ML_ST_hugh_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "782e8f80-78a9-4e0b-9384-896f365185cf",
   "metadata": {},
   "source": [
    "# Stack Traces Project (HuggingFace)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c515ef5d",
   "metadata": {},
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ea3aa8e-15cb-4ec1-b427-1144fcc9f458",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from pathlib import Path\n",
    "from typing import Tuple, List, Optional, Union, Tuple\n",
    "from pprint import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "from matplotlib import gridspec\n",
    "import string\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "from itertools import permutations\n",
    "import heapq\n",
    "import csv\n",
    "import pickle\n",
    "import matplotlib.ticker as mtick\n",
    "import collections\n",
    "import os\n",
    "from tabulate import tabulate\n",
    "from openpyxl import load_workbook\n",
    "\n",
    "# pd.options.display.max_colwidth = 500\n",
    "# pd.options.display.max_columns = None\n",
    "# pd.options.display.max_rows = None\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c925ffd5",
   "metadata": {},
   "source": [
    "## Setting up the project environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "725feedf-a5c7-4192-aa8f-3ca77a152adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# .csv configuration\n",
    "encoding = \"utf-8\"\n",
    "delimiter = None\n",
    "working_directory_path = \"../\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81c38ac-0fcf-425c-91bb-813366418dbf",
   "metadata": {},
   "source": [
    "## Loading the question dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b03769f4-5831-40d4-84ea-4262e234886b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pure_data = working_directory_path + \"question_tag.csv\"\n",
    "path = Path(pure_data)\n",
    "\n",
    "if path.suffix == \".csv\":\n",
    "    df_sotorrent = pd.read_csv(path, encoding=encoding)\n",
    "else:\n",
    "    raise ValueError(\"{data_file_path.suffix} extensions are not supported.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6396565-7035-4813-8fd1-5572a916c8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(163194, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sotorrent.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b96da99-56e0-4f72-a4a8-e6e322d724d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sotorrent['Source'] = 'old'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc44412-670e-4334-a794-bdab851a3cc2",
   "metadata": {},
   "source": [
    "Update the DB"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ca74c30c-7912-4bf4-81d2-ac3d0c8b17ca",
   "metadata": {},
   "source": [
    "Ref: https://data.stackexchange.com/stackoverflow/query/edit/1769158\n",
    "\n",
    "SELECT \n",
    "      Id, \n",
    "      PostTypeId, \n",
    "      AcceptedAnswerId, \n",
    "      CreationDate, \n",
    "      ViewCount, \n",
    "      AnswerCount, \n",
    "      CommentCount, \n",
    "      Score,\n",
    "      Title,\n",
    "      Body,\n",
    "      Tags\n",
    "FROM Posts \n",
    "WHERE (tags like '%tensorflow%' OR\n",
    "      tags LIKE '%pytorch%' OR\n",
    "      tags LIKE '%scikit-learn%' OR\n",
    "      tags LIKE '%keras%' OR\n",
    "      tags LIKE '%nltk%' OR\n",
    "      tags LIKE '%huggingface%' OR\n",
    "      tags LIKE '%spark-ml%') AND\n",
    "      creationDate > '2021-01-25 00:00:00' AND\n",
    "      PostTypeId=1\n",
    "ORDER BY [creationDate] DESC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d48a93b-ebe0-4b24-bd4f-06fbb539e688",
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_data = working_directory_path + \"proj1_update_qst.csv\"\n",
    "path = Path(pure_data)\n",
    "\n",
    "if path.suffix == \".csv\":\n",
    "    df_remained = pd.read_csv(path, encoding=encoding)\n",
    "else:\n",
    "    raise ValueError(\"{data_file_path.suffix} extensions are not supported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cda54ec5-02ae-44ff-872c-f9c36c5d2739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49918, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_remained.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca14074c-5c83-4061-ad66-9dd89502d91c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_remained['source'] = 'new'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9410ae5f-fc42-492e-8364-2f28261da453",
   "metadata": {},
   "source": [
    "Concat two data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "991c61fa-052a-42c6-827e-ca0e2761b819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(213112, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df_sotorrent, df_remained])\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fbb26916-2157-4203-9a68-0e2c8bf7c752",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates([\"Id\", \"PostTypeId\", \"AcceptedAnswerId\", \"ViewCount\", \"AnswerCount\", \"CommentCount\", \"Score\", \"Title\", 'Body', 'Tags'], ignore_index=False, inplace=True)\n",
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "051f4fba-bdb2-43a4-8286-ef7c4b789785",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of records (w/o considering specific tag set) : 171114\n"
     ]
    }
   ],
   "source": [
    "print(\"The total number of records (w/o considering specific tag set) :\", df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e408693",
   "metadata": {},
   "source": [
    "## Answering the first research question (RQ1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9a6e6e-46f4-432b-81c6-a5f646d6dd08",
   "metadata": {},
   "source": [
    "### Filtering the specific tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eae1d276-5b01-482b-b244-cd89bef86bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tag_filter(pref_tags: List, tags: str) -> bool:\n",
    "    regex = \"\"\n",
    "    for tag in pref_tags:\n",
    "        regex += '(?=.*\\\\b'+ tag +'([+-]?([0-9]*[.])?[0-9]*)\\\\b)'\n",
    "    regex = r\"^\" + regex + \".*$\"\n",
    "    tags = tags.strip().lower()\n",
    "    match_result = re.match(regex, tags, re.MULTILINE | re.IGNORECASE)\n",
    "    if match_result is None:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b05dfaf-8a60-4a86-b341-b78d16a32226",
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = [\"huggingface\", \"python\"]\n",
    "df['HasPreferableTags'] = df['Tags'].apply(lambda row_tags: tag_filter(tags, row_tags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40b740ac-83e0-45f1-a2be-e8825609ee76",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags = df[df['HasPreferableTags']==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a2d3ed2-b450-4151-8856-352d7e8a2463",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags = df_w_hug_tags.drop(['HasPreferableTags'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32db8e0e-ad52-40ee-aab0-229a72267751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1848, 13)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_w_hug_tags.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebb0623-d6d8-4261-90f3-c3eace22e119",
   "metadata": {},
   "source": [
    "We found there are some duplocated rows in our DB, so we eleminate those based on some columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eb8b23cc-bffd-4fc3-a8c6-1fae9798336f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags.drop_duplicates([\"Id\", \"PostTypeId\", \"AcceptedAnswerId\", \"ViewCount\", \"AnswerCount\", \"CommentCount\", \"Score\", \"Title\"], ignore_index=False, inplace=True)\n",
    "df_w_hug_tags = df_w_hug_tags.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b79a315e-28a4-4ae4-8fb7-dcfb510a291e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1848, 13)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_w_hug_tags.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c96c84-dedb-4f7f-a464-f8c8e8607970",
   "metadata": {
    "tags": []
   },
   "source": [
    "The new dataset that has a specific tag/s is reduced to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "15fdfa92-2d73-4f6c-af4d-4a814b7d11d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The orginal DB:  171114\n",
      "The new DB (sp):  1848\n",
      "The difference is:  169266\n"
     ]
    }
   ],
   "source": [
    "print(\"The orginal DB: \", df.shape[0])\n",
    "print(\"The new DB (sp): \", df_w_hug_tags.shape[0])\n",
    "print(\"The difference is: \", df.shape[0] - df_w_hug_tags.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2050397c-ca76-4c95-843d-71fbd9940991",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>PostTypeId</th>\n",
       "      <th>AcceptedAnswerId</th>\n",
       "      <th>CreationDate</th>\n",
       "      <th>ViewCount</th>\n",
       "      <th>AnswerCount</th>\n",
       "      <th>CommentCount</th>\n",
       "      <th>Score</th>\n",
       "      <th>Title</th>\n",
       "      <th>Body</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Source</th>\n",
       "      <th>source</th>\n",
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       "      <td>61913010</td>\n",
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       "      <td>61913108.0</td>\n",
       "      <td>2020-05-20 12:20:43</td>\n",
       "      <td>1792</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Can not import pipeline from transformers</td>\n",
       "      <td>&lt;p&gt;I have installed &lt;code&gt;pytorch&lt;/code&gt; with ...</td>\n",
       "      <td>&lt;python&gt;&lt;python-3.x&gt;&lt;pipeline&gt;&lt;huggingface-tra...</td>\n",
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       "      <td>118</td>\n",
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       "      <td>How to perform text classification inference u...</td>\n",
       "      <td>&lt;p&gt;To Whom It May Concern,&lt;/p&gt;\\n\\n&lt;p&gt;After tra...</td>\n",
       "      <td>&lt;python&gt;&lt;pytorch&gt;&lt;huggingface-transformers&gt;</td>\n",
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       "      <td>Hugging Face Transformer multiprocessing training</td>\n",
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       "      <td>0</td>\n",
       "      <td>Why does the evaluation loss increases when tr...</td>\n",
       "      <td>&lt;p&gt;Training a &lt;code&gt;huggingface transformers&lt;/...</td>\n",
       "      <td>&lt;python&gt;&lt;pytorch&gt;&lt;huggingface-transformers&gt;</td>\n",
       "      <td>old</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64727226</td>\n",
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       "      <td>How to get access intermediate layers paramete...</td>\n",
       "      <td>&lt;pre&gt;&lt;code&gt;import torch\\nimport transformers\\n...</td>\n",
       "      <td>&lt;python&gt;&lt;pytorch&gt;&lt;bert-language-model&gt;&lt;hugging...</td>\n",
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      "text/plain": [
       "         Id  PostTypeId  AcceptedAnswerId         CreationDate  ViewCount  AnswerCount  CommentCount  Score                                              Title                                               Body                                               Tags Source source\n",
       "0  61913010           1        61913108.0  2020-05-20 12:20:43       1792            2             0      2          Can not import pipeline from transformers  <p>I have installed <code>pytorch</code> with ...  <python><python-3.x><pipeline><huggingface-tra...    old    NaN\n",
       "1  60940110           1               NaN  2020-03-30 21:50:45        118            0             0      1  How to perform text classification inference u...  <p>To Whom It May Concern,</p>\\n\\n<p>After tra...        <python><pytorch><huggingface-transformers>    old    NaN\n",
       "2  62303645           1               NaN  2020-06-10 12:31:10        102            0             1      1  Hugging Face Transformer multiprocessing training  <p>I'm trying to train NER using distilbert on...  <python><deep-learning><pytorch><huggingface-t...    old    NaN\n",
       "3  64313576           1               NaN  2020-10-12 07:44:17         41            1             3      0  Why does the evaluation loss increases when tr...  <p>Training a <code>huggingface transformers</...        <python><pytorch><huggingface-transformers>    old    NaN\n",
       "4  64727226           1               NaN  2020-11-07 11:34:12         70            0             5      1  How to get access intermediate layers paramete...  <pre><code>import torch\\nimport transformers\\n...  <python><pytorch><bert-language-model><hugging...    old    NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_w_hug_tags.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28990026-83cc-4af5-94f6-5b2a75eb41f7",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Extracting the code parts from body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "23057f26-0779-428d-885f-7b166ea7ebca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_code_blocks(body: str, _id: int) -> List:\n",
    "    global index\n",
    "    # regex = r\"<pre><code>((.*?)|(\\n)*)*(<\\/code><\\/pre>|</pre></code>)\"\n",
    "    # regex = r\"(<pre>|(<pre((.*?)|(\\n)*)*><code>))((.*?)|(\\n)*)*(<\\/code><\\/pre>|</pre></code>|</pre>)\"\n",
    "    regex = r\"<pre(><code>|>|(((.*?)|(\\n)*)><code>)|((.*?)|(\\n)*)>)((.*?)|(\\n)*)*(<\\/code><\\/pre>|</pre></code>|</pre>)\"\n",
    "    matches = re.finditer(regex, body, re.MULTILINE | re.IGNORECASE)\n",
    "    result = []\n",
    "    \n",
    "    try:\n",
    "        for matchNum, match in enumerate(matches, start=1):\n",
    "            code = match.group()\n",
    "            code = re.sub('<pre(><code>|>|(((.*?)|(\\n)*)><code>)|((.*?)|(\\n)*)>)', '', code)\n",
    "            code = code.replace(\"<pre><code>\", \"\")\n",
    "            code = code.replace(\"</pre></code>\", \"\")\n",
    "            code = code.replace(\"</code></pre>\", \"\")\n",
    "            result.append(code)\n",
    "    except:\n",
    "        print(\"\\n Error(1): \", _id)\n",
    "        print(body)\n",
    "        return None\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f87db6b2-b24a-45d5-a7d2-e234bc3f0f73",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_w_hug_tags['Code'] = df_w_hug_tags.apply(lambda row: extract_code_blocks(row.Body, row.Id), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "25534f29-f886-4366-9c74-366cb41becd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(df_w_tags.iloc[7]['Body'])\n",
    "# print(type(extract_code_blocks(df_w_tags.iloc[7]['Body'])))\n",
    "# print(len(extract_code_blocks(df_w_tags.iloc[7]['Body'])))\n",
    "# print(extract_code_blocks(df_w_tags.iloc[7]['Body'])[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8b1e27-61be-4359-9b1c-c80828413913",
   "metadata": {},
   "source": [
    "Reset the index of dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e700b0aa-2f79-43c5-90e8-f969379e8f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags = df_w_hug_tags.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bdbd611",
   "metadata": {},
   "source": [
    "#### Finding the number of questions that have a code or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "717748e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "count__question_w_code  = 0\n",
    "count__question_wo_code = 0\n",
    "count__num_codes = 0\n",
    "\n",
    "def counting_w_or_wo_code(row_code: List) -> bool:\n",
    "    global count__question_w_code, count__question_wo_code, count__num_codes\n",
    "    \n",
    "    if row_code:\n",
    "        count__question_w_code = count__question_w_code + 1\n",
    "        count__num_codes += len(row_code)\n",
    "        return True\n",
    "    else:\n",
    "        count__question_wo_code = count__question_wo_code + 1\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9a723e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags['Has_code'] = df_w_hug_tags['Code'].apply(lambda row_code: counting_w_or_wo_code(row_code))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d8547f91-ce63-4b34-a349-f13a0815fb18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 1848 records in our DB, including 1629 posts that have code block in themselve and 219 w/o any code block.\n",
      "We have 3752 number of code blocks including stacktrace, snippet code, error message.\n"
     ]
    }
   ],
   "source": [
    "print(f\"We have {df_w_hug_tags.shape[0]} records in our DB, including {count__question_w_code} posts that have code block in themselve and {count__question_wo_code} w/o any code block.\")\n",
    "print(f\"We have {count__num_codes} number of code blocks including stacktrace, snippet code, error message.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd63ed7-eb63-42e3-b2a5-bc9641a5de68",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define two lists for storing the information of the questions w or w/o Code Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "42393cac-4d55-40c3-91b4-ff5b0c46e46a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Question_w_code_info = []\n",
    "Question_wo_code_info = []\n",
    "\n",
    "def counting_w_or_wo_code(row_id: int, \n",
    "                           row_cr: object, \n",
    "                           row_vc: int, \n",
    "                           row_ac: int, \n",
    "                           row_cc: int, \n",
    "                           row_sc: int, \n",
    "                           row_ac_an_id: float, \n",
    "                           has_code: bool) -> None:\n",
    "    \n",
    "    global Question_w_code_info, Question_wo_code_info\n",
    "\n",
    "    if has_code:\n",
    "        Question_w_code_info.append((row_id, row_cr, row_vc, row_ac, row_cc, row_sc, row_ac_an_id))\n",
