0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":651,\"../streamtube/calc\":1295}],1125:[function(t,e,r){\"use strict\";var n=t(\"gl-mesh3d\"),i=t(\"../../lib/gl_format_color\").parseColorScale,a=t(\"../../lib/str2rgbarray\"),o=t(\"../../components/colorscale\").extractOpts,s=t(\"../../plots/gl3d/zip3\"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var 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0),\"category descending\"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,m[e])}));var v,y=e.line;y?(i(e,\"line\")&&a(t,e,{vals:e.line.color,containerStr:\"line\",cLetter:\"c\"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,M={},A=m.map((function(t){return t.inds}));for(g=0,x=0;x<k;x++){var S=[];for(b=0;b<A.length;b++)S.push(A[b][x]);d=p[x%p.length],g+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),C=S+\"-\"+E.rawColor;void 0===M[C]&&(M[C]=f(S,E.color,E.rawColor)),h(M[C],x,d)}var L,I=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,g)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<I.length;b++){var P=I[b].containerInd,z=m[b].inds[x],O=I[b].categories;if(void 0===O[z]){var D=e.dimensions[P]._categoryarray[z],R=e.dimensions[P]._ticktext[z];O[z]={dimensionInd:b,categoryInd:L=z,categoryValue:D,displayInd:L,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(O[z],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(I,M,g))}},{\"../../components/colorscale/calc\":651,\"../../components/colorscale/helpers\":654,\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/filter_unique.js\":769,\"../../lib/gup\":775,\"fast-isnumeric\":241}],1145:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"./attributes\"),c=t(\"../parcoords/merge_length\");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r(\"values\"),a=r(\"visible\");if(i&&i.length||(a=e.visible=!1),a){r(\"label\"),r(\"displayindex\",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o=\"array\");var u=r(\"categoryorder\",o);\"array\"===u?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==u||(e.categoryorder=\"trace\")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:\"dimensions\",handleItemDefaults:u}),d=function(t,e,r,o,s){s(\"line.shape\"),s(\"line.hovertemplate\");var l=s(\"line.color\",o.colorway[0]);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,\"values\",d),h(\"hoveron\"),h(\"hovertemplate\"),h(\"arrangement\"),h(\"bundlecolors\"),h(\"sortpaths\"),h(\"counts\");var g={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",g);var m={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",m)}},{\"../../components/colorscale/defaults\":653,\"../../components/colorscale/helpers\":654,\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/domain\":855,\"../parcoords/merge_length\":1158,\"./attributes\":1142}],1146:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":1142,\"./base_plot\":1143,\"./calc\":1144,\"./defaults\":1145,\"./plot\":1148}],1147:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plot_api/plot_api\"),a=t(\"../../components/fx\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../components/drawing\"),c=t(\"tinycolor2\"),u=t(\"../../lib/svg_text_utils\");function f(t,e,r,i){var a=t.map(R.bind(0,e,r)),c=i.selectAll(\"g.parcatslayer\").data([null]);c.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var f=c.selectAll(\"g.trace.parcats\").data(a,h),v=f.enter().append(\"g\").attr(\"class\",\"trace parcats\");f.attr(\"transform\",(function(t){return s(t.x,t.y)})),v.append(\"g\").attr(\"class\",\"paths\");var y=f.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),h);y.attr(\"fill\",(function(t){return t.model.color}));var _=y.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);b(_),y.attr(\"d\",(function(t){return t.svgD})),_.empty()||y.sort(d),y.exit().remove(),y.on(\"mouseover\",g).on(\"mouseout\",m).on(\"click\",x),v.append(\"g\").attr(\"class\",\"dimensions\");var k=f.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),h);k.enter().append(\"g\").attr(\"class\",\"dimension\"),k.attr(\"transform\",(function(t){return s(t.x,0)})),k.exit().remove();var M=k.selectAll(\"g.category\").data((function(t){return t.categories}),h),A=M.enter().append(\"g\").attr(\"class\",\"category\");M.attr(\"transform\",(function(t){return s(0,t.y)})),A.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),M.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),w(A);var S=M.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),h);S.each((function(){o.raiseToTop(this)})),S.attr(\"fill\",(function(t){return t.color}));var z=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),T(z),S.exit().remove(),A.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var O=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return p(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",O+\" -1px  1px 2px, \"+O+\" 1px  1px 2px, \"+O+\"  1px -1px 2px, \"+O+\" -1px -1px 2px\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return p(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){l.font(n.select(this),t.parcatsViewModel.categorylabelfont),u.convertToTspans(n.select(this),e)})),A.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){l.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",E).on(\"mouseout\",C),M.exit().remove(),k.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",L).on(\"drag\",I).on(\"dragend\",P)),f.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),f.exit().remove()}function h(t){return t.key}function p(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function d(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){o.raiseToTop(this),_(n.select(this));var e=v(t),r=y(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,s,l,u=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),g=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=u&&u<=t.leftXs[l+1]+2){var m=t.parcatsViewModel.dimensions[l],x=t.parcatsViewModel.dimensions[l+1];i=(m.x+m.width+x.x)/2,s=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,w=t.parcatsViewModel.y+s,T=c.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,M=k/t.parcatsViewModel.model.count,A={countLabel:k,probabilityLabel:M.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",A.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",A.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),C=n.mouse(f)[0];a.loneHover({trace:h,x:b-d.left+g.left,y:w-d.top+g.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:C<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:A,eventData:[{data:h._input,fullData:h,count:k,probability:M}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function m(t){if(!t.parcatsViewModel.dragDimension&&(b(n.select(this)),a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(d),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=v(t),r=y(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function v(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function y(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function x(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=v(t),r=y(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function b(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function _(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return c.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function w(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function T(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function k(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){k(t).each((function(t){Array.prototype.push.apply(l,v(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function A(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=k(i),l=[];s.each((function(t){Array.prototype.push.apply(l,v(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var g=u.model.count,m=u.model.categoryLabel,v=g/u.parcatsViewModel.model.count,y={countLabel:g,categoryLabel:m,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", 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e=k(t);_(e),e.each((function(){o.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=S(r,s,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,g=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var m=f.model.categoryLabel,v=u.parcatsViewModel.model.count,y=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:m,probabilityLabel:_.toFixed(3)},M=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&M.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(M.push(\"P(color \\u2229 \"+m+\"): \"+k.probabilityLabel),M.push(\"P(\"+m+\" | color): \"+w.toFixed(3)),M.push(\"P(color | \"+m+\"): \"+T.toFixed(3)));var A=M.join(\"<br>\"),S=c.mostReadable(u.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(g-e.top),text:A,color:u.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:m,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,s,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(S(t,e,this))})),i}(r,s,this)),e&&a.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(b(e.pathSelection),w(e.dimensionSelection.selectAll(\"g.category\")),T(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),a.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(d),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?A(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function L(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,o.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}N(t.parcatsViewModel),B(t.parcatsViewModel),D(t.parcatsViewModel),O(t.parcatsViewModel)}}function P(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),a=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==a[e]}));o&&a.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?A(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,N(t.parcatsViewModel),B(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){D(t.parcatsViewModel,!0),O(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&i.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function O(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),h),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function D(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),h);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),h);r(t.dimensionSelection).attr(\"transform\",(function(t){return s(t.x,0)})),r(i).attr(\"transform\",(function(t){return s(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return p(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return p(t)?t.width+5:-5})).each((function(t){var e,r;p(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),h),l=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),T(l),a.each((function(){o.raiseToTop(this)})),a.exit().remove()}function R(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var g={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(N(g),B(g)),g}function F(t,e,r,i,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=n.interpolateNumber(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+i+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+i)+\" \"+l[s]+\",\"+(e[s]+i)+\" \"+(t[s]+r[s])+\",\"+(e[s]+i),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function B(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),g=0;g<c.length;g++){var m,v=c[g];m=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var _=v.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=m;var k=t.dimensions[T].categories[w],M=k.bands.length,A=k.bands[M-1];if(void 0===A||v.rawColor!==A.rawColor){var S=void 0===A?0:A.y+A.height;k.bands.push({key:S,color:v.color,rawColor:v.rawColor,height:m,width:k.width,count:v.count,y:S,categoryViewModel:k,parcatsViewModel:t})}else{var E=k.bands[M-1];E.height+=m,E.count+=v.count}}y=\"hspline\"===t.pathShape?F(s,x,l,m,.5):F(s,x,l,m,0),h[g]={key:v.valueInds[0],model:v,height:m,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=h}function N(t){var e=t.model.dimensions.map((function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}}));e.sort((function(t,e){return t.displayInd-e.displayInd}));var r=[];for(var n in e){var i=e[n].dimensionInd,a=t.model.dimensions[i];r.push(j(t,a))}t.dimensions=r}function j(t,e){var r,n=t.model.dimensions.length,i=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],f=t.model.maxCats,h=e.categories.length,p=e.count,d=t.height-8*(f-1),g=8*(f-h)/2,m=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(m.sort((function(t,e){return t.displayInd-e.displayInd})),c=0;c<h;c++)l=m[c].categoryInd,o=e.categories[l],a=p>0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:a,y:null!==o.dragY?o.dragY:g,bands:[],parcatsViewModel:t},g=g+a+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}e.exports=function(t,e,r,n){f(r,t,n,e)}},{\"../../components/drawing\":665,\"../../components/fx\":683,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plot_api/plot_api\":814,d3:169,tinycolor2:576}],1148:[function(t,e,r){\"use strict\";var n=t(\"./parcats\");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{\"./parcats\":1147}],1149:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/layout_attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/plot_template\":817,\"../../plots/cartesian/layout_attributes\":842,\"../../plots/domain\":855,\"../../plots/font_attributes\":856}],1150:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"d3\"),a=t(\"../../lib/gup\").keyFun,o=t(\"../../lib/gup\").repeat,s=t(\"../../lib\").sorterAsc,l=t(\"../../lib\").strTranslate,c=n.bar.snapRatio;function u(t,e){return t*(1-c)+e*c}var f=n.bar.snapClose;function h(t,e){return t*(1-f)+e*f}function p(t,e,r,n){if(function(t,e){for(var r=0;r<e.length;r++)if(t>=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],c=l,f=a;i*f<i*o;f+=i){var p=f+i,d=e[p];if(i*r<i*h(l,d))return u(l,c);if(i*r<i*d||p===o)return u(d,l);c=l,l=d}}function d(t){t.attr(\"x\",-n.bar.captureWidth/2).attr(\"width\",n.bar.captureWidth)}function g(t){t.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function m(t){if(!t.brush.filterSpecified)return\"0,\"+t.height;for(var 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k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||y()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&A(r)):A(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||A(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function M(t,e){return t[0]-e[0]}function A(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(M)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t){var e=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);e.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t){var e=t.selectAll(\".background\").data(o);e.enter().append(\"rect\").classed(\"background\",!0).call(d).call(g).style(\"pointer-events\",\"auto\").attr(\"transform\",l(0,n.verticalPadding)),e.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var r=t.selectAll(\".highlight-shadow\").data(o);r.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",n.bar.strokeColor).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),r.attr(\"y1\",(function(t){return t.height})).call(x);var i=t.selectAll(\".highlight\").data(o);i.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",(function(t){return t.height})).call(x)}(e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(M)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":778,\"../../lib/gup\":775,\"./constants\":1153,d3:169}],1151:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plots/get_data\").getModuleCalcData,a=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.width,height:this.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":754,\"../../plots/get_data\":865,\"./plot\":1160,d3:169}],1152:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,i=t(\"../../components/colorscale\"),a=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{\"../../components/colorscale\":655,\"../../lib\":778,\"../../lib/gup\":775}],1153:[function(t,e,r){\"use strict\";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:[\"contextLineLayer\",\"focusLineLayer\",\"pickLineLayer\"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:\"#777\",bar:{width:4,captureWidth:10,fillColor:\"magenta\",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeColor:\"white\",strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:\"axis-extent-text\",parcoordsLineLayers:\"parcoords-line-layers\",parcoordsLineLayer:\"parcoords-lines\",parcoords:\"parcoords\",parcoordsControlView:\"parcoords-control-view\",yAxis:\"y-axis\",axisOverlays:\"axis-overlays\",axis:\"axis\",axisHeading:\"axis-heading\",axisTitle:\"axis-title\",axisExtent:\"axis-extent\",axisExtentTop:\"axis-extent-top\",axisExtentTopText:\"axis-extent-top-text\",axisExtentBottom:\"axis-extent-bottom\",axisExtentBottomText:\"axis-extent-bottom-text\",axisBrush:\"axis-brush\"},id:{filterBarPattern:\"filter-bar-pattern\"}}},{}],1154:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"../../plots/cartesian/axes\"),c=t(\"./attributes\"),u=t(\"./axisbrush\"),f=t(\"./constants\").maxDimensionCount,h=t(\"./merge_length\");function p(t,e,r,i){function a(r,i){return n.coerce(t,e,c.dimensions,r,i)}var o=a(\"values\"),s=a(\"visible\");if(o&&o.length||(s=e.visible=!1),s){a(\"label\"),a(\"tickvals\"),a(\"ticktext\"),a(\"tickformat\");var f=a(\"range\");e._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:f},l.setConvert(e._ax,i.layout),a(\"multiselect\");var h=a(\"constraintrange\");h&&(e.constraintrange=u.cleanRanges(h,e))}}e.exports=function(t,e,r,l){function u(r,i){return n.coerce(t,e,c,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log(\"parcoords traces support up to \"+f+\" dimensions at the moment\"),d.splice(f));var g=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),m=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(g)&&g.length||(e.visible=!1),h(e,g,\"values\",m);var v={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(u,\"labelfont\",v),n.coerceFont(u,\"tickfont\",v),n.coerceFont(u,\"rangefont\",v),u(\"labelangle\"),u(\"labelside\")}},{\"../../components/colorscale/defaults\":653,\"../../components/colorscale/helpers\":654,\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"../../plots/domain\":855,\"./attributes\":1149,\"./axisbrush\":1150,\"./constants\":1153,\"./merge_length\":1158}],1155:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":778}],1156:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":1149,\"./base_plot\":1151,\"./calc\":1152,\"./defaults\":1154,\"./plot\":1160}],1157:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform sampler2D mask, palette;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(mask,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function g(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,c=r[64*a+l];63===l&&0===o&&(c*=-1),n[i++]=c}return n}function m(t){var e=\"0\"+t;return e.substr(e.length-2)}function v(t){return t<o?\"p\"+m(t+1)+\"_\"+m(t+4):\"colors\"}function y(t,e,r,n,i,a,o,l,c,u,f,h,p){for(var d=[[],[]],g=0;g<64;g++)d[0][g]=g===i?1:0,d[1][g]=g===a?1:0;var m=t.lines.canvasOverdrag,v=t.domain,y=t.canvasWidth,x=t.canvasHeight,b=t.deselectedLines.color;return s.extendFlat({key:f,resolution:[y,x],viewBoxPos:[o+m,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:d[0].slice(0,16),dim0B:d[0].slice(16,32),dim0C:d[0].slice(32,48),dim0D:d[0].slice(48,64),dim1A:d[1].slice(0,16),dim1B:d[1].slice(16,32),dim1C:d[1].slice(32,48),dim1D:d[1].slice(48,64),drwLayer:h,contextColor:[b[0]/255,b[1]/255,b[2]/255,b[3]<1?b[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+m)+(t.pad.l-m)+t.layoutWidth*v.x[0],scissorWidth:(n===r?y-o+m:c+.5)+(n===e?o+m:0),scissorY:l+t.pad.b+t.layoutHeight*v.y[0],scissorHeight:u,viewportX:t.pad.l-m+t.layoutWidth*v.x[0],viewportY:t.pad.b+t.layoutHeight*v.y[0],viewportWidth:y,viewportHeight:x},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,m,b,_=e.context,w=e.pick,T=e.regl,k={currentRafs:{},drawCompleted:!0,clearOnly:!1},M=function(t){for(var e={},r=0;r<=o;r+=4)e[v(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(T),A=T.texture(u),S=[];C(e);var E=T({profile:!1,blend:{enable:_,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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r=t._fullLayout;if(i(t)){var s={},l={},c={},u={},f=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=u[r]=n._fullInput.index;s[r]=t.data[i].dimensions,l[r]=t.data[i].dimensions.slice()}));n(t,e,{width:f.w,height:f.h,margin:{t:f.t,r:f.r,b:f.b,l:f.l}},{filterChanged:function(e,n,i){var a=l[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",f=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===f[s]){var h=a.constraintrange;f[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[u[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"plot\"},editType:\"plot\"},domain:i({name:\"pie\",trace:!0,editType:\"calc\"}),hole:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},sort:{valType:\"boolean\",dflt:!0,editType:\"calc\"},direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"],dflt:\"counterclockwise\",editType:\"calc\"},rotation:{valType:\"number\",min:-360,max:360,dflt:0,editType:\"calc\"},pull:{valType:\"number\",min:0,max:1,dflt:0,arrayOk:!0,editType:\"calc\"},_deprecated:{title:{valType:\"string\",dflt:\"\",editType:\"calc\"},titlefont:c({},u,{}),titleposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":642,\"../../lib/extend\":768,\"../../plots/attributes\":824,\"../../plots/domain\":855,\"../../plots/font_attributes\":856,\"../../plots/template_attributes\":906}],1162:[function(t,e,r){\"use strict\";var n=t(\"../../plots/plots\");r.name=\"pie\",r.plot=function(t,e,i,a){n.plotBasePlot(r.name,t,e,i,a)},r.clean=function(t,e,i,a){n.cleanBasePlot(r.name,t,e,i,a)}},{\"../../plots/plots\":891}],1163:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"tinycolor2\"),a=t(\"../../components/color\"),o={};function s(t){return function(e,r){return!!e&&(!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e))}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}e.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},g=s(o[\"_\"+e.type+\"colormap\"]),m=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;if((y=+y)<0)continue}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(m+=y),a.push({v:y,label:x,color:g(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(m+=y),!1===i.color&&u[r]&&(i.color=g(u[r],x)))}return(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=m),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":643,\"fast-isnumeric\":241,tinycolor2:576}],1164:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var g=c(\"textposition\");s(t,e,n,c,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&c(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&c(\"insidetextorientation\")}o(e,n,c);var m=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",m?\"middle center\":\"top center\");m||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":778,\"../../plots/domain\":855,\"../bar/defaults\":925,\"./attributes\":1161,\"fast-isnumeric\":241}],1165:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":679}],1166:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":778}],1167:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":1161,\"./base_plot\":1162,\"./calc\":1163,\"./defaults\":1164,\"./layout_attributes\":1168,\"./layout_defaults\":1169,\"./plot\":1170,\"./style\":1171,\"./style_one\":1172}],1168:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],1169:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":778,\"./layout_attributes\":1168}],1170:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,g=t(\"../bar/constants\").TEXTPAD,m=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.trace,s=i.cx,c=i.cy;\"_hasHoverLabel\"in o||(o._hasHoverLabel=!1),\"_hasHoverEvent\"in o||(o._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,u=e._fullData[o.index];if(!e._dragging&&!1!==r.hovermode){var f=u.hoverinfo;if(Array.isArray(f)&&(f=a.castHoverinfo({hoverinfo:[m.castOption(f,t.pts)],_module:o._module},r,0)),\"all\"===f&&(f=\"label+text+value+percent+name\"),u.hovertemplate||\"none\"!==f&&\"skip\"!==f&&f){var h=t.rInscribed||0,p=s+t.pxmid[0]*(1-h),d=c+t.pxmid[1]*(1-h),g=r.separators,y=[];if(f&&-1!==f.indexOf(\"label\")&&y.push(t.label),t.text=m.castOption(u.hovertext||u.text,t.pts),f&&-1!==f.indexOf(\"text\")){var x=t.text;l.isValidTextValue(x)&&y.push(x)}t.value=t.v,t.valueLabel=m.formatPieValue(t.v,g),f&&-1!==f.indexOf(\"value\")&&y.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=m.formatPiePercent(t.percent,g),f&&-1!==f.indexOf(\"percent\")&&y.push(t.percentLabel);var b=u.hoverlabel,_=b.font;a.loneHover({trace:o,x0:p-h*i.r,x1:p+h*i.r,y:d,text:y.join(\"<br>\"),name:u.hovertemplate||-1!==f.indexOf(\"name\")?u.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:m.castOption(b.bgcolor,t.pts)||t.color,borderColor:m.castOption(b.bordercolor,t.pts),fontFamily:m.castOption(_.family,t.pts),fontSize:m.castOption(_.size,t.pts),fontColor:m.castOption(_.color,t.pts),nameLength:m.castOption(b.namelength,t.pts),textAlign:m.castOption(b.align,t.pts),hovertemplate:m.castOption(u.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,u)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e}),o._hasHoverLabel=!0}o._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,u)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[o.index],s=n.select(this).datum();o._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(s,i)],event:n.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),o._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[o.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=m.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=m.castOption(t._input.textfont.color,e.pts));var i=m.castOption(t.insidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.insidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,g=[];if(!d){var m,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,g.push(a)}};if(f||h){for(m=4;m>=-4;m-=2)v(Math.PI*m,\"tan\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+1),\"tan\")}if(f||p){for(m=4;m>=-4;m-=2)v(Math.PI*(m+1.5),\"rad\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;g.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a));for(var x=0,b=0,_=0;_<g.length;_++){var w=g[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return g[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:M(a,o/e),rotate:A(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:M(a,o/e),rotate:A(i+Math.PI/2)}}function M(t,e){return Math.cos(e)-t*e}function A(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function C(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function L(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=P(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=I(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function I(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function P(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function z(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=I(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+P(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=I(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function O(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),g=n.separators;if(s=f?[e.label]:[],h){var v=m.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(m.formatPieValue(e.v,g)),d&&s.push(m.formatPiePercent(e.v/r.vTotal,g)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:m.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:m.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=m.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),z(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],g=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=m.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=O(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=O(a,s),r.midangle=s,s+=l*r.v/2,n=O(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=g.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),g.pull){var v=+m.castOption(g.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=g.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+L(i.px0,i.pxmid,!0,1)+L(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+L(i.px0,i.pxmid,!1,T)+L(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var M=L(i.px0,i.px1,!0,1);if(T){var A=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+L(i.px1,i.px0,!1,T)+\"l\"+A*i.px0[0]+\",\"+A*i.px0[1]+M+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}D(t,i,d);var S=m.castOption(g.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=m.castOption(t.outsidetextfont.color,e.pts)||m.castOption(t.textfont.color,e.pts)||r.color,i=m.castOption(t.outsidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.outsidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(g,i,r.font):b(g,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=C(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,g.outsidetextfont);u.call(s.font,x),v=C(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:O(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var M=v.targetY;i.yLabelMin=M-y.height/2,i.yLabelMid=M,i.yLabelMax=M+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(g.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function L(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(g.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=g.title.text;g._meta&&(i=l.templateString(i,g._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,g.title.font).call(f.convertToTspans,t),e=\"middle center\"===g.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):L(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function g(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,g=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(m.castOption(e.pull,t.pts)||0)>=(m.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(g+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?g:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,g),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr(\"transform\",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+c):s+=\"l\"+t.labelExtraX+\",\"+u+\"v\"+(f-u)+\"h\"+c}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+c;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(v,g),_&&g.automargin){var k=s.bBox(h.node()),M=g.domain,A=a.w*(M.x[1]-M.x[0]),S=a.h*(M.y[1]-M.y[0]),E=(.5*A-d.r)/a.w,I=(.5*S-d.r)/a.h;i.autoMargin(t,\"pie.\"+g.uid+\".automargin\",{xl:M.x[0]-E,xr:M.x[1]+E,yb:M.y[0]-I,yt:M.y[1]+I,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:L,prerenderTitles:_,layoutAreas:z,attachFxHandlers:x,computeTransform:R}},{\"../../components/color\":643,\"../../components/drawing\":665,\"../../components/fx\":683,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plots/plots\":891,\"../bar/constants\":923,\"../bar/uniform_text\":937,\"./event_data\":1165,\"./helpers\":1166,d3:169}],1171:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"./style_one\"),a=t(\"../bar/uniform_text\").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each((function(t){n.select(this).call(i,t,e)}))}))}},{\"../bar/uniform_text\":937,\"./style_one\":1172,d3:169}],1172:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"./helpers\").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},{\"../../components/color\":643,\"./helpers\":1166}],1173:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},{\"../scatter/attributes\":1187}],1174:[function(t,e,r){\"use strict\";var n=t(\"gl-pointcloud2d\"),i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new Int32Array(e),l=0;l<e;l++)o=c[l],s=u[l],r[l]=l,n[2*l]=o,n[2*l+1]=s,o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var g=i(t.marker.color),m=i(t.marker.border.color),v=t.opacity*t.marker.opacity;g[3]*=v,this.pointcloudOptions.color=g;var y=t.marker.blend;if(null===y){y=c.length<100||u.length<100}this.pointcloudOptions.blend=y,m[3]*=v,this.pointcloudOptions.borderColor=m;var x=t.marker.sizemin,b=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=b,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var _=this.scene.xaxis,w=this.scene.yaxis,T=b/2||.5;t._extremes[_._id]=a(_,[d[0],d[2]],{ppad:T}),t._extremes[w._id]=a(w,[d[1],d[3]],{ppad:T})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{\"../../lib/str2rgbarray\":802,\"../../plots/cartesian/autorange\":827,\"../scatter/get_trace_color\":1197,\"gl-pointcloud2d\":324}],1175:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},{\"../../lib\":778,\"./attributes\":1173}],1176:[function(t,e,r){\"use 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../plots/template_attributes\").hovertemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../plot_api/plot_template\").templatedArray,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../plot_api/edit_types\").overrideAll;t(\"../../constants/docs\").FORMAT_LINK;(e.exports=h({hoverinfo:f({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),customdata:void 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o=n[t=i[w]],s=0,l={};for(T=0;T<o.length;T++)l[(e=o[T]).label]||(l[e.label]=0),l[e.label]+=e.value,s+=e.value;for(T=0;T<o.length;T++)(e=o[T]).flow={value:s,labelConcentration:l[e.label]/s,concentration:e.value/s,links:o},e.concentrationscale&&(e.color=a(e.concentrationscale(e.flow.labelConcentration)))}var c=0;for(w=0;w<r.sourceLinks.length;w++)c+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/c;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),s.node.x.length&&s.node.y.length){for(_=0;_<Math.min(s.node.x.length,s.node.y.length,k.nodes.length);_++)if(s.node.x[_]&&s.node.y[_]){var C=[s.node.x[_]*g,s.node.y[_]*v];k.nodes[_].x0=C[0]-d/2,k.nodes[_].x1=C[0]+d/2;var L=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=C[1]-L/2,k.nodes[_].y1=C[1]+L/2}if(\"snap\"===s.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(y=k.nodes));i.update(k)}return{circular:b,key:r,trace:s,guid:f.randstr(),horizontal:h,width:g,height:v,nodePad:s.node.pad,nodeLineColor:s.node.line.color,nodeLineWidth:s.node.line.width,linkLineColor:s.link.line.color,linkLineWidth:s.link.line.width,valueFormat:s.valueformat,valueSuffix:s.valuesuffix,textFont:s.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:h?v:g,dragPerpendicular:h?g:v,arrangement:s.arrangement,sankey:i,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function b(t,e,r){var n=a(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:o.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:_,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function _(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,i=t.link.source.x1,a=t.link.target.x0,o=v(i,a),s=o(.5),l=o(.5),c=t.link.y0-t.link.width/2,u=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2;return\"M\"+i+\",\"+c+\"C\"+s+\",\"+c+\" \"+l+\",\"+f+\" \"+a+\",\"+f+\"L\"+a+\",\"+h+\"C\"+l+\",\"+h+\" \"+s+\",\"+u+\" \"+i+\",\"+u+\"Z\"}}function w(t,e){var r=a(e.color),i=n.nodePadAcross,s=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var l=e.dx,c=Math.max(.5,e.dy),u=\"node_\"+e.pointNumber;return e.group&&(u=f.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:u,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(l),visibleHeight:c,zoneX:-i,zoneY:-s,zoneWidth:l+2*i,zoneHeight:c+2*s,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:o.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,u].join(\"_\"),interactionState:t.interactionState,figure:t}}function T(t){t.attr(\"transform\",(function(t){return h(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function k(t){t.call(T)}function M(t,e){t.call(k),e.attr(\"d\",_())}function A(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function S(t){return t.link.width>1||t.linkLineWidth>0}function E(t){return h(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function C(t){return h(t.horizontal?0:t.labelY,t.horizontal?t.labelY:0)}function L(t){return i.svg.line()([[t.horizontal?t.left?-t.sizeAcross:t.visibleWidth+n.nodeTextOffsetHorizontal:n.nodeTextOffsetHorizontal,0],[t.horizontal?t.left?-n.nodeTextOffsetHorizontal:t.sizeAcross:t.visibleHeight-n.nodeTextOffsetHorizontal,0]])}function I(t){return t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\"}function P(t){return t.horizontal?\"scale(1 1)\":\"scale(-1 1)\"}function z(t){return t.darkBackground&&!t.horizontal?\"rgb(255,255,255)\":\"rgb(0,0,0)\"}function O(t){return t.horizontal&&t.left?\"100%\":\"0%\"}function D(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function R(t,e,r,a){var o=i.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(i){if(\"fixed\"!==i.arrangement&&(f.ensureSingle(a._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){a._fullLayout._dragCover=t})),f.raiseToTop(this),i.interactionState.dragInProgress=i.node,B(i.node),i.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,i.interactionState.hovered),i.interactionState.hovered=!1),\"snap\"===i.arrangement)){var o=i.traceId+\"|\"+i.key;i.forceLayouts[o]?i.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=u.forceSimulation(a).alphaDecay(0).force(\"collide\",u.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(n.forceIterations)).force(\"constrain\",function(t,e,r,i){return function(){for(var t=0,a=0;a<r.length;a++){var o=r[a];o===i.interactionState.dragInProgress?(o.x=o.lastDraggedX,o.y=o.lastDraggedY):(o.vx=(o.originalX-o.x)/n.forceTicksPerFrame,o.y=Math.min(i.size-o.dy/2,Math.max(o.dy/2,o.y))),t=Math.max(t,Math.abs(o.vx),Math.abs(o.vy))}!i.interactionState.dragInProgress&&t<.1&&i.forceLayouts[e].alpha()>0&&i.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,i),function(t,e,r,i,a){window.requestAnimationFrame((function o(){var s;for(s=0;s<n.forceTicksPerFrame;s++)r.forceLayouts[i].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),M(t.filter(N(r)),e),r.forceLayouts[i].alpha()>0)window.requestAnimationFrame(o);else{var l=r.node.originalX;r.node.x0=l-r.visibleWidth/2,r.node.x1=l+r.visibleWidth/2,F(r,a)}}))}(t,e,i,o,a)}})).on(\"drag\",(function(r){if(\"fixed\"!==r.arrangement){var n=i.event.x,a=i.event.y;\"snap\"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=a-r.visibleHeight/2,r.node.y1=a+r.visibleHeight/2):(\"freeform\"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),a=Math.max(0,Math.min(r.size-r.visibleHeight/2,a)),r.node.y0=a-r.visibleHeight/2,r.node.y1=a+r.visibleHeight/2),B(r.node),\"snap\"!==r.arrangement&&(r.sankey.update(r.graph),M(t.filter(N(r)),e))}})).on(\"dragend\",(function(t){if(\"fixed\"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;\"snap\"!==t.arrangement&&F(t,a)}}));t.on(\".drag\",null).call(o)}function F(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}y.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function B(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function N(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,l,c){var u=!1;f.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){u=!0}));var h=t._fullLayout._dragCover,p=r.filter((function(t){return m(t).trace.visible})).map(x.bind(null,l)),v=e.selectAll(\".\"+n.cn.sankey).data(p,d);v.exit().remove(),v.enter().append(\"g\").classed(n.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").attr(\"transform\",E),v.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;f.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=i.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),v.transition().ease(n.ease).duration(n.duration).attr(\"transform\",E);var y=v.selectAll(\".\"+n.cn.sankeyLinks).data(g,d);y.enter().append(\"g\").classed(n.cn.sankeyLinks,!0).style(\"fill\",\"none\");var k=y.selectAll(\".\"+n.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(b.bind(null,t))}),d);k.enter().append(\"path\").classed(n.cn.sankeyLink,!0).call(D,v,c.linkEvents),k.style(\"stroke\",(function(t){return S(t)?o.tinyRGB(a(t.linkLineColor)):t.tinyColorHue})).style(\"stroke-opacity\",(function(t){return S(t)?o.opacity(t.linkLineColor):t.tinyColorAlpha})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})).style(\"stroke-width\",(function(t){return S(t)?t.linkLineWidth:1})).attr(\"d\",_()),k.style(\"opacity\",(function(){return t._context.staticPlot||u||h?1:0})).transition().ease(n.ease).duration(n.duration).style(\"opacity\",1),k.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var M=v.selectAll(\".\"+n.cn.sankeyNodeSet).data(g,d);M.enter().append(\"g\").classed(n.cn.sankeyNodeSet,!0),M.style(\"cursor\",(function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}}));var F=M.selectAll(\".\"+n.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return t-e})),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.map(w.bind(null,t))}),d);F.enter().append(\"g\").classed(n.cn.sankeyNode,!0).call(T).style(\"opacity\",(function(e){return!t._context.staticPlot&&!u||e.partOfGroup?0:1})),F.call(D,v,c.nodeEvents).call(R,k,c,t),F.transition().ease(n.ease).duration(n.duration).call(T).style(\"opacity\",(function(t){return t.partOfGroup?0:1})),F.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var B=F.selectAll(\".\"+n.cn.nodeRect).data(g);B.enter().append(\"rect\").classed(n.cn.nodeRect,!0).call(A),B.style(\"stroke-width\",(function(t){return t.nodeLineWidth})).style(\"stroke\",(function(t){return o.tinyRGB(a(t.nodeLineColor))})).style(\"stroke-opacity\",(function(t){return o.opacity(t.nodeLineColor)})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),B.transition().ease(n.ease).duration(n.duration).call(A);var N=F.selectAll(\".\"+n.cn.nodeCapture).data(g);N.enter().append(\"rect\").classed(n.cn.nodeCapture,!0).style(\"fill-opacity\",0),N.attr(\"x\",(function(t){return t.zoneX})).attr(\"y\",(function(t){return t.zoneY})).attr(\"width\",(function(t){return t.zoneWidth})).attr(\"height\",(function(t){return t.zoneHeight}));var 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),g=p(\"b\"),m=Math.min(d.length,g.length);if(m){e._length=m,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",m<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":778,\"../scatter/constants\":1191,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/line_shape_defaults\":1202,\"../scatter/marker_defaults\":1206,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"./attributes\":1221}],1224:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],1225:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],1226:[function(t,e,r){\"use 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n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r,o){var s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":665,\"../../plots/cartesian/axes\":828,\"../scatter/plot\":1208}],1229:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,g=f(\"geojson\");(\"string\"==typeof g&&\"\"!==g||n.isPlainObject(g))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var m=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(m.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":778,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/marker_defaults\":1206,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"./attributes\":1229}],1232:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],1233:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},{\"../../plots/cartesian/axes\":828}],1234:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/get_trace_color\"),o=t(\"../../lib\").fillText,s=t(\"./attributes\");e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=h.projection.isLonLatOverEdges,d=h.project;if(n.getClosest(l,(function(t){var n=t.lonlat;if(n[0]===i)return 1/0;if(p(n))return 1/0;var a=d(n),o=d([e,r]),s=Math.abs(a[0]-o[0]),l=Math.abs(a[1]-o[1]),c=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-c,1-3/c)}),t),!1!==t.index){var g=l[t.index],m=g.lonlat,v=[u.c2p(m),f.c2p(m)],y=g.mrc||1;t.x0=v[0]-y,t.x1=v[0]+y,t.y0=v[1]-y,t.y1=v[1]+y,t.loc=g.loc,t.lon=m[0],t.lat=m[1];var x={};x[c.geo]={_subplot:h};var b=c._module.formatLabels(g,c,x);return 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T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),g=Math.max(g,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,g]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,g=m[p],v=m[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[g*y/h,x/h]}}return o}}},{\"../../components/drawing\":665,\"../../components/fx/helpers\":679,\"../../constants/interactions\":752,\"../../lib\":778,\"../../lib/gl_format_color\":774,\"../../plots/cartesian/axis_ids\":831,\"../../registry\":911,\"../scatter/make_bubble_size_func\":1204,\"../scatter/subtypes\":1212,\"./constants\":1241,\"./helpers\":1246,\"color-normalize\":125,\"fast-isnumeric\":241,\"svg-path-sdf\":574}],1243:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,g){function m(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,g,m);if(x){u(t,e,g,m);var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";m(\"text\"),m(\"hovertext\"),m(\"hovertemplate\"),m(\"mode\",b),l.hasLines(e)&&(m(\"connectgaps\"),h(t,e,r,g,m),m(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,g,m),m(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(m(\"texttemplate\"),d(t,e,g,m));var _=(e.line||{}).color,w=(e.marker||{}).color;m(\"fill\"),\"none\"!==e.fill&&p(t,e,r,m);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,m)}else e.visible=!1}},{\"../../lib\":778,\"../../registry\":911,\"../scatter/constants\":1191,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/marker_defaults\":1206,\"../scatter/period_defaults\":1207,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"../scatter/xy_defaults\":1214,\"./attributes\":1239,\"./helpers\":1246}],1244:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},g=c.textUnselectedOptions[u]||{},m=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=g.color,x=p.color,b=Array.isArray(x);for(m.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],m.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;m.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(m)}}},{\"../../components/color\":643,\"../../constants/interactions\":752,\"../../lib\":778}],1245:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":1196}],1246:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":1241}],1247:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var g=d&&d.line;g&&(h.mlc=Array.isArray(g.color)?g.color[f]:g.color,h.mlw=i.isArrayOrTypedArray(g.width)?g.width[f]:g.width);var m=d&&d.gradient;m&&\"none\"!==m.type&&(h.mgt=Array.isArray(m.type)?m.type[f]:m.type,h.mgc=Array.isArray(m.color)?m.color[f]:m.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,M=o._origY,A=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:M?M[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?A.text=h.htx:h.tx?A.text=h.tx:o.text&&(A.text=o.text),i.fillText(h,o,A),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,A),A}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d=t.cd,g=d[0].t,m=d[0].trace,v=t.xa,y=t.ya,x=g.x,b=g.y,_=v.c2p(e),w=y.c2p(r),T=t.distance;if(g.tree){var k=v.p2c(_-T),M=v.p2c(_+T),A=y.p2c(w-T),S=y.p2c(w+T);i=\"x\"===n?g.tree.range(Math.min(k,M),Math.min(y._rl[0],y._rl[1]),Math.max(k,M),Math.max(y._rl[0],y._rl[1])):g.tree.range(Math.min(k,M),Math.min(A,S),Math.max(k,M),Math.max(A,S))}else i=g.ids;var E=T;if(\"x\"===n)for(c=0;c<i.length;c++)s=x[i[c]],(u=Math.abs(v.c2p(s)-_))<E&&(E=u,f=y.c2p(b[i[c]])-w,p=Math.sqrt(u*u+f*f),a=i[c]);else for(c=i.length-1;c>-1;c--)s=x[i[c]],l=b[i[c]],u=v.c2p(s)-_,f=y.c2p(l)-w,(h=Math.sqrt(u*u+f*f))<E&&(E=p=h,a=i[c]);return t.index=a,t.distance=E,t.dxy=p,void 0===a?[t]:[o(t,x,b,m)]},calcHover:o}},{\"../../lib\":778,\"../../registry\":911,\"../scatter/get_trace_color\":1197}],1248:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":841,\"../scatter/cross_trace_defaults\":1193,\"../scatter/marker_colorbar\":1205,\"./attributes\":1239,\"./calc\":1240,\"./defaults\":1243,\"./format_labels\":1245,\"./hover\":1247,\"./plot\":1249,\"./select\":1251}],1249:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r){var n=t._size,i=t.width,a=t.height;return[n.l+e.domain[0]*n.w,n.b+r.domain[0]*n.h,i-n.r-(1-e.domain[1])*n.w,a-n.t-(1-r.domain[1])*n.h]}e.exports=function(t,e,r){if(r.length){var d,g,m=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=m._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_,{constPointSize:!0})),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var g=s._nexttrace;if(g){var m=v.lineOptions[e+1];if(m){var y=m.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=g.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var M=m.dragmode,A=l(M),S=m.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],C=E.trace,L=E.t,I=L.index,P=C._length,z=L.x,O=L.y;if(C.selectedpoints||A||S){if(A||(A=!0),C.selectedpoints){var D=v.selectBatch[I]=s.selIndices2selPoints(C),R={};for(g=0;g<D.length;g++)R[D[g]]=1;var F=[];for(g=0;g<P;g++)R[g]||F.push(g);v.unselectBatch[I]=F}var B=L.xpx=new Array(P),N=L.ypx=new Array(P);for(g=0;g<P;g++)B[g]=y.c2p(z[g]),N[g]=x.c2p(O[g])}else L.xpx=L.ypx=null}if(A){if(v.select2d||(v.select2d=n(m._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(m,y,x),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":661,\"../../lib\":778,\"../../lib/prepare_regl\":791,\"../scatter/link_traces\":1203,\"../scatter/subtypes\":1212,\"./edit_style\":1244,\"gl-text\":352,\"regl-error2d\":534,\"regl-line2d\":535,\"regl-scatter2d\":537}],1250:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else 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n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/mapbox/layout_attributes\":887,\"../../plots/template_attributes\":906,\"../scatter/attributes\":1187,\"../scattergeo/attributes\":1229}],1253:[function(t,e,r){\"use 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e,r,i,a,s,l,c,u,f,h,p,d,g=t._x,m=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,M=\"\";for(y&&(c=g[0],f=m[0],p=v[0]),y>1&&(u=g[y-1],h=m[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,g[e]),b=Math.min(b,g[e]),_=Math.max(_,m[e]),w=Math.min(w,m[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||g[e]===c||(a=!0,M+=\"x\"),s||m[e]===f||(s=!0,M+=\"y\"),l||v[e]===p||(l=!0,M+=\"z\");a||(M+=\"x\"),s||(M+=\"y\"),l||(M+=\"z\");var A=o(t._x),S=o(t._y),E=o(t._z);M=(M=(M=M.replace(\"x\",(c>u?\"-\":\"+\")+\"x\")).replace(\"y\",(f>h?\"-\":\"+\")+\"y\")).replace(\"z\",(p>d?\"-\":\"+\")+\"z\");var C=function(){y=0,A=[],S=[],E=[]};(!y||y<A.length*S.length*E.length)&&C();var L=function(t){return\"x\"===t?g:\"y\"===t?m:v},I=function(t){return\"x\"===t?A:\"y\"===t?S:E},P=function(t){return t[y-1]<t[0]?-1:1},z=L(M[1]),O=L(M[3]),D=L(M[5]),R=I(M[1]).length,F=I(M[3]).length,B=I(M[5]).length,N=!1,j=function(t,e,r){return 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M={};u.label&&(M.label=u.label),u.hasOwnProperty(\"v\")&&(M.value=u.v,M.valueLabel=x.formatValue(u.v,l)),M.currentPath=x.getPath(t.data),h||(M.percentParent=d/x.getValue(p),M.percentParentLabel=x.formatPercent(M.percentParent,l),M.parent=x.getPtLabel(p)),M.percentEntry=d/x.getValue(e),M.percentEntryLabel=x.formatPercent(M.percentEntry,l),M.entry=x.getPtLabel(e),M.percentRoot=d/x.getValue(f),M.percentRootLabel=x.formatPercent(M.percentRoot,l),M.root=x.getPtLabel(f),u.hasOwnProperty(\"color\")&&(M.color=u.color);var A=o.castOption(r,u.i,\"text\");return(o.isValidTextValue(A)||\"\"===A)&&(M.text=A),M.customdata=o.castOption(r,u.i,\"customdata\"),o.texttemplateString(k,M,i._d3locale,M,r._meta||{})}},{\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../bar/style\":935,\"../bar/uniform_text\":937,\"../pie/helpers\":1166,\"../pie/plot\":1170,\"./constants\":1302,\"./fx\":1304,\"./helpers\":1305,\"./style\":1310,d3:169,\"d3-hierarchy\":161}],1310:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":643,\"../../lib\":778,\"../bar/uniform_text\":937,d3:169}],1311:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;function c(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var u=e.exports=l(s({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:a(),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:c(),y:c(),z:c()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:s({},i.zauto,{}),zmin:s({},i.zmin,{}),zmax:s({},i.zmax,{})},hoverinfo:s({},o.hoverinfo),showlegend:s({},o.showlegend,{dflt:!1})}),\"calc\",\"nested\");u.x.editType=u.y.editType=u.z.editType=\"calc+clearAxisTypes\",u.transforms=void 0},{\"../../components/color\":643,\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/template_attributes\":906}],1312:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":651}],1313:[function(t,e,r){\"use strict\";var n=t(\"gl-surface3d\"),i=t(\"ndarray\"),a=t(\"ndarray-linear-interpolate\").d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function g(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function m(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(g(t,n))}return e}function v(t){for(var e=m(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=m(t),n=m(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),g=l.dataScale,m=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<m;r++)x[e][r]=[];for(r=0;r<m;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<m;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=g[e];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v])];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(m*v),[m,v]);for(r=0;r<m;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=g[2],w.intensityBounds[1]*=g[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var M=[!0,!0,!0],A=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[A[e]];M[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=M,p.enableHighlight=M,p.snapToData=!0,\"lighting\"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),\"lightposition\"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{\"../../components/colorscale\":655,\"../../lib\":778,\"../../lib/gl_format_color\":774,\"../../lib/str2rgbarray\":802,\"../heatmap/find_empties\":1071,\"../heatmap/interp2d\":1074,\"gl-surface3d\":351,ndarray:495,\"ndarray-linear-interpolate\":489}],1314:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var 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d=i.ensureSingle(h,\"g\",\"slicetext\"),T=i.ensureSingle(d,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),E=i.ensureUniformFontSize(t,u.determineTextFont(C,s,S.font,{onPathbar:!0}));T.text(s._text||\" \").classed(\"slicetext\",!0).attr(\"text-anchor\",\"start\").call(a.font,E).call(o.convertToTspans,t),s.textBB=a.bBox(T.node()),s.transform=b(s,{fontSize:E.size,onPathbar:!0}),s.transform.fontSize=E.size,w?T.transition().attrTween(\"transform\",(function(t){var e=M(t,!0,A,[g,m]);return function(t){return _(e(t))}})):T.attr(\"transform\",_(s))}))}},{\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../sunburst/fx\":1304,\"../sunburst/helpers\":1305,\"./constants\":1328,\"./partition\":1335,\"./style\":1337,d3:169}],1331:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../components/drawing\"),o=t(\"../../lib/svg_text_utils\"),s=t(\"./partition\"),l=t(\"./style\").styleOne,c=t(\"./constants\"),u=t(\"../sunburst/helpers\"),f=t(\"../sunburst/fx\"),h=t(\"../sunburst/plot\").formatSliceLabel;e.exports=function(t,e,r,p,d){var 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strict\";e.exports={attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../box/defaults\").crossTraceDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),crossTraceCalc:t(\"./cross_trace_calc\"),plot:t(\"./plot\"),style:t(\"./style\"),styleOnSelect:t(\"../scatter/style\").styleOnSelect,hoverPoints:t(\"./hover\"),selectPoints:t(\"../box/select\"),moduleType:\"trace\",name:\"violin\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"violinLayout\",\"zoomScale\"],meta:{}}},{\"../../plots/cartesian\":841,\"../box/defaults\":949,\"../box/select\":956,\"../scatter/style\":1211,\"./attributes\":1338,\"./calc\":1339,\"./cross_trace_calc\":1340,\"./defaults\":1341,\"./hover\":1343,\"./layout_attributes\":1345,\"./layout_defaults\":1346,\"./plot\":1347,\"./style\":1348}],1345:[function(t,e,r){\"use strict\";var 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f={\"stroke-width\":u+\"px\",\"stroke-dasharray\":2*u+\"px,\"+u+\"px\"};o.selectAll(\"path.mean\").style(f).call(i.stroke,c.color),o.selectAll(\"path.meanline\").style(f).call(i.stroke,c.color),a(o,r,t)}))}},{\"../../components/color\":643,\"../scatter/style\":1211,d3:169}],1349:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../isosurface/attributes\"),a=t(\"../surface/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,c=e.exports=l(s({x:i.x,y:i.y,z:i.z,value:i.value,isomin:i.isomin,isomax:i.isomax,surface:i.surface,spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:1}},slices:i.slices,caps:i.caps,text:i.text,hovertext:i.hovertext,hovertemplate:i.hovertemplate},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{colorbar:i.colorbar,opacity:i.opacity,opacityscale:a.opacityscale,lightposition:i.lightposition,lighting:i.lighting,flatshading:i.flatshading,contour:i.contour,hoverinfo:s({},o.hoverinfo),showlegend:s({},o.showlegend,{dflt:!1})}),\"calc\",\"nested\");c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 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+       "                            \n",
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+       "\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.choropleth(geoplot_2019,                        \n",
+    "                     locations=\"Country_code\",           \n",
+    "                     color=\"Respondents\",               \n",
+    "                     hover_name=\"Country\",              \n",
+    "                     projection=\"natural earth\",        \n",
+    "                     color_continuous_scale = 'Peach',  \n",
+    "                     range_color=[0,10000]     \n",
+    "                     )        \n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysing salary distribution among top ten countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 340,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (20, 10))\n",
+    "\n",
+    "countries = cleaned_df_2019['Country'].value_counts().sort_values(ascending = False)[:10].index.tolist()\n",
+    "\n",
+    "for i, country in enumerate(countries):\n",
+    "    plt.subplot(4, 3, i + 1)\n",
+    "    temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['Country'] == country, 'SalaryUSD']\n",
+    "\n",
+    "    ax = temp_salaries.plot(kind = 'kde')\n",
+    "    ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n",
+    "    ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n",
+    "    ax.set_xlabel('Annual Salary in USD')\n",
+    "    ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n",
+    "    ax.set_title('Annual Salary Distribution in {}'.format(country))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overall, the country which has the highest mean annual salary is the United States of America(240,000) Dollars. The second highest country which provides the highest mean salary is Australia(164,926) Dollars. Though India has a higher number of respondents, it has the lowest mean salary of $25,213.We can understand that the mean salary of a developed country is much higher than that of a developing country."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysing impact of education level on salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 341,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#removing outliers from Associate group\n",
+    "salary_edu = cleaned_df_2019.groupby(['EdLevel'])\n",
+    "associate_mean = salary_edu.get_group('Associate').mean()['SalaryUSD']\n",
+    "filt = (salary_edu.get_group('Associate')['SalaryUSD'] > associate_mean).to_frame()\n",
+    "filt = filt[filt['SalaryUSD'] == False]\n",
+    "cleaned_df_2019.drop(index=filt.index, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (20, 10))\n",
+    "\n",
+    "education_2019 = cleaned_df_2019['EdLevel'].value_counts().sort_values(ascending = False).index.tolist()\n",
+    "\n",
+    "for i, edu in enumerate(education_2019):\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['EdLevel'] == edu, 'SalaryUSD']\n",
+    "\n",
+    "    ax = temp_salaries.plot(kind = 'kde')\n",
+    "    ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n",
+    "    ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n",
+    "    ax.set_xlabel('Annual Salary in USD')\n",
+    "    ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n",
+    "    ax.set_title('Annual Salary Distribution in {}'.format(edu))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, the respondents who have done Doctorate have the highest mean salary among all other education levels. Secondly, the respondents who have done Bachelors degree have more salary than that of Masters degree holders. This may be due to years of professional coding experience and due to the higher number of respondents in that category than that of Masters degree(No of respondents in Bachelor degree is 35659 and number of respondents in masters degree is 16940)\n",
+    "\n",
+    "The most interesting is that the respondents who do not have any degree have a mean salary of $90k. This shows the improvement in online learning and advancement of technology that is shifting the company from relying on University degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distribution of respondents based on age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col =['Age', 'Country']\n",
+    "df_2020= cleaned_df_2019[col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 344,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df_2020['Age_range'] = 0\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=15) & (df_2020['Age']<=19), '15 - 19 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=20) & (df_2020['Age']<=24), '20 - 24 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=25) & (df_2020['Age']<=29), '25 - 29 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=30) & (df_2020['Age']<=34), '30 - 34 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=35) & (df_2020['Age']<=39), '35 - 39 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=40) & (df_2020['Age']<=45), '40 - 45 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=46), '46 and above years', df_2020.Age_range)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2020_age = df_2020.groupby(['Age_range']).size().reset_index(name='Count')\n",
+    "df_2020_age.sort_values(by=['Count'], ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (10, 6))\n",
+    "sns.set_style('white')\n",
+    "sns.set_context('paper', font_scale=1.5)\n",
+    "sns.barplot(x=\"Count\",  y=\"Age_range\", palette='inferno', data=df_2020_age).set(xlabel=\"Age\", ylabel = \"Count\")\n",
+    "plt.title('Distribution of respondents based on age')\n",
+    "\n",
+    "for y, x in enumerate(df_2020_age['Count']):\n",
+    "    label = \"{:,}\".format(int(x))\n",
+    "    plt.annotate(label, xy=(x, y), va='center')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Impact on the increase in popularity of a language in the current-year due to developer’s interest in the previous year.(Based on 2019 and 2020 dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#seperate language for getting individual one\n",
+    "cols = ['LanguageWorkedWith']\n",
+    "df_19 = survey_df_2019[cols]\n",
+    "df_20 = df2020[cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#splitting 'LanguageWorkedWith' and sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
+    "language_2019['Language'] = language_2019.index\n",
+    "language_2019.reset_index(drop=True, inplace=True)\n",
+    "language_2019 = language_2019[['Language', '2019']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 349,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
+    "language_2020['Language'] = language_2020.index\n",
+    "language_2020.reset_index(drop=True, inplace=True)\n",
+    "language_2020 = language_2020[['Language', '2020']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "language_all= pd.merge(language_2019, language_2020,on = ['Language'], how = 'outer')\n",
+    "language_all.fillna(0, inplace=True)\n",
+    "language_all['2019'] = language_all['2019']. astype(int)\n",
+    "language_all['2020'] = language_all['2020']. astype(int)\n",
+    "language_all.set_index('Language', inplace=True)\n",
+    "#language_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2019</th>\n",
+       "      <th>2020</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Language</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>JavaScript</th>\n",
+       "      <td>0.136468</td>\n",
+       "      <td>0.137808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HTML/CSS</th>\n",
+       "      <td>0.126595</td>\n",
+       "      <td>0.126495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SQL</th>\n",
+       "      <td>0.109882</td>\n",
+       "      <td>0.112110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Python</th>\n",
+       "      <td>0.081963</td>\n",
+       "      <td>0.086418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Java</th>\n",
+       "      <td>0.080446</td>\n",
+       "      <td>0.078374</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                2019      2020\n",
+       "Language                      \n",
+       "JavaScript  0.136468  0.137808\n",
+       "HTML/CSS    0.126595  0.126495\n",
+       "SQL         0.109882  0.112110\n",
+       "Python      0.081963  0.086418\n",
+       "Java        0.080446  0.078374"
+      ]
+     },
+     "execution_count": 351,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "language19_20=(language_all/language_all.sum())\n",
+    "language19_20.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 352,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "language19_20.plot(kind='bar', figsize=(12,8))\n",
+    "plt.title('Programming Language worked by Respondents in 2019 and 2020', fontsize = 18)\n",
+    "plt.xlabel('Languages', fontsize = 14)\n",
+    "plt.ylabel('Percentages', fontsize = 14)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The most language that worked in 2019 and 2020 is JavaScript.In 2020, people worked slightly in javascript compare to 2019. The 2nd highest working language is HTML/CSS. For HTML/CSS the percentage is slightly low in 2020. There are some language people worked in only one year. Elixir, Clojure, F#, Web assembly are those languages that people used in 2019. Respondent started to use Perl, Haskell, Julia in 2020 on a small scale."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Programming language desired to work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#language desire net year\n",
+    "cols_1 = ['LanguageDesireNextYear']\n",
+    "df_19 = survey_df_2019[cols_1]\n",
+    "df_20 = df2020[cols_1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "languagedesire_2019= df_19['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
+    "languagedesire_2019['Language'] = languagedesire_2019.index\n",
+    "languagedesire_2019.reset_index(drop=True, inplace=True)\n",
+    "languagedesire_2019 = languagedesire_2019[['Language', '2019']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "languagedesire_2020= df_20['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
+    "languagedesire_2020['Language'] = languagedesire_2020.index\n",
+    "languagedesire_2020.reset_index(drop=True, inplace=True)\n",
+    "languagedesire_2020= languagedesire_2020[['Language','2020']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 356,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "languagedesire_all = pd.merge(languagedesire_2019, languagedesire_2020,on = ['Language'], how = 'outer')\n",
+    "languagedesire_all.fillna(0, inplace=True)\n",
+    "languagedesire_all['2019'] = languagedesire_all['2019']. astype(int)\n",
+    "languagedesire_all['2020'] = languagedesire_all['2020']. astype(int)\n",
+    "languagedesire_all.set_index('Language', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2019</th>\n",
+       "      <th>2020</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Language</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>JavaScript</th>\n",
+       "      <td>0.113559</td>\n",
+       "      <td>0.111381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Python</th>\n",
+       "      <td>0.099827</td>\n",
+       "      <td>0.110728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HTML/CSS</th>\n",
+       "      <td>0.092490</td>\n",
+       "      <td>0.088983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SQL</th>\n",
+       "      <td>0.085326</td>\n",
+       "      <td>0.085879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TypeScript</th>\n",
+       "      <td>0.062380</td>\n",
+       "      <td>0.076744</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                2019      2020\n",
+       "Language                      \n",
+       "JavaScript  0.113559  0.111381\n",
+       "Python      0.099827  0.110728\n",
+       "HTML/CSS    0.092490  0.088983\n",
+       "SQL         0.085326  0.085879\n",
+       "TypeScript  0.062380  0.076744"
+      ]
+     },
+     "execution_count": 357,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "languagedesire19_20=(languagedesire_all/languagedesire_all.sum())\n",
+    "languagedesire19_20.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 358,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "languagedesire19_20.plot(kind='bar', figsize=(12,8))\n",
+    "plt.title('Programming Language desire to work', fontsize = 18)\n",
+    "plt.xlabel('Languages', fontsize = 14)\n",
+    "plt.ylabel('Percentages', fontsize = 14)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In 2019, respondents said that they wanted to work in javascript is around more than 10 % and the fewer respond have a desire to work on VBA next year. People started to work in Haskell, Julia, and pearl in 2019 though the amount was less around 5% of people have the desire to work in those languages in 2021. Here, phyton is the 2nd one in which people have the desire to work in both 2019 and 2020."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distribution of surveyors based on their developer role."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col = ['DevType']\n",
+    "dev_18=df[col]\n",
+    "dev_19 = survey_df_2019[col]\n",
+    "dev_20= df2020[col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "dev_2018= dev_18['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n",
+    "dev_2018['Developer'] = dev_2018.index\n",
+    "dev_2018.reset_index(drop=True, inplace=True)\n",
+    "dev_2018 = dev_2018[['Developer', '2018']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 361,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev_2019= dev_19['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
+    "dev_2019['Developer'] = dev_2019.index\n",
+    "dev_2019.reset_index(drop=True, inplace=True)\n",
+    "dev_2019 = dev_2019[['Developer', '2019']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev_2020= dev_20['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
+    "dev_2020['Developer'] = dev_2020.index\n",
+    "dev_2020.reset_index(drop=True, inplace=True)\n",
+    "dev_2020 = dev_2020[['Developer', '2020']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2018</th>\n",
+       "      <th>2019</th>\n",
+       "      <th>2020</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Developer, back-end</th>\n",
+       "      <td>27228</td>\n",
+       "      <td>37700</td>\n",
+       "      <td>21683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, full-stack</th>\n",
+       "      <td>27125</td>\n",
+       "      <td>39479</td>\n",
+       "      <td>21683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, front-end</th>\n",
+       "      <td>18296</td>\n",
+       "      <td>24527</td>\n",
+       "      <td>14356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, desktop or enterprise applications</th>\n",
+       "      <td>11784</td>\n",
+       "      <td>15748</td>\n",
+       "      <td>9172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, mobile</th>\n",
+       "      <td>9482</td>\n",
+       "      <td>13298</td>\n",
+       "      <td>7254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DevOps specialist</th>\n",
+       "      <td>5969</td>\n",
+       "      <td>8463</td>\n",
+       "      <td>4895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Database administrator</th>\n",
+       "      <td>5722</td>\n",
+       "      <td>8616</td>\n",
+       "      <td>4452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Designer</th>\n",
+       "      <td>5321</td>\n",
+       "      <td>7883</td>\n",
+       "      <td>3791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>System administrator</th>\n",
+       "      <td>5242</td>\n",
+       "      <td>8094</td>\n",
+       "      <td>4094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, embedded applications or devices</th>\n",
+       "      <td>4750</td>\n",
+       "      <td>6494</td>\n",
+       "      <td>3536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Data or business analyst</th>\n",
+       "      <td>4024</td>\n",
+       "      <td>5526</td>\n",
+       "      <td>2939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Data scientist or machine learning specialist</th>\n",
+       "      <td>3988</td>\n",
+       "      <td>5788</td>\n",
+       "      <td>2939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, QA or test</th>\n",
+       "      <td>3947</td>\n",
+       "      <td>5927</td>\n",
+       "      <td>3161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Engineer, data</th>\n",
+       "      <td>3738</td>\n",
+       "      <td>5240</td>\n",
+       "      <td>2776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Academic researcher</th>\n",
+       "      <td>3552</td>\n",
+       "      <td>5070</td>\n",
+       "      <td>2610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Educator</th>\n",
+       "      <td>2928</td>\n",
+       "      <td>3930</td>\n",
+       "      <td>2213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, game or graphics</th>\n",
+       "      <td>2789</td>\n",
+       "      <td>3902</td>\n",
+       "      <td>2118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Engineering manager</th>\n",
+       "      <td>2724</td>\n",
+       "      <td>3803</td>\n",
+       "      <td>1979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Product manager</th>\n",
+       "      <td>2497</td>\n",
+       "      <td>3630</td>\n",
+       "      <td>1891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scientist</th>\n",
+       "      <td>2086</td>\n",
+       "      <td>3149</td>\n",
+       "      <td>1573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Engineer, site reliability</th>\n",
+       "      <td>1940</td>\n",
+       "      <td>2684</td>\n",
+       "      <td>1491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Senior executive/VP</th>\n",
+       "      <td>1320</td>\n",
+       "      <td>1795</td>\n",
+       "      <td>850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Marketing or sales professional</th>\n",
+       "      <td>642</td>\n",
+       "      <td>789</td>\n",
+       "      <td>401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Student</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10113</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                2018   2019   2020\n",
+       "Developer                                                         \n",
+       "Developer, back-end                            27228  37700  21683\n",
+       "Developer, full-stack                          27125  39479  21683\n",
+       "Developer, front-end                           18296  24527  14356\n",
+       "Developer, desktop or enterprise applications  11784  15748   9172\n",
+       "Developer, mobile                               9482  13298   7254\n",
+       "DevOps specialist                               5969   8463   4895\n",
+       "Database administrator                          5722   8616   4452\n",
+       "Designer                                        5321   7883   3791\n",
+       "System administrator                            5242   8094   4094\n",
+       "Developer, embedded applications or devices     4750   6494   3536\n",
+       "Data or business analyst                        4024   5526   2939\n",
+       "Data scientist or machine learning specialist   3988   5788   2939\n",
+       "Developer, QA or test                           3947   5927   3161\n",
+       "Engineer, data                                  3738   5240   2776\n",
+       "Academic researcher                             3552   5070   2610\n",
+       "Educator                                        2928   3930   2213\n",
+       "Developer, game or graphics                     2789   3902   2118\n",
+       "Engineering manager                             2724   3803   1979\n",
+       "Product manager                                 2497   3630   1891\n",
+       "Scientist                                       2086   3149   1573\n",
+       "Engineer, site reliability                      1940   2684   1491\n",
+       "Senior executive/VP                             1320   1795    850\n",
+       "Marketing or sales professional                  642    789    401\n",
+       "Student                                            0  10113      0"
+      ]
+     },
+     "execution_count": 363,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df18_19 = pd.merge(dev_2018, dev_2019,on = ['Developer'], how = 'outer')\n",
+    "devtype_all = pd.merge(df18_19,dev_2020, on=[\"Developer\"], how=\"outer\")\n",
+    "devtype_all.fillna(0, inplace=True)\n",
+    "devtype_all['2018'] = devtype_all['2018']. astype(int)\n",
+    "devtype_all['2019'] = devtype_all['2019']. astype(int)\n",
+    "devtype_all['2020'] =devtype_all['2020'].astype(int)\n",
+    "devtype_all.set_index('Developer', inplace=True)\n",
+    "devtype_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 364,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2018</th>\n",
+       "      <th>2019</th>\n",
+       "      <th>2020</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>6545.583333</td>\n",
+       "      <td>9652.000000</td>\n",
+       "      <td>5077.375000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>7441.476051</td>\n",
+       "      <td>10257.524072</td>\n",
+       "      <td>5956.696034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>789.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2667.250000</td>\n",
+       "      <td>3877.250000</td>\n",
+       "      <td>1957.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3967.500000</td>\n",
+       "      <td>5857.500000</td>\n",
+       "      <td>2939.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5783.750000</td>\n",
+       "      <td>8990.250000</td>\n",
+       "      <td>4562.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>27228.000000</td>\n",
+       "      <td>39479.000000</td>\n",
+       "      <td>21683.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               2018          2019          2020\n",
+       "count     24.000000     24.000000     24.000000\n",
+       "mean    6545.583333   9652.000000   5077.375000\n",
+       "std     7441.476051  10257.524072   5956.696034\n",
+       "min        0.000000    789.000000      0.000000\n",
+       "25%     2667.250000   3877.250000   1957.000000\n",
+       "50%     3967.500000   5857.500000   2939.000000\n",
+       "75%     5783.750000   8990.250000   4562.750000\n",
+       "max    27228.000000  39479.000000  21683.000000"
+      ]
+     },
+     "execution_count": 364,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "devtype_all.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 365,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2018</th>\n",
+       "      <th>2019</th>\n",
+       "      <th>2020</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Developer, back-end</th>\n",
+       "      <td>0.173323</td>\n",
+       "      <td>0.162747</td>\n",
+       "      <td>0.177938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, full-stack</th>\n",
+       "      <td>0.172667</td>\n",
+       "      <td>0.170427</td>\n",
+       "      <td>0.177938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, front-end</th>\n",
+       "      <td>0.116465</td>\n",
+       "      <td>0.105880</td>\n",
+       "      <td>0.117810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, desktop or enterprise applications</th>\n",
+       "      <td>0.075012</td>\n",
+       "      <td>0.067982</td>\n",
+       "      <td>0.075269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Developer, mobile</th>\n",
+       "      <td>0.060359</td>\n",
+       "      <td>0.057406</td>\n",
+       "      <td>0.059529</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   2018      2019      2020\n",
+       "Developer                                                                  \n",
+       "Developer, back-end                            0.173323  0.162747  0.177938\n",
+       "Developer, full-stack                          0.172667  0.170427  0.177938\n",
+       "Developer, front-end                           0.116465  0.105880  0.117810\n",
+       "Developer, desktop or enterprise applications  0.075012  0.067982  0.075269\n",
+       "Developer, mobile                              0.060359  0.057406  0.059529"
+      ]
+     },
+     "execution_count": 365,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt_all=devtype_all/devtype_all.sum()\n",
+    "dt_all.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(devtype_all/devtype_all.sum()).plot(kind='bar', figsize=(12,8))\n",
+    "plt.title('Developer Types pertcentages', fontsize = 18)\n",
+    "plt.xlabel('Developer Types', fontsize = 14)\n",
+    "plt.ylabel('Percentages', fontsize = 14)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In developer types, developers who are full stack and working backends are the most in the three years. There is a presence of student developers only in 2019 the percentage is 7.5%. Those who are working back end and full stack their percentages increased throughout the three years. For those who are working as marketing and sales professionals, their percentage is lowest compare to others."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Impact of education/experience/responsibilities on gender inequalities.(Based on 2019 dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['Gender','EdLevel', 'Dependents', 'SalaryUSD', 'YearsCodePro', 'Age', 'Country']\n",
+    "df2019 = survey_df_2019[cols]\n",
+    "df2019 = df2019[df2019.Gender != \"Non-binary\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2019['exp_range'] = 0\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro >= 0) & (df2019.YearsCodePro <= 5), '0 - 5 years', df2019.exp_range)\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 5) & (df2019.YearsCodePro <= 10), '6 - 10 years', df2019.exp_range)\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 10) & (df2019.YearsCodePro <= 15), '11 - 15 years', df2019.exp_range)\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 15) & (df2019.YearsCodePro <= 20), '16 - 20 years', df2019.exp_range)\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 20), 'more that 20 years', df2019.exp_range)\n",
+    "#df2019"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2,2, figsize = (12, 8))\n",
+    "g1 = sns.countplot('Gender', data=df2019, ax=axes[0][0]).set(title = 'A. Gender')\n",
+    "g2 = sns.countplot('Dependents', data=df2019, ax=axes[0][1]).set( title = 'B. Dependents')\n",
+    "g3 = sns.countplot('EdLevel', data=df2019, ax=axes[1][0]).set(title = 'C. EdLevel')\n",
+    "g4 = sns.countplot('exp_range', data=df2019, ax=axes[1][1]).set(title = 'D. exp_range')\n",
+    "\n",
+    "axes[1][0].tick_params(axis='x', rotation=45)\n",
+    "axes[1][1].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='EdLevel', ylabel='count'>"
+      ]
+     },
+     "execution_count": 370,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot('EdLevel', hue='Gender', data=df2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 371,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Dependents', ylabel='count'>"
+      ]
+     },
+     "execution_count": 371,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ccMMNWbZs2Xu+f5EKAAAAYA9x/PHHlx75e/DBB3PyySfnoIMOyp/8yZ/kkUceye9+97v87ne/yz777JONGzfmkksuSVlZWY444oiMGTMmd955Z+lap556avr06ZPy8vL0798/xx13XPr06ZOKioocd9xxefHFF5Mk48aNy5QpU9K+ffusXr06nTp1ytq1a0vXadOmTb7yla+kXbt2Of7449O1a9c8//zz7/nefXA6AAAAwB7i+OOPz3e/+92sWLEia9euzUc+8pEkyYknnphHHnkkTU1N+bM/+7O88sor6dq1a9q1a1eae9hhh2X16tWl1wcccEDp97Zt26ZTp04tXu/YsSNJsnbt2kydOjXPPvtsjjzyyHzwgx9Mc3Nz6dz99tsvZWVlpdft2rUrzX0vuZMKAAAAYA9RU1OTJUuWZOHChfn4xz+efff9/f1FJ510Uh5//PE89thjOemkk9KtW7esXbs2TU1NpbkrVqzIBz/4wdLrNm3a7NJ7jh8/PieeeGJ+8YtfZM6cOTn99NPf0z3tKpEKAAAAYA/RsWPH9OnTJ7feemtOPvnk0vF+/fpl06ZNeeSRR3LiiSfmmGOOycEHH5zp06dny5YteeGFF/IP//APOe200972e77yyispLy9P27Zt8/zzz+c73/lOtm7d+h7uatd43A8AAADY67TZtyyHjl/Yau/1dpxwwgmZMWNGTjzxxNKxtm3b5uMf/3iefPLJHHnkkUmSm266KZMnT84JJ5yQ9u3b5zOf+UwuvPDCt72+KVOmZOrUqZk5c2YOOuigjBw5Mtdee21WrFjxtq/1brRpfu1DhpRUV1dn8eLFRS8DAGCvt+6lzRk6eW7Ry4C9zv1fOzNdO3coehnA+8yb9RaP+wEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFC4Vo1UP/vZz3Laaaelf//+GTx4cO64444kSVNTU/r06ZP+/fuXfs4999zSvPvuuy9DhgxJv379cv7552f9+vWlsVWrVuWcc84pXfOhhx4qjTU3N2fmzJmpqanJgAEDMnXq1Gzbtq31NgwAAADALtm3td5o7dq1GTt2bK6//vqcdNJJefrppzNq1KgcffTR2bFjRzp37pyf//znr5u3dOnSXHnllfnud7+bo48+Otdee23Gjx+fW2+9NUly8cUXp1+/frnpppvyq1/9KmPGjMlPfvKTfOhDH8qcOXOyYMGCzJs3L+3bt8+YMWNy44035sILL2ytbQMAAACwC1rtTqqDDjoov/jFL3LSSSdlx44d2bhxY/bZZ5907NgxTz/9dHr16vWG8+66664MGjQoAwYMSFlZWSZMmJBFixblueeeS319fZ566qmMHTs27du3T01NTQYNGpS5c+cmSebPn5+zzz47hxxySLp06ZKLLrooc+bMaa0tAwAAALCLWvVxv/322y8NDQ05+uijc+655+YLX/hCjjzyyDzzzDPZsGFDTjvttHzsYx/L2LFjs2bNmiS/v5OqqqqqdI2Kiop069YtS5YsybJly9KtW7d06NChNF5ZWZnFixe/4dzKysqsXbs2GzdubJ0NAwAAALBLWv2D08vKyvLEE09k7ty5ufPOO/Mv//IvqaioyLHHHpsf/OAHuf/++1NeXp4xY8YkSTZv3pyKiooW1ygvL09DQ0NeffXVlJeXtxirqKhIY2PjG87dee7OcQAAAAD2DK32mVQ7tW3bNu3bt8/RRx+dz33uc1m4cGFuvPHGFudcfvnlqampyerVq1tEp50aGxvTsWPHNDc3Z8uWLS3GGhoaSndW/eHcnb+/9s4rAAAAAIrXandSPfbYYznjjDNaHGtqakqnTp0ya9asLFu2rHR869atSX5/11XPnj1TX19fGmtoaMjq1atTVVWVqqqqrFq1qkWIWr58eXr27Jkkr5u7fPnydO3aNZ06ddotewQAAADgnWm1SNW7d++sWbMm3/ve97J9+/YsWrQod955Z84888wsXrw406ZNy6ZNm7Jp06ZMmTIlJ598crp06ZJhw4Zl4cKFqaurS1NTU6ZPn57evXunR48eqaysTK9evTJz5sw0NTXl0UcfzcKFCzNs2LAkyfDhwzN79uysXLkyGzZsSG1tbUaMGNFaWwYAAABgF7Xa4377779/br755kyePDnXX399unXrlsmTJ2fgwIH5kz/5k0yePDlDhgzJ9u3bc9JJJ+Wqq65KklRXV2fq1KmZNGlS1qxZk759+2bWrFml69bW1mbixImpqanJgQcemClTpuSoo45KkowaNSrr16/PyJEj09jYmKFDh2bcuHGttWUAAAAAdlGb5ubm5qIXsSeqrq4ufUsgAADFWffS5gydPLfoZcBe5/6vnZmunX2eL/DeerPe0urf7gcAAAAAf0ikAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAArXqpHqZz/7WU477bT0798/gwcPzh133JEkaWpqysSJEzNw4MB89KMfzU033dRi3n333ZchQ4akX79+Of/887N+/frS2KpVq3LOOeeUrvnQQw+VxpqbmzNz5szU1NRkwIABmTp1arZt29Y6mwUAAABgl7VapFq7dm3Gjh2bSy65JE888URmzZqVqVOn5umnn05tbW3q6+uzYMGCzJ07N/Pmzcv8+fOTJEuXLs2VV16Zq6++OnV1denevXvGjx9fuu7FF1+c6urq1NXV5aqrrsr48eOzYsWKJMmcOXOyYMGCzJs3Lw888EB+85vf5MYbb2ytLQMAAACwi1otUh100EH5xS9+kZNOOik7duzIxo0bs88++6Rjx46ZN29eRo8enc6dO+fwww/PeeedV7rL6q677sqgQYMyYMCAlJWVZcKECVm0aFGee+651NfX56mnnsrYsWPTvn371NTUZNCgQZk7d26SZP78+Tn77LNzyCGHpEuXLrnooosyZ86c1toyAAAAALto39Z8s/322y8NDQ0ZMGBAtm3blvPPPz9dunTJunXr0rNnz9J5PXr0yJIlS5L8/k6qPn36lMYqKirSrVu3LFmyJG3btk23bt3SoUOH0nhlZWWefPLJ0tyqqqoWY2vXrs3GjRtzwAEH7ObdAgAAALCrWjVSJUlZWVmeeOKJLF68OH/913+d8vLyJCn9Z/L7ENXY2Jgk2bx5cyoqKlpco7y8PA0NDa+b91Zzd567cxwAAACAPUOrR6q2bdumffv2Ofroo/O5z30uTz31VJJky5YtpXMaGhpKd0e9Njrt1NjYmI4dO6a5ubnFvLeau/P31955BQAAAEDxWu0zqR577LGcccYZLY41NTWlU6dO6dq1a5YvX146Xl9fX3r8r2fPnqmvry+NNTQ0ZPXq1amqqkpVVVVWrVrVIkQtX778j85dvnx5unbtmk6dOu2WPQIAAADwzrRapOrdu3fWrFmT733ve9m+fXsWLVqUO++8M2eeeWaGDx+eG264IRs2bMiLL76YW265JcOHD0+SDBs2LAsXLkxdXV2ampoyffr09O7dOz169EhlZWV69eqVmTNnpqmpKY8++mgWLlyYYcOGJUmGDx+e2bNnZ+XKldmwYUNqa2szYsSI1toyAAAAALuo1R7323///XPzzTdn8uTJuf7669OtW7dMnjw5AwcOTN++fTNt2rQMGzYsO3bsyFlnnZVRo0YlSaqrqzN16tRMmjQpa9asSd++fTNr1qzSdWtrazNx4sTU1NTkwAMPzJQpU3LUUUclSUaNGpX169dn5MiRaWxszNChQzNu3LjW2jIAAAAAu6hNc3Nzc9GL2BNVV1dn8eLFRS8DAGCvt+6lzRk6eW7Ry4C9zv1fOzNdO/s8X+C99Wa9pdUe9wMAAACAP0akAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDh9i16ARTjpc1b0rR1e9HLgL1O+3b7pHOHsqKXAQAAsMcRqfZSTVu3Z+jkuUUvA/Y693/tzKKXAAAAsEfyuB8AAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwrVqpPr5z3+eM844I8cee2yGDBmSO+64I0nS1NSUPn36pH///qWfc889tzTvvvvuy5AhQ9KvX7+cf/75Wb9+fWls1apVOeecc9K/f/8MHjw4Dz30UGmsubk5M2fOTE1NTQYMGJCpU6dm27ZtrbdhAAAAAHZJq0Wq1atX56KLLsoFF1yQxx9/PNOnT8+MGTPy8MMPZ/HixencuXOeeOKJ0s/s2bOTJEuXLs2VV16Zq6++OnV1denevXvGjx9fuu7FF1+c6urq1NXV5aqrrsr48eOzYsWKJMmcOXOyYMGCzJs3Lw888EB+85vf5MYbb2ytLQMAAACwi1otUq1cuTLDhg3LkCFD0rZt2xxzzDEZOHBgFi1alKeffjq9evV6w3l33XVXBg0alAEDBqSsrCwTJkzIokWL8txzz6W+vj5PPfVUxo4dm/bt26empiaDBg3K3LlzkyTz58/P2WefnUMOOSRdunTJRRddlDlz5rTWlgEAAADYRa0WqQYMGJC///u/L73euHFjHn/88Xz4wx/OM888kw0bNuS0007Lxz72sYwdOzZr1qxJ8vs7qaqqqkrzKioq0q1btyxZsiTLli1Lt27d0qFDh9J4ZWVlFi9e/IZzKysrs3bt2mzcuHE37xYAAACAt6OQD05/+eWXc8EFF6Rv37459dRTU1FRkWOPPTY/+MEPcv/996e8vDxjxoxJkmzevDkVFRUt5peXl6ehoSGvvvpqysvLW4xVVFSksbHxDefuPHfnOAAAAAB7hn1b+w3r6+vzla98JT179sy3v/3ttG3bNldccUWLcy6//PLU1NRk9erVLaLTTo2NjenYsWOam5uzZcuWFmMNDQ2lO6v+cO7O31975xUAAAAAxWvVO6l++ctf5nOf+1wGDx6c6667LmVlZUmSWbNmZdmyZaXztm7dmiQpKytLz549U19fXxpraGjI6tWrU1VVlaqqqqxatapFiFq+fHl69uyZJK+bu3z58nTt2jWdOnXarfsEAAAA4O1ptUj1wgsv5G/+5m8yduzYTJgwIW3atCmNLV68ONOmTcumTZuyadOmTJkyJSeffHK6dOmSYcOGZeHChamrq0tTU1OmT5+e3r17p0ePHqmsrEyvXr0yc+bMNDU15dFHH83ChQszbNiwJMnw4cMze/bsrFy5Mhs2bEhtbW1GjBjRWlsGAAAAYBe12uN+t912W1599dXMmDEjM2bMKB3//Oc/nylTpmTy5MkZMmRItm/fnpNOOilXXXVVkqS6ujpTp07NpEmTsmbNmvTt2zezZs0qza+trc3EiRNTU1OTAw88MFOmTMlRRx2VJBk1alTWr1+fkSNHprGxMUOHDs24ceNaa8sAAAAA7KI2zc3NzUUvYk9UXV1d+pbA96N1L23O0Mlzi14G7HXu/9qZ6drZ5+IBvB3+boFi+LsF2B3erLcU8u1+AAAAAPBaIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwuxypTj311Pzv//7v646vWbMmNTU17+miAAAAANi77PtmgwsXLsyvfvWrJMnKlStTW1ub8vLyFue88MILu291AAAAAOwV3jRS9erVKz/4wQ/S3NycJHnmmWfSrl270nibNm3SoUOHTJs2bfeuEgAAAID3tTeNVIcddlhuvfXWJMkVV1yRK6+8Mvvtt1+rLAwAAACAvcebRqrXuvrqq7Nt27Y8//zz2bZtW+nuqp169uz5ni8OAAAAgL3DLkeqBx98MFdccUU2btz4ukDVpk2b/Pd///d7vjgAAAAA9g67HKlmzJiRP/uzP8uYMWM88gcAAADAe2qXI9ULL7yQmTNnpqqqaneuBwAAAIC9UNtdPfHoo4/OkiVLdudaAAAAANhL7fKdVEOGDMnXv/71PPbYY+nevXvatWvXYvwLX/jCe744AAAAAPYOuxypvv/972f//ffPQw899LqxNm3aiFQAAAAAvGO7HKn+/d//fXeuAwAAAIC92C5HqoaGhjcdr6ioeNeLAQAAAGDvtMuRqn///mnTps0fHf/v//7v92RBAAAAAOx9djlS3XrrrS1eb9++PS+88EJmz56dSy+99D1fGAAAAAB7j12OVAMHDnzdsZqamhxxxBG59tprM3jw4Pd0YQAAAADsPdq+2wscdNBBWbp06XuxFgAAAAD2Urt8J9VDDz30umMvv/xyZs+enV69er2niwIAAABg77LLkepv/uZvXnesXbt2Ofroo/P1r3/9PV0UAAAAAHuXXY5Uv/3tb3fnOgAAAADYi+1ypEqSLVu25K677srSpUuzY8eOVFVV5ZOf/GQ6deq0u9YHAAAAwF5glyNVfX19zjvvvGzevDkf/vCH09zcnHvuuSfXX399brvttnTv3n13rhMAAACA97FdjlRTpkxJ79698+1vfzsVFRVJkoaGhlx22WW5+uqrc+ONN+62RQIAAADw/tZ2V098/PHH87d/+7elQJUkFRUVufDCC/PYY4/tlsUBAAAAsHfY5UjVqVOnbNq06XXHN23alHbt2r2niwIAAABg77LLkWrIkCH5xje+kWeeeaZ07Omnn843v/nNDBkyZLcsDgAAAIC9wy5/JtX48eMzduzYfOYznyndObV169YMGTIkl19++W5bIAAAAADvf7scqSoqKnLMMcdk0KBB6dKlS8rKynLFFVfkqKOOSocOHXbnGgEAAAB4n9vlx/2mTZuWu+++Oz169MgnP/nJnHrqqfm7v/u7zJ8/P7NmzdqdawQAAADgfW6XI9VPf/rTfPvb387HP/7x0rHTTz8906ZNy49//ONdusbPf/7znHHGGTn22GMzZMiQ3HHHHUmSpqamTJw4MQMHDsxHP/rR3HTTTS3m3XfffRkyZEj69euX888/P+vXry+NrVq1Kuecc0769++fwYMH56GHHiqNNTc3Z+bMmampqcmAAQMyderUbNu2bVe3DAAAAEAr2eVI1dDQ8IaP9XXu3DmvvPLKW85fvXp1LrroolxwwQV5/PHHM3369MyYMSMPP/xwamtrU19fnwULFmTu3LmZN29e5s+fnyRZunRprrzyylx99dWpq6tL9+7dM378+NJ1L7744lRXV6euri5XXXVVxo8fnxUrViRJ5syZkwULFmTevHl54IEH8pvf/CY33njjrm4ZAAAAgFayy5HquOOOy7XXXpuNGzeWjm3atCkzZ87MwIED33L+ypUrM2zYsAwZMiRt27bNMccck4EDB2bRokWZN29eRo8enc6dO+fwww/PeeedV7rL6q677sqgQYMyYMCAlJWVZcKECVm0aFGee+651NfX56mnnsrYsWPTvn371NTUZNCgQZk7d26SZP78+Tn77LNzyCGHpEuXLrnooosyZ86ct/lPBAAAAMDutssfnP61r30t55xzTk488cQceuihadOmTVatWpUjjjgi3/nOd95y/oABAzJgwIDS640bN+bxxx/PiBEjsm7duvTs2bM01qNHjyxZsiTJ7++k6tOnT2msoqIi3bp1y5IlS9K2bdt069atxR1elZWVefLJJ0tzq6qqWoytXbs2GzduzAEHHLCrWwcAAABgN9vlSHXooYfm7rvvzn/+539m2bJladeuXY488sgcf/zxadt2l2/ISpK8/PLLueCCC9K3b9/86Z/+aZKkvLy8NF5RUZHGxsYkyebNm1NRUdFifnl5eRoaGl43763m7jx35zgAAAAAe4ZdjlRJ0r59+5x88sk5+eST3/Eb1tfX5ytf+Up69uyZb3/726VgtGXLltI5r/38q9dGp50aGxvTsWPHNDc3t5j3VnN3/v5Gn60FAAAAQHHe3i1Q79Ivf/nLfO5zn8vgwYNz3XXXpaysLJ07d07Xrl2zfPny0nn19fWlx/969uyZ+vr60lhDQ0NWr16dqqqqVFVVZdWqVS1C1PLly//o3OXLl6dr167p1KnT7t4qAAAAAG9Dq0WqF154IX/zN3+TsWPHZsKECWnTpk1pbPjw4bnhhhuyYcOGvPjii7nlllsyfPjwJMmwYcOycOHC1NXVpampKdOnT0/v3r3To0ePVFZWplevXpk5c2aampry6KOPZuHChRk2bFjpurNnz87KlSuzYcOG1NbWZsSIEa21ZQAAAAB20dt63O/duO222/Lqq69mxowZmTFjRun45z//+YwbNy7Tpk3LsGHDsmPHjpx11lkZNWpUkqS6ujpTp07NpEmTsmbNmvTt2zezZs0qza+trc3EiRNTU1OTAw88MFOmTMlRRx2VJBk1alTWr1+fkSNHprGxMUOHDs24ceNaa8sAAAAA7KI2zc3NzUUvYk9UXV2dxYsXF72M3WbdS5szdPLcopcBe537v3Zmunb2uXgAb4e/W6AY/m4Bdoc36y2t+plUAAAAAPBGRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABSukEj15JNPpqampvS6qakpffr0Sf/+/Us/5557bmn8vvvuy5AhQ9KvX7+cf/75Wb9+fWls1apVOeecc9K/f/8MHjw4Dz30UGmsubk5M2fOTE1NTQYMGJCpU6dm27ZtrbNJAAAAAHZZq0aq5ubm/Mu//EvOPffcbN26tXR88eLF6dy5c5544onSz+zZs5MkS5cuzZVXXpmrr746dXV16d69e8aPH1+ae/HFF6e6ujp1dXW56qqrMn78+KxYsSJJMmfOnCxYsCDz5s3LAw88kN/85je58cYbW3PLAAAAAOyCVo1U1113XW6//fZccMEFLY4//fTT6dWr1xvOueuuuzJo0KAMGDAgZWVlmTBhQhYtWpTnnnsu9fX1eeqppzJ27Ni0b98+NTU1GTRoUObOnZskmT9/fs4+++wccsgh6dKlSy666KLMmTNnt+8TAAAAgLenVSPVyJEj8+Mf/zh9+vRpcfyZZ57Jhg0bctppp+VjH/tYxo4dmzVr1iT5/Z1UVVVVpXMrKirSrVu3LFmyJMuWLUu3bt3SoUOH0nhlZWUWL178hnMrKyuzdu3abNy4cTfuEgAAAIC3q1Uj1cEHH/yGxysqKnLsscfmBz/4Qe6///6Ul5dnzJgxSZLNmzenoqKixfnl5eVpaGjIq6++mvLy8tddq7Gx8Q3n7jx35zgAAAAAe4Z9i15AklxxxRUtXl9++eWpqanJ6tWrW0SnnRobG9OxY8c0Nzdny5YtLcYaGhpKd1b94dydv7/2zisAAAAAilfIt/v9oVmzZmXZsmWl1zs/VL2srCw9e/ZMfX19aayhoSGrV69OVVVVqqqqsmrVqhYhavny5enZs2eSvG7u8uXL07Vr13Tq1Gl3bwkAAACAt2GPiFSLFy/OtGnTsmnTpmzatClTpkzJySefnC5dumTYsGFZuHBh6urq0tTUlOnTp6d3797p0aNHKisr06tXr8ycOTNNTU159NFHs3DhwgwbNixJMnz48MyePTsrV67Mhg0bUltbmxEjRhS8WwAAAAD+0B7xuN+UKVMyefLkDBkyJNu3b89JJ52Uq666KklSXV2dqVOnZtKkSVmzZk369u2bWbNmlebW1tZm4sSJqampyYEHHpgpU6bkqKOOSpKMGjUq69evz8iRI9PY2JihQ4dm3LhxhewRAAAAgD+uTXNzc3PRi9gTVVdXl74l8P1o3UubM3Ty3KKXAXud+792Zrp29rl4AG+Hv1ugGP5uAXaHN+ste8TjfgAAAADs3UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUrpBI9eSTT6ampqb0uqmpKRMnTszAgQPz0Y9+NDfddFOL8++7774MGTIk/fr1y/nnn5/169eXxlatWpVzzjkn/fv3z+DBg/PQQw+VxpqbmzNz5szU1NRkwIABmTp1arZt27b7NwgAAADA29Kqkaq5uTn/8i//knPPPTdbt24tHa+trU19fX0WLFiQuXPnZt68eZk/f36SZOnSpbnyyitz9dVXp66uLt27d8/48eNLcy+++OJUV1enrq4uV111VcaPH58VK1YkSebMmZMFCxZk3rx5eeCBB/Kb3/wmN954Y2tuGQAAAIBd0KqR6rrrrsvtt9+eCy64oMXxefPmZfTo0encuXMOP/zwnHfeebnjjjuSJHfddVcGDRqUAQMGpKysLBMmTMiiRYvy3HPPpb6+Pk899VTGjh2b9u3bp6amJoMGDcrcuXOTJPPnz8/ZZ5+dQw45JF26dMlFF12UOXPmtOaWAQAAANgFrRqpRo4cmR//+Mfp06dP6dimTZuybt269OzZs3SsR48eWbJkSZLf30lVVVVVGquoqEi3bt2yZMmSLFu2LN26dUuHDh1K45WVlVm8ePEbzq2srMzatWuzcePG3bVFAAAAAN6BfVvzzQ4++ODXHdu8eXOSpLy8vHSsoqIijY2NpfGKiooWc8rLy9PQ0PC6eW81d+e5O8cBAAAA2DO0aqR6Izsj0pYtW0rHGhoaSndHvTY67dTY2JiOHTumubm5xby3mrvz99feeQUAAABA8Qr5dr/X6ty5c7p27Zrly5eXjtXX15ce/+vZs2fq6+tLYw0NDVm9enWqqqpSVVWVVatWtQhRy5cv/6Nzly9fnq5du6ZTp067e1sAAAAAvA2FR6okGT58eG644YZs2LAhL774Ym655ZYMHz48STJs2LAsXLgwdXV1aWpqyvTp09O7d+/06NEjlZWV6dWrV2bOnJmmpqY8+uijWbhwYYYNG1a67uzZs7Ny5cps2LAhtbW1GTFiRJFbBQAAAOANFP64X5KMGzcu06ZNy7Bhw7Jjx46cddZZGTVqVJKkuro6U6dOzaRJk7JmzZr07ds3s2bNKs2tra3NxIkTU1NTkwMPPDBTpkzJUUcdlSQZNWpU1q9fn5EjR6axsTFDhw7NuHHjCtkjAAAAAH9cm+bm5uaiF7Enqq6uLn1L4PvRupc2Z+jkuUUvA/Y693/tzHTt7HPxAN4Of7dAMfzdAuwOb9Zb9ojH/QAAAADYu4lUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOH2mEg1d+7c/Omf/mn69+9f+pk3b16ampoyceLEDBw4MB/96Edz0003tZh33333ZciQIenXr1/OP//8rF+/vjS2atWqnHPOOenfv38GDx6chx56qLW3BQAAAMAu2GMi1TPPPJNzzjknTzzxROnn05/+dGpra1NfX58FCxZk7ty5mTdvXubPn58kWbp0aa688spcffXVqaurS/fu3TN+/PjSNS+++OJUV1enrq4uV111VcaPH58VK1YUtEMAAAAA/pg9JlI9/fTT6d279+uOz5s3L6NHj07nzp1z+OGH57zzzssdd9yRJLnrrrsyaNCgDBgwIGVlZZkwYUIWLVqU5557LvX19XnqqacyduzYtG/fPjU1NRk0aFDmzp3b2lsDAAAA4C3sEZFq+/btWbx4cX7yk5/k+OOPz5AhQ3LzzTfnpZdeyrp169KzZ8/SuT169MiSJUuS/P5OqqqqqtJYRUVFunXrliVLlmTZsmXp1q1bOnToUBqvrKzM4sWLW29jAAAAAOySfYteQJJs2LAhffr0yemnn57rr78+y5Yty1e+8pU0NTUlScrLy0vnVlRUpLGxMUmyefPmVFRUtLhWeXl5GhoaXjfvD+cCAAAAsOfYIyJV165d88Mf/rD0unfv3vnLv/zL/Md//EeSZMuWLaWxhoaG0t1RbxSdGhsb07FjxzQ3N7eY94dzAQAAANhz7BGP+z377LO57rrrWhzbunVrysrK0rVr1yxfvrx0vL6+vvT4X8+ePVNfX18aa2hoyOrVq1NVVZWqqqqsWrWqRcRavnx5i0cHAQAAANgz7BGRqlOnTvne976XH/3oR9mxY0eeeuqp/NM//VPOOOOMDB8+PDfccEM2bNiQF198MbfcckuGDx+eJBk2bFgWLlyYurq6NDU1Zfr06endu3d69OiRysrK9OrVKzNnzkxTU1MeffTRLFy4MMOGDSt4twAAAAD8oT3icb+DDz443/nOd3Lttdfm6quvzoEHHpivfOUrGTp0aE455ZRMmzYtw4YNy44dO3LWWWdl1KhRSZLq6upMnTo1kyZNypo1a9K3b9/MmjWrdN3a2tpMnDgxNTU1OfDAAzNlypQcddRRRW0TAAAAgD+iTXNzc3PRi9gTVVdXv6+/CXDdS5szdPLcopcBe537v3Zmunb22XgAb4e/W6AY/m4Bdoc36y17xON+AAAAAOzdRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcPsWvQAAAAD2PJ3aNmbby68UvQzYK7XZtyz7VHQuehmtTqQCAADgddrs2JpVswYXvQzYKx06fmHRSyiEx/0AAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIUTqQAAAAAonEgFAAAAQOFEKgAAAAAKJ1IBAAAAUDiRCgAAAIDCiVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAonUgEAAABQOJEKAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcPsWvQCAvUmnto3Z9vIrRS8D9kpt9i3LPhWdi14GAAB/hEgF0Ira7NiaVbMGF70M2CsdOn5h0UsAAOBNeNwPAAAAgMKJVAAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQAAAFA4kQoAAACAwolUAAAAABROpAIAAACgcCIVAAAAAIV730eq3/72tznrrLPSr1+/nHbaaXnyySeLXhIAAAAAf+B9Hamampryla98JX/xF3+RX/7ylxk9enTOO++8vPLKK0UvDQAAAIDXeF9Hqsceeyxbt27Nl770pbRr1y6f+tSn0rNnz9x3331FLw0AAACA13hfR6qlS5emqqqqxbHKysosWbKkoBUBAAAA8Ebe15Fq8+bNKS8vb3GsoqIiDQ0NBa0IAAAAgDeyb9EL2J06dOiQLVu2tDjW0NCQDh067NL86urq3bEsYC929F1Til4C7L1uO6HoFQD8n+LvFijQXvp3y/s6UlVVVeX73/9+i2PLly/P6aef/pZzFy9evHsWBQAAAMDrvK8f9zvuuOPS3Nyc73//+9m6dWvuvffeLF68OEOGDCl6aQAAAAC8Rpvm5ubmohexOy1ZsiSTJk3Kb3/72xx++OH5u7/7u9TU1BS9LAAAAABe430fqQAAAADY872vH/cDAAAA4P8GkQoAAACAwolUAAAAABROpAJ4j4wbNy4jRozI1q1bWxxftGhR+vTpk0WLFhW0MgCAN1ZdXZ3LLrvsdce/+MUv5vvf/37rLwjYq4lUAO+Rb37zm/nf//3fXH/99aVjmzdvzmWXXZYLLrggxx57bIGrAwB4Y/Pnz89Pf/rTopcBIFIBvFcOOOCATJs2LbfcckuefPLJJMm3vvWtHHLIIRk9enRuvPHGnHrqqTnuuOMybty4bNiwIUnS2NiYSy+9NMcdd1xOOOGEjB07tjQGALC7nXXWWZk0aVLWrFnzhuOvvvpq/v7v/z7HH398Pvaxj+XSSy/1twqwW4hUAO+hj33sYxk1alQmTpyYX/ziF7n//vtz7bXX5tZbb81dd92V733ve3nooYfSpUuXjB8/Pknyz//8z/nd736XBx98MA888EBeffXV3H777QXvBADYW4waNSrHHntsLrvssjQ3N79u/Otf/3qeffbZzJ8/Pw888EC2bNmSSy+9tICVAu93IhXAe+ySSy7Jjh07Mnr06EyePDmHHHJIfvSjH+XCCy/MEUcckfLy8lx66aX55S9/meeeey77779/6uvrc++992bTpk357ne/mzFjxhS9DQBgLzJlypQsWbLkdZ9DtWXLlvzrv/5rLrnkknzwgx/Mfvvtl4kTJ+aRRx75o3deAbxTIhXAe6ysrCx/9Vd/lSOOOCKDBw9OkqxatSpXXnllBgwYkAEDBuTEE0/Mvvvum5UrV+bMM8/Ml7/85fzwhz/MySefnM985jP5r//6r2I3AQDsVT7wgQ9kypQpmTlzZhYvXlw6/tJLL2Xr1q059NBDS8e6du2a9u3bZ/Xq1UUsFXgf27foBQC8H+2zzz5p2/b//f8ABx10UL7+9a/nhBNOKB1bsmRJjjzyyCxbtiyDBg3Kl770pWzYsCE33HBDLr300ixYsKCIpQMAe6lTTjkln/70p3PJJZekQ4cOSZIPfvCDad++fVauXJmuXbsmSdasWZOmpqZ84AMfKHK5wPuQO6kAWsHpp5+eG264IatXr8727dtz88035wtf+EIaGxtzzz335Ktf/Wo2bNiQzp07p2PHjuncuXPRSwYA9kKXX355tm7dWrqru23bthk+fHimT5+e9evX55VXXsmUKVPSv3//fOhDHyp2scD7jjupAFrBX//1X2fbtm35whe+kI0bN+aoo47KLbfckk6dOmX06NFZs2ZNPvnJT2bLli3p06dPrrnmmqKXDADshSoqKvLtb387I0eOLB274oor8u1vfzsjRoxIY2NjTjjhhFx//fUFrhJ4v2rT/EZf3wAAAAAArcjjfgAAAAAUTqQCAAAAoHAiFQAAAACFE6kAAAAAKJxIBQAAAEDhRCoAAAAACidSAQC8DYMGDUp1dXXpp2/fvhkxYkTmzp1b9NLe1BlnnJHa2tr35Fpbt27Nbbfd9p5cCwBgp32LXgAAwP81F198cc4444w0NzfnlVdeySOPPJKrrroqL730Us4777yil7fb3XPPPbnuuuvyhS98oeilAADvIyIVAMDb1LFjx3Tt2jVJctBBB6WysjL77LNPvvWtb+X000/PBz7wgYJXuHs1NzcXvQQA4H3I434AAO+BM844I23atMnPfvazJMktt9ySU045Jf3798+oUaPyX//1X6VzL7/88nz961/PhAkT0rdv3wwZMiT33HNPi+vNnz8/n/jEJ9K3b998+tOfzoMPPlgaq62tzYUXXphrrrkmAwcOzPHHH5/Jkydn+/btpXNmz56dE088Mccee2xmzpz5uvW+0+vX1dXliiuuyMaNG1NdXZ26urqsWbMmo0ePzkc+8pEMGDAgY8eOzfr169+bf1gAYK8hUgEAvAcqKipy+OGHZ+nSpbnjjjty6623ZtKkSZk3b15OOumknH322VmxYkXp/B//+MfZb7/98uMf/zhf+MIXcumll6auri5J8vDDD2fy5MkZO3Zs7r777px11lkZO3ZsnnjiidL8Bx98MJs2bcodd9yRsWPH5rbbbsu//du/la5dW1ubyy67LD/60Y+yYsWKPP3006W57+b6/fv3z9/93d/lgAMOyCOPPJL+/fvnG9/4Rtq2bZu5c+fmhz/8YVauXJlp06bt7n9yAOB9xuN+AADvkf333z+vvPJKbrrppkyYMCEnn3xykmT06NF57LHH8s///M+57LLLkiSHHXZYJk2alLZt26aqqiqPPfZYbr/99hx33HG56aabct555+VTn/pUkuSII47I008/ne9973vp379/kqSsrCyTJk1K+/btU1lZmdtvvz1PP/10PvGJT+T222/PyJEjS/OnTJmSn//856V1vtvr77///klSeuRx5cqV+fCHP5zDDjss7du3z4wZM/Lqq6/u5n9tAOD9RqQCAHiPvPLKK9l///2zatWqTJw4MZMmTSqNNTU1pX379qXX/fv3T9u2/++m9mOOOSZ33313kuTZZ5/Nr3/969x8882l8a1bt6ZHjx6l14ceemiL6+23337ZunVraf7ZZ59dGquoqEjPnj1Lr9/t9f/QuHHjMn78+DzwwAOpqanJkCFDMmzYsDf7pwIAeB2RCgDgPdDY2Jj6+vqce+65SZJp06blwx/+cItzysvLS7/vs88+LcZ27NhRilbbt2/PhAkTcsopp7Q4Z999/9+fbu3atfuja2nTps3rPtz8tee/2+v/oVNPPTUPPvhgFi5cmIcffjjf+MY3ctddd2X27Nm7fA0AAJ9JBQDwHpg3b1723XffnHLKKenatWvWrFmT7t27l35++MMf5uGHHy6d/8wzz7SY/+STT6ZXr15JkqqqqqxcubLF/J/+9Ke59957d2ktRx11VH7961+XXjc1NeXZZ58tvX6312/Tpk3p9+bm5lxzzTVZu3ZtPvvZz+a6667L9ddfn5///Oc+PB0AeFtEKgCAt+nVV1/NunXrsm7duixfvjy33HJLrrnmmvzt3/5tDjjggHz5y1/Od77zndx3331ZsWJFvvOd7+S2225r8TjdM888k5kzZ6a+vj7f+9738vDDD+eLX/xikuTLX/5y7rjjjtx+++154YUX8qMf/Si1tbU57LDDdml9X/rSl/KjH/0o8+bNy/Lly/PNb34zGzZsKI2/2+t36NAhmzdvztKlS9PU1JRly5bl7//+7/PMM8/k+eefz7333pvDDjssBx544Nv4VwUA9nYe9wMAeJtmzJiRGTNmJEkOOOCAVFZWZurUqfnkJz+ZJPmrv/qrNDY25tprr83//M//5Mgjj8x1112Xj3zkI6VrHH/88Xn++eczYsSIHHHEEbn++utzzDHHJEmGDBmSiRMn5pZbbsmUKVNKH7K+84PO38pf/MVfZNOmTamtrc2GDRty2mmn5aMf/Whp/N1ev6amJh/+8Idz+umnZ/r06ZkyZUquuuqqnHPOOWlsbEz//v1z8803t/jMLQCAt9Km+Q8/sAAAgN3q8ssvz+bNm3PdddcVvRQAgD2G/3sLAAAAgMKJVAAAAAAUzuN+AAAAABTOnVQAAAAAFE6kAgAAAKBwIhUAAAAAhROpAAAAACicSAUAAABA4UQqAAAAAAr3/wNYX2QOo3+OJAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "sns.countplot('Dependents', hue='Gender', data=df2019)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Analysis**<br>\n",
+    "\n",
+    "\n",
+    "After exploring the 2019 dataset, we have found that we cannot answer this question since male and female observations are significantly unbalanced."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is the gender distribution among top 5 countries of respondents in 2019?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>15895</td>\n",
+       "      <td>17837</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>Woman</td>\n",
+       "      <td>1942</td>\n",
+       "      <td>17837</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>India</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>6632</td>\n",
+       "      <td>7046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>India</td>\n",
+       "      <td>Woman</td>\n",
+       "      <td>414</td>\n",
+       "      <td>7046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>4847</td>\n",
+       "      <td>5130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Woman</td>\n",
+       "      <td>283</td>\n",
+       "      <td>5130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Woman</td>\n",
+       "      <td>395</td>\n",
+       "      <td>4933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>4538</td>\n",
+       "      <td>4933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>Woman</td>\n",
+       "      <td>275</td>\n",
+       "      <td>2857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>2582</td>\n",
+       "      <td>2857</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Country Gender  Count  Total\n",
+       "252   United States    Man  15895  17837\n",
+       "253   United States  Woman   1942  17837\n",
+       "101           India    Man   6632   7046\n",
+       "102           India  Woman    414   7046\n",
+       "84          Germany    Man   4847   5130\n",
+       "85          Germany  Woman    283   5130\n",
+       "250  United Kingdom  Woman    395   4933\n",
+       "249  United Kingdom    Man   4538   4933\n",
+       "39           Canada  Woman    275   2857\n",
+       "38           Canada    Man   2582   2857"
+      ]
+     },
+     "execution_count": 372,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all = df2019.groupby(['Country','Gender']).size().reset_index(name ='Count')\n",
+    "all['Total'] = all.groupby(['Country'])['Count'].transform('sum')\n",
+    "all = all.sort_values(by=['Total'], ascending=False)\n",
+    "#all.set_index('Total')\n",
+    "Top = all[:10].sort_values(by=['Total'], ascending=False)\n",
+    "Top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# from raw value to percentage\n",
+    "total = Top.groupby(['Country'])['Count'].sum().reset_index()\n",
+    "total['Percentage'] = [i / j * 100 for i,j in zip(total['Count'], total['Count'])]\n",
+    "\n",
+    "woman = Top[Top.Gender=='Woman'].groupby(['Country'])['Count'].sum().reset_index()\n",
+    "woman['Percentage'] = [i / j * 100 for i,j in zip(woman['Count'], total['Count'])]\n",
+    "woman.sort_values(by=['Percentage'], ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>1942</td>\n",
+       "      <td>10.887481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>275</td>\n",
+       "      <td>9.625481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>395</td>\n",
+       "      <td>8.007298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>India</td>\n",
+       "      <td>414</td>\n",
+       "      <td>5.875674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>283</td>\n",
+       "      <td>5.516569</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Country  Count  Percentage\n",
+       "4   United States   1942   10.887481\n",
+       "0          Canada    275    9.625481\n",
+       "3  United Kingdom    395    8.007298\n",
+       "2           India    414    5.875674\n",
+       "1         Germany    283    5.516569"
+      ]
+     },
+     "execution_count": 374,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "woman"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>2857</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>5130</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>India</td>\n",
+       "      <td>7046</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>4933</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>17837</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Country  Count  Percentage\n",
+       "0          Canada   2857       100.0\n",
+       "1         Germany   5130       100.0\n",
+       "2           India   7046       100.0\n",
+       "3  United Kingdom   4933       100.0\n",
+       "4   United States  17837       100.0"
+      ]
+     },
+     "execution_count": 375,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (10, 6))\n",
+    "\n",
+    "# bar chart 1 -> top bars (group of 'Man')\n",
+    "bar1 = sns.barplot(x=\"Country\",  y=\"Percentage\", data=total, color='darkblue')\n",
+    "# bar chart 2 -> bottom bars (group of 'Woman')\n",
+    "bar2 = sns.barplot(x=\"Country\", y=\"Percentage\", data=woman, color='#5E96E9')\n",
+    "\n",
+    "# add legend\n",
+    "top_bar = mpatches.Patch(color='darkblue', label='Man')\n",
+    "bottom_bar = mpatches.Patch(color='#5E96E9', label='Woman')\n",
+    "plt.legend(handles=[top_bar, bottom_bar])\n",
+    "\n",
+    "# Fix the legend so it's not on top of the bars.\n",
+    "legend = ax.get_legend()\n",
+    "legend.set_bbox_to_anchor((1, 1))\n",
+    "\n",
+    "ax.set_ylabel('Percentage', fontsize = 12)\n",
+    "ax.set_xlabel('Top 5 Countries', fontsize = 12)\n",
+    "plt.title('Gender vs Top 5 Countries in 2019', fontsize = 14)\n",
+    "\n",
+    "def add_value_labels(bar2, spacing=5):\n",
+    "    \"\"\"Add labels to the end of each bar in a bar chart.\n",
+    "\n",
+    "    Arguments:\n",
+    "        ax (matplotlib.axes.Axes): The matplotlib object containing the axes\n",
+    "            of the plot to annotate.\n",
+    "        spacing (int): The distance between the labels and the bars.\n",
+    "    \"\"\"\n",
+    "    # For each bar: Place a label\n",
+    "    for rect in bar2.patches:\n",
+    "        # Get X and Y placement of label from rect.\n",
+    "        y_value = rect.get_height()\n",
+    "        x_value = rect.get_x() + rect.get_width() / 2\n",
+    "\n",
+    "        space = spacing  # Number of points between bar and label. Change to your liking.\n",
+    "        va = 'bottom' # Vertical alignment for positive values\n",
+    "        label = \"{:.1f}%\".format(y_value) # Use Y value as label and format number with one decimal place\n",
+    "\n",
+    "        # Create annotation\n",
+    "        bar2.annotate(\n",
+    "            label,                      # Use `label` as label\n",
+    "            (x_value, y_value),         # Place label at end of the bar\n",
+    "            xytext=(0, space),          # Vertically shift label by `space`\n",
+    "            textcoords=\"offset points\", # Interpret `xytext` as offset in points\n",
+    "            ha='center',                # Horizontally center label\n",
+    "            va=va,                      # Vertically align label differently for\n",
+    "            color='white', fontsize=12, style='italic')       \n",
+    "\n",
+    "#Add value bar\n",
+    "add_value_labels(bar2)\n",
+    "\n",
+    "plt.tight_layout(pad=0., w_pad=-16.5, h_pad=0.0)  \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Analysis**<br>\n",
+    "\n",
+    "\n",
+    "In terms of male and female statistics, it can be seen that the US has the relatively largest female percentage at about 10.9%. Follow by Canada, the UK at 9.6% and 8.0% respectively. India and Germany have the fewest female respondents among the top 5 at around 5%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Where are the most data scientist come from in 2019?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5788"
+      ]
+     },
+     "execution_count": 377,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creating data scientist scientist df\n",
+    "ds = survey_df_2019[survey_df_2019['DevType'].str.contains('Data scientist') == True ]\n",
+    "ds = ds.reset_index(drop=True)\n",
+    "len(ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 378,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>1550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>India</td>\n",
+       "      <td>543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>France</td>\n",
+       "      <td>169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>Russian Federation</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Country  Count\n",
+       "113       United States   1550\n",
+       "49                India    543\n",
+       "41              Germany    427\n",
+       "111      United Kingdom    339\n",
+       "18               Canada    195\n",
+       "39               France    169\n",
+       "74          Netherlands    148\n",
+       "14               Brazil    143\n",
+       "88   Russian Federation    123\n",
+       "5             Australia    119"
+      ]
+     },
+     "execution_count": 378,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_country = ds.groupby(['Country']).size().reset_index(name ='Count')\n",
+    "ds_country.sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "top_ds_country = ds_country[:10]\n",
+    "top_ds_country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 379,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (10, 6))\n",
+    "ax=sns.barplot(x=\"Count\",  y=\"Country\", data=top_ds_country )\n",
+    "ax.set_ylabel('Countries', fontsize = 12)\n",
+    "ax.set_xlabel('The Number of Data Scientists', fontsize = 12)\n",
+    "plt.title('Top 10 Countries of Data Scientists in 2019', fontsize = 14)\n",
+    "\n",
+    "for y, x in enumerate(top_ds_country['Count']):\n",
+    "    label = \"{:,}\".format(int(x))\n",
+    "    plt.annotate(label, xy=(x, y), va='center')\n",
+    "\n",
+    "plt.tight_layout()  \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis\n",
+    "\n",
+    "\n",
+    "There are 5,788 data scientists who responded to the Stackoverflow survey in 2019. Most data scientists are from the US with 1,550 people and it is 3 times higher than data scientists from India. Followed by Germany and the UK with 427 and 339 people respectively. The rest are Canada, France, Netherlands, Brazil, Russia, and Australia which have less than 200 data scientists."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Which countries pay the most to Data Scientists in 2019?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 380,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>Qatar</td>\n",
+       "      <td>1000000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>Myanmar</td>\n",
+       "      <td>333757.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>Ireland</td>\n",
+       "      <td>275851.466092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>Luxembourg</td>\n",
+       "      <td>272796.133333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>265211.014843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>Syrian Arab Republic</td>\n",
+       "      <td>2916.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>Madagascar</td>\n",
+       "      <td>1800.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>Venezuela, Bolivarian Republic of...</td>\n",
+       "      <td>1500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Cambodia</td>\n",
+       "      <td>816.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>400.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>120 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Country            Mean\n",
+       "85                                  Qatar  1000000.000000\n",
+       "72                                Myanmar   333757.333333\n",
+       "52                                Ireland   275851.466092\n",
+       "63                             Luxembourg   272796.133333\n",
+       "113                         United States   265211.014843\n",
+       "..                                    ...             ...\n",
+       "101                  Syrian Arab Republic     2916.000000\n",
+       "64                             Madagascar     1800.000000\n",
+       "116  Venezuela, Bolivarian Republic of...     1500.000000\n",
+       "16                               Cambodia      816.000000\n",
+       "118                                Zambia      400.000000\n",
+       "\n",
+       "[120 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 380,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_mean_salary = ds.groupby('Country')['SalaryUSD'].mean().reset_index(name ='Mean')\n",
+    "ds_mean_salary.sort_values(by=['Mean'], ascending=False, inplace=True)\n",
+    "ds_mean_salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 381,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plottig boxplot to check outliers after cleaning some outliers\n",
+    "sns.boxplot('Mean', data=ds_mean_salary)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 382,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Cleaning Age's outliers from each gender)\n",
+    "ds_mean_salary = ds_mean_salary[(ds_mean_salary['Mean'] <= 280000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 383,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plottig boxplot to check outliers after cleaning some outliers\n",
+    "sns.boxplot('Mean', data=ds_mean_salary)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 384,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>Ireland</td>\n",
+       "      <td>275851.466092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>Luxembourg</td>\n",
+       "      <td>272796.133333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>265211.014843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>169366.692664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>165462.430196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Cyprus</td>\n",
+       "      <td>150936.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>146803.174460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>Norway</td>\n",
+       "      <td>145948.523273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>125228.788666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>Japan</td>\n",
+       "      <td>118969.194525</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Country           Mean\n",
+       "52          Ireland  275851.466092\n",
+       "63       Luxembourg  272796.133333\n",
+       "113   United States  265211.014843\n",
+       "111  United Kingdom  169366.692664\n",
+       "100     Switzerland  165462.430196\n",
+       "25           Cyprus  150936.000000\n",
+       "5         Australia  146803.174460\n",
+       "78           Norway  145948.523273\n",
+       "18           Canada  125228.788666\n",
+       "56            Japan  118969.194525"
+      ]
+     },
+     "execution_count": 384,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Top_mean_salary = ds_mean_salary[:10]\n",
+    "Top_mean_salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 385,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (10, 6))\n",
+    "ax=sns.barplot(x=\"Mean\",  y=\"Country\", data=Top_mean_salary )\n",
+    "ax.set_ylabel('Country', fontsize = 12)\n",
+    "ax.set_xlabel('Average Salary in US$', fontsize = 12)\n",
+    "plt.title('The Top 10 highest paying data scientist country in 2019', fontsize = 14)\n",
+    "\n",
+    "for y, x in enumerate(Top_mean_salary['Mean']):\n",
+    "    label = \"${:,}\".format(int(x))\n",
+    "    plt.annotate(label, xy=(x, y), va='center')\n",
+    "\n",
+    "plt.tight_layout()  \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Analysis**<br>\n",
+    "\n",
+    "\n",
+    "In 2019, the top three countries which have a highest mean annual salary of a data scientist are Ireland (275,851), Luxembourg (272,769), and the USA (265,211). Apart from that, the mean salary of the rest of the countries is less than (200,000) per year. Japan provides the highest mean annual salary among Asian countries (118,969)<br>\n",
+    "*Figures in Dollars* **$**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mydf2018 = pd.read_csv(r\"C:\\Users\\Aneesh Angane\\Downloads\\stack-overflow-developer-survey-2018\\survey_results_public_2018.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Most Popular IDE's in 2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Split IDEs and explode into separate rows\n",
+    "individual_ides = mydf2018['IDE'].str.split(';').explode()\n",
+    "\n",
+    "# Count occurrences of each IDE and sort by value\n",
+    "ide_counts_value_sorted = individual_ides.value_counts().sort_values(ascending=False)\n",
+    "\n",
+    "# Plotting - Sorted by value\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(ide_counts_value_sorted.index, ide_counts_value_sorted.values, color='skyblue')\n",
+    "plt.title('IDE Usage')\n",
+    "plt.xlabel('IDE')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis of IDE Usage\n",
+    "\n",
+    "1. **Popular IDEs**: Visual Studio Code, Visual Studio, and Notepad++ are among the most widely used IDEs, with high user counts ranging from 25,870 to 26,280.\n",
+    "\n",
+    "2. **Text Editors**: Sublime Text, Vim, and IntelliJ are also popular choices, with user counts ranging from 19,477 to 21,810.\n",
+    "\n",
+    "3. **General-purpose Editors**: TextMate, Coda, and Light Table are also used, although they have lower user counts compared to other IDEs.\n",
+    "\n",
+    "4. **Emerging Trends**: IPython / Jupyter, Atom, and Emacs show significant adoption, indicating a growing interest in interactive computing environments, lightweight editors, and customizable text editors, respectively.\n",
+    "\n",
+    "5. **Industry Standard**: Xcode, primarily used for macOS and iOS development, maintains a substantial user base due to its integration with Apple's development ecosystem.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coders perception about AI in 2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "# Assuming df2018 is your DataFrame\n",
+    "df = df2018[['AIDangerous','AIInteresting','AIResponsible','AIFuture']]\n",
+    "\n",
+    "# Strip leading and trailing whitespace from all columns\n",
+    "df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
+    "\n",
+    "# Mapping for shorter versions\n",
+    "short_mapping = {\n",
+    "    'Algorithms making important decisions': 'Algorithms',\n",
+    "    'Artificial intelligence surpassing human intelligence (\"the singularity\")': 'AI Singularity',\n",
+    "    'Evolving definitions of \"fairness\" in algorithmic versus human decisions': 'Fairness Evolution',\n",
+    "    \"Increasing automation of jobs\": 'Automation',\n",
+    "    \"The developers or the people creating the AI\": 'Developers',\n",
+    "    \"A governmental or other regulatory body\": 'Government/Regulatory',\n",
+    "    \"Prominent industry leaders\": 'Industry Leaders',\n",
+    "    \"Nobody\": 'No Responsibility',\n",
+    "    \"I'm excited about the possibilities more than worried about the dangers.\": 'Excited about AI Future',\n",
+    "    \"I'm worried about the dangers more than I'm excited about the possibilities.\": 'Worried about AI Future',\n",
+    "    \"I don't care about it, or I haven't thought about it.\": 'Indifferent about AI Future'\n",
+    "}\n",
+    "\n",
+    "# Replace responses with shorter versions\n",
+    "df.replace(short_mapping, inplace=True)\n",
+    "\n",
+    "# Function to create value count plots for each column\n",
+    "def plot_value_counts(column_name, ax):\n",
+    "    colors = ['skyblue','yellow']\n",
+    "    df[column_name].value_counts().plot(kind='bar', color=random.choice(colors), ax=ax)\n",
+    "    ax.set_title(f'Value Counts for {column_name}')\n",
+    "    ax.set_xlabel('Response')\n",
+    "    ax.set_ylabel('Count')\n",
+    "    ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
+    "\n",
+    "# Plot value counts for each column\n",
+    "for i, column in enumerate(df.columns):\n",
+    "    plot_value_counts(column, axes[i//2, i%2])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "### AIDangerous:\n",
+    "- The most commonly cited concern is \"Algorithms making important decisions,\" followed closely by \"Artificial intelligence surpassing human intelligence\" and \"Evolving definitions of fairness.\"\n",
+    "- \"Increasing automation of jobs\" is also a significant concern but appears to be less frequently mentioned compared to the other categories.\n",
+    "\n",
+    "### AIInteresting:\n",
+    "- The most interesting aspect for respondents seems to be \"Increasing automation of jobs,\" followed by \"Algorithms making important decisions\" and \"Artificial intelligence surpassing human intelligence.\"\n",
+    "- \"Evolving definitions of fairness\" appears to be less intriguing to respondents compared to other categories.\n",
+    "\n",
+    "### AIResponsible:\n",
+    "- The majority of respondents believe that responsibility lies with \"The developers or the people creating the AI.\"\n",
+    "- Fewer respondents attribute responsibility to \"A governmental or other regulatory body,\" \"Prominent industry leaders,\" or \"Nobody.\"\n",
+    "\n",
+    "### AIFuture:\n",
+    "- A significant proportion of respondents express excitement about the future of AI, indicating that they are \"Excited about the possibilities more than worried about the dangers.\"\n",
+    "- However, there is also a notable percentage of respondents who are \"Worried about the dangers more than excited about the possibilities.\"\n",
+    "- A smaller portion of respondents either \"Don't care about it\" or \"Haven't thought about it.\"\n",
+    "\n",
+    "Overall, these results suggest a complex and varied perspective on AI technology. While many see great potential in AI, there are also concerns about its implications, particularly regarding decision-making, automation of jobs, and the ethical considerations surrounding its development and regulation.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 386,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['LanguageWorkedWith']\n",
+    "df_18 = df[cols]\n",
+    "df_19 = survey_df_2019[cols]\n",
+    "df_20 = df2020[cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 387,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#splitting 'LanguageWorkedWith' on ';' \n",
+    "language_2018= df_18['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n",
+    "language_2018['Language'] = language_2018.index\n",
+    "language_2018.reset_index(drop=True, inplace=True)\n",
+    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "language_2018 = language_2018[['Language', '2018']]\n",
+    "#language_2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 388,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#splitting 'LanguageWorkedWith' on ';' \n",
+    "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
+    "language_2019['Language'] = language_2019.index\n",
+    "language_2019.reset_index(drop=True, inplace=True)\n",
+    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "language_2019 = language_2019[['Language', '2019']]\n",
+    "#language_2019"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 389,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#splitting 'LanguageWorkedWith' on ';' \n",
+    "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
+    "language_2020['Language'] = language_2020.index\n",
+    "language_2020.reset_index(drop=True, inplace=True)\n",
+    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "language_2020 = language_2020[['Language', '2020']]\n",
+    "#language_2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 390,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compare_df = pd.merge(language_2018, language_2019,on = ['Language'], how = 'outer')\n",
+    "language_all = pd.merge(compare_df, language_2020,on = ['Language'], how = 'outer')\n",
+    "language_all.fillna(0, inplace=True)\n",
+    "language_all['2018'] = language_all['2018']. astype(int)\n",
+    "language_all['2019'] = language_all['2019']. astype(int)\n",
+    "language_all['2020'] = language_all['2020']. astype(int)\n",
+    "language_all.set_index('Language', inplace=True)\n",
+    "#language_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 391,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(language_all/language_all.sum()).plot(kind='bar', figsize=(12,8))\n",
+    "plt.title('Programming Language Use by Respondents', fontsize = 14)\n",
+    "plt.xlabel('Languages', fontsize = 12)\n",
+    "plt.ylabel('Percentages', fontsize = 12)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  **Analysing the growth of languages from 2018 to 2020 before predicting part**\n",
+    "\n",
+    "The most language the developers use between 2018 to 2020 is JavaScript(14%). The second and third highest working language is HTML/CSS(13%) and SQL(11%). JavaScript and SQL had the same steady increasing trend over the three years. The percentage of HTML/CSS was slightly increased from 2018 to 2019. However, it dropped to the same level as 2018 in 2020. Python was responsible for about 9% in 2018. After then, it decreased to 8% in 2019 and it rose 1% in 2020.\n",
+    "\n",
+    "There are some languages that were in only 2019; Elixir, Clojure, F#, Web assembly, and Erlang. Perl, Haskell, Julia was in the 2019 and 2020 surveys with small percentages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 392,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Preparing data for ML\n",
+    "df_language_2018 = language_2018[['Language', '2018']]\n",
+    "df_language_2018 = df_language_2018.rename(columns={'2018': 'Number'})\n",
+    "df_language_2018['Year'] = '2018'\n",
+    "df_language_2018['Year_Total'] = df_language_2018['Number'].sum()\n",
+    "df_language_2018['Fraction'] = df_language_2018['Number']/df_language_2018['Number'].sum()\n",
+    "df_language_2018 = df_language_2018[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
+    "df_language_2018.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
+    "#df_language_2018\n",
+    "df_language_2019 = language_2019[['Language', '2019']]\n",
+    "df_language_2019 = df_language_2019.rename(columns={'2019': 'Number'})\n",
+    "df_language_2019['Year'] = '2019'\n",
+    "df_language_2019['Year_Total'] = df_language_2019['Number'].sum()\n",
+    "df_language_2019['Fraction'] = df_language_2019['Number']/df_language_2019['Number'].sum()\n",
+    "df_language_2019 = df_language_2019[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
+    "df_language_2019.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
+    "#df_language_2019\n",
+    "df_language_2020 = language_2020[['Language', '2020']]\n",
+    "df_language_2020 = df_language_2020.rename(columns={'2020': 'Number'})\n",
+    "df_language_2020['Year'] = '2020'\n",
+    "df_language_2020['Year_Total'] = df_language_2020['Number'].sum()\n",
+    "df_language_2020['Fraction'] = df_language_2020['Number']/df_language_2020['Number'].sum()\n",
+    "df_language_2020 = df_language_2020[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
+    "df_language_2020.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
+    "#df_language_2020\n",
+    "\n",
+    "#Append Dataset 2018 x 2019 x 2020\n",
+    "df_language = pd.concat([df_language_2018[:10], df_language_2019[:10], df_language_2020[:10]] , axis=0)\n",
+    "#resetting the index values\n",
+    "df_language = df_language.reset_index(drop=True)\n",
+    "#df_language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 393,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['Language', 'Fraction']\n",
+    "df_language_2018_ = df_language_2018[cols][:10]\n",
+    "#df_language_2018_\n",
+    "cols = ['Language', 'Fraction']\n",
+    "df_language_2019_ = df_language_2019[cols][:10]\n",
+    "#df_language_2019_\n",
+    "cols = ['Language', 'Fraction']\n",
+    "df_language_2020_ = df_language_2020[cols][:10]\n",
+    "#df_language_2020_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 394,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Language</th>\n",
+       "      <th>JavaScript</th>\n",
+       "      <th>HTML/CSS</th>\n",
+       "      <th>SQL</th>\n",
+       "      <th>Python</th>\n",
+       "      <th>Java</th>\n",
+       "      <th>Bash/Shell/PowerShell</th>\n",
+       "      <th>C#</th>\n",
+       "      <th>PHP</th>\n",
+       "      <th>TypeScript</th>\n",
+       "      <th>C++</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01</th>\n",
+       "      <td>0.134797</td>\n",
+       "      <td>0.125627</td>\n",
+       "      <td>0.109071</td>\n",
+       "      <td>0.087801</td>\n",
+       "      <td>0.080117</td>\n",
+       "      <td>0.065902</td>\n",
+       "      <td>0.062641</td>\n",
+       "      <td>0.052107</td>\n",
+       "      <td>0.050617</td>\n",
+       "      <td>0.047593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-01-01</th>\n",
+       "      <td>0.136468</td>\n",
+       "      <td>0.126595</td>\n",
+       "      <td>0.109882</td>\n",
+       "      <td>0.081963</td>\n",
+       "      <td>0.080446</td>\n",
+       "      <td>0.074697</td>\n",
+       "      <td>0.062234</td>\n",
+       "      <td>0.051494</td>\n",
+       "      <td>0.044158</td>\n",
+       "      <td>0.044193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-01</th>\n",
+       "      <td>0.137808</td>\n",
+       "      <td>0.126495</td>\n",
+       "      <td>0.112110</td>\n",
+       "      <td>0.086418</td>\n",
+       "      <td>0.078374</td>\n",
+       "      <td>0.069555</td>\n",
+       "      <td>0.063638</td>\n",
+       "      <td>0.051339</td>\n",
+       "      <td>0.054607</td>\n",
+       "      <td>0.043372</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Language    JavaScript  HTML/CSS       SQL    Python      Java  \\\n",
+       "Year                                                             \n",
+       "2018-01-01    0.134797  0.125627  0.109071  0.087801  0.080117   \n",
+       "2019-01-01    0.136468  0.126595  0.109882  0.081963  0.080446   \n",
+       "2020-01-01    0.137808  0.126495  0.112110  0.086418  0.078374   \n",
+       "\n",
+       "Language    Bash/Shell/PowerShell        C#       PHP  TypeScript       C++  \n",
+       "Year                                                                         \n",
+       "2018-01-01               0.065902  0.062641  0.052107    0.050617  0.047593  \n",
+       "2019-01-01               0.074697  0.062234  0.051494    0.044158  0.044193  \n",
+       "2020-01-01               0.069555  0.063638  0.051339    0.054607  0.043372  "
+      ]
+     },
+     "execution_count": 394,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_language_2018_.set_index('Language', inplace = True)\n",
+    "df_language_2018_t = df_language_2018_.T\n",
+    "df_language_2018_t['Year'] = '2018'\n",
+    "df_language_2018_t.Year = pd.to_datetime(df_language_2018_t.Year)\n",
+    "df_language_2018_t = df_language_2018_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
+    "#df_language_2018_t\n",
+    "df_language_2019_.set_index('Language', inplace = True)\n",
+    "df_language_2019_t = df_language_2019_.T\n",
+    "df_language_2019_t['Year'] = '2019'\n",
+    "df_language_2019_t.Year = pd.to_datetime(df_language_2019_t.Year)\n",
+    "df_language_2019_t = df_language_2019_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
+    "#df_language_2019_t\n",
+    "df_language_2020_.set_index('Language', inplace = True)\n",
+    "df_language_2020_t = df_language_2020_.T\n",
+    "df_language_2020_t['Year'] = '2020'\n",
+    "df_language_2020_t.Year = pd.to_datetime(df_language_2020_t.Year)\n",
+    "df_language_2020_t = df_language_2020_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
+    "#df_language_2020_t\n",
+    "\n",
+    "#Append Dataset 2018 x 2019 x 2020\n",
+    "all_language = pd.concat([df_language_2018_t, df_language_2019_t, df_language_2020_t] , axis=0)\n",
+    "#resetting the index values\n",
+    "all_language = all_language.reset_index(drop=True)\n",
+    "all_language.set_index('Year', inplace = True)\n",
+    "all_language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 395,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java',\n",
+       "       'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++'],\n",
+       "      dtype='object', name='Language')"
+      ]
+     },
+     "execution_count": 395,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_language.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 396,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Fraction of total queries in the year (%)')"
+      ]
+     },
+     "execution_count": 396,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = all_language.plot(grid=True, lw=0.5, figsize=(14,6), marker='o')\n",
+    "\n",
+    "#Show the legend outside of the plot.\n",
+    "legend = ax.get_legend()\n",
+    "legend.set_bbox_to_anchor((1, 1))\n",
+    "plt.title('Fraction of total queries in the year for top programming languages', fontsize = 14)\n",
+    "plt.xlabel('Languages', fontsize = 12)\n",
+    "plt.ylabel('Fraction of total queries in the year (%)', fontsize = 12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are trying to answer the question \"Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020).\"\n",
+    "\n",
+    "Since we have only 3 years of datasets, there is not enough data to use the time series forecasting method to predict the future popularity of programming languages. With the very small number of observations, there is insufficient data to split the observations into training and testing. We need more observations to build the predictive model, this question we leave for further exploration in future projects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Can we predict the salary of Data Scientists?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 397,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Rename columns\n",
+    "cleaned_2018.rename(columns={'JobSatisfaction': 'CurrentJobSatis', 'JobSearchStatus': 'JobStatus', 'YearsCodingProf':'YearsCodePro'}, inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 398,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sal_df = ['Age', 'Country', 'EdLevel', 'DevType', 'YearsCodePro', 'SalaryUSD']\n",
+    "df1 = cleaned_2018\n",
+    "df2 = survey_df_2019\n",
+    "df3 = df2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 399,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(191693, 6)"
+      ]
+     },
+     "execution_count": 399,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Append Dataset 2018 x 2019 x 2020\n",
+    "df_sal = pd.concat([df1[sal_df], df2[sal_df], df3[sal_df]], axis=0)\n",
+    "#resetting the index values\n",
+    "df_sal = df_sal.reset_index(drop=True)\n",
+    "df_sal.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 400,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8727"
+      ]
+     },
+     "execution_count": 400,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creating data scientist scientist df\n",
+    "all_ds = df_sal[df_sal['DevType'].str.contains('Data scientist') == True ]\n",
+    "all_ds = all_ds.reset_index(drop=True)\n",
+    "len(all_ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 401,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Canada</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>3</td>\n",
+       "      <td>366420.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21</td>\n",
+       "      <td>Canada</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>4</td>\n",
+       "      <td>170292.187500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>Masters</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8400.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>19</td>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>Associate</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>1</td>\n",
+       "      <td>87994.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>6</td>\n",
+       "      <td>66750.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8722</th>\n",
+       "      <td>23</td>\n",
+       "      <td>Russian Federation</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>3</td>\n",
+       "      <td>33972.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8723</th>\n",
+       "      <td>27</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Masters</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>2</td>\n",
+       "      <td>97284.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8724</th>\n",
+       "      <td>47</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>22</td>\n",
+       "      <td>148951.282051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8725</th>\n",
+       "      <td>33</td>\n",
+       "      <td>Panama</td>\n",
+       "      <td>Masters</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>2</td>\n",
+       "      <td>72000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8726</th>\n",
+       "      <td>28</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Masters</td>\n",
+       "      <td>Data scientist</td>\n",
+       "      <td>5</td>\n",
+       "      <td>180000.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8727 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Age             Country    EdLevel         DevType YearsCodePro  \\\n",
+       "0     28              Canada  Bachelors  Data scientist            3   \n",
+       "1     21              Canada  No Degree  Data scientist            4   \n",
+       "2     25           Argentina    Masters  Data scientist            3   \n",
+       "3     19         Netherlands  Associate  Data scientist            1   \n",
+       "4     25       United States  Bachelors  Data scientist            6   \n",
+       "...   ..                 ...        ...             ...          ...   \n",
+       "8722  23  Russian Federation  Bachelors  Data scientist            3   \n",
+       "8723  27             Germany    Masters  Data scientist            2   \n",
+       "8724  47       United States  Bachelors  Data scientist           22   \n",
+       "8725  33              Panama    Masters  Data scientist            2   \n",
+       "8726  28       United States    Masters  Data scientist            5   \n",
+       "\n",
+       "          SalaryUSD  \n",
+       "0     366420.000000  \n",
+       "1     170292.187500  \n",
+       "2       8400.000000  \n",
+       "3      87994.000000  \n",
+       "4      66750.000000  \n",
+       "...             ...  \n",
+       "8722   33972.000000  \n",
+       "8723   97284.000000  \n",
+       "8724  148951.282051  \n",
+       "8725   72000.000000  \n",
+       "8726  180000.000000  \n",
+       "\n",
+       "[8727 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 401,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_ds['DevType'] = 'Data scientist'\n",
+    "all_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 402,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "54049.0"
+      ]
+     },
+     "execution_count": 402,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Divide SalaryUSD into 2 groups; SalaryUSD >= median and SalaryUSD < median \n",
+    "all_ds['greater than median'] = all_ds['SalaryUSD'] >= all_ds['SalaryUSD'].median()\n",
+    "all_ds['SalaryUSD'].median() #56616.0 USD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 403,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{False: 0, True: 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#Encoding the target\n",
+    "labelencoder = preprocessing.LabelEncoder()\n",
+    "all_ds['gt_median'] = labelencoder.fit_transform(all_ds['greater than median'])\n",
+    "\n",
+    "le_name_mapping = dict(zip(labelencoder.classes_, labelencoder.transform(labelencoder.classes_)))\n",
+    "print(le_name_mapping)\n",
+    "#{False: 0 (SalaryUSD < median), True: 1 (SalaryUSD >= median}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 404,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8727, 4)"
+      ]
+     },
+     "execution_count": 404,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = all_ds.drop(['SalaryUSD', 'greater than median', 'gt_median', 'DevType'], axis = 1)\n",
+    "y = all_ds['gt_median']\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 405,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8727, 225)"
+      ]
+     },
+     "execution_count": 405,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cats_lst = X.select_dtypes(include = ['object']).columns.tolist()\n",
+    "for col in cats_lst:\n",
+    "    X = pd.concat([X.drop(col, axis=1), pd.get_dummies(X[col], prefix=col, prefix_sep='_', drop_first=True)], axis=1)\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 406,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Splitting data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.30, random_state=142)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 407,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_metrics = {}\n",
+    "\n",
+    "def metrics_data(title, labels, predictions):\n",
+    "    \"\"\"\n",
+    "    INPUT:\n",
+    "        title - Display title for classification algorithm\n",
+    "        labels - Actual values for target variable\n",
+    "        predictions - Predicted values for target variable\n",
+    "        \n",
+    "    OUTPUT:\n",
+    "        metrics - Dictionary of classification metrics for given title\n",
+    "    \"\"\"\n",
+    "    metrics = {\n",
+    "        title: {\n",
+    "            \"model\": title,\n",
+    "            \"accuracy\": accuracy_score(labels, predictions),\n",
+    "            \"precision\": precision_score(labels, predictions),\n",
+    "            \"recall\": recall_score(labels, predictions),\n",
+    "            \"f1-score\": f1_score(labels, predictions),\n",
+    "            \"r2\":  r2_score(labels, predictions)\n",
+    "        }\n",
+    "    }\n",
+    "    print(metrics)\n",
+    "    return metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 408,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.11200189590454102\n",
+      "{'Decision Trees': {'model': 'Decision Trees', 'accuracy': 0.8300878197785414, 'precision': 0.8640611724723875, 'recall': 0.7811059907834101, 'f1-score': 0.8204921339249698, 'r2': 0.32032898397069154}}\n",
+      "Accuracy on train set: 0.823510150622135\n"
+     ]
+    }
+   ],
+   "source": [
+    "#DecisionTreeClassifier\n",
+    "start = time.time()\n",
+    "modelDC = DecisionTreeClassifier(max_depth = 12, min_samples_leaf = 10)\n",
+    "modelDC.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeDC = end - start\n",
+    "print('Time: ', TimeDC)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = modelDC.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Decision Trees\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = modelDC.predict(X_train)\n",
+    "accuracyDC2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyDC2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 409,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Multinomial Naive Bayes': {'model': 'Multinomial Naive Bayes', 'accuracy': 0.8335242458953799, 'precision': 0.8315467075038285, 'recall': 0.8341013824884793, 'f1-score': 0.8328220858895706, 'r2': 0.3340751393510596}}\n",
+      "Accuracy on train set: 0.8366077275703995\n"
+     ]
+    }
+   ],
+   "source": [
+    "#MultinomialNB\n",
+    "start = time.time()\n",
+    "modelNB = MultinomialNB(alpha=0.005)\n",
+    "modelNB.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeNB = end - start\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = modelNB.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Multinomial Naive Bayes\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = modelNB.predict(X_train)\n",
+    "accuracyNB2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyNB2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 410,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.040006399154663086\n",
+      "{'Gaussian Naive Bayes': {'model': 'Gaussian Naive Bayes', 'accuracy': 0.6380297823596792, 'precision': 0.5806745670009116, 'recall': 0.978494623655914, 'f1-score': 0.72883295194508, 'r2': -0.4479283667320999}}\n",
+      "Accuracy on train set: 0.64603798297315\n"
+     ]
+    }
+   ],
+   "source": [
+    "#GaussianNB\n",
+    "start = time.time()\n",
+    "modelGNB = GaussianNB()\n",
+    "modelGNB.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeGNB = end - start\n",
+    "print('Time: ', TimeGNB)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = modelGNB.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Gaussian Naive Bayes\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = modelGNB.predict(X_train)\n",
+    "accuracyGNB2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyGNB2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 411,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.27199530601501465\n",
+      "{'Logistic Regression': {'model': 'Logistic Regression', 'accuracy': 0.8518518518518519, 'precision': 0.8520801232665639, 'recall': 0.8494623655913979, 'f1-score': 0.8507692307692308, 'r2': 0.4073879680463558}}\n",
+      "Accuracy on train set: 0.8542894564505567\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Logistic Regression\n",
+    "start = time.time()\n",
+    "modelLR = LogisticRegression()\n",
+    "modelLR.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeLR = end - start\n",
+    "print('Time: ', TimeLR)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = modelLR.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Logistic Regression\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = modelLR.predict(X_train)\n",
+    "accuracyLR2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyLR2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 412,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  2.9950499534606934\n",
+      "{'Random Forest': {'model': 'Random Forest', 'accuracy': 0.8369606720122184, 'precision': 0.8480509148766905, 'recall': 0.8187403993855606, 'f1-score': 0.8331379445095739, 'r2': 0.34782129473142764}}\n",
+      "Accuracy on train set: 0.9616895874263262\n"
+     ]
+    }
+   ],
+   "source": [
+    "#RandomForestClassifier\n",
+    "start = time.time()\n",
+    "rfc = RandomForestClassifier()\n",
+    "rfc.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeRFC = end - start\n",
+    "print('Time: ', TimeRFC)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = rfc.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Random Forest\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = rfc.predict(X_train)\n",
+    "accuracyRFC2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyRFC2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.06399965286254883\n",
+      "{'LinearSVC': {'model': 'LinearSVC', 'accuracy': 0.8518518518518519, 'precision': 0.8395245170876672, 'recall': 0.8678955453149002, 'f1-score': 0.8534743202416918, 'r2': 0.4073879680463558}}\n",
+      "Accuracy on train set: 0.8551080550098232\n"
+     ]
+    }
+   ],
+   "source": [
+    "#LinearSVC\n",
+    "start = time.time()\n",
+    "svc = LinearSVC()\n",
+    "svc.fit(X_train, y_train) \n",
+    "end = time.time()\n",
+    "TimeSVC = end - start\n",
+    "print('Time: ', TimeSVC)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = svc.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"LinearSVC\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = svc.predict(X_train)\n",
+    "accuracySVC2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracySVC2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model performance comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 414,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Trees</td>\n",
+       "      <td>0.830088</td>\n",
+       "      <td>0.864061</td>\n",
+       "      <td>0.781106</td>\n",
+       "      <td>0.820492</td>\n",
+       "      <td>0.320329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Multinomial Naive Bayes</td>\n",
+       "      <td>0.833524</td>\n",
+       "      <td>0.831547</td>\n",
+       "      <td>0.834101</td>\n",
+       "      <td>0.832822</td>\n",
+       "      <td>0.334075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gaussian Naive Bayes</td>\n",
+       "      <td>0.63803</td>\n",
+       "      <td>0.580675</td>\n",
+       "      <td>0.978495</td>\n",
+       "      <td>0.728833</td>\n",
+       "      <td>-0.447928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.851852</td>\n",
+       "      <td>0.85208</td>\n",
+       "      <td>0.849462</td>\n",
+       "      <td>0.850769</td>\n",
+       "      <td>0.407388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>0.836961</td>\n",
+       "      <td>0.848051</td>\n",
+       "      <td>0.81874</td>\n",
+       "      <td>0.833138</td>\n",
+       "      <td>0.347821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>LinearSVC</td>\n",
+       "      <td>0.851852</td>\n",
+       "      <td>0.839525</td>\n",
+       "      <td>0.867896</td>\n",
+       "      <td>0.853474</td>\n",
+       "      <td>0.407388</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     model  accuracy precision    recall  f1-score        r2\n",
+       "0           Decision Trees  0.830088  0.864061  0.781106  0.820492  0.320329\n",
+       "1  Multinomial Naive Bayes  0.833524  0.831547  0.834101  0.832822  0.334075\n",
+       "2     Gaussian Naive Bayes   0.63803  0.580675  0.978495  0.728833 -0.447928\n",
+       "3      Logistic Regression  0.851852   0.85208  0.849462  0.850769  0.407388\n",
+       "4            Random Forest  0.836961  0.848051   0.81874  0.833138  0.347821\n",
+       "5                LinearSVC  0.851852  0.839525  0.867896  0.853474  0.407388"
+      ]
+     },
+     "execution_count": 414,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_metrics = pd.DataFrame(all_metrics).T\n",
+    "all_metrics = all_metrics.reset_index(drop=True)\n",
+    "all_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Accuracy_train</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Trees</td>\n",
+       "      <td>0.823510</td>\n",
+       "      <td>0.112002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Multinomial Naive Bayes</td>\n",
+       "      <td>0.836608</td>\n",
+       "      <td>0.031969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gaussian Naive Bayes</td>\n",
+       "      <td>0.646038</td>\n",
+       "      <td>0.040006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.854289</td>\n",
+       "      <td>0.271995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>0.961690</td>\n",
+       "      <td>2.995050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>LinearSVC</td>\n",
+       "      <td>0.855108</td>\n",
+       "      <td>0.064000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Model  Accuracy_train      Time\n",
+       "0           Decision Trees        0.823510  0.112002\n",
+       "1  Multinomial Naive Bayes        0.836608  0.031969\n",
+       "2     Gaussian Naive Bayes        0.646038  0.040006\n",
+       "3      Logistic Regression        0.854289  0.271995\n",
+       "4            Random Forest        0.961690  2.995050\n",
+       "5                LinearSVC        0.855108  0.064000"
+      ]
+     },
+     "execution_count": 415,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Creating new df to store model performances\n",
+    "Model = ['Decision Trees', 'Multinomial Naive Bayes', 'Gaussian Naive Bayes', 'Logistic Regression', 'Random Forest', 'LinearSVC']\n",
+    "Accuracy_train = [accuracyDC2, accuracyNB2, accuracyGNB2, accuracyLR2, accuracyRFC2, accuracySVC2]\n",
+    "Time = [TimeDC, TimeNB, TimeGNB, TimeLR, TimeRFC, TimeSVC]\n",
+    "\n",
+    "#Create df from lists\n",
+    "cols = ['Model', 'Accuracy_train', 'Time']\n",
+    "data = list(zip(Model, Accuracy_train, Time))\n",
+    "\n",
+    "performance = pd.DataFrame(data, columns=cols)\n",
+    "performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 416,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>Accuracy_train</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Trees</td>\n",
+       "      <td>0.830088</td>\n",
+       "      <td>0.864061</td>\n",
+       "      <td>0.781106</td>\n",
+       "      <td>0.820492</td>\n",
+       "      <td>0.320329</td>\n",
+       "      <td>0.823510</td>\n",
+       "      <td>0.112002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Multinomial Naive Bayes</td>\n",
+       "      <td>0.833524</td>\n",
+       "      <td>0.831547</td>\n",
+       "      <td>0.834101</td>\n",
+       "      <td>0.832822</td>\n",
+       "      <td>0.334075</td>\n",
+       "      <td>0.836608</td>\n",
+       "      <td>0.031969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gaussian Naive Bayes</td>\n",
+       "      <td>0.63803</td>\n",
+       "      <td>0.580675</td>\n",
+       "      <td>0.978495</td>\n",
+       "      <td>0.728833</td>\n",
+       "      <td>-0.447928</td>\n",
+       "      <td>0.646038</td>\n",
+       "      <td>0.040006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.851852</td>\n",
+       "      <td>0.85208</td>\n",
+       "      <td>0.849462</td>\n",
+       "      <td>0.850769</td>\n",
+       "      <td>0.407388</td>\n",
+       "      <td>0.854289</td>\n",
+       "      <td>0.271995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>0.836961</td>\n",
+       "      <td>0.848051</td>\n",
+       "      <td>0.81874</td>\n",
+       "      <td>0.833138</td>\n",
+       "      <td>0.347821</td>\n",
+       "      <td>0.961690</td>\n",
+       "      <td>2.995050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>LinearSVC</td>\n",
+       "      <td>0.851852</td>\n",
+       "      <td>0.839525</td>\n",
+       "      <td>0.867896</td>\n",
+       "      <td>0.853474</td>\n",
+       "      <td>0.407388</td>\n",
+       "      <td>0.855108</td>\n",
+       "      <td>0.064000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     model  accuracy precision    recall  f1-score        r2  \\\n",
+       "0           Decision Trees  0.830088  0.864061  0.781106  0.820492  0.320329   \n",
+       "1  Multinomial Naive Bayes  0.833524  0.831547  0.834101  0.832822  0.334075   \n",
+       "2     Gaussian Naive Bayes   0.63803  0.580675  0.978495  0.728833 -0.447928   \n",
+       "3      Logistic Regression  0.851852   0.85208  0.849462  0.850769  0.407388   \n",
+       "4            Random Forest  0.836961  0.848051   0.81874  0.833138  0.347821   \n",
+       "5                LinearSVC  0.851852  0.839525  0.867896  0.853474  0.407388   \n",
+       "\n",
+       "   Accuracy_train      Time  \n",
+       "0        0.823510  0.112002  \n",
+       "1        0.836608  0.031969  \n",
+       "2        0.646038  0.040006  \n",
+       "3        0.854289  0.271995  \n",
+       "4        0.961690  2.995050  \n",
+       "5        0.855108  0.064000  "
+      ]
+     },
+     "execution_count": 416,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Join result2018 with weather2018 to get the Maximum temperature (Degree C)\n",
+    "all_performance = pd.merge(left = all_metrics , right = performance ,\n",
+    "                 left_on = ['model'], right_on = ['Model'], how = 'left')\n",
+    "drop_cols = ['Model']\n",
+    "all_performance.drop(drop_cols, axis=1, inplace=True)\n",
+    "\n",
+    "all_performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unfortunately, none of the models has good enough r2 values. The best model is Logistic Regression with $R^2$ just approximately 0.4. We cannot confidently say that Logistic Regression is a good fit to predict the salary of Data Scientists.\n",
+    "\n",
+    "**This question we leave for further exploration in future projects.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hamming Loss (HL) and Jacard Score On Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Hamming loss is the fraction of labels that are incorrectly predicted ( evaluation metrics for a classifier model.) \n",
+    "- The Jaccard Index, also known as the Jaccard similarity coefficient, is a statistic used in understanding the similarities between sample sets. (To measure Similarity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 417,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clf:  RandomForestClassifier\n",
+      "Jacard score: 0.7062750333778371\n",
+      "Hamming loss: 0.16800305460099274\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "def avg_jacard(y_true,y_pred):\n",
+    "\n",
+    "    jacard = np.minimum(y_true,y_pred).sum(axis=0) / np.maximum(y_true,y_pred).sum(axis=0)\n",
+    "    \n",
+    "    return jacard.mean()\n",
+    "\n",
+    "def print_score(y_pred, clf):\n",
+    "    print(\"Clf: \", clf.__class__.__name__)\n",
+    "    print(\"Jacard score: {}\".format(avg_jacard(y_test, y_pred)))\n",
+    "    print(\"Hamming loss: {}\".format(hamming_loss(y_pred, y_test)))\n",
+    "    print(\"---\")    \n",
+    "\n",
+    "rfc = RandomForestClassifier()\n",
+    "rfc.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = rfc.predict(X_test)\n",
+    "\n",
+    "print_score(y_pred, rfc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 418,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clf:  MLPClassifier\n",
+      "Jacard score: 0.6990861618798956\n",
+      "Hamming loss: 0.1760213822069492\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlpc = MLPClassifier()\n",
+    "mlpc.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = mlpc.predict(X_test)\n",
+    "\n",
+    "print_score(y_pred, mlpc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 419,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clf:  SGDClassifier\n",
+      "Jacard score: 0.7312042581503659\n",
+      "Hamming loss: 0.15425735013363878\n",
+      "---\n",
+      "Clf:  LogisticRegression\n",
+      "Jacard score: 0.7402945113788487\n",
+      "Hamming loss: 0.14814814814814814\n",
+      "---\n",
+      "Clf:  MultinomialNB\n",
+      "Jacard score: 0.7124183006535948\n",
+      "Hamming loss: 0.16800305460099274\n",
+      "---\n",
+      "Clf:  LinearSVC\n",
+      "Jacard score: 0.7444005270092227\n",
+      "Hamming loss: 0.14814814814814814\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "sgd = SGDClassifier()\n",
+    "lr = LogisticRegression()\n",
+    "mn = MultinomialNB()\n",
+    "svc = LinearSVC()\n",
+    "\n",
+    "for classifier in [sgd, lr, mn, svc,]:\n",
+    "    clf = OneVsRestClassifier(classifier)\n",
+    "    clf.fit(X_train, y_train)\n",
+    "    y_pred = clf.predict(X_test)\n",
+    "    print_score(y_pred, classifier)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Findings: It has been found that better Hamming loss has been found in Logistic Regression and Linear SVC **which is 0.14815**<br>\n",
+    "Jaccard similarity scores give us the distribution of label sets when using the models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predicting what causing Job satisfaction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An examination of work satisfaction variables based on Stack Over Flow survey data from 2020. Job satisfaction can be defined by factors such as compensation, benefits, work environment, team members, work-life balance, education level, place, and so on. By analyzing the Stack Over Flow survey data from 2020, I will try to find some features that are negatively and positively affecting job satisfaction in various countries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 420,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Very satisfied                        12439\n",
+       "Slightly satisfied                    11953\n",
+       "Slightly dissatisfied                  6269\n",
+       "Neither satisfied nor dissatisfied     4669\n",
+       "Very dissatisfied                      3106\n",
+       "Name: CurrentJobSatis, dtype: int64"
+      ]
+     },
+     "execution_count": 420,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 421,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Very satisfied', 'Slightly satisfied', 'Slightly dissatisfied', 'Neither satisfied nor dissatisfied', 'Very dissatisfied']\n",
+      "[12439, 11953, 6269, 4669, 3106]\n"
+     ]
+    }
+   ],
+   "source": [
+    "participation_rate = df2020['CurrentJobSatis'].value_counts().keys().tolist()\n",
+    "print(participation_rate)\n",
+    "count = df2020['CurrentJobSatis'].value_counts().tolist()\n",
+    "print(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 422,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(count, explode = (0.01,0.01,0.1,0.1,0.2), labels = participation_rate, shadow=True, autopct=lambda p : f'{p:.2f}%  ({p * sum(count)/100:,.0f})', textprops={'fontsize':18, 'weight':'bold'})\n",
+    "plt.title(\"Propotion of Job Satisfaction from the Dataset\",fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**In further Analysis, we will split into two categories like Satisfied or Not Satisfied**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 423,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Applying one hot encoding\n",
+    "df_indicator = df.isnull().astype(int).add_suffix('_nan')\n",
+    "df = pd.concat([df, df_indicator], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 424,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "best mean cross-validation score: -0.262\n",
+      "best parameters: {'max_depth': 40, 'min_samples_leaf': 10}\n",
+      "test-set score: -0.258\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Grid search for good parameters, I used the mean absolute error as the main measure of quality\n",
+    "param_grid = {'min_samples_leaf': [10,15,20],'max_depth': [20,30,40]}\n",
+    "grid = GridSearchCV(RandomForestRegressor(n_estimators=100,n_jobs=-1, oob_score=True), param_grid=param_grid,\n",
+    "                    scoring='neg_mean_absolute_error',cv=5, return_train_score=True)\n",
+    "X_train_grit = X_train.sample(frac=0.5, random_state=42)\n",
+    "grid.fit(X_train_grit, y_train.loc[X_train_grit.index])\n",
+    "print(\"best mean cross-validation score: {:.3f}\".format(grid.best_score_))\n",
+    "print(\"best parameters: {}\".format(grid.best_params_))\n",
+    "print(\"test-set score: {:.3f}\".format(grid.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Here Random Forest is used to Predicting Job satisfaction, model did not yield much better output and turned out to be very complex to get insights.** Random forest Regressor, Logistic Regression which may yield good results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Trying with Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Used Sklearn library to create a Logistic Regression model.\n",
+    "\n",
+    "Before creating a model, need to create data, Using model coefficients, features that have negative and positive effects on job satisfaction to be calculated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 425,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numericals = [\"Age\",\"SalaryUSD\",\"YearsCodePro\"]\n",
+    "categoricals = [\"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 426,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 427,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Very satisfied                        12439\n",
+       "Slightly satisfied                    11953\n",
+       "Slightly dissatisfied                  6269\n",
+       "Neither satisfied nor dissatisfied     4669\n",
+       "Very dissatisfied                      3106\n",
+       "Name: CurrentJobSatis, dtype: int64"
+      ]
+     },
+     "execution_count": 427,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Performing further Spliting of CurrentJobSatis Coloumn**\n",
+    "- Delete \"Neither satisfied nor dissatisfied\"\n",
+    "- Combine \"Very satisfied\" and \"Slightly satisfied\", label as \"Satisfied\" -->1\n",
+    "- Combine \"Very dissatisfied\" and \"Slightly dissatisfied\", label as \"Dissatisfied\"-->0\n",
+    "- Delete rows \"Neither satisfied nor dissatisfied\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 428,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df = df2020.drop(df2020[df2020.CurrentJobSatis == \"Neither satisfied nor dissatisfied\"].index)\n",
+    "\n",
+    "df.CurrentJobSatis = [1 if each == \"Very satisfied\" else \n",
+    "             1 if each == \"Slightly satisfied\" else \n",
+    "             0 if each == \"Very dissatisfied\"else \n",
+    "             0 if each == \"Slightly dissatisfied\" else\n",
+    "             each for each in df.CurrentJobSatis]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 429,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dropping nan in Converted Salary if any\n",
+    "df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 430,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>36</td>\n",
+       "      <td>116000.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>32315.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Masters</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23</td>\n",
+       "      <td>40070.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49</td>\n",
+       "      <td>14268.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>53</td>\n",
+       "      <td>38916.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No major</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age  SalaryUSD  YearsCodePro         Country    EdLevel Employment  \\\n",
+       "1   36   116000.0          13.0   United States  Bachelors  Full-time   \n",
+       "2   22    32315.0           4.0  United Kingdom    Masters  Full-time   \n",
+       "3   23    40070.0           2.0  United Kingdom  Bachelors  Full-time   \n",
+       "4   49    14268.0           7.0           Spain  No Degree  Full-time   \n",
+       "5   53    38916.0          20.0     Netherlands  No Degree  Full-time   \n",
+       "\n",
+       "  Hobbyist    UndergradMajor  CurrentJobSatis  \n",
+       "1      Yes  Computer Science                0  \n",
+       "2      Yes         Math/Stat                1  \n",
+       "3      Yes  Computer Science                0  \n",
+       "4       No         Math/Stat                0  \n",
+       "5      Yes          No major                1  "
+      ]
+     },
+     "execution_count": 430,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols= [\"Age\",\"SalaryUSD\",\"YearsCodePro\", \"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\", \"CurrentJobSatis\"]\n",
+    "df = df[cols]\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 431,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# one hot encoding\n",
+    "df = pd.get_dummies(df,  columns = categoricals )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 432,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalization of numerical features\n",
+    "for each in numericals:\n",
+    "    df[each] = (df[each] - df[each].min()) / (df[each].max() - df[each].min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 433,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 434,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data into X and y\n",
+    "X = df.drop(\"CurrentJobSatis\", axis = 1)\n",
+    "y = df.CurrentJobSatis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 435,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>Country_Afghanistan</th>\n",
+       "      <th>Country_Albania</th>\n",
+       "      <th>Country_Algeria</th>\n",
+       "      <th>Country_Andorra</th>\n",
+       "      <th>Country_Angola</th>\n",
+       "      <th>Country_Argentina</th>\n",
+       "      <th>Country_Armenia</th>\n",
+       "      <th>Country_Australia</th>\n",
+       "      <th>Country_Austria</th>\n",
+       "      <th>Country_Azerbaijan</th>\n",
+       "      <th>Country_Bahamas</th>\n",
+       "      <th>Country_Bahrain</th>\n",
+       "      <th>Country_Bangladesh</th>\n",
+       "      <th>Country_Barbados</th>\n",
+       "      <th>Country_Belarus</th>\n",
+       "      <th>Country_Belgium</th>\n",
+       "      <th>Country_Benin</th>\n",
+       "      <th>Country_Bhutan</th>\n",
+       "      <th>Country_Bolivia</th>\n",
+       "      <th>Country_Bosnia and Herzegovina</th>\n",
+       "      <th>Country_Botswana</th>\n",
+       "      <th>Country_Brazil</th>\n",
+       "      <th>Country_Brunei Darussalam</th>\n",
+       "      <th>Country_Bulgaria</th>\n",
+       "      <th>Country_Burkina Faso</th>\n",
+       "      <th>Country_Cambodia</th>\n",
+       "      <th>Country_Cameroon</th>\n",
+       "      <th>Country_Canada</th>\n",
+       "      <th>Country_Chile</th>\n",
+       "      <th>Country_China</th>\n",
+       "      <th>Country_Colombia</th>\n",
+       "      <th>Country_Costa Rica</th>\n",
+       "      <th>Country_Croatia</th>\n",
+       "      <th>Country_Cuba</th>\n",
+       "      <th>Country_Cyprus</th>\n",
+       "      <th>Country_Czech Republic</th>\n",
+       "      <th>Country_Côte d'Ivoire</th>\n",
+       "      <th>Country_Democratic Republic of the Congo</th>\n",
+       "      <th>Country_Denmark</th>\n",
+       "      <th>Country_Dominican Republic</th>\n",
+       "      <th>Country_Ecuador</th>\n",
+       "      <th>Country_Egypt</th>\n",
+       "      <th>Country_El Salvador</th>\n",
+       "      <th>Country_Estonia</th>\n",
+       "      <th>Country_Ethiopia</th>\n",
+       "      <th>Country_Finland</th>\n",
+       "      <th>Country_France</th>\n",
+       "      <th>Country_Georgia</th>\n",
+       "      <th>Country_Germany</th>\n",
+       "      <th>Country_Ghana</th>\n",
+       "      <th>Country_Greece</th>\n",
+       "      <th>Country_Guatemala</th>\n",
+       "      <th>Country_Guinea</th>\n",
+       "      <th>Country_Guyana</th>\n",
+       "      <th>Country_Haiti</th>\n",
+       "      <th>Country_Honduras</th>\n",
+       "      <th>Country_Hong Kong (S.A.R.)</th>\n",
+       "      <th>Country_Hungary</th>\n",
+       "      <th>Country_Iceland</th>\n",
+       "      <th>Country_India</th>\n",
+       "      <th>Country_Indonesia</th>\n",
+       "      <th>Country_Iran</th>\n",
+       "      <th>Country_Iraq</th>\n",
+       "      <th>Country_Ireland</th>\n",
+       "      <th>Country_Isle of Man</th>\n",
+       "      <th>Country_Israel</th>\n",
+       "      <th>Country_Italy</th>\n",
+       "      <th>Country_Jamaica</th>\n",
+       "      <th>Country_Japan</th>\n",
+       "      <th>Country_Jordan</th>\n",
+       "      <th>Country_Kazakhstan</th>\n",
+       "      <th>Country_Kenya</th>\n",
+       "      <th>Country_Kosovo</th>\n",
+       "      <th>Country_Kuwait</th>\n",
+       "      <th>Country_Kyrgyzstan</th>\n",
+       "      <th>Country_Lao People's Democratic Republic</th>\n",
+       "      <th>Country_Latvia</th>\n",
+       "      <th>Country_Lebanon</th>\n",
+       "      <th>Country_Lithuania</th>\n",
+       "      <th>Country_Luxembourg</th>\n",
+       "      <th>Country_Madagascar</th>\n",
+       "      <th>Country_Malawi</th>\n",
+       "      <th>Country_Malaysia</th>\n",
+       "      <th>Country_Maldives</th>\n",
+       "      <th>Country_Malta</th>\n",
+       "      <th>Country_Mauritius</th>\n",
+       "      <th>Country_Mexico</th>\n",
+       "      <th>Country_Monaco</th>\n",
+       "      <th>Country_Mongolia</th>\n",
+       "      <th>Country_Montenegro</th>\n",
+       "      <th>Country_Morocco</th>\n",
+       "      <th>Country_Myanmar</th>\n",
+       "      <th>Country_Namibia</th>\n",
+       "      <th>Country_Nepal</th>\n",
+       "      <th>Country_Netherlands</th>\n",
+       "      <th>Country_New Zealand</th>\n",
+       "      <th>Country_Nicaragua</th>\n",
+       "      <th>Country_Niger</th>\n",
+       "      <th>Country_Nigeria</th>\n",
+       "      <th>Country_Nomadic</th>\n",
+       "      <th>Country_Norway</th>\n",
+       "      <th>Country_Oman</th>\n",
+       "      <th>Country_Pakistan</th>\n",
+       "      <th>Country_Panama</th>\n",
+       "      <th>Country_Paraguay</th>\n",
+       "      <th>Country_Peru</th>\n",
+       "      <th>Country_Philippines</th>\n",
+       "      <th>Country_Poland</th>\n",
+       "      <th>Country_Portugal</th>\n",
+       "      <th>Country_Qatar</th>\n",
+       "      <th>Country_Republic of Korea</th>\n",
+       "      <th>Country_Republic of Moldova</th>\n",
+       "      <th>Country_Romania</th>\n",
+       "      <th>Country_Russian Federation</th>\n",
+       "      <th>Country_Rwanda</th>\n",
+       "      <th>Country_San Marino</th>\n",
+       "      <th>Country_Saudi Arabia</th>\n",
+       "      <th>Country_Senegal</th>\n",
+       "      <th>Country_Serbia</th>\n",
+       "      <th>Country_Singapore</th>\n",
+       "      <th>Country_Slovakia</th>\n",
+       "      <th>Country_Slovenia</th>\n",
+       "      <th>Country_Somalia</th>\n",
+       "      <th>Country_South Africa</th>\n",
+       "      <th>Country_South Korea</th>\n",
+       "      <th>Country_Spain</th>\n",
+       "      <th>Country_Sri Lanka</th>\n",
+       "      <th>Country_Sudan</th>\n",
+       "      <th>Country_Swaziland</th>\n",
+       "      <th>Country_Sweden</th>\n",
+       "      <th>Country_Switzerland</th>\n",
+       "      <th>Country_Syrian Arab Republic</th>\n",
+       "      <th>Country_Taiwan</th>\n",
+       "      <th>Country_Thailand</th>\n",
+       "      <th>Country_The former Yugoslav Republic of Macedonia</th>\n",
+       "      <th>Country_Togo</th>\n",
+       "      <th>Country_Trinidad and Tobago</th>\n",
+       "      <th>Country_Tunisia</th>\n",
+       "      <th>Country_Turkey</th>\n",
+       "      <th>Country_Turkmenistan</th>\n",
+       "      <th>Country_Uganda</th>\n",
+       "      <th>Country_Ukraine</th>\n",
+       "      <th>Country_United Arab Emirates</th>\n",
+       "      <th>Country_United Kingdom</th>\n",
+       "      <th>Country_United Republic of Tanzania</th>\n",
+       "      <th>Country_United States</th>\n",
+       "      <th>Country_Uruguay</th>\n",
+       "      <th>Country_Uzbekistan</th>\n",
+       "      <th>Country_Venezuela, Bolivarian Republic of...</th>\n",
+       "      <th>Country_Viet Nam</th>\n",
+       "      <th>Country_Yemen</th>\n",
+       "      <th>Country_Zambia</th>\n",
+       "      <th>Country_Zimbabwe</th>\n",
+       "      <th>EdLevel_Associate</th>\n",
+       "      <th>EdLevel_Bachelors</th>\n",
+       "      <th>EdLevel_Doctorate</th>\n",
+       "      <th>EdLevel_Masters</th>\n",
+       "      <th>EdLevel_No Degree</th>\n",
+       "      <th>EdLevel_Professional</th>\n",
+       "      <th>Employment_Full-time</th>\n",
+       "      <th>Employment_Not employed</th>\n",
+       "      <th>Employment_Not employed, and not looking for work</th>\n",
+       "      <th>Employment_Part-time</th>\n",
+       "      <th>Employment_Retired</th>\n",
+       "      <th>Employment_Self-employed</th>\n",
+       "      <th>Employment_Student</th>\n",
+       "      <th>Hobbyist_No</th>\n",
+       "      <th>Hobbyist_Yes</th>\n",
+       "      <th>UndergradMajor_Arts and Science</th>\n",
+       "      <th>UndergradMajor_Business</th>\n",
+       "      <th>UndergradMajor_Computer Science</th>\n",
+       "      <th>UndergradMajor_Engineering</th>\n",
+       "      <th>UndergradMajor_Info Systems</th>\n",
+       "      <th>UndergradMajor_Math/Stat</th>\n",
+       "      <th>UndergradMajor_No major</th>\n",
+       "      <th>UndergradMajor_Other Science</th>\n",
+       "      <th>UndergradMajor_Web Design/Dev</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.466667</td>\n",
+       "      <td>0.580055</td>\n",
+       "      <td>0.260870</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.155556</td>\n",
+       "      <td>0.161590</td>\n",
+       "      <td>0.065217</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.177778</td>\n",
+       "      <td>0.200369</td>\n",
+       "      <td>0.021739</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.755556</td>\n",
+       "      <td>0.071347</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.844444</td>\n",
+       "      <td>0.194598</td>\n",
+       "      <td>0.413043</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>33767 rows × 180 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Age  SalaryUSD  YearsCodePro  Country_Afghanistan  \\\n",
+       "1      0.466667   0.580055      0.260870                    0   \n",
+       "2      0.155556   0.161590      0.065217                    0   \n",
+       "3      0.177778   0.200369      0.021739                    0   \n",
+       "4      0.755556   0.071347      0.130435                    0   \n",
+       "5      0.844444   0.194598      0.413043                    0   \n",
+       "...         ...        ...           ...                  ...   \n",
+       "41564  0.133333   0.041597      0.168261                    0   \n",
+       "41565  0.133333   0.041597      0.168261                    0   \n",
+       "41566  0.133333   0.026343      0.168261                    0   \n",
+       "41567  0.155556   0.048065      0.043478                    0   \n",
+       "41568  0.577778   0.001000      0.413043                    0   \n",
+       "\n",
+       "       Country_Albania  Country_Algeria  Country_Andorra  Country_Angola  \\\n",
+       "1                    0                0                0               0   \n",
+       "2                    0                0                0               0   \n",
+       "3                    0                0                0               0   \n",
+       "4                    0                0                0               0   \n",
+       "5                    0                0                0               0   \n",
+       "...                ...              ...              ...             ...   \n",
+       "41564                0                0                0               0   \n",
+       "41565                0                0                0               0   \n",
+       "41566                0                0                0               0   \n",
+       "41567                0                0                0               0   \n",
+       "41568                0                0                0               0   \n",
+       "\n",
+       "       Country_Argentina  Country_Armenia  Country_Australia  Country_Austria  \\\n",
+       "1                      0                0                  0                0   \n",
+       "2                      0                0                  0                0   \n",
+       "3                      0                0                  0                0   \n",
+       "4                      0                0                  0                0   \n",
+       "5                      0                0                  0                0   \n",
+       "...                  ...              ...                ...              ...   \n",
+       "41564                  0                0                  0                0   \n",
+       "41565                  0                0                  0                0   \n",
+       "41566                  0                0                  0                0   \n",
+       "41567                  0                0                  0                0   \n",
+       "41568                  0                0                  0                0   \n",
+       "\n",
+       "       Country_Azerbaijan  Country_Bahamas  Country_Bahrain  \\\n",
+       "1                       0                0                0   \n",
+       "2                       0                0                0   \n",
+       "3                       0                0                0   \n",
+       "4                       0                0                0   \n",