    "\n",
    "    else:\n",
    "        Question_wo_code_info.append((row_id, row_cr, row_vc, row_ac, row_cc, row_sc, row_ac_an_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "810867bb-9f10-4e57-b88c-ecf5a4213358",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = df_w_hug_tags.apply(lambda row: counting_w_or_wo_code(row.Id, \n",
    "                                                           row.CreationDate, \n",
    "                                                           row.ViewCount,\n",
    "                                                           row.AnswerCount,\n",
    "                                                           row.CommentCount,\n",
    "                                                           row.Score,\n",
    "                                                           row.AcceptedAnswerId,\n",
    "                                                           row.Has_code), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a6adf1-dc32-4dc1-b3b7-0519de400007",
   "metadata": {},
   "source": [
    "Create dfs based on the two lists, Question_w_code_info and Question_wo_code_info and filled their answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0602d311-b776-4158-ab8d-4ab7f176ecee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_q_w_code_v1 = pd.DataFrame(columns=['First_ans_time', 'First_acc_ans_time', 'Answers'])\n",
    "\n",
    "df_q_w_code_v1[\"Q_info\"] = Question_w_code_info\n",
    "\n",
    "df_q_w_code_v1[\"Q_id\"]          = df_q_w_code_v1['Q_info'].apply(lambda q_info: q_info[0])\n",
    "df_q_w_code_v1[\"Q_create_time\"] = df_q_w_code_v1['Q_info'].apply(lambda q_info: q_info[1])\n",
    "df_q_w_code_v1[\"View_count\"]    = df_q_w_code_v1['Q_info'].apply(lambda q_info: q_info[2])\n",
    "df_q_w_code_v1[\"Answer_count\"]  = df_q_w_code_v1['Q_info'].apply(lambda q_info: q_info[3])\n",
    "df_q_w_code_v1[\"Comment_count\"] = df_q_w_code_v1['Q_info'].apply(lambda q_info: q_info[4])\n",
    "df_q_w_code_v1[\"Score\"]         = df_q_w_code_v1['Q_info'].apply(lambda q_info: q_info[5])\n",
    "df_q_w_code_v1[\"Accepted_Answer_id\"] = df_q_w_code_v1['Q_info'].apply(lambda q_info: q_info[6])\n",
    "df_q_w_code_v1 = df_q_w_code_v1.drop(['Q_info'], axis='columns')\n",
    "df_q_w_code_v1[\"Q_create_time\"]      = pd.to_datetime(df_q_w_code_v1[\"Q_create_time\"])\n",
    "df_q_w_code_v1[\"First_acc_ans_time\"] = pd.to_datetime(df_q_w_code_v1[\"First_acc_ans_time\"])\n",
    "df_q_w_code_v1[\"First_ans_time\"]     = pd.to_datetime(df_q_w_code_v1[\"First_ans_time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e7e66981-b309-477f-8551-33461cf01f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store them\n",
    "df_q_w_code_v1[\"Q_id\"].to_csv('../code_output_csv/df_hf_q_w_code.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6986107d-0813-4c97-842f-9d39e842b4c7",
   "metadata": {},
   "source": [
    "SELECT df_hf_q_w_code.Q_id, all_results.Id, all_results.CreationDate, all_results.ParentId\n",
    "FROM df_hf_q_w_code\n",
    "INNER JOIN all_results\n",
    "ON df_hf_q_w_code.Q_id = all_results.ParentId;"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1b5c9d94-4cca-42fa-94d1-017705bf4022",
   "metadata": {},
   "source": [
    "with cte1 as (\n",
    "SELECT\n",
    "        p.Id, \n",
    "        p.PostTypeId, \n",
    "        p.AcceptedAnswerId, \n",
    "        p.CreationDate, \n",
    "        p.ViewCount, \n",
    "        p.AnswerCount, \n",
    "        p.CommentCount, \n",
    "        p.Score,\n",
    "        p.Title,\n",
    "        p.Body,\n",
    "        p.Tags,\n",
    "        p.parentid\n",
    "\n",
    "FROM Posts as p\n",
    "inner join posts q \n",
    "        on q.id = coalesce(p.parentid, p.id) -- only questions have tags\n",
    "\n",
    "WHERE p.CreationDate > '2018-01-01 00:00:00' AND      -- After this time\n",
    "      p.PostTypeId=1 AND                     -- Just answers\n",
    "      --(p.Tags LIKE '%tensorflow%' OR\n",
    "       -- p.Tags LIKE '%pytorch%' OR\n",
    "       -- p.Tags LIKE '%scikit-learn%' OR\n",
    "       -- p.Tags LIKE '%keras%' OR\n",
    "       -- p.Tags LIKE '%nltk%' OR\n",
    "       p.Tags LIKE '%huggingface%' AND\n",
    "       -- p.Tags LIKE '%spark-ml%')\n",
    "       p.Tags LIKE '%python%'\n",
    ")\n",
    "select\n",
    "        r.parentid as Q_id,\n",
    "        r.parentid as ParentId,\n",
    "        r.Id, \n",
    "        --r.AcceptedAnswerId, \n",
    "        --r.PostTypeId,\n",
    "        --cte1.Id, \n",
    "        --cte1.PostTypeId,\n",
    "        r.CreationDate\n",
    "        --cte1.CreationDate as Q_CreationDate\n",
    "        --r.Score,\n",
    "        --cte1.AcceptedAnswerId\n",
    "        --cte1.ViewCount, \n",
    "        --cte1.AnswerCount,\n",
    "        --cte1.CommentCount, \n",
    "        --cte1.Score\n",
    "        --cte1.Title,\n",
    "        --cte1.Body,\n",
    "        --cte1.Tags,\n",
    "from posts r \n",
    "inner join cte1\n",
    "       on cte1.id = r.parentid\n",
    "where\n",
    "      r.PostTypeId = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf74dd8-441a-4aa0-a2de-a28e98968990",
   "metadata": {},
   "source": [
    "The below csv file is the result of the top query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "24503305-cf71-4a86-959a-35ae11c8634d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_data = working_directory_path + \"db_results/\" + \"hugh_ans_all.csv\"\n",
    "path = Path(pure_data)\n",
    "\n",
    "if path.suffix == \".csv\":\n",
    "    df_ans_all = pd.read_csv(path, encoding=encoding)\n",
    "else:\n",
    "    raise ValueError(\"{data_file_path.suffix} extensions are not supported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6f25fe22-fa0d-4c38-93a1-5fa8821c6443",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_w = pd.merge(df_q_w_code_v1, df_ans_all, how='outer',left_on=['Q_id'], right_on=['ParentId']).reset_index(drop=True)\n",
    "\n",
    "pd_tmp_w[\"Answer_tup\"] = pd_tmp_w.apply(lambda x: (x.Id, x.CreationDate), axis=1)\n",
    "\n",
    "pd_tmp_new_w = pd_tmp_w.groupby([\"Q_id_x\", \"Q_create_time\", \"View_count\", \"Comment_count\", \"Score\", \"Answer_count\", \"Accepted_Answer_id\"], dropna=False)[\"Answer_tup\"].agg(list).reset_index()\n",
    "pd_tmp_new2_w = pd_tmp_new_w[pd_tmp_new_w.Answer_count != 0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89bb9d0b-08bd-47fb-a65f-480b2d5dc715",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning:  57248098.0 2.0 [(60574502.0, '2020-03-07 04:51:25')]\n",
      "Warning:  62040309.0 3.0 [(62053791.0, '2020-05-27 22:18:30'), (62130091.0, '2020-06-01 10:24:27')]\n"
     ]
    }
   ],
   "source": [
    "def checker_ans_and_ans_tup(row_indx: int, ans_count: int, ans_list: list) -> None:\n",
    "    if len(ans_list) < ans_count:\n",
    "        print(\"Warning: \", row_indx, ans_count, ans_list)\n",
    "        \n",
    "_ = pd_tmp_new2_w.apply(lambda row: checker_ans_and_ans_tup(row.Q_id_x, row.Answer_count, row.Answer_tup), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0b7d0537-47e2-4c11-83ef-080c32fd0c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new2_w.insert(len(pd_tmp_new2_w.columns), 'First_ans_time', np.nan)\n",
    "pd_tmp_new2_w.insert(len(pd_tmp_new2_w.columns), 'First_acc_ans_time', np.nan)\n",
    "pd_tmp_new3_w = pd_tmp_new2_w.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c8a77995-f0dc-4c9e-b2ef-34b28ed28de2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for index1, row_status in pd_tmp_new3_w.iterrows():   \n",
    "    flag = 0\n",
    "    fr_time = pd.to_datetime(datetime.datetime.now())\n",
    "    acc_time = pd.to_datetime(datetime.datetime.now())\n",
    "        \n",
    "    if not row_status[\"Answer_tup\"]:\n",
    "        pd_tmp_new3_w.at[index1,'First_ans_time'] = np.nan\n",
    "        pd_tmp_new3_w.at[index1,'First_acc_ans_time'] = np.nan\n",
    "        continue\n",
    "\n",
    "    for answer in row_status[\"Answer_tup\"]:\n",
    "        \n",
    "        anwer_time = pd.to_datetime(answer[1])\n",
    "        \n",
    "        if flag == 0:\n",
    "            fr_time = anwer_time\n",
    "            flag = 1\n",
    "        \n",
    "        if fr_time > anwer_time:\n",
    "            fr_time = anwer_time\n",
    "        \n",
    "        if row_status[\"Accepted_Answer_id\"] == answer[0]:\n",
    "            acc_time = anwer_time\n",
    "\n",
    "    pd_tmp_new3_w.at[index1,'First_ans_time'] = fr_time\n",
    "        \n",
    "    if pd.isna(row_status[\"Accepted_Answer_id\"]):\n",
    "        acc_time = np.nan\n",
    "    \n",
    "    pd_tmp_new3_w.at[index1,'First_acc_ans_time'] = acc_time\n",
    "\n",
    "pd_tmp_new3_w[\"Duration_ans\"] = pd_tmp_new3_w.apply(lambda row: (row.First_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_ans_time) else np.nan, axis=1)\n",
    "pd_tmp_new3_w[\"Duration_acc_ans\"] = pd_tmp_new3_w.apply(lambda row: (row.First_acc_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_acc_ans_time) else np.nan, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0705f9a8-d22f-4003-b098-451901ad3218",
   "metadata": {},
   "source": [
    "We did the same behaviour for the Question_wo_code_info list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b6e57dad-db68-4579-bfb2-aaa08da7881c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_q_wo_code_v1 = pd.DataFrame(columns=['First_ans_time', 'First_acc_ans_time', 'Answers'])\n",
    "\n",
    "df_q_wo_code_v1[\"Q_info\"] = Question_wo_code_info\n",
    "\n",
    "df_q_wo_code_v1[\"Q_id\"]          = df_q_wo_code_v1['Q_info'].apply(lambda q_info: q_info[0])\n",
    "df_q_wo_code_v1[\"Q_create_time\"] = df_q_wo_code_v1['Q_info'].apply(lambda q_info: q_info[1])\n",
    "df_q_wo_code_v1[\"View_count\"]    = df_q_wo_code_v1['Q_info'].apply(lambda q_info: q_info[2])\n",
    "df_q_wo_code_v1[\"Answer_count\"]  = df_q_wo_code_v1['Q_info'].apply(lambda q_info: q_info[3])\n",
    "df_q_wo_code_v1[\"Comment_count\"] = df_q_wo_code_v1['Q_info'].apply(lambda q_info: q_info[4])\n",
    "df_q_wo_code_v1[\"Score\"]         = df_q_wo_code_v1['Q_info'].apply(lambda q_info: q_info[5])\n",
    "df_q_wo_code_v1[\"Accepted_Answer_id\"] = df_q_wo_code_v1['Q_info'].apply(lambda q_info: q_info[6])\n",
    "df_q_wo_code_v1 = df_q_wo_code_v1.drop(['Q_info'], axis='columns')\n",
    "df_q_wo_code_v1[\"Q_create_time\"] = pd.to_datetime(df_q_wo_code_v1[\"Q_create_time\"])\n",
    "df_q_wo_code_v1[\"First_acc_ans_time\"] = pd.to_datetime(df_q_wo_code_v1[\"First_acc_ans_time\"])\n",
    "df_q_wo_code_v1[\"First_ans_time\"]     = pd.to_datetime(df_q_wo_code_v1[\"First_ans_time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e7ebc01b-7e00-4977-8a5f-114506b5a81b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store\n",
    "df_q_wo_code_v1[\"Q_id\"].to_csv('../code_output_csv/df_hf_q_wo_code.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e3c41fc5-ade4-4b12-9368-7262b638af3f",
   "metadata": {
    "tags": []
   },
   "source": [
    "SELECT df_hf_q_wo_code.Q_id, all_results.Id, all_results.CreationDate, all_results.ParentId\n",
    "FROM df_hf_q_wo_code\n",
    "INNER JOIN all_results\n",
    "ON df_hf_q_wo_code.Q_id = all_results.ParentId;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fede8654-6663-4603-b936-8b131e7aef11",
   "metadata": {},
   "source": [
    "We used the all answers csv file again, again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "431f3673-aab0-4b0f-84b9-6c71c75279d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_data = working_directory_path + \"db_results/\" + \"hugh_ans_all.csv\"\n",
    "path = Path(pure_data)\n",
    "\n",
    "if path.suffix == \".csv\":\n",
    "    df_ans_all = pd.read_csv(path, encoding=encoding)\n",
    "else:\n",
    "    raise ValueError(\"{data_file_path.suffix} extensions are not supported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6f328e1c-edaf-4d0f-b502-4d4c92e3c163",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ans_all = df_ans_all.reset_index(drop=True)\n",
    "df_q_wo_code_v1['Answers'] = df_q_wo_code_v1.apply(lambda x: [], axis=1)\n",
    "\n",
    "pd_tmp_wo = pd.merge(df_q_wo_code_v1, df_ans_all, how='left',left_on=['Q_id'],right_on=['ParentId']).reset_index(drop=True)\n",
    "\n",
    "pd_tmp_wo[\"Answer_tup\"] = pd_tmp_wo.apply(lambda x: (x.Id, x.CreationDate), axis=1)\n",
    "pd_tmp_new_wo = pd_tmp_wo.groupby([\"Q_id_x\", \"Q_create_time\", \"View_count\", \"Comment_count\", \"Score\", \"Answer_count\", \"Accepted_Answer_id\"], dropna=False)[\"Answer_tup\"].agg(list).reset_index()\n",
    "pd_tmp_new2_wo = pd_tmp_new_wo[pd_tmp_new_wo.Answer_count != 0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d272dd66-9d43-46e5-801f-7c8c8988d302",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = pd_tmp_new2_wo.apply(lambda row: checker_ans_and_ans_tup(row.name, row.Answer_count, row.Answer_tup), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fde1f33a-4863-4349-a4e4-7e5ff169f62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new2_wo.insert(len(pd_tmp_new2_wo.columns), 'First_ans_time', np.nan)\n",
    "pd_tmp_new2_wo.insert(len(pd_tmp_new2_wo.columns), 'First_acc_ans_time', np.nan)\n",
    "pd_tmp_new3_wo = pd_tmp_new2_wo.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7741c7cc-9ed9-4b7b-8af6-7c3c577d3717",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index1, row_status in pd_tmp_new3_wo.iterrows():   \n",
    "    flag = 0\n",
    "    fr_time = pd.to_datetime(datetime.datetime.now())\n",
    "    acc_time = pd.to_datetime(datetime.datetime.now())\n",
    "        \n",
    "    if not row_status[\"Answer_tup\"]:\n",
    "        pd_tmp_new3_wo.at[index1,'First_ans_time'] = np.nan\n",
    "        pd_tmp_new3_wo.at[index1,'First_acc_ans_time'] = np.nan\n",
    "        continue\n",
    "\n",
    "    for answer in row_status[\"Answer_tup\"]:\n",
    "        \n",
    "        anwer_time = pd.to_datetime(answer[1])\n",
    "        \n",
    "        if flag == 0:\n",
    "            fr_time = anwer_time\n",
    "            flag = 1\n",
    "        \n",
    "        if fr_time > anwer_time:\n",
    "            fr_time = anwer_time\n",
    "        \n",
    "        if row_status[\"Accepted_Answer_id\"] == answer[0]:\n",
    "            acc_time = anwer_time\n",
    "\n",
    "    pd_tmp_new3_wo.at[index1,'First_ans_time'] = fr_time\n",
    "        \n",
    "    if pd.isna(row_status[\"Accepted_Answer_id\"]):\n",
    "        acc_time = np.nan\n",
    "    \n",
    "    pd_tmp_new3_wo.at[index1,'First_acc_ans_time'] = acc_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3a000799-e925-4426-b0c3-a94e34749d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new3_wo[\"Duration_ans\"] = pd_tmp_new3_wo.apply(lambda row: (row.First_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_ans_time) else np.nan, axis=1)\n",
    "pd_tmp_new3_wo[\"Duration_acc_ans\"] = pd_tmp_new3_wo.apply(lambda row: (row.First_acc_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_acc_ans_time) else np.nan, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9edc7843-74df-43e9-81fb-0302a59f16f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store\n",
    "pd_tmp_new3_w.to_csv('./CSV_data/plt_hf_q_w_code.csv', encoding='utf-8')\n",
    "pd_tmp_new3_wo.to_csv('./CSV_data/plt_hf_q_wo_code.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76009349",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Extracting the text parts from body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "426b615b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_text_blocks(i: int, body: str) -> Optional[List]:\n",
    "    # It's bug\n",
    "    if i in [10005, 14212, 21959, 26677, 53279, 22356]: \n",
    "        body = body + \"</p>\"\n",
    "\n",
    "    regex = r\"(<p>((.*?)|(\\n)*)*<\\/p>)|(<ul>((.*?)|(\\n)*)*<\\/ul>)|(<ol>((.*?)|(\\n)*)*<\\/ol>)\"\n",
    "    matches = re.finditer(regex, body, re.MULTILINE | re.IGNORECASE)\n",
    "    result = []\n",
    "    \n",
    "    if i in [18397]:\n",
    "        return result\n",
    "\n",
    "    try:\n",
    "        for matchNum, match in enumerate(matches, start=1):\n",
    "            text = match.group()\n",
    "            text = text.replace(\"<p>\", \"\")\n",
    "            text = text.replace(\"<strong>\", \"\")\n",
    "            text = text.replace(\"<br>\", \"\")\n",
    "            text = text.replace(\"<ol>\", \"\")\n",
    "            text = text.replace(\"<ul>\", \"\")\n",
    "            text = text.replace(\"<li>\", \"\")\n",
    "            text = text.replace(\"</p>\", \"\")\n",
    "            text = text.replace(\"</strong>\", \"\")\n",
    "            text = text.replace(\"</ol>\", \"\")\n",
    "            text = text.replace(\"</ul>\", \"\")\n",
    "            text = text.replace(\"</li>\", \"\")\n",
    "            result.append(text)\n",
    "        return result\n",
    "    except:\n",
    "        print(\"Error(1): \", i)\n",
    "        print(body)\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "96ff4ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags['Text'] = df_w_hug_tags.apply(lambda row: extract_text_blocks(row.name, row.Body), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec3bb294",
   "metadata": {},
   "source": [
    "#### Finding the number of body words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "18933061",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_q_text_words(text_list: List) -> int:\n",
    "    word_count = 0\n",
    "    for text in text_list:\n",
    "        word_count += sum([i.strip(string.punctuation).isalpha() for i in text.split()])\n",