+       "5                       0                0                0   \n",
+       "...                   ...              ...              ...   \n",
+       "41564                   0                0                0   \n",
+       "41565                   0                0                0   \n",
+       "41566                   0                0                0   \n",
+       "41567                   0                0                0   \n",
+       "41568                   0                0                0   \n",
+       "\n",
+       "       Country_Bangladesh  Country_Barbados  Country_Belarus  Country_Belgium  \\\n",
+       "1                       0                 0                0                0   \n",
+       "2                       0                 0                0                0   \n",
+       "3                       0                 0                0                0   \n",
+       "4                       0                 0                0                0   \n",
+       "5                       0                 0                0                0   \n",
+       "...                   ...               ...              ...              ...   \n",
+       "41564                   0                 0                0                0   \n",
+       "41565                   0                 0                0                0   \n",
+       "41566                   0                 0                0                0   \n",
+       "41567                   0                 0                0                0   \n",
+       "41568                   0                 0                0                0   \n",
+       "\n",
+       "       Country_Benin  Country_Bhutan  Country_Bolivia  \\\n",
+       "1                  0               0                0   \n",
+       "2                  0               0                0   \n",
+       "3                  0               0                0   \n",
+       "4                  0               0                0   \n",
+       "5                  0               0                0   \n",
+       "...              ...             ...              ...   \n",
+       "41564              0               0                0   \n",
+       "41565              0               0                0   \n",
+       "41566              0               0                0   \n",
+       "41567              0               0                0   \n",
+       "41568              0               0                0   \n",
+       "\n",
+       "       Country_Bosnia and Herzegovina  Country_Botswana  Country_Brazil  \\\n",
+       "1                                   0                 0               0   \n",
+       "2                                   0                 0               0   \n",
+       "3                                   0                 0               0   \n",
+       "4                                   0                 0               0   \n",
+       "5                                   0                 0               0   \n",
+       "...                               ...               ...             ...   \n",
+       "41564                               0                 0               0   \n",
+       "41565                               0                 0               0   \n",
+       "41566                               0                 0               0   \n",
+       "41567                               0                 0               0   \n",
+       "41568                               0                 0               0   \n",
+       "\n",
+       "       Country_Brunei Darussalam  Country_Bulgaria  Country_Burkina Faso  \\\n",
+       "1                              0                 0                     0   \n",
+       "2                              0                 0                     0   \n",
+       "3                              0                 0                     0   \n",
+       "4                              0                 0                     0   \n",
+       "5                              0                 0                     0   \n",
+       "...                          ...               ...                   ...   \n",
+       "41564                          0                 0                     0   \n",
+       "41565                          0                 0                     0   \n",
+       "41566                          0                 0                     0   \n",
+       "41567                          0                 0                     0   \n",
+       "41568                          0                 0                     0   \n",
+       "\n",
+       "       Country_Cambodia  Country_Cameroon  Country_Canada  Country_Chile  \\\n",
+       "1                     0                 0               0              0   \n",
+       "2                     0                 0               0              0   \n",
+       "3                     0                 0               0              0   \n",
+       "4                     0                 0               0              0   \n",
+       "5                     0                 0               0              0   \n",
+       "...                 ...               ...             ...            ...   \n",
+       "41564                 0                 0               0              0   \n",
+       "41565                 0                 0               0              0   \n",
+       "41566                 0                 0               0              0   \n",
+       "41567                 0                 0               0              0   \n",
+       "41568                 0                 0               0              0   \n",
+       "\n",
+       "       Country_China  Country_Colombia  Country_Costa Rica  Country_Croatia  \\\n",
+       "1                  0                 0                   0                0   \n",
+       "2                  0                 0                   0                0   \n",
+       "3                  0                 0                   0                0   \n",
+       "4                  0                 0                   0                0   \n",
+       "5                  0                 0                   0                0   \n",
+       "...              ...               ...                 ...              ...   \n",
+       "41564              0                 0                   0                0   \n",
+       "41565              0                 0                   0                0   \n",
+       "41566              0                 0                   0                0   \n",
+       "41567              0                 0                   0                0   \n",
+       "41568              0                 0                   0                0   \n",
+       "\n",
+       "       Country_Cuba  Country_Cyprus  Country_Czech Republic  \\\n",
+       "1                 0               0                       0   \n",
+       "2                 0               0                       0   \n",
+       "3                 0               0                       0   \n",
+       "4                 0               0                       0   \n",
+       "5                 0               0                       0   \n",
+       "...             ...             ...                     ...   \n",
+       "41564             0               0                       0   \n",
+       "41565             0               0                       0   \n",
+       "41566             0               0                       0   \n",
+       "41567             0               0                       0   \n",
+       "41568             0               0                       0   \n",
+       "\n",
+       "       Country_Côte d'Ivoire  Country_Democratic Republic of the Congo  \\\n",
+       "1                          0                                         0   \n",
+       "2                          0                                         0   \n",
+       "3                          0                                         0   \n",
+       "4                          0                                         0   \n",
+       "5                          0                                         0   \n",
+       "...                      ...                                       ...   \n",
+       "41564                      0                                         0   \n",
+       "41565                      0                                         0   \n",
+       "41566                      0                                         0   \n",
+       "41567                      0                                         0   \n",
+       "41568                      0                                         0   \n",
+       "\n",
+       "       Country_Denmark  Country_Dominican Republic  Country_Ecuador  \\\n",
+       "1                    0                           0                0   \n",
+       "2                    0                           0                0   \n",
+       "3                    0                           0                0   \n",
+       "4                    0                           0                0   \n",
+       "5                    0                           0                0   \n",
+       "...                ...                         ...              ...   \n",
+       "41564                0                           0                0   \n",
+       "41565                0                           0                0   \n",
+       "41566                0                           0                0   \n",
+       "41567                0                           0                0   \n",
+       "41568                0                           0                0   \n",
+       "\n",
+       "       Country_Egypt  Country_El Salvador  Country_Estonia  Country_Ethiopia  \\\n",
+       "1                  0                    0                0                 0   \n",
+       "2                  0                    0                0                 0   \n",
+       "3                  0                    0                0                 0   \n",
+       "4                  0                    0                0                 0   \n",
+       "5                  0                    0                0                 0   \n",
+       "...              ...                  ...              ...               ...   \n",
+       "41564              0                    0                0                 0   \n",
+       "41565              0                    0                0                 0   \n",
+       "41566              0                    0                0                 0   \n",
+       "41567              0                    0                0                 0   \n",
+       "41568              0                    0                0                 0   \n",
+       "\n",
+       "       Country_Finland  Country_France  Country_Georgia  Country_Germany  \\\n",
+       "1                    0               0                0                0   \n",
+       "2                    0               0                0                0   \n",
+       "3                    0               0                0                0   \n",
+       "4                    0               0                0                0   \n",
+       "5                    0               0                0                0   \n",
+       "...                ...             ...              ...              ...   \n",
+       "41564                0               0                0                0   \n",
+       "41565                0               0                0                0   \n",
+       "41566                0               0                0                0   \n",
+       "41567                0               0                0                0   \n",
+       "41568                0               0                0                0   \n",
+       "\n",
+       "       Country_Ghana  Country_Greece  Country_Guatemala  Country_Guinea  \\\n",
+       "1                  0               0                  0               0   \n",
+       "2                  0               0                  0               0   \n",
+       "3                  0               0                  0               0   \n",
+       "4                  0               0                  0               0   \n",
+       "5                  0               0                  0               0   \n",
+       "...              ...             ...                ...             ...   \n",
+       "41564              0               0                  0               0   \n",
+       "41565              0               0                  0               0   \n",
+       "41566              0               0                  0               0   \n",
+       "41567              0               0                  0               0   \n",
+       "41568              0               0                  0               0   \n",
+       "\n",
+       "       Country_Guyana  Country_Haiti  Country_Honduras  \\\n",
+       "1                   0              0                 0   \n",
+       "2                   0              0                 0   \n",
+       "3                   0              0                 0   \n",
+       "4                   0              0                 0   \n",
+       "5                   0              0                 0   \n",
+       "...               ...            ...               ...   \n",
+       "41564               0              0                 0   \n",
+       "41565               0              0                 0   \n",
+       "41566               0              0                 0   \n",
+       "41567               0              0                 0   \n",
+       "41568               0              0                 0   \n",
+       "\n",
+       "       Country_Hong Kong (S.A.R.)  Country_Hungary  Country_Iceland  \\\n",
+       "1                               0                0                0   \n",
+       "2                               0                0                0   \n",
+       "3                               0                0                0   \n",
+       "4                               0                0                0   \n",
+       "5                               0                0                0   \n",
+       "...                           ...              ...              ...   \n",
+       "41564                           0                0                0   \n",
+       "41565                           0                0                0   \n",
+       "41566                           0                0                0   \n",
+       "41567                           0                0                0   \n",
+       "41568                           0                0                0   \n",
+       "\n",
+       "       Country_India  Country_Indonesia  Country_Iran  Country_Iraq  \\\n",
+       "1                  0                  0             0             0   \n",
+       "2                  0                  0             0             0   \n",
+       "3                  0                  0             0             0   \n",
+       "4                  0                  0             0             0   \n",
+       "5                  0                  0             0             0   \n",
+       "...              ...                ...           ...           ...   \n",
+       "41564              1                  0             0             0   \n",
+       "41565              1                  0             0             0   \n",
+       "41566              0                  1             0             0   \n",
+       "41567              0                  0             0             0   \n",
+       "41568              0                  0             0             0   \n",
+       "\n",
+       "       Country_Ireland  Country_Isle of Man  Country_Israel  Country_Italy  \\\n",
+       "1                    0                    0               0              0   \n",
+       "2                    0                    0               0              0   \n",
+       "3                    0                    0               0              0   \n",
+       "4                    0                    0               0              0   \n",
+       "5                    0                    0               0              0   \n",
+       "...                ...                  ...             ...            ...   \n",
+       "41564                0                    0               0              0   \n",
+       "41565                0                    0               0              0   \n",
+       "41566                0                    0               0              0   \n",
+       "41567                0                    0               0              0   \n",
+       "41568                0                    0               0              0   \n",
+       "\n",
+       "       Country_Jamaica  Country_Japan  Country_Jordan  Country_Kazakhstan  \\\n",
+       "1                    0              0               0                   0   \n",
+       "2                    0              0               0                   0   \n",
+       "3                    0              0               0                   0   \n",
+       "4                    0              0               0                   0   \n",
+       "5                    0              0               0                   0   \n",
+       "...                ...            ...             ...                 ...   \n",
+       "41564                0              0               0                   0   \n",
+       "41565                0              0               0                   0   \n",
+       "41566                0              0               0                   0   \n",
+       "41567                0              0               0                   0   \n",
+       "41568                0              0               0                   0   \n",
+       "\n",
+       "       Country_Kenya  Country_Kosovo  Country_Kuwait  Country_Kyrgyzstan  \\\n",
+       "1                  0               0               0                   0   \n",
+       "2                  0               0               0                   0   \n",
+       "3                  0               0               0                   0   \n",
+       "4                  0               0               0                   0   \n",
+       "5                  0               0               0                   0   \n",
+       "...              ...             ...             ...                 ...   \n",
+       "41564              0               0               0                   0   \n",
+       "41565              0               0               0                   0   \n",
+       "41566              0               0               0                   0   \n",
+       "41567              0               0               0                   0   \n",
+       "41568              0               0               0                   0   \n",
+       "\n",
+       "       Country_Lao People's Democratic Republic  Country_Latvia  \\\n",
+       "1                                             0               0   \n",
+       "2                                             0               0   \n",
+       "3                                             0               0   \n",
+       "4                                             0               0   \n",
+       "5                                             0               0   \n",
+       "...                                         ...             ...   \n",
+       "41564                                         0               0   \n",
+       "41565                                         0               0   \n",
+       "41566                                         0               0   \n",
+       "41567                                         0               0   \n",
+       "41568                                         0               0   \n",
+       "\n",
+       "       Country_Lebanon  Country_Lithuania  Country_Luxembourg  \\\n",
+       "1                    0                  0                   0   \n",
+       "2                    0                  0                   0   \n",
+       "3                    0                  0                   0   \n",
+       "4                    0                  0                   0   \n",
+       "5                    0                  0                   0   \n",
+       "...                ...                ...                 ...   \n",
+       "41564                0                  0                   0   \n",
+       "41565                0                  0                   0   \n",
+       "41566                0                  0                   0   \n",
+       "41567                0                  0                   0   \n",
+       "41568                0                  0                   0   \n",
+       "\n",
+       "       Country_Madagascar  Country_Malawi  Country_Malaysia  Country_Maldives  \\\n",
+       "1                       0               0                 0                 0   \n",
+       "2                       0               0                 0                 0   \n",
+       "3                       0               0                 0                 0   \n",
+       "4                       0               0                 0                 0   \n",
+       "5                       0               0                 0                 0   \n",
+       "...                   ...             ...               ...               ...   \n",
+       "41564                   0               0                 0                 0   \n",
+       "41565                   0               0                 0                 0   \n",
+       "41566                   0               0                 0                 0   \n",
+       "41567                   0               0                 0                 0   \n",
+       "41568                   0               0                 0                 0   \n",
+       "\n",
+       "       Country_Malta  Country_Mauritius  Country_Mexico  Country_Monaco  \\\n",
+       "1                  0                  0               0               0   \n",
+       "2                  0                  0               0               0   \n",
+       "3                  0                  0               0               0   \n",
+       "4                  0                  0               0               0   \n",
+       "5                  0                  0               0               0   \n",
+       "...              ...                ...             ...             ...   \n",
+       "41564              0                  0               0               0   \n",
+       "41565              0                  0               0               0   \n",
+       "41566              0                  0               0               0   \n",
+       "41567              0                  1               0               0   \n",
+       "41568              0                  0               0               0   \n",
+       "\n",
+       "       Country_Mongolia  Country_Montenegro  Country_Morocco  Country_Myanmar  \\\n",
+       "1                     0                   0                0                0   \n",
+       "2                     0                   0                0                0   \n",
+       "3                     0                   0                0                0   \n",
+       "4                     0                   0                0                0   \n",
+       "5                     0                   0                0                0   \n",
+       "...                 ...                 ...              ...              ...   \n",
+       "41564                 0                   0                0                0   \n",
+       "41565                 0                   0                0                0   \n",
+       "41566                 0                   0                0                0   \n",
+       "41567                 0                   0                0                0   \n",
+       "41568                 0                   0                0                0   \n",
+       "\n",
+       "       Country_Namibia  Country_Nepal  Country_Netherlands  \\\n",
+       "1                    0              0                    0   \n",
+       "2                    0              0                    0   \n",
+       "3                    0              0                    0   \n",
+       "4                    0              0                    0   \n",
+       "5                    0              0                    1   \n",
+       "...                ...            ...                  ...   \n",
+       "41564                0              0                    0   \n",
+       "41565                0              0                    0   \n",
+       "41566                0              0                    0   \n",
+       "41567                0              0                    0   \n",
+       "41568                0              0                    0   \n",
+       "\n",
+       "       Country_New Zealand  Country_Nicaragua  Country_Niger  Country_Nigeria  \\\n",
+       "1                        0                  0              0                0   \n",
+       "2                        0                  0              0                0   \n",
+       "3                        0                  0              0                0   \n",
+       "4                        0                  0              0                0   \n",
+       "5                        0                  0              0                0   \n",
+       "...                    ...                ...            ...              ...   \n",
+       "41564                    0                  0              0                0   \n",
+       "41565                    0                  0              0                0   \n",
+       "41566                    0                  0              0                0   \n",
+       "41567                    0                  0              0                0   \n",
+       "41568                    0                  0              0                0   \n",
+       "\n",
+       "       Country_Nomadic  Country_Norway  Country_Oman  Country_Pakistan  \\\n",
+       "1                    0               0             0                 0   \n",
+       "2                    0               0             0                 0   \n",
+       "3                    0               0             0                 0   \n",
+       "4                    0               0             0                 0   \n",
+       "5                    0               0             0                 0   \n",
+       "...                ...             ...           ...               ...   \n",
+       "41564                0               0             0                 0   \n",
+       "41565                0               0             0                 0   \n",
+       "41566                0               0             0                 0   \n",
+       "41567                0               0             0                 0   \n",
+       "41568                0               0             0                 0   \n",
+       "\n",
+       "       Country_Panama  Country_Paraguay  Country_Peru  Country_Philippines  \\\n",
+       "1                   0                 0             0                    0   \n",
+       "2                   0                 0             0                    0   \n",
+       "3                   0                 0             0                    0   \n",
+       "4                   0                 0             0                    0   \n",
+       "5                   0                 0             0                    0   \n",
+       "...               ...               ...           ...                  ...   \n",
+       "41564               0                 0             0                    0   \n",
+       "41565               0                 0             0                    0   \n",
+       "41566               0                 0             0                    0   \n",
+       "41567               0                 0             0                    0   \n",
+       "41568               0                 0             0                    0   \n",
+       "\n",
+       "       Country_Poland  Country_Portugal  Country_Qatar  \\\n",
+       "1                   0                 0              0   \n",
+       "2                   0                 0              0   \n",
+       "3                   0                 0              0   \n",
+       "4                   0                 0              0   \n",
+       "5                   0                 0              0   \n",
+       "...               ...               ...            ...   \n",
+       "41564               0                 0              0   \n",
+       "41565               0                 0              0   \n",
+       "41566               0                 0              0   \n",
+       "41567               0                 0              0   \n",
+       "41568               0                 0              0   \n",
+       "\n",
+       "       Country_Republic of Korea  Country_Republic of Moldova  \\\n",
+       "1                              0                            0   \n",
+       "2                              0                            0   \n",
+       "3                              0                            0   \n",
+       "4                              0                            0   \n",
+       "5                              0                            0   \n",
+       "...                          ...                          ...   \n",
+       "41564                          0                            0   \n",
+       "41565                          0                            0   \n",
+       "41566                          0                            0   \n",
+       "41567                          0                            0   \n",
+       "41568                          0                            0   \n",
+       "\n",
+       "       Country_Romania  Country_Russian Federation  Country_Rwanda  \\\n",
+       "1                    0                           0               0   \n",
+       "2                    0                           0               0   \n",
+       "3                    0                           0               0   \n",
+       "4                    0                           0               0   \n",
+       "5                    0                           0               0   \n",
+       "...                ...                         ...             ...   \n",
+       "41564                0                           0               0   \n",
+       "41565                0                           0               0   \n",
+       "41566                0                           0               0   \n",
+       "41567                0                           0               0   \n",
+       "41568                0                           0               0   \n",
+       "\n",
+       "       Country_San Marino  Country_Saudi Arabia  Country_Senegal  \\\n",
+       "1                       0                     0                0   \n",
+       "2                       0                     0                0   \n",
+       "3                       0                     0                0   \n",
+       "4                       0                     0                0   \n",
+       "5                       0                     0                0   \n",
+       "...                   ...                   ...              ...   \n",
+       "41564                   0                     0                0   \n",
+       "41565                   0                     0                0   \n",
+       "41566                   0                     0                0   \n",
+       "41567                   0                     0                0   \n",
+       "41568                   0                     0                0   \n",
+       "\n",
+       "       Country_Serbia  Country_Singapore  Country_Slovakia  Country_Slovenia  \\\n",
+       "1                   0                  0                 0                 0   \n",
+       "2                   0                  0                 0                 0   \n",
+       "3                   0                  0                 0                 0   \n",
+       "4                   0                  0                 0                 0   \n",
+       "5                   0                  0                 0                 0   \n",
+       "...               ...                ...               ...               ...   \n",
+       "41564               0                  0                 0                 0   \n",
+       "41565               0                  0                 0                 0   \n",
+       "41566               0                  0                 0                 0   \n",
+       "41567               0                  0                 0                 0   \n",
+       "41568               0                  0                 0                 0   \n",
+       "\n",
+       "       Country_Somalia  Country_South Africa  Country_South Korea  \\\n",
+       "1                    0                     0                    0   \n",
+       "2                    0                     0                    0   \n",
+       "3                    0                     0                    0   \n",
+       "4                    0                     0                    0   \n",
+       "5                    0                     0                    0   \n",
+       "...                ...                   ...                  ...   \n",
+       "41564                0                     0                    0   \n",
+       "41565                0                     0                    0   \n",
+       "41566                0                     0                    0   \n",
+       "41567                0                     0                    0   \n",
+       "41568                0                     0                    0   \n",
+       "\n",
+       "       Country_Spain  Country_Sri Lanka  Country_Sudan  Country_Swaziland  \\\n",
+       "1                  0                  0              0                  0   \n",
+       "2                  0                  0              0                  0   \n",
+       "3                  0                  0              0                  0   \n",
+       "4                  1                  0              0                  0   \n",
+       "5                  0                  0              0                  0   \n",
+       "...              ...                ...            ...                ...   \n",
+       "41564              0                  0              0                  0   \n",
+       "41565              0                  0              0                  0   \n",
+       "41566              0                  0              0                  0   \n",
+       "41567              0                  0              0                  0   \n",
+       "41568              0                  0              0                  0   \n",
+       "\n",
+       "       Country_Sweden  Country_Switzerland  Country_Syrian Arab Republic  \\\n",
+       "1                   0                    0                             0   \n",
+       "2                   0                    0                             0   \n",
+       "3                   0                    0                             0   \n",
+       "4                   0                    0                             0   \n",
+       "5                   0                    0                             0   \n",
+       "...               ...                  ...                           ...   \n",
+       "41564               0                    0                             0   \n",
+       "41565               0                    0                             0   \n",
+       "41566               0                    0                             0   \n",
+       "41567               0                    0                             0   \n",
+       "41568               0                    0                             0   \n",
+       "\n",
+       "       Country_Taiwan  Country_Thailand  \\\n",
+       "1                   0                 0   \n",
+       "2                   0                 0   \n",
+       "3                   0                 0   \n",
+       "4                   0                 0   \n",
+       "5                   0                 0   \n",
+       "...               ...               ...   \n",
+       "41564               0                 0   \n",
+       "41565               0                 0   \n",
+       "41566               0                 0   \n",
+       "41567               0                 0   \n",
+       "41568               0                 0   \n",
+       "\n",
+       "       Country_The former Yugoslav Republic of Macedonia  Country_Togo  \\\n",
+       "1                                                      0             0   \n",
+       "2                                                      0             0   \n",
+       "3                                                      0             0   \n",
+       "4                                                      0             0   \n",
+       "5                                                      0             0   \n",
+       "...                                                  ...           ...   \n",
+       "41564                                                  0             0   \n",
+       "41565                                                  0             0   \n",
+       "41566                                                  0             0   \n",
+       "41567                                                  0             0   \n",
+       "41568                                                  0             0   \n",
+       "\n",
+       "       Country_Trinidad and Tobago  Country_Tunisia  Country_Turkey  \\\n",
+       "1                                0                0               0   \n",
+       "2                                0                0               0   \n",
+       "3                                0                0               0   \n",
+       "4                                0                0               0   \n",
+       "5                                0                0               0   \n",
+       "...                            ...              ...             ...   \n",
+       "41564                            0                0               0   \n",
+       "41565                            0                0               0   \n",
+       "41566                            0                0               0   \n",
+       "41567                            0                0               0   \n",
+       "41568                            0                0               0   \n",
+       "\n",
+       "       Country_Turkmenistan  Country_Uganda  Country_Ukraine  \\\n",
+       "1                         0               0                0   \n",
+       "2                         0               0                0   \n",
+       "3                         0               0                0   \n",
+       "4                         0               0                0   \n",
+       "5                         0               0                0   \n",
+       "...                     ...             ...              ...   \n",
+       "41564                     0               0                0   \n",
+       "41565                     0               0                0   \n",
+       "41566                     0               0                0   \n",
+       "41567                     0               0                0   \n",
+       "41568                     0               0                0   \n",
+       "\n",
+       "       Country_United Arab Emirates  Country_United Kingdom  \\\n",
+       "1                                 0                       0   \n",
+       "2                                 0                       1   \n",
+       "3                                 0                       1   \n",
+       "4                                 0                       0   \n",
+       "5                                 0                       0   \n",
+       "...                             ...                     ...   \n",
+       "41564                             0                       0   \n",
+       "41565                             0                       0   \n",
+       "41566                             0                       0   \n",
+       "41567                             0                       0   \n",
+       "41568                             0                       0   \n",
+       "\n",
+       "       Country_United Republic of Tanzania  Country_United States  \\\n",
+       "1                                        0                      1   \n",
+       "2                                        0                      0   \n",
+       "3                                        0                      0   \n",
+       "4                                        0                      0   \n",
+       "5                                        0                      0   \n",
+       "...                                    ...                    ...   \n",
+       "41564                                    0                      0   \n",
+       "41565                                    0                      0   \n",
+       "41566                                    0                      0   \n",
+       "41567                                    0                      0   \n",
+       "41568                                    0                      1   \n",
+       "\n",
+       "       Country_Uruguay  Country_Uzbekistan  \\\n",
+       "1                    0                   0   \n",
+       "2                    0                   0   \n",
+       "3                    0                   0   \n",
+       "4                    0                   0   \n",
+       "5                    0                   0   \n",
+       "...                ...                 ...   \n",
+       "41564                0                   0   \n",
+       "41565                0                   0   \n",
+       "41566                0                   0   \n",
+       "41567                0                   0   \n",
+       "41568                0                   0   \n",
+       "\n",
+       "       Country_Venezuela, Bolivarian Republic of...  Country_Viet Nam  \\\n",
+       "1                                                 0                 0   \n",
+       "2                                                 0                 0   \n",
+       "3                                                 0                 0   \n",
+       "4                                                 0                 0   \n",
+       "5                                                 0                 0   \n",
+       "...                                             ...               ...   \n",
+       "41564                                             0                 0   \n",
+       "41565                                             0                 0   \n",
+       "41566                                             0                 0   \n",
+       "41567                                             0                 0   \n",
+       "41568                                             0                 0   \n",
+       "\n",
+       "       Country_Yemen  Country_Zambia  Country_Zimbabwe  EdLevel_Associate  \\\n",
+       "1                  0               0                 0                  0   \n",
+       "2                  0               0                 0                  0   \n",
+       "3                  0               0                 0                  0   \n",
+       "4                  0               0                 0                  0   \n",
+       "5                  0               0                 0                  0   \n",
+       "...              ...             ...               ...                ...   \n",
+       "41564              0               0                 0                  0   \n",
+       "41565              0               0                 0                  0   \n",
+       "41566              0               0                 0                  0   \n",
+       "41567              0               0                 0                  0   \n",
+       "41568              0               0                 0                  0   \n",
+       "\n",
+       "       EdLevel_Bachelors  EdLevel_Doctorate  EdLevel_Masters  \\\n",
+       "1                      1                  0                0   \n",
+       "2                      0                  0                1   \n",
+       "3                      1                  0                0   \n",
+       "4                      0                  0                0   \n",
+       "5                      0                  0                0   \n",
+       "...                  ...                ...              ...   \n",
+       "41564                  1                  0                0   \n",
+       "41565                  1                  0                0   \n",
+       "41566                  0                  0                0   \n",
+       "41567                  0                  0                1   \n",
+       "41568                  0                  0                0   \n",
+       "\n",
+       "       EdLevel_No Degree  EdLevel_Professional  Employment_Full-time  \\\n",
+       "1                      0                     0                     1   \n",
+       "2                      0                     0                     1   \n",
+       "3                      0                     0                     1   \n",
+       "4                      1                     0                     1   \n",
+       "5                      1                     0                     1   \n",
+       "...                  ...                   ...                   ...   \n",
+       "41564                  0                     0                     0   \n",
+       "41565                  0                     0                     0   \n",
+       "41566                  1                     0                     0   \n",
+       "41567                  0                     0                     1   \n",
+       "41568                  1                     0                     1   \n",
+       "\n",
+       "       Employment_Not employed  \\\n",
+       "1                            0   \n",
+       "2                            0   \n",
+       "3                            0   \n",
+       "4                            0   \n",
+       "5                            0   \n",
+       "...                        ...   \n",
+       "41564                        0   \n",
+       "41565                        0   \n",
+       "41566                        0   \n",
+       "41567                        0   \n",
+       "41568                        0   \n",
+       "\n",
+       "       Employment_Not employed, and not looking for work  \\\n",
+       "1                                                      0   \n",
+       "2                                                      0   \n",
+       "3                                                      0   \n",
+       "4                                                      0   \n",
+       "5                                                      0   \n",
+       "...                                                  ...   \n",
+       "41564                                                  0   \n",
+       "41565                                                  0   \n",
+       "41566                                                  0   \n",
+       "41567                                                  0   \n",
+       "41568                                                  0   \n",
+       "\n",
+       "       Employment_Part-time  Employment_Retired  Employment_Self-employed  \\\n",
+       "1                         0                   0                         0   \n",
+       "2                         0                   0                         0   \n",
+       "3                         0                   0                         0   \n",
+       "4                         0                   0                         0   \n",
+       "5                         0                   0                         0   \n",
+       "...                     ...                 ...                       ...   \n",
+       "41564                     0                   0                         0   \n",
+       "41565                     0                   0                         0   \n",
+       "41566                     0                   0                         0   \n",
+       "41567                     0                   0                         0   \n",
+       "41568                     0                   0                         0   \n",
+       "\n",
+       "       Employment_Student  Hobbyist_No  Hobbyist_Yes  \\\n",
+       "1                       0            0             1   \n",
+       "2                       0            0             1   \n",
+       "3                       0            0             1   \n",
+       "4                       0            1             0   \n",
+       "5                       0            0             1   \n",
+       "...                   ...          ...           ...   \n",
+       "41564                   1            0             1   \n",
+       "41565                   1            0             1   \n",
+       "41566                   1            0             1   \n",
+       "41567                   0            0             1   \n",
+       "41568                   0            0             1   \n",
+       "\n",
+       "       UndergradMajor_Arts and Science  UndergradMajor_Business  \\\n",
+       "1                                    0                        0   \n",
+       "2                                    0                        0   \n",
+       "3                                    0                        0   \n",
+       "4                                    0                        0   \n",
+       "5                                    0                        0   \n",
+       "...                                ...                      ...   \n",
+       "41564                                0                        0   \n",
+       "41565                                0                        0   \n",
+       "41566                                0                        0   \n",
+       "41567                                0                        0   \n",
+       "41568                                0                        0   \n",
+       "\n",
+       "       UndergradMajor_Computer Science  UndergradMajor_Engineering  \\\n",
+       "1                                    1                           0   \n",
+       "2                                    0                           0   \n",
+       "3                                    1                           0   \n",
+       "4                                    0                           0   \n",
+       "5                                    0                           0   \n",
+       "...                                ...                         ...   \n",
+       "41564                                1                           0   \n",
+       "41565                                1                           0   \n",
+       "41566                                0                           0   \n",
+       "41567                                1                           0   \n",
+       "41568                                1                           0   \n",
+       "\n",
+       "       UndergradMajor_Info Systems  UndergradMajor_Math/Stat  \\\n",
+       "1                                0                         0   \n",
+       "2                                0                         1   \n",
+       "3                                0                         0   \n",
+       "4                                0                         1   \n",
+       "5                                0                         0   \n",
+       "...                            ...                       ...   \n",
+       "41564                            0                         0   \n",
+       "41565                            0                         0   \n",
+       "41566                            0                         0   \n",
+       "41567                            0                         0   \n",
+       "41568                            0                         0   \n",
+       "\n",
+       "       UndergradMajor_No major  UndergradMajor_Other Science  \\\n",
+       "1                            0                             0   \n",
+       "2                            0                             0   \n",
+       "3                            0                             0   \n",
+       "4                            0                             0   \n",
+       "5                            1                             0   \n",
+       "...                        ...                           ...   \n",
+       "41564                        0                             0   \n",
+       "41565                        0                             0   \n",
+       "41566                        1                             0   \n",
+       "41567                        0                             0   \n",
+       "41568                        0                             0   \n",
+       "\n",
+       "       UndergradMajor_Web Design/Dev  \n",
+       "1                                  0  \n",
+       "2                                  0  \n",
+       "3                                  0  \n",
+       "4                                  0  \n",
+       "5                                  0  \n",
+       "...                              ...  \n",
+       "41564                              0  \n",
+       "41565                              0  \n",
+       "41566                              0  \n",
+       "41567                              0  \n",
+       "41568                              0  \n",
+       "\n",
+       "[33767 rows x 180 columns]"
+      ]
+     },
+     "execution_count": 435,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 436,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1        0\n",
+       "2        1\n",
+       "3        0\n",
+       "4        0\n",
+       "5        1\n",
+       "        ..\n",
+       "41564    1\n",
+       "41565    1\n",
+       "41566    1\n",
+       "41567    1\n",
+       "41568    1\n",
+       "Name: CurrentJobSatis, Length: 33767, dtype: int64"
+      ]
+     },
+     "execution_count": 436,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 437,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split data into train and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Checking Model Coefficent**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 438,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 72.11%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# define the model\n",
+    "model = LogisticRegression()\n",
+    "# fit the model\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# get importance\n",
+    "importance = model.coef_[0]\n",
+    "\n",
+    "# make predictions for test data and evaluate\n",
+    "y_pred = model.predict(X_test)\n",
+    "predictions = [round(value) for value in y_pred]\n",
+    "accuracy = accuracy_score(y_test, predictions)\n",
+    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have recieved **72% Accuracy** which is good enough to move ahead with predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting Features affecting Job Satisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 439,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x2160 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame()\n",
+    "results_df[\"Rates\"] = importance.tolist()\n",
+    "results_df[\"Columns\"] = X.columns\n",
+    "\n",
+    "new_index = results_df.Rates.sort_values(ascending = False).index\n",
+    "sorted_results = results_df.reindex(new_index)\n",
+    "filtered_results = sorted_results[np.abs(sorted_results.Rates) > 0.1]\n",
+    "\n",
+    "plt.figure(figsize =(20,30))\n",
+    "plt.barh(filtered_results.Columns, filtered_results.Rates)\n",
+    "plt.xlabel(\"Negative and Postive Features\", fontsize = 25)\n",
+    "plt.title(\"Features Affecting Job Satistaction\",fontsize = 25)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The top 2 features negatively affecting Job Satisfaction are age, country. So, in the elderly ages, job satisfaction may decrease because of the personal expectation increases. In the same way, as the professional coding years are increasing, satisfaction may decrease.\n",
+    "\n",
+    "- Among the countries; most dissatisfied countries are Angolia, Rwanda, Krygyzstan, Sudan.\n",
+    "- UndergradMajor and other Science, are mostly satisfied.\n",
+    "- Most satisfied countries Malta, Ghana, Cyprus."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overall, we performed various analyses on the Stack overflow developer survey and derived insights from it. We found which country has the highest no of respondents, which is the most popular language, education level of respondents, different roles of developers, and so on.<br>\n",
+    "Additionally, we performed machine learning models to predict the growth of languages, the salary of data scientists, what is causing job satisfaction. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 4576e551bbd277cca86be10cb57be4db116f21fd Mon Sep 17 00:00:00 2001
From: SwarnenduJUIT <163632954+SwarnenduJUIT@users.noreply.github.com>
Date: Tue, 14 May 2024 19:23:55 +0530
Subject: [PATCH 2/3] Add files via upload


From d9a2e99e98060ba16027b078759a2e12259cf752 Mon Sep 17 00:00:00 2001
From: SwarnenduJUIT <163632954+SwarnenduJUIT@users.noreply.github.com>
Date: Tue, 14 May 2024 19:29:41 +0530
Subject: [PATCH 3/3] Add files via upload

---
 Stackoverflow_Survey_Analysis.ipynb | 34504 ++++++++++++--------------
 1 file changed, 16206 insertions(+), 18298 deletions(-)

diff --git a/Stackoverflow_Survey_Analysis.ipynb b/Stackoverflow_Survey_Analysis.ipynb
index 85b1642..3257236 100644
--- a/Stackoverflow_Survey_Analysis.ipynb
+++ b/Stackoverflow_Survey_Analysis.ipynb
@@ -1,18298 +1,16206 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stackoverflow_Survey_Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction\n",
-    "Stack overflow is a professional community for developers. They conduct developer surveys every year since 2011. The collected data is available open-source on the web. The Dataset would help us to answer real-world questions with the help of proper analysis. The most popular language that developers use can be found through the analysis. We also can find the developer role which pays the highest salary. The aim of our project is to analyze the 2018,2019 and 2020 developer surveys datasets from where we collect valuable insights from them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import seaborn as sns\n",
-    "import warnings; \n",
-    "warnings.simplefilter('ignore')\n",
-    "import pycountry\n",
-    "import plotly.express as px\n",
-    "import matplotlib.patches as mpatches\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn import preprocessing\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
-    "from sklearn.metrics import r2_score\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "from sklearn.naive_bayes import MultinomialNB\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from sklearn.svm import LinearSVC\n",
-    "import time\n",
-    "from sklearn.metrics import hamming_loss\n",
-    "from sklearn.metrics import jaccard_score\n",
-    "from sklearn.linear_model import SGDClassifier\n",
-    "from sklearn.multiclass import OneVsRestClassifier\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "from sklearn.metrics import accuracy_score\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stackoverflow 2018 Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Respondent</th>\n",
-       "      <th>Hobby</th>\n",
-       "      <th>OpenSource</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Student</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>FormalEducation</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>CompanySize</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Exercise</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>SexualOrientation</th>\n",
-       "      <th>EducationParents</th>\n",
-       "      <th>RaceEthnicity</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Dependents</th>\n",
-       "      <th>MilitaryUS</th>\n",
-       "      <th>SurveyTooLong</th>\n",
-       "      <th>SurveyEasy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Kenya</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Employed part-time</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Mathematics or statistics</td>\n",
-       "      <td>20 to 99 employees</td>\n",
-       "      <td>Full-stack developer</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3 - 4 times per week</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Straight or heterosexual</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Black or of African descent</td>\n",
-       "      <td>25 - 34 years old</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>The survey was an appropriate length</td>\n",
-       "      <td>Very easy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Employed full-time</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
-       "      <td>10,000 or more employees</td>\n",
-       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Daily or almost every day</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Straight or heterosexual</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>The survey was an appropriate length</td>\n",
-       "      <td>Somewhat easy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Employed full-time</td>\n",
-       "      <td>Associate degree</td>\n",
-       "      <td>Computer science, computer engineering, or sof...</td>\n",
-       "      <td>20 to 99 employees</td>\n",
-       "      <td>Engineering manager;Full-stack developer</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3 rows × 129 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Respondent Hobby OpenSource         Country Student          Employment  \\\n",
-       "0           1   Yes         No           Kenya      No  Employed part-time   \n",
-       "1           3   Yes        Yes  United Kingdom      No  Employed full-time   \n",
-       "2           4   Yes        Yes   United States      No  Employed full-time   \n",
-       "\n",
-       "                            FormalEducation  \\\n",
-       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
-       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
-       "2                          Associate degree   \n",
-       "\n",
-       "                                      UndergradMajor  \\\n",
-       "0                          Mathematics or statistics   \n",
-       "1  A natural science (ex. biology, chemistry, phy...   \n",
-       "2  Computer science, computer engineering, or sof...   \n",
-       "\n",
-       "                CompanySize  \\\n",
-       "0        20 to 99 employees   \n",
-       "1  10,000 or more employees   \n",
-       "2        20 to 99 employees   \n",
-       "\n",
-       "                                             DevType  ...  \\\n",
-       "0                               Full-stack developer  ...   \n",
-       "1  Database administrator;DevOps specialist;Full-...  ...   \n",
-       "2           Engineering manager;Full-stack developer  ...   \n",
-       "\n",
-       "                    Exercise Gender         SexualOrientation  \\\n",
-       "0       3 - 4 times per week   Male  Straight or heterosexual   \n",
-       "1  Daily or almost every day   Male  Straight or heterosexual   \n",
-       "2                        NaN    NaN                       NaN   \n",
-       "\n",
-       "                           EducationParents                 RaceEthnicity  \\\n",
-       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   Black or of African descent   \n",
-       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)  White or of European descent   \n",
-       "2                                       NaN                           NaN   \n",
-       "\n",
-       "                 Age Dependents  MilitaryUS  \\\n",
-       "0  25 - 34 years old        Yes         NaN   \n",
-       "1  35 - 44 years old        Yes         NaN   \n",
-       "2                NaN        NaN         NaN   \n",
-       "\n",
-       "                          SurveyTooLong     SurveyEasy  \n",
-       "0  The survey was an appropriate length      Very easy  \n",
-       "1  The survey was an appropriate length  Somewhat easy  \n",
-       "2                                   NaN            NaN  \n",
-       "\n",
-       "[3 rows x 129 columns]"
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2018 = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2018.csv')\n",
-    "df2018.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(98855, 129)"
-      ]
-     },
-     "execution_count": 190,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2018.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#print(df2018.columns.tolist()      !--> Listing coloumsn in table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#dropping the columns\n",
-    "#drop_cols = ['Respondent', 'OpenSource', 'Student', 'FormalEducation', 'CompanySize', 'CareerSatisfaction', 'HopeFiveYears', 'LastNewJob', 'AssessJob1', 'AssessJob2', 'AssessJob3', 'AssessJob4', 'AssessJob5', 'AssessJob6', 'AssessJob7', 'AssessJob8', 'AssessJob9', 'AssessJob10', 'AssessBenefits1', 'AssessBenefits2', 'AssessBenefits3', 'AssessBenefits4', 'AssessBenefits5', 'AssessBenefits6', 'AssessBenefits7', 'AssessBenefits8', 'AssessBenefits9', 'AssessBenefits10', 'AssessBenefits11', 'JobContactPriorities1', 'JobContactPriorities2', 'JobContactPriorities3', 'JobContactPriorities4', 'JobContactPriorities5', 'JobEmailPriorities1', 'JobEmailPriorities2', 'JobEmailPriorities3', 'JobEmailPriorities4', 'JobEmailPriorities5', 'JobEmailPriorities6', 'JobEmailPriorities7', 'UpdateCV', 'CommunicationTools', 'TimeFullyProductive', 'EducationTypes', 'SelfTaughtTypes', 'TimeAfterBootcamp', 'HackathonReasons', 'AgreeDisagree1', 'AgreeDisagree2', 'AgreeDisagree3', 'DatabaseWorkedWith', 'DatabaseDesireNextYear', 'PlatformDesireNextYear', 'FrameworkWorkedWith', 'FrameworkDesireNextYear', 'IDE', 'NumberMonitors', 'Methodology', 'VersionControl', 'CheckInCode', 'AdBlocker', 'AdBlockerDisable', 'AdBlockerReasons', 'AdsAgreeDisagree1', 'AdsAgreeDisagree2', 'AdsAgreeDisagree3', 'AdsActions', 'AdsPriorities1', 'AdsPriorities2', 'AdsPriorities3', 'AdsPriorities4', 'AdsPriorities5', 'AdsPriorities6', 'AdsPriorities7', 'AIDangerous', 'AIInteresting', 'AIResponsible', 'AIFuture', 'EthicsChoice', 'EthicsReport', 'EthicsResponsible', 'EthicalImplications', 'StackOverflowRecommend', 'StackOverflowVisit', 'StackOverflowHasAccount', 'StackOverflowParticipate', 'StackOverflowJobs', 'StackOverflowDevStory', 'StackOverflowJobsRecommend', 'StackOverflowConsiderMember', 'HypotheticalTools1', 'HypotheticalTools2', 'HypotheticalTools3', 'HypotheticalTools4', 'HypotheticalTools5', 'WakeTime', 'HoursComputer', 'HoursOutside', 'SkipMeals', 'ErgonomicDevices', 'Exercise', 'SexualOrientation', 'EducationParents', 'Dependents', 'MilitaryUS', 'SurveyTooLong', 'SurveyEasy']\n",
-    "#df2018.drop(drop_cols, axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df2018.shape #checking rows and col after dropping the table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Filtering - Sorting & Renaming\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col=['Age','ConvertedSalary','Country','Currency','DevType','Employment','RaceEthnicity','Gender','SalaryType','Hobby','JobSatisfaction','JobSearchStatus','OperatingSystem','UndergradMajor','YearsCoding','YearsCodingProf','LanguageDesireNextYear','LanguageWorkedWith','FormalEducation']\n",
-    "df=df2018[col]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#renaming the colo\n",
-    "# 'ConvertedSalary': 'SalaryUSD'\n",
-    "df.rename(columns={'ConvertedSalary': 'SalaryUSD' }, inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Currency</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>FormalEducation</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Hobby</th>\n",
-       "      <th>JobSatisfaction</th>\n",
-       "      <th>JobSearchStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>OperatingSystem</th>\n",
-       "      <th>RaceEthnicity</th>\n",
-       "      <th>SalaryType</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCoding</th>\n",
-       "      <th>YearsCodingProf</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>25 - 34 years old</td>\n",
-       "      <td>Kenya</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Full-stack developer</td>\n",
-       "      <td>Employed part-time</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Extremely satisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>JavaScript;Python;HTML;CSS</td>\n",
-       "      <td>JavaScript;Python;HTML;CSS</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Black or of African descent</td>\n",
-       "      <td>Monthly</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Mathematics or statistics</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>3-5 years</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>British pounds sterling (£)</td>\n",
-       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
-       "      <td>Employed full-time</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately dissatisfied</td>\n",
-       "      <td>I am actively looking for a job</td>\n",
-       "      <td>Go;Python</td>\n",
-       "      <td>JavaScript;Python;Bash/Shell</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>70841.0</td>\n",
-       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
-       "      <td>30 or more years</td>\n",
-       "      <td>18-20 years</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Age         Country                     Currency  \\\n",
-       "0  25 - 34 years old           Kenya                          NaN   \n",
-       "1  35 - 44 years old  United Kingdom  British pounds sterling (£)   \n",
-       "\n",
-       "                                             DevType          Employment  \\\n",
-       "0                               Full-stack developer  Employed part-time   \n",
-       "1  Database administrator;DevOps specialist;Full-...  Employed full-time   \n",
-       "\n",
-       "                            FormalEducation Gender Hobby  \\\n",
-       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   Male   Yes   \n",
-       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)   Male   Yes   \n",
-       "\n",
-       "           JobSatisfaction                                    JobSearchStatus  \\\n",
-       "0      Extremely satisfied  I’m not actively looking, but I am open to new...   \n",
-       "1  Moderately dissatisfied                    I am actively looking for a job   \n",
-       "\n",
-       "       LanguageDesireNextYear            LanguageWorkedWith OperatingSystem  \\\n",
-       "0  JavaScript;Python;HTML;CSS    JavaScript;Python;HTML;CSS     Linux-based   \n",
-       "1                   Go;Python  JavaScript;Python;Bash/Shell     Linux-based   \n",
-       "\n",
-       "                  RaceEthnicity SalaryType  SalaryUSD  \\\n",
-       "0   Black or of African descent    Monthly        NaN   \n",
-       "1  White or of European descent     Yearly    70841.0   \n",
-       "\n",
-       "                                      UndergradMajor       YearsCoding  \\\n",
-       "0                          Mathematics or statistics         3-5 years   \n",
-       "1  A natural science (ex. biology, chemistry, phy...  30 or more years   \n",
-       "\n",
-       "  YearsCodingProf  \n",
-       "0       3-5 years  \n",
-       "1     18-20 years  "
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.sort_index(axis=1).head(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(98855, 19)"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#21 col has been selected rfom 129, compared the shape\n",
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                       34281\n",
-      "SalaryUSD                 51153\n",
-      "Country                     412\n",
-      "Currency                  36847\n",
-      "DevType                    6757\n",
-      "Employment                 3534\n",
-      "RaceEthnicity             41382\n",
-      "Gender                    34386\n",
-      "SalaryType                47785\n",
-      "Hobby                         0\n",
-      "JobSatisfaction           29579\n",
-      "JobSearchStatus           19367\n",
-      "OperatingSystem           22676\n",
-      "UndergradMajor            19819\n",
-      "YearsCoding                5020\n",
-      "YearsCodingProf           20952\n",
-      "LanguageDesireNextYear    25611\n",
-      "LanguageWorkedWith        20521\n",
-      "FormalEducation            4152\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df.isnull().sum()) #Finding Null Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                        object\n",
-       "SalaryUSD                 float64\n",
-       "Country                    object\n",
-       "Currency                   object\n",
-       "DevType                    object\n",
-       "Employment                 object\n",
-       "RaceEthnicity              object\n",
-       "Gender                     object\n",
-       "SalaryType                 object\n",
-       "Hobby                      object\n",
-       "JobSatisfaction            object\n",
-       "JobSearchStatus            object\n",
-       "OperatingSystem            object\n",
-       "UndergradMajor             object\n",
-       "YearsCoding                object\n",
-       "YearsCodingProf            object\n",
-       "LanguageDesireNextYear     object\n",
-       "LanguageWorkedWith         object\n",
-       "FormalEducation            object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes #data_types"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Validation - Total Cells vs Missing %"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  1878245\n",
-      "Total missing :  424234\n",
-      "Missing Percentage:  22.58672324430519 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = df.isnull().sum() #number of missing\n",
-    "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Missing Percentage column-wise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def missing(df,column,n):\n",
-    "    empty_cells=df[column].isnull().sum()\n",
-    "    return (empty_cells*100.0)/n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age : 34.68 %\n",
-      "SalaryUSD : 51.75 %\n",
-      "Country : 0.42 %\n",
-      "Currency : 37.27 %\n",
-      "DevType : 6.84 %\n",
-      "Employment : 3.57 %\n",
-      "RaceEthnicity : 41.86 %\n",
-      "Gender : 34.78 %\n",
-      "SalaryType : 48.34 %\n",
-      "Hobby : 0.00 %\n",
-      "JobSatisfaction : 29.92 %\n",
-      "JobSearchStatus : 19.59 %\n",
-      "OperatingSystem : 22.94 %\n",
-      "UndergradMajor : 20.05 %\n",
-      "YearsCoding : 5.08 %\n",
-      "YearsCodingProf : 21.19 %\n",
-      "LanguageDesireNextYear : 25.91 %\n",
-      "LanguageWorkedWith : 20.76 %\n",
-      "FormalEducation : 4.20 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "total_cells=df.shape[0]\n",
-    "for column in df.columns:\n",
-    "    res=missing(df,column,total_cells)\n",
-    "    print(column,\":\",\"{:.2f}\".format(res),\"%\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gender Filtering          \n",
-    "### Data Cleaning Starts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gender\n",
-       "Female                                                                        4025\n",
-       "Female;Male                                                                     98\n",
-       "Female;Male;Non-binary, genderqueer, or gender non-conforming                    3\n",
-       "Female;Male;Transgender                                                         14\n",
-       "Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming       50\n",
-       "Female;Non-binary, genderqueer, or gender non-conforming                        50\n",
-       "Female;Transgender                                                             145\n",
-       "Female;Transgender;Non-binary, genderqueer, or gender non-conforming            24\n",
-       "Male                                                                         59458\n",
-       "Male;Non-binary, genderqueer, or gender non-conforming                         128\n",
-       "Male;Transgender                                                                29\n",
-       "Male;Transgender;Non-binary, genderqueer, or gender non-conforming               5\n",
-       "Non-binary, genderqueer, or gender non-conforming                              284\n",
-       "Transgender                                                                    105\n",
-       "Transgender;Non-binary, genderqueer, or gender non-conforming                   51\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Gender: null = 13312 (21.6%)\n",
-    "df['Gender'].unique()\n",
-    "#count number of each gender\n",
-    "df.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#replace\n",
-    "df['Gender'] = df['Gender'].fillna('Non-binary, genderqueer, or gender non-conforming')\n",
-    "df['Gender'].replace('Female;Male;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Male;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
-    "df['Gender'].replace('Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
-    "df['Gender'].replace('Transgender;Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) ##not sure\n",
-    "df['Gender'].replace('Female;Male', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Male;Transgender', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Female;Transgender', 'Female', inplace =True)\n",
-    "df['Gender'].replace('Male;Transgender', 'Female', inplace =True) \n",
-    "df['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) #\n",
-    "df['Gender'].replace('Transgender', 'Male', inplace =True) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "lst=df.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(3,3))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Gender Distribution')  # Add a title\n",
-    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(98855, 19)"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isnull().sum()['Gender']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Country"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Country\n",
-       "Afghanistan                              64\n",
-       "Albania                                 109\n",
-       "Algeria                                 130\n",
-       "Andorra                                  15\n",
-       "Angola                                   11\n",
-       "                                       ... \n",
-       "Venezuela, Bolivarian Republic of...    123\n",
-       "Viet Nam                                331\n",
-       "Yemen                                    13\n",
-       "Zambia                                    9\n",
-       "Zimbabwe                                 39\n",
-       "Name: Country, Length: 183, dtype: int64"
-      ]
-     },
-     "execution_count": 209,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('Country')['Country'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "412"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Country'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 212,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 311,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst=df.groupby('Country')['Country'].count()\n",
-    "lst = lst.sort_values(ascending=False)\n",
-    "lst=lst[:50]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(15,4))\n",
-    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
-    "\n",
-    "# Adding labels and title\n",
-    "plt.xlabel('Country')  # Label for x-axis\n",
-    "plt.ylabel('Count')      # Label for y-axis\n",
-    "plt.title('Top 50 Countries according to participation')  # Title of the plot\n",
-    "plt.xticks(rotation=90)  # Rotate labels by 90 degrees\n",
-    "\n",
-    "# Display the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hobby"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 215,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Hobby'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hobby\n",
-       "No     18958\n",
-       "Yes    79897\n",
-       "Name: Hobby, dtype: int64"
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('Hobby')['Hobby'].count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## UndergradMajor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19819"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "UndergradMajor\n",
-       "Computer science, computer engineering, or software engineering          50336\n",
-       "Another engineering discipline (ex. civil, electrical, mechanical)        6945\n",
-       "Information systems, information technology, or system administration     6507\n",
-       "A natural science (ex. biology, chemistry, physics)                       3050\n",
-       "Mathematics or statistics                                                 2818\n",
-       "Web development or web design                                             2418\n",
-       "A business discipline (ex. accounting, finance, marketing)                1921\n",
-       "A humanities discipline (ex. literature, history, philosophy)             1590\n",
-       "A social science (ex. anthropology, psychology, political science)        1377\n",
-       "Fine arts or performing arts (ex. graphic design, music, studio art)      1135\n",
-       "I never declared a major                                                   693\n",
-       "A health science (ex. nursing, pharmacy, radiology)                        246\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 218,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['UndergradMajor'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def refactor_major(df):\n",
-    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n",
-    "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
-    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n",
-    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
-    "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)') \n",
-    "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'), \n",
-    "                        (df['UndergradMajor'] == 'Web development or web design'), \n",
-    "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'), \n",
-    "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
-    "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
-    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)'),\n",
-    "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
-    "    \n",
-    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
-    "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
-    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
-    "    return df\n",
-    "\n",
-    "df = refactor_major(df)\n",
-    "df['UndergradMajor'].replace('nan', 'No major', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst=df['UndergradMajor'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(5,5))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Undergrad Major')  # Add a title\n",
-    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['UndergradMajor'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 224,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Job Status"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobSearchStatus\n",
-       "I’m not actively looking, but I am open to new opportunities    47556\n",
-       "I am not interested in new job opportunities                    19296\n",
-       "I am actively looking for a job                                 12636\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 225,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSearchStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['JobSearchStatus'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# refactoring JobStatus\n",
-    "# changing the jobstatus to seeking and non seeking\n",
-    "def refactor_job(df):\n",
-    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
-    "    \n",
-    "    conditions_job = [(df['JobSearchStatus'] == 'I am actively looking for a job'),\n",
-    "                    (df['JobSearchStatus'] == 'I am not interested in new job opportunities')\n",
-    "                    | (df['JobSearchStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
-    "    \n",
-    "    choices_job = ['Seeking', 'Not seeking']\n",
-    "    \n",
-    "    df['JobSearchStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "df = refactor_job(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobSearchStatus\n",
-       "Not seeking    66852\n",
-       "Seeking        12636\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 228,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSearchStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 229,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSearchStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Employment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employment\n",
-       "Employed full-time                                      58551\n",
-       "Independent contractor, freelancer, or self-employed     7797\n",
-       "Not employed, but looking for work                       4604\n",
-       "Employed part-time                                       4170\n",
-       "Not employed, and not looking for work                   3210\n",
-       "Retired                                                   138\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 230,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1018"
-      ]
-     },
-     "execution_count": 231,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Employment'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#im not considering the retired person here\n",
-    "#Refactoring the employment\n",
-    "def refactor_emp(df):\n",
-    "    \n",
-    "    conditions_emp = [(df['Employment'] == 'Employed full-time'),\n",
-    "                    (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n",
-    "                    (df['Employment'] == 'Not employed, but looking for work'),\n",
-    "                    (df['Employment'] == 'Employed part-time')]\n",
-    "    \n",
-    "    choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n",
-    "    \n",
-    "    df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "df = refactor_emp(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst=df['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(4,4))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Employment Type')  # Add a title\n",
-    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JobSatisfaction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobSatisfaction\n",
-       "Moderately satisfied                  25908\n",
-       "Extremely satisfied                   12395\n",
-       "Slightly satisfied                     9973\n",
-       "Slightly dissatisfied                  7037\n",
-       "Moderately dissatisfied                6286\n",
-       "Neither satisfied nor dissatisfied     4935\n",
-       "Extremely dissatisfied                 2472\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 237,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSatisfaction'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10482"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSatisfaction'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['JobSatisfaction'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['JobSatisfaction'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 313,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lst=df['JobSatisfaction'].value_counts()\n",
-    "plt.figure(figsize=(12,4))\n",
-    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
-    "\n",
-    "# Adding labels and title\n",
-    "plt.xlabel('Satisfaction Level')  # Label for x-axis\n",
-    "plt.ylabel('Counts')      # Label for y-axis\n",
-    "plt.title('Job Satisfaction')  # Title of the plot\n",
-    "plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
-    "\n",
-    "# Display the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ethnicity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 242,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23578"
-      ]
-     },
-     "execution_count": 242,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['RaceEthnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RaceEthnicity\n",
-       "Black or of African descent                                                                                                                                 1204\n",
-       "Black or of African descent;East Asian                                                                                                                         7\n",
-       "Black or of African descent;East Asian;Hispanic or Latino/Latina                                                                                               2\n",
-       "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian                    1\n",
-       "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian        1\n",
-       "                                                                                                                                                           ...  \n",
-       "Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent                                                           2\n",
-       "Native American, Pacific Islander, or Indigenous Australian;White or of European descent                                                                     160\n",
-       "South Asian                                                                                                                                                 6112\n",
-       "South Asian;White or of European descent                                                                                                                      88\n",
-       "White or of European descent                                                                                                                               39320\n",
-       "Name: RaceEthnicity, Length: 71, dtype: int64"
-      ]
-     },
-     "execution_count": 243,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#count number of each Ethnicity\n",
-    "df.groupby('RaceEthnicity')['RaceEthnicity'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
-    "df.loc[df['RaceEthnicity'].str.match('Biracial') == True, 'RaceEthnicity'] = 'Biracial'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Black or of African descent') == True, 'RaceEthnicity'] = 'Black or African descent'\n",
-    "df.loc[df['RaceEthnicity'].str.match('East Asian') == True, 'RaceEthnicity'] = 'East Asian'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Hispanic or Latino') == True, 'RaceEthnicity'] = 'Hispanic or Latino'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Indigenous') == True, 'RaceEthnicity'] = 'Indigenous'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Middle Eastern') == True, 'RaceEthnicity'] = 'Middle Eastern'\n",
-    "df.loc[df['RaceEthnicity'].str.match('South') == True, 'RaceEthnicity'] = 'South Asian'\n",
-    "df.loc[df['RaceEthnicity'].str.match('White or of European descent') == True, 'RaceEthnicity'] = 'White or European descent'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Multiracial') == True, 'RaceEthnicity'] = 'Multiracial'\n",
-    "df.loc[df['RaceEthnicity'].str.match('Native American') == True, 'RaceEthnicity'] = 'Native American'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RaceEthnicity\n",
-       "Black or African descent      1549\n",
-       "East Asian                    2787\n",
-       "Hispanic or Latino            3592\n",
-       "Middle Eastern                2176\n",
-       "Native American                286\n",
-       "South Asian                   6200\n",
-       "White or European descent    39320\n",
-       "Name: RaceEthnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 245,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('RaceEthnicity')['RaceEthnicity'].count() #11 groups of Ethnicity after combining"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "23578"
-      ]
-     },
-     "execution_count": 246,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['RaceEthnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['RaceEthnicity']=df.groupby(['Country'])['RaceEthnicity'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 248,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['RaceEthnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lst=df.groupby('RaceEthnicity')['RaceEthnicity'].count()\n",
-    "plt.figure(figsize=(6,6))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Race/Ethnicity')  # Add a title\n",
-    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Developer Roles"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "728"
-      ]
-     },
-     "execution_count": 250,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['DevType'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DevType\n",
-       "Back-end developer                                                                                                                                                                                           5372\n",
-       "Back-end developer;C-suite executive (CEO, CTO, etc.)                                                                                                                                                          59\n",
-       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst                                                                                                                                  5\n",
-       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Data scientist or machine learning specialist                                                                                    1\n",
-       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Data scientist or machine learning specialist;Database administrator;Designer;Desktop or enterprise applications developer       1\n",
-       "                                                                                                                                                                                                             ... \n",
-       "QA or test developer;Student;System administrator                                                                                                                                                               5\n",
-       "QA or test developer;System administrator                                                                                                                                                                      10\n",
-       "Student                                                                                                                                                                                                      2523\n",
-       "Student;System administrator                                                                                                                                                                                   63\n",
-       "System administrator                                                                                                                                                                                          247\n",
-       "Name: DevType, Length: 8820, dtype: int64"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('DevType')['DevType'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
-    "df.loc[df['DevType'].str.match('Back-end developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Student') == True, 'DevType'] = 'Student'\n",
-    "df.loc[df['DevType'].str.match('QA or test developer') == True, 'DevType'] = 'Non developer'\n",
-    "df.loc[df['DevType'].str.match('Product manager') == True, 'DevType'] = 'Manager'\n",
-    "df.loc[df['DevType'].str.match('Mobile developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Marketing or sales professional') == True, 'DevType'] = 'Non developer'\n",
-    "\n",
-    "df.loc[df['DevType'].str.match('System administrator') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Game or graphics developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Full-stack developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Front-end developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Engineering manager') == True, 'DevType'] = 'Manager'\n",
-    "df.loc[df['DevType'].str.match('Embedded applications or devices developer') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Educator or academic researcher') == True, 'DevType'] = 'Student'\n",
-    "df.loc[df['DevType'].str.match('DevOps specialist') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Desktop or enterprise applications developer') == True, 'DevType'] = 'Developer'\n",
-    "\n",
-    "df.loc[df['DevType'].str.match('Designer') == True, 'DevType'] = 'Non developer'\n",
-    "df.loc[df['DevType'].str.match('Database administrator') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Data scientist or machine learning specialist') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('Data or business analyst') == True, 'DevType'] = 'Developer'\n",
-    "df.loc[df['DevType'].str.match('C-suite executive') == True, 'DevType'] = 'Developer'\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DevType\n",
-       "Developer        73032\n",
-       "Manager            665\n",
-       "Non developer     2791\n",
-       "Student           3000\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 254,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('DevType')['DevType'].count() #11 groups of Ethnicity after combining"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lst=df.groupby('DevType')['DevType'].count()\n",
-    "plt.figure(figsize=(6,6))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Developer Type')  # Add a title\n",
-    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Language to worked with"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LanguageWorkedWith\n",
-       "C#;JavaScript;SQL;HTML;CSS     1235\n",
-       "JavaScript;PHP;SQL;HTML;CSS    1095\n",
-       "Java                            855\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageWorkedWith'].value_counts().nlargest(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9985"
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LanguageWorkedWith\n",
-       "C#;JavaScript;SQL;HTML;CSS     1383\n",
-       "JavaScript;PHP;SQL;HTML;CSS    1226\n",
-       "Java                            989\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageWorkedWith'].value_counts().nlargest(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageDesireNextYear"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LanguageDesireNextYear\n",
-       "Python                                   718\n",
-       "C#;JavaScript;SQL;TypeScript;HTML;CSS    557\n",
-       "C#                                       522\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14147"
-      ]
-     },
-     "execution_count": 262,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 264,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LanguageDesireNextYear\n",
-       "Python                                   878\n",
-       "C#;JavaScript;SQL;TypeScript;HTML;CSS    690\n",
-       "C#                                       629\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 265,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Years of coding (Exp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCodingProf\n",
-       "0-2 years           22612\n",
-       "3-5 years           20883\n",
-       "6-8 years           11177\n",
-       "9-11 years           7456\n",
-       "12-14 years          4220\n",
-       "15-17 years          2987\n",
-       "18-20 years          2810\n",
-       "21-23 years          1352\n",
-       "30 or more years     1289\n",
-       "24-26 years           853\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCodingProf'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3349"
-      ]
-     },
-     "execution_count": 267,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCodingProf'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['YearsCodingProf'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 269,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCodingProf'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 270,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCodingProf\n",
-       "3-5 years           23773\n",
-       "0-2 years           22781\n",
-       "6-8 years           11274\n",
-       "9-11 years           7527\n",
-       "12-14 years          4267\n",
-       "15-17 years          3007\n",
-       "18-20 years          2841\n",
-       "21-23 years          1365\n",
-       "30 or more years     1294\n",
-       "24-26 years           856\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 270,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCodingProf'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Years of coding (Non Exp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCoding\n",
-       "3-5 years           19100\n",
-       "6-8 years           16537\n",
-       "9-11 years          10578\n",
-       "0-2 years            8022\n",
-       "12-14 years          7069\n",
-       "15-17 years          5459\n",
-       "18-20 years          4472\n",
-       "30 or more years     3136\n",
-       "21-23 years          2377\n",
-       "24-26 years          1671\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 271,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCoding'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 272,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "105"
-      ]
-     },
-     "execution_count": 272,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCoding'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['YearsCoding'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 274,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCoding'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCoding\n",
-       "3-5 years           19135\n",
-       "6-8 years           16554\n",
-       "9-11 years          10585\n",
-       "0-2 years            8043\n",
-       "12-14 years          7077\n",
-       "15-17 years          5462\n",
-       "18-20 years          4476\n",
-       "30 or more years     3144\n",
-       "21-23 years          2378\n",
-       "24-26 years          1671\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 275,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['YearsCoding'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Operating System"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OperatingSystem\n",
-       "Windows        34268\n",
-       "MacOS          18638\n",
-       "Linux-based    16069\n",
-       "BSD/Unix         139\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 276,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['OperatingSystem'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10374"
-      ]
-     },
-     "execution_count": 277,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['OperatingSystem'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['OperatingSystem'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 279,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['OperatingSystem'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lst=df['OperatingSystem'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(4,4))\n",
-    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
-    "plt.title('Oeratining System Used')  # Add a title\n",
-    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
-    "\n",
-    "# Display the pie chart\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Salary Type"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SalaryType\n",
-       "Monthly    26201\n",
-       "Yearly     22541\n",
-       "Weekly      2248\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 282,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28498"
-      ]
-     },
-     "execution_count": 283,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['SalaryType'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 285,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SalaryType\n",
-       "Monthly    40953\n",
-       "Yearly     34333\n",
-       "Weekly      4202\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 286,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Currency"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Currency\n",
-       "U.S. dollars ($)               20599\n",
-       "Euros (€)                      15201\n",
-       "Indian rupees (₹)               7908\n",
-       "British pounds sterling (£)     4856\n",
-       "Canadian dollars (C$)           2535\n",
-       "Russian rubles (₽)              1768\n",
-       "Brazilian reais (R$)            1663\n",
-       "Australian dollars (A$)         1571\n",
-       "Polish złoty (zł)               1434\n",
-       "Swedish kroner (SEK)             864\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Currency'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17483"
-      ]
-     },
-     "execution_count": 288,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Currency'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Currency'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 290,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Currency'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['Currency'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Currency\n",
-       "U.S. dollars ($)               26356\n",
-       "Euros (€)                      19465\n",
-       "Indian rupees (₹)              10152\n",
-       "British pounds sterling (£)     6194\n",
-       "Canadian dollars (C$)           3289\n",
-       "Russian rubles (₽)              2340\n",
-       "Brazilian reais (R$)            2122\n",
-       "Australian dollars (A$)         1970\n",
-       "Polish złoty (zł)               1856\n",
-       "Swedish kroner (SEK)            1101\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 292,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Currency'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Salary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SalaryUSD\n",
-       "0.0          842\n",
-       "120000.0     524\n",
-       "100000.0     497\n",
-       "80000.0      396\n",
-       "1000000.0    382\n",
-       "110000.0     371\n",
-       "90000.0      364\n",
-       "150000.0     357\n",
-       "60000.0      351\n",
-       "75000.0      337\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 293,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryUSD'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31786"
-      ]
-     },
-     "execution_count": 294,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['SalaryUSD'].isnull().sum() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DevType        Country      \n",
-       "Student        Saudi Arabia     1500000.0\n",
-       "Developer      Andorra           525089.5\n",
-       "Manager        Hungary           516000.0\n",
-       "               Netherlands       507175.0\n",
-       "Non developer  Algeria           360000.0\n",
-       "               Cyprus            293736.0\n",
-       "Developer      Liechtenstein     284028.0\n",
-       "Student        Finland           272212.0\n",
-       "Manager        Denmark           262920.6\n",
-       "Student        Israel            256522.4\n",
-       "Name: SalaryUSD, dtype: float64"
-      ]
-     },
-     "execution_count": 295,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean_salary = df.groupby(['DevType','Country'])['SalaryUSD'].mean() \n",
-    "mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "means = df.groupby(['YearsCodingProf','DevType', 'Country'])['SalaryUSD'].transform('mean')\n",
-    "df['SalaryUSD'] = df['SalaryUSD'].fillna(means)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "YearsCodingProf  DevType        Country                             \n",
-       "9-11 years       Student        Saudi Arabia                            1500000.0\n",
-       "12-14 years      Non developer  Norway                                  1000000.0\n",
-       "                 Student        Switzerland                             1000000.0\n",
-       "15-17 years      Non developer  Australia                               1000000.0\n",
-       "                                New Zealand                             1000000.0\n",
-       "21-23 years      Developer      Japan                                   1000000.0\n",
-       "                                Venezuela, Bolivarian Republic of...    1000000.0\n",
-       "                 Non developer  Sweden                                  1000000.0\n",
-       "                 Student        Finland                                 1000000.0\n",
-       "24-26 years      Manager        Canada                                  1000000.0\n",
-       "Name: SalaryUSD, dtype: float64"
-      ]
-     },
-     "execution_count": 297,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mean_salary = df.groupby(['YearsCodingProf','DevType','Country'])['SalaryUSD'].mean()\n",
-    "mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dropna(subset=['SalaryUSD'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age\n",
-       "25 - 34 years old     30969\n",
-       "18 - 24 years old     14847\n",
-       "35 - 44 years old     10980\n",
-       "45 - 54 years old      3072\n",
-       "Under 18 years old     1549\n",
-       "55 - 64 years old       865\n",
-       "65 years or older       144\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 299,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Age'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16374"
-      ]
-     },
-     "execution_count": 300,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Age'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Age'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Age'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lst=df['Age'].value_counts().nlargest(10)\n",
-    "plt.figure(figsize=(12,4))\n",
-    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
-    "\n",
-    "# Adding labels and title\n",
-    "plt.xlabel('Age Group')  # Label for x-axis\n",
-    "plt.ylabel('Counts')      # Label for y-axis\n",
-    "plt.title('Age')  # Title of the plot\n",
-    "#plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
-    "\n",
-    "# Display the plot\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 304,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                          0\n",
-      "SalaryUSD                    0\n",
-      "Country                      0\n",
-      "Currency                     0\n",
-      "DevType                      0\n",
-      "Employment                   0\n",
-      "RaceEthnicity                0\n",
-      "Gender                       0\n",
-      "SalaryType                   0\n",
-      "Hobby                        0\n",
-      "JobSatisfaction              0\n",
-      "JobSearchStatus              0\n",
-      "OperatingSystem              0\n",
-      "UndergradMajor               0\n",
-      "YearsCoding                  0\n",
-      "YearsCodingProf              0\n",
-      "LanguageDesireNextYear       0\n",
-      "LanguageWorkedWith           0\n",
-      "FormalEducation           1549\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 305,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1549"
-      ]
-     },
-     "execution_count": 305,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['FormalEducation'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "FormalEducation\n",
-       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                              36010\n",
-       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                           17529\n",
-       "Some college/university study without earning a degree                                 9737\n",
-       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     7088\n",
-       "Associate degree                                                                       2407\n",
-       "Other doctoral degree (Ph.D, Ed.D., etc.)                                              1754\n",
-       "Primary/elementary school                                                              1217\n",
-       "Professional degree (JD, MD, etc.)                                                     1073\n",
-       "I never completed any formal education                                                  436\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['FormalEducation'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 307,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "EdLevel\n",
-       "Bachelors       37559\n",
-       "No Degree       18478\n",
-       "Masters         17529\n",
-       "Associate        2407\n",
-       "Doctorate        1754\n",
-       "Professional     1073\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 307,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Changing column's name\n",
-    "df.rename(columns={'FormalEducation':'EdLevel'}, inplace =True)\n",
-    "#Refactoring EdLevel\n",
-    "def refactor_ed(df):\n",
-    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
-    "    conditions_ed = [(df['EdLevel'] == 'Associate degree'),\n",
-    "                    (df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
-    "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
-    "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
-    "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
-    "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
-    "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
-    "    \n",
-    "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
-    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
-    "    return df\n",
-    "\n",
-    "# applying function to subsets\n",
-    "df = refactor_ed(df)\n",
-    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
-    "df['EdLevel'].replace('nan', 'Bachelors', inplace=True)\n",
-    "\n",
-    "df['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleaned Dataset : 2018_Survey"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 308,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(78800, 19)"
-      ]
-     },
-     "execution_count": 308,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cleaned_2018 = df[df.notnull()]\n",
-    "cleaned_2018.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Currency</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>RaceEthnicity</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>SalaryType</th>\n",
-       "      <th>Hobby</th>\n",
-       "      <th>JobSatisfaction</th>\n",
-       "      <th>JobSearchStatus</th>\n",
-       "      <th>OperatingSystem</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCoding</th>\n",
-       "      <th>YearsCodingProf</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>EdLevel</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>70841.000000</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>British pounds sterling (£)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately dissatisfied</td>\n",
-       "      <td>Seeking</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Other Science</td>\n",
-       "      <td>30 or more years</td>\n",
-       "      <td>18-20 years</td>\n",
-       "      <td>Go;Python</td>\n",
-       "      <td>JavaScript;Python;Bash/Shell</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>153030.333333</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>British pounds sterling (£)</td>\n",
-       "      <td>Manager</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Non-conforming</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>24-26 years</td>\n",
-       "      <td>6-8 years</td>\n",
-       "      <td>Go;Python</td>\n",
-       "      <td>JavaScript;Python;Bash/Shell</td>\n",
-       "      <td>Associate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>165809.207657</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Neither satisfied nor dissatisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Windows</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>18-20 years</td>\n",
-       "      <td>12-14 years</td>\n",
-       "      <td>C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell</td>\n",
-       "      <td>C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>18 - 24 years old</td>\n",
-       "      <td>21426.000000</td>\n",
-       "      <td>South Africa</td>\n",
-       "      <td>South African rands (R)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Windows</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>6-8 years</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>Assembly;C;C++;Matlab;SQL;Bash/Shell</td>\n",
-       "      <td>C;C++;Java;Matlab;R;SQL;Bash/Shell</td>\n",
-       "      <td>No Degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>18 - 24 years old</td>\n",
-       "      <td>41671.000000</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>British pounds sterling (£)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately satisfied</td>\n",
-       "      <td>Seeking</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>6-8 years</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...</td>\n",
-       "      <td>Java;JavaScript;Python;TypeScript;HTML;CSS</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>18 - 24 years old</td>\n",
-       "      <td>120000.000000</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>MacOS</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>9-11 years</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>C;Go;JavaScript;Python;HTML;CSS</td>\n",
-       "      <td>JavaScript;HTML;CSS</td>\n",
-       "      <td>No Degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>25 - 34 years old</td>\n",
-       "      <td>93336.000000</td>\n",
-       "      <td>Nigeria</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Non developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Black or African descent</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Windows</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>Matlab;SQL;Kotlin;Bash/Shell</td>\n",
-       "      <td>JavaScript;TypeScript;HTML;CSS</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>250000.000000</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Moderately satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>MacOS</td>\n",
-       "      <td>Arts and Science</td>\n",
-       "      <td>30 or more years</td>\n",
-       "      <td>21-23 years</td>\n",
-       "      <td>Erlang;Go;Python;Rust;SQL</td>\n",
-       "      <td>Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua...</td>\n",
-       "      <td>No Degree</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>35 - 44 years old</td>\n",
-       "      <td>26023.003365</td>\n",
-       "      <td>India</td>\n",
-       "      <td>U.S. dollars ($)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>South Asian</td>\n",
-       "      <td>Non-conforming</td>\n",
-       "      <td>Yearly</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Extremely satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Linux-based</td>\n",
-       "      <td>Engineering</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>3-5 years</td>\n",
-       "      <td>Java;Python</td>\n",
-       "      <td>Java</td>\n",
-       "      <td>Bachelors</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>18 - 24 years old</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>Netherlands</td>\n",
-       "      <td>Euros (€)</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or European descent</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Monthly</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Neither satisfied nor dissatisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Windows</td>\n",
-       "      <td>No major</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>0-2 years</td>\n",
-       "      <td>Java;Python</td>\n",
-       "      <td>Java;JavaScript;PHP;VB.NET;HTML;CSS</td>\n",
-       "      <td>No Degree</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Age      SalaryUSD         Country  \\\n",
-       "1   35 - 44 years old   70841.000000  United Kingdom   \n",
-       "2   35 - 44 years old  153030.333333   United States   \n",
-       "3   35 - 44 years old  165809.207657   United States   \n",
-       "4   18 - 24 years old   21426.000000    South Africa   \n",
-       "5   18 - 24 years old   41671.000000  United Kingdom   \n",
-       "6   18 - 24 years old  120000.000000   United States   \n",
-       "7   25 - 34 years old   93336.000000         Nigeria   \n",
-       "8   35 - 44 years old  250000.000000   United States   \n",
-       "13  35 - 44 years old   26023.003365           India   \n",
-       "14  18 - 24 years old       0.000000     Netherlands   \n",
-       "\n",
-       "                       Currency        DevType Employment  \\\n",
-       "1   British pounds sterling (£)      Developer  Full-time   \n",
-       "2   British pounds sterling (£)        Manager  Full-time   \n",
-       "3              U.S. dollars ($)      Developer  Full-time   \n",
-       "4       South African rands (R)      Developer  Full-time   \n",
-       "5   British pounds sterling (£)      Developer  Full-time   \n",
-       "6              U.S. dollars ($)      Developer  Full-time   \n",
-       "7              U.S. dollars ($)  Non developer  Full-time   \n",
-       "8              U.S. dollars ($)      Developer  Full-time   \n",
-       "13             U.S. dollars ($)      Developer  Full-time   \n",
-       "14                    Euros (€)      Developer  Full-time   \n",
-       "\n",
-       "                RaceEthnicity          Gender SalaryType Hobby  \\\n",
-       "1   White or European descent            Male     Yearly   Yes   \n",
-       "2   White or European descent  Non-conforming     Yearly   Yes   \n",
-       "3   White or European descent            Male     Yearly    No   \n",
-       "4   White or European descent            Male     Yearly   Yes   \n",
-       "5   White or European descent            Male     Yearly   Yes   \n",
-       "6   White or European descent            Male     Yearly   Yes   \n",
-       "7    Black or African descent          Female     Yearly   Yes   \n",
-       "8   White or European descent            Male     Yearly   Yes   \n",
-       "13                South Asian  Non-conforming     Yearly    No   \n",
-       "14  White or European descent            Male    Monthly    No   \n",
-       "\n",
-       "                       JobSatisfaction JobSearchStatus OperatingSystem  \\\n",
-       "1              Moderately dissatisfied         Seeking     Linux-based   \n",
-       "2                 Moderately satisfied     Not seeking     Linux-based   \n",
-       "3   Neither satisfied nor dissatisfied     Not seeking         Windows   \n",
-       "4                   Slightly satisfied     Not seeking         Windows   \n",
-       "5                 Moderately satisfied         Seeking     Linux-based   \n",
-       "6                   Slightly satisfied     Not seeking           MacOS   \n",
-       "7                   Slightly satisfied     Not seeking         Windows   \n",
-       "8                 Moderately satisfied     Not seeking           MacOS   \n",
-       "13                 Extremely satisfied     Not seeking     Linux-based   \n",
-       "14  Neither satisfied nor dissatisfied     Not seeking         Windows   \n",
-       "\n",
-       "      UndergradMajor       YearsCoding YearsCodingProf  \\\n",
-       "1      Other Science  30 or more years     18-20 years   \n",
-       "2   Computer Science       24-26 years       6-8 years   \n",
-       "3   Computer Science       18-20 years     12-14 years   \n",
-       "4   Computer Science         6-8 years       0-2 years   \n",
-       "5   Computer Science         6-8 years       3-5 years   \n",
-       "6   Computer Science        9-11 years       0-2 years   \n",
-       "7   Computer Science         0-2 years       3-5 years   \n",
-       "8   Arts and Science  30 or more years     21-23 years   \n",
-       "13       Engineering         3-5 years       3-5 years   \n",
-       "14          No major         0-2 years       0-2 years   \n",
-       "\n",
-       "                               LanguageDesireNextYear  \\\n",
-       "1                                           Go;Python   \n",
-       "2                                           Go;Python   \n",
-       "3    C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell   \n",
-       "4                Assembly;C;C++;Matlab;SQL;Bash/Shell   \n",
-       "5   C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...   \n",
-       "6                     C;Go;JavaScript;Python;HTML;CSS   \n",
-       "7                        Matlab;SQL;Kotlin;Bash/Shell   \n",
-       "8                           Erlang;Go;Python;Rust;SQL   \n",
-       "13                                        Java;Python   \n",
-       "14                                        Java;Python   \n",
-       "\n",
-       "                                   LanguageWorkedWith    EdLevel  \n",
-       "1                        JavaScript;Python;Bash/Shell  Bachelors  \n",
-       "2                        JavaScript;Python;Bash/Shell  Associate  \n",
-       "3    C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell  Bachelors  \n",
-       "4                  C;C++;Java;Matlab;R;SQL;Bash/Shell  No Degree  \n",
-       "5          Java;JavaScript;Python;TypeScript;HTML;CSS  Bachelors  \n",
-       "6                                 JavaScript;HTML;CSS  No Degree  \n",
-       "7                      JavaScript;TypeScript;HTML;CSS  Bachelors  \n",
-       "8   Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua...  No Degree  \n",
-       "13                                               Java  Bachelors  \n",
-       "14                Java;JavaScript;PHP;VB.NET;HTML;CSS  No Degree  "
-      ]
-     },
-     "execution_count": 309,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cleaned_2018.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## After Cleaning Dataset 2018"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  1497200\n",
-      "Total missing :  0\n",
-      "Missing Percentage:  0.0 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = df.isnull().sum() #number of missing\n",
-    "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stackoverflow 2019 Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "na_vals = ['NA', 'Missing']\n",
-    "survey_main_df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2019.csv', na_values=na_vals)\n",
-    "schema_df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2019.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Cleaning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Selecting only the required columns for analysis\n",
-    "survey_df_2019 = survey_main_df[['Age', 'CareerSat', 'ConvertedComp', 'Country', 'Dependents', 'EdLevel', 'Employment', 'Ethnicity', 'Gender', 'Hobbyist', 'ImpSyn', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n",
-    " 'UndergradMajor', 'YearsCodePro', 'DevType']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#changing the name of columns for easier understanding\n",
-    "# 'MainBranch': 'Profession'\n",
-    "# 'ConvertedComp': 'SalaryUSD'\n",
-    "# 'CareerSat': 'JobSatisfaction'\n",
-    "# 'ImpSyn' : 'CompetenceLevel'\n",
-    "# 'JobSat' : 'CurrentJobSatis'\n",
-    "# 'JobSeek' : 'JobStatus'\n",
-    "\n",
-    "\n",
-    "survey_df_2019.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD',  'CareerSat': 'JobSatisfaction', 'ImpSyn' : 'CompetenceLevel', 'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>CompetenceLevel</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "      <th>Dependents</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>JobSatisfaction</th>\n",
-       "      <th>JobStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>Profession</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>14.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>No</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Primary/elementary school</td>\n",
-       "      <td>Not employed, and not looking for work</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL</td>\n",
-       "      <td>HTML/CSS;Java;JavaScript;Python</td>\n",
-       "      <td>I am a student who is learning to code</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>19.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Bosnia and Herzegovina</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Developer, desktop or enterprise applications;...</td>\n",
-       "      <td>Secondary school (e.g. American high school, G...</td>\n",
-       "      <td>Not employed, but looking for work</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>No</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>I am actively looking for a job</td>\n",
-       "      <td>C++;HTML/CSS;JavaScript;SQL</td>\n",
-       "      <td>C++;HTML/CSS;Python</td>\n",
-       "      <td>I am a student who is learning to code</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>Thailand</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
-       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
-       "      <td>Employed full-time</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>Elixir;HTML/CSS</td>\n",
-       "      <td>HTML/CSS</td>\n",
-       "      <td>I am not primarily a developer, but I write co...</td>\n",
-       "      <td>8820.0</td>\n",
-       "      <td>Web development or web design</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Age CompetenceLevel                 Country     CurrentJobSatis  \\\n",
-       "0  14.0             NaN          United Kingdom                 NaN   \n",
-       "1  19.0             NaN  Bosnia and Herzegovina                 NaN   \n",
-       "2  28.0         Average                Thailand  Slightly satisfied   \n",
-       "\n",
-       "  Dependents                                            DevType  \\\n",
-       "0         No                                                NaN   \n",
-       "1         No  Developer, desktop or enterprise applications;...   \n",
-       "2        Yes  Designer;Developer, back-end;Developer, front-...   \n",
-       "\n",
-       "                                             EdLevel  \\\n",
-       "0                          Primary/elementary school   \n",
-       "1  Secondary school (e.g. American high school, G...   \n",
-       "2           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
-       "\n",
-       "                               Employment Ethnicity Gender Hobbyist  \\\n",
-       "0  Not employed, and not looking for work       NaN    Man      Yes   \n",
-       "1      Not employed, but looking for work       NaN    Man       No   \n",
-       "2                      Employed full-time       NaN    Man      Yes   \n",
-       "\n",
-       "      JobSatisfaction                                          JobStatus  \\\n",
-       "0                 NaN                                                NaN   \n",
-       "1                 NaN                    I am actively looking for a job   \n",
-       "2  Slightly satisfied  I’m not actively looking, but I am open to new...   \n",
-       "\n",
-       "                            LanguageDesireNextYear  \\\n",
-       "0  C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL   \n",
-       "1                      C++;HTML/CSS;JavaScript;SQL   \n",
-       "2                                  Elixir;HTML/CSS   \n",
-       "\n",
-       "                LanguageWorkedWith  \\\n",
-       "0  HTML/CSS;Java;JavaScript;Python   \n",
-       "1              C++;HTML/CSS;Python   \n",
-       "2                         HTML/CSS   \n",
-       "\n",
-       "                                          Profession  SalaryUSD  \\\n",
-       "0             I am a student who is learning to code        NaN   \n",
-       "1             I am a student who is learning to code        NaN   \n",
-       "2  I am not primarily a developer, but I write co...     8820.0   \n",
-       "\n",
-       "                  UndergradMajor YearsCodePro  \n",
-       "0                            NaN          NaN  \n",
-       "1                            NaN          NaN  \n",
-       "2  Web development or web design            1  "
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#sorting the columns alphabetically\n",
-    "survey_df_2019.sort_index(axis=1).head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                       float64\n",
-       "JobSatisfaction            object\n",
-       "SalaryUSD                 float64\n",
-       "Country                    object\n",
-       "Dependents                 object\n",
-       "EdLevel                    object\n",
-       "Employment                 object\n",
-       "Ethnicity                  object\n",
-       "Gender                     object\n",
-       "Hobbyist                   object\n",
-       "CompetenceLevel            object\n",
-       "CurrentJobSatis            object\n",
-       "JobStatus                  object\n",
-       "LanguageDesireNextYear     object\n",
-       "LanguageWorkedWith         object\n",
-       "Profession                 object\n",
-       "UndergradMajor             object\n",
-       "YearsCodePro               object\n",
-       "DevType                    object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#datatype of survey data\n",
-    "survey_df_2019.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Validation - Total Cells vs Missing %"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  1688777\n",
-      "Total missing :  169969\n",
-      "Missing Percentage:  10.064620728491684 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = survey_df_2019.isnull().sum() #number of missing\n",
-    "total_cells = np.product(survey_df_2019.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleaning and Refactoring column values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Gender"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Man                                                            77919\n",
-       "Woman                                                           6344\n",
-       "Non-binary, genderqueer, or gender non-conforming                597\n",
-       "Man;Non-binary, genderqueer, or gender non-conforming            181\n",
-       "Woman;Non-binary, genderqueer, or gender non-conforming          163\n",
-       "Woman;Man                                                        132\n",
-       "Woman;Man;Non-binary, genderqueer, or gender non-conforming       70\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Gender'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#lets refactor Gender values to Male, female and Non binary\n",
-    "#For the purpose of our data analysis we are considering three gender category. This not to defame any gender.\n",
-    "#refactoring Gender\n",
-    "\n",
-    "def refactor_gender(df):\n",
-    "    '''function to change gender category to Male, Female, Non binary'''\n",
-    "    conditions = [(df['Gender'] == 'Man') | (df['Gender'] == 'Man;Non-binary, genderqueer, or gender non-conforming'),\n",
-    "                  (df['Gender'] == 'Woman') | (df['Gender'] == 'Woman;Non-binary, genderqueer, or gender non-conforming'),\n",
-    "                  (df['Gender'] == 'Non-binary, genderqueer, or gender non-conforming') \n",
-    "                  | (df['Gender'] == 'Woman;Man') \n",
-    "                  | (df['Gender'] == 'Woman;Man;Non-binary, genderqueer, or gender non-conforming')]\n",
-    "\n",
-    "    values = ['Man', 'Woman', 'Non-binary']\n",
-    "\n",
-    "    df['Gender'] = np.select(conditions, values, default = np.NaN)\n",
-    "    \n",
-    "    return df\n",
-    "            \n",
-    "survey_df_2019 = refactor_gender(survey_df_2019)\n",
-    "survey_df_2019['Gender'].replace('nan', 'Non-binary', inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Gender'] = survey_df_2019['Gender'].fillna('Non-binary')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n",
-    "survey_df_2019.isnull().sum()['Gender']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gender\n",
-       "Man           78100\n",
-       "Non-binary     4276\n",
-       "Woman          6507\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XLmzzE1JjYyNbt25t+RRdSjZs2IB9UPwRv217hw0b3i3JbRi1YcMG+vfvX+xqlKxVq1bFSpfur+n/haSZPXt21tHUwoULWwJj9DgAcOWVV+4y8ommj3OsuOqqq3KWmWvdmYH63nvv7dRTfr1iJOXujwNVBKOozHd+IPBLM3sSuA44LLLsAXd/2923AU8DB2aWbWbfMLPVZrZ606ZNXVL/TO7eamdZsWJFt6xXRLpHelSV6b777tslOKWlR0C50sc5VmSWEZ2OlhU9BnW13jKSAlgCXAuMA6JXD+cBD7r7KWZWBTwUWbY98vpDsmwvd78RuBGCa1KdWuMczCy9bsrLy5k4cWKbeSorK4HSviZVbN7vY4zsQdekmpqailwTyaWioiLr/AkTJlBfX581UFVVVeVNH+dYUVVV1SowRcuMlhU9BnW1XjGSCt0MXOnuT2TMHwi8HL4+u1trFIp7V1TfvkGMLC8vp7y8HICysjJqa2u7rG4i0toxxxwTK126v6b/F5Jm3rx5WdPX1ta2BIj0MSBtzpw5edPHOVbMmjUrZ5mZ606vPz0vbcqUKXnXUaheE6TcvdHd/z3Lon8DrjazPwB9urlaAMycOTPnsvQnqoqKCiZNmoSZcfLJJ1NdXY2ZUV1drVvQpV3acxdWLjU1NZ1WVhydWfdCXXvttS0H5swDdLb+Onny5Fbz06OTqqqqnGly3YI+dOjQlr4/adKkVmVluwU9mj7OsWLUqFE5y4yWFT0GZb73nX0Leo8PUu6+y7jZ3R9K337u7o+4+yh3P8HdZ7t7VTj/Vnc/P5Jnsrs/1FX1TI+mpk6dyrnnngvA9OnTmTt3LmVlZcybN4/a2lpGjx5NbW1tq9cixdad+2FmYIDge1IAe+65JwMHDgRg8ODBLTeIDBgwgH322QeAYcOGtUozbNgwIDglXl1dDQSjgdGjRwNw9NFHc8YZZwAftTP9wfLiiy9u1Xdz9dfo/FmzZjFgwADmzJmTM00+0TzRsuKkjyNfmbmOQdHt1tl6/PekulNHvifV1fQ9KX1PKq2U9wXpmfQ9KRERKUkKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIikli96QGzvVquX+2U3kf7gpQSBaleotR/A0k6j/YFKSU63SciIomlICUiIomlICUiIomlICUiIomlICUiIomlICUiIomlICUiIomlICUiIomlICUiIomlICUiIomlICUiIomlICUiIomlB8xKSSh7/036Pb20nXk3A7Q7f7QOsF+HyhCRwihISeJ19KclGht3AFBZ2dEAs59+5kKkmylISeLppyVEei9dkxIRkcRSkBIRkcRSkBIRkcRSkBIRkcRSkBIRkcRSkBIRkcRSkBIRkcRSkBIRkcRSkBIRkcRSkBIRkcRSkBIRkcTSs/skkerq6ti4cWPs9I2NjQBUVlZ2el1GjBih5weKFImClCTSxo0bWf/kYxxQ8WGs9O+92weAbTte7dR6vLilT6eWJyKFUZCSxDqg4kNmjd0SK+1VqysAYqePK12uiBSHrkmJiEhiKUiJiEhiKUiJiEhiKUiJiEhiKUiJiEhiKUiJiEhiKUiJiEhiKUiJiEhiKUiJiEhiKUiJiEhiKUiJiEhitRmkzKyPmd3fHZURERGJajNIufuHwPtmNrAb6iPdoK6ujrq6umJXQ7qR3nMpVXGfgr4NeMLM7gPeS890d/3ITgkq5HeapGfQey6lKm6QWhb+iYiIdJtYQcrdF5pZf+AAd3+mi+skIiICxLy7z8ymAOuA34bTR5rZki6sl4iISOxb0OcCxwBvAbj7OuATXVIjERGRUNwgtcPd386Y551dGRERkai4N048aWanA33MbCQwA/hj11VLREQk/kjqAuAwYDtwJ/AOMLOL6iQiIgLEv7vvfeA74Z+IiEi3yBukzOxe8lx7cvcvdHqNREREQm2d7rsWmA88D2wFbgr/tgBPdm3VepcFCxaQSqW4/vrraWpq4oILLmDz5s05069fv57q6mo9SUAKdvXVV5NKpbjmmmtavV61ahXjxo1jzZo13HDDDaRSKX7+85+3en3bbbeRSqW48847W82H1vtk9HU0T3QdudJHRedH8+YTrVc0T5x+FSdNKSi0He1pd/S9ufzyy0mlUsyaNau9Vc7J3Nu+Sc/MVrp7qq15Gcsd+JG7XxxOXwJUuPvcjlUZzOxWYKm7/ypj/lhgWrEe1zR27FhfvXp1u/KmUh9typqaGpYsWUJNTQ0XXXRR1vTTpk2joaGBqqoqFi1aVNC6ZswINk+Sn+U2Y8YMtjX8mVljt8RKf9XqCoDY6eO6anUF/ao+k+htFUf0PY/ua1EVFRVs2bKl5X8hVq5c2WqfBFpeNzQ0ZF3H0KFDs6aP7s/RMpuamlry1tfX56xLtH3R9Y0fP77NfjV//vw205SCQtvRnnZH35voe7xy5cqC62tma9x9bLZlcW+c2NvMPhkp8BPA3m3k2Q6camZDY66jw9x9dWcFKDPr0xnlxLFgwYJW0/fccw/uzvLly7N+slm/fn3LTtHQ0KDRlMR29dVX51yWDkyFBiiAa665ptU+GX2dax250qf358z9PJo312jqhhtuyLm+ZcuW5e1XTU1NLF++PG+aUlBoO9rT7sz3JqqzR1Nxb0H/FvCQmT0XTlcB57WRZwdwY5i31Q0XZnYgcDNBoNsEnOPuL4YjpHeAscB+wGWZo6WIk8zsQmBf4CJ3X2pm44BL3H2ymc0FDgA+Gf5f4O514frvBvYH+gH/7u43hvO3AD8CPgfUm9mR7n5KuGwCMN3dT22j3QVbvHhx1vk7d+5k4cKFu3yyueqqq1pNX3nllQWNphobG9m6dWvLp+sk2rBhA7s1F//nzl5/v4wPNmxI9LaKY8OGDfTv359169Z1Sfn33ntvp5WV3p8z9/Oo2bNnZx1N3XHHHTnz7NixA8jdrxYuXEj6zFKuNKWg0Ha0p9353pv2jKTyiXUUcPffAiOBC8O/g939v2Nk/Q/gjCw/83E9sMjdRwN3ANFzKcOAzwKTgf+bp+wq4ERgEvBTM+uXJc0hBAHnGOC7ZlYezv+au48hCIYzzGxIOH8A8KS7/x1wJXComaVHjOcAt2SuwMy+YWarzWz1pk2b8lS3cM3NzaxYsWKX+ZmfXDKnRUpZrk/oUe0Z7aXl6lf33Xcfzc3NedOUgkLb0Z52d+cxJ+5ICmAMQWDoCxxhZrh73o/v7v6OmS0i+PLv1sii44D0iOQ24N8iy+52953A02a2b57i7wrTbQhHeIdkSbPM3bcD283sDYJRVyNBYDolTLM/QQDeDHwI/Dqsu5vZbcCZZnZLWOdpWdp4I8GIkbFjx3bqUzjKy8uZOHHiLvMzzwGnz+nHVVlZCZTGNali23ePnfSrGpnobRVHeiTY1NRU5Jq0Lb0/Z+7nURUVFe0uP1e/mjBhAvX19TQ3N+dMUwoKbUd72p3vvelscR8wexvBnX6fBT4T/mW9yJXFAuDrBKOUXKIH9+3RVYfr/76ZrTOzdTnyZJvOLOtDoG94SvAk4Dh3PwJYS3DaD2Bb+COPabcAZwKnAb909x152tBup56a/QxiWVkZtbW1u8zPPOc7Z86crqiW9EDV1dVdUu6UKVM6raz0/pzv2sa8efOyzj/jjDNy5unbN/hMnqtf1dbWYmZ505SCQtvRnnbne29y3ZjTXnFP+o8FTnD3/+PuF4R/sU7Su/ubwF0EgSrtj8BXw9dnAL9vo4zvuPuR7n5kZPaXzazMzA4iuO4U9ydEBgJ/c/f3zewQ4Ng8630FeAWYBdwas/yCzZw5s9V0TU0NZkZ1dTVDhgzZJf2oUaNafdocMWJEV1VNephvf/vbOZelRyftGaVceumlrfbJ6Otc68iVPr0/Z+7n0bxjxozJWo/zzmt9qTyaZ9KkSXn71dChQ6murs6bphQU2o72tDvzvYnKd72qPeIGqScJbmRor/lA9C6/GcA5ZvY4cBbBda5CPQM8DCwHvunu22Lm+y3BiOpxYB7wpzbS3wG85O5Pt6OOsaVHU1OnTqW2tpbRo0fn/UQza9YsBgwYoFGUFCw9mpoyZUqr13PnzqWsrIx58+a1jEhqa2tbvT733HMBmD59eqv50HqfjL6O5omuI1f6qOj8aN58ovWK5onTr+KkKQWFtqM97Y6+N8cffzzQ+aMoiP89qQeBI4FVRE6h9YYnTpjZ9cBad/95W2k78j2p7qTvScXXE78nJZI0+b4nFffGibmdV53SYWZrgPeAi4tdFxGR3ijuA2YfDr/bNNLd7zezPYBu+7JrsYS3qYuISJHEvbvvXOBXQPrr3MOBu7uoTiIiIkD8Gyf+BTiB4GkQuPsGYJ+uqpSIiAjED1Lb3f2D9ISZ9UU/Hy8iIl0sbpB62MyuAPqHz7D7JdB5D+sSERHJIm6QupzgQbBPAN8geNyQfqVXRES6VN4gZWY1ZvYv7r7T3W8CDiR4+sQVZvalbqmhiIj0Wm2NpC4DlkSmdyN40Ow4YHoX1UlERARo+3tSu7n7S5Hp34fP4nvTzPI9MFYSTM/66330nkupaitIDY5OuPv5kcm2fplXEqrUf8BPCqf3XEpVW6f7Hg2/yNuKmZ1H8Bw/ERGRLtPWSOpbwN1mdjrwWDhvDLA78MUurJeIiEj+IOXubwDHm9k/AoeFs5e5+++6vGYiItLrxX3A7O8ABSYREelWcb/MKyIi0u0UpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLFifU9KpBhe3NKHq1ZXxEr7wrt9AGKnL6QOozq1RBEphIKUJFKhT+0e0NgIQL/Kyk6tx6h21EVEOo+ClCSSntotIqBrUiIikmAKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklgKUiIiklh6wKwkXl1dHRs3boydvjF8InplB56IPmLECD3kViQBFKQk8TZu3Mjap9bCoJgZ3g7+bbJN7VvhW+3LJiKdT0FKSsMg2DluZ6ykZQ8FZ7Hjps+VX0SKT71RREQSS0FKREQSS0FKREQSS0FKREQSS0FKREQSS0FKREQSS0FKREQSS0FKREQSS0FKREQSS0FKREQSS0FKREQSS0FKqKuro66urtjVkJj0fklvogfMSkE/gyHFp/dLehONpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLESHaTM7DozmxmZ/m8z+1lker6ZXVSUynWy9evXU11dzcaNG2lqauKCCy5g8+bNxa6WJNz48eNJpVKcdNJJAKRSqZa/CRMmkEqlmDhxYqvXNTU1pFIpTjnlFKZMmUIqlaKmpobTTz+dVCrFtGnTuOiii0ilUlx22WV85StfIZVKcfrpp3P55ZeTSqWYNWsWV199NalUimuuuQaABQsWkEqluP7667nttttIpVLceeedrFq1inHjxrFmzZpW86Ovo/t/NP39999PKpXiwQcf3KVfRNNF5eo/udYRR2aZcfpoR/pxqR0Dotu2s5m7d3qhncXMvgx82d2nmlkZ8GfgA3c/Llz+CDDT3R8tZj3Txo4d66tXr25X3mnTptHQ0EBVVRVHHHEES5Ysoaamhosu6voYPGPGDIDEPrR0xowZrH15LTvH7YyVvuyh4LNX3PTZ8h81/KhEbw8I3q9UKtUyf+XKla2mu1O+dVdUVLBly5aW/9lUVVW17P9NTU0t6bdt28aOHTvo27cvkyZNatUvTj755JZ09fX1LWXNnz8/a/+J9rHoOqJ5c8ksM9c68uUpREfyFkN02y5atKjg/Ga2xt3HZluW6JEU8Afg+PD1YcCTwLtmNtjMdgcOBQaZ2Voze8LMbg7nY2YNZvYDM3vEzFab2dHhSOxZM/tmmKbCzB4ws8fC/DXh/Coz+4uZ3WRmT5nZCjPr31WNXL9+PQ0NDQA0NDRQX1+Pu7N8+fKS+SQl3W/8+PGtposVoADOPPPMnMvSgSlXgAJa7f/R9Dt27ABgx44dLF26tKVfPPDAA63SpUdETU1NLF++fJf+k9nHsuXNJbPMDRs2ZF1HvjyF9OOO5C2GzG3b2aOpRI+kIAg2QAqoBgwYDjwCvA1cBwwDxrv7ejNbBDzm7gvCfD9095+Y2XXAeOAEoB/wlLvvY2Z9gT3c/R0zGwr8CRgJHAhsBMa6+zozuwtY4u6356tre0dS6U8hmcrLy5k0aVKXf5I69dRT2bp1KyNHjuzS9bTXhg0b2LJzCzsnd9NIamkZFWUVid4e/fv3p6mpqdhVKYry8nLcvSWAAS0jovnz51NfX09zc3Or/pOrj0Xz5pJZ5vDhw3n55Zd3WUe+PIX0447kLYbMbdue0VQpj6Tgo9HU8QTB6ZHI9MvA8+6+Pky7kCCgpS0J/z8BPOru77r7JmCbmQ0iCHo/MLPHgfsJAuC+YZ7n3X1d+HoNUJWtcmb2jXCktnrTpk3tamCuztPc3MyKFSvaVaZIT9Xc3NwqQMFHo7T77ruP5ubmlnTp/pOrj0Xz5pJZZkNDQ9Z15MtTSD/uSN5iyNy2+bZ1e5TC70n9kSAgHU5wuu8l4GLgHeAxYEKevNvD/zsjr9PTfYEzgL2BMe7eHI6++mXkBfgQyHq6z91vBG6EYCQVt1FR6fPxmcrLy5k4cWJ7iixIZWUlkPxrUt2mAkYOH5no7QFoJJUxkgKYMGFCq1FIuv/k6mPRvLlklpk5ksrWR3PVI46O5C2GzG1bVVXVqeWXykhqMvCmu3/o7m8Cg4DjgFuAKjMbEaY9C3i4gLIHAm+EAeofCE7zdbtZs2a1mi4vLwegrKyM2traYlRJSkB6P0mCAw44oMvX0adPHyDoF9/5zndaLZs3bx4AtbW1mFlLunT/yexj2fLmklnm7Nmzs64jX55C+nFH8hZD5radM2dOp5ZfCkHqCSB9vSg67213bwTOAX5pZk8QjJB+WkDZdwBjzWw1wajqr51T5cKMGjWq5dNHVVUVJ598MmZGdXU1Q4YMKUaVpAQ88MADraZXrlxZpJrA7bfnvlybHqnkG7FE9/9o+r59g5M9ffv2ZfLkyS39Yvz48a3SjRkzBoChQ4dSXV29S//J7GPZ8uaSWebIkSOzriNfnkL6cUfyFkPmth0xYkT+DAVKfJAKR08fc/dZkXlnu/vB4esH3P0odz/c3b/m7tvD+VXu3hS+vtXdz4/kr3L3pvDvOHcf6+7/7O6HuntD+PfpSPpr3X1uV7Zz1qxZDBgwgDlz5lBbW8vo0aMT/wlKii89mtptt912Wbb77rsD0K9fv1avBw8eDMCQIUMYOHAgAIMHD2457VtVVcXYscE17GOPPZZhw4YBwWnh448PbrZNpVJUV1cDMGXKFCC4AQdg6tSpnHvuuQBMnz6duXPnUlZWxrx581rNj76O7v/R9FdccQUAs2fP3qVfRNNF5eo/udYRR2aZcfpoR/pxqR0Dotu2syX+7r5S0pHvSRWTvie1a/5S+Z6USE9Q6nf3iYhIL6UgJSIiiaUgJSIiiaUgJSIiiaUgJSIiiaUgJSIiiaUgJSIiiaUgJSIiiVUKD5iVLtbZjzGRrqX3S3oTBSlpeYKBlAa9X9Kb6HSfiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgklh4wK6XhLSh7KOZnqreCf7HTZ8s/vH1ZRaRzKUhJ4hX60xSN3ghA5fDK9q1wuH4OQyQpFKQk8fTTFCK9l65JiYhIYilIiYhIYilIiYhIYilIiYhIYilIiYhIYpm7F7sOPYaZbQJeKCDLUKCpi6qTdL217Wp376J2x3Ogu++dbYGCVBGZ2Wp3H1vsehRDb2272t27qN0dp9N9IiKSWApSIiKSWApSxXVjsStQRL217Wp376J2d5CuSYmISGJpJCUiIomlICUiIomlIFUkZvZ5M3vGzDaa2eXFrk9XMbP9zexBM/uLmT1lZheG8/cys/vMbEP4f3Cx69oVzKyPma01s6XhdI9vt5kNMrNfmdlfw/f9uF7S7m+F+/iTZnanmfXrqe02s5vN7A0zezIyL2dbzezb4bHuGTP7XCHrUpAqAjPrA/wHUA18CjjNzD5V3Fp1mR3Axe5+KHAs8C9hWy8HHnD3kcAD4XRPdCHwl8h0b2j3vwO/dfdDgCMI2t+j221mw4EZwFh3/zTQB/gqPbfdtwKfz5iXta1hf/8qcFiY5z/DY2AsClLFcQyw0d2fc/cPgF8ANUWuU5dw91fd/bHw9bsEB6zhBO1dGCZbCHyxKBXsQmZWCUwCfhaZ3aPbbWYfA1LAzwHc/QN3f4se3u5QX6C/mfUF9gBeoYe2291XAm9mzM7V1hrgF+6+3d2fBzYSHANjUZAqjuHAS5HpRnrBD5abWRVwFPAosK+7vwpBIAP2KWLVusoC4DJgZ2ReT2/3J4FNwC3hac6fmdkAeni73f1l4FrgReBV4G13X0EPb3eGXG3t0PFOQao4LMu8Hv1dADOrAH4NzHT3d4pdn65mZpOBN9x9TbHr0s36AkcDP3H3o4D36DmnuHIKr7/UAJ8APg4MMLMzi1urxOjQ8U5Bqjgagf0j05UEpwZ6JDMrJwhQd7j74nD262Y2LFw+DHijWPXrIicAXzCzBoLTuf9oZrfT89vdCDS6+6Ph9K8IglZPb/dJwPPuvsndm4HFwPH0/HZH5Wprh453ClLF8WdgpJl9wsx2I7iouKTIdeoSZmYE1yf+4u4/iixaAtSGr2uBe7q7bl3J3b/t7pXuXkXw/v7O3c+k57f7NeAlMzs4nDUeeJoe3m6C03zHmtke4T4/nuD6a09vd1Suti4Bvmpmu5vZJ4CRwKq4heqJE0ViZicTXLPoA9zs7t8vbo26hpl9Fvgf4Ak+ujZzBcF1qbuAAwg6+JfdPfNCbI9gZuOAS9x9spkNoYe328yOJLhZZDfgOeAcgg/EPb3d3wO+QnBH61rgn4EKemC7zexOYBzBT3K8DnwXuJscbTWz7wBfI9g2M919eex1KUiJiEhS6XSfiIgkloKUiIgkloKUiIgkloKUiIgkloKUiIgkloKUSA9hZqeYmZvZIcWui0hnUZAS6TlOA35P8OVhkR5BQUqkBwifjXgC8HXCIGVmZWb2n+FvHC01s3oz+1K4bIyZPWxma8zsv9OPsxFJGgUpkZ7hiwS/4bQeeNPMjgZOBaqAwwmefnActDxL8cfAl9x9DHAz0COfeCKlr2+xKyAineI0gsdsQfBA29OAcuCX7r4TeM3MHgyXHwx8GrgveMwcfQh+XkIkcRSkREpc+DzAfwQ+bWZOEHQc+E2uLMBT7n5cN1VRpN10uk+k9H0JWOTuB7p7lbvvDzwPNAH/FF6b2pfggaAAzwB7m1nL6T8zO6wYFRdpi4KUSOk7jV1HTb8m+PG9RuBJ4AaCJ8+/7e4fEAS2H5rZ/wLrCH77SCRx9BR0kR7MzCrcfUt4SnAVcEL4m08iJUHXpER6tqVmNojgt53mKUBJqdFISkREEkvXpEREJLEUpEREJLEUpEREJLEUpEREJLEUpEREJLH+P0iVskUGtOHQAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n",
-    "plt.title(\"Before cleaning Age's outliers from genders\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#We are considering developes of age 15 to 60\n",
-    "filt = (survey_df_2019['Age'] >= 15) & (survey_df_2019['Age'] <= 60)\n",
-    "survey_df_2019 = survey_df_2019[filt]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n",
-    "plt.title(\"After cleaning Age's outliers from genders\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Age'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Profession column (Mainbranch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I am a developer by profession                                                   59247\n",
-       "I am a student who is learning to code                                            8382\n",
-       "I am not primarily a developer, but I write code sometimes as part of my work     6531\n",
-       "I code primarily as a hobby                                                       2370\n",
-       "I used to be a developer by profession, but no longer am                          1210\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Profession'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "255"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Profession'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Profession'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I am a developer by profession                                                   59502\n",
-       "I am a student who is learning to code                                            8382\n",
-       "I am not primarily a developer, but I write code sometimes as part of my work     6531\n",
-       "I code primarily as a hobby                                                       2370\n",
-       "I used to be a developer by profession, but no longer am                          1210\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Profession'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#Lets refactor column values of Profession column\n",
-    "#refactoring profession column\n",
-    "\n",
-    "def refactor_prof(df):\n",
-    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
-    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
-    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
-    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
-    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
-    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
-    "    \n",
-    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
-    "    \n",
-    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "survey_df_2019 = refactor_prof(survey_df_2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer        59502\n",
-       "Student           8382\n",
-       "Non developer     6531\n",
-       "Novoice           2370\n",
-       "Ex-Developer      1210\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Profession'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## EdLevel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                              34926\n",
-       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                           17305\n",
-       "Some college/university study without earning a degree                                 9571\n",
-       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     7638\n",
-       "Associate degree                                                                       2585\n",
-       "Other doctoral degree (Ph.D, Ed.D., etc.)                                              2032\n",
-       "Professional degree (JD, MD, etc.)                                                     1037\n",
-       "Primary/elementary school                                                               981\n",
-       "I never completed any formal education                                                  352\n",
-       "Name: EdLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1568"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['EdLevel'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#  Refactoring EdLevel\n",
-    "def refactor_ed(df):\n",
-    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
-    "    conditions_ed = [(df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
-    "                     (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
-    "                     (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
-    "                     (df['EdLevel'] == 'Associate degree'),\n",
-    "                     (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
-    "                     (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
-    "                     | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
-    "                     | (df['EdLevel'] == 'Primary/elementary school')\n",
-    "                     | (df['EdLevel'] == 'I never completed any formal education')]\n",
-    "\n",
-    "    choices_ed = ['Bachelors', 'Masters', 'Professional', 'Associate', 'Doctorate', 'No Degree']\n",
-    "\n",
-    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
-    "    \n",
-    "    return df\n",
-    "\n",
-    "# applying function to subsets\n",
-    "survey_df_2019 = refactor_ed(survey_df_2019)\n",
-    "survey_df_2019['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Bachelors       36494\n",
-       "No Degree       18542\n",
-       "Masters         17305\n",
-       "Associate        2585\n",
-       "Doctorate        2032\n",
-       "Professional     1037\n",
-       "Name: EdLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019.isnull().sum()['EdLevel']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Undergrad Major"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Computer science, computer engineering, or software engineering          42211\n",
-       "Another engineering discipline (ex. civil, electrical, mechanical)        5472\n",
-       "Information systems, information technology, or system administration     4646\n",
-       "Web development or web design                                             2975\n",
-       "A natural science (ex. biology, chemistry, physics)                       2866\n",
-       "Mathematics or statistics                                                 2557\n",
-       "A business discipline (ex. accounting, finance, marketing)                1633\n",
-       "A humanities discipline (ex. literature, history, philosophy)             1408\n",
-       "A social science (ex. anthropology, psychology, political science)        1246\n",
-       "Fine arts or performing arts (ex. graphic design, music, studio art)      1124\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10787"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['UndergradMajor'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Computer science, computer engineering, or software engineering          49010\n",
-       "Another engineering discipline (ex. civil, electrical, mechanical)        6368\n",
-       "Information systems, information technology, or system administration     5392\n",
-       "Web development or web design                                             3424\n",
-       "A natural science (ex. biology, chemistry, physics)                       3285\n",
-       "Mathematics or statistics                                                 2984\n",
-       "A business discipline (ex. accounting, finance, marketing)                1908\n",
-       "A humanities discipline (ex. literature, history, philosophy)             1627\n",
-       "A social science (ex. anthropology, psychology, political science)        1431\n",
-       "Fine arts or performing arts (ex. graphic design, music, studio art)      1327\n",
-       "I never declared a major                                                   922\n",
-       "A health science (ex. nursing, pharmacy, radiology)                        316\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].value_counts().nlargest(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019.dropna(subset=['UndergradMajor'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Refactoring UndergradMajor\n",
-    "def refactor_major(df):\n",
-    "    '''function to change undergrad major category to Computer Science, Engineering, Info Systems, Math/Stat, \n",
-    "        Other Science, Web Design/Dev, Business, Arts and Science'''\n",
-    "    \n",
-    "    \n",
-    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'),\n",
-    "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
-    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'),\n",
-    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
-    "                        (df['UndergradMajor'] == 'I never declared a major'),\n",
-    "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)')\n",
-    "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'),\n",
-    "                        (df['UndergradMajor'] == 'Web development or web design'),\n",
-    "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'),\n",
-    "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
-    "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
-    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)')]\n",
-    "\n",
-    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'No Major', 'Other Science',\n",
-    "                     'Web Design/Dev', 'Business', 'Arts and Science']\n",
-    "\n",
-    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
-    "    \n",
-    "    return df\n",
-    "\n",
-    "# applying function to subsets\n",
-    "survey_df_2019 = refactor_major(survey_df_2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Computer Science    49010\n",
-       "Engineering          6368\n",
-       "Info Systems         5392\n",
-       "Arts and Science     4385\n",
-       "Other Science        3601\n",
-       "Web Design/Dev       3424\n",
-       "Math/Stat            2984\n",
-       "Business             1908\n",
-       "No Major              922\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['UndergradMajor'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Job Status"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I’m not actively looking, but I am open to new opportunities    42258\n",
-       "I am not interested in new job opportunities                    19161\n",
-       "I am actively looking for a job                                 10491\n",
-       "Name: JobStatus, dtype: int64"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6084"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['JobStatus'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "I’m not actively looking, but I am open to new opportunities    45917\n",
-       "I am not interested in new job opportunities                    20712\n",
-       "I am actively looking for a job                                 11365\n",
-       "Name: JobStatus, dtype: int64"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019.dropna(subset=['JobStatus'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# refactoring JobStatus\n",
-    "# changing the jobstatus to seeking and non seeking\n",
-    "def refactor_job(df):\n",
-    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
-    "    \n",
-    "    conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n",
-    "                    (df['JobStatus'] == 'I am not interested in new job opportunities')\n",
-    "                    | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
-    "    \n",
-    "    choices_job = ['Seeking', 'Not seeking']\n",
-    "    \n",
-    "    df['JobStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "survey_df_2019 = refactor_job(survey_df_2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Not seeking    66629\n",
-       "Seeking        11365\n",
-       "Name: JobStatus, dtype: int64"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobStatus'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JobSatisfaction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        26584\n",
-       "Slightly satisfied                    22739\n",
-       "Slightly dissatisfied                  6843\n",
-       "Neither satisfied nor dissatisfied     6158\n",
-       "Very dissatisfied                      3203\n",
-       "Name: JobSatisfaction, dtype: int64"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12467"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        31507\n",
-       "Slightly satisfied                    26970\n",
-       "Slightly dissatisfied                  8343\n",
-       "Neither satisfied nor dissatisfied     7313\n",
-       "Very dissatisfied                      3861\n",
-       "Name: JobSatisfaction, dtype: int64"
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['JobSatisfaction'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Employment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employed full-time                                      58069\n",
-       "Independent contractor, freelancer, or self-employed     7305\n",
-       "Not employed, but looking for work                       4703\n",
-       "Employed part-time                                       3958\n",
-       "Not employed, and not looking for work                   2914\n",
-       "Retired                                                    76\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "969"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Employment'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 164,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employed full-time                                      58771\n",
-       "Independent contractor, freelancer, or self-employed     7397\n",
-       "Not employed, but looking for work                       4770\n",
-       "Employed part-time                                       4017\n",
-       "Not employed, and not looking for work                   2960\n",
-       "Retired                                                    79\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 165,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Refactoring the employment\n",
-    "def refactor_emp(df):\n",
-    "    '''function to change Employment category to Full-time, Self-employed, Not employed, Part-time '''\n",
-    "    conditions_emp = [(df['Employment'] == 'Employed full-time'),\n",
-    "                    (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n",
-    "                    (df['Employment'] == 'Not employed, but looking for work')\n",
-    "                    | (df['Employment'] == 'Not employed, and not looking for work')\n",
-    "                    | (df['Employment'] == 'Retired'),\n",
-    "                    (df['Employment'] == 'Employed part-time')]\n",
-    "    \n",
-    "    choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n",
-    "    \n",
-    "    df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n",
-    "                       \n",
-    "    return df\n",
-    "\n",
-    "\n",
-    "survey_df_2019 = refactor_emp(survey_df_2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Full-time        58771\n",
-       "Not employed      7809\n",
-       "Self-employed     7397\n",
-       "Part-time         4017\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Employment'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ethnicity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ethnicity_list = survey_df_2019['Ethnicity'].unique().tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[nan,\n",
-       " 'White or of European descent',\n",
-       " 'White or of European descent;Multiracial',\n",
-       " 'East Asian',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent',\n",
-       " 'Hispanic or Latino/Latina;Multiracial',\n",
-       " 'Hispanic or Latino/Latina',\n",
-       " 'Middle Eastern',\n",
-       " 'South Asian',\n",
-       " 'Multiracial',\n",
-       " 'East Asian;South Asian',\n",
-       " 'Biracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Black or of African descent;White or of European descent;Biracial',\n",
-       " 'Middle Eastern;White or of European descent',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Black or of African descent;White or of European descent',\n",
-       " 'White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;White or of European descent',\n",
-       " 'East Asian;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'East Asian;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;White or of European descent;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
-       " 'South Asian;White or of European descent;Multiracial',\n",
-       " 'South Asian;Biracial',\n",
-       " 'Middle Eastern;South Asian',\n",
-       " 'East Asian;South Asian;Multiracial',\n",
-       " 'White or of European descent;Biracial',\n",
-       " 'East Asian;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina',\n",
-       " 'East Asian;Hispanic or Latino/Latina;White or of European descent',\n",
-       " 'East Asian;White or of European descent;Multiracial',\n",
-       " 'South Asian;White or of European descent;Biracial',\n",
-       " 'East Asian;South Asian;White or of European descent;Multiracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;Biracial',\n",
-       " 'Hispanic or Latino/Latina;South Asian;Multiracial',\n",
-       " 'Black or of African descent;East Asian;South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'East Asian;Middle Eastern',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;Multiracial',\n",
-       " 'Middle Eastern;White or of European descent;Biracial',\n",
-       " 'East Asian;Middle Eastern;South Asian',\n",
-       " 'East Asian;Biracial',\n",
-       " 'Middle Eastern;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;Biracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
-       " 'Middle Eastern;Multiracial',\n",
-       " 'Black or of African descent;Middle Eastern',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Black or of African descent;South Asian;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina',\n",
-       " 'South Asian;Multiracial',\n",
-       " 'East Asian;South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Multiracial',\n",
-       " 'South Asian;White or of European descent',\n",
-       " 'Black or of African descent;East Asian',\n",
-       " 'Black or of African descent;Middle Eastern;Multiracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
-       " 'Black or of African descent;South Asian;White or of European descent;Multiracial',\n",
-       " 'East Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Middle Eastern;Biracial',\n",
-       " 'East Asian;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial',\n",
-       " 'Hispanic or Latino/Latina;Biracial;Multiracial',\n",
-       " 'Black or of African descent;South Asian',\n",
-       " 'Black or of African descent;East Asian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;White or of European descent;Biracial;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;South Asian',\n",
-       " 'East Asian;Middle Eastern;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
-       " 'Middle Eastern;South Asian;Multiracial',\n",
-       " 'Black or of African descent;Middle Eastern;White or of European descent',\n",
-       " 'Black or of African descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern',\n",
-       " 'Black or of African descent;Middle Eastern;Biracial',\n",
-       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;Biracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Middle Eastern;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'East Asian;Middle Eastern;South Asian;White or of European descent',\n",
-       " 'East Asian;Middle Eastern;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Biracial',\n",
-       " 'East Asian;Middle Eastern;White or of European descent',\n",
-       " 'East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern;White or of European descent',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;South Asian;White or of European descent',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian;Biracial',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'East Asian;South Asian;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'East Asian;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
-       " 'East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian',\n",
-       " 'Hispanic or Latino/Latina;South Asian;White or of European descent',\n",
-       " 'Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'Black or of African descent;East Asian;Biracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Biracial',\n",
-       " 'East Asian;South Asian;White or of European descent',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Middle Eastern;Biracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;South Asian',\n",
-       " 'East Asian;South Asian;Biracial;Multiracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;South Asian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;South Asian;Biracial',\n",
-       " 'East Asian;Middle Eastern;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Middle Eastern;South Asian;White or of European descent',\n",
-       " 'Middle Eastern;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'East Asian;Middle Eastern;South Asian;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;Biracial;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;South Asian;White or of European descent;Biracial;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Middle Eastern;White or of European descent;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;Middle Eastern;South Asian',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;Biracial;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n",
-       " 'South Asian;Biracial;Multiracial',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;South Asian',\n",
-       " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Multiracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Middle Eastern;South Asian',\n",
-       " 'Hispanic or Latino/Latina;Middle Eastern;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Multiracial',\n",
-       " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n",
-       " 'East Asian;Middle Eastern;South Asian;White or of European descent;Multiracial',\n",
-       " 'Black or of African descent;East Asian;South Asian',\n",
-       " 'Black or of African descent;Hispanic or Latino/Latina;Biracial;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Biracial',\n",
-       " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n",
-       " 'Black or of African descent;East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n",
-       " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n",
-       " 'Black or of African descent;South Asian;Biracial',\n",
-       " 'East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian',\n",
-       " 'East Asian;South Asian;White or of European descent;Biracial',\n",
-       " 'Black or of African descent;East Asian;Middle Eastern;South Asian;Multiracial']"
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#here, you can see that we have long list of values. lets refactor them\n",
-    "ethnicity_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "173"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(ethnicity_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#refactoring long list of values into categories.\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Biracial', na=False), 'Ethnicity'] = 'Biracial'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Black or of African descent', na=False), 'Ethnicity'] = 'Black or of African descent'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('East Asian', na=False), 'Ethnicity'] = 'East Asian'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Hispanic or Latino', na=False), 'Ethnicity'] = 'Hispanic or Latino'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Indigenous', na=False), 'Ethnicity'] = 'Indigenous'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Middle Eastern', na=False), 'Ethnicity'] = 'Middle Eastern'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('South Asian', na=False), 'Ethnicity'] = 'South Asian'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('White or of European descent', na=False), 'Ethnicity'] = 'White or of European descent'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Multiracial', na=False), 'Ethnicity'] = 'Multiracial'\n",
-    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Native American', na=False), 'Ethnicity'] = 'Native American'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7804"
-      ]
-     },
-     "execution_count": 172,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Ethnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent    47587\n",
-       "South Asian                      7417\n",
-       "Hispanic or Latino               4901\n",
-       "East Asian                       3698\n",
-       "Middle Eastern                   3057\n",
-       "Black or of African descent      2360\n",
-       "Multiracial                       572\n",
-       "Native American                   322\n",
-       "Biracial                          276\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 173,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Ethnicity'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Ethnicity']=survey_df_2019.groupby(['Country'])['Ethnicity'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 175,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Ethnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent    50883\n",
-       "South Asian                     10061\n",
-       "Hispanic or Latino               5204\n",
-       "East Asian                       4391\n",
-       "Middle Eastern                   3596\n",
-       "Black or of African descent      2570\n",
-       "Multiracial                       632\n",
-       "Native American                   355\n",
-       "Biracial                          302\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Ethnicity'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dependents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "No     46457\n",
-       "Yes    28918\n",
-       "Name: Dependents, dtype: int64"
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019[\"Dependents\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2619"
-      ]
-     },
-     "execution_count": 178,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019[\"Dependents\"].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Lets consider that people who didnt respond has no dependents for the purpose of analysis\n",
-    "survey_df_2019[\"Dependents\"].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 180,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019[\"Dependents\"].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "No     48085\n",
-       "Yes    29909\n",
-       "Name: Dependents, dtype: int64"
-      ]
-     },
-     "execution_count": 181,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019[\"Dependents\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DevType"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5025"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer, full-stack                                                                                           7636\n",
-       "Developer, back-end                                                                                             4387\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  2216\n",
-       "Developer, front-end                                                                                            1985\n",
-       "Developer, mobile                                                                                               1934\n",
-       "Developer, back-end;Developer, full-stack                                                                       1886\n",
-       "Student                                                                                                         1289\n",
-       "Developer, front-end;Developer, full-stack                                                                       940\n",
-       "Developer, desktop or enterprise applications                                                                    900\n",
-       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     815\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 183,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['DevType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['DevType'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer, full-stack                                             8147\n",
-       "Developer, back-end                                               4680\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack    2365\n",
-       "Developer, front-end                                              2129\n",
-       "Developer, mobile                                                 2086\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['DevType'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageWorkedWith"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "656"
-      ]
-     },
-     "execution_count": 187,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HTML/CSS;JavaScript;PHP;SQL              1345\n",
-       "C#;HTML/CSS;JavaScript;SQL               1282\n",
-       "HTML/CSS;JavaScript                      1098\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript     908\n",
-       "HTML/CSS;JavaScript;PHP                   821\n",
-       "Java                                      757\n",
-       "HTML/CSS;JavaScript;TypeScript            644\n",
-       "Python                                    634\n",
-       "HTML/CSS;Java;JavaScript;SQL              596\n",
-       "C#                                        484\n",
-       "Name: LanguageWorkedWith, dtype: int64"
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 190,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HTML/CSS;JavaScript;PHP;SQL              1366\n",
-       "C#;HTML/CSS;JavaScript;SQL               1288\n",
-       "HTML/CSS;JavaScript                      1108\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript     914\n",
-       "HTML/CSS;JavaScript;PHP                   831\n",
-       "Java                                      765\n",
-       "HTML/CSS;JavaScript;TypeScript            650\n",
-       "Python                                    640\n",
-       "HTML/CSS;Java;JavaScript;SQL              600\n",
-       "C#                                        489\n",
-       "Name: LanguageWorkedWith, dtype: int64"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## CompetenceLevel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "A little above average    29693\n",
-       "Average                   15532\n",
-       "Far above average         13840\n",
-       "A little below average     4837\n",
-       "Far below average          1322\n",
-       "Name: CompetenceLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 192,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CompetenceLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12770"
-      ]
-     },
-     "execution_count": 193,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CompetenceLevel'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign the null values based on forward fill.\n",
-    "survey_df_2019['CompetenceLevel'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 195,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CompetenceLevel'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "A little above average    35394\n",
-       "Average                   18436\n",
-       "Far above average         16821\n",
-       "A little below average     5739\n",
-       "Far below average          1604\n",
-       "Name: CompetenceLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CompetenceLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Current Job Satisfaction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Slightly satisfied                    22123\n",
-       "Very satisfied                        20452\n",
-       "Slightly dissatisfied                  9751\n",
-       "Neither satisfied nor dissatisfied     7547\n",