    "    return word_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "15bc8431",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags['Q_text_words_num'] = df_w_hug_tags[\"Text\"].apply(lambda text_list: find_q_text_words(text_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24d4e61-0992-43cf-bfc4-83cebb7d1bbf",
   "metadata": {},
   "source": [
    "Save the dataframe as csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3d8f131a-19f7-46b8-b8e3-399ba5537607",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df_w_Tens_tags.to_csv('./amin_result_v1.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a5ff90-afcd-45a3-9d2d-a2b58de44dd9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Find the Regular Expressions for Unix and Windows base Pathnames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3406a54-9a09-4255-8287-d16fab93833d",
   "metadata": {},
   "source": [
    "#### 1) Absolute and Relative Pathnames in UNIX OS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c90be3-66df-4290-972f-940233a71e7c",
   "metadata": {},
   "source": [
    "Stack/trace example: https://stackoverflow.com/questions/37337728/tensorflow-internalerror-blas-sgemm-launch-failed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e9d6c0-8adb-4939-9f06-329b036eb875",
   "metadata": {},
   "source": [
    "Find the restrictions and limitations related to the Unix pathnames: https://www.cyberciti.biz/faq/linuxunix-rules-for-naming-file-and-directory-names/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe32c563-d1db-4f0a-9820-62fd42a6591e",
   "metadata": {},
   "source": [
    "Online regular expression environment for testing: https://regex101.com/r/ZyEx5u/4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d9456a-4c9e-4a8f-bee5-86a5be847e82",
   "metadata": {},
   "source": [
    "> Regular Expression: \"[^\\n\\&\\:\\|\\>\\<\\/\\ \\\"\\;\\&]*(\\/[^\\n\\&\\:\\|\\>\\<\\/]+)+\\/([^\\n\\&\\:\\|\\>\\<\\/]+)(\\.pyc|\\.py)\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6bb6875-32a5-4f52-b33a-9cb522a49f72",
   "metadata": {},
   "source": [
    "#### 1) Pathnames in Windows OS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd9c86a-e55e-4200-85ff-ed3033b4ee64",
   "metadata": {},
   "source": [
    "Stack/trace example: https://stackoverflow.com/questions/49434031/tensorflow-on-windows-cpu-version-importerror-no-module-named-pywrap-tensorf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84dd40a8-d970-435c-a06d-6a32130d3055",
   "metadata": {},
   "source": [
    "Find the restrictions and limitations related to the Windows pathnames: <br /> \n",
    "https://docs.microsoft.com/en-us/dotnet/standard/io/file-path-formats <br />\n",
    "https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced5ffa1-d9f8-4e8c-b9a8-8f438af95a1a",
   "metadata": {},
   "source": [
    "Online regular expression environment for testing: https://regex101.com/r/L6xmCa/1 , https://regex101.com/r/wz1WqW/1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec1be13-b32c-4d50-9ea2-490eafe5b139",
   "metadata": {},
   "source": [
    "> Regular Expression: \"[a-zA-Z]:\\\\?([^\\<\\>\\:\\\"\\\\\\/\\/\\|\\?\\*\\n]+\\\\)+([^\\<\\>\\:\\\"\\\\\\/\\/\\|\\?\\*\\n]+)(\\.pyc|\\.py)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bc401c12-44a2-402c-9e4a-1de67941e502",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_pathnames_from_code_column(code_sec: List, _id: int) -> Tuple[str, List]: \n",
    "    try:\n",
    "        result_post_file_names = []\n",
    "        OS_flag = None\n",
    "        error_index = None\n",
    "        error_code = None\n",
    "        regex_unix    = r\"[^\\n\\&\\:\\|\\>\\<\\/\\ \\\"\\;\\&]*(\\/[^\\n\\&\\:\\|\\>\\<\\/]+)+\\/([^\\n\\&\\:\\|\\>\\<\\/\\\\]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d*)\\W*in\\s\\W*\\d*([a-zA-Z_$][a-zA-Z_$0-9]*)\"\n",
    "        regex_windows = r\"(([a-zA-Z]:)|\\~)\\\\?\\/?([^\\<\\>\\:\\\"\\\\\\|\\?\\*\\n]+\\\\)+([^\\<\\>\\:\\\"\\\\\\/\\/\\|\\?\\*\\n]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d*)\\W*in\\W+([a-zA-Z_$][a-zA-Z_$0-9]*)\"\n",
    "        \n",
    "        for idx, code in enumerate(code_sec):\n",
    "            pattern_unix  = re.compile(regex_unix)\n",
    "            pattern_windows  = re.compile(regex_windows)\n",
    "            if pattern_unix.search(code):\n",
    "                OS_flag = \"unix\"\n",
    "                break\n",
    "            elif pattern_windows.search(code):\n",
    "                OS_flag = \"windows\"\n",
    "                break\n",
    "                \n",
    "        if OS_flag == \"unix\":\n",
    "            for idx, code in enumerate(code_sec):\n",
    "                error_index = idx\n",
    "                code = code.replace(\"\\\\n\", \"\\n\")\n",
    "                code = code.replace('&lt;', '<')\n",
    "                code = code.replace('&gt;', '>')\n",
    "                code = code.replace('&quot;', '\"')\n",
    "                error_code = code\n",
    "                matches = re.finditer(regex_unix, code, re.MULTILINE)\n",
    "                file_names_for_each_code_part = []\n",
    "                \n",
    "                for matchNum, match in enumerate(matches, start=1):\n",
    "                    file_names_for_each_code_part.append((match.groups()[1].strip(), match.groups()[4].strip()))\n",
    "#                     file_names_for_each_code_part.append(match.groups()[1].strip())\n",
    "                    # print (\"Match {matchNum} was found at {start}-{end}: {match}\".format(matchNum = matchNum, start = match.start(), end = match.end(), match = match.group()))\n",
    "                    # for groupNum in range(0, len(match.groups())):\n",
    "                        # groupNum = groupNum + 1\n",
    "                        # print (\"Group {groupNum} found at {start}-{end}: {group}\".format(groupNum = groupNum, start = match.start(groupNum), end = match.end(groupNum), group = match.group(groupNum)))\n",
    "#                 print(file_names_for_each_code_part)\n",
    "#                 file_names_for_each_code_part = list(set(file_names_for_each_code_part))            # Create a unique list\n",
    "#                 print(file_names_for_each_code_part)\n",
    "                \n",
    "                if file_names_for_each_code_part:                                                   # Ignore the empty list\n",
    "                    result_post_file_names.append(file_names_for_each_code_part)\n",
    "                    \n",
    "        elif OS_flag == \"windows\":\n",
    "            for idx, code in enumerate(code_sec):\n",
    "                code = code.replace('&lt;', '<')\n",
    "                code = code.replace('&gt;', '>')\n",
    "                code = code.replace('&quot;', '\"')\n",
    "                error_code = code\n",
    "                matches = re.finditer(regex_windows, code, re.MULTILINE)                            \n",
    "                file_names_for_each_code_part = []\n",
    "\n",
    "                for matchNum, match in enumerate(matches, start=1):\n",
    "                    file_names_for_each_code_part.append((match.groups()[3].strip(), match.groups()[6].strip()))\n",
    "#                     file_names_for_each_code_part.append(match.groups()[1].strip())\n",
    "                    # print (\"Match {matchNum} was found at {start}-{end}: {match}\".format(matchNum = matchNum, start = match.start(), end = match.end(), match = match.group()))\n",
    "                    # for groupNum in range(0, len(match.groups())):\n",
    "                        # groupNum = groupNum + 1\n",
    "                        # print (\"Group {groupNum} found at {start}-{end}: {group}\".format(groupNum = groupNum, start = match.start(groupNum), end = match.end(groupNum), group = match.group(groupNum)))\n",
    "#                 file_names_for_each_code_part = [s.strip() for s in file_names_for_each_code_part]  # Strip the list\n",
    "#                 file_names_for_each_code_part = list(set(file_names_for_each_code_part))            # create a unique list\n",
    "                if file_names_for_each_code_part:                                                     # Ignore the empty list\n",
    "                    result_post_file_names.append(file_names_for_each_code_part)\n",
    "            \n",
    "    except:\n",
    "        print(\"\\n Error Id:\", _id, \" Error index:\", error_index, \"\\n\")\n",
    "        print(error_code)\n",
    "        return None, None\n",
    "\n",
    "    return OS_flag, result_post_file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fc583436-6446-47b3-8811-ee3b316c57cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('unix',\n",
       " [[('run', 'module'),\n",
       "   ('grad_mode', 'decorate_context'),\n",
       "   ('generation_utils', 'generate')]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example for Unix path\n",
    "# https://stackoverflow.com/questions/51839415/tensorflow-valueerror-rank-mismatch\n",
    "# df_w_hug_tags.iloc[9][:]\n",
    "extract_pathnames_from_code_column(df_w_hug_tags[\"Code\"][51], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8642e202-ad70-4f13-9440-57eeeb3fda8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Example for Windows path\n",
    "# df_w_hug_tags['Code'] = df_w_hug_tags['Body'].apply(lambda row_body: extract_code_blocks(row_body))\n",
    "# print(df_w_hug_tags[\"Code\"][9])\n",
    "# extract_pathnames_from_code_column(df_w_hug_tags[\"Code\"][84])\n",
    "# extract_pathnames_from_code_column(df_w_hug_tags[\"Code\"][606])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0c61970a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_w_hug_tags[\"Body\"][606]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0dd38400-fb42-4dc7-8f9d-deb13a98c1a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_w_hug_tags['Bugy_py_files'] = df_w_hug_tags.apply(lambda row: extract_pathnames_from_code_column(row.Code, row.Id), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e7d4de",
   "metadata": {},
   "source": [
    "Save the dataframe as csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4e4ea318-37e3-4d03-b3a2-566723d22160",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df_w_hug_tags.loc[df_w_PT_tags['Id'] == 46509039]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4718e5db-2866-4f96-8a3a-2a09e559cb99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_w_hug_tags[[\"Title\", \"Code\", \"Bugy_py_files\"]].head(100)\n",
    "# df_w_hug_tags.to_csv('./hf_Code_bug.csv', sep='\\n', encoding='utf-8')\n",
    "# df_w_hug_tags['Bugy_py_files'].to_csv('./hf_bugy_tuples.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a7e0c3f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_has_trace_col(cal_tuple) -> bool:\n",
    "    if cal_tuple[0] == \"windows\" or cal_tuple[0] == \"unix\":\n",
    "        return True\n",
    "    elif cal_tuple[0] is None:\n",
    "        return False\n",
    "    else:\n",
    "        print(\"Error!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "acd983ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags['Has_trace'] = df_w_hug_tags['Bugy_py_files'].apply(lambda cell_tuple: create_has_trace_col(cell_tuple))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e60aff5",
   "metadata": {},
   "source": [
    "#### Finding the number and type of LOC in the body of post:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33da7bf-c174-48ba-999c-766d91c33a7a",
   "metadata": {},
   "source": [
    "For example how many pairs did you find in a post and defining the OS of that stack trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9c25a136",
   "metadata": {},
   "outputs": [],
   "source": [
    "def finding_line_of_code(code_list: List) -> Tuple[int, int, int]:\n",
    "    line_count_trace_win   = 0\n",
    "    line_count_trace_unix  = 0\n",
    "    line_count_simple_code = 0\n",
    "    \n",
    "    for code in code_list:\n",
    "        regex_unix    = r\"[^\\n\\&\\:\\|\\>\\<\\/\\ \\\"\\;\\&]*(\\/[^\\n\\&\\:\\|\\>\\<\\/]+)+\\/([^\\n\\&\\:\\|\\>\\<\\/\\\\]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d*)\\W*in\\s\\W*\\d*([a-zA-Z_$][a-zA-Z_$0-9]*)\"\n",
    "        regex_windows = r\"(([a-zA-Z]:)|\\~)\\\\?\\/?([^\\<\\>\\:\\\"\\\\\\|\\?\\*\\n]+\\\\)+([^\\<\\>\\:\\\"\\\\\\/\\/\\|\\?\\*\\n]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d*)\\W*in\\W+([a-zA-Z_$][a-zA-Z_$0-9]*)\"\n",
    "        # regex_unix    = r\"[^\\n\\&\\:\\|\\>\\<\\/\\ \\\"\\;\\&]*(\\/[^\\n\\&\\:\\|\\>\\<\\/]+)+\\/([^\\n\\&\\:\\|\\>\\<\\/]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d*)\\W*in\\W+([a-zA-Z_$][a-zA-Z_$0-9]*)\" version 2\n",
    "        # regex_unix    = r\"[^\\n\\&\\:\\|\\>\\<\\/\\ \\\"\\;\\&]*(\\/[^\\n\\&\\:\\|\\>\\<\\/]+)+\\/([^\\n\\&\\:\\|\\>\\<\\/]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d+)\\W*in\\W*([a-zA-Z_$][a-zA-Z_$0-9]*)\" version 1\n",
    "        # regex_windows = r\"[a-zA-Z]:\\\\?([^\\<\\>\\:\\\"\\\\\\/\\/\\|\\?\\*\\n]+\\\\)+([^\\<\\>\\:\\\"\\\\\\/\\/\\|\\?\\*\\n]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d+)\\W*in\\W*([a-zA-Z_$][a-zA-Z_$0-9]*)\"\n",
    "        pattern_unix  = re.compile(regex_unix)\n",
    "        pattern_windows = re.compile(regex_windows)\n",
    "        if pattern_unix.search(code):\n",
    "            OS_flag = \"unix\"\n",
    "        elif pattern_windows.search(code):\n",
    "            OS_flag = \"windows\"\n",
    "        else:\n",
    "            OS_flag = \"nothing\"\n",
    "            \n",
    "        code_wo_empty_line = os.linesep.join([s for s in code.splitlines() if s])\n",
    "            \n",
    "        if OS_flag == \"unix\":\n",
    "            line_count_trace_unix += len(code_wo_empty_line.splitlines())\n",
    "        elif OS_flag == \"windows\":\n",
    "            line_count_trace_win += len(code_wo_empty_line.splitlines())\n",
    "        elif OS_flag == \"nothing\":\n",
    "            line_count_simple_code += len(code_wo_empty_line.splitlines()) \n",
    "        else:\n",
    "            print(\"Error!\")\n",
    "\n",
    "    return (line_count_trace_unix, line_count_trace_win, line_count_simple_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4054d8d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 20, 72)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finding_line_of_code(df_w_hug_tags.loc[19, 'Code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c2f3958c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags['Line_code_u_w_s'] = df_w_hug_tags['Code'].apply(lambda code_list: finding_line_of_code(code_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b3e57594",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags['Line_code_uix'] = df_w_hug_tags['Line_code_u_w_s'].apply(lambda loc_tuple: loc_tuple[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4a683106",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_w_hug_tags['Line_code_win'] = df_w_hug_tags['Line_code_u_w_s'].apply(lambda loc_tuple: loc_tuple[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b99bf6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Has a code (Python code) inside the post\n",
    "df_w_hug_tags['Line_code_simple_code'] = df_w_hug_tags['Line_code_u_w_s'].apply(lambda loc_tuple: loc_tuple[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3534efd4",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Id</th>\n",
       "      <th>PostTypeId</th>\n",
       "      <th>AcceptedAnswerId</th>\n",
       "      <th>CreationDate</th>\n",
       "      <th>ViewCount</th>\n",
       "      <th>AnswerCount</th>\n",
       "      <th>CommentCount</th>\n",
       "      <th>Score</th>\n",
       "      <th>Title</th>\n",
       "      <th>Body</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Source</th>\n",
       "      <th>source</th>\n",
       "      <th>Code</th>\n",
       "      <th>Has_code</th>\n",
       "      <th>Text</th>\n",
       "      <th>Q_text_words_num</th>\n",
       "      <th>Bugy_py_files</th>\n",
       "      <th>Has_trace</th>\n",
       "      <th>Line_code_u_w_s</th>\n",
       "      <th>Line_code_uix</th>\n",
       "      <th>Line_code_win</th>\n",
       "      <th>Line_code_simple_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61913010</td>\n",
       "      <td>1</td>\n",
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       "      <td>2020-05-20 12:20:43</td>\n",
       "      <td>1792</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Can not import pipeline from transformers</td>\n",
       "      <td>&lt;p&gt;I have installed &lt;code&gt;pytorch&lt;/code&gt; with ...</td>\n",
       "      <td>&lt;python&gt;&lt;python-3.x&gt;&lt;pipeline&gt;&lt;huggingface-tra...</td>\n",
       "      <td>old</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[from transformers import pipeline\\n----------...</td>\n",
       "      <td>True</td>\n",
       "      <td>[I have installed &lt;code&gt;pytorch&lt;/code&gt; with &lt;c...</td>\n",
       "      <td>49</td>\n",
       "      <td>(None, [])</td>\n",
       "      <td>False</td>\n",
       "      <td>(0, 0, 6)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60940110</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-30 21:50:45</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>How to perform text classification inference u...</td>\n",
       "      <td>&lt;p&gt;To Whom It May Concern,&lt;/p&gt;\\n\\n&lt;p&gt;After tra...</td>\n",
       "      <td>&lt;python&gt;&lt;pytorch&gt;&lt;huggingface-transformers&gt;</td>\n",
       "      <td>old</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[merges.txt\\ntokenizer_config.json\\nconfig.jso...</td>\n",
       "      <td>True</td>\n",
       "      <td>[To Whom It May Concern,, After training a &lt;co...</td>\n",
       "      <td>89</td>\n",
       "      <td>(None, [])</td>\n",
       "      <td>False</td>\n",