-       "Very dissatisfied                      4283\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13838"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CurrentJobSatis'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign the null values based on forward fill.\n",
-    "survey_df_2019['CurrentJobSatis'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CurrentJobSatis'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Slightly satisfied                    26780\n",
-       "Very satisfied                        24873\n",
-       "Slightly dissatisfied                 12043\n",
-       "Neither satisfied nor dissatisfied     9111\n",
-       "Very dissatisfied                      5187\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 201,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageDesireNextYear"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Python                                   1003\n",
-       "HTML/CSS;JavaScript                       624\n",
-       "HTML/CSS;JavaScript;TypeScript            569\n",
-       "C#                                        533\n",
-       "C#;HTML/CSS;JavaScript;SQL                525\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript     515\n",
-       "HTML/CSS;JavaScript;PHP;SQL               472\n",
-       "Java                                      457\n",
-       "Go                                        373\n",
-       "HTML/CSS;JavaScript;Python                354\n",
-       "Swift                                     348\n",
-       "Kotlin                                    335\n",
-       "HTML/CSS;JavaScript;PHP                   326\n",
-       "C++;Python                                324\n",
-       "C#;SQL                                    309\n",
-       "JavaScript                                307\n",
-       "C++                                       306\n",
-       "C#;HTML/CSS;JavaScript;TypeScript         297\n",
-       "Java;Kotlin                               280\n",
-       "JavaScript;Python                         275\n",
-       "Name: LanguageDesireNextYear, dtype: int64"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3424"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assign the null values based on forward fill.\n",
-    "survey_df_2019['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 205,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Python                                   1054\n",
-       "HTML/CSS;JavaScript                       656\n",
-       "HTML/CSS;JavaScript;TypeScript            597\n",
-       "C#                                        557\n",
-       "C#;HTML/CSS;JavaScript;SQL                553\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript     533\n",
-       "HTML/CSS;JavaScript;PHP;SQL               493\n",
-       "Java                                      484\n",
-       "Go                                        397\n",
-       "HTML/CSS;JavaScript;Python                370\n",
-       "Swift                                     360\n",
-       "Kotlin                                    360\n",
-       "HTML/CSS;JavaScript;PHP                   347\n",
-       "C++;Python                                336\n",
-       "C#;SQL                                    320\n",
-       "C++                                       319\n",
-       "JavaScript                                312\n",
-       "C#;HTML/CSS;JavaScript;TypeScript         305\n",
-       "Java;Kotlin                               298\n",
-       "JavaScript;Python                         289\n",
-       "Name: LanguageDesireNextYear, dtype: int64"
-      ]
-     },
-     "execution_count": 206,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## YearsCodePro"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<bound method IndexOpsMixin.value_counts of 2                       1\n",
-       "3        Less than 1 year\n",
-       "4                       9\n",
-       "5                       3\n",
-       "6                       4\n",
-       "               ...       \n",
-       "88870                 NaN\n",
-       "88871                   5\n",
-       "88872                   3\n",
-       "88876                  15\n",
-       "88882                   3\n",
-       "Name: YearsCodePro, Length: 77994, dtype: object>"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].value_counts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#changing the dtype to float\n",
-    "survey_df_2019['YearsCodePro'] = survey_df_2019['YearsCodePro'].apply(pd.to_numeric, errors='coerce')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0     7243\n",
-       "3.0     7164\n",
-       "5.0     5855\n",
-       "4.0     5764\n",
-       "6.0     4133\n",
-       "1.0     3995\n",
-       "10.0    3934\n",
-       "7.0     3374\n",
-       "8.0     3166\n",
-       "12.0    2008\n",
-       "Name: YearsCodePro, dtype: int64"
-      ]
-     },
-     "execution_count": 209,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].value_counts().head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14639"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['YearsCodePro'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 212,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019.dropna(subset=['YearsCodePro'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 214,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0     8853\n",
-       "3.0     8843\n",
-       "5.0     7186\n",
-       "4.0     7124\n",
-       "6.0     5103\n",
-       "1.0     4925\n",
-       "10.0    4830\n",
-       "7.0     4146\n",
-       "8.0     3910\n",
-       "12.0    2487\n",
-       "Name: YearsCodePro, dtype: int64"
-      ]
-     },
-     "execution_count": 215,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['YearsCodePro'].value_counts().head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Country"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "United States         18335\n",
-       "India                  7276\n",
-       "Germany                5316\n",
-       "United Kingdom         5130\n",
-       "Canada                 2976\n",
-       "France                 2225\n",
-       "Brazil                 1860\n",
-       "Poland                 1773\n",
-       "Netherlands            1687\n",
-       "Australia              1657\n",
-       "Russian Federation     1551\n",
-       "Spain                  1477\n",
-       "Italy                  1451\n",
-       "Sweden                 1165\n",
-       "Switzerland             884\n",
-       "Name: Country, dtype: int64"
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Country'].value_counts().nlargest(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['Country'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 219,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "United States         18335\n",
-       "India                  7276\n",
-       "Germany                5316\n",
-       "United Kingdom         5130\n",
-       "Canada                 2976\n",
-       "France                 2225\n",
-       "Brazil                 1860\n",
-       "Poland                 1773\n",
-       "Netherlands            1687\n",
-       "Australia              1657\n",
-       "Russian Federation     1551\n",
-       "Spain                  1477\n",
-       "Italy                  1451\n",
-       "Sweden                 1165\n",
-       "Switzerland             884\n",
-       "Name: Country, dtype: int64"
-      ]
-     },
-     "execution_count": 220,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['Country'].value_counts().nlargest(15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SalaryUSD"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000000.0    667\n",
-       "1000000.0    529\n",
-       "120000.0     475\n",
-       "100000.0     450\n",
-       "150000.0     399\n",
-       "Name: SalaryUSD, dtype: int64"
-      ]
-     },
-     "execution_count": 221,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['SalaryUSD'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24805"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['SalaryUSD'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019['SalaryUSD'] = survey_df_2019.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3537"
-      ]
-     },
-     "execution_count": 224,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "survey_df_2019['SalaryUSD'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000000.0    669\n",
-       "1000000.0    547\n",
-       "150000.0     494\n",
-       "120000.0     476\n",
-       "100000.0     450\n",
-       "Name: SalaryUSD, dtype: int64"
-      ]
-     },
-     "execution_count": 225,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "survey_df_2019['SalaryUSD'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "country_mean_salary = survey_df_2019.groupby('Country')['SalaryUSD'].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Country\n",
-       "Liechtenstein        811188.000000\n",
-       "San Marino           301788.000000\n",
-       "Ireland              247051.427005\n",
-       "Swaziland            242607.500000\n",
-       "United States        240269.159270\n",
-       "Timor-Leste          229500.000000\n",
-       "Qatar                203892.571429\n",
-       "Republic of Korea    174593.739130\n",
-       "Norway               173173.193026\n",
-       "Andorra              171862.000000\n",
-       "Name: SalaryUSD, dtype: float64"
-      ]
-     },
-     "execution_count": 227,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "country_mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "survey_df_2019.dropna(subset=['SalaryUSD'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleaned Dataset:2019_Survey"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                       0\n",
-       "JobSatisfaction           0\n",
-       "SalaryUSD                 0\n",
-       "Country                   0\n",
-       "Dependents                0\n",
-       "EdLevel                   0\n",
-       "Employment                0\n",
-       "Ethnicity                 0\n",
-       "Gender                    0\n",
-       "Hobbyist                  0\n",
-       "CompetenceLevel           0\n",
-       "CurrentJobSatis           0\n",
-       "JobStatus                 0\n",
-       "LanguageDesireNextYear    0\n",
-       "LanguageWorkedWith        0\n",
-       "Profession                0\n",
-       "UndergradMajor            0\n",
-       "YearsCodePro              0\n",
-       "DevType                   0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 229,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#handle all the null value\n",
-    "survey_df_2019.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#resetting the index values\n",
-    "survey_df_2019 = survey_df_2019.reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of rows before cleaning the data is 88883\n",
-      "Number of rows after cleaning the data is 74457\n"
-     ]
-    }
-   ],
-   "source": [
-    "cleaned_df_2019 = survey_df_2019[survey_df_2019.notnull()]\n",
-    "\n",
-    "print(f\"Number of rows before cleaning the data is {survey_main_df.shape[0]}\")\n",
-    "print(f\"Number of rows after cleaning the data is {cleaned_df_2019.shape[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cleaned_df_2019['Age']=cleaned_df_2019['Age'].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>JobSatisfaction</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Dependents</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>CompetenceLevel</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "      <th>JobStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>Profession</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>DevType</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>8820.0</td>\n",
-       "      <td>Thailand</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>East Asian</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Elixir;HTML/CSS</td>\n",
-       "      <td>HTML/CSS</td>\n",
-       "      <td>Non developer</td>\n",
-       "      <td>Web Design/Dev</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>22</td>\n",
-       "      <td>Very satisfied</td>\n",
-       "      <td>61000.0</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>No</td>\n",
-       "      <td>A little below average</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>C;C#;JavaScript;SQL</td>\n",
-       "      <td>C;C++;C#;Python;SQL</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Developer, full-stack</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>30</td>\n",
-       "      <td>Very dissatisfied</td>\n",
-       "      <td>33184.8</td>\n",
-       "      <td>Ukraine</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>A little above average</td>\n",
-       "      <td>Slightly dissatisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>HTML/CSS;Java;JavaScript;SQL;WebAssembly</td>\n",
-       "      <td>C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>Academic researcher;Developer, desktop or ente...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Very satisfied</td>\n",
-       "      <td>366420.0</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>East Asian</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>A little above average</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>Python;Scala;SQL</td>\n",
-       "      <td>Java;R;SQL</td>\n",
-       "      <td>Non developer</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>Data or business analyst;Data scientist or mac...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>42</td>\n",
-       "      <td>Slightly satisfied</td>\n",
-       "      <td>36000.0</td>\n",
-       "      <td>Ukraine</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Self-employed</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>Neither satisfied nor dissatisfied</td>\n",
-       "      <td>Not seeking</td>\n",
-       "      <td>HTML/CSS;JavaScript</td>\n",
-       "      <td>HTML/CSS;JavaScript</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Engineering</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Designer;Developer, front-end</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age     JobSatisfaction  SalaryUSD        Country Dependents    EdLevel  \\\n",
-       "0   28  Slightly satisfied     8820.0       Thailand        Yes  Bachelors   \n",
-       "1   22      Very satisfied    61000.0  United States         No  Bachelors   \n",
-       "2   30   Very dissatisfied    33184.8        Ukraine         No  Bachelors   \n",
-       "3   28      Very satisfied   366420.0         Canada         No  Bachelors   \n",
-       "4   42  Slightly satisfied    36000.0        Ukraine        Yes  Bachelors   \n",
-       "\n",
-       "      Employment                     Ethnicity Gender Hobbyist  \\\n",
-       "0      Full-time                    East Asian    Man      Yes   \n",
-       "1      Full-time  White or of European descent    Man       No   \n",
-       "2      Full-time  White or of European descent    Man      Yes   \n",
-       "3      Full-time                    East Asian    Man      Yes   \n",
-       "4  Self-employed  White or of European descent    Man       No   \n",
-       "\n",
-       "          CompetenceLevel                     CurrentJobSatis    JobStatus  \\\n",
-       "0                 Average                  Slightly satisfied  Not seeking   \n",
-       "1  A little below average                  Slightly satisfied  Not seeking   \n",
-       "2  A little above average               Slightly dissatisfied  Not seeking   \n",
-       "3  A little above average                  Slightly satisfied  Not seeking   \n",
-       "4                 Average  Neither satisfied nor dissatisfied  Not seeking   \n",
-       "\n",
-       "                     LanguageDesireNextYear  \\\n",
-       "0                           Elixir;HTML/CSS   \n",
-       "1                       C;C#;JavaScript;SQL   \n",
-       "2  HTML/CSS;Java;JavaScript;SQL;WebAssembly   \n",
-       "3                          Python;Scala;SQL   \n",
-       "4                       HTML/CSS;JavaScript   \n",
-       "\n",
-       "                            LanguageWorkedWith     Profession  \\\n",
-       "0                                     HTML/CSS  Non developer   \n",
-       "1                          C;C++;C#;Python;SQL      Developer   \n",
-       "2  C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA      Developer   \n",
-       "3                                   Java;R;SQL  Non developer   \n",
-       "4                          HTML/CSS;JavaScript      Developer   \n",
-       "\n",
-       "     UndergradMajor  YearsCodePro  \\\n",
-       "0    Web Design/Dev           1.0   \n",
-       "1  Computer Science           1.0   \n",
-       "2  Computer Science           9.0   \n",
-       "3         Math/Stat           3.0   \n",
-       "4       Engineering           4.0   \n",
-       "\n",
-       "                                             DevType  \n",
-       "0  Designer;Developer, back-end;Developer, front-...  \n",
-       "1                              Developer, full-stack  \n",
-       "2  Academic researcher;Developer, desktop or ente...  \n",
-       "3  Data or business analyst;Data scientist or mac...  \n",
-       "4                      Designer;Developer, front-end  "
-      ]
-     },
-     "execution_count": 233,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "cleaned_df_2019.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## After Cleaning  Dataset 2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  1414683\n",
-      "Total missing :  0\n",
-      "Missing Percentage:  0.0 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = survey_df_2019.isnull().sum() #number of missing\n",
-    "total_cells = np.product(survey_df_2019.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stackoverflow Survey Analysis 2020"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(r'C:\\Users\\User\\Stack_Data\\survey_results_public_2020.csv')\n",
-    "#df2020.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#drop unnecessary columns\n",
-    "drop_cols = [  'Age1stCode', 'CompFreq', 'CompTotal', 'CurrencyDesc', 'CurrencySymbol', 'NEWJobHunt','NEWJobHuntResearch', 'NEWLearn', \n",
-    "             'NEWOffTopic', 'NEWOnboardGood', 'NEWOtherComms', 'NEWOvertime', 'NEWPurchaseResearch', \n",
-    "             'NEWPurpleLink', 'NEWSOSites', 'NEWStuck', 'OpSys', 'OrgSize', 'PlatformDesireNextYear', 'PlatformWorkedWith',\n",
-    "             'PurchaseWhat', 'Respondent', 'SOAccount', 'SOComm', 'SOPartFreq', 'SOVisitFreq', 'Sexuality', 'SurveyEase', \n",
-    "             'SurveyLength', 'Trans', 'WebframeDesireNextYear', 'WebframeWorkedWith', 'WelcomeChange', 'WorkWeekHrs', 'YearsCode']\n",
-    "df.drop(drop_cols, axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Selecting only the required columns for analysis\n",
-    "cols =['Age','Gender', 'ConvertedComp', 'Country', 'DevType', 'Hobbyist', 'EdLevel', 'Employment', \n",
-    "       'Ethnicity', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n",
-    "        'UndergradMajor', 'YearsCodePro']\n",
-    "df2020 = df[cols]\n",
-    "#df2020.head()\n",
-    "#df2020.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#changing the name of columns for easier understanding\n",
-    "# 'MainBranch': 'Profession'\n",
-    "# 'ConvertedComp': 'SalaryUSD'\n",
-    "# 'JobSat' : 'CurrentJobSatis'\n",
-    "# 'JobSeek' : 'JobStatus'\n",
-    "\n",
-    "df2020.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD', \n",
-    "                                'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, \n",
-    "                      inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                       19015\n",
-      "Gender                    13904\n",
-      "SalaryUSD                 29705\n",
-      "Country                     389\n",
-      "DevType                   15091\n",
-      "Hobbyist                     45\n",
-      "EdLevel                    7030\n",
-      "Employment                  607\n",
-      "Ethnicity                 18513\n",
-      "CurrentJobSatis           19267\n",
-      "JobStatus                 12734\n",
-      "LanguageDesireNextYear    10348\n",
-      "LanguageWorkedWith         7083\n",
-      "Profession                  299\n",
-      "UndergradMajor            13466\n",
-      "YearsCodePro              18112\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df2020.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Validation - Total Cells vs Missing %"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total cell:  1031376\n",
-      "Total missing values:  371516\n",
-      "Missing:  36.02139278013062 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Finding % of missing data\n",
-    "missing_count = df.isnull().sum() #number of missing\n",
-    "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total cell: ', total_cells)\n",
-    "print('Total missing values: ', total_missing)\n",
-    "print('Missing: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gender"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 241,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13904"
-      ]
-     },
-     "execution_count": 241,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Gender'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 242,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gender\n",
-       "Man                                                            46013\n",
-       "Man;Non-binary, genderqueer, or gender non-conforming            121\n",
-       "Non-binary, genderqueer, or gender non-conforming                385\n",
-       "Woman                                                           3844\n",
-       "Woman;Man                                                         76\n",
-       "Woman;Man;Non-binary, genderqueer, or gender non-conforming       26\n",
-       "Woman;Non-binary, genderqueer, or gender non-conforming           92\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 242,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Counting number of each gender\n",
-    "df2020.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n",
-    "df2020['Gender'] = df['Gender'].fillna('Non-binary') \n",
-    "\n",
-    "#Grouping genders into 3 groups Man, Womanand Non-binary\n",
-    "df2020['Gender'].replace('Man;Non-binary, genderqueer, or gender non-conforming', 'Man', inplace =True)\n",
-    "df2020['Gender'].replace('Woman;Non-binary, genderqueer, or gender non-conforming', 'Woman', inplace =True)\n",
-    "df2020['Gender'].replace('Woman;Man;Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)\n",
-    "df2020['Gender'].replace('Woman;Man', 'Non-binary', inplace =True)\n",
-    "df2020['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Gender\n",
-       "Man           46134\n",
-       "Non-binary    14391\n",
-       "Woman          3936\n",
-       "Name: Gender, dtype: int64"
-      ]
-     },
-     "execution_count": 244,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Counting number of each gender after\n",
-    "df2020.groupby('Gender')['Gender'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "df shape after clean Gender:  (64461, 16)\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "print('df shape after clean Gender: ', df2020.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19015"
-      ]
-     },
-     "execution_count": 246,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Age'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Plottig boxplot to check outliers\n",
-    "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n",
-    "plt.title(\"Before cleaning Age's outliers from genders\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Cleaning Age's outliers from each gender)\n",
-    "df2020 = df2020[(df['Age'] >= 15) & (df2020['Age'] <= 60)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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llpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnl1qBJStI4Sb8aiWDMzMyyBk1SEbEBeF7SdiMQT93q6Oigo6Oj1mGYWYafl7VX7l3Q1wL3SFoAPFdYGBH+8ZsKGanfVjKz8vl5WXvlJqmfpQ8zM7MRU1aSioh5kiYAe0TEfVWOyczMDCjz6j5J7wDuBn6ezh8g6adVjMvMzKzsS9DbganAUwARcTfwyqpEZGZmlio3Sb0YEU/3WhaVDsbMzCyr3Asn/iLpBGCcpMlAG3BH9cIyMzMrvyd1BvBaYB1wFfAMcGaVYjIzMwPKv7rveeA/0oeZmdmIGDBJSbqRAT57ioh3VjwiMzOz1GDDfd8ELgAeANYAl6aPbuAv1Q3NzCyfZs6cSWtrKyeccEKfspNOOonW1lY+/OEP9yk7/fTTaW1t5cwzz+xTdvbZZ9Pa2srs2bOLlp977rm0trZy/vnn99lmoLILL7yQ1tZWLr744j5lixYtYtq0aSxZsqTsbYYax3ANmKQi4taIuBV4Q0TMjIgb08cJwGEVj6YXSf9H0pmZ+V9I+m5m/gJJn6l2HGZmWStXrgSgq6urT9ny5cuB0rdUWrp0KQB33nlnn7I77kiuRbvtttuKlt9yyy0ALFiwoM82A5Vdf/31AFx99dV9ytrb29m4cSPnnHNO2dsMNY7hKvfCiR0l7VWYkfRKYMeKR9PXHcAh6T4bgIkkF3AUHAL8bgTiMDMDkl5UVrY3ddJJJxWVZXtTp59+elFZtjd19tlnF5UVelPnnntu0fJsT2WgsgsvvLCoLNszWrRoEd3d3QB0d3f39KYG2maocVSCIgb/upOktwOXAP9MFzUDH4+IX1Q0mr773RVYFBFNkl4HfA7YBZgJPA88lk5/neTztT8Bp0XEOknLgSuBw4EtgVOBrwGTgP8dEd+W1Aj8BNghXWd2RPxEUjNwM3A7SSJ8GHhXRKwZKN6WlpZYvHjxkI51xowZrFmzhsmTJw9p+9Gms7OTrdY/w0Wtz1R9X3MWNwIwu6W76vsaqjNueykvbPnSuvn/jxadnZ1MmDChp4cB0Nra2me9Qu+n0mXV2NfRRx/dk6QAGhsbmT9/flX2VS5JSyKipVRZWT2piPg5MBn4dPrYp9oJKt3vI8CLkvYgSRa/B/4IHAy0APcD3wVmRsTrSBLVaZkqVkTEwcBvgbnAccCbgPPS8rXAeyLiQJJkdoEkpWWTgf8bEa8ludPGe0vFKOlUSYslLV61alVFjtvMrFqyCarUfN6U+2VegCkkPagtgNdLIiIur0pUxX5HkqAOAf4L2C2dfpqkh7M2Iu5P150HfAq4MJ0v3F/wHqAxIp4FnpW0VtL2JD878lVJrcDGtO6d0m0eSG//BLCE5Nj7iIhLSHqZtLS0DPkuHE1NTQB189s1bW1trF3+p1qHkRs7bb2R8c2T6+b/P1q0tY29XyNqbGzs05PKs3JvMHsFyZV+hwFvTB8lu2ZVUPhc6nUkVxT+gaQndQjQ99PHYuvSvxsz04X5LYAPkny2NiUiDiAZPhzfa1uADWxeQjezMWqXXXYpmi+8wQRobm4uKps0aVLP9P77719UduCBB/ZMH3LIIUVlhSG0ww8/vGj5kUce2TM9UNmMGTOKyt7//vf3TLe3txeVFT5DGmibocZRCeVeONECHBoRn4yIM9LHSL3F+B1wLPBkRGyIiCeB7UkS1feAZkmFM+FE4NbNqHs74PGIWC/pcGDPyoVtZmPRj370o6L5K6+8smf68suLB5cuu+yynunel3VnL1T4+te/XlQ2Z84cAL785S8XLc9ejTdQWe9L3LMXbUydOrWn99TY2MiUKVMG3WaocVRCuUnqL8DOFd1z+e4huarvD72WPR0RXcApwDWS7iHpIX17M+r+AdAiaTFJr+rvlQnZzMayQm8q24sqKPSmsr2ogkJvKtuLKij0pnpfiFDoqZTqoQxUVugZZXtEBe3t7TQ0NPS5Em+gbYYax3CVe3XfLcABwCIyw2C+40Sx4VzdVxj7rpfPJAqfSY3EFXej4eq+OYsbGd/8xrr5/48W9fa8rJWBru4r93OW9sqFY2ZmVp5ybzB7q6Q9gckR8StJWwPjqhuamZnVu3Kv7vsYcC3wnXTRbsANVYrJzMwMKP/CiU8Bh5L8jhQR0Qm8olpBmZmZQflJal1EvFCYkbQF/vl4MzOrsnKT1K2SvghMkHQkcA1wY/XCMjMzKz9JnQ2sIvl+0qnAzyLCv9JrZmZVNWCSkvQuSZ+KiI0RcSnJHRlagC9KOm5EIjQzs7o1WE/q82y6SSvAViQ3mp1G8d3GzczMKm6w70ltFRErMvO3p/fOe1LSNlWMq+6UuoWKmdWWn5e1N1iS2iE7ExHZn5YciV/mrRtj8ScBzEY7Py9rb7Dhvj+mX+QtIunjJPfxMzMzq5rBelJnATdIOoFNv900BXgJ8O4qxmVmZjZwkoqIx4FDJL0VeG26+GcR8ZuqR2ZmZnWv3BvM/gZwYjIzsxFV7pd5zczMRpyTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5VZZ35Myq4aHuscxZ3Fj1ffz4LPjAEZkX0P1UPc49q51EGY55CRlNTGSd5fepqsLgPFNTSO2z821N77jtlkpTlJWE767tJmVw59JmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbjlJmZlZbvkGs1ZVHR0dLFu2rKYxdKV3QW/K8V3QBzNp0iTflNfqkpOUVdWyZcu46693wfY1DOLp5M8qraphEMPwVK0DMKsdJymrvu1h47SNNdt9w8JkVLuWMQxHIX6zeuSz38zMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJyszMcstJKic6Ojro6OiodRhmViN+DSjNN5jNiVr/nIWZ1ZZfA0pzT8rMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHLLScrMzHKraklKUki6IDP/OUntFap7rqTjSixvkeSbX5nZmLFo0SKmTZvGkiVL+pRdccUVtLa2ctVVV/UpW716NWeccQZPPPFE2XXef//9TJ8+veQtmoZaNlzV7EmtA2ZImljFfRSJiMUR0VaJuiSNq0Q9ZmbD0d7ezsaNGznnnHP6lF166aUAfOtb3+pTNm/ePJYuXcq8efPKrnPOnDk899xznHfeeX22GWrZcFUzSb0IXAKc1btA0p6Sfi1pafp3j3T5XEkdku6Q9M9SvaWMIyT9VtL9ko5Nt58m6aZ0ul3SZZIWpnX1JC9JN0haIumvkk7NLO+WdJ6kPwKzJf04U3akpOuH2yhmZuVatGgR3d3dAHR3dxf1fK644oqidbO9qdWrV3PzzTcTEdx8881Fvan+6rz//vtZvnw5AMuXLy/qFQ21rBIUERWtsKdiqRvYFVgKvB74GNAYEe2SbgSujYh5kj4MvDMi3i1pLrANMBN4NfDTiJhUou65wM7A0cCrgFuAScCbgM9FxLHp0OJRwOHAtsB9wM4RsV7SyyLiSUkTgD8Bb4mIJyQFMDMirpYk4G/AmyNilaQrgasi4sb+jrmlpSUWL148pPaaMWMGa9asYfLkyUPaPq86Ozvp3tjNxmM31iyGhoXJe7GN02oXw3A03NRAY0PjmDs3rFhnZycTJkzg+us3vRc++uijexIKQGNjI/PnzwegtbW1Tx233XYbABdccAHz589n/fr1bLnllhxzzDF85jOfGbDOk046qSfZADQ3N3P55ZcDDLmsXJKWRERLqbKqXjgREc8AlwO9h+AOBq5Mp68ADsuU3RARGyPiXmCnAaq/Ol2vE/gnSVLr7WcRsS4iVgOPZ+prk/Rn4A/A7kDh2b8BuC6NPdLYPiRp+zTmm3vvQNKpkhZLWrxq1aoBwjUz2zzZZFJqvj8LFixg/fr1AKxfv55f/vKXg9aZTTS954daVgkj8XtSFwJ3At8bYJ1sd25dZloAkr4CHAMQEQeU2KbUfO+6NgBbSJoGHAEcHBHPS1oIjE/XWRsRGzLbfA+4EVgLXBMRL/YJPOISkmFNWlpahtwtbWpqAhhzP3rW1tbGXQ/fVeswRrdGmLzb5DF3blixtra+H6c3Njb26fWU48gjjyzqSR111FGD1tnc3NynR5SdHkpZJVT9EvSIeBK4GvhIZvEdwAfS6Q8Ctw9Sx39ExAGZBAXwPkkNkl4F7EUynFeO7YB/pQnq1SRDhP3t9xHgEWA2MLfM+s3MKqK9vb1o/vzzz++Z/tjHPlZUdtppp/VMz5o1i+QTC2hoaGDWrFmD1jl79uyi5V/60pd6podaVgkj9T2pC4DsVX5twCmSlgInAp8eQp33AbeSDMF9IiLWlrndz0l6VEuB80mG/AbyA2BFOvxoZjZipk6d2tPTaWxsZMqUKT1lJ554YtG6xx9/fM/0xIkTmT59OpKYPn06L3/5ywetc++99+7pBTU3NzNp0qbLAYZaVglVS1IR0ZiZfiwito6I9nR+eUS8NSL2j4i3RcRD6fKTI+LaUnX0qvvkiDgrIt4cEXtHxE3p8oURcWw63R4R38xss1+633URMT3d9/siYlpELBxgf4cBlw63PczMhqK9vZ2GhoaiXlRBoTeV7UUVzJo1i/3337+oFzVYnbNnz2abbbYp2RsaatlwjcRnUqOWpCXAc8Bnax2LmdWnqVOnsnDhwpJlJ554Yp8eVcHEiRO56KKLNqvOvffem5tv7nN92LDKhstJagARMWXwtczMrFp87z4zM8stJykzM8stJykzM8stJykzM8stJykzM8stJykzM8stJykzM8stf08qJyp9KxEzG138GlCak1ROlLoDspnVD78GlObhPjMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy0nKTMzyy3fYNaq7yloWFjD90NPJX9qGsNwPAXsVusgzGrDScqqKg8/P9AVXQA07dZU40iGaLd8tKNZLThJWVX55wfMbDhG6fiHmZnVAycpMzPLLScpMzPLLScpMzPLLScpMzPLLUVErWMYMyStAh6sdRzDNBFYXesgcsTtUcztsYnbothw2mPPiNixVIGTlBWRtDgiWmodR164PYq5PTZxWxSrVnt4uM/MzHLLScrMzHLLScp6u6TWAeSM26OY22MTt0WxqrSHP5MyM7Pcck/KzMxyy0nKzMxyy0mqjknaXdItkv4m6a+SPp0uf5mkBZI607871DrWapM0XtIiSX9O2+LL6fK6a4ssSeMk3SXppnS+bttD0nJJ90i6W9LidFldtoek7SVdK+nv6evHwdVqCyep+vYi8NmIeA3wJuBTkvYFzgZ+HRGTgV+n82PdOuCtEfF64ADg7ZLeRH22Rdangb9l5uu9PQ6PiAMy3weq1/b4b+DnEfFq4PUk50hV2sJJqo5FxMqIuDOdfpbkRNsNeBcwL11tHvDumgQ4giLRnc5umT6COmyLAklNwDHAdzOL67Y9+lF37SHppUAr8P8AIuKFiHiKKrWFk5QBIKkZeAPwR2CniFgJSSIDXlHD0EZMOrR1N/A4sCAi6rYtUhcCnwc2ZpbVc3sE8EtJSySdmi6rx/bYC1gFfC8dCv6upG2oUls4SRmSGoHrgDMj4plax1MrEbEhIg4AmoCpkvarcUg1I+lY4PGIWFLrWHLk0Ig4EJhOMjTeWuuAamQL4EDgWxHxBuA5qjjM6SRV5yRtSZKgfhAR16eLH5O0S1q+C0nPom6kQxcLgbdTv21xKPBOScuBHwJvlfR96rc9iIhH0r+PAz8GplKf7dEFdKUjDQDXkiStqrSFk1QdkySSceW/RcR/ZYp+CsxKp2cBPxnp2EaapB0lbZ9OTwCOAP5OHbYFQER8ISKaIqIZ+ADwm4j4EHXaHpK2kbRtYRo4CvgLddgeEfEosELSPumitwH3UqW28B0n6pikw4DfAvew6XOHL5J8LnU1sAfwEPC+iHiyJkGOEEn7k3zYO47kzdvVEXGepJdTZ23Rm6RpwOci4th6bQ9Je5H0niAZ7royIr5Sx+1xAMkFNVsB/wROIX3eUOG2cJIyM7Pc8nCfmZnllpOUmZnllpOUmZnllpOUmZnllpOUmZnllpOU2Rgh6T2SQtKrax2LWaU4SZmNHccDt5N8+dZsTHCSMhsD0vsvHgp8hDRJSWqQ9D/p72PdJGm+pOPSsimSbk1vlvqLwu1szPLGScpsbHg3ye/73A88KelAYAbQDLwO+ChwMPTcr/Ei4LiImAJcBnylBjGbDWqLWgdgZhVxPMlPa0ByQ9jjSX4T65qI2Ag8KumWtHwfYD9gQXL7RsYBK0c0WrMyOUmZjXLp/ePeCuwnKUiSTrDpXnN9NgH+GhEHj1CIZkPm4T6z0e844PKI2DMimiNid+ABYDXw3vSzqZ2Aaen69wE7SuoZ/pP02loEbjYYJymz0e94+vaargN2Jfntn78A3yG5u/3TEfECSWL7hqQ/A3cDh4xYtGabwXdBNxvDJDVGRHc6JLiI5NdlH611XGbl8mdSZmPbTemPOW4FnO8EZaONe1JmZpZb/kzKzMxyy0nKzMxyy0nKzMxyy0nKzMxyy0nKzMxy6/8D+DkkLlWihPUAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Plottig boxplot to check outliers after cleaning some outliers\n",
-    "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n",
-    "plt.title(\"After cleaning Age's outliers from genders\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#fill Age's null values with mean of each gender\n",
-    "means = df2020.groupby('Gender')['Age'].transform('mean')\n",
-    "df2020['Age'] = df2020['Age'].fillna(means)\n",
-    "\n",
-    "#convert from float to int\n",
-    "df2020['Age'] = df2020['Age'].apply(str).str[:2]\n",
-    "df2020['Age'] = df2020['Age'].apply(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "df shape after clean Age:  (44709, 16)\n"
-     ]
-    }
-   ],
-   "source": [
-    "#df before 64461\n",
-    "print('df shape after clean Age: ', df2020.shape) #no. of Ages' outliners = 64461-44709=19752 (30.6%)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## EdLevel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "933"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['EdLevel'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Bachelor’s degree (B.A., B.S., B.Eng., etc.)                                          20290\n",
-       "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)                                       10000\n",
-       "Some college/university study without earning a degree                                 5699\n",
-       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     3676\n",
-       "Associate degree (A.A., A.S., etc.)                                                    1455\n",
-       "Other doctoral degree (Ph.D., Ed.D., etc.)                                             1256\n",
-       "Primary/elementary school                                                               590\n",
-       "Professional degree (JD, MD, etc.)                                                      578\n",
-       "I never completed any formal education                                                  232\n",
-       "Name: EdLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 253,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Refactoring EdLevel\n",
-    "def refactor_ed(df):\n",
-    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
-    "    conditions_ed = [(df['EdLevel'] == 'Associate degree (A.A., A.S., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Bachelor’s degree (B.A., B.S., B.Eng., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)'),\n",
-    "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
-    "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D., Ed.D., etc.)'),\n",
-    "                    (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
-    "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
-    "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
-    "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
-    "    \n",
-    "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
-    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
-    "    return df\n",
-    "\n",
-    "# applying function to subsets\n",
-    "df2020 = refactor_ed(df2020)\n",
-    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
-    "df2020['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Bachelors       21223\n",
-       "No Degree       10197\n",
-       "Masters         10000\n",
-       "Associate        1455\n",
-       "Doctorate        1256\n",
-       "Professional      578\n",
-       "Name: EdLevel, dtype: int64"
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['EdLevel'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JobSat (CurrentJobSatis)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8690"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        11751\n",
-       "Slightly satisfied                    11198\n",
-       "Slightly dissatisfied                  5790\n",
-       "Neither satisfied nor dissatisfied     4373\n",
-       "Very dissatisfied                      2907\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020['CurrentJobSatis'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        14628\n",
-       "Slightly satisfied                    13834\n",
-       "Slightly dissatisfied                  7192\n",
-       "Neither satisfied nor dissatisfied     5446\n",
-       "Very dissatisfied                      3609\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JobSeek (JobStatus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2153"
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['JobStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobStatus\n",
-       "I am actively looking for a job                                  6980\n",
-       "I am not interested in new job opportunities                    10919\n",
-       "I’m not actively looking, but I am open to new opportunities    24657\n",
-       "Name: JobStatus, dtype: int64"
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('JobStatus')['JobStatus'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020['JobStatus'].fillna(method='ffill', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Refactoring JobStatus\n",
-    "#Changing the jobstatus to seeking and non seeking\n",
-    "def refactor_job(df):\n",
-    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
-    "    \n",
-    "    conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n",
-    "                    (df['JobStatus'] == 'I am not interested in new job opportunities')\n",
-    "                    | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
-    "    \n",
-    "    choices_job = ['Seeking', 'Not seeking']\n",
-    "    df['JobSeek'] = np.select(conditions_job, choices_job, default=np.nan)                      \n",
-    "    return df\n",
-    "\n",
-    "df2020 = refactor_job(df2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "JobSeek\n",
-       "Not seeking    37369\n",
-       "Seeking         7340\n",
-       "Name: JobSeek, dtype: int64"
-      ]
-     },
-     "execution_count": 264,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('JobSeek')['JobSeek'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 265,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['JobStatus'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DevType"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5954"
-      ]
-     },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer, full-stack                                                                                           3399\n",
-       "Developer, back-end                                                                                             2374\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  1838\n",
-       "Developer, back-end;Developer, full-stack                                                                       1216\n",
-       "Developer, front-end                                                                                            1071\n",
-       "Developer, mobile                                                                                                953\n",
-       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     668\n",
-       "Developer, front-end;Developer, full-stack                                                                       667\n",
-       "Developer, back-end;Developer, desktop or enterprise applications                                                528\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack;Developer, mobile                                 475\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 267,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['DevType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020['DevType'] = df2020['DevType'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer, full-stack                                                                                           3940\n",
-       "Developer, back-end                                                                                             2721\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack                                                  2146\n",
-       "Developer, back-end;Developer, full-stack                                                                       1411\n",
-       "Developer, front-end                                                                                            1229\n",
-       "Developer, mobile                                                                                               1074\n",
-       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack     779\n",
-       "Developer, front-end;Developer, full-stack                                                                       758\n",
-       "Developer, back-end;Developer, desktop or enterprise applications                                                617\n",
-       "Developer, back-end;Developer, front-end;Developer, full-stack;Developer, mobile                                 532\n",
-       "Name: DevType, dtype: int64"
-      ]
-     },
-     "execution_count": 269,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['DevType'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 270,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(64461, 26)"
-      ]
-     },
-     "execution_count": 270,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 271,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['DevType'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 272,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "      <th>JobStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>Profession</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>JobSeek</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [Age, Gender, SalaryUSD, Country, DevType, Hobbyist, EdLevel, Employment, Ethnicity, CurrentJobSatis, JobStatus, LanguageDesireNextYear, LanguageWorkedWith, Profession, UndergradMajor, YearsCodePro, JobSeek]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 272,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020[df2020['DevType'].isnull()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ethnicity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4051"
-      ]
-     },
-     "execution_count": 273,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['Ethnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent                           26552\n",
-       "South Asian                                             3707\n",
-       "Hispanic or Latino/a/x                                  2078\n",
-       "Middle Eastern                                          1417\n",
-       "Southeast Asian                                         1371\n",
-       "East Asian                                              1342\n",
-       "Black or of African descent                             1327\n",
-       "Hispanic or Latino/a/x;White or of European descent      720\n",
-       "Middle Eastern;White or of European descent              344\n",
-       "Multiracial                                              245\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 274,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#count number of each Ethnicity\n",
-    "df2020.groupby('Ethnicity')['Ethnicity'].count()\n",
-    "df2020['Ethnicity'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
-    "df2020.loc[df['Ethnicity'].str.match('Biracial') == True, 'Ethnicity'] = 'Biracial'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Black or of African descent') == True, 'Ethnicity'] = 'Black or of African descent'\n",
-    "df2020.loc[df['Ethnicity'].str.match('East Asian') == True, 'Ethnicity'] = 'East Asian'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Hispanic or Latino') == True, 'Ethnicity'] = 'Hispanic or Latino'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Indigenous') == True, 'Ethnicity'] = 'Indigenous'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Middle Eastern') == True, 'Ethnicity'] = 'Middle Eastern'\n",
-    "df2020.loc[df['Ethnicity'].str.match('South Asian') == True, 'Ethnicity'] = 'South Asian'\n",
-    "df2020.loc[df['Ethnicity'].str.match('White or of European descent') == True, 'Ethnicity'] = 'White or of European descent'\n",
-    "df2020.loc[df['Ethnicity'].str.match('Multiracial') == True, 'Ethnicity'] = 'Multiracial'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent    26848\n",
-       "South Asian                      3783\n",
-       "Hispanic or Latino               3072\n",
-       "Middle Eastern                   1840\n",
-       "East Asian                       1661\n",
-       "Black or of African descent      1633\n",
-       "Southeast Asian                  1371\n",
-       "Multiracial                       249\n",
-       "Biracial                          138\n",
-       "Indigenous                         63\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 276,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020.groupby('Ethnicity')['Ethnicity'].count() #11 groups of Ethnicity after combining \n",
-    "df2020['Ethnicity'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "df2020['Ethnicity']=df2020.groupby(['Country'])['Ethnicity'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "White or of European descent    28466\n",
-       "South Asian                      5101\n",
-       "Hispanic or Latino               3270\n",
-       "Middle Eastern                   2104\n",
-       "East Asian                       1907\n",
-       "Black or of African descent      1762\n",
-       "Southeast Asian                  1614\n",
-       "Multiracial                       263\n",
-       "Biracial                          151\n",
-       "Indigenous                         71\n",
-       "Name: Ethnicity, dtype: int64"
-      ]
-     },
-     "execution_count": 278,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#count number of each Ethnicity\n",
-    "df2020.groupby('Ethnicity')['Ethnicity'].count()\n",
-    "df2020['Ethnicity'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 279,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Ethnicity'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                           0\n",
-      "Gender                        0\n",
-      "SalaryUSD                 14358\n",
-      "Country                       0\n",
-      "DevType                       0\n",
-      "Hobbyist                      0\n",
-      "EdLevel                       0\n",
-      "Employment                  118\n",
-      "Ethnicity                     0\n",
-      "CurrentJobSatis               0\n",
-      "JobStatus                     0\n",
-      "LanguageDesireNextYear     2394\n",
-      "LanguageWorkedWith          396\n",
-      "Profession                   77\n",
-      "UndergradMajor             5522\n",
-      "YearsCodePro               8212\n",
-      "JobSeek                       0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "print(df2020.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageDesireNextYear"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2394"
-      ]
-     },
-     "execution_count": 281,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Python                                   773\n",
-       "Rust                                     417\n",
-       "HTML/CSS;JavaScript;TypeScript           405\n",
-       "C#                                       342\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript    339\n",
-       "HTML/CSS;JavaScript                      307\n",
-       "Go                                       300\n",
-       "HTML/CSS;JavaScript;PHP;SQL              229\n",
-       "TypeScript                               227\n",
-       "Java                                     224\n",
-       "Name: LanguageDesireNextYear, dtype: int64"
-      ]
-     },
-     "execution_count": 282,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df2020['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)\n",
-    "df2020['LanguageDesireNextYear']=df2020['LanguageDesireNextYear'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Python                                   802\n",
-       "Rust                                     432\n",
-       "HTML/CSS;JavaScript;TypeScript           425\n",
-       "C#                                       377\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript    372\n",
-       "HTML/CSS;JavaScript                      323\n",
-       "Go                                       310\n",
-       "HTML/CSS;JavaScript;PHP;SQL              245\n",
-       "Java                                     238\n",
-       "C#;HTML/CSS;JavaScript;SQL               236\n",
-       "Name: LanguageDesireNextYear, dtype: int64"
-      ]
-     },
-     "execution_count": 284,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 285,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageDesireNextYear'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LanguageWorkedWith"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "396"
-      ]
-     },
-     "execution_count": 286,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HTML/CSS;JavaScript;PHP;SQL              819\n",
-       "C#;HTML/CSS;JavaScript;SQL               669\n",
-       "HTML/CSS;JavaScript                      655\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript    624\n",
-       "HTML/CSS;JavaScript;TypeScript           568\n",
-       "Python                                   449\n",
-       "Java                                     392\n",
-       "HTML/CSS;JavaScript;PHP                  382\n",
-       "HTML/CSS;Java;JavaScript;SQL             301\n",
-       "C#                                       296\n",
-       "Name: LanguageWorkedWith, dtype: int64"
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageWorkedWith'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df2020['LanguageWorkedWith'].fillna(method='ffill', inplace=True)\n",
-    "df2020['LanguageWorkedWith']=df2020['LanguageWorkedWith'].bfill().ffill()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "HTML/CSS;JavaScript;PHP;SQL              822\n",
-       "C#;HTML/CSS;JavaScript;SQL               670\n",
-       "HTML/CSS;JavaScript                      658\n",
-       "C#;HTML/CSS;JavaScript;SQL;TypeScript    631\n",
-       "HTML/CSS;JavaScript;TypeScript           572\n",
-       "Python                                   450\n",
-       "Java                                     394\n",
-       "HTML/CSS;JavaScript;PHP                  385\n",
-       "HTML/CSS;Java;JavaScript;SQL             306\n",
-       "C#                                       298\n",
-       "Name: LanguageWorkedWith, dtype: int64"
-      ]
-     },
-     "execution_count": 289,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageWorkedWith'].value_counts().nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 290,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['LanguageWorkedWith'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## MainBranch (Profession)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "77"
-      ]
-     },
-     "execution_count": 291,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Profession'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Profession\n",
-       "I am a developer by profession                                                   34037\n",
-       "I am a student who is learning to code                                            4900\n",
-       "I am not primarily a developer, but I write code sometimes as part of my work     3718\n",
-       "I code primarily as a hobby                                                       1301\n",
-       "I used to be a developer by profession, but no longer am                           676\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 292,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('Profession')['Profession'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020.dropna(subset=['Profession'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Lets refactor column values of Profession column\n",
-    "#refactoring profession column\n",
-    "\n",
-    "def refactor_prof(df):\n",
-    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
-    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
-    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
-    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
-    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
-    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
-    "    \n",
-    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
-    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)          \n",
-    "    return df\n",
-    "\n",
-    "df2020 = refactor_prof(df2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Developer        34037\n",
-       "Student           4900\n",
-       "Non developer     3718\n",
-       "Novoice           1301\n",
-       "Ex-Developer       676\n",
-       "Name: Profession, dtype: int64"
-      ]
-     },
-     "execution_count": 295,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Profession'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 296,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Profession'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## UndergradMajor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5501"
-      ]
-     },
-     "execution_count": 297,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "UndergradMajor\n",
-       "A business discipline (such as accounting, finance, marketing, etc.)               1033\n",
-       "A health science (such as nursing, pharmacy, radiology, etc.)                       190\n",
-       "A humanities discipline (such as literature, history, philosophy, etc.)             815\n",
-       "A natural science (such as biology, chemistry, physics, etc.)                      1754\n",
-       "A social science (such as anthropology, psychology, political science, etc.)        733\n",
-       "Another engineering discipline (such as civil, electrical, mechanical, etc.)       3542\n",
-       "Computer science, computer engineering, or software engineering                   24429\n",
-       "Fine arts or performing arts (such as graphic design, music, studio art, etc.)      581\n",
-       "I never declared a major                                                            331\n",
-       "Information systems, information technology, or system administration              3074\n",
-       "Mathematics or statistics                                                          1419\n",
-       "Web development or web design                                                      1230\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 298,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('UndergradMajor')['UndergradMajor'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def refactor_major(df):\n",
-    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n",
-    "                        (df['UndergradMajor'] == 'Another engineering discipline (such as civil, electrical, mechanical, etc.)'),\n",
-    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n",
-    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
-    "                        (df['UndergradMajor'] == 'A natural science (such as biology, chemistry, physics, etc.)') \n",
-    "                        |(df['UndergradMajor'] == 'A health science (such as nursing, pharmacy, radiology, etc.)'), \n",
-    "                        (df['UndergradMajor'] == 'Web development or web design'), \n",
-    "                        (df['UndergradMajor'] == 'A business discipline (such as accounting, finance, marketing, etc.)'), \n",
-    "                        (df['UndergradMajor'] == 'A humanities discipline (such as literature, history, philosophy, etc.)')\n",
-    "                        | (df['UndergradMajor'] == 'A social science (such as anthropology, psychology, political science, etc.)')\n",
-    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (such as graphic design, music, studio art, etc.)'),\n",
-    "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
-    "    \n",
-    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
-    "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
-    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
-    "    return df\n",
-    "\n",
-    "df2020 = refactor_major(df2020)\n",
-    "df2020['UndergradMajor'].replace('nan', 'No major', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "UndergradMajor\n",
-       "Arts and Science     2129\n",
-       "Business             1033\n",
-       "Computer Science    24429\n",
-       "Engineering          3542\n",
-       "Info Systems         3074\n",
-       "Math/Stat            1419\n",
-       "No major             5832\n",
-       "Other Science        1944\n",
-       "Web Design/Dev       1230\n",
-       "Name: UndergradMajor, dtype: int64"
-      ]
-     },
-     "execution_count": 300,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('UndergradMajor')['UndergradMajor'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 301,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['UndergradMajor'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Employment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "111"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employment\n",
-       "Employed full-time                                      32474\n",
-       "Employed part-time                                       1489\n",
-       "Independent contractor, freelancer, or self-employed     3859\n",
-       "Not employed, and not looking for work                    181\n",
-       "Not employed, but looking for work                       1500\n",
-       "Retired                                                    32\n",
-       "Student                                                  4986\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 303,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020.groupby('Employment')['Employment'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 304,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020.dropna(subset=['Employment'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 305,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Refactoring Employment\n",
-    "df2020['Employment'].replace('Employed full-time', 'Full-time', inplace =True)\n",
-    "df2020['Employment'].replace('Employed part-time', 'Part-time',inplace =True)\n",
-    "df2020['Employment'].replace('Independent contractor, freelancer, or self-employed', 'Self-employed', inplace =True)\n",
-    "df2020['Employment'].replace('Not employed, but looking for work', 'Not employed', inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Employment\n",
-       "Full-time                                 32474\n",
-       "Not employed                               1500\n",
-       "Not employed, and not looking for work      181\n",
-       "Part-time                                  1489\n",
-       "Retired                                      32\n",
-       "Self-employed                              3859\n",
-       "Student                                    4986\n",
-       "Name: Employment, dtype: int64"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('Employment')['Employment'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 307,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 307,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Employment'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Country"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 308,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 308,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Country'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Country\n",
-       "Afghanistan                              22\n",
-       "Albania                                  29\n",
-       "Algeria                                  47\n",
-       "Andorra                                   3\n",
-       "Angola                                    2\n",
-       "                                       ... \n",
-       "Venezuela, Bolivarian Republic of...     53\n",
-       "Viet Nam                                159\n",
-       "Yemen                                     2\n",
-       "Zambia                                   10\n",
-       "Zimbabwe                                 19\n",
-       "Name: Country, Length: 170, dtype: int64"
-      ]
-     },
-     "execution_count": 309,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020.groupby('Country')['Country'].count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## YearsCodePro"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8123"
-      ]
-     },
-     "execution_count": 310,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 311,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age                         int64\n",
-       "Gender                     object\n",
-       "SalaryUSD                 float64\n",
-       "Country                    object\n",
-       "DevType                    object\n",
-       "Hobbyist                   object\n",
-       "EdLevel                    object\n",
-       "Employment                 object\n",
-       "Ethnicity                  object\n",
-       "CurrentJobSatis            object\n",
-       "JobStatus                  object\n",
-       "LanguageDesireNextYear     object\n",
-       "LanguageWorkedWith         object\n",
-       "Profession                 object\n",
-       "UndergradMajor             object\n",
-       "YearsCodePro               object\n",
-       "JobSeek                    object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 311,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#convert YearsCodePro data type from obj to int\n",
-    "df2020[\"YearsCodePro\"]=pd.to_numeric(df2020[\"YearsCodePro\"],errors='coerce')\n",
-    "\n",
-    "#fill YearsCodePro's null values with mean\n",
-    "means = df2020['YearsCodePro'].mean() #means 8.673142457693764\n",
-    "df2020['YearsCodePro'] = df2020['YearsCodePro'].fillna(means)\n",
-    "df2020['YearsCodePro'] = df2020['YearsCodePro'].round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 313,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 313,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['YearsCodePro'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hobbyist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 314,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 314,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['Hobbyist'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 315,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hobbyist\n",
-       "No      9583\n",
-       "Yes    34938\n",
-       "Name: Hobbyist, dtype: int64"
-      ]
-     },
-     "execution_count": 315,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.groupby('Hobbyist')['Hobbyist'].count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                           0\n",
-      "Gender                        0\n",
-      "SalaryUSD                 14202\n",
-      "Country                       0\n",
-      "DevType                       0\n",
-      "Hobbyist                      0\n",
-      "EdLevel                       0\n",
-      "Employment                    0\n",
-      "Ethnicity                     0\n",
-      "CurrentJobSatis               0\n",
-      "JobStatus                     0\n",
-      "LanguageDesireNextYear        0\n",
-      "LanguageWorkedWith            0\n",
-      "Profession                    0\n",
-      "UndergradMajor                0\n",
-      "YearsCodePro                  0\n",
-      "JobSeek                       0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df2020.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ConvertedComp (SalaryUSD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14202"
-      ]
-     },
-     "execution_count": 317,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['SalaryUSD'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "120000.0     284\n",
-       "100000.0     254\n",
-       "64859.0      224\n",
-       "150000.0     221\n",
-       "2000000.0    216\n",
-       "Name: SalaryUSD, dtype: int64"
-      ]
-     },
-     "execution_count": 318,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['SalaryUSD'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
-    "mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df2020['SalaryUSD'] = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))\n",
-    "\n",
-    "means = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform('mean')\n",
-    "df2020['SalaryUSD'] = df2020['SalaryUSD'].fillna(means)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age  EdLevel       Country      \n",
-       "40   Professional  United States    2000000.0\n",
-       "37   Masters       Nomadic          1320000.0\n",
-       "41   Masters       Israel           1200000.0\n",
-       "47   Professional  United States    1047500.0\n",
-       "33   Doctorate     Italy            1018376.5\n",
-       "15   Bachelors     Germany          1000000.0\n",
-       "20   Associate     Australia        1000000.0\n",
-       "25   Bachelors     Paraguay         1000000.0\n",
-       "28   Doctorate     Singapore        1000000.0\n",
-       "32   No Degree     Ireland          1000000.0\n",
-       "Name: SalaryUSD, dtype: float64"
-      ]
-     },
-     "execution_count": 320,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
-    "mean_salary.nlargest(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "120000.0     286\n",
-       "100000.0     255\n",
-       "64859.0      239\n",
-       "150000.0     227\n",
-       "1000000.0    219\n",
-       "Name: SalaryUSD, dtype: int64"
-      ]
-     },
-     "execution_count": 321,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['SalaryUSD'].value_counts().nlargest()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2952"
-      ]
-     },
-     "execution_count": 322,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "df2020['SalaryUSD'].isnull().sum() #2952 out of 64461 -> 4.6%"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2020.dropna(subset=['SalaryUSD'], inplace = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 324,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['SalaryUSD'].isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cleaned Dataset:2020_Survey"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Age                       0\n",
-      "Gender                    0\n",
-      "SalaryUSD                 0\n",
-      "Country                   0\n",
-      "DevType                   0\n",
-      "Hobbyist                  0\n",
-      "EdLevel                   0\n",
-      "Employment                0\n",
-      "Ethnicity                 0\n",
-      "CurrentJobSatis           0\n",
-      "JobStatus                 0\n",
-      "LanguageDesireNextYear    0\n",
-      "LanguageWorkedWith        0\n",
-      "Profession                0\n",
-      "UndergradMajor            0\n",
-      "YearsCodePro              0\n",
-      "JobSeek                   0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df2020.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#resetting the index values\n",
-    "df2020 = df2020.reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Ethnicity</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "      <th>JobStatus</th>\n",
-       "      <th>LanguageDesireNextYear</th>\n",
-       "      <th>LanguageWorkedWith</th>\n",
-       "      <th>Profession</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>JobSeek</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>31</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>214247.736842</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Slightly dissatisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>Java;Ruby;Scala</td>\n",
-       "      <td>HTML/CSS;Ruby;SQL</td>\n",
-       "      <td>Ex-Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>Not seeking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>36</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>116000.000000</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Slightly dissatisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>JavaScript</td>\n",
-       "      <td>Python;SQL</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>Not seeking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>22</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>32315.000000</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Database administrator;Developer, full-stack;D...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Very satisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>HTML/CSS;Java;JavaScript;Python;R;SQL</td>\n",
-       "      <td>HTML/CSS;Java;JavaScript;Python;SQL</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>Not seeking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>23</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>40070.000000</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Slightly dissatisfied</td>\n",
-       "      <td>I am actively looking for a job</td>\n",
-       "      <td>Go;JavaScript;Swift;TypeScript</td>\n",
-       "      <td>C#;JavaScript;Swift</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Seeking</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>49</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>14268.000000</td>\n",
-       "      <td>Spain</td>\n",
-       "      <td>Designer;Developer, front-end</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No Degree</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>White or of European descent</td>\n",
-       "      <td>Very dissatisfied</td>\n",
-       "      <td>I’m not actively looking, but I am open to new...</td>\n",
-       "      <td>HTML/CSS;JavaScript</td>\n",
-       "      <td>HTML/CSS;JavaScript</td>\n",
-       "      <td>Developer</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Not seeking</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age Gender      SalaryUSD         Country  \\\n",
-       "0   31    Man  214247.736842   United States   \n",
-       "1   36    Man  116000.000000   United States   \n",
-       "2   22    Man   32315.000000  United Kingdom   \n",
-       "3   23    Man   40070.000000  United Kingdom   \n",
-       "4   49    Man   14268.000000           Spain   \n",
-       "\n",
-       "                                             DevType Hobbyist    EdLevel  \\\n",
-       "0  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
-       "1  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
-       "2  Database administrator;Developer, full-stack;D...      Yes    Masters   \n",
-       "3  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
-       "4                      Designer;Developer, front-end       No  No Degree   \n",
-       "\n",
-       "  Employment                     Ethnicity        CurrentJobSatis  \\\n",
-       "0  Full-time  White or of European descent  Slightly dissatisfied   \n",
-       "1  Full-time  White or of European descent  Slightly dissatisfied   \n",
-       "2  Full-time  White or of European descent         Very satisfied   \n",
-       "3  Full-time  White or of European descent  Slightly dissatisfied   \n",
-       "4  Full-time  White or of European descent      Very dissatisfied   \n",
-       "\n",
-       "                                           JobStatus  \\\n",
-       "0  I’m not actively looking, but I am open to new...   \n",
-       "1  I’m not actively looking, but I am open to new...   \n",
-       "2  I’m not actively looking, but I am open to new...   \n",
-       "3                    I am actively looking for a job   \n",
-       "4  I’m not actively looking, but I am open to new...   \n",
-       "\n",
-       "                  LanguageDesireNextYear                   LanguageWorkedWith  \\\n",
-       "0                        Java;Ruby;Scala                    HTML/CSS;Ruby;SQL   \n",
-       "1                             JavaScript                           Python;SQL   \n",
-       "2  HTML/CSS;Java;JavaScript;Python;R;SQL  HTML/CSS;Java;JavaScript;Python;SQL   \n",
-       "3         Go;JavaScript;Swift;TypeScript                  C#;JavaScript;Swift   \n",
-       "4                    HTML/CSS;JavaScript                  HTML/CSS;JavaScript   \n",
-       "\n",
-       "     Profession    UndergradMajor  YearsCodePro      JobSeek  \n",
-       "0  Ex-Developer  Computer Science           8.0  Not seeking  \n",
-       "1     Developer  Computer Science          13.0  Not seeking  \n",
-       "2     Developer         Math/Stat           4.0  Not seeking  \n",
-       "3     Developer  Computer Science           2.0      Seeking  \n",
-       "4     Developer         Math/Stat           7.0  Not seeking  "
-      ]
-     },
-     "execution_count": 327,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 41569 entries, 0 to 41568\n",
-      "Data columns (total 17 columns):\n",
-      " #   Column                  Non-Null Count  Dtype  \n",
-      "---  ------                  --------------  -----  \n",
-      " 0   Age                     41569 non-null  int64  \n",
-      " 1   Gender                  41569 non-null  object \n",
-      " 2   SalaryUSD               41569 non-null  float64\n",
-      " 3   Country                 41569 non-null  object \n",
-      " 4   DevType                 41569 non-null  object \n",
-      " 5   Hobbyist                41569 non-null  object \n",
-      " 6   EdLevel                 41569 non-null  object \n",
-      " 7   Employment              41569 non-null  object \n",
-      " 8   Ethnicity               41569 non-null  object \n",
-      " 9   CurrentJobSatis         41569 non-null  object \n",
-      " 10  JobStatus               41569 non-null  object \n",
-      " 11  LanguageDesireNextYear  41569 non-null  object \n",
-      " 12  LanguageWorkedWith      41569 non-null  object \n",
-      " 13  Profession              41569 non-null  object \n",
-      " 14  UndergradMajor          41569 non-null  object \n",
-      " 15  YearsCodePro            41569 non-null  float64\n",
-      " 16  JobSeek                 41569 non-null  object \n",
-      "dtypes: float64(2), int64(1), object(14)\n",
-      "memory usage: 5.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df2020.info()#after cleaning the dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### After Cleaning Dataset 2020"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 329,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total :  706673\n",
-      "Total missing :  0\n",
-      "Missing Percentage:  0.0 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Find % of missing data\n",
-    "missing_count = df2020.isnull().sum() #number of missing\n",
-    "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n",
-    "total_missing = missing_count.sum()\n",
-    "missing_percent = (total_missing*100)/total_cells\n",
-    "\n",
-    "print('Total : ', total_cells)\n",
-    "print('Total missing : ', total_missing)\n",
-    "print('Missing Percentage: ', missing_percent, '%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization\n",
-    "After cleaning the datasets, we started visualizations to analyze the datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## To find whether there is any difference between men and women's income from latest stack overflow survey (2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 330,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.style.use('seaborn-darkgrid')\n",
-    "plt.rcParams[\"figure.figsize\"] = (20,10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#sns.boxplot('SalaryUSD', data=df2020, width=0.3) \n",
-    "#Cleaning SalaryUSD's outliers\n",
-    "df2020 = df2020[(df2020['SalaryUSD'] < 200000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Income vs Gender')"
-      ]
-     },
-     "execution_count": 332,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(x ='Gender', y='SalaryUSD', data=df2020)\n",
-    "plt.title('Income vs Gender', fontsize = 14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Analysis**<br>\n",
-    "There is a little bit of difference between Gender and income they received respectively. Men tend to receive more salary than women from the above analysis."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Impact on participation rate due to different ethnicity based on country."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 333,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['White or of European descent', 'South Asian', 'Hispanic or Latino', 'Middle Eastern', 'East Asian', 'Southeast Asian', 'Black or of African descent', 'Multiracial', 'Biracial', 'Indigenous']\n",
-      "[24573, 4585, 2877, 1757, 1539, 1348, 1336, 226, 133, 62]\n"
-     ]
-    }
-   ],
-   "source": [
-    "participation_rate = df2020['Ethnicity'].value_counts().keys().tolist()\n",
-    "print(participation_rate)\n",
-    "count = df2020['Ethnicity'].value_counts().tolist()\n",
-    "print(count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 334,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()    \n",
-    " \n",
-    "ax.bar(participation_rate,count)\n",
-    "plt.title('Ethnicity VS Participation',size=20)\n",
-    "plt.xlabel('Total Count',size = 20)\n",
-    "plt.ylabel('# Of Developer Did Survey',size = 20) \n",
-    "for i, v in enumerate(count):\n",
-    "    ax.text(i-.15, \n",
-    "              v+3,\n",
-    "              count[i],\n",
-    "              style = 'italic',\n",
-    "              fontsize=14,\n",
-    "              color = 'magenta')\n",
-    "ax.grid(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 335,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1080x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(15, 5))\n",
-    "sns.barplot(x = count, y = participation_rate, palette = 'Set1')\n",
-    "plt.xlabel('Ethnicity', size = 16)\n",
-    "for i, v in enumerate(count):\n",
-    "    ax.text(  v+3,\n",
-    "              i-.15,\n",
-    "              f'{count[i]*100/sum(count):.2f}%',\n",
-    "              style = 'italic',\n",
-    "              fontsize=14,\n",
-    "              )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**From the Survey Analysis, more particpation has been happened from White or of European Ethnicity which is 24573 participation which is very high comparing to others.<br>\n",
-    "The least has been recorded as only 0.16% from Indigenous.<br>\n",
-    "The second top survey contributors are from South Asians which is 11.93% of the respondents.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Geographical plot to show number of respondents in each country in 2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 336,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#geoplot_2019=cleaned_df_2019.groupby('Country').agg('count')\n",
-    "geoplot_2019=cleaned_df_2019.groupby('Country').size()\n",
-    "geoplot_2019=geoplot_2019.to_frame('Respondents')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 337,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_country_code(name):\n",
-    "    try:\n",
-    "        return pycountry.countries.lookup(name).alpha_3\n",
-    "    except:\n",
-    "        return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 338,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geoplot_2019['Country'] = geoplot_2019.index\n",
-    "geoplot_2019['Country_code'] = geoplot_2019['Country'].apply(get_country_code)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 339,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "        require.undef(\"plotly\");\n",
-       "        define('plotly', function(require, exports, module) {\n",
-       "            /**\n",
-       "* plotly.js v1.58.4\n",
-       "* Copyright 2012-2020, Plotly, Inc.\n",
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-       "* Licensed under the MIT license\n",
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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,M=0|o[5],A=8191&M,S=M>>>13,E=0|o[6],C=8191&E,L=E>>>13,I=0|o[7],P=8191&I,z=I>>>13,O=0|o[8],D=8191&O,R=O>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(g,U)|0,a=Math.imul(g,V);var vt=(c+(n=n+Math.imul(f,H)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,H)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,H)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,H)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,H)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(g,J)|0,a=a+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(A,U),i=(i=Math.imul(A,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,V),n=n+Math.imul(A,H)|0,i=(i=i+Math.imul(A,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,U),i=(i=Math.imul(P,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(A,W)|0,i=(i=i+Math.imul(A,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,X)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,X)|0,n=n+Math.imul(A,J)|0,i=(i=i+Math.imul(A,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,W)|0,i=(i=i+Math.imul(P,X)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,X)|0,n=n+Math.imul(C,J)|0,i=(i=i+Math.imul(C,K)|0)+Math.imul(L,J)|0,a=a+Math.imul(L,K)|0,n=n+Math.imul(A,$)|0,i=(i=i+Math.imul(A,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(g,ft)|0,a=a+Math.imul(g,ht)|0;var Mt=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,gt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(P,J)|0,i=(i=i+Math.imul(P,K)|0)+Math.imul(z,J)|0,a=a+Math.imul(z,K)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(A,rt)|0,i=(i=i+Math.imul(A,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var At=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(P,$)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(A,at)|0,i=(i=i+Math.imul(A,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(A,lt)|0,i=(i=i+Math.imul(A,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(A,ft)|0,i=(i=i+Math.imul(A,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Ct=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ft)|0,i=(i=i+Math.imul(C,ht)|0)+Math.imul(L,ft)|0,a=a+Math.imul(L,ht)|0;var Lt=(c+(n=n+Math.imul(A,dt)|0)|0)+((8191&(i=(i=i+Math.imul(A,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(P,ft)|0,i=(i=i+Math.imul(P,ht)|0)+Math.imul(z,ft)|0,a=a+Math.imul(z,ht)|0;var It=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,gt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var Ot=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=Mt,l[10]=At,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=It,l[16]=Pt,l[17]=zt,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=h),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?h(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=f*m-h*v;v=f*v+h*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number 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numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var m={k256:null,p224:null,p192:null,p25519:null};function v(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){v.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff 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r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},T.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(\"undefined\"==typeof e||e,this)},{buffer:108}],100:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],101:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function 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-       "/*!\n",
-       " * The buffer module from node.js, for the browser.\n",
-       " *\n",
-       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
-       " * @license  MIT\n",
-       " */\n",
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a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function g(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],248:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function a(t){if(t<0)return Number(\"0/0\");for(var e=i[0],r=i.length-1;r>0;--r)e+=i[r]/(t+r);var n=t+607/128+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(a(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var o=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(o,e+.5)*Math.exp(-o)*r},e.exports.log=a},{}],249:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e||{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf(\"webgl\")&&a.push(\"experimental-\"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],250:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],m={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=f[M],this.lastCubeProps.axis[M]=h[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,f,h);e=this.gl;var S,E=g;for(M=0;M<3;++M)this.backgroundEnable[M]?E[M]=h[M]:E[M]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(M=0;M<3;++M){var C=[0,0,0];h[M]>0?C[M]=a[1][M]:C[M]=a[0][M];for(var L=0;L<2;++L){var I=(M+1+L)%3,P=(M+1+(1^L))%3;this.gridEnable[I]&&this._lines.drawGrid(I,P,this.bounds,C,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(L=0;L<2;++L){I=(M+1+L)%3,P=(M+1+(1^L))%3;this.zeroEnable[P]&&Math.min(a[0][P],a[1][P])<=0&&Math.max(a[0][P],a[1][P])>=0&&this._lines.drawZero(I,P,this.bounds,C,this.zeroLineColor[P],this.zeroLineWidth[P]*this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio);var z=c(v,A[M].primalMinor),O=c(y,A[M].mirrorMinor),D=this.lineTickLength;for(L=0;L<3;++L){var R=k/r[5*L];z[L]*=D[L]*R,O[L]*=D[L]*R}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,z,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,O,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(M=0;M<3;++M){var U=A[M].primalMinor,V=A[M].mirrorMinor,q=c(x,A[M].primalOffset);for(L=0;L<3;++L)this.lineTickEnable[M]&&(q[L]+=k*U[L]*Math.max(this.lineTickLength[L],0)/r[5*L]);var H=[0,0,0];if(H[M]=1,this.tickEnable[M]){-3600===this.tickAngle[M]?(this.tickAngle[M]=0,this.tickAlign[M]=\"auto\"):this.tickAlign[M]=-1,F=1,\"auto\"===(S=[this.tickAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(M,U,V);for(L=0;L<3;++L)q[L]+=k*U[L]*this.tickPad[L]/r[5*L];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],q,this.tickColor[M],H,B,S)}if(this.labelEnable[M]){F=0,B=[0,0,0],this.labels[M].length>4&&(N(M),F=1),\"auto\"===(S=[this.labelAlign[M],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(L=0;L<3;++L)q[L]+=k*U[L]*this.labelPad[L]/r[5*L];q[M]+=.5*(a[0][M]+a[1][M]),this._text.drawLabel(M,this.labelSize[M],this.labelAngle[M],q,this.labelColor[M],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":251,\"./lib/cube.js\":252,\"./lib/lines.js\":253,\"./lib/text.js\":255,\"./lib/ticks.js\":256}],251:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var g=-1;g<=1;g+=2)f[u]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var m=c;c=u,u=m}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":254,\"gl-buffer\":259,\"gl-vao\":358}],252:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,S=(M+1)%3,E=-1,C=-1,L=0;L<2;++L){var I=(z=L<<M)+(L<<A)+(1-L<<S),P=z+(1-L<<A)+(L<<S);o(c[z],c[I],c[P],f)<0||(L?E=1:C=1)}if(E<0||C<0)C>E&&(w|=1<<M);else{for(L=0;L<2;++L){I=(z=L<<M)+(L<<A)+(1-L<<S),P=z+(1-L<<A)+(L<<S);var z,O=d([l[z],l[I],l[P],l[z+(1<<A)+(1<<S)]]);L?E=O:C=O}C>E&&(w|=1<<M)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=g;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=m,q=w;for(M=0;M<3;++M)V[M]=q&1<<M?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{\"bit-twiddle\":97,\"gl-mat4/multiply\":295,\"robust-orientation\":548,\"split-polygon\":566}],253:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;s[f]=h,l[f]=m-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){g=+r[f][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}m=o.length/3|0;c[f]=h,u[f]=m-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":254,\"gl-buffer\":259,\"gl-vao\":358}],254:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":335,glslify:257}],255:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var 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n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],m=[0,0,0]},{\"./lib/cube.js\":252,\"extract-frustum-planes\":240,\"gl-mat4/multiply\":295,\"gl-mat4/transpose\":306,\"gl-vec4/transformMat4\":429,\"split-polygon\":566}],259:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&\"undefined\"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:495,\"ndarray-ops\":490,\"typedarray-pool\":595}],260:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,g=[],m=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(m=Math.min(m,_),v=!1):v=!0}v||(p=x,d=b),g.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(m)||(m=1),a.vectorScale=m;var M=t.coneSize||.5;t.absoluteConeSize&&(M=t.absoluteConeSize*k),a.coneScale=M;y=0;for(var A=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],C=x[2],L=g[y],I=n.length(L)*k,P=0;P<8;P++){a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.positions.push([S,E,C,A++]),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vectors.push(L),a.vertexIntensity.push(I,I,I),a.vertexIntensity.push(I,I,I);var z=a.positions.length;a.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":261,\"./lib/shaders\":262,\"gl-vec3\":377}],261:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function g(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var m=0;m<p.length;++m){var v=p[m];d=Math.min(d,v),g=Math.max(g,v)}else for(m=0;m<n.length;++m){v=n[m][2];d=Math.min(d,v),g=Math.max(g,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(m=0;m<n.length;++m)for(var y=n[m],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(m=0;m<r.length;++m){var _=r[m];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var M,A=h;3===A.length?s.push(A[0],A[1],A[2],1):s.push(A[0],A[1],A[2],A[3]),M=p?[(p[T]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(M[0],M[1]),f.push(m)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=g(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,m,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-shader\":335,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495}],262:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:263}],263:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],264:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\"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p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":335,glslify:267}],269:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new 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t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var g=d.prototype;function m(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,m(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":353}],270:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));h[g]=d}}var m=o(e).split(\"\\n\");for(p=0;p<m.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=m[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":66,\"gl-constants/lookup\":265,\"glsl-shader-name\":431,\"sprintf-js\":567}],271:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],g=t.colorValues||[0,0,0,1],m=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),M=a.mallocUint8(2*w),A=a.mallocUint32(w),S=0,E=l?b-1:b,C=l?_-1:_,L=0;L<C;++L){var I,P;l?(I=x*(o[L]-u),P=x*(o[L+1]-u)):(I=L<_-1?x*(o[L]-(o[L+1]-o[L])/2-u):x*(o[L]-(o[L]-o[L-1])/2-u),P=L<_-1?x*(o[L]+(o[L+1]-o[L])/2-u):x*(o[L]+(o[L]-o[L-1])/2-u));for(var z=0;z<E;++z){var O,D;l?(O=y*(r[z]-c),D=y*(r[z+1]-c)):(O=z<b-1?y*(r[z]-(r[z+1]-r[z])/2-c):y*(r[z]-(r[z]-r[z-1])/2-c),D=z<b-1?y*(r[z]+(r[z+1]-r[z])/2-c):y*(r[z]+(r[z]-r[z-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],q=s[l?(L+V)*b+(z+U):L*b+z],H=n.le(d,q);if(H<0)F=g[0],B=g[1],N=g[2],j=g[3];else if(H===m-1)F=g[4*m-4],B=g[4*m-3],N=g[4*m-2],j=g[4*m-1];else{var G=(q-d[H])/(d[H+1]-d[H]),Y=1-G,W=4*H,X=4*(H+1);F=Y*g[W]+G*g[X],B=Y*g[W+1]+G*g[X+1],N=Y*g[W+2]+G*g[X+2],j=Y*g[W+3]+G*g[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*O+.5*D,k[2*S+1]=.5*I+.5*P,M[2*S]=U,M[2*S+1]=V,A[S]=L*b+z,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(M),this.colorBuffer.update(T),this.idBuffer.update(A),a.free(k),a.free(T),a.free(M),a.free(A)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":272,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335,\"iota-array\":463,\"typedarray-pool\":595}],272:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:273}],273:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],274:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":335,glslify:276}],275:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var g=new v(e,r,o,s,l,d);return g.update(t),g};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function g(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function m(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:g(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:g(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,m=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,m=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var 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t[0]=1-(p+g),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=h+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],285:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],286:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],287:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],288:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],289:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],290:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),c=1/(a-o);return t[0]=2*a*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*a*l,t[6]=0,t[7]=0,t[8]=(r+e)*s,t[9]=(i+n)*l,t[10]=(o+a)*c,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*a*2*c,t[15]=0,t}},{}],291:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],292:[function(t,e,r){e.exports={create:t(\"./create\"),clone:t(\"./clone\"),copy:t(\"./copy\"),identity:t(\"./identity\"),transpose:t(\"./transpose\"),invert:t(\"./invert\"),adjoint:t(\"./adjoint\"),determinant:t(\"./determinant\"),multiply:t(\"./multiply\"),translate:t(\"./translate\"),scale:t(\"./scale\"),rotate:t(\"./rotate\"),rotateX:t(\"./rotateX\"),rotateY:t(\"./rotateY\"),rotateZ:t(\"./rotateZ\"),fromRotation:t(\"./fromRotation\"),fromRotationTranslation:t(\"./fromRotationTranslation\"),fromScaling:t(\"./fromScaling\"),fromTranslation:t(\"./fromTranslation\"),fromXRotation:t(\"./fromXRotation\"),fromYRotation:t(\"./fromYRotation\"),fromZRotation:t(\"./fromZRotation\"),fromQuat:t(\"./fromQuat\"),frustum:t(\"./frustum\"),perspective:t(\"./perspective\"),perspectiveFromFieldOfView:t(\"./perspectiveFromFieldOfView\"),ortho:t(\"./ortho\"),lookAt:t(\"./lookAt\"),str:t(\"./str\")}},{\"./adjoint\":277,\"./clone\":278,\"./copy\":279,\"./create\":280,\"./determinant\":281,\"./fromQuat\":282,\"./fromRotation\":283,\"./fromRotationTranslation\":284,\"./fromScaling\":285,\"./fromTranslation\":286,\"./fromXRotation\":287,\"./fromYRotation\":288,\"./fromZRotation\":289,\"./frustum\":290,\"./identity\":291,\"./invert\":293,\"./lookAt\":294,\"./multiply\":295,\"./ortho\":296,\"./perspective\":297,\"./perspectiveFromFieldOfView\":298,\"./rotate\":299,\"./rotateX\":300,\"./rotateY\":301,\"./rotateZ\":302,\"./scale\":303,\"./str\":304,\"./translate\":305,\"./transpose\":306}],293:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*g-f*d,M=u*m-h*d,A=u*v-p*d,S=f*m-h*g,E=f*v-p*g,C=h*v-p*m,L=y*C-x*E+b*S+_*A-w*M+T*k;if(!L)return null;return L=1/L,t[0]=(s*C-l*E+c*S)*L,t[1]=(i*E-n*C-a*S)*L,t[2]=(g*T-m*w+v*_)*L,t[3]=(h*w-f*T-p*_)*L,t[4]=(l*A-o*C-c*M)*L,t[5]=(r*C-i*A+a*M)*L,t[6]=(m*b-d*T-v*x)*L,t[7]=(u*T-h*b+p*x)*L,t[8]=(o*E-s*A+c*k)*L,t[9]=(n*A-r*E-a*k)*L,t[10]=(d*w-g*b+v*y)*L,t[11]=(f*b-u*w-p*y)*L,t[12]=(s*M-o*S-l*k)*L,t[13]=(r*S-n*M+i*k)*L,t[14]=(g*x-d*_-m*y)*L,t[15]=(u*_-f*x+h*y)*L,t}},{}],294:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,g=e[0],m=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=g-_,h=m-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{\"./identity\":291}],295:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*g,t[1]=x*i+b*l+_*h+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*g,t[5]=x*i+b*l+_*h+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*g,t[9]=x*i+b*l+_*h+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],296:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],297:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],298:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],299:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S,E=n[0],C=n[1],L=n[2],I=Math.sqrt(E*E+C*C+L*L);if(Math.abs(I)<1e-6)return null;E*=I=1/I,C*=I,L*=I,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=C*E*o+L*i,_=L*E*o-C*i,w=E*C*o-L*i,T=C*C*o+a,k=L*C*o+E*i,M=E*L*o+C*i,A=C*L*o-E*i,S=L*L*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*M+f*A+g*S,t[9]=l*M+h*A+m*S,t[10]=c*M+p*A+v*S,t[11]=u*M+d*A+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],300:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],301:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],302:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],303:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],304:[function(t,e,r){e.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},{}],305:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],306:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],307:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,g=0;g<2;++g)d+=Math.pow(u[f][g]-e[g],2);d<p&&(p=d,h=f)}var m=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(m[f]<-.001||m[f]>1.0001)return null;v+=m[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,m),m]}},{barycentric:78,\"polytope-closest-point/lib/closest_point_2d.js\":525}],308:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:310}],309:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),m=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,T,k,M,A,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=M,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function M(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function A(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function C(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function L(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function I(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],m=i[f],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*M(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,A=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,A)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,C=t.meshColor||[1,1,1,1],L=t.vertexUVs,I=t.vertexIntensity,P=t.cellUVs,z=t.cellIntensity,O=1/0,D=-1/0;if(!L&&!P)if(I)if(t.vertexIntensityBounds)O=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];O=Math.min(O,F),D=Math.max(D,F)}else if(z)if(t.cellIntensityBounds)O=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<z.length;++R){F=z[R];O=Math.min(O,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];O=Math.min(O,F),D=Math.max(D,F)}this.intensity=I||(z||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(z||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:C,this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:C,this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],g.push(Z[0],Z[1]),m.push(R)}q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:C)?this.opacityscale&&I?a.push(X[0],X[1],X[2],this.opacity*M((I[W]-O)/(D-O),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=g(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=A(t),s=S(t),l=E(t),c=C(t),f=L(t),h=I(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),P=i(t),z=i(t),O=i(t),D=i(t),R=i(t),F=a(t,[{buffer:P,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:O,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,g,m,v,x,b,k,_,w,M,P,R,z,O,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":307,\"./lib/shaders\":308,colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-shader\":335,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495,normals:498,\"simplicial-complex-contour\":556,\"typedarray-pool\":595}],310:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],311:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"gl-buffer\":259,\"gl-shader\":335}],312:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=h[g];var _=6*n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,M=.5*(d[w+2]+d[w]),A=d[w+2]-d[w];e[w]=2*k/A,t[w]=2*(T-M)/A}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,C=s.tickMarkWidth,L=s.tickMarkColor,I=6*f[0].length,P=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),z=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),O=0+6*P,D=6*Math.max(0,z-P),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=I+6*R,N=6*Math.max(0,F-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=C[1]*m/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=C[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,O,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=C[3]*m/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=C[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,O,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":314,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335}],313:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"gl-buffer\":259,\"gl-shader\":335}],314:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:316}],315:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,m=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),M=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var A=2/h[2+(1^t)]-h[1^t];u[1^t]=A*s[1^t]-1,d[t]&&(u[1^t]-=A*p*g[t],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t]&&T&&(u[1^t]-=A*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=A*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=A*p*g[t+2],k<M&&i[M]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t+2]&&T&&(u[1^t]+=A*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],g=a[o+2]-f,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,h[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,g=p.text,m=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(m,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":314,\"binary-search-bounds\":96,\"gl-buffer\":259,\"gl-shader\":335,\"text-cache\":575}],316:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],317:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":311,\"./lib/grid\":312,\"./lib/line\":313,\"./lib/text\":315,\"gl-select-static\":334}],318:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,g=!!a.shift,m=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,M=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&m&&!h&&!p&&!g||m&&!h&&!p&&g)&&c.rotate(M,w*k*b,-T*k*_,0),(l&&m&&!h&&!p&&!g||v||m&&h&&!p&&!g)&&c.pan(M,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&m&&!h&&!p&&!g||y||m&&!h&&p&&!g)){var A=-d.zoomSpeed*_/window.innerHeight*(M-c.lastT())*100;c.pan(M,0,0,f*(Math.exp(A)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":54,\"has-passive-events\":441,\"mouse-change\":483,\"mouse-event-offset\":484,\"mouse-wheel\":486,\"right-now\":542}],319:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":335,glslify:320}],320:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],321:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function m(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new g,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},M=i(r,k);M.enable=!k.disable;var A=t.spikes||{},S=o(r,A),E=[],C=[],L=[],I=[],P=!0,z=!0,O=new Array(16),D=new Array(16),R={view:null,projection:O,model:D,_ortho:!1},F=(z=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:M,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,z=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",P=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=I.length,n=0;n<e;++n)L[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(L[o]+a<255){C[n]=o,i.setPickBase(L[o]+1),L[o]+=a;continue t}var l=s(r,F);C[n]=e,I.push(l),L.push(a),i.setPickBase(1),e+=1}else C[n]=-1}for(;e>0&&0===L[e-1];)L.pop(),I.pop().dispose()}function q(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},P=!0,z=!0)},N.add=function(t){N._stopped||(t.axes=M,E.push(t),C.push(-1),P=!0,z=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),C.pop(),P=!0,z=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",q),N.mouseListener.enabled=!1,!N.contextLost)){M.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<I.length;++t)I[t].dispose();_.dispose(),r=null,M=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=I.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(z=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=I[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(C[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),P=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),P=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",q);var H=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[H[0].slice(),H[1].slice()];function Y(){if(!q()){U();var t=N.camera.tick();R.view=N.camera.matrix,P=P||t,z=z||t,M.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=H[0],i=H[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(L=E[o]).pixelRatio=N.pixelRatio,L.axes=N.axes,P=P||!!L.dirty,z=z||!!L.dirty;var s=L.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var g=.05*(i[p]-n[p]);n[p]=n[p]-g,i[p]=i[p]+g}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(z=z||v,P=P||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=m((d[1][o]-d[0][o])/10);M.autoTicks?M.update({bounds:d,tickSpacing:y}):M.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var g=0;for(d=0;d<3;++d)g=Math.max(g,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/g,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(L=E[o]).axesBounds=d,N.clipToBounds&&(L.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),z&&(z=!1,function(){if(!q()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=I.length,n=0;n<e;++n){var i=I[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(C[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var A=!1;M.enable&&(A=A||M.isTransparent(),M.draw(R)),S.axes=M,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(L=E[o]).axes=M,L.pixelRatio=N.pixelRatio,L.isOpaque&&L.isOpaque()&&L.draw(R),L.isTransparent&&L.isTransparent()&&(A=!0)}if(A){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),M.enable&&M.isTransparent()&&M.drawTransparent(R);for(o=0;o<e;++o){(L=E[o]).isOpaque&&L.isOpaque()&&L.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),M.isTransparent()&&M.drawTransparent(R);for(o=0;o<e;++o){var L;(L=E[o]).isTransparent&&L.isTransparent()&&L.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}P=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(P=!0,Y())},N},createCamera:n}},{\"./camera.js\":318,\"./lib/shader\":319,\"a-big-triangle\":64,\"gl-axes3d\":250,\"gl-axes3d/properties\":258,\"gl-fbo\":269,\"gl-mat4/ortho\":296,\"gl-mat4/perspective\":297,\"gl-select-static\":334,\"gl-spikes3d\":345,\"is-mobile\":467,\"mouse-change\":483}],322:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:323}],323:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],324:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":322,\"gl-buffer\":259,\"gl-shader\":335,\"typedarray-pool\":595}],325:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+f*g+h*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*g,t[3]=s*h+l*m,t}},{}],326:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],327:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":600}],328:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{\"gl-shader\":335,glslify:329}],329:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],330:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t||t>1?1:t}function m(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),g=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new m(e,r,n,o,f,h,p,d,g,s,c,u);return v.update(t),v};var v=m.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],M=[[0,0,0],[0,0,0]];function A(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function C(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,g=function(t){for(var e=M,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var v=T,C=0;C<16;++C)v[C]=0;for(C=0;C<4;++C)v[5*C]=1;v[5*m]=0,i[m]<0?v[12+m]=d[0][m]:v[12+m]=d[1][m],s(v,c,v),l.model=v;var L=(m+1)%3,I=(m+2)%3,P=A(x),z=A(b);P[L]=1,z[I]=1;var O=p(0,0,0,S(_,P)),D=p(0,0,0,S(w,z));if(Math.abs(O[1])>Math.abs(D[1])){var R=O;O=D,D=R,R=P,P=z,z=R;var F=L;L=I,I=F}O[0]<0&&(P[L]=-1),D[1]>0&&(z[I]=-1);var B=0,N=0;for(C=0;C<4;++C)B+=Math.pow(c[4*L+C],2),N+=Math.pow(c[4*I+C],2);P[L]/=Math.sqrt(B),z[I]/=Math.sqrt(N),l.axes[0]=P,l.axes[1]=z,l.fragClipBounds[0]=E(k,g[0],m,-1e8),l.fragClipBounds[1]=E(k,g[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function I(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&C(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=L,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){I(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,m=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=P(h,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;x+=3*k.cells.length,b+=2*M.edges.length}}var S=x+b,E=o.mallocFloat(3*S),C=o.mallocFloat(4*S),L=o.mallocFloat(2*S),I=o.mallocUint32(S);if(S>0){var z=0,O=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var q=0;Array.isArray(m)?q=n<m.length?+m[n]:0:m&&(q=+m);var H=Math.cos(q),G=Math.sin(q);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-A[0][0]:Y<0?1+A[1][0]:1,W*=W>0?1-A[0][1]:W<0?1+A[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)C[4*z+$]=D[$];I[z]=y;var tt=J[K[Q]];L[2*z]=V*(H*tt[0]-G*tt[1]+X[0]),L[2*z+1]=V*(G*tt[0]+H*tt[1]+X[1]),z+=1}for(Z=M.edges,J=M.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)C[4*O+$]=R[$];I[O]=y;tt=J[K[Q]];L[2*O]=V*(H*tt[0]-G*tt[1]+X[0]),L[2*O+1]=V*(G*tt[0]+H*tt[1]+X[1]),O+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(C),this.glyphBuffer.update(L),this.idBuffer.update(I),o.free(E),o.free(C),o.free(L),o.free(I)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":326,\"./lib/glyphs\":327,\"./lib/shaders\":328,\"gl-buffer\":259,\"gl-mat4/multiply\":295,\"gl-vao\":358,\"is-string-blank\":470,\"typedarray-pool\":595}],331:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:332}],332:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],333:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var g=s[2]-s[0],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,h,g,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":331,\"gl-buffer\":259,\"gl-shader\":335}],334:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function 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a=[t.id,e.id,r.join(\":\"),i.join(\":\")].join(\"@\"),o=this.programs[a];return o&&this.gl.isProgram(o)||(this.programs[a]=o=function(t,e,r,i,a){var o=t.createProgram();t.attachShader(o,e),t.attachShader(o,r);for(var s=0;s<i.length;++s)t.bindAttribLocation(o,a[s],i[s]);if(t.linkProgram(o),!t.getProgramParameter(o,t.LINK_STATUS)){var l=t.getProgramInfoLog(o);throw new n(l,\"Error linking program: \"+l)}return o}(this.gl,t.shader,e.shader,r,i),t.programs.push(a),e.programs.push(a)),o}},{\"./GLError\":336,\"gl-format-compiler-error\":270,\"weakmap-shim\":605}],342:[function(t,e,r){\"use strict\";function n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}e.exports=function(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r};var i=n.prototype;i.update=function(t){t=t||{},this.enable=(t.enable||[!0,!0,!1,!1]).slice(),this.width=(t.width||[1,1,1,1]).slice(),this.color=(t.color||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map((function(t){return t.slice()})),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},i.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],343:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],344:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta = weight * clipOffset * screenShape;\\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":335,glslify:343}],345:[function(t,e,r){\"use strict\";var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":344,\"gl-buffer\":259,\"gl-vao\":358}],346:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:347}],347:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],348:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],g=0,m=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),m=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(g,m,m,m,g,g);var M=c.length;u.push([M-6,M-5,M-4],[M-3,M-2,M-1])}var A=h;h=p,p=A;var S=y;y=v,v=S;var E=g;g=m,m=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var g=u[s],m=f.length;f=f.concat(g.positions),p=p.concat(g.vectors),d=d.concat(g.vertexIntensity);for(c=0;c<g.cells.length;c++){var v=g.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+m)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),g=s(a[1],c),m=s(a[2],u),v=d+1,y=g+1,x=m+1;if(d=l(d,0,f-1),v=l(v,0,f-1),g=l(g,0,h-1),y=l(y,0,h-1),m=l(m,0,p-1),x=l(x,0,p-1),d<0||g<0||m<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,M,A=a[0][d],S=a[0][v],E=a[1][g],C=a[1][y],L=a[2][m],I=(o-A)/(S-A),P=(c-E)/(C-E),z=(u-L)/(a[2][x]-L);switch(isFinite(I)||(I=.5),isFinite(P)||(P=.5),isFinite(z)||(z=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(g=h-1-g,y=h-1-y),r.reversedZ&&(m=p-1-m,x=p-1-x),r.filled){case 5:k=m,M=x,w=g*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=m,M=x,b=d*p,_=v*p,w=g*p*f,T=y*p*f;break;case 3:w=g,T=y,k=m*h,M=x*h,b=d*h*p,_=v*h*p;break;case 2:w=g,T=y,b=d*h,_=v*h,k=m*h*f,M=x*h*f;break;case 1:b=d,_=v,k=m*f,M=x*f,w=g*f*p,T=y*f*p;break;default:b=d,_=v,w=g*f,T=y*f,k=m*f*h,M=x*f*h}var O=i[b+w+k],D=i[b+w+M],R=i[b+T+k],F=i[b+T+M],B=i[_+w+k],N=i[_+w+M],j=i[_+T+k],U=i[_+T+M],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,O,B,I),n.lerp(q,D,N,I),n.lerp(H,R,j,I),n.lerp(G,F,U,I);var Y=n.create(),W=n.create();n.lerp(Y,V,H,P),n.lerp(W,q,G,P);var X=n.create();return n.lerp(X,Y,W,z),X}(e,t,p)},g=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},m=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,M=k*k,A=1,S=0,E=r.length;E>1&&(A=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),g=c(r),m=c(n),v=Math.min(d,g,m);return isFinite(v)?v:1}(r));for(var C=0;C<E;C++){var L=n.create();n.copy(L,r[C]);var I=[L],P=[],z=d(L),O=L;P.push(z);var D=[],R=g(L,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),m.push({points:I,velocities:P,divergences:D});for(var B=0;B<100*i&&I.length<i&&T(L);){B++;var N=n.clone(z),j=n.squaredLength(N);if(0===j)break;if(j>M&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,L),z=d(N),n.squaredDistance(O,N)-M>-1e-4*M){I.push(N),O=N,P.push(z);R=g(N,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}L=N}}var U=o(m,t.colormap,S,A);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*A/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":346,\"gl-cone3d\":260,\"gl-vec3\":377,\"gl-vec4\":413}],349:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":335,glslify:350}],350:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],351:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new A(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",m.update(v),m};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),m=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function A(t,e,r,n,i,a,o,l,c,u,h,p,d,g,m){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=m,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=A.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],C={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function L(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=C.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=C.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return C.showSurface=o,C.showContour=s,C}var I={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},P=w.slice(),z=[1,0,0,0,1,0,0,0,1];function O(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=P;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=L(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return O.call(this,t,!1)},S.drawTransparent=function(t){return O.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=z;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=L(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,m=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var g=[0,0];g[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],g,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)m(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),M=x.get(1,o,v,1),A=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-M*A,C=A*w-S*_,L=_*M-w*k,I=Math.sqrt(E*E+C*C+L*L);I<1e-8?(I=Math.max(Math.abs(E),Math.abs(C),Math.abs(L)))<1e-8?(L=1,C=E=0,I=1):I=1/I:I=1/Math.sqrt(I),b.set(o,v,0,E*I),b.set(o,v,1,C*I),b.set(o,v,2,L*I)}s.free(x.data);var P=[1/0,1/0,1/0],z=[-1/0,-1/0,-1/0],O=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+v+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var Y=o+T[H][0],W=v+T[H][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),C=b.get(Y+1,W+1,1),L=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=C,N[j++]=L,P[0]=Math.min(P[0],X+this.objectOffset[0]),P[1]=Math.min(P[1],Z+this.objectOffset[1]),P[2]=Math.min(P[2],G+this.objectOffset[2]),O=Math.min(O,J),z[0]=Math.max(z[0],X+this.objectOffset[0]),z[1]=Math.max(z[1],Z+this.objectOffset[1]),z[2]=Math.max(z[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(O=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-O)/(D-O);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[P,z],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===O&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[O,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var gt=($+dt+1)%3;for(V=0;V<2;++V){var mt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var vt=q?ht:1-ht;if(W=0|Math.min(Math.max(ft+q,0),a[1]),G=dt<2?this._field[gt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=mt*vt;nt[dt]+=yt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,M=+x[1],A=0|M,S=0|Math.min(A+1,i[1]),E=M-A,C=1-E,L=k*C,I=k*E,P=T*C,z=T*E,O=L*f.get(_,A)+I*f.get(_,S)+P*f.get(w,A)+z*f.get(w,S),D=L*p.get(_,A)+I*p.get(_,S)+P*p.get(w,A)+z*p.get(w,S);if(isNaN(O)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=O,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":349,\"binary-search-bounds\":96,\"bit-twiddle\":97,colormap:131,\"gl-buffer\":259,\"gl-mat4/invert\":293,\"gl-mat4/multiply\":295,\"gl-texture2d\":353,\"gl-vao\":358,ndarray:495,\"ndarray-gradient\":488,\"ndarray-ops\":490,\"ndarray-pack\":491,\"surface-nets\":570,\"typedarray-pool\":595}],352:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),g=t(\"detect-kerning\"),m=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\t\"+(T.normalViewport?\"\":\"vec2 positionOffset = vec2(positionOffset.x,- positionOffset.y);\")+\"\\n\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ positionOffset))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\t\"+(T.normalViewport?\"position.y = 1. - position.y;\":\"\")+\"\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),T.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);m(t.kerning,g(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),M=0,A=0;M<this.counts.length;M++){var S=this.counts[M];if(w)for(var E=0;E<S;E++)k[A++]=t.position[2*M],k[A++]=t.position[2*M+1];else for(var C=0;C<S;C++)k[A++]=t.position[M][0],k[A++]=t.position[M][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var L=u.mallocUint8(this.count),I=u.mallocFloat(2*this.count);this.textWidth=[];for(var P=0,z=0;P<this.counts.length;P++){for(var O=this.counts[P],D=this.font[P]||this.font[0],R=this.fontAtlas[P]||this.fontAtlas[0],F=0;F<O;F++){var B=this.text.charAt(z),N=this.text.charAt(z-1);if(L[z]=R.ids[B],I[2*z]=D.width[B],F){var j=I[2*z-2],U=I[2*z],V=I[2*z-1]+.5*j+.5*U;if(this.kerning){var q=D.kerning[N+B];q&&(V+=.001*q)}I[2*z+1]=V}else I[2*z+1]=.5*I[2*z];z++}this.textWidth.push(I.length?.5*I[2*z-2]+I[2*z-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:L,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:I,type:\"float\",usage:\"stream\"}),u.freeUint8(L),u.freeFloat(I),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],T.normalViewport||(i*=-1),i}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)H.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;H=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.normalViewport=!1,T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{\"bit-twiddle\":97,\"color-normalize\":125,\"css-font\":144,\"detect-kerning\":172,\"es6-weak-map\":233,\"flatten-vertex-data\":244,\"font-atlas\":245,\"font-measure\":246,\"gl-util/context\":354,\"is-plain-obj\":469,\"object-assign\":499,\"parse-rect\":504,\"parse-unit\":506,\"pick-by-alias\":511,regl:540,\"to-px\":578,\"typedarray-pool\":595}],353:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function g(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function m(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=g(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var 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strict\";e.exports=function(t,e){var r=t.length;if(0===r)throw new Error(\"Must have at least d+1 points\");var i=t[0].length;if(r<=i)throw new Error(\"Must input at least d+1 points\");var o=t.slice(0,i+1),s=n.apply(void 0,o);if(0===s)throw new Error(\"Input not in general position\");for(var l=new Array(i+1),u=0;u<=i;++u)l[u]=u;s<0&&(l[0]=1,l[1]=0);var f=new a(l,new Array(i+1),!1),h=f.adjacent,p=new Array(i+2);for(u=0;u<=i;++u){for(var d=l.slice(),g=0;g<=i;++g)g===u&&(d[g]=-1);var m=d[0];d[0]=d[1],d[1]=m;var v=new a(d,new Array(i+1),!0);h[u]=v,p[u]=v}p[i+1]=f;for(u=0;u<=i;++u){d=h[u].vertices;var y=h[u].adjacent;for(g=0;g<=i;++g){var x=d[g];if(x<0)y[g]=f;else for(var b=0;b<=i;++b)h[b].vertices.indexOf(x)<0&&(y[g]=h[b])}}var _=new c(i,o,p),w=!!e;for(u=i+1;u<r;++u)_.insert(t[u],w);return _.boundary()};var n=t(\"robust-orientation\"),i=t(\"simplicial-complex\").compareCells;function a(t,e,r){this.vertices=t,this.adjacent=e,this.boundary=r,this.lastVisited=-1}function 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s=t.adjacent,l=0;l<=r;++l){var c=s[l];if(c.boundary&&!(c.lastVisited<=-n)){for(var u=c.vertices,f=0;f<=r;++f){var h=u[f];i[f]=h<0?e:a[h]}var p=this.orient();if(p>0)return c;c.lastVisited=-n,0===p&&o.push(c)}}}return null},u.walk=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,a=this.tuple,o=e?this.interior.length*Math.random()|0:this.interior.length-1,s=this.interior[o];t:for(;!s.boundary;){for(var l=s.vertices,c=s.adjacent,u=0;u<=n;++u)a[u]=i[l[u]];s.lastVisited=r;for(u=0;u<=n;++u){var f=c[u];if(!(f.lastVisited>=r)){var h=a[u];a[u]=t;var p=this.orient();if(a[u]=h,p<0){s=f;continue t}f.boundary?f.lastVisited=-r:f.lastVisited=r}}return}return s},u.addPeaks=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,l=this.tuple,c=this.interior,u=this.simplices,f=[e];e.lastVisited=r,e.vertices[e.vertices.indexOf(-1)]=r,e.boundary=!1,c.push(e);for(var h=[];f.length>0;){var p=(e=f.pop()).vertices,d=e.adjacent,g=p.indexOf(r);if(!(g<0))for(var m=0;m<=n;++m)if(m!==g){var v=d[m];if(v.boundary&&!(v.lastVisited>=r)){var y=v.vertices;if(v.lastVisited!==-r){for(var x=0,b=0;b<=n;++b)y[b]<0?(x=b,l[b]=t):l[b]=i[y[b]];if(this.orient()>0){y[x]=r,v.boundary=!1,c.push(v),f.push(v),v.lastVisited=r;continue}v.lastVisited=-r}var _=v.adjacent,w=p.slice(),T=d.slice(),k=new a(w,T,!0);u.push(k);var M=_.indexOf(e);if(!(M<0)){_[M]=k,T[g]=v,w[m]=-1,T[m]=e,d[m]=k,k.flip();for(b=0;b<=n;++b){var A=w[b];if(!(A<0||A===r)){for(var S=new Array(n-1),E=0,C=0;C<=n;++C){var L=w[C];L<0||C===b||(S[E++]=L)}h.push(new o(S,k,b))}}}}}}h.sort(s);for(m=0;m+1<h.length;m+=2){var I=h[m],P=h[m+1],z=I.index,O=P.index;z<0||O<0||(I.cell.adjacent[I.index]=P.cell,P.cell.adjacent[P.index]=I.cell)}},u.insert=function(t,e){var r=this.vertices;r.push(t);var n=this.walk(t,e);if(n){for(var i=this.dimension,a=this.tuple,o=0;o<=i;++o){var s=n.vertices[o];a[o]=s<0?t:r[s]}var l=this.orient(a);l<0||(0!==l||(n=this.handleBoundaryDegeneracy(n,t)))&&this.addPeaks(t,n)}},u.boundary=function(){for(var t=this.dimension,e=[],r=this.simplices,n=r.length,i=0;i<n;++i){var a=r[i];if(a.boundary){for(var o=new Array(t),s=a.vertices,l=0,c=0,u=0;u<=t;++u)s[u]>=0?o[l++]=s[u]:c=1&u;if(c===(1&t)){var f=o[0];o[0]=o[1],o[1]=f}e.push(o)}}return e}},{\"robust-orientation\":548,\"simplicial-complex\":558}],460:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\");function i(t,e,r,n,i){this.mid=t,this.left=e,this.right=r,this.leftPoints=n,this.rightPoints=i,this.count=(e?e.count:0)+(r?r.count:0)+n.length}e.exports=function(t){if(!t||0===t.length)return new v(null);return new v(m(t))};var a=i.prototype;function o(t,e){t.mid=e.mid,t.left=e.left,t.right=e.right,t.leftPoints=e.leftPoints,t.rightPoints=e.rightPoints,t.count=e.count}function s(t,e){var r=m(e);t.mid=r.mid,t.left=r.left,t.right=r.right,t.leftPoints=r.leftPoints,t.rightPoints=r.rightPoints,t.count=r.count}function l(t,e){var r=t.intervals([]);r.push(e),s(t,r)}function c(t,e){var r=t.intervals([]),n=r.indexOf(e);return n<0?0:(r.splice(n,1),s(t,r),1)}function u(t,e,r){for(var n=0;n<t.length&&t[n][0]<=e;++n){var i=r(t[n]);if(i)return i}}function f(t,e,r){for(var n=t.length-1;n>=0&&t[n][1]>=e;--n){var i=r(t[n]);if(i)return i}}function h(t,e){for(var r=0;r<t.length;++r){var n=e(t[r]);if(n)return n}}function p(t,e){return t-e}function d(t,e){var r=t[0]-e[0];return r||t[1]-e[1]}function g(t,e){var r=t[1]-e[1];return r||t[0]-e[0]}function m(t){if(0===t.length)return null;for(var e=[],r=0;r<t.length;++r)e.push(t[r][0],t[r][1]);e.sort(p);var n=e[e.length>>1],a=[],o=[],s=[];for(r=0;r<t.length;++r){var l=t[r];l[1]<n?a.push(l):n<l[0]?o.push(l):s.push(l)}var c=s,u=s.slice();return c.sort(d),u.sort(g),new i(n,m(a),m(o),c,u)}function v(t){this.root=t}a.intervals=function(t){return 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this.leftPoints[0]===t?2:0;if(1===this.leftPoints.length&&this.leftPoints[0]===t){if(this.left&&this.right){for(var r=this,i=this.left;i.right;)r=i,i=i.right;if(r===this)i.right=this.right;else{var a=this.left,s=this.right;r.count-=i.count,r.right=i.left,i.left=a,i.right=s}o(this,i),this.count=(this.left?this.left.count:0)+(this.right?this.right.count:0)+this.leftPoints.length}else this.left?o(this,this.left):o(this,this.right);return 1}for(a=n.ge(this.leftPoints,t,d);a<this.leftPoints.length&&this.leftPoints[a][0]===t[0];++a)if(this.leftPoints[a]===t){this.count-=1,this.leftPoints.splice(a,1);for(s=n.ge(this.rightPoints,t,g);s<this.rightPoints.length&&this.rightPoints[s][1]===t[1];++s)if(this.rightPoints[s]===t)return this.rightPoints.splice(s,1),1}return 0},a.queryPoint=function(t,e){if(t<this.mid){if(this.left)if(r=this.left.queryPoint(t,e))return r;return u(this.leftPoints,t,e)}if(t>this.mid){var r;if(this.right)if(r=this.right.queryPoint(t,e))return r;return f(this.rightPoints,t,e)}return h(this.leftPoints,e)},a.queryInterval=function(t,e,r){var n;if(t<this.mid&&this.left&&(n=this.left.queryInterval(t,e,r)))return n;if(e>this.mid&&this.right&&(n=this.right.queryInterval(t,e,r)))return n;return e<this.mid?u(this.leftPoints,e,r):t>this.mid?f(this.rightPoints,t,r):h(this.leftPoints,r)};var y=v.prototype;y.insert=function(t){this.root?this.root.insert(t):this.root=new i(t[0],null,null,[t],[t])},y.remove=function(t){if(this.root){var e=this.root.remove(t);return 2===e&&(this.root=null),0!==e}return!1},y.queryPoint=function(t,e){if(this.root)return this.root.queryPoint(t,e)},y.queryInterval=function(t,e,r){if(t<=e&&this.root)return this.root.queryInterval(t,e,r)},Object.defineProperty(y,\"count\",{get:function(){return this.root?this.root.count:0}}),Object.defineProperty(y,\"intervals\",{get:function(){return this.root?this.root.intervals([]):[]}})},{\"binary-search-bounds\":461}],461:[function(t,e,r){arguments[4][243][0].apply(r,arguments)},{dup:243}],462:[function(t,e,r){\"use strict\";e.exports=function(t,e){e=e||new Array(t.length);for(var r=0;r<t.length;++r)e[t[r]]=r;return e}},{}],463:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=r;return e}},{}],464:[function(t,e,r){e.exports=!0},{}],465:[function(t,e,r){function n(t){return!!t.constructor&&\"function\"==typeof t.constructor.isBuffer&&t.constructor.isBuffer(t)}\n",
-       "/*!\n",
-       " * Determine if an object is a Buffer\n",
-       " *\n",
-       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
-       " * @license  MIT\n",
-       " */\n",
-       "e.exports=function(t){return null!=t&&(n(t)||function(t){return\"function\"==typeof t.readFloatLE&&\"function\"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],466:[function(t,e,r){\"use strict\";e.exports=\"undefined\"!=typeof navigator&&(/MSIE/.test(navigator.userAgent)||/Trident\\//.test(navigator.appVersion))},{}],467:[function(t,e,r){\"use strict\";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series[46]0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||\"undefined\"==typeof navigator||(e=navigator.userAgent),e&&e.headers&&\"string\"==typeof e.headers[\"user-agent\"]&&(e=e.headers[\"user-agent\"]),\"string\"!=typeof e)return!1;var r=t.tablet?i.test(e):n.test(e);return!r&&t.tablet&&t.featureDetect&&navigator&&navigator.maxTouchPoints>1&&-1!==e.indexOf(\"Macintosh\")&&-1!==e.indexOf(\"Safari\")&&(r=!0),r}},{}],468:[function(t,e,r){\"use strict\";e.exports=function(t){var e=typeof t;return null!==t&&(\"object\"===e||\"function\"===e)}},{}],469:[function(t,e,r){\"use strict\";var n=Object.prototype.toString;e.exports=function(t){var e;return\"[object Object]\"===n.call(t)&&(null===(e=Object.getPrototypeOf(t))||e===Object.getPrototypeOf({}))}},{}],470:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e,r=t.length,n=0;n<r;n++)if(((e=t.charCodeAt(n))<9||e>13)&&32!==e&&133!==e&&160!==e&&5760!==e&&6158!==e&&(e<8192||e>8205)&&8232!==e&&8233!==e&&8239!==e&&8287!==e&&8288!==e&&12288!==e&&65279!==e)return!1;return!0}},{}],471:[function(t,e,r){\"use strict\";e.exports=function(t){return\"string\"==typeof t&&(t=t.trim(),!!(/^[mzlhvcsqta]\\s*[-+.0-9][^mlhvzcsqta]+/i.test(t)&&/[\\dz]$/i.test(t)&&t.length>4))}},{}],472:[function(t,e,r){e.exports=function(t,e,r){return t*(1-r)+e*r}},{}],473:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():(t=t||self).mapboxgl=n()}(this,(function(){\"use strict\";var t,e,r;function n(n,i){if(t)if(e){var a=\"var sharedChunk = {}; (\"+t+\")(sharedChunk); (\"+e+\")(sharedChunk);\",o={};t(o),(r=i(o)).workerUrl=window.URL.createObjectURL(new Blob([a],{type:\"text/javascript\"}))}else e=i;else t=i}return n(0,(function(t){function e(t,e){return t(e={exports:{}},e.exports),e.exports}var r=n;function n(t,e,r,n){this.cx=3*t,this.bx=3*(r-t)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*e,this.by=3*(n-e)-this.cy,this.ay=1-this.cy-this.by,this.p1x=t,this.p1y=n,this.p2x=r,this.p2y=n}n.prototype.sampleCurveX=function(t){return((this.ax*t+this.bx)*t+this.cx)*t},n.prototype.sampleCurveY=function(t){return((this.ay*t+this.by)*t+this.cy)*t},n.prototype.sampleCurveDerivativeX=function(t){return(3*this.ax*t+2*this.bx)*t+this.cx},n.prototype.solveCurveX=function(t,e){var r,n,i,a,o;for(void 0===e&&(e=1e-6),i=t,o=0;o<8;o++){if(a=this.sampleCurveX(i)-t,Math.abs(a)<e)return i;var s=this.sampleCurveDerivativeX(i);if(Math.abs(s)<1e-6)break;i-=a/s}if((i=t)<(r=0))return r;if(i>(n=1))return n;for(;r<n;){if(a=this.sampleCurveX(i),Math.abs(a-t)<e)return i;t>a?r=i:n=i,i=.5*(n-r)+r}return i},n.prototype.solve=function(t,e){return this.sampleCurveY(this.solveCurveX(t,e))};var i=a;function a(t,e){this.x=t,this.y=e}function o(t,e,n,i){var a=new r(t,e,n,i);return function(t){return 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r={transparent:[0,0,0,0],aliceblue:[240,248,255,1],antiquewhite:[250,235,215,1],aqua:[0,255,255,1],aquamarine:[127,255,212,1],azure:[240,255,255,1],beige:[245,245,220,1],bisque:[255,228,196,1],black:[0,0,0,1],blanchedalmond:[255,235,205,1],blue:[0,0,255,1],blueviolet:[138,43,226,1],brown:[165,42,42,1],burlywood:[222,184,135,1],cadetblue:[95,158,160,1],chartreuse:[127,255,0,1],chocolate:[210,105,30,1],coral:[255,127,80,1],cornflowerblue:[100,149,237,1],cornsilk:[255,248,220,1],crimson:[220,20,60,1],cyan:[0,255,255,1],darkblue:[0,0,139,1],darkcyan:[0,139,139,1],darkgoldenrod:[184,134,11,1],darkgray:[169,169,169,1],darkgreen:[0,100,0,1],darkgrey:[169,169,169,1],darkkhaki:[189,183,107,1],darkmagenta:[139,0,139,1],darkolivegreen:[85,107,47,1],darkorange:[255,140,0,1],darkorchid:[153,50,204,1],darkred:[139,0,0,1],darksalmon:[233,150,122,1],darkseagreen:[143,188,143,1],darkslateblue:[72,61,139,1],darkslategray:[47,79,79,1],darkslategrey:[47,79,79,1],darkturquoise:[0,206,209,1],darkviolet:[148,0,211,1],deeppink:[255,20,147,1],deepskyblue:[0,191,255,1],dimgray:[105,105,105,1],dimgrey:[105,105,105,1],dodgerblue:[30,144,255,1],firebrick:[178,34,34,1],floralwhite:[255,250,240,1],forestgreen:[34,139,34,1],fuchsia:[255,0,255,1],gainsboro:[220,220,220,1],ghostwhite:[248,248,255,1],gold:[255,215,0,1],goldenrod:[218,165,32,1],gray:[128,128,128,1],green:[0,128,0,1],greenyellow:[173,255,47,1],grey:[128,128,128,1],honeydew:[240,255,240,1],hotpink:[255,105,180,1],indianred:[205,92,92,1],indigo:[75,0,130,1],ivory:[255,255,240,1],khaki:[240,230,140,1],lavender:[230,230,250,1],lavenderblush:[255,240,245,1],lawngreen:[124,252,0,1],lemonchiffon:[255,250,205,1],lightblue:[173,216,230,1],lightcoral:[240,128,128,1],lightcyan:[224,255,255,1],lightgoldenrodyellow:[250,250,210,1],lightgray:[211,211,211,1],lightgreen:[144,238,144,1],lightgrey:[211,211,211,1],lightpink:[255,182,193,1],lightsalmon:[255,160,122,1],lightseagreen:[32,178,170,1],lightskyblue:[135,206,250,1],lightslategray:[119,136,153,1],lightslategrey:[119,136,153,1],lightsteelblue:[176,196,222,1],lightyellow:[255,255,224,1],lime:[0,255,0,1],limegreen:[50,205,50,1],linen:[250,240,230,1],magenta:[255,0,255,1],maroon:[128,0,0,1],mediumaquamarine:[102,205,170,1],mediumblue:[0,0,205,1],mediumorchid:[186,85,211,1],mediumpurple:[147,112,219,1],mediumseagreen:[60,179,113,1],mediumslateblue:[123,104,238,1],mediumspringgreen:[0,250,154,1],mediumturquoise:[72,209,204,1],mediumvioletred:[199,21,133,1],midnightblue:[25,25,112,1],mintcream:[245,255,250,1],mistyrose:[255,228,225,1],moccasin:[255,228,181,1],navajowhite:[255,222,173,1],navy:[0,0,128,1],oldlace:[253,245,230,1],olive:[128,128,0,1],olivedrab:[107,142,35,1],orange:[255,165,0,1],orangered:[255,69,0,1],orchid:[218,112,214,1],palegoldenrod:[238,232,170,1],palegreen:[152,251,152,1],paleturquoise:[175,238,238,1],palevioletred:[219,112,147,1],papayawhip:[255,239,213,1],peachpuff:[255,218,185,1],peru:[205,133,63,1],pink:[255,192,203,1],plum:[221,160,221,1],powderblue:[176,224,230,1],purple:[128,0,128,1],rebeccapurple:[102,51,153,1],red:[255,0,0,1],rosybrown:[188,143,143,1],royalblue:[65,105,225,1],saddlebrown:[139,69,19,1],salmon:[250,128,114,1],sandybrown:[244,164,96,1],seagreen:[46,139,87,1],seashell:[255,245,238,1],sienna:[160,82,45,1],silver:[192,192,192,1],skyblue:[135,206,235,1],slateblue:[106,90,205,1],slategray:[112,128,144,1],slategrey:[112,128,144,1],snow:[255,250,250,1],springgreen:[0,255,127,1],steelblue:[70,130,180,1],tan:[210,180,140,1],teal:[0,128,128,1],thistle:[216,191,216,1],tomato:[255,99,71,1],turquoise:[64,224,208,1],violet:[238,130,238,1],wheat:[245,222,179,1],white:[255,255,255,1],whitesmoke:[245,245,245,1],yellow:[255,255,0,1],yellowgreen:[154,205,50,1]};function n(t){return(t=Math.round(t))<0?0:t>255?255:t}function i(t){return n(\"%\"===t[t.length-1]?parseFloat(t)/100*255:parseInt(t))}function a(t){return(e=\"%\"===t[t.length-1]?parseFloat(t)/100:parseFloat(t))<0?0:e>1?1:e;var e}function o(t,e,r){return r<0?r+=1:r>1&&(r-=1),6*r<1?t+(e-t)*r*6:2*r<1?e:3*r<2?t+(e-t)*(2/3-r)*6:t}try{e.parseCSSColor=function(t){var e,s=t.replace(/ /g,\"\").toLowerCase();if(s in r)return r[s].slice();if(\"#\"===s[0])return 4===s.length?(e=parseInt(s.substr(1),16))>=0&&e<=4095?[(3840&e)>>4|(3840&e)>>8,240&e|(240&e)>>4,15&e|(15&e)<<4,1]:null:7===s.length&&(e=parseInt(s.substr(1),16))>=0&&e<=16777215?[(16711680&e)>>16,(65280&e)>>8,255&e,1]:null;var l=s.indexOf(\"(\"),c=s.indexOf(\")\");if(-1!==l&&c+1===s.length){var u=s.substr(0,l),f=s.substr(l+1,c-(l+1)).split(\",\"),h=1;switch(u){case\"rgba\":if(4!==f.length)return null;h=a(f.pop());case\"rgb\":return 3!==f.length?null:[i(f[0]),i(f[1]),i(f[2]),h];case\"hsla\":if(4!==f.length)return 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Qt=function(t,e,r){this.sensitivity=t?e?\"variant\":\"case\":e?\"accent\":\"base\",this.locale=r,this.collator=new Intl.Collator(this.locale?this.locale:[],{sensitivity:this.sensitivity,usage:\"search\"})};Qt.prototype.compare=function(t,e){return this.collator.compare(t,e)},Qt.prototype.resolvedLocale=function(){return new Intl.Collator(this.locale?this.locale:[]).resolvedOptions().locale};var $t=function(t,e,r,n,i){this.text=t,this.image=e,this.scale=r,this.fontStack=n,this.textColor=i},te=function(t){this.sections=t};te.fromString=function(t){return new te([new $t(t,null,null,null,null)])},te.prototype.isEmpty=function(){return 0===this.sections.length||!this.sections.some((function(t){return 0!==t.text.length||t.image&&0!==t.image.name.length}))},te.factory=function(t){return t instanceof te?t:te.fromString(t)},te.prototype.toString=function(){return 0===this.sections.length?\"\":this.sections.map((function(t){return t.text})).join(\"\")},te.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];if(n.image)t.push([\"image\",n.image.name]);else{t.push(n.text);var i={};n.fontStack&&(i[\"text-font\"]=[\"literal\",n.fontStack.split(\",\")]),n.scale&&(i[\"font-scale\"]=n.scale),n.textColor&&(i[\"text-color\"]=[\"rgba\"].concat(n.textColor.toArray())),t.push(i)}}return t};var ee=function(t){this.name=t.name,this.available=t.available};function re(t,e,r,n){return\"number\"==typeof t&&t>=0&&t<=255&&\"number\"==typeof e&&e>=0&&e<=255&&\"number\"==typeof r&&r>=0&&r<=255?void 0===n||\"number\"==typeof n&&n>=0&&n<=1?null:\"Invalid rgba value [\"+[t,e,r,n].join(\", \")+\"]: 'a' must be between 0 and 1.\":\"Invalid rgba value [\"+(\"number\"==typeof n?[t,e,r,n]:[t,e,r]).join(\", \")+\"]: 'r', 'g', and 'b' must be between 0 and 255.\"}function ne(t){if(null===t)return!0;if(\"string\"==typeof t)return!0;if(\"boolean\"==typeof t)return!0;if(\"number\"==typeof t)return!0;if(t instanceof Kt)return!0;if(t instanceof Qt)return!0;if(t instanceof te)return!0;if(t instanceof ee)return!0;if(Array.isArray(t)){for(var e=0,r=t;e<r.length;e+=1)if(!ne(r[e]))return!1;return!0}if(\"object\"==typeof t){for(var n in t)if(!ne(t[n]))return!1;return!0}return!1}function ie(t){if(null===t)return Ot;if(\"string\"==typeof t)return Rt;if(\"boolean\"==typeof t)return Ft;if(\"number\"==typeof t)return Dt;if(t instanceof Kt)return Bt;if(t instanceof Qt)return Ut;if(t instanceof te)return Vt;if(t instanceof ee)return qt;if(Array.isArray(t)){for(var e,r=t.length,n=0,i=t;n<i.length;n+=1){var a=ie(i[n]);if(e){if(e===a)continue;e=jt;break}e=a}return Ht(e||jt,r)}return Nt}function ae(t){var e=typeof t;return null===t?\"\":\"string\"===e||\"number\"===e||\"boolean\"===e?String(t):t instanceof Kt||t instanceof te||t instanceof ee?t.toString():JSON.stringify(t)}ee.prototype.toString=function(){return this.name},ee.fromString=function(t){return t?new ee({name:t,available:!1}):null},ee.prototype.serialize=function(){return[\"image\",this.name]};var oe=function(t,e){this.type=t,this.value=e};oe.parse=function(t,e){if(2!==t.length)return e.error(\"'literal' expression requires exactly one argument, but found \"+(t.length-1)+\" instead.\");if(!ne(t[1]))return e.error(\"invalid value\");var r=t[1],n=ie(r),i=e.expectedType;return\"array\"!==n.kind||0!==n.N||!i||\"array\"!==i.kind||\"number\"==typeof i.N&&0!==i.N||(n=i),new oe(n,r)},oe.prototype.evaluate=function(){return this.value},oe.prototype.eachChild=function(){},oe.prototype.outputDefined=function(){return!0},oe.prototype.serialize=function(){return\"array\"===this.type.kind||\"object\"===this.type.kind?[\"literal\",this.value]:this.value instanceof Kt?[\"rgba\"].concat(this.value.toArray()):this.value instanceof te?this.value.serialize():this.value};var se=function(t){this.name=\"ExpressionEvaluationError\",this.message=t};se.prototype.toJSON=function(){return this.message};var le={string:Rt,number:Dt,boolean:Ft,object:Nt},ce=function(t,e){this.type=t,this.args=e};ce.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r,n=1,i=t[0];if(\"array\"===i){var a,o;if(t.length>2){var s=t[1];if(\"string\"!=typeof s||!(s in le)||\"object\"===s)return e.error('The item type argument of \"array\" must be one of string, number, boolean',1);a=le[s],n++}else a=jt;if(t.length>3){if(null!==t[2]&&(\"number\"!=typeof t[2]||t[2]<0||t[2]!==Math.floor(t[2])))return e.error('The length argument to \"array\" must be a positive integer literal',2);o=t[2],n++}r=Ht(a,o)}else r=le[i];for(var l=[];n<t.length;n++){var c=e.parse(t[n],n,jt);if(!c)return null;l.push(c)}return new ce(r,l)},ce.prototype.evaluate=function(t){for(var e=0;e<this.args.length;e++){var r=this.args[e].evaluate(t);if(!Wt(this.type,ie(r)))return r;if(e===this.args.length-1)throw new se(\"Expected value to be of type \"+Gt(this.type)+\", but found \"+Gt(ie(r))+\" instead.\")}return null},ce.prototype.eachChild=function(t){this.args.forEach(t)},ce.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},ce.prototype.serialize=function(){var t=this.type,e=[t.kind];if(\"array\"===t.kind){var r=t.itemType;if(\"string\"===r.kind||\"number\"===r.kind||\"boolean\"===r.kind){e.push(r.kind);var n=t.N;(\"number\"==typeof n||this.args.length>1)&&e.push(n)}}return e.concat(this.args.map((function(t){return t.serialize()})))};var ue=function(t){this.type=Vt,this.sections=t};ue.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[1];if(!Array.isArray(r)&&\"object\"==typeof r)return e.error(\"First argument must be an image or text section.\");for(var n=[],i=!1,a=1;a<=t.length-1;++a){var o=t[a];if(i&&\"object\"==typeof o&&!Array.isArray(o)){i=!1;var s=null;if(o[\"font-scale\"]&&!(s=e.parse(o[\"font-scale\"],1,Dt)))return null;var l=null;if(o[\"text-font\"]&&!(l=e.parse(o[\"text-font\"],1,Ht(Rt))))return null;var c=null;if(o[\"text-color\"]&&!(c=e.parse(o[\"text-color\"],1,Bt)))return null;var u=n[n.length-1];u.scale=s,u.font=l,u.textColor=c}else{var f=e.parse(t[a],1,jt);if(!f)return null;var h=f.type.kind;if(\"string\"!==h&&\"value\"!==h&&\"null\"!==h&&\"resolvedImage\"!==h)return e.error(\"Formatted text type must be 'string', 'value', 'image' or 'null'.\");i=!0,n.push({content:f,scale:null,font:null,textColor:null})}}return new ue(n)},ue.prototype.evaluate=function(t){return new te(this.sections.map((function(e){var r=e.content.evaluate(t);return ie(r)===qt?new $t(\"\",r,null,null,null):new $t(ae(r),null,e.scale?e.scale.evaluate(t):null,e.font?e.font.evaluate(t).join(\",\"):null,e.textColor?e.textColor.evaluate(t):null)})))},ue.prototype.eachChild=function(t){for(var e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t(n.content),n.scale&&t(n.scale),n.font&&t(n.font),n.textColor&&t(n.textColor)}},ue.prototype.outputDefined=function(){return!1},ue.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t.push(n.content.serialize());var i={};n.scale&&(i[\"font-scale\"]=n.scale.serialize()),n.font&&(i[\"text-font\"]=n.font.serialize()),n.textColor&&(i[\"text-color\"]=n.textColor.serialize()),t.push(i)}return t};var fe=function(t){this.type=qt,this.input=t};fe.parse=function(t,e){if(2!==t.length)return e.error(\"Expected two arguments.\");var r=e.parse(t[1],1,Rt);return r?new fe(r):e.error(\"No image name provided.\")},fe.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=ee.fromString(e);return r&&t.availableImages&&(r.available=t.availableImages.indexOf(e)>-1),r},fe.prototype.eachChild=function(t){t(this.input)},fe.prototype.outputDefined=function(){return!1},fe.prototype.serialize=function(){return[\"image\",this.input.serialize()]};var he={\"to-boolean\":Ft,\"to-color\":Bt,\"to-number\":Dt,\"to-string\":Rt},pe=function(t,e){this.type=t,this.args=e};pe.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[0];if((\"to-boolean\"===r||\"to-string\"===r)&&2!==t.length)return e.error(\"Expected one argument.\");for(var n=he[r],i=[],a=1;a<t.length;a++){var o=e.parse(t[a],a,jt);if(!o)return null;i.push(o)}return new pe(n,i)},pe.prototype.evaluate=function(t){if(\"boolean\"===this.type.kind)return Boolean(this.args[0].evaluate(t));if(\"color\"===this.type.kind){for(var e,r,n=0,i=this.args;n<i.length;n+=1){if(r=null,(e=i[n].evaluate(t))instanceof Kt)return e;if(\"string\"==typeof e){var a=t.parseColor(e);if(a)return a}else if(Array.isArray(e)&&!(r=e.length<3||e.length>4?\"Invalid rbga value \"+JSON.stringify(e)+\": expected an array containing either three or four numeric values.\":re(e[0],e[1],e[2],e[3])))return new Kt(e[0]/255,e[1]/255,e[2]/255,e[3])}throw new se(r||\"Could not parse color from value '\"+(\"string\"==typeof e?e:String(JSON.stringify(e)))+\"'\")}if(\"number\"===this.type.kind){for(var o=null,s=0,l=this.args;s<l.length;s+=1){if(null===(o=l[s].evaluate(t)))return 0;var c=Number(o);if(!isNaN(c))return c}throw new se(\"Could not convert \"+JSON.stringify(o)+\" to number.\")}return\"formatted\"===this.type.kind?te.fromString(ae(this.args[0].evaluate(t))):\"resolvedImage\"===this.type.kind?ee.fromString(ae(this.args[0].evaluate(t))):ae(this.args[0].evaluate(t))},pe.prototype.eachChild=function(t){this.args.forEach(t)},pe.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},pe.prototype.serialize=function(){if(\"formatted\"===this.type.kind)return new ue([{content:this.args[0],scale:null,font:null,textColor:null}]).serialize();if(\"resolvedImage\"===this.type.kind)return new fe(this.args[0]).serialize();var t=[\"to-\"+this.type.kind];return this.eachChild((function(e){t.push(e.serialize())})),t};var de=[\"Unknown\",\"Point\",\"LineString\",\"Polygon\"],ge=function(){this.globals=null,this.feature=null,this.featureState=null,this.formattedSection=null,this._parseColorCache={},this.availableImages=null,this.canonical=null};ge.prototype.id=function(){return this.feature&&\"id\"in this.feature?this.feature.id:null},ge.prototype.geometryType=function(){return this.feature?\"number\"==typeof this.feature.type?de[this.feature.type]:this.feature.type:null},ge.prototype.geometry=function(){return this.feature&&\"geometry\"in this.feature?this.feature.geometry:null},ge.prototype.canonicalID=function(){return this.canonical},ge.prototype.properties=function(){return this.feature&&this.feature.properties||{}},ge.prototype.parseColor=function(t){var e=this._parseColorCache[t];return e||(e=this._parseColorCache[t]=Kt.parse(t)),e};var me=function(t,e,r,n){this.name=t,this.type=e,this._evaluate=r,this.args=n};me.prototype.evaluate=function(t){return 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a=Ne(e,n),o=rr.interpolationFactor(this.interpolation,n,e[a],e[a+1]),s=r[a].evaluate(t),l=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ve[this.type.kind.toLowerCase()](s,l,o):\"interpolate-hcl\"===this.operator?tr.reverse(tr.interpolate(tr.forward(s),tr.forward(l),o)):$e.reverse($e.interpolate($e.forward(s),$e.forward(l),o))},rr.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1)t(r[e])},rr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},rr.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var ir=function(t,e){this.type=t,this.args=e};ir.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=e.parse(o[a],1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!s)return null;r=r||s.type,i.push(s)}var l=n&&i.some((function(t){return Wt(n,t.type)}));return new ir(l?jt:r,i)},ir.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length&&(n++,(r=a[i].evaluate(t))&&r instanceof ee&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null===r);i+=1);return r},ir.prototype.eachChild=function(t){this.args.forEach(t)},ir.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},ir.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var ar=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};ar.prototype.evaluate=function(t){return this.result.evaluate(t)},ar.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1)t(r[e][1]);t(this.result)},ar.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new ar(r,o):null},ar.prototype.outputDefined=function(){return this.result.outputDefined()},ar.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e];t.push(n[0],n[1].serialize())}return t.push(this.result.serialize()),t};var or=function(t,e,r){this.type=t,this.index=e,this.input=r};or.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Dt),n=e.parse(t[2],2,Ht(e.expectedType||jt));return r&&n?new or(n.type.itemType,r,n):null},or.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new se(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new se(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new se(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},or.prototype.eachChild=function(t){t(this.index),t(this.input)},or.prototype.outputDefined=function(){return!1},or.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var sr=function(t,e){this.type=Ft,this.needle=t,this.haystack=e};sr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,jt),n=e.parse(t[2],2,jt);return r&&n?Xt(r.type,[Ft,Rt,Dt,Ot,jt])?new sr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(r.type)+\" instead\"):null},sr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Zt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new se(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(ie(e))+\" instead.\");if(!Zt(r,[\"string\",\"array\"]))throw new se(\"Expected second argument to be of type array or string, but found \"+Gt(ie(r))+\" instead.\");return r.indexOf(e)>=0},sr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},sr.prototype.outputDefined=function(){return!0},sr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var lr=function(t,e,r){this.type=Dt,this.needle=t,this.haystack=e,this.fromIndex=r};lr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,jt),n=e.parse(t[2],2,jt);if(!r||!n)return null;if(!Xt(r.type,[Ft,Rt,Dt,Ot,jt]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Dt);return i?new lr(r,n,i):null}return new lr(r,n)},lr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Zt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new se(\"Expected first argument to be of type boolean, string, number or null, but found \"+Gt(ie(e))+\" instead.\");if(!Zt(r,[\"string\",\"array\"]))throw new se(\"Expected second argument to be of type array or string, but found \"+Gt(ie(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},lr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},lr.prototype.outputDefined=function(){return!1},lr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var cr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};cr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of arguments.\");var r,n;e.expectedType&&\"value\"!==e.expectedType.kind&&(n=e.expectedType);for(var i={},a=[],o=2;o<t.length-1;o+=2){var s=t[o],l=t[o+1];Array.isArray(s)||(s=[s]);var 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e=this.input.evaluate(t);return(ie(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},cr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},cr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},cr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];e.push(1===h.length?s(h[0]):h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var ur=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};ur.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 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fr=function(t,e,r,n){this.type=t,this.input=e,this.beginIndex=r,this.endIndex=n};function hr(t,e){return\"==\"===t||\"!=\"===t?\"boolean\"===e.kind||\"string\"===e.kind||\"number\"===e.kind||\"null\"===e.kind||\"value\"===e.kind:\"string\"===e.kind||\"number\"===e.kind||\"value\"===e.kind}function pr(t,e,r,n){return 0===n.compare(e,r)}function dr(t,e,r){var n=\"==\"!==t&&\"!=\"!==t;return function(){function i(t,e,r){this.type=Ft,this.lhs=t,this.rhs=e,this.collator=r,this.hasUntypedArgument=\"value\"===t.type.kind||\"value\"===e.type.kind}return i.parse=function(t,e){if(3!==t.length&&4!==t.length)return e.error(\"Expected two or three arguments.\");var r=t[0],a=e.parse(t[1],1,jt);if(!a)return null;if(!hr(r,a.type))return e.concat(1).error('\"'+r+\"\\\" comparisons are not supported for type '\"+Gt(a.type)+\"'.\");var o=e.parse(t[2],2,jt);if(!o)return null;if(!hr(r,o.type))return e.concat(2).error('\"'+r+\"\\\" comparisons are not supported for type 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0,o={}),e=e.concat(Xr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:Jr,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Vr(It(a[1]))?e.concat([new St(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(bn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=zr(t.value),l=Lt(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new St(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new St(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return Lr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new St(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new St(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new St(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new St(t.key,c,\"integer expected, found \"+l)]}}function Qr(t){var e=(\"property\"===t.expressionContext?Yr:qr)(It(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new St(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new St(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". 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en={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function rn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};tn(t)||(t=an(t));var e=qr(t,en);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function nn(t,e){return t<e?-1:t>e?1:0}function an(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?on(t[1],t[2],\"==\"):\"!=\"===r?cn(on(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?on(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(an))):\"all\"===r?[\"all\"].concat(t.slice(1).map(an)):\"none\"===r?[\"all\"].concat(t.slice(1).map(an).map(cn)):\"in\"===r?sn(t[1],t.slice(2)):\"!in\"===r?cn(sn(t[1],t.slice(2))):\"has\"===r?ln(t[1]):\"!has\"===r?cn(ln(t[1])):\"within\"!==r||t}function on(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function sn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(nn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function ln(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function cn(t){return[\"!\",t]}function un(t){return tn(It(t.value))?Qr(Ct({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==zr(r))return[new St(n,r,\"array expected, \"+zr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new St(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat($r({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Lt(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Lt(r[1])&&o.push(new St(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new St(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=zr(r[1]))&&o.push(new St(n+\"[1]\",r[1],\"string expected, \"+i+\" found\"));for(var s=2;s<r.length;s++)i=zr(r[s]),\"$type\"===Lt(r[1])?o=o.concat($r({key:n+\"[\"+s+\"]\",value:r[s],valueSpec:a.geometry_type,style:e.style,styleSpec:e.styleSpec})):\"string\"!==i&&\"number\"!==i&&\"boolean\"!==i&&o.push(new St(n+\"[\"+s+\"]\",r[s],\"string, number, or boolean expected, \"+i+\" found\"));break;case\"any\":case\"all\":case\"none\":for(var l=1;l<r.length;l++)o=o.concat(t({key:n+\"[\"+l+\"]\",value:r[l],style:e.style,styleSpec:e.styleSpec}));break;case\"has\":case\"!has\":i=zr(r[1]),2!==r.length?o.push(new St(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"string\"!==i&&o.push(new St(n+\"[1]\",r[1],\"string expected, \"+i+\" found\"));break;case\"within\":i=zr(r[1]),2!==r.length?o.push(new St(n,r,'filter array for \"'+r[0]+'\" operator must have 2 elements')):\"object\"!==i&&o.push(new St(n+\"[1]\",r[1],\"object expected, \"+i+\" found\"))}return o}(t)}function fn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return bn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new St(r,a,'unknown property \"'+o+'\"')];if(\"string\"===zr(a)&&Lr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new St(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new St(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Or(It(a))&&\"identity\"===Lt(a.type)&&f.push(new St(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Lt(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new St(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new St(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new St(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new St(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new St(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new St(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new St(n,r,'missing required property \"source\"'));return e=e.concat(Xr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(){return[]},type:function(){return bn({key:n+\".type\",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:\"type\"})},filter:un,layout:function(t){return Xr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return pn(Ct({layerType:s},t))}}})},paint:function(t){return Xr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return hn(Ct({layerType:s},t))}}})}}}))}function gn(t){var e=t.value,r=t.key,n=zr(e);return\"string\"!==n?[new St(r,e,\"string expected, \"+n+\" found\")]:[]}var mn={promoteId:function(t){var e=t.key,r=t.value;if(\"string\"===zr(r))return gn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,gn({key:e+\".\"+i,value:r[i]}));return n}};function vn(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new St(r,e,'\"type\" is required')];var a,o=Lt(e.type);switch(o){case\"vector\":case\"raster\":case\"raster-dem\":return Xr({key:r,value:e,valueSpec:n[\"source_\"+o.replace(\"-\",\"_\")],style:t.style,styleSpec:n,objectElementValidators:mn});case\"geojson\":if(a=Xr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:mn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=\"string\"==typeof c?[c,[\"accumulated\"],[\"get\",s]]:c;a.push.apply(a,Qr({key:r+\".\"+s+\".map\",value:l[1],expressionContext:\"cluster-map\"})),a.push.apply(a,Qr({key:r+\".\"+s+\".reduce\",value:u,expressionContext:\"cluster-reduce\"}))}return a;case\"video\":return Xr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case\"image\":return Xr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case\"canvas\":return[new St(r,null,\"Please use runtime APIs to add canvas sources, rather than including them in stylesheets.\",\"source.canvas\")];default:return $r({key:r+\".type\",value:e.type,valueSpec:{values:[\"vector\",\"raster\",\"raster-dem\",\"geojson\",\"video\",\"image\"]},style:i,styleSpec:n})}}function yn(t){var e=t.value,r=t.styleSpec,n=r.light,i=t.style,a=[],o=zr(e);if(void 0===e)return a;if(\"object\"!==o)return a.concat([new St(\"light\",e,\"object expected, \"+o+\" found\")]);for(var s in e){var l=s.match(/^(.*)-transition$/);a=a.concat(l&&n[l[1]]&&n[l[1]].transition?bn({key:s,value:e[s],valueSpec:r.transition,style:i,styleSpec:r}):n[s]?bn({key:s,value:e[s],valueSpec:n[s],style:i,styleSpec:r}):[new St(s,e[s],'unknown property \"'+s+'\"')])}return a}var xn={\"*\":function(){return[]},array:Zr,boolean:function(t){var e=t.value,r=t.key,n=zr(e);return\"boolean\"!==n?[new St(r,e,\"boolean expected, \"+n+\" found\")]:[]},number:Jr,color:function(t){var e=t.key,r=t.value,n=zr(r);return\"string\"!==n?[new St(e,r,\"color expected, \"+n+\" 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r=r.concat(bn({key:\"\",value:t,valueSpec:e.$root,styleSpec:e,style:t,objectElementValidators:{glyphs:_n,\"*\":function(){return[]}}})),t.constants&&(r=r.concat(Et({key:\"constants\",value:t.constants,style:t,styleSpec:e}))),Tn(r)}function Tn(t){return[].concat(t).sort((function(t,e){return t.line-e.line}))}function kn(t){return function(){for(var e=[],r=arguments.length;r--;)e[r]=arguments[r];return Tn(t.apply(this,e))}}wn.source=kn(vn),wn.light=kn(yn),wn.layer=kn(dn),wn.filter=kn(un),wn.paintProperty=kn(hn),wn.layoutProperty=kn(pn);var Mn=wn,An=Mn.light,Sn=Mn.paintProperty,En=Mn.layoutProperty;function Cn(t,e){var r=!1;if(e&&e.length)for(var n=0,i=e;n<i.length;n+=1)t.fire(new kt(new Error(i[n].message))),r=!0;return r}var Ln=In;function In(t,e,r){var n=this.cells=[];if(t instanceof ArrayBuffer){this.arrayBuffer=t;var i=new Int32Array(this.arrayBuffer);t=i[0],this.d=(e=i[1])+2*(r=i[2]);for(var a=0;a<this.d*this.d;a++){var o=i[3+a],s=i[3+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[3+n.length+1];this.keys=i.subarray(i[3+n.length],l),this.bboxes=i.subarray(l),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var c=0;c<this.d*this.d;c++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var u=r/e*t;this.min=-u,this.max=t+u}In.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},In.prototype._insertReadonly=function(){throw\"Cannot insert into a GridIndex created from an ArrayBuffer.\"},In.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},In.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},In.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var c=this.keys,u=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(u[p+0],u[p+1],u[p+2],u[p+3]):t<=u[p+2]&&e<=u[p+3]&&r>=u[p+0]&&n>=u[p+1])?(o[h]=!0,a.push(c[h])):o[h]=!1}}},In.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},In.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},In.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},In.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=3+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[3+o]=a,i.set(s,a),a+=s.length}return i[3+t.length]=a,i.set(this.keys,a),i[3+t.length+1]=a+=this.keys.length,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var Pn=self.ImageData,zn=self.ImageBitmap,On={};function Dn(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),On[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var Rn in Dn(\"Object\",Object),Ln.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},Ln.deserialize=function(t){return new Ln(t.buffer)},Dn(\"Grid\",Ln),Dn(\"Color\",Kt),Dn(\"Error\",Error),Dn(\"ResolvedImage\",ee),Dn(\"StylePropertyFunction\",Wr),Dn(\"StyleExpression\",Ur,{omit:[\"_evaluator\"]}),Dn(\"ZoomDependentExpression\",Gr),Dn(\"ZoomConstantExpression\",Hr),Dn(\"CompoundExpression\",me,{omit:[\"_evaluate\"]}),Tr)Tr[Rn]._classRegistryKey||Dn(\"Expression_\"+Rn,Tr[Rn]);function Fn(t){return t&&\"undefined\"!=typeof ArrayBuffer&&(t instanceof ArrayBuffer||t.constructor&&\"ArrayBuffer\"===t.constructor.name)}function Bn(t){return zn&&t instanceof zn}function Nn(t,e){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp)return t;if(Fn(t)||Bn(t))return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof Pn)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1)n.push(Nn(a[i],e));return n}if(\"object\"==typeof t){var o=t.constructor,s=o._classRegistryKey;if(!s)throw new Error(\"can't serialize object of unregistered class\");var l=o.serialize?o.serialize(t,e):{};if(!o.serialize){for(var c in t)if(t.hasOwnProperty(c)&&!(On[s].omit.indexOf(c)>=0)){var u=t[c];l[c]=On[s].shallow.indexOf(c)>=0?u:Nn(u,e)}t instanceof Error&&(l.message=t.message)}if(l.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==s&&(l.$name=s),l}throw new Error(\"can't serialize object of type \"+typeof t)}function jn(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||Fn(t)||Bn(t)||ArrayBuffer.isView(t)||t instanceof Pn)return t;if(Array.isArray(t))return t.map(jn);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=On[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class \"+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i<a.length;i+=1){var o=a[i];if(\"$name\"!==o){var s=t[o];n[o]=On[e].shallow.indexOf(o)>=0?s:jn(s)}}return n}throw new Error(\"can't deserialize object of type \"+typeof t)}var Un=function(){this.first=!0};Un.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))};var Vn={\"Latin-1 Supplement\":function(t){return t>=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic Supplement\":function(t){return t>=1872&&t<=1919},\"Arabic Extended-A\":function(t){return t>=2208&&t<=2303},\"Hangul Jamo\":function(t){return t>=4352&&t<=4607},\"Unified Canadian Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram Symbols\":function(t){return t>=19904&&t<=19967},\"CJK Unified Ideographs\":function(t){return t>=19968&&t<=40959},\"Yi Syllables\":function(t){return t>=40960&&t<=42127},\"Yi Radicals\":function(t){return t>=42128&&t<=42191},\"Hangul Jamo Extended-A\":function(t){return t>=43360&&t<=43391},\"Hangul Syllables\":function(t){return t>=44032&&t<=55215},\"Hangul Jamo Extended-B\":function(t){return t>=55216&&t<=55295},\"Private Use Area\":function(t){return t>=57344&&t<=63743},\"CJK Compatibility Ideographs\":function(t){return t>=63744&&t<=64255},\"Arabic Presentation Forms-A\":function(t){return t>=64336&&t<=65023},\"Vertical Forms\":function(t){return t>=65040&&t<=65055},\"CJK Compatibility Forms\":function(t){return t>=65072&&t<=65103},\"Small Form Variants\":function(t){return t>=65104&&t<=65135},\"Arabic Presentation Forms-B\":function(t){return t>=65136&&t<=65279},\"Halfwidth and Fullwidth Forms\":function(t){return t>=65280&&t<=65519}};function qn(t){for(var e=0,r=t;e<r.length;e+=1)if(Hn(r[e].charCodeAt(0)))return!0;return!1}function Hn(t){return!(746!==t&&747!==t&&(t<4352||!(Vn[\"Bopomofo Extended\"](t)||Vn.Bopomofo(t)||Vn[\"CJK Compatibility 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0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},ii.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var ai=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Or(t))return new Wr(t,e);if(Vr(t)){var r=Yr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=Kt.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};ai.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},ai.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var oi=function(t){this.property=t,this.value=new ai(t,void 0)};oi.prototype.transitioned=function(t,e){return new li(this.property,this.value,e,u({},t.transition,this.transition),t.now)},oi.prototype.untransitioned=function(){return new li(this.property,this.value,null,{},0)};var si=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};si.prototype.getValue=function(t){return x(this._values[t].value.value)},si.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new oi(this._values[t].property)),this._values[t].value=new ai(this._values[t].property,null===e?void 0:x(e))},si.prototype.getTransition=function(t){return x(this._values[t].transition)},si.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new oi(this._values[t].property)),this._values[t].transition=x(e)||void 0},si.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},si.prototype.transitioned=function(t,e){for(var r=new ci(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},si.prototype.untransitioned=function(){for(var t=new ci(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var li=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};li.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var ci=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};ci.prototype.possiblyEvaluate=function(t,e,r){for(var n=new hi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},ci.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1)if(this._values[e[t]].prior)return!0;return!1};var ui=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};ui.prototype.getValue=function(t){return x(this._values[t].value)},ui.prototype.setValue=function(t,e){this._values[t]=new ai(this._values[t].property,null===e?void 0:x(e))},ui.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},ui.prototype.possiblyEvaluate=function(t,e,r){for(var n=new hi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var fi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};fi.prototype.isConstant=function(){return\"constant\"===this.value.kind},fi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},fi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var hi=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};hi.prototype.get=function(t){return this._values[t]};var pi=function(t){this.specification=t};pi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},pi.prototype.interpolate=function(t,e,r){var n=Ve[this.specification.type];return n?n(t,e,r):t};var di=function(t,e){this.specification=t,this.overrides=e};di.prototype.possiblyEvaluate=function(t,e,r,n){return new fi(this,\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)}:t.expression,e)},di.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new fi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ve[this.specification.type];return n?new fi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},di.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new fi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new fi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new fi(this,{kind:\"constant\",value:s},e)}return new fi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(di),mi=function(t){this.specification=t};mi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new ii(Math.floor(e.zoom-1),e)),t.expression.evaluate(new ii(Math.floor(e.zoom),e)),t.expression.evaluate(new ii(Math.floor(e.zoom+1),e)),e)}},mi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},mi.prototype.interpolate=function(t){return t};var vi=function(t){this.specification=t};vi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},vi.prototype.interpolate=function(){return!1};var yi=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new ai(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new oi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Dn(\"DataDrivenProperty\",di),Dn(\"DataConstantProperty\",pi),Dn(\"CrossFadedDataDrivenProperty\",gi),Dn(\"CrossFadedProperty\",mi),Dn(\"ColorRampProperty\",vi);var xi=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(this.metadata=(e=e).metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new ui(r.layout)),r.paint)){for(var n in this._transitionablePaint=new si(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new hi(r.paint)}}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){void 0===r&&(r={}),null!=e&&this._validate(En,\"layers.\"+this.id+\".layout.\"+t,t,e,r)||(\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e)},e.prototype.getPaintProperty=function(t){return m(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e&&this._validate(Sn,\"layers.\"+this.id+\".paint.\"+t,t,e,r))return!1;if(m(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var n=this._transitionablePaint._values[t],i=\"cross-faded-data-driven\"===n.property.specification[\"property-type\"],a=n.value.isDataDriven(),o=n.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var s=this._transitionablePaint._values[t].value;return s.isDataDriven()||a||i||this._handleOverridablePaintPropertyUpdate(t,o,s)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Cn(this,t.call(Mn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:At,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof fi&&Lr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(Mt),bi={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},_i=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},wi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ti(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i=bi[t.type].BYTES_PER_ELEMENT,a=r=ki(r,Math.max(e,i)),o=t.components||1;return n=Math.max(n,i),r+=i*o,{name:t.name,type:t.type,components:o,offset:a}})),size:ki(r,Math.max(n,e)),alignment:e}}function ki(t,e){return Math.ceil(t/e)*e}wi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},wi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},wi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},wi.prototype.clear=function(){this.length=0},wi.prototype.resize=function(t){this.reserve(t),this.length=t},wi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},wi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var Mi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(wi);Mi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout2i4\",Mi);var Ai=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(wi);Ai.prototype.bytesPerElement=8,Dn(\"StructArrayLayout4i8\",Ai);var Si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(wi);Si.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i4i12\",Si);var Ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(wi);Ei.prototype.bytesPerElement=8,Dn(\"StructArrayLayout2i4ub8\",Ei);var Ci=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(wi);Ci.prototype.bytesPerElement=18,Dn(\"StructArrayLayout8ui2ub18\",Ci);var Li=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(wi);Li.prototype.bytesPerElement=24,Dn(\"StructArrayLayout4i4ui4i24\",Li);var Ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(wi);Ii.prototype.bytesPerElement=12,Dn(\"StructArrayLayout3f12\",Ii);var Pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.uint32[1*t+0]=e,t},e}(wi);Pi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1ul4\",Pi);var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(wi);zi.prototype.bytesPerElement=20,Dn(\"StructArrayLayout6i1ul2ui20\",zi);var Oi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(wi);Oi.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i2i2i12\",Oi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(wi);Di.prototype.bytesPerElement=16,Dn(\"StructArrayLayout2f1f2i16\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(wi);Ri.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2ub2f12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(wi);Fi.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3ui6\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=g,this.uint32[x+10]=m,this.int16[y+22]=v,t},e}(wi);Bi.prototype.bytesPerElement=48,Dn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S,E){var C=34*t,L=17*t;return this.int16[C+0]=e,this.int16[C+1]=r,this.int16[C+2]=n,this.int16[C+3]=i,this.int16[C+4]=a,this.int16[C+5]=o,this.int16[C+6]=s,this.int16[C+7]=l,this.uint16[C+8]=c,this.uint16[C+9]=u,this.uint16[C+10]=f,this.uint16[C+11]=h,this.uint16[C+12]=p,this.uint16[C+13]=d,this.uint16[C+14]=g,this.uint16[C+15]=m,this.uint16[C+16]=v,this.uint16[C+17]=y,this.uint16[C+18]=x,this.uint16[C+19]=b,this.uint16[C+20]=_,this.uint16[C+21]=w,this.uint16[C+22]=T,this.uint32[L+12]=k,this.float32[L+13]=M,this.float32[L+14]=A,this.float32[L+15]=S,this.float32[L+16]=E,t},e}(wi);Ni.prototype.bytesPerElement=68,Dn(\"StructArrayLayout8i15ui1ul4f68\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.float32[1*t+0]=e,t},e}(wi);ji.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1f4\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(wi);Ui.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3i6\",Ui);var 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a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},Sa.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Sa.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var Ea=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return\"u_\"+t+\"_t\"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:\"a_\"+t,type:\"Float32\",components:\"color\"===r?4:2,offset:0}})),this.paintVertexArray=new a};Ea.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new ii(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new ii(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},Ea.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},Ea.prototype._setPaintValue=function(t,e,r,n){if(\"color\"===this.type)for(var i=ka(r),a=ka(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},Ea.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Ea.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ea.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=l(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},Ea.prototype.getBinding=function(t,e,r){return new va(t,e)};var Ca=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};Ca.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},Ca.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},Ca.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=n[r.min],a=n[r.mid],o=n[r.max];if(i&&a&&o)for(var s=t;s<e;s++)this.zoomInPaintVertexArray.emplace(s,a.tl[0],a.tl[1],a.br[0],a.br[1],i.tl[0],i.tl[1],i.br[0],i.br[1],a.pixelRatio,i.pixelRatio),this.zoomOutPaintVertexArray.emplace(s,a.tl[0],a.tl[1],a.br[0],a.br[1],o.tl[0],o.tl[1],o.br[0],o.br[1],a.pixelRatio,o.pixelRatio)}},Ca.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,oa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,oa.members,this.expression.isStateDependent))},Ca.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var La=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof fi&&Lr(o.property.specification)){var s=Pa(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new Aa(l.value,s):new Ma(l.value,s,c),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=za(a,c,\"source\");this.binders[a]=h?new Ca(l,c,u,e,p,t.id):new Sa(l,s,c,p),i.push(\"/a_\"+a)}else{var d=za(a,c,\"composite\");this.binders[a]=new Ea(l,s,c,u,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};La.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Sa||e instanceof Ea?e.maxValue:0},La.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Sa||o instanceof Ea||o instanceof Ca)&&o.populatePaintArray(t,e,r,n,i)}},La.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof Aa&&n.setConstantPatternPositions(t,e)}},La.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof Sa||h instanceof Ea||h instanceof Ca)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},La.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof Ma||r instanceof Aa)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},La.prototype.getPaintVertexBuffers=function(){return this._buffers},La.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof Ma||i instanceof Aa||i instanceof Ea)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},La.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property;this.binders[l].setUniform(o.binding,n,r.get(l),s)}},La.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Ca){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Sa||r instanceof Ea)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},La.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Sa||r instanceof Ea||r instanceof Ca)&&r.upload(t)}this.updatePaintBuffers()},La.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Sa||e instanceof Ea||e instanceof Ca)&&e.destroy()}};var Ia=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new La(o,r,n,t)}this.needsUpload=!1,this._featureMap=new fa,this._bufferOffset=0};function Pa(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function za(t,e,r){var n={color:{source:Gi,composite:Yi},number:{source:ji,composite:Gi}},i=function(t){return{\"line-pattern\":{source:Ci,composite:Ci},\"fill-pattern\":{source:Ci,composite:Ci},\"fill-extrusion-pattern\":{source:Ci,composite:Ci}}[t]}(t);return i&&i[r]||n[e][r]}Ia.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},Ia.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},Ia.prototype.get=function(t){return this.programConfigurations[t]},Ia.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},Ia.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},Dn(\"ConstantBinder\",Ma),Dn(\"CrossFadedConstantBinder\",Aa),Dn(\"SourceExpressionBinder\",Sa),Dn(\"CrossFadedCompositeBinder\",Ca),Dn(\"CompositeExpressionBinder\",Ea),Dn(\"ProgramConfiguration\",La,{omit:[\"_buffers\"]}),Dn(\"ProgramConfigurationSet\",Ia);var Oa={min:-1*Math.pow(2,14),max:Math.pow(2,14)-1};function Da(t){for(var e=8192/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a];o.x=Math.round(o.x*e),o.y=Math.round(o.y*e),(o.x<Oa.min||o.x>Oa.max||o.y<Oa.min||o.y>Oa.max)&&(_(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\"),o.x=l(o.x,Oa.min,Oa.max),o.y=l(o.y,Oa.min,Oa.max))}return r}function Ra(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var Fa=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Mi,this.indexArray=new Fi,this.segments=new ia,this.programConfigurations=new Ia(na,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function Ba(t,e){for(var r=0;r<t.length;r++)if(Wa(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Wa(t,e[n]))return!0;return!!Va(t,e)}function Na(t,e,r){return!!Wa(t,e)||!!Ha(e,t,r)}function ja(t,e){if(1===t.length)return Ya(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Wa(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Ya(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(Va(t,e[o]))return!0;return!1}function Ua(t,e,r){if(t.length>1){if(Va(t,e))return!0;for(var n=0;n<e.length;n++)if(Ha(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(Ha(t[i],e,r))return!0;return!1}function Va(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(qa(n,i,e[a],e[a+1]))return!0;return!1}function qa(t,e,r,n){return w(t,r,n)!==w(e,r,n)&&w(t,e,r)!==w(t,e,n)}function Ha(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Ga(t,e[i-1],e[i])<n)return!0;return!1}function Ga(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return t.distSqr(i<0?e:i>1?r:r.sub(e)._mult(i)._add(e))}function Ya(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)(n=r[s]).y>e.y!=(i=r[l]).y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Wa(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function Xa(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=w(t,e,r[0]);return a!==w(t,e,r[1])||a!==w(t,e,r[2])||a!==w(t,e,r[3])}function Za(t,e,r){var n=e.paint.get(t).value;return\"constant\"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function Ja(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function Ka(t,e,r,n,a){if(!e[0]&&!e[1])return t;var o=i.convert(e)._mult(a);\"viewport\"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++)s.push(t[l].sub(o));return s}Fa.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;\"circle\"===n.type&&(a=n.layout.get(\"circle-sort-key\"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],c=l.feature,u=l.id,f=l.index,h=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d={type:c.type,id:u,properties:c.properties,geometry:p?Da(c):[]};if(this.layers[0]._featureFilter.filter(new ii(this.zoom),d,r)){p||(d.geometry=Da(c));var g=a?a.evaluate(d,{},r):void 0;i.push({id:u,properties:c.properties,type:c.type,sourceLayerIndex:h,index:f,geometry:d.geometry,patterns:{},sortKey:g})}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var m=0,v=i;m<v.length;m+=1){var y=v[m],x=y.geometry,b=y.index,_=y.sourceLayerIndex,w=t[b].feature;this.addFeature(y,x,b,r),e.featureIndex.insert(w,x,b,_,this.index)}},Fa.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Fa.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Fa.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Fa.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,na),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Fa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Fa.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],c=l.x,u=l.y;if(!(c<0||c>=8192||u<0||u>=8192)){var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),h=f.vertexLength;Ra(this.layoutVertexArray,c,u,-1,-1),Ra(this.layoutVertexArray,c,u,1,-1),Ra(this.layoutVertexArray,c,u,1,1),Ra(this.layoutVertexArray,c,u,-1,1),this.indexArray.emplaceBack(h,h+1,h+2),this.indexArray.emplaceBack(h,h+3,h+2),f.vertexLength+=4,f.primitiveLength+=2}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{},n)},Dn(\"CircleBucket\",Fa,{omit:[\"layers\"]});var Qa=new yi({\"circle-sort-key\":new di(At.layout_circle[\"circle-sort-key\"])}),$a={paint:new yi({\"circle-radius\":new di(At.paint_circle[\"circle-radius\"]),\"circle-color\":new di(At.paint_circle[\"circle-color\"]),\"circle-blur\":new di(At.paint_circle[\"circle-blur\"]),\"circle-opacity\":new di(At.paint_circle[\"circle-opacity\"]),\"circle-translate\":new pi(At.paint_circle[\"circle-translate\"]),\"circle-translate-anchor\":new 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2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=us(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var fs=hs;function hs(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(ps,this,e),this.length=this._features.length}function ps(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function ds(t,e,r){if(3===t){var n=new fs(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}hs.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new ss(this._pbf,e,this.extent,this._keys,this._values)};var gs={VectorTile:function(t,e){this.layers=t.readFields(ds,{},e)},VectorTileFeature:ss,VectorTileLayer:fs},ms=gs.VectorTileFeature.types,vs=Math.pow(2,13);function ys(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*vs)+o,i*vs*2,a*vs*2,Math.round(s))}var xs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Si,this.indexArray=new Fi,this.programConfigurations=new Ia(os,t.layers,t.zoom),this.segments=new ia,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function bs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}xs.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ts(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Da(o):[]};if(this.layers[0]._featureFilter.filter(new ii(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Da(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(es(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},xs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},xs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},xs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},xs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},xs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,os),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},xs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},xs.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Qo(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1)l+=u[c].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((P=d).every((function(t){return t.x<0}))||P.every((function(t){return t.x>8192}))||P.every((function(t){return t.y<0}))||P.every((function(t){return t.y>8192}))))for(var g=0,m=0;m<d.length;m++){var v=d[m];if(m>=1){var y=d[m-1];if(!bs(v,y)){f.vertexLength+4>ia.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);g+b>32768&&(g=0),ys(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,g),ys(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,g),ys(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,g+=b),ys(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,g);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>ia.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===ms[t.type]){for(var w=[],T=[],k=f.vertexLength,M=0,A=s;M<A.length;M+=1){var S=A[M];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var C=S[E];ys(this.layoutVertexArray,C.x,C.y,0,0,1,1,0),w.push(C.x),w.push(C.y)}}}for(var 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a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Ts(s,s),f=Ts(s,l),h=Ts(l,l),p=Ts(c,s),d=Ts(c,l),g=u*h-f*f,m=(h*p-f*d)/g,v=(u*d-f*p)/g,y=i.z*(1-m-v)+r.z*m+a.z*v;if(isFinite(y))return y}return 1/0}for(var x=1/0,b=0,_=e;b<_.length;b+=1)x=Math.min(x,_[b].z);return x}var Ms=Ti([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,As=gs.VectorTileFeature.types,Ss=Math.cos(Math.PI/180*37.5),Es=Math.pow(2,14)/.5,Cs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Ei,this.indexArray=new Fi,this.programConfigurations=new Ia(Ms,t.layers,t.zoom),this.segments=new ia,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return 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a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1)this.addLine(f[u],t,o,s,l,c);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Cs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===As[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 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o=e.y*n-e.x,s=-e.y-e.x*n;this.addHalfVertex(t,e.x+e.y*r,e.y-e.x*r,a,!1,r,i),this.addHalfVertex(t,o,s,a,!0,-n,i),this.distance>Es/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},Cs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((t.x<<1)+(n?1:0),(t.y<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&s)<<2,s>>6);var l=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,l),o.primitiveLength++),i?this.e2=l:this.e1=l},Cs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Es-1):this.distance},Cs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Dn(\"LineBucket\",Cs,{omit:[\"layers\",\"patternFeatures\"]});var Ls=new yi({\"line-cap\":new 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n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&Zs(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),qs(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),qs(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&Zs(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,Gs.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,Js,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Ks,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,tl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Qs,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,$s,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,el,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,rl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,nl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,il,e)},writeBytesField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var hl=function(t,e){var r=e.pixelRatio,n=e.version,i=e.stretchX,a=e.stretchY,o=e.content;this.paddedRect=t,this.pixelRatio=r,this.stretchX=i,this.stretchY=a,this.content=o,this.version=n},pl={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};pl.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},pl.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},pl.tlbr.get=function(){return this.tl.concat(this.br)},pl.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(hl.prototype,pl);var dl=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var i=[];this.addImages(t,r,i),this.addImages(e,n,i);var a=fl(i),o=new po({width:a.w||1,height:a.h||1});for(var s in t){var l=t[s],c=r[s].paddedRect;po.copy(l.data,o,{x:0,y:0},{x:c.x+1,y:c.y+1},l.data)}for(var u in e){var f=e[u],h=n[u].paddedRect,p=h.x+1,d=h.y+1,g=f.data.width,m=f.data.height;po.copy(f.data,o,{x:0,y:0},{x:p,y:d},f.data),po.copy(f.data,o,{x:0,y:m-1},{x:p,y:d-1},{width:g,height:1}),po.copy(f.data,o,{x:0,y:0},{x:p,y:d+m},{width:g,height:1}),po.copy(f.data,o,{x:g-1,y:0},{x:p-1,y:d},{width:1,height:m}),po.copy(f.data,o,{x:0,y:0},{x:p+g,y:d},{width:1,height:m})}this.image=o,this.iconPositions=r,this.patternPositions=n};dl.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new hl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},dl.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},dl.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl;r.update(e.data,void 0,{x:n[0],y:n[1]})}},Dn(\"ImagePosition\",hl),Dn(\"ImageAtlas\",dl);var gl={horizontal:1,vertical:2,horizontalOnly:3},ml=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};ml.forText=function(t,e){var r=new ml;return r.scale=t||1,r.fontStack=e,r},ml.forImage=function(t){var e=new ml;return e.imageName=t,e};var vl=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function yl(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=vl.fromFeature(t,i);f===gl.vertical&&v.verticalizePunctuation();var y=ni.processBidirectionalText,x=ni.processStyledBidirectionalText;if(y&&1===v.sections.length){m=[];for(var b=0,_=y(v.toString(),Ml(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new vl;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);m.push(T)}}else if(x){m=[];for(var M=0,A=x(v.text,v.sectionIndex,Ml(v,c,a,e,n,p,d));M<A.length;M+=1){var S=A[M],E=new vl;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,m.push(E)}}else m=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,Ml(v,c,a,e,n,p,d));var C=[],L={positionedLines:C,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,g=0,m=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,M=0;if(b.length()){for(var A=0;A<b.length();A++){var S=b.getSection(A),E=b.getSectionIndex(A),C=b.getCharCode(A),L=0,I=null,P=null,z=null,O=24,D=!(l===gl.horizontal||!u&&!Hn(C)||u&&(xl[C]||(H=C,Vn.Arabic(H)||Vn[\"Arabic Supplement\"](H)||Vn[\"Arabic Extended-A\"](H)||Vn[\"Arabic Presentation Forms-A\"](H)||Vn[\"Arabic Presentation Forms-B\"](H))));if(S.imageName){var R=n[S.imageName];if(!R)continue;z=S.imageName,t.iconsInText=t.iconsInText||!0,P=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,L=w+(24-F[1]*S.scale),O=(I={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]}).advance;var B=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;B>0&&B>M&&(M=B)}else{var N=r[S.fontStack],j=N&&N[C];if(j&&j.rect)P=j.rect,I=j.metrics;else{var U=e[S.fontStack],V=U&&U[C];if(!V)continue;I=V.metrics}L=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:C,imageName:z,x:h,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),h+=O*S.scale+c):(k.push({glyph:C,imageName:z,x:h,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),h+=I.advance*S.scale+c)}0!==k.length&&(d=Math.max(h-c,d),Sl(k,0,k.length-1,m,M)),h=0;var q=a*_+M;T.lineOffset=Math.max(M,w),p+=q,g=Math.max(q,g),++v}else p+=a,++v}var H,G=p- -17,Y=Al(o),W=Y.horizontalAlign,X=Y.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c,u=(e-r)*i;c=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var g=d[p];g.x+=u,g.y+=c}})(t.positionedLines,m,W,X,d,g,a,G,i.length),t.top+=-X*G,t.bottom=t.top+G,t.left+=-W*d,t.right=t.left+d}(L,e,r,n,m,o,s,l,f,c,h,g),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(C)&&L}vl.fromFeature=function(t,e){for(var r=new vl,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},vl.prototype.length=function(){return this.text.length},vl.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},vl.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},vl.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},vl.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;e+=n&&Gn(n)&&!Us[t[r+1]]||i&&Gn(i)&&!Us[t[r-1]]||!Us[t[r]]?t[r]:Us[t[r]]}return e}(this.text)},vl.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&xl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&xl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},vl.prototype.substring=function(t,e){var r=new vl;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},vl.prototype.toString=function(){return this.text},vl.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},vl.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(ml.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},vl.prototype.addImageSection=function(t){var e=t.image?t.image.name:\"\";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(ml.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_(\"Reached maximum number of images 6401\")}else _(\"Can't add FormattedSection with an empty image.\")},vl.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var xl={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},bl={};function _l(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*24/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function wl(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function Tl(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function kl(t,e,r,n,i,a){for(var o=null,s=wl(e,r,i,a),l=0,c=n;l<c.length;l+=1){var u=c[l],f=wl(e-u.x,r,i,a)+u.badness;f<=s&&(o=u,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function Ml(t,e,r,n,i,a,o){if(\"point\"!==a)return[];if(!t)return[];for(var s,l=[],c=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=_l(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf(\"\\u200b\")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(xl[d]||(f+=_l(d,p,n,i,e,o)),h<t.length()-1){var g=!((s=d)<11904||!(Vn[\"Bopomofo Extended\"](s)||Vn.Bopomofo(s)||Vn[\"CJK Compatibility Forms\"](s)||Vn[\"CJK Compatibility Ideographs\"](s)||Vn[\"CJK Compatibility\"](s)||Vn[\"CJK Radicals Supplement\"](s)||Vn[\"CJK Strokes\"](s)||Vn[\"CJK Symbols and Punctuation\"](s)||Vn[\"CJK Unified Ideographs Extension A\"](s)||Vn[\"CJK Unified 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o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,g=e.right*a;\"width\"===r||\"both\"===r?(p=i[0]+d-n[3],f=i[0]+g+n[1]):f=(p=i[0]+(d+g-s.displaySize[0])/2)+s.displaySize[0];var m=e.top*a,v=e.bottom*a;return\"height\"===r||\"both\"===r?(u=i[1]+m-n[0],h=i[1]+v+n[2]):h=(u=i[1]+(m+v-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:u,right:f,bottom:h,left:p,collisionPadding:o}}bl[10]=!0,bl[32]=!0,bl[38]=!0,bl[40]=!0,bl[41]=!0,bl[43]=!0,bl[45]=!0,bl[47]=!0,bl[173]=!0,bl[183]=!0,bl[8203]=!0,bl[8208]=!0,bl[8211]=!0,bl[8231]=!0;var Cl=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(i);function Ll(t,e){var r=e.expression;if(\"constant\"===r.kind)return{kind:\"constant\",layoutSize:r.evaluate(new ii(t+1))};if(\"source\"===r.kind)return{kind:\"source\"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return\"composite\"===r.kind?{kind:\"composite\",minZoom:s,maxZoom:l,interpolationType:i}:{kind:\"camera\",minZoom:s,maxZoom:l,minSize:r.evaluate(new ii(s)),maxSize:r.evaluate(new ii(l)),interpolationType:i}}function Il(t,e,r){var n=e.uSize,i=r.lowerSize;return\"source\"===t.kind?i/128:\"composite\"===t.kind?Ue(i/128,r.upperSize/128,e.uSizeT):n}function Pl(t,e){var r=0,n=0;if(\"constant\"===t.kind)n=t.layoutSize;else if(\"source\"!==t.kind){var i=t.interpolationType,a=i?l(rr.interpolationFactor(i,e,t.minZoom,t.maxZoom),0,1):0;\"camera\"===t.kind?n=Ue(t.minSize,t.maxSize,a):r=a}return{uSizeT:r,uSize:n}}Dn(\"Anchor\",Cl);var 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h=Hl(n.stretch-x,b,u,t.left),p=Gl(n.fixed-T,k,n.stretch,g),d=Hl(a.stretch-_,w,f,t.top),v=Gl(a.fixed-M,A,a.stretch,m),y=Hl(l.stretch-x,b,u,t.left),S=Gl(l.fixed-T,k,l.stretch,g),E=Hl(c.stretch-_,w,f,t.top),C=Gl(c.fixed-M,A,c.stretch,m),L=new i(h,d),I=new i(y,d),P=new i(y,E),z=new i(h,E),O=new i(p/s,v/s),D=new i(S/s,C/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];L._matMult(N),I._matMult(N),z._matMult(N),P._matMult(N)}var j=n.stretch+n.fixed,U=a.stretch+a.fixed;return{tl:L,tr:I,bl:z,br:P,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+U,w:l.stretch+l.fixed-j,h:c.stretch+c.fixed-U},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:O,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:A/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var C=ql(h,v,g),L=ql(p,y,m),I=0;I<C.length-1;I++)for(var P=C[I],z=C[I+1],O=0;O<L.length-1;O++)a.push(E(P,L[O],z,L[O+1]));else 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k=s[m.imageName];x=k.sdf,y=1/(b=k.pixelRatio)}var M=a?[m.x+T,m.y]:[0,0],A=a?[0,0]:[m.x+T+r[0],m.y+r[1]-_],S=[0,0];w&&(S=A,A=[0,0]);var E=(m.metrics.left-y)*m.scale-T+A[0],C=(-m.metrics.top-y)*m.scale+A[1],L=E+v.w*m.scale/b,I=C+v.h*m.scale/b,P=new i(E,C),z=new i(L,C),O=new i(E,I),D=new i(L,I);if(w){var R=new i(-T,T- -17),F=-Math.PI/2,B=12-T,N=new i(22-B,-(m.imageName?B:0)),j=new(Function.prototype.bind.apply(i,[null].concat(S)));P._rotateAround(F,R)._add(N)._add(j),z._rotateAround(F,R)._add(N)._add(j),O._rotateAround(F,R)._add(N)._add(j),D._rotateAround(F,R)._add(N)._add(j)}if(c){var U=Math.sin(c),V=Math.cos(c),q=[V,-U,U,V];P._matMult(q),z._matMult(q),O._matMult(q),D._matMult(q)}var H=new i(0,0),G=new i(0,0);u.push({tl:P,tr:z,bl:O,br:D,tex:v,writingMode:e.writingMode,glyphOffset:M,sectionIndex:m.sectionIndex,isSDF:x,pixelOffsetTL:H,pixelOffsetBR:G,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. 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Fi,this.programConfigurations=t,this.segments=new ia,this.dynamicLayoutVertexArray=new Ii,this.opacityVertexArray=new Pi,this.placedSymbolArray=new Ji};cc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},cc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ds.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Rs.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,ac,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},cc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Dn(\"SymbolBuffers\",cc);var uc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new ia,this.collisionVertexArray=new Ri};uc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Fs.members,!0)},uc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Dn(\"CollisionBuffers\",uc);var fc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=eo([]),this.placementViewportMatrix=eo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Ll(this.zoom,e[\"text-size\"]),this.iconSizeData=Ll(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1),this.sortFeaturesByY=(\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey)&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return gl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};fc.prototype.createArrays=function(){this.text=new cc(new Ia(Ds.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new cc(new Ia(Ds.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new $i,this.lineVertexArray=new ta,this.symbolInstances=new Qi},fc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=Us[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},fc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof te&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new ii(this.zoom),g=0,m=t;g<m.length;g+=1){var v=m[g],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Da(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Da(y));var k=void 0;if(l){var M=n.getValueAndResolveTokens(\"text-field\",T,r,p),A=te.factory(M);lc(A)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ei()||this.hasRTLText&&ni.isParsed())&&(k=js(A,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ee?E:ee.fromString(E)}if(k||S){var C=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,L={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Da(y),properties:y.properties,type:ic[y.type],sortKey:C};if(this.features.push(L),S&&(f[S.name]=!0),k){var I=a.evaluate(T,{},r).join(\",\"),P=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(gl.vertical)>=0;for(var z=0,O=k.sections;z<O.length;z+=1){var D=O[z];if(D.image)f[D.image.name]=!0;else{var R=qn(k.toString()),F=D.fontStack||I,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,P,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,f),m=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[m].geometry,!0)]=m,n[g].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},fc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},fc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},fc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},fc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},fc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},fc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},fc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},fc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),g=this.glyphOffsetArray.length,m=d.vertexLength,v=this.allowVerticalPlacement&&o===gl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,M=b.tex,A=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,C=b.minFontScaleY,L=b.glyphOffset,I=b.isSDF,P=b.sectionIndex,z=d.vertexLength,O=L[1];oc(p,s.x,s.y,_.x,O+_.y,M.x,M.y,r,I,A.x,A.y,E,C),oc(p,s.x,s.y,w.x,O+w.y,M.x+M.w,M.y,r,I,S.x,A.y,E,C),oc(p,s.x,s.y,T.x,O+T.y,M.x,M.y+M.h,r,I,A.x,S.y,E,C),oc(p,s.x,s.y,k.x,O+k.y,M.x+M.w,M.y+M.h,r,I,S.x,S.y,E,C),sc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(z,z+1,z+2),h.emplaceBack(z+1,z+2,z+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(L[0]),x!==e.length-1&&P===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[P])}t.placedSymbolArray.emplaceBack(s.x,s.y,g,this.glyphOffsetArray.length-g,m,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},fc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},fc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},fc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i);this.addCollisionDebugVertices(a.x1,a.y1,a.x2,a.y2,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},fc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new uc(Oi,Bs.members,qi),this.iconCollisionBox=new uc(Oi,Bs.members,qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},fc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var g=t.get(d);c.iconBox={x1:g.x1,y1:g.y1,x2:g.x2,y2:g.y2,anchorPointX:g.anchorPointX,anchorPointY:g.anchorPointY},c.iconFeatureIndex=g.featureIndex;break}for(var m=s;m<l;m++){var v=t.get(m);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},fc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},fc.prototype.hasTextData=function(){return this.text.segments.get().length>0},fc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},fc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},fc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},fc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},fc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},fc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},fc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},fc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=this.symbolInstances.get(n[r]);this.featureSortOrder.push(i.featureIndex),[i.rightJustifiedTextSymbolIndex,i.centerJustifiedTextSymbolIndex,i.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),i.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,i.verticalPlacedTextSymbolIndex),i.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.placedIconSymbolIndex),i.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Dn(\"SymbolBucket\",fc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),fc.MAX_GLYPHS=65535,fc.addDynamicAttributes=sc;var hc=new yi({\"symbol-placement\":new pi(At.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new pi(At.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new pi(At.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new di(At.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new pi(At.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new pi(At.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new pi(At.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new pi(At.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new pi(At.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new di(At.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new pi(At.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new pi(At.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new di(At.layout_symbol[\"icon-image\"]),\"icon-rotate\":new di(At.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new pi(At.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new pi(At.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new di(At.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new di(At.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new pi(At.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new pi(At.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new pi(At.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new di(At.layout_symbol[\"text-field\"]),\"text-font\":new di(At.layout_symbol[\"text-font\"]),\"text-size\":new di(At.layout_symbol[\"text-size\"]),\"text-max-width\":new di(At.layout_symbol[\"text-max-width\"]),\"text-line-height\":new pi(At.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new di(At.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new di(At.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new di(At.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new pi(At.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new di(At.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new pi(At.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new pi(At.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new di(At.layout_symbol[\"text-rotate\"]),\"text-padding\":new pi(At.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new pi(At.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new di(At.layout_symbol[\"text-transform\"]),\"text-offset\":new di(At.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new pi(At.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new pi(At.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new pi(At.layout_symbol[\"text-optional\"])}),pc={paint:new yi({\"icon-opacity\":new di(At.paint_symbol[\"icon-opacity\"]),\"icon-color\":new di(At.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new di(At.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new di(At.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new di(At.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new pi(At.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new pi(At.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new di(At.paint_symbol[\"text-opacity\"]),\"text-color\":new di(At.paint_symbol[\"text-color\"],{runtimeType:Bt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new di(At.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new di(At.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new di(At.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new pi(At.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new pi(At.paint_symbol[\"text-translate-anchor\"])}),layout:hc},dc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ot,this.defaultValue=t};dc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},dc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},dc.prototype.outputDefined=function(){return!1},dc.prototype.serialize=function(){return null},Dn(\"FormatSectionOverride\",dc,{omit:[\"defaultValue\"]});var gc=function(t){function e(e){t.call(this,e,pc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(this.layout._values[\"icon-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(this.layout._values[\"text-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Vr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new fc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=pc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new dc(a),s=new Ur(o,a.property.specification);i=\"constant\"===a.value.kind||\"source\"===a.value.kind?new Hr(\"source\",s):new Gr(\"composite\",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new fi(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=pc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1)if(n.overrides&&n.overrides.hasOverride(r[e]))return void(i=!0)};if(\"constant\"===r.value.kind&&r.value.value instanceof te)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){i||(t instanceof oe&&ie(t.value)===Vt?a(t.value.sections):t instanceof ue?a(t.sections):t.eachChild(o))},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(xi),mc={paint:new yi({\"background-color\":new pi(At.paint_background[\"background-color\"]),\"background-pattern\":new mi(At.paint_background[\"background-pattern\"]),\"background-opacity\":new pi(At.paint_background[\"background-opacity\"])})},vc=function(t){function e(e){t.call(this,e,mc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(xi),yc={paint:new yi({\"raster-opacity\":new pi(At.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new pi(At.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new pi(At.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new pi(At.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new pi(At.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new pi(At.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new pi(At.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new pi(At.paint_raster[\"raster-fade-duration\"])})},xc=function(t){function e(e){t.call(this,e,yc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(xi),bc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(xi),_c={circle:oo,heatmap:vo,hillshade:xo,fill:as,\"fill-extrusion\":ws,line:zs,symbol:gc,background:vc,raster:xc},wc=self.HTMLImageElement,Tc=self.HTMLCanvasElement,kc=self.HTMLVideoElement,Mc=self.ImageData,Ac=self.ImageBitmap,Sc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Sc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof wc||t instanceof Tc||t instanceof kc||t instanceof Mc||Ac&&t instanceof Ac?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof wc||t instanceof Tc||t instanceof kc||t instanceof Mc||Ac&&t instanceof Ac?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Sc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Sc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Sc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Ec=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Ec.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Ec.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Cc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g([\"receive\",\"process\"],this),this.invoker=new Ec(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Lc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Cc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Nn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(jn(e.error)):n(null,jn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Nn(e):null,data:Nn(n,a)},a)}:function(t){i=!0},s=null,l=jn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Ic=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Ic.prototype.setNorthEast=function(t){return this._ne=t instanceof Pc?new Pc(t.lng,t.lat):Pc.convert(t),this},Ic.prototype.setSouthWest=function(t){return this._sw=t instanceof Pc?new Pc(t.lng,t.lat):Pc.convert(t),this},Ic.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Pc)e=t,r=t;else{if(!(t instanceof Ic))return Array.isArray(t)?4===t.length||t.every(Array.isArray)?this.extend(Ic.convert(t)):this.extend(Pc.convert(t)):this;if(r=t._ne,!(e=t._sw)||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Pc(e.lng,e.lat),this._ne=new Pc(r.lng,r.lat)),this},Ic.prototype.getCenter=function(){return new Pc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Ic.prototype.getSouthWest=function(){return this._sw},Ic.prototype.getNorthEast=function(){return this._ne},Ic.prototype.getNorthWest=function(){return new Pc(this.getWest(),this.getNorth())},Ic.prototype.getSouthEast=function(){return new Pc(this.getEast(),this.getSouth())},Ic.prototype.getWest=function(){return this._sw.lng},Ic.prototype.getSouth=function(){return this._sw.lat},Ic.prototype.getEast=function(){return this._ne.lng},Ic.prototype.getNorth=function(){return this._ne.lat},Ic.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Ic.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Ic.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Ic.prototype.contains=function(t){var e=Pc.convert(t),r=e.lng,n=e.lat,i=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(i=this._sw.lng>=r&&r>=this._ne.lng),this._sw.lat<=n&&n<=this._ne.lat&&i},Ic.convert=function(t){return!t||t instanceof Ic?t:new Ic(t)};var Pc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Pc.prototype.wrap=function(){return new Pc(c(this.lng,-180,180),this.lat)},Pc.prototype.toArray=function(){return[this.lng,this.lat]},Pc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Pc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Pc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Ic(new Pc(this.lng-r,this.lat-e),new Pc(this.lng+r,this.lat+e))},Pc.convert=function(t){if(t instanceof Pc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Pc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Pc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var zc=2*Math.PI*6371008.8;function Oc(t){return zc*Math.cos(t*Math.PI/180)}function Dc(t){return(180+t)/360}function Rc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Fc(t,e){return t/Oc(e)}function Bc(t){return 360/Math.PI*Math.atan(Math.exp((180-360*t)*Math.PI/180))-90}var Nc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Nc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Pc.convert(t);return new Nc(Dc(r.lng),Rc(r.lat),Fc(e,r.lat))},Nc.prototype.toLngLat=function(){return new Pc(360*this.x-180,Bc(this.y))},Nc.prototype.toAltitude=function(){return this.z*Oc(Bc(this.y))},Nc.prototype.meterInMercatorCoordinateUnits=function(){return 1/zc*(t=Bc(this.y),1/Math.cos(t*Math.PI/180));var t};var jc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=qc(0,t,t,e,r)};jc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},jc.prototype.url=function(t,e){var r,n,i,a,o,s=(n=this.y,i=this.z,a=Lc(256*(r=this.x),256*(n=Math.pow(2,i)-n-1),i),o=Lc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},jc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},jc.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var Uc=function(t,e){this.wrap=t,this.canonical=e,this.key=qc(t,e.z,e.z,e.x,e.y)},Vc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new jc(r,+n,+i),this.key=qc(e,t,r,n,i)};function qc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Vc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Vc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Vc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Vc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Vc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?qc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):qc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Vc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Vc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Vc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Vc(e,this.wrap,e,r,n),new Vc(e,this.wrap,e,r+1,n),new Vc(e,this.wrap,e,r,n+1),new Vc(e,this.wrap,e,r+1,n+1)]},Vc.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Vc.prototype.wrapped=function(){return new Vc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Vc.prototype.unwrapTo=function(t){return new Vc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Vc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Vc.prototype.toUnwrapped=function(){return new Uc(this.wrap,this.canonical)},Vc.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Vc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Nc(t.x-this.wrap,t.y))},Dn(\"CanonicalTileID\",jc),Dn(\"OverscaledTileID\",Vc,{omit:[\"posMatrix\"]});var Hc=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Hc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Hc.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Hc.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Hc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Hc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Hc.prototype.getPixels=function(){return new po({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Hc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Dn(\"DEMData\",Hc);var Gc=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Gc.prototype.encode=function(t){return this._stringToNumber[t]},Gc.prototype.decode=function(t){return this._numberToString[t]};var Yc=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},Wc={geometry:{configurable:!0}};Wc.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Wc.geometry.set=function(t){this._geometry=t},Yc.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Yc.prototype,Wc);var Xc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Xc.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Xc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Xc.prototype.getState=function(t,e){var r=String(e),n=u({},(this.state[t]||{})[r],(this.stateChanges[t]||{})[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var i=this.deletedStates[t][e];if(null===i)return{};for(var a in i)delete n[a]}return n},Xc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Xc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1)delete this.state[o][c][h[f]];s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var p in t)t[p].setFeatureState(r,e)};var Zc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new Ln(8192,16,0),this.grid3D=new Ln(8192,16,0),this.featureIndexArray=new ra,this.promoteId=e};function Jc(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof hi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Kc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function Qc(t,e){return e-t}Zc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},Zc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new gs.VectorTile(new Hs(this.rawTileData)).layers,this.sourceLayerCoder=new Gc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Zc.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=rn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=Kc(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=Kc(t.cameraQueryGeometry),d=0,g=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1)if(Wa(t,f[u]))return!0;for(var h=0;h<t.length-1;h++)if(Xa(t[h],t[h+1],c))return!0;return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)}));d<g.length;d+=1)h.push(g[d]);h.sort(Qc);for(var m,v={},y=function(i){var u=h[i];if(u!==m){m=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(v,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Da(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},x=0;x<h.length;x++)y(x);return v},Zc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new ii(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),g=0;g<f.length;g++){var m=f[g];if(!(a&&a.indexOf(m)<0)){var v=s[m];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[m];x.paint=Jc(x.paint,v.paint,p,y,o),x.layout=Jc(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new Yc(p,this.z,this.x,this.y,d);_.layer=x;var w=t[m];void 0===w&&(w=t[m]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},Zc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=rn(i),u=0,f=t;u<f.length;u+=1)this.loadMatchingFeature(l,r,n,f[u],c,a,o,s,e);return l},Zc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Zc.prototype.getId=function(t,e){var r=t.id;return this.promoteId&&\"boolean\"==typeof(r=t.properties[\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e]])&&(r=Number(r)),r},Dn(\"FeatureIndex\",Zc,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var $c=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};$c.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},$c.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},$c.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1)r[s[o].id]=t}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof fc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof fc&&o.hasRTLText){this.hasRTLText=!0,ni.isLoading()||ni.isLoaded()||\"deferred\"!==ei()||ri();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new Xi},$c.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},$c.prototype.getBucket=function(t){return this.buckets[t.id]},$c.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Sc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Sc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},$c.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},$c.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},$c.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=rn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new ii(this.tileID.overscaledZ),p)){var d=r.getId(p,i),g=new Yc(p,l,c,u,d);g.tile=f,t.push(g)}}}},$c.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},$c.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},$c.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=M(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},$c.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},$c.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},$c.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},$c.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},$c.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},$c.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},$c.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},$c.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=this.dependencies[n[r]];if(i)for(var a=0,o=e;a<o.length;a+=1)if(i[o[a]])return!0}return!1};var tu=self.performance,eu=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},tu.mark(this._marks.start)};eu.prototype.finish=function(){tu.mark(this._marks.end);var t=tu.getEntriesByName(this._marks.measure);return 0===t.length&&(tu.measure(this._marks.measure,this._marks.start,this._marks.end),t=tu.getEntriesByName(this._marks.measure),tu.clearMarks(this._marks.start),tu.clearMarks(this._marks.end),tu.clearMeasures(this._marks.measure)),t},t.Actor=Cc,t.AlphaImage=ho,t.CanonicalTileID=jc,t.CollisionBoxArray=Xi,t.Color=Kt,t.DEMData=Hc,t.DataConstantProperty=pi,t.DictionaryCoder=Gc,t.EXTENT=8192,t.ErrorEvent=kt,t.EvaluationParameters=ii,t.Event=Tt,t.Evented=Mt,t.FeatureIndex=Zc,t.FillBucket=rs,t.FillExtrusionBucket=xs,t.ImageAtlas=dl,t.ImagePosition=hl,t.LineBucket=Cs,t.LngLat=Pc,t.LngLatBounds=Ic,t.MercatorCoordinate=Nc,t.ONE_EM=24,t.OverscaledTileID=Vc,t.Point=i,t.Point$1=i,t.Properties=yi,t.Protobuf=Hs,t.RGBAImage=po,t.RequestManager=q,t.RequestPerformance=eu,t.ResourceType=ht,t.SegmentVector=ia,t.SourceFeatureState=Xc,t.StructArrayLayout1ui2=Hi,t.StructArrayLayout2f1f2i16=Di,t.StructArrayLayout2i4=Mi,t.StructArrayLayout3ui6=Fi,t.StructArrayLayout4i8=Ai,t.SymbolBucket=fc,t.Texture=Sc,t.Tile=$c,t.Transitionable=si,t.Uniform1f=va,t.Uniform1i=ma,t.Uniform2f=ya,t.Uniform3f=xa,t.Uniform4f=ba,t.UniformColor=_a,t.UniformMatrix4f=Ta,t.UnwrappedTileID=Uc,t.ValidationError=St,t.WritingMode=gl,t.ZoomHistory=Un,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=sc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=g,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ut>ot&&(t.getActor().send(\"enforceCacheSizeLimit\",at),ut=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete(\"mapbox-tiles\");t&&e.catch(t).then((function(){return t()}))},t.clipLine=jl,t.clone=function(t){var e=new to(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new to(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ns,t.config=F,t.create=function(){var t=new to(16);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new to(9);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new to(4);return to!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=qr,t.createLayout=Ti,t.createStyleLayer=function(t){return\"custom\"===t.type?new bc(t):new _c[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if(\"object\"==typeof e&&null!==e&&null!==r){if(\"object\"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=s,t.emitValidationErrors=Cn,t.endsWith=m,t.enforceCacheSizeLimit=function(t){st(),Q&&Q.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Il,t.evaluateSizeForZoom=Pl,t.evaluateVariableOffset=$l,t.evented=ti,t.extend=u,t.featureFilter=rn,t.filterObject=y,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=Al,t.getAnchorJustification=tc,t.getArrayBuffer=yt,t.getImage=bt,t.getJSON=function(t,e){return vt(u(t,{type:\"json\"}),e)},t.getRTLTextPluginStatus=ei,t.getReferrer=mt,t.getVideo=function(t,e){var r,n,i=self.document.createElement(\"video\");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=self.document.createElement(\"source\");r=t[a],n=void 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r}function ft(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function ht(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(pt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(pt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function pt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function dt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,gt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function gt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a||\"MultiPoint\"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if(\"LineString\"===a)mt(o,i,t,r,!1,!1);else if(\"MultiLineString\"===a||\"Polygon\"===a)for(s=0;s<i.length;s++)mt(o,i[s],t,r,\"Polygon\"===a,0===s);else if(\"MultiPolygon\"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)mt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if(\"LineString\"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function mt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function vt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)J(r,t.features[n],e,n);else J(r,\"Feature\"===t.type?t:{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=rt(t,1,-1-r,r,0,-1,2,e),a=rt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=rt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=ut(i,1).concat(n)),a&&(n=n.concat(ut(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function yt(t,e,r){return 32*((1<<t)*r+e)+t}function xt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=b(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}F.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(N(t[l],l));this.trees[i+1]=new D(s,G,Y,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new D(s,G,Y,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},F.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(V(r),q(a),V(i),q(n));u<f.length;u+=1){var h=l.points[f[u]];c.push(h.numPoints?j(h):this.points[h.index])}return c},F.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=i.points[c[l]];u.parentId===t&&s.push(u.numPoints?j(u):this.points[u.index])}if(0===s.length)throw new Error(n);return s},F.prototype.getLeaves=function(t,e,r){var n=[];return this._appendLeaves(n,t,e=e||10,r=r||0,0),n},F.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.radius/a.extent,s=(r-o)/i,l=(r+1+o)/i,c={features:[]};return this._addTileFeatures(n.range((e-o)/i,s,(e+1+o)/i,l),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-o/i,s,1,l),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,s,o/i,l),n.points,-1,r,i,c),c.features.length?c:null},F.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},F.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},F.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?U(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},F.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},F.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.reduce,a=n.radius/(n.extent*Math.pow(2,e)),o=0;o<t.length;o++){var s=t[o];if(!(s.zoom<=e)){s.zoom=e;for(var l=this.trees[e+1],c=l.within(s.x,s.y,a),u=s.numPoints||1,f=s.x*u,h=s.y*u,p=i&&u>1?this._map(s,!0):null,d=(o<<5)+(e+1)+this.points.length,g=0,m=c;g<m.length;g+=1){var v=l.points[m[g]];if(!(v.zoom<=e)){v.zoom=e;var y=v.numPoints||1;f+=v.x*y,h+=v.y*y,u+=y,v.parentId=d,i&&(p||(p=this._map(s,!0)),i(p,this._map(v)))}}1===u?r.push(s):(s.parentId=d,r.push(B(f/u,h/u,d,u,p)))}}return r},F.prototype._getOriginId=function(t){return t-this.points.length>>5},F.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},F.prototype._map=function(t,e){if(t.numPoints)return e?H({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?H({},n):n},vt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},vt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=yt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=dt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;g=m=v=y=null,x=rt(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=rt(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(g=rt(x,u,n-_,n+T,1,h.minY,h.maxY,l),m=rt(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=rt(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=rt(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},vt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=yt(t,e=(e%o+o)%o,r);if(this.tiles[s])return ht(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[yt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?ht(this.tiles[s],i):null):null};var bt=function(e){function r(t,r,n,i){e.call(this,t,r,n,xt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));!function t(e,r){var n,i=e&&e.type;if(\"FeatureCollection\"===i)for(n=0;n<e.features.length;n++)t(e.features[n],r);else if(\"GeometryCollection\"===i)for(n=0;n<e.geometries.length;n++)t(e.geometries[n],r);else if(\"Feature\"===i)t(e.geometry,r);else if(\"Polygon\"===i)f(e.coordinates,r);else if(\"MultiPolygon\"===i)for(n=0;n<e.coordinates.length;n++)f(e.coordinates[n],r);return e}(o,!0);try{e._geoJSONIndex=n.cluster?new F(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=t.createExpression(h[1]),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=d.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new vt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded;return n&&n[t.uid]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l),_t=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:bt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return _t.prototype.setReferrer=function(t,e){this.referrer=e},_t.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in i)i[a].availableImages=e}r()},_t.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},_t.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},_t.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},_t.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},_t.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},_t.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},_t.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},_t.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},_t.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},_t.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},_t.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error(\"RTL Text Plugin failed to import scripts from \"+i),a)}}catch(t){n(t.toString())}},_t.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},_t.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},_t.prototype.getWorkerSource=function(t,e,r){var n=this;return this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),this.workerSources[t][e][r]||(this.workerSources[t][e][r]=new this.workerSourceTypes[e]({send:function(e,r,i){n.actor.send(e,r,i,t)}},this.getLayerIndex(t),this.getAvailableImages(t))),this.workerSources[t][e][r]},_t.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new u),this.demWorkerSources[t][e]},_t.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},\"undefined\"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new _t(t.window)),_t})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return\"undefined\"==typeof window||\"undefined\"==typeof document?\"not a browser\":Array.prototype&&Array.prototype.every&&Array.prototype.filter&&Array.prototype.forEach&&Array.prototype.indexOf&&Array.prototype.lastIndexOf&&Array.prototype.map&&Array.prototype.some&&Array.prototype.reduce&&Array.prototype.reduceRight&&Array.isArray?Function.prototype&&Function.prototype.bind?Object.keys&&Object.create&&Object.getPrototypeOf&&Object.getOwnPropertyNames&&Object.isSealed&&Object.isFrozen&&Object.isExtensible&&Object.getOwnPropertyDescriptor&&Object.defineProperty&&Object.defineProperties&&Object.seal&&Object.freeze&&Object.preventExtensions?\"JSON\"in window&&\"parse\"in JSON&&\"stringify\"in JSON?function(){if(!(\"Worker\"in window&&\"Blob\"in window&&\"URL\"in window))return!1;var t,e,r=new Blob([\"\"],{type:\"text/javascript\"}),n=URL.createObjectURL(r);try{e=new Worker(n),t=!0}catch(e){t=!1}return e&&e.terminate(),URL.revokeObjectURL(n),t}()?\"Uint8ClampedArray\"in window?ArrayBuffer.isView?function(){var t=document.createElement(\"canvas\");t.width=t.height=1;var e=t.getContext(\"2d\");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?(void 0===n[r=t&&t.failIfMajorPerformanceCaveat]&&(n[r]=function(t){var r=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)||r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)||r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)||r.getContext(\"experimental-webgl\",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);return!(!n||r.isContextLost())&&(r.shaderSource(n,\"void main() {}\"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS))}(r)),n[r]?void 0:\"insufficient WebGL support\"):\"insufficient Canvas/getImageData support\":\"insufficient ArrayBuffer support\":\"insufficient Uint8ClampedArray support\":\"insufficient worker support\":\"insufficient JSON support\":\"insufficient Object support\":\"insufficient Function support\":\"insufficent Array support\";var r}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]=\"none\")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",c,c),t.window.removeEventListener(\"test\",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),t.addEventListener(e,r,\"passive\"in n&&l?n:n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),t.removeEventListener(e,r,\"passive\"in n&&l?n:n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",u,!0)};function f(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",u,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e];this._notify(n.ids,n.callback)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!(t&&(4!==t.length||t[0]<0||e.data.width<t[0]||t[1]<0||e.data.height<t[1]||t[2]<0||e.data.width<t[2]||t[3]<0||e.data.height<t[3]||t[2]<t[0]||t[3]<t[1]))},r.prototype.updateImage=function(t,e){e.version=this.images[t].version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1)this.images[i[n]]||(r=!1);this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. 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o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented),p=m,d=m,g=1e20;function m(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-g,n[1]=+g;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+g}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}m.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?g:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?g:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++)r[n]=Math.max(0,Math.min(255,Math.round(255-255*((this.gridOuter[n]-this.gridInner[n])/this.radius+this.cutoff))));return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1)i.push({stack:a,id:s[o]});t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){var c=t[l];n.push({left:i=s*r,right:a=(s+=c)*r,isDash:o=!o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.width*(this.nextRow+r+i),o=0,s=t[o],l=0;l<this.width;l++){l/s.right>1&&(s=t[++o]);var c=Math.abs(l-s.left),u=Math.abs(l-s.right),f=Math.min(c,u),h=void 0,p=i/r*(n+1);if(s.isDash){var d=n-Math.abs(p);h=Math.sqrt(f*f+d*d)}else h=n-Math.sqrt(f*f+p*p);this.data[a+l]=Math.max(0,Math.min(255,h+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f);this.data[o+c]=Math.max(0,Math.min(255,(l.isDash?h:-h)+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=new e.Actor(i[a],n,this.id);o.name=\"Worker \"+a,this.actors.push(o)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n=n||function(){})},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var A=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};A.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},A.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new A(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new A(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor,e.request=this.actor.send(i,{type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId},(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),I=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),P=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=Math.max(i-r,a-n),u=Math.max(0,Math.floor(-Math.log(c)/Math.LN2)),f=Math.pow(2,u);return new t.CanonicalTileID(u,Math.floor((r+i)/2*f),Math.floor((n+a)/2*f))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented),z=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1)this.urls.push(this.map._requestManager.transformRequest(i[n],t.ResourceType.Source).url);t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(P),O=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,I.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(P),D={vector:S,raster:E,\"raster-dem\":C,geojson:L,video:z,image:P,canvas:O};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var g=d[p];f[g.featureIndex]||(f[g.featureIndex]=!0,h.push(g))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=e.attributes[this.attributes[r].name];void 0!==n&&t.enableVertexAttribArray(n)}},V.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[U[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},V.prototype.destroy=function(){this.buffer&&(this.context.gl.deleteBuffer(this.buffer),delete this.buffer)};var q=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};q.prototype.get=function(){return this.current},q.prototype.set=function(t){},q.prototype.getDefault=function(){return this.default},q.prototype.setDefault=function(){this.set(this.default)};var H=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var 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e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(q),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(q),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(q),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var 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e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(q),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),_t=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getDefault=function(){return null},e}(q),wt=function(t){function 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t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var At=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};At.disabled=new At({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Ct=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new H(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new mt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Ct.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Ct.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Ct.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Ct.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Ct.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Ct.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Ct.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Ct.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Ct.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Ct.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Ct.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Ct.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Lt=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=Math.ceil(t.width/this._source.tileSize)+1,r=Math.ceil(t.height/this._source.tileSize)+1,n=Math.floor(e*r*5),i=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,n):n;this._cache.setMaxSize(i)},r.prototype.handleWrapJump=function(t){var e=Math.round((t-(void 0===this._prevLng?t:this._prevLng))/360);if(this._prevLng=t,e){var r={};for(var n in this._tiles){var i=this._tiles[n];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+e),r[i.tileID.key]=i}for(var a in this._tiles=r,this._timers)clearTimeout(this._timers[a]),delete this._timers[a];for(var o in this._tiles)this._setTileReloadTimer(o,this._tiles[o])}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Pt(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],g=this._tiles[p];if(g&&!(g.fadeEndTime&&g.fadeEndTime<=t.browser.now())){var m=this.findLoadedParent(d,o);m&&(this._addTile(m.tileID),c[m.tileID.key]=m.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],g=this._tiles[d.key];if(!g.hasData()){if(e+1>this._source.maxzoom){var m=d.children(this._source.maxzoom)[0],v=this.getTile(m);if(v&&v.hasData()){n[m.key]=m;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=g.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1)this._loadedParentTiles[o[a]]=r}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,g=0,m=c;g<m.length;g+=1){var v=m[g];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,g=Math.pow(2,o.zoom-n.tileID.overscaledZ),m=r*n.queryPadding*t.EXTENT/n.tileSize/g,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-m<t.EXTENT&&v[0].y-m<t.EXTENT&&v[1].x+m>=0&&v[1].y+m>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:g})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Pt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){this._state.updateState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.removeFeatureState=function(t,e,r){this._state.removeFeatureState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.getFeatureState=function(t,e){return this._state.getState(t=t||\"_geojsonTileLayer\",e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Pt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(Yi.workerUrl)}Lt.maxOverzooming=10,Lt.maxUnderzooming=3;var Ot=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new zt);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[Ot]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function qt(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function Ht(t,e,r,n){qt(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}var Zt=function(t,e){this.reset(t,e)};Zt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Zt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Jt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Kt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function Qt(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function $t(e,r){var n=[e.x,e.y,0,1];ue(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function te(t,e){return.5+t/e*.5}function ee(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function re(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!m)ce(y.numGlyphs,h);else{m=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),ee(x,f)){var b=te(n.transform.cameraToCenterDistance,x[3]),_=t.evaluateSizeForFeature(c,u,y),w=s?_/b:_*b,T=new t.Point(y.anchorX,y.anchorY),k=$t(T,a).point,M={},A=ae(y,w,!1,l,r,a,o,e.glyphOffsetArray,p,h,k,T,M,g);m=A.useVertical,(A.notEnoughRoom||m||A.needsFlipping&&ae(y,w,!0,l,r,a,o,e.glyphOffsetArray,p,h,k,T,M,g).notEnoughRoom)&&ce(y.numGlyphs,h)}else ce(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ne(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),g=e.getoffsetX(f-1),m=se(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!m)return null;var v=se(t*g,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:m,last:v}:null}function ie(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function ae(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=r/24,v=e.lineOffsetX*m,y=e.lineOffsetY*m;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ne(m,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=$t(w.first.point,s).point,k=$t(w.last.point,s).point;if(i&&!n){var M=ie(e.writingMode,T,k,d);if(M)return M}g=[w.first];for(var A=e.glyphStartIndex+1;A<x-1;A++)g.push(se(m*l.getoffsetX(A),v,y,n,f,h,e.segment,b,_,c,o,p));g.push(w.last)}else{if(i&&!n){var S=$t(h,a).point,E=e.lineStartIndex+e.segment+1,C=new t.Point(c.getx(E),c.gety(E)),L=$t(C,a),I=L.signedDistanceFromCamera>0?L.point:oe(h,C,S,1,a),P=ie(e.writingMode,S,I,d);if(P)return P}var z=se(m*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!z)return{notEnoughRoom:!0};g=[z]}for(var O=0,D=g;O<D.length;O+=1){var R=D[O];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function oe(t,e,r,n,i){var a=$t(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function se(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,g=0;i&&(d*=-1,g=Math.PI),d<0&&(g+=Math.PI);for(var m=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((m+=d)<l||m>=c)return null;if(y=v,w.push(v),void 0===(v=h[m])){var T=new t.Point(u.getx(m),u.gety(m)),k=$t(T,f);if(k.signedDistanceFromCamera>0)v=h[m]=k.point;else{var M=m-d;v=oe(0===x?o:new t.Point(u.getx(M),u.gety(M)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var A=(_-x)/b,S=v.sub(y),E=S.mult(A)._add(y);E._add(S._unit()._perp()._mult(n*d));var C=g+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:C,path:w}}Jt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Jt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Jt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Jt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Jt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Jt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}return this._forEachCell(t,e,r,n,this._queryCell,o,{hitTest:i,seenUids:{box:{},circle:{}}},a),i?o.length>0:o},Jt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[];return this._forEachCell(a,s,o,l,this._queryCellCircle,c,{hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}},i),n?c.length>0:c},Jt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Jt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Jt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Jt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(m[b],m[b+1],m[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=m[b],w=m[b+1],T=m[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Jt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var g=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[g+0],f[g+1],f[g+2],f[g+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var m=this.circleCells[i];if(null!==m)for(var v=this.circles,y=0,x=m;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Jt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++)if(i.call(this,t,e,r,n,this.xCellCount*p+h,a,o,s))return},Jt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Jt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Jt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Jt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var le=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ce(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(le,3*n)}}function ue(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var fe=function(t,e,r){void 0===e&&(e=new Jt(t.width+200,t.height+200,25)),void 0===r&&(r=new Jt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function he(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}fe.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},fe.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=new t.Point(r.anchorX,r.anchorY),m=$t(g,o),v=te(this.transform.cameraToCenterDistance,m.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=$t(g,s).point,b=ne(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,g,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,M=new t.Point(-100,-100),A=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Zt,E=b.first,C=b.last,L=[],I=E.path.length-1;I>=1;I--)L.push(E.path[I]);for(var P=1;P<C.path.length;P++)L.push(C.path[P]);var z=2.5*k;if(l){var O=L.map((function(t){return $t(t,l)}));L=O.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:O.map((function(t){return t.point}))}var D=[];if(L.length>0){for(var R=L[0].clone(),F=L[0].clone(),B=1;B<L.length;B++)R.x=Math.min(R.x,L[B].x),R.y=Math.min(R.y,L[B].y),F.x=Math.max(F.x,L[B].x),F.y=Math.max(F.y,L[B].y);D=R.x>=M.x&&F.x<=A.x&&R.y>=M.y&&F.y<=A.y?[L]:F.x<M.x||R.x>A.x||F.y<M.y||R.y>A.y?[]:t.clipLine([L],M.x,M.y,A.x,A.y)}for(var N=0,j=D;N<j.length;N+=1){var U;S.reset(j[N],.25*k),U=S.length<=.5*k?1:Math.ceil(S.paddedLength/z)+1;for(var V=0;V<U;V++){var q=V/Math.max(U-1,1),H=S.lerp(q),G=H.x+100,Y=H.y+100;d.push(G,Y,k,0);var W=G-k,X=Y-k,Z=G+k,J=Y+k;if(T=T&&this.isOffscreen(W,X,Z,J),w=w||this.isInsideGrid(W,X,Z,J),!e&&this.grid.hitTestCircle(G,Y,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},fe.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],m=g.key;if(void 0===f[m.bucketInstanceId]&&(f[m.bucketInstanceId]={}),!f[m.bucketInstanceId][m.featureIndex]){var v=[new t.Point(g.x1,g.y1),new t.Point(g.x2,g.y1),new t.Point(g.x2,g.y2),new t.Point(g.x1,g.y2)];t.polygonIntersectsPolygon(r,v)&&(f[m.bucketInstanceId][m.featureIndex]=!0,void 0===h[m.bucketInstanceId]&&(h[m.bucketInstanceId]=[]),h[m.bucketInstanceId].push(m.featureIndex))}}return h},fe.prototype.insertCollisionBox=function(t,e,r,n,i){(e?this.ignoredGrid:this.grid).insert({bucketInstanceId:r,featureIndex:n,collisionGroupID:i},t[0],t[1],t[2],t[3])},fe.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},fe.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return ue(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},fe.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},fe.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},fe.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var pe=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};pe.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var de=function(t,e,r,n,i){this.text=new pe(t?t.text:null,e,r,i),this.icon=new pe(t?t.icon:null,e,n,i)};de.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},me=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ve=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},ye=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function xe(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function be(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}ye.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var _e=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new fe(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new ye(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function we(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}_e.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=he(n,1,this.transform.zoom),g=Kt(f,h,p,this.transform,d),m=null;if(h){var v=Qt(f,h,p,this.transform,d);m=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ve(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:g,labelToScreenMatrix:m,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x];e.push({sortKey:_.sortKey,symbolInstanceStart:_.symbolInstanceStart,symbolInstanceEnd:_.symbolInstanceEnd,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},_e.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=[f.textOffset0,f.textOffset1],v=xe(t,r,n,m,i),y=this.collisionIndex.placeCollisionBox(be(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(be(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return y.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(g=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:m,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:g},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}):void 0},_e.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,g=a.collisionGroup,m=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),M=x&&(y||!o.hasTextData()||m);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var A=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,A=!1,S=!0,E=null,C={box:null,offscreen:null},L={box:null,offscreen:null},I=null,P=null,z=0,O=0,D=0;a.textFeatureIndex?z=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(z=e.featureIndex),a.verticalTextFeatureIndex&&(O=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,i.markUsedOrientation(o,n=a,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(C=n(),L=C):C=r(),!(C&&C.box&&C.box.length));i+=1);else C=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var m=i.attemptAnchorPlacement(N[d%N.length],t,a,s,c,b,_,f,l,g,d>=N.length,e,o,n,u);if(m&&(h=m.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=m.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&!(C&&C.box&&C.box.length)&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),C&&(T=C.box,S=C.offscreen);var V=F(C&&C.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,g.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(C&&C.box&&C.box.length)}}if(T=(p=C)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\");I=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,g.predicate,e.collisionCircleDiameter,W),T=y||I.circles.length>0&&!I.collisionDetected,S=S&&I.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?be(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,g.predicate)};A=L&&L.box&&L.box.length&&a.verticalIconBox?(P=X(a.verticalIconBox)).box.length>0:(P=X(a.iconBox)).box.length>0,S=S&&P.offscreen}var Z=m||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=v||0===e.numIconVertices;if(Z||J?J?Z||(A=A&&T):T=A&&T:A=T=A&&T,T&&p&&p.box&&i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,L&&L.box&&O?O:z,g.ID),A&&P&&i.collisionIndex.insertCollisionBox(P.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,g.ID),I&&(T&&i.collisionIndex.insertCollisionCircles(I.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,g.ID),n)){var K=o.bucketInstanceId,Q=i.collisionCircleArrays[K];void 0===Q&&(Q=i.collisionCircleArrays[K]=new me);for(var $=0;$<I.circles.length;$+=4)Q.circles.push(I.circles[$+0]),Q.circles.push(I.circles[$+1]),Q.circles.push(I.circles[$+2]),Q.circles.push(I.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,A||M,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var C=S[E];A(o.symbolInstances.get(C),o.collisionArrays[C])}else for(var L=e.symbolInstanceStart;L<e.symbolInstanceEnd;L++)A(o.symbolInstances.get(L),o.collisionArrays[L]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var I=this.collisionCircleArrays[o.bucketInstanceId];t.invert(I.invProjMatrix,l),I.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},_e.prototype.markUsedJustification=function(e,r,n,i){var a;a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:{left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex}[t.getAnchorJustification(r)];for(var o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];o<s.length;o+=1){var l=s[o];l>=0&&(e.text.placedSymbolArray.get(l).crossTileID=a>=0&&l!==a?0:n.crossTileID)}},_e.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1)e.text.placedSymbolArray.get(s[o]).placedOrientation=i;n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},_e.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new de(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new de(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new de(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},_e.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},_e.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new de(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new de(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},g=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,g=a.crossTileID,m=i.opacities[g];r[g]?m=o:m||(i.opacities[g]=m=p),r[g]=!0;var v=a.numIconVertices>0,y=i.placedOrientations[a.crossTileID],x=y===t.WritingMode.vertical,b=y===t.WritingMode.horizontal||y===t.WritingMode.horizontalOnly;if(s>0||l>0){var _=Le(m.text);d(e.text,s,x?Ie:_),d(e.text,l,b?Ie:_);var w=m.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=w||x?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=w||b?1:0);var T=i.variableOffsets[a.crossTileID];T&&i.markUsedJustification(e,T.anchor,a,y);var k=i.placedOrientations[a.crossTileID];k&&(i.markUsedJustification(e,\"left\",a,k),i.markUsedOrientation(e,k,a))}if(v){var M=Le(m.icon),A=!(h&&a.verticalPlacedIconSymbolIndex&&x);a.placedIconSymbolIndex>=0&&(d(e.icon,a.numIconVertices,A?M:Ie),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=m.icon.isHidden()),a.verticalPlacedIconSymbolIndex>=0&&(d(e.icon,a.numVerticalIconVertices,A?Ie:M),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=m.icon.isHidden())}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var S=e.collisionArrays[n];if(S){var E=new t.Point(0,0);if(S.textBox||S.verticalTextBox){var C=!0;if(c){var L=i.variableOffsets[g];L?(E=xe(L.anchor,L.width,L.height,L.textOffset,L.textBoxScale),u&&E._rotate(f?i.transform.angle:-i.transform.angle)):C=!1}S.textBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||x,E.x,E.y),S.verticalTextBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||b,E.x,E.y)}var I=Boolean(!b&&S.verticalIconBox);S.iconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,I,h?E.x:0,h?E.y:0),S.verticalIconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,!I,h?E.x:0,h?E.y:0)}}},m=0;m<e.symbolInstances.length;m++)g(m);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},_e.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},_e.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},_e.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},_e.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},_e.prototype.setStale=function(){this.stale=!0};var Te=Math.pow(2,25),ke=Math.pow(2,24),Me=Math.pow(2,17),Ae=Math.pow(2,16),Se=Math.pow(2,9),Ee=Math.pow(2,8),Ce=Math.pow(2,1);function Le(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Te+e*ke+r*Me+e*Ae+r*Se+e*Ee+r*Ce+e}var Ie=0,Pe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Pe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;)if(e.getBucketParts(a,n,t[this._currentTileIndex],this._sortAcrossTiles),this._currentTileIndex++,i())return!0;for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;)if(e.placeLayerBucketPart(a[this._currentPartIndex],this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0;return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new _e(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Pe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Oe=512/t.EXTENT/2,De=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};De.prototype.getScaledCoordinates=function(e,r){var n=Oe/Math.pow(2,r.canonical.z-this.tileID.canonical.z);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*n),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*n)}},De.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Re=function(){this.maxCrossTileID=0};Re.prototype.generate=function(){return++this.maxCrossTileID};var Fe=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Fe.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Fe.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new De(t,e.symbolInstances,e.bucketInstanceId),!0},Fe.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1)delete this.usedCrossTileIDs[t][i[n].crossTileID]},Fe.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Be=function(){this.layerIndexes={},this.crossTileIDs=new Re,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Be.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Fe);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Be.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var Ne=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},je=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ue=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Ve=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i,a=t.styleSpec.$root[n];a.required&&null!=(i=\"version\"===n?r:\"array\"===a.type?[]:{})&&(e[n]=i)}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Be,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){o.dispatcher.broadcast(\"syncRTLPluginState\",{pluginStatus:e.pluginStatus,pluginURL:e.pluginURL},(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(Ve,!1)},r.prototype._load=function(e,r){if(!r||!Ne(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,g=l.stretchX,m=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:g,stretchY:m,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=this._layers[n[r]];\"custom\"!==i.type&&e.push(i.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=this._layers[u[c]];f.recalculate(e,this._availableImages),!f.isHidden(e.zoom)&&f.source&&(this.sourceCaches[f.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),Ne(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=function(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||qt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):Ht(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),g=f.slice(),m=Object.create(null);for(i=0,a=0;i<f.length;i++)d.hasOwnProperty(o=f[i])?a++:(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)g[g.length-1-i]!==(o=h[h.length-1-i])&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,n.push({command:Ut.addLayer,args:[d[o],c=g[g.length-i]]}),g.splice(g.length-i,0,o),m[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!m[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}(this.serialize(),e).filter((function(t){return!(t.command in Ue)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in je)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Lt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(Ne(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1)i.push(f[u])}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var h=[],p=this._order.length-1;p>=0;p--){var d=this._order[p];if(r(d))for(var g=i.length-1;g>=0;g--){var m=i[g].feature;if(n[m.layer.id]<p)break;h.push(m),i.pop()}else for(var v=0,y=t;v<y.length;v+=1){var x=y[v][d];if(x)for(var b=0,_=x;b<_.length;b+=1)h.push(_[b].feature)}}return h},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1)c.push(o[f[u]]);c.sort(B);for(var h=function(){var r=d[p],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1)o.push(f[u])}},p=0,d=c;p<d.length;p+=1)h();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=r[t[e].source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=a}))};for(var m in s)g(m);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&Ne(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=this._layers[u[c]];if(\"symbol\"===f.type){if(!l[f.source]){var h=this.sourceCaches[f.source];l[f.source]=h.getRenderableIds(!0).map((function(t){return h.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var p=this.crossTileSymbolIndex.addLayer(f,l[f.source],e.center.lng);o=o||p}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var d=0,g=this._order;d<g.length;d+=1){var m=this._layers[g[d]];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var He=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ge=vr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),Ye=vr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),We=vr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Xe=vr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Ze=vr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Je=vr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Ke=vr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),Qe=vr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),$e=vr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),tr=vr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),er=vr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),rr=vr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),nr=vr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=vr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ar=vr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),or=vr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),sr=vr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),lr=vr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),cr=vr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),ur=vr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=vr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),hr=vr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),pr=vr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),dr=vr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=vr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),mr=vr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function vr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var yr=Object.freeze({__proto__:null,prelude:Ge,background:Ye,backgroundPattern:We,circle:Xe,clippingMask:Ze,heatmap:Je,heatmapTexture:Ke,collisionBox:Qe,collisionCircle:$e,debug:tr,fill:er,fillOutline:rr,fillOutlinePattern:nr,fillPattern:ir,fillExtrusion:ar,fillExtrusionPattern:or,hillshadePrepare:sr,hillshade:lr,line:cr,lineGradient:ur,linePattern:fr,lineSDF:hr,raster:pr,symbolIcon:dr,symbolSDF:gr,symbolTextAndIcon:mr}),xr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};xr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);t.extVertexArrayObject&&this.vao&&this.boundProgram===e&&this.boundLayoutVertexBuffer===r&&!l&&this.boundIndexBuffer===i&&this.boundVertexOffset===a&&this.boundDynamicVertexBuffer===o&&this.boundDynamicVertexBuffer2===s?(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind()):this.freshBind(e,r,n,i,a,o,s)},xr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(u,t);a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var m=g[d];m.bind(),m.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},xr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var br=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ge.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ge.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var g=a.getActiveAttrib(this.program,d);g&&(this.attributes[g.name]=a.getAttribLocation(this.program,g.name))}for(var m=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<m;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function _r(t,e,r){var n=1/he(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}br.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(m={},m[v.LINES]=2,m[v.TRIANGLES]=3,m[v.LINE_STRIP]=1,m)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new xr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var wr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},Tr=function(e,r,n,i,a,o,s){return t.extend(wr(e,r,n,i),_r(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},kr=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(kr(e),_r(n,r,i))},Ar=function(t,e){return{u_matrix:t,u_world:e}},Sr=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Er=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=he(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=he(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Lr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Pr=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:he(e,1,r),u_intensity:n}},Or=function(e,r,n){var i=e.transform;return{u_matrix:Nr(e,r,n),u_ratio:1/he(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Dr=function(e,r,n){return t.extend(Or(e,r,n),{u_image:0})},Rr=function(e,r,n,i){var a=e.transform,o=Br(r,a);return{u_matrix:Nr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/he(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Fr=function(e,r,n,i,a){var o=e.lineAtlas,s=Br(r,e.transform),l=\"round\"===n.layout.get(\"line-cap\"),c=o.getDash(i.from,l),u=o.getDash(i.to,l),f=c.width*a.fromScale,h=u.width*a.toScale;return t.extend(Or(e,r,n),{u_patternscale_a:[s/f,-c.height/2],u_patternscale_b:[s/h,-u.height/2],u_sdfgamma:o.width/(256*Math.min(f,h)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:u.y,u_mix:a.t})};function Br(t,e){return 1/he(t,1,e.tileZoom)}function Nr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var jr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Ur(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Ur(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Vr,qr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Hr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(qr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Gr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Hr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Yr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Wr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/he(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Xr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Zr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var g=i[d],m=r.getTile(g),v=m.getBucket(n);if(v){var y=g.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(g.posMatrix,m,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,m),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var M=0,A=0,S=f;A<S.length;A+=1)for(var E=S[A],C=0;C<E.circleArray.length/4;C++){var L=4*C,I=E.circleArray[L+0],P=E.circleArray[L+1],z=E.circleArray[L+2],O=E.circleArray[L+3];k.emplace(M++,I,P,z,O,0),k.emplace(M++,I,P,z,O,1),k.emplace(M++,I,P,z,O,2),k.emplace(M++,I,P,z,O,3)}(!Vr||Vr.length<2*h)&&(Vr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(Vr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Lr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Jr=t.identity(new Float32Array(16));function Kr(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function Qr(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,g={};p.clear();for(var m=0;m<h.length;m++){var v=h.get(m),y=v.hidden||!v.crossTileID||e.allowVerticalPlacement&&!v.placedOrientation?null:i[v.crossTileID];if(y){var x=new t.Point(v.anchorX,v.anchorY),b=$t(x,n?l:s),_=te(o.cameraToCenterDistance,b.signedDistanceFromCamera),w=a.evaluateSizeForFeature(e.textSizeData,u,v)*_/t.ONE_EM;n&&(w*=e.tilePixelRatio/c);for(var T=Kr(y.anchor,y.width,y.height,y.textOffset,y.textBoxScale,w),k=n?$t(x.add(T),s).point:b.point.add(r?T.rotate(-o.angle):T),M=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,A=0;A<v.numGlyphs;A++)t.addDynamicAttributes(p,k,M);f&&v.associatedIconIndex>=0&&(g[v.associatedIconIndex]={shiftedAnchor:k,angle:M})}else ce(v.numGlyphs,p)}if(f){d.clear();for(var S=e.icon.placedSymbolArray,E=0;E<S.length;E++){var C=S.get(E);if(C.hidden)ce(C.numGlyphs,d);else{var L=g[E];if(L)for(var I=0;I<C.numGlyphs;I++)t.addDynamicAttributes(d,L.shiftedAnchor,L.angle);else ce(C.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function $r(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function tn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,g=e.transform,m=\"map\"===l,v=\"map\"===c,y=m&&\"point\"!==n.layout.get(\"symbol-placement\"),x=m&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,Mt.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,M=i;k<M.length;k+=1){var A=M[k],S=r.getTile(A),E=S.getBucket(n);if(E){var C=a?E.text:E.icon;if(C&&C.segments.get().length){var L=C.programConfigurations.get(n.id),I=a||E.sdfIcons,P=a?E.textSizeData:E.iconSizeData,z=v||0!==g.pitch,O=e.useProgram($r(I,a,E),L),D=t.evaluateSizeForZoom(P,g.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a)B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText&&(F=S.imageAtlasTexture.size,j=S.imageAtlasTexture,U=z||e.options.rotating||e.options.zooming||\"composite\"===P.kind||\"camera\"===P.kind?d.LINEAR:d.NEAREST);else{var V=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=I||e.options.rotating||e.options.zooming||V||z?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=he(S,1,e.transform.zoom),H=Kt(A.posMatrix,v,m,e.transform,q),G=Qt(A.posMatrix,v,m,e.transform,q),Y=w&&E.hasTextData(),W=\"none\"!==n.layout.get(\"icon-text-fit\")&&Y&&E.hasIconData();y&&re(E,A.posMatrix,e,a,H,G,v,u);var X=e.translatePosMatrix(A.posMatrix,S,o,s),Z=y||a&&w||W?Jr:H,J=e.translatePosMatrix(G,S,o,s,!0),K=I&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),Q={program:O,buffers:C,uniformValues:I?E.iconsInText?Gr(P.kind,D,x,v,e,X,Z,J,R,F):Hr(P.kind,D,x,v,e,X,Z,J,a,R,!0):qr(P.kind,D,x,v,e,X,Z,J,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:I,hasHalo:K};if(b)for(var $=0,tt=C.segments.get();$<tt.length;$+=1){var et=tt[$];T.push({segments:new t.SegmentVector([et]),sortKey:et.sortKey,state:Q})}else T.push({segments:C.segments,sortKey:0,state:Q})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var rt=0,nt=T;rt<nt.length;rt+=1){var it=nt[rt],at=it.state;if(p.activeTexture.set(d.TEXTURE0),at.atlasTexture.bind(at.atlasInterpolation,d.CLAMP_TO_EDGE),at.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),at.atlasTextureIcon&&at.atlasTextureIcon.bind(at.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),at.isSDF){var ot=at.uniformValues;at.hasHalo&&(ot.u_is_halo=1,en(at.buffers,it.segments,n,e,at.program,_,f,h,ot)),ot.u_is_halo=0}en(at.buffers,it.segments,n,e,at.program,_,f,h,at.uniformValues)}}function en(t,e,r,n,i,a,o,s,l){var c=n.context;i.draw(c,c.gl.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function rn(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),g=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var m=0,v=n;m<v.length;m+=1){var y=v[m],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(g));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,M=k.patternPositions[T.to.toString()],A=k.patternPositions[T.from.toString()];M&&A&&_.setConstantPatternPositions(M,A)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Sr(S,t,g,x,E):Ar(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,g,x):kr(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function nn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var g=d[p],m=e.getTile(g),v=m.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),m.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&m.imageAtlas){var _=m.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(g.posMatrix,m,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),M=r.paint.get(\"fill-extrusion-vertical-gradient\"),A=u?Tr(k,t,M,h,g,f,m):wr(k,t,M,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,A,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function an(e,r,n,i,a,o){var s=e.context,l=s.gl,c=r.fbo;if(c){var u=e.useProgram(\"hillshade\");s.activeTexture.set(l.TEXTURE0),l.bindTexture(l.TEXTURE_2D,c.colorAttachment.get());var f=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,c,u,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,c=Math.pow(2,l.canonical.z),u=l.canonical.y,[new t.MercatorCoordinate(0,u/c).toLngLat().lat,new t.MercatorCoordinate(0,(u+1)/c).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}}(e,r,n);u.draw(s,l.TRIANGLES,i,a,o,Et.disabled,f,n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments)}}function on(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var g=r.fbo;if(!g){var m=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);m.bind(c.LINEAR,c.CLAMP_TO_EDGE),(g=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(m.texture)}l.bindFramebuffer.set(g.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function sn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var ln=new t.Color(1,0,0,1),cn=new t.Color(0,1,0,1),un=new t.Color(0,0,1,1),fn=new t.Color(1,0,1,1),hn=new t.Color(0,1,1,1);function pn(t,e,r,n){gn(t,0,e+r/2,t.transform.width,r,n)}function dn(t,e,r,n){gn(t,e-r/2,0,r,t.transform.height,n)}function gn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function mn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,c=At.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=Math.floor((f&&f.byteLength||0)/1024),p=r.getTile(n).tileSize,d=512/Math.min(p,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+h+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,d),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var vn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=At.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),g=d.getBucket(n);if(g&&g.text&&g.text.segments.get().length){var m=t.evaluateSizeForZoom(g.textSizeData,l.zoom),v=he(d,1,r.transform.zoom),y=Kt(p.posMatrix,u,c,r.transform,v),x=\"none\"!==n.layout.get(\"icon-text-fit\")&&g.hasIconData();if(m){var b=Math.pow(2,l.zoom-d.tileID.overscaledZ);Qr(g,c,u,s,t.symbolSize,l,y,p.posMatrix,b,m,x)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&tn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&tn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Zr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Zr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=At.disabled,p=e.colorModeForRenderPass(),d=[],g=0;g<i.length;g++){var m=i[g],v=r.getTile(m),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Er(e,m,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,M=d;k<M.length;k+=1){var A=M[k],S=A.state;S.program.draw(c,u.TRIANGLES,f,h,p,Et.disabled,S.uniformValues,n.id,S.layoutVertexBuffer,S.indexBuffer,A.segments,n.paint,e.transform.zoom,S.programConfiguration)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=At.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id);e.useProgram(\"heatmap\",p).draw(a,o.TRIANGLES,Mt.disabled,s,l,Et.disabled,zr(u.posMatrix,f,e.transform.zoom,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:0,u_color_ramp:1,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",g=e.context,m=g.gl,v=!0;if(h){g.activeTexture.set(m.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(g,n.gradient,m.RGBA)),y.bind(m.LINEAR,m.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),M=e.context.program.get(),A=e.useProgram(d,k),S=v||A.program!==M,E=u.constantOr(null);if(E&&w.imageAtlas){var C=w.imageAtlas,L=C.patternPositions[E.to.toString()],I=C.patternPositions[E.from.toString()];L&&I&&k.setConstantPatternPositions(L,I)}var P=f?Rr(e,w,n,p):c?Fr(e,w,n,c,p):h?Dr(e,w,n):Or(e,w,n);f?(g.activeTexture.set(m.TEXTURE0),w.imageAtlasTexture.bind(m.LINEAR,m.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(g.activeTexture.set(m.TEXTURE0),e.lineAtlas.bind(g)),A.draw(g,m.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,P,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);rn(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,Mt.ReadOnly);rn(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))nn(t,e,r,n,a,At.disabled,St.disabled),nn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();nn(t,e,r,n,a,At.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,Mt.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?on(t,p,r,a,o,At.disabled,s):\"translucent\"===t.renderPass&&an(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof P?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,g=f;d<g.length;d+=1){var m=g[d],v=t.depthModeForSublayer(m.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),y=e.getTile(m),x=t.transform.calculatePosMatrix(m.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(m,0),_=sn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var M=jr(x,T||[0,0],w||1,_,r);o instanceof P?s.draw(i,a.TRIANGLES,v,At.disabled,l,Et.disabled,M,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[m.overscaledZ],l,Et.disabled,M,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=At.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),g=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var m=r.getCrossfadeParameters(),v=0,y=g;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Wr(b,i,t,c,{tileID:x,tileSize:l},m):Yr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)mn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(At.disabled);var o=\"3d\"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},yn=function(t,e){this.context=new Ct(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Lt.maxUnderzooming+Lt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Be,this.gpuTimers={}};yn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1)this.style._layers[i[n]].resize()},yn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,He.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,He.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,I.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,He.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new At({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},yn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,St.disabled,Et.disabled,Pr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},yn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new At({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Pr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},yn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new At({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},yn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new At({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},yn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new At({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=At.disabled,e),n]},yn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;return this._showOverdrawInspector?new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0]):\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},yn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},yn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},yn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var g=0;g<i.length;g++)if(this.style._layers[i[g]].is3D()){this.opaquePassCutoff=g;break}this.renderPass=\"offscreen\";for(var m=0,v=i;m<v.length;m+=1){var y=this.style._layers[v[m]];if(y.hasOffscreenPass()&&!y.isHidden(this.transform.zoom)){var x=f[y.source];(\"custom\"===y.type||x.length)&&this.renderLayer(this,a[y.source],y,x)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var b=this.style._layers[i[this.currentLayer]],_=a[b.source],w=u[b.source];this._renderTileClippingMasks(b,w),this.renderLayer(this,_,b,w)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var T=this.style._layers[i[this.currentLayer]],k=a[T.source],M=(\"symbol\"===T.type?h:f)[T.source];this._renderTileClippingMasks(T,u[T.source]),this.renderLayer(this,k,T,M)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&vn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&function(t){var e=t.transform.padding;pn(t,t.transform.height-(e.top||0),3,ln),pn(t,e.bottom||0,3,cn),dn(t,e.left||0,3,un),dn(t,t.transform.width-(e.right||0),3,fn);var r=t.transform.centerPoint;!function(t,e,r,n){gn(t,e-1,r-10,2,20,n),gn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,hn)}(this),this.context.setDefault()},yn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),vn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},yn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},yn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},yn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},yn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},yn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:he(r,n[0],this.transform.zoom),a?n[1]:he(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},yn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},yn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},yn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},yn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new br(this.context,yr[t],e,Xr[t],this._showOverdrawInspector)),this.cache[r]},yn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},yn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},yn.prototype.initDebugOverlayCanvas=function(){null==this.debugOverlayCanvas&&(this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))},yn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var xn=function(t,e){this.points=t,this.planes=e};xn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new xn(a,o)};var bn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};bn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new bn(n,i)},bn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},bn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},bn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var _n=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};_n.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},_n.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},_n.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},_n.prototype.clone=function(){return new _n(this.top,this.bottom,this.left,this.right)},_n.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var wn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new _n,this._posMatrixCache={},this._alignedPosMatrixCache={}},Tn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};wn.prototype.clone=function(){var t=new wn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Tn.minZoom.get=function(){return this._minZoom},Tn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Tn.maxZoom.get=function(){return this._maxZoom},Tn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Tn.minPitch.get=function(){return this._minPitch},Tn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Tn.maxPitch.get=function(){return this._maxPitch},Tn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Tn.renderWorldCopies.get=function(){return this._renderWorldCopies},Tn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Tn.worldSize.get=function(){return this.tileSize*this.scale},Tn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Tn.size.get=function(){return new t.Point(this.width,this.height)},Tn.bearing.get=function(){return-this.angle/Math.PI*180},Tn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Tn.pitch.get=function(){return this._pitch/Math.PI*180},Tn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Tn.fov.get=function(){return this._fov/Math.PI*180},Tn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Tn.zoom.get=function(){return this._zoom},Tn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Tn.center.get=function(){return this._center},Tn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Tn.padding.get=function(){return this._edgeInsets.toJSON()},Tn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Tn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},wn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},wn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},wn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},wn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},wn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=xn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new bn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var g=u.pop(),m=g.x,v=g.y,y=g.fullyVisible;if(!y){var x=g.aabb.intersects(s);if(0===x)continue;y=2===x}var b=g.aabb.distanceX(o),_=g.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_));if(g.zoom===h||w>3+(1<<h-g.zoom)-2&&g.zoom>=l)f.push({tileID:new t.OverscaledTileID(g.zoom===h?p:g.zoom,g.wrap,g.zoom,m,v),distanceSq:t.sqrLen([o[0]-.5-m,o[1]-.5-v])});else for(var T=0;T<4;T++){var k=(m<<1)+T%2,M=(v<<1)+(T>>1);u.push({aabb:g.aabb.quadrant(T),zoom:g.zoom+1,x:k,y:M,wrap:g.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},wn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Tn.unmodified.get=function(){return this._unmodified},wn.prototype.zoomScale=function(t){return Math.pow(2,t)},wn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},wn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},wn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Tn.point.get=function(){return this.project(this.center)},wn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},wn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},wn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},wn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},wn.prototype.coordinateLocation=function(t){return t.toLngLat()},wn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[1]/i,s=n[1]/a,l=r[2]/i,c=n[2]/a,u=l===c?0:(0-l)/(c-l);return new t.MercatorCoordinate(t.number(r[0]/i,n[0]/a,u)/this.worldSize,t.number(o,s,u)/this.worldSize)},wn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},wn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},wn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},wn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},wn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},wn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},wn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var g=p.y,m=c.y/2;g-m<a&&(i=a+m),g+m>o&&(i=o-m)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},wn.prototype._calcMatrices=function(){if(this.height){var e=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height;var r=Math.PI/2+this._pitch,n=this._fov*(.5+e.y/this.height),i=Math.sin(n)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-r-n,.01,Math.PI-.01)),a=this.point,o=a.x,s=a.y,l=1.01*(Math.cos(Math.PI/2-this._pitch)*i+this.cameraToCenterDistance),c=this.height/50,u=new Float64Array(16);t.perspective(u,this._fov,this.width/this.height,c,l),u[8]=2*-e.x/this.width,u[9]=2*e.y/this.height,t.scale(u,u,[1,-1,1]),t.translate(u,u,[0,0,-this.cameraToCenterDistance]),t.rotateX(u,u,this._pitch),t.rotateZ(u,u,this.angle),t.translate(u,u,[-o,-s,0]),this.mercatorMatrix=t.scale([],u,[this.worldSize,this.worldSize,this.worldSize]),t.scale(u,u,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=u,this.invProjMatrix=t.invert([],this.projMatrix);var f=this.width%2/2,h=this.height%2/2,p=Math.cos(this.angle),d=Math.sin(this.angle),g=o-Math.round(o)+p*f+d*h,m=s-Math.round(s)+p*h+d*f,v=new Float64Array(u);if(t.translate(v,v,[g>.5?g-1:g,m>.5?m-1:m,0]),this.alignedProjMatrix=v,u=t.create(),t.scale(u,u,[this.width/2,-this.height/2,1]),t.translate(u,u,[1,-1,0]),this.labelPlaneMatrix=u,u=t.create(),t.scale(u,u,[1,-1,1]),t.translate(u,u,[-1,-1,0]),t.scale(u,u,[2/this.width,2/this.height,1]),this.glCoordMatrix=u,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(u=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=u,this._posMatrixCache={},this._alignedPosMatrixCache={}}},wn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},wn.prototype.getCameraPoint=function(){var e=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,e))},wn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(wn.prototype,Tn);var kn=function(e){var r,n,i,a;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=!1,i=null,a=function(){i=null,n&&(r(),i=setTimeout(a,300),n=!1)},function(){return n=!0,i||a(),i})};kn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},kn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},kn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},kn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},kn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},kn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var Mn={linearity:.3,easing:t.bezier(0,0,.3,1)},An=t.extend({deceleration:2500,maxSpeed:1400},Mn),Sn=t.extend({deceleration:20,maxSpeed:1400},Mn),En=t.extend({deceleration:1e3,maxSpeed:360},Mn),Cn=t.extend({deceleration:1e3,maxSpeed:90},Mn),Ln=function(t){this._map=t,this.clear()};function In(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Pn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Ln.prototype.clear=function(){this._inertiaBuffer=[]},Ln.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Ln.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Ln.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Pn(r.pan.mag(),o,t.extend({},An,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,In(s,l)}if(r.zoom){var c=Pn(r.zoom,o,Sn);s.zoom=this._map.transform.zoom+c.amount,In(s,c)}if(r.bearing){var u=Pn(r.bearing,o,En);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),In(s,u)}if(r.pitch){var f=Pn(r.pitch,o,Cn);s.pitch=this._map.transform.pitch+f.amount,In(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var zn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),On=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Dn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Rn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Rn.prototype.reset=function(){delete this._mousedownPos},Rn.prototype.wheel=function(t){return this._firePreventable(new Dn(t.type,this._map,t))},Rn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseup=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.dblclick=function(t){return this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseover=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.mouseout=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.touchstart=function(t){return this._firePreventable(new On(t.type,this._map,t))},Rn.prototype.touchmove=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchend=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchcancel=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Rn.prototype.isEnabled=function(){return!0},Rn.prototype.isActive=function(){return!1},Rn.prototype.enable=function(){},Rn.prototype.disable=function(){};var Fn=function(t){this._map=t};Fn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Fn.prototype.mousemove=function(t){this._map.fire(new zn(t.type,this._map,t))},Fn.prototype.mousedown=function(){this._delayContextMenu=!0},Fn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new zn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Fn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new zn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Fn.prototype.isEnabled=function(){return!0},Fn.prototype.isActive=function(){return!1},Fn.prototype.enable=function(){},Fn.prototype.disable=function(){};var Bn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Nn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Bn.prototype.isEnabled=function(){return!!this._enabled},Bn.prototype.isActive=function(){return!!this._active},Bn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Bn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Bn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Bn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Bn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Bn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Bn.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Bn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var jn=function(t){this.reset(),this.numTouches=t.numTouches};jn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},jn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1)r._add(i[n]);return r.div(e.length)}(r),this.touches=Nn(n,r)))},jn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Nn(r,e);for(var i in this.touches){var a=n[i];(!a||a.dist(this.touches[i])>30)&&(this.aborted=!0)}}},jn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Un=function(t){this.singleTap=new jn(t),this.numTaps=t.numTaps,this.reset()};Un.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Un.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Un.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Un.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Vn=function(){this._zoomIn=new Un({numTouches:1,numTaps:2}),this._zoomOut=new Un({numTouches:2,numTaps:1}),this.reset()};Vn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Vn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Vn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Vn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Vn.prototype.touchcancel=function(){this.reset()},Vn.prototype.enable=function(){this._enabled=!0},Vn.prototype.disable=function(){this._enabled=!1,this.reset()},Vn.prototype.isEnabled=function(){return this._enabled},Vn.prototype.isActive=function(){return this._active};var qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},qn.prototype._correctButton=function(t,e){return!1},qn.prototype._move=function(t,e){return{}},qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},qn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},qn.prototype.enable=function(){this._enabled=!0},qn.prototype.disable=function(){this._enabled=!1,this.reset()},qn.prototype.isEnabled=function(){return this._enabled},qn.prototype.isActive=function(){return this._active};var Hn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(qn),Gn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(qn),Yn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(qn),Wn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Wn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Wn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Wn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Wn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Wn.prototype.touchcancel=function(){this.reset()},Wn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Nn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Wn.prototype.enable=function(){this._enabled=!0},Wn.prototype.disable=function(){this._enabled=!1,this.reset()},Wn.prototype.isEnabled=function(){return this._enabled},Wn.prototype.isActive=function(){return this._active};var Xn=function(){this.reset()};function Zn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}function Jn(t,e){return Math.log(t/e)/Math.LN2}Xn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Xn.prototype._start=function(t){},Xn.prototype._move=function(t,e,r){return{}},Xn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Xn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[1],a=Zn(r,e,n[0]),o=Zn(r,e,i);if(a&&o){var s=this._aroundCenter?null:a.add(o).div(2);return this._move([a,o],s,t)}}},Xn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[1],o=Zn(n,e,i[0]),s=Zn(n,e,a);o&&s||(this._active&&r.suppressClick(),this.reset())}},Xn.prototype.touchcancel=function(){this.reset()},Xn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Xn.prototype.disable=function(){this._enabled=!1,this.reset()},Xn.prototype.isEnabled=function(){return this._enabled},Xn.prototype.isActive=function(){return this._active};var Kn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(Jn(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:Jn(this._distance,r),pinchAround:e}},e}(Xn);function Qn(t,e){return 180*t.angleWith(e)/Math.PI}var $n=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:Qn(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=Qn(t,this._startVector);return Math.abs(r)<e},e}(Xn);function ti(t){return Math.abs(t.y)>Math.abs(t.x)}var ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ti(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ti(t)&&ti(e)&&a}},e}(Xn),ri={panStep:100,bearingStep:15,pitchStep:10},ni=function(){var t=ri;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function ii(t){return t*(2-t)}ni.prototype.reset=function(){this._active=!1},ni.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:ii,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ai=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};ai.prototype.setZoomRate=function(t){this._defaultZoomRate=t},ai.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},ai.prototype.isEnabled=function(){return!!this._enabled},ai.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},ai.prototype.isZooming=function(){return!!this._zooming},ai.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},ai.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},ai.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},ai.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},ai.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},ai.prototype.renderFrame=function(){return this._onScrollFrame()},ai.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},ai.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},ai.prototype.reset=function(){this._active=!1};var oi=function(t,e){this._clickZoom=t,this._tapZoom=e};oi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},oi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},oi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},oi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var si=function(){this.reset()};si.prototype.reset=function(){this._active=!1},si.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},si.prototype.enable=function(){this._enabled=!0},si.prototype.disable=function(){this._enabled=!1,this.reset()},si.prototype.isEnabled=function(){return this._enabled},si.prototype.isActive=function(){return this._active};var li=function(){this._tap=new Un({numTouches:1,numTaps:1}),this.reset()};li.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},li.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},li.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},li.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},li.prototype.touchcancel=function(){this.reset()},li.prototype.enable=function(){this._enabled=!0},li.prototype.disable=function(){this._enabled=!1,this.reset()},li.prototype.isEnabled=function(){return this._enabled},li.prototype.isActive=function(){return this._active};var ci=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};ci.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},ci.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},ci.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},ci.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var ui=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};ui.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},ui.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},ui.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},ui.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var fi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};fi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},fi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},fi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},fi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},fi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},fi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var hi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(t.Event);function di(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var gi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Ln(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0];r.addEventListener(l,s[1],l===t.window.document?this.handleWindowEvent:this.handleEvent,s[2])}};gi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0];r.removeEventListener(a,i[1],a===t.window.document?this.handleWindowEvent:this.handleEvent,i[2])}},gi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Rn(e,t));var n=e.boxZoom=new Bn(e,t);this._add(\"boxZoom\",n);var i=new Vn,a=new si;e.doubleClickZoom=new oi(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new li;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ei;this._add(\"touchPitch\",s);var l=new Gn(t),c=new Yn(t);e.dragRotate=new ui(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Hn(t),f=new Wn(t);e.dragPan=new ci(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new $n,p=new Kn;e.touchZoomRotate=new fi(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new ai(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var g=e.keyboard=new ni;this._add(\"keyboard\",g),this._add(\"blockableMapEvent\",new Fn(e));for(var m=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];m<v.length;m+=1){var y=v[m];t.interactive&&t[y]&&e[y].enable(t[y])}},gi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},gi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1)e[t].handler.reset();this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},gi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},gi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},gi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},gi.prototype.isMoving=function(){return Boolean(hi(this._eventsInProgress))||this.isZooming()},gi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},gi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},gi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r];this._el.contains(i.target)&&e.push(i)}return e},gi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var g=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(g=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,g,h,n),g&&g.needsRenderFrame&&this._triggerRenderFrame()),(g||p.isActive())&&(o[h]=p)}}var m={};for(var v in this._previousActiveHandlers)o[v]||(m[v]=n);this._previousActiveHandlers=o,(Object.keys(m).length||di(i))&&(this._changes.push([i,a,m]),this._triggerRenderFrame()),(Object.keys(o).length||di(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},gi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},gi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},gi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!di(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},gi.prototype._fireEvents=function(e,r){var n=this,i=hi(this._eventsInProgress),a=hi(e),o={};for(var s in e)this._eventsInProgress[s]||(o[s+\"start\"]=e[s].originalEvent),this._eventsInProgress[s]=e[s];for(var l in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(l,o[l]);for(var c in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e)this._fireEvent(c,e[c].originalEvent);var u,f={};for(var h in this._eventsInProgress){var p=this._eventsInProgress[h],d=p.handlerName,g=p.originalEvent;this._handlersById[d].isActive()||(delete this._eventsInProgress[h],f[h+\"end\"]=u=r[d]||g)}for(var m in f)this._fireEvent(m,f[m]);var v=hi(this._eventsInProgress);if((i||a)&&!v){this._updatingCamera=!0;var y=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),x=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};y?(x(y.bearing||this._map.getBearing())&&(y.bearing=0),this._map.easeTo(y,{originalEvent:u})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:u})),x(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},gi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},gi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new pi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var mi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),g=p.sub(d),m=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/g.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/g.y;if(!(v<0||m<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(m,v)),i.maxZoom),x=t.Point.convert(i.offset),b=new t.Point(x.x+(i.padding.left-i.padding.right)/2,x.y+(i.padding.top-i.padding.bottom)/2).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(b)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),g=i.pointLocation(d),m=t.LngLat.convert(e.center||g);this._normalizeCenter(m);var v,y,x=i.project(g),b=i.project(m).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var g=i.zoomScale(i.zoom-a),m=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(m,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(g));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),g=t.Point.convert(e.offset),m=a.centerPoint.add(g),v=a.pointLocation(m),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var M=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),A=w/a.zoomScale(M-o);_=Math.sqrt(A/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function C(t){return(Math.exp(t)-Math.exp(-t))/2}function L(t){return(Math.exp(t)+Math.exp(-t))/2}var I=E(0),P=function(t){return L(I)/L(I+_*t)},z=function(t){return w*((L(I)*(C(e=I+_*t)/L(e))-C(I))/S)/k;var e},O=(E(1)-I)/_;if(Math.abs(k)<1e-6||!isFinite(O)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;O=Math.abs(Math.log(T/w))/_,z=function(){return 0},P=function(t){return Math.exp(D*_*t)}}return e.duration=\"duration\"in e?+e.duration:1e3*O/(\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed),e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*O,d=1/P(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),m=a.centerPoint.add(g));var v=1===e?y:a.unproject(x.add(b.mult(z(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,m),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),vi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};vi.prototype.getDefaultPosition=function(){return\"bottom-right\"},vi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},vi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},vi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},vi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},vi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},vi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var yi=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};yi.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},yi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},yi.prototype.getDefaultPosition=function(){return\"bottom-left\"},yi.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},yi.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},yi.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var xi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};xi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},xi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},xi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},xi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var bi={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},_i=t.window.HTMLImageElement,wi=t.window.HTMLElement,Ti=t.window.ImageBitmap,ki={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Mi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},ki,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new wn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new xi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},bi,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof wi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new gi(this,e),this._hash=e.hash&&new kn(\"string\"==typeof e.hash&&e.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new vi({customAttribution:e.customAttribution})),this.addControl(new yi,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),(i.prototype=Object.create(n&&n.prototype)).constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new zn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new zn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new zn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;return void 0===r?n.prototype.off.call(this,t,e):(this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this)},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;if(this._lazyInitEmptyStyle(),r instanceof _i||Ti&&r instanceof Ti){var c=t.browser.getImageData(r);this.style.addImage(e,{data:new t.RGBAImage({width:c.width,height:c.height},c.data),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var u=r;this.style.addImage(e,{data:new t.RGBAImage({width:r.width,height:r.height},new Uint8Array(r.data)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:u}),u.onAdd&&u.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof _i||Ti&&r instanceof Ti?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;return void 0===a||void 0===o?this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\"))):a!==n.data.width||o!==n.data.height?this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\"))):(n.data.replace(s,!(r instanceof _i||Ti&&r instanceof Ti)),void this.style.updateImage(e,n))},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new yn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Ai(this._canvasContainer),Ai(this._controlContainer),Ai(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(mi);function Ai(t){t.parentNode&&t.parentNode.removeChild(t)}var Si={showCompass:!0,showZoom:!0,visualizePitch:!1},Ei=function(e){var n=this;this.options=t.extend({},Si,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Ei.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Ei.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Ei.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ci(this._map,this._compass,this.options.visualizePitch)),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Ei.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Ei.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ci=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Gn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Yn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Li(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ci.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ci.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ci.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ci.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ci.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ci.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ci.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ci.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ci.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ci.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ci.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var Ii={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Pi(t,e,r){var n=t.classList;for(var i in Ii)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var zi,Oi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");g.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(g);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"opacity\",\"0.25\"),m.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),m.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(m),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Pi(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Li(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,Ii[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Di={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},Ri=0,Fi=!1,Bi=function(e){function n(r){e.call(this),this.options=t.extend({},Di,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==zi?n(zi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){n(zi=\"denied\"!==t.state)})):n(zi=!!t.window.navigator.geolocation),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ri=0,Fi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Fi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Oi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Oi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||e.originalEvent&&\"resize\"===e.originalEvent.type||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ri--,Fi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ri>1?(e={maximumAge:6e5,timeout:0},Fi=!0):(e=this.options.positionOptions,Fi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ni={maxWidth:100,unit:\"metric\"},ji=function(e){this.options=t.extend({},Ni,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Ui(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?Vi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):Vi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?Vi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?Vi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Vi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Vi(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*(o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o)));t.style.width=e*(s/r)+\"px\",t.innerHTML=s+\"&nbsp;\"+n}ji.prototype.getDefaultPosition=function(){return\"bottom-left\"},ji.prototype._onMove=function(){Ui(this._map,this._container,this.options)},ji.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},ji.prototype.setUnit=function(t){this.options.unit=t,Ui(this._map,this._container,this.options)};var qi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};qi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},qi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},qi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},qi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},qi.prototype._isFullscreen=function(){return this._fullscreen},qi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},qi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Hi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Gi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Hi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this;if(this._map&&(this._lngLat||this._trackPointer)&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Li(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var i=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),a=this.options.anchor,o=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!a){var s,l=this._container.offsetWidth,c=this._container.offsetHeight;s=i.y+o.bottom.y<c?[\"top\"]:i.y>this._map.transform.height-c?[\"bottom\"]:[],i.x<l/2?s.push(\"left\"):i.x>this._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var u=i.add(o[a]).round();r.setTransform(this._container,Ii[a]+\" translate(\"+u.x+\"px,\"+u.y+\"px)\"),Pi(this._container,a,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented),Yi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Mi,NavigationControl:Ei,GeolocateControl:Bi,AttributionControl:vi,ScaleControl:ji,FullscreenControl:qi,Popup:Gi,Marker:Oi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(Ot)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Ot),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Yi})),r}))},{}],474:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=1<<t+1,r=new Array(e),n=0;n<e;++n)r[n]=a(t,n);return r};var n=t(\"convex-hull\");function i(t,e,r){for(var n=new Array(t),i=0;i<t;++i)n[i]=0,i===e&&(n[i]+=.5),i===r&&(n[i]+=.5);return n}function a(t,e){if(0===e||e===(1<<t+1)-1)return[];for(var r=[],a=[],o=0;o<=t;++o)if(e&1<<o){r.push(i(t,o-1,o-1)),a.push(null);for(var s=0;s<=t;++s)~e&1<<s&&(r.push(i(t,o-1,s-1)),a.push([o,s]))}var l=n(r),c=[];t:for(o=0;o<l.length;++o){var u=l[o],f=[];for(s=0;s<u.length;++s){if(!a[u[s]])continue t;f.push(a[u[s]].slice())}c.push(f)}return c}},{\"convex-hull\":135}],475:[function(t,e,r){var n=t(\"./normalize\"),i=t(\"gl-mat4/create\"),a=t(\"gl-mat4/clone\"),o=t(\"gl-mat4/determinant\"),s=t(\"gl-mat4/invert\"),l=t(\"gl-mat4/transpose\"),c={length:t(\"gl-vec3/length\"),normalize:t(\"gl-vec3/normalize\"),dot:t(\"gl-vec3/dot\"),cross:t(\"gl-vec3/cross\")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function g(t,e,r,n,i){t[0]=e[0]*n+r[0]*i,t[1]=e[1]*n+r[1]*i,t[2]=e[2]*n+r[2]*i}e.exports=function(t,e,r,i,m,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),m||(m=[0,0,0,1]),v||(v=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,T,k,M=u[3],A=u[7],S=u[11],E=u[12],C=u[13],L=u[14],I=u[15];if(0!==M||0!==A||0!==S){if(h[0]=M,h[1]=A,h[2]=S,h[3]=I,!s(f,f))return!1;l(f,f),y=m,b=f,_=(x=h)[0],w=x[1],T=x[2],k=x[3],y[0]=b[0]*_+b[4]*w+b[8]*T+b[12]*k,y[1]=b[1]*_+b[5]*w+b[9]*T+b[13]*k,y[2]=b[2]*_+b[6]*w+b[10]*T+b[14]*k,y[3]=b[3]*_+b[7]*w+b[11]*T+b[15]*k}else m[0]=m[1]=m[2]=0,m[3]=1;if(e[0]=E,e[1]=C,e[2]=L,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),g(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),g(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),g(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var P=0;P<3;P++)r[P]*=-1,p[P][0]*=-1,p[P][1]*=-1,p[P][2]*=-1;return v[0]=.5*Math.sqrt(Math.max(1+p[0][0]-p[1][1]-p[2][2],0)),v[1]=.5*Math.sqrt(Math.max(1-p[0][0]+p[1][1]-p[2][2],0)),v[2]=.5*Math.sqrt(Math.max(1-p[0][0]-p[1][1]+p[2][2],0)),v[3]=.5*Math.sqrt(Math.max(1+p[0][0]+p[1][1]+p[2][2],0)),p[2][1]>p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{\"./normalize\":476,\"gl-mat4/clone\":278,\"gl-mat4/create\":280,\"gl-mat4/determinant\":281,\"gl-mat4/invert\":293,\"gl-mat4/transpose\":306,\"gl-vec3/cross\":365,\"gl-vec3/dot\":370,\"gl-vec3/length\":380,\"gl-vec3/normalize\":387}],476:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],477:[function(t,e,r){var n=t(\"gl-vec3/lerp\"),i=t(\"mat4-recompose\"),a=t(\"mat4-decompose\"),o=t(\"gl-mat4/determinant\"),s=t(\"quat-slerp\"),l=f(),c=f(),u=f();function f(){return{translate:h(),scale:h(1),skew:h(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function h(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,f){if(0===o(e)||0===o(r))return!1;var h=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!h||!p)&&(n(u.translate,l.translate,c.translate,f),n(u.skew,l.skew,c.skew,f),n(u.scale,l.scale,c.scale,f),n(u.perspective,l.perspective,c.perspective,f),s(u.quaternion,l.quaternion,c.quaternion,f),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),!0)}},{\"gl-mat4/determinant\":281,\"gl-vec3/lerp\":381,\"mat4-decompose\":475,\"mat4-recompose\":478,\"quat-slerp\":527}],478:[function(t,e,r){var n={identity:t(\"gl-mat4/identity\"),translate:t(\"gl-mat4/translate\"),multiply:t(\"gl-mat4/multiply\"),create:t(\"gl-mat4/create\"),scale:t(\"gl-mat4/scale\"),fromRotationTranslation:t(\"gl-mat4/fromRotationTranslation\")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{\"gl-mat4/create\":280,\"gl-mat4/fromRotationTranslation\":284,\"gl-mat4/identity\":291,\"gl-mat4/multiply\":295,\"gl-mat4/scale\":303,\"gl-mat4/translate\":305}],479:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],480:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\"),i=t(\"mat4-interpolate\"),a=t(\"gl-mat4/invert\"),o=t(\"gl-mat4/rotateX\"),s=t(\"gl-mat4/rotateY\"),l=t(\"gl-mat4/rotateZ\"),c=t(\"gl-mat4/lookAt\"),u=t(\"gl-mat4/translate\"),f=(t(\"gl-mat4/scale\"),t(\"gl-vec3/normalize\")),h=[0,0,0];function p(t){this._components=t.slice(),this._time=[0],this.prevMatrix=t.slice(),this.nextMatrix=t.slice(),this.computedMatrix=t.slice(),this.computedInverse=t.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}e.exports=function(t){return new p((t=t||{}).matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])};var d=p.prototype;d.recalcMatrix=function(t){var e=this._time,r=n.le(e,t),o=this.computedMatrix;if(!(r<0)){var s=this._components;if(r===e.length-1)for(var l=16*r,c=0;c<16;++c)o[c]=s[l++];else{var u=e[r+1]-e[r],h=(l=16*r,this.prevMatrix),p=!0;for(c=0;c<16;++c)h[c]=s[l++];var d=this.nextMatrix;for(c=0;c<16;++c)d[c]=s[l++],p=p&&h[c]===d[c];if(u<1e-6||p)for(c=0;c<16;++c)o[c]=h[c];else i(o,h,d,(t-e[r])/u)}var g=this.computedUp;g[0]=o[1],g[1]=o[5],g[2]=o[9],f(g,g);var m=this.computedInverse;a(m,o);var v=this.computedEye,y=m[15];v[0]=m[12]/y,v[1]=m[13]/y,v[2]=m[14]/y;var 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-       "/*\n",
-       "object-assign\n",
-       "(c) Sindre Sorhus\n",
-       "@license MIT\n",
-       "*/\n",
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-       "/*\n",
-       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
-       " * @license MIT\n",
-       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
-       " */\n",
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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":70,\"color-normalize\":125,\"flatten-vertex-data\":244,\"object-assign\":499,\"pick-by-alias\":511,\"to-float32\":577,\"update-diff\":599}],535:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),g=t(\"parse-rect\");function m(t,e){if(!(this instanceof m))return new m(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=m.shaders.has(t)?m.shaders.get(t):m.shaders.set(t,m.createShaders(t)).get(t),this.update(e)}e.exports=m,m.dashMult=2,m.maxPatternLength=256,m.precisionThreshold=3e6,m.maxPoints=1e4,m.maxLines=2048,m.shaders=new d,m.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashPattern:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashSize:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\n\\nuniform float dashSize, pixelRatio, thickness, opacity, id;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},m.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},m.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},m.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>m.precisionThreshold||e.scale[1]*e.viewport.height>m.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=m.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},m.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},m.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<m.maxLines&&(d.depth=2*(m.maxLines-1-f%m.maxLines)/m.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=g(t.viewport)),d.viewport||(d.viewport=g([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var v,y;if(t.positions.x&&t.positions.y){var x=t.positions.x,b=t.positions.y;y=d.count=Math.max(x.length,b.length),v=new Float64Array(2*y);for(var _=0;_<y;_++)v[2*_]=x[_],v[2*_+1]=b[_]}else v=l(t.positions,\"float64\"),y=d.count=Math.floor(v.length/2);var w=d.bounds=i(v,2);if(d.fill){for(var T=[],k={},M=0,A=0,S=0,E=d.count;A<E;A++){var C=v[2*A],L=v[2*A+1];isNaN(C)||isNaN(L)||null==C||null==L?(C=v[2*M],L=v[2*M+1],k[A]=M):M=A,T[S++]=C,T[S++]=L}for(var I=c(T,d.hole||[]),P=0,z=I.length;P<z;P++)null!=k[I[P]]&&(I[P]=k[I[P]]);d.triangles=I}var O=new Float64Array(v);u(O,2,w);var D=new Float64Array(2*y+6);d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[0]=O[2*y-4],D[1]=O[2*y-3]):(D[0]=O[2*y-2],D[1]=O[2*y-1]):(D[0]=O[0],D[1]=O[1]),D.set(O,2),d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[2*y+2]=O[2],D[2*y+3]=O[3],d.count-=1):(D[2*y+2]=O[0],D[2*y+3]=O[1],D[2*y+4]=O[2],D[2*y+5]=O[3]):(D[2*y+2]=O[2*y-2],D[2*y+3]=O[2*y-1],D[2*y+4]=O[2*y-2],D[2*y+5]=O[2*y-1]),d.positionBuffer(h(D)),d.positionFractBuffer(p(D))}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var R=d.bounds,F=R[2]-R[0],B=R[3]-R[1],N=d.range[2]-d.range[0],j=d.range[3]-d.range[1];d.scale=[F/N,B/j],d.translate=[-d.range[0]/N+R[0]/N||0,-d.range[1]/j+R[1]/j||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var U,V=0;if(!t.dashes||t.dashes.length<2)V=1,U=new Uint8Array([255,255,255,255,255,255,255,255]);else{V=0;for(var q=0;q<t.dashes.length;++q)V+=t.dashes[q];U=new Uint8Array(V*m.dashMult);for(var H=0,G=255,Y=0;Y<2;Y++)for(var W=0;W<t.dashes.length;++W){for(var X=0,Z=t.dashes[W]*m.dashMult*.5;X<Z;++X)U[H++]=G;G^=255}}d.dashLength=V,d.dashTexture({channels:1,data:U,width:U.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var J=d.count,K=t.color;K||(K=\"transparent\");var Q=new Uint8Array(4*J+4);if(Array.isArray(K)&&\"number\"!=typeof K[0]){for(var $=0;$<J;$++){var tt=n(K[$],\"uint8\");Q.set(tt,4*$)}Q.set(n(K[0],\"uint8\"),4*J)}else for(var et=n(K,\"uint8\"),rt=0;rt<J+1;rt++)Q.set(et,4*rt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:Q})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var v=[],y=0;y<this.passes.length;y++)null!==this.passes[y]&&v.push(this.passes[y]);return this.passes=v,this}},m.prototype.destroy=function(){return this.passes.forEach((function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()})),this.passes.length=0,this}},{\"array-bounds\":70,\"array-normalize\":71,\"color-normalize\":125,earcut:177,\"es6-weak-map\":233,\"flatten-vertex-data\":244,glslify:536,\"object-assign\":499,\"parse-rect\":504,\"pick-by-alias\":511,\"to-float32\":577}],536:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],537:[function(t,e,r){\"use strict\";function n(t,e){return function(t){if(Array.isArray(t))return t}(t)||function(t,e){if(\"undefined\"==typeof Symbol||!(Symbol.iterator in Object(t)))return;var r=[],n=!0,i=!1,a=void 0;try{for(var o,s=t[Symbol.iterator]();!(n=(o=s.next()).done)&&(r.push(o.value),!e||r.length!==e);n=!0);}catch(t){i=!0,a=t}finally{try{n||null==s.return||s.return()}finally{if(i)throw a}}return r}(t,e)||a(t,e)||function(){throw new TypeError(\"Invalid attempt to destructure non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function i(t){return function(t){if(Array.isArray(t))return o(t)}(t)||function(t){if(\"undefined\"!=typeof Symbol&&Symbol.iterator in Object(t))return Array.from(t)}(t)||a(t)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function a(t,e){if(t){if(\"string\"==typeof t)return o(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return\"Object\"===r&&t.constructor&&(r=t.constructor.name),\"Map\"===r||\"Set\"===r?Array.from(t):\"Arguments\"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?o(t,e):void 0}}function o(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var s=t(\"color-normalize\"),l=t(\"array-bounds\"),c=t(\"color-id\"),u=t(\"@plotly/point-cluster\"),f=t(\"object-assign\"),h=t(\"glslify\"),p=t(\"pick-by-alias\"),d=t(\"update-diff\"),g=t(\"flatten-vertex-data\"),m=t(\"is-iexplorer\"),v=t(\"to-float32\"),y=t(\"parse-rect\"),x=b;function b(t,e){var r=this;if(!(this instanceof b))return new b(t,e);\"function\"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=m,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),f(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,pixelRatio:t.context(\"pixelRatio\"),palette:n,paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),opacity:t.prop(\"opacity\"),marker:t.prop(\"markerTexture\")},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop(\"elements\"),count:t.prop(\"count\"),offset:t.prop(\"offset\"),primitive:\"points\"},s=f({},o);s.frag=h([\"precision 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fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform float pixelRatio;\\nuniform bool constPointSize;\\nuniform sampler2D palette;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\n\\nuniform float opacity;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio;\\nuniform bool constPointSize;\\nuniform sampler2D palette;\\nuniform vec2 paletteSize;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(palette,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),m&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),g=0;g<r.length;g++){var m=r[g];d[m]=p?p[m]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new 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t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=g(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}return i({data:v.float(t),usage:\"dynamic\"}),a({data:v.fract(t),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,M=_.sizeBuffer,A=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)A[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),A[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);M({data:A,usage:\"dynamic\"})}if(x){var E,C=n,L=C.count,I=C.color,P=C.borderColor,z=C.colorBuffer;if(t.tooManyColors){if(I.length||P.length){E=new Uint8Array(8*L);for(var O=0;O<L;O++){var D=I[O];E[8*O]=h[4*D],E[8*O+1]=h[4*D+1],E[8*O+2]=h[4*D+2],E[8*O+3]=h[4*D+3];var R=P[O];E[8*O+4]=h[4*R],E[8*O+5]=h[4*R+1],E[8*O+6]=h[4*R+2],E[8*O+7]=h[4*R+3]}}}else if(I.length||P.length){E=new Uint8Array(4*L+2);for(var F=0;F<L;F++)null!=I[F]&&(E[4*F]=I[F]%c,E[4*F+1]=Math.floor(I[F]/c)),null!=P[F]&&(E[4*F+2]=P[F]%c,E[4*F+3]=Math.floor(P[F]/c))}z({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":57,\"array-bounds\":70,\"color-id\":123,\"color-normalize\":125,\"flatten-vertex-data\":244,glslify:538,\"is-iexplorer\":466,\"object-assign\":499,\"parse-rect\":504,\"pick-by-alias\":511,\"to-float32\":577,\"update-diff\":599}],538:[function(t,e,r){arguments[4][257][0].apply(r,arguments)},{dup:257}],539:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var 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u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var g=s.columns,m=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/g,w=y/g;s.passes=[];for(var T=0;T<g;T++)for(var k=0;k<g;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var M=f(s.id,T,k),A=this.passes[M]||(this.passes[M]={});if(o.data&&(o.transpose?A.positions={x:{buffer:s.buffer,offset:k,count:m,stride:g},y:{buffer:s.buffer,offset:T,count:m,stride:g}}:A.positions={x:{buffer:s.buffer,offset:k*m,count:m},y:{buffer:s.buffer,offset:T*m,count:m}},A.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),C=E[0],L=E[1],I=E[2],P=E[3];A.viewport=[x+C*v+S[0],b+L*y+S[1],x+I*v-S[2],b+P*y-S[3]]}else A.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(A.color=s.color),o.size&&(A.size=s.size),o.marker&&(A.marker=s.marker),o.borderSize&&(A.borderSize=s.borderSize),o.borderColor&&(A.borderColor=s.borderColor),o.opacity&&(A.opacity=s.opacity),o.range&&(A.range=n?h(s.range,T,k):s.range||A.bounds),s.passes.push(M)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return this.traces.forEach((function(t){t.buffer&&t.buffer.destroy&&t.buffer.destroy()})),this.traces=null,this.passes=null,this.scatter.destroy(),this}},{\"array-bounds\":70,\"array-range\":72,\"flatten-vertex-data\":244,\"parse-rect\":504,\"pick-by-alias\":511,raf:528,\"regl-scatter2d\":537}],540:[function(t,e,r){!function(t,n){\"object\"==typeof r&&\"undefined\"!=typeof e?e.exports=n():t.createREGL=n()}(this,(function(){function t(t,e){this.id=V++,this.type=t,this.data=e}function e(t){return\"[\"+function t(e){if(0===e.length)return[];var r=e.charAt(0),n=e.charAt(e.length-1);if(1<e.length&&r===n&&('\"'===r||\"'\"===r))return['\"'+e.substr(1,e.length-2).replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];if(r=/\\[(false|true|null|\\d+|'[^']*'|\"[^\"]*\")\\]/.exec(e))return 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r,n=parseInt(e);if(isNaN(n))\"\\u95f0\"===e[0]&&(r=!0,e=e.substring(1)),\"\\u6708\"===e[e.length-1]&&(e=e.substring(0,e.length-1)),n=1+[\"\\u4e00\",\"\\u4e8c\",\"\\u4e09\",\"\\u56db\",\"\\u4e94\",\"\\u516d\",\"\\u4e03\",\"\\u516b\",\"\\u4e5d\",\"\\u5341\",\"\\u5341\\u4e00\",\"\\u5341\\u4e8c\"].indexOf(e);else{var i=e[e.length-1];r=\"i\"===i||\"I\"===i}return this.toMonthIndex(t,n,r)},dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},_validateYear:function(t,e){if(t.year&&(t=t.year()),\"number\"!=typeof t||t<1888||t>2111)throw e.replace(/\\{0\\}/,this.local.name);return t},toMonthIndex:function(t,e,r){var i=this.intercalaryMonth(t);if(r&&e!==i||e<1||e>12)throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return i?!r&&e<=i?e-1:e:e-1},toChineseMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);if(e<0||e>(r?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r?e<r?e+1:e:e+1},intercalaryMonth:function(t){return t=this._validateYear(t),f[t-f[0]]>>13},isIntercalaryMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);return!!r&&r===e},leapYear:function(t){return 0!==this.intercalaryMonth(t)},weekOfYear:function(t,e,r){var i,o=this._validateYear(t,n.local.invalidyear),s=h[o-h[0]],l=s>>9&4095,c=s>>5&15,u=31&s;(i=a.newDate(l,c,u)).add(4-(i.dayOfWeek()||7),\"d\");var f=this.toJD(t,e,r)-i.toJD();return 1+Math.floor(f/7)},monthsInYear:function(t){return this.leapYear(t)?13:12},daysInMonth:function(t,e){t.year&&(e=t.month(),t=t.year()),t=this._validateYear(t);var r=f[t-f[0]];if(e>(r>>13?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r&1<<12-e?30:29},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,s,r,n.local.invalidDate);t=this._validateYear(i.year()),e=i.month(),r=i.day();var o=this.isIntercalaryMonth(t,e),s=this.toChineseMonth(t,e),l=function(t,e,r,n,i){var a,o,s;if(\"object\"==typeof t)o=t,a=e||{};else{var l;if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Lunar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Lunar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=30))throw new Error(\"Lunar day outside range 1 - 30\");\"object\"==typeof n?(l=!1,a=n):(l=!!n,a=i||{}),o={year:t,month:e,day:r,isIntercalary:l}}s=o.day-1;var c,u=f[o.year-f[0]],p=u>>13;c=p&&(o.month>p||o.isIntercalary)?o.month:o.month-1;for(var d=0;d<c;d++){s+=u&1<<12-d?30:29}var g=h[o.year-h[0]],m=new Date(g>>9&4095,(g>>5&15)-1,(31&g)+s);return a.year=m.getFullYear(),a.month=1+m.getMonth(),a.day=m.getDate(),a}(t,s,r,o);return a.toJD(l.year,l.month,l.day)},fromJD:function(t){var e=a.fromJD(t),r=function(t,e,r,n){var i,a;if(\"object\"==typeof t)i=t,a=e||{};else{if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Solar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Solar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=31))throw new Error(\"Solar day outside range 1 - 31\");i={year:t,month:e,day:r},a=n||{}}var o=h[i.year-h[0]],s=i.year<<9|i.month<<5|i.day;a.year=s>=o?i.year:i.year-1,o=h[a.year-h[0]];var l,c=new Date(o>>9&4095,(o>>5&15)-1,31&o),u=new Date(i.year,i.month-1,i.day);l=Math.round((u-c)/864e5);var p,d=f[a.year-f[0]];for(p=0;p<13;p++){var g=d&1<<12-p?30:29;if(l<g)break;l-=g}var m=d>>13;!m||p<m?(a.isIntercalary=!1,a.month=1+p):p===m?(a.isIntercalary=!0,a.month=p):(a.isIntercalary=!1,a.month=p);return 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Coptic\",jdEpoch:1825029.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Coptic\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Thout\",\"Paopi\",\"Hathor\",\"Koiak\",\"Tobi\",\"Meshir\",\"Paremhat\",\"Paremoude\",\"Pashons\",\"Paoni\",\"Epip\",\"Mesori\",\"Pi Kogi Enavot\"],monthNamesShort:[\"Tho\",\"Pao\",\"Hath\",\"Koi\",\"Tob\",\"Mesh\",\"Pat\",\"Pad\",\"Pash\",\"Pao\",\"Epi\",\"Meso\",\"PiK\"],dayNames:[\"Tkyriaka\",\"Pesnau\",\"Pshoment\",\"Peftoou\",\"Ptiou\",\"Psoou\",\"Psabbaton\"],dayNamesShort:[\"Tky\",\"Pes\",\"Psh\",\"Pef\",\"Pti\",\"Pso\",\"Psa\"],dayNamesMin:[\"Tk\",\"Pes\",\"Psh\",\"Pef\",\"Pt\",\"Pso\",\"Psa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.coptic=a},{\"../main\":621,\"object-assign\":499}],609:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Discworld\",jdEpoch:1721425.5,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":621,\"object-assign\":499}],610:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{\"../main\":621,\"object-assign\":499}],611:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{\"../main\":621,\"object-assign\":499}],612:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Islamic\",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012bs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":621,\"object-assign\":499}],613:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":621,\"object-assign\":499}],614:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":621,\"object-assign\":499}],615:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{\"../main\":621,\"object-assign\":499}],616:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return 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c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)\"undefined\"==typeof 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":621,\"object-assign\":499}],619:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":621,\"object-assign\":499}],620:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":778,\"../../plot_api/plot_template\":817,\"../../registry\":911}],629:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),g=r.hoverlabel||{};if(d){var m=a(\"hoverlabel.bgcolor\",g.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",g.bordercolor||i.contrast(m));n.coerceFont(a,\"hoverlabel.font\",{family:g.font.family,size:g.font.size,color:g.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":778,\"../color\":643}],630:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":805,\"fast-isnumeric\":241}],631:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var g=f[d],m=i.coerceRef(t,e,p,g,\"\",\"paper\");if(\"paper\"!==m)i.getFromId(p,m)._annIndices.push(e._index);if(i.coercePosition(e,p,a,m,g,.5),u){var v=\"a\"+g,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==m&&(y=e[v]=\"pixel\");var x=\"pixel\"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(g+\"anchor\"),a(g+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),u&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),c){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:\"annotations\",handleItemDefaults:l})}},{\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"./attributes\":626,\"./common_defaults\":629}],632:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../registry\"),a=t(\"../../plots/plots\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../plots/cartesian/axes\"),c=t(\"../color\"),u=t(\"../drawing\"),f=t(\"../fx\"),h=t(\"../../lib/svg_text_utils\"),p=t(\"../../lib/setcursor\"),d=t(\"../dragelement\"),g=t(\"../../plot_api/plot_template\").arrayEditor,m=t(\"./draw_arrow_head\");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var M=g(t.layout,_,e),A=M.modifyBase,S=M.modifyItem,E=M.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var C=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var L={x:{},y:{}},I=+e.textangle||0,P=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),z=P.append(\"g\").classed(\"annotation-text-g\",!0),O=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||O,R=z.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+C).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",C).append(\"rect\"),V.exit().remove();var q=e.font,H=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(H);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&A(v._name+\".autorange\",!0),x&&x.autorange&&A(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+C).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,q).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),g=u.bBox((f?n:G).node()),b=g.width,_=g.height,M=e.width||b,D=e.height||_,B=Math.round(M+2*N),q=Math.round(D+2*N);function H(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(I+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=q*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=L[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var gt=e[\"a\"+et];if(tt=ot*H(.5,e.xanchor)-st*H(.5,e.yanchor),nt===rt){var mt=l.getRefType(nt);\"domain\"===mt?(\"y\"===et&&(gt=1-gt),ft.tail=it._offset+it._length*gt):\"paper\"===mt?\"y\"===et?(gt=1-gt,ft.tail=T.t+T.h*gt):ft.tail=T.l+T.w*gt:ft.tail=it._offset+it.r2p(gt),K=tt}else ft.tail=J+gt,K=tt+gt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*H($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(M-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?C:null,t);else{var wt=N+_t-g.top,Tt=N+bt-g.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?C:null,t)}V.select(\"rect\").call(u.setRect,N,N,M,D),j.call(u.setRect,F/2,F/2,B-F,q-F),R.call(u.setTranslate,Math.round(L.x.text-B/2),Math.round(L.y.text-q/2)),z.attr({transform:\"rotate(\"+I+\",\"+L.x.text+\",\"+L.y.text+\")\"});var kt,Mt=function(r,n){P.selectAll(\".annotation-arrow-g\").remove();var l=L.x.head,f=L.y.head,h=L.x.tail+r,p=L.y.tail+n,g=L.x.text+r,b=L.y.text+n,_=o.rotationXYMatrix(I,g,b),w=o.apply2DTransform(_),M=o.apply2DTransform2(_),C=+j.attr(\"width\"),O=+j.attr(\"height\"),D=g-.5*C,F=D+C,B=b-.5*O,N=B+O,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(M);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,q=e.arrowcolor,H=e.arrowside,G=P.append(\"g\").style({opacity:c.opacity(q)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(q));if(m(Y,H,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var 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n=t(\"../annotations/attributes\"),i=t(\"../../plot_api/edit_types\").overrideAll,a=t(\"../../plot_api/plot_template\").templatedArray;e.exports=i(a(\"annotation\",{visible:n.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),\"calc\",\"from-root\")},{\"../../plot_api/edit_types\":810,\"../../plot_api/plot_template\":817,\"../annotations/attributes\":626}],636:[function(t,e,r){\"use 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n.coerce(t,e,s,r,i)}function c(t){var n=t+\"axis\",a={_fullLayout:{}};return a._fullLayout[n]=r[n],i.coercePosition(e,a,l,t,t,.5)}l(\"visible\")&&(o(t,e,a.fullLayout,l),c(\"x\"),c(\"y\"),c(\"z\"),n.noneOrAll(t,e,[\"x\",\"y\",\"z\"]),e.xref=\"x\",e.yref=\"y\",e.zref=\"z\",l(\"xanchor\"),l(\"yanchor\"),l(\"xshift\"),l(\"yshift\"),e.showarrow&&(e.axref=\"pixel\",e.ayref=\"pixel\",l(\"ax\",-10),l(\"ay\",-30),n.noneOrAll(t,e,[\"ax\",\"ay\"])))}e.exports=function(t,e,r){a(t,e,{name:\"annotations\",handleItemDefaults:l,fullLayout:r.fullLayout})}},{\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"../annotations/common_defaults\":629,\"./attributes\":635}],638:[function(t,e,r){\"use strict\";var n=t(\"../annotations/draw\").drawRaw,i=t(\"../../plots/gl3d/project\"),a=[\"x\",\"y\",\"z\"];e.exports=function(t){for(var e=t.fullSceneLayout,r=t.dataScale,o=e.annotations,s=0;s<o.length;s++){for(var l=o[s],c=!1,u=0;u<3;u++){var 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If `target` is a string (eg *x*) we use the\",\"corresponding trace attribute (eg `xcalendar`) if it exists,\",\"even if `targetcalendar` is provided.\"].join(\" \"))}}},layoutAttributes:l,handleDefaults:c,handleTraceDefaults:function(t,e,r,n){for(var i=0;i<r.length;i++)c(t,e,r[i]+\"calendar\",n.calendar)},CANONICAL_SUNDAY:{chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},CANONICAL_TICK:{chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},DFLTRANGE:{chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},getCal:h,worldCalFmt:function(t,e,r){for(var 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T={y:e.y,b:u.height*g[w],t:u.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*g[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*g[b]),i.autoMargin(t,m(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var g=c.lineCount(l),m=(a.currentValueMaxLines+1-g)*e.currentvalue.font.size*p;return 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r=t[e[n]],s=(r._extremes||{})[o];if(!0===r.visible&&s){for(i=0;i<s.min.length;i++)a=s.min[i],d(u,a.val,a.pad,{extrapad:a.extrapad});for(i=0;i<s.max.length;i++)a=s.max[i],g(f,a.val,a.pad,{extrapad:a.extrapad})}}}if(h(s,e._traceIndices),h(c.annotations||[],e._annIndices||[]),h(c.shapes||[],e._shapeIndices||[]),e._matchGroup&&!r)for(var m in e._matchGroup)if(m!==e._id){var v=l(t,m),y=p(t,v,!0),x=e._length/v._length;for(i=0;i<y.min.length;i++)a=y.min[i],d(u,a.val,a.pad*x,{extrapad:a.extrapad});for(i=0;i<y.max.length;i++)a=y.max[i],g(f,a.val,a.pad*x,{extrapad:a.extrapad})}return{min:u,max:f}}function d(t,e,r,n){m(t,e,r,n,y)}function g(t,e,r,n){m(t,e,r,n,x)}function m(t,e,r,n,i){for(var a=n.tozero,o=n.extrapad,s=!0,l=0;l<t.length&&s;l++){var c=t[l];if(i(c.val,e)&&c.pad>=r&&(c.extrapad||!o)){s=!1;break}i(e,c.val)&&c.pad<=r&&(o||!c.extrapad)&&(t.splice(l,1),l--)}if(s){var u=a&&0===e;t.push({val:e,pad:u?0:r,extrapad:!u&&o})}}function v(t){return n(t)&&Math.abs(t)<a}function y(t,e){return 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n,a=t.nticks;a||(\"category\"===t.type||\"multicategory\"===t.type?(n=t.tickfont?1.2*(t.tickfont.size||12):15,a=t._length/n):(n=\"y\"===t._id.charAt(0)?40:80,a=s.constrain(t._length/n,4,9)+1),\"radialaxis\"===t._name&&(a*=2)),\"array\"===t.tickmode&&(a*=100),t._roughDTick=Math.abs(r[1]-r[0])/a,B.autoTicks(t,t._roughDTick),t._minDtick>0&&t.dtick<2*t._minDtick&&(t.dtick=t._minDtick,t.tick0=t.l2r(t._forceTick0))}\"period\"===t.ticklabelmode&&function(t){var e;function r(){return!(i(t.dtick)||\"M\"!==t.dtick.charAt(0))}var n=r(),a=B.getTickFormat(t);if(a){var 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c=\"category\"===t.type?t.d2l_noadd:t.d2l;\"log\"===t.type&&\"L\"!==String(t.dtick).charAt(0)&&(t.dtick=\"L\"+Math.pow(10,Math.floor(Math.min(t.range[0],t.range[1]))-1));for(var u=0;u<e.length;u++){var f=c(e[u]);f>a&&f<o&&(void 0===r[u]?n[l]=B.tickText(t,f):n[l]=it(t,f,String(r[u])),l++)}l<e.length&&n.splice(l,e.length-l);t.rangebreaks&&(n=n.filter((function(e){return t.maskBreaks(e.x)!==P})));return n}(t);var n=H(r),a=n[0],o=n[1],l=r[1]<r[0],c=Math.min(r[0],r[1]),u=Math.max(r[0],r[1]),f=\"log\"===t.type&&!(i(t.dtick)||\"L\"===t.dtick.charAt(0)),h=\"period\"===t.ticklabelmode;if(t._tmin=B.tickFirst(t,e),t._tmin<a!==l)return[];\"category\"!==t.type&&\"multicategory\"!==t.type||(o=l?Math.max(-.5,o):Math.min(t._categories.length-.5,o));var p=t._tmin;t.rangebreaks&&t._tick0Init!==t.tick0&&(p=wt(p,t),l||(p=B.tickIncrement(p,t.dtick,!l,t.calendar))),h&&(p=B.tickIncrement(p,t.dtick,!l,t.calendar));for(var d,g=Math.max(1e3,t._length||0),C=[],L=null;l?p>=o:p<=o;p=B.tickIncrement(p,t.dtick,l,t.calendar)){if(t.rangebreaks&&!l){if(p<a)continue;if(t.maskBreaks(p)===P&&wt(p,t)>=u)break}if(C.length>g||p===L)break;L=p;var I=!1;f&&p!==(0|p)&&(I=!0),C.push({minor:I,value:p})}if(h&&function(t,e,r){for(var n=0;n<t.length;n++){var i=t[n].value,a=n,o=n+1;n<t.length-1?(a=n,o=n+1):n>0?(a=n-1,o=n):(a=n,o=n);var s,l=t[a].value,c=t[o].value,u=Math.abs(c-l),f=r||u,h=0;f>=y?h=u>=y&&u<=m?u:v:r===b&&f>=_?h=u>=_&&u<=x?u:b:f>=k?h=u>=k&&u<=w?u:T:r===M&&f>=M?h=M:f>=A?h=A:r===S&&f>=S?h=S:r===E&&f>=E&&(h=E),h>=u&&(h=u,s=!0);var p=i+h;if(e.rangebreaks&&h>0){for(var d=0,g=0;g<84;g++){var C=(g+.5)/84;e.maskBreaks(i*(1-C)+C*p)!==P&&d++}(h*=d/84)||(t[n].drop=!0),s&&u>M&&(h=u)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var z=\"y\"===t._id.charAt(0),O=1;\"auto\"===t.tickmode&&(O=t.tickfont?t.tickfont.size:12);var 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w=e._anchorAxis;w&&w.autorange&&-1!==(e.ticklabelposition||\"\").indexOf(\"inside\")&&!V(a,e._id)&&(a._insideTickLabelsAutorange||(a._insideTickLabelsAutorange={}),a._insideTickLabelsAutorange[w._name+\".autorange\"]=w.autorange,_.push((function(){v.each((function(t,r){var n=dt(this);e._vals[r].bb=h.bBox(n.node())}))})));var T=s.syncOrAsync(_);return T&&T.then&&t._promises.push(T),T},B.getPxPosition=function(t,e){var r,n=t._fullLayout._size,i=e._id.charAt(0),a=e.side;return\"free\"!==e.anchor?r=e._anchorAxis:\"x\"===i?r={_offset:n.t+(1-(e.position||0))*n.h,_length:0}:\"y\"===i&&(r={_offset:n.l+(e.position||0)*n.w,_length:0}),\"top\"===a||\"left\"===a?r._offset:\"bottom\"===a||\"right\"===a?r._offset+r._length:void 0},B.shouldShowZeroLine=function(t,e,r){var n=s.simpleMap(e.range,e.r2l);return n[0]*n[1]<=0&&e.zeroline&&(\"linear\"===e.type||\"-\"===e.type)&&!(e.rangebreaks&&e.maskBreaks(0)===P)&&(pt(e,0)||!function(t,e,r,n){var i=r._mainAxis;if(!i)return;var 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r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":834,\"has-passive-events\":441,\"mouse-change\":483,\"mouse-event-offset\":484,\"mouse-wheel\":486}],867:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":802,\"../cartesian/axes\":828}],868:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":684,\"../../constants/xmlns_namespaces\":754,\"../../plot_api/edit_types\":810,\"../cartesian\":841,\"../cartesian/attributes\":826,\"../cartesian/constants\":834,\"../get_data\":865,\"../layout_attributes\":882,\"./scene2d\":869}],869:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"gl-plot2d\"),c=t(\"gl-spikes2d\"),u=t(\"gl-select-box\"),f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),g=t(\"../cartesian/constraints\"),m=g.enforce,v=g.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var M=k.prototype;M.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},M.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},M.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},M.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},M.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},M.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},M.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},M.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},M.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},M.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();m(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},M.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},M.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},M.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},M.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},M.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},M.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var m=this.fullData[g.trace.index]||{},v=g.pointIndex,y=s.castHoverinfo(m,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(m,v,\"bgcolor\")||g.color,borderColor:s.castHoverOption(m,v,\"bordercolor\"),fontFamily:s.castHoverOption(m,v,\"font.family\"),fontSize:s.castHoverOption(m,v,\"font.size\"),fontColor:s.castHoverOption(m,v,\"font.color\"),nameLength:s.castHoverOption(m,v,\"namelength\"),textAlign:s.castHoverOption(m,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},M.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},M.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/dragelement/helpers\":661,\"../../components/fx\":683,\"../../lib/show_no_webgl_msg\":800,\"../../plots/cartesian/axes\":828,\"../../registry\":911,\"../cartesian/autorange\":827,\"../cartesian/constants\":834,\"../cartesian/constraints\":835,\"./camera\":866,\"./convert\":867,\"gl-plot2d\":317,\"gl-select-box\":333,\"gl-spikes2d\":342,\"webgl-context\":606}],870:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":684,\"../../constants/xmlns_namespaces\":754,\"../../lib\":778,\"../../plot_api/edit_types\":810,\"../get_data\":865,\"./layout/attributes\":871,\"./layout/defaults\":875,\"./layout/layout_attributes\":876,\"./scene\":880}],871:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],872:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":643,\"../../../lib/extend\":768,\"../../../plot_api/edit_types\":810,\"../../cartesian/layout_attributes\":842}],873:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":778,\"../../../plot_api/plot_template\":817,\"../../cartesian/axis_defaults\":830,\"../../cartesian/type_defaults\":853,\"./axis_attributes\":872,tinycolor2:576}],874:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":778,\"../../../lib/str2rgbarray\":802}],875:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var m=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(m&&v&&y?y/Math.sqrt(m*m+v*v+y*y)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":643,\"../../../lib\":778,\"../../../registry\":911,\"../../get_data\":865,\"../../subplot_defaults\":905,\"./axis_defaults\":873,\"./layout_attributes\":876}],876:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":778,\"../../../lib/extend\":768,\"../../domain\":855,\"./axis_attributes\":872}],877:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":802}],878:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":778,\"../../cartesian/axes\":828}],879:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],880:[function(t,e,r){\"use strict\";var n,i,a=t(\"gl-plot3d\"),o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),g=t(\"../../lib/str2rgbarray\"),m=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return m(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c,u=Object.keys(e.traces),h=null,g=e.glplot.selection,m=0;m<u.length;++m)\"skip\"!==(t=e.traces[u[m]]).data.hoverinfo&&t.handlePick(g)&&(h=t),t.setContourLevels&&t.setContourLevels();function y(t,r){var n=e.fullSceneLayout[t];return p.tickText(n,n.d2l(r),\"hover\").text}if(null!==h){var x=v(e.glplot.cameraParams,g.dataCoordinate);t=h.data;var _,w=r._fullData[t.index],T=g.index,k={xLabel:y(\"xaxis\",g.traceCoordinate[0]),yLabel:y(\"yaxis\",g.traceCoordinate[1]),zLabel:y(\"zaxis\",g.traceCoordinate[2])},M=d.castHoverinfo(w,e.fullLayout,T),A=(M||\"\").split(\"+\"),S=M&&\"all\"===M;w.hovertemplate||S||(-1===A.indexOf(\"x\")&&(k.xLabel=void 0),-1===A.indexOf(\"y\")&&(k.yLabel=void 0),-1===A.indexOf(\"z\")&&(k.zLabel=void 0),-1===A.indexOf(\"text\")&&(g.textLabel=void 0),-1===A.indexOf(\"name\")&&(h.name=void 0));var E=[];\"cone\"===t.type||\"streamtube\"===t.type?(k.uLabel=y(\"xaxis\",g.traceCoordinate[3]),(S||-1!==A.indexOf(\"u\"))&&E.push(\"u: \"+k.uLabel),k.vLabel=y(\"yaxis\",g.traceCoordinate[4]),(S||-1!==A.indexOf(\"v\"))&&E.push(\"v: \"+k.vLabel),k.wLabel=y(\"zaxis\",g.traceCoordinate[5]),(S||-1!==A.indexOf(\"w\"))&&E.push(\"w: \"+k.wLabel),k.normLabel=g.traceCoordinate[6].toPrecision(3),(S||-1!==A.indexOf(\"norm\"))&&E.push(\"norm: \"+k.normLabel),\"streamtube\"===t.type&&(k.divergenceLabel=g.traceCoordinate[7].toPrecision(3),(S||-1!==A.indexOf(\"divergence\"))&&E.push(\"divergence: \"+k.divergenceLabel)),g.textLabel&&E.push(g.textLabel),_=E.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.tickText(e._mockAxis,e._mockAxis.d2l(g.traceCoordinate[3]),\"hover\").text,E.push(\"value: \"+k.valueLabel),g.textLabel&&E.push(g.textLabel),_=E.join(\"<br>\")):_=g.textLabel;var C={x:g.traceCoordinate[0],y:g.traceCoordinate[1],z:g.traceCoordinate[2],data:w._input,fullData:w,curveNumber:w.index,pointNumber:T};d.appendArrayPointValue(C,w,T),t._module.eventData&&(C=w._module.eventData(C,g,w,{},T));var L={points:[C]};e.fullSceneLayout.hovermode&&d.loneHover({trace:w,x:(.5+.5*x[0]/x[3])*s,y:(.5-.5*x[1]/x[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:_,name:h.name,color:d.castHoverOption(w,T,\"bgcolor\")||h.color,borderColor:d.castHoverOption(w,T,\"bordercolor\"),fontFamily:d.castHoverOption(w,T,\"font.family\"),fontSize:d.castHoverOption(w,T,\"font.size\"),fontColor:d.castHoverOption(w,T,\"font.color\"),nameLength:d.castHoverOption(w,T,\"namelength\"),textAlign:d.castHoverOption(w,T,\"align\"),hovertemplate:f.castOption(w,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},C,k),eventData:[C]},{container:n,gd:r}),g.buttons&&g.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),c=L}else d.loneUnhover(n),r.emit(\"plotly_unhover\",c);e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function M(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(g(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&M(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,m=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){m[0][a]=1/0,m[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,A=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?m[0][a]=Math.min(m[0][a],s._lowerLogErrorBound):m[0][a]=Math.min(m[0][a],T[0][a]/h[a]-A),m[1][a]=Math.max(m[1][a],T[1][a]/h[a]+A)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);m[0][a]=Math.min(m[0][a],E),m[1][a]=Math.max(m[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var C=m[1][a]-m[0][a];m[0][a]-=C/32,m[1][a]+=C/32}if(\"reversed\"===s.autorange){var L=m[0][a];m[0][a]=m[1][a],m[1][a]=L}}else{var I=s.range;m[0][a]=s.r2l(I[0]),m[1][a]=s.r2l(I[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.setBounds(a,{min:m[0][a]*h[a],max:m[1][a]*h[a]})}var P=c.aspectmode;if(\"cube\"===P)d=[1,1,1];else if(\"manual\"===P){var z=c.aspectratio;d=[z.x,z.y,z.z]}else{if(\"auto\"!==P&&\"data\"!==P)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var O=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];O[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===P||Math.max.apply(null,O)/Math.min.apply(null,O)<=4?O:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var 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r=t.visible;return\"undefined\"==typeof r||!0===r})),d=!1,g=p.map((function(t,e){return d=d||\"undefined\"!=typeof t.groupId,t}));if(d){var m=n.nest().key((function(t,e){return\"undefined\"!=typeof t.groupId?t.groupId:\"unstacked\"})).entries(g),v=[],y=m.map((function(t,e){if(\"unstacked\"===t.key)return t.values;var r=t.values[0].r.map((function(t,e){return 0}));return t.values.forEach((function(t,e,n){t.yStack=[r],v.push(r),r=o.util.sumArrays(t.r,r)})),t.values}));p=n.merge(y)}p.forEach((function(t,e){t.t=Array.isArray(t.t[0])?t.t:[t.t],t.r=Array.isArray(t.r[0])?t.r:[t.r]}));var x=Math.min(h.width-h.margin.left-h.margin.right,h.height-h.margin.top-h.margin.bottom)/2;x=Math.max(10,x);var b,_=[h.margin.left+x,h.margin.top+x];d?b=[0,n.max(o.util.sumArrays(o.util.arrayLast(p).r[0],o.util.arrayLast(v)))]:b=n.extent(o.util.flattenArray(p.map((function(t,e){return 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P=n.range.apply(this,C);if(P=P.map((function(t,e){return parseFloat(t.toPrecision(12))})),s=n.scale.linear().domain(C.slice(0,2)).range(\"clockwise\"===h.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=s.domain(),u.layout.angularAxis.endPadding=A?E:0,\"undefined\"==typeof(t=n.select(this).select(\"svg.chart-root\"))||t.empty()){var z=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g class='legend-group'></g>' + '<g class='tooltips-group'></g>' + '<g class='title-group'><text></text></g>' + '</g>' + 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o=n.hsl(a.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",f=i||\"\";e.style({fill:u,\"font-size\":a.fontSize+\"px\"}).text(f);var h=a.padding,p=e.node().getBBox(),d={fill:a.color,stroke:s,\"stroke-width\":\"2px\"},g=p.width+2*h+l,m=p.height+2*h;return r.attr({d:\"M\"+[[l,-m/2],[l,-m/4],[a.hasTick?0:l,0],[l,m/4],[l,m/2],[g,m/2],[g,-m/2]].join(\"L\")+\"Z\"}).style(d),t.attr({transform:\"translate(\"+[l,-m/2+2*h]+\")\"}),t.style({display:\"block\"}),c},c.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),c},c.hide=function(){if(t)return t.style({display:\"none\"}),c},c.show=function(){if(t)return t.style({display:\"block\"}),c},c.config=function(t){return i(a,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map((function(t,r){var 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s=i({},t.layout);if([[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach((function(t,r){o.util.translator.apply(null,t.concat(e))})),e?(\"undefined\"!=typeof 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n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strRotate,l=o.strTranslate,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../plots\"),h=t(\"../../plots/cartesian/axes\"),p=t(\"../cartesian/set_convert\"),d=t(\"./set_convert\"),g=t(\"../cartesian/autorange\").doAutoRange,m=t(\"../cartesian/dragbox\"),v=t(\"../../components/dragelement\"),y=t(\"../../components/fx\"),x=t(\"../../components/titles\"),b=t(\"../cartesian/select\").prepSelect,_=t(\"../cartesian/select\").selectOnClick,w=t(\"../cartesian/select\").clearSelect,T=t(\"../../lib/setcursor\"),k=t(\"../../lib/clear_gl_canvases\"),M=t(\"../../plot_api/subroutines\").redrawReglTraces,A=t(\"../../constants/alignment\").MID_SHIFT,S=t(\"./constants\"),E=t(\"./helpers\"),C=o._,L=o.mod,I=o.deg2rad,P=o.rad2deg;function 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e,r,n,i,a=t[0],o=t[1]-a,s=L(a,360),l=s+o,c=Math.cos(I(s)),u=Math.sin(I(s)),f=Math.cos(I(l)),h=Math.sin(I(l));i=s<=90&&l>=90||s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(p),b=x[2]-x[0],_=x[3]-x[1],w=h/f,T=Math.abs(_/b);w>T?(d=f,y=(h-(g=f*T))/n.h/2,m=[o[0],o[1]],v=[s[0]+y,s[1]-y]):(g=h,y=(f-(d=h/T))/n.w/2,m=[o[0]+y,o[1]-y],v=[s[0],s[1]]),this.xLength2=d,this.yLength2=g,this.xDomain2=m,this.yDomain2=v;var 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e=m(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[f+u*r,p-u*n]+\"L\"+[f+a*r,p-a*n]},transFn:o.noop,crisp:!1}),h.drawLabels(n,g,{vals:w,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:function(t){return v(m(t))},labelFns:x})}F(i[\"angular-line\"].select(\"path\"),d.showline,{d:r.pathSubplot(),transform:l(f,p)}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},O.updateFx=function(t,e){this.gd._context.staticPlot||(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1),this.updateMainDrag(t))},O.updateMainDrag=function(t){var e,r,s=this,c=s.gd,u=s.layers,f=t._zoomlayer,h=S.MINZOOM,p=S.OFFEDGE,d=s.radius,g=s.innerRadius,x=s.cx,T=s.cy,k=s.cxx,M=s.cyy,A=s.sectorInRad,C=s.vangles,L=s.radialAxis,I=E.clampTiny,P=E.findXYatLength,z=E.findEnclosingVertexAngles,O=S.cornerHalfWidth,D=S.cornerLen/2,R=m.makeDragger(u,\"path\",\"maindrag\",\"crosshair\");n.select(R).