       "      <td>(0, 0, 7)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  PostTypeId  AcceptedAnswerId         CreationDate  ViewCount  AnswerCount  CommentCount  Score                                              Title                                               Body                                               Tags Source source                                               Code  Has_code                                               Text  Q_text_words_num Bugy_py_files  Has_trace Line_code_u_w_s  Line_code_uix  Line_code_win  Line_code_simple_code\n",
       "0  61913010           1        61913108.0  2020-05-20 12:20:43       1792            2             0      2          Can not import pipeline from transformers  <p>I have installed <code>pytorch</code> with ...  <python><python-3.x><pipeline><huggingface-tra...    old    NaN  [from transformers import pipeline\\n----------...      True  [I have installed <code>pytorch</code> with <c...                49    (None, [])      False       (0, 0, 6)              0              0                      6\n",
       "1  60940110           1               NaN  2020-03-30 21:50:45        118            0             0      1  How to perform text classification inference u...  <p>To Whom It May Concern,</p>\\n\\n<p>After tra...        <python><pytorch><huggingface-transformers>    old    NaN  [merges.txt\\ntokenizer_config.json\\nconfig.jso...      True  [To Whom It May Concern,, After training a <co...                89    (None, [])      False       (0, 0, 7)              0              0                      7"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_w_hug_tags.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dcbe8b5-700b-4819-b0a8-f40cf9ba41c0",
   "metadata": {},
   "source": [
    "#### To find some examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "479a469f-d29d-4d36-951a-3a54373bcea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ex_tmp_w_st_df = df_w_hug_tags[(df_w_hug_tags.Has_trace == True)&(~df_w_hug_tags.AcceptedAnswerId.isna())].copy()\n",
    "ex_tmp_w_c_df = df_w_hug_tags[(df_w_hug_tags.Has_code == True)&(df_w_hug_tags.Has_trace == False)].copy()\n",
    "ex_tmp_wo_c_df = df_w_hug_tags[(df_w_hug_tags.Has_code == False)&(df_w_hug_tags.AnswerCount == 0)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "76adae84-9da4-4050-8f77-0ec7237f5c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84, 23)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex_tmp_wo_c_df.shape\n",
    "# ex_tmp_w_st_df.shape\n",
    "# ex_tmp_w_c_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "07abe9dd-c1fe-40f7-b2b8-62dc27a439b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "from transformers import BertTokenizer, BertModel\n",
    "from itertools import product\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import random\n",
    "\n",
    "# Tokenize the titles\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
    "\n",
    "def tokenize_text(text):\n",
    "    return tokenizer.encode(text, add_special_tokens=True)\n",
    "\n",
    "# Convert titles to BERT embeddings\n",
    "bert_model = BertModel.from_pretrained('bert-base-uncased')\n",
    "bert_model.eval()\n",
    "\n",
    "def get_pooled_bert_embedding(text):\n",
    "    input_ids = torch.tensor([tokenize_text(text)])\n",
    "    with torch.no_grad():\n",
    "        outputs = bert_model(input_ids)\n",
    "    # Use mean pooling across all tokens in the sequence\n",
    "    pooled_embedding = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()\n",
    "    return pooled_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "92515a81-b5d5-442d-9938-724022ffafbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get BERT embeddings for each title\n",
    "bert_embeddings = {\n",
    "    row['Id']: get_pooled_bert_embedding(row['Title'][:512]) for _, row in pd.concat([ex_tmp_w_st_df[['Id', 'Title']], ex_tmp_wo_c_df[['Id', 'Title']]]).iterrows()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "96616203-6ee0-4197-a289-bd445b6583cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create all combinations of IDs\n",
    "all_combinations = list(product(ex_tmp_w_st_df[\"Id\"], ex_tmp_wo_c_df[\"Id\"]))\n",
    "\n",
    "# Sample 50% of combinations\n",
    "sampled_combinations = random.sample(all_combinations, k=int(0.9 * len(all_combinations)))\n",
    "\n",
    "# Calculate cosine similarity for sampled combinations\n",
    "similarity_scores = [\n",
    "    (id1, id2, cosine_similarity([bert_embeddings[id1]], [bert_embeddings[id2]])[0][0])\n",
    "    for id1, id2 in sampled_combinations\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "d7615ff8-a181-4f60-808c-93c628c04f14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text 76625249 and Text 72718143 have a similarity of 0.9255744218826294\n",
      "\n",
      "Text 66293355 and Text 69208695 have a similarity of 0.9094135761260986\n",
      "\n",
      "Text 76663419 and Text 75721769 have a similarity of 0.8970029950141907\n",
      "\n",
      "Text 75237628 and Text 72718143 have a similarity of 0.8932217359542847\n",
      "\n",
      "Text 76340743 and Text 77102352 have a similarity of 0.8922630548477173\n",
      "\n",
      "Text 74304875 and Text 69208695 have a similarity of 0.892057478427887\n",
      "\n",
      "Text 77861915 and Text 69208695 have a similarity of 0.8919897079467773\n",
      "\n",
      "Text 71755917 and Text 76298231 have a similarity of 0.8911118507385254\n",
      "\n",
      "Text 75229395 and Text 69208695 have a similarity of 0.8903745412826538\n",
      "\n",
      "Text 77673353 and Text 71476873 have a similarity of 0.889037549495697\n",
      "\n",
      "Text 75237628 and Text 69208695 have a similarity of 0.8878755569458008\n",
      "\n",
      "Text 70518826 and Text 69208695 have a similarity of 0.8872841596603394\n",
      "\n",
      "Text 70709572 and Text 77102352 have a similarity of 0.8834724426269531\n",
      "\n",
      "Text 76663419 and Text 61390271 have a similarity of 0.8810780644416809\n",
      "\n",
      "Text 77311999 and Text 69208695 have a similarity of 0.8796153664588928\n",
      "\n",
      "Text 70518826 and Text 72718143 have a similarity of 0.8786507248878479\n",
      "\n",
      "Text 71078218 and Text 77102352 have a similarity of 0.8778653740882874\n",
      "\n",
      "Text 70698407 and Text 77102352 have a similarity of 0.877296507358551\n",
      "\n",
      "Text 70709572 and Text 72718143 have a similarity of 0.8759708404541016\n",
      "\n",
      "Text 71078218 and Text 69208695 have a similarity of 0.8759374618530273\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Sort by similarity score in descending order\n",
    "sorted_pairs = sorted(similarity_scores, key=lambda x: x[2], reverse=True)\n",
    "    \n",
    "# Get the top two pairs\n",
    "top_two_pairs = sorted_pairs[:20]\n",
    "\n",
    "# Output the top two pairs with IDs and Titles\n",
    "for pair in top_two_pairs:\n",
    "    id1, id2, similarity = pair\n",
    "    # title1, title2 = tokenized_titles[id1], tokenized_titles[id2]\n",
    "    print(f\"Text {id1} and Text {id2} have a similarity of {similarity}\\n\")    \n",
    "    # print(f\"Text {id1} ({title1}) and Text {id2} ({title2}) have a similarity of {similarity}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06fde1f5",
   "metadata": {},
   "source": [
    "#### Question Post's Length (body of the post): Defining the lists for plotting their comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bd5dee83",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_num_words_w_tra  = []\n",
    "list_num_words_wo_tra = []\n",
    "\n",
    "for index1, row in df_w_hug_tags.iterrows():\n",
    "    if row.Has_trace is True:\n",
    "        list_num_words_w_tra.append(row.Q_text_words_num)\n",
    "    elif row.Has_trace is False:\n",
    "        list_num_words_wo_tra.append(row.Q_text_words_num)\n",
    "    else:\n",
    "        print(\"Error!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "684ae0b5-da13-4ad7-b853-44783e21104b",
   "metadata": {},
   "source": [
    "#### OS Stack Traces: Defining the number of stack straces the belongs to the Unix or Windows OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bd3530e5-831c-4db8-b250-089f05938c10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found the 68 of Stack Overflow's posts that belong to the Windows OS.\n",
      "We found the 313 of Stack Overflow's posts that belong to the Unix OS.\n"
     ]
    }
   ],
   "source": [
    "counter_win  = 0\n",
    "counter_unix = 0\n",
    "# dim_win  = []\n",
    "# dim_unix = []\n",
    "\n",
    "for tp in df_w_hug_tags[\"Bugy_py_files\"]:\n",
    "    if tp[0] == \"windows\":\n",
    "        counter_win += 1\n",
    "#         np_array = np.array(tuple[1], dtype=object)\n",
    "#         dim_win.append(np_array.shape)\n",
    "        \n",
    "    elif tp[0] == \"unix\":\n",
    "        counter_unix += 1\n",
    "#         np_array = np.array(tuple[1], dtype=object)\n",
    "#         dim_unix.append(np_array.shape)\n",
    "\n",
    "print(f\"We found the {counter_win} of Stack Overflow's posts that belong to the Windows OS.\")\n",
    "print(f\"We found the {counter_unix} of Stack Overflow's posts that belong to the Unix OS.\")\n",
    "# print(\"The dimensions of Windows labels is: \", set(dim_win))\n",
    "# print(\"The dimensions of Unix labels is: \", set(dim_unix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "862bb743-f63d-4f4f-9b62-e38bb4711f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                         int64\n",
       "PostTypeId                 int64\n",
       "AcceptedAnswerId         float64\n",
       "CreationDate              object\n",
       "ViewCount                  int64\n",
       "AnswerCount                int64\n",
       "CommentCount               int64\n",
       "Score                      int64\n",
       "Title                     object\n",
       "Body                      object\n",
       "Tags                      object\n",
       "Source                    object\n",
       "source                    object\n",
       "Code                      object\n",
       "Has_code                    bool\n",
       "Text                      object\n",
       "Q_text_words_num           int64\n",
       "Bugy_py_files             object\n",
       "Has_trace                   bool\n",
       "Line_code_u_w_s           object\n",
       "Line_code_uix              int64\n",
       "Line_code_win              int64\n",
       "Line_code_simple_code      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_w_hug_tags.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e4c57c-d11e-4e1d-ac85-9c155d748630",
   "metadata": {},
   "source": [
    "#### Define two lists for storing the information of the questions w or w/o stack traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7a685f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_w_t  = 0\n",
    "count_wo_t = 0\n",
    "count_win  = 0\n",
    "count_unix = 0\n",
    "\n",
    "Question_with_trace_info = []\n",
    "Question_with_wo_trace_info = []\n",
    "\n",
    "def counting_w_or_wo_trace(row_id: int, \n",
    "                           row_cr: object, \n",
    "                           row_vc: int, \n",
    "                           row_ac: int, \n",
    "                           row_cc: int, \n",
    "                           row_sc: int, \n",
    "                           row_ac_an_id: float, \n",
    "                           row_t: object, \n",
    "                           has_code: bool) -> None:\n",
    "    \n",
    "    global count_w_t, count_wo_t, count_win, count_unix, Question_with_trace_info, Question_with_wo_trace_info\n",
    "\n",
    "    if has_code:\n",
    "        if row_t[0] is not None:\n",
    "            count_w_t = count_w_t + 1\n",
    "            Question_with_trace_info.append((row_id, row_cr, row_vc, row_ac, row_cc, row_sc, row_ac_an_id))\n",
    "            \n",
    "            if row_t[0] == 'unix':\n",
    "                count_unix = count_unix + 1\n",
    "            else:\n",
    "                count_win = count_win + 1\n",
    "        else:\n",
    "            count_wo_t = count_wo_t + 1\n",
    "            Question_with_wo_trace_info.append((row_id, row_cr, row_vc, row_ac, row_cc, row_sc, row_ac_an_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ee29fdfd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "_ = df_w_hug_tags.apply(lambda row: counting_w_or_wo_trace(row.Id, \n",
    "                                                               row.CreationDate, \n",
    "                                                               row.ViewCount,\n",
    "                                                               row.AnswerCount,\n",
    "                                                               row.CommentCount,\n",
    "                                                               row.Score,\n",
    "                                                               row.AcceptedAnswerId,\n",
    "                                                               row.Bugy_py_files, \n",
    "                                                               row.Has_code), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255f73aa-307c-4ca5-ad1f-9c80e0067c0f",
   "metadata": {},
   "source": [
    "#### Creating a string matrix of pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfe9422-3706-4fc3-9135-8cbd87353226",
   "metadata": {
    "tags": []
   },
   "source": [
    "https://stackoverflow.com/questions/32037893/numpy-fix-array-with-rows-of-different-lengths-by-filling-the-empty-elements-wi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3390d5ca-986e-4ddf-a48f-eb4e45ab46dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_fillna(data: List) -> np.ndarray:\n",
    "    # Get lengths of each row of data\n",
    "    lens = np.array([len(i) for i in data])\n",
    "\n",
    "    # Mask of valid places in each row\n",
    "    mask = np.arange(lens.max()) < lens[:, None]\n",
    "\n",
    "    # Setup output array and put elements from data into masked positions\n",
    "    out = np.zeros(mask.shape, dtype='object')\n",
    "    out[mask] = np.concatenate(data)\n",
    "    \n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "043411f3-4d8b-43b5-89e2-7a9b5d9e1385",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "_2D_array_with_str = []\n",
    "\n",
    "# The below list store the post Id for each instances (or row in _2D_array). \n",
    "# We have to mention that the index of _2D_array and _Id_array helps us to find the id pattern quickly.\n",
    "_Id_array = []\n",
    "\n",
    "for index1, row in df_w_hug_tags.iterrows():\n",
    "    row_tuple = row.Bugy_py_files\n",
    "    row_id = row.Id\n",
    "    \n",
    "    # row: (some rows have multiple patterns)\n",
    "    # ('unix', [[('estimator', 'train'), ('estimator', '_train_model'), \n",
    "    #            ('estimator', '_train_model_default'), ('estimator', '_call_model_fn'), ('nn_ops', 'sparse_softmax_cross_entropy_with_logits')]])\n",
    "    \n",
    "    if row_tuple[0] is None: continue\n",
    "    \n",
    "    for element in row_tuple[1]:\n",
    "        _Id_array.append(row_id)\n",
    "        element_tmp = element[:]\n",
    "        _2D_array_with_str.append(element_tmp)\n",
    "\n",
    "# _2D_array_pad = numpy_fillna(_2D_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fffa51f-6992-42c2-9cf0-fd1888ae4047",
   "metadata": {},
   "source": [
    "#### Creating a numerical matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2ab766-8031-4eca-9cef-455c127c6283",
   "metadata": {},
   "source": [
    "https://www.geeksforgeeks.org/python-pandas-factorize/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4cbea84-d201-4010-9759-c32f05b80aa9",
   "metadata": {},
   "source": [
    "##### Creating a dictionary based on pairs and assigning a unique number to that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8257c9af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict pair : int\n",
    "\n",
    "dic = {} \n",
    "specific_val = 1\n",
    "for row in _2D_array_with_str:\n",
    "    for element in row:\n",
    "        if element not in dic:\n",
    "            dic[element] = specific_val\n",
    "            specific_val += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "82ffd8ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of unique pairs is:  1093\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of unique pairs is: \", max(dic.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedfa8c9-9b6c-4643-b0b7-6b39e7fe40e4",
   "metadata": {},
   "source": [
    "##### Converting strings on the _2D_array into numbers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a2bf0be4-4828-4126-8816-81c1a4a3f4e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# _2D_array_with_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7a556c82-ad7d-43c9-b7f1-c852da489f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# _2D_array = list()\n",
    "# _2D_array_str = _2D_array_with_str.copy()\n",
    "# _2D_array = _2D_array_with_str[:]\n",
    "_2D_array = _2D_array_with_str[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0ff5062e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in enumerate(_2D_array):\n",
    "    for j in range(len(row)):\n",
    "        _2D_array[i][j] = dic[_2D_array[i][j]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b3971037-915c-4994-9107-5b36a9d62e33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# _2D_array_with_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ec5559a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# _2D_array[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f97135b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uniqe_dic int : pair\n",