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(x,T));var F,B,N,j,U,V,q,H,G,Y={element:R,gd:c,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function W(t,e){return Math.sqrt(t*t+e*e)}function X(t,e){return W(t-k,e-M)}function Z(t,e){return Math.atan2(M-e,t-k)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function K(t,e){if(0===t)return s.pathSector(2*O);var r=D/t,n=e-r,i=e+r,a=Math.max(0,Math.min(t,d)),o=a-O,l=a+O;return\"M\"+J(o,n)+\"A\"+[o,o]+\" 0,0,0 \"+J(o,i)+\"L\"+J(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+J(l,n)+\"Z\"}function Q(t,e,r){if(0===t)return s.pathSector(2*O);var n,i,a=J(t,e),o=J(t,r),l=I((a[0]+o[0])/2),c=I((a[1]+o[1])/2);if(l&&c){var u=c/l,f=-1/u,h=P(O,u,l,c);n=P(D,f,h[0][0],h[0][1]),i=P(D,f,h[1][0],h[1][1])}else{var p,d;c?(p=D,d=O):(p=O,d=D),n=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+n.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function $(t,e){return 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e=L._rl,r=(e[1]-e[0])/(1-g/d)/d,n=[e[0]+(N-g)*r,e[0]+(j-g)*r];t[s.id+\".radialaxis.range\"]=n}function ot(t,e){var r=c._fullLayout.clickmode;if(m.removeZoombox(c),2===t){var n={};for(var i in s.viewInitial)n[s.id+\".\"+i]=s.viewInitial[i];c.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",c,n)}r.indexOf(\"select\")>-1&&1===t&&_(e,c,[s.xaxis],[s.yaxis],s.id,Y),r.indexOf(\"event\")>-1&&y.click(c,e,s.id)}Y.prepFn=function(t,n,a){var l=c._fullLayout.dragmode,u=R.getBoundingClientRect();c._fullLayout._calcInverseTransform(c);var h=c._fullLayout._invTransform;e=c._fullLayout._invScaleX,r=c._fullLayout._invScaleY;var p=o.apply3DTransform(h)(n-u.left,a-u.top);if(F=p[0],B=p[1],C){var g=E.findPolygonOffset(d,A[0],A[1],C);F+=k+g[0],B+=M+g[1]}switch(l){case\"zoom\":Y.moveFn=C?nt:et,Y.clickFn=ot,Y.doneFn=it,function(){N=null,j=null,U=s.pathSubplot(),V=!1;var t=c._fullLayout[s.id];q=i(t.bgcolor).getLuminance(),(H=m.makeZoombox(f,q,x,T,U)).attr(\"fill-rule\",\"evenodd\"),G=m.makeCorners(f,x,T),w(c)}();break;case\"select\":case\"lasso\":b(t,n,a,Y,l)}},R.onmousemove=function(t){y.hover(c,t,s.id),c._fullLayout._lasthover=R,c._fullLayout._hoversubplot=s.id},R.onmouseout=function(t){c._dragging||v.unhover(c,t)},v.init(Y)},O.updateRadialDrag=function(t,e,r){var i=this,c=i.gd,u=i.layers,f=i.radius,h=i.innerRadius,p=i.cx,d=i.cy,g=i.radialAxis,y=S.radialDragBoxSize,x=y/2;if(g.visible){var b,_,T,A=I(i.radialAxisAngle),E=g._rl,C=E[0],L=E[1],z=E[r],O=.75*(E[1]-E[0])/(1-e.hole)/f;r?(b=p+(f+x)*Math.cos(A),_=d-(f+x)*Math.sin(A),T=\"radialdrag\"):(b=p+(h-x)*Math.cos(A),_=d-(h-x)*Math.sin(A),T=\"radialdrag-inner\");var D,B,N,j=m.makeRectDragger(u,T,\"crosshair\",-x,-x,y,y),U={element:j,gd:c};F(n.select(j),g.visible&&h<f,{transform:l(b,_)}),U.prepFn=function(){D=null,B=null,N=null,U.moveFn=V,U.doneFn=q,w(c)},U.clampFn=function(t,e){return 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v=e.gd._fullLayout,y=v[e.id],x=_(A+c*t._invScaleX,E+m*t._invScaleY),b=P(x-z);if(L=C+b,i.frontplot.attr(\"transform\",l(e.xOffset2,e.yOffset2)+s([-b,d,g])),e.vangles){I=e.radialAxisAngle+b;var w=l(h,p)+s(-b),T=l(h,p)+s(-I);i.bg.attr(\"transform\",w),i[\"radial-grid\"].attr(\"transform\",w),i[\"radial-axis\"].attr(\"transform\",T),i[\"radial-line\"].select(\"line\").attr(\"transform\",T),e.updateRadialAxisTitle(v,y,I)}else e.clipPaths.forTraces.select(\"path\").attr(\"transform\",l(d,g)+s(b));D.each((function(){var t=n.select(this),e=u.getTranslate(t);t.attr(\"transform\",l(e.x,e.y)+s([b]))})),R.each((function(){var t=n.select(this),e=t.select(\"text\"),r=u.getTranslate(t);t.attr(\"transform\",s([b,e.attr(\"x\"),e.attr(\"y\")])+l(r.x,r.y))})),f.rotation=o.modHalf(L,360),e.updateAngularAxis(v,y),e._hasClipOnAxisFalse&&!o.isFullCircle(e.sectorInRad)&&O.call(u.hideOutsideRangePoints,e);var S=!1;for(var F in e.traceHash)if(a.traceIs(F,\"gl\")){var 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x={};x[h]=[v/d,(v+1)/d],a(l,e,m,x),o.id=y,f(s,l,m,o)}}},{\"../lib\":778,\"../plot_api/plot_template\":817,\"./domain\":855}],906:[function(t,e,r){\"use strict\";['Variables are inserted using %{variable}, for example \"y: %{y}\".','Numbers are formatted using d3-format\\'s syntax %{variable:d3-format}, for example \"Price: %{y:$.2f}\".',t(\"../constants/docs\").FORMAT_LINK,\"for details on the formatting syntax.\",'Dates are formatted using d3-time-format\\'s syntax %{variable|d3-time-format}, for example \"Day: %{2019-01-01|%A}\".',t(\"../constants/docs\").DATE_FORMAT_LINK,\"for details on the date formatting syntax.\"].join(\" \");function n(t){var e=t.description?\" \"+t.description:\"\",r=t.keys||[];if(r.length>0){for(var n=[],i=0;i<r.length;i++)n[i]=\"`\"+r[i]+\"`\";e+=\"Finally, the template string has access to \",e=1===r.length?\"variable \"+n[0]:\"variables \"+n.slice(0,-1).join(\", \")+\" and \"+n.slice(-1)+\".\"}return e}r.hovertemplateAttrs=function(t,e){t=t||{};n(e=e||{});var r={valType:\"string\",dflt:\"\",editType:t.editType||\"none\"};return!1!==t.arrayOk&&(r.arrayOk=!0),r},r.texttemplateAttrs=function(t,e){t=t||{};n(e=e||{});var r={valType:\"string\",dflt:\"\",editType:t.editType||\"calc\"};return!1!==t.arrayOk&&(r.arrayOk=!0),r}},{\"../constants/docs\":748}],907:[function(t,e,r){\"use strict\";var n=t(\"./ternary\"),i=t(\"../../plots/get_data\").getSubplotCalcData,a=t(\"../../lib\").counterRegex;r.name=\"ternary\";var o=r.attr=\"subplot\";r.idRoot=\"ternary\",r.idRegex=r.attrRegex=a(\"ternary\"),(r.attributes={})[o]={valType:\"subplotid\",dflt:\"ternary\",editType:\"calc\"},r.layoutAttributes=t(\"./layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout_defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t.calcdata,a=e._subplots.ternary,o=0;o<a.length;o++){var s=a[o],l=i(r,\"ternary\",s),c=e[s]._subplot;c||(c=new 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n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../cartesian/layout_attributes\"),o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../lib/extend\").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:\"number\",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:\"ternary\"}),bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},\"plot\",\"from-root\");c.uirevision={valType:\"any\",editType:\"none\"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":642,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../cartesian/layout_attributes\":842,\"../domain\":855}],909:[function(t,e,r){\"use 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n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strTranslate,l=o._,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,p=t(\"../plots\"),d=t(\"../cartesian/axes\"),g=t(\"../../components/dragelement\"),m=t(\"../../components/fx\"),v=t(\"../../components/dragelement/helpers\"),y=v.freeMode,x=v.rectMode,b=t(\"../../components/titles\"),_=t(\"../cartesian/select\").prepSelect,w=t(\"../cartesian/select\").selectOnClick,T=t(\"../cartesian/select\").clearSelect,k=t(\"../cartesian/select\").clearSelectionsCache,M=t(\"../cartesian/constants\");function A(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=A;var S=A.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select(\"path\").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below 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E=Math.sqrt(4/3);S.adjustLayout=function(t,e){var r,n,i,a,o,l,p=this,d=t.domain,g=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*g-i/2,n=e.t+e.h*(1-m)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[g-o/2,g+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[m-l/2,m+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var M=p.yaxis.domain[0],A=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[M,M+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(A,p.graphDiv._fullLayout),A.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var C=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[M,M+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(C,p.graphDiv._fullLayout),C.setScale();var L=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",L),p.layers.plotbg.select(\"path\").attr(\"d\",L);var I=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",I);var P=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",P),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var z=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",z),p.layers.bgrid.attr(\"transform\",z);var O=s(r+i/2,n)+\"rotate(30)\"+s(0,-A._offset);p.layers.aaxis.attr(\"transform\",O),p.layers.agrid.attr(\"transform\",O);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-C._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",A.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,A.linecolor||\"#000\").style(\"stroke-width\",(A.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",C.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,C.linecolor||\"#000\").style(\"stroke-width\",(C.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var 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n=t(\"../pie/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../lib/extend\").extendFlat;e.exports={labels:n.labels,label0:n.label0,dlabel:n.dlabel,values:n.values,marker:{colors:n.marker.colors,line:{color:l({},n.marker.line.color,{dflt:null}),width:l({},n.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},text:n.text,hovertext:n.hovertext,scalegroup:l({},n.scalegroup,{}),textinfo:l({},n.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:s({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hoverinfo:l({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:o({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),textposition:l({},n.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:n.textfont,insidetextfont:n.insidetextfont,title:{text:n.title.text,font:n.title.font,position:l({},n.title.position,{values:[\"top 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText,s=t(\"../pie/defaults\").handleLabelsAndValues;e.exports=function(t,e,r,l){function c(r,a){return n.coerce(t,e,i,r,a)}var u=c(\"labels\"),f=c(\"values\"),h=s(u,f),p=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),p){e._length=p,c(\"marker.line.width\")&&c(\"marker.line.color\",l.paper_bgcolor),c(\"marker.colors\"),c(\"scalegroup\");var d,g=c(\"text\"),m=c(\"texttemplate\");if(m||(d=c(\"textinfo\",Array.isArray(g)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),m||d&&\"none\"!==d){var v=c(\"textposition\");o(t,e,l,c,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}a(e,l,c),c(\"title.text\")&&(c(\"title.position\"),n.coerceFont(c,\"title.font\",l.font)),c(\"aspectratio\"),c(\"baseratio\")}else 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n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":1168}],1062:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":778,\"./layout_attributes\":1061}],1063:[function(t,e,r){\"use strict\";var 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0===d._deltaLastValue&&(d._deltaLastValue=a(r[0]));var l=C.select(\"text.delta\");function p(){l.text(o(a(r[0]),i)).call(m.fill,s(r[0])).call(f.convertToTspans,t)}return l.call(c.font,d.delta.font).call(m.fill,s({delta:d._deltaLastValue})),b(S)?l.transition().duration(S.duration).ease(S.easing).tween(\"text\",(function(){var t=n.select(this),e=a(r[0]),l=d._deltaLastValue,c=P(d.delta.valueformat,i,l,e),u=n.interpolateNumber(l,e);return d._deltaLastValue=e,function(e){t.text(o(u(e),c)),t.call(m.fill,s({delta:u(e)}))}})).each(\"end\",(function(){p(),E&&E()})).each(\"interrupt\",(function(){p(),E&&E()})):p(),u=k(o(a(r[0]),i),d.delta.font,A,t),l}(),O+=d.delta.position+d.delta.font.size+d.delta.font.family+d.delta.valueformat,O+=d.delta.increasing.symbol+d.delta.decreasing.symbol,p=u);d._hasNumber&&(!function(){var e=w(t,{tickformat:d.number.valueformat},d._range);e.setScale(),h.prepTicks(e);var i=function(t){return h.tickText(e,t).text},a=d.number.suffix,o=d.number.prefix,s=C.select(\"text.number\");function u(){var e=\"number\"==typeof r[0].y?o+i(r[0].y)+a:\"-\";s.text(e).call(c.font,d.number.font).call(f.convertToTspans,t)}b(S)?s.transition().duration(S.duration).ease(S.easing).each(\"end\",(function(){u(),E&&E()})).each(\"interrupt\",(function(){u(),E&&E()})).attrTween(\"text\",(function(){var t=n.select(this),e=n.interpolateNumber(r[0].lastY,r[0].y);d._lastValue=r[0].y;var s=P(d.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(o+s(e(r))+a)}})):u(),l=k(o+i(r[0].y)+a,d.number.font,A,t)}(),O+=d.number.font.size+d.number.font.family+d.number.valueformat+d.number.suffix+d.number.prefix,p=l);if(d._hasDelta&&d._hasNumber){var D,R,F=[(l.left+l.right)/2,(l.top+l.bottom)/2],B=[(u.left+u.right)/2,(u.top+u.bottom)/2],N=.75*d.delta.font.size;\"left\"===d.delta.position&&(D=M(d,\"deltaPos\",0,-1*(l.width*y[d.align]+u.width*(1-y[d.align])+N),O,Math.min),R=F[1]-B[1],p={width:l.width+u.width+N,height:Math.max(l.height,u.height),left:u.left+D,right:l.right,top:Math.min(l.top,u.top+R),bottom:Math.max(l.bottom,u.bottom+R)}),\"right\"===d.delta.position&&(D=M(d,\"deltaPos\",0,l.width*(1-y[d.align])+u.width*y[d.align]+N,O,Math.max),R=F[1]-B[1],p={width:l.width+u.width+N,height:Math.max(l.height,u.height),left:l.left,right:u.right+D,top:Math.min(l.top,u.top+R),bottom:Math.max(l.bottom,u.bottom+R)}),\"bottom\"===d.delta.position&&(D=null,R=u.height,p={width:Math.max(l.width,u.width),height:l.height+u.height,left:Math.min(l.left,u.left),right:Math.max(l.right,u.right),top:l.bottom-l.height,bottom:l.bottom+u.height}),\"top\"===d.delta.position&&(D=null,R=l.top,p={width:Math.max(l.width,u.width),height:l.height+u.height,left:Math.min(l.left,u.left),right:Math.max(l.right,u.right),top:l.bottom-l.height-u.height,bottom:l.bottom}),z.attr({dx:D,dy:R})}(d._hasNumber||d._hasDelta)&&C.attr(\"transform\",(function(){var t=s.numbersScaler(p);O+=t[2];var e,r=M(d,\"numbersScale\",1,t[0],O,Math.min);d._scaleNumbers||(r=1),e=d._isAngular?_-r*p.bottom:_-r*(p.top+p.bottom)/2,d._numbersTop=r*p.top+e;var n=p[T];\"center\"===T&&(n=(p.left+p.right)/2);var i=g-r*n;return i=M(d,\"numbersTranslate\",0,i,O,Math.max),o(i,e)+a(r)}))}(t,I,e,{numbersX:p,numbersY:A,numbersScaler:S,transitionOpts:r,onComplete:d}),P&&(E={range:L.gauge.axis.range,color:L.gauge.bgcolor,line:{color:L.gauge.bordercolor,width:0},thickness:1},C={range:L.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:L.gauge.bordercolor,width:L.gauge.borderwidth},thickness:1});var H=I.selectAll(\"g.angular\").data(z?e:[]);H.exit().remove();var G=I.selectAll(\"g.angularaxis\").data(z?e:[]);G.exit().remove(),z&&function(t,e,r,i){var a,c,u,f,p=r[0].trace,d=i.size,g=i.radius,m=i.innerRadius,v=i.gaugeBg,y=i.gaugeOutline,x=[d.l+d.w/2,d.t+d.h/2+g/2],T=i.gauge,k=i.layer,M=i.transitionOpts,A=i.onComplete,S=Math.PI/2;function E(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-S;return r<-S?-S:r>S?S:r}function C(t){return n.svg.arc().innerRadius((m+g)/2-t/2*(g-m)).outerRadius((m+g)/2+t/2*(g-m)).startAngle(-S)}function L(t){t.attr(\"d\",(function(t){return C(t.thickness).startAngle(E(t.range[0])).endAngle(E(t.range[1]))()}))}T.enter().append(\"g\").classed(\"angular\",!0),T.attr(\"transform\",o(x[0],x[1])),k.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),k.selectAll(\"g.xangularaxistick,path,text\").remove(),(a=w(t,p.gauge.axis)).type=\"linear\",a.range=p.gauge.axis.range,a._id=\"xangularaxis\",a.setScale();var I=function(t){return(a.range[0]-t.x)/(a.range[1]-a.range[0])*Math.PI+Math.PI},P={},z=h.makeLabelFns(a,0).labelStandoff;P.xFn=function(t){var e=I(t);return Math.cos(e)*z},P.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*l)},P.anchorFn=function(t){var e=I(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},P.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var O=function(t){return o(x[0]+g*Math.cos(t),x[1]-g*Math.sin(t))};u=function(t){return O(I(t))};if(c=h.calcTicks(a),f=h.getTickSigns(a)[2],a.visible){f=\"inside\"===a.ticks?-1:1;var D=(a.linewidth||1)/2;h.drawTicks(t,a,{vals:c,layer:k,path:\"M\"+f*D+\",0h\"+f*a.ticklen,transFn:function(t){var e=I(t);return O(e)+\"rotate(\"+-s(e)+\")\"}}),h.drawLabels(t,a,{vals:c,layer:k,transFn:u,labelFns:P})}var R=[v].concat(p.gauge.steps),F=T.selectAll(\"g.bg-arc\").data(R);F.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),F.select(\"path\").call(L).call(_),F.exit().remove();var B=C(p.gauge.bar.thickness),N=T.selectAll(\"g.value-arc\").data([p.gauge.bar]);N.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var j=N.select(\"path\");b(M)?(j.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){A&&A()})).each(\"interrupt\",(function(){A&&A()})).attrTween(\"d\",(U=B,V=E(r[0].lastY),q=E(r[0].y),function(){var t=n.interpolate(V,q);return function(e){return U.endAngle(t(e))()}})),p._lastValue=r[0].y):j.attr(\"d\",\"number\"==typeof r[0].y?B.endAngle(E(r[0].y)):\"M0,0Z\");var U,V,q;j.call(_),N.exit().remove(),R=[];var H=p.gauge.threshold.value;H&&R.push({range:[H,H],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var G=T.selectAll(\"g.threshold-arc\").data(R);G.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),G.select(\"path\").call(L).call(_),G.exit().remove();var Y=T.selectAll(\"g.gauge-outline\").data([y]);Y.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),Y.select(\"path\").call(L).call(_),Y.exit().remove()}(t,0,e,{radius:N,innerRadius:j,gauge:H,layer:G,size:R,gaugeBg:E,gaugeOutline:C,transitionOpts:r,onComplete:d});var Y=I.selectAll(\"g.bullet\").data(O?e:[]);Y.exit().remove();var W=I.selectAll(\"g.bulletaxis\").data(O?e:[]);W.exit().remove(),O&&function(t,e,r,n){var i,a,s,l,c,f=r[0].trace,p=n.gauge,d=n.layer,g=n.gaugeBg,v=n.gaugeOutline,y=n.size,x=f.domain,T=n.transitionOpts,k=n.onComplete;p.enter().append(\"g\").classed(\"bullet\",!0),p.attr(\"transform\",o(y.l,y.t)),d.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),d.selectAll(\"g.xbulletaxistick,path,text\").remove();var M=y.h,A=f.gauge.bar.thickness*M,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(f._hasNumber||f._hasDelta?1-u.bulletNumberDomainSize:1);(i=w(t,f.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=h.calcTicks(i),s=h.makeTransTickFn(i),l=h.getTickSigns(i)[2],c=y.t+y.h,i.visible&&(h.drawTicks(t,i,{vals:\"inside\"===i.ticks?h.clipEnds(i,a):a,layer:d,path:h.makeTickPath(i,c,l),transFn:s}),h.drawLabels(t,i,{vals:a,layer:d,transFn:s,labelFns:h.makeLabelFns(i,c)}));function C(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*M})).attr(\"height\",(function(t){return t.thickness*M}))}var L=[g].concat(f.gauge.steps),I=p.selectAll(\"g.bg-bullet\").data(L);I.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),I.select(\"rect\").call(C).call(_),I.exit().remove();var P=p.selectAll(\"g.value-bullet\").data([f.gauge.bar]);P.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),P.select(\"rect\").attr(\"height\",A).attr(\"y\",(M-A)/2).call(_),b(T)?P.select(\"rect\").transition().duration(T.duration).ease(T.easing).each(\"end\",(function(){k&&k()})).each(\"interrupt\",(function(){k&&k()})).attr(\"width\",Math.max(0,i.c2p(Math.min(f.gauge.axis.range[1],r[0].y)))):P.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(f.gauge.axis.range[1],r[0].y))):0);P.exit().remove();var z=r.filter((function(){return f.gauge.threshold.value})),O=p.selectAll(\"g.threshold-bullet\").data(z);O.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),O.select(\"line\").attr(\"x1\",i.c2p(f.gauge.threshold.value)).attr(\"x2\",i.c2p(f.gauge.threshold.value)).attr(\"y1\",(1-f.gauge.threshold.thickness)/2*M).attr(\"y2\",(1-(1-f.gauge.threshold.thickness)/2)*M).call(m.stroke,f.gauge.threshold.line.color).style(\"stroke-width\",f.gauge.threshold.line.width),O.exit().remove();var D=p.selectAll(\"g.gauge-outline\").data([v]);D.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),D.select(\"rect\").call(C).call(_),D.exit().remove()}(t,0,e,{gauge:Y,layer:W,size:R,gaugeBg:E,gaugeOutline:C,transitionOpts:r,onComplete:d});var X=I.selectAll(\"text.title\").data(e);X.exit().remove(),X.enter().append(\"text\").classed(\"title\",!0),X.attr(\"text-anchor\",(function(){return O?v.right:v[L.title.align]})).text(L.title.text).call(c.font,L.title.font).call(f.convertToTspans,t),X.attr(\"transform\",(function(){var t,e=R.l+R.w*y[L.title.align],r=u.titlePadding,n=c.bBox(X.node());if(P){if(z)if(L.gauge.axis.visible)t=c.bBox(G.node()).top-r-n.bottom;else t=R.t+R.h/2-N/2-n.bottom-r;O&&(t=A-(n.top+n.bottom)/2,e=R.l-u.bulletPadding*R.w)}else t=L._numbersTop-r-n.bottom;return o(e,t)}))}))}},{\"../../components/color\":643,\"../../components/drawing\":665,\"../../constants/alignment\":745,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plots/cartesian/axes\":828,\"../../plots/cartesian/axis_defaults\":830,\"../../plots/cartesian/layout_attributes\":842,\"../../plots/cartesian/position_defaults\":845,\"./constants\":1119,d3:169}],1123:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../mesh3d/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;var c=e.exports=l(s({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:i(),showlegend:s({},o.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:a.opacity,lightposition:a.lightposition,lighting:a.lighting,flatshading:a.flatshading,contour:a.contour,hoverinfo:s({},o.hoverinfo)}),\"calc\",\"nested\");c.flatshading.dflt=!0,c.lighting.facenormalsepsilon.dflt=0,c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 0},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/template_attributes\":906,\"../mesh3d/attributes\":1128}],1124:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\"),i=t(\"../streamtube/calc\").processGrid,a=t(\"../streamtube/calc\").filter;e.exports=function(t,e){e._len=Math.min(e.x.length,e.y.length,e.z.length,e.value.length),e._x=a(e.x,e._len),e._y=a(e.y,e._len),e._z=a(e.z,e._len),e._value=a(e.value,e._len);var r=i(e);e._gridFill=r.fill,e._Xs=r.Xs,e._Ys=r.Ys,e._Zs=r.Zs,e._len=r.len;for(var o=1/0,s=-1/0,l=0;l<e._len;l++){var c=e._value[l];o=Math.min(o,c),s=Math.max(s,c)}e._minValues=o,e._maxValues=s,e._vMin=void 0===e.isomin||null===e.isomin?o:e.isomin,e._vMax=void 0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":651,\"../streamtube/calc\":1295}],1125:[function(t,e,r){\"use strict\";var n=t(\"gl-mesh3d\"),i=t(\"../../lib/gl_format_color\").parseColorScale,a=t(\"../../lib/str2rgbarray\"),o=t(\"../../components/colorscale\").extractOpts,s=t(\"../../plots/gl3d/zip3\"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var 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0),\"category descending\"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,m[e])}));var v,y=e.line;y?(i(e,\"line\")&&a(t,e,{vals:e.line.color,containerStr:\"line\",cLetter:\"c\"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,M={},A=m.map((function(t){return t.inds}));for(g=0,x=0;x<k;x++){var S=[];for(b=0;b<A.length;b++)S.push(A[b][x]);d=p[x%p.length],g+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),C=S+\"-\"+E.rawColor;void 0===M[C]&&(M[C]=f(S,E.color,E.rawColor)),h(M[C],x,d)}var L,I=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,g)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<I.length;b++){var P=I[b].containerInd,z=m[b].inds[x],O=I[b].categories;if(void 0===O[z]){var D=e.dimensions[P]._categoryarray[z],R=e.dimensions[P]._ticktext[z];O[z]={dimensionInd:b,categoryInd:L=z,categoryValue:D,displayInd:L,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(O[z],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(I,M,g))}},{\"../../components/colorscale/calc\":651,\"../../components/colorscale/helpers\":654,\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/filter_unique.js\":769,\"../../lib/gup\":775,\"fast-isnumeric\":241}],1145:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"./attributes\"),c=t(\"../parcoords/merge_length\");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r(\"values\"),a=r(\"visible\");if(i&&i.length||(a=e.visible=!1),a){r(\"label\"),r(\"displayindex\",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o=\"array\");var u=r(\"categoryorder\",o);\"array\"===u?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==u||(e.categoryorder=\"trace\")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:\"dimensions\",handleItemDefaults:u}),d=function(t,e,r,o,s){s(\"line.shape\"),s(\"line.hovertemplate\");var l=s(\"line.color\",o.colorway[0]);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,\"values\",d),h(\"hoveron\"),h(\"hovertemplate\"),h(\"arrangement\"),h(\"bundlecolors\"),h(\"sortpaths\"),h(\"counts\");var g={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",g);var m={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",m)}},{\"../../components/colorscale/defaults\":653,\"../../components/colorscale/helpers\":654,\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/domain\":855,\"../parcoords/merge_length\":1158,\"./attributes\":1142}],1146:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":1142,\"./base_plot\":1143,\"./calc\":1144,\"./defaults\":1145,\"./plot\":1148}],1147:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plot_api/plot_api\"),a=t(\"../../components/fx\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../components/drawing\"),c=t(\"tinycolor2\"),u=t(\"../../lib/svg_text_utils\");function f(t,e,r,i){var a=t.map(R.bind(0,e,r)),c=i.selectAll(\"g.parcatslayer\").data([null]);c.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var f=c.selectAll(\"g.trace.parcats\").data(a,h),v=f.enter().append(\"g\").attr(\"class\",\"trace parcats\");f.attr(\"transform\",(function(t){return s(t.x,t.y)})),v.append(\"g\").attr(\"class\",\"paths\");var y=f.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),h);y.attr(\"fill\",(function(t){return t.model.color}));var _=y.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);b(_),y.attr(\"d\",(function(t){return t.svgD})),_.empty()||y.sort(d),y.exit().remove(),y.on(\"mouseover\",g).on(\"mouseout\",m).on(\"click\",x),v.append(\"g\").attr(\"class\",\"dimensions\");var k=f.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),h);k.enter().append(\"g\").attr(\"class\",\"dimension\"),k.attr(\"transform\",(function(t){return s(t.x,0)})),k.exit().remove();var M=k.selectAll(\"g.category\").data((function(t){return t.categories}),h),A=M.enter().append(\"g\").attr(\"class\",\"category\");M.attr(\"transform\",(function(t){return s(0,t.y)})),A.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),M.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),w(A);var S=M.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),h);S.each((function(){o.raiseToTop(this)})),S.attr(\"fill\",(function(t){return t.color}));var z=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),T(z),S.exit().remove(),A.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var O=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return p(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",O+\" -1px  1px 2px, \"+O+\" 1px  1px 2px, \"+O+\"  1px -1px 2px, \"+O+\" -1px -1px 2px\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return p(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){l.font(n.select(this),t.parcatsViewModel.categorylabelfont),u.convertToTspans(n.select(this),e)})),A.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){l.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",E).on(\"mouseout\",C),M.exit().remove(),k.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",L).on(\"drag\",I).on(\"dragend\",P)),f.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),f.exit().remove()}function h(t){return t.key}function p(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function d(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){o.raiseToTop(this),_(n.select(this));var e=v(t),r=y(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,s,l,u=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),g=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=u&&u<=t.leftXs[l+1]+2){var m=t.parcatsViewModel.dimensions[l],x=t.parcatsViewModel.dimensions[l+1];i=(m.x+m.width+x.x)/2,s=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,w=t.parcatsViewModel.y+s,T=c.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,M=k/t.parcatsViewModel.model.count,A={countLabel:k,probabilityLabel:M.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",A.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",A.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),C=n.mouse(f)[0];a.loneHover({trace:h,x:b-d.left+g.left,y:w-d.top+g.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:C<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:A,eventData:[{data:h._input,fullData:h,count:k,probability:M}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function m(t){if(!t.parcatsViewModel.dragDimension&&(b(n.select(this)),a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(d),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=v(t),r=y(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function v(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function y(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function x(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=v(t),r=y(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function b(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function _(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return c.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function w(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function T(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function k(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){k(t).each((function(t){Array.prototype.push.apply(l,v(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function A(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=k(i),l=[];s.each((function(t){Array.prototype.push.apply(l,v(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var g=u.model.count,m=u.model.categoryLabel,v=g/u.parcatsViewModel.model.count,y={countLabel:g,categoryLabel:m,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", 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e=k(t);_(e),e.each((function(){o.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=S(r,s,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,g=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var m=f.model.categoryLabel,v=u.parcatsViewModel.model.count,y=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:m,probabilityLabel:_.toFixed(3)},M=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&M.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(M.push(\"P(color \\u2229 \"+m+\"): \"+k.probabilityLabel),M.push(\"P(\"+m+\" | color): \"+w.toFixed(3)),M.push(\"P(color | \"+m+\"): \"+T.toFixed(3)));var A=M.join(\"<br>\"),S=c.mostReadable(u.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(g-e.top),text:A,color:u.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:m,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,s,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(S(t,e,this))})),i}(r,s,this)),e&&a.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(b(e.pathSelection),w(e.dimensionSelection.selectAll(\"g.category\")),T(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),a.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(d),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?A(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function L(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,o.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}N(t.parcatsViewModel),B(t.parcatsViewModel),D(t.parcatsViewModel),O(t.parcatsViewModel)}}function P(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),a=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==a[e]}));o&&a.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?A(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,N(t.parcatsViewModel),B(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){D(t.parcatsViewModel,!0),O(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&i.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function O(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),h),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function D(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),h);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),h);r(t.dimensionSelection).attr(\"transform\",(function(t){return s(t.x,0)})),r(i).attr(\"transform\",(function(t){return s(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return p(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return p(t)?t.width+5:-5})).each((function(t){var e,r;p(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),h),l=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),T(l),a.each((function(){o.raiseToTop(this)})),a.exit().remove()}function R(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var g={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(N(g),B(g)),g}function F(t,e,r,i,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=n.interpolateNumber(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+i+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+i)+\" \"+l[s]+\",\"+(e[s]+i)+\" \"+(t[s]+r[s])+\",\"+(e[s]+i),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function B(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),g=0;g<c.length;g++){var m,v=c[g];m=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var _=v.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=m;var k=t.dimensions[T].categories[w],M=k.bands.length,A=k.bands[M-1];if(void 0===A||v.rawColor!==A.rawColor){var S=void 0===A?0:A.y+A.height;k.bands.push({key:S,color:v.color,rawColor:v.rawColor,height:m,width:k.width,count:v.count,y:S,categoryViewModel:k,parcatsViewModel:t})}else{var E=k.bands[M-1];E.height+=m,E.count+=v.count}}y=\"hspline\"===t.pathShape?F(s,x,l,m,.5):F(s,x,l,m,0),h[g]={key:v.valueInds[0],model:v,height:m,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=h}function N(t){var e=t.model.dimensions.map((function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}}));e.sort((function(t,e){return t.displayInd-e.displayInd}));var r=[];for(var n in e){var i=e[n].dimensionInd,a=t.model.dimensions[i];r.push(j(t,a))}t.dimensions=r}function j(t,e){var r,n=t.model.dimensions.length,i=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],f=t.model.maxCats,h=e.categories.length,p=e.count,d=t.height-8*(f-1),g=8*(f-h)/2,m=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(m.sort((function(t,e){return t.displayInd-e.displayInd})),c=0;c<h;c++)l=m[c].categoryInd,o=e.categories[l],a=p>0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:a,y:null!==o.dragY?o.dragY:g,bands:[],parcatsViewModel:t},g=g+a+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}e.exports=function(t,e,r,n){f(r,t,n,e)}},{\"../../components/drawing\":665,\"../../components/fx\":683,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plot_api/plot_api\":814,d3:169,tinycolor2:576}],1148:[function(t,e,r){\"use strict\";var n=t(\"./parcats\");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{\"./parcats\":1147}],1149:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/layout_attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/plot_template\":817,\"../../plots/cartesian/layout_attributes\":842,\"../../plots/domain\":855,\"../../plots/font_attributes\":856}],1150:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"d3\"),a=t(\"../../lib/gup\").keyFun,o=t(\"../../lib/gup\").repeat,s=t(\"../../lib\").sorterAsc,l=t(\"../../lib\").strTranslate,c=n.bar.snapRatio;function u(t,e){return t*(1-c)+e*c}var f=n.bar.snapClose;function h(t,e){return t*(1-f)+e*f}function p(t,e,r,n){if(function(t,e){for(var r=0;r<e.length;r++)if(t>=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],c=l,f=a;i*f<i*o;f+=i){var p=f+i,d=e[p];if(i*r<i*h(l,d))return u(l,c);if(i*r<i*d||p===o)return u(d,l);c=l,l=d}}function d(t){t.attr(\"x\",-n.bar.captureWidth/2).attr(\"width\",n.bar.captureWidth)}function g(t){t.attr(\"visibility\",\"visible\").style(\"visibility\",\"visible\").attr(\"fill\",\"yellow\").attr(\"opacity\",0)}function m(t){if(!t.brush.filterSpecified)return\"0,\"+t.height;for(var 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k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||y()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&A(r)):A(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||A(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function M(t,e){return t[0]-e[0]}function A(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(M)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t){var e=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);e.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t){var e=t.selectAll(\".background\").data(o);e.enter().append(\"rect\").classed(\"background\",!0).call(d).call(g).style(\"pointer-events\",\"auto\").attr(\"transform\",l(0,n.verticalPadding)),e.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var r=t.selectAll(\".highlight-shadow\").data(o);r.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",n.bar.strokeColor).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),r.attr(\"y1\",(function(t){return t.height})).call(x);var i=t.selectAll(\".highlight\").data(o);i.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",(function(t){return t.height})).call(x)}(e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(M)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":778,\"../../lib/gup\":775,\"./constants\":1153,d3:169}],1151:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plots/get_data\").getModuleCalcData,a=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.width,height:this.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":754,\"../../plots/get_data\":865,\"./plot\":1160,d3:169}],1152:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,i=t(\"../../components/colorscale\"),a=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{\"../../components/colorscale\":655,\"../../lib\":778,\"../../lib/gup\":775}],1153:[function(t,e,r){\"use strict\";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:[\"contextLineLayer\",\"focusLineLayer\",\"pickLineLayer\"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:\"#777\",bar:{width:4,captureWidth:10,fillColor:\"magenta\",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeColor:\"white\",strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:\"axis-extent-text\",parcoordsLineLayers:\"parcoords-line-layers\",parcoordsLineLayer:\"parcoords-lines\",parcoords:\"parcoords\",parcoordsControlView:\"parcoords-control-view\",yAxis:\"y-axis\",axisOverlays:\"axis-overlays\",axis:\"axis\",axisHeading:\"axis-heading\",axisTitle:\"axis-title\",axisExtent:\"axis-extent\",axisExtentTop:\"axis-extent-top\",axisExtentTopText:\"axis-extent-top-text\",axisExtentBottom:\"axis-extent-bottom\",axisExtentBottomText:\"axis-extent-bottom-text\",axisBrush:\"axis-brush\"},id:{filterBarPattern:\"filter-bar-pattern\"}}},{}],1154:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/helpers\").hasColorscale,a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"../../plots/cartesian/axes\"),c=t(\"./attributes\"),u=t(\"./axisbrush\"),f=t(\"./constants\").maxDimensionCount,h=t(\"./merge_length\");function p(t,e,r,i){function a(r,i){return n.coerce(t,e,c.dimensions,r,i)}var o=a(\"values\"),s=a(\"visible\");if(o&&o.length||(s=e.visible=!1),s){a(\"label\"),a(\"tickvals\"),a(\"ticktext\"),a(\"tickformat\");var f=a(\"range\");e._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:f},l.setConvert(e._ax,i.layout),a(\"multiselect\");var h=a(\"constraintrange\");h&&(e.constraintrange=u.cleanRanges(h,e))}}e.exports=function(t,e,r,l){function u(r,i){return n.coerce(t,e,c,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log(\"parcoords traces support up to \"+f+\" dimensions at the moment\"),d.splice(f));var g=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),m=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(g)&&g.length||(e.visible=!1),h(e,g,\"values\",m);var v={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(u,\"labelfont\",v),n.coerceFont(u,\"tickfont\",v),n.coerceFont(u,\"rangefont\",v),u(\"labelangle\"),u(\"labelside\")}},{\"../../components/colorscale/defaults\":653,\"../../components/colorscale/helpers\":654,\"../../lib\":778,\"../../plots/array_container_defaults\":823,\"../../plots/cartesian/axes\":828,\"../../plots/domain\":855,\"./attributes\":1149,\"./axisbrush\":1150,\"./constants\":1153,\"./merge_length\":1158}],1155:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":778}],1156:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":1149,\"./base_plot\":1151,\"./calc\":1152,\"./defaults\":1154,\"./plot\":1160}],1157:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform sampler2D mask, palette;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(mask,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function g(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,c=r[64*a+l];63===l&&0===o&&(c*=-1),n[i++]=c}return n}function m(t){var e=\"0\"+t;return e.substr(e.length-2)}function v(t){return t<o?\"p\"+m(t+1)+\"_\"+m(t+4):\"colors\"}function y(t,e,r,n,i,a,o,l,c,u,f,h,p){for(var d=[[],[]],g=0;g<64;g++)d[0][g]=g===i?1:0,d[1][g]=g===a?1:0;var m=t.lines.canvasOverdrag,v=t.domain,y=t.canvasWidth,x=t.canvasHeight,b=t.deselectedLines.color;return s.extendFlat({key:f,resolution:[y,x],viewBoxPos:[o+m,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:d[0].slice(0,16),dim0B:d[0].slice(16,32),dim0C:d[0].slice(32,48),dim0D:d[0].slice(48,64),dim1A:d[1].slice(0,16),dim1B:d[1].slice(16,32),dim1C:d[1].slice(32,48),dim1D:d[1].slice(48,64),drwLayer:h,contextColor:[b[0]/255,b[1]/255,b[2]/255,b[3]<1?b[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+m)+(t.pad.l-m)+t.layoutWidth*v.x[0],scissorWidth:(n===r?y-o+m:c+.5)+(n===e?o+m:0),scissorY:l+t.pad.b+t.layoutHeight*v.y[0],scissorHeight:u,viewportX:t.pad.l-m+t.layoutWidth*v.x[0],viewportY:t.pad.b+t.layoutHeight*v.y[0],viewportWidth:y,viewportHeight:x},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,m,b,_=e.context,w=e.pick,T=e.regl,k={currentRafs:{},drawCompleted:!0,clearOnly:!1},M=function(t){for(var e={},r=0;r<=o;r+=4)e[v(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(T),A=T.texture(u),S=[];C(e);var E=T({profile:!1,blend:{enable:_,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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r=t._fullLayout;if(i(t)){var s={},l={},c={},u={},f=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=u[r]=n._fullInput.index;s[r]=t.data[i].dimensions,l[r]=t.data[i].dimensions.slice()}));n(t,e,{width:f.w,height:f.h,margin:{t:f.t,r:f.r,b:f.b,l:f.l}},{filterChanged:function(e,n,i){var a=l[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",f=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===f[s]){var h=a.constraintrange;f[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[u[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"plot\"},editType:\"plot\"},domain:i({name:\"pie\",trace:!0,editType:\"calc\"}),hole:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},sort:{valType:\"boolean\",dflt:!0,editType:\"calc\"},direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"],dflt:\"counterclockwise\",editType:\"calc\"},rotation:{valType:\"number\",min:-360,max:360,dflt:0,editType:\"calc\"},pull:{valType:\"number\",min:0,max:1,dflt:0,arrayOk:!0,editType:\"calc\"},_deprecated:{title:{valType:\"string\",dflt:\"\",editType:\"calc\"},titlefont:c({},u,{}),titleposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":642,\"../../lib/extend\":768,\"../../plots/attributes\":824,\"../../plots/domain\":855,\"../../plots/font_attributes\":856,\"../../plots/template_attributes\":906}],1162:[function(t,e,r){\"use strict\";var n=t(\"../../plots/plots\");r.name=\"pie\",r.plot=function(t,e,i,a){n.plotBasePlot(r.name,t,e,i,a)},r.clean=function(t,e,i,a){n.cleanBasePlot(r.name,t,e,i,a)}},{\"../../plots/plots\":891}],1163:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"tinycolor2\"),a=t(\"../../components/color\"),o={};function s(t){return function(e,r){return!!e&&(!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e))}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}e.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},g=s(o[\"_\"+e.type+\"colormap\"]),m=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;if((y=+y)<0)continue}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(m+=y),a.push({v:y,label:x,color:g(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(m+=y),!1===i.color&&u[r]&&(i.color=g(u[r],x)))}return(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=m),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":643,\"fast-isnumeric\":241,tinycolor2:576}],1164:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var g=c(\"textposition\");s(t,e,n,c,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&c(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&c(\"insidetextorientation\")}o(e,n,c);var m=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",m?\"middle center\":\"top center\");m||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":778,\"../../plots/domain\":855,\"../bar/defaults\":925,\"./attributes\":1161,\"fast-isnumeric\":241}],1165:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":679}],1166:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":778}],1167:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":1161,\"./base_plot\":1162,\"./calc\":1163,\"./defaults\":1164,\"./layout_attributes\":1168,\"./layout_defaults\":1169,\"./plot\":1170,\"./style\":1171,\"./style_one\":1172}],1168:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],1169:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":778,\"./layout_attributes\":1168}],1170:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,g=t(\"../bar/constants\").TEXTPAD,m=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.trace,s=i.cx,c=i.cy;\"_hasHoverLabel\"in o||(o._hasHoverLabel=!1),\"_hasHoverEvent\"in o||(o._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,u=e._fullData[o.index];if(!e._dragging&&!1!==r.hovermode){var f=u.hoverinfo;if(Array.isArray(f)&&(f=a.castHoverinfo({hoverinfo:[m.castOption(f,t.pts)],_module:o._module},r,0)),\"all\"===f&&(f=\"label+text+value+percent+name\"),u.hovertemplate||\"none\"!==f&&\"skip\"!==f&&f){var h=t.rInscribed||0,p=s+t.pxmid[0]*(1-h),d=c+t.pxmid[1]*(1-h),g=r.separators,y=[];if(f&&-1!==f.indexOf(\"label\")&&y.push(t.label),t.text=m.castOption(u.hovertext||u.text,t.pts),f&&-1!==f.indexOf(\"text\")){var x=t.text;l.isValidTextValue(x)&&y.push(x)}t.value=t.v,t.valueLabel=m.formatPieValue(t.v,g),f&&-1!==f.indexOf(\"value\")&&y.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=m.formatPiePercent(t.percent,g),f&&-1!==f.indexOf(\"percent\")&&y.push(t.percentLabel);var b=u.hoverlabel,_=b.font;a.loneHover({trace:o,x0:p-h*i.r,x1:p+h*i.r,y:d,text:y.join(\"<br>\"),name:u.hovertemplate||-1!==f.indexOf(\"name\")?u.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:m.castOption(b.bgcolor,t.pts)||t.color,borderColor:m.castOption(b.bordercolor,t.pts),fontFamily:m.castOption(_.family,t.pts),fontSize:m.castOption(_.size,t.pts),fontColor:m.castOption(_.color,t.pts),nameLength:m.castOption(b.namelength,t.pts),textAlign:m.castOption(b.align,t.pts),hovertemplate:m.castOption(u.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,u)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e}),o._hasHoverLabel=!0}o._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,u)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[o.index],s=n.select(this).datum();o._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(s,i)],event:n.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),o._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[o.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=m.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=m.castOption(t._input.textfont.color,e.pts));var i=m.castOption(t.insidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.insidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,g=[];if(!d){var m,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,g.push(a)}};if(f||h){for(m=4;m>=-4;m-=2)v(Math.PI*m,\"tan\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+1),\"tan\")}if(f||p){for(m=4;m>=-4;m-=2)v(Math.PI*(m+1.5),\"rad\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;g.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a));for(var x=0,b=0,_=0;_<g.length;_++){var w=g[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return g[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:M(a,o/e),rotate:A(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:M(a,o/e),rotate:A(i+Math.PI/2)}}function M(t,e){return Math.cos(e)-t*e}function A(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function C(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function L(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=P(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=I(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function I(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function P(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function z(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=I(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+P(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=I(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function O(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),g=n.separators;if(s=f?[e.label]:[],h){var v=m.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(m.formatPieValue(e.v,g)),d&&s.push(m.formatPiePercent(e.v/r.vTotal,g)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:m.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:m.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=m.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),z(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],g=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=m.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=O(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=O(a,s),r.midangle=s,s+=l*r.v/2,n=O(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=g.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),g.pull){var v=+m.castOption(g.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=g.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+L(i.px0,i.pxmid,!0,1)+L(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+L(i.px0,i.pxmid,!1,T)+L(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var M=L(i.px0,i.px1,!0,1);if(T){var A=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+L(i.px1,i.px0,!1,T)+\"l\"+A*i.px0[0]+\",\"+A*i.px0[1]+M+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}D(t,i,d);var S=m.castOption(g.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=m.castOption(t.outsidetextfont.color,e.pts)||m.castOption(t.textfont.color,e.pts)||r.color,i=m.castOption(t.outsidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.outsidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(g,i,r.font):b(g,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=C(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,g.outsidetextfont);u.call(s.font,x),v=C(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:O(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var M=v.targetY;i.yLabelMin=M-y.height/2,i.yLabelMid=M,i.yLabelMax=M+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(g.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function L(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(g.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=g.title.text;g._meta&&(i=l.templateString(i,g._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,g.title.font).call(f.convertToTspans,t),e=\"middle center\"===g.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):L(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function g(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,g=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(m.castOption(e.pull,t.pts)||0)>=(m.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(g+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?g:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,g),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr(\"transform\",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+c):s+=\"l\"+t.labelExtraX+\",\"+u+\"v\"+(f-u)+\"h\"+c}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+c;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(v,g),_&&g.automargin){var k=s.bBox(h.node()),M=g.domain,A=a.w*(M.x[1]-M.x[0]),S=a.h*(M.y[1]-M.y[0]),E=(.5*A-d.r)/a.w,I=(.5*S-d.r)/a.h;i.autoMargin(t,\"pie.\"+g.uid+\".automargin\",{xl:M.x[0]-E,xr:M.x[1]+E,yb:M.y[0]-I,yt:M.y[1]+I,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:L,prerenderTitles:_,layoutAreas:z,attachFxHandlers:x,computeTransform:R}},{\"../../components/color\":643,\"../../components/drawing\":665,\"../../components/fx\":683,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../../plots/plots\":891,\"../bar/constants\":923,\"../bar/uniform_text\":937,\"./event_data\":1165,\"./helpers\":1166,d3:169}],1171:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"./style_one\"),a=t(\"../bar/uniform_text\").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each((function(t){n.select(this).call(i,t,e)}))}))}},{\"../bar/uniform_text\":937,\"./style_one\":1172,d3:169}],1172:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"./helpers\").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},{\"../../components/color\":643,\"./helpers\":1166}],1173:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},{\"../scatter/attributes\":1187}],1174:[function(t,e,r){\"use strict\";var n=t(\"gl-pointcloud2d\"),i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new Int32Array(e),l=0;l<e;l++)o=c[l],s=u[l],r[l]=l,n[2*l]=o,n[2*l+1]=s,o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var g=i(t.marker.color),m=i(t.marker.border.color),v=t.opacity*t.marker.opacity;g[3]*=v,this.pointcloudOptions.color=g;var y=t.marker.blend;if(null===y){y=c.length<100||u.length<100}this.pointcloudOptions.blend=y,m[3]*=v,this.pointcloudOptions.borderColor=m;var x=t.marker.sizemin,b=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=b,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var _=this.scene.xaxis,w=this.scene.yaxis,T=b/2||.5;t._extremes[_._id]=a(_,[d[0],d[2]],{ppad:T}),t._extremes[w._id]=a(w,[d[1],d[3]],{ppad:T})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{\"../../lib/str2rgbarray\":802,\"../../plots/cartesian/autorange\":827,\"../scatter/get_trace_color\":1197,\"gl-pointcloud2d\":324}],1175:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},{\"../../lib\":778,\"./attributes\":1173}],1176:[function(t,e,r){\"use 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../plots/template_attributes\").hovertemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../plot_api/plot_template\").templatedArray,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../plot_api/edit_types\").overrideAll;t(\"../../constants/docs\").FORMAT_LINK;(e.exports=h({hoverinfo:f({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),customdata:void 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o=n[t=i[w]],s=0,l={};for(T=0;T<o.length;T++)l[(e=o[T]).label]||(l[e.label]=0),l[e.label]+=e.value,s+=e.value;for(T=0;T<o.length;T++)(e=o[T]).flow={value:s,labelConcentration:l[e.label]/s,concentration:e.value/s,links:o},e.concentrationscale&&(e.color=a(e.concentrationscale(e.flow.labelConcentration)))}var c=0;for(w=0;w<r.sourceLinks.length;w++)c+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/c;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),s.node.x.length&&s.node.y.length){for(_=0;_<Math.min(s.node.x.length,s.node.y.length,k.nodes.length);_++)if(s.node.x[_]&&s.node.y[_]){var C=[s.node.x[_]*g,s.node.y[_]*v];k.nodes[_].x0=C[0]-d/2,k.nodes[_].x1=C[0]+d/2;var L=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=C[1]-L/2,k.nodes[_].y1=C[1]+L/2}if(\"snap\"===s.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(y=k.nodes));i.update(k)}return{circular:b,key:r,trace:s,guid:f.randstr(),horizontal:h,width:g,height:v,nodePad:s.node.pad,nodeLineColor:s.node.line.color,nodeLineWidth:s.node.line.width,linkLineColor:s.link.line.color,linkLineWidth:s.link.line.width,valueFormat:s.valueformat,valueSuffix:s.valuesuffix,textFont:s.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:h?v:g,dragPerpendicular:h?g:v,arrangement:s.arrangement,sankey:i,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function b(t,e,r){var n=a(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:o.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:_,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function _(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,i=t.link.source.x1,a=t.link.target.x0,o=v(i,a),s=o(.5),l=o(.5),c=t.link.y0-t.link.width/2,u=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2;return\"M\"+i+\",\"+c+\"C\"+s+\",\"+c+\" \"+l+\",\"+f+\" \"+a+\",\"+f+\"L\"+a+\",\"+h+\"C\"+l+\",\"+h+\" \"+s+\",\"+u+\" \"+i+\",\"+u+\"Z\"}}function w(t,e){var r=a(e.color),i=n.nodePadAcross,s=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var l=e.dx,c=Math.max(.5,e.dy),u=\"node_\"+e.pointNumber;return e.group&&(u=f.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:u,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(l),visibleHeight:c,zoneX:-i,zoneY:-s,zoneWidth:l+2*i,zoneHeight:c+2*s,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:o.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,u].join(\"_\"),interactionState:t.interactionState,figure:t}}function T(t){t.attr(\"transform\",(function(t){return h(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function k(t){t.call(T)}function M(t,e){t.call(k),e.attr(\"d\",_())}function A(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function S(t){return t.link.width>1||t.linkLineWidth>0}function E(t){return h(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function C(t){return h(t.horizontal?0:t.labelY,t.horizontal?t.labelY:0)}function L(t){return i.svg.line()([[t.horizontal?t.left?-t.sizeAcross:t.visibleWidth+n.nodeTextOffsetHorizontal:n.nodeTextOffsetHorizontal,0],[t.horizontal?t.left?-n.nodeTextOffsetHorizontal:t.sizeAcross:t.visibleHeight-n.nodeTextOffsetHorizontal,0]])}function I(t){return t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\"}function P(t){return t.horizontal?\"scale(1 1)\":\"scale(-1 1)\"}function z(t){return t.darkBackground&&!t.horizontal?\"rgb(255,255,255)\":\"rgb(0,0,0)\"}function O(t){return t.horizontal&&t.left?\"100%\":\"0%\"}function D(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function R(t,e,r,a){var o=i.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(i){if(\"fixed\"!==i.arrangement&&(f.ensureSingle(a._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){a._fullLayout._dragCover=t})),f.raiseToTop(this),i.interactionState.dragInProgress=i.node,B(i.node),i.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,i.interactionState.hovered),i.interactionState.hovered=!1),\"snap\"===i.arrangement)){var o=i.traceId+\"|\"+i.key;i.forceLayouts[o]?i.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=u.forceSimulation(a).alphaDecay(0).force(\"collide\",u.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(n.forceIterations)).force(\"constrain\",function(t,e,r,i){return function(){for(var t=0,a=0;a<r.length;a++){var o=r[a];o===i.interactionState.dragInProgress?(o.x=o.lastDraggedX,o.y=o.lastDraggedY):(o.vx=(o.originalX-o.x)/n.forceTicksPerFrame,o.y=Math.min(i.size-o.dy/2,Math.max(o.dy/2,o.y))),t=Math.max(t,Math.abs(o.vx),Math.abs(o.vy))}!i.interactionState.dragInProgress&&t<.1&&i.forceLayouts[e].alpha()>0&&i.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,i),function(t,e,r,i,a){window.requestAnimationFrame((function o(){var s;for(s=0;s<n.forceTicksPerFrame;s++)r.forceLayouts[i].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),M(t.filter(N(r)),e),r.forceLayouts[i].alpha()>0)window.requestAnimationFrame(o);else{var l=r.node.originalX;r.node.x0=l-r.visibleWidth/2,r.node.x1=l+r.visibleWidth/2,F(r,a)}}))}(t,e,i,o,a)}})).on(\"drag\",(function(r){if(\"fixed\"!==r.arrangement){var n=i.event.x,a=i.event.y;\"snap\"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=a-r.visibleHeight/2,r.node.y1=a+r.visibleHeight/2):(\"freeform\"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),a=Math.max(0,Math.min(r.size-r.visibleHeight/2,a)),r.node.y0=a-r.visibleHeight/2,r.node.y1=a+r.visibleHeight/2),B(r.node),\"snap\"!==r.arrangement&&(r.sankey.update(r.graph),M(t.filter(N(r)),e))}})).on(\"dragend\",(function(t){if(\"fixed\"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;\"snap\"!==t.arrangement&&F(t,a)}}));t.on(\".drag\",null).call(o)}function F(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}y.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function B(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function N(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,l,c){var u=!1;f.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){u=!0}));var h=t._fullLayout._dragCover,p=r.filter((function(t){return m(t).trace.visible})).map(x.bind(null,l)),v=e.selectAll(\".\"+n.cn.sankey).data(p,d);v.exit().remove(),v.enter().append(\"g\").classed(n.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").attr(\"transform\",E),v.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;f.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=i.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),v.transition().ease(n.ease).duration(n.duration).attr(\"transform\",E);var y=v.selectAll(\".\"+n.cn.sankeyLinks).data(g,d);y.enter().append(\"g\").classed(n.cn.sankeyLinks,!0).style(\"fill\",\"none\");var k=y.selectAll(\".\"+n.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(b.bind(null,t))}),d);k.enter().append(\"path\").classed(n.cn.sankeyLink,!0).call(D,v,c.linkEvents),k.style(\"stroke\",(function(t){return S(t)?o.tinyRGB(a(t.linkLineColor)):t.tinyColorHue})).style(\"stroke-opacity\",(function(t){return S(t)?o.opacity(t.linkLineColor):t.tinyColorAlpha})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})).style(\"stroke-width\",(function(t){return S(t)?t.linkLineWidth:1})).attr(\"d\",_()),k.style(\"opacity\",(function(){return t._context.staticPlot||u||h?1:0})).transition().ease(n.ease).duration(n.duration).style(\"opacity\",1),k.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var M=v.selectAll(\".\"+n.cn.sankeyNodeSet).data(g,d);M.enter().append(\"g\").classed(n.cn.sankeyNodeSet,!0),M.style(\"cursor\",(function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}}));var F=M.selectAll(\".\"+n.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return t-e})),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.map(w.bind(null,t))}),d);F.enter().append(\"g\").classed(n.cn.sankeyNode,!0).call(T).style(\"opacity\",(function(e){return!t._context.staticPlot&&!u||e.partOfGroup?0:1})),F.call(D,v,c.nodeEvents).call(R,k,c,t),F.transition().ease(n.ease).duration(n.duration).call(T).style(\"opacity\",(function(t){return t.partOfGroup?0:1})),F.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var B=F.selectAll(\".\"+n.cn.nodeRect).data(g);B.enter().append(\"rect\").classed(n.cn.nodeRect,!0).call(A),B.style(\"stroke-width\",(function(t){return t.nodeLineWidth})).style(\"stroke\",(function(t){return o.tinyRGB(a(t.nodeLineColor))})).style(\"stroke-opacity\",(function(t){return o.opacity(t.nodeLineColor)})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),B.transition().ease(n.ease).duration(n.duration).call(A);var N=F.selectAll(\".\"+n.cn.nodeCapture).data(g);N.enter().append(\"rect\").classed(n.cn.nodeCapture,!0).style(\"fill-opacity\",0),N.attr(\"x\",(function(t){return t.zoneX})).attr(\"y\",(function(t){return t.zoneY})).attr(\"width\",(function(t){return t.zoneWidth})).attr(\"height\",(function(t){return t.zoneHeight}));var 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),g=p(\"b\"),m=Math.min(d.length,g.length);if(m){e._length=m,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",m<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":778,\"../scatter/constants\":1191,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/line_shape_defaults\":1202,\"../scatter/marker_defaults\":1206,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"./attributes\":1221}],1224:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],1225:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],1226:[function(t,e,r){\"use 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n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r,o){var s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":665,\"../../plots/cartesian/axes\":828,\"../scatter/plot\":1208}],1229:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,g=f(\"geojson\");(\"string\"==typeof g&&\"\"!==g||n.isPlainObject(g))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var m=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(m.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":778,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/marker_defaults\":1206,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"./attributes\":1229}],1232:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],1233:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},{\"../../plots/cartesian/axes\":828}],1234:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/get_trace_color\"),o=t(\"../../lib\").fillText,s=t(\"./attributes\");e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=h.projection.isLonLatOverEdges,d=h.project;if(n.getClosest(l,(function(t){var n=t.lonlat;if(n[0]===i)return 1/0;if(p(n))return 1/0;var a=d(n),o=d([e,r]),s=Math.abs(a[0]-o[0]),l=Math.abs(a[1]-o[1]),c=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-c,1-3/c)}),t),!1!==t.index){var g=l[t.index],m=g.lonlat,v=[u.c2p(m),f.c2p(m)],y=g.mrc||1;t.x0=v[0]-y,t.x1=v[0]+y,t.y0=v[1]-y,t.y1=v[1]+y,t.loc=g.loc,t.lon=m[0],t.lat=m[1];var x={};x[c.geo]={_subplot:h};var b=c._module.formatLabels(g,c,x);return 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T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),g=Math.max(g,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,g]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,g=m[p],v=m[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[g*y/h,x/h]}}return o}}},{\"../../components/drawing\":665,\"../../components/fx/helpers\":679,\"../../constants/interactions\":752,\"../../lib\":778,\"../../lib/gl_format_color\":774,\"../../plots/cartesian/axis_ids\":831,\"../../registry\":911,\"../scatter/make_bubble_size_func\":1204,\"../scatter/subtypes\":1212,\"./constants\":1241,\"./helpers\":1246,\"color-normalize\":125,\"fast-isnumeric\":241,\"svg-path-sdf\":574}],1243:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,g){function m(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,g,m);if(x){u(t,e,g,m);var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";m(\"text\"),m(\"hovertext\"),m(\"hovertemplate\"),m(\"mode\",b),l.hasLines(e)&&(m(\"connectgaps\"),h(t,e,r,g,m),m(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,g,m),m(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(m(\"texttemplate\"),d(t,e,g,m));var _=(e.line||{}).color,w=(e.marker||{}).color;m(\"fill\"),\"none\"!==e.fill&&p(t,e,r,m);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,m)}else e.visible=!1}},{\"../../lib\":778,\"../../registry\":911,\"../scatter/constants\":1191,\"../scatter/fillcolor_defaults\":1195,\"../scatter/line_defaults\":1200,\"../scatter/marker_defaults\":1206,\"../scatter/period_defaults\":1207,\"../scatter/subtypes\":1212,\"../scatter/text_defaults\":1213,\"../scatter/xy_defaults\":1214,\"./attributes\":1239,\"./helpers\":1246}],1244:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},g=c.textUnselectedOptions[u]||{},m=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=g.color,x=p.color,b=Array.isArray(x);for(m.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],m.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;m.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(m)}}},{\"../../components/color\":643,\"../../constants/interactions\":752,\"../../lib\":778}],1245:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":1196}],1246:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":1241}],1247:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var g=d&&d.line;g&&(h.mlc=Array.isArray(g.color)?g.color[f]:g.color,h.mlw=i.isArrayOrTypedArray(g.width)?g.width[f]:g.width);var m=d&&d.gradient;m&&\"none\"!==m.type&&(h.mgt=Array.isArray(m.type)?m.type[f]:m.type,h.mgc=Array.isArray(m.color)?m.color[f]:m.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,M=o._origY,A=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:M?M[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?A.text=h.htx:h.tx?A.text=h.tx:o.text&&(A.text=o.text),i.fillText(h,o,A),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,A),A}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d=t.cd,g=d[0].t,m=d[0].trace,v=t.xa,y=t.ya,x=g.x,b=g.y,_=v.c2p(e),w=y.c2p(r),T=t.distance;if(g.tree){var k=v.p2c(_-T),M=v.p2c(_+T),A=y.p2c(w-T),S=y.p2c(w+T);i=\"x\"===n?g.tree.range(Math.min(k,M),Math.min(y._rl[0],y._rl[1]),Math.max(k,M),Math.max(y._rl[0],y._rl[1])):g.tree.range(Math.min(k,M),Math.min(A,S),Math.max(k,M),Math.max(A,S))}else i=g.ids;var E=T;if(\"x\"===n)for(c=0;c<i.length;c++)s=x[i[c]],(u=Math.abs(v.c2p(s)-_))<E&&(E=u,f=y.c2p(b[i[c]])-w,p=Math.sqrt(u*u+f*f),a=i[c]);else for(c=i.length-1;c>-1;c--)s=x[i[c]],l=b[i[c]],u=v.c2p(s)-_,f=y.c2p(l)-w,(h=Math.sqrt(u*u+f*f))<E&&(E=p=h,a=i[c]);return t.index=a,t.distance=E,t.dxy=p,void 0===a?[t]:[o(t,x,b,m)]},calcHover:o}},{\"../../lib\":778,\"../../registry\":911,\"../scatter/get_trace_color\":1197}],1248:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":841,\"../scatter/cross_trace_defaults\":1193,\"../scatter/marker_colorbar\":1205,\"./attributes\":1239,\"./calc\":1240,\"./defaults\":1243,\"./format_labels\":1245,\"./hover\":1247,\"./plot\":1249,\"./select\":1251}],1249:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r){var n=t._size,i=t.width,a=t.height;return[n.l+e.domain[0]*n.w,n.b+r.domain[0]*n.h,i-n.r-(1-e.domain[1])*n.w,a-n.t-(1-r.domain[1])*n.h]}e.exports=function(t,e,r){if(r.length){var d,g,m=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=m._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_,{constPointSize:!0})),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var g=s._nexttrace;if(g){var m=v.lineOptions[e+1];if(m){var y=m.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=g.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var M=m.dragmode,A=l(M),S=m.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],C=E.trace,L=E.t,I=L.index,P=C._length,z=L.x,O=L.y;if(C.selectedpoints||A||S){if(A||(A=!0),C.selectedpoints){var D=v.selectBatch[I]=s.selIndices2selPoints(C),R={};for(g=0;g<D.length;g++)R[D[g]]=1;var F=[];for(g=0;g<P;g++)R[g]||F.push(g);v.unselectBatch[I]=F}var B=L.xpx=new Array(P),N=L.ypx=new Array(P);for(g=0;g<P;g++)B[g]=y.c2p(z[g]),N[g]=x.c2p(O[g])}else L.xpx=L.ypx=null}if(A){if(v.select2d||(v.select2d=n(m._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(m,y,x),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":661,\"../../lib\":778,\"../../lib/prepare_regl\":791,\"../scatter/link_traces\":1203,\"../scatter/subtypes\":1212,\"./edit_style\":1244,\"gl-text\":352,\"regl-error2d\":534,\"regl-line2d\":535,\"regl-scatter2d\":537}],1250:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else 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n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/mapbox/layout_attributes\":887,\"../../plots/template_attributes\":906,\"../scatter/attributes\":1187,\"../scattergeo/attributes\":1229}],1253:[function(t,e,r){\"use 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e,r,i,a,s,l,c,u,f,h,p,d,g=t._x,m=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,M=\"\";for(y&&(c=g[0],f=m[0],p=v[0]),y>1&&(u=g[y-1],h=m[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,g[e]),b=Math.min(b,g[e]),_=Math.max(_,m[e]),w=Math.min(w,m[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||g[e]===c||(a=!0,M+=\"x\"),s||m[e]===f||(s=!0,M+=\"y\"),l||v[e]===p||(l=!0,M+=\"z\");a||(M+=\"x\"),s||(M+=\"y\"),l||(M+=\"z\");var A=o(t._x),S=o(t._y),E=o(t._z);M=(M=(M=M.replace(\"x\",(c>u?\"-\":\"+\")+\"x\")).replace(\"y\",(f>h?\"-\":\"+\")+\"y\")).replace(\"z\",(p>d?\"-\":\"+\")+\"z\");var C=function(){y=0,A=[],S=[],E=[]};(!y||y<A.length*S.length*E.length)&&C();var L=function(t){return\"x\"===t?g:\"y\"===t?m:v},I=function(t){return\"x\"===t?A:\"y\"===t?S:E},P=function(t){return t[y-1]<t[0]?-1:1},z=L(M[1]),O=L(M[3]),D=L(M[5]),R=I(M[1]).length,F=I(M[3]).length,B=I(M[5]).length,N=!1,j=function(t,e,r){return 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M={};u.label&&(M.label=u.label),u.hasOwnProperty(\"v\")&&(M.value=u.v,M.valueLabel=x.formatValue(u.v,l)),M.currentPath=x.getPath(t.data),h||(M.percentParent=d/x.getValue(p),M.percentParentLabel=x.formatPercent(M.percentParent,l),M.parent=x.getPtLabel(p)),M.percentEntry=d/x.getValue(e),M.percentEntryLabel=x.formatPercent(M.percentEntry,l),M.entry=x.getPtLabel(e),M.percentRoot=d/x.getValue(f),M.percentRootLabel=x.formatPercent(M.percentRoot,l),M.root=x.getPtLabel(f),u.hasOwnProperty(\"color\")&&(M.color=u.color);var A=o.castOption(r,u.i,\"text\");return(o.isValidTextValue(A)||\"\"===A)&&(M.text=A),M.customdata=o.castOption(r,u.i,\"customdata\"),o.texttemplateString(k,M,i._d3locale,M,r._meta||{})}},{\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../bar/style\":935,\"../bar/uniform_text\":937,\"../pie/helpers\":1166,\"../pie/plot\":1170,\"./constants\":1302,\"./fx\":1304,\"./helpers\":1305,\"./style\":1310,d3:169,\"d3-hierarchy\":161}],1310:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":643,\"../../lib\":778,\"../bar/uniform_text\":937,d3:169}],1311:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;function c(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var u=e.exports=l(s({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:a(),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:c(),y:c(),z:c()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:s({},i.zauto,{}),zmin:s({},i.zmin,{}),zmax:s({},i.zmax,{})},hoverinfo:s({},o.hoverinfo),showlegend:s({},o.showlegend,{dflt:!1})}),\"calc\",\"nested\");u.x.editType=u.y.editType=u.z.editType=\"calc+clearAxisTypes\",u.transforms=void 0},{\"../../components/color\":643,\"../../components/colorscale/attributes\":650,\"../../lib/extend\":768,\"../../plot_api/edit_types\":810,\"../../plots/attributes\":824,\"../../plots/template_attributes\":906}],1312:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":651}],1313:[function(t,e,r){\"use strict\";var n=t(\"gl-surface3d\"),i=t(\"ndarray\"),a=t(\"ndarray-linear-interpolate\").d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function g(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function m(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(g(t,n))}return e}function v(t){for(var e=m(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=m(t),n=m(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),g=l.dataScale,m=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<m;r++)x[e][r]=[];for(r=0;r<m;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<m;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=g[e];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v])];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(m*v),[m,v]);for(r=0;r<m;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=g[2],w.intensityBounds[1]*=g[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var M=[!0,!0,!0],A=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[A[e]];M[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=M,p.enableHighlight=M,p.snapToData=!0,\"lighting\"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),\"lightposition\"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{\"../../components/colorscale\":655,\"../../lib\":778,\"../../lib/gl_format_color\":774,\"../../lib/str2rgbarray\":802,\"../heatmap/find_empties\":1071,\"../heatmap/interp2d\":1074,\"gl-surface3d\":351,ndarray:495,\"ndarray-linear-interpolate\":489}],1314:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var 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d=i.ensureSingle(h,\"g\",\"slicetext\"),T=i.ensureSingle(d,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),E=i.ensureUniformFontSize(t,u.determineTextFont(C,s,S.font,{onPathbar:!0}));T.text(s._text||\" \").classed(\"slicetext\",!0).attr(\"text-anchor\",\"start\").call(a.font,E).call(o.convertToTspans,t),s.textBB=a.bBox(T.node()),s.transform=b(s,{fontSize:E.size,onPathbar:!0}),s.transform.fontSize=E.size,w?T.transition().attrTween(\"transform\",(function(t){var e=M(t,!0,A,[g,m]);return function(t){return _(e(t))}})):T.attr(\"transform\",_(s))}))}},{\"../../components/drawing\":665,\"../../lib\":778,\"../../lib/svg_text_utils\":803,\"../sunburst/fx\":1304,\"../sunburst/helpers\":1305,\"./constants\":1328,\"./partition\":1335,\"./style\":1337,d3:169}],1331:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../components/drawing\"),o=t(\"../../lib/svg_text_utils\"),s=t(\"./partition\"),l=t(\"./style\").styleOne,c=t(\"./constants\"),u=t(\"../sunburst/helpers\"),f=t(\"../sunburst/fx\"),h=t(\"../sunburst/plot\").formatSliceLabel;e.exports=function(t,e,r,p,d){var 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strict\";e.exports={attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../box/defaults\").crossTraceDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),crossTraceCalc:t(\"./cross_trace_calc\"),plot:t(\"./plot\"),style:t(\"./style\"),styleOnSelect:t(\"../scatter/style\").styleOnSelect,hoverPoints:t(\"./hover\"),selectPoints:t(\"../box/select\"),moduleType:\"trace\",name:\"violin\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"violinLayout\",\"zoomScale\"],meta:{}}},{\"../../plots/cartesian\":841,\"../box/defaults\":949,\"../box/select\":956,\"../scatter/style\":1211,\"./attributes\":1338,\"./calc\":1339,\"./cross_trace_calc\":1340,\"./defaults\":1341,\"./hover\":1343,\"./layout_attributes\":1345,\"./layout_defaults\":1346,\"./plot\":1347,\"./style\":1348}],1345:[function(t,e,r){\"use strict\";var 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f={\"stroke-width\":u+\"px\",\"stroke-dasharray\":2*u+\"px,\"+u+\"px\"};o.selectAll(\"path.mean\").style(f).call(i.stroke,c.color),o.selectAll(\"path.meanline\").style(f).call(i.stroke,c.color),a(o,r,t)}))}},{\"../../components/color\":643,\"../scatter/style\":1211,d3:169}],1349:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../isosurface/attributes\"),a=t(\"../surface/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,c=e.exports=l(s({x:i.x,y:i.y,z:i.z,value:i.value,isomin:i.isomin,isomax:i.isomax,surface:i.surface,spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:1}},slices:i.slices,caps:i.caps,text:i.text,hovertext:i.hovertext,hovertemplate:i.hovertemplate},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{colorbar:i.colorbar,opacity:i.opacity,opacityscale:a.opacityscale,lightposition:i.lightposition,lighting:i.lighting,flatshading:i.flatshading,contour:i.contour,hoverinfo:s({},o.hoverinfo),showlegend:s({},o.showlegend,{dflt:!1})}),\"calc\",\"nested\");c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 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-   "source": [
-    "fig = px.choropleth(geoplot_2019,                        \n",
-    "                     locations=\"Country_code\",           \n",
-    "                     color=\"Respondents\",               \n",
-    "                     hover_name=\"Country\",              \n",
-    "                     projection=\"natural earth\",        \n",
-    "                     color_continuous_scale = 'Peach',  \n",
-    "                     range_color=[0,10000]     \n",
-    "                     )        \n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Analysing salary distribution among top ten countries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 340,
-   "metadata": {},
-   "outputs": [
-    {
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",
-      "text/plain": [
-       "<Figure size 1440x720 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize = (20, 10))\n",
-    "\n",
-    "countries = cleaned_df_2019['Country'].value_counts().sort_values(ascending = False)[:10].index.tolist()\n",
-    "\n",
-    "for i, country in enumerate(countries):\n",
-    "    plt.subplot(4, 3, i + 1)\n",
-    "    temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['Country'] == country, 'SalaryUSD']\n",
-    "\n",
-    "    ax = temp_salaries.plot(kind = 'kde')\n",
-    "    ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n",
-    "    ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n",
-    "    ax.set_xlabel('Annual Salary in USD')\n",
-    "    ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n",
-    "    ax.set_title('Annual Salary Distribution in {}'.format(country))\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overall, the country which has the highest mean annual salary is the United States of America(240,000) Dollars. The second highest country which provides the highest mean salary is Australia(164,926) Dollars. Though India has a higher number of respondents, it has the lowest mean salary of $25,213.We can understand that the mean salary of a developed country is much higher than that of a developing country."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Analysing impact of education level on salary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 341,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#removing outliers from Associate group\n",
-    "salary_edu = cleaned_df_2019.groupby(['EdLevel'])\n",
-    "associate_mean = salary_edu.get_group('Associate').mean()['SalaryUSD']\n",
-    "filt = (salary_edu.get_group('Associate')['SalaryUSD'] > associate_mean).to_frame()\n",
-    "filt = filt[filt['SalaryUSD'] == False]\n",
-    "cleaned_df_2019.drop(index=filt.index, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 342,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize = (20, 10))\n",
-    "\n",
-    "education_2019 = cleaned_df_2019['EdLevel'].value_counts().sort_values(ascending = False).index.tolist()\n",
-    "\n",
-    "for i, edu in enumerate(education_2019):\n",
-    "    plt.subplot(3, 2, i + 1)\n",
-    "    temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['EdLevel'] == edu, 'SalaryUSD']\n",
-    "\n",
-    "    ax = temp_salaries.plot(kind = 'kde')\n",
-    "    ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n",
-    "    ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n",
-    "    ax.set_xlabel('Annual Salary in USD')\n",
-    "    ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n",
-    "    ax.set_title('Annual Salary Distribution in {}'.format(edu))\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see, the respondents who have done Doctorate have the highest mean salary among all other education levels. Secondly, the respondents who have done Bachelors degree have more salary than that of Masters degree holders. This may be due to years of professional coding experience and due to the higher number of respondents in that category than that of Masters degree(No of respondents in Bachelor degree is 35659 and number of respondents in masters degree is 16940)\n",
-    "\n",
-    "The most interesting is that the respondents who do not have any degree have a mean salary of $90k. This shows the improvement in online learning and advancement of technology that is shifting the company from relying on University degrees."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Distribution of respondents based on age"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 343,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col =['Age', 'Country']\n",
-    "df_2020= cleaned_df_2019[col]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 344,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "df_2020['Age_range'] = 0\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=15) & (df_2020['Age']<=19), '15 - 19 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=20) & (df_2020['Age']<=24), '20 - 24 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=25) & (df_2020['Age']<=29), '25 - 29 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=30) & (df_2020['Age']<=34), '30 - 34 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=35) & (df_2020['Age']<=39), '35 - 39 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=40) & (df_2020['Age']<=45), '40 - 45 years', df_2020.Age_range)\n",
-    "df_2020['Age_range']= np.where((df_2020['Age']>=46), '46 and above years', df_2020.Age_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 345,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_2020_age = df_2020.groupby(['Age_range']).size().reset_index(name='Count')\n",
-    "df_2020_age.sort_values(by=['Count'], ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 346,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (10, 6))\n",
-    "sns.set_style('white')\n",
-    "sns.set_context('paper', font_scale=1.5)\n",
-    "sns.barplot(x=\"Count\",  y=\"Age_range\", palette='inferno', data=df_2020_age).set(xlabel=\"Age\", ylabel = \"Count\")\n",
-    "plt.title('Distribution of respondents based on age')\n",
-    "\n",
-    "for y, x in enumerate(df_2020_age['Count']):\n",
-    "    label = \"{:,}\".format(int(x))\n",
-    "    plt.annotate(label, xy=(x, y), va='center')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Impact on the increase in popularity of a language in the current-year due to developer’s interest in the previous year.(Based on 2019 and 2020 dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 347,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#seperate language for getting individual one\n",
-    "cols = ['LanguageWorkedWith']\n",
-    "df_19 = survey_df_2019[cols]\n",
-    "df_20 = df2020[cols]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 348,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#splitting 'LanguageWorkedWith' and sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
-    "language_2019['Language'] = language_2019.index\n",
-    "language_2019.reset_index(drop=True, inplace=True)\n",
-    "language_2019 = language_2019[['Language', '2019']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 349,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
-    "language_2020['Language'] = language_2020.index\n",
-    "language_2020.reset_index(drop=True, inplace=True)\n",
-    "language_2020 = language_2020[['Language', '2020']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 350,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "language_all= pd.merge(language_2019, language_2020,on = ['Language'], how = 'outer')\n",
-    "language_all.fillna(0, inplace=True)\n",
-    "language_all['2019'] = language_all['2019']. astype(int)\n",
-    "language_all['2020'] = language_all['2020']. astype(int)\n",
-    "language_all.set_index('Language', inplace=True)\n",
-    "#language_all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 351,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Language</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>JavaScript</th>\n",
-       "      <td>0.136468</td>\n",
-       "      <td>0.137808</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>HTML/CSS</th>\n",
-       "      <td>0.126595</td>\n",
-       "      <td>0.126495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SQL</th>\n",
-       "      <td>0.109882</td>\n",
-       "      <td>0.112110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Python</th>\n",
-       "      <td>0.081963</td>\n",
-       "      <td>0.086418</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Java</th>\n",
-       "      <td>0.080446</td>\n",
-       "      <td>0.078374</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                2019      2020\n",
-       "Language                      \n",
-       "JavaScript  0.136468  0.137808\n",
-       "HTML/CSS    0.126595  0.126495\n",
-       "SQL         0.109882  0.112110\n",
-       "Python      0.081963  0.086418\n",
-       "Java        0.080446  0.078374"
-      ]
-     },
-     "execution_count": 351,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "language19_20=(language_all/language_all.sum())\n",
-    "language19_20.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 352,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "language19_20.plot(kind='bar', figsize=(12,8))\n",
-    "plt.title('Programming Language worked by Respondents in 2019 and 2020', fontsize = 18)\n",
-    "plt.xlabel('Languages', fontsize = 14)\n",
-    "plt.ylabel('Percentages', fontsize = 14)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The most language that worked in 2019 and 2020 is JavaScript.In 2020, people worked slightly in javascript compare to 2019. The 2nd highest working language is HTML/CSS. For HTML/CSS the percentage is slightly low in 2020. There are some language people worked in only one year. Elixir, Clojure, F#, Web assembly are those languages that people used in 2019. Respondent started to use Perl, Haskell, Julia in 2020 on a small scale."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Programming language desired to work"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 353,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#language desire net year\n",
-    "cols_1 = ['LanguageDesireNextYear']\n",
-    "df_19 = survey_df_2019[cols_1]\n",
-    "df_20 = df2020[cols_1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 354,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "languagedesire_2019= df_19['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
-    "languagedesire_2019['Language'] = languagedesire_2019.index\n",
-    "languagedesire_2019.reset_index(drop=True, inplace=True)\n",
-    "languagedesire_2019 = languagedesire_2019[['Language', '2019']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 355,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "languagedesire_2020= df_20['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
-    "languagedesire_2020['Language'] = languagedesire_2020.index\n",
-    "languagedesire_2020.reset_index(drop=True, inplace=True)\n",
-    "languagedesire_2020= languagedesire_2020[['Language','2020']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 356,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "languagedesire_all = pd.merge(languagedesire_2019, languagedesire_2020,on = ['Language'], how = 'outer')\n",
-    "languagedesire_all.fillna(0, inplace=True)\n",
-    "languagedesire_all['2019'] = languagedesire_all['2019']. astype(int)\n",
-    "languagedesire_all['2020'] = languagedesire_all['2020']. astype(int)\n",
-    "languagedesire_all.set_index('Language', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 357,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Language</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>JavaScript</th>\n",
-       "      <td>0.113559</td>\n",
-       "      <td>0.111381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Python</th>\n",
-       "      <td>0.099827</td>\n",
-       "      <td>0.110728</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>HTML/CSS</th>\n",
-       "      <td>0.092490</td>\n",
-       "      <td>0.088983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SQL</th>\n",
-       "      <td>0.085326</td>\n",
-       "      <td>0.085879</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TypeScript</th>\n",
-       "      <td>0.062380</td>\n",
-       "      <td>0.076744</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                2019      2020\n",
-       "Language                      \n",
-       "JavaScript  0.113559  0.111381\n",
-       "Python      0.099827  0.110728\n",
-       "HTML/CSS    0.092490  0.088983\n",
-       "SQL         0.085326  0.085879\n",
-       "TypeScript  0.062380  0.076744"
-      ]
-     },
-     "execution_count": 357,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "languagedesire19_20=(languagedesire_all/languagedesire_all.sum())\n",
-    "languagedesire19_20.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 358,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "languagedesire19_20.plot(kind='bar', figsize=(12,8))\n",
-    "plt.title('Programming Language desire to work', fontsize = 18)\n",
-    "plt.xlabel('Languages', fontsize = 14)\n",
-    "plt.ylabel('Percentages', fontsize = 14)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In 2019, respondents said that they wanted to work in javascript is around more than 10 % and the fewer respond have a desire to work on VBA next year. People started to work in Haskell, Julia, and pearl in 2019 though the amount was less around 5% of people have the desire to work in those languages in 2021. Here, phyton is the 2nd one in which people have the desire to work in both 2019 and 2020."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distribution of surveyors based on their developer role."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 359,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "col = ['DevType']\n",
-    "dev_18=df[col]\n",
-    "dev_19 = survey_df_2019[col]\n",
-    "dev_20= df2020[col]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 360,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "dev_2018= dev_18['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n",
-    "dev_2018['Developer'] = dev_2018.index\n",
-    "dev_2018.reset_index(drop=True, inplace=True)\n",
-    "dev_2018 = dev_2018[['Developer', '2018']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 361,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dev_2019= dev_19['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
-    "dev_2019['Developer'] = dev_2019.index\n",
-    "dev_2019.reset_index(drop=True, inplace=True)\n",
-    "dev_2019 = dev_2019[['Developer', '2019']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 362,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dev_2020= dev_20['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
-    "dev_2020['Developer'] = dev_2020.index\n",
-    "dev_2020.reset_index(drop=True, inplace=True)\n",
-    "dev_2020 = dev_2020[['Developer', '2020']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 363,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Developer, back-end</th>\n",
-       "      <td>27228</td>\n",
-       "      <td>37700</td>\n",
-       "      <td>21683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, full-stack</th>\n",
-       "      <td>27125</td>\n",
-       "      <td>39479</td>\n",
-       "      <td>21683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, front-end</th>\n",
-       "      <td>18296</td>\n",
-       "      <td>24527</td>\n",
-       "      <td>14356</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, desktop or enterprise applications</th>\n",
-       "      <td>11784</td>\n",
-       "      <td>15748</td>\n",
-       "      <td>9172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, mobile</th>\n",
-       "      <td>9482</td>\n",
-       "      <td>13298</td>\n",
-       "      <td>7254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DevOps specialist</th>\n",
-       "      <td>5969</td>\n",
-       "      <td>8463</td>\n",
-       "      <td>4895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Database administrator</th>\n",
-       "      <td>5722</td>\n",
-       "      <td>8616</td>\n",
-       "      <td>4452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Designer</th>\n",
-       "      <td>5321</td>\n",
-       "      <td>7883</td>\n",
-       "      <td>3791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>System administrator</th>\n",
-       "      <td>5242</td>\n",
-       "      <td>8094</td>\n",
-       "      <td>4094</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, embedded applications or devices</th>\n",
-       "      <td>4750</td>\n",
-       "      <td>6494</td>\n",
-       "      <td>3536</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Data or business analyst</th>\n",
-       "      <td>4024</td>\n",
-       "      <td>5526</td>\n",
-       "      <td>2939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Data scientist or machine learning specialist</th>\n",
-       "      <td>3988</td>\n",
-       "      <td>5788</td>\n",
-       "      <td>2939</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, QA or test</th>\n",
-       "      <td>3947</td>\n",
-       "      <td>5927</td>\n",
-       "      <td>3161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Engineer, data</th>\n",
-       "      <td>3738</td>\n",
-       "      <td>5240</td>\n",
-       "      <td>2776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Academic researcher</th>\n",
-       "      <td>3552</td>\n",
-       "      <td>5070</td>\n",
-       "      <td>2610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Educator</th>\n",
-       "      <td>2928</td>\n",
-       "      <td>3930</td>\n",
-       "      <td>2213</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, game or graphics</th>\n",
-       "      <td>2789</td>\n",
-       "      <td>3902</td>\n",
-       "      <td>2118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Engineering manager</th>\n",
-       "      <td>2724</td>\n",
-       "      <td>3803</td>\n",
-       "      <td>1979</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Product manager</th>\n",
-       "      <td>2497</td>\n",
-       "      <td>3630</td>\n",
-       "      <td>1891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Scientist</th>\n",
-       "      <td>2086</td>\n",
-       "      <td>3149</td>\n",
-       "      <td>1573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Engineer, site reliability</th>\n",
-       "      <td>1940</td>\n",
-       "      <td>2684</td>\n",
-       "      <td>1491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Senior executive/VP</th>\n",
-       "      <td>1320</td>\n",
-       "      <td>1795</td>\n",
-       "      <td>850</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Marketing or sales professional</th>\n",
-       "      <td>642</td>\n",
-       "      <td>789</td>\n",
-       "      <td>401</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Student</th>\n",
-       "      <td>0</td>\n",
-       "      <td>10113</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                2018   2019   2020\n",
-       "Developer                                                         \n",
-       "Developer, back-end                            27228  37700  21683\n",
-       "Developer, full-stack                          27125  39479  21683\n",
-       "Developer, front-end                           18296  24527  14356\n",
-       "Developer, desktop or enterprise applications  11784  15748   9172\n",
-       "Developer, mobile                               9482  13298   7254\n",
-       "DevOps specialist                               5969   8463   4895\n",
-       "Database administrator                          5722   8616   4452\n",
-       "Designer                                        5321   7883   3791\n",
-       "System administrator                            5242   8094   4094\n",
-       "Developer, embedded applications or devices     4750   6494   3536\n",
-       "Data or business analyst                        4024   5526   2939\n",
-       "Data scientist or machine learning specialist   3988   5788   2939\n",
-       "Developer, QA or test                           3947   5927   3161\n",
-       "Engineer, data                                  3738   5240   2776\n",
-       "Academic researcher                             3552   5070   2610\n",
-       "Educator                                        2928   3930   2213\n",
-       "Developer, game or graphics                     2789   3902   2118\n",
-       "Engineering manager                             2724   3803   1979\n",
-       "Product manager                                 2497   3630   1891\n",
-       "Scientist                                       2086   3149   1573\n",
-       "Engineer, site reliability                      1940   2684   1491\n",
-       "Senior executive/VP                             1320   1795    850\n",
-       "Marketing or sales professional                  642    789    401\n",
-       "Student                                            0  10113      0"
-      ]
-     },
-     "execution_count": 363,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df18_19 = pd.merge(dev_2018, dev_2019,on = ['Developer'], how = 'outer')\n",
-    "devtype_all = pd.merge(df18_19,dev_2020, on=[\"Developer\"], how=\"outer\")\n",
-    "devtype_all.fillna(0, inplace=True)\n",
-    "devtype_all['2018'] = devtype_all['2018']. astype(int)\n",
-    "devtype_all['2019'] = devtype_all['2019']. astype(int)\n",
-    "devtype_all['2020'] =devtype_all['2020'].astype(int)\n",
-    "devtype_all.set_index('Developer', inplace=True)\n",
-    "devtype_all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 364,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>24.000000</td>\n",
-       "      <td>24.000000</td>\n",
-       "      <td>24.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>6545.583333</td>\n",
-       "      <td>9652.000000</td>\n",
-       "      <td>5077.375000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>7441.476051</td>\n",
-       "      <td>10257.524072</td>\n",
-       "      <td>5956.696034</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>789.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2667.250000</td>\n",
-       "      <td>3877.250000</td>\n",
-       "      <td>1957.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3967.500000</td>\n",
-       "      <td>5857.500000</td>\n",
-       "      <td>2939.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>5783.750000</td>\n",
-       "      <td>8990.250000</td>\n",
-       "      <td>4562.750000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>27228.000000</td>\n",
-       "      <td>39479.000000</td>\n",
-       "      <td>21683.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               2018          2019          2020\n",
-       "count     24.000000     24.000000     24.000000\n",
-       "mean    6545.583333   9652.000000   5077.375000\n",
-       "std     7441.476051  10257.524072   5956.696034\n",
-       "min        0.000000    789.000000      0.000000\n",
-       "25%     2667.250000   3877.250000   1957.000000\n",
-       "50%     3967.500000   5857.500000   2939.000000\n",
-       "75%     5783.750000   8990.250000   4562.750000\n",
-       "max    27228.000000  39479.000000  21683.000000"
-      ]
-     },
-     "execution_count": 364,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "devtype_all.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 365,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Developer, back-end</th>\n",
-       "      <td>0.173323</td>\n",
-       "      <td>0.162747</td>\n",
-       "      <td>0.177938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, full-stack</th>\n",
-       "      <td>0.172667</td>\n",
-       "      <td>0.170427</td>\n",
-       "      <td>0.177938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, front-end</th>\n",
-       "      <td>0.116465</td>\n",
-       "      <td>0.105880</td>\n",
-       "      <td>0.117810</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, desktop or enterprise applications</th>\n",
-       "      <td>0.075012</td>\n",
-       "      <td>0.067982</td>\n",
-       "      <td>0.075269</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Developer, mobile</th>\n",
-       "      <td>0.060359</td>\n",
-       "      <td>0.057406</td>\n",
-       "      <td>0.059529</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   2018      2019      2020\n",
-       "Developer                                                                  \n",
-       "Developer, back-end                            0.173323  0.162747  0.177938\n",
-       "Developer, full-stack                          0.172667  0.170427  0.177938\n",
-       "Developer, front-end                           0.116465  0.105880  0.117810\n",
-       "Developer, desktop or enterprise applications  0.075012  0.067982  0.075269\n",
-       "Developer, mobile                              0.060359  0.057406  0.059529"
-      ]
-     },
-     "execution_count": 365,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dt_all=devtype_all/devtype_all.sum()\n",
-    "dt_all.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 366,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(devtype_all/devtype_all.sum()).plot(kind='bar', figsize=(12,8))\n",
-    "plt.title('Developer Types pertcentages', fontsize = 18)\n",
-    "plt.xlabel('Developer Types', fontsize = 14)\n",
-    "plt.ylabel('Percentages', fontsize = 14)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In developer types, developers who are full stack and working backends are the most in the three years. There is a presence of student developers only in 2019 the percentage is 7.5%. Those who are working back end and full stack their percentages increased throughout the three years. For those who are working as marketing and sales professionals, their percentage is lowest compare to others."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  Impact of education/experience/responsibilities on gender inequalities.(Based on 2019 dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 367,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['Gender','EdLevel', 'Dependents', 'SalaryUSD', 'YearsCodePro', 'Age', 'Country']\n",
-    "df2019 = survey_df_2019[cols]\n",
-    "df2019 = df2019[df2019.Gender != \"Non-binary\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 368,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2019['exp_range'] = 0\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro >= 0) & (df2019.YearsCodePro <= 5), '0 - 5 years', df2019.exp_range)\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 5) & (df2019.YearsCodePro <= 10), '6 - 10 years', df2019.exp_range)\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 10) & (df2019.YearsCodePro <= 15), '11 - 15 years', df2019.exp_range)\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 15) & (df2019.YearsCodePro <= 20), '16 - 20 years', df2019.exp_range)\n",
-    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 20), 'more that 20 years', df2019.exp_range)\n",
-    "#df2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 369,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(2,2, figsize = (12, 8))\n",
-    "g1 = sns.countplot('Gender', data=df2019, ax=axes[0][0]).set(title = 'A. Gender')\n",
-    "g2 = sns.countplot('Dependents', data=df2019, ax=axes[0][1]).set( title = 'B. Dependents')\n",
-    "g3 = sns.countplot('EdLevel', data=df2019, ax=axes[1][0]).set(title = 'C. EdLevel')\n",
-    "g4 = sns.countplot('exp_range', data=df2019, ax=axes[1][1]).set(title = 'D. exp_range')\n",
-    "\n",
-    "axes[1][0].tick_params(axis='x', rotation=45)\n",
-    "axes[1][1].tick_params(axis='x', rotation=45)\n",
-    "    \n",
-    "fig.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 370,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='EdLevel', ylabel='count'>"
-      ]
-     },
-     "execution_count": 370,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.countplot('EdLevel', hue='Gender', data=df2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 371,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='Dependents', ylabel='count'>"
-      ]
-     },
-     "execution_count": 371,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "sns.countplot('Dependents', hue='Gender', data=df2019)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Analysis**<br>\n",
-    "\n",
-    "\n",
-    "After exploring the 2019 dataset, we have found that we cannot answer this question since male and female observations are significantly unbalanced."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What is the gender distribution among top 5 countries of respondents in 2019?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 372,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Total</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>252</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>15895</td>\n",
-       "      <td>17837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>253</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>1942</td>\n",
-       "      <td>17837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>India</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>6632</td>\n",
-       "      <td>7046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>India</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>414</td>\n",
-       "      <td>7046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>4847</td>\n",
-       "      <td>5130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>283</td>\n",
-       "      <td>5130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>250</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>395</td>\n",
-       "      <td>4933</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>249</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>4538</td>\n",
-       "      <td>4933</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>Woman</td>\n",
-       "      <td>275</td>\n",
-       "      <td>2857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>Man</td>\n",
-       "      <td>2582</td>\n",
-       "      <td>2857</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Country Gender  Count  Total\n",
-       "252   United States    Man  15895  17837\n",
-       "253   United States  Woman   1942  17837\n",
-       "101           India    Man   6632   7046\n",
-       "102           India  Woman    414   7046\n",
-       "84          Germany    Man   4847   5130\n",
-       "85          Germany  Woman    283   5130\n",
-       "250  United Kingdom  Woman    395   4933\n",
-       "249  United Kingdom    Man   4538   4933\n",
-       "39           Canada  Woman    275   2857\n",
-       "38           Canada    Man   2582   2857"
-      ]
-     },
-     "execution_count": 372,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all = df2019.groupby(['Country','Gender']).size().reset_index(name ='Count')\n",
-    "all['Total'] = all.groupby(['Country'])['Count'].transform('sum')\n",
-    "all = all.sort_values(by=['Total'], ascending=False)\n",
-    "#all.set_index('Total')\n",
-    "Top = all[:10].sort_values(by=['Total'], ascending=False)\n",
-    "Top"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 373,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# from raw value to percentage\n",
-    "total = Top.groupby(['Country'])['Count'].sum().reset_index()\n",
-    "total['Percentage'] = [i / j * 100 for i,j in zip(total['Count'], total['Count'])]\n",
-    "\n",
-    "woman = Top[Top.Gender=='Woman'].groupby(['Country'])['Count'].sum().reset_index()\n",
-    "woman['Percentage'] = [i / j * 100 for i,j in zip(woman['Count'], total['Count'])]\n",
-    "woman.sort_values(by=['Percentage'], ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 374,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>1942</td>\n",
-       "      <td>10.887481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>275</td>\n",
-       "      <td>9.625481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>395</td>\n",
-       "      <td>8.007298</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>India</td>\n",
-       "      <td>414</td>\n",
-       "      <td>5.875674</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>283</td>\n",
-       "      <td>5.516569</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Country  Count  Percentage\n",
-       "4   United States   1942   10.887481\n",
-       "0          Canada    275    9.625481\n",
-       "3  United Kingdom    395    8.007298\n",
-       "2           India    414    5.875674\n",
-       "1         Germany    283    5.516569"
-      ]
-     },
-     "execution_count": 374,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "woman"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 375,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>2857</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>5130</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>India</td>\n",
-       "      <td>7046</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>4933</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>17837</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Country  Count  Percentage\n",
-       "0          Canada   2857       100.0\n",
-       "1         Germany   5130       100.0\n",
-       "2           India   7046       100.0\n",
-       "3  United Kingdom   4933       100.0\n",
-       "4   United States  17837       100.0"
-      ]
-     },
-     "execution_count": 375,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 376,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize = (10, 6))\n",
-    "\n",
-    "# bar chart 1 -> top bars (group of 'Man')\n",
-    "bar1 = sns.barplot(x=\"Country\",  y=\"Percentage\", data=total, color='darkblue')\n",
-    "# bar chart 2 -> bottom bars (group of 'Woman')\n",
-    "bar2 = sns.barplot(x=\"Country\", y=\"Percentage\", data=woman, color='#5E96E9')\n",
-    "\n",
-    "# add legend\n",
-    "top_bar = mpatches.Patch(color='darkblue', label='Man')\n",
-    "bottom_bar = mpatches.Patch(color='#5E96E9', label='Woman')\n",
-    "plt.legend(handles=[top_bar, bottom_bar])\n",
-    "\n",
-    "# Fix the legend so it's not on top of the bars.\n",
-    "legend = ax.get_legend()\n",
-    "legend.set_bbox_to_anchor((1, 1))\n",
-    "\n",
-    "ax.set_ylabel('Percentage', fontsize = 12)\n",
-    "ax.set_xlabel('Top 5 Countries', fontsize = 12)\n",
-    "plt.title('Gender vs Top 5 Countries in 2019', fontsize = 14)\n",
-    "\n",
-    "def add_value_labels(bar2, spacing=5):\n",
-    "    \"\"\"Add labels to the end of each bar in a bar chart.\n",
-    "\n",
-    "    Arguments:\n",
-    "        ax (matplotlib.axes.Axes): The matplotlib object containing the axes\n",
-    "            of the plot to annotate.\n",
-    "        spacing (int): The distance between the labels and the bars.\n",
-    "    \"\"\"\n",
-    "    # For each bar: Place a label\n",
-    "    for rect in bar2.patches:\n",
-    "        # Get X and Y placement of label from rect.\n",
-    "        y_value = rect.get_height()\n",
-    "        x_value = rect.get_x() + rect.get_width() / 2\n",
-    "\n",
-    "        space = spacing  # Number of points between bar and label. Change to your liking.\n",
-    "        va = 'bottom' # Vertical alignment for positive values\n",
-    "        label = \"{:.1f}%\".format(y_value) # Use Y value as label and format number with one decimal place\n",
-    "\n",
-    "        # Create annotation\n",
-    "        bar2.annotate(\n",
-    "            label,                      # Use `label` as label\n",
-    "            (x_value, y_value),         # Place label at end of the bar\n",
-    "            xytext=(0, space),          # Vertically shift label by `space`\n",
-    "            textcoords=\"offset points\", # Interpret `xytext` as offset in points\n",
-    "            ha='center',                # Horizontally center label\n",
-    "            va=va,                      # Vertically align label differently for\n",
-    "            color='white', fontsize=12, style='italic')       \n",
-    "\n",
-    "#Add value bar\n",
-    "add_value_labels(bar2)\n",
-    "\n",
-    "plt.tight_layout(pad=0., w_pad=-16.5, h_pad=0.0)  \n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Analysis**<br>\n",
-    "\n",
-    "\n",
-    "In terms of male and female statistics, it can be seen that the US has the relatively largest female percentage at about 10.9%. Follow by Canada, the UK at 9.6% and 8.0% respectively. India and Germany have the fewest female respondents among the top 5 at around 5%."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Where are the most data scientist come from in 2019?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 377,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5788"
-      ]
-     },
-     "execution_count": 377,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#creating data scientist scientist df\n",
-    "ds = survey_df_2019[survey_df_2019['DevType'].str.contains('Data scientist') == True ]\n",
-    "ds = ds.reset_index(drop=True)\n",
-    "len(ds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 378,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>113</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>1550</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>India</td>\n",
-       "      <td>543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>Germany</td>\n",
-       "      <td>427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>111</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>France</td>\n",
-       "      <td>169</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>Netherlands</td>\n",
-       "      <td>148</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Brazil</td>\n",
-       "      <td>143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>Russian Federation</td>\n",
-       "      <td>123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Australia</td>\n",
-       "      <td>119</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                Country  Count\n",
-       "113       United States   1550\n",
-       "49                India    543\n",
-       "41              Germany    427\n",
-       "111      United Kingdom    339\n",
-       "18               Canada    195\n",
-       "39               France    169\n",
-       "74          Netherlands    148\n",
-       "14               Brazil    143\n",
-       "88   Russian Federation    123\n",
-       "5             Australia    119"
-      ]
-     },
-     "execution_count": 378,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_country = ds.groupby(['Country']).size().reset_index(name ='Count')\n",
-    "ds_country.sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "top_ds_country = ds_country[:10]\n",
-    "top_ds_country"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 379,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize = (10, 6))\n",
-    "ax=sns.barplot(x=\"Count\",  y=\"Country\", data=top_ds_country )\n",
-    "ax.set_ylabel('Countries', fontsize = 12)\n",
-    "ax.set_xlabel('The Number of Data Scientists', fontsize = 12)\n",
-    "plt.title('Top 10 Countries of Data Scientists in 2019', fontsize = 14)\n",
-    "\n",
-    "for y, x in enumerate(top_ds_country['Count']):\n",
-    "    label = \"{:,}\".format(int(x))\n",
-    "    plt.annotate(label, xy=(x, y), va='center')\n",
-    "\n",
-    "plt.tight_layout()  \n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "\n",
-    "\n",
-    "There are 5,788 data scientists who responded to the Stackoverflow survey in 2019. Most data scientists are from the US with 1,550 people and it is 3 times higher than data scientists from India. Followed by Germany and the UK with 427 and 339 people respectively. The rest are Canada, France, Netherlands, Brazil, Russia, and Australia which have less than 200 data scientists."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#  Which countries pay the most to Data Scientists in 2019?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 380,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>Qatar</td>\n",
-       "      <td>1000000.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>Myanmar</td>\n",
-       "      <td>333757.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>275851.466092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>272796.133333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>113</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>265211.014843</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>Syrian Arab Republic</td>\n",
-       "      <td>2916.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>Madagascar</td>\n",
-       "      <td>1800.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>116</th>\n",
-       "      <td>Venezuela, Bolivarian Republic of...</td>\n",
-       "      <td>1500.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>Cambodia</td>\n",
-       "      <td>816.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>Zambia</td>\n",
-       "      <td>400.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>120 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  Country            Mean\n",
-       "85                                  Qatar  1000000.000000\n",
-       "72                                Myanmar   333757.333333\n",
-       "52                                Ireland   275851.466092\n",
-       "63                             Luxembourg   272796.133333\n",
-       "113                         United States   265211.014843\n",
-       "..                                    ...             ...\n",
-       "101                  Syrian Arab Republic     2916.000000\n",
-       "64                             Madagascar     1800.000000\n",
-       "116  Venezuela, Bolivarian Republic of...     1500.000000\n",
-       "16                               Cambodia      816.000000\n",
-       "118                                Zambia      400.000000\n",
-       "\n",
-       "[120 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 380,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_mean_salary = ds.groupby('Country')['SalaryUSD'].mean().reset_index(name ='Mean')\n",
-    "ds_mean_salary.sort_values(by=['Mean'], ascending=False, inplace=True)\n",
-    "ds_mean_salary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 381,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Plottig boxplot to check outliers after cleaning some outliers\n",
-    "sns.boxplot('Mean', data=ds_mean_salary)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 382,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Cleaning Age's outliers from each gender)\n",
-    "ds_mean_salary = ds_mean_salary[(ds_mean_salary['Mean'] <= 280000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 383,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Plottig boxplot to check outliers after cleaning some outliers\n",
-    "sns.boxplot('Mean', data=ds_mean_salary)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 384,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Ireland</td>\n",
-       "      <td>275851.466092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>272796.133333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>113</th>\n",
-       "      <td>United States</td>\n",
-       "      <td>265211.014843</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>111</th>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>169366.692664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100</th>\n",
-       "      <td>Switzerland</td>\n",
-       "      <td>165462.430196</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>Cyprus</td>\n",
-       "      <td>150936.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Australia</td>\n",
-       "      <td>146803.174460</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>Norway</td>\n",
-       "      <td>145948.523273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>125228.788666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>Japan</td>\n",
-       "      <td>118969.194525</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Country           Mean\n",
-       "52          Ireland  275851.466092\n",
-       "63       Luxembourg  272796.133333\n",
-       "113   United States  265211.014843\n",
-       "111  United Kingdom  169366.692664\n",
-       "100     Switzerland  165462.430196\n",
-       "25           Cyprus  150936.000000\n",
-       "5         Australia  146803.174460\n",
-       "78           Norway  145948.523273\n",
-       "18           Canada  125228.788666\n",
-       "56            Japan  118969.194525"
-      ]
-     },
-     "execution_count": 384,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Top_mean_salary = ds_mean_salary[:10]\n",
-    "Top_mean_salary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 385,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize = (10, 6))\n",
-    "ax=sns.barplot(x=\"Mean\",  y=\"Country\", data=Top_mean_salary )\n",
-    "ax.set_ylabel('Country', fontsize = 12)\n",
-    "ax.set_xlabel('Average Salary in US$', fontsize = 12)\n",
-    "plt.title('The Top 10 highest paying data scientist country in 2019', fontsize = 14)\n",
-    "\n",
-    "for y, x in enumerate(Top_mean_salary['Mean']):\n",
-    "    label = \"${:,}\".format(int(x))\n",
-    "    plt.annotate(label, xy=(x, y), va='center')\n",
-    "\n",
-    "plt.tight_layout()  \n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Analysis**<br>\n",
-    "\n",
-    "\n",
-    "In 2019, the top three countries which have a highest mean annual salary of a data scientist are Ireland (275,851), Luxembourg (272,769), and the USA (265,211). Apart from that, the mean salary of the rest of the countries is less than (200,000) per year. Japan provides the highest mean annual salary among Asian countries (118,969)<br>\n",
-    "*Figures in Dollars* **$**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mydf2018 = pd.read_csv(r\"C:\\Users\\Aneesh Angane\\Downloads\\stack-overflow-developer-survey-2018\\survey_results_public_2018.csv\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Most Popular IDE's in 2018"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Split IDEs and explode into separate rows\n",
-    "individual_ides = mydf2018['IDE'].str.split(';').explode()\n",
-    "\n",
-    "# Count occurrences of each IDE and sort by value\n",
-    "ide_counts_value_sorted = individual_ides.value_counts().sort_values(ascending=False)\n",
-    "\n",
-    "# Plotting - Sorted by value\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.bar(ide_counts_value_sorted.index, ide_counts_value_sorted.values, color='skyblue')\n",
-    "plt.title('IDE Usage')\n",
-    "plt.xlabel('IDE')\n",
-    "plt.ylabel('Count')\n",
-    "plt.xticks(rotation=45, ha='right')\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis of IDE Usage\n",
-    "\n",
-    "1. **Popular IDEs**: Visual Studio Code, Visual Studio, and Notepad++ are among the most widely used IDEs, with high user counts ranging from 25,870 to 26,280.\n",
-    "\n",
-    "2. **Text Editors**: Sublime Text, Vim, and IntelliJ are also popular choices, with user counts ranging from 19,477 to 21,810.\n",
-    "\n",
-    "3. **General-purpose Editors**: TextMate, Coda, and Light Table are also used, although they have lower user counts compared to other IDEs.\n",
-    "\n",
-    "4. **Emerging Trends**: IPython / Jupyter, Atom, and Emacs show significant adoption, indicating a growing interest in interactive computing environments, lightweight editors, and customizable text editors, respectively.\n",
-    "\n",
-    "5. **Industry Standard**: Xcode, primarily used for macOS and iOS development, maintains a substantial user base due to its integration with Apple's development ecosystem.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Coders perception about AI in 2018"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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0avjw4SpXrlxq2LBhauzYsUqn0ylnZ2dtfdeuXZWVlZVatWqVNlxPiP9U0mFgM2bMUI0bN1ZVqlRRzs7O6ubNm0oppU6fPq1++OEH1aJFC3XixIlPXiM0NFQ1bNhQnT59OsXiTqsSh2gEBQWpwoULqzNnziillDp06JAyNjZWq1ev1rYNDw9XQ4YMUfXq1ZOhHd8BDw8PlS9fPjVhwgRtKE90dLQqUaKEsrOzU6dPn9Y+z9u3b1dt2rRRLi4u2vfDX/8vhPg2fe58/fbtWzVjxgyl0+nU9OnTteUBAQGqXLlyqmnTpio2NlbO9SJFfK6d3b9/X7m6uqqyZcuqadOmacsXLVqkChQooIYMGSLfP8KgkrbbxJ+jo6PV4MGDlampqQoICFBKKRUVFaW2bt2qMmfOrNq1a2eQWD9HklIixR09elTZ2tqqo0ePKqWU2rx5szI3N1fe3t7JtmvatGmyca9C/Lfc3d2VtbW18vT0VC4uLqpOnToqS5Ys6uLFi0oppc6cOaOKFy+uatSooa5evZps34SEBBUVFWWIsNOEOXPmqODg4GTLduzYoSpXrqyUUmrdunXK0tJSLVq0SCmlVEREhPr9999VQkKCevXqlfbFKzcrqc/gwYPVnj17tN+nTp2qbGxs1IQJE7RaMn9NTCXWP4iMjNT2S1oTQdqBEN++/fv3q1WrViVb9ubNGzVz5kyl0+mUp6entnzr1q3a+UCIlHLy5Em1cuXKZMvu37+vhgwZouzs7NTs2bO15cuWLUtWc0oIQzlw4ID2QPdziaktW7YopT5eQ+3YsUPduXPHYLH+lSSlRIrbsGGD+vHHH5VSSgUGBqoMGTKoJUuWKKU+Pi3bsWOHtu3fFTwW4p8KCQlRJUuWVNu2bdOW3bt3T7Vu3VrlyZNH3b9/Xyml1KlTp1T79u2lzaWgU6dOqQIFCqhOnTol64n222+/qUqVKqk9e/aojBkzqgULFmjrtm3bpvr06aMeP36sLZNEROoTEhKievXq9UmRzSlTpnw2MVWyZElVpkwZdfTo0c8+DRRCfHuSfj4Tv1tjYmLU4MGDlU6nU2vXrk22fXh4uOrUqdMnPaaE+Fo+9x0SGxur2rVrp+zt7ZP10FZKqYcPH6oaNWooGxsbNXHixJQKU4hkkrbbxOuoDx8+qLZt2yqdTqfOnTuXbLuIiAjVpEkTZW1trTZt2pTyAf8DUnhDpBj1/+v0ZMyYkaxZs+Lj48Mvv/zCrFmz6NWrFwBnzpxh48aN3Lt3D0CrISXEP6X+UqQvPDycu3fvYm1trS0rWLAgY8eOJUeOHBw+fBilFJUqVcLf31/aXAqqVKkS8+bN486dO8ybN48TJ04A0K5dO16+fEmjRo3w8vLCxcUFgOjoaBYvXszbt2/JlSuX9jpS1Dp1UUpRsGBBlixZgrGxMWvXrmX16tUAuLu707dvX5YtW8bKlSu5f/8+pqamnD9/nkePHrFkyZJkx1uOvRDfJvX/Jw0JCwsjJCQEvV7P5s2bOX78OEOHDmX48OH07t2bNWvWaPtYWVlRvHhxChQowMyZM3n58qVBC++K759Op+Ply5eEh4cDsHXrVs6cOcOECROwt7fH29sbPz8/bXsbGxuqVq2KqakpR48e5cWLF4YKXaRRSc+tERERGBsbs3fvXs6cOcOcOXNo3749tWvX5ty5c9o1UoYMGfjhhx+Ii4ujX79+REREGPhdfEqqBoqvJmlRc/jXzUPOnDm5evUqu3btYvr06fTu3RuAqKgoZs2aRdasWSlUqJC2nxQtFv/Uo0ePsLGxAcDX15cePXpQokQJypYty549eyhXrhxmZmbodDqKFy9OTEwM9+7d++TGVtrc1xcbG4uJiQnNmjXj1atXeHt7M3/+fExMTKhQoQJz5sxhwIAB+Pn5kTt3bl6+fMmKFSt48uQJW7du1QrSS1Ii9Uk8ZkopXr58ydy5c7G0tMTMzIzWrVvj7u4OgLe3NwBdu3Ylf/78PH36VI63EKmETqcjPDycdu3aUbp0aYoWLUr//v1Zs2YNNWvWpH///sTHx9OvXz/0ej0dOnQAPk5IMnz4cDp16kTGjBkN/C7E90wpxevXrylatChubm5kz56d7t27s3btWqpUqcKwYcPw8PBg6dKlAHTp0gVAu7F3cnJK9sBTiJSQmEjt0qULRYsWpWLFinTt2pWtW7eSO3du5syZQ1xcHHXq1OHgwYOUL18eACMjI1atWkXVqlWxtLQ08Lv4DEN10RLft6TdCn19fdW4ceOUm5ubunXrllLqY80YnU6nevfurfz8/NT27dtV3bp1VenSpbVuiDIsQ/wnDh48qMqVK6eOHDmiDQ0ICQlR8fHxauDAgapSpUrKz89P2/79+/eqcuXKysvLy4BRp01JP9sTJkxQffr0Ufnz51c6nU61bNlSXbhwQSn1sf5cpUqVVP78+VWlSpVU+/bttUKicXFxhghd/I8+VwPs8uXLqmHDhqpevXpq/fr12vJp06ap/Pnzq6FDh6qnT59qy+XYC5F6LFq0SNna2iqdTqdmzZqVbN2DBw/U8OHDxPvCrwAAe9BJREFUlV6vV02bNlVNmzZVmTNn1q4VhUgJK1asUKampsrIyEirX5n4HXX58mX1yy+/qJIlS6pmzZopJycnZWVlJTWkhEFFR0crDw8P9cMPPygTExO1ePFipdS/hkmHhoaqDh06KAsLC9WjRw/VqlUrZW1trW7fvm3IsP8tnVLSL1Z8WUl7SA0bNgxfX1/KlCnD8+fPefDgAdOnT6dv375s3ryZ+fPnc+nSJUqUKEGOHDnw9/fHxMSE+Ph4jIyMDPxORGry9u1bGjVqxIMHD4iIiODw4cOULVsWgJiYGDp37sydO3fInz8/5cuX58CBA7x8+ZKLFy/KVNMGMnfuXMaNG8eWLVvInj07p06dYsaMGZQpU4aRI0dSpkwZAP78808yZ85MxowZ0el0xMXFyTFLhWJiYrSph58+fUqmTJlISEggQ4YMXLlyhaFDhwLQs2dP2rZtC3wcznfjxg0CAwOll5QQqUjidVxISAhVq1bFxMSEdu3a0adPH2xtbbXt3r59y969e/Hz8yNr1qy4urpSqlQpA0Yu0orE+5X79+9TsGBBAGbOnEn37t3JnDmztl1ISAj79u1j/fr1ZM2alV9//ZXSpUsbKmyRxiW223PnzlG/fn0yZsxIq1atGDduHJaWltoogtjYWGbOnMmRI0cwNzdnwoQJ33S7laSU+GrCwsLo378/o0aNonTp0hgZGTFs2DBWrlzJ3Llz6dSpE69eveLDhw9YWFiQKVMmueEU/5XENjNlyhQmTZpEsWLF8PT0pHr16piYmAAfh4stXryYI0eO8Pr1awoUKMCiRYskCWogSilatWqFtbU1y5Yt05Zv3LiR/v37U61aNUaMGEGlSpU+2U+SE6mLj48PrVu3xsrKCoBx48YRGBgIQLZs2Zg9ezZly5bl2rVruLq6otPp6NmzJ23atAH+dczl2AuRuiQOqb937x579uzBx8eH6tWrM2jQoGSJKfiYtNbr9XL9J1LUixcvyJo1K/fv32f//v306tWLiRMn4uLikiwxlejDhw+YmZkZIFIh/uXly5dYWlpy7tw5goODCQwMpGLFikydOhVLS8tk9zXx8fEkJCRo90PfKimcIr6KZcuWUa5cOe7fv0/27Nm1G4lZs2bRpk0bhg0bxrt378iSJQu5c+fGyspKu+mQCxLxn0psM9WrV+fo0aNYWVkxZswY9u7dS3x8PAAmJiYMGDCAjRs3smXLFpYtW4aJiQlxcXGSkEphickFCwsLIiMjAbTj1KZNG3r27MmBAweYMGECV69eTbavJCVSl40bN+Lh4cHYsWOJj49n3bp1LFiwgKFDh9K1a1csLS2pVq0au3fvpmTJksyaNQu9Xo+HhweHDh0CkISUEKnQjRs3aNiwIZ6entja2uLi4kKXLl0IDg5m/vz52oQ2Hh4e7N+/H1NTU7n+Eynq7NmzNG3alA0bNpAvXz569uzJvHnzGDt2LIsXL9aKn8+ePZtdu3YBSEJKGNy1a9coVaoUu3btokqVKgwcOJAmTZpw5swZfv31V969e4eRkRHe3t4cO3YMIyOjbz4hBVLoXHwFCQkJWFtbkydPHu7evYuJiQl6vZ7IyEgsLCwYOXIkmzZt4vTp09SuXTvZvnLTIf6ppMNE4+Pj0el01KhRA4CAgAAcHR2ZOnUqRkZGNGrUCIBJkybx66+/kj59egBJgqaQvyYUEn8uX7487u7unD17lgoVKmjrs2TJQsmSJSlUqBAlSpRI8XjFl9OsWTPu3LnD9u3bcXV1RSnFjBkzcHJyAqB///64urrSunVrrly5QunSpfHw8OC3336jVq1a2uvId4MQqYupqSlVqlRh3bp1AAwZMoQhQ4ag0+nw9/fn5s2bZM+endWrV3P+/HkDRyvSojx58qDX61m6dClKKdq2bcuAAQOAj+317t27xMXF4e/vz5kzZwwcrRD/4uDgwNChQ1FK0aJFC9zc3ADYs2cPbdu2pUiRIsyfP59r164ZONJ/Tobvif/ZX2fZg48z6QUFBdGvXz9y5crF8ePHtXW3b9/GwcEBf39/LYkgxH8iaZvz8vLi4sWL3L17F1dXV8qXL0++fPl49eoVjo6OxMbG0rBhQ86cOcPRo0d5+fKl9IxKQUmP1aNHj4iKiiJv3rza08ZWrVpx9OhR1q1bR7FixbCysqJDhw40bdqUHj16oNPpPnuOEd+2zp0707t3b6pXr05sbCweHh4cPHiQa9euMW/ePDp27EhCQgI6nY7Xr1/TqFEjateuzaRJk5J9PuXYC5E6fK434x9//MGcOXM4evQonTt3ZsiQIQD89ttvnDx5kkePHuHh4YGdnZ0hQhZpQOJt7t+VBwkNDaVLly7ExMTQr18/2rZti06nY/ny5QQGBpKQkMC0adOwt7c3RPhCfPbcev36debMmcPevXuZN28eLVq0IDo6mhUrVnDo0CGeP3/OvHnzvukaUn8lSSnxP0l6w3Du3DmioqLInDkzJUuWBGDv3r307t2bbNmyMX78eIyMjJg/fz5Pnz7lzJkzkhwQ/5G/nphHjRqFr68vffr04cmTJxw5coRmzZrRq1cvfvjhB8LDw3FxceHVq1cYGxuzefNmTExM5EY3hST9O48dO5Y9e/Zw5coVmjdvTv369enevTuRkZE4Ozuzbds2cuTIgU6nw8jIiKtXr2JsbCzDtlKh69ev4+fnx8SJE7Uu49HR0Xh6ejJ37lxKlSpFYGAgGTNm1G4YGjVqRP78+VmyZIkhQxdC/EOJ5/eXL19ibW0NwOnTpwkJCaF9+/badiEhIcybN49Dhw7Ru3dv+vfvD3ysBRkfH0+6dOkMEr/4/iW20cS6UQDnz58nIiKCmjVratuFhobyyy+/8ObNG4YPH06rVq3Q6XS8ffsWU1NTGbInUlRiu339+jWmpqZYWFhonTt++uknbbvEh3y7d+/G29ubpk2bavsmjk5KVVJolj/xHUo6pbebm5uysbFRefPmVcbGxsrZ2Vmb1n3Pnj3qhx9+UDqdTvXq1UuNGTNGvX//XiklU3uL/1zidKerV69WBQsWVGfPnlVKKXX06FGl0+lUkSJFVP/+/dXdu3eVUh+nTX39+rXWXmNjYw0TeBo2btw4lT17drVlyxZ18eJFVbt2bVW8eHE1b948bZtt27YpPz8/tXTpUu0Yyfkh9Vu8eLHavHmzUkqpmJgY5eHhocqUKaOcnZ1VTEyMUurjcf7xxx+Vq6urASMVQvxTid/D58+fV0ZGRur48ePq7du3qk2bNqps2bJq/fr1ybYPCQlRP/30k8qfP7+aMWOGIUIWaUxiG71w4YKysLBQR44cUe/evVMlSpRQNWrUUEeOHEm2/YsXL1SBAgVU1apV1W+//WaIkIXQ2u25c+dU/vz51aVLl1R4eLhydHRUhQsXVidOnEi2/cWLF9WPP/6o8ubNq11rpVaSlBL/lcQPjVJKLVy4UGXLlk39/vvv6sGDB2rLli2qdOnSqn379urWrVsqPj5ebd++XVWqVEnVrFlT2y8yMtIAkYvUqEuXLqpfv37a7zExMWrTpk1q+vTpSimltmzZoqysrNTy5cvVjBkzlLm5uRo4cKC6fv16stdJmkgVKePYsWPK3t5eBQUFKaWU+v3335WZmZmqUaOGKlmypPLy8vrsfpKQSp2Sfjc8fvxYNW3aVBUtWlTt2rVLKfUxSTxp0iRVuHBhVaJECdW5c2fVtm1bVbRoUUkYC5EKJH7GL168qCwtLdWwYcO0dSdPnlQdO3ZUVatWVf7+/sn2GzZsmCpYsKCqV6+eevnyZYrGLNKWpG3UwsJCjRo1Slt3/fp1VbZsWdWgQQP1+++/J9uve/fuKkOGDKpFixbqzZs3KRqzEEnbbYYMGdSQIUO0dfv371etW7dWZcqUUcePH0+2X/fu3VXWrFlVqVKl1Nu3b1PtvY4kpcR/5MCBA9rPiTeNXbp0Uc7Ozp9sZ2Njo8aPH6+UUioqKkrt2LFDFStWTDVo0CDlAhap3rt379TUqVOVtbW1cnd315Y/efJEPXv2TIWGhqpKlSqpWbNmKaWUioiIUDY2NipXrlxqzpw5BopaJAoLC1NeXl7qw4cPav/+/Spbtmxq+fLl6tWrV6pYsWKqcOHCasKECYYOU3wBSRNS0dHRSimlTp06pbp06aLs7OzUzp07tXUzZsxQhQoVUsWKFVP+/v7a94kkpoT4diXe7Fy+fFmZm5ursWPHJlv/4cMHFRISotq2batq1Kih1q1bp60bNmyYmj17tnrx4kWKxizSlsTvob9ro0op9eeff6pSpUqpRo0aaQ/MlFJq+PDhat26derBgwcpFq8QSv3r3Hrp0qVPEqmJ6y9duqRatGihypUrp06dOqWtGzJkiPL29k7151apKSX+sVmzZuHj48Pw4cPp0aMH8LEmQJs2bbCysmLFihXExsZiZGSkTem9YMECrl27RqZMmYiNjeXgwYM4OTlRuXJltmzZYtg3JFKN8PBw1qxZw9ixY+nduzfTpk3T1l25coUWLVqwYsUKqlevzq1bt5g+fTqVK1emR48eUrcsBf1dra7379+TLl06OnXqROHChZkwYQLGxsa0a9eOGzdu4ODgwNy5c6V2VCqW9NjPmDGDly9fMnz4cLJmzcqpU6fw8vLi8uXLTJ8+ncaNGxMTE8OYMWOIjY3F09NTCtoLkUqEhoZSpUoV8ubNy5EjR7TlM2bM4NSpUwQEBHD+/Hk8PT25ePEiJUqUwNLSks2bN3P+/HkKFChguOBFmvDo0SOKFStGgwYNCAgI0JbPmDGDJ0+eMHfuXG7fvk379u2xsrKiUKFCpEuXjjVr1nD9+nVy585twOhFWhUaGkrZsmWpUKEC27dv15ZPnTqVGzdu4Ofnx++//86CBQs4duwYnTp14sWLF+zdu5cTJ06QP39+A0b/v5OrP/GPderUidKlS7Nq1Sp8fX0BMDY2pkaNGqxevZrLly9rRW3h47TuBQoUwNzcHAATExNq166Nn58fs2fPNsh7EKlLfHw8AJkzZ6ZTp06MHz+eJUuWMGrUKG2bt2/fYmJiwrFjxzhy5AjDhg3j7du39OrVCyMjI+01xNeVNKFw/Phxtm7dypkzZwgLCyN9+vTo9Xr+/PNPPnz4gLGxMXFxcRgZGTF69GgtISXPSFKvxGPv5ubG3LlzyZ8/PzExMQD8+OOPDBo0iFKlSjFixAh2796NqakpU6ZMkYSUEKnMhw8fKF++PHq9nmXLlgEfH1pOmTKFPn36AFCuXDnGjh1Lr169CA0NJTw8nKCgIElIiRSRPn16ChYsyNu3b9m7dy8As2fPZvz48TRt2hSAH374gYCAAEqUKMG9e/e4efMmR44ckYSUMJjQ0FAqV67M48eP2bVrFwAeHh7MmjWLzp07A1CzZk3GjRtHz5492b9/P8+ePWPXrl2pPiEFMvue+Ifi4+MxMjIiLCwMFxcXbaYKZ2dnlFK0atWK4OBgtm7dSvHixTExMaFVq1ZkyJCBTZs2SQ8I8T9JnN3n1atXrF69mvHjxyfrMeXq6sr27duJiYnBxsaG33//HRMTE5m5zQBGjhzJ+vXryZQpE9HR0RQuXJjhw4fz008/0a9fP+7cuUOJEiW4efMmr1694ty5c+j1eklKfAfWrl2Lq6sr+/bt06bPfv/+PTExMWTOnJkbN24wZcoU9u3bx9atW6lSpQrw+emOhRDfrjt37jB16lTu3btH5syZOX78OBs2bMDBweGzn+eoqCjtAaUQX1PS+5XmzZuTIUMG8ubNy9atWwkICKBWrVrAvx6kxcTEYGpqyvv370mfPr1hgxdp3rlz55g3bx6XLl2idOnS7Nu3Dz8/P+rXr//JuTUiIgJjY+Pv5twqdwDiH0nscZI9e3YWLFhAjhw5WLlyJb6+vuh0Ory8vKhfvz4ODg5UrlyZH3/8kdDQUNatWyc9IMT/xN/fn7x583L9+nWyZMlC586dtR5Tbm5uAMyZM4eAgAB27tzJ0aNHMTExIS4uTm50U9jixYtZuXIlfn5+XLx4kZYtWxIUFKT1jho0aBBFihTh5s2bZM2aldOnT0tC6jvy5MkTatSogb29PdevX2fOnDmUK1cOBwcHxo8fT7Fixejbty8uLi5UqlRJ208+p0KkLkWKFGHUqFHY2tpy9OhROnTogIODA0Cy673En7+Xmybx7Ut6v7J161Y+fPjAb7/9xtChQ7WElFJKu+ZIHOEhCSlhSInnyvLlyzNw4EDs7e0JCAjAxcWF+vXrf3YfS0vL7+rcKj2lxL/115vFuLg4jI2NCQ0NZcCAATx+/JhevXrh5OQEwM6dO3n58qVWL8bIyEjbR4j/xrNnz+jQoQMhISHs3r2b4sWLaz2mJkyYgLOzMx4eHsn2SXxSJlJG4tObnj17kjNnTiZPnsyWLVvo2rUrM2bMoHfv3rx//x4TExOMjY3R6/XaPnJ+SJ0+1xvCy8uLwYMH4+rqyo4dO7C3t+fHH3/k0aNHbNu2jaNHj5IrVy5te/mcCpG63bt3j8mTJ3P79m26du1Kr169gL+vLyhESkn8fnn58iXNmjXDxMSEkSNH0qBBA+1huTwQEYaWtB0m/fns2bN4eXlx4cIFPDw8aNKkySfbfG8kKSX+VtKLCl9fX65cuUJoaCht27alZcuWvHz5kr59+/LkyROcnJzo2bPnJ68hNx3iP/HXC9nE358/f06nTp24fv06+/fv1xJTa9euZeDAgSxatEirZSFSXuKXpKOjI05OTlhbW9O4cWNmzZpF7969iYuLY8WKFWTOnJnmzZtrTya/5y/X71nSz+mzZ89Ily4dVlZWAIwfP56zZ8/StGlT6tWrh62trVZQds2aNRQvXtyAkQsh/onEc3NERASmpqakS5fub7e9ffs206ZN49atW3Tv3v2z14JCfGmJbTQyMhILC4vPXk/8dSifiYkJo0ePpn79+nLtIQwi8frp3bt3mJqaYmpq+n8mps6fP8/MmTNp1KiRIUP/6uQxhvhbSQvXjh8/ng8fPlCgQAFat27NlClTsLa2ZsGCBeTOnZvVq1czb968T15DElLiP5HY5nx8fHjz5o3WoyZbtmzaDW2DBg24efMmWbJkoX379mzYsEEugg0s8Qu0UKFCdOrUiQYNGrBkyRJ69+4NfBz3vm7dOu7du5dsMgS5KEydEj+n48ePp1atWtSvX1/rITF+/Hj8/f3p06cPtra2REdHM2jQILJly0bRokUNGbYQ4j8QFhZG8eLFWbVqFR8+fPjb7X744QdGjRpFiRIl8PT0ZOXKlSkYpUirdDodYWFh2NrasmXLls+WCvnrUD6lFMOHD+fQoUMGilqkdXq9nsePH9O0aVPWrVtHTExMsrab9OcKFSowcOBAKlasSI8ePdi/f78hQ//qJCkl/q0DBw6wfv16AgMDWbx4Mc2aNQPQqvxnz56d+fPno9fruXXrltSOEv+VpO3m5cuXTJgwgZ9++om3b99qJ+hs2bKxdOlS9Ho9HTp04OrVq2TNmpXWrVtrs7mJr+/169fAx6c9iRKP36hRo3BwcCBDhgzUrVuXN2/e8OTJEzp27Mi7d+8YOnSoIUIWX0jSY75q1SoWLlzIkCFDqFu3LocPH6ZmzZrAxzoHb9++xcvLi6ZNmxIaGsqOHTu0+mFCiG+bTqcje/bs1K1bl6FDh7Jx48b/MzE1dOhQHBwctPOAEF+bmZkZ9evXp1OnTuzatev/TExt3LiRnDlzUrhwYQNFLATkzJmThIQE5s+fz+bNm4mOjv63ianevXvTvHlzbG1tDRn2VyfD90Qyf+3+un79elavXs327dvZsGEDPXr00IbkvH79mqdPn2pDqaysrJLVihHiPxUdHU26dOm4du0aXbp0IT4+nuDgYDJmzAhAZGQkLVq0ICgoiIYNG7J161YDR5y2nDhxglq1anH8+HHKly//yXBLpRRHjx7F3d2dy5cvkytXLiwtLTEyMiI4OBgTExMZ0vsd2L59Oy9evMDCwoJ27doRFxdHcHAwXbt2xdbWlsOHDwMwb948bt26hZeXl5Y4lvphQnz7kp7b+/fvj6+vL0uXLqVNmzaYmZl9sn3idV9sbGyy3rBCfG1v3rxh9OjRLF26lC1bttC4ceN/O5RP6p0JQ0psh/Hx8Tg6OvLw4UPc3d1p3rw56dKl+9uhfB8+fPjsufd7Ikkp8VmJHxpfX18WL17M4MGD6devHx4eHvTt2xf4mLBav349ixcvJnv27IAUtxT/maTtZerUqTx//pzRo0eTNWtWrl+/Tvv27dHpdBw5coRMmTIRFxdH9+7dGTp0KKVKlZK2lsKePn2Ks7Mzp0+fZu/evZQtW1Y7hkm/PBMSEti4cSPv378nW7ZsNG7cWCY9+E5cv36dn376iXfv3rF69Wrat28PfPzOOHLkCN26daNw4cIcOHAg2X6SjBTi2/W5a7ek5+t/kpgS4mv6u4mX4GMP7jFjxvyfiSmQWpYiZSVtt4k/JybvExIScHR05MGDB4waNQpHR8dPElNpidzRiU/4+vrSsGFDEhISqFq1KunSpaN79+6MGjVKS0hFRUWxdu1aMmXKRLZs2bR9JUkg/qmkJ+qQkBBCQkKYN28e3t7ehIeHU6JECdatW4eRkRF2dnYMGDCA6tWrc+PGDezs7NDr9cTHxxv4XaQtuXLlwtfXlxo1alC7dm0uXLigDclK+gX6+vVratSoQffu3fn555+1p0KSkEr9bGxsWLBgATY2Nqxbt05bbmRkRI0aNfjtt984cuQI/fv3T7afJKSE+DYppdDr9dy9e5fdu3dry42NjYmOjgZgwYIF9OjRg169ev2fQ/mE+NIS2+jDhw+1Bx6J1xNKKaysrJg8eTK9evXC0dHxb4fygdSyFCknsd2GhIRw/fp1YmNjATAxMSEmJga9Xs+WLVvIly8f06ZNY8uWLZ8M5UtL5A5BJJOQkEB0dDSvX7/m8ePHFCtWjKZNm/Lq1SuuX7/OkSNHeP36Nd7e3jx58oSAgACZWlX8VxITUkOHDmXXrl3Url2bqlWrMm7cOCIjIxkxYgQlSpTg8OHDDBkyhMePH1OkSBF8fX21Lthyo5vycuTIwYIFC+jfvz+1a9fm0KFDlC1bVjsHhIWFUadOHdKnT8/Jkye15KMcq9Tnc7NhZsyYkVatWmFkZMSgQYPo3Lkzq1evBj4mnqpXr86pU6coXbq0ocIWQvwHdDodr169okSJEsTFxdGnTx/y5s3L8OHDk826t2DBApRS9OrVC71eT8uWLTE3Nzdg5CKt0Ol0vHz5kjJlyhATE0OtWrXo2bMnlStXJkeOHABYWVkxYcIElFI4Ojqybds2GjZsKPcnwmB0Oh2hoaEULlyYPHny0K5dOwoVKkS/fv20Yc6JianmzZszbdo09Ho9P//8c5rsjSrD99K4z52sX716RZkyZWjZsiVz584FYM6cOezdu5dDhw5RoUIFcuTIwYYNG6RGjPif7N69mw4dOrB//34qVqwIwJIlS+jbty8jR47E1dVV64mXtFaFDAMzvNDQUPr378/Bgwc5fPgwZcqU4enTp7Rr147Xr19z7tw5qS2SiiVNSC1evJhr167x559/4uTkRI0aNciWLRv+/v4MGzaM2rVr4+fn98lryHeDEKnHoEGDSJ8+PVmyZGHnzp08fPiQvn37UqtWLcqXL69t5+Ligre3N/7+/rRr186AEYu05MmTJ/Tt25dffvmFM2fOcO3aNa5evcq0adMoUaKE9iAkIiKCUaNGsWjRIvbu3Uu9evUMHLlI637++WcyZMjATz/9xMyZM7Gzs6NmzZr06dOHTJkyAR+vl1q2bMmFCxeYN28eLVq0MHDUKU/GWqVxn3t6kCVLFqZPn86+ffs4efIkAK6uruzatYurV6+yZ88eAgMDMTExIS4uTm46xH8tMjKSnDlzYmtrq83K1bt3b+bMmcOMGTNYunQpz549A9ASHEopSUilgP9rlrScOXOyYMEC6tSpQ+3atTlw4ABOTk68ePFCS0jJjIipV2JCys3NjV9//ZW3b98C4OzszLhx47hz5w7t27dn1qxZHDlyhCZNmnzyGvLdIETqoJTC2tqaO3fuMGzYMA4fPkz//v25ffs2tWrVYurUqWzbtg2AhQsXMmrUKOkNKVJU7ty5KViwIHPmzMHDw4MVK1YwaNAgfHx8aNOmDePHj+fOnTtYWlri5eWFq6srNjY2hg5bpGEJCQkopahRowY5cuRgwIABnD17lqpVq3Lq1ClKlSrF3LlzCQoKwsjIiM2bN+Pg4IC9vb2hQzcMJdK8yZMnqy5duqjdu3dry27cuKHs7e3VggULlFJKxcfHf7Lf55YJ8XcSEhKS/V8ppXbs2KH0er26du2aUkqp6OhopZRSt2/fVpkyZVImJibKw8Pjk/1Eyrlw4YKKjo7+279/aGio6tChg9LpdKpYsWIqJiZGKaVUbGxsSoYpvoLg4GCVN29edfr0aW2Zn5+fKl26tBoyZIiKi4tTb9++VT4+Pqp58+bynSBEKhYdHa0KFy6sJk2apC1r2rSpypIli3JwcFAlSpRQ9vb26sSJEwaMUqRFidcf7969U3Xq1FGrV6/W1tWuXVsVLFhQ5c+fX/3000+qRo0aKiQkxFChCvGJK1euqIwZMyo/Pz9tWfPmzZWlpaVq2LChypEjh2rdurW6dOmSAaM0POkplQapv4zYLFGiBDdv3sTd3Z2aNWty+vRpihUrxoABAxgzZgyPHz/+bAFzKWou/qmkhbCjo6O1XjhNmjShYcOGdOrUibt372JqagqAmZkZzs7OeHh4MHr0aE6fPi01AQxgx44dNGrUSDt+fz13wMcaUx4eHsyaNYsrV65oPaSkN1vqFxsbi06nw8rKSvvMdu7cmUGDBuHt7c2tW7ewtLSkc+fObNmyRSt6L4RIXeLj4zE1NcXFxYUHDx4A0LVrV86ePcvZs2e1mZazZs1KlixZDBytSGsSr/+MjY2xsbEhKCgI+NhGr127xuHDh7ly5QrdunXD1NRUKygthKEppbCzs8PNzY3Dhw8D0K1bN86cOcPZs2fx8fFhwYIF3L17lwwZMhg4WsOSmlJpzF8L10ZGRmJhYcH79++5ePEiEydO5P79++TLl4/WrVuzatUqHB0dcXV1laEY4r+StM15eXlx5MgRIiMjKViwIPPmzePy5cu4u7tz9+5dZsyYgbGxMYsWLUIpRWBgIKVLl6ZPnz4MHz7cwO8k7YmPj6do0aJ07NiRiRMn/qN9JCGVOqn/X19QJakzeODAAVq2bElwcDD29vZ8+PABMzMzEhISKFCgABMnTsTJycmwgQshvpjTp0/TuHFj8uTJw+vXrwkMDExWT0oIQ7t16xY1a9YkW7ZsvHjxgl27dlG2bFltvVyDiG/Rzp07GTlyJFZWVjx48IBNmzZptXRB2i1ITak0JWlyYPbs2XTo0IFy5coxc+ZM7ty5Q9WqVdm7dy/Tpk2jePHi9O/fn+PHj3Pr1i1JSIn/WmKbGzlyJFOmTKFatWo0b96ctWvX0qhRI+zt7Zk0aRI1a9akW7duuLm5ERUVxc6dO7GwsMDS0lKbXUV8PX99PhETE4NOp6NLly5cvHiRiIiIf/Q6af1LNTVK2pMxPj5eW163bl2qVauGo6MjL1++1GaDCQsLw9zcHGtra4PEK4T4OipVqkSfPn0IDQ1l5cqVkpAS35SEhAQKFy5M8+bNeffu3ScJKZBrEPFtatKkCfb29pw9e5aAgIBkCSmQGpwgSak0JTE54O7uzsyZM6lUqRJDhgxh6tSpTJkyhSdPngDQokUL5s2bx7Fjx5g0aRLe3t6GDFt8B65evcrOnTvZtGkTgwcPJleuXCQkJNC8eXOMjIyoWLEivr6+XLhwgePHjxMUFISxsTHu7u5ERERQs2ZNQ7+F71rS3jGXL18GwNTUFL1eT/Pmzdm/fz9bt241ZIjiK1FKJevJ2LFjR3r16sW8efOAjzPv5cyZkxIlSuDr68uKFSvo0aMH6dOnp3HjxoYMXQjxDyQdUps4rOnfDW+qWrUq2bNn14bTJ01UC/E1JH0o9u+GgOv1eoyMjKhfvz5hYWHaBDgybFwYwufOrZ8bgJa4XadOnShXrhxRUVGf7C8lSiQpleacO3eOzZs3s3nzZlxdXSlXrhwRERE0a9aM3Llzax8QpRQVK1Zk9OjRGBsbyyxa4r+S2J7CwsKIjIykevXqbNu2jU6dOjFjxgz69+9PRESENp18oUKFyJIlC2fPnsXFxYWlS5eyadMm8ufPb8i38V1L2ksmICCAjh070rx5c06dOsWrV68oU6YMgwcPZvXq1dpMiOL7kDQZOXnyZEaPHo21tTWPHj1i9uzZ/Pzzz+TLl4/du3fTtGlTpk+fzoIFCzAxMeHUqVMYGRnJDasQ3zi9Xs/Dhw958uQJJiYmbNu2jZUrV/5tYqpRo0bY2Njg6uoKyBN88fXpdDqePXvGvXv30Ov1BAYGsmfPnr/dvlWrVjRo0IARI0bw/v17qXErDEKv1/P48WPg4wzhu3fvZsWKFZ8kSRPbZ9WqVYmKimLx4sXJlouP5K+RxsTFxWFhYUGVKlXYsGEDDg4OzJ8/ny5duvDu3TuCgoKIjIz8JGMr3WHFP/XkyRMePXpEWFiYdsLNmjUrhQsXxtPTk06dOjF79mx69+4NwPXr19m5cydXrlzRXiNr1qyULVuWkydPftI1W3xZicdo4sSJBAQE4OnpSXh4OP3796dRo0bs27ePvHnzEhYWxosXLwB5Kvm9SDzPnz17litXrrBlyxa8vb3ZsmULy5cv58KFC7Rp0wYrKyt8fX0JDg4mKCiIzZs3awXt5YZViG9bREQELi4utGnThiVLluDo6EjGjBm1XiZJJSaZe/bsSXR0NGFhYSkdrkiDwsPDad++PXPnzmXhwoW0bt2aly9ffnbbxJ4oZcqUISwsTIqaC4OJiIjg559/pmnTpmzevJkmTZqQOXPmzyabEhISyJgxI4MHDyY4OJjw8PDP9qpK01J8vj+RYp4+faouX76s/Pz81JUrV9SrV6/UpUuXVLZs2dTSpUtVpkyZ1MKFC7XtDxw4oFq2bKlu3bplwKhFarZmzRpVoUIFVbBgQWVjY6N+//13pZRST548UWXLllU6nU5NnDhR2z4yMlI1atRItWrV6pPp5GV6+a8r6d93x44dytbWVp0+fVpbdujQITVw4EBVoEAB1b59e6XT6VSbNm3kuHxnVq9erapUqaKKFy+u7t27py2PiYlRgYGBqkiRIurgwYNKKaXi4uK09dIOhEg9du/erYoXL66MjY3V3LlzlVJKxcbG/u32L1++VE+ePEmp8IRQS5YsUba2tkqn06mZM2cqpZRKSEj42+3j4uLU/fv3Uyo8IT4RFRWl9u7dq7Jnz67SpUun1qxZo5T6eP30d+7fv68eP36cUiGmKtJT6jsVGBhIjx49qF+/Pi4uLlSqVAknJyciIyNp164dvXv3xtXVlX79+gEQHR3N3Llz0el0FC5c2MDRi9RoyZIldO/ene7duzN69GgKFixIkyZNuHPnDrly5WLNmjVYWVlx/Phxpk6dyvLly2nSpAmPHj1i3bp1n0wnL91av67Ev+/u3bvZvn07Xbp0oWLFikRHRwPg4ODAvHnzWL16NXXq1NEKNCb2aFPyhCdV+msvt4IFC2JsbMzt27fZt2+fttzExITy5cvz+vVrrd5g0l5R8vkU4tumlNI+70WLFiUqKor8+fOza9cuHj16hLGx8WeH3yYkJJAlSxZy5cqV0iGLNCixDdarV4/IyEhsbGx48uQJt2/f1maE/dw+RkZG5MuXL6XDFQKlFEopzMzMyJs3LxEREZiZmbFlyxYArSf5X/cByJcvH7lz507pkFMFuar8Di1btoyePXtSu3ZtVq9ezf379xk1ahS3b9/GycmJLFmy0K5dO1asWMGqVauYO3cuzZo1448//sDf3/+T5IAQ/xc/Pz/69u3L7t276du3Lz169MDR0ZGYmBju3LkDQPHixTl8+DBWVlasXLmS1atXU6BAAc6dO6fVLZMb3a9LKZXsJiQsLIzhw4ezcuVKnj59CkC6dOlISEjQvkCrVq1Kz549CQ4OxtTUlDVr1gBSlDE1SjoD6969ewkPD+enn37C29ubGjVq4O/vT2BgoLZ95syZsba2lpqCQqRCOp0OvV7PgwcPKFiwIPv372fOnDlER0fTtWtXHj16lKwu3IcPHwBJOIuUZWRkxMOHDylYsCDHjh1j9OjRBAUFMW/ePO7cufPZaw0ZNi4MSafTodPpePHiBUWLFuXkyZP4+/tz+vRpWrZsCZCsHnN8fLxcM/8ThuukJb6GpUuXKlNTUxUQEPDJunXr1qkKFSqoGjVqqHXr1ql+/fqpvHnzKgcHB9WjRw+tK/e/69ItxF89e/ZMVahQQdna2qrw8HBtedOmTZVOp1MDBw5Uvr6+6sSJE9q6yMhIFR0drf0ubS5lPHv2TPt5xYoV6vnz5+r06dOqatWqqmjRomrnzp3a+qTd5hO7Is+ePVs5ODio9+/fp1zQ4otIejzd3d1V4cKF1dy5c7Vje/78eeXg4KBKlCihhg0bppYsWaKaNWumihYtmmzYnhAi9QgJCUk2HEoppdavX69q1aql6tatqx49eqSUUmrOnDnKx8dHhuWKFHfjxg1VsmRJNWPGDG3Z3LlzVdmyZdWAAQO0kiJTp05Ve/bsMVSYQiRz/fp1ZWJiorZs2aKUUur9+/dq06ZNqkCBAqply5badkuWLFErVqwwUJSpiySlviOHDx9WOp1OTZgwQSn18SYkISEh2Q3/vHnzVKZMmbSkVVhYWLLXkOSA+E/s2bNHvXv3Tu3atUs5ODioevXqqTdv3qiOHTuqggULqjlz5qiRI0cqR0dHZWpqqurVq6c6duyYrN39u5oB4ss5efKk0ul06urVq2rYsGEqR44c6o8//lBKKXX8+HFVtWpV1bx5c7V//35tn78em9atW6tq1aqpqKiolAxdfEG//vqrypo1qzp+/Lh69epVsnWXLl1StWrVUjqdTjVp0kRNmzZNWyeJKSFSn9evX6tx48YpExMT5enpqS3fsGGDqlOnjipUqJDq3r270ul06vLlywaMVKRVISEhqlevXqpixYrJElPz5s1TFStWVPXq1VOdO3dWOp1OXbhwwXCBCpFEVFSU6tatm8qQIYPavn27UurjA/fExFSFChVUv379lE6nU9euXTNwtKmDTKn2HcmTJw/VqlXj/PnzBAcHU716deBjF8LEYRsDBw5k2bJlHDhwgJYtW2JlZaXtr5SSWfbEf2Ts2LGULFmS5cuXExMTw5w5cyhQoADW1tZcvXoVCwsLbduDBw9y+vRpgoKCyJIli7ZcurSmjCJFitC9e3cqV66MkZERly5dIn/+/CQkJFClShWmTZvGqFGjWLhwITqdjjp16iQ7Nm/fviUsLIzZs2djZmZmwHci/lsPHjxg3759+Pn5UaVKFcLCwjh37hz+/v7UqVOHRo0asWDBAgYMGIClpSUlS5bU9pUhPUJ8+5RSyc7bmTJlYsiQIaRLl46hQ4cC4OrqSps2bciaNSvbtm3j4cOHXL58GTs7O0OFLdKQv7bRggULMmrUKObOnYu/vz8Aw4cPZ+DAgVhZWXHy5Elpo8LgkrZb9f/rSXl7e2Nubk7r1q3ZtGkTTZs2pWnTpuTIkQNPT0+ePHnCxYsXKVGihIGjTyUMmhITX9zt27dVw4YNVYMGDVRwcLC2PLHHw5s3b1ThwoWTzYAmxH9r9+7dqkWLFur69etKKaU2btyoatasqapUqaL1hvq7WShkmEDKSNrbaebMmUqn0ylzc3N1/vx5pdTHHjCJ2xw5ckTVqFFDVatWTZ05c+aT1/p3M4qIb9+jR49U5syZ1dKlS9W5c+dU165dVenSpZWdnZ3S6/Vq8+bNSimlTp8+rRwcHFSTJk3Upk2bDBu0EOI/cuDAAbVu3bpky16/fq2mTZumdDqdmj9/frJ1SYfSC5ESTp06pVavXp1sWUhIiBo8eLCys7NT8+bN05bHxsZKGxXfhIMHD6pLly4ppf51bR0VFaVcXFyUmZlZshIYSn3sOSX+OXn0+Z0pUqQIXl5e6HQ6Jk+ezLFjx5KtDwkJwcbGhsqVKwMyg5b431SoUIFXr16xbNkyAFq3bk3//v1Jly4d7du35/Hjx5+dhQKk50VKSEhI0J7svHnzho4dO3L+/Hk6depEtWrVOHr0KEZGRsTGxgJQvXp1PDw8sLOzo1y5cp+8nomJSYrGL/57n5usIk+ePPTq1Qs3NzeqVatG5syZmTJlCleuXKF27doEBQUBULFiRWbPns2TJ09Yv3497969S+HohRD/jejoaLZu3UqHDh3YtGmTtjxTpkz06tWLdu3aMXDgQObNm6etMzU1NUSoIo169+4ds2fPZtasWaxbt05bXrBgQQYPHoyVlRWzZs1i+vTpwMfRHtJGhaHFxMQwb948ypcvz5UrV7SZIc3MzJg6dSo1a9akW7du7NixQ9vH3NzcgBGnPjolWYnv0p07dxg4cCBKKUaPHk316tWJi4ujefPm6PV6tm7dKkkB8UWcPn0aR0dHfHx8aNy4MQABAQEsXLgQY2NjfHx8ZNpeA0g605qHhwdhYWF0794dOzs7bda9TZs2ceDAAapUqQLAtGnT6NGjB9mzZ//kNUTqkfS4bdq0iT/++IOoqCg6deqEra0tly9fJiEhgTJlygAfZ4apXbs2zZs3Z8iQIVo39UuXLmFlZUX+/PkN+G6EEP+Je/fusXDhQnx8fPDx8aFt27bauokTJ7J8+XLevXvH7du3yZw5swyhFylCJRn+dPr0aebPn88ff/xBv3796Nixo7bdiBEj2LhxI0WKFGHt2rVYW1sbKmQhknn06BGDBg3i8OHDBAUFUbp0aW3doEGDWL58ORkyZODOnTtkyJDBgJGmTnK38Z1K2mPKw8ODY8eO0a5dO/78808CAwPR6/WffZIuxN+5c+eO9vPixYt5/vw5SinKlStHp06dCAoKIjIyEoBWrVoxYMAAnj59yuzZsw0VcpqWmJRwc3PD09OT8uXLkzVrVgCyZ8/O3LlzadmyJbVq1WLWrFnUqlULf3//ZBeAkpBKnRKP2/Dhw3FzcyMoKIiLFy9SpEgRtm3bRunSpSlTpgyRkZFcuHCB5s2b8+bNGwYOHKi9hlIKe3t7SUgJ8Q1LfK789u1bXrx4AYCtrS3u7u507doVZ2fnZD2mIiMjGTVqFCEhIWTJkkUSUuKrS2yj796948OHDwBUqlQJNzc38ubNi7e3N2vXrk22vYuLiySkhEElttvIyEjCw8MBsLGxwcfHh6pVq+Lg4MDly5e17U1MTPD39+fKlSuSkPpvGWTQoEgxt2/fVk2aNFEmJiaqaNGiWk0YmWVP/CdOnDihypUrp3777Tc1aNAgpdPp1N27d7X1gYGBqkCBAurKlSvJ9gsKCpJZuwxow4YNKnfu3MlmVQoPD1e3b9/Wfh82bJiqUKGCatWqlXZ+kHpfqd+GDRtUrly5tNpg27ZtUzqdTqs1k5CQoAIDA1Xjxo2Vg4ODduzl8ypE6pBY02Tr1q2qSpUqytbWVlWqVEktXLhQvXnzRr1+/Vr7vnZ0dFTNmjVTWbJkUbdu3TJw5CKtSGyj27dvVzVr1lTlypVTlSpVUrt27VJKKXXjxg3VuXNnVbJkSdWyZUvVrVs3ZWVlpc0MLIQhJLbbbdu2qfr166tChQqp9u3bqyVLliilPtZndnR0VBkyZFC9e/dWbdq0UdbW1urOnTuGDDvVk+F7acDNmzdZtGgRnp6eGBsbExcXJ7Psif9IREQE/fr149ChQ0RERBAUFES5cuWIjY3V6gz17t2bW7dusXPnTtKnT59s//j4eIyMjAwRepqi/jKrjY+PD/7+/hw4cIDbt2+zdetWFi9ejIWFBaVLl2bNmjXodDpevHiBtbU1Op1Ozg+pXGIbmDNnDrdv38bb25tNmzbRrVs3Zs+eTa9evXj79q02xO/s2bPUrFkTIyMjOfZCpDJ79+6lWbNmuLu7U7JkSbZs2cLNmzepXr06EydOJF26dGzatIm1a9eSNWtWhg4dSqlSpQwdtkhDdu/ejaOjIyNHjiRr1qwcO3aMffv2MX78eAYOHMi9e/fYs2cPGzduxNramnHjxiUbFiWEIezatYsWLVowbNgwsmfPzuHDh3nw4AENGjRg2rRpAIwbN44TJ05gbm7OpEmTpN3+jyQplcbITYf4TyUmlBYtWsSoUaMoUKAAgwcPplOnTpiamhITE4OpqSmXLl1i/Pjx9OzZkyZNmkg9IgNKTEz4+PgwceJEqlWrxqlTp6hSpQr29vakT5+e2bNns2nTJsqWLfvJfiJ1iY+PJzY2FjMzM23ZqFGjuHfvHh07duSXX35h+vTp9O3bFwBfX1+uXLmCh4eHto98XoVIPZRSxMbG0qVLF3LkyIGXl5e2bsaMGfj7+zNw4EC6desGQGxsLDqdTq7/RIpJbKNt2rShQIECyYrrjx49moULF7J582YcHBy05R8+fEj2PSZESlNKaTU4S5QowZQpUwAIDw9nyZIlbNiwgUGDBtG1a1fg4+QSer1eJgL6AuQKNI2RCxLxTyXWHEvs4VSlShWOHDlCmTJlWLZsGT4+PsTFxWmzopQsWRJra2uWLFkCSD0iQ/H09KRp06YA9OzZkwEDBpA+fXrGjh3L1KlTGT58OA4ODlhaWn4yo40kpFKfbdu24eTkRK1atZgxYwZRUVEAVK5cmVu3btGxY0cmTZqkJaQiIiLYsmULQLKLf/m8CpF66HQ6TE1N+fDhg/aZj4+PBz7WESxUqBCrVq3StjcxMZHrP5GidDoder2ely9farWhoqOjAZgyZQoODg5MnjwZ+FfblYSUMDSdToeFhQVhYWFERERoyzNnzkyfPn3ImjUrwcHB2vJ06dJJQuoLkatQIcQnkvaauHTpEg8ePCB37tzY29vj6elJgQIFWL16Nb6+vto+CxcuZOjQoVy4cIHAwEBDhZ7m2dnZceTIETp06AB8LHa9cOFCunbtSt68eXn37h3Dhw/H2tqa4sWLGzha8b9YunQp3bp1w8LCgkKFCjFy5EimTp0KQNOmTSlbtixZs2YlPj6eu3fvcubMGdq2bcujR4+YNWsW8K9inkKI1CPxoVGWLFm4cOECHz58wMjISLu5r1evHhEREdrkI0IYgrGxMXny5GHr1q3Axxv4xMSUvb29tp2UdxCGlngtlJCQQFRUFAULFuTRo0e8fv1aO99aWVlRtWpVLl26pD0MEF+ODN8TQvytUaNGsXr1agBKlSrFiBEjqFmzJq9evWLAgAHcu3ePUqVK8fjxY44fP054eDiHDx8mW7ZsUrciBfxdra7ff/+dFi1aUL9+fdauXYter+f9+/csXLiQw4cP8+zZM06dOoWJiYkM20qlli1bxoABA1i7di0tW7bk/fv3NG3alKtXr3Lu3Dny5ctHXFwczs7OXLx4kcuXL1OhQgUsLS3ZvXs3JiYmUutNiFQgISEBpRRGRkY8e/YMCwsLoqOjyZo1K6GhoZQpU4YaNWrw22+/YW5ujk6nw9nZmUePHrFlyxbSpUtn6Lcg0oDE75PEmcosLS0xNjbm/PnzdO7cGXt7e/z9/bXtnZ2def78OevXr8fU1FR6aosUFxsbi7GxMTqdjlevXmFpaUlcXBzm5uYcO3YMBwcHBg8ezKhRo8icOTMATk5OREZGsmbNGukh9YVJUkoIoUlaUygoKIhffvmF3377jTt37hAUFMTp06dZvHgx9erVIzw8nOnTp3P79m30ej3+/v6YmJjw7t07mQ71K/trMuHQoUPUrl072TZBQUG0bNmSxo0bs3LlSoyMjJg/fz5//vkn06dPl0kPUrHLly9TpkwZBg0axJw5c7TlFStWJCQkhOPHjxMdHa0V3Xz37h3nz58nT548FCxYEL1eL8deiG/cxo0byZ8/P5UqVQIgICCA8ePHExUVRdasWXFycqJPnz4EBwfTunVrcuXKha2tLSYmJuzcuZNjx45J4V3xVe3btw8bGxtKlCgBQGBgILNmzeLRo0dUr16dWrVq4ezszLp165gwYQLGxsbUrl2bZ8+esXPnTo4fPy4PMEWK27BhA3Xq1NGGlW7dupUJEyYAYG5uzvjx46lXrx47duygZcuW1K9fn8yZM2NiYsLGjRvl3PqVSFJKCPEJPz8/rl27hrW1NcOHDwfgwoULzJ49m2PHjrFs2TLq1q1LTEwM8LFehczcljK6du1K06ZNcXR0xMTEhCtXrmBvb8/gwYPx9PRMtu327dtp0aIFffv2Zf78+cnWSS+Z1OvOnTvMnj2bgIAAVq5cSePGjWnVqhVHjx6lfPnypE+fnn379lGzZk3KlStHs2bNKFWqlPZUT3rHCfFtu3nzJl26dCF79uzMmjULCwsL7O3tGT16NBkyZODBgwfMmDGDMWPGMGHCBF68eMGkSZN48+YN5ubmDBgwQEsUCPGlKaW4ceMG5cuXx8nJiVGjRvHixQvq1KnD4MGDSZ8+PVevXuXQoUM4Ozvz66+/cu3aNWbPns3Lly+xsrLCzc2NkiVLGvqtiDTm2rVrtGnThnz58rF582aePHlCiRIlGD16NObm5ly+fBl/f3+8vLzo168fJ0+exM/Pj/v375MjRw5cXV2xs7Mz9Nv4LklSSgiRTEhICM7Ozpw6dYrhw4czbtw4bd2FCxfw9PTkxIkTzJ8/n0aNGmnrZOa2lOHg4MDVq1dZsWIFDRs2xNjYmJUrV9KvXz/69evHzJkztW3v379PrVq1uH//PqNHj2bSpEkGjFx8Sffv32fGjBmsWbMGW1tbjIyMWL16NT/88AMfPnzg5s2brFixgm3btlG8eHF27twpn08hUpENGzbg4+ODpaUlJUuW5M2bN8lmMFuzZg1dunRh6dKl9OzZU1suDxxESlm7di3u7u60bt0aKysr3r9/z7Rp0wB4/vw5/v7+eHp6Mn78eJycnLT95MGIMJT4+Hj8/f1ZsmQJVlZWNGzYkCdPnmiz7AFMnjyZcePGcfDgQWrVqkVsbCwmJiba/8XXIUkpIdK4zyWT9uzZw+zZs7l+/To7d+6kTJky2rqLFy9qTxQ2bdqUwtGmXUkv4tq1a8f+/ftZuXIlDRs2xMTEhNWrV9OjRw8GDBigFbEODw9n9OjRdO3alQoVKsiNynfmzz//xMvLi4ULFzJ37lxthr3ECyellFa8U24AhEgdkp7rN27cyIoVK7h27Rq1atVi5cqVwL8ST4MHD+bKlSsEBgaSIUMGjIyM5AGR+OqStjF/f39GjBhBdHQ0rVu3ZuHChdp2z58/p3///lhaWrJs2TJAZvkVhpPYbuPj41m3bh2+vr5cuXKFdu3asWDBgmRJp7Zt2/L69Wu2b9+OsbGxnFtTgIyzESINS3rx+/r1a96+fUu+fPlo2LAhmTJlYsKECfTu3ZulS5dqM6WUKVOGuXPnYmtra8jQ07SVK1dSs2ZN3NzcAGjcuDGdO3cGPhYPffDgAbVq1WLLli3ExcVRqVIlGV75HSpQoAAuLi7ExsYyatQocubMSYsWLTA2NiYhIUGbkhuk94QQqYVer9c+r23atEGv1+Ph4cGePXs4c+YMFStW1D7LuXLl4vDhw5ibm2vL5KZJfG06nU67Qe/QoQPm5ub06NGD06dPc+nSJe16MVu2bBQrVoyAgAAiIyNJnz69gSMXaVliQsrIyIj27dujlGLWrFns2bOH8PBwMmfOrCWmihQpwtGjR5NNFCHn1q9LHp0KkUYppbQb1kmTJvHzzz9TsWJFHB0d2bBhA1WqVMHNzY3s2bPTq1cvLl++rO1bpEgR9Hq9Nk2q+PoSj5WrqystWrQgY8aMPH/+nJ49e7Jr1y7i4+Pp3Lkzu3fv5urVq6xatQqAvXv3aheQkpD6/tja2jJ06FC6dOlCt27d2Lx5s5aMSnoBJQkpIb59iT0bk35eW7Vqhbu7O0WLFmXs2LGcPXtWW/fo0SMyZMhAdHR0iscq0rak3y+Ojo4sXryYx48f4+3tzcWLF7V1jx8/Jk+ePNJbV3wTEs+tiYmpkSNHkiFDBq1nVGJPqefPn6PX64mMjEQGlaUMGb4nRBo3YcIEvL29mTdvHhUrVqRJkyaYmZkRGBhIwYIFOXDgAF5eXly5coUDBw5IDykDWrVqFQMHDuTw4cPY2Nig0+no3r07x44d47fffqN+/fqkS5eOyMhIYmJiyJQpk/SQSsUSn0T/ky7j9+/fx9PTk/nz53P48GFq1qyZQlEKIb6ExM/56dOnOXnyJEZGRtjZ2Wmf5fXr17NgwQKuX79OrVq1sLa2ZvPmzezbt4+yZcsaOHqRFiS20bNnz3Lnzh0iIiJo3LgxefLkQafTsWbNGoYPH07WrFmpUKEC6dKlIzAwkL179yYrAyFESkpstxcuXODcuXOYm5tTrFgxypcvT1xcHOvWrWPGjBm8ffuWatWqYW1tja+vL8eOHdN6/YmvT+5ShEijlFI8ffqUXbt2sWzZMn7++WeCg4N58OAB8+bNo2DBggDUrVuX6OhogoKCKFCggGGDTuNCQ0MpVaoUdnZ2GBsbo9Pp2LZtG40aNaJ///4sXLiQ2rVrY2FhgYWFBfBxiKYkpFKfpENrP3z4gLm5ebJ6CH/t+ZQ/f34GDhxIgQIFqFq1qiFCFkL8lxI/24GBgTg7O1OhQgWeP3+Oubk5bdq0YfDgwbRr1w5TU1NmzZrF8ePHGTp0KBcvXiRPnjyGDl+kAX9to6VLl+batWts2rSJzp0706VLFzp16kS6dOno378/u3fvxs3NjZMnT2rXk0KktKTt1sXFhXz58mFsbEx4eDgzZsygadOmtG/fHmNjY+bMmcOWLVvw9vZm2LBh5M2b19Dhpy1KCJFmPXr0SJUoUUIlJCSorVu3qgwZMihvb2+llFLv379Xq1evVs+fP0+2T1xcnCFCTdMSEhKUUkpNmDBB2djYaMsjIyOVUkrt3r1b6XQ6lS5dOnX06FGDxCi+nPj4eO1nT09P5ejoqOrWratcXV1VWFiYUur//hzGxsZ+1RiFEF9WcHCwyp07t1q8eLFSSqljx46pTJkyKRsbGzVp0iRtu5UrV6q2bduqx48fGypUkUYFBQWp7Nmzq2XLlimllLp06ZIyNjZWP/74o1q6dKl2reLn56fs7OzUixcvDBmuEEoppX7//XeVLVs27f7m4MGDyszMTGXIkEH5+/srpT5eMy1dulQ1a9ZMPXz40JDhplkyfE+INEJ9ZghQeHg4pUuXpn79+gQEBODh4UGfPn0AuH79OgMGDGDkyJHUq1fPECGnWX83XfLjx4/56aefqFu3Lr6+vtry4OBgtm/fjpGREZMmTZKeUd+JUaNGsWzZMlxdXbl79y43b97kyZMnHD9+nDx58kjxciG+E0oppk6dyv3791m6dCn379/HwcGBypUrY21tzYYNGxg1ahSDBw8G4O3bt2TMmNGwQYs0JS4ujpkzZxIWFsacOXMICQmhXr16VK5cmRcvXhASEoK7uztdu3ZFr9fz5s0bMmXKZOiwRRoXExPD2LFjUUoxffp0Hj16RLVq1ahWrRomJiZs3LiRdevW0bRpU+Li4nj//r20WwORpJQQaUDSJEdYWBjW1tZaIeRZs2YxefJkmjRpwpo1a1BK8eHDB9q0aUNcXBw7d+6UG98UlDR5uHLlSi5dukSpUqWoVq0aRYoUYfny5cycORN7e3umTZvG27dvGTlyJIUKFdKmYpZkReqT+BlNPP63b9+mWbNmzJ07l4YNGwJw48YNBg4cyMOHDzl58iRWVlaGDVoI8cW8e/eOq1evUqZMGerWrcsPP/zA8uXLuXr1KjVq1CAqKopff/0Vd3d3Q4cq0qgbN24AkC9fPho0aEDRokXx9fXl3r17lC9fnrx58+Lq6kr37t3/US1EIVLC7du3efbsGWXLlqVu3bqULl2apUuXcvjwYerVq0dCQgLr16+nTZs2hg41TZPH6UKkAYkJqfHjx7N3716io6NxdXWlSZMmdOnShT/++IPVq1fj5OSEmZkZt2/f5vnz55w/fx4jI6O/7bkjvqykF3G//vor8+fPp2LFivj5+VGjRg2GDBlC9+7dyZAhA7/++iv29vZkypSJnDlzsm3bNu11JCGV+oSGhpI7d26tDbx584YHDx6QO3dubZuiRYsyZcoUnJ2dOXDgAK1btzZgxEKI/8bnbtaVUmTIkIHKlStz/vx5IiIiGDZsGACmpqZUq1aNKlWq0L59e0OELNKgz7XTokWLotfrOXbsGG/fvtXa6KtXr6hQoQK5cuWibt26AJKQEt+MH374gR9++IFTp06hlMLNzQ2AbNmy8fPPP1O6dGlKly5t4CiF3GUKkUasXLmSpUuX0r17d/Lnz4+HhwfTp0/HxMSE6dOn4+3tzf3794mMjKRq1apcuHABExMT4uLiJCGVApJeAJ4/f567d++ye/du9u/fz/r163n79i1TpkzhyJEjtG3bllu3brF9+3Y2b97MyZMntWMlUp+LFy9iY2NDQECA9lmztbXlhx9+YM+ePcTHxwMfk8t2dna8f/+ekJAQQ4YshPgP+fr6EhwcrM2omdRfb+CfPHnCiRMngI/f3UZGRvTp00cKRouvasuWLQQHBwN8tp0mfj9FRkYSGRnJvXv3UEqxc+dOChUqhLe3N/ny5UvxuIVIlNhmPzcQLCIigjNnzvDo0SMA/P39ARg6dChFixZNuSDFZ0lPKSG+U3/t3RQdHc3YsWNxdnbG2dmZadOmsWHDBhISEnB1daVjx460b98+2T7x8fFSnyiFJN6UrFq1irVr1xIfH4+dnR0AtWvXRqfTMXXqVGbMmEFkZCQNGzbUpgoHOVapWa5cuejVqxcdO3Zkw4YNNG/eHAsLC8qWLcv27duxtbWlVatWwMcLLWtrazJnzmzgqIUQ/9TDhw/x9/fn2bNn+Pj48OOPP/7t8KY8efLg6OjImDFjmD17Nk+fPuXw4cPymRdf1d27d3Fzc6NMmTKYmpry448/aompv7bTUqVKkTNnToYOHcrIkSN58uQJBw8eJEOGDAaKXqRFifc5SUtWhIaGkitXrs+eW+3t7enQoQNNmjShVKlSXLt2jaNHj0p9vm+E1JQS4juU9CJizZo1PHv2jAsXLtCgQQM6d+6sbTdt2jQ2btxI3bp16dOnD4UKFfpkf/F1HTx4kBMnTjBmzBgAVqxYgYeHB69fv2bLli1UqVJF2/bw4cNMnz6dN2/e4OXlRcWKFQ0VtvjCnj17xtSpU5k/fz4BAQG0aNGCly9f0qlTJ169ekWRIkWoWLEiW7du5cWLF1y4cEGSkEKkIocPH2bBggX88ccfLFiwgJ9++ulvv2vv3bvH1atXefjwIY0aNcLW1tYAEYu0ZuvWrXh4eFCwYEFcXFyoWrUqkPyaMDER8ODBA3bt2sWHDx9o0qQJRYoUMWToIo1KHDUwbNgwNm7ciIuLC+fPn8fGxuZvtz9+/DihoaG0bt1a2u03RK5ohfjOJO0h5ebmxpIlS8iXLx83btzg7t27VKtWjQIFCgAfZ/fS6/XMnz+f/Pnz4+LiAkgtgJQSHR3Nhg0bOHHiBKampri5udGtWzcyZ87MuHHjmD9/PqamppQvXx4ABwcHoqOj2bNnj7ZMpE6PHj3C3Nwca2trAHLkyMGoUaNISEigVatWbNiwgdatW7N27VoWL15MUFAQW7ZsIV++fOzbtw9jY2MpaC9EKpD4OXVwcODDhw+sWrWK/v374+vrS9myZT+bmLK1tZVElEgx8fHx6PV6mjdvTnR0NF5eXixcuJB06dJRoUKFZD2m9Ho9CQkJ5MuXT5utWQhDOXr0KG5ubpw/f54NGzbg6+v72YRUYvstWrSoDNX7RklPKSG+U7dv38bDwwMXFxfKlCmDj48Pq1atomDBgkyZMoX8+fNr2/r5+dGxY0e5wTWAJ0+eMGPGDE6ePEmzZs20mZX8/f3x9PSkePHiDB48mHLlyn2yrxSgT50CAgLo2bMnuXPnxtnZmRw5ctChQwfg4/TFw4cPZ/78+dpsMInHOTIyEgsLC+Dj9NzSU0qIb1/izdDu3btZtWoVDx484MSJE5QpUwZvb+9/O5RPiJSQ2P527NjB0aNH2bp1K3fu3OHnn39m1KhRVKpUKdl2QnxLnJ2dWb58OY6OjmzatEnaaColdzNCfIfWr19Po0aNuH37NoUKFcLIyIjevXvzyy+/cP/+fUaPHs2DBw+07bt06YKRkZFWUFmknNy5czNy5EgqVqzItm3bmDp1KgAdOnRgyJAh3Lx5k/nz53Py5MlP9pWEVOoTExPDoUOHiIuL48WLF2zevJmRI0dSqlQpWrVqxZEjR3B0dGTUqFG0b9+effv2acc5MSGllJKElBCphE6n48iRIzRt2pRq1arh5eXF4sWLyZQpE3379uX06dOfLSotRErR6XQcPnyY5s2bky9fPubNm8eCBQu4fPkynp6enD59WttO2qn4VsTGxgIfr4k6dOjA5s2bmTx5Mq9fv/7s9tJ2v21yVSvEdyg+Pp68efNy+fJloqKitAKpvXv3RqfTsXbtWvr06cOKFSvIkSOHtp/0lDKMnDlzMnr0aKZMmcK2bdsAcHd3p0OHDuh0OkaOHEmhQoWoXLmygSMV/ytTU1PGjh2LiYkJf/zxByVLlmTTpk1s3ryZPXv20KtXL6Kjo7G1tUWn09GwYUPOnDmTbLimPAUUInVI7Fly8OBB6tWrpw2RL1++PAULFmTixIn06dOH5cuXU6ZMGemJIlJcYpvbsmUL9erVo1+/ftq67Nmz4+rqyrRp0xgzZgzly5eX9ikM7q/nSR8fHwB+/PFHBg0ahFKKQYMGkSlTJgDu3LlDkSJFpO1+4yQpJUQq97khXB07diR9+vRMnDiRdu3asWbNGm2a3l69evH+/Xvu3LlDtmzZDBGy+Iy/JqZ0Op3WW8ba2pratWsbOkTxheTIkQM3NzemTp3K/v37yZMnDy4uLvTq1YubN28SGhrKb7/9RmxsLC9evMDe3t7QIQsh/guJN0FmZmaEhIQQHh6uPSSqV68eN27cYPDgwbRo0YL169drw6SESGkWFhZ8+PCB2NhYjIyM0Ov1tGzZkgcPHjB69Gji4uIYM2YMP/74o6FDFWlY0kR/QEAAFhYW/Pzzz9SoUYMBAwYAMGjQIHQ6HR07dmTdunV4e3tz/fp1LC0tJTH1DZOklBCpWNKE1I4dO4iNjSV9+vTUr1+f5s2bk5CQwPz58+natSurVq0ib968ALi6umondqlL9O1ITExNnTqVHTt28PbtW6ZNm0a9evUApLD1dyR37tzasV61ahVv3rzB3d2dYsWKUaxYMWrWrJmsuKzUkBIi9SpVqhRKKfbt20ezZs0wNzfXllerVo2SJUvKQyJhEIk36T/88IM2VK9q1arad0+uXLkoUKAACQkJ2jWkEIai0+nYv38/jRo1ok2bNmzdupWTJ0/i6OjIkCFDGDBgAEZGRgwfPpzNmzfz4MED9uzZQ8aMGQ0duvg/SKFzIVKppN1Xhw8fztKlS8mZMyd//PEHI0aMYNKkSQBs3ryZBQsWoNPp8PHx0Wbe++triG9HaGgobm5umJmZsWTJEjlG37HQ0FCmTJnCmTNnaNGiBSNGjACSFzKXxLEQqUPid+rFixd5+vSpNu24paUl3bt35+DBg0ydOpW6deuSI0cO3N3dCQ0NxdPTEysrK0OHL9KAxDZ65coVwsLCeP78Oe3atUOn0+Hk5MTOnTsJCAigXLlyZMiQAXd3d8zNzenfv7/Wy08IQ3n06BFz587F1taWvn37Eh4ejpubG9evX6dZs2YMHz4cvV5PcHAwb968wc7OLtl9j/h2SVJKiFTu4cOHODo6snz5ciwtLTl69CjOzs7069ePOXPmALBlyxbGjh1LzZo1mT9/voEjFv/Eq1evsLKyQq/XS/LwOxcaGsrUqVM5d+4cDg4OTJ482dAhCSH+SwEBAbi4uFCyZElu375Nrly5GDRoEJ06daJTp05cuHCBt2/fki9fPi5evMipU6coVaqUocMWaUDitURAQABDhw7F2tqayMhIYmJiWL58OcWLF2fYsGFs2LABOzs7TExMuHjxIqdPn5Y2Kgzu/PnzjB49mqdPnzJ37lxq1aoFwMuXL3F3d+fKlSu0aNGCoUOHyoO8VEjGAgiRyiRNUEybNo2rV69SsWJF7OzsMDIyolChQpiZmdGlSxd0Oh2enp44OjqSOXNmqlWrZuDoxT+VJUsWQHrJpAU5c+bE3d0dNzc3wsLCJAkpRCp19uxZXFxcmDZtGt26deP27dsUK1aMp0+fArBmzRqCgoK4evUqsbGxrFy5kiJFihg4apFW6HQ6Tp48ibOzM7Nnz6Zbt26EhIRQuHBhrl27Rs2aNVm1ahVNmjTh/v37REZG8ttvv1G0aFFDhy4EWbJkISEhgdu3b3Ps2DEtKWVtba0V41+xYgVmZmZafSmRekhPKSFSkaQ3q2fOnOHMmTMMHjyYihUrcuzYsWTbbtiwgW7dutG+fXt8fX215VKXSIhvk/SOEyJ1W716NX5+fuzdu5dbt27RuHFjateuzbJly4CPT/Stra0NHKVIK65evYqdnV2yZb/99hv79+9nzZo13Llzh/r161O/fn2WLFlioCiF+LzPXQc9evSIgQMH8uTJE1xcXOjSpYu27uXLl0yZMoWBAwfKkL1USB6/C5FKJD05u7u706tXL2rXrs2cOXM4efIks2fPTrZ927ZtWbRoEffu3SMhIUFbLgkpIb5NWbJkQa/Xk5CQIAkpIb5RSb9PE3+OiIgA4I8//iBjxowopahbty5169bVbva3bt3K8uXLiYyMTPmgRZqzadMmateuzdu3b5Mtv3DhAu/evePt27fUqVOH+vXr4+3tDXxMWI0fP94A0QqRXOI9z4kTJ/Dy8mLMmDEEBwdjY2PDggULyJEjB76+vvj5+Wn7WFtbM2vWLElIpVKSlBIilUjaQ+rChQt4e3tTrFgxevbsyYwZMxg+fDhz585Ntk/Xrl0JCgrSbnSFEN8+Ga4pxLdLr9fz559/smvXLvR6PRs3btRu5Bs2bMi+fftInz49rVq1YsmSJdrn+cCBA5w+fVq+i0WKaN26NefPnydjxoyEhYVpy1u2bEloaCj58uWjUaNGyXpIXbp0iVu3bvHu3TtDhCyEJrH2WfPmzdm1axd37tyhZs2aTJ06ldy5c7NgwQIyZcrEypUr8fHx0faT66fUS46cEKmIn58fU6ZMQafTUaZMGZRSpEuXDhcXF2bOnMmwYcPw8vL67L5yohZCCCH+N+/evWPmzJn06dOHUaNG0a5dO0qXLg1A8eLF6du3L5kyZdLq8Dx8+BB3d3fWrl3LhAkTyJAhgyHDF2lAYmUWGxsb7ty5Q548edi0aRMAhQsXJm/evGTNmpUff/wRgLCwMMaMGcOaNWsYO3astFFhcNevX2fw4MFMmTKFPXv2aGVIoqKiiI+PJ2/evMyfP5+4uDi2bt36SY9AkfpITSkhUpEpU6awaNEi4uPjCQ4OTlYg9cOHD3h7ezN06FDWrVtH27ZtDRipEEII8f1YtWoVv/zyCwB37tyhZ8+eBAcHM3jwYDw9PbXtrl+/zooVK1iwYAG5cuUiY8aMREZGsn79esqWLWuo8EUakjj06dSpU+TOnZtp06axZs0aVq5ciaOjIzdu3ODXX3/l/PnzxMTEkDdvXkJDQwkMDJQ2Kr4JR48eZfz48Rw4cIC7d+9Sq1YtmjRpovXse/ToETY2Njx8+BCAvHnzGjJc8QVIUkqIb9Tfzbq2dOlSpk+fjoODA6NGjcLW1lZbFxUVxdatW2ndujXGxjK5phBCCPG/unDhAh06dGD37t0ULFiQ9+/f07x5c969e0dkZCSTJk2iefPm2vZRUVHcvXuXU6dOkS9fPkqWLEmePHkM+A5EWrN37146derEqlWraNy4MS4uLixfvpy1a9fSokULQkNDefToEUFBQdjZ2VGyZEm5sRcGk5hIPXDgADlz5uTPP/9kyJAhBAQE0KxZM632mV6v58CBA3h7e7Nw4UJy5sxp6NDFFyJJKSG+QUkTUvv37ycyMpI3b95oT2kXLlyIj48PNWrUYPDgwRQsWPCT14iLi5PElBBCCPE/ioqKIjo6GisrKy5duoS9vT2xsbFcv36dOXPmcObMGaZOnZosMRUbG4uJiYkBoxZp1bNnzxg/fjw//PADrq6u2vLExJS/vz+Ojo6GC1CIzzhy5AgNGzbEx8eHqlWr0qNHD06ePEmLFi3w8/PT7o1GjRrFuXPn8Pf3l9lMvyNyxyrENygxITVixAg2btxIjhw5CA0NxcvLi5UrV+Li4kJcXByrVq1Cr9fTr1+/ZEP5AElICSGEEP8jpRTm5uaYm5sTGhpKgwYNqFy5Mlu2bMHe3p6+ffsCMGbMGJRSODo6MmnSJN69e8fEiRNJly6dgd+B+N4lnZ35zJkzDBo0iMjISC3xlHgzv3DhQuDjJDg+Pj60adPGUCELkcyDBw/YvXs348ePp2PHjgDUrl2b+/fvkyFDBh48eMDr169Zu3YtS5YsITg4WBJS3xm5axXiG7V48WJ+++039uzZQ9myZfH396dTp048efKEkiVLMmjQIJRSzJo1i/z58zN48GBDhyyEEEJ8lxISErQpx8eOHUu7du1Yv369Vizax8eHrl27UqlSJX7//XdOnDghCSmRok6dOkXWrFkxMTHh8uXLnDlzhvr162szMCcmpt69e8fgwYNp1KiRFDUXBnft2jX69OnDkydPGDt2rLbc3d2duLg49uzZQ6FChbCzs0MpxeHDh7GzszNgxOJrkOF7Qnyjhg8fjqWlJWPHjmX9+vX07t0bDw8P+vTpQ0REBJaWlgCsW7eONm3aYGRkZOCIhRBCiO9HYg+U4OBgbt++TePGjcmaNSsBAQG4ublRpUoV1q9fD8CtW7c4deoUFy9epHfv3trse0KkhEOHDlG3bl2Cg4Oxs7Ojbdu2PH36lIkTJ/Lzzz9jZGSUrDREaGio1OMR34SYmBj69u3Lhg0baNasGcuXL0+W0H/16hWXLl0ib968ZMqUiWzZshkwWvG1SFJKiG9A0q7X8LEeVO3atalTpw716tWjQYMG2hTUCQkJjB07ljx58mjDBgDi4+MlMSWEEEJ8QQEBATg5OTFs2DBat25NyZIliYyMZNu2bZ8kpoQwhD///JOzZ8/y8OFDrYZUREQEzZo1IyoqCnd3d5o0aYKRkdEn15tCpLTPtcHY2FiGDh3KwYMHcXJywsXFBQsLCwNFKAzh06m9hBApLvHk7OnpyY4dOzA2NuaXX34hMDAQBwcH5s6dS58+fQB4//49Fy5cIDQ0NNlrSEJKCCGE+HLOnz9Pv379mDdvHr/++islS5YEwMLCgmbNmjFz5kzOnDlD48aNDRypSKv++OMPypQpQ69evbTeJTExMVhaWrJt2zbMzc2ZMWMGgYGBxMfHS0JKGFRiQurIkSOMHj2a3r174+vri4mJCZ6entSoUYONGzeyaNEioqKigI9Dp8X3T5JSQnwjXr9+zaFDh9i2bRsA5cqVI3PmzNjZ2ZErVy4A7t69S/v27Xn+/Dm//vqrIcMVQgghvmuXL18mX758tGzZUhv2lHiDZGFhQatWrZgwYQIPHjzg8ePHhgxVpFHm5uYMGzYMvV7PrVu3ADA1NU2WmIqMjGTp0qXaTb4QhqLT6QgMDMTR0ZG7d+9iaWmJs7Mzzs7O6HQ6vLy8KFeuHIGBgcyaNYuoqCjt3Cu+b3KUhfhGWFlZ0aBBA7Zv387Dhw8pV64cY8eOJVeuXDg7O2NjY0ObNm14/fo1x44dw9jYmPj4eEOHLYQQQnwX/lrRIiQkhKioKKysrICPw+QTb5DOnj3Lixcv6NChAydOnCBPnjwpHa5Ig/7aRnPmzEmvXr1wdXVl0aJFTJ8+HUiemAoODsbHx0eKmosU5+XlxZEjR7Tf//jjD0aMGMHkyZNZv349EyZMIH369GTKlAkAExMTvLy8yJ8/P7///rskUtMQqSklhAEkLTb5VzVq1CBv3rysXr0anU7Hw4cPCQ0N5erVq9ja2lK1alWMjIyIi4vD2Fgm0BRCCCG+lN27dwPQqFEjjhw5Qt26dVm2bBldu3bVtomOjmbEiBFUrFiRTp06GSpUkcYkDn06ffo0N2/e5MmTJ7Rr1468efMSGxuLp6cnM2bMYPTo0bi5uQEfh/KZmpoaOHKRFv36668sWbKEU6dOUbBgQQBu3LhB9+7dOXHiBH/88QfVq1enSZMmLFmyBIBLly5hb29PTEwML1++1EaKiO+f9JQSwgASE1Le3t6cO3eO58+fAx+TVZ06deLu3bvcu3cPgDx58lCxYkW6detGjRo1MDIyIj4+XhJSQgghxBcUExPDvHnzOHnyJAB2dnb07t2b8ePH4+vrC3yctWzKlCmsXbuWH3/80ZDhijQkMSG1adMm6tWrx8KFC1mwYAE//fSTNsxp8ODBjBgxghkzZjB+/HgASUiJFKeU4vXr1wQFBTF8+HAKFizIzZs3efbsGe/fv+fJkyfs37+fOnXq0KRJExYtWgR87H3q5ubG9evXMTU1lYRUGiNJKSEM5N27d/j6+tKhQwc6duzI1q1b0ev1dOnShfDwcO0k/bkeVVLUXAghhPiyTE1NyZQpExcuXAAgS5YsuLi40L59e/r160fhwoWpW7cuK1asYO/evRQuXNjAEYvvVWLtsg8fPgAfa/Fcv36dQYMGMW/ePA4dOsSjR4/o3r07/v7+LF++HAsLC3r27Enfvn1ZsWIFL1++/GS4nxBf24MHD7CyssLIyIi7d+/i5eXFTz/9RFhYGLa2tpQrV44WLVpQoUIFlixZot3TbN68maioKLJmzWrgdyAMQYbvCZFC/m7I3tatWzlw4ABLlizB0dGRZs2aYWZmxqRJk1i1ahX29vYGiFYIIYT4/iSdjjxxGHx4eDhmZmaYm5vj5eXFwYMH2bp1K/Hx8RgZGfH+/Xtu377NqVOnyJYtGxUrViRfvnwGfifie/f48WPq1avH3r17yZs3L4cPH6ZHjx7s27cPW1tbrR2PGDGCVatWcfHiRXLkyMGLFy/Q6XRYW1sb+B2ItGb48OHcvHmT7du3c+7cORwcHIiOjmbcuHG4u7sDsHLlSmbMmEHx4sUZMGAAer2eLVu24Ovry5EjRyhdurSB34UwBBn/I0QKSJqQ2r9/P+/fv+fDhw+0b9+e5s2b07x5c1q3bs3GjRsZM2YM4eHhREREcO3aNUlKCSGEEF9A4ndxaGgoOXPmxNjYmPPnz1OtWjV++OEHihUrxtOnT7l9+zZBQUEULVqUXLlykT59esqWLUvZsmUN/RZEGqLT6YiJicHNzY1Vq1aRkJDA27dvSZcuHTqdjsjISCwsLJg+fTrLly9n69at9OrVS3qaCIPYtGkTCxYs4Pz58wCkS5eOd+/eYW5uTlhYGHfu3KFIkSJ07dqVuLg4tmzZQr169ShRogTm5ub8/vvvkpBKw6SnlBApaNiwYaxduxZLS0uePn2KnZ0dU6ZMoVq1apiYmBAZGcm7d+9wd3fnxYsXbNq0SWpHCSGEEP+jxITUxYsXcXR0xMfHh7p16xIWFkZwcDBxcXEcOHCAuLg4Vq5cScaMGcmTJw/GxsZky5aNzp0707VrV613ihBfWtJefEoplFLMnTtX61lSr149SpcuTc6cOTlw4IC234sXL6hduzaTJ0+mWbNmhgpfpHErV67Ex8eHoKAg9u7dy7Nnz7CzsyMiIoIWLVrQqVMnXF1dKVKkCACRkZHcv38fa2trTE1NtVlORdokSSkhUsiKFSsYMWIE+/btI1euXCilaN68OQkJCSxZsoRy5colm1Ev8eJEZtkTQggh/nuJCalLly5RuXJlXF1dmTp16me3DQ8Pp2XLlnTu3JmSJUsSFBTE8+fPcXJyolSpUikcuUgrEtvoq1evyJIli7b8zZs3ODg4kCVLFg4cOMDx48dp27YtRYoUYeHChcTGxhIYGMiSJUs4efIkBQoUMNybEGnawYMHadeuHQ4ODgQEBBAQEECLFi2Aj6VKunbtSseOHRk6dCi2trYGjlZ8a6TQuRAp5M6dO1SpUoUyZcpgbW1Nzpw5+f3334mMjGTixIkAGBsba0UpdTodSilJSAkhhBD/paQJqSpVqnySkLpx40ay7c3Nzbl//z5v3ryhcuXKjBw5ktmzZ0tCSnxVer2ekJAQihQpQrNmzQgNDeX9+/dkypQJHx8fjh07xuzZs/npp5/YuHEjr1+/xsHBgRYtWrB27Vp27dolCSmR4lavXs27d+8AqFOnDo6OjmzZsoVGjRpRt25dbbvmzZuzatUq1q5dy9y5c7l7966hQhbfKElKCfEVBAcH4+npiaenJ6GhoQA8e/aMFy9eAB+TT1FRUZiZmTFr1ixOnjzJH3/8kazrNiDDBIQQQoj/gV6v5+7du1SuXJmhQ4cydepU7eHPlClTGDp0KGFhYcDHBJaZmRnVqlXj4cOHhgxbpEHx8fEkJCSwY8cOunbtyuLFi7l8+TLlypVjwIAB+Pn5cfr0aapUqcKFCxcIDAwkMDCQ4OBgypUrZ+jwRRoTEhKCm5sbL1++BD7OFHn//n3atWvH0aNHmThxYrLzaLNmzfDz82PhwoUsXryYuLg4Q4UuvkGSlBLiC/Pz86Nnz57cv3+f9OnTkzNnTgCcnZ25ceMGs2fPBj4+jQWIjY0la9aspE+fXpJQQgghxBeUkJDA8uXLsbS01GYj0+l0TJs2jZkzZzJo0CCyZ88OoE1IYmVlxfHjx0lISECqXIivKSEhAfg4E2SRIkWYMGECgwcPplKlSty9excnJyd27NhB69atiYuLY+fOndrNfNWqVSlTpox2nSlESklISKBQoULcvn2b/Pnzc+nSJXQ6Hdu2bWP16tV4eXmxZMkSvLy8ePTokbbfzz//zM6dO3F2dpaRICIZaQ1CfEF+fn707t2bJUuW0KJFCzJkyADAnDlzsLKyYtiwYSxbtowPHz7Qv39/wsPDWbZsGTY2NmTLls3A0QshhBDfF71eT//+/YmMjGTdunWYmZnx9u1bZs+ezfr162nQoMEn+7Rp00abqlyIryGxZ3xkZCQZMmTQbtDt7e3ZvXs3AwcOxMHBAV9fX7p06cLo0aOxsbHBy8uLli1byszMwmDmzJlDvnz5aNasGRkyZODVq1f89NNPNGjQgBUrVmBubk7Xrl0BGDBgAACDBw8mT548ADRq1MhgsYtvlxQ6F+ILuXHjBu3ataN///706tVLW96mTRsCAgJwdHTEwcGBhIQEJk2ahE6nw8rKisyZM3Ps2DFMTEy02hdCCCGE+HJCQ0OZMmUK+/fv5969e+zdu5fatWsnm0xk7NixhIeHM3/+fANHK9KC0NBQKlWqRJcuXejduzf58uUDYPLkycybN4+LFy+SJ08ejh07xsqVK3ny5Am7du2iUaNGbNu2Db1eLz3sRYqrV68ep0+fZu3atdSvXx8TExMOHz5MmzZtaNiwIYsWLSJjxowArFq1CldXV9q1a8eYMWPInTu3gaMX3yq5+xXiC3n48CERERHUqFFD647t4uLCxYsX2b59OxEREfz+++/kzZuX27dvs2LFCpYtW8aJEycwMTEhLi5OElJCCCHEV5AzZ07GjBlDgwYNKFGiBBcuXADQElLjxo1j5syZODk5GTBKkZaYmZnRs2dPFi1aRPfu3Zk7dy4AY8aMoUmTJowePZo3b95QtWpVJk+ejJubG02aNGHatGkYGRlJQkqkqMR+LPv376dRo0Z06dKFPXv2EBUVhYODA5s3b2b79u307duXN2/eAPDLL7/g4eHBjh07ZLie+Lekp5QQX8iUKVOYM2eOVswc4OnTp8THx2NjY8P169fp1asXsbGx7Nq1S6ttAR+LWxoZGRkibCGEECLNSOwxdebMGVq0aMGIESOYMmUKkydP5ujRo5QvX97QIYo05vr164wbN46LFy9iY2OjFTjfuXMnnTt3TjaL2V8nxBEiJcXExGBqagp87DH14MEDZs2aRf369UmXLh3BwcE0bdqUpk2b4u3trfWYevv2rfazEJ8j3TKE+EIKFy5MVFQU+/fv15blypULGxsbEhISKFGiBM2aNSNr1qyYmZkl21cSUkIIIcTXlzNnTkaPHk3FihXZuXMnP/74oySkhEGVKFGCJUuWMGfOHN68eUPjxo05f/48V69eZePGjcm2lYSUMBSlFKampqxbt44mTZqQPn16/vzzT3r16sW+ffuIiYmhevXq7Nixg71799K5c2fevn0LgKWlpYGjF986SUoJ8YVUrFgRY2NjlixZwoMHD5Kt0+v1REREEBwcTNGiRUmfPr2BohRCCCHStsTEVOHChXn16hUnTpyQhJQwqCxZstC0aVPOnz9Ps2bNuHjxIqGhoSxbtgwfHx9DhycEOp2O06dP07NnT1q3bs3s2bO5fv06FSpUwMnJib179xIdHU316tXZsGEDly5d4t27d9q+Qvw7MnxPiC/I39+fbt260bp1a4YNG0aZMmUAuH//Ps7OzoSFhXH27FmMjY2lC7YQQghhQM+fPychIYEcOXIYOhQhkl0XBgUFsWfPHhYtWsTp06cpVqyYgaMT4uN9zqRJkzh27BiZM2fWlicmVJcuXUqdOnUwNzcnKioKc3NzA0YrUhNJSgnxBcXHx7NixQr69etHjhw5sLOzIy4ujoiICACCg4MxMTGRGlJCCCGEECKZvz6wlFo84luybNkyRo4cSVhYGEZGRlri6dKlS1SoUIEMGTLg7+9Pw4YNDR2qSGVk+J4QX5CRkRE9e/bk9OnTNG/enPj4ePLnz88vv/zCsWPHtFn2JCElhBBCCCGS+msPeklICUP5XL+Vtm3bYmlpSffu3QG0nlDGxsZ06NCBevXqUbBgwRSNU3wfpKeUEClIekgJIYQQQgghvlWJPfZOnTrFsWPHsLW1pUyZMuTPn59Vq1Yxbdo0KlSogLe3NxERESxatEgrzG9sbGzo8EUqJK1GiK/kczWjJCElhBBCCCGE+FbpdDq2bdtGu3btsLOz4/r16/z8888MGDCAX375Bb1ez9ixY8mTJw/ZsmXj9evX7Nu3TxJS4r8mPaWEEEIIIYQQQog0LPGB+qNHj3B3d6d69eo4OzuzZ88eZs2ahampKaNGjaJ69epER0ezefNmMmbMSIkSJShQoIChwxepmCSlhBBCCCGEEEKINCQhIQG9PnmJ6VOnTrF48WIeP37M0qVLtWTToUOHmDZtGiYmJgwePJj69esbIGLxvZJC50IIIYQQQgghRBqi1+t58OABXl5e2rLbt2/z+++/c+rUKf744w9tee3atXF3d0cpxaRJkzh8+LAhQhbfKRn4KYQQQgghhBBCpCHx8fEsWrSIzZs3Ex0dzfDhw+nSpQsZM2bk119/ZdGiRWTMmJHy5csD4ODgQGxsLIsXL6Zw4cIGjl58T2T4nhBCCCGEEEIIkcY8fvyYmTNncvLkSZo1a4a7uzsA69atY/bs2RQvXpzBgwdTrlw5bZ/IyEgsLCwMFbL4DklSSgghhBBCCCGESINCQ0OZMmUKZ86cSZaY8vf3x9PTk1KlStGnTx8qVapk4EjF90qG7wkhhBBCCCGEEGlQzpw5GT16NFOmTGHbtm0AuLu706FDB/R6PaNHjyZdunTY29uTLl06A0crvkfSU0oIIYQQQgghhEjD/q7HVEBAAOXKlaNgwYIGjlB8r2T2PSFEquXk5IROp0On02FsbEy+fPno27cv4eHhhg5NCCGEEEKIVCOxx1TFihXZtWsXY8aMAaBVq1aSkBJflSSlhBCpWsOGDXn69Cl//vknPj4+bN++nX79+hk6LCGEEEIIIVKVxMRUkSJFOHXqFC9fvjR0SCINkKSUECJVS5cuHTlz5sTGxob69evTrl079u3bp61fsWIFxYsXx8zMjGLFirFo0SJtXUxMDP379ydXrlyYmZlRoEABpk2bpq3X6XR4e3vTqFEjzM3NKViwIBs3bkz271+5coXatWtjbm6OtbU1vXr14t27d9p6JycnHB0dmTVrFrly5cLa2hoXFxdiY2O1bRYtWkSRIkUwMzMjR44ctG7dWlunlGLGjBkUKlQIc3Nz7O3t2bRp0xf9GwohhBBCCAEfE1MeHh6sXr0aa2trQ4cj0gApdC6E+G6EhISwZ88eTExMAFi2bBnjxo1jwYIFlC1blgsXLuDs7Ez69Onp2rUrXl5ebNu2jQ0bNpAvXz4ePnzIw4cPk73mr7/+ioeHB/PmzcPPz48OHTpgZ2dH8eLFiYyMpGHDhlSuXJkzZ84QFhZGz5496d+/P7/99pv2GocPHyZXrlwcPnyYu3fv0q5dO8qUKYOzszNnz55l4MCB+Pn58dNPP/Hq1SuCg4O1fceMGUNgYCDe3t4UKVKEI0eO0LlzZ7Jly0bNmjVT5O8qhBBCCCHSjhw5chg6BJGGSKFzIUSq5eTkxOrVqzEzMyM+Pp4PHz4A4OnpiaurK/ny5WP69Ol06NBB22fy5Mns2rWL48ePM3DgQK5du8aBAwfQ6XSfvL5Op6NPnz54e3tryypXrky5cuVYtGgRy5YtY8SIETx8+JD06dMDsGvXLn7++WeePHlCjhw5cHJyIigoiHv37mFkZARA27Zt0ev1rFu3jsDAQLp168ajR4+wtLRM9u+/f/+erFmzcujQIapUqaIt79mzJ5GRkaxdu/bL/TGFEEIIIYQQIoVJTykhRKrm4OCAt7c3kZGR+Pj4cPv2bQYMGMDz5895+PAhPXr0wNnZWds+Li6OTJkyAR+TWvXq1aNo0aI0bNiQpk2bUr9+/WSvnzQZlPj7xYsXAbhx4wb29vZaQgqgatWqJCQkcOvWLe0pU8mSJbWEFECuXLm4cuUKAPXq1SN//vwUKlSIhg0b0rBhQ1q0aIGFhQXXr1/nw4cP1KtXL1kMMTExlC1b9n/8ywkhhBBCCCGEYUlSSgiRqqVPn57ChQsD4OXlhYODAxMmTKB///7AxyF8P/74Y7J9EhNE5cqV448//mD37t0cOHCAtm3bUrdu3f+zZlNiryql1Gd7WCXdBtCGEyZdl5CQAIClpSXnz58nKCiIffv2MXbsWMaPH8+ZM2e0bXbu3EmePHmSvUa6dOn+bYxCCCGEEEII8a2TQudCiO/KuHHjmDVrFvHx8eTJk4eQkBAKFy6c7L+k09pmzJiRdu3asWzZMtavX09AQACvXr3S1p88eTLZ6588eZJixYoBUKJECS5evMj79++19ceOHUOv1/PDDz/845iNjY2pW7cuM2bM4PLly/z5558cOnSIEiVKkC5dOh48ePDJe8ibN+9/+ycSQgghhBBCiG+C9JQSQnxXatWqRcmSJZk6dSrjx49n4MCBZMyYkUaNGhEdHc3Zs2cJDw9nyJAhzJkzh1y5clGmTBn0ej0bN24kZ86cWFlZaa+3ceNGKlSoQLVq1VizZg2nT5/G19cXgE6dOjFu3Di6du3K+PHjef78OQMGDKBLly7/uEDkjh07CAkJoUaNGmTOnJldu3aRkJBA0aJFsbS0ZNiwYbi6upKQkEC1atV4+/Ytx48fJ0OGDHTt2vVr/AmFEEIIIYQQIkVIUkoI8d0ZMmQI3bp14+7du/j4+DBz5kzc3NxInz49pUqVYvDgwQBkyJCB6dOnc+fOHYyMjKhYsSK7du1Cr/9XJ9IJEyawbt06+vXrR86cOVmzZg0lSpQAwMLCgr179zJo0CAqVqyIhYUFrVq1wtPT8x/HamVlRWBgIOPHj+fDhw8UKVIEf39/SpYsCcCkSZPInj0706ZNIyQkBCsrK8qVK4e7u/uX+4MJIYQQQgghhAHI7HtCCPE3dDodmzdvxtHR0dChCCGEEEIIIcR3R2pKCSGEEEIIIYQQQogUJ0kpIYQQQgghhEgjnJyc0Ol06HQ6jI2NyZcvH3379iU8PNzQoQkh0iCpKSWEEH9DRjcLIYQQ4nvUsGFDVqxYQVxcHNevX6d79+68fv0af39/Q4cmhEhjpKeUEEIIIYQQQqQh6dKlI2fOnNjY2FC/fn3atWvHvn37tPUrVqygePHimJmZUaxYMRYtWqSti4mJoX///uTKlQszMzMKFCjAtGnTtPU6nQ5vb28aNWqEubk5BQsWZOPGjcn+/StXrlC7dm3Mzc2xtramV69evHv3Tlvv5OSEo6Mjs2bNIleuXFhbW+Pi4kJsbKy2zaJFiyhSpAhmZmbkyJGD1q1ba+uUUsyYMYNChQphbm6Ovb09mzZt+qJ/QyHElyE9pYQQQgghhBAijQoJCWHPnj2YmJgAsGzZMsaNG8eCBQsoW7YsFy5cwNnZmfTp09O1a1e8vLzYtm0bGzZsIF++fDx8+JCHDx8me81ff/0VDw8P5s2bh5+fHx06dMDOzo7ixYsTGRlJw4YNqVy5MmfOnCEsLIyePXvSv39/fvvtN+01Dh8+TK5cuTh8+DB3796lXbt2lClTBmdnZ86ePcvAgQPx8/Pjp59+4tWrVwQHB2v7jhkzhsDAQLy9vSlSpAhHjhyhc+fOZMuWjZo1a6bI31UI8c/I7HtCCCGEEEIIkUY4OTmxevVqzMzMiI+P58OHDwB4enri6upKvnz5mD59Oh06dND2mTx5Mrt27eL48eMMHDiQa9euceDAAXQ63Sevr9Pp6NOnD97e3tqyypUrU65cORYtWsSyZcsYMWIEDx8+JH369ADs2rWLn3/+mSdPnpAjRw6cnJwICgri3r17GBkZAdC2bVv0ej3r1q0jMDCQbt268ejRIywtLZP9++/fvydr1qwcOnSIKlWqaMt79uxJZGQka9eu/XJ/TCHE/0x6SgkhhBBCCCFEGuLg4PD/2rubkCi3OI7j38ZAGF+aNuYwRBiTygxiSdIiiQInJmhRCLWIkIg24VsSLVrkiGD0QkGbFtlSLKTalGFEtEmkIiShSGMSXQ4kBIVJTS2iucq93Lpdm8vF7weexXDOc/g/Z/njnP9w+fJlPnz4QF9fHxMTE7S2tpLJZJiZmeHw4cMcOXIkN//Tp0+sWrUK+BZqJRIJqqqqSCaT7N69m507dy5af2EY9P332NgYAC9fvqS2tjYXSAFs3bqVbDbLq1evWLNmDQDxeDwXSAGEw2HGx8cBSCQSrFu3jvXr15NMJkkmk+zdu5dgMMiLFy+Ym5sjkUgsqmF+fp5Nmzb9y52TtNQMpSRJkiRpGSkqKiIajQJw6dIlduzYQXd3Ny0tLcC3K3xbtmxZ9M73gKiuro43b95w9+5d7t+/z759+2hsbPxhz6bvp6q+fPnylyesFs4BctcJF45ls1kASkpKePbsGQ8fPuTevXucOnWKVCrFkydPcnPu3LlDJBJZtEZhYeHf1igp/2x0LkmSJEnLWFdXF+fPn+fz589EIhHS6TTRaHTRU1FRkZtfWlrK/v37uXLlCtevX+fGjRu8ffs2Nz46Orpo/dHRUaqrqwGIxWKMjY3x/v373PijR48IBAJUVlb+dM0rV66ksbGRs2fP8vz5c6ampnjw4AGxWIzCwkKmp6f/9A1r16791S2S9Jt4UkqSJEmSlrHt27cTj8fp7e0llUrR1tZGaWkpu3bt4uPHjzx9+pTZ2Vk6Ozu5ePEi4XCYjRs3EggEGBwcpLy8nFAolFtvcHCQzZs309DQQH9/P48fP+bq1asAHDhwgK6uLpqbm0mlUmQyGVpbWzl48GDu6t6P3L59m3Q6zbZt21i9ejVDQ0Nks1mqqqooKSnh+PHjHDt2jGw2S0NDA+/evWNkZITi4mKam5t/xxZK+kWGUpIkSZK0zHV2dnLo0CFev35NX18f586d48SJExQVFVFTU0NHRwcAxcXFnDlzhsnJSQoKCqivr2doaIhA4I9LON3d3Vy7do2jR49SXl5Of38/sVgMgGAwyPDwMO3t7dTX1xMMBmlqauLChQs/XWsoFOLmzZukUinm5ubYsGEDAwMDxONxAHp6eigrK+P06dOk02lCoRB1dXWcPHly6TZM0pLw3/ckSZIkSUtixYoV3Lp1iz179vzXpUj6H7CnlCRJkiRJkvLOUEqSJEmSJEl5Z08pSZIkSdKSsDuMpH/Ck1KSJEmSJEnKO0MpSZIkSZIk5Z2hlCRJkiRJkvLOUEqSJEmSJEl5ZyglSZIkSZKkvDOUkiRJkiRJUt4ZSkmSJEmSJCnvDKUkSZIkSZKUd4ZSkiRJkiRJyruv6im6QUY8OxUAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1200x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import random\n",
-    "# Assuming df2018 is your DataFrame\n",
-    "df = df2018[['AIDangerous','AIInteresting','AIResponsible','AIFuture']]\n",
-    "\n",
-    "# Strip leading and trailing whitespace from all columns\n",
-    "df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
-    "\n",
-    "# Mapping for shorter versions\n",
-    "short_mapping = {\n",
-    "    'Algorithms making important decisions': 'Algorithms',\n",
-    "    'Artificial intelligence surpassing human intelligence (\"the singularity\")': 'AI Singularity',\n",
-    "    'Evolving definitions of \"fairness\" in algorithmic versus human decisions': 'Fairness Evolution',\n",
-    "    \"Increasing automation of jobs\": 'Automation',\n",
-    "    \"The developers or the people creating the AI\": 'Developers',\n",
-    "    \"A governmental or other regulatory body\": 'Government/Regulatory',\n",
-    "    \"Prominent industry leaders\": 'Industry Leaders',\n",
-    "    \"Nobody\": 'No Responsibility',\n",
-    "    \"I'm excited about the possibilities more than worried about the dangers.\": 'Excited about AI Future',\n",
-    "    \"I'm worried about the dangers more than I'm excited about the possibilities.\": 'Worried about AI Future',\n",
-    "    \"I don't care about it, or I haven't thought about it.\": 'Indifferent about AI Future'\n",
-    "}\n",
-    "\n",
-    "# Replace responses with shorter versions\n",
-    "df.replace(short_mapping, inplace=True)\n",
-    "\n",
-    "# Function to create value count plots for each column\n",
-    "def plot_value_counts(column_name, ax):\n",
-    "    colors = ['skyblue','yellow']\n",
-    "    df[column_name].value_counts().plot(kind='bar', color=random.choice(colors), ax=ax)\n",
-    "    ax.set_title(f'Value Counts for {column_name}')\n",
-    "    ax.set_xlabel('Response')\n",
-    "    ax.set_ylabel('Count')\n",
-    "    ax.tick_params(axis='x', rotation=45)\n",
-    "\n",
-    "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
-    "\n",
-    "# Plot value counts for each column\n",
-    "for i, column in enumerate(df.columns):\n",
-    "    plot_value_counts(column, axes[i//2, i%2])\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analysis\n",
-    "\n",
-    "### AIDangerous:\n",
-    "- The most commonly cited concern is \"Algorithms making important decisions,\" followed closely by \"Artificial intelligence surpassing human intelligence\" and \"Evolving definitions of fairness.\"\n",
-    "- \"Increasing automation of jobs\" is also a significant concern but appears to be less frequently mentioned compared to the other categories.\n",
-    "\n",
-    "### AIInteresting:\n",
-    "- The most interesting aspect for respondents seems to be \"Increasing automation of jobs,\" followed by \"Algorithms making important decisions\" and \"Artificial intelligence surpassing human intelligence.\"\n",
-    "- \"Evolving definitions of fairness\" appears to be less intriguing to respondents compared to other categories.\n",
-    "\n",
-    "### AIResponsible:\n",
-    "- The majority of respondents believe that responsibility lies with \"The developers or the people creating the AI.\"\n",
-    "- Fewer respondents attribute responsibility to \"A governmental or other regulatory body,\" \"Prominent industry leaders,\" or \"Nobody.\"\n",
-    "\n",
-    "### AIFuture:\n",
-    "- A significant proportion of respondents express excitement about the future of AI, indicating that they are \"Excited about the possibilities more than worried about the dangers.\"\n",
-    "- However, there is also a notable percentage of respondents who are \"Worried about the dangers more than excited about the possibilities.\"\n",
-    "- A smaller portion of respondents either \"Don't care about it\" or \"Haven't thought about it.\"\n",
-    "\n",
-    "Overall, these results suggest a complex and varied perspective on AI technology. While many see great potential in AI, there are also concerns about its implications, particularly regarding decision-making, automation of jobs, and the ethical considerations surrounding its development and regulation.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##  Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 386,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['LanguageWorkedWith']\n",
-    "df_18 = df[cols]\n",
-    "df_19 = survey_df_2019[cols]\n",
-    "df_20 = df2020[cols]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 387,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#splitting 'LanguageWorkedWith' on ';' \n",
-    "language_2018= df_18['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n",
-    "language_2018['Language'] = language_2018.index\n",
-    "language_2018.reset_index(drop=True, inplace=True)\n",
-    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "language_2018 = language_2018[['Language', '2018']]\n",
-    "#language_2018"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 388,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#splitting 'LanguageWorkedWith' on ';' \n",
-    "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
-    "language_2019['Language'] = language_2019.index\n",
-    "language_2019.reset_index(drop=True, inplace=True)\n",
-    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "language_2019 = language_2019[['Language', '2019']]\n",
-    "#language_2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 389,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#splitting 'LanguageWorkedWith' on ';' \n",
-    "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
-    "language_2020['Language'] = language_2020.index\n",
-    "language_2020.reset_index(drop=True, inplace=True)\n",
-    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
-    "language_2020 = language_2020[['Language', '2020']]\n",
-    "#language_2020"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 390,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compare_df = pd.merge(language_2018, language_2019,on = ['Language'], how = 'outer')\n",
-    "language_all = pd.merge(compare_df, language_2020,on = ['Language'], how = 'outer')\n",
-    "language_all.fillna(0, inplace=True)\n",
-    "language_all['2018'] = language_all['2018']. astype(int)\n",
-    "language_all['2019'] = language_all['2019']. astype(int)\n",
-    "language_all['2020'] = language_all['2020']. astype(int)\n",
-    "language_all.set_index('Language', inplace=True)\n",
-    "#language_all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 391,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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q4cmTJ/j+++/ldT548AABAQEfDXpyZsHLyMjAP//8A2NjYwAfAghHR0cEBwfj4sWL6NSp08de6jzU1NQwb948vHv3Dt7e3gA+9PxoaGggISFB3r4KFSpg0aJFePHiBW7fvg1/f39YWVlh4sSJOHbsGCpVqoQLFy7Iyw0LC1N4vStVqoSKFSvC2NgYd+/eVWjD7du3UatWrc+21cjICFpaWvj777/l+x48eCD/vVatWnj16hXKlSsnb/fr16/h7e2t1P9hziBdIpFgwYIFUFNTQ58+fbBhwwaMGDECQUFBny2HiL5+DJ6I6JvVrFkz/Pjjj5g0aRLCwsIQEhKCBQsWoGPHjihfvjy6d++O+/fvY82aNYiKisLatWtx48aNfHtDgA939dXV1REUFIQXL17g+PHj8PPzA/DxoXVFoVOnTkhNTcXChQsRGRmJ3bt3f/bCUCqV4sKFCwo/N2/ehI6ODnR1dXHy5Ek8f/4cly5dwrx58+SPKWhb6tevj+TkZAQEBCA6OhpLly5VGDZZrlw5nD17FtHR0bh58yYmTZokr/u7775D165dsWjRIty5cwd37tyBl5cXgA8Xy8bGxmjZsiUmTZqEu3fvIiwsDJMnT8bbt29hYGCQb3v79esHPz8/HDx4EM+fP8f9+/cxc+ZMvHz5Er/++iuAD70pS5cuxfHjx/H8+XMcPnwYUqkU5ubmcHFxgVQqxYwZMxAREYHLly9j3rx5KFu27Edfo9u3b2P16tWIiIjAggULIJPJ4OzsLD/u7OyMgwcP4rvvvkODBg0++5rnVK1aNQwZMgS7du3CP//8A319fXTr1g3z58/H1atXERERgcmTJ+Phw4f48ccfUapUKfj7+2PHjh14/vw5zpw5g5cvXyoMaVu4cCH+/vtvXL16FStXrkSvXr2gpqaGXr164eHDh/Dx8UFUVBQOHDiAbdu24bfffvtsO/X19eHi4gIvLy/cuXMH165dg7+/v/x4ixYt8MMPP2DChAkICwvD7du3MWPGjI/2eOWmp6eH9+/fIyoqChoaGrh16xbmz5+PiIgIhIeH48KFC6hbt+5/em2J6OvE4ImIvlnq6upYvXo11NTU0KNHD4wZMwZt27aVX2B///33WLlyJfbv3w9nZ2fcunULDg4OH81kVrVqVXkGvU6dOmHt2rWYMWMGtLS0FO6SF7XvvvsOa9aswc2bN+Hi4oJ9+/bB2dk53/k22RISEuDh4aHwM3v2bGhpaWHZsmU4ffo0OnbsiIULF8LT0xNVqlSRzz8qSFtq1qyJyZMnIzAwEJ07d4ZMJlPoXVm4cCEePnyITp06YfLkyfj5559hYWEhr3vy5MkwNzfHgAEDMHLkSHnQkf03XLp0KWrWrImBAwfit99+g4GBgcJFeW59+vTBuHHjsH79enTq1Anu7u54/fo1tm7dikqVKgEA7OzsMGbMGCxduhQ///wzNm3aBG9vbxgZGUFfXx/r16/Hixcv0KVLF0yePBldunTB2LFjP1pnly5dcPv2bbi6uuLevXtYu3Yt9PT05McbN26M8uXL/6dep5wGDhyI6tWrY968eRAEAVOnTkWLFi0wduxY/Prrr5BIJNiwYQNKlSqFOnXqYNGiRfjzzz/RoUMHLFq0CJMnT0azZs3k5XXq1Amenp4YO3YsfvnlFwwdOhTAh/fH2rVr5en//f39MXnyZHTr1k2pds6aNQuNGzfGwIEDMW3aNIWgS0NDA/7+/tDQ0EDPnj3h6ekJKysrLFiwQKmymzZtCiMjI7i4uMiXA5BIJOjevTt69eqF6tWrY+bMmf/hVSWir5WaUJzGkhARFSMPHz5ERkaGfDgWAAwePBj169cv0XMfoqOjERsbqzC5fu7cuUhLS5Onaf9a2nL69GnY2triu+++AwDcu3cPvXr1wu3bt5VO513cpaWloVmzZtizZ498OF9ReP78Oezt7XHy5MnPzj0kIiqp2PNERPQRz549Q//+/XH58mW8ePECu3fvxtWrV9GuXbuiblqBJCcno3///jh+/DhevHiBkydP4uDBg/JMhV9TW1atWgUvLy88ffoU//zzD5YtWwY7O7uvJnA6fvw4Zs+eDXNz8yINnIiIvhXseSIi+oQ//vgDO3fuxNu3b1GrVi2MGjUKDg4ORd2sAtu9ezcCAgLw8uVLVKtWDYMGDVJ6+FRJasvjx48xf/583Lt3D9ra2rCzs8O0adPyZJIrqRwdHZGRkQF/f3+YmZkVaVvY80RE3wIGT0RERERERErgsD0iIiIiIiIlMHgiIiIiIiJSQv7LmX8linr8NxERERERlTzh4eH57v+qgyfg40+ciIiIiIgot091wHDYHhERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKUGlwVNYWBh69OgBCwsLODs74969e588Pzo6Gk2aNMG7d+/yPb5x40bY2dmpoqlERERERESfpLLgSSqVYtiwYejQoQNCQ0Ph6ekJd3d3JCcn53v+6dOn0atXr48GTmFhYVixYoWqmktERERERPRJKguerl+/DplMhv79+0NLSwudOnWCiYkJgoKC8py7Z88eLF26FCNGjMi3rPT0dEycOBG9e/dWVXOJiIiIiIg+SWXB0+PHj2FsbKywz8jICA8fPsxzbps2bXDs2DE0b94837KWLl0KOzs7NGrUSCVtJSIiIiIi+hxNVRWcmpqKUqVKKezT1dVFWlpannMrVar00XLOnz+Pu3fvYseOHTh//rzo7SQiIiIi+holpkohychSeT06muoop6et9PmXL1+Gt7c3njx5gooVK8Ld3R09e/aEVCrF/PnzceLECairq2PAgAEYMmRInscHBgbi+vXr8Pf3l++LjIzEnDlz8M8//+C7775Dz549MXToUFGeX04qC5709PQgkUgU9qWlpUFPT0/pMt6+fYu5c+ciICAAWlpaYjeRiIiIiOirJcnIgs3CYJXXEzLNXulzX758iZEjR2LJkiWwt7fH/fv3MWjQIHz//fe4fv06oqKicOrUKbx//x6DBg1ClSpV4OrqCgBISUnBqlWrsGnTpjxJ5CZMmID27dsjMDAQz549Q69evWBqagp7e+XbpgyVDdszNjZGVFSUwr7IyEiYmJgoXcalS5fw9u1b9OjRA1ZWVpgwYQJiYmJgZWWFmJgYsZtMREREREQq9OLFCzg5OaFdu3ZQV1dHgwYNYG1tjVu3bmH//v3w9PRE2bJlUb16dbi7u2PHjh3yxw4ZMgQvXrxAjx498pSbHXcIggA1NTWoqalBR0dH9ParLHiysbGBIAgIDAyETCbD0aNHER4ejnbt2ildRufOnXH37l3cuHEDN27cwPLly1GtWjXcuHED1apVU1XTiYiIiIhIBaysrDBv3jz5dmJiIm7cuIGffvoJr1+/VuhoqVWrlkK+BG9vb6xcuTLfKT/Dhg2Dn58f6tevj/bt28PJyQktWrQQvf0qC560tbUREBCAEydOwNraGmvWrMHq1atRoUIFHDp0CJaWlqqqmoiIiIiIirn3799j6NChaNiwIerWrQsACjkTdHV1kZ6eLt+uUqXKR8tSU1PD5MmTcfv2bRw8eBCnTp3C7t27RW+zyuY8AYCpqSm2b9+eZ7+LiwtcXFzy7K9evTrCw8M/Wp6DgwMcHBxEbSMRERERERWuqKgoDBs2DCYmJli+fLk8SMqZM0HZfAl///03AgMDcenSJQCAubk53N3dsX37dnTr1k3Udqus56mkS5IkIS41Tv6TJEkq6iYREREREZV4oaGh6N69OxwcHLBy5Uro6OigbNmyqFy5MiIjI+XnRUVFKZUv4dWrV5DJZBAEQb5PU1MTmpri9xMxePoISaYE9rvt5T+STMnnH0RERERERB/17NkzDBkyBKNGjcL48eOhpqYmP+bi4oLVq1cjPj4ez58/x4YNG/IdrZZbo0aNkJWVhZUrVyIjIwNPnjzBxo0b0alTJ9Hbr9Jhe0RERERERNm2bt2KlJQU+Pj4wMfHR76/V69eGD16NBYvXgwnJydkZWWhR48ecHNz+2yZFStWREBAAJYuXYotW7agTJky6NGjB/r06SN6+9WEnP1bXxkzM7NPzqFSkBoPZPzbuxSnoQb7Pf9mBgzuFgwDPQOxm0hEREREpBLFdZHc4u5TMQR7nrJlSAAf83+3J4QVXVuIiIiIiAroawpoigvOeSIiIiIiIlICgyciIiIiIiIlfLPD9nKPAa0sCIwkiYiIiIjoo77Z4EmSkQWbhcHy7YiplkXYGiIiIiIiKu7Y2UJERERERKQEBk9ERERERERKYPBERERERESkBAZPRERERERESvhmE0YQEREREX3VUuOBDInq69HUAfQqKH365cuX4e3tjSdPnqBixYpwd3dHz549IZVKMX/+fJw4cQLq6uoYMGAAhgwZkufxgYGBuH79Ovz9/eX7Hjx4gAULFuDBgwcwMDDA8OHD4ezsLMrTy4nBExERERHR1yhDAviYq76ecWFKn/ry5UuMHDkSS5Ysgb29Pe7fv49Bgwbh+++/x/Xr1xEVFYVTp07h/fv3GDRoEKpUqQJXV1cAQEpKClatWoVNmzbBzs5OXmZycjI8PDzg4uKCjRs3Ijo6GoMGDUKFChXQvHlzUZ8qh+0REREREVGhePHiBZycnNCuXTuoq6ujQYMGsLa2xq1bt7B//354enqibNmyqF69Otzd3bFjxw75Y4cMGYIXL16gR48eCmXevHkTmZmZmDBhAnR0dGBiYoJevXph586dorefPU9ERERERFQorKysYGVlJd9OTEzEjRs30LlzZ7x+/RomJibyY7Vq1cLDhw/l297e3qhSpQr8/Pzw+vVr+X5BEKCjowN19X/7hTQ0NPDkyRPR28+eJyIiIiIiKnTv37/H0KFD0bBhQ9StWxcAUKpUKflxXV1dpKeny7erVKmSbzmNGzeGTCbDunXrIJVK8fjxY+zcuRMSifjzvRg8ERERERFRoYqKikL37t1RqVIlrFy5Et999x0AKAQ8aWlp0NPT+2xZpUuXRkBAAC5cuICWLVti7ty5cHV1RZkyZURvN4MnIiIiIiIqNKGhoejevTscHBywcuVK6OjooGzZsqhcuTIiIyPl50VFRSkM4/sYqVSKzMxM/PXXXwgJCcGWLVuQlpYm780SE4MnIiIiIiIqFM+ePcOQIUMwatQojB8/HmpqavJjLi4uWL16NeLj4/H8+XNs2LABLi4uny0zMzMT/fr1w4kTJ5CVlYWQkBDs2rULPXv2FL39TBhBRERERESFYuvWrUhJSYGPjw98fHzk+3v16oXRo0dj8eLFcHJyQlZWFnr06AE3N7fPlqmrqws/Pz8sXrwYU6ZMQY0aNbB06VKYm4ufpl1NEARB9FKLCTMzM4SHh+d7LPZdOmwWBsu3I6ZaQsO3jnw7bkIY7Pe2l28HdwuGgZ6B6hpLRERERCSmYrpIbnH3qRiCPU9ERERERF+jryigKS4454mIiIiIiEgJDJ6IiIiIiIiUwOCJiIiIiIhICQyeiIiIiIiIlMCEEYUpd8aTrywzCRERERHR14zBU2HKkAA+/+abT5r0GJLUOIVTdDR0UFanbGG3jIiIiIiIPoPBUxGSZGUorCUFfFhPioiIiIiIih8GTyqUmCqFJCNLvl1ZEDjJjIiIiIgKRZIkCZJM1S+S+y2NnGLwpEKSjCzYLPy3JyliqmURtoaIiIiIviWSTAnsd9urvJ7/OnLq8uXL8Pb2xpMnT1CxYkW4u7ujZ8+ekEqlmD9/Pk6cOAF1dXUMGDAAQ4YMkT/uzz//xObNm5GYmIhatWphypQpsLKyAgDExMRg+vTpuHPnDipWrIiZM2eidevWoj5PgMETEREREREVkpcvX2LkyJFYsmQJ7O3tcf/+fQwaNAjff/89rl+/jqioKJw6dQrv37/HoEGDUKVKFbi6uuLkyZNYv349Nm3aBCMjI+zfvx9DhgzBqVOnUKFCBYwbNw4WFhZYu3Ytbt68ieHDh+PgwYP44YcfRG0/R5EREREREVGhePHiBZycnNCuXTuoq6ujQYMGsLa2xq1bt7B//354enqibNmyqF69Otzd3bFjxw4AwOvXr+Hp6QkTExOoq6vjl19+gYaGBsLDwxEVFYX79+9j1KhR0NbWhq2tLezs7LBnzx7R28+eJyIiIiIiKhRWVlbyoXYAkJiYiBs3bqBz5854/fo1TExM5Mdq1aqFhw8fAgB69+6tUE5oaChSU1NRu3Zt3LlzB4aGhtDT05MfNzIywr1790RvP3ueiIiIiIio0L1//x5Dhw5Fw4YNUbduXQBAqVKl5Md1dXWRnp6e53EPHz7E2LFjMXr0aFSqVAkpKSkKj/vUYwuKwRMRERERERWqqKgodO/eHZUqVcLKlSvx3XffAQAkkn+zA6alpSn0JgHA2bNn0bt3b/Tt2xceHh4AAD09PYXHfeyxYmDwREREREREhSY0NBTdu3eHg4MDVq5cCR0dHZQtWxaVK1dGZGSk/LyoqCiFYXx//vknxo0bhwULFmDw4MHy/cbGxoiJiVHoaYqMjFR4rFgYPBERERERUaF49uwZhgwZglGjRmH8+PFQU1OTH3NxccHq1asRHx+P58+fY8OGDXBxcQEABAUFwdfXF4GBgXB0dFQo08jICObm5vD19YVUKsW1a9cQHBwMJycn0dvPhBFERERERFQotm7dipSUFPj4+MDHx0e+v1evXhg9ejQWL14MJycnZGVloUePHnBzcwMABAQEQCqVon///grl+fj4oG3btvDz88PMmTNha2uL8uXLw8vLC6ampqK3X6XBU1hYGGbPno3w8HD88MMP8PLyQoMGDT56fnR0NLp27Yrg4GCUKVMGwIfxiosWLcKZM2cglUrRuHFjzJw5E9WqVVNl04mIiIiISjQdDZ3/vIDtl9ajrKlTp2Lq1KkfPT579mzMnj07z/79+/d/slxDQ0OsX79e6XZ8KZUFT1KpFMOGDUPfvn3x119/4eTJk3B3d8fZs2ehr6+f5/zTp09j7ty5ePfuncJ+b29vPHv2DIcPH4auri68vLwwbtw4ec53IiIiIiLKq6xO2aJuwldHZXOerl+/DplMhv79+0NLSwudOnWCiYkJgoKC8py7Z88eLF26FCNGjMhzTCKRYMSIEShfvjxKlSqF3r174+7du8jIyFBV04mIiIiIiPJQWc/T48ePYWxsrLDPyMhIvtBVTm3atEGXLl3w8uXLPMfmz5+vsH369GnUrl0bmpqcrkVERERERIVHZRFIampqvotVpaWl5Tm3UqVKSpV59OhRbNiwAevWrROljURERERERMpSWfAk5mJVgiBg9erV+PPPP7F69Wo0adJErGYSEREREREpRWVznoyNjREVFaWw70sWq5LJZBg3bhwOHDiAbdu2oVmzZmI2k4iIiIiISCkqC55sbGwgCAICAwMhk8lw9OhRhIeHo127dv+pnIULFyIsLAy7du1C7dq1VdRaIiIiIiKiT1NZ8KStrY2AgACcOHEC1tbWWLNmDVavXo0KFSrg0KFDsLS0/GwZ7969w44dOxAdHQ17e3tYWlrKf96/f6+qphMREREREeWh0pR1pqam2L59e579Li4ucHFxybO/evXqCA8Pl2+XKVMGDx48UGUTiYiIiIiIlKKyniciIiIiIqKvCYMnIiIiIiIiJTB4IiIiIiIiUgKDJyIiIiIiIiUweCIiIiIiIlICgyciIiIiIiIlMHgiIiIiIiJSAoMnIiIiIiIiJTB4IiIiIiIiUgKDJyIiIiIiIiUweCIiIiIiIlICgyciIiIiIiIlMHgiIiIiIiJSAoMnIiIiIiIiJTB4IiIiIiIiUoJmUTeAVC9JkgRJpkS+raOhg7I6ZYuwRUREREREJQ+Dp2+AJFMC+9328u3gbsFF2BoiIiIiopKJw/aIiIiIiIiUwOCJiIiIiIhICQyeiIiIiIiIlMA5T1+j1Hgg498EEdBQK7q2EBERERF9JRg8fY0yJICP+b/bE8KKri1ERERERF8JBk8lXGKqFJKMLIV9lQWB4zGJiIiIiETG4KmEk2RkwWahYurxiKmWRdQaIiIiIqKvFzsoiIiIiIiIlMDgiYiIiIiISAkMnoiIiIiIiJTA4ImIiIiIiEgJDJ6IiIiIiIiUwOCJiIiIiIhICQyeiIiIiIiIlMDgiYiIiIiISAkMnoiIiIiIiJTA4ImIiIiIiEgJDJ6IiIiIiIiUwOCJiIiIiIhICQyeiIiIiIiIlMDgiYiIiIiISAkMnoiIiIiIiJTA4ImIiIiIiEgJDJ6IiIiIiIiUoNLgKSwsDD169ICFhQWcnZ1x7969T54fHR2NJk2a4N27d/J9giDA19cXtra2sLKywsKFC5GRkaHKZhMREREREeWhsuBJKpVi2LBh6NChA0JDQ+Hp6Ql3d3ckJyfne/7p06fRq1cvhcAJAHbu3IlTp05h//79OHnyJP7++2+sWbNGVc0mIiIiIiLKl8qCp+vXr0Mmk6F///7Q0tJCp06dYGJigqCgoDzn7tmzB0uXLsWIESPyHDtw4AD69euHqlWrokKFChg5ciR27typqmYTERERERHlS1NVBT9+/BjGxsYK+4yMjPDw4cM857Zp0wZdunTBy5cvP1uOkZER4uLikJiYiHLlyonebiIiIiIiovyoLHhKTU1FqVKlFPbp6uoiLS0tz7mVKlX6ZDm6urry7ewy09PTRWopERERERHR56ls2J6enh4kEonCvrS0NOjp6f2ncnR1dRUCpezf/2s5REREREREBaGy4MnY2BhRUVEK+yIjI2FiYvKfyjExMVEoJzIyEpUrV0aZMmVEaScREREREZEyVBY82djYQBAEBAYGQiaT4ejRowgPD0e7du3+UzkuLi7YuHEjXrx4gfj4ePj5+aFz584qajUREREREVH+VBY8aWtrIyAgACdOnIC1tTXWrFmD1atXo0KFCjh06BAsLS2VKsfNzQ0///wzevbsCUdHR5iYmGD06NGqajYREREREVG+VJYwAgBMTU2xffv2PPtdXFzg4uKSZ3/16tURHh6usE9dXR2jRo3CqFGjVNZOIiIiIiKiz1FZzxMREREREdHXhMETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREjSLugFUMiSmSiHJyJJv62iqo5yedhG2iIiIiIiocDF4IqVIMrJgszBYvh0yzb4IW0NEREREVPg4bI+IiIiIiEgJDJ6IiIiIiIiUwGF7JIokSRIkmRL5to6GDsrqlC3CFhERERERiYvBE4lCkimB/e5/50EFdwv+xNlERERERCUPh+0REREREREpgcETERERERGREhg8ERERERERKUGp4CkiIgK7d++GIAgYM2YMHBwccO3aNVW3jYiIiIiIqNhQKniaPXs2dHR0cO7cOcTGxsLLywu+vr6qbhsREREREVGxoVTwJJFI4OLigkuXLqFDhw6wsbGBTCZTdduIiIiIiIiKDaWCJ6lUijdv3uDcuXNo1qwZ3rx5A4lE8vkHEhERERERfSWUCp569OiBtm3bonHjxjAxMcGvv/6Kfv36qbptRERERERExYZSi+T26tULPXv2hLr6h1hr//79KF++vEobRkREREREVJwo1fOUkpKCBQsWoF+/fkhMTISvry9SUlJU3TYqxsqrpQDvXv77I2QVdZOIiIiIiFRKqeBpwYIFKF26NN6+fQsdHR0kJydj1qxZn31cWFgYevToAQsLCzg7O+PevXv5nhcTE4MBAwbA0tISDg4OOH/+vPxYZmYmFi5ciObNm8Pa2hpDhw5FbGyskk+PVEUjSwL4mP/7IwhF3SQiIiIiIpVSKnh68OABxo4dC01NTejq6mL58uV48ODBJx8jlUoxbNgwdOjQAaGhofD09IS7uzuSk5PznDtu3DiYmZkhJCQE8+fPx9ixYxEdHQ0A2L59O27fvo0jR47gwoUL0NPTw/z587/gqRIREREREX05pYKn7LlO2TIzM/Psy+369euQyWTo378/tLS00KlTJ5iYmCAoKEjhvKioKNy/fx+jRo2CtrY2bG1tYWdnhz179siPC4IA4f97NtTV1aGjo6P0EyQiIiIiIhKDUgkjmjRpgmXLliE9PR0XL17E1q1bYWNj88nHPH78GMbGxgr7jIyM8PDhQ4V9ERERMDQ0hJ6ensJ52UP8unfvjlOnTsHW1hbq6uqoWbMmtm3bptSToxIuNR7IyJESX1MH0KtQdO0hIiIiom+aUj1PEyZMgJ6eHkqXLg1fX1+YmZlh0qRJn3xMamoqSpUqpbBPV1cXaWlpCvtSUlLyPS89PR0AIJPJ0KpVK5w/fx6hoaFo2LAhRo4cqUyzqYRJTJUi9l26/CdLlq44ryqDa4sRERERUdFRqudJS0sLw4cPx/Dhw5UuWE9PL89CumlpaQo9TMqcN2XKFEyZMgVVq1YFAMyaNQtWVlYIDw+HmZmZ0u2h4k+SkQWbhcHy7YiplkXYGiIiIiIiRUoFT3Z2dlBTU5Nvq6mpQVdXF7Vr18aUKVNgYGCQ5zHGxsYIDAxU2BcZGQlXV9c858XExCA9PV3eAxUZGQkTExMAwMuXLyGVSuXna2hoQE1NDZqaSjWdiIiIiIhIFEoN23NwcEDTpk3h5+eH1atXo02bNqhXrx4aNGjw0ZTlNjY2EAQBgYGBkMlkOHr0KMLDw9GuXTuF84yMjGBubg5fX19IpVJcu3YNwcHBcHJyAgC0adMGfn5+eP36NdLT07FkyRKYm5ujVq1aBXzqREREREREylMqeLpx4wa8vLzw008/wdzcHDNmzMCjR4/Qv39/vHjxIt/HaGtrIyAgACdOnIC1tTXWrFmD1atXo0KFCjh06BAsLf8dkuXn54eIiAjY2tpixowZ8PLygqmpKQBgzpw5qF+/Prp06YLWrVvj9evX8Pf3/2y2P/r6JGlqIi41TuEnSZJU1M0iIiIiom+EUmPfUlJSkJycDH19fQBAcnKyPKHDp5iammL79u159ru4uMDFxUW+bWhoiPXr1+dbRunSpTFv3jzMmzdPmabSV0ySlQH7ve0V9gV3C/7I2URERERE4lIqePrll1/QvXt3/PzzzxAEASdPnkS3bt2wZcsWGBkZqbqNRERERERERU6p4Gnw4MGoU6cOLly4AE1NTcycORNNmzbF/fv30aVLF1W3kYiIiIiIqMgpnbKufv36MDExgSAIyMzMxOXLl9G8eXNVto2IiIiIiKjYUCp4WrFiBdatW/fhAZqakEqlMDExweHDh1XaOCIiIiIiouJCqZR1Bw8exNmzZ+Ho6IgTJ05g0aJF8nWYiIiIiIiIvgVKBU8VKlSAgYEBjIyMEBYWBldXVzx8+FDVbSMiIiIiIio2lAqeNDU18ezZMxgZGeHGjRvIyMiARCJRdduIiIiIiIiKDaWCpyFDhmDmzJlo06YNTp06hTZt2qBp06aqbhsREREREVGxoVTCiJ9++gl//vknAODAgQN4+vQp1NWViruIir/UeCAjR0+qpg6gV6Ho2kNERERExdInI6DExEQkJibCw8MDSUlJSExMhEQiQaVKlTBq1KjCaiORamVIAB/zf38yOCSViIiIiPL6ZM/T+PHjcfnyZQCAjY3Nvw/S1ISjo6NqW0ZERERERFSMfDJ42rBhAwBg6tSpWLRoUaE0iIiIiIiIqDhSas7TokWL8OLFCyQlJUEQBPn+unXrqqxhRERERERExYlSwdPKlSuxYcMGVKxYUb5PTU0NwcHBKmsYERERERFRcaJU8HTgwAGcPHkSVapUUXV7iIiIiIiIiiWl8o0bGhoycCIiIiIiom+aUj1Ptra2WLp0Kezt7VGqVCn5fs55IiIiIiKib4VSwdO+ffsAAMePH5fv45wnIiIiIiL6ligVPJ05c0bV7SAiIiIiIirWlJrzlJKSgnnz5qFfv35ITEzErFmzkJKSouq2ERERERERFRtKBU8LFixA6dKl8fbtW+jo6CA5ORmzZs1SdduIiIiIiIiKDaWCpwcPHmDs2LHQ1NSErq4uli9fjgcPHqi6bURFIklTE3GpcfKfJElSUTeJiIiIiIoBpeY8qasrxliZmZl59hF9LSRZGbDf216+HdyNiVGIiIiISMngqUmTJli2bBnS09Nx8eJF/PXXX7CxsVF124iIiIiIiIoNpbqPJkyYAD09PZQuXRq+vr4wNzfHpEmTVN02IiIiIiKiYkOpnictLS1YW1tj+PDhSExMxI0bN6Cjo6PqthERERERERUbSvU8+fr6YuXKlQCA9PR0rFu3Dv7+/iptGBERERERUXGiVPAUHByMjRs3AgCqVq2Kv/76C0FBQSptGBERERERUXGiVPAkk8mgpaUl39bS0oKamprKGkVERERERFTcKDXnqVGjRhg/fjx+/fVXqKmp4cCBA2jYsKGq20ZERERERFRsKBU8zZw5EytXrsSiRYugqakJW1tbjBgxQtVtIxJdYqoUkowshX2VBUG5LlgiIiIi+qYpFTz98ccfmDJliqrbQqRykows2CxUXPQ2YqplEbWGiIiIiEoSpW64nzt3TsXNICIiIiIiKt6U6nmqXr06Bg4ciEaNGuG7776T7x8wYIDKGkZERERERFScKBU8lStXDgDw4sULVbaFiIiIiIio2FIqeFq0aBEA4N27dyhTpoxKG0RERERERFQcKTXnKSoqCh07dkSnTp0QGxuLDh06ICIiQtVtI/pqJUmSEJcaJ/9JkiQVdZOIiIiI6DOUCp7mz5+P6dOno2LFiqhSpQp+++03zJo1S9VtI/pqSTIlsN9tL/+RZEqKuklERERE9BlKBU+JiYlo3ry5fLt3795ITk5WWaOIiIiIiIiKG6XmPAGARCKBmpoaAOD169fIysr6zCOIvl25F+PV0VRHOT3tImwRERERERWUUsFTr1694O7ujrdv38Lb2xtHjx7FoEGDVN02ohIr92K8D6c3Bd5J/z1BQ60IWkVEREREBfHZYXsPHz5EmTJlMHr0aDg7OyMjIwPz589Hr169Plt4WFgYevToAQsLCzg7O+PevXv5nhcTE4MBAwbA0tISDg4OOH/+vMLx3bt3w97eHpaWlujZsyfCwsKUfHpExYNGlgTwMf/3RxCKuklERERE9B99Mnjau3cvfvvtNwQEBGDEiBFo2rQpJk+erDD/6WOkUimGDRuGDh06IDQ0FJ6ennB3d893rtS4ceNgZmaGkJAQzJ8/H2PHjkV0dDQA4Pz58/D29sbvv/+OGzduoHnz5hg9evQXPl0iIiIiIqIv88ngacuWLTh8+DB2796NNWvWICAgQOmCr1+/DplMhv79+0NLSwudOnWCiYkJgoKCFM6LiorC/fv3MWrUKGhra8PW1hZ2dnbYs2ePvA2enp6oX78+NDQ0MGTIEPj4+HDOFRERERERFarPDturUqUKAMDS0hIJCQlKF/z48WMYGxsr7DMyMsLDhw8V9kVERMDQ0BB6enoK54WHhwMA/ve//0FdXR1ubm6wsbHB0KFDUbp0aairK5UokIiIiIiISBSfjECys+tl09DQULrg1NRUlCpVSmGfrq4u0tLSFPalpKTke156ejoAICkpCVu3bsX8+fNx4cIFGBsbw9PTExkZGUq3hYiIiIiIqKD+U/dN7mDqU/T09CCRKC78mZaWptDDpMx52tra6N27N0xMTKCjo4MJEybgyZMniIyM/C9NJyIiIiIiKpBPpioPDw9Ho0aN5Nvp6elo1KgRBEGAmpoabt269dHHGhsbIzAwUGFfZGQkXF1d85wXExOD9PR0eQ9UZGQkTExMAHwYwvfu3Tv5+VlZWRCYqYyIiIiIiArZJ4OnU6dOfXHBNjY2EAQBgYGB6N27N06ePInw8HC0a9dO4TwjIyOYm5vD19cX48ePx61btxAcHIydO3cCAH755ResWrUKDg4OMDIywvLly2FiYoLatWt/cduIiIiIiIj+q08GT99///0XF6ytrY2AgADMnj0bK1asQPXq1bF69WpUqFABhw4dwuzZs3H79m0AgJ+fH2bOnAlbW1uUL18eXl5eMDU1BfBhgd7MzEyMHj0acXFxaNCgAVavXv2fhhASEREREREV1CeDp4IyNTXF9u3b8+x3cXGBi4uLfNvQ0BDr16/Ptww1NTX07dsXffv2VVk7iYiIiIiIPof5vomIiIiIiJTA4ImIiIiIiEgJDJ6IiIiIiIiUwOCJiIiIiIhICQyeiIiIiIiIlMDgiYiIiIiISAkMnoiIiIiIiJTA4ImIiIiIiEgJKl0kl4i+fkmSJEgyJfJtHQ0dlNUpW4QtIiIiIlINBk9EVCCSTAnsd9vLt4O7BRdha4iIiIhUh8P2iIiIiIiIlMDgiYiIiIiISAkMnoiIiIiIiJTA4ImIiIiIiEgJDJ6IiIiIiIiUwGx7RCVUYqoUkows+baOpjrK6WkXYYuIiIiIvm4MnohKKElGFmwW/psW/OH0psA76b8naOoAehWKoGVEREREXycGT0RfCY0sCeBbR76dNOkxJKlxCudwAVsiIiKiL8fgiegrJcnKgP3e9gr7/usCthwaSERERPQvBk9E9FG5hwaGTLMvwtYQERERFS1m2yMiIiIiIlICgyciIiIiIiIlMHgiIiIiIiJSAoMnIiIiIiIiJTB4IiIiIiIiUgKDJyIiIiIiIiUwVTkRFTmuJ0VEREQlAYMnIipyXE+KiIiISgIO2yMiIiIiIlICgyciIiIiIiIlMHgiIiIiIiJSAoMnIiIiIiIiJTB4IiIiIiIiUgKDJyIiIiIiIiUweCIiIiIiIlIC13kiIqWVV0sB3iUo7tRQU309mjqAXgXR6yEiIiL6Lxg8EZHSNLIkgG8dxZ0TwlRfzzjx6yAiIiL6rxg8EVGxl6SpCUlqnHxbR0MHZXXK/qcyElOlkGRk/VuGpjrK6WmL1kYiIiL6+jF4IqJiT5KVAfu97eXbwd2C/3sZGVmwWfjv40Km2YvSNiIiIvp2MGEEERERERGREhg8ERERERERKUGlwVNYWBh69OgBCwsLODs74969e/meFxMTgwEDBsDS0hIODg44f/58vudt3LgRdnZ2qmwyERERERFRvlQWPEmlUgwbNgwdOnRAaGgoPD094e7ujuTk5Dznjhs3DmZmZggJCcH8+fMxduxYREdHK5wTFhaGFStWqKq5REREREREn6Sy4On69euQyWTo378/tLS00KlTJ5iYmCAoKEjhvKioKNy/fx+jRo2CtrY2bG1tYWdnhz179sjPSU9Px8SJE9G7d29VNZeIiIiIiOiTVBY8PX78GMbGxgr7jIyM8PDhQ4V9ERERMDQ0hJ6ensJ54eHh8u2lS5fCzs4OjRo1UlVziYiIiIiIPkllwVNqaipKlSqlsE9XVxdpaWkK+1JSUvI9Lz09HQBw/vx53L17FyNGjFBVU4mIiIiIiD5LZes86enpQSKRKOxLS0tT6GH63Hlv377F3LlzERAQAC0tLVU1lYiIiIiI6LNUFjwZGxsjMDBQYV9kZCRcXV3znBcTE4P09HR5D1RkZCRMTExw6dIlvH37Fj169AAAZGRkID09HVZWVjh06BCqVaumquYT0TcmSZIESabijRwdDR2U1SlbRC0iIiKi4kZlwZONjQ0EQUBgYCB69+6NkydPIjw8HO3atVM4z8jICObm5vD19cX48eNx69YtBAcHY+fOnTA1NUXnzp3l554+fRoLFy7EmTNnVNVsIvpGSTIlsN9tr7AvuFtwEbWGiIiIiiOVzXnS1tZGQEAATpw4AWtra6xZswarV69GhQoVcOjQIVhaWsrP9fPzQ0REBGxtbTFjxgx4eXnB1NRUVU0jIiIiIiL6z1TW8wQApqam2L59e579Li4ucHFxkW8bGhpi/fr1ny3PwcEBDg4OoraRiL5N5dVSgHcJ/+7QUCu6xhAREVGJoNLgiYiouNLIkgC+df7dMSGs6BpDREREJYLKhu0RERERERF9TRg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESGDwREREREREpgcETERERERGREhg8ERERERERKYHBExERERERkRIYPBERERERESmBwRMREREREZESVBo8hYWFoUePHrCwsICzszPu3buX73kxMTEYMGAALC0t4eDggPPnz8uPpaWlYdasWWjRogWsra0xdOhQxMTEqLLZREREREREeagseJJKpRg2bBg6dOiA0NBQeHp6wt3dHcnJyXnOHTduHMzMzBASEoL58+dj7NixiI6OBgB4e3vj2bNnOHz4MC5cuIBKlSph3Lhxqmo2ERERERFRvlQWPF2/fh0ymQz9+/eHlpYWOnXqBBMTEwQFBSmcFxUVhfv372PUqFHQ1taGra0t7OzssGfPHgCARCLBiBEjUL58eZQqVQq9e/fG3bt3kZGRoaqmExERERER5aGpqoIfP34MY2NjhX1GRkZ4+PChwr6IiAgYGhpCT09P4bzsIX7z589XOP/06dOoXbs2NDVV1nQiIiIiIqI8VBaBpKamolSpUgr7dHV1kZaWprAvJSUl3/PS09PzlHn06FFs2LAB69atE7/BREREREREn6Cy4ElPTw8SiURhX1pamkIPk7LnCYKA1atX488//8Tq1avRpEkTVTWbiIiIiIgoXyqb82RsbIyoqCiFfZGRkTAxMclzXkxMjEJPU87zZDIZxo0bhwMHDmDbtm1o1qyZqppMRERERET0USoLnmxsbCAIAgIDAyGTyXD06FGEh4ejXbt2CucZGRnB3Nwcvr6+kEqluHbtGoKDg+Hk5AQAWLhwIcLCwrBr1y7Url1bVc0lIiIiIiL6JJUFT9ra2ggICMCJEydgbW2NNWvWYPXq1ahQoQIOHToES0tL+bl+fn6IiIiAra0tZsyYAS8vL5iamuLdu3fYsWMHoqOjYW9vD0tLS/nP+/fvVdV0IiIiIiKiPFSass7U1BTbt2/Ps9/FxQUuLi7ybUNDQ6xfvz7PeWXKlMGDBw9U2UQiIiIiIiKlMN83EZFIElOlkGRkybd1NNVRTk9b4ZwkSRIkmf8mydHR0EFZnbKF1kYiom9OajyQkSM5maYOoFeh6NpDJRqDJyIikUgysmCzMFi+HTLNPu85mRLY7/53f3C34DznFEu8+CCikipDAviY/7s9Lqzo2kIlHoMnIiL6PF58EBERMXgiIlKV8mopwLsExZ0aakXTGCIiIiowBk9ERCqikSUBfOso7pyg+h4bzqsiIiJSDQZPRERfmRI7r4qIiKiYY/BERFSC5M7oB+Sf1Y+IiIjEx+CJiKgEyZ3RD8g/qx8RERGJj8ETERH9Z0mampCkxsm3Oa+KiIi+BQyeiIgoj9zDAysLAtRzHJdkZcB+b3v5NudVERHRt4DBExER5ZF7eGDEVMsibA0REVHxoP75U4iIiIiIiIg9T0REJVyexXi5EG/RS40HMv5dawuaOoBehaJrDxERiYLBExFRCZdnMd5CWIiXPiNDAviY/7s9jn8TIqKvAYMnIiIqErmTUqhivSqui0VERGJi8EREREUid1KKh9ObAu+kiicVcLhbYa2L9bnshERE9HVg8ERERMVCnuGHQKEMd0uSJEGS+e/8pC9Zs+pz2QnFWBcrd4BWUT0Fmlk5gk3OqyIiUjkGT0RE9E2TZEpgv/vf3ihVrFklxrpY+QZoOYLNpEmPFQI0gIsXExGJjcETEREVW2L02HwrcgdoABcvJiISG4MnIiIqtsToscmNqd0/TpkEG2IMcyQiKqkYPBER0TeFqd0/TpkEG4UxzJGIqLhi8ERERERFi4sKE1EJweCJiIiIihYXFSaiEoLLUBARERERESmBwRMREREREZESOGyPiIiIClXurH6VBUHhbq4qFhXOnTVQZTh/i+irxuCJiIiIClW+C/7mPK6CRYVzZw3MnXIdECntOudvEX3VGDwRERHRNyd3ynXgvwdp+a2LpYpeNCIqPhg8EREREX2B/NbFUkUvGhEVHwyeiIiI6KPKq6UA7xL+3aGhVnSN+UYV2fwtIsqDwRMRERF9lEaWBPCt8++OCZzDU9g+N3+LiAoPgyciIiKiEix38gvOqyJSHQZPRERERCVY7uQXnFdFpDpcJJeIiIiIiEgJ7HkiIiIi+sYxKQWRchg8EREREX3jimxRYaIShsETERERffWYcr1gxFhUmOhrwOCJiIiIvnpMuU5EYmDwRERERERUEKnxQEaOYY2aOoBeBdXWoap66JMYPBERERERFUSGBPAx/3d7nAp6NnPXoap66JMYPBERERER0QdF0YtWgnrQVLrOU1hYGHr06AELCws4Ozvj3r17+Z4XExODAQMGwNLSEg4ODjh//rz8mCAI8PX1ha2tLaysrLBw4UJkZGSostlEREREJLLEVCli36XLfxJTpXnOSZIkIS41Tv6TJEkqgpZ+47J7uLJ/cg8VLCl1pMYD714q/qTGF7hYlfU8SaVSDBs2DH379sVff/2FkydPwt3dHWfPnoW+vr7CuePGjYOFhQXWrl2LmzdvYvjw4Th48CB++OEH7Ny5E6dOncL+/fuhra2N4cOHY82aNRgxYoSqmk5EREREIvtcOnQgb1Y/ZvSjL6aiYY4q63m6fv06ZDIZ+vfvDy0tLXTq1AkmJiYICgpSOC8qKgr379/HqFGjoK2tDVtbW9jZ2WHPnj0AgAMHDqBfv36oWrUqKlSogJEjR2Lnzp2qajYREREREVG+VBY8PX78GMbGxgr7jIyM8PDhQ4V9ERERMDQ0hJ6ensJ54eHh+ZZjZGSEuLg4JCYmqqrpREREREREeagJgiCoomB/f3/cu3cPa9aske9bsGAB0tLS4OXlJd938OBBrF+/HocPH5bv27RpE86fP4/AwED89NNP2L17N+rWrQsASExMhI2NDc6fP4+qVat+sg1mZmYiPysiIiIiIvraZXfk5KayOU96enqQSBQnf6WlpSn0MClznq6uLtLT0+XHsn/PXU5+PvakiYiIiIiI/iuVDdszNjZGVFSUwr7IyEiYmJjkOS8mJkYhQMp5nomJiUI5kZGRqFy5MsqUKaOqphMREREREeWhsuDJxsYGgiAgMDAQMpkMR48eRXh4ONq1a6dwnpGREczNzeHr6wupVIpr164hODgYTk5OAAAXFxds3LgRL168QHx8PPz8/NC5c2dVNZuIiIiIiChfKpvzBAAPHz7E7NmzERYWhurVq2PatGmwtbXFoUOHMHv2bNy+fRsA8PLlS8ycORO3b99G+fLlMW7cOHTs2BEAkJWVhVWrVmH37t1IT0/Hzz//jJkzZ0JbW1tVzSYiIiIiIspDpcETERERERHR10Jlw/aIiIiIiIi+JgyeiIiIiIiIlMDgiYiIiIiISAkMnoiIqMgkJSUVdROIiIiUxuCJiL4Zt27dwp49e+TbUqkUAwYMwI0bN4qwVd+2li1bYsSIETh58iRkMlmh13/37t1Cr1NVUlNTC6We5OTkQqmHiL4uX8vNsm8+256rqysOHDiQZ3/btm1x9uzZwm+QyGJjY9G+fXtRLxCSk5Px8uVLZGZmKuw3NzcXrY6srCyFCwGpVIpHjx7BxsamwGWHhYV99pyCPpfMzExoaGhg8uTJWLJkCQBg6tSpWLRoUYHKzS0iIgIRERFo2LAhqlSpImrZOW3YsAHu7u559vv6+mLs2LGi1nXr1i00atRI1DIBIDQ0FIMHD4anpyeGDBkC4MP/8tKlS3Ho0CFs2LABjRs3FqWurKwsrF+/Hnv37sWrV69QsWJFuLi4YOTIkdDQ0BCljmyqfj8uWbIErq6uMDMzE6W83GJiYnD06FEcPXoUL1++hKOjI1xcXGBlZaWS+gAgIyMDQUFB2Lx5M/73v//hwYMHopWt6vfKpUuX8OjRI1hbW6Nu3bry/ZcvX8bMmTNx5syZAteRzdraGtevX1fYJwgCrKyscPPmzQKXn5WVhWfPnuHHH3+U7zt06BB+/vlnUZcjKazv+fv37yMuLg7Zl1UymQyPHj3CyJEjRatDlZT5fpo6daoodWVmZuL06dP5vl6LFy8WpQ4AuHnzZr6f6x/b/yX69u0Lf39/6Ovri1Lex2RlZSEkJAQvXryAs7MzYmNjUaNGDVHriIyMhJGRUZ7fxdKgQQO0atUKLi4uaNu2LbS0tEQtP1t+73cA0NLSQvny5dGwYUN89913X1y+5hc/sgR7/vw5lixZAkEQ8PjxY4wYMULh+Pv375GVlSVKXVevXkVKSgocHBzw/v17zJkzBw8fPoSDgwNGjRoFNTU1Uer5FIlEIlpZO3fuhJeXF6RSqcJ+NTU10S5AgoKCMGvWLKSkpCjsL1++PK5cuVLg8l1dXT95XIzn0qpVKzRq1AgXLlyAm5sbfvrpJwQHBxeozNxOnTqFsWPHonTp0khPT4efnx9atGghWvlv377FnTt3AAB+fn6oVasWct5ref/+PTZv3ix68OTp6Znngk0Mq1atwrRp09CtWzf5Pn19fcybNw+1atXCqlWrsGnTJlHq8vf3x7FjxzB69Gh8//33ePbsGdasWQM1NTWMHj1alDqAwnk/JiUloU+fPqhSpQo6d+4MZ2dnUQP1atWqwcPDAx4eHoiIiMCJEycwd+5cpKamwsXFBV27dsUPP/wgSl1v3rzBjh07sGPHDgCAs7OzKDc0Cuu9smbNGvj7+8PIyAg+Pj5Ys2YNbG1t4eXlhW3btqFr164FKh/48P04atQoCIKA5ORkdOnSReF4SkoKDAwMClxPYmIi+vfvjxo1amDlypUAgPj4eHh5eSEwMBAbN25EuXLlvrj8wvyeB4Dly5cjMDAQpUuXRlZWFrKyspCcnIxmzZqJUv7w4cM/e72watWqAtXx7t27Aj3+v5g5cybOnj2L8uXLQyKRQF9fH+Hh4XBychK1nkGDBsnXFFVm/5eIjIwUpZxPiY6OxuDBg5GcnIzk5GRYWlrC1dUVf/zxhyjf++vXr0fLli3h5uaGW7duAQB69uwp+nfx8ePHcfToUfj7+2PmzJkqu1m2c+dO3LlzBwYGBqhatSpiY2MRGxsLQ0NDSCQSyGQyrFmz5otv1H6zPU/btm1DfHw81qxZA09PT4Vj2traaNOmDUxNTQtUR1BQEKZPn46xY8eib9++mDJlCu7duwdPT09s374dbdu2xeDBgwtUx+fExsaiTZs2ol1ItW/fHh4eHujSpQs0NVUTezs6OqJPnz7Q1dXF1atX4e7ujuXLl8PGxkblr5dYJBIJQkJCMGzYMFhZWSEsLAzJycno3r07zM3NYW5ujgYNGhSojq5du2LYsGFwcHDA7t27sW/fPmzfvl2kZ/DhOfz22294+/YtXr58CUNDQ4Xj2tra6Nq1q+h/kyZNmiA0NFTUMgHAxsYGly9fzvf/Ni0tDW3atEFISIgoddnb2yMwMFDhov/Zs2fo3bs3Ll68KEodQOG8H4EPPb8XLlzAkSNHcOHCBTRo0ACdO3dG+/btC3T3Lqf4+HgcP34cJ06cwL1799CiRQtUrVoVR48exeDBg9G/f/8vLvvevXv4888/cfr0aVhbW+PWrVs4fvw4KleuLErbC+u9Ymdnh/nz56N58+YICgrC/v37oaOjg/v378PLywvNmzcvUPnZzp07h/j4eMyZMwdz585VOKatrY0mTZoUOICaPXs2EhISsGTJEujq6sr3p6amYtSoUahZsyZmzpxZoDoK43s+W/PmzbFmzRqkpaVh9+7dWLZsGby9vfH27VssXLiwwOUrExjlDhCLMxsbG+zevRtv375FYGAgVqxYgS1btuD69evw8/MTrZ783o+f2v8lpk6dinv37qFNmzYwMDBQCHL79u0rSh0eHh5o0qQJPDw8YG1tjdDQUBw+fBgbN27E/v37C1y+n58fLl26hHv37qFZs2Zo1KgRNmzYgODgYJQvX16EZ5BX9s2yY8eOiX6zbPr06ahRo4Z8lAkABAYGIjIyEvPmzcOOHTuwd+9e7N69+8sqEL5xJ06cUFnZv/76q3Du3DlBEAQhLS1NaNCggXDmzBlBEATh8ePHgqOjo8rqzvbq1SvB3NxctPIaN24sZGZmilZefiwsLARB+ND2rl27CoIgCLGxsYK9vb0o5T948OCTP2FhYQWuI/s1yn4uMplMsLS0FPbu3SssWLBA6NOnT4HraNSokfx3iUQiWFtbF7jMjxk+fLjKys42ZcoUYcqUKUK9evXkv0+ZMkW08q2trQWZTJbvsYyMDFFfP2tra0EqlSrsU8XfqDDejzllZWUJFy9eFDp37iyYmZkJjRo1EqZMmSLExcV9cZl79uwRBg4cKNStW1fo1auXsH37diEpKUl+PCQkRLC0tPzi8rt16ya0aNFC8Pb2Fp49eyYIgiA0b95cePPmzReXmZ/du3cLgqDa90r254kgfPif/emnn4S+ffsKiYmJKqnv3r17KilXEAShVatWH/2/efbsmdCmTRvR6jp06JAgkUhEKy8/jRs3FgRBEBISEoSOHTsKgiAIqampQvPmzUWt5/fffxeSk5NFLTPbwoULP/sjluzPwvfv3wvt2rUTBEEQpFKp0KxZM9HqyCk1NVV4+vSpEB0dLfr/wm+//Zbvjxjf89lyfn81adJEvj/ndYAYLCwshNDQUGHNmjVC3bp1hbZt2wotW7YUPDw8RK3n7du3wtatW4W+ffsKFhYWwogRI4QFCxYItra2wqZNmwpcvo2NjZCRkaGwL+f3fFZWVoFeu29y2F5O7du3R1BQEI4ePYo3b96gatWq6Nq1K1q3bl3gsqOiotCqVSsAHyYlZ2VlybvwjYyMEBsbW+A6AHxyOFhCQoIodWRr164d9u7dqzD0SWwGBgZITk5GlSpV8Pz5cwiCgMqVKyM+Pl6U8gtj2F7Lli3RqFEjZGVl4fbt26hbty40NTVFGVaTH21tbYVhQmJbtWoVUlNTceXKFbx58waGhoawtbUVdV7C999/D+DD65/9u5h++uknXLx4EW3bts1z7Pz58wrzLgrK0tISPj4+mDBhAjQ0NJCRkQFfX19YWFiIVgdQOO9HALh9+zaOHj2K48ePQ0NDA506dcKSJUtQuXJl/P777xg8ePAX3/0MCAiAi4sL5s6di+rVq+c5/uOPPxZoqOOTJ0/QuHFjGBsbq3Re4KJFi/Drr7+KMrT4Y3Le0dbQ0ICmpia8vb1RtmxZldRXvXp1/PHHHxg6dCju3r2LSZMmoWzZsli8eHGB50IkJyd/tOfvhx9+EHVi+YIFC+Do6ChaefmpVq0aoqKiUKtWLcTHxyM5ORkaGhqiJ/HYtm2bynqYCnPY3g8//IDbt2/D0tISaWlpePPmDTQ1NUWdZpCRkYH9+/dj586dePDggXxeqKamJho1agRXV1d07ty5wPNQt2zZIkZzP6l8+fJ4/PixwlzWiIgIVKpUSZTys4ftqampwcrKClZWVtiwYQPOnDmDpKQkhIeHi1LP3r17ERQUhJCQEDRs2BDOzs7w8/NDmTJlAHz4TvP09CzQSAMAKFu2LK5cuYKWLVvK9127dk0+UuLFixfyOr/ENx88rVu3Dn/++Se6deuGli1bIiYmBlOmTMHYsWPRvXv3ApeflZUFDQ0NhIaGon79+tDR0QHwYdy4WBeeXl5enzwuVtc08GEY4MyZM7Fy5co8b1oxuo6BD4GHh4cH/P39YWFhAS8vL2hra+d7YfUllEkYUVBnz55FaGgozp49i5UrV+LBgwdITU3FvHnzRBu2p8pgKbfs4aba2tqoUqUKXr58CQ0NDaxfvx7Gxsai1JF9QbB582aVXBwMHDgQU6ZMwfz589G6dWtoaGggMzMT58+fx6xZszB9+nTR6po2bRoGDhyIPXv2oHLlyoiLi0OVKlWwZs0a0eoACuf92LZtW7x79w7t2rXDsmXL0LRpU4WL+N9++w39+vX74vKPHz/+yeMGBgYFKv/ixYs4duwYtm/fjoULF6Jz586QSqWizzetVKkS+vbtC6lU+tH/34LOSclNR0dHtIun/MyePRupqakQBAFz585Fy5Yt8d1332HOnDnYvHlzgcquUqUKnj59ipo1a+Y59uTJE1SoUKFA5efUqFEjHDx4EE5OTgpDBMXUs2dP9OzZEwcPHkT79u3h7u4OLS0tWFpailqPk5MTpk+fjo4dO+YZOlnQJDFiJzT6FA8PDwwcOBBHjhzBL7/8Ajc3N6irqytc7BZEaGgo5s6dCyMjIwwYMAANGzaEgYEBsrKyEBcXhzt37uDkyZMICAjAvHnz0KRJk/9cx/bt2+Hm5vbJ94JYw/YGDhwIDw8P9OvXDzKZDLt27cLGjRvRp08fUcpPT0/HnDlzkJ6ejoEDB8LS0hIZGRlISEhA+fLlYW1tLUo9qr5Zlm38+PEYMWIEmjVrhqpVq+Lly5cICQnBokWLEBERgb59+xZoGPU3O+cpW4sWLbBx40aFcc8PHjzA8OHDC5y1aODAgejYsSM6deqErl27okuXLvI/1qZNm3Dp0iVs2LChQHUUtk9dkOWeWPylpFIpNm3aBDc3N7x//x6zZs1CcnIypk+fXuCAI7fk5GQcO3YMr169wsCBA/G///1PtA8J4EMPxO3bt5GVlYUmTZpgxowZCAsLw8OHDwucnKB+/fqYOHGifNvb2xvjx49XOEesD+6uXbvC2dkZAwYMkO9bt24dzp8/j61bt4pSRzZVzXkCgD179mDJkiWQyWQoW7YsEhMTUapUKYwfP16UmyXAh7udSUlJKFOmDLZv34709HRkZWWhWrVqcHFxEaWObIXxfjxy5AgcHBxQqlSpfI8LgvBFgYirq+tnHydWAJgtPDwcu3btwqFDh2BoaAgXFxc4OTmhatWqBS778ePHOHXqFFavXp1nfk22gt4UsLS0xI4dO+Q3Tnr37o1t27Yp3EgRM+tp69atcfLkSSQlJaFNmza4fPkyypQpA2tr6wJn21u9ejXu3r2LVatWKdxIlEgkGDVqFIyNjTFp0qSCPgUAH+bRPn36FGpqatDX11f4vxNzQvzt27fx008/QU1NDZs2bUJycjIGDBggaiD4sb+vmEligA9ZD3fs2IG4uDj89ddfWLFiBWbPnv3Rz4EvERMTg8qVK0NLSwtHjhyRJyjJvslcEFOnTsWYMWM+29scExODFStWyLPi/hceHh4ICAj4aACjpqZW4JsMOZ08eRK7d+9GTEwMDAwM5D1nYrK0tMT69etx69YtrFixAlWqVIFMJoO5uTnWrVsnal2qFhUVhaCgIHmiCGdnZ1SvXh0vX75EXFwcGjZs+MVlf/PBk42NDc6dO6dwN0oqlcLOzg6XLl0qUNl///033N3dkZqaiho1amDXrl3Q19fHiBEj5IGTWKkygQ8Tri9cuIC4uDhUqlQJLVu2ROXKlXH16lWUKlVK1DtgcXFx8hTMYg+xCg0N/aK7QP/VgwcPMHDgQNSsWRPh4eE4cOAAXFxcsGDBAjg7Oxe4/Fu3buHmzZvw8PAA8CGByO7duzF8+HBRMst87o6TmB/clpaWuHHjhsLwhoyMDNjY2IiSsrgwpaen4/bt24iPj0flypVhaWkpWrrU169fo2/fvnB0dMSYMWNgYWGBBg0aQBAE3L59G3/99ZfoQ/cKw//+9z8cPXoUcXFx+P7779G5c+cCD9tSJjASKwDMLT09HUeOHMGuXbtw//59/PPPP6KV7e/vj2HDholWXk7m5uZQU1P7aK+z2BfQTZs2xYULF3D48GH89ddf2L9/PxISEtCxY0dcvXq1QGVLpVJ4eHggMjISbdq0QcWKFfHmzRtcvHgRP/zwA9avXy/ahfqnAiQxb5Z9LQIDA7Fz5070798fy5Ytw+nTpzF06FDUrl0b8+bNK1DZycnJ0NfX/+RaYapO+V1Y0tPTRfsfXrFiBQYNGiRagp7csnvROnfujIMHDwL4cNN03759eP/+PcLDwwt03VIUN8uADwlocmfVFOP/65sPntavX4+7d+9i+vTpqFq1KuLj47F8+XKUK1dO4QvwS1/s5ORkREZGwtzcXH53zdfXF+3bt1dYo6Og/vrrL/kcBAMDA7x58wZxcXEYPXo0Dh8+DC8vL1HqS0hIwIQJE3DlyhVoaWlBJpPB0tISq1atEu3uWvPmzVGqVCm4uLjA1dU132EdYujVqxfc3Nzg7Ows7+24evUq5s2bh2PHjhWo7MJcU6gweHh4wNXVFZ06dZLvu3DhArZs2YKAgABR6woICJAHnCXNjBkzoK6uLr/AyNmLtmzZMsTExMDX17fA9bi5uWH79u2f/EIS64vowIEDmDNnDhwcHGBoaIiYmBicOXMGvr6+aNOmjSh1AB++5F6/fg1DQ0NR59J9TlhYmCi9NdkXH3/++edH/yZi9QQXlkmTJiE2NhaRkZEYNGgQ7OzsMGnSJBgZGX12uLgysrKycOTIEVy8eBHx8fEwMDBA69at4ejoWCjLeDx//rzAw8GbNGny2baKne5Z1SMm2rdvj/Xr16NGjRrytb7i4+Ph5ORU4Dl9jRo1wq1bt+Q3AnLK7sUW8wYAANy4cQNWVlZ4/fo1Vq5cibJly2LYsGHQ09MTpfyhQ4di0aJFCqn179y5g8mTJ+PEiROi1GFjY4MrV66Ivk5gtuy/SzZPT09Rh5kX9s2y06dPY+7cuXjz5o18n5j/X9988NSgQQP5+PfsORDZL0n2HT5VvJnFdP78eUydOhU+Pj5o2rSpfP+NGzcwYsQING/eHN7e3qLUNWnSJEgkEsycOROVKlVCbGwsvLy8oKWlJVodWVlZuHTpEo4cOYLTp0/D1NQUrq6u6NixY4Em+OXWpEkThISEQF1dXWExyMaNGxe4N6Vfv35wcnLKdyL/pk2bcOHChQIP24uJifnsOdWqVStQHdnGjBmDkydPokmTJqhZsyZiY2Nx+fJlNGjQQCFoFmNOR34Lc5YUrVu3xo4dO+TzDHMGTy9fvkS3bt0K3KMNAIcPH4azszP27dv30Qs3sb6Ifv75ZyxYsEDhruOVK1fg5eWFo0ePFrj8+Ph4TJ8+HefOnYMgCFBXV4eTkxNmzZol2h3op0+f4sGDB7C2tka5cuWwePFiXLlyBY0aNcL06dNFGSZU2EN4CoNEIsG2bdugr6+Pbt26ISIiAkeOHIGnp6cor1luYi5cmtO1a9fg5eWVZ0FWmUyG+/fvF6hsZT6rxAxsVD1iAvjQ43jx4kVoaWnJP8OkUilatWqFa9euFajs7BThL168+Og5Yo5mWbp0KYKCgnDu3DkMHToU79+/h5aWFqpUqSLaYrxjxozBzZs3sWTJElhbW2PlypXYvHkzBg0aJNr83fnz5yMlJQUdOnTIMxRRjJs/2VMMsqn6e1jVN8ucnJzka0jlXsZDjP+vbz54+tQbOKcvfbGzc8h369YNsbGxGD16NMLDw9GuXTvMnz9flC+gfv36oWfPnujQoYPC/mfPnqFr164wMzMTbV5KixYtcOLECYWu4+TkZNjZ2ankjZaeno6TJ0/i999/x5s3b3Dv3j3Ryu7cuTOmTZsGGxsb+QfFvXv3MH36dBw+fLhAZRfGmkI5h/DkN5RHzKBf2aBIjC+Kkhw85f4CWrVqlcJrkvvuXknQvHlznDt3TmFoo1QqRdOmTUV5LtmLhY8bNw6GhoZ4/vw5li9fjjJlyohycRMUFIRJkybBwMAAKSkpcHZ2xq1bt/DLL7/g8OHDqFOnDmbPnl3gerJFR0eLtqjvt0ZV7w8XFxe0aNECZcqUwf379+Hq6gp/f3+4uLgUOKvX54jRu5WTKkdMZBsxYgS+//57TJ48GU2bNsX169fxxx9/4O7duwXujfjUcL1sYg7ba9++PbZs2QJ9fX3Y2NjgyJEjqFy5Muzs7ERb1w/4MEds4cKF+O6771C5cmUsWLAAJiYmopWv6rluud97qvoeLoybZUD+Uw3E9M1m28sed/upFK8F/UNu3boV/v7+mDx5MgDIh/KsX78emzZtgp+fHyZMmFCgOoAPw07atWuXZ//NmzfRu3dvURdOBT7cjcwZPEmlUtHvHAiCgGvXruHIkSM4deoUatasCXd3d1HrGDVqFIYOHSrPvuXr64s9e/aImnUtP2K9Vtk9GoIgwM7ODmfPnhWl3PwUxuKLdnZ2UFNTQ3JyMuzt7eX7P5WKv7jR19fHq1ev5AkIcr5ub968ES2ldGGOH//111/h4+ODcePGQUtLC1lZWVi3bp1oE5VDQkJw4cIF+Y0kIyMjLF26VOF/oCBWr16N1atXo3Xr1jh79iyGDRuGY8eO4ccff0S7du3QtWtXUYOnzp07o1q1arCzs4ODg4PoSW4K06eGpKniwkpV93KfPXuGCRMmICYmBufOnYODgwNq164tSkrkbKrs3crp0aNH8uHT2X8bW1tbxMXFiVbHzJkz4enpCWtra6SmpqJVq1bQ19fH2rVrC1y2lZXVR/+nVDHSJzExEVWqVEFwcDCqVKmCH3/8ETKZLM88mIJKSEhARkYGdHV18f79e6WCxP+iMLIEF4Y5c+ZAW1sbx48fV7hZtmDBAtF6AoEP74nLly/LlwsS2zcbPLVq1Qq3bt3K940s1ht4x44dWL16NSwsLJCcnIzz58/L57pkp98VI3gSBAEymSxPL0eXLl2QmpoqavDUvn17jBkzBhMnTkS1atXw4sULLF++HO3btxetjoULF+LYsWPQ0NCAs7Mztm/fLlo67Jzs7e3x559/Yu/evbC2tsbbt2+xYsUKUZI5FMaaQqVLl5b/rq6urrAtlvHjx8Pb2xvDhw//6BeeWOmXFy9eDEEQ5OPHSyJra2vs3bsXw4cPz3Ns165d8nXeCqogqbuVlX3hnJWVheTkZOzYsQOVKlVCQkICkpOTRetd+eGHH/Ds2TPUrl1bvu/169eirWofExMjX7evbdu20NTUlL//DAwMkJaWJko92UJCQhAaGooLFy5g0qRJSE5ORps2bWBvb5/v50Fxtnr1aoXthIQEbN26FT///HMRtejLVKxYERkZGahWrRqePn0KAKhZs6aoAcfChQvRsmXLfHu3xFStWjWEhobCxsZGvu/evXuiDdEGPqSR37t3L+7fv48XL17AwMAADRo0ECWxTmHfDDMzM4Ovry9CQkJgb2+P5ORk/P7776hfv75odXTr1g1v376Fv78/rK2tERgYiAEDBuDXX38V7Wbsp4InMYbtZWZm4syZMwqBf85tAKLc0FL1zbJsurq6GDZsGOrVq5dnOQcxrlm+2eApe6z+iRMn8h1aJYYXL17IM2vdvn0bWlpa8vHcP/zwg2gL2NapUwcXLlzIdxHACxcuiJq6dsKECZg5cybc3NyQkZEBbW1tuLi45EmRXRDx8fFYvHgxmjVrpvJJw/Xr1xf1QzRbYa4ppErZKfzr1Kmj8rqy5wVoaGiU2AxYnp6e6NmzJzIyMvDrr7+iSpUqeP36Nfbu3YvNmzdj7969otSTPZ9pw4YN+fbIipGUIveFs9iy5/+Ym5tjwIAB6N27N6pVq4Y3b95g27ZtogUauT9Dcq/zI3Zvh5aWFpo1a4ZmzZqhb9++2LVrl/wmTXGeO5uf/N6H1tbW6NmzJ3r16iV6fWInn8nWuHFjTJgwAV5eXjAxMcGGDRtEXyOrMHq3gMIZMXHq1Cm0a9cODRo0kPecRkdHY+bMmQgMDCxQ2dlTIMTumfkYLy8vLFy4ED/88ANGjx6NBw8e4MGDB6L2cpiYmGDTpk3y0Ur9+/dHy5Yt5aOOxODq6ppnn7q6OgwNDUUJSCtWrIgFCxbIt8uXL6+wraamJkqAo+qbZdlq1ar10SUjxPDNBk/ZE7qHDBmCPXv2qCQ1pqampnxIW2hoKCwsLOSBWnx8vGiL9Q0YMAAzZ85EuXLlFO5GXblyBQsWLMDChQtFqQcA9PT04O3tjUWLFiEpKQmVKlUSPcBZvnw5srKykJKSIt8nlUrx6NEjhedXUM+ePYOvry+eP3+OjIwMhWMFHfLUsmVLjB07FpMnT853TaHc89OKq+xMgZmZmSpNk5pTScpCmFvt2rWxadMmzJ8/H3/88Yf8vWFubo4NGzaI0lvz9u1b3LlzBwDg5+eHWrVqKQQA79+/x+bNmzF27NgC1aPqAPbUqVPy32vVqqWQxatatWp4+PChSutXlcOHDyMkJAQhISFISEiAlZUVxowZA1tb26JumigyMzMRHx8vapk3b97E9u3bERcXBx8fH+zYseOTvd3/1cyZM7Fs2TLIZDJMnToV48aNQ3JysigZA7MVRu8WoNoRE9lmzJgBNTU1ODg4AAD+/PNP/P7776IOgfrU8D0xbjJkjyCqUaOGwjwtKyurPHPAv3Stumz5jZQwNjbGrl27vrjM3HL3PCUkJGDt2rWiBR0FXdf0cwrrZlk2VU81+OYTRtjb22Pbtm2fXUjtS4wYMQLm5uZwcnJC//794eHhgd69ewP4kLb42bNn8PPzE6WuLVu2YNmyZShfvjyqVq2KmJgYJCYmYsqUKfI6xSCRSLBv3758A46pU6eKUkdQUBBmzZqlEDwBH+6EFDRNak49e/ZE2bJlYW9vn2c4gliZylS5plBOqk6yoOo0qcCHdbEiIyPx66+/AvgQMA8ZMkS0dbEK26tXrxAbG4uKFSuKOmFcIpHgt99+w9u3b+WZq3LS1tZG165dC7R6ek6FPe9FbObm5gpZOt+9e6ew/f79e1F7hMzNzVGqVCn88ssv6N+/f4lOHpH7olAmk+HChQto0KABfHx8RKnjyJEjWLx4Mbp27YqtW7fi2LFj6NOnDxwdHTFu3DhR6igMkyZNQnp6Ory8vDBs2DC0bt0apUqVwpYtW0RLVy0IApKSkhRSYqvCnTt3MHToUAwfPhyHDx9GXFwcZs2aJeoFbu5kXQkJCdi0aROaN2+Orl27Frj83377DcOHD//sTYtz585h3bp12LZt23+uozCXjMiPTCZD27ZtRcngqmqFuS4l8OH7NyAgIM8cxMePH4vSU/fNB09jx45FSEgIrK2tYWBgoPAGKGgw8OzZM3h4eODZs2ewtrbG+vXroaWlha5duyI6Ohrbtm1T6LosqPj4eJw/fx6vX79GxYoV0bZtW1FXNgeAcePG4ebNm7CyssqT+ECseSqOjo7o06cPdHV1cfXqVbi7u2P58uWwsbER7YIQ+JBd5tq1a4W6poyYcl7U5r4gzCbWxW12mtSOHTvCwMBA4ZgYw0K/tnWxCsuIESNEm3P2Mbn/h3LOexFj6FZqair279+P58+f55nELcYNmTVr1qBGjRqfHKIlZi/b27dvceXKFVy5cgXXrl2DlpYWmjZtiqZNm6Jjx46i1VMYcr/+6urq+PHHH+Hm5ibaaI2OHTti6dKlqFevnjx7XHR0NHr16oWLFy8WuPzsG36hoaF4//49DA0NUb9+fXTu3FnUz/73799j2bJlGDNmDGJjYzF27Fh571b2nLuCePToETw8PBAbGwsTExOsWrVKZWsgAh8Wxh44cCDq1KmDP/74Q7SRMp+SmpoKJycnUXpBXr16hTlz5iAqKgo///wz6tevDwMDAwiCgLi4ONy+fRunT5+GkZERZs2a9UVzxgpzyYj83Lp1C6NGjSoRwVNhGzBgADIzM1G+fHm8fv0adevWxcGDB+Hm5lbgURkAg6dPfjmLEQwIgoCEhASFIGbXrl1o3bq1aL1dmZmZ8PPzg56eHgYPHozHjx9j0KBBiI2NRePGjbFq1SrR7lQ1atQIJ0+eFHWseG7Z6Z5jY2MxbNgw7N27F3FxcejVqxdOnz4tWj1ubm5YtGiRKMkbikJhri+i6jSphbEu1tcqOTkZ586dw8uXL1G5cmW0bt1a9PHjuSUkJKBnz56i3FHPvsFkYWEBdXV1hWNifAb37NkT//vf/9CuXTv06tWrUHsx3717h507d2L9+vV49+5diZvz9DGpqamiLTBqbW2Na9euKay3l5mZCVtb2wLf/ImLi0OfPn0gk8nQsmVLlCtXDgkJCbh8+TL09PSwZcsWUXtxJBIJ1NXVoaWlhZiYGJQvX160oGPQoEEwMzNDly5dsHnzZrx58wb+/v6ilJ0t9/vt8ePHuH79Orp16yYfMSHWCJP8REVFoWfPnqKmEP/777+xZ88ehISEIDY2FmpqajA0NIStrS1cXFxKTDbM3L1bMpkMT548weDBgzFq1KgibNl/o+qbZdksLS1x8eJFxMTEYMmSJdiwYQOuXr2KFStWYMeOHQUu/5ud85Rt0aJFkEgkUFNTg7a2tugfeGpqannSE7ds2VLUYYJ//PEHTpw4IZ8wOn/+fJibm2Pnzp3w9/eHj4+PPE16QVWtWlXld6AMDAyQnJyMKlWq4Pnz5xAEAZUrVxZtnH1217CRkRH69OmD7t275/kb9e3bV5S6VMna2rrQhnGoOk1qWFgYNmzYkO+xnj17irrS+dckPDwc7u7uKF26NL7//ns8f/4cixYtwsaNG1G3bl2V1SvmvJebN2/i3Llzoi6AndOOHTvw+PFj7N27F6NGjUL58uXRq1cvdO7cWSVzXW/cuCHveQoLC0PDhg3h6elZ4jLtXbp0ST7P9KeffpLvv3LlCmbMmCHaHIm6deti8+bNCkkVDh06BDMzswKXvWzZMjRo0ACLFy9WGHKcmZmJyZMnw9fXF3Pnzi1wPcCHv/vw4cOxbt06NGzYEHv37sW2bduwdu1aUS7Q79y5g7Vr10JDQwPjx4+XpysX07t37xS2DQwM4OTkhLS0NNGzUuae0yaTyXD79m3Rn5eOjo5of+PcCnPJiNwZVtXV1VGrVq0SE/xlGz169EdvlolJX18f+vr6+PHHH/Ho0SMAH9KXjxkzRpTyv/ngSZUfeIIgYP78+fLhR8CHoXX29vbo3r07Zs+eLcqE2MOHD8Pf3x/GxsaIj4/H9evXsXPnTlSpUgXDhw/HL7/8UuA6si+e27dvj6FDh2LEiBF5LnbEyurXsmVLeHh4wN/fHxYWFvDy8oK2trZo80ZyTlT/8ccf89zdVFNTKxHBU85hHLVr14afn5/KhnGkpKRgyZIlGDBgAGrVqoWAgAC8ePECkydPVnkwXVKHVRaGhQsXYuDAgRg4cKB834YNG7Bw4ULRFsb+2LyXli1bilK+sbHxR4edisXExASTJ0/GuHHjEBwcjD179sDHxwcdO3aEm5ubQnBQUCNHjkSrVq3Qt29ftGrVCjKZTOU9gWJbs2YN/P39YWRkBB8fH6xZswa2trbw8vLCtm3bRJmTkm369OkYOHAgdu/ejdTUVPTs2RPR0dFYv359gcu+cuUKDh8+nGeupoaGBiZOnAg3N7cC15Ft4cKFmDp1Kho2bAjgw/9BzZo1MX/+fOzevbvA5QuCIH8eZcuWhVQqLXCZuRXmEhG5M7iqq6ujc+fO+a5ZWRB9+/ZFcHCwSpIdFcaSEdlUOfyvMKn6Zlk2U1NTbNq0CX379oW+vj7u3r0LHR0d0eZtf/PD9rp27Yq+ffsqpIE8dOgQtmzZUuAPvHXr1uHgwYNYsmQJ6tWrJ99/+/ZtTJo0CW5ubgoXPV8qe5gbAJw+fRpTp07F9evX5YFZzuNfytzcHGpqah9N6yvmwnZSqRSbNm2Cm5sb3r9/L08eMW3atBJ3l0WVCmMYR7axY8ciLS0NXl5eqFixIiIiIuDj44PSpUuLkvJ1wIAB6Nu3b75358+cOYO1a9di586dBa7na2NtbY2rV68qfCFkZGTA2tpaYbX4glD1vJd79+5h7NixcHBwyLNWmSozJsXGxmL58uU4cuSIKJ9d2cOndXV1MWTIEDx+/BgeHh549eqV6MOnVc3Ozg7z589H8+bNERQUhP3790NHRwf379+Hl5cXmjdvLmp9qampOHfuHGJiYmBgYIDWrVuLsqD05777GjdujJs3bxa4HuDDkPbc7zlBENCkSRPcuHFD9PJVmSQoMTER27dvx9ChQ3H37l1MmjQJ5cqVw+LFi1GrVq0Clf2xAK1UqVIwMzMTfV6gm5sbRowYIfr/bGFRJimFuro6KlasiAEDBhT7rJ7dunWDr6+vqEmU8hMeHo7Ro0cjICAAf//9NyZOnAjgQ6r/7HnVBfHN9zw9efIkT/58Z2dnUYa57du3DytWrMgz/MDS0hKLFy/GzJkzRQmedHV1kZycDH19fVy/fh2NGzeWv8levnwpygVOYaxunftDNXudmXr16sHMzEzUwCkxMRFv376VL767a9cuhIeHw8HBodh/+GQrjGEc2a5cuYLz58+jVKlSAD70Foi5sN3Xsi5WYStbtiwePXqk0Ov7+PFjUeckfuxiJzU1VZTy/f39kZGRgadPnyoEgapa4+3169c4ePAgDhw4gNTUVNHmC+Q3fNrMzAw7duwQffi0qiUkJMgvNh0dHTFx4kRYWVnh4MGDogQ1OU2dOhVdunRRSTKNz/0PiXnv+IcffsCZM2dgZ2cn33fp0iX5ukYFJQgCwsPD5W3OzMxU2AbEG/0xa9YspKamQhAEzJ07Fy1btsR3332H2bNnFzgjWu6hgdnevn2LAwcO4O7du6LPqxo0aBDKly+fJylYQYfUubu7Kww3379/v0IPkRgBbnZSnk/1cgmCgEePHmHixInFPnnEzJkz0a9fP5XfLDMzM8Px48cBfHhvNm7cGCkpKTAyMhKl/G8+eFLlB97r168/Om67UaNGiI2NLXAdwIdhbkuXLoWjoyMOHTqEKVOmAPjw4fr777+LesfF1dUVBw4cyLO/bdu2OHv2bIHKLqwP1X/++UeeoGD27NlYs2YNAgIC0KFDB0yaNAmzZ8+Wr29RnBXGMI5smpqaSEpKkgdPwIehfNmrhBfU17IuVmHr27cvBg8ejL59+6JatWp48eIFtmzZgkGDBolSfmHMewkJCcH58+dVOoxDKpUiODgY+/fvx9WrV9G8eXOMHz8ebdq0ES1IK4zh04Ul52uioaEBTU1NeHt7ix44AUCZMmUwYcIEaGpqwsXFBa6urqIl8ckdcOR3XCxjxozB6NGj0axZMxgaGuLly5cICQnB77//Lkr5aWlpcHV1VWhz586d5b+LOfrj7t27OHnyJF6/fi2fj1qmTBlREhB9amhgTEwMunTpImrw1L17d3Tv3l208nLK3au5aNEiheBJJpMVuA5nZ2cA/w7bi4uLw6tXr1CxYkWF61SJRIKoqKgC16dqqr5Z9rk05FFRUaLc9P3mg6fcH3ivXr3CtWvXRPnAK126NBISEvId756YmCjaXJFJkyZh7NixGD58ODp06CDvSWvdujW0tbULPPfh+fPnWLJkCQRBwOPHj/PcHXj//n2erClforA+VFesWAF3d3d4enoiKysLf/75JyZPnozu3bsjJCQE3t7eJSZ4KiydO3eGp6cnhg4diqpVqyI2NhZr166Fk5OTaHX8+uuvcHJyKpR1sb4Wffr0gY6ODg4ePIi3b9+iWrVqmDBhAlxcXApcdmHNe/nxxx+RnJyssuBpzpw5OHbsmHz9qzlz5nxRWuLPiYuLk/dk37p1C/r6+qhfvz6Af5PglFQ6Ojoqy7A6depUTJkyBdeuXcPhw4fRrVs3GBkZwdXVtcBzkvILOHISs3ezbdu22Lt3L44fP47Xr1+jYcOGmDJlimiBYGGM/siWnUTr4sWLMDMzQ/ny5ZGQkKDy+ae5eyLEkB10yGQyxMTEoEaNGgBU07Od+/9MzDoSEhIwYcIEXLlyBVpaWpDJZLC0tMSqVatQoUIF6OjolIjESqq+Wfa5ha/V1NQYPIkh5wfemzdvUL9+fUyaNKnA43oBoFWrVti4cSPGjx+f59imTZtESyNdsWLFfLvSFy1aBCsrqwIHadWrV4etrS3i4+Nx7ty5PEMDtLW1VT6sSswP1Tt37sgXJw4LC0NiYqJ8kqqlpSUeP34sWl2qVJjDOMaOHQtNTU0sXrwYb968QZUqVeDs7AxPT09Rys9WqlSpEjNssig9efJEflGW353VQ4cOFTiA2rVrF/744w/5vJfAwEBs374d9+/fx/r160Xr0XZ0dISbmxu6du2KcuXKKVxwiJG45cmTJ5g7dy4cHBygqam6r7zCGD5dWArzswX4cEFja2uLRo0aoVmzZvDx8cGiRYsKHDwVRsCRe6kQNTU1DBo0CK9evcLly5dL1Fy3bK1atYKHhwciIyMxaNAgREdHY9KkSQojdMS2detWbN++XfSslOnp6Zg/fz72798PbW1t7N27F8OGDcP69etFX8BaVUONgQ/Xc/r6+rh48SIqVaqE2NhYeHl5wcvLC97e3iqrV2yqvlmWPRoiPDxclIydH/PNJ4zISRAEJCYmipYZ6fXr1+jSpQssLCzg6OiISpUq4fXr1zh58iRu3bqFnTt3lrjV50+dOiV6NpzPyf5QrVevnijJCXJOIt6yZQt27dqFw4cPA/hwd6pp06aiTSJWpcJM4kHFi52dHXbu3InKlSsr7M/IyMCCBQuwa9cu/PPPPwWqI+f7JDMzEw0aNICVlRVWrlwp6vCtj608L/aK86o2efJk6OjowNHREePHj8eUKVPg6uqKzMxMTJs2DWpqaqJ8fhWGwvxsycrKwqVLl3DkyBEEBwfDxMQEnTt3RqdOnVQyTFBsq1atwtGjRzF9+nS0aNEC/fr1g66uLubOnQt/f38IglBi5rplk0gk2LZtG/T19dGtWzdERETgyJEj8PT0FG2odm4HDhxASkoKunXrJmoP16xZs5CQkICxY8eiR48euHLlChYvXoynT58WOKPj55J45JdE5Eu1aNECJ06cUMgamJycDDs7O5UlDlGFNWvWYPv27Sq7WZatadOmKsuyCHzDPU/5LSwrdmakypUrY9++fVi5ciWWL18uH4rUpk0bHDhwAAYGBuI8mUI0fvx4tGrVCp07d0abNm0KZUjVd999Bzc3t3wXUP0SNWvWxJ07d2BhYYHTp08r3EE/f/58iVk0t7CGcURERCAhIQFWVlbIyMiAn5+fPLnGr7/+WihtIEVWVlYYOHAgtm3bJu+VffXqFUaOHImYmBisXbu2wHUU1ryXLVu2iFpeUVH18OnCVJhDxJo3b45SpUrB2dkZu3fvFm1Cd2H5mua6ZdPR0cGAAQPk28bGxhg9ejSeP3+usixpuRN3ieXMmTM4fvw49PX1oaamBi0tLUyePBktWrQocNmf66EVu29CIpEoBANSqbTELeVx+fJl1KhRI08GSrGXiKlVqxbu3LmjsiyL32zwVBiZka5evQpra2ssWLBAjCYXCydOnMCRI0ewevVqzJgxA46OjnBxcYGVlZXK6hT7Q9Xd3R1DhgxBrVq1EB4eLv/7BAYGYt26dfkOs/xWXb16FUOGDIG7uzusrKywbNkyBAUFoVevXtiwYQMEQRAtqCXlLV68GGPGjIGHhwcCAwNx48YNTJgwAebm5jhw4ECeHikxqHLey82bN7Fjxw7ExsbCx8cHO3bsyLOIZnGn6uHTXytvb2/Y2tqWqL91Tl/jXLerV69i4cKFiIuLkwcAMpkMMpkM9+/fL+LW/TeampryZErZzyU1NVWU96MySTzE0r59e4wZMwYTJ06UJwdavnw52rdvL1odhaEwb5apKssi8A0HT4Vxt2jHjh2YNm0aLC0tYWdnhzZt2pSoce/5MTQ0hIeHBzw8PBAREYETJ05g7ty5SE1NhYuLC7p27VrshyI6OzujSpUquHfvHhYsWCBv76lTpzBs2LASeadQVfz9/TFnzhx07doVMpkMe/bswfz589GxY0e0aNECM2bMYPBUBNTV1eHt7S3/f3327BmGDRsGT09P0b6wC2vey5EjR7B48WJ07doVZ86cQVZWFg4fPgypVIpx48YVuPyiJtZiwl+b7du3w83NDY8fP/7oPNOSsFj51zTXLduiRYvQsmVLlClTBvfv34erqyv8/f1FSURT2BwdHTF69Gj5Oj9PnjzBsmXLRJl+UJg9tBMmTMDMmTPh5uaGjIwMaGtrw8XFpUTe7C2Mm2WqzLIIfMNzngpjYVngw92akJAQnDlzBhcvXoShoSHs7Oxgb29f7IOMT4mPj8fx48dx4sQJ3Lt3Dy1atEDVqlVx9OhRDB48GP379y/qJn7Wrl274OLiopB+mxQ1adIE165dg4aGBu7cuYPevXsjJCQE+vr6yMzMhJWVlSjvE/oyEokEgwYNgra2tsJ6I2IorHkvHTt2xNKlS1GvXj00adIEoaGhiI6ORq9evXDx4sUCl0/Fk4eHBwICAkr8nLevaa5bNgsLC9y6dQsxMTGYMGECduzYgadPn8LT0xPHjh0r6ub9J1KpFEuXLsWePXuQnp4ObW1tODs7Y9q0aSqbD6NKUqkUSUlJqFSpUonsrc15s2zr1q04duwY+vTpA0dHxxJ1s+yb7XkqrLtFWlpaaNGihXx8bVhYGIKDgzF27FhIJBJ5ooKSYu/evQgKCkJISAgaNmwIZ2dn+Pn5yTOntGvXDp6eniUieFq+fLlo6Za/VpmZmVBXVwcA3LhxA+bm5vL3hkwmU2n2Mvq4nHc8s4dyzJs3T+FOW0F7hQrrruqbN2/ka0hlfwZXq1YNEomkUOqnohEQEAAA8PHxyXeY6aNHjwq7SV/ka5rrlq1ixYrIyMhAtWrV8PTpUwAf5grHxcUVccv+O21tbcyYMQMzZsxAfHw8ypUrJ/9OKwk+tYRLNrEXFVYlf39/rFmzBvXq1cP27dthYGCA9evXo1evXqIET4WxcDHwDQdPhbmwbE7m5uYwNzfHoEGDcOrUKZXUoUoBAQFwcXHB3Llz8504+uOPP2L06NFF0LL/ztHREatWrYKzs3OeMbElcaiFKpiamuL8+fNo3bo1jh8/jlatWsmPHTt2DKampkXYum+Xq6trnl6hbdu2Ydu2bQBKVqbFunXrYvPmzQo3XA4dOqTSNLNUfDg6OubJSCaVStG9e/cS0av9Nc51a9y4MSZMmAAvLy+YmJhgw4YNKp3zqGoXLlzA8+fPkZGRobC/JAwLfffuXVE3QVSqvllWGAsXA9/wsL23b99i7NixuHfvHjp06CCP7lu0aCG/W2RoaKiy+mNjY9GmTZsSc4EDfJgDkZSUVOLWrPgYS0tLpKWlAfj3TSwIQom68FS1S5cuYeTIkShbtiwyMjJw4MABVKpUCQsXLsTu3bvh7e2t0rU/6Ov3+PFjDBw4EKVLl8bTp09Rr149REdHY/369ahTp05RN49U4Pnz5+jevTsyMjLw7t27PGu+SKVSmJubY8eOHUXUwm/b+/fvsWzZMowZMwavXr3CuHHjkJycDC8vL7Ru3bqom/efzJs3D/v374eZmZlCduCSMiwU+DBHKCoqSp7dViqVYvDgwRgxYoRKk3WpwoABA9C6dWv0799f3gu0f/9+7Nu3T5RkErnTw2cPBf/Y8S/1zQZPH3Px4sVCuVsUGxuL1q1bF+qEw4J49OgRPDw8EBsbi9q1a8PPzw81a9Ys6mYVyIsXLz567Pvvvy/ElhRv0dHRuH//PmxsbFChQgUAwMSJE9GhQwcGTiSK1NRUnDt3DjExMTAwMEDr1q1LxBo/9OXCwsKQlJSEwYMHy4fwZdPW1oaZmVmJ7LWh4sXKygq7du0qcSnws4WGhmLw4MHw9PTEkCFDAHxY32np0qU4dOgQNmzYgMaNGxdxK5Wn6ptlhbX2FoMnfFikLzU1Vb4tlUrx6NEj2NjYqKzOktbzNGjQIJiZmaFLly7YvHkz3rx5A39//6JuVoFlZWUhJCQEL168gLOzM2JjY1GjRo2iblaxVhLXlqDiLS0tDbq6usjIyMCxY8dQrlw5Zqn7RsTHx0MqlaJs2bLQ1dVFWFgYSpcuzRtYReDAgQOfPUdV6zGpioODA4KCgkrsd1a/fv3g5OSUb1bbTZs24cKFC9i0aVMRtOzLqfJmWWEFT9/snKdsQUFBmDVrFlJSUhT2ly9fHleuXCmiVhU/d+7cwdq1a6GhoYHx48ejU6dORd2kAouOjsbgwYORnJyM5ORkWFpawtXVFX/88YcoC+h9TbKysrB27Vps374d79+/x6FDhzB9+nT8/vvv8t4ooi9x8OBBeHl54fr161i2bBmOHDkCNTU19OnTR36nlb5eN2/exMSJE7Ft2zb89NNPuHbtGlatWgVvb+8SN0SspAsMDJT//ujRI9SuXVvhuJqaWokJnrLX1/rll18wbdo0jB07Ns8FekmY2xwWFvbRTKo9e/bEmjVrCrlFBaempoaOHTvKb5bdu3dPtJtlhbVw8TcfPK1YsQJjxoyBrq4url69Cnd3dyxfvlyUXqdP5a1PT08vcPmFSRAEaGhoAADKli0rX3SuJJs3bx66dOkCDw8PWP9fe3ceFWW9/wH8PSCiiJooYnRNb2qQKwTDooICaoAiZF0hqKM3xQ3uOdmiIjdLUMzURC1cwtxSUlBZhGulAxbIqqaSSsnBBRVQJoFhFMYZfn90nZ+4hZflmeF5v87pnOZ5xu+8D6bN5/kuHwcHDBgwAFFRUVizZg2Lp4esW7cOeXl5iIqKwvz582FmZobu3bsjIiIC0dHRQscjPbZ161asW7cOarUa+/fvx+bNm2FhYYGgoCAWTyLwxRdfYOPGjdpN5NOnT4eVlZVe7q/Rdw/OPEml0ibNROkqe3v7RofqpKamau+1l73N+jib1toPy9qqcbHoi6eKigq8/fbbKC8vx549e/DKK69gxYoVCAwMxKxZs5o19l+t37SxsWnW+G2pPa7uPHPmDDZu3AiJRKL9A+Xj44NPP/1U2GA6KDk5GfHx8dreEl26dMGKFSvg4eEhdDTSc2VlZXB2dkZBQQEMDQ216/dramoETkZtoays7JGHlY6OjigrKxMoEQEt9yVTKEePHhU6QosYPHgwfv75Z7i5uT1y79ixY+jfv3/bh2qG1n5Y1lbnCIi+eOrduzcUCgUsLCxQWlqKhoYGmJubQy6XN3vs0NDQFkioG/5qKhRofl+ZttajRw9cvHixUe7i4mK9PY61NalUKpiYmAD4/0LawMCAfZ6o2Xr37o2ff/4Zqamp2hYRaWlpet1EnJrOysoK+/btQ0BAgPbawYMHMXDgQAFTkb67v2du7ty52Lhx4yP3g4KC9KIH17vvvotFixYhMjISY8aMgaGhIdRqNY4dO4YlS5YgPDxc6IjPpL08LBP9Nx8XFxcEBwcjJiYGNjY2WL58OTp27PjYHkbP6ssvv/zL9+hLgdWUqVB9mwJ/9913ERwcjGnTpkGlUmHfvn345ptvntjxXsxcXFwQHh6Ojz/+GBKJRNu13dnZWehopOc+/PBDvP/++zA1NcU333yD7OxshIeHY8OGDUJHozawcOFCzJo1S9sepKysDOXl5Y+cwEfUVNeuXdMeQ56ZmflIo9mamhoUFxcLEe2Zubi4YP78+Vi4cCFUKhW6d++O27dvo1OnTvjggw/g5eUldMRn0l4elon+tL36+nps27YNb731FmpqarBkyRIoFAqEh4dj+PDhzRrb2toapqamsLe31+4XepBEImlSgUWt54cffkB8fLz21Bc/P79GRSH9qbq6GgsWLEBGRgYAwNDQEA4ODlizZg0PjKBmu78HAQDq6uqgUqn0YjM3tQy5XI6MjAzcunULFhYWGDNmTLvpJ6hPHlzyFBQUhD179jyyZF9fVpisWrUKcrkcKSkp8PHxaXTPyMgIXl5eevXw7+7duzh16hTkcjnMzc1ha2vbqG+VvsjIyMBHH32kfVhWVlaGefPmYcOGDXq111z0xVN+fj6kUmmrjJ2cnIyUlBRcuHABXl5e8PX1xZAhQ1rls+jZJSQkwM3NDT179hQ6it6orKzEtWvXYGFhAQsLC6HjUDtQX1+Pw4cPY/LkySgpKUFkZCSee+45hIWFwdzcXOh41AbYMkI3WFtbNzpk4WH6uMJk+/btUCgUuHXrFnr16gUPDw823xZYe3hYJvriadSoUejUqRMmT54MPz+/Vmn8WllZiUOHDiEpKQn19fWYPHkyfH19+eVTYDNmzMDJkydhZWUFd3d3eHh4YMCAAULH0lmnT59Gamoqbt26hT59+sDX1xdWVlZCxyI9t3jxYpw7dw6JiYl45513YGZmhk6dOqGmpqZd9JKjp3u4ZURCQgJbRlCLWLlyJXbt2gVbW1uYm5vj+vXrKCwsRFBQEMLCwoSOJ0rt5WGZ6IsnjUaDzMxMHDp0CEeOHMHLL78MPz8/eHt7o1u3bi3+ecXFxUhOTkZqair69u2rd83N2hulUomcnBxkZGQgMzMTHTp0gIeHBxYuXCh0NJ1y4MABREREYPz48ejTpw+uX78OmUyGVatWYdy4cULHIz3m7u6OxMREaDQajBw5Eunp6ejRowdGjhyJgoICoeNRKwsODoZUKtW2jMjPz0dKSgq++eYbHDx4UOh4pKeSkpKwYcMGxMbGNjqRrqioCCEhIQgNDdWbnlXtSXt5WCb6AyMMDAzg6uoKV1dX3L17Fz/88AOio6MRFRWFM2fOtOhnqdVqXL16FaWlpZDL5Xj++edbdHx6diYmJhg8eDCqq6tRX1+P77//HklJSSyeHvLll18iNjYW9vb22mvZ2dlYunQpiydqFoVCAVNTUxw+fBj9+/eHhYUFlErlY/eJUvvDlhHUGvbs2YPIyMhHjvK2srLCJ598gi+//JLFkwBycnKQmJiI27dv48SJE40elukT0RdPwJ/rL3NycnDo0CH8+OOP6NevH2bMmNFi499/kvb999+jd+/e8PHxwYcffsjiSWAff/wxcnNzIZfLYWdnBycnJ0yfPl1vNsS2paqqqkcOUJFKpbh9+7YwgajdePXVV7Fo0SIUFhbCy8sLFRUVWLZsWYs0Kifdx5YR1BpKSkrg5OT02HuOjo6YP39+GycioP08LBN98RQVFYX//Oc/MDQ0hI+PD+Li4lps38vnn3+OtLQ0AMDEiROxY8cOfjHXIQUFBaioqMCECRMwcuRIODs7cx/aE7z55pv4/PPPsWDBAnTs2BEajQYbN27EpEmThI5Gei4qKgoxMTGYMGEC5s6di6KiIkgkEnTt2lXoaNQG2DKCWoNGo0Ftbe1jDyKoq6vTuy/r7cXDD8tu3ryJyMhIODg4CB3tmYh+z9OHH36I119/HSNHjmzxjtrW1tZ47rnnYGtr+8Q/qDyqXFgVFRU4fvw4jh8/jry8PBgbG8PZ2ZlLRh7i6emJS5cuoXPnzujduzfkcjlqamrQpUuXRv9t5+XlCZiS9J1MJsPOnTuRk5ODoUOHIiEhQehI1AbYMoJa2owZM/Daa69h6tSpj9zbu3cv0tPTsWnTJgGSiZtcLkdMTAxMTU0REhKC3377DTt27MDixYv1qj2B6Isn4M8nFEqlUvu6vr4ev//+e7OXjbSnJrntmVKpRG5uLrKyspCSkoJOnTrh2LFjQsfSKU0tivTt6REJr6amBgkJCdi9ezeuXbuGoKAg+Pv7Y9CgQUJHIyI9lZ+fj9DQUERERGDcuHEwNDSESqVCUlISVq1ahS1btmDEiBFCxyQAFy9exK5du7B06VKhozSZ6IuntLQ0LFmyBLW1tY2u9+jRA8ePH2/W2CdOnICdnV2zxqDWExMTg+PHj+PMmTOwsrLCmDFj4Obmxl5cj7Fy5Ur4+fnxaHJqMcXFxdi5cyeSk5MxbNgwBAQEYNmyZUhJSWHvNRH44IMPsGbNGoSEhDxx1YeBgQF69uwJf39/LnmnZ5aWlobIyEjcuXMH3bt3R2VlJbp166YtqEhYMpkMu3btQk5ODoYMGaJXKw1Ev+dp3bp1eO+999C5c2dkZ2djxowZWL16dYtsVg4ODsbJkydbICW1hnPnzsHPzw/R0dHcnPwXqqur8c4778DCwgK+vr7w8fHh/jBqlkmTJmHKlCnYv38/XnrpJQB/7n8icXj55ZcB4KkNSxsaGlBcXIzQ0FAcOXKkraJRO+Ht7Q0PDw/88ssvuHnzJnr27Ak7Ozt07NhR6GiidX+lwbfffovr168jKCgIixcv1ruVBqKfebK1tcWpU6dQXl6OefPmYf/+/aioqEBgYGCz/7K+PzbprqtXryItLQ03btxAr1694OXlxUa5T1BfX4+ffvoJhw4dwk8//YThw4fD19cXEyZMQJcuXYSOR3pmyZIlOHz4MAYPHoypU6diwoQJGDt2LJKSkjjzJFL37t1Dhw6Nn+nW1tYiICAAKSkpAqUiouZqbysNDIQOILTevXtDoVDAwsICpaWlaGhogLm5OeRyebPHlkgk2q7pT/qHhJOXl4fJkycjPz8fDQ0NOHXqFKZMmYKsrCyho+mkjh07Yty4cVi7di3Wr1+P27dvIywsDK6urggLC8PNmzeFjkh6JCIiAhkZGfDy8kJsbCxcXV1RXV2N4uJioaNRG7pz5w6WLVsGJycnDBs2DC4uLli3bh1UKhUAoEuXLiyciPTcpEmTcO/ePezfvx87d+6Et7c3DAz0twQR/czTsmXL8OuvvyImJgaLFi1C37590bFjR2RmZiI5OblZY1tbWz9xLXdDQwMkEgnOnz/frM+g/93UqVMxffp0eHt7a6+lpqZi69atOHDggIDJdNOpU6eQmpqKw4cPw9DQEBMnToSvry/Mzc0RHR2Ns2fP4uDBg0LHJD119uxZ7N27F6mpqbC0tISPjw/mzJkjdCxqZeHh4bh06RJCQkLQp08fXLt2DV999RVGjBiBsLAwoeMRUQtobysNRF881dfXY9u2bXjrrbdQU1OjPTxi8eLFjzQFfVa2trY4dOjQU9/zwgsvNOsz6H8nlUqRm5vb6OmHRqOBvb0996r916xZs7Blyxa4ubmhuroa48ePh6+vL5ycnBo9GPjtt98wbdo0ZGdnC5iW2gOFQoHk5GTs27cPiYmJQsehVjZy5EgcPnwY3bp1016rrKzE5MmTuQqAqB1RKpVISUnB3r17UVZWBoVCgdjYWL08pVe0B0asWLGi0euvvvoKADB06FBYWVk1u3AC/ly2x+JId/Xp0wcnTpyAVCrVXisoKIClpaWAqXRLQUEBgD9Pxho3bhw6der02PcNGjSIhRO1CFNTUwQGBiIwMFDoKNQGevToAaVS2ah4UqvV3NRP1M6YmJjA398f/v7+2pUGs2fP1suVBqItnqqrqx97vbKyEomJiTh9+nSzlwyIfFJP582ZMwezZ8+Gr68vLC0tce3aNaSkpCAyMlLoaDpn0qRJT73f0g2miah9O3r0KABg9OjRmDlzJmbNmgVLS0vcunULW7Zs+cu/c4hIfw0bNgzDhg3DokWLtCsN9Kl4Ev2yvce5fv06Xn/9deTm5jZrnIKCAtjb27dQKmoN9/e2VVZWwtLSEr6+vvw9e8DQoUMRFBT01PdwXwIRPSt3d/dGr+8/gLn/lUQikWgLLCJqP+rq6pCTk4OKigq88MILkEqlMDIyEjrWMxHtzNPTdO3atUXGKS0tRWlp6VPf4+fn1yKfRc+mqqoKpaWlkEqlGD16tNBxdNqTZmmJiP5XMpkMAHDhwgUcOXIEN2/ehLm5OTw8PJ7a+4mI9FdhYSHmzJkDQ0ND9OnTBzdu3ICxsTG2bNmCv//970LHazLOPD1k9+7diIuLw9ChQ/HZZ581a6wHC6Pff//9kSZgEomEp5MJoKCgALNmzYJSqUSvXr2wadMmDB06VOhYOunVV1/l4RlE1CpWrlyJXbt2wdbWFubm5rh+/ToKCwsRFBTEGW2idiggIACenp6YPn269tqmTZuQlZWFXbt2CRfsGbF4ekhiYiJqa2vxj3/8o0U3rEqlUuTn57fYePS/CwoKgpeXF9544w1s3boVJ06cwLZt24SOpZPY6JmIWkNSUhI2bNiA2NhY9O/fX3u9qKgIISEhCA0N5coMonbGwcEB2dnZMDQ01F67d+8eHBwc9OpBrf52qGolfn5+CAoKavGTfrihXndcuHABb7/9Njp37ox3330XFy5cEDqSzuL+LyJqDXv27EFkZGSjwgkArKys8MknnyAuLk6YYETUahwdHXHkyJFG17KyslrkhOu2xD1PJGomJiZQq9VCx9BZX3/9tdARiKgdKikpgZOT02PvOTo6Yv78+W2ciIhaS0hICCQSCaqqqjB//nw4OjpqT9fMzs6Gi4uL0BGfCYsnEh2uVCUiEpZGo0FtbS1MTU0fuVdXV9doWQ8R6bcHD4FxdHTU/vvzzz+PYcOGCRGpWVg8taIHl4Op1WoUFRU98sXd2tq6rWOJnlqthkwm0/5eqFSqRq8BwMPDQ6h4RETt3ogRI5CWloapU6c+ci8tLQ22trYCpCKi1hAaGvrItfr6er1ths0DI1qRtbU1JBLJE2c6JBIJzp8/38ap6OH+Ig9jfxEiotaVn5+P0NBQREREYNy4cTA0NIRKpUJSUhJWrVqFLVu2YMSIEULHJKIWpNFosHnzZsTFxaGmpgbJyckIDw9HdHQ0zMzMhI7XZCyeSHTu3LmDzp07Cx2DiEjU0tLSEBkZiTt37qB79+6orKxEt27dtAUVEbUva9euRV5eHkJCQjB//nxkZGRg0aJFMDQ0RHR0tNDxmozFE4mOu7s7Bg4cCHd3d7i5ucHCwkLoSEREolRXV4dffvkFN2/eRM+ePWFnZ6e3S3mI6Onc3NwQHx+PXr16wcHBAXl5eVAoFPDw8EBubq7Q8ZqMe55IdI4ePYrTp09DJpNh5syZ6NixI9zc3NjZnoiojRkbGzfaQE5E7ZdKpYKJiQmA/z+8y8DAAB066Fc5wpknEr2rV69CJpNBJpPh+vXrcHFxwZIlS4SORURERNRuhIWF4e7du/j444/h6emJzMxMREVFQaFQYPXq1ULHazIWT0QPqKmpwU8//YSJEycKHYWIiIhI712+fBn9+vVDdXU1Fi5ciPT0dACAoaEhHBwcsGbNGh4YQaTLVqxY8ZfvCQsLa4MkRERERO3bK6+8gr59+8LV1RWurq4YOHAgbt26BQsLC73cd65fiwyJWkB1dbXQEYiIiIhEISsrCzk5OcjJycHy5ctRXl4OqVSKMWPGwNXVFS+++KLQEZ8JZ56IiIiIiKhNlJeXIzs7G/n5+cjKyoKxsTG+//57oWM1GWeeSHS4bI+IiIio7SkUCpw8eRL5+fkoKChAXV0d7O3thY71TFg8kehw2R4RERFR27h8+TLS09Mhk8lw8uRJvPzyy3B1dcVnn30GGxsbSCQSoSM+Ey7bIyIiIiKiVmFtbQ0bGxtMnToVLi4uMDc3FzpSsxgIHYBISMnJyQgMDMS4ceNQVlam7UFARERERM03ceJElJSUYNu2bdi+fTsKCgqg0WiEjvU/48wTidb27duxd+9eTJ8+HatWrcKRI0cwd+5cDBo0CBEREULHIyIiImoXNBoNTp48ifT0dGRkZODmzZsYNWqU9sQ99nki0gMTJkxAbGwsXnzxRTg4OCAvLw9yuRyTJk3C8ePHhY5HRERE1C6Vlpbi6NGj2LlzJ27cuIFz584JHanJeGAEiVZ1dTWef/55AMD9ZwimpqZ6PZVMREREpItqa2tx4sQJFBQUIC8vD0VFRRg6dCj8/f2FjvZMWDyRaNnb22P16tVYuHCh9qSXrVu3wsbGRthgRERERO3EypUrtcWSpaUlRo0aheDgYDg7O8PExEToeM+My/ZItMrLyzFnzhxcvXoVSqUSvXr1gqmpKTZv3oy+ffsKHY+IiIhI782ePRsuLi5wcXFBv379hI7TbCyeSNQ0Gg0KCwtx7do19O7dG8OHD4eRkZHQsYiIiIhIB7F4IlFTKBQoLy+HSqVqdN3a2lqgRERERESkq7jniUQrPj4eS5cuxb179xpdl0gkOH/+vECpiIiIiEhXceaJRMvV1RUffPABvL29uVSPiIiIiP4SZ55ItNRqNXx9fYWOQURERER6wkDoAERCGT9+PHbv3i10DCIiIiLSE1y2R6Lj5+cHiUSCu3fvoqSkBJaWlujevXuj9xw8eFCgdERERESkq7hsj0Rn2rRpQkcgIiIiIj3EmSciIiIiIqIm4J4nEqX4+HjEx8cDAMrLyxEQEABbW1ssWLAAdXV1AqcjIiIiIl3E4olEZ/fu3YiOjoaxsTEAICIiAgAQGxsLpVKJ9evXCxmPiIiIiHQUl+2R6Pj4+CAyMhI2NjZQKBRwcnLC1q1b4ejoiKtXr2LatGmQyWRCxyQiIiIiHcOZJxKda9euwcbGBgBw6tQpGBkZwc7ODgDQt29f/PHHHwKmIyIiIiJdxeKJRKdDhw6or68HAOTn58PGxgYdOvx58KRcLkfnzp2FjEdEREREOorFE4mOg4MDtmzZgkuXLiE5ORnjxo3T3tu6dat2FoqIiIiI6EHc80Sic+XKFQQHB+Py5ctwdHREbGwsjIyMMGXKFFy9ehV79uzBoEGDhI5JRERERDqGxROJUkNDA/744w+YmZlpr+3btw9jxoyBhYWFgMmIiIiISFexeCLRCQwMhLu7O9zd3fHSSy8JHYeIiIiI9ASLJxKdixcvQiaTQSaToaqqCmPHjoW7uzvs7OxgYMBtgERERET0eCyeSNTkcjnS09Mhk8lw7tw5SKVSuLu7w9PTU+hoRERERKRjWDwR/Vd9fT2OHz+O9PR0LF26VOg4RERERKRjOggdgEhIxcXFKC0txb1797TXXF1dBUxERERERLqKxROJVkxMDDZs2IBevXrByMhIe10ikcDDw0PAZERERESki7hsj0TLyckJGzZsgFQqFToKEREREekBHi1GomVsbAw7OzuhYxARERGRnmDxRKI1ZcoUfPHFF432OxERERERPQmX7ZHoSKVSSCQSqNVq1NbWokOHDjAxMWn0nry8PIHSEREREZGu4oERJDpfffWV0BGIiIiISA9x5onov06ePInu3btjwIABQkchIiIiIh3EPU8kWj///DNee+01AMDmzZsxffp0vPHGG0hISBA4GRERERHpIhZPJFrr169HcHAwNBoNdu7ciQ0bNiAuLg6bNm0SOhoRERER6SDueSLRunLlCt58800UFhZCqVRi9OjRMDQ0hFwuFzoaEREREekgzjyRaHXr1g3FxcVITU2Fs7MzDA0NkZ+fD3Nzc6GjEREREZEO4swTidacOXMwefJkGBkZYceOHThx4gSCg4MREREhdDQiIiIi0kE8bY9ETS6Xw9jYGF26dEF1dTXkcjn69+8vdCwiIiIi0kEsnkjUCgsLUVFRgft/DFQqFX7//Xf861//EjgZEREREekaLtsj0Vq9ejW2b9+Orl27QqPRQKPRQKFQYOTIkUJHIyIiIiIdxOKJROvgwYOIi4vDnTt3EB8fj1WrVmHNmjWorKwUOhoRERER6SAWTyRadXV1GDZsGG7fvo1z584BAObNm4fx48cLnIyIiIiIdBGPKifRsrS0RElJCZ577jnI5XIoFAoAgFKpFDgZEREREekizjyRaAUEBCAgIABJSUmYMGECZsyYASMjI9ja2godjYiIiIh0EE/bI1E7deoUBg8eDIlEgm3btkGhUOCf//wnzMzMhI5GRERERDqGxRPRf8nlchZNRERERPRE3PNEorR//35EREQgKysLVVVV8PPzw6hRo+Dt7Y0rV64IHY+IiIiIdBBnnkh01q5di6SkJDg5OSEvLw+WlpZ44YUXMHPmTMTFxaGsrAwxMTFCxyQiIiIiHcPiiURn7Nix2LFjB/r164eLFy/Cx8cHubm56NatG5RKJdzc3JCbmyt0TCIiIiLSMVy2R6JTVVWFfv36AQAGDhyITp06oVu3bgAAExMT3Lt3T8h4RERERKSjWDyR6EgkkkavjYyMGr3mZCwRERERPQ77PJHoNDQ0oKioSFskqdXqRq9ZPBERERHR43DPE4mOtbU1JBLJE4skiUSC8+fPt3EqIiIiItJ1LJ6IiIiIiIiagHueiIiIiIiImoDFExERERERUROweCIiIsGVlpbC1tZW6BhERERPxeKJiIiIiIioCXhUORER6aySkhJERESgtrYWN2/ehLW1NaKjo2FsbIxhw4Zh1qxZyMrKQkVFBWbOnInAwECo1Wp8/vnnkMlk6Nq1K4YPH47i4mLs2rUL77zzDoKCguDp6QkAjV4nJCRg7969UKlUqKqqQnBw8F+OV1NTg+XLl+O3336DSqWCs7MzFixYgA4dOmD9+vX48ccfYWRkhB49emDFihXo3bu3wD9RIiJqDs48ERGRztq3bx/8/Pywb98+/PDDDygtLUVGRgYAoL6+Hj169MB3332H9evXY8WKFairq0N8fDx+/fVXHDp0CN999x2uXr36l59TW1uL+Ph4bNmyBYmJiVi7di1WrVoFAE8dLyoqCkOGDMGBAweQmJiIP/74A9u2bcONGzewY8cO7N+/HwcOHMCoUaNw5syZVvkZERFR2+HMExER6ayPPvoIWVlZ+Prrr3Hp0iVUVFRAqVRq73t4eAAAhgwZgvr6eiiVShw7dgy+vr4wNjYGAPj7+2PXrl1P/ZwuXbpg06ZNOHbsGC5duoQLFy5oP+dp42VkZODs2bNISEgAANy9excAYGFhAWtra7z++utwdXWFq6srnJ2dW/AnQ0REQmDxREREOuv999+HWq2Gl5cXxo4dixs3bjRqcH2/oJFIJACAhoYGdOjQ+H9tBgaNF1k8+OtVKhUAoKysDP7+/pg6dSrs7Ozg6emJ9PR0AHjqeBqNBuvWrcOAAQMAANXV1ZBIJDAwMMC3336Ls2fPIjs7G1FRUXBxccGCBQua9fMgIiJhcdkeERHprMzMTISEhMDb2xsAcPr0aajV6qf+mjFjxiA5ORn19fW4d+8eDh48qL1nZmaGwsJCAMDFixdRVFQEACgsLISZmRnmzZuH0aNHawsntVr91PFGjx6N7du3o6GhAfX19Zg7dy6+/fZbXLhwAZMmTcKAAQMwe/ZsTJ8+HWfPnm3Rnw0REbU9zjwREZFOUCqVjxxX/t577yEkJAQmJiYwNTWFVCrFlStXnjrOlClTUFJSAj8/P5iYmOBvf/sbOnfuDACYO3cuFi1ahGPHjuGll16Cvb09AGDUqFFISEiAp6cnJBIJHBwcYGZmhsuXLz91vPDwcCxfvhw+Pj5QqVQYOXIkZs6cCSMjI3h5eeGNN96AiYkJOnXqhH//+9+t8FMjIqK2JGl4cP0CERGRnsvMzERlZSV8fX0BAMuWLYOxsTE++ugjnRiPiIj0F4snIiJqV8rLy7Fo0SLcunULGo0G1tbW+PTTT9G1a1edGI+IiPQXiyciIiIiIqIm4IERRERERERETcDiiYiIiIiIqAlYPBERERERETUBiyciIiIiIqImYPFERERERETUBCyeiIiIiIiImuD/AHHRYCm08kRJAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(language_all/language_all.sum()).plot(kind='bar', figsize=(12,8))\n",
-    "plt.title('Programming Language Use by Respondents', fontsize = 14)\n",
-    "plt.xlabel('Languages', fontsize = 12)\n",
-    "plt.ylabel('Percentages', fontsize = 12)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###  **Analysing the growth of languages from 2018 to 2020 before predicting part**\n",
-    "\n",
-    "The most language the developers use between 2018 to 2020 is JavaScript(14%). The second and third highest working language is HTML/CSS(13%) and SQL(11%). JavaScript and SQL had the same steady increasing trend over the three years. The percentage of HTML/CSS was slightly increased from 2018 to 2019. However, it dropped to the same level as 2018 in 2020. Python was responsible for about 9% in 2018. After then, it decreased to 8% in 2019 and it rose 1% in 2020.\n",
-    "\n",
-    "There are some languages that were in only 2019; Elixir, Clojure, F#, Web assembly, and Erlang. Perl, Haskell, Julia was in the 2019 and 2020 surveys with small percentages."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 392,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Preparing data for ML\n",
-    "df_language_2018 = language_2018[['Language', '2018']]\n",
-    "df_language_2018 = df_language_2018.rename(columns={'2018': 'Number'})\n",
-    "df_language_2018['Year'] = '2018'\n",
-    "df_language_2018['Year_Total'] = df_language_2018['Number'].sum()\n",
-    "df_language_2018['Fraction'] = df_language_2018['Number']/df_language_2018['Number'].sum()\n",
-    "df_language_2018 = df_language_2018[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
-    "df_language_2018.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
-    "#df_language_2018\n",
-    "df_language_2019 = language_2019[['Language', '2019']]\n",
-    "df_language_2019 = df_language_2019.rename(columns={'2019': 'Number'})\n",
-    "df_language_2019['Year'] = '2019'\n",
-    "df_language_2019['Year_Total'] = df_language_2019['Number'].sum()\n",
-    "df_language_2019['Fraction'] = df_language_2019['Number']/df_language_2019['Number'].sum()\n",
-    "df_language_2019 = df_language_2019[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
-    "df_language_2019.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
-    "#df_language_2019\n",
-    "df_language_2020 = language_2020[['Language', '2020']]\n",
-    "df_language_2020 = df_language_2020.rename(columns={'2020': 'Number'})\n",
-    "df_language_2020['Year'] = '2020'\n",
-    "df_language_2020['Year_Total'] = df_language_2020['Number'].sum()\n",
-    "df_language_2020['Fraction'] = df_language_2020['Number']/df_language_2020['Number'].sum()\n",
-    "df_language_2020 = df_language_2020[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
-    "df_language_2020.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
-    "#df_language_2020\n",
-    "\n",
-    "#Append Dataset 2018 x 2019 x 2020\n",
-    "df_language = pd.concat([df_language_2018[:10], df_language_2019[:10], df_language_2020[:10]] , axis=0)\n",
-    "#resetting the index values\n",
-    "df_language = df_language.reset_index(drop=True)\n",
-    "#df_language"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 393,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols = ['Language', 'Fraction']\n",
-    "df_language_2018_ = df_language_2018[cols][:10]\n",
-    "#df_language_2018_\n",
-    "cols = ['Language', 'Fraction']\n",
-    "df_language_2019_ = df_language_2019[cols][:10]\n",
-    "#df_language_2019_\n",
-    "cols = ['Language', 'Fraction']\n",
-    "df_language_2020_ = df_language_2020[cols][:10]\n",
-    "#df_language_2020_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 394,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Language</th>\n",
-       "      <th>JavaScript</th>\n",
-       "      <th>HTML/CSS</th>\n",
-       "      <th>SQL</th>\n",
-       "      <th>Python</th>\n",
-       "      <th>Java</th>\n",
-       "      <th>Bash/Shell/PowerShell</th>\n",
-       "      <th>C#</th>\n",
-       "      <th>PHP</th>\n",
-       "      <th>TypeScript</th>\n",
-       "      <th>C++</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01</th>\n",
-       "      <td>0.134797</td>\n",
-       "      <td>0.125627</td>\n",
-       "      <td>0.109071</td>\n",
-       "      <td>0.087801</td>\n",
-       "      <td>0.080117</td>\n",
-       "      <td>0.065902</td>\n",
-       "      <td>0.062641</td>\n",
-       "      <td>0.052107</td>\n",
-       "      <td>0.050617</td>\n",
-       "      <td>0.047593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-01-01</th>\n",
-       "      <td>0.136468</td>\n",
-       "      <td>0.126595</td>\n",
-       "      <td>0.109882</td>\n",
-       "      <td>0.081963</td>\n",
-       "      <td>0.080446</td>\n",
-       "      <td>0.074697</td>\n",
-       "      <td>0.062234</td>\n",
-       "      <td>0.051494</td>\n",
-       "      <td>0.044158</td>\n",
-       "      <td>0.044193</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-01</th>\n",
-       "      <td>0.137808</td>\n",
-       "      <td>0.126495</td>\n",
-       "      <td>0.112110</td>\n",
-       "      <td>0.086418</td>\n",
-       "      <td>0.078374</td>\n",
-       "      <td>0.069555</td>\n",
-       "      <td>0.063638</td>\n",
-       "      <td>0.051339</td>\n",
-       "      <td>0.054607</td>\n",
-       "      <td>0.043372</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Language    JavaScript  HTML/CSS       SQL    Python      Java  \\\n",
-       "Year                                                             \n",
-       "2018-01-01    0.134797  0.125627  0.109071  0.087801  0.080117   \n",
-       "2019-01-01    0.136468  0.126595  0.109882  0.081963  0.080446   \n",
-       "2020-01-01    0.137808  0.126495  0.112110  0.086418  0.078374   \n",
-       "\n",
-       "Language    Bash/Shell/PowerShell        C#       PHP  TypeScript       C++  \n",
-       "Year                                                                         \n",
-       "2018-01-01               0.065902  0.062641  0.052107    0.050617  0.047593  \n",
-       "2019-01-01               0.074697  0.062234  0.051494    0.044158  0.044193  \n",
-       "2020-01-01               0.069555  0.063638  0.051339    0.054607  0.043372  "
-      ]
-     },
-     "execution_count": 394,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_language_2018_.set_index('Language', inplace = True)\n",
-    "df_language_2018_t = df_language_2018_.T\n",
-    "df_language_2018_t['Year'] = '2018'\n",
-    "df_language_2018_t.Year = pd.to_datetime(df_language_2018_t.Year)\n",
-    "df_language_2018_t = df_language_2018_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
-    "#df_language_2018_t\n",
-    "df_language_2019_.set_index('Language', inplace = True)\n",
-    "df_language_2019_t = df_language_2019_.T\n",
-    "df_language_2019_t['Year'] = '2019'\n",
-    "df_language_2019_t.Year = pd.to_datetime(df_language_2019_t.Year)\n",
-    "df_language_2019_t = df_language_2019_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
-    "#df_language_2019_t\n",
-    "df_language_2020_.set_index('Language', inplace = True)\n",
-    "df_language_2020_t = df_language_2020_.T\n",
-    "df_language_2020_t['Year'] = '2020'\n",
-    "df_language_2020_t.Year = pd.to_datetime(df_language_2020_t.Year)\n",
-    "df_language_2020_t = df_language_2020_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
-    "#df_language_2020_t\n",
-    "\n",
-    "#Append Dataset 2018 x 2019 x 2020\n",
-    "all_language = pd.concat([df_language_2018_t, df_language_2019_t, df_language_2020_t] , axis=0)\n",
-    "#resetting the index values\n",
-    "all_language = all_language.reset_index(drop=True)\n",
-    "all_language.set_index('Year', inplace = True)\n",
-    "all_language"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 395,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java',\n",
-       "       'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++'],\n",
-       "      dtype='object', name='Language')"
-      ]
-     },
-     "execution_count": 395,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_language.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 396,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Fraction of total queries in the year (%)')"
-      ]
-     },
-     "execution_count": 396,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1008x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = all_language.plot(grid=True, lw=0.5, figsize=(14,6), marker='o')\n",
-    "\n",
-    "#Show the legend outside of the plot.\n",
-    "legend = ax.get_legend()\n",
-    "legend.set_bbox_to_anchor((1, 1))\n",
-    "plt.title('Fraction of total queries in the year for top programming languages', fontsize = 14)\n",
-    "plt.xlabel('Languages', fontsize = 12)\n",
-    "plt.ylabel('Fraction of total queries in the year (%)', fontsize = 12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are trying to answer the question \"Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020).\"\n",
-    "\n",
-    "Since we have only 3 years of datasets, there is not enough data to use the time series forecasting method to predict the future popularity of programming languages. With the very small number of observations, there is insufficient data to split the observations into training and testing. We need more observations to build the predictive model, this question we leave for further exploration in future projects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Can we predict the salary of Data Scientists?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 397,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Rename columns\n",
-    "cleaned_2018.rename(columns={'JobSatisfaction': 'CurrentJobSatis', 'JobSearchStatus': 'JobStatus', 'YearsCodingProf':'YearsCodePro'}, inplace =True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 398,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sal_df = ['Age', 'Country', 'EdLevel', 'DevType', 'YearsCodePro', 'SalaryUSD']\n",
-    "df1 = cleaned_2018\n",
-    "df2 = survey_df_2019\n",
-    "df3 = df2020"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 399,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(191693, 6)"
-      ]
-     },
-     "execution_count": 399,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Append Dataset 2018 x 2019 x 2020\n",
-    "df_sal = pd.concat([df1[sal_df], df2[sal_df], df3[sal_df]], axis=0)\n",
-    "#resetting the index values\n",
-    "df_sal = df_sal.reset_index(drop=True)\n",
-    "df_sal.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 400,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8727"
-      ]
-     },
-     "execution_count": 400,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#creating data scientist scientist df\n",
-    "all_ds = df_sal[df_sal['DevType'].str.contains('Data scientist') == True ]\n",
-    "all_ds = all_ds.reset_index(drop=True)\n",
-    "len(all_ds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 401,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>DevType</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>3</td>\n",
-       "      <td>366420.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>21</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>No Degree</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>4</td>\n",
-       "      <td>170292.187500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>25</td>\n",
-       "      <td>Argentina</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8400.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>19</td>\n",
-       "      <td>Netherlands</td>\n",
-       "      <td>Associate</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>1</td>\n",
-       "      <td>87994.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>25</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>6</td>\n",
-       "      <td>66750.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8722</th>\n",
-       "      <td>23</td>\n",
-       "      <td>Russian Federation</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>3</td>\n",
-       "      <td>33972.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8723</th>\n",
-       "      <td>27</td>\n",
-       "      <td>Germany</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>2</td>\n",
-       "      <td>97284.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8724</th>\n",
-       "      <td>47</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>22</td>\n",
-       "      <td>148951.282051</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8725</th>\n",
-       "      <td>33</td>\n",
-       "      <td>Panama</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>2</td>\n",
-       "      <td>72000.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8726</th>\n",
-       "      <td>28</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Data scientist</td>\n",
-       "      <td>5</td>\n",
-       "      <td>180000.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8727 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Age             Country    EdLevel         DevType YearsCodePro  \\\n",
-       "0     28              Canada  Bachelors  Data scientist            3   \n",
-       "1     21              Canada  No Degree  Data scientist            4   \n",
-       "2     25           Argentina    Masters  Data scientist            3   \n",
-       "3     19         Netherlands  Associate  Data scientist            1   \n",
-       "4     25       United States  Bachelors  Data scientist            6   \n",
-       "...   ..                 ...        ...             ...          ...   \n",
-       "8722  23  Russian Federation  Bachelors  Data scientist            3   \n",
-       "8723  27             Germany    Masters  Data scientist            2   \n",
-       "8724  47       United States  Bachelors  Data scientist           22   \n",
-       "8725  33              Panama    Masters  Data scientist            2   \n",
-       "8726  28       United States    Masters  Data scientist            5   \n",
-       "\n",
-       "          SalaryUSD  \n",
-       "0     366420.000000  \n",
-       "1     170292.187500  \n",
-       "2       8400.000000  \n",
-       "3      87994.000000  \n",
-       "4      66750.000000  \n",
-       "...             ...  \n",
-       "8722   33972.000000  \n",
-       "8723   97284.000000  \n",
-       "8724  148951.282051  \n",
-       "8725   72000.000000  \n",
-       "8726  180000.000000  \n",
-       "\n",
-       "[8727 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 401,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_ds['DevType'] = 'Data scientist'\n",
-    "all_ds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 402,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "54049.0"
-      ]
-     },
-     "execution_count": 402,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Divide SalaryUSD into 2 groups; SalaryUSD >= median and SalaryUSD < median \n",
-    "all_ds['greater than median'] = all_ds['SalaryUSD'] >= all_ds['SalaryUSD'].median()\n",
-    "all_ds['SalaryUSD'].median() #56616.0 USD"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 403,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{False: 0, True: 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "#Encoding the target\n",
-    "labelencoder = preprocessing.LabelEncoder()\n",
-    "all_ds['gt_median'] = labelencoder.fit_transform(all_ds['greater than median'])\n",
-    "\n",
-    "le_name_mapping = dict(zip(labelencoder.classes_, labelencoder.transform(labelencoder.classes_)))\n",
-    "print(le_name_mapping)\n",
-    "#{False: 0 (SalaryUSD < median), True: 1 (SalaryUSD >= median}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 404,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(8727, 4)"
-      ]
-     },
-     "execution_count": 404,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X = all_ds.drop(['SalaryUSD', 'greater than median', 'gt_median', 'DevType'], axis = 1)\n",
-    "y = all_ds['gt_median']\n",
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 405,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(8727, 225)"
-      ]
-     },
-     "execution_count": 405,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cats_lst = X.select_dtypes(include = ['object']).columns.tolist()\n",
-    "for col in cats_lst:\n",
-    "    X = pd.concat([X.drop(col, axis=1), pd.get_dummies(X[col], prefix=col, prefix_sep='_', drop_first=True)], axis=1)\n",
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 406,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Splitting data\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.30, random_state=142)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 407,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_metrics = {}\n",
-    "\n",
-    "def metrics_data(title, labels, predictions):\n",
-    "    \"\"\"\n",
-    "    INPUT:\n",
-    "        title - Display title for classification algorithm\n",
-    "        labels - Actual values for target variable\n",
-    "        predictions - Predicted values for target variable\n",
-    "        \n",
-    "    OUTPUT:\n",
-    "        metrics - Dictionary of classification metrics for given title\n",
-    "    \"\"\"\n",
-    "    metrics = {\n",
-    "        title: {\n",
-    "            \"model\": title,\n",
-    "            \"accuracy\": accuracy_score(labels, predictions),\n",
-    "            \"precision\": precision_score(labels, predictions),\n",
-    "            \"recall\": recall_score(labels, predictions),\n",
-    "            \"f1-score\": f1_score(labels, predictions),\n",
-    "            \"r2\":  r2_score(labels, predictions)\n",
-    "        }\n",
-    "    }\n",
-    "    print(metrics)\n",
-    "    return metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 408,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  0.11200189590454102\n",
-      "{'Decision Trees': {'model': 'Decision Trees', 'accuracy': 0.8300878197785414, 'precision': 0.8640611724723875, 'recall': 0.7811059907834101, 'f1-score': 0.8204921339249698, 'r2': 0.32032898397069154}}\n",
-      "Accuracy on train set: 0.823510150622135\n"
-     ]
-    }
-   ],
-   "source": [
-    "#DecisionTreeClassifier\n",
-    "start = time.time()\n",
-    "modelDC = DecisionTreeClassifier(max_depth = 12, min_samples_leaf = 10)\n",
-    "modelDC.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeDC = end - start\n",
-    "print('Time: ', TimeDC)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = modelDC.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Decision Trees\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = modelDC.predict(X_train)\n",
-    "accuracyDC2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyDC2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 409,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'Multinomial Naive Bayes': {'model': 'Multinomial Naive Bayes', 'accuracy': 0.8335242458953799, 'precision': 0.8315467075038285, 'recall': 0.8341013824884793, 'f1-score': 0.8328220858895706, 'r2': 0.3340751393510596}}\n",
-      "Accuracy on train set: 0.8366077275703995\n"
-     ]
-    }
-   ],
-   "source": [
-    "#MultinomialNB\n",
-    "start = time.time()\n",
-    "modelNB = MultinomialNB(alpha=0.005)\n",
-    "modelNB.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeNB = end - start\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = modelNB.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Multinomial Naive Bayes\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = modelNB.predict(X_train)\n",
-    "accuracyNB2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyNB2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 410,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  0.040006399154663086\n",
-      "{'Gaussian Naive Bayes': {'model': 'Gaussian Naive Bayes', 'accuracy': 0.6380297823596792, 'precision': 0.5806745670009116, 'recall': 0.978494623655914, 'f1-score': 0.72883295194508, 'r2': -0.4479283667320999}}\n",
-      "Accuracy on train set: 0.64603798297315\n"
-     ]
-    }
-   ],
-   "source": [
-    "#GaussianNB\n",
-    "start = time.time()\n",
-    "modelGNB = GaussianNB()\n",
-    "modelGNB.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeGNB = end - start\n",
-    "print('Time: ', TimeGNB)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = modelGNB.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Gaussian Naive Bayes\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = modelGNB.predict(X_train)\n",
-    "accuracyGNB2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyGNB2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 411,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  0.27199530601501465\n",
-      "{'Logistic Regression': {'model': 'Logistic Regression', 'accuracy': 0.8518518518518519, 'precision': 0.8520801232665639, 'recall': 0.8494623655913979, 'f1-score': 0.8507692307692308, 'r2': 0.4073879680463558}}\n",
-      "Accuracy on train set: 0.8542894564505567\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Logistic Regression\n",
-    "start = time.time()\n",
-    "modelLR = LogisticRegression()\n",
-    "modelLR.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeLR = end - start\n",
-    "print('Time: ', TimeLR)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = modelLR.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Logistic Regression\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = modelLR.predict(X_train)\n",
-    "accuracyLR2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyLR2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 412,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  2.9950499534606934\n",
-      "{'Random Forest': {'model': 'Random Forest', 'accuracy': 0.8369606720122184, 'precision': 0.8480509148766905, 'recall': 0.8187403993855606, 'f1-score': 0.8331379445095739, 'r2': 0.34782129473142764}}\n",
-      "Accuracy on train set: 0.9616895874263262\n"
-     ]
-    }
-   ],
-   "source": [
-    "#RandomForestClassifier\n",
-    "start = time.time()\n",
-    "rfc = RandomForestClassifier()\n",
-    "rfc.fit(X_train, y_train)\n",
-    "end = time.time()\n",
-    "TimeRFC = end - start\n",
-    "print('Time: ', TimeRFC)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = rfc.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"Random Forest\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = rfc.predict(X_train)\n",
-    "accuracyRFC2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracyRFC2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 413,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time:  0.06399965286254883\n",
-      "{'LinearSVC': {'model': 'LinearSVC', 'accuracy': 0.8518518518518519, 'precision': 0.8395245170876672, 'recall': 0.8678955453149002, 'f1-score': 0.8534743202416918, 'r2': 0.4073879680463558}}\n",
-      "Accuracy on train set: 0.8551080550098232\n"
-     ]
-    }
-   ],
-   "source": [
-    "#LinearSVC\n",
-    "start = time.time()\n",
-    "svc = LinearSVC()\n",
-    "svc.fit(X_train, y_train) \n",
-    "end = time.time()\n",
-    "TimeSVC = end - start\n",
-    "print('Time: ', TimeSVC)\n",
-    "\n",
-    "#Evaluating model on test set\n",
-    "y_pred = svc.predict(X_test)\n",
-    "all_metrics.update(metrics_data(\"LinearSVC\", y_test, y_pred))\n",
-    "\n",
-    "#Evaluating model on train set\n",
-    "y_pred = svc.predict(X_train)\n",
-    "accuracySVC2 = accuracy_score(y_train, y_pred)\n",
-    "print('Accuracy on train set: {}'.format(accuracySVC2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model performance comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 414,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1-score</th>\n",
-       "      <th>r2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Decision Trees</td>\n",
-       "      <td>0.830088</td>\n",
-       "      <td>0.864061</td>\n",
-       "      <td>0.781106</td>\n",
-       "      <td>0.820492</td>\n",
-       "      <td>0.320329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Multinomial Naive Bayes</td>\n",
-       "      <td>0.833524</td>\n",
-       "      <td>0.831547</td>\n",
-       "      <td>0.834101</td>\n",
-       "      <td>0.832822</td>\n",
-       "      <td>0.334075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Gaussian Naive Bayes</td>\n",
-       "      <td>0.63803</td>\n",
-       "      <td>0.580675</td>\n",
-       "      <td>0.978495</td>\n",
-       "      <td>0.728833</td>\n",
-       "      <td>-0.447928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Logistic Regression</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.85208</td>\n",
-       "      <td>0.849462</td>\n",
-       "      <td>0.850769</td>\n",
-       "      <td>0.407388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Random Forest</td>\n",
-       "      <td>0.836961</td>\n",
-       "      <td>0.848051</td>\n",
-       "      <td>0.81874</td>\n",
-       "      <td>0.833138</td>\n",
-       "      <td>0.347821</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>LinearSVC</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.839525</td>\n",
-       "      <td>0.867896</td>\n",
-       "      <td>0.853474</td>\n",
-       "      <td>0.407388</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     model  accuracy precision    recall  f1-score        r2\n",
-       "0           Decision Trees  0.830088  0.864061  0.781106  0.820492  0.320329\n",
-       "1  Multinomial Naive Bayes  0.833524  0.831547  0.834101  0.832822  0.334075\n",
-       "2     Gaussian Naive Bayes   0.63803  0.580675  0.978495  0.728833 -0.447928\n",
-       "3      Logistic Regression  0.851852   0.85208  0.849462  0.850769  0.407388\n",
-       "4            Random Forest  0.836961  0.848051   0.81874  0.833138  0.347821\n",
-       "5                LinearSVC  0.851852  0.839525  0.867896  0.853474  0.407388"
-      ]
-     },
-     "execution_count": 414,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_metrics = pd.DataFrame(all_metrics).T\n",
-    "all_metrics = all_metrics.reset_index(drop=True)\n",
-    "all_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 415,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Model</th>\n",
-       "      <th>Accuracy_train</th>\n",
-       "      <th>Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Decision Trees</td>\n",
-       "      <td>0.823510</td>\n",
-       "      <td>0.112002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Multinomial Naive Bayes</td>\n",
-       "      <td>0.836608</td>\n",
-       "      <td>0.031969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Gaussian Naive Bayes</td>\n",
-       "      <td>0.646038</td>\n",
-       "      <td>0.040006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Logistic Regression</td>\n",
-       "      <td>0.854289</td>\n",
-       "      <td>0.271995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Random Forest</td>\n",
-       "      <td>0.961690</td>\n",
-       "      <td>2.995050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>LinearSVC</td>\n",
-       "      <td>0.855108</td>\n",
-       "      <td>0.064000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     Model  Accuracy_train      Time\n",
-       "0           Decision Trees        0.823510  0.112002\n",
-       "1  Multinomial Naive Bayes        0.836608  0.031969\n",
-       "2     Gaussian Naive Bayes        0.646038  0.040006\n",
-       "3      Logistic Regression        0.854289  0.271995\n",
-       "4            Random Forest        0.961690  2.995050\n",
-       "5                LinearSVC        0.855108  0.064000"
-      ]
-     },
-     "execution_count": 415,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Creating new df to store model performances\n",
-    "Model = ['Decision Trees', 'Multinomial Naive Bayes', 'Gaussian Naive Bayes', 'Logistic Regression', 'Random Forest', 'LinearSVC']\n",
-    "Accuracy_train = [accuracyDC2, accuracyNB2, accuracyGNB2, accuracyLR2, accuracyRFC2, accuracySVC2]\n",
-    "Time = [TimeDC, TimeNB, TimeGNB, TimeLR, TimeRFC, TimeSVC]\n",
-    "\n",
-    "#Create df from lists\n",
-    "cols = ['Model', 'Accuracy_train', 'Time']\n",
-    "data = list(zip(Model, Accuracy_train, Time))\n",
-    "\n",
-    "performance = pd.DataFrame(data, columns=cols)\n",
-    "performance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 416,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>precision</th>\n",
-       "      <th>recall</th>\n",
-       "      <th>f1-score</th>\n",
-       "      <th>r2</th>\n",
-       "      <th>Accuracy_train</th>\n",
-       "      <th>Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Decision Trees</td>\n",
-       "      <td>0.830088</td>\n",
-       "      <td>0.864061</td>\n",
-       "      <td>0.781106</td>\n",
-       "      <td>0.820492</td>\n",
-       "      <td>0.320329</td>\n",
-       "      <td>0.823510</td>\n",
-       "      <td>0.112002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Multinomial Naive Bayes</td>\n",
-       "      <td>0.833524</td>\n",
-       "      <td>0.831547</td>\n",
-       "      <td>0.834101</td>\n",
-       "      <td>0.832822</td>\n",
-       "      <td>0.334075</td>\n",
-       "      <td>0.836608</td>\n",
-       "      <td>0.031969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Gaussian Naive Bayes</td>\n",
-       "      <td>0.63803</td>\n",
-       "      <td>0.580675</td>\n",
-       "      <td>0.978495</td>\n",
-       "      <td>0.728833</td>\n",
-       "      <td>-0.447928</td>\n",
-       "      <td>0.646038</td>\n",
-       "      <td>0.040006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Logistic Regression</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.85208</td>\n",
-       "      <td>0.849462</td>\n",
-       "      <td>0.850769</td>\n",
-       "      <td>0.407388</td>\n",
-       "      <td>0.854289</td>\n",
-       "      <td>0.271995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Random Forest</td>\n",
-       "      <td>0.836961</td>\n",
-       "      <td>0.848051</td>\n",
-       "      <td>0.81874</td>\n",
-       "      <td>0.833138</td>\n",
-       "      <td>0.347821</td>\n",
-       "      <td>0.961690</td>\n",
-       "      <td>2.995050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>LinearSVC</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.839525</td>\n",
-       "      <td>0.867896</td>\n",
-       "      <td>0.853474</td>\n",
-       "      <td>0.407388</td>\n",
-       "      <td>0.855108</td>\n",
-       "      <td>0.064000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     model  accuracy precision    recall  f1-score        r2  \\\n",
-       "0           Decision Trees  0.830088  0.864061  0.781106  0.820492  0.320329   \n",
-       "1  Multinomial Naive Bayes  0.833524  0.831547  0.834101  0.832822  0.334075   \n",
-       "2     Gaussian Naive Bayes   0.63803  0.580675  0.978495  0.728833 -0.447928   \n",
-       "3      Logistic Regression  0.851852   0.85208  0.849462  0.850769  0.407388   \n",
-       "4            Random Forest  0.836961  0.848051   0.81874  0.833138  0.347821   \n",
-       "5                LinearSVC  0.851852  0.839525  0.867896  0.853474  0.407388   \n",
-       "\n",
-       "   Accuracy_train      Time  \n",
-       "0        0.823510  0.112002  \n",
-       "1        0.836608  0.031969  \n",
-       "2        0.646038  0.040006  \n",
-       "3        0.854289  0.271995  \n",
-       "4        0.961690  2.995050  \n",
-       "5        0.855108  0.064000  "
-      ]
-     },
-     "execution_count": 416,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Join result2018 with weather2018 to get the Maximum temperature (Degree C)\n",
-    "all_performance = pd.merge(left = all_metrics , right = performance ,\n",
-    "                 left_on = ['model'], right_on = ['Model'], how = 'left')\n",
-    "drop_cols = ['Model']\n",
-    "all_performance.drop(drop_cols, axis=1, inplace=True)\n",
-    "\n",
-    "all_performance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Unfortunately, none of the models has good enough r2 values. The best model is Logistic Regression with $R^2$ just approximately 0.4. We cannot confidently say that Logistic Regression is a good fit to predict the salary of Data Scientists.\n",
-    "\n",
-    "**This question we leave for further exploration in future projects.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hamming Loss (HL) and Jacard Score On Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Hamming loss is the fraction of labels that are incorrectly predicted ( evaluation metrics for a classifier model.) \n",
-    "- The Jaccard Index, also known as the Jaccard similarity coefficient, is a statistic used in understanding the similarities between sample sets. (To measure Similarity)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 417,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clf:  RandomForestClassifier\n",
-      "Jacard score: 0.7062750333778371\n",
-      "Hamming loss: 0.16800305460099274\n",
-      "---\n"
-     ]
-    }
-   ],
-   "source": [
-    "def avg_jacard(y_true,y_pred):\n",
-    "\n",
-    "    jacard = np.minimum(y_true,y_pred).sum(axis=0) / np.maximum(y_true,y_pred).sum(axis=0)\n",
-    "    \n",
-    "    return jacard.mean()\n",
-    "\n",
-    "def print_score(y_pred, clf):\n",
-    "    print(\"Clf: \", clf.__class__.__name__)\n",
-    "    print(\"Jacard score: {}\".format(avg_jacard(y_test, y_pred)))\n",
-    "    print(\"Hamming loss: {}\".format(hamming_loss(y_pred, y_test)))\n",
-    "    print(\"---\")    \n",
-    "\n",
-    "rfc = RandomForestClassifier()\n",
-    "rfc.fit(X_train, y_train)\n",
-    "\n",
-    "y_pred = rfc.predict(X_test)\n",
-    "\n",
-    "print_score(y_pred, rfc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 418,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clf:  MLPClassifier\n",
-      "Jacard score: 0.6990861618798956\n",
-      "Hamming loss: 0.1760213822069492\n",
-      "---\n"
-     ]
-    }
-   ],
-   "source": [
-    "mlpc = MLPClassifier()\n",
-    "mlpc.fit(X_train, y_train)\n",
-    "\n",
-    "y_pred = mlpc.predict(X_test)\n",
-    "\n",
-    "print_score(y_pred, mlpc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 419,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clf:  SGDClassifier\n",
-      "Jacard score: 0.7312042581503659\n",
-      "Hamming loss: 0.15425735013363878\n",
-      "---\n",
-      "Clf:  LogisticRegression\n",
-      "Jacard score: 0.7402945113788487\n",
-      "Hamming loss: 0.14814814814814814\n",
-      "---\n",
-      "Clf:  MultinomialNB\n",
-      "Jacard score: 0.7124183006535948\n",
-      "Hamming loss: 0.16800305460099274\n",
-      "---\n",
-      "Clf:  LinearSVC\n",
-      "Jacard score: 0.7444005270092227\n",
-      "Hamming loss: 0.14814814814814814\n",
-      "---\n"
-     ]
-    }
-   ],
-   "source": [
-    "sgd = SGDClassifier()\n",
-    "lr = LogisticRegression()\n",
-    "mn = MultinomialNB()\n",
-    "svc = LinearSVC()\n",
-    "\n",
-    "for classifier in [sgd, lr, mn, svc,]:\n",
-    "    clf = OneVsRestClassifier(classifier)\n",
-    "    clf.fit(X_train, y_train)\n",
-    "    y_pred = clf.predict(X_test)\n",
-    "    print_score(y_pred, classifier)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Findings: It has been found that better Hamming loss has been found in Logistic Regression and Linear SVC **which is 0.14815**<br>\n",
-    "Jaccard similarity scores give us the distribution of label sets when using the models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predicting what causing Job satisfaction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An examination of work satisfaction variables based on Stack Over Flow survey data from 2020. Job satisfaction can be defined by factors such as compensation, benefits, work environment, team members, work-life balance, education level, place, and so on. By analyzing the Stack Over Flow survey data from 2020, I will try to find some features that are negatively and positively affecting job satisfaction in various countries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 420,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        12439\n",
-       "Slightly satisfied                    11953\n",
-       "Slightly dissatisfied                  6269\n",
-       "Neither satisfied nor dissatisfied     4669\n",
-       "Very dissatisfied                      3106\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 420,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 421,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Very satisfied', 'Slightly satisfied', 'Slightly dissatisfied', 'Neither satisfied nor dissatisfied', 'Very dissatisfied']\n",
-      "[12439, 11953, 6269, 4669, 3106]\n"
-     ]
-    }
-   ],
-   "source": [
-    "participation_rate = df2020['CurrentJobSatis'].value_counts().keys().tolist()\n",
-    "print(participation_rate)\n",
-    "count = df2020['CurrentJobSatis'].value_counts().tolist()\n",
-    "print(count)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 422,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pie(count, explode = (0.01,0.01,0.1,0.1,0.2), labels = participation_rate, shadow=True, autopct=lambda p : f'{p:.2f}%  ({p * sum(count)/100:,.0f})', textprops={'fontsize':18, 'weight':'bold'})\n",
-    "plt.title(\"Propotion of Job Satisfaction from the Dataset\",fontsize = 20)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**In further Analysis, we will split into two categories like Satisfied or Not Satisfied**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 423,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Applying one hot encoding\n",
-    "df_indicator = df.isnull().astype(int).add_suffix('_nan')\n",
-    "df = pd.concat([df, df_indicator], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 424,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "best mean cross-validation score: -0.262\n",
-      "best parameters: {'max_depth': 40, 'min_samples_leaf': 10}\n",
-      "test-set score: -0.258\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Grid search for good parameters, I used the mean absolute error as the main measure of quality\n",
-    "param_grid = {'min_samples_leaf': [10,15,20],'max_depth': [20,30,40]}\n",
-    "grid = GridSearchCV(RandomForestRegressor(n_estimators=100,n_jobs=-1, oob_score=True), param_grid=param_grid,\n",
-    "                    scoring='neg_mean_absolute_error',cv=5, return_train_score=True)\n",
-    "X_train_grit = X_train.sample(frac=0.5, random_state=42)\n",
-    "grid.fit(X_train_grit, y_train.loc[X_train_grit.index])\n",
-    "print(\"best mean cross-validation score: {:.3f}\".format(grid.best_score_))\n",
-    "print(\"best parameters: {}\".format(grid.best_params_))\n",
-    "print(\"test-set score: {:.3f}\".format(grid.score(X_test, y_test)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Here Random Forest is used to Predicting Job satisfaction, model did not yield much better output and turned out to be very complex to get insights.** Random forest Regressor, Logistic Regression which may yield good results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Trying with Logistic Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Used Sklearn library to create a Logistic Regression model.\n",
-    "\n",
-    "Before creating a model, need to create data, Using model coefficients, features that have negative and positive effects on job satisfaction to be calculated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 425,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numericals = [\"Age\",\"SalaryUSD\",\"YearsCodePro\"]\n",
-    "categoricals = [\"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 426,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('display.max_columns', None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 427,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Very satisfied                        12439\n",
-       "Slightly satisfied                    11953\n",
-       "Slightly dissatisfied                  6269\n",
-       "Neither satisfied nor dissatisfied     4669\n",
-       "Very dissatisfied                      3106\n",
-       "Name: CurrentJobSatis, dtype: int64"
-      ]
-     },
-     "execution_count": 427,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2020['CurrentJobSatis'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Performing further Spliting of CurrentJobSatis Coloumn**\n",
-    "- Delete \"Neither satisfied nor dissatisfied\"\n",
-    "- Combine \"Very satisfied\" and \"Slightly satisfied\", label as \"Satisfied\" -->1\n",
-    "- Combine \"Very dissatisfied\" and \"Slightly dissatisfied\", label as \"Dissatisfied\"-->0\n",
-    "- Delete rows \"Neither satisfied nor dissatisfied\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 428,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "df = df2020.drop(df2020[df2020.CurrentJobSatis == \"Neither satisfied nor dissatisfied\"].index)\n",
-    "\n",
-    "df.CurrentJobSatis = [1 if each == \"Very satisfied\" else \n",
-    "             1 if each == \"Slightly satisfied\" else \n",
-    "             0 if each == \"Very dissatisfied\"else \n",
-    "             0 if each == \"Slightly dissatisfied\" else\n",
-    "             each for each in df.CurrentJobSatis]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 429,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dropping nan in Converted Salary if any\n",
-    "df = df.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 430,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SalaryUSD</th>\n",
-       "      <th>YearsCodePro</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>EdLevel</th>\n",
-       "      <th>Employment</th>\n",
-       "      <th>Hobbyist</th>\n",
-       "      <th>UndergradMajor</th>\n",
-       "      <th>CurrentJobSatis</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>36</td>\n",
-       "      <td>116000.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>22</td>\n",
-       "      <td>32315.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>23</td>\n",
-       "      <td>40070.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Computer Science</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>49</td>\n",
-       "      <td>14268.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Spain</td>\n",
-       "      <td>No Degree</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Math/Stat</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>53</td>\n",
-       "      <td>38916.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>Netherlands</td>\n",
-       "      <td>No Degree</td>\n",
-       "      <td>Full-time</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No major</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age  SalaryUSD  YearsCodePro         Country    EdLevel Employment  \\\n",
-       "1   36   116000.0          13.0   United States  Bachelors  Full-time   \n",
-       "2   22    32315.0           4.0  United Kingdom    Masters  Full-time   \n",
-       "3   23    40070.0           2.0  United Kingdom  Bachelors  Full-time   \n",
-       "4   49    14268.0           7.0           Spain  No Degree  Full-time   \n",
-       "5   53    38916.0          20.0     Netherlands  No Degree  Full-time   \n",
-       "\n",
-       "  Hobbyist    UndergradMajor  CurrentJobSatis  \n",
-       "1      Yes  Computer Science                0  \n",
-       "2      Yes         Math/Stat                1  \n",
-       "3      Yes  Computer Science                0  \n",
-       "4       No         Math/Stat                0  \n",
-       "5      Yes          No major                1  "
-      ]
-     },
-     "execution_count": 430,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cols= [\"Age\",\"SalaryUSD\",\"YearsCodePro\", \"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\", \"CurrentJobSatis\"]\n",
-    "df = df[cols]\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 431,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# one hot encoding\n",
-    "df = pd.get_dummies(df,  columns = categoricals )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 432,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Normalization of numerical features\n",
-    "for each in numericals:\n",
-    "    df[each] = (df[each] - df[each].min()) / (df[each].max() - df[each].min())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 433,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 434,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Split data into X and y\n",
-    "X = df.drop(\"CurrentJobSatis\", axis = 1)\n",
-    "y = df.CurrentJobSatis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 435,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>Age</th>\n",
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-       "      <th>Country_Ireland</th>\n",
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-       "      <th>Country_Italy</th>\n",
-       "      <th>Country_Jamaica</th>\n",
-       "      <th>Country_Japan</th>\n",
-       "      <th>Country_Jordan</th>\n",
-       "      <th>Country_Kazakhstan</th>\n",
-       "      <th>Country_Kenya</th>\n",
-       "      <th>Country_Kosovo</th>\n",
-       "      <th>Country_Kuwait</th>\n",
-       "      <th>Country_Kyrgyzstan</th>\n",
-       "      <th>Country_Lao People's Democratic Republic</th>\n",
-       "      <th>Country_Latvia</th>\n",
-       "      <th>Country_Lebanon</th>\n",
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-       "      <th>Country_Mongolia</th>\n",
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-       "      <th>Country_Myanmar</th>\n",
-       "      <th>Country_Namibia</th>\n",
-       "      <th>Country_Nepal</th>\n",
-       "      <th>Country_Netherlands</th>\n",
-       "      <th>Country_New Zealand</th>\n",
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-       "      <th>Country_Niger</th>\n",
-       "      <th>Country_Nigeria</th>\n",
-       "      <th>Country_Nomadic</th>\n",
-       "      <th>Country_Norway</th>\n",
-       "      <th>Country_Oman</th>\n",
-       "      <th>Country_Pakistan</th>\n",
-       "      <th>Country_Panama</th>\n",
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-       "      <th>Country_Portugal</th>\n",
-       "      <th>Country_Qatar</th>\n",
-       "      <th>Country_Republic of Korea</th>\n",
-       "      <th>Country_Republic of Moldova</th>\n",
-       "      <th>Country_Romania</th>\n",
-       "      <th>Country_Russian Federation</th>\n",
-       "      <th>Country_Rwanda</th>\n",
-       "      <th>Country_San Marino</th>\n",
-       "      <th>Country_Saudi Arabia</th>\n",
-       "      <th>Country_Senegal</th>\n",
-       "      <th>Country_Serbia</th>\n",
-       "      <th>Country_Singapore</th>\n",
-       "      <th>Country_Slovakia</th>\n",
-       "      <th>Country_Slovenia</th>\n",
-       "      <th>Country_Somalia</th>\n",
-       "      <th>Country_South Africa</th>\n",
-       "      <th>Country_South Korea</th>\n",
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-       "      <th>Country_Sweden</th>\n",
-       "      <th>Country_Switzerland</th>\n",
-       "      <th>Country_Syrian Arab Republic</th>\n",
-       "      <th>Country_Taiwan</th>\n",
-       "      <th>Country_Thailand</th>\n",
-       "      <th>Country_The former Yugoslav Republic of Macedonia</th>\n",
-       "      <th>Country_Togo</th>\n",
-       "      <th>Country_Trinidad and Tobago</th>\n",
-       "      <th>Country_Tunisia</th>\n",
-       "      <th>Country_Turkey</th>\n",
-       "      <th>Country_Turkmenistan</th>\n",
-       "      <th>Country_Uganda</th>\n",
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-       "      <th>Country_United Arab Emirates</th>\n",
-       "      <th>Country_United Kingdom</th>\n",
-       "      <th>Country_United Republic of Tanzania</th>\n",
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-       "      <th>Country_Uruguay</th>\n",
-       "      <th>Country_Uzbekistan</th>\n",
-       "      <th>Country_Venezuela, Bolivarian Republic of...</th>\n",
-       "      <th>Country_Viet Nam</th>\n",
-       "      <th>Country_Yemen</th>\n",
-       "      <th>Country_Zambia</th>\n",
-       "      <th>Country_Zimbabwe</th>\n",
-       "      <th>EdLevel_Associate</th>\n",
-       "      <th>EdLevel_Bachelors</th>\n",
-       "      <th>EdLevel_Doctorate</th>\n",
-       "      <th>EdLevel_Masters</th>\n",
-       "      <th>EdLevel_No Degree</th>\n",
-       "      <th>EdLevel_Professional</th>\n",
-       "      <th>Employment_Full-time</th>\n",
-       "      <th>Employment_Not employed</th>\n",
-       "      <th>Employment_Not employed, and not looking for work</th>\n",
-       "      <th>Employment_Part-time</th>\n",
-       "      <th>Employment_Retired</th>\n",
-       "      <th>Employment_Self-employed</th>\n",
-       "      <th>Employment_Student</th>\n",
-       "      <th>Hobbyist_No</th>\n",
-       "      <th>Hobbyist_Yes</th>\n",
-       "      <th>UndergradMajor_Arts and Science</th>\n",
-       "      <th>UndergradMajor_Business</th>\n",
-       "      <th>UndergradMajor_Computer Science</th>\n",
-       "      <th>UndergradMajor_Engineering</th>\n",
-       "      <th>UndergradMajor_Info Systems</th>\n",
-       "      <th>UndergradMajor_Math/Stat</th>\n",
-       "      <th>UndergradMajor_No major</th>\n",
-       "      <th>UndergradMajor_Other Science</th>\n",
-       "      <th>UndergradMajor_Web Design/Dev</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.466667</td>\n",
-       "      <td>0.580055</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.155556</td>\n",
-       "      <td>0.161590</td>\n",
-       "      <td>0.065217</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.177778</td>\n",
-       "      <td>0.200369</td>\n",
-       "      <td>0.021739</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.755556</td>\n",
-       "      <td>0.071347</td>\n",
-       "      <td>0.130435</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.844444</td>\n",
-       "      <td>0.194598</td>\n",
-       "      <td>0.413043</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>33767 rows × 180 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Age  SalaryUSD  YearsCodePro  Country_Afghanistan  \\\n",
-       "1      0.466667   0.580055      0.260870                    0   \n",
-       "2      0.155556   0.161590      0.065217                    0   \n",
-       "3      0.177778   0.200369      0.021739                    0   \n",
-       "4      0.755556   0.071347      0.130435                    0   \n",
-       "5      0.844444   0.194598      0.413043                    0   \n",
-       "...         ...        ...           ...                  ...   \n",
-       "41564  0.133333   0.041597      0.168261                    0   \n",
-       "41565  0.133333   0.041597      0.168261                    0   \n",
-       "41566  0.133333   0.026343      0.168261                    0   \n",
-       "41567  0.155556   0.048065      0.043478                    0   \n",
-       "41568  0.577778   0.001000      0.413043                    0   \n",
-       "\n",
-       "       Country_Albania  Country_Algeria  Country_Andorra  Country_Angola  \\\n",
-       "1                    0                0                0               0   \n",
-       "2                    0                0                0               0   \n",
-       "3                    0                0                0               0   \n",
-       "4                    0                0                0               0   \n",
-       "5                    0                0                0               0   \n",
-       "...                ...              ...              ...             ...   \n",
-       "41564                0                0                0               0   \n",
-       "41565                0                0                0               0   \n",
-       "41566                0                0                0               0   \n",
-       "41567                0                0                0               0   \n",
-       "41568                0                0                0               0   \n",
-       "\n",
-       "       Country_Argentina  Country_Armenia  Country_Australia  Country_Austria  \\\n",
-       "1                      0                0                  0                0   \n",
-       "2                      0                0                  0                0   \n",
-       "3                      0                0                  0                0   \n",
-       "4                      0                0                  0                0   \n",
-       "5                      0                0                  0                0   \n",
-       "...                  ...              ...                ...              ...   \n",
-       "41564                  0                0                  0                0   \n",
-       "41565                  0                0                  0                0   \n",
-       "41566                  0                0                  0                0   \n",
-       "41567                  0                0                  0                0   \n",
-       "41568                  0                0                  0                0   \n",
-       "\n",
-       "       Country_Azerbaijan  Country_Bahamas  Country_Bahrain  \\\n",
-       "1                       0                0                0   \n",
-       "2                       0                0                0   \n",
-       "3                       0                0                0   \n",
-       "4                       0                0                0   \n",
-       "5                       0                0                0   \n",
-       "...                   ...              ...              ...   \n",
-       "41564                   0                0                0   \n",
-       "41565                   0                0                0   \n",
-       "41566                   0                0                0   \n",
-       "41567                   0                0                0   \n",
-       "41568                   0                0                0   \n",
-       "\n",
-       "       Country_Bangladesh  Country_Barbados  Country_Belarus  Country_Belgium  \\\n",
-       "1                       0                 0                0                0   \n",
-       "2                       0                 0                0                0   \n",
-       "3                       0                 0                0                0   \n",
-       "4                       0                 0                0                0   \n",
-       "5                       0                 0                0                0   \n",
-       "...                   ...               ...              ...              ...   \n",
-       "41564                   0                 0                0                0   \n",
-       "41565                   0                 0                0                0   \n",
-       "41566                   0                 0                0                0   \n",
-       "41567                   0                 0                0                0   \n",
-       "41568                   0                 0                0                0   \n",
-       "\n",
-       "       Country_Benin  Country_Bhutan  Country_Bolivia  \\\n",
-       "1                  0               0                0   \n",
-       "2                  0               0                0   \n",
-       "3                  0               0                0   \n",
-       "4                  0               0                0   \n",
-       "5                  0               0                0   \n",
-       "...              ...             ...              ...   \n",
-       "41564              0               0                0   \n",
-       "41565              0               0                0   \n",
-       "41566              0               0                0   \n",
-       "41567              0               0                0   \n",
-       "41568              0               0                0   \n",
-       "\n",
-       "       Country_Bosnia and Herzegovina  Country_Botswana  Country_Brazil  \\\n",
-       "1                                   0                 0               0   \n",
-       "2                                   0                 0               0   \n",
-       "3                                   0                 0               0   \n",
-       "4                                   0                 0               0   \n",
-       "5                                   0                 0               0   \n",
-       "...                               ...               ...             ...   \n",
-       "41564                               0                 0               0   \n",
-       "41565                               0                 0               0   \n",
-       "41566                               0                 0               0   \n",
-       "41567                               0                 0               0   \n",
-       "41568                               0                 0               0   \n",
-       "\n",
-       "       Country_Brunei Darussalam  Country_Bulgaria  Country_Burkina Faso  \\\n",
-       "1                              0                 0                     0   \n",
-       "2                              0                 0                     0   \n",
-       "3                              0                 0                     0   \n",
-       "4                              0                 0                     0   \n",
-       "5                              0                 0                     0   \n",
-       "...                          ...               ...                   ...   \n",
-       "41564                          0                 0                     0   \n",
-       "41565                          0                 0                     0   \n",
-       "41566                          0                 0                     0   \n",
-       "41567                          0                 0                     0   \n",
-       "41568                          0                 0                     0   \n",
-       "\n",
-       "       Country_Cambodia  Country_Cameroon  Country_Canada  Country_Chile  \\\n",
-       "1                     0                 0               0              0   \n",
-       "2                     0                 0               0              0   \n",
-       "3                     0                 0               0              0   \n",
-       "4                     0                 0               0              0   \n",
-       "5                     0                 0               0              0   \n",
-       "...                 ...               ...             ...            ...   \n",
-       "41564                 0                 0               0              0   \n",
-       "41565                 0                 0               0              0   \n",
-       "41566                 0                 0               0              0   \n",
-       "41567                 0                 0               0              0   \n",
-       "41568                 0                 0               0              0   \n",
-       "\n",
-       "       Country_China  Country_Colombia  Country_Costa Rica  Country_Croatia  \\\n",
-       "1                  0                 0                   0                0   \n",
-       "2                  0                 0                   0                0   \n",
-       "3                  0                 0                   0                0   \n",
-       "4                  0                 0                   0                0   \n",
-       "5                  0                 0                   0                0   \n",
-       "...              ...               ...                 ...              ...   \n",
-       "41564              0                 0                   0                0   \n",
-       "41565              0                 0                   0                0   \n",
-       "41566              0                 0                   0                0   \n",
-       "41567              0                 0                   0                0   \n",
-       "41568              0                 0                   0                0   \n",
-       "\n",
-       "       Country_Cuba  Country_Cyprus  Country_Czech Republic  \\\n",
-       "1                 0               0                       0   \n",
-       "2                 0               0                       0   \n",
-       "3                 0               0                       0   \n",
-       "4                 0               0                       0   \n",
-       "5                 0               0                       0   \n",
-       "...             ...             ...                     ...   \n",
-       "41564             0               0                       0   \n",
-       "41565             0               0                       0   \n",
-       "41566             0               0                       0   \n",
-       "41567             0               0                       0   \n",
-       "41568             0               0                       0   \n",
-       "\n",
-       "       Country_Côte d'Ivoire  Country_Democratic Republic of the Congo  \\\n",
-       "1                          0                                         0   \n",
-       "2                          0                                         0   \n",
-       "3                          0                                         0   \n",
-       "4                          0                                         0   \n",
-       "5                          0                                         0   \n",
-       "...                      ...                                       ...   \n",
-       "41564                      0                                         0   \n",
-       "41565                      0                                         0   \n",
-       "41566                      0                                         0   \n",
-       "41567                      0                                         0   \n",
-       "41568                      0                                         0   \n",
-       "\n",
-       "       Country_Denmark  Country_Dominican Republic  Country_Ecuador  \\\n",
-       "1                    0                           0                0   \n",
-       "2                    0                           0                0   \n",
-       "3                    0                           0                0   \n",
-       "4                    0                           0                0   \n",
-       "5                    0                           0                0   \n",
-       "...                ...                         ...              ...   \n",
-       "41564                0                           0                0   \n",
-       "41565                0                           0                0   \n",
-       "41566                0                           0                0   \n",
-       "41567                0                           0                0   \n",
-       "41568                0                           0                0   \n",
-       "\n",
-       "       Country_Egypt  Country_El Salvador  Country_Estonia  Country_Ethiopia  \\\n",
-       "1                  0                    0                0                 0   \n",
-       "2                  0                    0                0                 0   \n",
-       "3                  0                    0                0                 0   \n",
-       "4                  0                    0                0                 0   \n",
-       "5                  0                    0                0                 0   \n",
-       "...              ...                  ...              ...               ...   \n",
-       "41564              0                    0                0                 0   \n",
-       "41565              0                    0                0                 0   \n",
-       "41566              0                    0                0                 0   \n",
-       "41567              0                    0                0                 0   \n",
-       "41568              0                    0                0                 0   \n",
-       "\n",
-       "       Country_Finland  Country_France  Country_Georgia  Country_Germany  \\\n",
-       "1                    0               0                0                0   \n",
-       "2                    0               0                0                0   \n",
-       "3                    0               0                0                0   \n",
-       "4                    0               0                0                0   \n",
-       "5                    0               0                0                0   \n",
-       "...                ...             ...              ...              ...   \n",
-       "41564                0               0                0                0   \n",
-       "41565                0               0                0                0   \n",
-       "41566                0               0                0                0   \n",
-       "41567                0               0                0                0   \n",
-       "41568                0               0                0                0   \n",
-       "\n",
-       "       Country_Ghana  Country_Greece  Country_Guatemala  Country_Guinea  \\\n",
-       "1                  0               0                  0               0   \n",
-       "2                  0               0                  0               0   \n",
-       "3                  0               0                  0               0   \n",
-       "4                  0               0                  0               0   \n",
-       "5                  0               0                  0               0   \n",
-       "...              ...             ...                ...             ...   \n",
-       "41564              0               0                  0               0   \n",
-       "41565              0               0                  0               0   \n",
-       "41566              0               0                  0               0   \n",
-       "41567              0               0                  0               0   \n",
-       "41568              0               0                  0               0   \n",
-       "\n",
-       "       Country_Guyana  Country_Haiti  Country_Honduras  \\\n",
-       "1                   0              0                 0   \n",
-       "2                   0              0                 0   \n",
-       "3                   0              0                 0   \n",
-       "4                   0              0                 0   \n",
-       "5                   0              0                 0   \n",
-       "...               ...            ...               ...   \n",
-       "41564               0              0                 0   \n",
-       "41565               0              0                 0   \n",
-       "41566               0              0                 0   \n",
-       "41567               0              0                 0   \n",
-       "41568               0              0                 0   \n",
-       "\n",
-       "       Country_Hong Kong (S.A.R.)  Country_Hungary  Country_Iceland  \\\n",
-       "1                               0                0                0   \n",
-       "2                               0                0                0   \n",
-       "3                               0                0                0   \n",
-       "4                               0                0                0   \n",
-       "5                               0                0                0   \n",
-       "...                           ...              ...              ...   \n",
-       "41564                           0                0                0   \n",
-       "41565                           0                0                0   \n",
-       "41566                           0                0                0   \n",
-       "41567                           0                0                0   \n",
-       "41568                           0                0                0   \n",
-       "\n",
-       "       Country_India  Country_Indonesia  Country_Iran  Country_Iraq  \\\n",
-       "1                  0                  0             0             0   \n",
-       "2                  0                  0             0             0   \n",
-       "3                  0                  0             0             0   \n",
-       "4                  0                  0             0             0   \n",
-       "5                  0                  0             0             0   \n",
-       "...              ...                ...           ...           ...   \n",
-       "41564              1                  0             0             0   \n",
-       "41565              1                  0             0             0   \n",
-       "41566              0                  1             0             0   \n",
-       "41567              0                  0             0             0   \n",
-       "41568              0                  0             0             0   \n",
-       "\n",
-       "       Country_Ireland  Country_Isle of Man  Country_Israel  Country_Italy  \\\n",
-       "1                    0                    0               0              0   \n",
-       "2                    0                    0               0              0   \n",
-       "3                    0                    0               0              0   \n",
-       "4                    0                    0               0              0   \n",
-       "5                    0                    0               0              0   \n",
-       "...                ...                  ...             ...            ...   \n",
-       "41564                0                    0               0              0   \n",
-       "41565                0                    0               0              0   \n",
-       "41566                0                    0               0              0   \n",
-       "41567                0                    0               0              0   \n",
-       "41568                0                    0               0              0   \n",
-       "\n",
-       "       Country_Jamaica  Country_Japan  Country_Jordan  Country_Kazakhstan  \\\n",
-       "1                    0              0               0                   0   \n",
-       "2                    0              0               0                   0   \n",
-       "3                    0              0               0                   0   \n",
-       "4                    0              0               0                   0   \n",
-       "5                    0              0               0                   0   \n",
-       "...                ...            ...             ...                 ...   \n",
-       "41564                0              0               0                   0   \n",
-       "41565                0              0               0                   0   \n",
-       "41566                0              0               0                   0   \n",
-       "41567                0              0               0                   0   \n",
-       "41568                0              0               0                   0   \n",
-       "\n",
-       "       Country_Kenya  Country_Kosovo  Country_Kuwait  Country_Kyrgyzstan  \\\n",
-       "1                  0               0               0                   0   \n",
-       "2                  0               0               0                   0   \n",
-       "3                  0               0               0                   0   \n",
-       "4                  0               0               0                   0   \n",
-       "5                  0               0               0                   0   \n",
-       "...              ...             ...             ...                 ...   \n",
-       "41564              0               0               0                   0   \n",
-       "41565              0               0               0                   0   \n",
-       "41566              0               0               0                   0   \n",
-       "41567              0               0               0                   0   \n",
-       "41568              0               0               0                   0   \n",
-       "\n",
-       "       Country_Lao People's Democratic Republic  Country_Latvia  \\\n",
-       "1                                             0               0   \n",
-       "2                                             0               0   \n",
-       "3                                             0               0   \n",
-       "4                                             0               0   \n",
-       "5                                             0               0   \n",
-       "...                                         ...             ...   \n",
-       "41564                                         0               0   \n",
-       "41565                                         0               0   \n",
-       "41566                                         0               0   \n",
-       "41567                                         0               0   \n",
-       "41568                                         0               0   \n",
-       "\n",
-       "       Country_Lebanon  Country_Lithuania  Country_Luxembourg  \\\n",
-       "1                    0                  0                   0   \n",
-       "2                    0                  0                   0   \n",
-       "3                    0                  0                   0   \n",
-       "4                    0                  0                   0   \n",
-       "5                    0                  0                   0   \n",
-       "...                ...                ...                 ...   \n",
-       "41564                0                  0                   0   \n",
-       "41565                0                  0                   0   \n",
-       "41566                0                  0                   0   \n",
-       "41567                0                  0                   0   \n",
-       "41568                0                  0                   0   \n",
-       "\n",
-       "       Country_Madagascar  Country_Malawi  Country_Malaysia  Country_Maldives  \\\n",
-       "1                       0               0                 0                 0   \n",
-       "2                       0               0                 0                 0   \n",
-       "3                       0               0                 0                 0   \n",
-       "4                       0               0                 0                 0   \n",
-       "5                       0               0                 0                 0   \n",
-       "...                   ...             ...               ...               ...   \n",
-       "41564                   0               0                 0                 0   \n",
-       "41565                   0               0                 0                 0   \n",
-       "41566                   0               0                 0                 0   \n",
-       "41567                   0               0                 0                 0   \n",
-       "41568                   0               0                 0                 0   \n",
-       "\n",
-       "       Country_Malta  Country_Mauritius  Country_Mexico  Country_Monaco  \\\n",
-       "1                  0                  0               0               0   \n",
-       "2                  0                  0               0               0   \n",
-       "3                  0                  0               0               0   \n",
-       "4                  0                  0               0               0   \n",
-       "5                  0                  0               0               0   \n",
-       "...              ...                ...             ...             ...   \n",
-       "41564              0                  0               0               0   \n",
-       "41565              0                  0               0               0   \n",
-       "41566              0                  0               0               0   \n",
-       "41567              0                  1               0               0   \n",
-       "41568              0                  0               0               0   \n",
-       "\n",
-       "       Country_Mongolia  Country_Montenegro  Country_Morocco  Country_Myanmar  \\\n",
-       "1                     0                   0                0                0   \n",
-       "2                     0                   0                0                0   \n",
-       "3                     0                   0                0                0   \n",
-       "4                     0                   0                0                0   \n",
-       "5                     0                   0                0                0   \n",
-       "...                 ...                 ...              ...              ...   \n",
-       "41564                 0                   0                0                0   \n",
-       "41565                 0                   0                0                0   \n",
-       "41566                 0                   0                0                0   \n",
-       "41567                 0                   0                0                0   \n",
-       "41568                 0                   0                0                0   \n",
-       "\n",
-       "       Country_Namibia  Country_Nepal  Country_Netherlands  \\\n",
-       "1                    0              0                    0   \n",
-       "2                    0              0                    0   \n",
-       "3                    0              0                    0   \n",
-       "4                    0              0                    0   \n",
-       "5                    0              0                    1   \n",
-       "...                ...            ...                  ...   \n",
-       "41564                0              0                    0   \n",
-       "41565                0              0                    0   \n",
-       "41566                0              0                    0   \n",
-       "41567                0              0                    0   \n",
-       "41568                0              0                    0   \n",
-       "\n",
-       "       Country_New Zealand  Country_Nicaragua  Country_Niger  Country_Nigeria  \\\n",
-       "1                        0                  0              0                0   \n",
-       "2                        0                  0              0                0   \n",
-       "3                        0                  0              0                0   \n",
-       "4                        0                  0              0                0   \n",
-       "5                        0                  0              0                0   \n",
-       "...                    ...                ...            ...              ...   \n",
-       "41564                    0                  0              0                0   \n",
-       "41565                    0                  0              0                0   \n",
-       "41566                    0                  0              0                0   \n",
-       "41567                    0                  0              0                0   \n",
-       "41568                    0                  0              0                0   \n",
-       "\n",
-       "       Country_Nomadic  Country_Norway  Country_Oman  Country_Pakistan  \\\n",
-       "1                    0               0             0                 0   \n",
-       "2                    0               0             0                 0   \n",
-       "3                    0               0             0                 0   \n",
-       "4                    0               0             0                 0   \n",
-       "5                    0               0             0                 0   \n",
-       "...                ...             ...           ...               ...   \n",
-       "41564                0               0             0                 0   \n",
-       "41565                0               0             0                 0   \n",
-       "41566                0               0             0                 0   \n",
-       "41567                0               0             0                 0   \n",
-       "41568                0               0             0                 0   \n",
-       "\n",
-       "       Country_Panama  Country_Paraguay  Country_Peru  Country_Philippines  \\\n",
-       "1                   0                 0             0                    0   \n",
-       "2                   0                 0             0                    0   \n",
-       "3                   0                 0             0                    0   \n",
-       "4                   0                 0             0                    0   \n",
-       "5                   0                 0             0                    0   \n",
-       "...               ...               ...           ...                  ...   \n",
-       "41564               0                 0             0                    0   \n",
-       "41565               0                 0             0                    0   \n",
-       "41566               0                 0             0                    0   \n",
-       "41567               0                 0             0                    0   \n",
-       "41568               0                 0             0                    0   \n",
-       "\n",
-       "       Country_Poland  Country_Portugal  Country_Qatar  \\\n",
-       "1                   0                 0              0   \n",
-       "2                   0                 0              0   \n",
-       "3                   0                 0              0   \n",
-       "4                   0                 0              0   \n",
-       "5                   0                 0              0   \n",
-       "...               ...               ...            ...   \n",
-       "41564               0                 0              0   \n",
-       "41565               0                 0              0   \n",
-       "41566               0                 0              0   \n",
-       "41567               0                 0              0   \n",
-       "41568               0                 0              0   \n",
-       "\n",
-       "       Country_Republic of Korea  Country_Republic of Moldova  \\\n",
-       "1                              0                            0   \n",
-       "2                              0                            0   \n",
-       "3                              0                            0   \n",
-       "4                              0                            0   \n",
-       "5                              0                            0   \n",
-       "...                          ...                          ...   \n",
-       "41564                          0                            0   \n",
-       "41565                          0                            0   \n",
-       "41566                          0                            0   \n",
-       "41567                          0                            0   \n",
-       "41568                          0                            0   \n",
-       "\n",
-       "       Country_Romania  Country_Russian Federation  Country_Rwanda  \\\n",
-       "1                    0                           0               0   \n",
-       "2                    0                           0               0   \n",
-       "3                    0                           0               0   \n",
-       "4                    0                           0               0   \n",
-       "5                    0                           0               0   \n",
-       "...                ...                         ...             ...   \n",
-       "41564                0                           0               0   \n",
-       "41565                0                           0               0   \n",
-       "41566                0                           0               0   \n",
-       "41567                0                           0               0   \n",
-       "41568                0                           0               0   \n",
-       "\n",
-       "       Country_San Marino  Country_Saudi Arabia  Country_Senegal  \\\n",
-       "1                       0                     0                0   \n",
-       "2                       0                     0                0   \n",
-       "3                       0                     0                0   \n",
-       "4                       0                     0                0   \n",
-       "5                       0                     0                0   \n",
-       "...                   ...                   ...              ...   \n",
-       "41564                   0                     0                0   \n",
-       "41565                   0                     0                0   \n",
-       "41566                   0                     0                0   \n",
-       "41567                   0                     0                0   \n",
-       "41568                   0                     0                0   \n",
-       "\n",
-       "       Country_Serbia  Country_Singapore  Country_Slovakia  Country_Slovenia  \\\n",
-       "1                   0                  0                 0                 0   \n",
-       "2                   0                  0                 0                 0   \n",
-       "3                   0                  0                 0                 0   \n",
-       "4                   0                  0                 0                 0   \n",
-       "5                   0                  0                 0                 0   \n",
-       "...               ...                ...               ...               ...   \n",
-       "41564               0                  0                 0                 0   \n",
-       "41565               0                  0                 0                 0   \n",
-       "41566               0                  0                 0                 0   \n",
-       "41567               0                  0                 0                 0   \n",
-       "41568               0                  0                 0                 0   \n",
-       "\n",
-       "       Country_Somalia  Country_South Africa  Country_South Korea  \\\n",
-       "1                    0                     0                    0   \n",
-       "2                    0                     0                    0   \n",
-       "3                    0                     0                    0   \n",
-       "4                    0                     0                    0   \n",
-       "5                    0                     0                    0   \n",
-       "...                ...                   ...                  ...   \n",
-       "41564                0                     0                    0   \n",
-       "41565                0                     0                    0   \n",
-       "41566                0                     0                    0   \n",
-       "41567                0                     0                    0   \n",
-       "41568                0                     0                    0   \n",
-       "\n",
-       "       Country_Spain  Country_Sri Lanka  Country_Sudan  Country_Swaziland  \\\n",
-       "1                  0                  0              0                  0   \n",
-       "2                  0                  0              0                  0   \n",
-       "3                  0                  0              0                  0   \n",
-       "4                  1                  0              0                  0   \n",
-       "5                  0                  0              0                  0   \n",
-       "...              ...                ...            ...                ...   \n",
-       "41564              0                  0              0                  0   \n",
-       "41565              0                  0              0                  0   \n",
-       "41566              0                  0              0                  0   \n",
-       "41567              0                  0              0                  0   \n",
-       "41568              0                  0              0                  0   \n",
-       "\n",
-       "       Country_Sweden  Country_Switzerland  Country_Syrian Arab Republic  \\\n",
-       "1                   0                    0                             0   \n",
-       "2                   0                    0                             0   \n",
-       "3                   0                    0                             0   \n",
-       "4                   0                    0                             0   \n",
-       "5                   0                    0                             0   \n",
-       "...               ...                  ...                           ...   \n",
-       "41564               0                    0                             0   \n",
-       "41565               0                    0                             0   \n",
-       "41566               0                    0                             0   \n",
-       "41567               0                    0                             0   \n",
-       "41568               0                    0                             0   \n",
-       "\n",
-       "       Country_Taiwan  Country_Thailand  \\\n",
-       "1                   0                 0   \n",
-       "2                   0                 0   \n",
-       "3                   0                 0   \n",
-       "4                   0                 0   \n",
-       "5                   0                 0   \n",
-       "...               ...               ...   \n",
-       "41564               0                 0   \n",
-       "41565               0                 0   \n",
-       "41566               0                 0   \n",
-       "41567               0                 0   \n",
-       "41568               0                 0   \n",
-       "\n",
-       "       Country_The former Yugoslav Republic of Macedonia  Country_Togo  \\\n",
-       "1                                                      0             0   \n",
-       "2                                                      0             0   \n",
-       "3                                                      0             0   \n",
-       "4                                                      0             0   \n",
-       "5                                                      0             0   \n",
-       "...                                                  ...           ...   \n",
-       "41564                                                  0             0   \n",
-       "41565                                                  0             0   \n",
-       "41566                                                  0             0   \n",
-       "41567                                                  0             0   \n",
-       "41568                                                  0             0   \n",
-       "\n",
-       "       Country_Trinidad and Tobago  Country_Tunisia  Country_Turkey  \\\n",
-       "1                                0                0               0   \n",
-       "2                                0                0               0   \n",
-       "3                                0                0               0   \n",
-       "4                                0                0               0   \n",
-       "5                                0                0               0   \n",
-       "...                            ...              ...             ...   \n",
-       "41564                            0                0               0   \n",
-       "41565                            0                0               0   \n",
-       "41566                            0                0               0   \n",
-       "41567                            0                0               0   \n",
-       "41568                            0                0               0   \n",
-       "\n",
-       "       Country_Turkmenistan  Country_Uganda  Country_Ukraine  \\\n",
-       "1                         0               0                0   \n",
-       "2                         0               0                0   \n",
-       "3                         0               0                0   \n",
-       "4                         0               0                0   \n",
-       "5                         0               0                0   \n",
-       "...                     ...             ...              ...   \n",
-       "41564                     0               0                0   \n",
-       "41565                     0               0                0   \n",
-       "41566                     0               0                0   \n",
-       "41567                     0               0                0   \n",
-       "41568                     0               0                0   \n",
-       "\n",
-       "       Country_United Arab Emirates  Country_United Kingdom  \\\n",
-       "1                                 0                       0   \n",
-       "2                                 0                       1   \n",
-       "3                                 0                       1   \n",
-       "4                                 0                       0   \n",
-       "5                                 0                       0   \n",
-       "...                             ...                     ...   \n",
-       "41564                             0                       0   \n",
-       "41565                             0                       0   \n",
-       "41566                             0                       0   \n",
-       "41567                             0                       0   \n",
-       "41568                             0                       0   \n",
-       "\n",
-       "       Country_United Republic of Tanzania  Country_United States  \\\n",
-       "1                                        0                      1   \n",
-       "2                                        0                      0   \n",
-       "3                                        0                      0   \n",
-       "4                                        0                      0   \n",
-       "5                                        0                      0   \n",
-       "...                                    ...                    ...   \n",
-       "41564                                    0                      0   \n",
-       "41565                                    0                      0   \n",
-       "41566                                    0                      0   \n",
-       "41567                                    0                      0   \n",
-       "41568                                    0                      1   \n",
-       "\n",
-       "       Country_Uruguay  Country_Uzbekistan  \\\n",
-       "1                    0                   0   \n",
-       "2                    0                   0   \n",
-       "3                    0                   0   \n",
-       "4                    0                   0   \n",
-       "5                    0                   0   \n",
-       "...                ...                 ...   \n",
-       "41564                0                   0   \n",
-       "41565                0                   0   \n",
-       "41566                0                   0   \n",
-       "41567                0                   0   \n",
-       "41568                0                   0   \n",
-       "\n",
-       "       Country_Venezuela, Bolivarian Republic of...  Country_Viet Nam  \\\n",
-       "1                                                 0                 0   \n",
-       "2                                                 0                 0   \n",
-       "3                                                 0                 0   \n",
-       "4                                                 0                 0   \n",
-       "5                                                 0                 0   \n",
-       "...                                             ...               ...   \n",
-       "41564                                             0                 0   \n",
-       "41565                                             0                 0   \n",
-       "41566                                             0                 0   \n",
-       "41567                                             0                 0   \n",
-       "41568                                             0                 0   \n",
-       "\n",
-       "       Country_Yemen  Country_Zambia  Country_Zimbabwe  EdLevel_Associate  \\\n",
-       "1                  0               0                 0                  0   \n",
-       "2                  0               0                 0                  0   \n",
-       "3                  0               0                 0                  0   \n",
-       "4                  0               0                 0                  0   \n",
-       "5                  0               0                 0                  0   \n",
-       "...              ...             ...               ...                ...   \n",
-       "41564              0               0                 0                  0   \n",
-       "41565              0               0                 0                  0   \n",
-       "41566              0               0                 0                  0   \n",
-       "41567              0               0                 0                  0   \n",
-       "41568              0               0                 0                  0   \n",
-       "\n",
-       "       EdLevel_Bachelors  EdLevel_Doctorate  EdLevel_Masters  \\\n",
-       "1                      1                  0                0   \n",
-       "2                      0                  0                1   \n",
-       "3                      1                  0                0   \n",
-       "4                      0                  0                0   \n",
-       "5                      0                  0                0   \n",
-       "...                  ...                ...              ...   \n",
-       "41564                  1                  0                0   \n",
-       "41565                  1                  0                0   \n",
-       "41566                  0                  0                0   \n",
-       "41567                  0                  0                1   \n",
-       "41568                  0                  0                0   \n",
-       "\n",
-       "       EdLevel_No Degree  EdLevel_Professional  Employment_Full-time  \\\n",
-       "1                      0                     0                     1   \n",
-       "2                      0                     0                     1   \n",
-       "3                      0                     0                     1   \n",
-       "4                      1                     0                     1   \n",
-       "5                      1                     0                     1   \n",
-       "...                  ...                   ...                   ...   \n",
-       "41564                  0                     0                     0   \n",
-       "41565                  0                     0                     0   \n",
-       "41566                  1                     0                     0   \n",
-       "41567                  0                     0                     1   \n",
-       "41568                  1                     0                     1   \n",
-       "\n",
-       "       Employment_Not employed  \\\n",
-       "1                            0   \n",
-       "2                            0   \n",
-       "3                            0   \n",
-       "4                            0   \n",
-       "5                            0   \n",
-       "...                        ...   \n",
-       "41564                        0   \n",
-       "41565                        0   \n",
-       "41566                        0   \n",
-       "41567                        0   \n",
-       "41568                        0   \n",
-       "\n",
-       "       Employment_Not employed, and not looking for work  \\\n",
-       "1                                                      0   \n",
-       "2                                                      0   \n",
-       "3                                                      0   \n",
-       "4                                                      0   \n",
-       "5                                                      0   \n",
-       "...                                                  ...   \n",
-       "41564                                                  0   \n",
-       "41565                                                  0   \n",
-       "41566                                                  0   \n",
-       "41567                                                  0   \n",
-       "41568                                                  0   \n",
-       "\n",
-       "       Employment_Part-time  Employment_Retired  Employment_Self-employed  \\\n",
-       "1                         0                   0                         0   \n",
-       "2                         0                   0                         0   \n",
-       "3                         0                   0                         0   \n",
-       "4                         0                   0                         0   \n",
-       "5                         0                   0                         0   \n",
-       "...                     ...                 ...                       ...   \n",
-       "41564                     0                   0                         0   \n",
-       "41565                     0                   0                         0   \n",
-       "41566                     0                   0                         0   \n",
-       "41567                     0                   0                         0   \n",
-       "41568                     0                   0                         0   \n",
-       "\n",
-       "       Employment_Student  Hobbyist_No  Hobbyist_Yes  \\\n",
-       "1                       0            0             1   \n",
-       "2                       0            0             1   \n",
-       "3                       0            0             1   \n",
-       "4                       0            1             0   \n",
-       "5                       0            0             1   \n",
-       "...                   ...          ...           ...   \n",
-       "41564                   1            0             1   \n",
-       "41565                   1            0             1   \n",
-       "41566                   1            0             1   \n",
-       "41567                   0            0             1   \n",
-       "41568                   0            0             1   \n",
-       "\n",
-       "       UndergradMajor_Arts and Science  UndergradMajor_Business  \\\n",
-       "1                                    0                        0   \n",
-       "2                                    0                        0   \n",
-       "3                                    0                        0   \n",
-       "4                                    0                        0   \n",
-       "5                                    0                        0   \n",
-       "...                                ...                      ...   \n",
-       "41564                                0                        0   \n",
-       "41565                                0                        0   \n",
-       "41566                                0                        0   \n",
-       "41567                                0                        0   \n",
-       "41568                                0                        0   \n",
-       "\n",
-       "       UndergradMajor_Computer Science  UndergradMajor_Engineering  \\\n",
-       "1                                    1                           0   \n",
-       "2                                    0                           0   \n",
-       "3                                    1                           0   \n",
-       "4                                    0                           0   \n",
-       "5                                    0                           0   \n",
-       "...                                ...                         ...   \n",
-       "41564                                1                           0   \n",
-       "41565                                1                           0   \n",
-       "41566                                0                           0   \n",
-       "41567                                1                           0   \n",
-       "41568                                1                           0   \n",
-       "\n",
-       "       UndergradMajor_Info Systems  UndergradMajor_Math/Stat  \\\n",
-       "1                                0                         0   \n",
-       "2                                0                         1   \n",
-       "3                                0                         0   \n",
-       "4                                0                         1   \n",
-       "5                                0                         0   \n",
-       "...                            ...                       ...   \n",
-       "41564                            0                         0   \n",
-       "41565                            0                         0   \n",
-       "41566                            0                         0   \n",
-       "41567                            0                         0   \n",
-       "41568                            0                         0   \n",
-       "\n",
-       "       UndergradMajor_No major  UndergradMajor_Other Science  \\\n",
-       "1                            0                             0   \n",
-       "2                            0                             0   \n",
-       "3                            0                             0   \n",
-       "4                            0                             0   \n",
-       "5                            1                             0   \n",
-       "...                        ...                           ...   \n",
-       "41564                        0                             0   \n",
-       "41565                        0                             0   \n",
-       "41566                        1                             0   \n",
-       "41567                        0                             0   \n",
-       "41568                        0                             0   \n",
-       "\n",
-       "       UndergradMajor_Web Design/Dev  \n",
-       "1                                  0  \n",
-       "2                                  0  \n",
-       "3                                  0  \n",
-       "4                                  0  \n",
-       "5                                  0  \n",
-       "...                              ...  \n",
-       "41564                              0  \n",
-       "41565                              0  \n",
-       "41566                              0  \n",
-       "41567                              0  \n",
-       "41568                              0  \n",
-       "\n",
-       "[33767 rows x 180 columns]"
-      ]
-     },
-     "execution_count": 435,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 436,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1        0\n",
-       "2        1\n",
-       "3        0\n",
-       "4        0\n",
-       "5        1\n",
-       "        ..\n",
-       "41564    1\n",
-       "41565    1\n",
-       "41566    1\n",
-       "41567    1\n",
-       "41568    1\n",
-       "Name: CurrentJobSatis, Length: 33767, dtype: int64"
-      ]
-     },
-     "execution_count": 436,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 437,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# split data into train and test sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Checking Model Coefficent**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 438,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 72.11%\n"
-     ]
-    }
-   ],
-   "source": [
-    "# define the model\n",
-    "model = LogisticRegression()\n",
-    "# fit the model\n",
-    "model.fit(X, y)\n",
-    "\n",
-    "# get importance\n",
-    "importance = model.coef_[0]\n",
-    "\n",
-    "# make predictions for test data and evaluate\n",
-    "y_pred = model.predict(X_test)\n",
-    "predictions = [round(value) for value in y_pred]\n",
-    "accuracy = accuracy_score(y_test, predictions)\n",
-    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have recieved **72% Accuracy** which is good enough to move ahead with predictions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting Features affecting Job Satisfaction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 439,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x2160 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "results_df = pd.DataFrame()\n",
-    "results_df[\"Rates\"] = importance.tolist()\n",
-    "results_df[\"Columns\"] = X.columns\n",
-    "\n",
-    "new_index = results_df.Rates.sort_values(ascending = False).index\n",
-    "sorted_results = results_df.reindex(new_index)\n",
-    "filtered_results = sorted_results[np.abs(sorted_results.Rates) > 0.1]\n",
-    "\n",
-    "plt.figure(figsize =(20,30))\n",
-    "plt.barh(filtered_results.Columns, filtered_results.Rates)\n",
-    "plt.xlabel(\"Negative and Postive Features\", fontsize = 25)\n",
-    "plt.title(\"Features Affecting Job Satistaction\",fontsize = 25)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The top 2 features negatively affecting Job Satisfaction are age, country. So, in the elderly ages, job satisfaction may decrease because of the personal expectation increases. In the same way, as the professional coding years are increasing, satisfaction may decrease.\n",
-    "\n",
-    "- Among the countries; most dissatisfied countries are Angolia, Rwanda, Krygyzstan, Sudan.\n",
-    "- UndergradMajor and other Science, are mostly satisfied.\n",
-    "- Most satisfied countries Malta, Ghana, Cyprus."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Conclusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overall, we performed various analyses on the Stack overflow developer survey and derived insights from it. We found which country has the highest no of respondents, which is the most popular language, education level of respondents, different roles of developers, and so on.<br>\n",
-    "Additionally, we performed machine learning models to predict the growth of languages, the salary of data scientists, what is causing job satisfaction. "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow_Survey_Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "Stack overflow is a professional community for developers. They conduct developer surveys every year since 2011. The collected data is available open-source on the web. The Dataset would help us to answer real-world questions with the help of proper analysis. The most popular language that developers use can be found through the analysis. We also can find the developer role which pays the highest salary. The aim of our project is to analyze the 2018,2019 and 2020 developer surveys datasets from where we collect valuable insights from them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 827,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "import warnings; \n",
+    "warnings.simplefilter('ignore')\n",
+    "import pycountry\n",
+    "import plotly.express as px\n",
+    "import matplotlib.patches as mpatches\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.svm import LinearSVC\n",
+    "import time\n",
+    "from sklearn.metrics import hamming_loss\n",
+    "from sklearn.metrics import jaccard_score\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "from sklearn.multiclass import OneVsRestClassifier\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow 2018 Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 828,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Respondent</th>\n",
+       "      <th>Hobby</th>\n",
+       "      <th>OpenSource</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Student</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>FormalEducation</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>CompanySize</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>YearsCoding</th>\n",
+       "      <th>YearsCodingProf</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>CareerSatisfaction</th>\n",
+       "      <th>HopeFiveYears</th>\n",
+       "      <th>JobSearchStatus</th>\n",
+       "      <th>LastNewJob</th>\n",
+       "      <th>AssessJob1</th>\n",
+       "      <th>AssessJob2</th>\n",
+       "      <th>AssessJob3</th>\n",
+       "      <th>AssessJob4</th>\n",
+       "      <th>AssessJob5</th>\n",
+       "      <th>AssessJob6</th>\n",
+       "      <th>AssessJob7</th>\n",
+       "      <th>AssessJob8</th>\n",
+       "      <th>AssessJob9</th>\n",
+       "      <th>AssessJob10</th>\n",
+       "      <th>AssessBenefits1</th>\n",
+       "      <th>AssessBenefits2</th>\n",
+       "      <th>AssessBenefits3</th>\n",
+       "      <th>AssessBenefits4</th>\n",
+       "      <th>AssessBenefits5</th>\n",
+       "      <th>AssessBenefits6</th>\n",
+       "      <th>AssessBenefits7</th>\n",
+       "      <th>AssessBenefits8</th>\n",
+       "      <th>AssessBenefits9</th>\n",
+       "      <th>AssessBenefits10</th>\n",
+       "      <th>AssessBenefits11</th>\n",
+       "      <th>JobContactPriorities1</th>\n",
+       "      <th>JobContactPriorities2</th>\n",
+       "      <th>JobContactPriorities3</th>\n",
+       "      <th>JobContactPriorities4</th>\n",
+       "      <th>JobContactPriorities5</th>\n",
+       "      <th>JobEmailPriorities1</th>\n",
+       "      <th>JobEmailPriorities2</th>\n",
+       "      <th>JobEmailPriorities3</th>\n",
+       "      <th>JobEmailPriorities4</th>\n",
+       "      <th>JobEmailPriorities5</th>\n",
+       "      <th>JobEmailPriorities6</th>\n",
+       "      <th>JobEmailPriorities7</th>\n",
+       "      <th>UpdateCV</th>\n",
+       "      <th>Currency</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>SalaryType</th>\n",
+       "      <th>ConvertedSalary</th>\n",
+       "      <th>CurrencySymbol</th>\n",
+       "      <th>CommunicationTools</th>\n",
+       "      <th>TimeFullyProductive</th>\n",
+       "      <th>EducationTypes</th>\n",
+       "      <th>SelfTaughtTypes</th>\n",
+       "      <th>TimeAfterBootcamp</th>\n",
+       "      <th>HackathonReasons</th>\n",
+       "      <th>AgreeDisagree1</th>\n",
+       "      <th>AgreeDisagree2</th>\n",
+       "      <th>AgreeDisagree3</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>DatabaseWorkedWith</th>\n",
+       "      <th>DatabaseDesireNextYear</th>\n",
+       "      <th>PlatformWorkedWith</th>\n",
+       "      <th>PlatformDesireNextYear</th>\n",
+       "      <th>FrameworkWorkedWith</th>\n",
+       "      <th>FrameworkDesireNextYear</th>\n",
+       "      <th>IDE</th>\n",
+       "      <th>OperatingSystem</th>\n",
+       "      <th>NumberMonitors</th>\n",
+       "      <th>Methodology</th>\n",
+       "      <th>VersionControl</th>\n",
+       "      <th>CheckInCode</th>\n",
+       "      <th>AdBlocker</th>\n",
+       "      <th>AdBlockerDisable</th>\n",
+       "      <th>AdBlockerReasons</th>\n",
+       "      <th>AdsAgreeDisagree1</th>\n",
+       "      <th>AdsAgreeDisagree2</th>\n",
+       "      <th>AdsAgreeDisagree3</th>\n",
+       "      <th>AdsActions</th>\n",
+       "      <th>AdsPriorities1</th>\n",
+       "      <th>AdsPriorities2</th>\n",
+       "      <th>AdsPriorities3</th>\n",
+       "      <th>AdsPriorities4</th>\n",
+       "      <th>AdsPriorities5</th>\n",
+       "      <th>AdsPriorities6</th>\n",
+       "      <th>AdsPriorities7</th>\n",
+       "      <th>AIDangerous</th>\n",
+       "      <th>AIInteresting</th>\n",
+       "      <th>AIResponsible</th>\n",
+       "      <th>AIFuture</th>\n",
+       "      <th>EthicsChoice</th>\n",
+       "      <th>EthicsReport</th>\n",
+       "      <th>EthicsResponsible</th>\n",
+       "      <th>EthicalImplications</th>\n",
+       "      <th>StackOverflowRecommend</th>\n",
+       "      <th>StackOverflowVisit</th>\n",
+       "      <th>StackOverflowHasAccount</th>\n",
+       "      <th>StackOverflowParticipate</th>\n",
+       "      <th>StackOverflowJobs</th>\n",
+       "      <th>StackOverflowDevStory</th>\n",
+       "      <th>StackOverflowJobsRecommend</th>\n",
+       "      <th>StackOverflowConsiderMember</th>\n",
+       "      <th>HypotheticalTools1</th>\n",
+       "      <th>HypotheticalTools2</th>\n",
+       "      <th>HypotheticalTools3</th>\n",
+       "      <th>HypotheticalTools4</th>\n",
+       "      <th>HypotheticalTools5</th>\n",
+       "      <th>WakeTime</th>\n",
+       "      <th>HoursComputer</th>\n",
+       "      <th>HoursOutside</th>\n",
+       "      <th>SkipMeals</th>\n",
+       "      <th>ErgonomicDevices</th>\n",
+       "      <th>Exercise</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SexualOrientation</th>\n",
+       "      <th>EducationParents</th>\n",
+       "      <th>RaceEthnicity</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>MilitaryUS</th>\n",
+       "      <th>SurveyTooLong</th>\n",
+       "      <th>SurveyEasy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Kenya</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed part-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Mathematics or statistics</td>\n",
+       "      <td>20 to 99 employees</td>\n",
+       "      <td>Full-stack developer</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>Extremely satisfied</td>\n",
+       "      <td>Extremely satisfied</td>\n",
+       "      <td>Working as a founder or co-founder of my own c...</td>\n",
+       "      <td>I’m not actively looking, but I am open to n...</td>\n",
+       "      <td>Less than a year ago</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>My job status or other personal status changed</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>KES</td>\n",
+       "      <td>Slack</td>\n",
+       "      <td>One to three months</td>\n",
+       "      <td>Taught yourself a new language, framework, or ...</td>\n",
+       "      <td>The official documentation and/or standards fo...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>To build my professional network</td>\n",
+       "      <td>Strongly agree</td>\n",
+       "      <td>Strongly agree</td>\n",
+       "      <td>Neither Agree nor Disagree</td>\n",
+       "      <td>JavaScript;Python;HTML;CSS</td>\n",
+       "      <td>JavaScript;Python;HTML;CSS</td>\n",
+       "      <td>Redis;SQL Server;MySQL;PostgreSQL;Amazon RDS/A...</td>\n",
+       "      <td>Redis;SQL Server;MySQL;PostgreSQL;Amazon RDS/A...</td>\n",
+       "      <td>AWS;Azure;Linux;Firebase</td>\n",
+       "      <td>AWS;Azure;Linux;Firebase</td>\n",
+       "      <td>Django;React</td>\n",
+       "      <td>Django;React</td>\n",
+       "      <td>Komodo;Vim;Visual Studio Code</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Agile;Scrum</td>\n",
+       "      <td>Git</td>\n",
+       "      <td>Multiple times per day</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Strongly agree</td>\n",
+       "      <td>Strongly agree</td>\n",
+       "      <td>Strongly agree</td>\n",
+       "      <td>Saw an online advertisement and then researche...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Artificial intelligence surpassing human intel...</td>\n",
+       "      <td>Algorithms making important decisions</td>\n",
+       "      <td>The developers or the people creating the AI</td>\n",
+       "      <td>I'm excited about the possibilities more than ...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes, and publicly</td>\n",
+       "      <td>Upper management at the company/organization</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>10 (Very Likely)</td>\n",
+       "      <td>Multiple times per day</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>I have never participated in Q&amp;A on Stack Over...</td>\n",
+       "      <td>No, I knew that Stack Overflow had a jobs boar...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extremely interested</td>\n",
+       "      <td>Extremely interested</td>\n",
+       "      <td>Extremely interested</td>\n",
+       "      <td>Extremely interested</td>\n",
+       "      <td>Extremely interested</td>\n",
+       "      <td>Between 5:00 - 6:00 AM</td>\n",
+       "      <td>9 - 12 hours</td>\n",
+       "      <td>1 - 2 hours</td>\n",
+       "      <td>Never</td>\n",
+       "      <td>Standing desk</td>\n",
+       "      <td>3 - 4 times per week</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Black or of African descent</td>\n",
+       "      <td>25 - 34 years old</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Very easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>10,000 or more employees</td>\n",
+       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
+       "      <td>30 or more years</td>\n",
+       "      <td>18-20 years</td>\n",
+       "      <td>Moderately dissatisfied</td>\n",
+       "      <td>Neither satisfied nor dissatisfied</td>\n",
+       "      <td>Working in a different or more specialized tec...</td>\n",
+       "      <td>I am actively looking for a job</td>\n",
+       "      <td>More than 4 years ago</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>I saw an employer’s advertisement</td>\n",
+       "      <td>British pounds sterling (£)</td>\n",
+       "      <td>51000.0</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>70841.0</td>\n",
+       "      <td>GBP</td>\n",
+       "      <td>Confluence;Office / productivity suite (Micros...</td>\n",
+       "      <td>One to three months</td>\n",
+       "      <td>Taught yourself a new language, framework, or ...</td>\n",
+       "      <td>The official documentation and/or standards fo...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Agree</td>\n",
+       "      <td>Agree</td>\n",
+       "      <td>Neither Agree nor Disagree</td>\n",
+       "      <td>JavaScript;Python;Bash/Shell</td>\n",
+       "      <td>Go;Python</td>\n",
+       "      <td>Redis;PostgreSQL;Memcached</td>\n",
+       "      <td>PostgreSQL</td>\n",
+       "      <td>Linux</td>\n",
+       "      <td>Linux</td>\n",
+       "      <td>Django</td>\n",
+       "      <td>React</td>\n",
+       "      <td>IPython / Jupyter;Sublime Text;Vim</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Git;Subversion</td>\n",
+       "      <td>A few times per week</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>The website I was visiting asked me to disable it</td>\n",
+       "      <td>Somewhat agree</td>\n",
+       "      <td>Neither agree nor disagree</td>\n",
+       "      <td>Neither agree nor disagree</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Increasing automation of jobs</td>\n",
+       "      <td>Increasing automation of jobs</td>\n",
+       "      <td>The developers or the people creating the AI</td>\n",
+       "      <td>I'm excited about the possibilities more than ...</td>\n",
+       "      <td>Depends on what it is</td>\n",
+       "      <td>Depends on what it is</td>\n",
+       "      <td>Upper management at the company/organization</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>10 (Very Likely)</td>\n",
+       "      <td>A few times per month or weekly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>A few times per month or weekly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No, I have one but it's out of date</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>A little bit interested</td>\n",
+       "      <td>A little bit interested</td>\n",
+       "      <td>A little bit interested</td>\n",
+       "      <td>A little bit interested</td>\n",
+       "      <td>A little bit interested</td>\n",
+       "      <td>Between 6:01 - 7:00 AM</td>\n",
+       "      <td>5 - 8 hours</td>\n",
+       "      <td>30 - 59 minutes</td>\n",
+       "      <td>Never</td>\n",
+       "      <td>Ergonomic keyboard or mouse</td>\n",
+       "      <td>Daily or almost every day</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Somewhat easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Associate degree</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>20 to 99 employees</td>\n",
+       "      <td>Engineering manager;Full-stack developer</td>\n",
+       "      <td>24-26 years</td>\n",
+       "      <td>6-8 years</td>\n",
+       "      <td>Moderately satisfied</td>\n",
+       "      <td>Moderately satisfied</td>\n",
+       "      <td>Working as a founder or co-founder of my own c...</td>\n",
+       "      <td>I’m not actively looking, but I am open to n...</td>\n",
+       "      <td>Less than a year ago</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Respondent Hobby OpenSource         Country Student          Employment  \\\n",
+       "0           1   Yes         No           Kenya      No  Employed part-time   \n",
+       "1           3   Yes        Yes  United Kingdom      No  Employed full-time   \n",
+       "2           4   Yes        Yes   United States      No  Employed full-time   \n",
+       "\n",
+       "                              FormalEducation  \\\n",
+       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "2                            Associate degree   \n",
+       "\n",
+       "                                      UndergradMajor  \\\n",
+       "0                          Mathematics or statistics   \n",
+       "1  A natural science (ex. biology, chemistry, phy...   \n",
+       "2  Computer science, computer engineering, or sof...   \n",
+       "\n",
+       "                CompanySize  \\\n",
+       "0        20 to 99 employees   \n",
+       "1  10,000 or more employees   \n",
+       "2        20 to 99 employees   \n",
+       "\n",
+       "                                             DevType       YearsCoding  \\\n",
+       "0                               Full-stack developer         3-5 years   \n",
+       "1  Database administrator;DevOps specialist;Full-...  30 or more years   \n",
+       "2           Engineering manager;Full-stack developer       24-26 years   \n",
+       "\n",
+       "  YearsCodingProf          JobSatisfaction  \\\n",
+       "0       3-5 years      Extremely satisfied   \n",
+       "1     18-20 years  Moderately dissatisfied   \n",
+       "2       6-8 years     Moderately satisfied   \n",
+       "\n",
+       "                   CareerSatisfaction  \\\n",
+       "0                 Extremely satisfied   \n",
+       "1  Neither satisfied nor dissatisfied   \n",
+       "2                Moderately satisfied   \n",
+       "\n",
+       "                                       HopeFiveYears  \\\n",
+       "0  Working as a founder or co-founder of my own c...   \n",
+       "1  Working in a different or more specialized tec...   \n",
+       "2  Working as a founder or co-founder of my own c...   \n",
+       "\n",
+       "                                     JobSearchStatus             LastNewJob  \\\n",
+       "0  I’m not actively looking, but I am open to n...   Less than a year ago   \n",
+       "1                    I am actively looking for a job  More than 4 years ago   \n",
+       "2  I’m not actively looking, but I am open to n...   Less than a year ago   \n",
+       "\n",
+       "   AssessJob1  AssessJob2  AssessJob3  AssessJob4  AssessJob5  AssessJob6  \\\n",
+       "0        10.0         7.0         8.0         1.0         2.0         5.0   \n",
+       "1         1.0         7.0        10.0         8.0         2.0         5.0   \n",
+       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
+       "\n",
+       "   AssessJob7  AssessJob8  AssessJob9  AssessJob10  AssessBenefits1  \\\n",
+       "0         3.0         4.0         9.0          6.0              NaN   \n",
+       "1         4.0         3.0         6.0          9.0              1.0   \n",
+       "2         NaN         NaN         NaN          NaN              NaN   \n",
+       "\n",
+       "   AssessBenefits2  AssessBenefits3  AssessBenefits4  AssessBenefits5  \\\n",
+       "0              NaN              NaN              NaN              NaN   \n",
+       "1              5.0              3.0              7.0             10.0   \n",
+       "2              NaN              NaN              NaN              NaN   \n",
+       "\n",
+       "   AssessBenefits6  AssessBenefits7  AssessBenefits8  AssessBenefits9  \\\n",
+       "0              NaN              NaN              NaN              NaN   \n",
+       "1              4.0             11.0              9.0              6.0   \n",
+       "2              NaN              NaN              NaN              NaN   \n",
+       "\n",
+       "   AssessBenefits10  AssessBenefits11  JobContactPriorities1  \\\n",
+       "0               NaN               NaN                    3.0   \n",
+       "1               2.0               8.0                    3.0   \n",
+       "2               NaN               NaN                    NaN   \n",
+       "\n",
+       "   JobContactPriorities2  JobContactPriorities3  JobContactPriorities4  \\\n",
+       "0                    1.0                    4.0                    2.0   \n",
+       "1                    1.0                    5.0                    2.0   \n",
+       "2                    NaN                    NaN                    NaN   \n",
+       "\n",
+       "   JobContactPriorities5  JobEmailPriorities1  JobEmailPriorities2  \\\n",
+       "0                    5.0                  5.0                  6.0   \n",
+       "1                    4.0                  1.0                  3.0   \n",
+       "2                    NaN                  NaN                  NaN   \n",
+       "\n",
+       "   JobEmailPriorities3  JobEmailPriorities4  JobEmailPriorities5  \\\n",
+       "0                  7.0                  2.0                  1.0   \n",
+       "1                  4.0                  5.0                  2.0   \n",
+       "2                  NaN                  NaN                  NaN   \n",
+       "\n",
+       "   JobEmailPriorities6  JobEmailPriorities7  \\\n",
+       "0                  4.0                  3.0   \n",
+       "1                  6.0                  7.0   \n",
+       "2                  NaN                  NaN   \n",
+       "\n",
+       "                                         UpdateCV  \\\n",
+       "0  My job status or other personal status changed   \n",
+       "1             I saw an employer’s advertisement   \n",
+       "2                                             NaN   \n",
+       "\n",
+       "                       Currency   Salary SalaryType  ConvertedSalary  \\\n",
+       "0                           NaN      NaN    Monthly              NaN   \n",
+       "1  British pounds sterling (£)  51000.0     Yearly          70841.0   \n",