    "uniqe_dic = dict([(value, key) for key, value in dic.items()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a621bac-8178-438b-8464-73225fe218a7",
   "metadata": {},
   "source": [
    "By the below function we deleted duplicated rows in 2d_array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ca3c46e1-7f42-4369-8ea3-6a25dbadc990",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_2d_array_dup(_2d_array: list) -> Tuple[int, list]:\n",
    "    \n",
    "    dup_count = 0\n",
    "    _2d_array_tmp = _2d_array.copy()\n",
    "    _2d_array_tmp_next = []\n",
    "    \n",
    "    idx = 0\n",
    "    while idx != len(_2d_array_tmp):\n",
    "        \n",
    "        for indx_previous in range(0, idx+1):\n",
    "            _2d_array_tmp_next.append(_2d_array_tmp[indx_previous])\n",
    "        \n",
    "        for indx_next in range(idx+1, len(_2d_array_tmp)):\n",
    "#             print(idx, indx_next)\n",
    "#             print(_2d_array_tmp)\n",
    "#             print(_2d_array_tmp_next)\n",
    "\n",
    "            if _2d_array_tmp[idx] == _2d_array_tmp[indx_next]:\n",
    "                dup_count += 1\n",
    "            else:\n",
    "                _2d_array_tmp_next.append(_2d_array_tmp[indx_next])\n",
    "#         print(_2d_array_tmp_next)\n",
    "#         print(len(_2d_array_tmp_next))\n",
    "#         break\n",
    "        _2d_array_tmp = _2d_array_tmp_next\n",
    "        _2d_array_tmp_next = []\n",
    "        idx += 1\n",
    "    \n",
    "    return dup_count, _2d_array_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cd20b1db-3f52-4f69-ad94-f91eff4c5a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# duplication_row, _2D_array_new = find_2d_array_dup(_2D_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "038074a7-a09a-4288-9368-db126c18d2d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(f\"We decresed the number of patterns from {len(_2D_array)} to {len(_2D_array_new)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa324359",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Answering the second research question (RQ2): Finding Patterns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31312935",
   "metadata": {},
   "source": [
    "## Contiguous Sequential Pattern Mining"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e4c26c",
   "metadata": {},
   "source": [
    "https://www.cc.gatech.edu/~hic/CS7616/pdf/lecture13.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8ff498",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Approach (1):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c9bae7",
   "metadata": {},
   "source": [
    "The shortest yet efficient implementation of the famous frequent sequential pattern mining algorithm PrefixSpan, the famous frequent closed sequential pattern mining algorithm BIDE (in closed.py), and the frequent generator sequential pattern mining algorithm FEAT (in generator.py), as a unified and holistic algorithm framework."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96570036",
   "metadata": {},
   "source": [
    "https://github.com/chuanconggao/PrefixSpan-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5eaa5c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install -U prefixspan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "35702190",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from prefixspan import PrefixSpan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c509adf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ps = PrefixSpan(_2D_array_pad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "df9fc672",
   "metadata": {},
   "outputs": [],
   "source": [
    "# k = ps.frequent(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b69e8fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# file = open(\"/home-students/amghad/sample.txt\", \"w\")\n",
    "# str_list = repr(k)\n",
    "# file.write(str_list)\n",
    "# file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f2979a",
   "metadata": {},
   "source": [
    "### Approach (2): "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566a8e0a",
   "metadata": {},
   "source": [
    "pymining is a small collection of data mining algorithms implemented in Python. I did not design any of the algorithms, but I use them in my own research so I thought other developers might be interested to use them as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94392e0f",
   "metadata": {},
   "source": [
    "https://github.com/bartdag/pymining"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411e8d57",
   "metadata": {},
   "source": [
    "### Approach (3): By this approach we simply check all possible status for each row (or pattern)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec37188-5c46-407b-b193-55c54b2e1841",
   "metadata": {},
   "source": [
    "We want to create a dict based on the vectors on the numerical matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f60f00-0840-41a0-8398-8f863bc2bc68",
   "metadata": {},
   "source": [
    "By the below function, we tried to find and search the patterns for each vector based on the window_size"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0d8aae18-d1d3-4282-9bf7-f7fe45ca5d6b",
   "metadata": {},
   "source": [
    "Input Vector: [1, 2, 6, 3]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "822ce776-1a3b-476d-92e6-be663d91ff05",
   "metadata": {},
   "source": [
    "{0: [(2, [(1, 2), (2, 6), (6, 3)]),\n",
    "    (3, [(1, 2, 6), (2, 6, 3)]),\n",
    "    (4, [(1, 2, 6, 3)])]\n",
    " ...}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9233612b-f3b4-4328-88ed-7843b13d0957",
   "metadata": {},
   "source": [
    "0: The index of vector <br>\n",
    "list: Contains tuples with different windows_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "03fb9196",
   "metadata": {},
   "outputs": [],
   "source": [
    "def append_to_dicc_new(dicc: dict, vec_id: int, list_: list) -> dict:\n",
    "    if vec_id not in dicc:\n",
    "        # key not exist\n",
    "        dicc[vec_id] = list_\n",
    "    else:\n",
    "        # key exist\n",
    "        for tuple_ in list_:\n",
    "            for item in dicc[vec_id]:\n",
    "                if item[0] == tuple_[0]:\n",
    "                    item[1].append(tuple_[1][0])\n",
    "                    break\n",
    "    return dicc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a2a340-fad0-4464-a15b-01f01995d3b0",
   "metadata": {},
   "source": [
    "The \"low\" parameter can help us control the lower bound of the window_size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1c3380c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "low = 0    # <~~~~~~~ define the lower bound threshold\n",
    "up = len(uniqe_dic)\n",
    "dicc = {}     \n",
    "\n",
    "for i_v, vector in enumerate(_2D_array):    \n",
    "    for index, element in enumerate(vector):\n",
    "        list_ = []\n",
    "        for wind_size in range(low, len(vector)+1-index):\n",
    "            list_.append((wind_size, [(*vector[index:index+wind_size], )]))\n",
    "        dicc = append_to_dicc_new(dicc, i_v, list_)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "88e6b3a4-6f49-462d-aac4-afa5cb3d4725",
   "metadata": {},
   "outputs": [],
   "source": [
    "# _2D_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ddd8bf1e-a7ad-45d8-99ab-a22e4b3f53c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dicc.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f09aefad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_duplication(dup_list: list) -> list:\n",
    "    final_list = []\n",
    "    for item in dup_list:\n",
    "        if item not in final_list:\n",
    "            final_list.append(item)\n",
    "        else:\n",
    "            indx = final_list.index(item)\n",
    "            counter = final_list[indx][0] + 1\n",
    "            pair = final_list[indx][1]\n",
    "            final_list.remove(item)\n",
    "            final_list.append((counter, pair))\n",
    "    \n",
    "    return final_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a95a41ac-d183-4807-a0d1-fa9328e359cf",
   "metadata": {},
   "source": [
    "dic_count is a dictionary that key is window_size, and value is a list that contains tuples. Each tuple shows a pair and the number of pair's iteration on the matrix."
   ]
  },
  {
   "cell_type": "raw",
   "id": "4befab67-c057-41a7-84c5-aa6b89e363ef",
   "metadata": {},
   "source": [
    "{2: [(1, (5, 1)),\n",
    "  (1, (1, 6)),\n",
    "  (1, (1, 2)),\n",
    "  (1, (2, 6)),\n",
    "  (2, (6, 3)),\n",
    "  (1, (6, 1)),\n",
    "  (2, (1, 3)),\n",
    "  (3, (4, 5)),\n",
    "  (5, (3, 4)),\n",
    "  (2, (4, 3)),\n",
    "  (1, (3, 6)),\n",
    "  (1, (2, 3)),\n",
    "  (1, (3, 1)),\n",
    "  (2, (1, 4)),\n",
    "  (1, (4, 6))],\n",
    "  ...}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9ef3ffab",
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_count = {}\n",
    "\n",
    "for vec_i, list_tuples in dicc.items():\n",
    "#     print(vec_i, list_tuples,\"\\n\")\n",
    "    for tuples_ in list_tuples:\n",
    "#         print(tuples_)\n",
    "        \n",
    "        if tuples_[0] not in dic_count:\n",
    "            lst_ = [(1, tuple_) for tuple_ in tuples_[1]] \n",
    "            \n",
    "            if len(lst_) != len(set(lst_)):\n",
    "                lst_ = remove_duplication(lst_)\n",
    "            \n",
    "            dic_count[tuples_[0]] = lst_  \n",
    "        else:\n",
    "            #(2, [(1, 3), (1, 4), (3, 4), (4, 5), (5, 1)]\n",
    "            #break\n",
    "            for tuple_ in tuples_[1]:\n",
    "                flag = 0 \n",
    "                for item in dic_count[tuples_[0]]:\n",
    "                    if item[1] == tuple_:\n",
    "                        counter = item[0] + 1\n",
    "                        dic_count[tuples_[0]].remove(item)\n",
    "                        dic_count[tuples_[0]].append((counter, item[1]))\n",
    "                        flag = 1\n",
    "                        break\n",
    "                # tuple is new\n",
    "                if flag == 0:\n",
    "                    dic_count[tuples_[0]].append((1, tuple_))\n",
    "#                 else: \n",
    "#                     print(\"Error100\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85825caa-0ea6-49f4-afb9-467ca6dcd2f8",
   "metadata": {},
   "source": [
    "dic_count is a dictionary that key is window_size, and value is a list that contains tuples. Each tuple shows a pair and the number of pair's iteration on the matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1f87b4-36e2-4376-8f3f-ba55164a1b72",
   "metadata": {},
   "source": [
    "For each window_size we count the number of patterns that we found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "735ff20e-0a61-4acb-b2e4-3c9e4e9886cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(439, ()), (441, ()), (441, ()), (441, ()), (441, ())]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic_count.pop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2db34be8-e770-4e6f-a243-1f70b1766461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inst_length = dic_count.keys()\n",
    "inst_count = [len(value) for key, value in dic_count.items()]\n",
    "comulative_inst_count = np.cumsum(inst_count)\n",
    "\n",
    "per_comulative_inst_count = []\n",
    "for element in comulative_inst_count:\n",
    "    per_comulative_inst_count.append((element*100)/comulative_inst_count[-1])\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4), dpi=100)\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.plot(inst_length, per_comulative_inst_count, '--o', color='#4b8262', linewidth=2)\n",
    "plt.xlabel('Instance\\'s length')\n",
    "plt.ylabel('Cumulative % of stack trace instances')\n",
    "ax1.set_title('hug RQ2')\n",
    "\n",
    "# plt.vlines(x=5, ymin=60, ymax=95, color='red')\n",
    "\n",
    "# plt.text(5.5, 65, 'sentence', rotation=0)\n",
    "ax1.axis(ymin=0, ymax=102)\n",
    "ax1.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "42ac2495-df65-4f9b-a88d-c2fc13e51d38",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x420 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "# import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize =(10, 6), dpi=70)\n",
    "ax.barh(list(inst_length), inst_count, align='center', alpha=0.5, log=True)\n",
    "\n",
    "for i in ax.patches:\n",
    "    plt.text(i.get_width()+0.2, i.get_y()+0.3,\n",
    "             str(round((i.get_width()), 2)),\n",
    "             fontsize = 10, fontweight ='bold',\n",
    "             color ='grey')\n",
    "\n",
    "# Add Plot Title\n",
    "ax.set_title('The frequency of stack trace intances for each instance\\'s length',loc ='center')\n",
    "# ax.set_yticks(y_pos, objects)\n",
    "ax.set_xlabel('Frequency')\n",
    "ax.set_ylabel('Instance\\'s length')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa8dc6b-c1d8-45fc-a851-184118ca1a3a",
   "metadata": {},
   "source": [
    "### Approach (4): CC-Span"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "87d8aea6-501b-4a56-98ea-2cdad58ed6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def append_to_dicc_new(dicc, vec_id, list_):\n",
    "    if vec_id not in dicc:\n",
    "        # key not exist\n",
    "        dicc[vec_id] = list_\n",
    "    else:\n",
    "        # key exist\n",
    "        for tuple_ in list_:\n",
    "            for item in dicc[vec_id]:\n",
    "                if item[0] == tuple_[0]:\n",
    "                    item[1].append(tuple_[1][0])\n",
    "                    break\n",
    "    return dicc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b8570828-2fb9-4945-b5df-70c17a4ebf81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "394"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(_2D_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f9370071-2ec9-4fbb-8b33-8e66e38b8c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Searching Pattern\n",
    "low = 1     # <~~~~~~~ define the lower bound threshold\n",
    "up = len(uniqe_dic)\n",
    "dicc = {}     \n",
    "\n",
    "for i_v, vector in enumerate(_2D_array):    \n",
    "    for index, element in enumerate(vector):\n",
    "        list_ = []\n",
    "        for wind_size in range(low, len(vector)+1-index):\n",
    "            list_.append((wind_size, [(*vector[index:index+wind_size], )]))\n",
    "        dicc = append_to_dicc_new(dicc, i_v, list_)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a64a3b0e-ffa9-4999-96cf-5bdc766df1a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Counting\n",
    "def remove_duplication(dup_list):\n",
    "    final_list = []\n",
    "    for item in dup_list:\n",
    "        if item not in final_list:\n",
    "            final_list.append(item)\n",
    "        else:\n",
    "            indx = final_list.index(item)\n",
    "            counter = final_list[indx][0] + 1\n",
    "            pair = final_list[indx][1]\n",
    "            final_list.remove(item)\n",
    "            final_list.append((counter, pair))\n",
    "    \n",
    "    return final_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ca4b370c-9b01-4e16-a3a6-a7a58bc463e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_count = {}\n",
    "\n",
    "for vec_i, list_tuples in dicc.items():\n",
    "#     print(vec_i, list_tuples,\"\\n\")\n",
    "    for tuples_ in list_tuples:\n",
    "#         print(tuples_)\n",
    "        \n",
    "        if tuples_[0] not in dic_count:\n",
    "            lst_ = [(1, tuple_) for tuple_ in tuples_[1]] \n",
    "            \n",
    "            if len(lst_) != len(set(lst_)):\n",
    "                lst_ = remove_duplication(lst_)\n",
    "            \n",
    "            dic_count[tuples_[0]] = lst_  \n",
    "        else:\n",
    "            #(2, [(1, 3), (1, 4), (3, 4), (4, 5), (5, 1)]\n",
    "            #break\n",
    "            for tuple_ in tuples_[1]:\n",
    "                flag = 0 \n",
    "                for item in dic_count[tuples_[0]]:\n",
    "                    if item[1] == tuple_:\n",
    "                        counter = item[0] + 1\n",
    "                        dic_count[tuples_[0]].remove(item)\n",
    "                        dic_count[tuples_[0]].append((counter, item[1]))\n",
    "                        flag = 1\n",
    "                        break\n",
    "                # tuple is new\n",
    "                if flag == 0:\n",
    "                    dic_count[tuples_[0]].append((1, tuple_))\n",
    "#                 else: \n",
    "#                     print(\"Error100\")\n",
    "#     print(\"salam\", dic_count[2], \"\\n\\n\" )\n",
    "#     if vec_i == 1:\n",
    "#         break\n",
    "\n",
    "# _2d_array = [[1, 3, 4, 3, 4, 5, 1, 4],\n",
    "#              [1, 6, 3, 4, 5],\n",
    "#              [1, 2, 6, 3],\n",
    "#              [6, 1, 3, 4, 5],\n",
    "#              [3, 4, 3, 6],\n",
    "#              [2, 3, 1, 4, 6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cb0c9fd7-b3c5-4e61-84bd-bd84f14b392a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_subsequence(needle: list, haystack: list) -> bool:\n",
    "    # >>> is_subsequence([2, 3, 4], [1, 2, 3, 4, 5, 6])\n",
    "    return any(\n",
    "        haystack[i:i+len(needle)] == needle\n",
    "        for i in range(len(haystack) - len(needle) + 1)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "777e6228-2296-47e7-bc0a-aa9f658346a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CloConSeqGen(dic_count: dict, threshold: int) -> list:\n",
    "    closed_list = []\n",
    "    for key, v_list in dic_count.items():\n",
    "        #print(key, v_list)\n",
    "        for item_tup in v_list:\n",
    "            tmp_flag = False\n",
    "            for key, v_list_search in dic_count.items():\n",
    "                for item_tup_search in v_list_search:\n",
    "                    #print(\"compare with: \", item_tup_search[1])\n",
    "                    if len(item_tup_search[1]) >= len(item_tup[1]) and is_subsequence(item_tup[1], item_tup_search[1]) and item_tup[1] != item_tup_search[1]:\n",
    "                        if item_tup[0] == item_tup_search[0]:\n",
    "                            tmp_flag = True\n",
    "            if not tmp_flag and item_tup[0] > threshold:\n",
    "\n",
    "                closed_list.append(item_tup)\n",
    "    return closed_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "dfbd276c-0735-4163-910b-27bd57d50fa9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic_count.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "70d292da-2e31-4ae1-9590-255586e51611",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CloConSeqGen_v2(dic_count: dict) -> list:\n",
    "    closed_list = []\n",
    "    flag_first_step = True\n",
    "    for win_size in list(reversed(list(dic_count))):\n",
    "        # Just add the last or the biggest window_size to the list \n",
    "        if flag_first_step:\n",
    "            flag_first_step = False\n",
    "            for item in dic_count[win_size]:\n",
    "                closed_list.append(item)\n",
    "            continue\n",
    "        \n",
    "        # For other window sizes\n",
    "        for item_tup_new in dic_count[win_size]:\n",
    "            flag_visit = False\n",
    "            for item_tup_old in closed_list:\n",
    "                if item_tup_new[0] == item_tup_old[0] and is_subsequence(item_tup_new[1], item_tup_old[1]):\n",
    "                    flag_visit = True\n",
    "                    break\n",
    "            if not flag_visit:\n",
    "                closed_list.append(item_tup_new)\n",
    "\n",
    "    return len(closed_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cbc205ce-b533-4d9e-81c6-623cc7d67941",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CloConSeqGen_v3(dic_count: dict, threshold: int) -> list:\n",
    "    closed_list = []\n",
    "    removed_closed_list = []\n",
    "    flag_first_step = True\n",
    "    for win_size in list(reversed(list(dic_count))):\n",
    "        # Just add the last or the biggest window_size to the list \n",
    "        if flag_first_step:\n",
    "            for item in dic_count[win_size]:\n",
    "                if item[0] >= threshold:\n",
    "                    flag_first_step = False\n",
    "                    closed_list.append(item)\n",
    "                else:\n",
    "                    removed_closed_list.append(item)\n",
    "            continue\n",
    "        \n",
    "        # For other window sizes\n",
    "        for item_tup_new in dic_count[win_size]:\n",
    "            flag_visit = False\n",
    "            for item_tup_old in closed_list:\n",
    "                if item_tup_new[0] == item_tup_old[0] and is_subsequence(item_tup_new[1], item_tup_old[1]):\n",
    "                    flag_visit = True\n",
    "                    break  \n",
    "            if (not flag_visit) and (item_tup_new[0] >= threshold):\n",
    "                closed_list.append(item_tup_new)\n",
    "            else:\n",
    "                removed_closed_list.append(item_tup_new)\n",
    "\n",
    "    return closed_list, removed_closed_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "57c0af36-4426-4796-9124-a3fbffa9354d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(CloConSeqGen_v2(dic_count))\n",
    "# print(CloConSeqGen_v3(dic_count, 1))\n",
    "# print(CloConSeqGen_v3(dic_count, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "227bf25d-7b45-42cf-818e-d417cf39afd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(CloConSeqGen_v2(dic_count))\n",
    "# print(CloConSeqGen_v3(dic_count, threshold=1))\n",
    "# print(CloConSeqGen_v3(dic_count, threshold=1))\n",
    "# result, result_remove = CloConSeqGen_v3(dic_count, threshold=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "280e0927-13ed-4dd6-8676-f197e10c93ce",
   "metadata": {},
   "source": [
    "### Prepare data for support and pattern length when support is 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6c02d3d2-9bfa-4b1e-b6e2-3d6b3dac5f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "result, result_remove = CloConSeqGen_v3(dic_count, threshold=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fca52ca7-29f4-48d1-bb3b-ca0d6ce60884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "733"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "69eb864c-5179-4c3f-b5bb-142276ed3a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the low and high boundries of CloConSeqGen_v3 result\n",
    "for element in result:\n",
    "    if result.index(element) == 0:\n",
    "        low = element[0] \n",
    "        high = element[0]\n",
    "        continue\n",
    "    if low > element[0]:\n",
    "        low = element[0]\n",
    "    elif high < element[0]:\n",
    "        high = element[0]\n",
    "        \n",
    "sorted_result = sorted(result, key=lambda tup: tup[0])\n",
    "\n",
    "sup_x = sorted(set([element[0] for element in result]))\n",
    "len_sup_y = []\n",
    "\n",
    "for sup in sup_x:\n",
    "    len_sup_y.append(len(sorted_result) - [y[0] for y in sorted_result].index(sup))\n",
    "    \n",
    "per_len_sup_y = [(x*100)/len_sup_y[0] for x in len_sup_y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "05a0269f-32a3-4658-bf39-0c35a9285720",
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_tf_sup = {\"x\":sup_x, \"y\":per_len_sup_y}\n",
    "pickle.dump(dic_tf_sup, open(\"./Pickle_data/dic_hugh_sup.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "413da7a7-3709-4598-adff-2cb27736f486",
   "metadata": {},
   "outputs": [],
   "source": [
    "win_size_list = []\n",
    "for element in result:\n",
    "    win_size_list.append(len(element[1]))\n",
    "win_size_set = set(win_size_list)\n",
    "counter=collections.Counter(win_size_list)\n",
    "\n",
    "counter = dict(sorted(counter.items()))\n",
    "comulative_y = np.cumsum(list(counter.values()))\n",
    "\n",
    "per_list_patterns = []\n",
    "for element in comulative_y:\n",
    "    per_list_patterns.append((element*100)/comulative_y[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "fd4b44ab-229d-46f1-b6aa-5ecdc6e5e69e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_tf_win = {\"x\":list(counter.keys()), \"y\":per_list_patterns}\n",
    "pickle.dump(dic_tf_win, open(\"./Pickle_data/dic_hugh_win.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd26a58-26cc-4dd4-9dec-54ba8d8a118c",
   "metadata": {},
   "source": [
    "### Prepare data for support and pattern length when support is 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "39a8dd67-f3d0-485c-9c3f-2fc8d7823bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "result, result_remove = CloConSeqGen_v3(dic_count, threshold=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "827b1c72-a704-40d2-9727-01c506a5fdc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "426"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "99737655-095a-4ece-a00a-c3ecdb82596e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def convert_num_string(input_list: list, unique_dic: dict) -> list:\n",
    "    convert_lst = []\n",
    "    for pattern in input_list:\n",
    "        string_pattern_tuple = ()\n",
    "        for item in pattern[1]:\n",
    "            string_pattern_tuple = string_pattern_tuple + (unique_dic[item],)\n",
    "        convert_lst.append((pattern[0], string_pattern_tuple))\n",
    "    return convert_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "689e9c2f-0259-45f6-98af-5406f01570ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def order_sublist(lst_big: list, lst_small: list) -> bool:\n",
    "    return lst_small in [lst_big[i:len(lst_small)+i] for i in range(len(lst_big))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "828d1c0b-318d-45c7-84e3-e9870bd333c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_str = convert_num_string(result, uniqe_dic)\n",
    "assert len(result) == len(result_str), \"It should be the same.\"\n",
    "pattern_str_w_qid = []\n",
    "\n",
    "for pattern in result_str:\n",
    "    for q_id in _Id_array:\n",
    "        # assert not df_w_sklearn_tags.loc[df_w_sklearn_tags['Id'] == q_id].Bugy_py_files.empty, \"It's not possible!\"\n",
    "        exit_flag_q_id = False\n",
    "        \n",
    "        for item in df_w_hug_tags['Bugy_py_files'].to_numpy()[df_w_hug_tags['Id'].to_numpy() == q_id].item()[1]:\n",
    "           \n",
    "            if order_sublist(item, list(pattern[1])):\n",
    "                # pattern_str_w_qid.append((pattern[0], pattern[1]))\n",
    "                \n",
    "                exist = False\n",
    "                for ele in pattern_str_w_qid:\n",
    "                    if ele[1] == pattern[0] and ele[2] == pattern[1]:\n",
    "                        exist = True\n",
    "                        ele[0].append(q_id)\n",
    "                        \n",
    "                        if len(ele[0]) ==  pattern[0]: exit_flag_q_id = True\n",
    "                if not exist:\n",
    "                    pattern_str_w_qid.append((list([q_id]), pattern[0], pattern[1]))\n",
    "                    if pattern[0] == 1: exit_flag_q_id = True\n",
    "        \n",
    "        if exit_flag_q_id: break\n",
    "            \n",
    "    # print(\"\\n\\n\")\n",
    "pickle.dump(pattern_str_w_qid, open(\"./Pickle_data/hug_ccspan_2_result_pure.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "722f9e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_patterns(patterns_lst: list, top: int) -> list:\n",
    "    assert top != 0, \"The top value is wrong!\"\n",
    "    top_list = []\n",
    "    support_set = set()\n",
    "    \n",
    "    # Find the support list\n",
    "    for pattern in patterns_lst:\n",
    "        support_set.add(pattern[0])\n",
    "        \n",
    "    top_support_list = sorted(list(support_set))[0:top]\n",
    "    \n",
    "    for pattern in patterns_lst:\n",
    "        if pattern[0] in top_support_list:\n",
    "            top_list.append(pattern)\n",
    "            \n",
    "    return top_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a455b92-37e7-4d77-8ea1-4312b0a48faf",
   "metadata": {},
   "source": [
    "The below function based on the dic_count dictionary trys to find x top patterns for each window_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d07908ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_pattern_num = get_top_patterns(result, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "3fa153b3-af93-4c74-907f-36fefcce2096",
   "metadata": {},
   "outputs": [],
   "source": [
    "# top_pattern_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7279c556",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def convert_num_string(input_list: list, unique_dic: dict) -> list:\n",
    "    convert_lst = []\n",
    "    for pattern in input_list:\n",
    "        string_pattern_tuple = ()\n",
    "        for item in pattern[1]:\n",
    "            string_pattern_tuple = string_pattern_tuple + (unique_dic[item],)\n",
    "        convert_lst.append((pattern[0], string_pattern_tuple))\n",
    "    return convert_lst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf82bce5-7af6-4782-b604-28cfe546b864",
   "metadata": {},
   "source": [
    "The below code converts the number pairs to the string pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d86e89e0-774f-4435-99cf-1b1f6cbbb396",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_pattern_str = convert_num_string(top_pattern_num, uniqe_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "f4c8d3ff-a0b4-4464-ba4e-80170401506d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# top_pattern_str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffdac07b-dca6-4c30-9d08-786f01535355",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Find the Id of a post based on its pattern:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "314c9c2e-1104-466f-ae09-ffb379fbc9d7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "assert len(_2D_array_with_str) == len(_2D_array), \"Always should be the same.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "f4fff9b0-4939-474b-9db2-03740e518292",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(394, 426)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(_Id_array), len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "618652dd-8e19-44fd-bb01-91f6810eeafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_q_id_based_on_str_pattern(total_IDs: list, pattern_str_lst: list) -> dict:\n",
    "    dict_pattern_and_qid = {}\n",
    "    \n",
    "    for pattern in pattern_str_lst:\n",
    "        for q_id in total_IDs:\n",
    "            assert not df_w_hug_tags.loc[df_w_hug_tags['Id'] == q_id].Bugy_py_files.empty, \"It's not possible!\"\n",
    "\n",
    "            for item in df_w_hug_tags.loc[df_w_hug_tags['Id'] == q_id].Bugy_py_files.values[0][1]:\n",
    "                if order_sublist(item, list(pattern[1])):\n",
    "                    pattern_tpl = (*pattern[1], )\n",
    "                    if pattern_tpl not in dict_pattern_and_qid.keys():\n",
    "                        dict_pattern_and_qid[pattern_tpl] = set({q_id})\n",
    "                    else:\n",
    "                        dict_pattern_and_qid[pattern_tpl].add(q_id)\n",
    "    return dict_pattern_and_qid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4795d690-d2e1-4f04-b37a-8f0eab767651",
   "metadata": {},
   "outputs": [],
   "source": [
    "result, result_remove = CloConSeqGen_v3(dic_count, threshold=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "13058c47-80ac-4588-a919-5c49c129fc42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "426"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e5510c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_q_id_based_on_str_pattern(total_IDs: list, pattern_str_lst: list) -> dict:\n",
    "    dict_pattern_and_qid = {}\n",
    "    \n",
    "    for pattern in pattern_str_lst:\n",
    "        for q_id in total_IDs:\n",
    "            assert not df_w_hug_tags.loc[df_w_hug_tags['Id'] == q_id].Bugy_py_files.empty, \"It's not possible!\"\n",
    "\n",
    "            for item in df_w_hug_tags.loc[df_w_hug_tags['Id'] == q_id].Bugy_py_files.values[0][1]:\n",
    "                if order_sublist(item, list(pattern[1])):\n",
    "                    pattern_tpl = (*pattern[1], )\n",
    "                    if pattern_tpl not in dict_pattern_and_qid.keys():\n",
    "                        dict_pattern_and_qid[pattern_tpl] = set({q_id})\n",
    "                    else:\n",
    "                        dict_pattern_and_qid[pattern_tpl].add(q_id)\n",
    "    return dict_pattern_and_qid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "af140a17-af6e-40a5-a774-a111c2db2a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_q_number(dict_pattern_and_qid: dict) -> int:\n",
    "    q_total_set = set({})\n",
    "    \n",
    "    for key, value in dict_pattern_and_qid.items():\n",
    "        # print(value)\n",
    "        q_total_set.update(value)\n",
    "\n",
    "    return len(q_total_set), q_total_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "d8345bd6-b985-49e1-a114-0f5b7accbe53",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "302\n"
     ]
    }
   ],
   "source": [
    "dict_pattern_and_qid = find_q_id_based_on_str_pattern(_Id_array, convert_num_string(result, uniqe_dic))\n",
    "len_q_total_set, q_total_sett = get_q_number(dict_pattern_and_qid)\n",
    "print(len_q_total_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "fb5074b1-ea39-4121-841c-b147b04e6cdc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "lst_q_id_has_pattern = []\n",
    "for top_pattern in top_pattern_str:\n",
    "    # print(\"Pattern(small set): \\t\", top_pattern[1])\n",
    "    for q_id in _Id_array:\n",
    "        # print(q_id)\n",
    "        assert not df_w_hug_tags.loc[df_w_hug_tags['Id'] == q_id].Bugy_py_files.empty, \"It's pot possible!\"\n",
    "        \n",
    "        for item in df_w_hug_tags.loc[df_w_hug_tags['Id'] == q_id].Bugy_py_files.values[0][1]:\n",
    "            # print(\"Big set: \\t\", item)\n",
    "            if order_sublist(item, list(top_pattern[1])):\n",
    "                # print(\"Big Set: \\t\", item)\n",
    "                # print(\"Small Set: \\t\", list(top_pattern[1]))\n",
    "                # print(\"\\n\\n\")\n",
    "                lst_q_id_has_pattern.append(q_id)\n",
    "    # print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "4e23791a-9002-46e6-aeb8-2d6b65bb6d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert lst_q_id_has_pattern, \"It not should be empty.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "6a60bb8a-137e-4bc5-89e3-6bc3c180c48d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern = df_w_hug_tags.loc[df_w_hug_tags['Id'].isin(lst_q_id_has_pattern)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "e62807f4-5baf-4a4e-9b22-5e5a030e5f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern.rename(columns = {'Id':'Q_id', 'CreationDate':'Q_create_time'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "7ffc07a5-f62c-488d-b873-b22c99368924",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pure_data = working_directory_path + \"db_results/\" + \"hugh_ans_all.csv\"\n",
    "path = Path(pure_data)\n",
    "\n",
    "if path.suffix == \".csv\":\n",
    "    df_night = pd.read_csv(path, encoding=encoding)\n",
    "else:\n",