+       "2                           NaN      NaN        NaN              NaN   \n",
+       "\n",
+       "  CurrencySymbol                                 CommunicationTools  \\\n",
+       "0            KES                                              Slack   \n",
+       "1            GBP  Confluence;Office / productivity suite (Micros...   \n",
+       "2            NaN                                                NaN   \n",
+       "\n",
+       "   TimeFullyProductive                                     EducationTypes  \\\n",
+       "0  One to three months  Taught yourself a new language, framework, or ...   \n",
+       "1  One to three months  Taught yourself a new language, framework, or ...   \n",
+       "2                  NaN                                                NaN   \n",
+       "\n",
+       "                                     SelfTaughtTypes TimeAfterBootcamp  \\\n",
+       "0  The official documentation and/or standards fo...               NaN   \n",
+       "1  The official documentation and/or standards fo...               NaN   \n",
+       "2                                                NaN               NaN   \n",
+       "\n",
+       "                   HackathonReasons  AgreeDisagree1  AgreeDisagree2  \\\n",
+       "0  To build my professional network  Strongly agree  Strongly agree   \n",
+       "1                               NaN           Agree           Agree   \n",
+       "2                               NaN             NaN             NaN   \n",
+       "\n",
+       "               AgreeDisagree3            LanguageWorkedWith  \\\n",
+       "0  Neither Agree nor Disagree    JavaScript;Python;HTML;CSS   \n",
+       "1  Neither Agree nor Disagree  JavaScript;Python;Bash/Shell   \n",
+       "2                         NaN                           NaN   \n",
+       "\n",
+       "       LanguageDesireNextYear  \\\n",
+       "0  JavaScript;Python;HTML;CSS   \n",
+       "1                   Go;Python   \n",
+       "2                         NaN   \n",
+       "\n",
+       "                                  DatabaseWorkedWith  \\\n",
+       "0  Redis;SQL Server;MySQL;PostgreSQL;Amazon RDS/A...   \n",
+       "1                         Redis;PostgreSQL;Memcached   \n",
+       "2                                                NaN   \n",
+       "\n",
+       "                              DatabaseDesireNextYear  \\\n",
+       "0  Redis;SQL Server;MySQL;PostgreSQL;Amazon RDS/A...   \n",
+       "1                                         PostgreSQL   \n",
+       "2                                                NaN   \n",
+       "\n",
+       "         PlatformWorkedWith    PlatformDesireNextYear FrameworkWorkedWith  \\\n",
+       "0  AWS;Azure;Linux;Firebase  AWS;Azure;Linux;Firebase        Django;React   \n",
+       "1                     Linux                     Linux              Django   \n",
+       "2                       NaN                       NaN                 NaN   \n",
+       "\n",
+       "  FrameworkDesireNextYear                                 IDE OperatingSystem  \\\n",
+       "0            Django;React       Komodo;Vim;Visual Studio Code     Linux-based   \n",
+       "1                   React  IPython / Jupyter;Sublime Text;Vim     Linux-based   \n",
+       "2                     NaN                                 NaN             NaN   \n",
+       "\n",
+       "  NumberMonitors  Methodology  VersionControl             CheckInCode  \\\n",
+       "0              1  Agile;Scrum             Git  Multiple times per day   \n",
+       "1              2          NaN  Git;Subversion    A few times per week   \n",
+       "2            NaN          NaN             NaN                     NaN   \n",
+       "\n",
+       "  AdBlocker AdBlockerDisable  \\\n",
+       "0       Yes               No   \n",
+       "1       Yes              Yes   \n",
+       "2       NaN              NaN   \n",
+       "\n",
+       "                                    AdBlockerReasons AdsAgreeDisagree1  \\\n",
+       "0                                                NaN    Strongly agree   \n",
+       "1  The website I was visiting asked me to disable it    Somewhat agree   \n",
+       "2                                                NaN               NaN   \n",
+       "\n",
+       "            AdsAgreeDisagree2           AdsAgreeDisagree3  \\\n",
+       "0              Strongly agree              Strongly agree   \n",
+       "1  Neither agree nor disagree  Neither agree nor disagree   \n",
+       "2                         NaN                         NaN   \n",
+       "\n",
+       "                                          AdsActions  AdsPriorities1  \\\n",
+       "0  Saw an online advertisement and then researche...             1.0   \n",
+       "1                                                NaN             3.0   \n",
+       "2                                                NaN             NaN   \n",
+       "\n",
+       "   AdsPriorities2  AdsPriorities3  AdsPriorities4  AdsPriorities5  \\\n",
+       "0             5.0             4.0             7.0             2.0   \n",
+       "1             5.0             1.0             4.0             6.0   \n",
+       "2             NaN             NaN             NaN             NaN   \n",
+       "\n",
+       "   AdsPriorities6  AdsPriorities7  \\\n",
+       "0             6.0             3.0   \n",
+       "1             7.0             2.0   \n",
+       "2             NaN             NaN   \n",
+       "\n",
+       "                                         AIDangerous  \\\n",
+       "0  Artificial intelligence surpassing human intel...   \n",
+       "1                      Increasing automation of jobs   \n",
+       "2                                                NaN   \n",
+       "\n",
+       "                           AIInteresting  \\\n",
+       "0  Algorithms making important decisions   \n",
+       "1          Increasing automation of jobs   \n",
+       "2                                    NaN   \n",
+       "\n",
+       "                                  AIResponsible  \\\n",
+       "0  The developers or the people creating the AI   \n",
+       "1  The developers or the people creating the AI   \n",
+       "2                                           NaN   \n",
+       "\n",
+       "                                            AIFuture           EthicsChoice  \\\n",
+       "0  I'm excited about the possibilities more than ...                     No   \n",
+       "1  I'm excited about the possibilities more than ...  Depends on what it is   \n",
+       "2                                                NaN                    NaN   \n",
+       "\n",
+       "            EthicsReport                             EthicsResponsible  \\\n",
+       "0      Yes, and publicly  Upper management at the company/organization   \n",
+       "1  Depends on what it is  Upper management at the company/organization   \n",
+       "2                    NaN                                           NaN   \n",
+       "\n",
+       "  EthicalImplications StackOverflowRecommend               StackOverflowVisit  \\\n",
+       "0                 Yes       10 (Very Likely)           Multiple times per day   \n",
+       "1                 Yes       10 (Very Likely)  A few times per month or weekly   \n",
+       "2                 NaN                    NaN                              NaN   \n",
+       "\n",
+       "  StackOverflowHasAccount                           StackOverflowParticipate  \\\n",
+       "0                     Yes  I have never participated in Q&A on Stack Over...   \n",
+       "1                     Yes                    A few times per month or weekly   \n",
+       "2                     NaN                                                NaN   \n",
+       "\n",
+       "                                   StackOverflowJobs  \\\n",
+       "0  No, I knew that Stack Overflow had a jobs boar...   \n",
+       "1                                                Yes   \n",
+       "2                                                NaN   \n",
+       "\n",
+       "                 StackOverflowDevStory StackOverflowJobsRecommend  \\\n",
+       "0                                  Yes                        NaN   \n",
+       "1  No, I have one but it's out of date                          7   \n",
+       "2                                  NaN                        NaN   \n",
+       "\n",
+       "  StackOverflowConsiderMember       HypotheticalTools1  \\\n",
+       "0                         Yes     Extremely interested   \n",
+       "1                         Yes  A little bit interested   \n",
+       "2                         NaN                      NaN   \n",
+       "\n",
+       "        HypotheticalTools2       HypotheticalTools3       HypotheticalTools4  \\\n",
+       "0     Extremely interested     Extremely interested     Extremely interested   \n",
+       "1  A little bit interested  A little bit interested  A little bit interested   \n",
+       "2                      NaN                      NaN                      NaN   \n",
+       "\n",
+       "        HypotheticalTools5                WakeTime HoursComputer  \\\n",
+       "0     Extremely interested  Between 5:00 - 6:00 AM  9 - 12 hours   \n",
+       "1  A little bit interested  Between 6:01 - 7:00 AM   5 - 8 hours   \n",
+       "2                      NaN                     NaN           NaN   \n",
+       "\n",
+       "      HoursOutside SkipMeals             ErgonomicDevices  \\\n",
+       "0      1 - 2 hours     Never                Standing desk   \n",
+       "1  30 - 59 minutes     Never  Ergonomic keyboard or mouse   \n",
+       "2              NaN       NaN                          NaN   \n",
+       "\n",
+       "                    Exercise Gender         SexualOrientation  \\\n",
+       "0       3 - 4 times per week   Male  Straight or heterosexual   \n",
+       "1  Daily or almost every day   Male  Straight or heterosexual   \n",
+       "2                        NaN    NaN                       NaN   \n",
+       "\n",
+       "                             EducationParents                 RaceEthnicity  \\\n",
+       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   Black or of African descent   \n",
+       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)  White or of European descent   \n",
+       "2                                         NaN                           NaN   \n",
+       "\n",
+       "                 Age Dependents MilitaryUS  \\\n",
+       "0  25 - 34 years old        Yes        NaN   \n",
+       "1  35 - 44 years old        Yes        NaN   \n",
+       "2                NaN        NaN        NaN   \n",
+       "\n",
+       "                          SurveyTooLong     SurveyEasy  \n",
+       "0  The survey was an appropriate length      Very easy  \n",
+       "1  The survey was an appropriate length  Somewhat easy  \n",
+       "2                                   NaN            NaN  "
+      ]
+     },
+     "execution_count": 828,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2018 = pd.read_csv(r'D:\\project\\Stackoverflow-Analysis\\Data\\survey_results_sample_2018.csv')\n",
+    "df2018.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 829,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99, 129)"
+      ]
+     },
+     "execution_count": 829,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2018.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 830,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(df2018.columns.tolist()      !--> Listing coloumsn in table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 831,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dropping the columns\n",
+    "#drop_cols = ['Respondent', 'OpenSource', 'Student', 'FormalEducation', 'CompanySize', 'CareerSatisfaction', 'HopeFiveYears', 'LastNewJob', 'AssessJob1', 'AssessJob2', 'AssessJob3', 'AssessJob4', 'AssessJob5', 'AssessJob6', 'AssessJob7', 'AssessJob8', 'AssessJob9', 'AssessJob10', 'AssessBenefits1', 'AssessBenefits2', 'AssessBenefits3', 'AssessBenefits4', 'AssessBenefits5', 'AssessBenefits6', 'AssessBenefits7', 'AssessBenefits8', 'AssessBenefits9', 'AssessBenefits10', 'AssessBenefits11', 'JobContactPriorities1', 'JobContactPriorities2', 'JobContactPriorities3', 'JobContactPriorities4', 'JobContactPriorities5', 'JobEmailPriorities1', 'JobEmailPriorities2', 'JobEmailPriorities3', 'JobEmailPriorities4', 'JobEmailPriorities5', 'JobEmailPriorities6', 'JobEmailPriorities7', 'UpdateCV', 'CommunicationTools', 'TimeFullyProductive', 'EducationTypes', 'SelfTaughtTypes', 'TimeAfterBootcamp', 'HackathonReasons', 'AgreeDisagree1', 'AgreeDisagree2', 'AgreeDisagree3', 'DatabaseWorkedWith', 'DatabaseDesireNextYear', 'PlatformDesireNextYear', 'FrameworkWorkedWith', 'FrameworkDesireNextYear', 'IDE', 'NumberMonitors', 'Methodology', 'VersionControl', 'CheckInCode', 'AdBlocker', 'AdBlockerDisable', 'AdBlockerReasons', 'AdsAgreeDisagree1', 'AdsAgreeDisagree2', 'AdsAgreeDisagree3', 'AdsActions', 'AdsPriorities1', 'AdsPriorities2', 'AdsPriorities3', 'AdsPriorities4', 'AdsPriorities5', 'AdsPriorities6', 'AdsPriorities7', 'AIDangerous', 'AIInteresting', 'AIResponsible', 'AIFuture', 'EthicsChoice', 'EthicsReport', 'EthicsResponsible', 'EthicalImplications', 'StackOverflowRecommend', 'StackOverflowVisit', 'StackOverflowHasAccount', 'StackOverflowParticipate', 'StackOverflowJobs', 'StackOverflowDevStory', 'StackOverflowJobsRecommend', 'StackOverflowConsiderMember', 'HypotheticalTools1', 'HypotheticalTools2', 'HypotheticalTools3', 'HypotheticalTools4', 'HypotheticalTools5', 'WakeTime', 'HoursComputer', 'HoursOutside', 'SkipMeals', 'ErgonomicDevices', 'Exercise', 'SexualOrientation', 'EducationParents', 'Dependents', 'MilitaryUS', 'SurveyTooLong', 'SurveyEasy']\n",
+    "#df2018.drop(drop_cols, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 832,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2018.shape #checking rows and col after dropping the table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Filtering - Sorting & Renaming\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 833,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col=['Age','ConvertedSalary','Country','Currency','DevType','Employment','RaceEthnicity','Gender','SalaryType','Hobby','JobSatisfaction','JobSearchStatus','OperatingSystem','UndergradMajor','YearsCoding','YearsCodingProf','LanguageDesireNextYear','LanguageWorkedWith','FormalEducation']\n",
+    "df=df2018[col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 834,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#renaming the colo\n",
+    "# 'ConvertedSalary': 'SalaryUSD'\n",
+    "df.rename(columns={'ConvertedSalary': 'SalaryUSD' }, inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 835,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Currency</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>FormalEducation</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Hobby</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>JobSearchStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>OperatingSystem</th>\n",
+       "      <th>RaceEthnicity</th>\n",
+       "      <th>SalaryType</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCoding</th>\n",
+       "      <th>YearsCodingProf</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25 - 34 years old</td>\n",
+       "      <td>Kenya</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Full-stack developer</td>\n",
+       "      <td>Employed part-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extremely satisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to n...</td>\n",
+       "      <td>JavaScript;Python;HTML;CSS</td>\n",
+       "      <td>JavaScript;Python;HTML;CSS</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Black or of African descent</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mathematics or statistics</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>British pounds sterling (£)</td>\n",
+       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately dissatisfied</td>\n",
+       "      <td>I am actively looking for a job</td>\n",
+       "      <td>Go;Python</td>\n",
+       "      <td>JavaScript;Python;Bash/Shell</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>70841.0</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>30 or more years</td>\n",
+       "      <td>18-20 years</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Age         Country                      Currency  \\\n",
+       "0  25 - 34 years old           Kenya                           NaN   \n",
+       "1  35 - 44 years old  United Kingdom  British pounds sterling (£)   \n",
+       "\n",
+       "                                             DevType          Employment  \\\n",
+       "0                               Full-stack developer  Employed part-time   \n",
+       "1  Database administrator;DevOps specialist;Full-...  Employed full-time   \n",
+       "\n",
+       "                              FormalEducation Gender Hobby  \\\n",
+       "0  Bachelor’s degree (BA, BS, B.Eng., etc.)   Male   Yes   \n",
+       "1  Bachelor’s degree (BA, BS, B.Eng., etc.)   Male   Yes   \n",
+       "\n",
+       "           JobSatisfaction                                    JobSearchStatus  \\\n",
+       "0      Extremely satisfied  I’m not actively looking, but I am open to n...   \n",
+       "1  Moderately dissatisfied                    I am actively looking for a job   \n",
+       "\n",
+       "       LanguageDesireNextYear            LanguageWorkedWith OperatingSystem  \\\n",
+       "0  JavaScript;Python;HTML;CSS    JavaScript;Python;HTML;CSS     Linux-based   \n",
+       "1                   Go;Python  JavaScript;Python;Bash/Shell     Linux-based   \n",
+       "\n",
+       "                  RaceEthnicity SalaryType  SalaryUSD  \\\n",
+       "0   Black or of African descent    Monthly        NaN   \n",
+       "1  White or of European descent     Yearly    70841.0   \n",
+       "\n",
+       "                                      UndergradMajor       YearsCoding  \\\n",
+       "0                          Mathematics or statistics         3-5 years   \n",
+       "1  A natural science (ex. biology, chemistry, phy...  30 or more years   \n",
+       "\n",
+       "  YearsCodingProf  \n",
+       "0       3-5 years  \n",
+       "1     18-20 years  "
+      ]
+     },
+     "execution_count": 835,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_index(axis=1).head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 836,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99, 19)"
+      ]
+     },
+     "execution_count": 836,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#21 col has been selected rfom 129, compared the shape\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 837,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                       32\n",
+      "SalaryUSD                 53\n",
+      "Country                    0\n",
+      "Currency                  39\n",
+      "DevType                    1\n",
+      "Employment                 0\n",
+      "RaceEthnicity             38\n",
+      "Gender                    32\n",
+      "SalaryType                50\n",
+      "Hobby                      0\n",
+      "JobSatisfaction           18\n",
+      "JobSearchStatus           16\n",
+      "OperatingSystem           26\n",
+      "UndergradMajor            11\n",
+      "YearsCoding                0\n",
+      "YearsCodingProf           15\n",
+      "LanguageDesireNextYear    29\n",
+      "LanguageWorkedWith        25\n",
+      "FormalEducation            2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum()) #Finding Null Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 838,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                        object\n",
+       "SalaryUSD                 float64\n",
+       "Country                    object\n",
+       "Currency                   object\n",
+       "DevType                    object\n",
+       "Employment                 object\n",
+       "RaceEthnicity              object\n",
+       "Gender                     object\n",
+       "SalaryType                 object\n",
+       "Hobby                      object\n",
+       "JobSatisfaction            object\n",
+       "JobSearchStatus            object\n",
+       "OperatingSystem            object\n",
+       "UndergradMajor             object\n",
+       "YearsCoding                object\n",
+       "YearsCodingProf            object\n",
+       "LanguageDesireNextYear     object\n",
+       "LanguageWorkedWith         object\n",
+       "FormalEducation            object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 838,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes #data_types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Validation - Total Cells vs Missing %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 839,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  1881\n",
+      "Total missing :  387\n",
+      "Missing Percentage:  20.574162679425836 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = df.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing Percentage column-wise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 840,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def missing(df,column,n):\n",
+    "    empty_cells=df[column].isnull().sum()\n",
+    "    return (empty_cells*100.0)/n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 841,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age : 32.32 %\n",
+      "SalaryUSD : 53.54 %\n",
+      "Country : 0.00 %\n",
+      "Currency : 39.39 %\n",
+      "DevType : 1.01 %\n",
+      "Employment : 0.00 %\n",
+      "RaceEthnicity : 38.38 %\n",
+      "Gender : 32.32 %\n",
+      "SalaryType : 50.51 %\n",
+      "Hobby : 0.00 %\n",
+      "JobSatisfaction : 18.18 %\n",
+      "JobSearchStatus : 16.16 %\n",
+      "OperatingSystem : 26.26 %\n",
+      "UndergradMajor : 11.11 %\n",
+      "YearsCoding : 0.00 %\n",
+      "YearsCodingProf : 15.15 %\n",
+      "LanguageDesireNextYear : 29.29 %\n",
+      "LanguageWorkedWith : 25.25 %\n",
+      "FormalEducation : 2.02 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_cells=df.shape[0]\n",
+    "for column in df.columns:\n",
+    "    res=missing(df,column,total_cells)\n",
+    "    print(column,\":\",\"{:.2f}\".format(res),\"%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gender Filtering          \n",
+    "### Data Cleaning Starts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 842,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Female                                                                        5\n",
+       "Female;Male                                                                   1\n",
+       "Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming     1\n",
+       "Male                                                                         59\n",
+       "Male;Non-binary, genderqueer, or gender non-conforming                        1\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 842,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Gender: null = 13312 (21.6%)\n",
+    "df['Gender'].unique()\n",
+    "#count number of each gender\n",
+    "df.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 843,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#replace\n",
+    "df['Gender'] = df['Gender'].fillna('Non-binary, genderqueer, or gender non-conforming')\n",
+    "df['Gender'].replace('Female;Male;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Male;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
+    "df['Gender'].replace('Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
+    "df['Gender'].replace('Transgender;Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) ##not sure\n",
+    "df['Gender'].replace('Female;Male', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Male;Transgender', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Transgender', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Male;Transgender', 'Female', inplace =True) \n",
+    "df['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) #\n",
+    "df['Gender'].replace('Transgender', 'Male', inplace =True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 844,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "lst=df.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 845,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(3,3))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Gender Distribution')  # Add a title\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 846,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99, 19)"
+      ]
+     },
+     "execution_count": 846,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 847,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 847,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()['Gender']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 848,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "Algeria                1\n",
+       "Argentina              2\n",
+       "Australia              1\n",
+       "Belgium                1\n",
+       "Brazil                 1\n",
+       "Bulgaria               1\n",
+       "Chile                  1\n",
+       "China                  2\n",
+       "Colombia               1\n",
+       "Croatia                1\n",
+       "Denmark                1\n",
+       "Dominican Republic     1\n",
+       "Finland                1\n",
+       "France                 3\n",
+       "Germany                5\n",
+       "Greece                 1\n",
+       "India                 16\n",
+       "Indonesia              2\n",
+       "Ireland                1\n",
+       "Israel                 1\n",
+       "Japan                  1\n",
+       "Kenya                  1\n",
+       "Latvia                 1\n",
+       "Netherlands            1\n",
+       "Nigeria                1\n",
+       "Poland                 3\n",
+       "Romania                1\n",
+       "Russian Federation     4\n",
+       "South Africa           1\n",
+       "Spain                  2\n",
+       "Sweden                 3\n",
+       "Ukraine                1\n",
+       "United Kingdom         7\n",
+       "United States         28\n",
+       "Name: Country, dtype: int64"
+      ]
+     },
+     "execution_count": 848,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Country')['Country'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 849,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 849,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 850,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Country'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 851,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 851,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 852,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst=df.groupby('Country')['Country'].count()\n",
+    "lst = lst.sort_values(ascending=False)\n",
+    "lst=lst[:50]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 853,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,4))\n",
+    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('Country')  # Label for x-axis\n",
+    "plt.ylabel('Count')      # Label for y-axis\n",
+    "plt.title('Top 50 Countries according to participation')  # Title of the plot\n",
+    "plt.xticks(rotation=90)  # Rotate labels by 90 degrees\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hobby"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 854,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 854,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Hobby'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 855,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Hobby\n",
+       "No     20\n",
+       "Yes    79\n",
+       "Name: Hobby, dtype: int64"
+      ]
+     },
+     "execution_count": 855,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Hobby')['Hobby'].count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## UndergradMajor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 856,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 856,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 857,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "Computer science, computer engineering, or software engineering          56\n",
+       "A natural science (ex. biology, chemistry, physics)                       7\n",
+       "Another engineering discipline (ex. civil, electrical, mechanical)        7\n",
+       "A business discipline (ex. accounting, finance, marketing)                5\n",
+       "Fine arts or performing arts (ex. graphic design, music, studio art)      4\n",
+       "Information systems, information technology, or system administration     3\n",
+       "Mathematics or statistics                                                 2\n",
+       "Web development or web design                                             2\n",
+       "A social science (ex. anthropology, psychology, political science)        1\n",
+       "A humanities discipline (ex. literature, history, philosophy)             1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 857,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 858,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def refactor_major(df):\n",
+    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n",
+    "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
+    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n",
+    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
+    "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)') \n",
+    "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'), \n",
+    "                        (df['UndergradMajor'] == 'Web development or web design'), \n",
+    "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'), \n",
+    "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
+    "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
+    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)'),\n",
+    "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
+    "    \n",
+    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
+    "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
+    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
+    "    return df\n",
+    "\n",
+    "df = refactor_major(df)\n",
+    "df['UndergradMajor'].replace('nan', 'No major', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 859,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst=df['UndergradMajor'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 860,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,5))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Undergrad Major')  # Add a title\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 861,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 861,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 862,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['UndergradMajor'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 863,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 863,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Job Status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 864,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSearchStatus\n",
+       "I’m not actively looking, but I am open to new opportunities    54\n",
+       "I am not interested in new job opportunities                      18\n",
+       "I am actively looking for a job                                   11\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 864,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSearchStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 865,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['JobSearchStatus'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 866,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# refactoring JobStatus\n",
+    "# changing the jobstatus to seeking and non seeking\n",
+    "def refactor_job(df):\n",
+    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
+    "    \n",
+    "    conditions_job = [(df['JobSearchStatus'] == 'I am actively looking for a job'),\n",
+    "                    (df['JobSearchStatus'] == 'I am not interested in new job opportunities')\n",
+    "                    | (df['JobSearchStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
+    "    \n",
+    "    choices_job = ['Seeking', 'Not seeking']\n",
+    "    \n",
+    "    df['JobSearchStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "df = refactor_job(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 867,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSearchStatus\n",
+       "nan            54\n",
+       "Not seeking    18\n",
+       "Seeking        11\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 867,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSearchStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 868,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 868,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSearchStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Employment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 869,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Employed full-time    77\n",
+       "Employed part-time     6\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 869,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 870,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 870,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 871,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Employment'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 872,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#im not considering the retired person here\n",
+    "#Refactoring the employment\n",
+    "def refactor_emp(df):\n",
+    "    \n",
+    "    conditions_emp = [(df['Employment'] == 'Employed full-time'),\n",
+    "                    (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n",
+    "                    (df['Employment'] == 'Not employed, but looking for work'),\n",
+    "                    (df['Employment'] == 'Employed part-time')]\n",
+    "    \n",
+    "    choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n",
+    "    \n",
+    "    df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "df = refactor_emp(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 873,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst=df['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 874,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0kgirrGBfShZ2nMnAicv20DybkQ+r7B5/OqF+RrQJD8Rd4UHo3LQWWtUPgJdegskqw6gToZMYsER3wjAluq7AYoNBJyLPZMPOM5lIPHkVO89k4tjlXHjaX0l4iDfaNgxCXHgQOkXUQnQdfyiqCun6uC0RlcQwJY9VNFlHEIBdZ69hzeHLSDyRjlPp+VqX5nT0koBOTUPQr1VdDGxdD4E+esiKynFXousYpuRRzFYZkijg/LVC/HHwAjaduIp9KdfcpsvWUWLq+qNPizoY3LY+mof5wWSV4WPkfEbyXAxTcntWWYGqAjmFVizZk4ple9Nw4kqe1mW5jVB/I3rHhOH+tvXRuWkIrLICIy/LIQ/DMCW3pCgqzLICRVGx4sAFLN1zHntSrnnc2Kejeesl3N28NgbF1sOA1nWhqCq8DWyxkvtjmJJbKZqBu+HoFfy8OxUJx9PZhasRX4OEIXc1wIR7mqJhsA9EAZwZTG6LYUouz2yVIUkC9qVk4cedKVidfBl5ZpvWZdFN4sKDML5bEwyMrQebrLC1Sm6HYUouq9Bin0z0464UfLnpNM5fK9S6JLqDQG89RrZviCfvbopafgboRJFjq+QWGKbkcgotMmyKgnmJZzB/61lkFVi1LokqoWtELTzerQl6tQiDVVbhzctsyIUxTMllFFpl5BZa8dn6k1i6JxUmq6J1SVQNQv2MGN0pHH/p3gySKMDbwFAl18MwJaemKCqsioLUzEL8e+1xrEq6VHwXFnIvvgYJ47o2xrM9mzNUyeUwTMkp2WQFKoB9KVn4dN1xbDmZoXVJ5CA+xaEaCZ0oMlTJJTBMyemYbTIOpGbj7RXJSL6Qo3U5pBEfg4Tx8U3wXK/mEARw6UJyagxTchqFFhlZhRZM/y0Ja49c0bocchKB3npM7hWJ8fFNoKgqjDqGKjkfhilpzmyToarAzDXH8e2Ws7DInFhEpdUL9MKr/aMxuE19AFwAgpwLw5Q0IysqVFXF8gMXMGPlUaTnmbUuiVxA8zA/fDiyDVrWC2DXLzkNhilpwmSVcfJKHqYtO4RDadlal0Mu6KEO4XjrgZbQiQIM7PoljTFMyaFMVhn5ZhveXnEYyw9c0LoccnGhfka8P7w1ekSFwaBjty9ph2FKDiErKmRFxRcJpzBn4ykUWmWtSyI30rdlHfxzRBt4GyR2/ZImGKZU40xWGeevFWDSD3tx/DLvI0o1w9+ow+uDWmBk+4aQBAEi1/wlB2KYUo1RFBWyam+NfrruBG+FRg7RoXEwZo66C6H+RrZSyWEYplQjTFYZV/PMePaHvThwnhOMyLEMkojnekdiYo9mEAWBd6ahGscwpWpnsSlYsjsV7/xxGGYbrxkl7UTX8ce8xzogzN8II1upVIMYplRtrDYFFlnBCz/tx5rDl7UuhwiAfQH9f4+Owz3Na7Pbl2oMw5Sqhckq4+ilXExcsAeXckxal0NUyoS7m+K1ATHQiQIEgd2+VL0YplRlVlnBnA0n8em6E+Dd0ciZdWwSjK/GdYCPQcfrUqlaMUyp0mRFhdkmY8L83dh6irdII9cQ6mfEV491QExdf3b7UrVhmFKlWGwyMvOteHTedpxKz9e6HKIK0YkCpg2MwdguTdhCpWrBMKUKM1llnLici3Hf7MS1AqvW5RBV2oDWdTFz1F3QSQJ0IkOVKo9hShVisspYd+QK/vrzfl72Qm4horYvvnu8I8ICvNjtS5XGMKVys9gUzE04hU/WHNe6FKJq5WuQ8N0TnRDbIJCBSpXCMKU7UlUVVlnFK0sP4Pf9vNMLuSeDJGLu2HaIb8brUaniGKZ0WzZZgcmq4PHvdmLX2Wtal0NUoyRRwCcPtUW/VnUZqFQhDFO6JbNNxtVcCx6Ztx3nMgq0LofIIQQBeGtwK4zuFA4jbzpO5cQwpTKZrTJOpufhka92ILuQM3bJ87zYpzmeuTeSl85QuTBMqRSzTcaZq/l48PNtyDXbtC6HSDOPd2uC1we2gF5ioNLtMUypBItNQUpmAYZ/vgU5hQxSouHtGuDDEW0YqHRbDFMqZrHJSMsyYficLVyMgegm97Wsg9mPtGOXL90Sw5QAABZZwaVsE4b9Zwsy8i1al0PkdOKb1cI34ztyli+ViR+zCFZZQXqOGSM+38ogJbqFracy8OT8XbBy5S8qA8PUw1llBVfzzBj++Rak55q1LofIqW05mYHnf9wHq8xApZIYph7MJiu4lm/BiDlbcTmHQUpUHquSLuHvy5NhYQuVbsIw9VA2WUF2oRXDP9+KC9kmrcshcimLdqRg1roTMNtkrUshJ8Ew9UCqqqLAKmP451tx/lqh1uUQuaRZG07ix52pMFkZqMQw9Ug2RcUT3+7iEoFEVfT2imRsPXWVgUoMU09jscn4++9J2H2Oi9YTVZWiApN+2IuzV/M5hurhGKYexGSVsWxvGhbtTNW6FCK3YbIqGPP1DmQVWCArvGzfUzFMPYTFJuPopRxM/z1J61KI3M7VPAse/moHJyR5MIapB5AVBdmFNjz53W5YZX5yJqoJp9LzMGH+bl6D6qEYph5AVlQ89s1Orm5EVMO2nsrA3IRTnJDkgRimbs5iU/DiTwdw+GKO1qUQeYSZa47jyMUcTkjyMAxTN2ayyvg68TT+e+ii1qUQeQxFBSYu3MPxUw/DMHVTZquMXWcz8c/Vx7QuhcjjXM4x47lFXMPXkzBM3ZCiqMgssGDSwr3gDfaItLHxeDrmbz3L8VMPwTB1Q7KqYtLCvcg127QuhcijfbDqKE6n57OF6gEYpm7GZJXxRcIp7EvN0roUIo9nU1Q8vYCXy3gChqkbsckKUjIL8O+1J7QuhYiuO3+tEC/+dICze90cw9SNKCrwzMI9sHFJMyKnsjr5Epbs4R1m3BnD1E2YrDLeX3kEp9LztS6FiMrwzorDuJBVCFlhC9UdMUzdgE1WcOxSLuZvO6t1KUR0C2abgr/+fADsOHJPDFM3oKjACz/t52UwRE5uf2oWft2XBjO7e90Ow9TFmawyZq49jjNX2b1L5AreX3mEN5xwQwxTFyYrKlIzC/DlptNal0JE5ZRVYMXbfyRzMpKbYZi6MEVVMeXH/bwhMZGLWbrnPE5eyePfrhthmLook1XGoh0pvBsMkQtSVeDlJQegcKKD22CYuihVBWauPa51GURUSUcv5eKHHefY3esmGKYuqNAq47N1x5FVYNW6FCKqgo9XH2eYugmGqQsqMNvwzZazWpdBRFWUZ7bhb78l8VIZN8AwdTFFKx2Zuc4nkVv44+BFHDyfDRsXw3dpDFMXoqoqLmYX4td9aVqXQkTV6LVfDmpdAlURw9SFWGUVf/89mcuREbmZ01fzsfLQRd5ZxoUxTF2ETVFw4HwWNp24qnUpRFQD/r32BERB6yqoshimrkIF3lqerHUVRFRDTl/Nx5rDl3kjcRfFMHUBFpuC/yVdQvIFLtBA5M4+WXMcAlunLolh6gIEAZix6qjWZRBRDTtxJQ8bj6ZzZq8LYpg6ObNNxo87U5CWVah1KUTkAP9ac0zrEqgSGKZOTieK+GrzGa3LICIHOXIxF1tOZbB16mIYpk5MUVRsPXkVKZkFWpdCRA708epj4BVwroVh6sSsioLPE05pXQYROdihtGzsOpvJW7S5EIapE7uUbcLWUxlal0FEGvh49THeos2FMEydVKFFxucb2Sol8lR7U7KwPzULClunLoFh6qQUVcVv+7kGL5Enm73+JCyciOQSGKZOyGSVsWD7OZis/CMi8mSbT6Qj12TTugwqB4apE9JJAuZvPat1GUSkMUUFvt92ljcQdwEMUydjkxWsP3IFF7NNWpdCRE7g592p0Et8q3Z2fIWcjKICcxNOa10GETmJyzlmJJ5I50QkJ8cwdTJnM/KxN+Wa1mUQkRP5dutZTkRycgxTJ1JgtuF7jpUS0Z9sOp7OcVMnxzB1Ika9hP8lX9K6DCJyMooKLN1zHhYbA9VZMUydyKHzWbiaZ9G6DCJyQkv3nIfIm506LYapkyiw2PDLXi7SQERlO3opF+ev8VaMzoph6iS8dOziJaLbW7jjHAot7Op1RgxTJ5F8IRvpuWatyyAiJ/b7/gsw6Pi27Ywq/KrMmjUL0dHRt/23bNmych9v2bJliI6OxqZNmwAAO3bsQHR0NBYvXlyux2dmZiIvL6/466lTpyI6Ohpms+sEE7t4iag80nPNOHIxR+syqAy6yj5w4sSJiIiIKPN77dq1q3RBFZGQkIBXXnkFixcvhp+fHwBg1KhR6Nq1K/R6vUNqqA5GnYRVSRe1LoOIXMD/ki8hMswPXnpJ61LoJpUO0/j4eHTu3Lk6a6mwgwcPIjs7u8S2uLg4xMXFaVRR5Ry9lIPLOa7TkiYi7SQcS8fzvZprXQb9CTvfNVZokfHLnvNal0FELiLpQjbMvN7U6dRYmN5q7PLjjz9GdHQ0zp+vWoBMnToVs2fPBgAMHDgQY8eOLfO8s2bNQsuWLXH27Fk8/fTTiIuLQ5cuXfDBBx/AZrNh5cqVuP/++9G2bVsMHToU27ZtK3EeVVUxf/58DBo0CLGxsejWrRveeOMNXL16tUr1FzHoRKxK4ixeIiofVbWviKSqXKvXmVS6mzc3NxeZmZmltvv6+sJoNFapqPIYNWoU8vLysGbNGrzyyito0aLFLfdVVRVjx45Ft27d8Nprr2H16tX49ttvcfLkSSQnJ2PcuHHw9vbGl19+icmTJ2PNmjUICQkBAEyfPh1Lly7F4MGDMWbMGKSlpeGHH37A9u3bsXTpUgQHB1fp5zh+OZd3iCGiCll75ArujQ6Dr7HSb+FUzSr9Sjz77LNlbp82bRrGjx9f2cOWW1xcHKKjo7FmzRr07NkTzZo1u+W+iqKgd+/eeOuttwDYW7Jdu3ZFYmIilixZgtjYWACAj48Ppk+fjv3796NXr17YtWsXlixZUupnGjBgAB588EF88cUXmDp1aqV/BpNVxh8HOfGIiCpm84l0eBs4AcmZVDpMX3vtNcTExJTa3rRp0yoVVFP69etX/P+AgADUqlULOp2uOEgBIDw8HACQnp4OAFi9ejUAoFevXiVa4fXq1UPz5s2xYcOGKoWpAGD76YxKP56IPNPVPAvOpOejWZif1qXQdZUO01atWjlkNm9ubi5MppLdoP7+/vDy8qrQcWrVqlXia51OV2qbKNqHkBXFfqujc+fOAQD69u1b5jGrevmNIAg4eD6rSscgIs+0OvkSJoREcBEHJ+HwDndZrtgstPfeew+//vpriW0zZszA8OHDK3QcSSrdJSLcYdFoRVFgNBoxd+7cCp2rvJLSsmGVOYmAiCpu4/F0PHm3c/YEeqIaC9OiVp7FYikxIamis2AnTJiABx54oMS2yMjIqhdYDg0aNEBiYiIiIyMRFhZW4nvr169HUFBQpY9ttsrYcOxKFSskIk+199w18KO486ix/oHQ0FAAwOHDh4u3ZWdnIzExsULHiYyMRHx8fIl/RcFWFNg1NUW8d+/eAIA5c+aU2L5//35MmjQJ8+fPr9Lxd5wpPRuaiKg8bIrKORdOpMZapoMGDcIXX3yBV155BU888QRUVcWPP/6IwMDAMi+pqYyiy1e+/fZb9OrVqzj8qkuPHj1w3333YfHixbh48SK6d++OjIwMLFy4EAEBAZgyZUqljy2JAg6kZlVfsUTkcdYfvYJOTUPgY+AlMlqrsZZpVFQUZs2aheDgYHz88cdYsGABRowYgYkTJ1bbOQYNGoT4+HgsX74cH3/8cbUd92YzZ87ESy+9hNTUVMyYMQM///wzunTpgsWLF99ybeLyOJWeD7NNqcZKicjTHL6QwxuGOwlB5TIaDicrChZuT8HflydrXQoRuTA/ow5Jb/e7845U4zinWgMmq4I9565pXQYRubg8sw3puVxBzRmwo10DXnoJB7S6vtScB92R/0G8mARYC6H6hUJu0hVK066AcNNnK6sJ0rE1kC4cBAqyAJ0RSu0IyDH9oAY1KN+5CjKhS14J8cpx+7l8QqA07gi5eU9AvOlSJUWGdOh3SKl7AFEHuWEc5Fb3A1LJX0/xzFboDi2H5b7XAa+Aqj8XRG7g8IUc9Iiu2HX3VP3YMtVAoVXGuYwCx5/YnAvDxn9DOrMVqncQ5CbxgN4b+gO/QLdroX0FbQCwWaDfPBu6ExugGv0hN7sHSlgUxEuHoU/4FELGmTufqzALho2fQjq/F0pIY8jN7gEkCbrDK6Hb/UOJXaVTm6E7nQglNApKnRaQTm2GdHhlyePJVuiOroEc0Y1BSnSTvSlZsHD+hebYMtVAclr2nXeqAbqkPyAUZMIWcTfkNsMAQYAMQEpaAd2JDVDqxEBp3AnSqc0Qsy/A1uwe+37XCVdPQp84F7r9S2Ht/crtz3V8PQRzLmyxQyBH9gAAyMog6BM/h5S2H/LVeKi17dcLi+d2QKkTA1vnx+yPFSVIZ7dBjr1xfbF0ZitgM0Nu3quanxUi13bkYg6sssKVkDTGZ9/BZEVF8oUcx59YkSFeOAhV72PvQr1pBqDcoj9UnRHSyQQAgHjxIFQIkFsMKHEItXYk1NrNIOZcBAqzbns64VqK/diNb1pyUpQgN+5k/2/muRv7FmRCCah/o9SgBhBsZsCcZ99gM0M6tg5yZHfA6FvhH53InR2+mANvPRe91xpbpg5mtsk4m5Hv+BNb8iHYzFBqNwN0hpLfk/RQ/UIhZKUBVhPkJvEQ6uUC+tLjMKpo/5URbJbbrr6iGuyhJxRkQg28EZSCKfv6929aoFvvDUG+cd9bwWqCCgHQ2c8vndoEqArkyHsr8AMTeYbz1wphlhV4iwxULbFl6mCqCqRoMV5a9Icm28r+vtUEASqEwmtQmnSGHN2n9D7mPIgZp6FKBqg+Ibc9ndK0K1QI0O39CUJ2GmAzQ7xwCNLxDVC9g6A0aHtj35DGENMOQsi5BBRcg5iyC2pwQ/sEJEshpBMb7ZOWygh3IgJOXcnTugSPx5apg3npJZzL1CBMDb5QfULswZafAfjeuGOOkHMJQv71Zcmst55mr0taAcFmhtw0vtRM2z9T6rWGrfNj0O35EYb1/7qxPbABrF2eKBGMcosBEDfPgWHdPwEAqt4L1q5PAwCkExvsM3yb3X3j4KpScuYxkYfbn5qFlvUCIIpcwEErDFMHEwTg/DUNwhSArfm90B9YBv32r2G760GogfUhZKVBt+9nQNIDsuXGjN4/kY6ugZSyC6pPMGwtB97xXEJeOqTDqwDZCrnBXVC9AiFmnIaYlQrdod9g6zC2OJDVgLqw9H7FfrkOVCh1WwLeQYA5F9KpTZBbDgR0Rohnd0B3eCVgzoMaGglru9GAT3A1PkNErikpLRuFVhm+Rr6la4XPvINl5ls0u+2a0rQbbHnpkE4lwrBpVvF2Obwd1NBI+4zZP4+nApAOr4Lu2BqoBl9Yuz4FGHxufyJVgW7bPAj5mbDeMwlqLfttomRVhXToN+hObYZ6ZBXk1oNvPMbL336t683nPbYOMPhAbhoPIecSdPt+hhzRDWqdFpCSVkC/+wdYu0+u/BNC5CaOXMyBTmKrVEsMUwdL1aKLt4ggQG4zDErjzhDTTwCqCqV2M6jB4dDtsN8BRzXeNDFIVaDbtwTSuR1QjX6wdvsL1IC6dz5NxlmIeemQwzsUB2nx+VsPhpSyG9K5XSXD9M8KsyCd2Qpbm6GApIN4dgdg8IXcZqi9i1eVod/+DYSci1AD6lXu+SByE6nXCmHUcQKSlhimDnbisvYTBdTA+pBvmmELAGJWKlS9l717FQBkG3Q750O6lAzVJ8QepH6h5Tq+cP2yGdW/TulvijqovrUhZqUCstXevVwG3dH/A7wDoVy/tEbIT4fqG1I8VlpUi5CXwTAlj3etwAJFUTlmqiHO4nAgk1XG6avahalu1wIYVr1ln8BzEyHrvP1az7Bo+wZVhW73AkiXkqH414Wl+3PlDlIAUL387cfNSy/9TUW2Xy6j975lkCLvKsRzO2GLua94FrKgKiXrLpqVzPcOIqgqkGOyal2GR2OYOpCsqNosI3id6hcGwZQDMXXvjY02C3QHfwWA4tWFpNObIV04BMW3Nqz3TAK8Ayt2npCmUL0CIJ7fW7x4QxHp6P9BsORDaRh3y8frjq6G6lsbSnj74m2KXyiE3CuAtRAAIFw7d/1nKn/IE7mzawUMUy2xm9eB9JKIFA3HTOXIHpBSdkG39ycoV45DNfpBungIQn4GbC36Qw0OB2QbpKNrAABqYD1IpxPLPlbT+OI1cqWTCYC1EHKzHoDBG5B0sLZ7GPrtX0O/aTaU+rH22byZ5yBmnoHiX+eWM4KFnEsQU/fC1nFsictflEadIJ3eAv3mOVBqR0A6txNKWFTZXclEHig914SmtblCmFYYpg5k0Imatkyh94Kl+3PQJf8BMf04YDNDDagHW6vBUBq0AQAIuZchWOwrNEkXDgEXDpV5KKVeLNSiMD21CULBNciNOtnDFIBaJxrWHs9DOroG4uVjgM0MeAfCFnkv5Ji+gN67zONKR1ZBDaxXYlEHAFCDGsDWcSx0SSsgndkGJTQKtnYPVcvTQuQOLmbzVmxa4s3BHajQKqPF9P9pXQYRuaGp/WPwVPemkESO3mmBz7oDmSyy1iUQkZtKzzPDYmPbSCsMUwcqtDJMiahmpOeaobCjUTMMUwcqZMuUiGpIep755jsrkoMxTB2ogC1TIqohV3PN0Et8S9cKn3kHyjff4vZnRERVlJ7HMNUSn3kHymOYElENyS60QlE4ZqoVhqkD5XK5LyKqIaoK2BimmmGYOlBOIVumRFRzOJtXOwxTB5EVhWOmRFSjGKbaYZg6iE1Wkc9LY4ioBnHMVDsMUwdRVKDQwpYpEdUcmVmqGS507yCKypYpVUyvmDC82Ke51mWQC/E1SFqX4LEYpg7E4QyqiOgwH7Su6w1BZwTSjwIn1wHgLxHdRp0JgOSldRUeiWHqIIIA+PBTI1XA55vOYuneC/hoZBv0aNYEaNYLwvp/AMdW8pMZla3to4CeYaoFjpk6iCQIDFOqsPQ8C8Z/txvtZ2zGmvRgKCO+gTp5D9BqWImbpxMBABfn1Q7/Gh1EEgX4GtkRQJWTWWDB0wv2oO17CfjjvDeUIZ9DfX4/0OYhQOSHNLqOH7A0w2feQXSSCD+GKVVRrsmG5xbvQ+t3N2LZaQny/Z9CfSEJiBsDiPz98niSXusKPBbD1IECvPlmR9WjwKLgpSUH0OqdBPx4xApb/4+g/vUw0OEJQDJoXR5pQRABvbfWVXgshqkD+XvxUyNVL5NNwbRfkxDz9gbMP1gIa593of71KND5L4COE1E8isFP6wo8GsPUgdjNSzXFpgBvLU9G9Dsb8eXuLFju/TvUl45BjX8OMPhqXR45glcAZ3lriGHqQJyARDVNUYAZq44i6u2NmLX1Ciz3TLWH6j0vA0Z/rcujmmQMABTemUorDFMH4uok5EifrDmB6LcT8PGGNBR2edEeqve+DngFaV0a1QSjP6BwyVKtCKrKfgFHuZhdiK4z1mtdBnmoJ7o1wV97NrL3kOyYC2HrLKAgQ+uySoheXPeO+3zfKxOd61huu09Spg5zkvywJ92AfJuAuj4y+oebMKl1Pnx0N97ybArwwT5//H7GG0ZJxcDGJrzcNhd//tz740lvfLDXH2sGX0Wot1Kpn63GNe8LPPgdx041wn5HB/LWs2VK2vlmy1l8s+UsHuncCK/1mYCALpOAXV9D2DITyLuidXkAgMmt88rcnmESsfikD2oZZUQE3L71tf2yARM2BgMA+oWbEOatYNcVPb464oftlw34oU8mjNf/FBcc98GC477oF26Cr07BguM+kATgtbjc4uOZZWBOkh8ejSpw3iAF7N28bBpphmHqQF4MU3ICi3akYNGOFIxs1wCv9xuD4E4TgD3fQ0j8F5BzQdPanostO0yf2RQEASo+6pp9x0B7e3cAVBVY3DcTbWrZxxBVFXhzVwB+PuWDRSd88HhMAQBg6Slv3FPPjM/uzgIA6CXgp5PeJcJ08Qkf5NsETGiRXw0/YQ3yDeWiDRriM+9AXnoJXno+5eQclu5NQ7sZm/Hcj0m42vwhqFMOQn1gNhDUSOvSSlh+1gvr07zwYLNCdKt3++7dk9kSTufo0LuhuThIAfsqe89eb/VuumAs3n4+X4eYoBv7tQiyIt8mItNsX5Yv3yrgi8O+GB9dgGCjkzf7AhsCOuOd96MawXd2B7LJChoE+WhdBlEJfxy6iI4fbsZTC/fjcuMHoD63D+qwL4GQCK1Lg1kGZh7wh79ewV/b5t5xfz+9ipfb5mJEREGp7xlEexgW2G6sXxtgUEp8nWcVIQoq/K6Pq35/3AeKKuDxGCdvlQJArUguLakhhqkDmW0KGgZzhRJyTmuPXEGXjxIx7rs9OF+/H9Rnd0Ed+R1QO0qzmhad8MGFAgkTWuSXq2VY10fBUy3z0aN+6RbsmvP2RSwiA2+Mud5Vy4rVqV44mS3hYr6IX894o1WwFQYJyLEI+OaIL55skQ8/vZO3SgGn61HwNAxTB2sQxDAl57b5xFXc868tGPXVTpwN7Ql10jaooxcBdVo5tA5ZAb4/5gtfnYJHmpduaVbE1UIRnx2yz3IdFVlYvH1Km1woKjBoZSjuXR6GK4Ui3mhvbwF/fcQXBknFmKgb51acOVP962ldgUfjBCQHMupEtkzJZew8ew09Z25B24aB+GhEPJo/vQk4vdF+T9WL+2v8/OvTjLhQIOHx6HwEGCqfYrkWAU9vCsZVk4SxUfklxlIjA2WsGJCB9WlGyCrQs4EZdX0UZJhEfH/cBy+0yYOPTsXSU96YedAPmWYRncMseL9zNur7OtHMXlECvIO0rsKjsWXqQDpJREQol3Yj13LgfDbu+3QrBszeisPe7aBOWAv1sRVAw441et7fzto/eD4UWflWaaZJwGPrQ5CcqUfP+iZMjSs97lrbW8FDkYV4uHkh6vrYA/KLw74IMKh4OLIAJ7J1+NvOAAxoZMLc7tdw1STi1W1Bla6pRrBVqjmGqYM1rc0Lqsk1Hb2Yi0Gzt6HPv7fggNgK6uOroD7xf0DjbtV+LrMMbL1kQFSgFREBcqWOkZIrYdSaWki+pkevBiZ8dncWdOV4x7tUIGLxCR880yoPBsl++UywUcG0uFz0qG/Bi23ysCvdgONZTtSxF9gQsJm1rsKjMUwdLDyE3bzk2k6l52Po59vR/ePN2GWLgDr2N6hPbQAielbbOXZeMaDAJqJfI1OlHn/kmg6j14QgJU+HYU0LMevurFKrGt3KnCQ/1PGRMSLCPrZ6LldCuJ8M6fq7ZZPri0ak5DnRzNlakYBauQ8dVD0Ypg7mY9Chth/vN0muL/VaIR76cifi/7kZWwsaQn3kJ6gTE4Hm91X52Aeu2m9X2L52xRduP5cr4YkNwcgw28dbZ3TOLleLFLC3Zn857Y3JrfNQdEm4TRVgU29cPmOR7f8XyjqAVuq0AnT8oK4lhqmDFVpkRISyq5fcx8UcEx79ehc6frAJG66FQX1oAdRndwIx99tXS6iEw9fsYdoqpGJhqqjAX7cGIdMsYVxUPqa2y61QCbOS/NDIX8bgxjdaxE39bTiTIyHXYj/Q/utB3+QOyxo6VP12vMZUY07U6e8ZFFVFRG1f7DyTqXUpRNXqap4FT8zfjSAfHT4Y3gb3jfgaQs4F++zfw78Bavlnv6bmSfCS1DvO4p11/XKXomUI1543IilTD4OowkevFn//ZrW9ZDzcvLDU9pPZEv4454V/xWcXd+kCwPCIQvxwwgfj1oegfagFv5z2Rre6ZjSr5FhujQiN1roCj8cwdTAvvYjIMLZMyX1lFdgwceFe+Hvp8N7Q1rh/yBwIfd+xh2rSUkC5cwhds4jw1985fGcnlQzTXVfsQygWRcDc5LL/zmKCrGWG6acH/REVaMOA8JLjtC2CbfgkPgsf7ffHTyd9EF/XjHc75dyxNofxDrb/I03xFmwa2HEmA6O+2K51GUQO4WMQ8c4DsRjWJgSiKQvChveBgz8CMm9kXS0axwNjlgF6jplqiWOmGmhdP7CyQ0lELqfAouDlpQfQ4p0ELDpiha3/P6G+eBjoOIELs1eHsJYV6kK/2dSpUxEdHV3iX0xMDOLi4jB06FDMnz8filK9i1OkpKSUe9/MzEzk5d24k1BRvWaz810GxG5eDRh1IpqF+uHklbJvN0Xkjiw2BW/8moS//w68PrAlxvZ+B7qeb0BI+BDYOx+wlu56pXKoGwvoq3YDjWnTpiE42N5VrKoqCgoKsHbtWrz//vtIS0vD66+/Xh2V4s0338SxY8fw008/3XHfhIQEvPLKK1i8eDH8/K4vBTlqFLp27Qq9Xl8t9VQnhqkGzDYFHZsEM0zJI9kU4J0/DuPdlYfx6n3ReOLeN6Hv+Tqw6WMIu78GLC5whxZn0ii+0rOmi/Tp0wcNGzYssW3UqFEYPXo0Fi5ciAkTJiAsLKxK5wCAxMRE1K5du1z7Hjx4ENnZ2SW2xcXFIS4ursp11AR282rAxyCha0Qtrcsg0pSiAB/87xii3t6If2++DPPdr0F96TjU7q8AxgCty3MNem+gVrMaObQkSRgwYABkWcaBAwdq5BzuhGGqAUEQ0IVhSlTs03UnEPNOAj5cn4rCzlOgvnQMas83OEv1TurdBci3v2F6VYiiPSKsVvtksbVr1+Kxxx5Dx44d0bp1a3Tv3h3Tp09HVlZW8WOWLVuG6OhorFq1Cv369UNsbCzefPNNREdHIy0tDQcOHEB0dDSWLVt2y/NOnToVs2fPBgAMHDgQY8eOLd5+85jprFmz0LJlS5w9exZPP/004uLi0KVLF3zwwQew2WxYuXIl7r//frRt2xZDhw7Ftm3bSpxHVVXMnz8fgwYNQmxsLLp164Y33ngDV69erfBzxW5ejdT2NyLU34j0XOcbSCfSytyE05ibcBrj45vgpV7PwC/+eWDHXAjbZgH5FX+Dc3vhnWr08Fu3bgUAtGrVCsuWLcO0adPQrVs3vPDCCwCALVu24Oeff0Z6ejrmzp1b4rFvvPEGRo0ahbp166JRo0Zo3749ZsyYAX9/f0yePBnt2rW75XlHjRqFvLw8rFmzBq+88gpatGhxy31VVcXYsWPRrVs3vPbaa1i9ejW+/fZbnDx5EsnJyRg3bhy8vb3x5ZdfYvLkyVizZg1CQkIAANOnT8fSpUsxePBgjBkzBmlpafjhhx+wfft2LF26tHgcuTwYphoptMjo0DgYq5IuaV0KkdP5butZfLf1LB7uFI6pfZ5AQJdngN3fQkj8BMi7rHV5zqNZr2q5JCYnJweZmfaFZBRFwcWLF/HLL78gISEB/fr1Q+PGjTFp0iS0aNEC8+bNK26xPvrooxg1ahQSExOhqiqEm8Zue/Xqhddee63EeT799FMEBwdjyJAht60nLi4O0dHRWLNmDXr27IlmzW7dla0oCnr37o233noLgL0l27VrVyQmJmLJkiWIjY0FAPj4+GD69OnYv38/evXqhV27dmHJkiWYNm0axo8fX3y8AQMG4MEHH8QXX3yBqVOnlvs5ZJhqxKgT0alpCMOU6DYW70zF4p2pGBbXANP7P4Lgjk8C+xZA2PQxkJOmdXnaEoRquw3esGHDSm3T6XQYOnQopk+fDgD47bffUFBQUBykgP3SFT8/P1itVlitVhgMN9Yd79KlS7XUVh79+vUr/n9AQABq1aoFnU5XHKQAEB4eDgBIT08HAKxevRqAPfSLPkgAQL169dC8eXNs2LCBYeoKdJKIu5uXb1Ybkaf7dV8aft2XhoGxdfHWwBEIjRsLHPwJQsI/gaxzWpenjbCWgFQ9N8346KOPimfZCoIAf39/REREwMfnxiU3er0ex44dw4oVK3D69GmkpKTgypUrxd//8/o/5Zm1m5ubC5Op5IpT/v7+8PLyqlD9tWqVnIOi0+lKbSv6EFB03ey5c/bfm759+5Z5zIpefsMw1VCz2n7w1ksotDrRGp9ETmzloUtYeegSesWE4b3Bg1H3udFA8jIICR8AGae0Ls+xmtxtX0VKqvo1l+3atSt1acyfvffee/j+++8RFRWFuLg4DBgwAG3atMGCBQuwfPnyUvvf3IK93TF//fXXEttmzJiB4cOHV6h+SSq9yL9wh8uFFEWB0WgsNdZbWQxTDZltCu5qFIRtpzK0LoXIpaw/egVdj15Bt2a1MGPofQh/djhw9L/2pQrTj2pdnmO0eAAwVG2xhvJKS0vD999/jwEDBmDmzJklgiojo/LvXxMmTMADDzxQYltkZGSlj1cRDRo0QGJiIiIjI0tdQ7t+/XoEBQVV6Hi8NEZDogB0bMKp/0SVteVUBrr/awse/GInzoR0h/rMFqgP/wjUaa11aTVL7wOEd3bY6YoWT4iIiCgRpMnJydi5cycAwGa78y3pRFEssTxhZGQk4uPjS/wrCrailm1NLR/fu3dvAMCcOXNKbN+/fz8mTZqE+fPnV+h4bJlqyKiX0L9VPXy27qTWpRC5tN3nrqHXv7eiTYNAfDSiC6KeTgDOJEBY/y5wYa/W5VW/pt2rrYu3PCIjI9GgQQN88803kGUZDRs2xPHjx7F06dLi0MvPz4evr+9tjxMSEoITJ07ghx9+QOfOnW/bCi26fOXbb79Fr169isOvuvTo0QP33XcfFi9ejIsXL6J79+7IyMjAwoULERAQgClTplToeGyZaiy6rj/qBVZssJ2IynYwLRv9PtuKAbO2Itl4F9Qn/w/q+P86tBXnEDGDAF31TD4qD4PBgK+++godO3bE4sWL8cEHH2D79u2YOHEiPvnkEwA3rkm9neeeew7BwcGYMWMG1qxZc9t9Bw0ahPj4eCxfvhwff/xxtfwcfzZz5ky89NJLSE1NxYwZM/Dzzz+jS5cuWLx4MSIiIip0LN6CTWOFFhs+Wn0M32w5q3UpRG4nItQX/xrRGneFBwAX9kNY9zZwNlHrsqruldOAL1dRcyYMUyeQnJaNQbPc4A+cyEmFB3vj4wdj0alRIHA52X6j8lPrtC6rcsJaAn/Z5LAuXiofdvM6gZh6AagTwPs6EtWU1GuFGPXlTsT/czMS8xtAfXgx1Ge2AFH9tS6t4qL6AcqdJ/uQYzFMnYDZJmNA63pal0Hk9i7mmDD2m13o+MEmrM8MhfLgfKjP7rJfZlLF25g5TOxD1bKEIFUvdvM6iUPnszB49hatyyDyKAFeOnw4og36xQRDyL1o7/5N/hVQlTs/WAshEcDk3YBYepEC0hZbpk6iVf1AhPqzq5fIkXJMNjzzw17E/iMBy88ZoDzwH6hTDgJtH3bOwIp9sEZvuUaVx5apkyiw2PDBqqP4fpuHrjNK5AS8dCLeGdoaI2JrQbTk2FdUOrDIfk2nM3gxGQi8/bJ/pA2GqRM5kJqFIf9hVy+R1gw6EdMHtcTD7WpDshVA2PgBsG8BYNPw/sP144An13AWr5NimDoRRVHR+f11SM/jDcOJnIFOBKYNaIFxHetAp5ghbPoI2PMtYC10fDH9PwQ6POHQxRqo/BimTqTAYsP7K49i4XZ29RI5E1EEXu4bjSe71INBkIHN/4Kwax5gyXNMAYIIvHoa8OZa3s6KYepkjl7KQf9/b9a6DCK6hed7R2JStwYwSgC2fgZhx1zAlF2zJ43oCTzyI6Dj0qPOimHqZKyygofmbsO+1CytSyGi23i6e1NM6REOH70EbJ8DYdtsoPBazZzskZ+ByD7OOcOYADBMnY5NVrDu6BX8ZcEerUshonJ4LL4xXu7ZGH7eemDnlxC2fArkX62+EwQ0AF44CIi8yZczY5g6IZui4O4PNuBSjknrUoionEZ3CMfU+5og0McL2PMthMRPgNxLVT9w7zeBLs8CenbxOjOGqRMyWWV8t/UsPlh1VOtSiKiChtxVH2/2b4YQf29g30IImz8Gss9X7mCSAXjlJOAVWL1FUrVjmDqpfLMN7d9dA5PVSZc1I6LbGtC6Lt4aFImwAB/g4BIImz4Erp2t2EFiRwIPzAL0PjVSI1UfhqmTKrTIeOePZCzemap1KURUBT2jw/DeA81RL8gPOPwbhI0zgIyT5XvwxESgbmzNFkjVgmHqxFIy8tH9o41al0FE1SC+WS3MGBKDRrX9gKMrIWx8H7hy5NYPqNsGeGo9VzxyEVzo3onVCfRCt8haWpdBRNVg66kM9PhkCx78YidOh9wD9S+JUB/52R6aZbnnr44tkKqELVMnpigqtp3OwKPzdmhdChFVs9gGAfhoRCtE1/EHziZCWP8OkLbX/s3gJsBze3g5jAthmDo5WVHR+18bcTajQOtSiKgGxNTxx0cjW6F1/QAgdac9VO8aA7R5CNDxtoyugmHq5Cw2GUt2n8cbvyVpXQoR1aCmtXzwrwdjERceAIg6CAJH4VwJXy0nZ9BJeKhjOOoE8BMqkTs7k1GA4XN34Jd9l2GV2cZxNQxTF6AoKl66L1rrMoiohtUP9MKQuAYw6LgGr6thmLoAo17C8LgGaFrbV+tSiKgGTenTHApH3lwSw9RFqACmDojRugwiqiENg70xol1DGNkqdUkMUxehl0T0jglDq/oBWpdCRDXghT5RbJW6MIapKxGA6fe31LoKIqpmzUL9MPSu+hwrdWEMUxeiE0W0axSMHlGhWpdCRNXonyNvsQoSuQyGqYvRSwLeHdoakihoXQoRVYMH2tZH6/oB0El8O3ZlfPVcjCAICPU34pFOjbQuhYiqyN+owztDWsGoZ/euq2OYuiAvvYTXBsQgwJvrdhK5spf7RcOLQeoWGKYuSicKeLFPlNZlEFEltajnj0c6N2KYugmGqYvy0ksY06UxL5UhckGCAHw0si1EgXMf3AXD1IWJgoDZj7SDgRMXiFzKyHYNERnmx4mEboTvwi5MEgXUC/TCC32aa10KEZVToLcebw5uye5dN8MwdXFeeglPdY9Am4aBWpdCROXw+sAY6Nmb5Hb4iroBSRDwn0fawajjy0nkzNo2DMTwdg3ZKnVDfPd1A6Jov/b05X68TRuRszLqRMwcdRc4TOqeGKZuwksvYXx8E7RrFKx1KURUhr8Pbon6Qd6QRL7tuiO+qm5EEgTMfiQOXnq+rETOpH/ruhjZPpzdu26M77puRBQFhPgaeN9TIifSMNgbMx+6CwbOaXBrfHXdjJdewqOdG6NT0xCtSyHyeDpRwJdjO0AncaDU3TFM3ZBOFPDZ6Dj4Gbl2L5GWpg6IQUSoLy+F8QB8hd2QIAgI9tFjzqPtwNXKiLRxb1QoHotvwnFSD8EwdVNGvYROTUPwyn28XIbI0cL8jZj1SBxbpB6Er7Qb89JLmHBPBAbF1tO6FCKPIQrA3DHteY9SD8MwdXMGnYhPRrVFy3q8uwyRI0zp0xwt6wfwBhQehq+2B9CJAuY/0Qm1fA1al0Lk1rpF1sKkeyM5TuqBGKYeQBJFBHrr8PX4jtBzij5RjWhRzx/zHuvIcVIPxVfdQxh0EmLq+uMfQ1prXQqR22kQ5I1FT3WBgR9WPRbD1IN46SUMa9cAj3ZupHUpRG4j0FuPH5/uAj+jjuvuejC+8h7GqJPw1gOtuEISUTUw6kQseLITwgKM7N71cHz1PZBOFPD1Yx3QuJaP1qUQuSxRAD4f0w5Rdfxh1HHCkadjmHogQRDgrZfwyzPxaBjsrXU5RC7p3aGtEd+sNmfuEgCGqcfSSSKCvPVY9kw86gd6aV0OkUt59t5mGNGuIYOUijFMPZhOEhHsa8Avk+JRJ8CodTlELmFYXANM6RPFFY6oBIaph9NLImr7GbHsmW4I9WegEt3O3ZG18c+RbXhvUiqFvxEEvSQiNMCIX56JR20/rpJEVJauEbUw77EOnLVLZeJvBQEADJKIugFeWDoxHsE+eq3LIXIqPaJCMf+JThwjpVtimFIxg05E/SBvLJkYj0BvBioRAPRtWQdfjevArl26Lf52UAkGnYhGId5Y8peuCPDSaV0OkaYGxdbDnEfbMUjpjvgbQqUYdBIa1/bBTwxU8mAj2zfEv0ffxTFSKhdBVVVV6yLIOZltMi5nmzD6y+24kG3Suhwih3mmRzO82DeKLVIqN4Yp3ZZVVpBntmHMvB1IvpCjdTlENUoQgL/f3xIPd27EJQKpQhimdEeKosIiK3j6+93YdOKq1uUQ1Qi9JODfo+LQu0UYZ+1ShTFMqdyssoK//ZaEn3alal0KUbXyNUiY91hHxDUKYpBSpTBMqUIsNgULtp3FeyuPQOFvDrmBZqF++O7xjgj1NzJIqdIYplRhJquMPeeu4S8L9iDPbNO6HKJKGxRbD/96qC30kghJFLQuh1wYw5QqxWyTcSXHjDFf78C5jAKtyyGqEJ0o4G/3t8AjnRpzxi5VC4YpVZpNVmC2KXjq+93YeipD63KIyiXU34h54zoguq4/u3Wp2jBMqcqssoK5Cafw6doTsHEglZxYxybB+GpcB/gYdGyRUrVimFK1MFllnLmaj2cW7sFZdvuSE3rqnqZ4tX8MdKIAQeD4KFUvhilVG5usQFZUvLk8mZfPkNPwNUiYOeou9IgK5Q29qcYwTKnama0ytp3OwF9/PoDMfIvW5ZAHi67jj3mPdeBlL1TjGKZUI8w2GSargimL92Hj8XStyyEPY9SJmNK7OZ7qHgFREHjZC9U4hinVKKus4KddqfjHH4dhtilal0MeoGOTYMwcdRdq+7E1So7DMKUaZ7LKSM81Y+LCPVwsn2qMv1GHNwa1wIj2DSEJAkS2RsmBGKbkELKiQFGBuRtP4T8bT8JkZSuVqk/flnXw4Yg28DFIbI2SJhim5FAmq4x8sw1vrziM5QcuaF0OubhQPyNmDI9F96hQXjdKmmKYkibMVhknruRh2rJDOJSWrXU55IIe7NAQbz/QCjpRgIH3HiWNMUxJM7KiQlVV/HHwIt5beQTpuWatSyIXEFPXH+8ObY3WDQLZpUtOg2FKmjNbZagAPl17HF8nnoVF5ngqlRZR2xevDYhB75gwAIBOYrcuOQ+GKTmNQquM7AIr3vw9Cf93+LLW5ZCTaBDkjZf7RWFwm/pQAegZouSEGKbkdMw2GYfOZ+Pvy5N5KY0HC/U34oXezfFQx3CoqspxUXJqDFNySrbrXb17U7Lw6brj2HKSt3jzFEE+ejzbMxKPdW0CVVW5ni65BIYpOTVFUWFVFKRmFuLTtcexMukSZN7mzS35GXV4unsEnu4eAQCcXEQuhWFKLsNklZFjsmL2+pNYsvs8Cq2y1iVRNajla8CjXRrhL92bQRIFhii5JIYpuZxCiwxZVfF14hnM33qWd6ZxUW0aBuKpe5piQOt6sCkqQ5RcGsOUXJbJKkMSBPy8OxVzN51Camah1iXRHeglAQNa18PEHs3QvI4fBPASF3IPDFNyeRabDEkUsPPMNSzacQ7/d/gy71DjZJqF+uGRTuF4qGM4dKIIbwNboeReGKbkNlRVLV5Af8WBC/hxVyr2plzTuCrP5a2XMDC2Lh7v1hQx9fwhKyqMvLyF3BTDlNySVVagqkBGvhlLdqdixYGLOHElT+uy3J6PQcI9zUMxMLYu7mtVF1DBVih5BIYpuT2TVYZOFHAh24Rf9tiD9fTVfK3Lchth/kb0aVEH97eth05NQmCRFXjpJN5PlDwKw5Q8iskqQycJOJ9ZiJVJF7H1ZAZ2n8vk/VUrKKauP/q2rIPBbesjMtQPJpsMH4NO67KINMMwJY9lkRXIsgqDTsSRizlYd/Qytp3KwL6ULE5g+hOdKKBT0xD0a1UXA2PrIdhHz8tZiG7CMCW6zmyToaqAJApISsvGuqNXsO1UBg6ez4JV9qw/k0YhPrgrPAh3hQehc0QImof5Q1FVSKLAheaJysAwJboFk1WGAEAQBBxIzcKmE+k4cSUPJ6/k4VxGvtsEbKifEW3DA9E2PAhdmtZCy/oB8NJLMFllGHUirwMlKgeGKVE5mW0ybLIKo06EKAi4lGPCySt5SLqQjZPXQ/bUlTzkW5xvmUNBsC/bVz/IG/WDvNG0li86NQ1B2/AgBPvoUWiVoZNEGBicRJXCMCWqIpuswGxToJMEGHUSMvMtOJ2eh8MXcpCWVYjsQiuyCq3ILrQip9CKrAL7//PMtmqrwc+oQ71ALzQI8ka9IC/UD/JG4xBfNK7lg3qBXgjxNUAniTBbZdgUlWvgElUzhilRDVEU1T7J6fpdbgTBPh5rkEQIggBFUVFgsSHPbF/A/1qBBVkFVsjXw04UAFEQ7P9EAUadCC+9BG+9BG+9CKNeglEnwlsvwaiXYJUVWGwKBAAGds8SORTDlIiIqIr40ZWIiKiKGKZERERVxDAlIiKqIoYpERFRFTFMiYiIqohhSkREVEUMUyIioipimBIREVURw5SIiKiKGKZERERVxDAlIiKqIoYpERFRFTFMiYiIqohhSkREVEUMUyIioipimBIREVURw5SIiKiKGKZERERVxDAlIiKqIoYpERFRFTFMiYiIqohhSkREVEX/DyYnqqHgSxGJAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(4,4))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Employment Type')  # Add a title\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 875,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 875,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JobSatisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 876,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSatisfaction\n",
+       "Moderately satisfied                  26\n",
+       "Slightly satisfied                    17\n",
+       "Neither satisfied nor dissatisfied    11\n",
+       "Extremely satisfied                    9\n",
+       "Slightly dissatisfied                  9\n",
+       "Moderately dissatisfied                6\n",
+       "Extremely dissatisfied                 2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 876,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSatisfaction'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 877,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 877,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSatisfaction'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 878,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['JobSatisfaction'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 879,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 879,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['JobSatisfaction'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 880,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df['JobSatisfaction'].value_counts()\n",
+    "plt.figure(figsize=(12,4))\n",
+    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('Satisfaction Level')  # Label for x-axis\n",
+    "plt.ylabel('Counts')      # Label for y-axis\n",
+    "plt.title('Job Satisfaction')  # Title of the plot\n",
+    "plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ethnicity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 881,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 881,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['RaceEthnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 882,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RaceEthnicity\n",
+       "Black or of African descent                                                                                                                                                              3\n",
+       "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent     1\n",
+       "Black or of African descent;Hispanic or Latino/Latina                                                                                                                                    1\n",
+       "East Asian                                                                                                                                                                               2\n",
+       "Hispanic or Latino/Latina                                                                                                                                                                1\n",
+       "Hispanic or Latino/Latina;White or of European descent                                                                                                                                   1\n",
+       "South Asian                                                                                                                                                                              8\n",
+       "White or of European descent                                                                                                                                                            41\n",
+       "Name: RaceEthnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 882,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#count number of each Ethnicity\n",
+    "df.groupby('RaceEthnicity')['RaceEthnicity'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 883,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
+    "df.loc[df['RaceEthnicity'].str.match('Biracial') == True, 'RaceEthnicity'] = 'Biracial'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Black or of African descent') == True, 'RaceEthnicity'] = 'Black or African descent'\n",
+    "df.loc[df['RaceEthnicity'].str.match('East Asian') == True, 'RaceEthnicity'] = 'East Asian'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Hispanic or Latino') == True, 'RaceEthnicity'] = 'Hispanic or Latino'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Indigenous') == True, 'RaceEthnicity'] = 'Indigenous'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Middle Eastern') == True, 'RaceEthnicity'] = 'Middle Eastern'\n",
+    "df.loc[df['RaceEthnicity'].str.match('South') == True, 'RaceEthnicity'] = 'South Asian'\n",
+    "df.loc[df['RaceEthnicity'].str.match('White or of European descent') == True, 'RaceEthnicity'] = 'White or European descent'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Multiracial') == True, 'RaceEthnicity'] = 'Multiracial'\n",
+    "df.loc[df['RaceEthnicity'].str.match('Native American') == True, 'RaceEthnicity'] = 'Native American'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 884,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RaceEthnicity\n",
+       "Black or African descent      5\n",
+       "East Asian                    2\n",
+       "Hispanic or Latino            2\n",
+       "South Asian                   8\n",
+       "White or European descent    41\n",
+       "Name: RaceEthnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 884,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('RaceEthnicity')['RaceEthnicity'].count() #11 groups of Ethnicity after combining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 885,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 885,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['RaceEthnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 886,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['RaceEthnicity']=df.groupby(['Country'])['RaceEthnicity'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 887,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 887,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['RaceEthnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 888,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df.groupby('RaceEthnicity')['RaceEthnicity'].count()\n",
+    "plt.figure(figsize=(6,6))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Race/Ethnicity')  # Add a title\n",
+    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Developer Roles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 889,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 889,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 890,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['DevType'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 891,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Back-end developer                                                                                                                                                                       2\n",
+       "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Database administrator;DevOps specialist;Engineering manager;Full-stack developer;System administrator    1\n",
+       "Back-end developer;C-suite executive (CEO, CTO, etc.);Database administrator;Designer;Front-end developer;Full-stack developer;Mobile developer                                          1\n",
+       "Back-end developer;C-suite executive (CEO, CTO, etc.);Database administrator;DevOps specialist;Engineering manager;Full-stack developer;System administrator                             1\n",
+       "Back-end developer;Data or business analyst;Database administrator;DevOps specialist;Front-end developer;Full-stack developer;System administrator                                       1\n",
+       "Back-end developer;Data or business analyst;Designer;Front-end developer;Game or graphics developer;Mobile developer;Student                                                             1\n",
+       "Back-end developer;Data scientist or machine learning specialist;Desktop or enterprise applications developer;Front-end developer;Full-stack developer                                   1\n",
+       "Back-end developer;Data scientist or machine learning specialist;DevOps specialist;Educator or academic researcher;QA or test developer;System administrator                             1\n",
+       "Back-end developer;Data scientist or machine learning specialist;Full-stack developer;Game or graphics developer;Student                                                                 1\n",
+       "Back-end developer;Database administrator;Designer;Desktop or enterprise applications developer;Front-end developer;Full-stack developer                                                 1\n",
+       "Back-end developer;Database administrator;Designer;Front-end developer;Full-stack developer;Mobile developer;System administrator                                                        1\n",
+       "Back-end developer;Database administrator;Desktop or enterprise applications developer;DevOps specialist;Full-stack developer;QA or test developer                                       1\n",
+       "Back-end developer;Database administrator;DevOps specialist;Front-end developer;Full-stack developer;Mobile developer                                                                    1\n",
+       "Back-end developer;Database administrator;DevOps specialist;Front-end developer;Full-stack developer;QA or test developer;System administrator                                           1\n",
+       "Back-end developer;Database administrator;Front-end developer                                                                                                                            1\n",
+       "Back-end developer;Database administrator;Front-end developer;Full-stack developer                                                                                                       1\n",
+       "Back-end developer;Database administrator;Front-end developer;Full-stack developer;Mobile developer                                                                                      1\n",
+       "Back-end developer;Database administrator;Front-end developer;Student;System administrator                                                                                               1\n",
+       "Back-end developer;Designer;Educator or academic researcher;Front-end developer                                                                                                          1\n",
+       "Back-end developer;Designer;Front-end developer;Full-stack developer;Marketing or sales professional;Mobile developer                                                                    1\n",
+       "Back-end developer;Desktop or enterprise applications developer;Embedded applications or devices developer                                                                               2\n",
+       "Back-end developer;Desktop or enterprise applications developer;Embedded applications or devices developer;Front-end developer;Full-stack developer;System administrator                 1\n",
+       "Back-end developer;Desktop or enterprise applications developer;Front-end developer;Full-stack developer                                                                                 2\n",
+       "Back-end developer;Desktop or enterprise applications developer;Front-end developer;Full-stack developer;Game or graphics developer;Student                                              1\n",
+       "Back-end developer;Desktop or enterprise applications developer;QA or test developer                                                                                                     1\n",
+       "Back-end developer;DevOps specialist                                                                                                                                                     2\n",
+       "Back-end developer;DevOps specialist;Front-end developer;Full-stack developer;Mobile developer                                                                                           1\n",
+       "Back-end developer;Embedded applications or devices developer                                                                                                                            1\n",
+       "Back-end developer;Embedded applications or devices developer;Full-stack developer                                                                                                       1\n",
+       "Back-end developer;Engineering manager                                                                                                                                                   1\n",
+       "Back-end developer;Front-end developer                                                                                                                                                   1\n",
+       "Back-end developer;Front-end developer;Full-stack developer                                                                                                                              5\n",
+       "Back-end developer;Front-end developer;Full-stack developer;Mobile developer                                                                                                             1\n",
+       "Back-end developer;Front-end developer;Student                                                                                                                                           2\n",
+       "Back-end developer;Full-stack developer                                                                                                                                                  5\n",
+       "Back-end developer;Full-stack developer;QA or test developer                                                                                                                             1\n",
+       "Back-end developer;Full-stack developer;System administrator                                                                                                                             1\n",
+       "Data or business analyst;Data scientist or machine learning specialist;Database administrator;DevOps specialist                                                                          1\n",
+       "Data or business analyst;Database administrator;DevOps specialist;System administrator                                                                                                   1\n",
+       "Data or business analyst;Desktop or enterprise applications developer;Game or graphics developer;QA or test developer;Student                                                            1\n",
+       "Data scientist or machine learning specialist                                                                                                                                            1\n",
+       "Database administrator;DevOps specialist;Full-stack developer;System administrator                                                                                                       1\n",
+       "Database administrator;Full-stack developer;Mobile developer                                                                                                                             1\n",
+       "Designer;Front-end developer                                                                                                                                                             2\n",
+       "Designer;Front-end developer;Marketing or sales professional                                                                                                                             1\n",
+       "Designer;Front-end developer;QA or test developer                                                                                                                                        1\n",
+       "Desktop or enterprise applications developer;Embedded applications or devices developer;Full-stack developer;Game or graphics developer;Mobile developer                                 1\n",
+       "Desktop or enterprise applications developer;Front-end developer;Product manager                                                                                                         1\n",
+       "Embedded applications or devices developer                                                                                                                                               1\n",
+       "Embedded applications or devices developer;Engineering manager                                                                                                                           1\n",
+       "Engineering manager;Full-stack developer                                                                                                                                                 1\n",
+       "Engineering manager;Mobile developer                                                                                                                                                     1\n",
+       "Front-end developer                                                                                                                                                                      1\n",
+       "Front-end developer;Full-stack developer                                                                                                                                                 1\n",
+       "Front-end developer;Student                                                                                                                                                              1\n",
+       "Full-stack developer                                                                                                                                                                     8\n",
+       "Full-stack developer;Product manager                                                                                                                                                     1\n",
+       "Mobile developer                                                                                                                                                                         2\n",
+       "QA or test developer                                                                                                                                                                     1\n",
+       "Student                                                                                                                                                                                  2\n",
+       "Name: DevType, dtype: int64"
+      ]
+     },
+     "execution_count": 891,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('DevType')['DevType'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 892,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
+    "df.loc[df['DevType'].str.match('Back-end developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Student') == True, 'DevType'] = 'Student'\n",
+    "df.loc[df['DevType'].str.match('QA or test developer') == True, 'DevType'] = 'Non developer'\n",
+    "df.loc[df['DevType'].str.match('Product manager') == True, 'DevType'] = 'Manager'\n",
+    "df.loc[df['DevType'].str.match('Mobile developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Marketing or sales professional') == True, 'DevType'] = 'Non developer'\n",
+    "\n",
+    "df.loc[df['DevType'].str.match('System administrator') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Game or graphics developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Full-stack developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Front-end developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Engineering manager') == True, 'DevType'] = 'Manager'\n",
+    "df.loc[df['DevType'].str.match('Embedded applications or devices developer') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Educator or academic researcher') == True, 'DevType'] = 'Student'\n",
+    "df.loc[df['DevType'].str.match('DevOps specialist') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Desktop or enterprise applications developer') == True, 'DevType'] = 'Developer'\n",
+    "\n",
+    "df.loc[df['DevType'].str.match('Designer') == True, 'DevType'] = 'Non developer'\n",
+    "df.loc[df['DevType'].str.match('Database administrator') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Data scientist or machine learning specialist') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('Data or business analyst') == True, 'DevType'] = 'Developer'\n",
+    "df.loc[df['DevType'].str.match('C-suite executive') == True, 'DevType'] = 'Developer'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 893,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Developer        74\n",
+       "Manager           2\n",
+       "Non developer     5\n",
+       "Student           2\n",
+       "Name: DevType, dtype: int64"
+      ]
+     },
+     "execution_count": 893,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('DevType')['DevType'].count() #11 groups of Ethnicity after combining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 894,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df.groupby('DevType')['DevType'].count()\n",
+    "plt.figure(figsize=(6,6))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Developer Type')  # Add a title\n",
+    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Language to worked with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 895,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWorkedWith\n",
+       "Java;JavaScript;PHP;SQL;TypeScript;HTML;CSS    2\n",
+       "JavaScript;PHP;HTML;CSS                        2\n",
+       "C;F#;Haskell;Python;Scala                      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 895,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageWorkedWith'].value_counts().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 896,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 896,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 897,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 898,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 898,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 899,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWorkedWith\n",
+       "Java;JavaScript;PHP;VB.NET;HTML;CSS                3\n",
+       "Assembly;C;C++;Java;Python;Delphi/Object Pascal    2\n",
+       "Java;JavaScript;PHP;SQL;TypeScript;HTML;CSS        2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 899,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageWorkedWith'].value_counts().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageDesireNextYear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 900,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageDesireNextYear\n",
+       "C#;JavaScript;PHP;SQL;HTML;CSS    2\n",
+       "JavaScript;Python;HTML;CSS        1\n",
+       "C#;JavaScript;TypeScript          1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 900,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 901,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 901,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 902,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 903,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 903,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 904,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageDesireNextYear\n",
+       "Java;Python                                    4\n",
+       "JavaScript;PHP;SQL;Swift;CSS;Bash/Shell        2\n",
+       "C#;Java;JavaScript;Ruby;TypeScript;HTML;CSS    2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 904,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['LanguageDesireNextYear'].value_counts().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Years of coding (Exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 905,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodingProf\n",
+       "3-5 years      26\n",
+       "0-2 years      20\n",
+       "6-8 years      12\n",
+       "9-11 years      9\n",
+       "12-14 years     6\n",
+       "18-20 years     3\n",
+       "21-23 years     2\n",
+       "24-26 years     2\n",
+       "15-17 years     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 905,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 906,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 906,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 907,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['YearsCodingProf'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 908,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 908,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 909,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodingProf\n",
+       "3-5 years      27\n",
+       "0-2 years      21\n",
+       "6-8 years      12\n",
+       "9-11 years      9\n",
+       "12-14 years     6\n",
+       "18-20 years     3\n",
+       "21-23 years     2\n",
+       "24-26 years     2\n",
+       "15-17 years     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 909,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Years of coding (Non Exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 910,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCoding\n",
+       "6-8 years           17\n",
+       "3-5 years           15\n",
+       "9-11 years          15\n",
+       "0-2 years           11\n",
+       "15-17 years          7\n",
+       "12-14 years          6\n",
+       "24-26 years          5\n",
+       "18-20 years          4\n",
+       "30 or more years     3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 910,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 911,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 911,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 912,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['YearsCoding'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 913,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 913,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 914,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCoding\n",
+       "6-8 years           17\n",
+       "3-5 years           15\n",
+       "9-11 years          15\n",
+       "0-2 years           11\n",
+       "15-17 years          7\n",
+       "12-14 years          6\n",
+       "24-26 years          5\n",
+       "18-20 years          4\n",
+       "30 or more years     3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 914,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operating System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 915,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OperatingSystem\n",
+       "Windows        32\n",
+       "MacOS          20\n",
+       "Linux-based    15\n",
+       "BSD/Unix        1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 915,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['OperatingSystem'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 916,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 916,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['OperatingSystem'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 917,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['OperatingSystem'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 918,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 918,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['OperatingSystem'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 919,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lst=df['OperatingSystem'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 920,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(4,4))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Oeratining System Used')  # Add a title\n",
+    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "# Display the pie chart\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Salary Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 921,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryType\n",
+       "Monthly    25\n",
+       "Yearly     22\n",
+       "Weekly      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 921,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 922,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35"
+      ]
+     },
+     "execution_count": 922,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 923,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['SalaryType'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 924,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 924,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 925,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryType\n",
+       "Monthly    42\n",
+       "Yearly     39\n",
+       "Weekly      2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 925,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Currency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 926,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Currency\n",
+       "U.S. dollars ($)                20\n",
+       "Euros (€)                     11\n",
+       "British pounds sterling (£)     7\n",
+       "Indian rupees (‚Çπ)              7\n",
+       "Swedish kroner (SEK)             3\n",
+       "Russian rubles (‚ÇΩ)             3\n",
+       "Polish złoty (zł)              2\n",
+       "Chinese yuan renminbi (¥)       2\n",
+       "South African rands (R)          1\n",
+       "Australian dollars (A$)          1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 926,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Currency'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 927,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23"
+      ]
+     },
+     "execution_count": 927,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Currency'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 928,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Currency'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 929,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 929,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Currency'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 930,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['Currency'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 931,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Currency\n",
+       "U.S. dollars ($)                27\n",
+       "Euros (€)                     16\n",
+       "British pounds sterling (£)    10\n",
+       "Indian rupees (‚Çπ)              8\n",
+       "Chinese yuan renminbi (¥)       5\n",
+       "Swedish kroner (SEK)             4\n",
+       "Russian rubles (‚ÇΩ)             4\n",
+       "Polish złoty (zł)              2\n",
+       "Brazilian reais (R$)             2\n",
+       "South African rands (R)          1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 931,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Currency'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 932,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryUSD\n",
+       "120000.0    3\n",
+       "30000.0     2\n",
+       "115000.0    2\n",
+       "70841.0     1\n",
+       "36000.0     1\n",
+       "90000.0     1\n",
+       "73428.0     1\n",
+       "128507.0    1\n",
+       "13212.0     1\n",
+       "48955.0     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 932,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryUSD'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 933,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 933,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryUSD'].isnull().sum() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 934,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType        Country       \n",
+       "Developer      United States     146687.5\n",
+       "               Ireland           128507.0\n",
+       "Non developer  India             123984.0\n",
+       "Developer      Australia          95968.0\n",
+       "               Colombia           64116.0\n",
+       "               Germany            61191.5\n",
+       "               Sweden             60257.5\n",
+       "               China              52604.0\n",
+       "               Greece             51408.0\n",
+       "               United Kingdom     48144.8\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 934,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_salary = df.groupby(['DevType','Country'])['SalaryUSD'].mean() \n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 935,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "means = df.groupby(['YearsCodingProf','DevType', 'Country'])['SalaryUSD'].transform('mean')\n",
+    "df['SalaryUSD'] = df['SalaryUSD'].fillna(means)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 936,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodingProf  DevType        Country       \n",
+       "21-23 years      Developer      United States     250000.000000\n",
+       "0-2 years        Developer      United States     244000.000000\n",
+       "15-17 years      Developer      Ireland           128507.000000\n",
+       "0-2 years        Non developer  India             123984.000000\n",
+       "9-11 years       Developer      United States     115000.000000\n",
+       "12-14 years      Developer      Australia          95968.000000\n",
+       "18-20 years      Developer      United States      95000.000000\n",
+       "6-8 years        Developer      United States      91333.333333\n",
+       "3-5 years        Developer      China              85708.000000\n",
+       "9-11 years       Developer      United Kingdom     82648.000000\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 936,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_salary = df.groupby(['YearsCodingProf','DevType','Country'])['SalaryUSD'].mean()\n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 937,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['SalaryUSD'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 938,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age\n",
+       "25 - 34 years old    22\n",
+       "35 - 44 years old    13\n",
+       "18 - 24 years old    13\n",
+       "45 - 54 years old     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 938,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Age'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 939,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 939,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 940,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Age'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 941,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 941,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 942,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df['Age'].value_counts().nlargest(10)\n",
+    "plt.figure(figsize=(12,4))\n",
+    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('Age Group')  # Label for x-axis\n",
+    "plt.ylabel('Counts')      # Label for y-axis\n",
+    "plt.title('Age')  # Title of the plot\n",
+    "#plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 943,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                       0\n",
+      "SalaryUSD                 0\n",
+      "Country                   0\n",
+      "Currency                  0\n",
+      "DevType                   0\n",
+      "Employment                0\n",
+      "RaceEthnicity             0\n",
+      "Gender                    0\n",
+      "SalaryType                0\n",
+      "Hobby                     0\n",
+      "JobSatisfaction           0\n",
+      "JobSearchStatus           0\n",
+      "OperatingSystem           0\n",
+      "UndergradMajor            0\n",
+      "YearsCoding               0\n",
+      "YearsCodingProf           0\n",
+      "LanguageDesireNextYear    0\n",
+      "LanguageWorkedWith        0\n",
+      "FormalEducation           0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 944,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 944,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['FormalEducation'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 945,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FormalEducation\n",
+       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                            25\n",
+       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                         17\n",
+       "Some college/university study without earning a degree                                12\n",
+       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     3\n",
+       "Associate degree                                                                       2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 945,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['FormalEducation'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 946,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EdLevel\n",
+       "Bachelors    42\n",
+       "No Degree    15\n",
+       "Associate     2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 946,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Changing column's name\n",
+    "df.rename(columns={'FormalEducation':'EdLevel'}, inplace =True)\n",
+    "#Refactoring EdLevel\n",
+    "def refactor_ed(df):\n",
+    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
+    "    conditions_ed = [(df['EdLevel'] == 'Associate degree'),\n",
+    "                    (df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
+    "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
+    "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
+    "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
+    "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
+    "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
+    "    \n",
+    "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
+    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
+    "    return df\n",
+    "\n",
+    "# applying function to subsets\n",
+    "df = refactor_ed(df)\n",
+    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
+    "df['EdLevel'].replace('nan', 'Bachelors', inplace=True)\n",
+    "\n",
+    "df['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaned Dataset : 2018_Survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 947,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(59, 19)"
+      ]
+     },
+     "execution_count": 947,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_2018 = df[df.notnull()]\n",
+    "cleaned_2018.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 948,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Currency</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>RaceEthnicity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SalaryType</th>\n",
+       "      <th>Hobby</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>JobSearchStatus</th>\n",
+       "      <th>OperatingSystem</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCoding</th>\n",
+       "      <th>YearsCodingProf</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>EdLevel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>70841.0</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>British pounds sterling (£)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately dissatisfied</td>\n",
+       "      <td>Seeking</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Other Science</td>\n",
+       "      <td>30 or more years</td>\n",
+       "      <td>18-20 years</td>\n",
+       "      <td>Go;Python</td>\n",
+       "      <td>JavaScript;Python;Bash/Shell</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>21426.0</td>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>South African rands (R)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>Windows</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>6-8 years</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>Assembly;C;C++;Matlab;SQL;Bash/Shell</td>\n",
+       "      <td>C;C++;Java;Matlab;R;SQL;Bash/Shell</td>\n",
+       "      <td>No Degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>41671.0</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>British pounds sterling (£)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately satisfied</td>\n",
+       "      <td>Seeking</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>6-8 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...</td>\n",
+       "      <td>Java;JavaScript;Python;TypeScript;HTML;CSS</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>120000.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>U.S. dollars ($)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>MacOS</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>9-11 years</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>C;Go;JavaScript;Python;HTML;CSS</td>\n",
+       "      <td>JavaScript;HTML;CSS</td>\n",
+       "      <td>No Degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>250000.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>U.S. dollars ($)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>MacOS</td>\n",
+       "      <td>Arts and Science</td>\n",
+       "      <td>30 or more years</td>\n",
+       "      <td>21-23 years</td>\n",
+       "      <td>Erlang;Go;Python;Rust;SQL</td>\n",
+       "      <td>Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua...</td>\n",
+       "      <td>No Degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>8767.0</td>\n",
+       "      <td>India</td>\n",
+       "      <td>U.S. dollars ($)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>South Asian</td>\n",
+       "      <td>Non-conforming</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extremely satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>Linux-based</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>Java;Python</td>\n",
+       "      <td>Java</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>Euros (€)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Neither satisfied nor dissatisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>Windows</td>\n",
+       "      <td>No major</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>Java;Python</td>\n",
+       "      <td>Java;JavaScript;PHP;VB.NET;HTML;CSS</td>\n",
+       "      <td>No Degree</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>47904.0</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>Swedish kroner (SEK)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or European descent</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Moderately satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>Windows</td>\n",
+       "      <td>Business</td>\n",
+       "      <td>6-8 years</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>C#;F#;Haskell;SQL;Ocaml</td>\n",
+       "      <td>C#;SQL;HTML;CSS;Bash/Shell</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>8767.0</td>\n",
+       "      <td>India</td>\n",
+       "      <td>Swedish kroner (SEK)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>South Asian</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>Windows</td>\n",
+       "      <td>No major</td>\n",
+       "      <td>0-2 years</td>\n",
+       "      <td>3-5 years</td>\n",
+       "      <td>Python;R</td>\n",
+       "      <td>C;C++;C#</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>95968.0</td>\n",
+       "      <td>Australia</td>\n",
+       "      <td>Australian dollars (A$)</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>South Asian</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>MacOS</td>\n",
+       "      <td>Engineering</td>\n",
+       "      <td>15-17 years</td>\n",
+       "      <td>12-14 years</td>\n",
+       "      <td>C;C++;Go;Python;SQL;Swift;Kotlin</td>\n",
+       "      <td>C;C++;Go;Python;SQL;Swift</td>\n",
+       "      <td>Bachelors</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Age  SalaryUSD         Country  \\\n",
+       "1   35 - 44 years old    70841.0  United Kingdom   \n",
+       "4   18 - 24 years old    21426.0    South Africa   \n",
+       "5   18 - 24 years old    41671.0  United Kingdom   \n",
+       "6   18 - 24 years old   120000.0   United States   \n",
+       "8   35 - 44 years old   250000.0   United States   \n",
+       "13  35 - 44 years old     8767.0           India   \n",
+       "14  18 - 24 years old        0.0     Netherlands   \n",
+       "17  35 - 44 years old    47904.0          Sweden   \n",
+       "18  35 - 44 years old     8767.0           India   \n",
+       "20  35 - 44 years old    95968.0       Australia   \n",
+       "\n",
+       "                        Currency    DevType Employment  \\\n",
+       "1   British pounds sterling (£)  Developer  Full-time   \n",
+       "4        South African rands (R)  Developer  Full-time   \n",
+       "5   British pounds sterling (£)  Developer  Full-time   \n",
+       "6               U.S. dollars ($)  Developer  Full-time   \n",
+       "8               U.S. dollars ($)  Developer  Full-time   \n",
+       "13              U.S. dollars ($)  Developer  Full-time   \n",
+       "14                   Euros (€)  Developer  Full-time   \n",
+       "17          Swedish kroner (SEK)  Developer  Full-time   \n",
+       "18          Swedish kroner (SEK)  Developer  Full-time   \n",
+       "20       Australian dollars (A$)  Developer  Full-time   \n",
+       "\n",
+       "                RaceEthnicity          Gender SalaryType Hobby  \\\n",
+       "1   White or European descent            Male     Yearly   Yes   \n",
+       "4   White or European descent            Male     Yearly   Yes   \n",
+       "5   White or European descent            Male     Yearly   Yes   \n",
+       "6   White or European descent            Male     Yearly   Yes   \n",
+       "8   White or European descent            Male     Yearly   Yes   \n",
+       "13                South Asian  Non-conforming     Yearly    No   \n",
+       "14  White or European descent            Male    Monthly    No   \n",
+       "17  White or European descent            Male    Monthly   Yes   \n",
+       "18                South Asian          Female    Monthly   Yes   \n",
+       "20                South Asian            Male     Yearly   Yes   \n",
+       "\n",
+       "                       JobSatisfaction JobSearchStatus OperatingSystem  \\\n",
+       "1              Moderately dissatisfied         Seeking     Linux-based   \n",
+       "4                   Slightly satisfied             nan         Windows   \n",
+       "5                 Moderately satisfied         Seeking     Linux-based   \n",
+       "6                   Slightly satisfied             nan           MacOS   \n",
+       "8                 Moderately satisfied             nan           MacOS   \n",
+       "13                 Extremely satisfied             nan     Linux-based   \n",
+       "14  Neither satisfied nor dissatisfied             nan         Windows   \n",
+       "17                Moderately satisfied             nan         Windows   \n",
+       "18                  Slightly satisfied             nan         Windows   \n",
+       "20                  Slightly satisfied             nan           MacOS   \n",
+       "\n",
+       "      UndergradMajor       YearsCoding YearsCodingProf  \\\n",
+       "1      Other Science  30 or more years     18-20 years   \n",
+       "4   Computer Science         6-8 years       0-2 years   \n",
+       "5   Computer Science         6-8 years       3-5 years   \n",
+       "6   Computer Science        9-11 years       0-2 years   \n",
+       "8   Arts and Science  30 or more years     21-23 years   \n",
+       "13       Engineering         3-5 years       3-5 years   \n",
+       "14          No major         0-2 years       0-2 years   \n",
+       "17          Business         6-8 years       0-2 years   \n",
+       "18          No major         0-2 years       3-5 years   \n",
+       "20       Engineering       15-17 years     12-14 years   \n",
+       "\n",
+       "                               LanguageDesireNextYear  \\\n",
+       "1                                           Go;Python   \n",
+       "4                Assembly;C;C++;Matlab;SQL;Bash/Shell   \n",
+       "5   C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...   \n",
+       "6                     C;Go;JavaScript;Python;HTML;CSS   \n",
+       "8                           Erlang;Go;Python;Rust;SQL   \n",
+       "13                                        Java;Python   \n",
+       "14                                        Java;Python   \n",
+       "17                            C#;F#;Haskell;SQL;Ocaml   \n",
+       "18                                           Python;R   \n",
+       "20                   C;C++;Go;Python;SQL;Swift;Kotlin   \n",
+       "\n",
+       "                                   LanguageWorkedWith    EdLevel  \n",
+       "1                        JavaScript;Python;Bash/Shell  Bachelors  \n",
+       "4                  C;C++;Java;Matlab;R;SQL;Bash/Shell  No Degree  \n",
+       "5          Java;JavaScript;Python;TypeScript;HTML;CSS  Bachelors  \n",
+       "6                                 JavaScript;HTML;CSS  No Degree  \n",
+       "8   Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua...  No Degree  \n",
+       "13                                               Java  Bachelors  \n",
+       "14                Java;JavaScript;PHP;VB.NET;HTML;CSS  No Degree  \n",
+       "17                         C#;SQL;HTML;CSS;Bash/Shell  Bachelors  \n",
+       "18                                           C;C++;C#  Bachelors  \n",
+       "20                          C;C++;Go;Python;SQL;Swift  Bachelors  "
+      ]
+     },
+     "execution_count": 948,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_2018.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## After Cleaning Dataset 2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 949,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  1121\n",
+      "Total missing :  0\n",
+      "Missing Percentage:  0.0 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = df.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow 2019 Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 950,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "na_vals = ['NA', 'Missing']\n",
+    "survey_main_df = pd.read_csv(r\"D:\\project\\Stackoverflow-Analysis\\Data\\survey_results_sample_2019.csv\", na_values=na_vals)\n",
+    "schema_df = pd.read_csv(r\"D:\\project\\Stackoverflow-Analysis\\Data\\survey_results_sample_2019.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 951,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Selecting only the required columns for analysis\n",
+    "survey_df_2019 = survey_main_df[['Age', 'CareerSat', 'ConvertedComp', 'Country', 'Dependents', 'EdLevel', 'Employment', 'Ethnicity', 'Gender', 'Hobbyist', 'ImpSyn', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n",
+    " 'UndergradMajor', 'YearsCodePro', 'DevType']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 952,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#changing the name of columns for easier understanding\n",
+    "# 'MainBranch': 'Profession'\n",
+    "# 'ConvertedComp': 'SalaryUSD'\n",
+    "# 'CareerSat': 'JobSatisfaction'\n",
+    "# 'ImpSyn' : 'CompetenceLevel'\n",
+    "# 'JobSat' : 'CurrentJobSatis'\n",
+    "# 'JobSeek' : 'JobStatus'\n",
+    "\n",
+    "\n",
+    "survey_df_2019.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD',  'CareerSat': 'JobSatisfaction', 'ImpSyn' : 'CompetenceLevel', 'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 953,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>CompetenceLevel</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>JobStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Primary/elementary school</td>\n",
+       "      <td>Not employed, and not looking for work</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL</td>\n",
+       "      <td>HTML/CSS;Java;JavaScript;Python</td>\n",
+       "      <td>I am a student who is learning to code</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bosnia and Herzegovina</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Developer, desktop or enterprise applications;...</td>\n",
+       "      <td>Secondary school (e.g. American high school, G...</td>\n",
+       "      <td>Not employed, but looking for work</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>I am actively looking for a job</td>\n",
+       "      <td>C++;HTML/CSS;JavaScript;SQL</td>\n",
+       "      <td>C++;HTML/CSS;Python</td>\n",
+       "      <td>I am a student who is learning to code</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>Thailand</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to n...</td>\n",
+       "      <td>Elixir;HTML/CSS</td>\n",
+       "      <td>HTML/CSS</td>\n",
+       "      <td>I am not primarily a developer, but I write co...</td>\n",
+       "      <td>8820.0</td>\n",
+       "      <td>Web development or web design</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Age CompetenceLevel                 Country     CurrentJobSatis  \\\n",
+       "0  14.0             NaN          United Kingdom                 NaN   \n",
+       "1  19.0             NaN  Bosnia and Herzegovina                 NaN   \n",
+       "2  28.0         Average                Thailand  Slightly satisfied   \n",
+       "\n",
+       "  Dependents                                            DevType  \\\n",
+       "0         No                                                NaN   \n",
+       "1         No  Developer, desktop or enterprise applications;...   \n",
+       "2        Yes  Designer;Developer, back-end;Developer, front-...   \n",
+       "\n",
+       "                                             EdLevel  \\\n",
+       "0                          Primary/elementary school   \n",
+       "1  Secondary school (e.g. American high school, G...   \n",
+       "2         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "\n",
+       "                               Employment Ethnicity Gender Hobbyist  \\\n",
+       "0  Not employed, and not looking for work       NaN    Man      Yes   \n",
+       "1      Not employed, but looking for work       NaN    Man       No   \n",
+       "2                      Employed full-time       NaN    Man      Yes   \n",
+       "\n",
+       "      JobSatisfaction                                          JobStatus  \\\n",
+       "0                 NaN                                                NaN   \n",
+       "1                 NaN                    I am actively looking for a job   \n",
+       "2  Slightly satisfied  I’m not actively looking, but I am open to n...   \n",
+       "\n",
+       "                            LanguageDesireNextYear  \\\n",
+       "0  C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL   \n",
+       "1                      C++;HTML/CSS;JavaScript;SQL   \n",
+       "2                                  Elixir;HTML/CSS   \n",
+       "\n",
+       "                LanguageWorkedWith  \\\n",
+       "0  HTML/CSS;Java;JavaScript;Python   \n",
+       "1              C++;HTML/CSS;Python   \n",
+       "2                         HTML/CSS   \n",
+       "\n",
+       "                                          Profession  SalaryUSD  \\\n",
+       "0             I am a student who is learning to code        NaN   \n",
+       "1             I am a student who is learning to code        NaN   \n",
+       "2  I am not primarily a developer, but I write co...     8820.0   \n",
+       "\n",
+       "                  UndergradMajor YearsCodePro  \n",
+       "0                            NaN          NaN  \n",
+       "1                            NaN          NaN  \n",
+       "2  Web development or web design            1  "
+      ]
+     },
+     "execution_count": 953,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sorting the columns alphabetically\n",
+    "survey_df_2019.sort_index(axis=1).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 954,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                       float64\n",
+       "JobSatisfaction            object\n",
+       "SalaryUSD                 float64\n",
+       "Country                    object\n",
+       "Dependents                 object\n",
+       "EdLevel                    object\n",
+       "Employment                 object\n",
+       "Ethnicity                  object\n",
+       "Gender                     object\n",
+       "Hobbyist                   object\n",
+       "CompetenceLevel            object\n",
+       "CurrentJobSatis            object\n",
+       "JobStatus                  object\n",
+       "LanguageDesireNextYear     object\n",
+       "LanguageWorkedWith         object\n",
+       "Profession                 object\n",
+       "UndergradMajor             object\n",
+       "YearsCodePro               object\n",
+       "DevType                    object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 954,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#datatype of survey data\n",
+    "survey_df_2019.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Validation - Total Cells vs Missing %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 955,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  1881\n",
+      "Total missing :  201\n",
+      "Missing Percentage:  10.685805422647528 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = survey_df_2019.isnull().sum() #number of missing\n",
+    "total_cells = np.product(survey_df_2019.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning and Refactoring column values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 956,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man                                                  87\n",
+       "Woman                                                 7\n",
+       "Non-binary, genderqueer, or gender non-conforming     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 956,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 957,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#lets refactor Gender values to Male, female and Non binary\n",
+    "#For the purpose of our data analysis we are considering three gender category. This not to defame any gender.\n",
+    "#refactoring Gender\n",
+    "\n",
+    "def refactor_gender(df):\n",
+    "    '''function to change gender category to Male, Female, Non binary'''\n",
+    "    conditions = [(df['Gender'] == 'Man') | (df['Gender'] == 'Man;Non-binary, genderqueer, or gender non-conforming'),\n",
+    "                  (df['Gender'] == 'Woman') | (df['Gender'] == 'Woman;Non-binary, genderqueer, or gender non-conforming'),\n",
+    "                  (df['Gender'] == 'Non-binary, genderqueer, or gender non-conforming') \n",
+    "                  | (df['Gender'] == 'Woman;Man') \n",
+    "                  | (df['Gender'] == 'Woman;Man;Non-binary, genderqueer, or gender non-conforming')]\n",
+    "\n",
+    "    values = ['Man', 'Woman', 'Non-binary']\n",
+    "\n",
+    "    df['Gender'] = np.select(conditions, values, default = np.NaN)\n",
+    "    \n",
+    "    return df\n",
+    "            \n",
+    "survey_df_2019 = refactor_gender(survey_df_2019)\n",
+    "survey_df_2019['Gender'].replace('nan', 'Non-binary', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 958,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Gender'] = survey_df_2019['Gender'].fillna('Non-binary')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 959,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 959,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n",
+    "survey_df_2019.isnull().sum()['Gender']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 960,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man           87\n",
+       "Non-binary     5\n",
+       "Woman          7\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 960,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 961,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n",
+    "plt.title(\"Before cleaning Age's outliers from genders\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 962,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#We are considering developes of age 15 to 60\n",
+    "filt = (survey_df_2019['Age'] >= 15) & (survey_df_2019['Age'] <= 60)\n",
+    "survey_df_2019 = survey_df_2019[filt]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 963,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n",
+    "plt.title(\"After cleaning Age's outliers from genders\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 964,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 964,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Profession column (Mainbranch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 965,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Profession\n",
+       "I am a developer by profession                                                   62\n",
+       "I am not primarily a developer, but I write code sometimes as part of my work    10\n",
+       "I am a student who is learning to code                                            8\n",
+       "I code primarily as a hobby                                                       6\n",
+       "I used to be a developer by profession, but no longer am                          2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 965,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 966,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 966,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Profession'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 967,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Profession'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 968,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Profession\n",
+       "I am a developer by profession                                                   62\n",
+       "I am not primarily a developer, but I write code sometimes as part of my work    10\n",
+       "I am a student who is learning to code                                            8\n",
+       "I code primarily as a hobby                                                       6\n",
+       "I used to be a developer by profession, but no longer am                          2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 968,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 969,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Lets refactor column values of Profession column\n",
+    "#refactoring profession column\n",
+    "\n",
+    "def refactor_prof(df):\n",
+    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
+    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
+    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
+    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
+    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
+    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
+    "    \n",
+    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
+    "    \n",
+    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "survey_df_2019 = refactor_prof(survey_df_2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 970,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Profession\n",
+       "Developer        62\n",
+       "Non developer    10\n",
+       "Student           8\n",
+       "Novoice           6\n",
+       "Ex-Developer      2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 970,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EdLevel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 971,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EdLevel\n",
+       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                            42\n",
+       "Some college/university study without earning a degree                                15\n",
+       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                         10\n",
+       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     9\n",
+       "Associate degree                                                                       5\n",
+       "Primary/elementary school                                                              3\n",
+       "Other doctoral degree (Ph.D, Ed.D., etc.)                                              2\n",
+       "Professional degree (JD, MD, etc.)                                                     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 971,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 972,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 972,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['EdLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 973,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  Refactoring EdLevel\n",
+    "def refactor_ed(df):\n",
+    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
+    "    conditions_ed = [(df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n",
+    "                     (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n",
+    "                     (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
+    "                     (df['EdLevel'] == 'Associate degree'),\n",
+    "                     (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n",
+    "                     (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
+    "                     | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
+    "                     | (df['EdLevel'] == 'Primary/elementary school')\n",
+    "                     | (df['EdLevel'] == 'I never completed any formal education')]\n",
+    "\n",
+    "    choices_ed = ['Bachelors', 'Masters', 'Professional', 'Associate', 'Doctorate', 'No Degree']\n",
+    "\n",
+    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# applying function to subsets\n",
+    "survey_df_2019 = refactor_ed(survey_df_2019)\n",
+    "survey_df_2019['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 974,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EdLevel\n",
+       "Bachelors       53\n",
+       "No Degree       27\n",
+       "Associate        5\n",
+       "Doctorate        2\n",
+       "Professional     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 974,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 975,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 975,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019.isnull().sum()['EdLevel']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Undergrad Major"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 976,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "Computer science, computer engineering, or software engineering          36\n",
+       "Information systems, information technology, or system administration    15\n",
+       "Web development or web design                                             5\n",
+       "Mathematics or statistics                                                 4\n",
+       "Another engineering discipline (ex. civil, electrical, mechanical)        4\n",
+       "A business discipline (ex. accounting, finance, marketing)                3\n",
+       "A natural science (ex. biology, chemistry, physics)                       2\n",
+       "A social science (ex. anthropology, psychology, political science)        2\n",
+       "A humanities discipline (ex. literature, history, philosophy)             1\n",
+       "Fine arts or performing arts (ex. graphic design, music, studio art)      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 976,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 977,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 977,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 978,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['UndergradMajor'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 979,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "Computer science, computer engineering, or software engineering          43\n",
+       "Information systems, information technology, or system administration    19\n",
+       "Web development or web design                                             6\n",
+       "Another engineering discipline (ex. civil, electrical, mechanical)        5\n",
+       "Mathematics or statistics                                                 4\n",
+       "A natural science (ex. biology, chemistry, physics)                       3\n",
+       "A business discipline (ex. accounting, finance, marketing)                3\n",
+       "A social science (ex. anthropology, psychology, political science)        2\n",
+       "A humanities discipline (ex. literature, history, philosophy)             1\n",
+       "Fine arts or performing arts (ex. graphic design, music, studio art)      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 979,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].value_counts().nlargest(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 980,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 980,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 981,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019.dropna(subset=['UndergradMajor'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 982,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 982,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 983,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Refactoring UndergradMajor\n",
+    "def refactor_major(df):\n",
+    "    '''function to change undergrad major category to Computer Science, Engineering, Info Systems, Math/Stat, \n",
+    "        Other Science, Web Design/Dev, Business, Arts and Science'''\n",
+    "    \n",
+    "    \n",
+    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'),\n",
+    "                        (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n",
+    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'),\n",
+    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
+    "                        (df['UndergradMajor'] == 'I never declared a major'),\n",
+    "                        (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)')\n",
+    "                        |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'),\n",
+    "                        (df['UndergradMajor'] == 'Web development or web design'),\n",
+    "                        (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'),\n",
+    "                        (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n",
+    "                        | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n",
+    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)')]\n",
+    "\n",
+    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'No Major', 'Other Science',\n",
+    "                     'Web Design/Dev', 'Business', 'Arts and Science']\n",
+    "\n",
+    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# applying function to subsets\n",
+    "survey_df_2019 = refactor_major(survey_df_2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 984,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "Computer Science    43\n",
+       "Info Systems        19\n",
+       "Web Design/Dev       6\n",
+       "Engineering          5\n",
+       "Math/Stat            4\n",
+       "Arts and Science     4\n",
+       "Other Science        3\n",
+       "Business             3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 984,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['UndergradMajor'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Job Status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 985,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobStatus\n",
+       "I’m not actively looking, but I am open to new opportunities    44\n",
+       "I am not interested in new job opportunities                      23\n",
+       "I am actively looking for a job                                   12\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 985,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 986,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 986,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 987,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['JobStatus'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 988,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 988,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 989,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobStatus\n",
+       "I’m not actively looking, but I am open to new opportunities    50\n",
+       "I am not interested in new job opportunities                      24\n",
+       "I am actively looking for a job                                   13\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 989,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 990,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019.dropna(subset=['JobStatus'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 991,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# refactoring JobStatus\n",
+    "# changing the jobstatus to seeking and non seeking\n",
+    "def refactor_job(df):\n",
+    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
+    "    \n",
+    "    conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n",
+    "                    (df['JobStatus'] == 'I am not interested in new job opportunities')\n",
+    "                    | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
+    "    \n",
+    "    choices_job = ['Seeking', 'Not seeking']\n",
+    "    \n",
+    "    df['JobStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "survey_df_2019 = refactor_job(survey_df_2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 992,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobStatus\n",
+       "nan            50\n",
+       "Not seeking    24\n",
+       "Seeking        13\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 992,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JobSatisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 993,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSatisfaction\n",
+       "Very satisfied                        33\n",
+       "Slightly satisfied                    21\n",
+       "Slightly dissatisfied                  8\n",
+       "Neither satisfied nor dissatisfied     6\n",
+       "Very dissatisfied                      4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 993,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 994,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 994,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 995,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 996,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 996,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 997,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSatisfaction\n",
+       "Very satisfied                        41\n",
+       "Slightly satisfied                    26\n",
+       "Slightly dissatisfied                 10\n",
+       "Neither satisfied nor dissatisfied     6\n",
+       "Very dissatisfied                      4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 997,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['JobSatisfaction'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Employment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 998,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Employed full-time                                      64\n",
+       "Independent contractor, freelancer, or self-employed    12\n",
+       "Employed part-time                                       5\n",
+       "Not employed, but looking for work                       4\n",
+       "Not employed, and not looking for work                   1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 998,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 999,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 999,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1000,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Employment'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1001,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1001,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1002,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Employed full-time                                      65\n",
+       "Independent contractor, freelancer, or self-employed    12\n",
+       "Employed part-time                                       5\n",
+       "Not employed, but looking for work                       4\n",
+       "Not employed, and not looking for work                   1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1002,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1003,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring the employment\n",
+    "def refactor_emp(df):\n",
+    "    '''function to change Employment category to Full-time, Self-employed, Not employed, Part-time '''\n",
+    "    conditions_emp = [(df['Employment'] == 'Employed full-time'),\n",
+    "                    (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n",
+    "                    (df['Employment'] == 'Not employed, but looking for work')\n",
+    "                    | (df['Employment'] == 'Not employed, and not looking for work')\n",
+    "                    | (df['Employment'] == 'Retired'),\n",
+    "                    (df['Employment'] == 'Employed part-time')]\n",
+    "    \n",
+    "    choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n",
+    "    \n",
+    "    df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n",
+    "                       \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "survey_df_2019 = refactor_emp(survey_df_2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1004,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Full-time        65\n",
+       "Self-employed    12\n",
+       "Not employed      5\n",
+       "Part-time         5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1004,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ethnicity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1005,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ethnicity_list = survey_df_2019['Ethnicity'].unique().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1006,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[nan,\n",
+       " 'White or of European descent',\n",
+       " 'White or of European descent;Multiracial',\n",
+       " 'East Asian',\n",
+       " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial;Multiracial',\n",
+       " 'Black or of African descent',\n",
+       " 'Hispanic or Latino/Latina;Multiracial',\n",
+       " 'Hispanic or Latino/Latina',\n",
+       " 'Middle Eastern',\n",
+       " 'South Asian',\n",
+       " 'Multiracial',\n",
+       " 'East Asian;South Asian',\n",
+       " 'Biracial']"
+      ]
+     },
+     "execution_count": 1006,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#here, you can see that we have long list of values. lets refactor them\n",
+    "ethnicity_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1007,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 1007,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(ethnicity_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1008,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#refactoring long list of values into categories.\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Biracial', na=False), 'Ethnicity'] = 'Biracial'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Black or of African descent', na=False), 'Ethnicity'] = 'Black or of African descent'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('East Asian', na=False), 'Ethnicity'] = 'East Asian'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Hispanic or Latino', na=False), 'Ethnicity'] = 'Hispanic or Latino'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Indigenous', na=False), 'Ethnicity'] = 'Indigenous'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Middle Eastern', na=False), 'Ethnicity'] = 'Middle Eastern'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('South Asian', na=False), 'Ethnicity'] = 'South Asian'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('White or of European descent', na=False), 'Ethnicity'] = 'White or of European descent'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Multiracial', na=False), 'Ethnicity'] = 'Multiracial'\n",
+    "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Native American', na=False), 'Ethnicity'] = 'Native American'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1009,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 1009,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1010,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ethnicity\n",
+       "White or of European descent    45\n",
+       "South Asian                      8\n",
+       "Hispanic or Latino               6\n",
+       "East Asian                       4\n",
+       "Black or of African descent      4\n",
+       "Middle Eastern                   3\n",
+       "Multiracial                      1\n",
+       "Biracial                         1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1010,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1011,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Ethnicity']=survey_df_2019.groupby(['Country'])['Ethnicity'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1012,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 1012,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1013,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ethnicity\n",
+       "White or of European descent    52\n",
+       "South Asian                     14\n",
+       "Hispanic or Latino               6\n",
+       "East Asian                       5\n",
+       "Black or of African descent      4\n",
+       "Middle Eastern                   3\n",
+       "Multiracial                      1\n",
+       "Biracial                         1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1013,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dependents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1014,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dependents\n",
+       "No     55\n",
+       "Yes    27\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1014,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019[\"Dependents\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1015,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 1015,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019[\"Dependents\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1016,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Lets consider that people who didnt respond has no dependents for the purpose of analysis\n",
+    "survey_df_2019[\"Dependents\"].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1017,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1017,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019[\"Dependents\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1018,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dependents\n",
+       "No     58\n",
+       "Yes    29\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1018,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019[\"Dependents\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DevType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1019,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 1019,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1020,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Developer, full-stack                                                      12\n",
+       "Developer, front-end                                                        4\n",
+       "Developer, back-end;DevOps specialist                                       3\n",
+       "Designer;Developer, back-end;Developer, front-end;Developer, full-stack     2\n",
+       "Student                                                                     2\n",
+       "Developer, full-stack;Engineer, data                                        2\n",
+       "Developer, desktop or enterprise applications                               2\n",
+       "System administrator                                                        1\n",
+       "Developer, front-end;Developer, mobile                                      1\n",
+       "Developer, full-stack;Student                                               1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1020,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['DevType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1021,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['DevType'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1022,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1022,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1023,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Developer, full-stack                                                      12\n",
+       "Developer, front-end                                                        4\n",
+       "Developer, desktop or enterprise applications                               3\n",
+       "Developer, back-end;DevOps specialist                                       3\n",
+       "Designer;Developer, back-end;Developer, front-end;Developer, full-stack     2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1023,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['DevType'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageWorkedWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1024,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 1024,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1025,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWorkedWith\n",
+       "HTML/CSS;JavaScript                                       5\n",
+       "C#;HTML/CSS;JavaScript;SQL                                3\n",
+       "HTML/CSS;JavaScript;PHP;TypeScript                        2\n",
+       "HTML/CSS;JavaScript;PHP;SQL                               2\n",
+       "HTML/CSS                                                  1\n",
+       "C;C#;HTML/CSS;Java;JavaScript;PHP;Python;SQL;VBA          1\n",
+       "SQL                                                       1\n",
+       "C#;HTML/CSS;JavaScript;PHP;SQL;TypeScript                 1\n",
+       "HTML/CSS;JavaScript;PHP;Python;SQL;VBA                    1\n",
+       "C#;HTML/CSS;Java;JavaScript;Objective-C;SQL;TypeScript    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1025,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1026,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1027,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1027,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1028,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWorkedWith\n",
+       "HTML/CSS;JavaScript                                 5\n",
+       "C#;HTML/CSS;JavaScript;SQL                          3\n",
+       "HTML/CSS;JavaScript;PHP;TypeScript                  2\n",
+       "HTML/CSS;JavaScript;PHP;SQL                         2\n",
+       "Bash/Shell/PowerShell;JavaScript;SQL                2\n",
+       "HTML/CSS                                            1\n",
+       "C;C#;HTML/CSS;Java;JavaScript;PHP;Python;SQL;VBA    1\n",
+       "SQL                                                 1\n",
+       "C#;HTML/CSS;JavaScript;PHP;SQL;TypeScript           1\n",
+       "HTML/CSS;JavaScript;PHP;Python;SQL;VBA              1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1028,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CompetenceLevel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1029,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CompetenceLevel\n",
+       "A little above average    30\n",
+       "Average                   21\n",
+       "Far above average         16\n",
+       "A little below average     5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1029,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CompetenceLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1030,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 1030,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CompetenceLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1031,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assign the null values based on forward fill.\n",
+    "survey_df_2019['CompetenceLevel'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1032,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1032,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CompetenceLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1033,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CompetenceLevel\n",
+       "A little above average    38\n",
+       "Average                   24\n",
+       "Far above average         20\n",
+       "A little below average     5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1033,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CompetenceLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Current Job Satisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1034,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CurrentJobSatis\n",
+       "Very satisfied                        31\n",
+       "Slightly satisfied                    19\n",
+       "Slightly dissatisfied                 12\n",
+       "Neither satisfied nor dissatisfied     6\n",
+       "Very dissatisfied                      4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1034,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1035,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 1035,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CurrentJobSatis'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1036,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assign the null values based on forward fill.\n",
+    "survey_df_2019['CurrentJobSatis'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1037,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1037,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CurrentJobSatis'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1038,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CurrentJobSatis\n",
+       "Very satisfied                        39\n",
+       "Slightly satisfied                    25\n",
+       "Slightly dissatisfied                 12\n",
+       "Neither satisfied nor dissatisfied     7\n",
+       "Very dissatisfied                      4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1038,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageDesireNextYear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1039,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageDesireNextYear\n",
+       "HTML/CSS;JavaScript                                                                  3\n",
+       "Go                                                                                   2\n",
+       "C#                                                                                   2\n",
+       "Elixir;HTML/CSS                                                                      1\n",
+       "Elixir;Go;HTML/CSS;Java;JavaScript;Kotlin;Python;R;Scala;TypeScript;WebAssembly      1\n",
+       "Java;Kotlin;Python                                                                   1\n",
+       "SQL                                                                                  1\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript                                                1\n",
+       "C#;HTML/CSS;JavaScript;PHP;Ruby;SQL                                                  1\n",
+       "C#;HTML/CSS;JavaScript;Python;SQL;TypeScript                                         1\n",
+       "C++;C#;Java;SQL                                                                      1\n",
+       "C#;HTML/CSS;R;Rust;TypeScript                                                        1\n",
+       "Python                                                                               1\n",
+       "Kotlin                                                                               1\n",
+       "Bash/Shell/PowerShell;C++;C#;Rust;SQL                                                1\n",
+       "Clojure;Elixir;Java                                                                  1\n",
+       "Bash/Shell/PowerShell;C;C++;C#;HTML/CSS;Java;JavaScript;PHP;Python;Ruby;SQL;Swift    1\n",
+       "Bash/Shell/PowerShell;C++;Go;Kotlin;Python;TypeScript                                1\n",
+       "HTML/CSS;JavaScript;PHP;SQL                                                          1\n",
+       "C#;F#;Java;Kotlin;SQL                                                                1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1039,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1040,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1040,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1041,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assign the null values based on forward fill.\n",
+    "survey_df_2019['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1042,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1042,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1043,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageDesireNextYear\n",
+       "HTML/CSS;JavaScript                                                                  3\n",
+       "Bash/Shell/PowerShell;Clojure;JavaScript;Python;R;TypeScript                         2\n",
+       "Bash/Shell/PowerShell;C#;JavaScript;Objective-C;Ruby;SQL;Swift;TypeScript            2\n",
+       "Go                                                                                   2\n",
+       "C#                                                                                   2\n",
+       "Erlang;Go;Scala;TypeScript                                                           2\n",
+       "HTML/CSS;JavaScript;PHP;Python;Ruby;SQL;WebAssembly                                  2\n",
+       "Kotlin                                                                               1\n",
+       "C#;HTML/CSS;R;Rust;TypeScript                                                        1\n",
+       "Elixir;Go;HTML/CSS;Java;JavaScript;Kotlin;Python;R;Scala;TypeScript;WebAssembly      1\n",
+       "Bash/Shell/PowerShell;C++;C#;Rust;SQL                                                1\n",
+       "Python                                                                               1\n",
+       "Clojure;Elixir;Java                                                                  1\n",
+       "C#;HTML/CSS;JavaScript;Python;SQL;TypeScript                                         1\n",
+       "C#;HTML/CSS;JavaScript;PHP;Ruby;SQL                                                  1\n",
+       "Bash/Shell/PowerShell;C;C++;C#;HTML/CSS;Java;JavaScript;PHP;Python;Ruby;SQL;Swift    1\n",
+       "C#;HTML/CSS;JavaScript;SQL;TypeScript                                                1\n",
+       "Bash/Shell/PowerShell;C++;Go;Kotlin;Python;TypeScript                                1\n",
+       "SQL                                                                                  1\n",
+       "Java;Kotlin;Python                                                                   1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1043,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## YearsCodePro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1044,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method IndexOpsMixin.value_counts of 2                    1\n",
+       "3     Less than 1 year\n",
+       "4                    9\n",
+       "5                    3\n",
+       "6                    4\n",
+       "            ...       \n",
+       "94                   8\n",
+       "95                  19\n",
+       "96                   6\n",
+       "97                 NaN\n",
+       "98                 NaN\n",
+       "Name: YearsCodePro, Length: 87, dtype: object>"
+      ]
+     },
+     "execution_count": 1044,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].value_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1045,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#changing the dtype to float\n",
+    "survey_df_2019['YearsCodePro'] = survey_df_2019['YearsCodePro'].apply(pd.to_numeric, errors='coerce')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1046,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodePro\n",
+       "2.0     16\n",
+       "1.0      8\n",
+       "3.0      8\n",
+       "8.0      6\n",
+       "5.0      4\n",
+       "4.0      3\n",
+       "13.0     3\n",
+       "14.0     3\n",
+       "9.0      3\n",
+       "23.0     3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1046,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1047,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 1047,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1048,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['YearsCodePro'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1049,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1049,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1050,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019.dropna(subset=['YearsCodePro'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1051,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1051,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1052,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YearsCodePro\n",
+       "2.0     18\n",
+       "1.0      9\n",
+       "3.0      9\n",
+       "8.0      7\n",
+       "4.0      6\n",
+       "23.0     5\n",
+       "14.0     5\n",
+       "5.0      5\n",
+       "10.0     4\n",
+       "20.0     3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1052,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['YearsCodePro'].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1053,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "United States     21\n",
+       "India             10\n",
+       "Canada             5\n",
+       "Germany            5\n",
+       "United Kingdom     4\n",
+       "New Zealand        3\n",
+       "Czech Republic     2\n",
+       "Poland             2\n",
+       "Argentina          2\n",
+       "Switzerland        2\n",
+       "Australia          2\n",
+       "Brazil             2\n",
+       "China              2\n",
+       "Ukraine            2\n",
+       "Sri Lanka          2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1053,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Country'].value_counts().nlargest(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1054,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1054,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1055,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['Country'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1056,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1056,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1057,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "United States     21\n",
+       "India             10\n",
+       "Canada             5\n",
+       "Germany            5\n",
+       "United Kingdom     4\n",
+       "New Zealand        3\n",
+       "Czech Republic     2\n",
+       "Poland             2\n",
+       "Argentina          2\n",
+       "Switzerland        2\n",
+       "Australia          2\n",
+       "Brazil             2\n",
+       "China              2\n",
+       "Ukraine            2\n",
+       "Sri Lanka          2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1057,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['Country'].value_counts().nlargest(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SalaryUSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1058,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryUSD\n",
+       "100000.0     2\n",
+       "51150.0      1\n",
+       "2000000.0    1\n",
+       "648.0        1\n",
+       "107000.0     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1058,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1059,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 1059,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1060,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019['SalaryUSD'] = survey_df_2019.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1061,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 1061,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "survey_df_2019['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1062,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryUSD\n",
+       "100000.0     2\n",
+       "47300.0      2\n",
+       "114575.0     1\n",
+       "2000000.0    1\n",
+       "648.0        1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1062,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "survey_df_2019['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1063,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_mean_salary = survey_df_2019.groupby('Country')['SalaryUSD'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1064,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "Canada            366420.000000\n",
+       "United States     246281.578947\n",
+       "United Kingdom    179262.000000\n",
+       "Ireland           114575.000000\n",
+       "New Zealand       102765.500000\n",
+       "France             97389.000000\n",
+       "Netherlands        87994.000000\n",
+       "Sweden             68268.000000\n",
+       "Serbia             60000.000000\n",
+       "Austria            57287.000000\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 1064,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "country_mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1065,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "survey_df_2019.dropna(subset=['SalaryUSD'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaned Dataset:2019_Survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1066,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                       0\n",
+       "JobSatisfaction           0\n",
+       "SalaryUSD                 0\n",
+       "Country                   0\n",
+       "Dependents                0\n",
+       "EdLevel                   0\n",
+       "Employment                0\n",
+       "Ethnicity                 1\n",
+       "Gender                    0\n",
+       "Hobbyist                  0\n",
+       "CompetenceLevel           0\n",
+       "CurrentJobSatis           0\n",
+       "JobStatus                 0\n",
+       "LanguageDesireNextYear    0\n",
+       "LanguageWorkedWith        0\n",
+       "Profession                0\n",
+       "UndergradMajor            0\n",
+       "YearsCodePro              0\n",
+       "DevType                   0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 1066,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#handle all the null value\n",
+    "survey_df_2019.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1067,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#resetting the index values\n",
+    "survey_df_2019 = survey_df_2019.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1068,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows before cleaning the data is 99\n",
+      "Number of rows after cleaning the data is 62\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_df_2019 = survey_df_2019[survey_df_2019.notnull()]\n",
+    "\n",
+    "print(f\"Number of rows before cleaning the data is {survey_main_df.shape[0]}\")\n",
+    "print(f\"Number of rows after cleaning the data is {cleaned_df_2019.shape[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1069,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_df_2019['Age']=cleaned_df_2019['Age'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1070,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>JobSatisfaction</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>CompetenceLevel</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "      <th>JobStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>DevType</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>8820.0</td>\n",
+       "      <td>Thailand</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>Elixir;HTML/CSS</td>\n",
+       "      <td>HTML/CSS</td>\n",
+       "      <td>Non developer</td>\n",
+       "      <td>Web Design/Dev</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>61000.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>No</td>\n",
+       "      <td>A little below average</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>C;C#;JavaScript;SQL</td>\n",
+       "      <td>C;C++;C#;Python;SQL</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Developer, full-stack</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>366420.0</td>\n",
+       "      <td>Canada</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>East Asian</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>A little above average</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Python;Scala;SQL</td>\n",
+       "      <td>Java;R;SQL</td>\n",
+       "      <td>Non developer</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Data or business analyst;Data scientist or mac...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>95179.0</td>\n",
+       "      <td>New Zealand</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>A little above average</td>\n",
+       "      <td>Slightly satisfied</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>Bash/Shell/PowerShell;C;HTML/CSS;JavaScript;Ru...</td>\n",
+       "      <td>Bash/Shell/PowerShell;C#;HTML/CSS;JavaScript;P...</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Database administrator;Developer, back-end;Dev...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>90000.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>A little above average</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>Not seeking</td>\n",
+       "      <td>Bash/Shell/PowerShell;HTML/CSS;JavaScript;Rust...</td>\n",
+       "      <td>Bash/Shell/PowerShell;HTML/CSS;JavaScript;PHP;...</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>Data or business analyst;Database administrato...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age     JobSatisfaction  SalaryUSD        Country Dependents    EdLevel  \\\n",
+       "0   28  Slightly satisfied     8820.0       Thailand        Yes  Bachelors   \n",
+       "1   22      Very satisfied    61000.0  United States         No  Bachelors   \n",
+       "2   28      Very satisfied   366420.0         Canada         No  Bachelors   \n",
+       "3   23  Slightly satisfied    95179.0    New Zealand         No  No Degree   \n",
+       "4   28      Very satisfied    90000.0  United States        Yes  Bachelors   \n",
+       "\n",
+       "  Employment                     Ethnicity Gender Hobbyist  \\\n",
+       "0  Full-time                           NaN    Man      Yes   \n",
+       "1  Full-time  White or of European descent    Man       No   \n",
+       "2  Full-time                    East Asian    Man      Yes   \n",
+       "3  Full-time  White or of European descent    Man      Yes   \n",
+       "4  Full-time  White or of European descent    Man      Yes   \n",
+       "\n",
+       "          CompetenceLevel     CurrentJobSatis    JobStatus  \\\n",
+       "0                 Average  Slightly satisfied          nan   \n",
+       "1  A little below average  Slightly satisfied  Not seeking   \n",
+       "2  A little above average  Slightly satisfied  Not seeking   \n",
+       "3  A little above average  Slightly satisfied          nan   \n",
+       "4  A little above average      Very satisfied  Not seeking   \n",
+       "\n",
+       "                              LanguageDesireNextYear  \\\n",
+       "0                                    Elixir;HTML/CSS   \n",
+       "1                                C;C#;JavaScript;SQL   \n",
+       "2                                   Python;Scala;SQL   \n",
+       "3  Bash/Shell/PowerShell;C;HTML/CSS;JavaScript;Ru...   \n",
+       "4  Bash/Shell/PowerShell;HTML/CSS;JavaScript;Rust...   \n",
+       "\n",
+       "                                  LanguageWorkedWith     Profession  \\\n",
+       "0                                           HTML/CSS  Non developer   \n",
+       "1                                C;C++;C#;Python;SQL      Developer   \n",
+       "2                                         Java;R;SQL  Non developer   \n",
+       "3  Bash/Shell/PowerShell;C#;HTML/CSS;JavaScript;P...      Developer   \n",
+       "4  Bash/Shell/PowerShell;HTML/CSS;JavaScript;PHP;...      Developer   \n",
+       "\n",
+       "     UndergradMajor  YearsCodePro  \\\n",
+       "0    Web Design/Dev           1.0   \n",
+       "1  Computer Science           1.0   \n",
+       "2         Math/Stat           3.0   \n",
+       "3  Computer Science           4.0   \n",
+       "4  Computer Science           8.0   \n",
+       "\n",
+       "                                             DevType  \n",
+       "0  Designer;Developer, back-end;Developer, front-...  \n",
+       "1                              Developer, full-stack  \n",
+       "2  Data or business analyst;Data scientist or mac...  \n",
+       "3  Database administrator;Developer, back-end;Dev...  \n",
+       "4  Data or business analyst;Database administrato...  "
+      ]
+     },
+     "execution_count": 1070,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "cleaned_df_2019.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## After Cleaning  Dataset 2019"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1071,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  1178\n",
+      "Total missing :  1\n",
+      "Missing Percentage:  0.08488964346349745 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = survey_df_2019.isnull().sum() #number of missing\n",
+    "total_cells = np.product(survey_df_2019.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stackoverflow Survey Analysis 2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1072,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r\"D:\\project\\Stackoverflow-Analysis\\Data\\survey_results_sample_2020.csv\")\n",
+    "#df2020.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1073,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#drop unnecessary columns\n",
+    "drop_cols = [  'Age1stCode', 'CompFreq', 'CompTotal', 'CurrencyDesc', 'CurrencySymbol', 'NEWJobHunt','NEWJobHuntResearch', 'NEWLearn', \n",
+    "             'NEWOffTopic', 'NEWOnboardGood', 'NEWOtherComms', 'NEWOvertime', 'NEWPurchaseResearch', \n",
+    "             'NEWPurpleLink', 'NEWSOSites', 'NEWStuck', 'OpSys', 'OrgSize', 'PlatformDesireNextYear', 'PlatformWorkedWith',\n",
+    "             'PurchaseWhat', 'Respondent', 'SOAccount', 'SOComm', 'SOPartFreq', 'SOVisitFreq', 'Sexuality', 'SurveyEase', \n",
+    "             'SurveyLength', 'Trans', 'WebframeDesireNextYear', 'WebframeWorkedWith', 'WelcomeChange', 'WorkWeekHrs', 'YearsCode']\n",
+    "df.drop(drop_cols, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1074,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Selecting only the required columns for analysis\n",
+    "cols =['Age','Gender', 'ConvertedComp', 'Country', 'DevType', 'Hobbyist', 'EdLevel', 'Employment', \n",
+    "       'Ethnicity', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n",
+    "        'UndergradMajor', 'YearsCodePro']\n",
+    "df2020 = df[cols]\n",
+    "#df2020.head()\n",
+    "#df2020.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1075,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#changing the name of columns for easier understanding\n",
+    "# 'MainBranch': 'Profession'\n",
+    "# 'ConvertedComp': 'SalaryUSD'\n",
+    "# 'JobSat' : 'CurrentJobSatis'\n",
+    "# 'JobSeek' : 'JobStatus'\n",
+    "\n",
+    "df2020.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD', \n",
+    "                                'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, \n",
+    "                      inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1076,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                       24\n",
+      "Gender                    11\n",
+      "SalaryUSD                 46\n",
+      "Country                    0\n",
+      "DevType                   20\n",
+      "Hobbyist                   0\n",
+      "EdLevel                    6\n",
+      "Employment                 2\n",
+      "Ethnicity                 17\n",
+      "CurrentJobSatis           21\n",
+      "JobStatus                  4\n",
+      "LanguageDesireNextYear     9\n",
+      "LanguageWorkedWith         1\n",
+      "Profession                 0\n",
+      "UndergradMajor            13\n",
+      "YearsCodePro              21\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2020.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Validation - Total Cells vs Missing %"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1077,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total cell:  1584\n",
+      "Total missing values:  446\n",
+      "Missing:  28.156565656565657 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Finding % of missing data\n",
+    "missing_count = df.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total cell: ', total_cells)\n",
+    "print('Total missing values: ', total_missing)\n",
+    "print('Missing: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1078,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 1078,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Gender'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1079,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man                                                      79\n",
+       "Man;Non-binary, genderqueer, or gender non-conforming     1\n",
+       "Woman                                                     8\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 1079,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Counting number of each gender\n",
+    "df2020.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1080,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n",
+    "df2020['Gender'] = df['Gender'].fillna('Non-binary') \n",
+    "\n",
+    "#Grouping genders into 3 groups Man, Womanand Non-binary\n",
+    "df2020['Gender'].replace('Man;Non-binary, genderqueer, or gender non-conforming', 'Man', inplace =True)\n",
+    "df2020['Gender'].replace('Woman;Non-binary, genderqueer, or gender non-conforming', 'Woman', inplace =True)\n",
+    "df2020['Gender'].replace('Woman;Man;Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)\n",
+    "df2020['Gender'].replace('Woman;Man', 'Non-binary', inplace =True)\n",
+    "df2020['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1081,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man           80\n",
+       "Non-binary    11\n",
+       "Woman          8\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 1081,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Counting number of each gender after\n",
+    "df2020.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1082,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df shape after clean Gender:  (99, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print('df shape after clean Gender: ', df2020.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1083,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 1083,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Age'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1084,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plottig boxplot to check outliers\n",
+    "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n",
+    "plt.title(\"Before cleaning Age's outliers from genders\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1085,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Cleaning Age's outliers from each gender)\n",
+    "df2020 = df2020[(df['Age'] >= 15) & (df2020['Age'] <= 60)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1086,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plottig boxplot to check outliers after cleaning some outliers\n",
+    "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n",
+    "plt.title(\"After cleaning Age's outliers from genders\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1087,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fill Age's null values with mean of each gender\n",
+    "means = df2020.groupby('Gender')['Age'].transform('mean')\n",
+    "df2020['Age'] = df2020['Age'].fillna(means)\n",
+    "\n",
+    "#convert from float to int\n",
+    "df2020['Age'] = df2020['Age'].apply(str).str[:2]\n",
+    "df2020['Age'] = df2020['Age'].apply(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1088,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df shape after clean Age:  (75, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#df before 64461\n",
+    "print('df shape after clean Age: ', df2020.shape) #no. of Ages' outliners = 64461-44709=19752 (30.6%)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EdLevel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1089,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 1089,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['EdLevel'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1090,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EdLevel\n",
+       "Bachelor’s degree (B.A., B.S., B.Eng., etc.)                                        34\n",
+       "Some college/university study without earning a degree                                11\n",
+       "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)                                     10\n",
+       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     7\n",
+       "Associate degree (A.A., A.S., etc.)                                                    5\n",
+       "Professional degree (JD, MD, etc.)                                                     3\n",
+       "Other doctoral degree (Ph.D., Ed.D., etc.)                                             2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1090,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1091,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring EdLevel\n",
+    "def refactor_ed(df):\n",
+    "    '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n",
+    "    conditions_ed = [(df['EdLevel'] == 'Associate degree (A.A., A.S., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Bachelor’s degree (B.A., B.S., B.Eng., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)'),\n",
+    "                    (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'),   \n",
+    "                    (df['EdLevel'] == 'Other doctoral degree (Ph.D., Ed.D., etc.)'),\n",
+    "                    (df['EdLevel'] == 'Some college/university study without earning a degree') \n",
+    "                    | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n",
+    "                    | (df['EdLevel'] == 'Primary/elementary school')\n",
+    "                    | (df['EdLevel'] == 'I never completed any formal education')]\n",
+    "    \n",
+    "    choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional',  'Doctorate', 'No Degree']\n",
+    "    df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n",
+    "    return df\n",
+    "\n",
+    "# applying function to subsets\n",
+    "df2020 = refactor_ed(df2020)\n",
+    "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n",
+    "df2020['EdLevel'].replace('nan', 'Bachelors', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1092,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EdLevel\n",
+       "Bachelors       47\n",
+       "No Degree       18\n",
+       "Associate        5\n",
+       "Professional     3\n",
+       "Doctorate        2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1092,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['EdLevel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JobSat (CurrentJobSatis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1093,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 1093,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1094,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CurrentJobSatis\n",
+       "Very satisfied                        27\n",
+       "Slightly satisfied                    12\n",
+       "Slightly dissatisfied                  9\n",
+       "Very dissatisfied                      7\n",
+       "Neither satisfied nor dissatisfied     5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1094,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1095,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020['CurrentJobSatis'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1096,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CurrentJobSatis\n",
+       "Very satisfied                        32\n",
+       "Slightly satisfied                    13\n",
+       "Slightly dissatisfied                 12\n",
+       "Neither satisfied nor dissatisfied    10\n",
+       "Very dissatisfied                      8\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1096,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JobSeek (JobStatus)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1097,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 1097,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['JobStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1098,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobStatus\n",
+       "I am actively looking for a job                                   12\n",
+       "I am not interested in new job opportunities                      23\n",
+       "I’m not actively looking, but I am open to new opportunities    38\n",
+       "Name: JobStatus, dtype: int64"
+      ]
+     },
+     "execution_count": 1098,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('JobStatus')['JobStatus'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1099,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020['JobStatus'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring JobStatus\n",
+    "#Changing the jobstatus to seeking and non seeking\n",
+    "def refactor_job(df):\n",
+    "    '''function to change JobStatus category to Seeking and Non Seeking'''\n",
+    "    \n",
+    "    conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n",
+    "                    (df['JobStatus'] == 'I am not interested in new job opportunities')\n",
+    "                    | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n",
+    "    \n",
+    "    choices_job = ['Seeking', 'Not seeking']\n",
+    "    df['JobSeek'] = np.select(conditions_job, choices_job, default=np.nan)                      \n",
+    "    return df\n",
+    "\n",
+    "df2020 = refactor_job(df2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "JobSeek\n",
+       "Not seeking    24\n",
+       "Seeking        12\n",
+       "nan            39\n",
+       "Name: JobSeek, dtype: int64"
+      ]
+     },
+     "execution_count": 1101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('JobSeek')['JobSeek'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['JobStatus'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DevType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 1103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Developer, full-stack                                                                                                                                                                                                                                                                        9\n",
+       "Developer, back-end                                                                                                                                                                                                                                                                          5\n",
+       "Designer;Developer, front-end                                                                                                                                                                                                                                                                2\n",
+       "Developer, back-end;Developer, front-end;Developer, full-stack                                                                                                                                                                                                                               2\n",
+       "Developer, back-end;Developer, desktop or enterprise applications;Developer, full-stack                                                                                                                                                                                                      2\n",
+       "Academic researcher;Database administrator;Developer, back-end;Developer, desktop or enterprise applications;Developer, embedded applications or devices;Developer, front-end;Developer, full-stack;Developer, game or graphics;Developer, mobile;Educator;Scientist;System administrator    1\n",
+       "Database administrator;Developer, back-end;Developer, desktop or enterprise applications;Developer, embedded applications or devices;Developer, front-end;Developer, full-stack;Developer, game or graphics;DevOps specialist;Educator                                                       1\n",
+       "Data or business analyst;Developer, back-end;Developer, front-end;Developer, full-stack;Developer, QA or test                                                                                                                                                                                1\n",
+       "Designer;Developer, back-end;Developer, front-end;Developer, mobile;Developer, QA or test                                                                                                                                                                                                    1\n",
+       "Developer, back-end;Developer, full-stack;Educator                                                                                                                                                                                                                                           1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['DevType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020['DevType'] = df2020['DevType'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DevType\n",
+       "Developer, full-stack                                                                                                                 12\n",
+       "Developer, back-end                                                                                                                    5\n",
+       "Developer, back-end;Developer, desktop or enterprise applications;Developer, game or graphics                                          3\n",
+       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack;Developer, QA or test     2\n",
+       "Developer, back-end;Developer, front-end;Developer, full-stack                                                                         2\n",
+       "Developer, full-stack;Product manager;Senior executive/VP                                                                              2\n",
+       "Data scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, full-stack;Engineer, data          2\n",
+       "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack;DevOps specialist         2\n",
+       "Developer, back-end;Developer, full-stack;Developer, mobile                                                                            2\n",
+       "Developer, back-end;Developer, desktop or enterprise applications;Developer, full-stack                                                2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['DevType'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99, 26)"
+      ]
+     },
+     "execution_count": 1107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['DevType'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "      <th>JobStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>JobSeek</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Age, Gender, SalaryUSD, Country, DevType, Hobbyist, EdLevel, Employment, Ethnicity, CurrentJobSatis, JobStatus, LanguageDesireNextYear, LanguageWorkedWith, Profession, UndergradMajor, YearsCodePro, JobSeek]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 1109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020[df2020['DevType'].isnull()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ethnicity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 1110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ethnicity\n",
+       "White or of European descent                                                                                     56\n",
+       "Hispanic or Latino/a/x                                                                                            5\n",
+       "East Asian                                                                                                        2\n",
+       "Middle Eastern                                                                                                    1\n",
+       "White or of European descent;Indigenous (such as Native American, Pacific Islander, or Indigenous Australian)     1\n",
+       "South Asian                                                                                                       1\n",
+       "Indigenous (such as Native American, Pacific Islander, or Indigenous Australian)                                  1\n",
+       "Hispanic or Latino/a/x;White or of European descent                                                               1\n",
+       "Multiracial                                                                                                       1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#count number of each Ethnicity\n",
+    "df2020.groupby('Ethnicity')['Ethnicity'].count()\n",
+    "df2020['Ethnicity'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n",
+    "df2020.loc[df['Ethnicity'].str.match('Biracial') == True, 'Ethnicity'] = 'Biracial'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Black or of African descent') == True, 'Ethnicity'] = 'Black or of African descent'\n",
+    "df2020.loc[df['Ethnicity'].str.match('East Asian') == True, 'Ethnicity'] = 'East Asian'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Hispanic or Latino') == True, 'Ethnicity'] = 'Hispanic or Latino'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Indigenous') == True, 'Ethnicity'] = 'Indigenous'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Middle Eastern') == True, 'Ethnicity'] = 'Middle Eastern'\n",
+    "df2020.loc[df['Ethnicity'].str.match('South Asian') == True, 'Ethnicity'] = 'South Asian'\n",
+    "df2020.loc[df['Ethnicity'].str.match('White or of European descent') == True, 'Ethnicity'] = 'White or of European descent'\n",
+    "df2020.loc[df['Ethnicity'].str.match('Multiracial') == True, 'Ethnicity'] = 'Multiracial'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ethnicity\n",
+       "White or of European descent    57\n",
+       "Hispanic or Latino               6\n",
+       "East Asian                       2\n",
+       "Middle Eastern                   1\n",
+       "South Asian                      1\n",
+       "Indigenous                       1\n",
+       "Multiracial                      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020.groupby('Ethnicity')['Ethnicity'].count() #11 groups of Ethnicity after combining \n",
+    "df2020['Ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df2020['Ethnicity']=df2020.groupby(['Country'])['Ethnicity'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ethnicity\n",
+       "White or of European descent    63\n",
+       "Hispanic or Latino               6\n",
+       "East Asian                       2\n",
+       "Middle Eastern                   1\n",
+       "South Asian                      1\n",
+       "Indigenous                       1\n",
+       "Multiracial                      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#count number of each Ethnicity\n",
+    "df2020.groupby('Ethnicity')['Ethnicity'].count()\n",
+    "df2020['Ethnicity'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                        0\n",
+      "Gender                     0\n",
+      "SalaryUSD                 28\n",
+      "Country                    0\n",
+      "DevType                    0\n",
+      "Hobbyist                   0\n",
+      "EdLevel                    0\n",
+      "Employment                 1\n",
+      "Ethnicity                  0\n",
+      "CurrentJobSatis            0\n",
+      "JobStatus                  0\n",
+      "LanguageDesireNextYear     5\n",
+      "LanguageWorkedWith         1\n",
+      "Profession                 0\n",
+      "UndergradMajor             9\n",
+      "YearsCodePro              14\n",
+      "JobSeek                    0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(df2020.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageDesireNextYear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 1118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageDesireNextYear\n",
+       "Python;TypeScript                                                                        2\n",
+       "C#                                                                                       2\n",
+       "HTML/CSS;Java;JavaScript;Python;R;SQL                                                    2\n",
+       "C++;Python                                                                               2\n",
+       "Go;Kotlin;TypeScript                                                                     2\n",
+       "Go                                                                                       2\n",
+       "Assembly;Bash/Shell/PowerShell;C;C#;C++;Go;Haskell;Java;JavaScript;Kotlin;Python;Rust    1\n",
+       "Kotlin;Python;Swift                                                                      1\n",
+       "Julia;Python;Rust;Swift                                                                  1\n",
+       "HTML/CSS;JavaScript;PHP;TypeScript                                                       1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2020['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)\n",
+    "df2020['LanguageDesireNextYear']=df2020['LanguageDesireNextYear'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageDesireNextYear\n",
+       "Java;Ruby;Scala                                2\n",
+       "JavaScript;Swift                               2\n",
+       "Python;TypeScript                              2\n",
+       "Java;Kotlin                                    2\n",
+       "Go;Kotlin;TypeScript                           2\n",
+       "Julia;Python;Rust;Swift                        2\n",
+       "Bash/Shell/PowerShell;C#;Dart;Go;TypeScript    2\n",
+       "C++;Python                                     2\n",
+       "C#                                             2\n",
+       "Go                                             2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageDesireNextYear'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LanguageWorkedWith"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 1123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWorkedWith\n",
+       "HTML/CSS;JavaScript;PHP;SQL                                                      5\n",
+       "C#;HTML/CSS;JavaScript;SQL                                                       2\n",
+       "Bash/Shell/PowerShell;C#;C++                                                     2\n",
+       "Assembly;Bash/Shell/PowerShell;C;C#;HTML/CSS;Java;JavaScript;PHP;Python          1\n",
+       "Assembly;Bash/Shell/PowerShell;C;C++;Python                                      1\n",
+       "Assembly;Bash/Shell/PowerShell;C;C#;C++;Go;HTML/CSS;JavaScript;SQL;TypeScript    1\n",
+       "Bash/Shell/PowerShell;JavaScript;Python;Swift                                    1\n",
+       "C#;HTML/CSS;Java;JavaScript;PHP;Ruby;TypeScript                                  1\n",
+       "HTML/CSS;Python                                                                  1\n",
+       "HTML/CSS;JavaScript;Ruby                                                         1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2020['LanguageWorkedWith'].fillna(method='ffill', inplace=True)\n",
+    "df2020['LanguageWorkedWith']=df2020['LanguageWorkedWith'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanguageWorkedWith\n",
+       "HTML/CSS;JavaScript;PHP;SQL                    5\n",
+       "HTML/CSS;Ruby;SQL                              2\n",
+       "Bash/Shell/PowerShell;C#;C++                   2\n",
+       "C#;HTML/CSS;JavaScript;SQL                     2\n",
+       "Bash/Shell/PowerShell;HTML/CSS;JavaScript      1\n",
+       "Assembly;Bash/Shell/PowerShell;C;C++;Python    1\n",
+       "C#;HTML/CSS;JavaScript;PHP;Ruby                1\n",
+       "C#;C++;HTML/CSS;Java;Python;Ruby;SQL;Swift     1\n",
+       "C++;Python                                     1\n",
+       "C;C++;HTML/CSS;Java;JavaScript;Python          1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageWorkedWith'].value_counts().nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['LanguageWorkedWith'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MainBranch (Profession)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Profession'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Profession\n",
+       "I am a developer by profession                                                   59\n",
+       "I am a student who is learning to code                                           10\n",
+       "I am not primarily a developer, but I write code sometimes as part of my work     4\n",
+       "I code primarily as a hobby                                                       1\n",
+       "I used to be a developer by profession, but no longer am                          1\n",
+       "Name: Profession, dtype: int64"
+      ]
+     },
+     "execution_count": 1129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('Profession')['Profession'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020.dropna(subset=['Profession'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Lets refactor column values of Profession column\n",
+    "#refactoring profession column\n",
+    "\n",
+    "def refactor_prof(df):\n",
+    "    '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n",
+    "    conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n",
+    "                       (df['Profession'] == 'I am a student who is learning to code'),\n",
+    "                       (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n",
+    "                       (df['Profession'] == 'I code primarily as a hobby'),\n",
+    "                       (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n",
+    "    \n",
+    "    choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n",
+    "    df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)          \n",
+    "    return df\n",
+    "\n",
+    "df2020 = refactor_prof(df2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Profession\n",
+       "Developer        59\n",
+       "Student          10\n",
+       "Non developer     4\n",
+       "Ex-Developer      1\n",
+       "Novoice           1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Profession'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Profession'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## UndergradMajor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 1134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "A health science (such as nursing, pharmacy, radiology, etc.)                      1\n",
+       "A humanities discipline (such as literature, history, philosophy, etc.)            1\n",
+       "A natural science (such as biology, chemistry, physics, etc.)                      3\n",
+       "A social science (such as anthropology, psychology, political science, etc.)       1\n",
+       "Another engineering discipline (such as civil, electrical, mechanical, etc.)       5\n",
+       "Computer science, computer engineering, or software engineering                   43\n",
+       "Fine arts or performing arts (such as graphic design, music, studio art, etc.)     2\n",
+       "I never declared a major                                                           1\n",
+       "Information systems, information technology, or system administration              3\n",
+       "Mathematics or statistics                                                          3\n",
+       "Web development or web design                                                      3\n",
+       "Name: UndergradMajor, dtype: int64"
+      ]
+     },
+     "execution_count": 1135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('UndergradMajor')['UndergradMajor'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def refactor_major(df):\n",
+    "    conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n",
+    "                        (df['UndergradMajor'] == 'Another engineering discipline (such as civil, electrical, mechanical, etc.)'),\n",
+    "                        (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n",
+    "                        (df['UndergradMajor'] == 'Mathematics or statistics'),\n",
+    "                        (df['UndergradMajor'] == 'A natural science (such as biology, chemistry, physics, etc.)') \n",
+    "                        |(df['UndergradMajor'] == 'A health science (such as nursing, pharmacy, radiology, etc.)'), \n",
+    "                        (df['UndergradMajor'] == 'Web development or web design'), \n",
+    "                        (df['UndergradMajor'] == 'A business discipline (such as accounting, finance, marketing, etc.)'), \n",
+    "                        (df['UndergradMajor'] == 'A humanities discipline (such as literature, history, philosophy, etc.)')\n",
+    "                        | (df['UndergradMajor'] == 'A social science (such as anthropology, psychology, political science, etc.)')\n",
+    "                        | (df['UndergradMajor'] == 'Fine arts or performing arts (such as graphic design, music, studio art, etc.)'),\n",
+    "                        (df['UndergradMajor'] == 'I never declared a major') ]\n",
+    "    \n",
+    "    choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n",
+    "                     'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n",
+    "    df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n",
+    "    return df\n",
+    "\n",
+    "df2020 = refactor_major(df2020)\n",
+    "df2020['UndergradMajor'].replace('nan', 'No major', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "UndergradMajor\n",
+       "Arts and Science     4\n",
+       "Computer Science    43\n",
+       "Engineering          5\n",
+       "Info Systems         3\n",
+       "Math/Stat            3\n",
+       "No major            10\n",
+       "Other Science        4\n",
+       "Web Design/Dev       3\n",
+       "Name: UndergradMajor, dtype: int64"
+      ]
+     },
+     "execution_count": 1137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('UndergradMajor')['UndergradMajor'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['UndergradMajor'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Employment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 1139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Employed full-time                                      52\n",
+       "Employed part-time                                       2\n",
+       "Independent contractor, freelancer, or self-employed     6\n",
+       "Not employed, but looking for work                       4\n",
+       "Student                                                 10\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 1140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020.groupby('Employment')['Employment'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020.dropna(subset=['Employment'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Refactoring Employment\n",
+    "df2020['Employment'].replace('Employed full-time', 'Full-time', inplace =True)\n",
+    "df2020['Employment'].replace('Employed part-time', 'Part-time',inplace =True)\n",
+    "df2020['Employment'].replace('Independent contractor, freelancer, or self-employed', 'Self-employed', inplace =True)\n",
+    "df2020['Employment'].replace('Not employed, but looking for work', 'Not employed', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employment\n",
+       "Full-time        52\n",
+       "Not employed      4\n",
+       "Part-time         2\n",
+       "Self-employed     6\n",
+       "Student          10\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 1143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('Employment')['Employment'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Employment'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Country'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country\n",
+       "Austria            1\n",
+       "Belgium            2\n",
+       "Brazil             2\n",
+       "Canada             8\n",
+       "Czech Republic     1\n",
+       "France             7\n",
+       "Germany            3\n",
+       "Greece             1\n",
+       "India              1\n",
+       "Indonesia          1\n",
+       "Israel             1\n",
+       "Italy              1\n",
+       "Mexico             2\n",
+       "Netherlands        1\n",
+       "Poland             1\n",
+       "Spain              2\n",
+       "Tunisia            1\n",
+       "Ukraine            1\n",
+       "United Kingdom    10\n",
+       "United States     27\n",
+       "Name: Country, dtype: int64"
+      ]
+     },
+     "execution_count": 1146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020.groupby('Country')['Country'].count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## YearsCodePro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 1147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age                         int64\n",
+       "Gender                     object\n",
+       "SalaryUSD                 float64\n",
+       "Country                    object\n",
+       "DevType                    object\n",
+       "Hobbyist                   object\n",
+       "EdLevel                    object\n",
+       "Employment                 object\n",
+       "Ethnicity                  object\n",
+       "CurrentJobSatis            object\n",
+       "JobStatus                  object\n",
+       "LanguageDesireNextYear     object\n",
+       "LanguageWorkedWith         object\n",
+       "Profession                 object\n",
+       "UndergradMajor             object\n",
+       "YearsCodePro               object\n",
+       "JobSeek                    object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 1148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#convert YearsCodePro data type from obj to int\n",
+    "df2020[\"YearsCodePro\"]=pd.to_numeric(df2020[\"YearsCodePro\"],errors='coerce')\n",
+    "\n",
+    "#fill YearsCodePro's null values with mean\n",
+    "means = df2020['YearsCodePro'].mean() #means 8.673142457693764\n",
+    "df2020['YearsCodePro'] = df2020['YearsCodePro'].fillna(means)\n",
+    "df2020['YearsCodePro'] = df2020['YearsCodePro'].round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['YearsCodePro'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hobbyist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['Hobbyist'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Hobbyist\n",
+       "No     17\n",
+       "Yes    57\n",
+       "Name: Hobbyist, dtype: int64"
+      ]
+     },
+     "execution_count": 1152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.groupby('Hobbyist')['Hobbyist'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1153,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                        0\n",
+      "Gender                     0\n",
+      "SalaryUSD                 27\n",
+      "Country                    0\n",
+      "DevType                    0\n",
+      "Hobbyist                   0\n",
+      "EdLevel                    0\n",
+      "Employment                 0\n",
+      "Ethnicity                  0\n",
+      "CurrentJobSatis            0\n",
+      "JobStatus                  0\n",
+      "LanguageDesireNextYear     0\n",
+      "LanguageWorkedWith         0\n",
+      "Profession                 0\n",
+      "UndergradMajor             0\n",
+      "YearsCodePro               0\n",
+      "JobSeek                    0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2020.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ConvertedComp (SalaryUSD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 1154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryUSD\n",
+       "130000.0    2\n",
+       "37816.0     2\n",
+       "116000.0    1\n",
+       "94500.0     1\n",
+       "16488.0     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df2020['SalaryUSD'] = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))\n",
+    "\n",
+    "means = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform('mean')\n",
+    "df2020['SalaryUSD'] = df2020['SalaryUSD'].fillna(means)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age  EdLevel       Country       \n",
+       "34   Bachelors     United States     1.176000e+06\n",
+       "44   Bachelors     United States     8.900000e+05\n",
+       "25   Bachelors     France            5.945390e+05\n",
+       "32   Bachelors     United States     4.948333e+05\n",
+       "35   Professional  United States     2.500000e+05\n",
+       "28   Bachelors     United States     1.300000e+05\n",
+       "38   Bachelors     United States     1.250000e+05\n",
+       "34   Bachelors     United Kingdom    1.240920e+05\n",
+       "48   Associate     United States     1.170000e+05\n",
+       "36   Bachelors     United States     1.160000e+05\n",
+       "Name: SalaryUSD, dtype: float64"
+      ]
+     },
+     "execution_count": 1157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n",
+    "mean_salary.nlargest(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SalaryUSD\n",
+       "79000.0     3\n",
+       "37816.0     3\n",
+       "130000.0    2\n",
+       "116000.0    1\n",
+       "117000.0    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['SalaryUSD'].value_counts().nlargest()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 1159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df2020['SalaryUSD'].isnull().sum() #2952 out of 64461 -> 4.6%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1160,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2020.dropna(subset=['SalaryUSD'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['SalaryUSD'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaned Dataset:2020_Survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1162,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Age                       0\n",
+      "Gender                    0\n",
+      "SalaryUSD                 0\n",
+      "Country                   0\n",
+      "DevType                   0\n",
+      "Hobbyist                  0\n",
+      "EdLevel                   0\n",
+      "Employment                0\n",
+      "Ethnicity                 0\n",
+      "CurrentJobSatis           0\n",
+      "JobStatus                 0\n",
+      "LanguageDesireNextYear    0\n",
+      "LanguageWorkedWith        0\n",
+      "Profession                0\n",
+      "UndergradMajor            0\n",
+      "YearsCodePro              0\n",
+      "JobSeek                   0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2020.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1163,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#resetting the index values\n",
+    "df2020 = df2020.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Ethnicity</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "      <th>JobStatus</th>\n",
+       "      <th>LanguageDesireNextYear</th>\n",
+       "      <th>LanguageWorkedWith</th>\n",
+       "      <th>Profession</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>JobSeek</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>36</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>116000.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Slightly dissatisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to n...</td>\n",
+       "      <td>JavaScript</td>\n",
+       "      <td>Python;SQL</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>32315.0</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Database administrator;Developer, full-stack;D...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to n...</td>\n",
+       "      <td>HTML/CSS;Java;JavaScript;Python;R;SQL</td>\n",
+       "      <td>HTML/CSS;Java;JavaScript;Python;SQL</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>40070.0</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Developer, back-end;Developer, desktop or ente...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Slightly dissatisfied</td>\n",
+       "      <td>I am actively looking for a job</td>\n",
+       "      <td>Go;JavaScript;Swift;TypeScript</td>\n",
+       "      <td>C#;JavaScript;Swift</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Seeking</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>49</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>14268.0</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Designer;Developer, front-end</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Very dissatisfied</td>\n",
+       "      <td>I’m not actively looking, but I am open to n...</td>\n",
+       "      <td>HTML/CSS;JavaScript</td>\n",
+       "      <td>HTML/CSS;JavaScript</td>\n",
+       "      <td>Developer</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>53</td>\n",
+       "      <td>Man</td>\n",
+       "      <td>38916.0</td>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>Designer;Developer, back-end</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>Very satisfied</td>\n",
+       "      <td>I am not interested in new job opportunities</td>\n",
+       "      <td>Python</td>\n",
+       "      <td>C;JavaScript;Python</td>\n",
+       "      <td>Non developer</td>\n",
+       "      <td>No major</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Not seeking</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age Gender  SalaryUSD         Country  \\\n",
+       "0   36    Man   116000.0   United States   \n",
+       "1   22    Man    32315.0  United Kingdom   \n",
+       "2   23    Man    40070.0  United Kingdom   \n",
+       "3   49    Man    14268.0           Spain   \n",
+       "4   53    Man    38916.0     Netherlands   \n",
+       "\n",
+       "                                             DevType Hobbyist    EdLevel  \\\n",
+       "0  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
+       "1  Database administrator;Developer, full-stack;D...      Yes  Bachelors   \n",
+       "2  Developer, back-end;Developer, desktop or ente...      Yes  Bachelors   \n",
+       "3                      Designer;Developer, front-end       No  No Degree   \n",
+       "4                       Designer;Developer, back-end      Yes  No Degree   \n",
+       "\n",
+       "  Employment                     Ethnicity        CurrentJobSatis  \\\n",
+       "0  Full-time  White or of European descent  Slightly dissatisfied   \n",
+       "1  Full-time  White or of European descent         Very satisfied   \n",
+       "2  Full-time  White or of European descent  Slightly dissatisfied   \n",
+       "3  Full-time  White or of European descent      Very dissatisfied   \n",
+       "4  Full-time  White or of European descent         Very satisfied   \n",
+       "\n",
+       "                                           JobStatus  \\\n",
+       "0  I’m not actively looking, but I am open to n...   \n",
+       "1  I’m not actively looking, but I am open to n...   \n",
+       "2                    I am actively looking for a job   \n",
+       "3  I’m not actively looking, but I am open to n...   \n",
+       "4       I am not interested in new job opportunities   \n",
+       "\n",
+       "                  LanguageDesireNextYear                   LanguageWorkedWith  \\\n",
+       "0                             JavaScript                           Python;SQL   \n",
+       "1  HTML/CSS;Java;JavaScript;Python;R;SQL  HTML/CSS;Java;JavaScript;Python;SQL   \n",
+       "2         Go;JavaScript;Swift;TypeScript                  C#;JavaScript;Swift   \n",
+       "3                    HTML/CSS;JavaScript                  HTML/CSS;JavaScript   \n",
+       "4                                 Python                  C;JavaScript;Python   \n",
+       "\n",
+       "      Profession    UndergradMajor  YearsCodePro      JobSeek  \n",
+       "0      Developer  Computer Science          13.0          nan  \n",
+       "1      Developer         Math/Stat           4.0          nan  \n",
+       "2      Developer  Computer Science           2.0      Seeking  \n",
+       "3      Developer         Math/Stat           7.0          nan  \n",
+       "4  Non developer          No major          20.0  Not seeking  "
+      ]
+     },
+     "execution_count": 1164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1165,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 50 entries, 0 to 49\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                  Non-Null Count  Dtype  \n",
+      "---  ------                  --------------  -----  \n",
+      " 0   Age                     50 non-null     int64  \n",
+      " 1   Gender                  50 non-null     object \n",
+      " 2   SalaryUSD               50 non-null     float64\n",
+      " 3   Country                 50 non-null     object \n",
+      " 4   DevType                 50 non-null     object \n",
+      " 5   Hobbyist                50 non-null     object \n",
+      " 6   EdLevel                 50 non-null     object \n",
+      " 7   Employment              50 non-null     object \n",
+      " 8   Ethnicity               50 non-null     object \n",
+      " 9   CurrentJobSatis         50 non-null     object \n",
+      " 10  JobStatus               50 non-null     object \n",
+      " 11  LanguageDesireNextYear  50 non-null     object \n",
+      " 12  LanguageWorkedWith      50 non-null     object \n",
+      " 13  Profession              50 non-null     object \n",
+      " 14  UndergradMajor          50 non-null     object \n",
+      " 15  YearsCodePro            50 non-null     float64\n",
+      " 16  JobSeek                 50 non-null     object \n",
+      "dtypes: float64(2), int64(1), object(14)\n",
+      "memory usage: 6.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df2020.info()#after cleaning the dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After Cleaning Dataset 2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1166,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total :  850\n",
+      "Total missing :  0\n",
+      "Missing Percentage:  0.0 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Find % of missing data\n",
+    "missing_count = df2020.isnull().sum() #number of missing\n",
+    "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n",
+    "total_missing = missing_count.sum()\n",
+    "missing_percent = (total_missing*100)/total_cells\n",
+    "\n",
+    "print('Total : ', total_cells)\n",
+    "print('Total missing : ', total_missing)\n",
+    "print('Missing Percentage: ', missing_percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization\n",
+    "After cleaning the datasets, we started visualizations to analyze the datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## To find whether there is any difference between men and women's income from latest stack overflow survey (2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1167,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('seaborn-darkgrid')\n",
+    "plt.rcParams[\"figure.figsize\"] = (20,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sns.boxplot('SalaryUSD', data=df2020, width=0.3) \n",
+    "#Cleaning SalaryUSD's outliers\n",
+    "df2020 = df2020[(df2020['SalaryUSD'] < 200000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Income vs Gender')"
+      ]
+     },
+     "execution_count": 1169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x ='Gender', y='SalaryUSD', data=df2020)\n",
+    "plt.title('Income vs Gender', fontsize = 14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Analysis**<br>\n",
+    "There is a little bit of difference between Gender and income they received respectively. Men tend to receive more salary than women from the above analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Impact on participation rate due to different ethnicity based on country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1170,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['White or of European descent', 'Hispanic or Latino', 'Indigenous', 'East Asian', 'Multiracial']\n",
+      "[40, 2, 1, 1, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "participation_rate = df2020['Ethnicity'].value_counts().keys().tolist()\n",
+    "print(participation_rate)\n",
+    "count = df2020['Ethnicity'].value_counts().tolist()\n",
+    "print(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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plCRp2LBhjZdJu/nmmzN8+PAkc0KOXXfdtfrc97///fztb39Lz549M2rUqFx55ZX5/ve/X6P7VlVVpVmzZrnpppuqw5E2bdrk5JNPTps2bfKnP/0pQ4cOzYMPPpgdd9wxyZyl+m6//fYkc/blOf3006vv16pVq6yxxhrp1KlTdtxxx0yfPj2DBw9Oz549k6T6M13kGZx//vmZMGFCGjVqlBtuuCGbbLJJ9bnvfe972WKLLXLsscfmwQcfzDXXXJMePXpk/fXXX+B7vuWWW/Ktb32r+tgee+yRFVZYIUcccURmzZqVQYMG5dBDD61RXQAAVDYzWmBpG5854UmnLxxvmuThJD9I8uskJyfZMMnzSbb7KgsEgLpr9OjR2XzzzWv0tdtuuxXuZ/31119gkNChQ4fqoOPL/nX6YYcdVh2yfN7czdvL5fIC9/H4ok8//TSDBw9OkhxyyCHzhCxzbbrpptlll13y3e9+d54QpYjPz1JZ0GyAp59+unqPlJouG/Z5c8OVp59+eoF71YwcOTIvvvhikjk/bF+Yo446KpdffnnWWGONeY5/8MEHuffee3PGGWeka9eu2XfffTNkyJDFrvOLpk+fnilTpszzNXny5Lz//vu57777sv/++1fPcNp5553nmenTvHnzHHTQQdltt93mCTI+b8stt6x+vbCZRKVSaZ4ApzbMnRXSuXPnBda2/PLL56CDDsrGG2+c1VZbbZ5lwBbl0EMPrQ5ZPu+QQw6pXv7t87NSJk2alN69e2eXXXbJIYccssB7rrTSSll77bWTLPw5LY5PPvkkDzzwQJI5M2g+H7LMVb9+/fz+979Po0aNUi6Xc9ttt83XJkm23XbbeUKWub7//e9XL+/2/vvvL3HNAABUBjNaYGlrlWRhfwddJcktX2EtAEAh3/nOdxZ6rk2bNnnnnXcyderUhbZZ2JJarVu3rn69qA3Bk2To0KHVG3/PDWkW5MuWNFpcPXr0yEsvvZRnn302H3zwQVZdddXqc3N/ML755psvcLbJovTs2TMXXXRRPvvsswwcOHC+H6jPnc2ywgorZKeddvrSe3Xt2jU77LBDhg0blocffjhDhgzJG2+8kdmzZ1e3efHFF9O7d+8ceOCB88yQWFxHHHFEjdp16dIlf/jDH+Y51qlTp3Tq9MV/gfM/EydOrJ6Rk8yZNbQg7dq1y/LLL1+jOmpi0qRJefXVV5MkO+yww0Lb9e7dO717917s+y8sVKpXr16233773HTTTfOEYG3bts1JJ5200PvNnDkzL730UqZNm5ZkziyiJfXss89Wh0dfFmK1bt06nTt3zuOPP77QJQM322yzBR5v2LBhVlhhhYwdO7a6dgAA6j5BCwAAddbqq69evVH60rTiiisu9NzcDbg//wP9L1rYkmCf37j8y66f6/OzPtZaa61Ftq8Nu+66a84555xUVVVl4MCB1UsdzZw5M4MGDUpSbDZLkqyyyirp0qVLHn300dxzzz3zBC3lcjn9+/evvn/jxo0Xeb9SqZQtttgiW2yxRZI5M4CGDRuWJ598Mg888ED187vpppvSrl27hc6UKKJ+/fpp3rx5VlpppWyyySbZbbfdsu2221YvX/VFs2fPzvPPP5+XX345b7/9dt57772MHDkyo0ePnmemyMJmjdRkmbnF8dFHH1X3VdufrYYNG2adddZZ6Pm5M7MmTpyYadOmzbPEX5KMGDEiw4YNy8iRI/Puu+/mnXfeydtvv10r4crnjRkzpvr15/fVWZB11103jz/++DzXfN6X/frM/X2/ODOCAACobIIWAABYhAUt+/VVXj/XxIkTq19/8YfRS0urVq3SpUuX/Otf/8qAAQOqg5ZHHnkkkyZNSsOGDbPLLrsUvv/ee++dRx99NK+++mpGjBhR/QPup556KqNHj65uU8Tyyy+frl27pmvXrjn55JNz8803509/+lM+++yzXHnllTnwwAML/drccMMN8yzvtbieeuqpnHHGGXnnnXfmO9euXbt06dJloUtSzVWT4GlxTJgwofp1bX+2WrRosdDA6Yv9TZo0qfr7UaNG5de//nWeeuqp+a5p2bJlttxyy7z22mu1tqn85MmTq183a9bsS9vOrXFhM9kaNPBXbQCAbxL/9wcAAHXE538gPW3atLRo0eIr6bdHjx7517/+lVdeeSXvvPNO1lxzzeo9W7bddtu0bNmy8L132GGHtGrVKuPHj88999yT448/Pkly9913J0k22WSTbLjhhgu89sUXX8zDDz+ccePG5YwzzphnhtAXNWzYML17985HH32Ua6+9Np988knefffdRc5cqG0vvfRSDjvssFRVVaV58+bZaaedsummm2a99dbLBhtskFatWmXWrFmLDFpq2+eDhdpe0mrGjBlfen7KlClJ5sxImvtZ+vTTT9OrV6+MGjUq9erVy7bbbptOnTpl/fXXz7rrrlu9VN1+++1Xa0HL55/B1KlTs9xyyy2y5q8q8AQAoLIJWgAAoI5YbbXVql+/99572WijjRbYbsiQIXn22WfTvn379OjRY4n77dq1a5ZbbrlMmjQpDz/8cA444IA89thjSYovGzZXw4YN06NHj/z1r3/Nvffem+OPPz7Tp0/P/fffnyTZZ599FnrtK6+8kiuuuCJJsueee+a73/3uIvvr3Llzrr322iSLDgCWhr/85S+pqqrKcsstl759+y5wb5tPPvnkK69rlVVWqX797rvvLrTd6NGjc+utt6Z9+/bZaaedvnRZvbmmTp2a8ePHL3Q5rZEjRyaZs7n93LDslltuyahRo5IkF110UX7wgx8s8NrafFarr7569esRI0bk29/+9kLbjhgxIsmcvWQAAKDesi4AAACWlS9bzqgSfec736muefDgwQttd+utt+aSSy7J5Zdfvsh71uQZNG7cODvvvHOS5OGHH87TTz9d/S/+u3btWsPqF26vvfZKMic8+ve//51HH300U6dOTdOmTfPDH/5wodd9Pli5+eaba9TX3OW6GjZsWL03yFfp+eefT5J8//vfX2DIkmSeTeFrsndPbWjVqlXWXnvtJF/+2frXv/6Vq666Kqeffvpi7ZEyN5j7os8++yyPPvpokmT77bevPj73Oa244ooLDVk++OCDvP3220lq5zltvvnmqVdvzl+R5wZ9CzJu3LgMHTo0yZzfkwAAIGgBAOAb6/P7KFRVVS3DSmpm5ZVXTpcuXZIk1113XfXm7p/38ssv5+GHH06S7Lbbbou8Z02fwdyZMc8991z1JvU777zzly7XVVPrr79+OnbsmCQZNGhQ9Q+5d9555y9dHm3DDTes3vh+wIABueKKK750g/H33nsvV199dZJk9913T/PmzZe49sVVv379JHNmcSwoHBgzZkzOO++86u+Lfi7n/rouzvU/+tGPkiSPP/74AoORyZMnV88G6ty5c1ZaaaUa3/vSSy+dZ4+hua688srqDeV79uxZfXzuc5o4cWLGjh0733UzZszIb37zm+pf7wW9z8V9Bq1atcqOO+6YJLnpppvyyiuvzNdm9uzZOfPMM1NVVZVSqVR4/yAAAL5eBC0AANRZ5XI5U6ZMWayvz1thhRWqX//zn//MhAkTFvjD4Epy8sknp0mTJvnkk0/yk5/8JP3798/YsWPz/vvv54477sgRRxyRqqqqrLLKKundu/ci71fTZ9CpU6esvvrqmTVrVu65554kS75s2OfN/YH1wIED88gjj8xz7Mv86U9/ql6+6cILL8xee+2Vm2++OcOHD8/48ePz0Ucf5dlnn83555+fnj17ZuzYsWnXrl1+8Ytf1Frti2NuUDZ8+PCcdNJJ+fe//51PPvkkI0aMyDXXXJOePXvOE6B98TNbU3P3Ohk8eHA+/PDDjB8/fpHX9OrVq3rPmp///Oe58sor895772Xs2LF59NFHc8ABB2TUqFGpX79+TjrppMWq57333sv++++fRx55pPr9/r//9/9y0UUXJZl/6be5z2n27Nk58sgjM2TIkIwbNy7vvfde7r777uy99955/PHHq9sv6DkVeQa/+tWvsvzyy2fmzJk5+OCDc+211+a9997LJ598kqeffjqHHnpodRB4yCGHVAeEAAB8s9mjBQCAOmv06NHZfPPNF+uaoUOHZvnll0+SNGnSJN/+9rfzwgsv5I477sgdd9yRzp0758Ybb1wa5daK9ddfP//3f/+XY489NqNHj84vf/nL+dqsssoqufrqq790M++5avoMSqVSdt999+pZI23btq2eTVIbdtttt5x99tnV+3Wsvfba6dSp0yKva9u2bW644YacfvrpGTJkSF599dW8+uqrC22/xRZb5Oyzz16s2Ri16aSTTsqwYcMyduzYDBgwIAMGDJivzQ477JCPP/44L7/8cvVSZ4tryy23zIsvvpjXXnst2267bRo2bJjnn38+DRs2XOg1TZs2zVVXXZUjjjgiI0aMyAUXXJALLrhgnjaNGzfOH/7wh2y22WaLVc8ee+yR/v3758gjj5zv3C677JIzzzxznmN77713Bg4cmKFDh+bVV19dYGi4zjrr5Fvf+lbuvffejBo1KlVVVfO8vyLPoH379rn22mvzs5/9LGPHjs2f/vSn/OlPf5qv3aGHHpoTTzxxMZ4AAABfZ2a0AADwjXbhhRdWb/beuHHjTJ06dVmXtEhbb7117r///hx22GFZf/3106xZszRu3Djrr79+jj766PTv3z8dOnSo8f1q+gzmLh+WJD/84Q9rdY+bFi1apHv37tXfL86STO3bt8/f/va3XH/99endu3c23njjrLzyymnUqFGaN2+etdZaKz179swVV1yRG2+8caF7o3wV2rVrl759+6ZXr15ZY4010rBhwzRq1CirrbZaunXrlssuuyxXXHFF9d43w4YNq9FMjC/q06dPevXqlZVWWikNGzZMq1at8sEHH9Sovn79+uU3v/lNvvOd72T55ZdPw4YN07Zt2+y9997p27dvdt9998Wu57jjjsull16azTffPM2aNUv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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()    \n",
+    " \n",
+    "ax.bar(participation_rate,count)\n",
+    "plt.title('Ethnicity VS Participation',size=20)\n",
+    "plt.xlabel('Total Count',size = 20)\n",
+    "plt.ylabel('# Of Developer Did Survey',size = 20) \n",
+    "for i, v in enumerate(count):\n",
+    "    ax.text(i-.15, \n",
+    "              v+3,\n",
+    "              count[i],\n",
+    "              style = 'italic',\n",
+    "              fontsize=14,\n",
+    "              color = 'magenta')\n",
+    "ax.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(15, 5))\n",
+    "sns.barplot(x = count, y = participation_rate, palette = 'Set1')\n",
+    "plt.xlabel('Ethnicity', size = 16)\n",
+    "for i, v in enumerate(count):\n",
+    "    ax.text(  v+3,\n",
+    "              i-.15,\n",
+    "              f'{count[i]*100/sum(count):.2f}%',\n",
+    "              style = 'italic',\n",
+    "              fontsize=14,\n",
+    "              )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**From the Survey Analysis, more particpation has been happened from White or of European Ethnicity which is 24573 participation which is very high comparing to others.<br>\n",
+    "The least has been recorded as only 0.16% from Indigenous.<br>\n",
+    "The second top survey contributors are from South Asians which is 11.93% of the respondents.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Geographical plot to show number of respondents in each country in 2019"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1173,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#geoplot_2019=cleaned_df_2019.groupby('Country').agg('count')\n",
+    "geoplot_2019=cleaned_df_2019.groupby('Country').size()\n",
+    "geoplot_2019=geoplot_2019.to_frame('Respondents')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_country_code(name):\n",
+    "    try:\n",
+    "        return pycountry.countries.lookup(name).alpha_3\n",
+    "    except:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geoplot_2019['Country'] = geoplot_2019.index\n",
+    "geoplot_2019['Country_code'] = geoplot_2019['Country'].apply(get_country_code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "coloraxis": "coloraxis",
+         "geo": "geo",
+         "hovertemplate": "<b>%{hovertext}</b><br><br>Country_code=%{location}<br>Respondents=%{z}<extra></extra>",
+         "hovertext": [
+          "Argentina",
+          "Australia",
+          "Austria",
+          "Brazil",
+          "Canada",
+          "China",
+          "Czech Republic",
+          "France",
+          "Germany",
+          "India",
+          "Ireland",
+          "Israel",
+          "Lithuania",
+          "Netherlands",
+          "New Zealand",
+          "Pakistan",
+          "Philippines",
+          "Poland",
+          "Serbia",
+          "South Africa",
+          "Spain",
+          "Sweden",
+          "Switzerland",
+          "Thailand",
+          "United Kingdom",
+          "United States"
+         ],
+         "locations": [
+          "ARG",
+          "AUS",
+          "AUT",
+          "BRA",
+          "CAN",
+          "CHN",
+          "CZE",
+          "FRA",
+          "DEU",
+          "IND",
+          "IRL",
+          "ISR",
+          "LTU",
+          "NLD",
+          "NZL",
+          "PAK",
+          "PHL",
+          "POL",
+          "SRB",
+          "ZAF",
+          "ESP",
+          "SWE",
+          "CHE",
+          "THA",
+          "GBR",
+          "USA"
+         ],
+         "name": "",
+         "type": "choropleth",
+         "z": [
+          2,
+          2,
+          1,
+          2,
+          1,
+          2,
+          2,
+          1,
+          5,
+          6,
+          1,
+          1,
+          1,
+          1,
+          2,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          2,
+          1,
+          3,
+          19
+         ]
+        }
+       ],
+       "layout": {
+        "autosize": true,
+        "coloraxis": {
+         "cmax": 10000,
+         "cmin": 0,
+         "colorbar": {
+          "title": {
+           "text": "Respondents"
+          }
+         },
+         "colorscale": [
+          [
+           0,
+           "rgb(253, 224, 197)"
+          ],
+          [
+           0.16666666666666666,
+           "rgb(250, 203, 166)"
+          ],
+          [
+           0.3333333333333333,
+           "rgb(248, 181, 139)"
+          ],
+          [
+           0.5,
+           "rgb(245, 158, 114)"
+          ],
+          [
+           0.6666666666666666,
+           "rgb(242, 133, 93)"
+          ],
+          [
+           0.8333333333333334,
+           "rgb(239, 106, 76)"
+          ],
+          [
+           1,
+           "rgb(235, 74, 64)"
+          ]
+         ]
+        },
+        "geo": {
+         "center": {},
+         "domain": {
+          "x": [
+           0,
+           1
+          ],
+          "y": [
+           0,
+           1
+          ]
+         },
+         "projection": {
+          "type": "natural earth"
+         }
+        },
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.choropleth(geoplot_2019,                        \n",
+    "                     locations=\"Country_code\",           \n",
+    "                     color=\"Respondents\",               \n",
+    "                     hover_name=\"Country\",              \n",
+    "                     projection=\"natural earth\",        \n",
+    "                     color_continuous_scale = 'Peach',  \n",
+    "                     range_color=[0,10000]     \n",
+    "                     )        \n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysing salary distribution among top ten countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (20, 10))\n",
+    "\n",
+    "countries = cleaned_df_2019['Country'].value_counts().sort_values(ascending = False)[:10].index.tolist()\n",
+    "\n",
+    "for i, country in enumerate(countries):\n",
+    "    plt.subplot(4, 3, i + 1)\n",
+    "    temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['Country'] == country, 'SalaryUSD']\n",
+    "\n",
+    "    ax = temp_salaries.plot(kind = 'kde')\n",
+    "    ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n",
+    "    ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n",
+    "    ax.set_xlabel('Annual Salary in USD')\n",
+    "    ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n",
+    "    ax.set_title('Annual Salary Distribution in {}'.format(country))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overall, the country which has the highest mean annual salary is the United States of America(240,000) Dollars. The second highest country which provides the highest mean salary is Australia(164,926) Dollars. Though India has a higher number of respondents, it has the lowest mean salary of $25,213.We can understand that the mean salary of a developed country is much higher than that of a developing country."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysing impact of education level on salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1178,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Could not convert ['Very satisfiedSlightly satisfiedVery satisfied'\n 'South AfricaUnited StatesNetherlands' 'NoNoNo'\n 'AssociateAssociateAssociate' 'Full-timeFull-timeFull-time'\n 'White or of European descentWhite or of European descentWhite or of European descent'\n 'ManManMan' 'NoNoYes' 'AverageA little above averageFar above average'\n 'Very satisfiedSlightly satisfiedSlightly dissatisfied'\n 'Not seekingSeekingnan'\n 'HTML/CSS;JavaScript;PHP;SQL;TypeScriptBash/Shell/PowerShell;HTML/CSS;JavaScript;Python;Other(s):Bash/Shell/PowerShell;C;C++;C#;HTML/CSS;Java;JavaScript;PHP;Python;Ruby;SQL;Swift'\n 'HTML/CSS;JavaScript;PHP;SQL;TypeScriptBash/Shell/PowerShell;HTML/CSS;JavaScript;PHP;PythonBash/Shell/PowerShell;C++;Go;HTML/CSS;Java;JavaScript;Kotlin;PHP;Python;Ruby;SQL;TypeScript;VBA'\n 'DeveloperDeveloperDeveloper'\n 'Computer ScienceInfo SystemsComputer Science'\n 'Designer;Developer, back-end;Developer, front-end;Developer, full-stackDeveloper, full-stackData or business analyst;Data scientist or machine learning specialist;Database administrator;Designer;Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack;Educator;Marketing or sales professional;Student;System administrator'] to numeric",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\nanops.py:1680\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1679\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1680\u001b[0m     x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mcomplex128)\n\u001b[0;32m   1681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n",
+      "\u001b[1;31mValueError\u001b[0m: complex() arg is a malformed string",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\nanops.py:1683\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1682\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1683\u001b[0m     x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[0;32m   1684\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m   1685\u001b[0m     \u001b[38;5;66;03m# GH#29941 we get here with object arrays containing strs\u001b[39;00m\n",
+      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Very satisfiedSlightly satisfiedVery satisfied'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1178], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#removing outliers from Associate group\u001b[39;00m\n\u001b[0;32m      2\u001b[0m salary_edu \u001b[38;5;241m=\u001b[39m cleaned_df_2019\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEdLevel\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m----> 3\u001b[0m associate_mean \u001b[38;5;241m=\u001b[39m \u001b[43msalary_edu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_group\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAssociate\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSalaryUSD\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      4\u001b[0m filt \u001b[38;5;241m=\u001b[39m (salary_edu\u001b[38;5;241m.\u001b[39mget_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAssociate\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSalaryUSD\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m associate_mean)\u001b[38;5;241m.\u001b[39mto_frame()\n\u001b[0;32m      5\u001b[0m filt \u001b[38;5;241m=\u001b[39m filt[filt[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSalaryUSD\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m]\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\generic.py:11556\u001b[0m, in \u001b[0;36mNDFrame._add_numeric_operations.<locals>.mean\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11539\u001b[0m \u001b[38;5;129m@doc\u001b[39m(\n\u001b[0;32m  11540\u001b[0m     _num_doc,\n\u001b[0;32m  11541\u001b[0m     desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn the mean of the values over the requested axis.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  11554\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  11555\u001b[0m ):\n\u001b[1;32m> 11556\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\generic.py:11201\u001b[0m, in \u001b[0;36mNDFrame.mean\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11194\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[0;32m  11195\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  11196\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  11199\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  11200\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[1;32m> 11201\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  11202\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m  11203\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\generic.py:11158\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[1;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  11154\u001b[0m     nv\u001b[38;5;241m.\u001b[39mvalidate_stat_func((), kwargs, fname\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m  11156\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m> 11158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  11159\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[0;32m  11160\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\frame.py:10519\u001b[0m, in \u001b[0;36mDataFrame._reduce\u001b[1;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[0;32m  10515\u001b[0m     df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m  10517\u001b[0m \u001b[38;5;66;03m# After possibly _get_data and transposing, we are now in the\u001b[39;00m\n\u001b[0;32m  10518\u001b[0m \u001b[38;5;66;03m#  simple case where we can use BlockManager.reduce\u001b[39;00m\n\u001b[1;32m> 10519\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblk_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m  10520\u001b[0m out \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39m_constructor(res)\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m  10521\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\internals\\managers.py:1534\u001b[0m, in \u001b[0;36mBlockManager.reduce\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m   1532\u001b[0m res_blocks: \u001b[38;5;28mlist\u001b[39m[Block] \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m   1533\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks:\n\u001b[1;32m-> 1534\u001b[0m     nbs \u001b[38;5;241m=\u001b[39m \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1535\u001b[0m     res_blocks\u001b[38;5;241m.\u001b[39mextend(nbs)\n\u001b[0;32m   1537\u001b[0m index \u001b[38;5;241m=\u001b[39m Index([\u001b[38;5;28;01mNone\u001b[39;00m])  \u001b[38;5;66;03m# placeholder\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\internals\\blocks.py:339\u001b[0m, in \u001b[0;36mBlock.reduce\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m    333\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    334\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreduce\u001b[39m(\u001b[38;5;28mself\u001b[39m, func) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[0;32m    335\u001b[0m     \u001b[38;5;66;03m# We will apply the function and reshape the result into a single-row\u001b[39;00m\n\u001b[0;32m    336\u001b[0m     \u001b[38;5;66;03m#  Block with the same mgr_locs; squeezing will be done at a higher level\u001b[39;00m\n\u001b[0;32m    337\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m--> 339\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    341\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    342\u001b[0m         \u001b[38;5;66;03m# TODO(EA2D): special case not needed with 2D EAs\u001b[39;00m\n\u001b[0;32m    343\u001b[0m         res_values \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([[result]])\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\frame.py:10482\u001b[0m, in \u001b[0;36mDataFrame._reduce.<locals>.blk_func\u001b[1;34m(values, axis)\u001b[0m\n\u001b[0;32m  10480\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39m_reduce(name, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m  10481\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m> 10482\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\nanops.py:96\u001b[0m, in \u001b[0;36mdisallow.__call__.<locals>._f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     95\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(invalid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m---> 96\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     98\u001b[0m     \u001b[38;5;66;03m# we want to transform an object array\u001b[39;00m\n\u001b[0;32m     99\u001b[0m     \u001b[38;5;66;03m# ValueError message to the more typical TypeError\u001b[39;00m\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;66;03m# e.g. this is normally a disallowed function on\u001b[39;00m\n\u001b[0;32m    101\u001b[0m     \u001b[38;5;66;03m# object arrays that contain strings\u001b[39;00m\n\u001b[0;32m    102\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_object_dtype(args[\u001b[38;5;241m0\u001b[39m]):\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\nanops.py:158\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[1;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[0;32m    156\u001b[0m         result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    157\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\nanops.py:421\u001b[0m, in \u001b[0;36m_datetimelike_compat.<locals>.new_func\u001b[1;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[0;32m    418\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    419\u001b[0m     mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[1;32m--> 421\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    423\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[0;32m    424\u001b[0m     result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\nanops.py:727\u001b[0m, in \u001b[0;36mnanmean\u001b[1;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[0;32m    724\u001b[0m     dtype_count \u001b[38;5;241m=\u001b[39m dtype\n\u001b[0;32m    726\u001b[0m count \u001b[38;5;241m=\u001b[39m _get_counts(values\u001b[38;5;241m.\u001b[39mshape, mask, axis, dtype\u001b[38;5;241m=\u001b[39mdtype_count)\n\u001b[1;32m--> 727\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype_sum\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    729\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(the_sum, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m    730\u001b[0m     count \u001b[38;5;241m=\u001b[39m cast(np\u001b[38;5;241m.\u001b[39mndarray, count)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\nanops.py:1686\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1683\u001b[0m         x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[0;32m   1684\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m   1685\u001b[0m         \u001b[38;5;66;03m# GH#29941 we get here with object arrays containing strs\u001b[39;00m\n\u001b[1;32m-> 1686\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not convert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   1687\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1688\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(np\u001b[38;5;241m.\u001b[39mimag(x)):\n",
+      "\u001b[1;31mTypeError\u001b[0m: Could not convert ['Very satisfiedSlightly satisfiedVery satisfied'\n 'South AfricaUnited StatesNetherlands' 'NoNoNo'\n 'AssociateAssociateAssociate' 'Full-timeFull-timeFull-time'\n 'White or of European descentWhite or of European descentWhite or of European descent'\n 'ManManMan' 'NoNoYes' 'AverageA little above averageFar above average'\n 'Very satisfiedSlightly satisfiedSlightly dissatisfied'\n 'Not seekingSeekingnan'\n 'HTML/CSS;JavaScript;PHP;SQL;TypeScriptBash/Shell/PowerShell;HTML/CSS;JavaScript;Python;Other(s):Bash/Shell/PowerShell;C;C++;C#;HTML/CSS;Java;JavaScript;PHP;Python;Ruby;SQL;Swift'\n 'HTML/CSS;JavaScript;PHP;SQL;TypeScriptBash/Shell/PowerShell;HTML/CSS;JavaScript;PHP;PythonBash/Shell/PowerShell;C++;Go;HTML/CSS;Java;JavaScript;Kotlin;PHP;Python;Ruby;SQL;TypeScript;VBA'\n 'DeveloperDeveloperDeveloper'\n 'Computer ScienceInfo SystemsComputer Science'\n 'Designer;Developer, back-end;Developer, front-end;Developer, full-stackDeveloper, full-stackData or business analyst;Data scientist or machine learning specialist;Database administrator;Designer;Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack;Educator;Marketing or sales professional;Student;System administrator'] to numeric"
+     ]
+    }
+   ],
+   "source": [
+    "#removing outliers from Associate group\n",
+    "salary_edu = cleaned_df_2019.groupby(['EdLevel'])\n",
+    "associate_mean = salary_edu.get_group('Associate').mean()['SalaryUSD']\n",
+    "filt = (salary_edu.get_group('Associate')['SalaryUSD'] > associate_mean).to_frame()\n",
+    "filt = filt[filt['SalaryUSD'] == False]\n",
+    "cleaned_df_2019.drop(index=filt.index, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize = (20, 10))\n",
+    "\n",
+    "education_2019 = cleaned_df_2019['EdLevel'].value_counts().sort_values(ascending = False).index.tolist()\n",
+    "\n",
+    "for i, edu in enumerate(education_2019):\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['EdLevel'] == edu, 'SalaryUSD']\n",
+    "\n",
+    "    ax = temp_salaries.plot(kind = 'kde')\n",
+    "    ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n",
+    "    ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n",
+    "    ax.set_xlabel('Annual Salary in USD')\n",
+    "    ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n",
+    "    ax.set_title('Annual Salary Distribution in {}'.format(edu))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, the respondents who have done Doctorate have the highest mean salary among all other education levels. Secondly, the respondents who have done Bachelors degree have more salary than that of Masters degree holders. This may be due to years of professional coding experience and due to the higher number of respondents in that category than that of Masters degree(No of respondents in Bachelor degree is 35659 and number of respondents in masters degree is 16940)\n",
+    "\n",
+    "The most interesting is that the respondents who do not have any degree have a mean salary of $90k. This shows the improvement in online learning and advancement of technology that is shifting the company from relying on University degrees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distribution of respondents based on age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col =['Age', 'Country']\n",
+    "df_2020= cleaned_df_2019[col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df_2020['Age_range'] = 0\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=15) & (df_2020['Age']<=19), '15 - 19 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=20) & (df_2020['Age']<=24), '20 - 24 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=25) & (df_2020['Age']<=29), '25 - 29 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=30) & (df_2020['Age']<=34), '30 - 34 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=35) & (df_2020['Age']<=39), '35 - 39 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=40) & (df_2020['Age']<=45), '40 - 45 years', df_2020.Age_range)\n",
+    "df_2020['Age_range']= np.where((df_2020['Age']>=46), '46 and above years', df_2020.Age_range)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2020_age = df_2020.groupby(['Age_range']).size().reset_index(name='Count')\n",
+    "df_2020_age.sort_values(by=['Count'], ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize = (10, 6))\n",
+    "sns.set_style('white')\n",
+    "sns.set_context('paper', font_scale=1.5)\n",
+    "sns.barplot(x=\"Count\",  y=\"Age_range\", palette='inferno', data=df_2020_age).set(xlabel=\"Age\", ylabel = \"Count\")\n",
+    "plt.title('Distribution of respondents based on age')\n",
+    "\n",
+    "for y, x in enumerate(df_2020_age['Count']):\n",
+    "    label = \"{:,}\".format(int(x))\n",
+    "    plt.annotate(label, xy=(x, y), va='center')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Impact on the increase in popularity of a language in the current-year due to developer’s interest in the previous year.(Based on 2019 and 2020 dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#seperate language for getting individual one\n",
+    "cols = ['LanguageWorkedWith']\n",
+    "df_19 = survey_df_2019[cols]\n",
+    "df_20 = df2020[cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#splitting 'LanguageWorkedWith' and sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
+    "language_2019['Language'] = language_2019.index\n",
+    "language_2019.reset_index(drop=True, inplace=True)\n",
+    "language_2019 = language_2019[['Language', '2019']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
+    "language_2020['Language'] = language_2020.index\n",
+    "language_2020.reset_index(drop=True, inplace=True)\n",
+    "language_2020 = language_2020[['Language', '2020']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "language_all= pd.merge(language_2019, language_2020,on = ['Language'], how = 'outer')\n",
+    "language_all.fillna(0, inplace=True)\n",
+    "language_all['2019'] = language_all['2019']. astype(int)\n",
+    "language_all['2020'] = language_all['2020']. astype(int)\n",
+    "language_all.set_index('Language', inplace=True)\n",
+    "#language_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "language19_20=(language_all/language_all.sum())\n",
+    "language19_20.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "language19_20.plot(kind='bar', figsize=(12,8))\n",
+    "plt.title('Programming Language worked by Respondents in 2019 and 2020', fontsize = 18)\n",
+    "plt.xlabel('Languages', fontsize = 14)\n",
+    "plt.ylabel('Percentages', fontsize = 14)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The most language that worked in 2019 and 2020 is JavaScript.In 2020, people worked slightly in javascript compare to 2019. The 2nd highest working language is HTML/CSS. For HTML/CSS the percentage is slightly low in 2020. There are some language people worked in only one year. Elixir, Clojure, F#, Web assembly are those languages that people used in 2019. Respondent started to use Perl, Haskell, Julia in 2020 on a small scale."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Programming language desired to work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#language desire net year\n",
+    "cols_1 = ['LanguageDesireNextYear']\n",
+    "df_19 = survey_df_2019[cols_1]\n",
+    "df_20 = df2020[cols_1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "languagedesire_2019= df_19['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
+    "languagedesire_2019['Language'] = languagedesire_2019.index\n",
+    "languagedesire_2019.reset_index(drop=True, inplace=True)\n",
+    "languagedesire_2019 = languagedesire_2019[['Language', '2019']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "languagedesire_2020= df_20['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
+    "languagedesire_2020['Language'] = languagedesire_2020.index\n",
+    "languagedesire_2020.reset_index(drop=True, inplace=True)\n",
+    "languagedesire_2020= languagedesire_2020[['Language','2020']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "languagedesire_all = pd.merge(languagedesire_2019, languagedesire_2020,on = ['Language'], how = 'outer')\n",
+    "languagedesire_all.fillna(0, inplace=True)\n",
+    "languagedesire_all['2019'] = languagedesire_all['2019']. astype(int)\n",
+    "languagedesire_all['2020'] = languagedesire_all['2020']. astype(int)\n",
+    "languagedesire_all.set_index('Language', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "languagedesire19_20=(languagedesire_all/languagedesire_all.sum())\n",
+    "languagedesire19_20.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "languagedesire19_20.plot(kind='bar', figsize=(12,8))\n",
+    "plt.title('Programming Language desire to work', fontsize = 18)\n",
+    "plt.xlabel('Languages', fontsize = 14)\n",
+    "plt.ylabel('Percentages', fontsize = 14)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In 2019, respondents said that they wanted to work in javascript is around more than 10 % and the fewer respond have a desire to work on VBA next year. People started to work in Haskell, Julia, and pearl in 2019 though the amount was less around 5% of people have the desire to work in those languages in 2021. Here, phyton is the 2nd one in which people have the desire to work in both 2019 and 2020."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distribution of surveyors based on their developer role."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col = ['DevType']\n",
+    "dev_18=df[col]\n",
+    "dev_19 = survey_df_2019[col]\n",
+    "dev_20= df2020[col]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "dev_2018= dev_18['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n",
+    "dev_2018['Developer'] = dev_2018.index\n",
+    "dev_2018.reset_index(drop=True, inplace=True)\n",
+    "dev_2018 = dev_2018[['Developer', '2018']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev_2019= dev_19['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
+    "dev_2019['Developer'] = dev_2019.index\n",
+    "dev_2019.reset_index(drop=True, inplace=True)\n",
+    "dev_2019 = dev_2019[['Developer', '2019']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev_2020= dev_20['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
+    "dev_2020['Developer'] = dev_2020.index\n",
+    "dev_2020.reset_index(drop=True, inplace=True)\n",
+    "dev_2020 = dev_2020[['Developer', '2020']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df18_19 = pd.merge(dev_2018, dev_2019,on = ['Developer'], how = 'outer')\n",
+    "devtype_all = pd.merge(df18_19,dev_2020, on=[\"Developer\"], how=\"outer\")\n",
+    "devtype_all.fillna(0, inplace=True)\n",
+    "devtype_all['2018'] = devtype_all['2018']. astype(int)\n",
+    "devtype_all['2019'] = devtype_all['2019']. astype(int)\n",
+    "devtype_all['2020'] =devtype_all['2020'].astype(int)\n",
+    "devtype_all.set_index('Developer', inplace=True)\n",
+    "devtype_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "devtype_all.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt_all=devtype_all/devtype_all.sum()\n",
+    "dt_all.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(devtype_all/devtype_all.sum()).plot(kind='bar', figsize=(12,8))\n",
+    "plt.title('Developer Types pertcentages', fontsize = 18)\n",
+    "plt.xlabel('Developer Types', fontsize = 14)\n",
+    "plt.ylabel('Percentages', fontsize = 14)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In developer types, developers who are full stack and working backends are the most in the three years. There is a presence of student developers only in 2019 the percentage is 7.5%. Those who are working back end and full stack their percentages increased throughout the three years. For those who are working as marketing and sales professionals, their percentage is lowest compare to others."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Impact of education/experience/responsibilities on gender inequalities.(Based on 2019 dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['Gender','EdLevel', 'Dependents', 'SalaryUSD', 'YearsCodePro', 'Age', 'Country']\n",
+    "df2019 = survey_df_2019[cols]\n",
+    "df2019 = df2019[df2019.Gender != \"Non-binary\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2019['exp_range'] = 0\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro >= 0) & (df2019.YearsCodePro <= 5), '0 - 5 years', df2019.exp_range)\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 5) & (df2019.YearsCodePro <= 10), '6 - 10 years', df2019.exp_range)\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 10) & (df2019.YearsCodePro <= 15), '11 - 15 years', df2019.exp_range)\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 15) & (df2019.YearsCodePro <= 20), '16 - 20 years', df2019.exp_range)\n",
+    "df2019['exp_range'] = np.where((df2019.YearsCodePro > 20), 'more that 20 years', df2019.exp_range)\n",
+    "#df2019"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(2,2, figsize = (12, 8))\n",
+    "g1 = sns.countplot('Gender', data=df2019, ax=axes[0][0]).set(title = 'A. Gender')\n",
+    "g2 = sns.countplot('Dependents', data=df2019, ax=axes[0][1]).set( title = 'B. Dependents')\n",
+    "g3 = sns.countplot('EdLevel', data=df2019, ax=axes[1][0]).set(title = 'C. EdLevel')\n",
+    "g4 = sns.countplot('exp_range', data=df2019, ax=axes[1][1]).set(title = 'D. exp_range')\n",
+    "\n",
+    "axes[1][0].tick_params(axis='x', rotation=45)\n",
+    "axes[1][1].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.countplot('EdLevel', hue='Gender', data=df2019)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "sns.countplot('Dependents', hue='Gender', data=df2019)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Analysis**<br>\n",
+    "\n",
+    "\n",
+    "After exploring the 2019 dataset, we have found that we cannot answer this question since male and female observations are significantly unbalanced."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is the gender distribution among top 5 countries of respondents in 2019?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all = df2019.groupby(['Country','Gender']).size().reset_index(name ='Count')\n",
+    "all['Total'] = all.groupby(['Country'])['Count'].transform('sum')\n",
+    "all = all.sort_values(by=['Total'], ascending=False)\n",
+    "#all.set_index('Total')\n",
+    "Top = all[:10].sort_values(by=['Total'], ascending=False)\n",
+    "Top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# from raw value to percentage\n",
+    "total = Top.groupby(['Country'])['Count'].sum().reset_index()\n",
+    "total['Percentage'] = [i / j * 100 for i,j in zip(total['Count'], total['Count'])]\n",
+    "\n",
+    "woman = Top[Top.Gender=='Woman'].groupby(['Country'])['Count'].sum().reset_index()\n",
+    "woman['Percentage'] = [i / j * 100 for i,j in zip(woman['Count'], total['Count'])]\n",
+    "woman.sort_values(by=['Percentage'], ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "woman"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (10, 6))\n",
+    "\n",
+    "# bar chart 1 -> top bars (group of 'Man')\n",
+    "bar1 = sns.barplot(x=\"Country\",  y=\"Percentage\", data=total, color='darkblue')\n",
+    "# bar chart 2 -> bottom bars (group of 'Woman')\n",
+    "bar2 = sns.barplot(x=\"Country\", y=\"Percentage\", data=woman, color='#5E96E9')\n",
+    "\n",
+    "# add legend\n",
+    "top_bar = mpatches.Patch(color='darkblue', label='Man')\n",
+    "bottom_bar = mpatches.Patch(color='#5E96E9', label='Woman')\n",
+    "plt.legend(handles=[top_bar, bottom_bar])\n",
+    "\n",
+    "# Fix the legend so it's not on top of the bars.\n",
+    "legend = ax.get_legend()\n",
+    "legend.set_bbox_to_anchor((1, 1))\n",
+    "\n",
+    "ax.set_ylabel('Percentage', fontsize = 12)\n",
+    "ax.set_xlabel('Top 5 Countries', fontsize = 12)\n",
+    "plt.title('Gender vs Top 5 Countries in 2019', fontsize = 14)\n",
+    "\n",
+    "def add_value_labels(bar2, spacing=5):\n",
+    "    \"\"\"Add labels to the end of each bar in a bar chart.\n",
+    "\n",
+    "    Arguments:\n",
+    "        ax (matplotlib.axes.Axes): The matplotlib object containing the axes\n",
+    "            of the plot to annotate.\n",
+    "        spacing (int): The distance between the labels and the bars.\n",
+    "    \"\"\"\n",
+    "    # For each bar: Place a label\n",
+    "    for rect in bar2.patches:\n",
+    "        # Get X and Y placement of label from rect.\n",
+    "        y_value = rect.get_height()\n",
+    "        x_value = rect.get_x() + rect.get_width() / 2\n",
+    "\n",
+    "        space = spacing  # Number of points between bar and label. Change to your liking.\n",
+    "        va = 'bottom' # Vertical alignment for positive values\n",
+    "        label = \"{:.1f}%\".format(y_value) # Use Y value as label and format number with one decimal place\n",
+    "\n",
+    "        # Create annotation\n",
+    "        bar2.annotate(\n",
+    "            label,                      # Use `label` as label\n",
+    "            (x_value, y_value),         # Place label at end of the bar\n",
+    "            xytext=(0, space),          # Vertically shift label by `space`\n",
+    "            textcoords=\"offset points\", # Interpret `xytext` as offset in points\n",
+    "            ha='center',                # Horizontally center label\n",
+    "            va=va,                      # Vertically align label differently for\n",
+    "            color='white', fontsize=12, style='italic')       \n",
+    "\n",
+    "#Add value bar\n",
+    "add_value_labels(bar2)\n",
+    "\n",
+    "plt.tight_layout(pad=0., w_pad=-16.5, h_pad=0.0)  \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Analysis**<br>\n",
+    "\n",
+    "\n",
+    "In terms of male and female statistics, it can be seen that the US has the relatively largest female percentage at about 10.9%. Follow by Canada, the UK at 9.6% and 8.0% respectively. India and Germany have the fewest female respondents among the top 5 at around 5%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Where are the most data scientist come from in 2019?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#creating data scientist scientist df\n",
+    "ds = survey_df_2019[survey_df_2019['DevType'].str.contains('Data scientist') == True ]\n",
+    "ds = ds.reset_index(drop=True)\n",
+    "len(ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_country = ds.groupby(['Country']).size().reset_index(name ='Count')\n",
+    "ds_country.sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "top_ds_country = ds_country[:10]\n",
+    "top_ds_country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (10, 6))\n",
+    "ax=sns.barplot(x=\"Count\",  y=\"Country\", data=top_ds_country )\n",
+    "ax.set_ylabel('Countries', fontsize = 12)\n",
+    "ax.set_xlabel('The Number of Data Scientists', fontsize = 12)\n",
+    "plt.title('Top 10 Countries of Data Scientists in 2019', fontsize = 14)\n",
+    "\n",
+    "for y, x in enumerate(top_ds_country['Count']):\n",
+    "    label = \"{:,}\".format(int(x))\n",
+    "    plt.annotate(label, xy=(x, y), va='center')\n",
+    "\n",
+    "plt.tight_layout()  \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis\n",
+    "\n",
+    "\n",
+    "There are 5,788 data scientists who responded to the Stackoverflow survey in 2019. Most data scientists are from the US with 1,550 people and it is 3 times higher than data scientists from India. Followed by Germany and the UK with 427 and 339 people respectively. The rest are Canada, France, Netherlands, Brazil, Russia, and Australia which have less than 200 data scientists."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Which countries pay the most to Data Scientists in 2019?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_mean_salary = ds.groupby('Country')['SalaryUSD'].mean().reset_index(name ='Mean')\n",
+    "ds_mean_salary.sort_values(by=['Mean'], ascending=False, inplace=True)\n",
+    "ds_mean_salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Plottig boxplot to check outliers after cleaning some outliers\n",
+    "sns.boxplot('Mean', data=ds_mean_salary)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Cleaning Age's outliers from each gender)\n",
+    "ds_mean_salary = ds_mean_salary[(ds_mean_salary['Mean'] <= 280000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Plottig boxplot to check outliers after cleaning some outliers\n",
+    "sns.boxplot('Mean', data=ds_mean_salary)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Top_mean_salary = ds_mean_salary[:10]\n",
+    "Top_mean_salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (10, 6))\n",
+    "ax=sns.barplot(x=\"Mean\",  y=\"Country\", data=Top_mean_salary )\n",
+    "ax.set_ylabel('Country', fontsize = 12)\n",
+    "ax.set_xlabel('Average Salary in US$', fontsize = 12)\n",
+    "plt.title('The Top 10 highest paying data scientist country in 2019', fontsize = 14)\n",
+    "\n",
+    "for y, x in enumerate(Top_mean_salary['Mean']):\n",
+    "    label = \"${:,}\".format(int(x))\n",
+    "    plt.annotate(label, xy=(x, y), va='center')\n",
+    "\n",
+    "plt.tight_layout()  \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Analysis**<br>\n",
+    "\n",
+    "\n",
+    "In 2019, the top three countries which have a highest mean annual salary of a data scientist are Ireland (275,851), Luxembourg (272,769), and the USA (265,211). Apart from that, the mean salary of the rest of the countries is less than (200,000) per year. Japan provides the highest mean annual salary among Asian countries (118,969)<br>\n",
+    "*Figures in Dollars* **$**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mydf2018 = pd.read_csv(r\"C:\\Users\\Aneesh Angane\\Downloads\\stack-overflow-developer-survey-2018\\survey_results_public_2018.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Most Popular IDE's in 2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split IDEs and explode into separate rows\n",
+    "individual_ides = mydf2018['IDE'].str.split(';').explode()\n",
+    "\n",
+    "# Count occurrences of each IDE and sort by value\n",
+    "ide_counts_value_sorted = individual_ides.value_counts().sort_values(ascending=False)\n",
+    "\n",
+    "# Plotting - Sorted by value\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.bar(ide_counts_value_sorted.index, ide_counts_value_sorted.values, color='skyblue')\n",
+    "plt.title('IDE Usage')\n",
+    "plt.xlabel('IDE')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis of IDE Usage\n",
+    "\n",
+    "1. **Popular IDEs**: Visual Studio Code, Visual Studio, and Notepad++ are among the most widely used IDEs, with high user counts ranging from 25,870 to 26,280.\n",
+    "\n",
+    "2. **Text Editors**: Sublime Text, Vim, and IntelliJ are also popular choices, with user counts ranging from 19,477 to 21,810.\n",
+    "\n",
+    "3. **General-purpose Editors**: TextMate, Coda, and Light Table are also used, although they have lower user counts compared to other IDEs.\n",
+    "\n",
+    "4. **Emerging Trends**: IPython / Jupyter, Atom, and Emacs show significant adoption, indicating a growing interest in interactive computing environments, lightweight editors, and customizable text editors, respectively.\n",
+    "\n",
+    "5. **Industry Standard**: Xcode, primarily used for macOS and iOS development, maintains a substantial user base due to its integration with Apple's development ecosystem.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coders perception about AI in 2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "# Assuming df2018 is your DataFrame\n",
+    "df = df2018[['AIDangerous','AIInteresting','AIResponsible','AIFuture']]\n",
+    "\n",
+    "# Strip leading and trailing whitespace from all columns\n",
+    "df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
+    "\n",
+    "# Mapping for shorter versions\n",
+    "short_mapping = {\n",
+    "    'Algorithms making important decisions': 'Algorithms',\n",
+    "    'Artificial intelligence surpassing human intelligence (\"the singularity\")': 'AI Singularity',\n",
+    "    'Evolving definitions of \"fairness\" in algorithmic versus human decisions': 'Fairness Evolution',\n",
+    "    \"Increasing automation of jobs\": 'Automation',\n",
+    "    \"The developers or the people creating the AI\": 'Developers',\n",
+    "    \"A governmental or other regulatory body\": 'Government/Regulatory',\n",
+    "    \"Prominent industry leaders\": 'Industry Leaders',\n",
+    "    \"Nobody\": 'No Responsibility',\n",
+    "    \"I'm excited about the possibilities more than worried about the dangers.\": 'Excited about AI Future',\n",
+    "    \"I'm worried about the dangers more than I'm excited about the possibilities.\": 'Worried about AI Future',\n",
+    "    \"I don't care about it, or I haven't thought about it.\": 'Indifferent about AI Future'\n",
+    "}\n",
+    "\n",
+    "# Replace responses with shorter versions\n",
+    "df.replace(short_mapping, inplace=True)\n",
+    "\n",
+    "# Function to create value count plots for each column\n",
+    "def plot_value_counts(column_name, ax):\n",
+    "    colors = ['skyblue','yellow']\n",
+    "    df[column_name].value_counts().plot(kind='bar', color=random.choice(colors), ax=ax)\n",
+    "    ax.set_title(f'Value Counts for {column_name}')\n",
+    "    ax.set_xlabel('Response')\n",
+    "    ax.set_ylabel('Count')\n",
+    "    ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
+    "\n",
+    "# Plot value counts for each column\n",
+    "for i, column in enumerate(df.columns):\n",
+    "    plot_value_counts(column, axes[i//2, i%2])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "### AIDangerous:\n",
+    "- The most commonly cited concern is \"Algorithms making important decisions,\" followed closely by \"Artificial intelligence surpassing human intelligence\" and \"Evolving definitions of fairness.\"\n",
+    "- \"Increasing automation of jobs\" is also a significant concern but appears to be less frequently mentioned compared to the other categories.\n",
+    "\n",
+    "### AIInteresting:\n",
+    "- The most interesting aspect for respondents seems to be \"Increasing automation of jobs,\" followed by \"Algorithms making important decisions\" and \"Artificial intelligence surpassing human intelligence.\"\n",
+    "- \"Evolving definitions of fairness\" appears to be less intriguing to respondents compared to other categories.\n",
+    "\n",
+    "### AIResponsible:\n",
+    "- The majority of respondents believe that responsibility lies with \"The developers or the people creating the AI.\"\n",
+    "- Fewer respondents attribute responsibility to \"A governmental or other regulatory body,\" \"Prominent industry leaders,\" or \"Nobody.\"\n",
+    "\n",
+    "### AIFuture:\n",
+    "- A significant proportion of respondents express excitement about the future of AI, indicating that they are \"Excited about the possibilities more than worried about the dangers.\"\n",
+    "- However, there is also a notable percentage of respondents who are \"Worried about the dangers more than excited about the possibilities.\"\n",
+    "- A smaller portion of respondents either \"Don't care about it\" or \"Haven't thought about it.\"\n",
+    "\n",
+    "Overall, these results suggest a complex and varied perspective on AI technology. While many see great potential in AI, there are also concerns about its implications, particularly regarding decision-making, automation of jobs, and the ethical considerations surrounding its development and regulation.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['LanguageWorkedWith']\n",
+    "df_18 = df[cols]\n",
+    "df_19 = survey_df_2019[cols]\n",
+    "df_20 = df2020[cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#splitting 'LanguageWorkedWith' on ';' \n",
+    "language_2018= df_18['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n",
+    "language_2018['Language'] = language_2018.index\n",
+    "language_2018.reset_index(drop=True, inplace=True)\n",
+    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "language_2018 = language_2018[['Language', '2018']]\n",
+    "#language_2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#splitting 'LanguageWorkedWith' on ';' \n",
+    "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n",
+    "language_2019['Language'] = language_2019.index\n",
+    "language_2019.reset_index(drop=True, inplace=True)\n",
+    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "language_2019 = language_2019[['Language', '2019']]\n",
+    "#language_2019"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#splitting 'LanguageWorkedWith' on ';' \n",
+    "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n",
+    "language_2020['Language'] = language_2020.index\n",
+    "language_2020.reset_index(drop=True, inplace=True)\n",
+    "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n",
+    "language_2020 = language_2020[['Language', '2020']]\n",
+    "#language_2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compare_df = pd.merge(language_2018, language_2019,on = ['Language'], how = 'outer')\n",
+    "language_all = pd.merge(compare_df, language_2020,on = ['Language'], how = 'outer')\n",
+    "language_all.fillna(0, inplace=True)\n",
+    "language_all['2018'] = language_all['2018']. astype(int)\n",
+    "language_all['2019'] = language_all['2019']. astype(int)\n",
+    "language_all['2020'] = language_all['2020']. astype(int)\n",
+    "language_all.set_index('Language', inplace=True)\n",
+    "#language_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(language_all/language_all.sum()).plot(kind='bar', figsize=(12,8))\n",
+    "plt.title('Programming Language Use by Respondents', fontsize = 14)\n",
+    "plt.xlabel('Languages', fontsize = 12)\n",
+    "plt.ylabel('Percentages', fontsize = 12)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  **Analysing the growth of languages from 2018 to 2020 before predicting part**\n",
+    "\n",
+    "The most language the developers use between 2018 to 2020 is JavaScript(14%). The second and third highest working language is HTML/CSS(13%) and SQL(11%). JavaScript and SQL had the same steady increasing trend over the three years. The percentage of HTML/CSS was slightly increased from 2018 to 2019. However, it dropped to the same level as 2018 in 2020. Python was responsible for about 9% in 2018. After then, it decreased to 8% in 2019 and it rose 1% in 2020.\n",
+    "\n",
+    "There are some languages that were in only 2019; Elixir, Clojure, F#, Web assembly, and Erlang. Perl, Haskell, Julia was in the 2019 and 2020 surveys with small percentages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Preparing data for ML\n",
+    "df_language_2018 = language_2018[['Language', '2018']]\n",
+    "df_language_2018 = df_language_2018.rename(columns={'2018': 'Number'})\n",
+    "df_language_2018['Year'] = '2018'\n",
+    "df_language_2018['Year_Total'] = df_language_2018['Number'].sum()\n",
+    "df_language_2018['Fraction'] = df_language_2018['Number']/df_language_2018['Number'].sum()\n",
+    "df_language_2018 = df_language_2018[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
+    "df_language_2018.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
+    "#df_language_2018\n",
+    "df_language_2019 = language_2019[['Language', '2019']]\n",
+    "df_language_2019 = df_language_2019.rename(columns={'2019': 'Number'})\n",
+    "df_language_2019['Year'] = '2019'\n",
+    "df_language_2019['Year_Total'] = df_language_2019['Number'].sum()\n",
+    "df_language_2019['Fraction'] = df_language_2019['Number']/df_language_2019['Number'].sum()\n",
+    "df_language_2019 = df_language_2019[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
+    "df_language_2019.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
+    "#df_language_2019\n",
+    "df_language_2020 = language_2020[['Language', '2020']]\n",
+    "df_language_2020 = df_language_2020.rename(columns={'2020': 'Number'})\n",
+    "df_language_2020['Year'] = '2020'\n",
+    "df_language_2020['Year_Total'] = df_language_2020['Number'].sum()\n",
+    "df_language_2020['Fraction'] = df_language_2020['Number']/df_language_2020['Number'].sum()\n",
+    "df_language_2020 = df_language_2020[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n",
+    "df_language_2020.sort_values(by=['Fraction'], ascending=False, inplace=True)\n",
+    "#df_language_2020\n",
+    "\n",
+    "#Append Dataset 2018 x 2019 x 2020\n",
+    "df_language = pd.concat([df_language_2018[:10], df_language_2019[:10], df_language_2020[:10]] , axis=0)\n",
+    "#resetting the index values\n",
+    "df_language = df_language.reset_index(drop=True)\n",
+    "#df_language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['Language', 'Fraction']\n",
+    "df_language_2018_ = df_language_2018[cols][:10]\n",
+    "#df_language_2018_\n",
+    "cols = ['Language', 'Fraction']\n",
+    "df_language_2019_ = df_language_2019[cols][:10]\n",
+    "#df_language_2019_\n",
+    "cols = ['Language', 'Fraction']\n",
+    "df_language_2020_ = df_language_2020[cols][:10]\n",
+    "#df_language_2020_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_language_2018_.set_index('Language', inplace = True)\n",
+    "df_language_2018_t = df_language_2018_.T\n",
+    "df_language_2018_t['Year'] = '2018'\n",
+    "df_language_2018_t.Year = pd.to_datetime(df_language_2018_t.Year)\n",
+    "df_language_2018_t = df_language_2018_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
+    "#df_language_2018_t\n",
+    "df_language_2019_.set_index('Language', inplace = True)\n",
+    "df_language_2019_t = df_language_2019_.T\n",
+    "df_language_2019_t['Year'] = '2019'\n",
+    "df_language_2019_t.Year = pd.to_datetime(df_language_2019_t.Year)\n",
+    "df_language_2019_t = df_language_2019_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
+    "#df_language_2019_t\n",
+    "df_language_2020_.set_index('Language', inplace = True)\n",
+    "df_language_2020_t = df_language_2020_.T\n",
+    "df_language_2020_t['Year'] = '2020'\n",
+    "df_language_2020_t.Year = pd.to_datetime(df_language_2020_t.Year)\n",
+    "df_language_2020_t = df_language_2020_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n",
+    "#df_language_2020_t\n",
+    "\n",
+    "#Append Dataset 2018 x 2019 x 2020\n",
+    "all_language = pd.concat([df_language_2018_t, df_language_2019_t, df_language_2020_t] , axis=0)\n",
+    "#resetting the index values\n",
+    "all_language = all_language.reset_index(drop=True)\n",
+    "all_language.set_index('Year', inplace = True)\n",
+    "all_language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_language.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = all_language.plot(grid=True, lw=0.5, figsize=(14,6), marker='o')\n",
+    "\n",
+    "#Show the legend outside of the plot.\n",
+    "legend = ax.get_legend()\n",
+    "legend.set_bbox_to_anchor((1, 1))\n",
+    "plt.title('Fraction of total queries in the year for top programming languages', fontsize = 14)\n",
+    "plt.xlabel('Languages', fontsize = 12)\n",
+    "plt.ylabel('Fraction of total queries in the year (%)', fontsize = 12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are trying to answer the question \"Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020).\"\n",
+    "\n",
+    "Since we have only 3 years of datasets, there is not enough data to use the time series forecasting method to predict the future popularity of programming languages. With the very small number of observations, there is insufficient data to split the observations into training and testing. We need more observations to build the predictive model, this question we leave for further exploration in future projects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Can we predict the salary of Data Scientists?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Rename columns\n",
+    "cleaned_2018.rename(columns={'JobSatisfaction': 'CurrentJobSatis', 'JobSearchStatus': 'JobStatus', 'YearsCodingProf':'YearsCodePro'}, inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sal_df = ['Age', 'Country', 'EdLevel', 'DevType', 'YearsCodePro', 'SalaryUSD']\n",
+    "df1 = cleaned_2018\n",
+    "df2 = survey_df_2019\n",
+    "df3 = df2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Append Dataset 2018 x 2019 x 2020\n",
+    "df_sal = pd.concat([df1[sal_df], df2[sal_df], df3[sal_df]], axis=0)\n",
+    "#resetting the index values\n",
+    "df_sal = df_sal.reset_index(drop=True)\n",
+    "df_sal.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#creating data scientist scientist df\n",
+    "all_ds = df_sal[df_sal['DevType'].str.contains('Data scientist') == True ]\n",
+    "all_ds = all_ds.reset_index(drop=True)\n",
+    "len(all_ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_ds['DevType'] = 'Data scientist'\n",
+    "all_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Divide SalaryUSD into 2 groups; SalaryUSD >= median and SalaryUSD < median \n",
+    "all_ds['greater than median'] = all_ds['SalaryUSD'] >= all_ds['SalaryUSD'].median()\n",
+    "all_ds['SalaryUSD'].median() #56616.0 USD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Encoding the target\n",
+    "labelencoder = preprocessing.LabelEncoder()\n",
+    "all_ds['gt_median'] = labelencoder.fit_transform(all_ds['greater than median'])\n",
+    "\n",
+    "le_name_mapping = dict(zip(labelencoder.classes_, labelencoder.transform(labelencoder.classes_)))\n",
+    "print(le_name_mapping)\n",
+    "#{False: 0 (SalaryUSD < median), True: 1 (SalaryUSD >= median}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = all_ds.drop(['SalaryUSD', 'greater than median', 'gt_median', 'DevType'], axis = 1)\n",
+    "y = all_ds['gt_median']\n",
+    "X.shape\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cats_lst = X.select_dtypes(include = ['object']).columns.tolist()\n",
+    "for col in cats_lst:\n",
+    "    X = pd.concat([X.drop(col, axis=1), pd.get_dummies(X[col], prefix=col, prefix_sep='_', drop_first=True)], axis=1)\n",
+    "X.shape\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Splitting data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.30, random_state=142)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1251,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_metrics = {}\n",
+    "\n",
+    "def metrics_data(title, labels, predictions):\n",
+    "    \"\"\"\n",
+    "    INPUT:\n",
+    "        title - Display title for classification algorithm\n",
+    "        labels - Actual values for target variable\n",
+    "        predictions - Predicted values for target variable\n",
+    "        \n",
+    "    OUTPUT:\n",
+    "        metrics - Dictionary of classification metrics for given title\n",
+    "    \"\"\"\n",
+    "    metrics = {\n",
+    "        title: {\n",
+    "            \"model\": title,\n",
+    "            \"accuracy\": accuracy_score(labels, predictions),\n",
+    "            \"precision\": precision_score(labels, predictions),\n",
+    "            \"recall\": recall_score(labels, predictions),\n",
+    "            \"f1-score\": f1_score(labels, predictions),\n",
+    "            \"r2\":  r2_score(labels, predictions)\n",
+    "        }\n",
+    "    }\n",
+    "    print(metrics)\n",
+    "    return metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1252,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.003900289535522461\n",
+      "{'Decision Trees': {'model': 'Decision Trees', 'accuracy': 0.6428571428571429, 'precision': 0.6428571428571429, 'recall': 1.0, 'f1-score': 0.782608695652174, 'r2': -0.5555555555555556}}\n",
+      "Accuracy on train set: 0.6785714285714286\n"
+     ]
+    }
+   ],
+   "source": [
+    "#DecisionTreeClassifier\n",
+    "start = time.time()\n",
+    "modelDC = DecisionTreeClassifier(max_depth = 12, min_samples_leaf = 10)\n",
+    "modelDC.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeDC = end - start\n",
+    "print('Time: ', TimeDC)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = modelDC.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Decision Trees\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = modelDC.predict(X_train)\n",
+    "accuracyDC2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyDC2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1253,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Multinomial Naive Bayes': {'model': 'Multinomial Naive Bayes', 'accuracy': 0.5714285714285714, 'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'r2': -0.8666666666666667}}\n",
+      "Accuracy on train set: 0.8214285714285714\n"
+     ]
+    }
+   ],
+   "source": [
+    "#MultinomialNB\n",
+    "start = time.time()\n",
+    "modelNB = MultinomialNB(alpha=0.005)\n",
+    "modelNB.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeNB = end - start\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = modelNB.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Multinomial Naive Bayes\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = modelNB.predict(X_train)\n",
+    "accuracyNB2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyNB2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1254,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.008631229400634766\n",
+      "{'Gaussian Naive Bayes': {'model': 'Gaussian Naive Bayes', 'accuracy': 0.42857142857142855, 'precision': 0.6, 'recall': 0.3333333333333333, 'f1-score': 0.42857142857142855, 'r2': -1.488888888888889}}\n",
+      "Accuracy on train set: 0.7857142857142857\n"
+     ]
+    }
+   ],
+   "source": [
+    "#GaussianNB\n",
+    "start = time.time()\n",
+    "modelGNB = GaussianNB()\n",
+    "modelGNB.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeGNB = end - start\n",
+    "print('Time: ', TimeGNB)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = modelGNB.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Gaussian Naive Bayes\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = modelGNB.predict(X_train)\n",
+    "accuracyGNB2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyGNB2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1255,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.009811878204345703\n",
+      "{'Logistic Regression': {'model': 'Logistic Regression', 'accuracy': 0.7142857142857143, 'precision': 0.6923076923076923, 'recall': 1.0, 'f1-score': 0.8181818181818181, 'r2': -0.24444444444444446}}\n",
+      "Accuracy on train set: 0.7857142857142857\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Logistic Regression\n",
+    "start = time.time()\n",
+    "modelLR = LogisticRegression()\n",
+    "modelLR.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeLR = end - start\n",
+    "print('Time: ', TimeLR)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = modelLR.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Logistic Regression\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = modelLR.predict(X_train)\n",
+    "accuracyLR2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyLR2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1256,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.23129034042358398\n",
+      "{'Random Forest': {'model': 'Random Forest', 'accuracy': 0.6428571428571429, 'precision': 0.6666666666666666, 'recall': 0.8888888888888888, 'f1-score': 0.761904761904762, 'r2': -0.5555555555555556}}\n",
+      "Accuracy on train set: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#RandomForestClassifier\n",
+    "start = time.time()\n",
+    "rfc = RandomForestClassifier()\n",
+    "rfc.fit(X_train, y_train)\n",
+    "end = time.time()\n",
+    "TimeRFC = end - start\n",
+    "print('Time: ', TimeRFC)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = rfc.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Random Forest\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = rfc.predict(X_train)\n",
+    "accuracyRFC2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracyRFC2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1257,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.0054416656494140625\n",
+      "{'LinearSVC': {'model': 'LinearSVC', 'accuracy': 0.5714285714285714, 'precision': 0.6363636363636364, 'recall': 0.7777777777777778, 'f1-score': 0.7000000000000001, 'r2': -0.8666666666666667}}\n",
+      "Accuracy on train set: 0.8928571428571429\n"
+     ]
+    }
+   ],
+   "source": [
+    "#LinearSVC\n",
+    "start = time.time()\n",
+    "svc = LinearSVC()\n",
+    "svc.fit(X_train, y_train) \n",
+    "end = time.time()\n",
+    "TimeSVC = end - start\n",
+    "print('Time: ', TimeSVC)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = svc.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"LinearSVC\", y_test, y_pred))\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = svc.predict(X_train)\n",
+    "accuracySVC2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracySVC2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1258,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  0.1327650547027588\n",
+      "{'Gradient Boosting Classifier': {'model': 'Gradient Boosting Classifier', 'accuracy': 0.5714285714285714, 'precision': 0.6363636363636364, 'recall': 0.7777777777777778, 'f1-score': 0.7000000000000001, 'r2': -0.8666666666666667}}\n",
+      "Accuracy on train set: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Gradient Boosting Classifier\n",
+    "start = time.time()\n",
+    "\n",
+    "grb= GradientBoostingClassifier()\n",
+    "grb.fit(X_train,y_train)\n",
+    "end = time.time()\n",
+    "Timegrb = end - start\n",
+    "print('Time: ', Timegrb)\n",
+    "\n",
+    "#Evaluating model on test set\n",
+    "y_pred = grb.predict(X_test)\n",
+    "all_metrics.update(metrics_data(\"Gradient Boosting Classifier\", y_test, y_pred))\n",
+    "\n",
+    "\n",
+    "#Evaluating model on train set\n",
+    "y_pred = grb.predict(X_train)\n",
+    "accuracygrb2 = accuracy_score(y_train, y_pred)\n",
+    "print('Accuracy on train set: {}'.format(accuracygrb2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model performance comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1259,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Trees</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.782609</td>\n",
+       "      <td>-0.555556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Multinomial Naive Bayes</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>-0.866667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gaussian Naive Bayes</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>-1.488889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>-0.244444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>0.761905</td>\n",
+       "      <td>-0.555556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>LinearSVC</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>0.777778</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>-0.866667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Gradient Boosting Classifier</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>0.777778</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>-0.866667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          model  accuracy precision    recall  f1-score  \\\n",
+       "0                Decision Trees  0.642857  0.642857       1.0  0.782609   \n",
+       "1       Multinomial Naive Bayes  0.571429  0.666667  0.666667  0.666667   \n",
+       "2          Gaussian Naive Bayes  0.428571       0.6  0.333333  0.428571   \n",
+       "3           Logistic Regression  0.714286  0.692308       1.0  0.818182   \n",
+       "4                 Random Forest  0.642857  0.666667  0.888889  0.761905   \n",
+       "5                     LinearSVC  0.571429  0.636364  0.777778       0.7   \n",
+       "6  Gradient Boosting Classifier  0.571429  0.636364  0.777778       0.7   \n",
+       "\n",
+       "         r2  \n",
+       "0 -0.555556  \n",
+       "1 -0.866667  \n",
+       "2 -1.488889  \n",
+       "3 -0.244444  \n",
+       "4 -0.555556  \n",
+       "5 -0.866667  \n",
+       "6 -0.866667  "
+      ]
+     },
+     "execution_count": 1259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_metrics = pd.DataFrame(all_metrics).T\n",
+    "all_metrics = all_metrics.reset_index(drop=True)\n",
+    "all_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1260,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Accuracy_train</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Trees</td>\n",
+       "      <td>0.678571</td>\n",
+       "      <td>0.003900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Multinomial Naive Bayes</td>\n",
+       "      <td>0.821429</td>\n",
+       "      <td>0.010132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gaussian Naive Bayes</td>\n",
+       "      <td>0.785714</td>\n",
+       "      <td>0.008631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.785714</td>\n",
+       "      <td>0.009812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.231290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>LinearSVC</td>\n",
+       "      <td>0.892857</td>\n",
+       "      <td>0.005442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Gradient Boosting Classifier</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.132765</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Model  Accuracy_train      Time\n",
+       "0                Decision Trees        0.678571  0.003900\n",
+       "1       Multinomial Naive Bayes        0.821429  0.010132\n",
+       "2          Gaussian Naive Bayes        0.785714  0.008631\n",
+       "3           Logistic Regression        0.785714  0.009812\n",
+       "4                 Random Forest        1.000000  0.231290\n",
+       "5                     LinearSVC        0.892857  0.005442\n",
+       "6  Gradient Boosting Classifier        1.000000  0.132765"
+      ]
+     },
+     "execution_count": 1260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Creating new df to store model performances\n",
+    "Model = ['Decision Trees', 'Multinomial Naive Bayes', 'Gaussian Naive Bayes', 'Logistic Regression', 'Random Forest', 'LinearSVC','Gradient Boosting Classifier']\n",
+    "Accuracy_train = [accuracyDC2, accuracyNB2, accuracyGNB2, accuracyLR2, accuracyRFC2, accuracySVC2,accuracygrb2]\n",
+    "Time = [TimeDC, TimeNB, TimeGNB, TimeLR, TimeRFC, TimeSVC,Timegrb]\n",
+    "\n",
+    "#Create df from lists\n",
+    "cols = ['Model', 'Accuracy_train', 'Time']\n",
+    "data = list(zip(Model, Accuracy_train, Time))\n",
+    "\n",
+    "performance = pd.DataFrame(data, columns=cols)\n",
+    "performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1261,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>Accuracy_train</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Trees</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.782609</td>\n",
+       "      <td>-0.555556</td>\n",
+       "      <td>0.678571</td>\n",
+       "      <td>0.003900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Multinomial Naive Bayes</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>-0.866667</td>\n",
+       "      <td>0.821429</td>\n",
+       "      <td>0.010132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gaussian Naive Bayes</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>-1.488889</td>\n",
+       "      <td>0.785714</td>\n",
+       "      <td>0.008631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>-0.244444</td>\n",
+       "      <td>0.785714</td>\n",
+       "      <td>0.009812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>0.761905</td>\n",
+       "      <td>-0.555556</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.231290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>LinearSVC</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>0.777778</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>-0.866667</td>\n",
+       "      <td>0.892857</td>\n",
+       "      <td>0.005442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Gradient Boosting Classifier</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>0.777778</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>-0.866667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.132765</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          model  accuracy precision    recall  f1-score  \\\n",
+       "0                Decision Trees  0.642857  0.642857       1.0  0.782609   \n",
+       "1       Multinomial Naive Bayes  0.571429  0.666667  0.666667  0.666667   \n",
+       "2          Gaussian Naive Bayes  0.428571       0.6  0.333333  0.428571   \n",
+       "3           Logistic Regression  0.714286  0.692308       1.0  0.818182   \n",
+       "4                 Random Forest  0.642857  0.666667  0.888889  0.761905   \n",
+       "5                     LinearSVC  0.571429  0.636364  0.777778       0.7   \n",
+       "6  Gradient Boosting Classifier  0.571429  0.636364  0.777778       0.7   \n",
+       "\n",
+       "         r2  Accuracy_train      Time  \n",
+       "0 -0.555556        0.678571  0.003900  \n",
+       "1 -0.866667        0.821429  0.010132  \n",
+       "2 -1.488889        0.785714  0.008631  \n",
+       "3 -0.244444        0.785714  0.009812  \n",
+       "4 -0.555556        1.000000  0.231290  \n",
+       "5 -0.866667        0.892857  0.005442  \n",
+       "6 -0.866667        1.000000  0.132765  "
+      ]
+     },
+     "execution_count": 1261,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Join result2018 with weather2018 to get the Maximum temperature (Degree C)\n",
+    "all_performance = pd.merge(left = all_metrics , right = performance ,\n",
+    "                 left_on = ['model'], right_on = ['Model'], how = 'left')\n",
+    "drop_cols = ['Model']\n",
+    "all_performance.drop(drop_cols, axis=1, inplace=True)\n",
+    "\n",
+    "all_performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unfortunately, none of the models has good enough r2 values. The best model is Logistic Regression with $R^2$ just approximately 0.4. We cannot confidently say that Logistic Regression is a good fit to predict the salary of Data Scientists.But at the same time even after applying a boosting algorithm like Gradient Boosting Classifier logistic regression and Random Forest still gets a lead in case of model accuracy.\n",
+    "\n",
+    "**This questionS we leave for further exploration in future projects.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hamming Loss (HL) and Jacard Score On Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Hamming loss is the fraction of labels that are incorrectly predicted ( evaluation metrics for a classifier model.) \n",
+    "- The Jaccard Index, also known as the Jaccard similarity coefficient, is a statistic used in understanding the similarities between sample sets. (To measure Similarity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1216,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clf:  RandomForestClassifier\n",
+      "Jacard score: 0.6666666666666666\n",
+      "Hamming loss: 0.2857142857142857\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "def avg_jacard(y_true,y_pred):\n",
+    "\n",
+    "    jacard = np.minimum(y_true,y_pred).sum(axis=0) / np.maximum(y_true,y_pred).sum(axis=0)\n",
+    "    \n",
+    "    return jacard.mean()\n",
+    "\n",
+    "def print_score(y_pred, clf):\n",
+    "    print(\"Clf: \", clf.__class__.__name__)\n",
+    "    print(\"Jacard score: {}\".format(avg_jacard(y_test, y_pred)))\n",
+    "    print(\"Hamming loss: {}\".format(hamming_loss(y_pred, y_test)))\n",
+    "    print(\"---\")    \n",
+    "\n",
+    "rfc = RandomForestClassifier()\n",
+    "rfc.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = rfc.predict(X_test)\n",
+    "\n",
+    "print_score(y_pred, rfc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1217,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clf:  MLPClassifier\n",
+      "Jacard score: 0.5384615384615384\n",
+      "Hamming loss: 0.42857142857142855\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlpc = MLPClassifier()\n",
+    "mlpc.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = mlpc.predict(X_test)\n",
+    "\n",
+    "print_score(y_pred, mlpc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1266,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clf:  SGDClassifier\n",
+      "Jacard score: 0.6153846153846154\n",
+      "Hamming loss: 0.35714285714285715\n",
+      "---\n",
+      "Clf:  LogisticRegression\n",
+      "Jacard score: 0.6923076923076923\n",
+      "Hamming loss: 0.2857142857142857\n",
+      "---\n",
+      "Clf:  MultinomialNB\n",
+      "Jacard score: 0.6923076923076923\n",
+      "Hamming loss: 0.2857142857142857\n",
+      "---\n",
+      "Clf:  LinearSVC\n",
+      "Jacard score: 0.5384615384615384\n",
+      "Hamming loss: 0.42857142857142855\n",
+      "---\n",
+      "Clf:  GradientBoostingClassifier\n",
+      "Jacard score: 0.5384615384615384\n",
+      "Hamming loss: 0.42857142857142855\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "sgd = SGDClassifier()\n",
+    "lr = LogisticRegression()\n",
+    "mn = MultinomialNB()\n",
+    "svc = LinearSVC()\n",
+    "grb= GradientBoostingClassifier()\n",
+    "\n",
+    "\n",
+    "\n",
+    "for classifier in [sgd, lr, mn, svc,grb,]:\n",
+    "    clf = OneVsRestClassifier(classifier)\n",
+    "    clf.fit(X_train, y_train)\n",
+    "    y_pred = clf.predict(X_test)\n",
+    "    print_score(y_pred, classifier)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Findings: It has been found that better Hamming loss has been found in Logistic Regression and MultinomialNB **which is 0.28571**<br>\n",
+    "Jaccard similarity scores give us the distribution of label sets when using the models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predicting what causing Job satisfaction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An examination of work satisfaction variables based on Stack Over Flow survey data from 2020. Job satisfaction can be defined by factors such as compensation, benefits, work environment, team members, work-life balance, education level, place, and so on. By analyzing the Stack Over Flow survey data from 2020, I will try to find some features that are negatively and positively affecting job satisfaction in various countries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1267,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CurrentJobSatis\n",
+       "Very satisfied                        19\n",
+       "Slightly satisfied                     9\n",
+       "Slightly dissatisfied                  8\n",
+       "Very dissatisfied                      6\n",
+       "Neither satisfied nor dissatisfied     3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1268,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Very satisfied', 'Slightly satisfied', 'Slightly dissatisfied', 'Very dissatisfied', 'Neither satisfied nor dissatisfied']\n",
+      "[19, 9, 8, 6, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "participation_rate = df2020['CurrentJobSatis'].value_counts().keys().tolist()\n",
+    "print(participation_rate)\n",
+    "count = df2020['CurrentJobSatis'].value_counts().tolist()\n",
+    "print(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1269,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(count, explode = (0.01,0.01,0.1,0.1,0.2), labels = participation_rate, shadow=True, autopct=lambda p : f'{p:.2f}%  ({p * sum(count)/100:,.0f})', textprops={'fontsize':18, 'weight':'bold'})\n",
+    "plt.title(\"Propotion of Job Satisfaction from the Dataset\",fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**In further Analysis, we will split into two categories like Satisfied or Not Satisfied**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1270,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Applying one hot encoding\n",
+    "df_indicator = df.isnull().astype(int).add_suffix('_nan')\n",
+    "df = pd.concat([df, df_indicator], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1271,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "best mean cross-validation score: -0.358\n",
+      "best parameters: {'max_depth': 40, 'min_samples_leaf': 20}\n",
+      "test-set score: -0.422\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Grid search for good parameters, I used the mean absolute error as the main measure of quality\n",
+    "param_grid = {'min_samples_leaf': [10,15,20],'max_depth': [20,30,40]}\n",
+    "grid = GridSearchCV(RandomForestRegressor(n_estimators=100,n_jobs=-1, oob_score=True), param_grid=param_grid,\n",
+    "                    scoring='neg_mean_absolute_error',cv=5, return_train_score=True)\n",
+    "X_train_grit = X_train.sample(frac=0.5, random_state=42)\n",
+    "grid.fit(X_train_grit, y_train.loc[X_train_grit.index])\n",
+    "print(\"best mean cross-validation score: {:.3f}\".format(grid.best_score_))\n",
+    "print(\"best parameters: {}\".format(grid.best_params_))\n",
+    "print(\"test-set score: {:.3f}\".format(grid.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Here Random Forest is used to Predicting Job satisfaction, model did not yield much better output and turned out to be very complex to get insights.** Random forest Regressor, Logistic Regression which may yield good results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Trying with Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Used Sklearn library to create a Logistic Regression model.\n",
+    "\n",
+    "Before creating a model, need to create data, Using model coefficients, features that have negative and positive effects on job satisfaction to be calculated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1272,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numericals = [\"Age\",\"SalaryUSD\",\"YearsCodePro\"]\n",
+    "categoricals = [\"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1273,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1274,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CurrentJobSatis\n",
+       "Very satisfied                        19\n",
+       "Slightly satisfied                     9\n",
+       "Slightly dissatisfied                  8\n",
+       "Very dissatisfied                      6\n",
+       "Neither satisfied nor dissatisfied     3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 1274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2020['CurrentJobSatis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Performing further Spliting of CurrentJobSatis Coloumn**\n",
+    "- Delete \"Neither satisfied nor dissatisfied\"\n",
+    "- Combine \"Very satisfied\" and \"Slightly satisfied\", label as \"Satisfied\" -->1\n",
+    "- Combine \"Very dissatisfied\" and \"Slightly dissatisfied\", label as \"Dissatisfied\"-->0\n",
+    "- Delete rows \"Neither satisfied nor dissatisfied\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1275,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df = df2020.drop(df2020[df2020.CurrentJobSatis == \"Neither satisfied nor dissatisfied\"].index)\n",
+    "\n",
+    "df.CurrentJobSatis = [1 if each == \"Very satisfied\" else \n",
+    "             1 if each == \"Slightly satisfied\" else \n",
+    "             0 if each == \"Very dissatisfied\"else \n",
+    "             0 if each == \"Slightly dissatisfied\" else\n",
+    "             each for each in df.CurrentJobSatis]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1276,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dropping nan in Converted Salary if any\n",
+    "df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1277,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>EdLevel</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>Hobbyist</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>CurrentJobSatis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>36</td>\n",
+       "      <td>116000.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22</td>\n",
+       "      <td>32315.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23</td>\n",
+       "      <td>40070.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Computer Science</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>49</td>\n",
+       "      <td>14268.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Math/Stat</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>53</td>\n",
+       "      <td>38916.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>No Degree</td>\n",
+       "      <td>Full-time</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No major</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age  SalaryUSD  YearsCodePro         Country    EdLevel Employment  \\\n",
+       "0   36   116000.0          13.0   United States  Bachelors  Full-time   \n",
+       "1   22    32315.0           4.0  United Kingdom  Bachelors  Full-time   \n",
+       "2   23    40070.0           2.0  United Kingdom  Bachelors  Full-time   \n",
+       "3   49    14268.0           7.0           Spain  No Degree  Full-time   \n",
+       "4   53    38916.0          20.0     Netherlands  No Degree  Full-time   \n",
+       "\n",
+       "  Hobbyist    UndergradMajor  CurrentJobSatis  \n",
+       "0      Yes  Computer Science                0  \n",
+       "1      Yes         Math/Stat                1  \n",
+       "2      Yes  Computer Science                0  \n",
+       "3       No         Math/Stat                0  \n",
+       "4      Yes          No major                1  "
+      ]
+     },
+     "execution_count": 1277,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols= [\"Age\",\"SalaryUSD\",\"YearsCodePro\", \"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\", \"CurrentJobSatis\"]\n",
+    "df = df[cols]\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1278,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# one hot encoding\n",
+    "df = pd.get_dummies(df,  columns = categoricals )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1279,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalization of numerical features\n",
+    "for each in numericals:\n",
+    "    df[each] = (df[each] - df[each].min()) / (df[each].max() - df[each].min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1280,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1281,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data into X and y\n",
+    "X = df.drop(\"CurrentJobSatis\", axis = 1)\n",
+    "y = df.CurrentJobSatis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1282,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SalaryUSD</th>\n",
+       "      <th>YearsCodePro</th>\n",
+       "      <th>Country_Belgium</th>\n",
+       "      <th>Country_Brazil</th>\n",
+       "      <th>Country_Canada</th>\n",
+       "      <th>Country_Czech Republic</th>\n",
+       "      <th>Country_France</th>\n",
+       "      <th>Country_Germany</th>\n",
+       "      <th>Country_Greece</th>\n",
+       "      <th>Country_Indonesia</th>\n",
+       "      <th>Country_Israel</th>\n",
+       "      <th>Country_Italy</th>\n",
+       "      <th>Country_Netherlands</th>\n",
+       "      <th>Country_Poland</th>\n",
+       "      <th>Country_Spain</th>\n",
+       "      <th>Country_United Kingdom</th>\n",
+       "      <th>Country_United States</th>\n",
+       "      <th>EdLevel_Associate</th>\n",
+       "      <th>EdLevel_Bachelors</th>\n",
+       "      <th>EdLevel_Doctorate</th>\n",
+       "      <th>EdLevel_No Degree</th>\n",
+       "      <th>EdLevel_Professional</th>\n",
+       "      <th>Employment_Full-time</th>\n",
+       "      <th>Employment_Not employed</th>\n",
+       "      <th>Employment_Part-time</th>\n",
+       "      <th>Employment_Self-employed</th>\n",
+       "      <th>Employment_Student</th>\n",
+       "      <th>Hobbyist_No</th>\n",
+       "      <th>Hobbyist_Yes</th>\n",
+       "      <th>UndergradMajor_Arts and Science</th>\n",
+       "      <th>UndergradMajor_Computer Science</th>\n",
+       "      <th>UndergradMajor_Engineering</th>\n",
+       "      <th>UndergradMajor_Info Systems</th>\n",
+       "      <th>UndergradMajor_Math/Stat</th>\n",
+       "      <th>UndergradMajor_No major</th>\n",
+       "      <th>UndergradMajor_Other Science</th>\n",
+       "      <th>UndergradMajor_Web Design/Dev</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.484848</td>\n",
+       "      <td>0.719681</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.186533</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.235939</td>\n",
+       "      <td>0.037037</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.878788</td>\n",
+       "      <td>0.071558</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.228587</td>\n",
+       "      <td>0.703704</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.212121</td>\n",
+       "      <td>0.401137</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.757576</td>\n",
+       "      <td>0.672383</td>\n",
+       "      <td>0.814815</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.151515</td>\n",
+       "      <td>0.483958</td>\n",
+       "      <td>0.074074</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.121212</td>\n",
+       "      <td>0.511990</td>\n",
+       "      <td>0.074074</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.757576</td>\n",
+       "      <td>0.566034</td>\n",
+       "      <td>0.703704</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.183023</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.575758</td>\n",
+       "      <td>0.385617</td>\n",
+       "      <td>0.481481</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.424242</td>\n",
+       "      <td>0.474759</td>\n",
+       "      <td>0.074074</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.454545</td>\n",
+       "      <td>0.600469</td>\n",
+       "      <td>0.407407</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.808873</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.060606</td>\n",
+       "      <td>0.214807</td>\n",
+       "      <td>0.037037</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.121212</td>\n",
+       "      <td>0.221694</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.458283</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.777019</td>\n",
+       "      <td>0.259259</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.311944</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.242424</td>\n",
+       "      <td>0.557109</td>\n",
+       "      <td>0.185185</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.727273</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.814815</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.085701</td>\n",
+       "      <td>0.518519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.424242</td>\n",
+       "      <td>0.581809</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.151515</td>\n",
+       "      <td>0.133005</td>\n",
+       "      <td>0.185185</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>0.151515</td>\n",
+       "      <td>0.483958</td>\n",
+       "      <td>0.281111</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0.242424</td>\n",
+       "      <td>0.221579</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.582707</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0.848485</td>\n",
+       "      <td>0.726052</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.281111</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0.151515</td>\n",
+       "      <td>0.483958</td>\n",
+       "      <td>0.037037</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0.181818</td>\n",
+       "      <td>0.669351</td>\n",
+       "      <td>0.074074</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>0.030303</td>\n",
+       "      <td>0.221579</td>\n",
+       "      <td>0.281111</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.242424</td>\n",
+       "      <td>0.808873</td>\n",
+       "      <td>0.037037</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>0.242424</td>\n",
+       "      <td>0.372315</td>\n",
+       "      <td>0.037037</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.118397</td>\n",
+       "      <td>0.518519</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>0.030303</td>\n",
+       "      <td>0.221579</td>\n",
+       "      <td>0.037037</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0.272727</td>\n",
+       "      <td>0.203817</td>\n",
+       "      <td>0.037037</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>0.303030</td>\n",
+       "      <td>0.462495</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.272727</td>\n",
+       "      <td>0.245483</td>\n",
+       "      <td>0.148148</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.303030</td>\n",
+       "      <td>0.486589</td>\n",
+       "      <td>0.296296</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.573151</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Age  SalaryUSD  YearsCodePro  Country_Belgium  Country_Brazil  \\\n",
+       "0   0.484848   0.719681      0.444444            False           False   \n",
+       "1   0.060606   0.186533      0.111111            False           False   \n",
+       "2   0.090909   0.235939      0.037037            False           False   \n",
+       "3   0.878788   0.071558      0.222222            False           False   \n",
+       "4   1.000000   0.228587      0.703704            False           False   \n",
+       "5   0.212121   0.401137      0.000000            False           False   \n",
+       "6   0.757576   0.672383      0.814815            False           False   \n",
+       "7   0.151515   0.483958      0.074074            False           False   \n",
+       "9   0.121212   0.511990      0.074074            False           False   \n",
+       "10  0.757576   0.566034      0.703704            False           False   \n",
+       "11  0.333333   0.183023      0.333333            False           False   \n",
+       "12  0.575758   0.385617      0.481481             True           False   \n",
+       "13  0.424242   0.474759      0.074074            False           False   \n",
+       "14  0.454545   0.600469      0.407407            False           False   \n",
+       "15  0.363636   0.808873      0.333333            False           False   \n",
+       "17  0.060606   0.214807      0.037037            False           False   \n",
+       "18  0.121212   0.221694      0.111111            False           False   \n",
+       "19  1.000000   0.458283      1.000000            False           False   \n",
+       "20  0.545455   0.777019      0.259259            False           False   \n",
+       "21  0.090909   0.311944      0.111111            False           False   \n",
+       "22  0.242424   0.557109      0.185185            False           False   \n",
+       "23  0.727273   1.000000      0.814815            False           False   \n",
+       "25  0.545455   0.085701      0.518519            False            True   \n",
+       "26  0.424242   0.581809      0.444444            False           False   \n",
+       "30  0.151515   0.133005      0.185185            False           False   \n",
+       "31  0.151515   0.483958      0.281111            False           False   \n",
+       "33  0.242424   0.221579      0.222222            False           False   \n",
+       "34  0.363636   0.582707      0.222222            False           False   \n",
+       "35  0.848485   0.726052      0.888889            False           False   \n",
+       "36  0.000000   0.000000      0.281111            False           False   \n",
+       "37  0.151515   0.483958      0.037037            False           False   \n",
+       "38  0.181818   0.669351      0.074074            False           False   \n",
+       "39  0.030303   0.221579      0.281111            False           False   \n",
+       "40  0.242424   0.808873      0.037037            False           False   \n",
+       "41  0.242424   0.372315      0.037037            False           False   \n",
+       "43  0.666667   0.118397      0.518519            False           False   \n",
+       "44  0.030303   0.221579      0.037037            False           False   \n",
+       "45  0.272727   0.203817      0.037037            False           False   \n",
+       "46  0.303030   0.462495      0.111111            False           False   \n",
+       "47  0.272727   0.245483      0.148148            False           False   \n",
+       "48  0.303030   0.486589      0.296296            False           False   \n",
+       "49  0.666667   0.573151      0.370370            False           False   \n",
+       "\n",
+       "    Country_Canada  Country_Czech Republic  Country_France  Country_Germany  \\\n",
+       "0            False                   False           False            False   \n",
+       "1            False                   False           False            False   \n",
+       "2            False                   False           False            False   \n",
+       "3            False                   False           False            False   \n",
+       "4            False                   False           False            False   \n",
+       "5            False                   False           False            False   \n",
+       "6            False                   False           False            False   \n",
+       "7            False                   False           False            False   \n",
+       "9            False                   False           False            False   \n",
+       "10           False                   False           False             True   \n",
+       "11            True                   False           False            False   \n",
+       "12           False                   False           False            False   \n",
+       "13           False                   False           False            False   \n",
+       "14           False                   False           False            False   \n",
+       "15           False                   False           False            False   \n",
+       "17           False                   False            True            False   \n",
+       "18           False                   False            True            False   \n",
+       "19           False                   False           False            False   \n",
+       "20           False                   False           False            False   \n",
+       "21           False                   False           False            False   \n",
+       "22           False                   False           False            False   \n",
+       "23           False                   False           False            False   \n",
+       "25           False                   False           False            False   \n",
+       "26           False                   False           False            False   \n",
+       "30           False                   False           False            False   \n",
+       "31           False                   False           False            False   \n",
+       "33            True                   False           False            False   \n",
+       "34           False                   False           False            False   \n",
+       "35           False                   False           False            False   \n",
+       "36           False                   False           False            False   \n",
+       "37           False                   False           False            False   \n",
+       "38           False                   False            True            False   \n",
+       "39            True                   False           False            False   \n",
+       "40           False                   False           False            False   \n",
+       "41           False                   False           False            False   \n",
+       "43           False                   False           False            False   \n",
+       "44            True                   False           False            False   \n",
+       "45           False                   False           False            False   \n",
+       "46            True                   False           False            False   \n",
+       "47           False                    True           False            False   \n",
+       "48            True                   False           False            False   \n",
+       "49           False                   False           False            False   \n",
+       "\n",
+       "    Country_Greece  Country_Indonesia  Country_Israel  Country_Italy  \\\n",
+       "0            False              False           False          False   \n",
+       "1            False              False           False          False   \n",
+       "2            False              False           False          False   \n",
+       "3            False              False           False          False   \n",
+       "4            False              False           False          False   \n",
+       "5            False              False           False          False   \n",
+       "6            False              False           False          False   \n",
+       "7            False              False           False          False   \n",
+       "9            False              False           False          False   \n",
+       "10           False              False           False          False   \n",
+       "11           False              False           False          False   \n",
+       "12           False              False           False          False   \n",
+       "13           False              False           False          False   \n",
+       "14           False              False           False           True   \n",
+       "15           False              False           False          False   \n",
+       "17           False              False           False          False   \n",
+       "18           False              False           False          False   \n",
+       "19           False              False           False          False   \n",
+       "20           False              False           False          False   \n",
+       "21           False              False           False          False   \n",
+       "22           False              False           False          False   \n",
+       "23           False              False           False          False   \n",
+       "25           False              False           False          False   \n",
+       "26           False              False           False          False   \n",
+       "30           False              False           False          False   \n",
+       "31           False              False           False          False   \n",
+       "33           False              False           False          False   \n",
+       "34           False              False           False          False   \n",
+       "35           False              False           False          False   \n",
+       "36           False              False           False          False   \n",
+       "37           False              False           False          False   \n",
+       "38           False              False           False          False   \n",
+       "39           False              False           False          False   \n",
+       "40           False              False           False          False   \n",
+       "41           False               True           False          False   \n",
+       "43            True              False           False          False   \n",
+       "44           False              False           False          False   \n",
+       "45           False              False            True          False   \n",
+       "46           False              False           False          False   \n",
+       "47           False              False           False          False   \n",
+       "48           False              False           False          False   \n",
+       "49           False              False           False          False   \n",
+       "\n",
+       "    Country_Netherlands  Country_Poland  Country_Spain  \\\n",
+       "0                 False           False          False   \n",
+       "1                 False           False          False   \n",
+       "2                 False           False          False   \n",
+       "3                 False           False           True   \n",
+       "4                  True           False          False   \n",
+       "5                 False           False          False   \n",
+       "6                 False           False          False   \n",
+       "7                 False           False          False   \n",
+       "9                 False           False          False   \n",
+       "10                False           False          False   \n",
+       "11                False           False          False   \n",
+       "12                False           False          False   \n",
+       "13                False           False          False   \n",
+       "14                False           False          False   \n",
+       "15                False           False          False   \n",
+       "17                False           False          False   \n",
+       "18                False           False          False   \n",
+       "19                False           False          False   \n",
+       "20                False           False          False   \n",
+       "21                False           False          False   \n",
+       "22                False           False          False   \n",
+       "23                False           False          False   \n",
+       "25                False           False          False   \n",
+       "26                False           False          False   \n",
+       "30                False           False          False   \n",
+       "31                False           False          False   \n",
+       "33                False           False          False   \n",
+       "34                False           False          False   \n",
+       "35                False           False          False   \n",
+       "36                False            True          False   \n",
+       "37                False           False          False   \n",
+       "38                False           False          False   \n",
+       "39                False           False          False   \n",
+       "40                False           False          False   \n",
+       "41                False           False          False   \n",
+       "43                False           False          False   \n",
+       "44                False           False          False   \n",
+       "45                False           False          False   \n",
+       "46                False           False          False   \n",
+       "47                False           False          False   \n",
+       "48                False           False          False   \n",
+       "49                False           False          False   \n",
+       "\n",
+       "    Country_United Kingdom  Country_United States  EdLevel_Associate  \\\n",
+       "0                    False                   True              False   \n",
+       "1                     True                  False              False   \n",
+       "2                     True                  False              False   \n",
+       "3                    False                  False              False   \n",
+       "4                    False                  False              False   \n",
+       "5                    False                   True               True   \n",
+       "6                     True                  False              False   \n",
+       "7                    False                   True              False   \n",
+       "9                    False                   True              False   \n",
+       "10                   False                  False              False   \n",
+       "11                   False                  False              False   \n",
+       "12                   False                  False              False   \n",
+       "13                    True                  False              False   \n",
+       "14                   False                  False              False   \n",
+       "15                   False                   True              False   \n",
+       "17                   False                  False              False   \n",
+       "18                   False                  False              False   \n",
+       "19                    True                  False              False   \n",
+       "20                   False                   True              False   \n",
+       "21                   False                   True              False   \n",
+       "22                    True                  False              False   \n",
+       "23                   False                   True              False   \n",
+       "25                   False                  False              False   \n",
+       "26                    True                  False              False   \n",
+       "30                    True                  False              False   \n",
+       "31                   False                   True              False   \n",
+       "33                   False                  False              False   \n",
+       "34                   False                   True              False   \n",
+       "35                   False                   True               True   \n",
+       "36                   False                  False              False   \n",
+       "37                   False                   True              False   \n",
+       "38                   False                  False              False   \n",
+       "39                   False                  False              False   \n",
+       "40                   False                   True              False   \n",
+       "41                   False                  False              False   \n",
+       "43                   False                  False              False   \n",
+       "44                   False                  False              False   \n",
+       "45                   False                  False              False   \n",
+       "46                   False                  False              False   \n",
+       "47                   False                  False              False   \n",
+       "48                   False                  False              False   \n",
+       "49                   False                   True               True   \n",
+       "\n",
+       "    EdLevel_Bachelors  EdLevel_Doctorate  EdLevel_No Degree  \\\n",
+       "0                True              False              False   \n",
+       "1                True              False              False   \n",
+       "2                True              False              False   \n",
+       "3               False              False               True   \n",
+       "4               False              False               True   \n",
+       "5               False              False              False   \n",
+       "6                True              False              False   \n",
+       "7                True              False              False   \n",
+       "9                True              False              False   \n",
+       "10              False              False              False   \n",
+       "11               True              False              False   \n",
+       "12               True              False              False   \n",
+       "13              False              False               True   \n",
+       "14               True              False              False   \n",
+       "15               True              False              False   \n",
+       "17               True              False              False   \n",
+       "18               True              False              False   \n",
+       "19              False               True              False   \n",
+       "20               True              False              False   \n",
+       "21               True              False              False   \n",
+       "22               True              False              False   \n",
+       "23               True              False              False   \n",
+       "25               True              False              False   \n",
+       "26              False              False               True   \n",
+       "30               True              False              False   \n",
+       "31               True              False              False   \n",
+       "33               True              False              False   \n",
+       "34               True              False              False   \n",
+       "35              False              False              False   \n",
+       "36              False              False               True   \n",
+       "37               True              False              False   \n",
+       "38               True              False              False   \n",
+       "39              False              False               True   \n",
+       "40               True              False              False   \n",
+       "41              False              False               True   \n",
+       "43              False               True              False   \n",
+       "44              False              False               True   \n",
+       "45               True              False              False   \n",
+       "46               True              False              False   \n",
+       "47               True              False              False   \n",
+       "48               True              False              False   \n",
+       "49              False              False              False   \n",
+       "\n",
+       "    EdLevel_Professional  Employment_Full-time  Employment_Not employed  \\\n",
+       "0                  False                  True                    False   \n",
+       "1                  False                  True                    False   \n",
+       "2                  False                  True                    False   \n",
+       "3                  False                  True                    False   \n",
+       "4                  False                  True                    False   \n",
+       "5                  False                  True                    False   \n",
+       "6                  False                 False                    False   \n",
+       "7                  False                  True                    False   \n",
+       "9                  False                  True                    False   \n",
+       "10                  True                  True                    False   \n",
+       "11                 False                  True                    False   \n",
+       "12                 False                  True                    False   \n",
+       "13                 False                  True                    False   \n",
+       "14                 False                  True                    False   \n",
+       "15                 False                  True                    False   \n",
+       "17                 False                  True                    False   \n",
+       "18                 False                  True                    False   \n",
+       "19                 False                  True                    False   \n",
+       "20                 False                  True                    False   \n",
+       "21                 False                  True                    False   \n",
+       "22                 False                  True                    False   \n",
+       "23                 False                  True                    False   \n",
+       "25                 False                  True                    False   \n",
+       "26                 False                  True                    False   \n",
+       "30                 False                  True                    False   \n",
+       "31                 False                 False                     True   \n",
+       "33                 False                  True                    False   \n",
+       "34                 False                  True                    False   \n",
+       "35                 False                  True                    False   \n",
+       "36                 False                  True                    False   \n",
+       "37                 False                  True                    False   \n",
+       "38                 False                 False                    False   \n",
+       "39                 False                 False                    False   \n",
+       "40                 False                  True                    False   \n",
+       "41                 False                  True                    False   \n",
+       "43                 False                  True                    False   \n",
+       "44                 False                  True                    False   \n",
+       "45                 False                 False                    False   \n",
+       "46                 False                  True                    False   \n",
+       "47                 False                  True                    False   \n",
+       "48                 False                  True                    False   \n",
+       "49                 False                  True                    False   \n",
+       "\n",
+       "    Employment_Part-time  Employment_Self-employed  Employment_Student  \\\n",
+       "0                  False                     False               False   \n",
+       "1                  False                     False               False   \n",
+       "2                  False                     False               False   \n",
+       "3                  False                     False               False   \n",
+       "4                  False                     False               False   \n",
+       "5                  False                     False               False   \n",
+       "6                  False                      True               False   \n",
+       "7                  False                     False               False   \n",
+       "9                  False                     False               False   \n",
+       "10                 False                     False               False   \n",
+       "11                 False                     False               False   \n",
+       "12                 False                     False               False   \n",
+       "13                 False                     False               False   \n",
+       "14                 False                     False               False   \n",
+       "15                 False                     False               False   \n",
+       "17                 False                     False               False   \n",
+       "18                 False                     False               False   \n",
+       "19                 False                     False               False   \n",
+       "20                 False                     False               False   \n",
+       "21                 False                     False               False   \n",
+       "22                 False                     False               False   \n",
+       "23                 False                     False               False   \n",
+       "25                 False                     False               False   \n",
+       "26                 False                     False               False   \n",
+       "30                 False                     False               False   \n",
+       "31                 False                     False               False   \n",
+       "33                 False                     False               False   \n",
+       "34                 False                     False               False   \n",
+       "35                 False                     False               False   \n",
+       "36                 False                     False               False   \n",
+       "37                 False                     False               False   \n",
+       "38                 False                      True               False   \n",
+       "39                 False                     False                True   \n",
+       "40                 False                     False               False   \n",
+       "41                 False                     False               False   \n",
+       "43                 False                     False               False   \n",
+       "44                 False                     False               False   \n",
+       "45                  True                     False               False   \n",
+       "46                 False                     False               False   \n",
+       "47                 False                     False               False   \n",
+       "48                 False                     False               False   \n",
+       "49                 False                     False               False   \n",
+       "\n",
+       "    Hobbyist_No  Hobbyist_Yes  UndergradMajor_Arts and Science  \\\n",
+       "0         False          True                            False   \n",
+       "1         False          True                            False   \n",
+       "2         False          True                            False   \n",
+       "3          True         False                            False   \n",
+       "4         False          True                            False   \n",
+       "5         False          True                            False   \n",
+       "6         False          True                            False   \n",
+       "7         False          True                            False   \n",
+       "9          True         False                            False   \n",
+       "10        False          True                            False   \n",
+       "11        False          True                            False   \n",
+       "12        False          True                            False   \n",
+       "13        False          True                            False   \n",
+       "14         True         False                            False   \n",
+       "15         True         False                            False   \n",
+       "17        False          True                            False   \n",
+       "18        False          True                            False   \n",
+       "19        False          True                            False   \n",
+       "20         True         False                             True   \n",
+       "21         True         False                            False   \n",
+       "22         True         False                            False   \n",
+       "23         True         False                            False   \n",
+       "25         True         False                            False   \n",
+       "26        False          True                            False   \n",
+       "30        False          True                            False   \n",
+       "31        False          True                            False   \n",
+       "33         True         False                            False   \n",
+       "34        False          True                            False   \n",
+       "35        False          True                            False   \n",
+       "36        False          True                            False   \n",
+       "37        False          True                            False   \n",
+       "38        False          True                            False   \n",
+       "39         True         False                            False   \n",
+       "40        False          True                             True   \n",
+       "41        False          True                            False   \n",
+       "43        False          True                            False   \n",
+       "44        False          True                             True   \n",
+       "45        False          True                            False   \n",
+       "46        False          True                            False   \n",
+       "47         True         False                            False   \n",
+       "48        False          True                            False   \n",
+       "49        False          True                            False   \n",
+       "\n",
+       "    UndergradMajor_Computer Science  UndergradMajor_Engineering  \\\n",
+       "0                              True                       False   \n",
+       "1                             False                       False   \n",
+       "2                              True                       False   \n",
+       "3                             False                       False   \n",
+       "4                             False                       False   \n",
+       "5                              True                       False   \n",
+       "6                              True                       False   \n",
+       "7                              True                       False   \n",
+       "9                              True                       False   \n",
+       "10                            False                        True   \n",
+       "11                             True                       False   \n",
+       "12                             True                       False   \n",
+       "13                             True                       False   \n",
+       "14                             True                       False   \n",
+       "15                             True                       False   \n",
+       "17                             True                       False   \n",
+       "18                             True                       False   \n",
+       "19                            False                       False   \n",
+       "20                            False                       False   \n",
+       "21                             True                       False   \n",
+       "22                             True                       False   \n",
+       "23                             True                       False   \n",
+       "25                             True                       False   \n",
+       "26                             True                       False   \n",
+       "30                            False                       False   \n",
+       "31                            False                       False   \n",
+       "33                             True                       False   \n",
+       "34                            False                       False   \n",
+       "35                             True                       False   \n",
+       "36                            False                       False   \n",
+       "37                             True                       False   \n",
+       "38                             True                       False   \n",
+       "39                             True                       False   \n",
+       "40                            False                       False   \n",
+       "41                            False                       False   \n",
+       "43                            False                       False   \n",
+       "44                            False                       False   \n",
+       "45                             True                       False   \n",
+       "46                             True                       False   \n",
+       "47                             True                       False   \n",
+       "48                             True                       False   \n",
+       "49                            False                       False   \n",
+       "\n",
+       "    UndergradMajor_Info Systems  UndergradMajor_Math/Stat  \\\n",
+       "0                         False                     False   \n",
+       "1                         False                      True   \n",
+       "2                         False                     False   \n",
+       "3                         False                      True   \n",
+       "4                         False                     False   \n",
+       "5                         False                     False   \n",
+       "6                         False                     False   \n",
+       "7                         False                     False   \n",
+       "9                         False                     False   \n",
+       "10                        False                     False   \n",
+       "11                        False                     False   \n",
+       "12                        False                     False   \n",
+       "13                        False                     False   \n",
+       "14                        False                     False   \n",
+       "15                        False                     False   \n",
+       "17                        False                     False   \n",
+       "18                        False                     False   \n",
+       "19                        False                     False   \n",
+       "20                        False                     False   \n",
+       "21                        False                     False   \n",
+       "22                        False                     False   \n",
+       "23                        False                     False   \n",
+       "25                        False                     False   \n",
+       "26                        False                     False   \n",
+       "30                        False                     False   \n",
+       "31                        False                      True   \n",
+       "33                        False                     False   \n",
+       "34                         True                     False   \n",
+       "35                        False                     False   \n",
+       "36                        False                     False   \n",
+       "37                        False                     False   \n",
+       "38                        False                     False   \n",
+       "39                        False                     False   \n",
+       "40                        False                     False   \n",
+       "41                        False                     False   \n",
+       "43                        False                     False   \n",
+       "44                        False                     False   \n",
+       "45                        False                     False   \n",
+       "46                        False                     False   \n",
+       "47                        False                     False   \n",
+       "48                        False                     False   \n",
+       "49                        False                     False   \n",
+       "\n",
+       "    UndergradMajor_No major  UndergradMajor_Other Science  \\\n",
+       "0                     False                         False   \n",
+       "1                     False                         False   \n",
+       "2                     False                         False   \n",
+       "3                     False                         False   \n",
+       "4                      True                         False   \n",
+       "5                     False                         False   \n",
+       "6                     False                         False   \n",
+       "7                     False                         False   \n",
+       "9                     False                         False   \n",
+       "10                    False                         False   \n",
+       "11                    False                         False   \n",
+       "12                    False                         False   \n",
+       "13                    False                         False   \n",
+       "14                    False                         False   \n",
+       "15                    False                         False   \n",
+       "17                    False                         False   \n",
+       "18                    False                         False   \n",
+       "19                    False                          True   \n",
+       "20                    False                         False   \n",
+       "21                    False                         False   \n",
+       "22                    False                         False   \n",
+       "23                    False                         False   \n",
+       "25                    False                         False   \n",
+       "26                    False                         False   \n",
+       "30                     True                         False   \n",
+       "31                    False                         False   \n",
+       "33                    False                         False   \n",
+       "34                    False                         False   \n",
+       "35                    False                         False   \n",
+       "36                     True                         False   \n",
+       "37                    False                         False   \n",
+       "38                    False                         False   \n",
+       "39                    False                         False   \n",
+       "40                    False                         False   \n",
+       "41                    False                          True   \n",
+       "43                     True                         False   \n",
+       "44                    False                         False   \n",
+       "45                    False                         False   \n",
+       "46                    False                         False   \n",
+       "47                    False                         False   \n",
+       "48                    False                         False   \n",
+       "49                    False                         False   \n",
+       "\n",
+       "    UndergradMajor_Web Design/Dev  \n",
+       "0                           False  \n",
+       "1                           False  \n",
+       "2                           False  \n",
+       "3                           False  \n",
+       "4                           False  \n",
+       "5                           False  \n",
+       "6                           False  \n",
+       "7                           False  \n",
+       "9                           False  \n",
+       "10                          False  \n",
+       "11                          False  \n",
+       "12                          False  \n",
+       "13                          False  \n",
+       "14                          False  \n",
+       "15                          False  \n",
+       "17                          False  \n",
+       "18                          False  \n",
+       "19                          False  \n",
+       "20                          False  \n",
+       "21                          False  \n",
+       "22                          False  \n",
+       "23                          False  \n",
+       "25                          False  \n",
+       "26                          False  \n",
+       "30                          False  \n",
+       "31                          False  \n",
+       "33                          False  \n",
+       "34                          False  \n",
+       "35                          False  \n",
+       "36                          False  \n",
+       "37                          False  \n",
+       "38                          False  \n",
+       "39                          False  \n",
+       "40                          False  \n",
+       "41                          False  \n",
+       "43                          False  \n",
+       "44                          False  \n",
+       "45                          False  \n",
+       "46                          False  \n",
+       "47                          False  \n",
+       "48                          False  \n",
+       "49                           True  "
+      ]
+     },
+     "execution_count": 1282,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1283,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     0\n",
+       "1     1\n",
+       "2     0\n",
+       "3     0\n",
+       "4     1\n",
+       "5     1\n",
+       "6     1\n",
+       "7     0\n",
+       "9     0\n",
+       "10    1\n",
+       "11    1\n",
+       "12    0\n",
+       "13    1\n",
+       "14    1\n",
+       "15    1\n",
+       "17    1\n",
+       "18    1\n",
+       "19    1\n",
+       "20    0\n",
+       "21    1\n",
+       "22    1\n",
+       "23    1\n",
+       "25    1\n",
+       "26    1\n",
+       "30    0\n",
+       "31    1\n",
+       "33    0\n",
+       "34    1\n",
+       "35    1\n",
+       "36    1\n",
+       "37    1\n",
+       "38    0\n",
+       "39    0\n",
+       "40    1\n",
+       "41    1\n",
+       "43    1\n",
+       "44    1\n",
+       "45    0\n",
+       "46    0\n",
+       "47    1\n",
+       "48    0\n",
+       "49    1\n",
+       "Name: CurrentJobSatis, dtype: int64"
+      ]
+     },
+     "execution_count": 1283,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1284,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split data into train and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Checking Model Coefficent**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1285,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 78.57%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# define the model\n",
+    "model = LogisticRegression()\n",
+    "# fit the model\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# get importance\n",
+    "importance = model.coef_[0]\n",
+    "\n",
+    "# make predictions for test data and evaluate\n",
+    "y_pred = model.predict(X_test)\n",
+    "predictions = [round(value) for value in y_pred]\n",
+    "accuracy = accuracy_score(y_test, predictions)\n",
+    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1286,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (3488427352.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[1286], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    We have recieved **78.57% Accuracy** which is good enough to move ahead with predictions.\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "We have recieved **78.57% Accuracy** which is good enough to move ahead with predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting Features affecting Job Satisfaction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x3000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame()\n",
+    "results_df[\"Rates\"] = importance.tolist()\n",
+    "results_df[\"Columns\"] = X.columns\n",
+    "\n",
+    "new_index = results_df.Rates.sort_values(ascending = False).index\n",
+    "sorted_results = results_df.reindex(new_index)\n",
+    "filtered_results = sorted_results[np.abs(sorted_results.Rates) > 0.1]\n",
+    "\n",
+    "plt.figure(figsize =(20,30))\n",
+    "plt.barh(filtered_results.Columns, filtered_results.Rates)\n",
+    "plt.xlabel(\"Negative and Postive Features\", fontsize = 25)\n",
+    "plt.title(\"Features Affecting Job Satistaction\",fontsize = 25)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The top 2 features negatively affecting Job Satisfaction are age, country. So, in the elderly ages, job satisfaction may decrease because of the personal expectation increases. In the same way, as the professional coding years are increasing, satisfaction may decrease.\n",
+    "\n",
+    "- Among the countries; most dissatisfied countries are Angolia, Rwanda, Krygyzstan, Sudan.\n",
+    "- UndergradMajor and other Science, are mostly satisfied.\n",
+    "- Most satisfied countries Malta, Ghana, Cyprus."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overall, we performed various analyses on the Stack overflow developer survey and derived insights from it. We found which country has the highest no of respondents, which is the most popular language, education level of respondents, different roles of developers, and so on.<br>\n",
+    "Additionally, we performed machine learning models to predict the growth of languages, the salary of data scientists, what is causing job satisfaction. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}