    "    raise ValueError(\"{data_file_path.suffix} extensions are not supported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "fa58c7cb-163b-4c69-a8ef-d51ec98d694f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_w = pd.merge(df_pattern, df_night, how='outer',left_on=['Q_id'], right_on=['ParentId']).reset_index(drop=True)\n",
    "pd_tmp_w[\"Answer_tup\"] = pd_tmp_w.apply(lambda x: (x.Id, x.CreationDate), axis=1)\n",
    "pd_tmp_new_w = pd_tmp_w.groupby([\"Q_id_x\", \"Q_create_time\", \"ViewCount\", \"CommentCount\", \"Score\", \"AnswerCount\", \"AcceptedAnswerId\"], dropna=False)[\"Answer_tup\"].agg(list).reset_index()\n",
    "df_pattern_ans = pd_tmp_new_w[pd_tmp_new_w.AnswerCount != 0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "cd2e8a96-1d56-4914-b24a-77278e0eb99e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern_ans = df_pattern_ans[df_pattern_ans.AcceptedAnswerId > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "a2e1b206-dfda-47ed-9469-06dc34e4d446",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_patt_ans(lst_ans_id_1, lst_ans_id_2):\n",
    "    assert set(lst_ans_id_1) == set(lst_ans_id_2), \"We couldn't find all answers.\"\n",
    "    assert len(lst_ans_id_1) == len(lst_ans_id_2), \"The length of answer lists are not the same.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "73a6b524-d003-41d2-a51d-7b36e1306f17",
   "metadata": {},
   "outputs": [],
   "source": [
    "check_patt_ans(df_pattern_ans.AcceptedAnswerId, df_pattern[df_pattern.AcceptedAnswerId > 0].AcceptedAnswerId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "73f97050-f8ad-4ac2-8339-7f1addc3f7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern_ans.insert(len(df_pattern_ans.columns), 'First_ans_time', np.nan)\n",
    "df_pattern_ans.insert(len(df_pattern_ans.columns), 'First_acc_ans_time', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "57cb3a82-8e38-4310-a2ed-1c89601e205d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_the_answers(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    for index1, row_status in df.iterrows():   \n",
    "        flag = 0\n",
    "        fr_time = pd.to_datetime(datetime.datetime.now())\n",
    "        acc_time = pd.to_datetime(datetime.datetime.now())\n",
    "\n",
    "        if not row_status[\"Answer_tup\"]:\n",
    "            pd_tmp_new3_w.at[index1,'First_ans_time'] = np.nan\n",
    "            pd_tmp_new3_w.at[index1,'First_acc_ans_time'] = np.nan\n",
    "            continue\n",
    "\n",
    "        for answer in row_status[\"Answer_tup\"]:\n",
    "\n",
    "            anwer_time = pd.to_datetime(answer[1])\n",
    "\n",
    "            if flag == 0:\n",
    "                fr_time = anwer_time\n",
    "                flag = 1\n",
    "\n",
    "            if fr_time > anwer_time:\n",
    "                fr_time = anwer_time\n",
    "\n",
    "            if row_status[\"AcceptedAnswerId\"] == answer[0]:\n",
    "                acc_time = anwer_time\n",
    "\n",
    "        df.at[index1,'First_ans_time'] = fr_time\n",
    "\n",
    "        if pd.isna(row_status[\"AcceptedAnswerId\"]):\n",
    "            acc_time = np.nan\n",
    "\n",
    "        df.at[index1,'First_acc_ans_time'] = acc_time\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "16ceb7d8-c5bc-44eb-815b-b3ebd36a92f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern_ans = assign_the_answers(df_pattern_ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ce318475-189c-4af8-9566-054f69be6435",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern_ans[\"Q_create_time\"]      = pd.to_datetime(df_pattern_ans[\"Q_create_time\"])\n",
    "df_pattern_ans[\"First_acc_ans_time\"] = pd.to_datetime(df_pattern_ans[\"First_acc_ans_time\"])\n",
    "df_pattern_ans[\"First_ans_time\"]     = pd.to_datetime(df_pattern_ans[\"First_ans_time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d51bf41c-e69c-4e36-bbdd-b95db19b00b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern_ans[\"Duration_ans\"] = df_pattern_ans.apply(lambda row: (row.First_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_ans_time) else np.nan, axis=1)\n",
    "df_pattern_ans[\"Duration_acc_ans\"] = df_pattern_ans.apply(lambda row: (row.First_acc_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_acc_ans_time) else np.nan, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "af4dc3e2-21f2-421d-8432-9acad1c4fc2e",
   "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>Q_id_x</th>\n",
       "      <th>Q_create_time</th>\n",
       "      <th>ViewCount</th>\n",
       "      <th>CommentCount</th>\n",
       "      <th>Score</th>\n",
       "      <th>AnswerCount</th>\n",
       "      <th>AcceptedAnswerId</th>\n",
       "      <th>Answer_tup</th>\n",
       "      <th>First_ans_time</th>\n",
       "      <th>First_acc_ans_time</th>\n",
       "      <th>Duration_ans</th>\n",
       "      <th>Duration_acc_ans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59656096.0</td>\n",
       "      <td>2020-01-09 01:41:04</td>\n",
       "      <td>497.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59684161.0</td>\n",
       "      <td>[(59684161.0, 2020-01-10 15:00:10)]</td>\n",
       "      <td>2020-01-10 15:00:10</td>\n",
       "      <td>2020-01-10 15:00:10</td>\n",
       "      <td>37.318333</td>\n",
       "      <td>37.318333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60459292.0</td>\n",
       "      <td>2020-02-28 21:11:27</td>\n",
       "      <td>223.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60480429.0</td>\n",
       "      <td>[(60480429.0, 2020-03-01 22:35:41)]</td>\n",
       "      <td>2020-03-01 22:35:41</td>\n",
       "      <td>2020-03-01 22:35:41</td>\n",
       "      <td>49.403889</td>\n",
       "      <td>49.403889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>66207138.0</td>\n",
       "      <td>2021-02-15 11:23:51</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66260061.0</td>\n",
       "      <td>[(66260061.0, 2021-02-18 12:33:39)]</td>\n",
       "      <td>2021-02-18 12:33:39</td>\n",
       "      <td>2021-02-18 12:33:39</td>\n",
       "      <td>73.163333</td>\n",
       "      <td>73.163333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66249631.0</td>\n",
       "      <td>2021-02-17 20:41:16</td>\n",
       "      <td>572.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66250147.0</td>\n",
       "      <td>[(66250147.0, 2021-02-17 21:22:50)]</td>\n",
       "      <td>2021-02-17 21:22:50</td>\n",
       "      <td>2021-02-17 21:22:50</td>\n",
       "      <td>0.692778</td>\n",
       "      <td>0.692778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66602656.0</td>\n",
       "      <td>2021-03-12 15:21:51</td>\n",
       "      <td>5341.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66603865.0</td>\n",
       "      <td>[(66603056.0, 2021-03-12 15:47:59), (66603865....</td>\n",
       "      <td>2021-03-12 15:47:59</td>\n",
       "      <td>2021-03-12 16:44:06</td>\n",
       "      <td>0.435556</td>\n",
       "      <td>1.370833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Q_id_x       Q_create_time  ViewCount  CommentCount  Score  AnswerCount  AcceptedAnswerId                                         Answer_tup      First_ans_time  First_acc_ans_time  Duration_ans  Duration_acc_ans\n",
       "0   59656096.0 2020-01-09 01:41:04      497.0           3.0    4.0          1.0        59684161.0                [(59684161.0, 2020-01-10 15:00:10)] 2020-01-10 15:00:10 2020-01-10 15:00:10     37.318333         37.318333\n",
       "1   60459292.0 2020-02-28 21:11:27      223.0           1.0    0.0          1.0        60480429.0                [(60480429.0, 2020-03-01 22:35:41)] 2020-03-01 22:35:41 2020-03-01 22:35:41     49.403889         49.403889\n",
       "7   66207138.0 2021-02-15 11:23:51      170.0           1.0    0.0          1.0        66260061.0                [(66260061.0, 2021-02-18 12:33:39)] 2021-02-18 12:33:39 2021-02-18 12:33:39     73.163333         73.163333\n",
       "8   66249631.0 2021-02-17 20:41:16      572.0           3.0    0.0          1.0        66250147.0                [(66250147.0, 2021-02-17 21:22:50)] 2021-02-17 21:22:50 2021-02-17 21:22:50      0.692778          0.692778\n",
       "10  66602656.0 2021-03-12 15:21:51     5341.0           7.0    5.0          2.0        66603865.0  [(66603056.0, 2021-03-12 15:47:59), (66603865.... 2021-03-12 15:47:59 2021-03-12 16:44:06      0.435556          1.370833"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pattern_ans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1ffe601d-a840-4d65-871b-c9a3aed88c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result = pd.DataFrame(columns=['Index', 'Q_id', 'Title', 'OS', '# ST-Post', '# ST', 'Link'])\n",
    "\n",
    "for _indx, row in df_pattern.iterrows():\n",
    "    _c = 0\n",
    "    for item in row.Bugy_py_files[1]:\n",
    "        _c += len(item)\n",
    "    _link = \"https://stackoverflow.com/questions/\" + str(row.Q_id)\n",
    "    tmp_dic = {'Index': _indx+1, 'Q_id': row.Q_id, 'Title': row.Title, 'OS': row.Bugy_py_files[0], '# ST-Post': len(row.Bugy_py_files[1]), '# ST': _c, 'Link': _link}\n",
    "    df2 = pd.DataFrame(tmp_dic, index={len(tmp_dic)+1})\n",
    "    df_result = pd.concat([df_result, df2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "c4cf331b-860e-4f6a-91bc-57c99ab10afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "regex_unix    = r\"[^\\n\\&\\:\\|\\>\\<\\/\\ \\\"\\;\\&]*(\\/[^\\n\\&\\:\\|\\>\\<\\/]+)+\\/([^\\n\\&\\:\\|\\>\\<\\/\\\\]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d*)\\W*in\\s\\W*\\d*([a-zA-Z_$][a-zA-Z_$0-9]*)\"\n",
    "regex_windows = r\"(([a-zA-Z]:)|\\~)\\\\?\\/?([^\\<\\>\\:\\\"\\\\\\|\\?\\*\\n]+\\\\)+([^\\<\\>\\:\\\"\\\\\\/\\/\\|\\?\\*\\n]+)(\\.pyc|\\.py)[^\\d\\n]+(\\d*)\\W*in\\W+([a-zA-Z_$][a-zA-Z_$0-9]*)\"\n",
    "# https://regex101.com/r/5yCAdA/1   Unix\n",
    "# https://regex101.com/r/50Vcmo/1 Windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "b139b576-02c8-4019-9829-c5387d7464aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Bugy_py_files, dtype: object)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_colwidth = 500\n",
    "df_pattern.loc[df_pattern.Q_id == 54219826].Bugy_py_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "024b8cd9-ab58-4448-a5f6-15b0fb6cb3f3",
   "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>Q_id</th>\n",
       "      <th>PostTypeId</th>\n",
       "      <th>AcceptedAnswerId</th>\n",
       "      <th>Q_create_time</th>\n",
       "      <th>ViewCount</th>\n",
       "      <th>AnswerCount</th>\n",
       "      <th>CommentCount</th>\n",
       "      <th>Score</th>\n",
       "      <th>Title</th>\n",
       "      <th>Body</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Source</th>\n",
       "      <th>source</th>\n",
       "      <th>Code</th>\n",
       "      <th>Has_code</th>\n",
       "      <th>Text</th>\n",
       "      <th>Q_text_words_num</th>\n",
       "      <th>Bugy_py_files</th>\n",
       "      <th>Has_trace</th>\n",
       "      <th>Line_code_u_w_s</th>\n",
       "      <th>Line_code_uix</th>\n",
       "      <th>Line_code_win</th>\n",
       "      <th>Line_code_simple_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Q_id, PostTypeId, AcceptedAnswerId, Q_create_time, ViewCount, AnswerCount, CommentCount, Score, Title, Body, Tags, Source, source, Code, Has_code, Text, Q_text_words_num, Bugy_py_files, Has_trace, Line_code_u_w_s, Line_code_uix, Line_code_win, Line_code_simple_code]\n",
       "Index: []"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pattern.loc[df_pattern.Q_id == 54219826]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "5766c065-c694-422e-b157-19a55f52f36b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_id_has_trace_wo_err_mess = []\n",
    "c_has_trace   = 0\n",
    "c_has_err_mes = 0\n",
    "\n",
    "def extract_error_messages(id: int, Has_code: bool, Has_trace: bool, code_sec: List) -> Optional[List]:\n",
    "    try:\n",
    "        global lst_id_has_trace_wo_err_mess, c_has_trace, c_has_err_mes\n",
    "        \n",
    "        if Has_trace: c_has_trace += 1\n",
    "        # if not (Has_trace and Has_code): print(f\"Error (2): It's not possiable. Has_code: {Has_code}, Has_trace: {Has_trace}\")\n",
    "            \n",
    "        result_total = []\n",
    "        \n",
    "        for code in code_sec:\n",
    "            # regex = r\"([A-Z]\\w+(Error|Warning|Exception))([^:]*):\\s(.*)$\"\n",
    "            # V3\n",
    "            regex = r\"^([A-Z]\\w+Error)(\\w|\\s|\\(|\\)|\\d)*:\\s(.*)$\"\n",
    "            # regex = r\"([A-Z]\\w+Error):\\s(.*)$\"\n",
    "            code = code.replace(\"\\\\n\", \"\\n\")\n",
    "            code = code.replace('&lt;', '<')\n",
    "            code = code.replace('&gt;', '>')\n",
    "            code = code.replace('&quot;', '\"')\n",
    "            matches = re.finditer(regex, code, re.MULTILINE)\n",
    "            result_each_code_part = []\n",
    "\n",
    "            for matchNum, match in enumerate(matches, start=1):\n",
    "                result_each_code_part.append((match.groups()[0].strip(), match.groups()[2].strip()))\n",
    "            \n",
    "            if result_each_code_part:   # Ignore the empty list\n",
    "                result_total.append(result_each_code_part)\n",
    "\n",
    "        if (not result_total) and Has_trace: \n",
    "            # print(\"Warning!: We have to check this is ID: \", id)\n",
    "            lst_id_has_trace_wo_err_mess.append(id)\n",
    "        \n",
    "        if result_total:\n",
    "            c_has_err_mes += len(result_total)\n",
    "            \n",
    "        return result_total\n",
    "    \n",
    "    except:\n",
    "        print(\"Error(1): \", id)\n",
    "        print(code_sec)\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "ea3133ee-8feb-4741-9663-10ba2d5aae6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_pattern[\"Err_msg\"] = df_pattern.apply(lambda row: extract_error_messages(row.Q_id, row.Has_code, row.Has_trace, row.Code), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "dcbf43e9-7d3c-485a-9a1f-0a2d5da10956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We captured 185 stack traces, however 166 stacktraces have a one type of error messages. I mean, 19 stack traces didn't have any error message.\n"
     ]
    }
   ],
   "source": [
    "print(f\"We captured {c_has_trace} stack traces, however {c_has_trace-len(lst_id_has_trace_wo_err_mess)} stacktraces have a one type of error messages. I mean, {len(lst_id_has_trace_wo_err_mess)} stack traces didn't have any error message.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "96c34043-59ca-49ec-add3-929367024323",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = df_pattern[df_pattern['Err_msg'].map(lambda err_msg_list: len(err_msg_list)) > 0]\n",
    "df_tmp.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "4609acf2-489e-42c5-bd10-dcebd3796e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern_err_msg = pd.DataFrame(columns=['Q_id', 'ErrorType', 'ErrorMessage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "f43bcfba-7be1-46f4-95b9-6a44c09d9194",
   "metadata": {},
   "outputs": [],
   "source": [
    "def message_extractor(msg_q_id: str, msg_list: list) -> None:\n",
    "    global df_pattern_err_msg\n",
    "    for msg_t in msg_list:\n",
    "        for message in msg_t:\n",
    "            df_pattern_err_msg = pd.concat([df_pattern_err_msg, pd.DataFrame.from_records([{'Q_id': msg_q_id, 'ErrorType': message[0], 'ErrorMessage': message[1]}])], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "13526342-f7cf-43a0-b013-5acd281f3038",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "_ = df_tmp.apply(lambda row: message_extractor(row.Q_id, row.Err_msg), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "2fbfe0ce-8a87-473e-bc51-b06e22ca6817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(223, 3)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pattern_err_msg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "8b4d6c77-2c1a-462a-bf95-3e7aec229d15",
   "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>Q_id</th>\n",
       "      <th>ErrorType</th>\n",
       "      <th>ErrorMessage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59656096</td>\n",
       "      <td>NotImplementedError</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62979062</td>\n",
       "      <td>RuntimeError</td>\n",
       "      <td>An attempt has been made to start a new process before the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65091635</td>\n",
       "      <td>ValueError</td>\n",
       "      <td>logits and labels must have the same shape ((1, 21) vs (21, 1))</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65091635</td>\n",
       "      <td>ValueError</td>\n",
       "      <td>in user code:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63465109</td>\n",
       "      <td>TypeError</td>\n",
       "      <td>Class advice impossible in Python3.  Use the @implementer class decorator instead.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q_id            ErrorType                                                                        ErrorMessage\n",
       "0  59656096  NotImplementedError                                                                                    \n",
       "1  62979062         RuntimeError                          An attempt has been made to start a new process before the\n",
       "2  65091635           ValueError                     logits and labels must have the same shape ((1, 21) vs (21, 1))\n",
       "3  65091635           ValueError                                                                       in user code:\n",
       "4  63465109            TypeError  Class advice impossible in Python3.  Use the @implementer class decorator instead."
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pattern_err_msg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "b010615a-39a7-4a9b-b211-565e51094f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure all the elements are string\n",
    "df_pattern_err_msg = df_pattern_err_msg.applymap(str)\n",
    "\n",
    "# Notice: strip() function will remove leading and trailing whitespaces.\n",
    "df_pattern_err_msg['ErrorType'] = df_pattern_err_msg['ErrorType'].apply(lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "898083b2-8efa-4536-be80-156b6925b254",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7203/1919839101.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df2 = df2.append({'ErrorType': 'Other', 'count': df_tmp['count'].iloc[TOP:].sum()}, ignore_index=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-1.1053354186406947,\n",
       " 1.1002540675543189,\n",
       " -1.1018804290351176,\n",
       " 1.1195931541067654)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOP = 6\n",
    "df_tmp = df_pattern_err_msg.groupby(['ErrorType'])['ErrorType'].count().reset_index(name='count').sort_values(['count'], ascending=False)\n",
    "df2 = df_tmp.iloc[:TOP]\n",
    "df2 = df2.append({'ErrorType': 'Other', 'count': df_tmp['count'].iloc[TOP:].sum()}, ignore_index=True)\n",
    "colors = ['tab:blue', 'tab:cyan', 'tab:gray', 'tab:orange', 'tab:red', 'burlywood', 'y']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "patches, texts, pcts =  ax.pie(df2.set_index('ErrorType')['count'],\n",
    "                               labels=df2['ErrorType'],\n",
    "                                autopct='%1.1f%%', \n",
    "                                pctdistance=0.7, \n",
    "                                labeldistance=1.1, \n",
    "                                # textprops={'fontsize': 12},\n",
    "                                textprops={'size': 'x-large'},\n",
    "                                colors=colors,\n",
    "                                wedgeprops={'linewidth': 3.0, 'edgecolor': 'white'},)\n",
    "\n",
    "s = df2.groupby('ErrorType')['count'].sum().map(lambda x : x).sort_values(ascending = False)\n",
    "labels = [f'{name}, {percentage*100:0.1f}%' for name, percentage in zip(s.index, s / s.sum())]\n",
    "ax.legend(bbox_to_anchor=(1.2, 1), \n",
    "           loc='upper left', \n",
    "           # labels=labels,\n",
    "           title=\"Error Types\",\n",
    "           # labelcolor=colors,\n",
    "           fontsize=14)\n",
    "\n",
    "\n",
    "plt.setp(pcts, color='white', fontweight='bold')\n",
    "ax.set_title(\"Hug Error Distribution\", fontsize=18)\n",
    "for i, patch in enumerate(patches):\n",
    "    texts[i].set_color(patch.get_facecolor())\n",
    "    \n",
    "plt.setp(texts, fontweight=600)\n",
    "\n",
    "ax.axis('equal')  \n",
    "# plt.tight_layout()\n",
    "# ax.figure.savefig('piechart.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "fd039f6a-c399-4022-b617-f3c029525e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              ErrorType  count\n",
      "22         RuntimeError     42\n",
      "29           ValueError     41\n",
      "26            TypeError     30\n",
      "12             KeyError     21\n",
      "1        AttributeError     18\n",
      "17              OSError     15\n",
      "10           IndexError     10\n",
      "9           ImportError      9\n",
      "14  ModuleNotFoundError      4\n",
      "27   UnicodeDecodeError      3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(223, 3)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the frequency of each message type\n",
    "print(df_pattern_err_msg.groupby(['ErrorType'])['ErrorType'].count().reset_index(name='count').sort_values(['count'], ascending=False).nlargest(10, 'count'))\n",
    "df_pattern_err_msg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "bd469d64-2c15-4fa9-81df-790273d8e48a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score median:  1.0\n",
      "View median:  616.0\n",
      "Answer_count median:  1.0\n",
      "Comment median:  1.0\n",
      "# Accepted answer: 53   Total Accepted answer: 185   Percentage of Accepted answer: 28.65\n",
      "LOC median:  30.0\n",
      "LOP median:  69.0\n",
      "Answer duration median:  15.13\n",
      "Accepted Answer duration median:  18.08\n"
     ]
    }
   ],
   "source": [
    "print(\"Score median: \", df_pattern.Score.median())\n",
    "print(\"View median: \", df_pattern.ViewCount.median())\n",
    "print(\"Answer_count median: \", df_pattern.AnswerCount.median())\n",
    "print(\"Comment median: \", df_pattern.CommentCount.median())\n",
    "print(\"# Accepted answer:\", df_pattern.AcceptedAnswerId.count(), \"  Total Accepted answer:\", df_pattern.AcceptedAnswerId.shape[0], \"  Percentage of Accepted answer:\", \"{:.2f}\".format(df_pattern.AcceptedAnswerId.count() *100 / df_pattern.AcceptedAnswerId.shape[0]))\n",
    "print(\"LOC median: \", (df_pattern[df_pattern.Has_trace == True].Line_code_win + df_pattern[df_pattern.Has_trace == True].Line_code_uix).median())\n",
    "print(\"LOP median: \", df_pattern.Q_text_words_num.median())\n",
    "print(\"Answer duration median: \", \"{:.2f}\".format(df_pattern_ans.Duration_ans.median()))\n",
    "print(\"Accepted Answer duration median: \", \"{:.2f}\".format(df_pattern_ans.Duration_acc_ans.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "130682a1-c135-4957-aac8-e4f9af3deed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pattern_err_msg.Q_id = df_pattern_err_msg.Q_id.astype(int)\n",
    "df_result.Q_id = df_result.Q_id.astype(int)\n",
    "df_result_mrg = pd.merge(df_pattern_err_msg, df_result, on=[\"Q_id\"], how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "84a25154-9b96-4755-a17c-b3ad44a30f13",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# print(tabulate(df_pattern_err_msg, headers='keys', tablefmt='psql', showindex=False))\n",
    "# print(tabulate(df_result, headers='keys', tablefmt='psql', showindex=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "b8c42ffb-7294-4b1a-a55a-e414bbf27d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\n",
    "                       truncate_sheet=False, \n",
    "                       **to_excel_kwargs):\n",
    "    \"\"\"\n",
    "    Append a DataFrame [df] to existing Excel file [filename]\n",
    "    into [sheet_name] Sheet.\n",
    "    If [filename] doesn't exist, then this function will create it.\n",
    "\n",
    "    @param filename: File path or existing ExcelWriter\n",
    "                     (Example: '/path/to/file.xlsx')\n",
    "    @param df: DataFrame to save to workbook\n",
    "    @param sheet_name: Name of sheet which will contain DataFrame.\n",
    "                       (default: 'Sheet1')\n",
    "    @param startrow: upper left cell row to dump data frame.\n",
    "                     Per default (startrow=None) calculate the last row\n",
    "                     in the existing DF and write to the next row...\n",
    "    @param truncate_sheet: truncate (remove and recreate) [sheet_name]\n",
    "                           before writing DataFrame to Excel file\n",
    "    @param to_excel_kwargs: arguments which will be passed to `DataFrame.to_excel()`\n",
    "                            [can be a dictionary]\n",
    "    @return: None\n",
    "\n",
    "    Usage examples:\n",
    "\n",
    "    >>> append_df_to_excel('d:/temp/test.xlsx', df)\n",
    "\n",
    "    >>> append_df_to_excel('d:/temp/test.xlsx', df, header=None, index=False)\n",
    "\n",
    "    >>> append_df_to_excel('d:/temp/test.xlsx', df, sheet_name='Sheet2',\n",
    "                           index=False)\n",
    "\n",
    "    >>> append_df_to_excel('d:/temp/test.xlsx', df, sheet_name='Sheet2', \n",
    "                           index=False, startrow=25)\n",
    "\n",
    "    (c) [MaxU](https://stackoverflow.com/users/5741205/maxu?tab=profile)\n",
    "    \"\"\"\n",
    "    # Excel file doesn't exist - saving and exiting\n",
    "    if not os.path.isfile(filename):\n",
    "        df.to_excel(\n",
    "            filename,\n",
    "            sheet_name=sheet_name, \n",
    "            startrow=startrow if startrow is not None else 0, \n",
    "            **to_excel_kwargs)\n",
    "        return\n",
    "    \n",
    "    # ignore [engine] parameter if it was passed\n",
    "    if 'engine' in to_excel_kwargs:\n",
    "        to_excel_kwargs.pop('engine')\n",
    "\n",
    "    writer = pd.ExcelWriter(filename, engine='openpyxl', mode='a', if_sheet_exists='replace')\n",
    "\n",
    "    # try to open an existing workbook\n",
    "    writer.book = load_workbook(filename)\n",
    "    \n",
    "    # get the last row in the existing Excel sheet\n",
    "    # if it was not specified explicitly\n",
    "    if startrow is None and sheet_name in writer.book.sheetnames:\n",
    "        startrow = writer.book[sheet_name].max_row\n",
    "\n",
    "    # truncate sheet\n",
    "    if truncate_sheet and sheet_name in writer.book.sheetnames:\n",
    "        # index of [sheet_name] sheet\n",
    "        idx = writer.book.sheetnames.index(sheet_name)\n",
    "        # remove [sheet_name]\n",
    "        writer.book.remove(writer.book.worksheets[idx])\n",
    "        # create an empty sheet [sheet_name] using old index\n",
    "        writer.book.create_sheet(sheet_name, idx)\n",
    "    \n",
    "    # copy existing sheets\n",
    "    writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\n",
    "\n",
    "    if startrow is None:\n",
    "        startrow = 0\n",
    "\n",
    "    # write out the new sheet\n",
    "    df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\n",
    "\n",
    "    # save the workbook\n",
    "    writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "f2caacef-a6c5-47b9-8999-082adfee161c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_result_mrg.drop(\"Index\", axis=1, inplace=True)\n",
    "# df_result_mrg.to_excel(\"../Emp_st_p_1.xlsx\", sheet_name='huggingFace') \n",
    "df_result_mrg.drop(\"Index\", axis=1, inplace=True)\n",
    "append_df_to_excel(\"../Emp_st_p_1.xlsx\", df_result_mrg, sheet_name='huggingFace')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10b2e551-4402-40bd-a56d-23c54cf0a797",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Create covered question plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c94c8c1-b1f9-4f75-a3ad-437cd67470ec",
   "metadata": {},
   "source": [
    "Create the x-axist: sorted based on the support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "54901995-127d-492c-8a08-7bd18f0b7c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "426"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "49283624-95e6-43b8-b288-e87be24069c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_str_rev_sorted = sorted(result_str, key=lambda tup: tup[0], reverse=True)\n",
    "x_number_of_patterns = list(range(1, len(result_str_rev_sorted)+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f76549f-19fd-48e2-8a16-431f5f2354c7",
   "metadata": {},
   "source": [
    "Create the y-axist: % of coverd questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "58be51cb-6f84-4cf7-adf8-d35166aff3b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_unique_questions, set_of_unique_questions = get_q_number(dict_pattern_and_qid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b5d43f54-7b81-4367-89d4-4f07ff3b07ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "visited_questions = set()\n",
    "y_axis_peresentage_of_coverage = []\n",
    "for support, pattern in result_str_rev_sorted:    \n",
    "    visited_questions.update(dict_pattern_and_qid[pattern])\n",
    "    if len(visited_questions) < len(set_of_unique_questions):\n",
    "        y_axis_peresentage_of_coverage.append(len(visited_questions)*100/len(set_of_unique_questions))\n",
    "    elif len(visited_questions) > len(set_of_unique_questions):\n",
    "        print(\"Error(100)!\")\n",
    "    elif len(visited_questions) == len(set_of_unique_questions):\n",
    "        y_axis_peresentage_of_coverage.append(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "454393cb-8d99-4fa7-81a3-9c662dee22f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x660 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6), dpi=110)\n",
    "\n",
    "ax.plot(x_number_of_patterns, y_axis_peresentage_of_coverage, '-o', color='green', linewidth=2)\n",
    "\n",
    "ax.set_title('Hugging Face', fontname=\"Times New Roman\", fontsize=16)\n",
    "\n",
    "ax.set_xlabel('The number of patterns', labelpad=10, fontname=\"Times New Roman\", fontsize=14)\n",
    "ax.set_xticks(range(1, max(x_number_of_patterns)+1, 5))\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "ax.set_ylabel('The percentage of covered questions', labelpad=10, fontname=\"Times New Roman\", fontsize=14)\n",
    "ax.set_yticks(range(0, 101, 20))\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "# plt.savefig('sp_le_rq1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b0560cdd-e0f4-41ba-a79b-08d586b36064",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt_dic_hf_covered_ques = {\"x\":x_number_of_patterns, \"y\":y_axis_peresentage_of_coverage}\n",
    "pickle.dump(plt_dic_hf_covered_ques, open(\"./Pickle_data/plt_dic_hf_covered_ques.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4bc342",
   "metadata": {},
   "source": [
    "## Answer time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448de367",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Loading Answer Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "6b763dbb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pure_ans_data = working_directory_path + \"ansSample.csv\"\n",
    "# path_ans = Path(pure_ans_data)\n",
    "\n",
    "# if path_ans.suffix == \".csv\":\n",
    "#     df_ans = pd.read_csv(path_ans, encoding=encoding)\n",
    "# else:\n",
    "#     raise ValueError(\"{data_file_path.suffix} extensions are not supported\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0daf259d-561a-4487-9908-da7e4da40191",
   "metadata": {},
   "source": [
    "## Create DF based on the questions contain stack traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "31d5963c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_w_stack = pd.DataFrame(columns=['First_ans_time', 'First_acc_ans_time', 'Answers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "522f93f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_w_stack[\"Q_info\"] = Question_with_trace_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "3f03ba0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59656096, '2020-01-09 01:41:04', 497, 1, 3, 4, 59684161.0)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Question_with_trace_info[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "b3e2b596",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_w_stack[\"Q_id\"]          = df_status_w_stack['Q_info'].apply(lambda q_info: q_info[0])\n",
    "df_status_w_stack[\"Q_create_time\"] = df_status_w_stack['Q_info'].apply(lambda q_info: q_info[1])\n",
    "df_status_w_stack[\"View_count\"]    = df_status_w_stack['Q_info'].apply(lambda q_info: q_info[2])\n",
    "df_status_w_stack[\"Answer_count\"]  = df_status_w_stack['Q_info'].apply(lambda q_info: q_info[3])\n",
    "df_status_w_stack[\"Comment_count\"] = df_status_w_stack['Q_info'].apply(lambda q_info: q_info[4])\n",
    "df_status_w_stack[\"Score\"]         = df_status_w_stack['Q_info'].apply(lambda q_info: q_info[5])\n",
    "df_status_w_stack[\"Accepted_Answer_id\"] = df_status_w_stack['Q_info'].apply(lambda q_info: q_info[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "28deb778",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_w_stack = df_status_w_stack.drop(['Q_info'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "ba5793bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_w_stack[\"Q_create_time\"]      = pd.to_datetime(df_status_w_stack[\"Q_create_time\"])\n",
    "df_status_w_stack[\"First_acc_ans_time\"] = pd.to_datetime(df_status_w_stack[\"First_acc_ans_time\"])\n",
    "df_status_w_stack[\"First_ans_time\"]     = pd.to_datetime(df_status_w_stack[\"First_ans_time\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfd1bbb",
   "metadata": {},
   "source": [
    "#### Filling the Answers column: A list contains the id and time of answers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba7d931-c4d3-492a-8276-abc6de000ea3",
   "metadata": {},
   "source": [
    "We have to prepare and find answers that has sp parrentID, so first we catch the questions and store as table in DB. Before this job we have to create a table with all answrs and apply inner join to those tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "f4d8093c-3c26-4d31-adab-ad1c4bd66959",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_w_stack[\"Q_id\"].to_csv('../code_output_csv/df_hf_w_stack.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "61f7376f-6f99-46ba-ac0e-296c646279bd",
   "metadata": {},
   "source": [
    "SELECT df_hf_w_stack.Q_id, all_results.Id, all_results.CreationDate, all_results.ParentId\n",
    "FROM df_hf_w_stack\n",
    "INNER JOIN all_results\n",
    "ON df_hf_w_stack.Q_id = all_results.ParentId;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "510bf191",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pure_data = working_directory_path + \"db_results/\" + \"hugh_ans_all.csv\"\n",
    "path = Path(pure_data)\n",
    "\n",
    "if path.suffix == \".csv\":\n",
    "    df_night = pd.read_csv(path, encoding=encoding)\n",
    "else:\n",
    "    raise ValueError(\"{data_file_path.suffix} extensions are not supported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "fd4de2ff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df_status_w_stack['Answers'] = df_status_w_stack.apply(lambda x: [], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "ba9770ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for index1, row_status in df_status_w_stack.iterrows():\n",
    "#     for index2, row_night in df_night.iterrows():\n",
    "#             if row_night[\"ParentId\"] == row_status[\"Q_id\"]:\n",
    "#                     row_status['Answers'].append((row_night[\"Id\"], row_night[\"CreationDate\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "ba9e9b00-b12d-498a-a816-9284be6bf1b8",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>65140400</td>\n",
       "      <td>2020-12-04 08:42:57</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63906559</td>\n",
       "      <td>2020-09-15 16:55:29</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64406166</td>\n",
       "      <td>2020-10-17 18:52:55</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62772819</td>\n",
       "      <td>2020-07-07 10:06:24</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64514791</td>\n",
       "      <td>2020-10-24 15:04:55</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77903161</td>\n",
       "      <td>2024-01-29 23:29:40</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77866423</td>\n",
       "      <td>2024-01-23 12:47:32</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77861915</td>\n",
       "      <td>2024-01-22 18:14:55</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>77862615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77836708</td>\n",
       "      <td>2024-01-18 03:21:22</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77825337</td>\n",
       "      <td>2024-01-16 11:12:17</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77820479</td>\n",
       "      <td>2024-01-15 14:30:00</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77814471</td>\n",
       "      <td>2024-01-14 09:18:30</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77810413</td>\n",
       "      <td>2024-01-13 05:31:57</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77738486</td>\n",
       "      <td>2023-12-31 07:13:50</td>\n",
       "      <td>241</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77729879</td>\n",
       "      <td>2023-12-29 01:30:14</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First_ans_time First_acc_ans_time Answers      Q_id       Q_create_time  View_count  Answer_count  Comment_count  Score  Accepted_Answer_id\n",
       "0             NaT                NaT     NaN  59656096 2020-01-09 01:41:04         497             1              3      4          59684161.0\n",
       "1             NaT                NaT     NaN  62979062 2020-07-19 10:28:28         136             1              2      0                 NaN\n",
       "2             NaT                NaT     NaN  64818504 2020-11-13 09:32:47          53             0              2      0                 NaN\n",
       "3             NaT                NaT     NaN  65023526 2020-11-26 14:01:21          52             1              6     -1          65052927.0\n",
       "4             NaT                NaT     NaN  61019485 2020-04-03 19:54:41         379             1              0      0          61019599.0\n",
       "5             NaT                NaT     NaN  65091635 2020-12-01 13:42:28          35             0              3      0                 NaN\n",
       "6             NaT                NaT     NaN  64370953 2020-10-15 11:49:09          36             0              0      0                 NaN\n",
       "7             NaT                NaT     NaN  63465109 2020-08-18 08:52:59         428             1              2      1                 NaN\n",
       "8             NaT                NaT     NaN  65095129 2020-12-01 17:07:32          39             1              1      1                 NaN\n",
       "9             NaT                NaT     NaN  61382917 2020-04-23 08:42:41         246             1              2      1                 NaN\n",
       "10            NaT                NaT     NaN  62936905 2020-07-16 14:23:59         194             0              0      1                 NaN\n",
       "11            NaT                NaT     NaN  60459292 2020-02-28 21:11:27         223             1              1      0          60480429.0\n",
       "12            NaT                NaT     NaN  64044200 2020-09-24 10:08:21         111             1              0      1          64044374.0\n",
       "13            NaT                NaT     NaN  60848403 2020-03-25 11:59:40          62             1              3     -1                 NaN\n",
       "14            NaT                NaT     NaN  60530393 2020-03-04 16:18:38         192             0              2      0                 NaN\n",
       "15            NaT                NaT     NaN  61863504 2020-05-18 06:20:21         687             0              4      0                 NaN\n",
       "16            NaT                NaT     NaN  60833301 2020-03-24 14:42:14         260             1              0      0          60833512.0\n",
       "17            NaT                NaT     NaN  62434075 2020-06-17 16:41:46         406             1              0      0          62434868.0\n",
       "18            NaT                NaT     NaN  64124757 2020-09-29 17:33:32         167             1              3     -2                 NaN\n",
       "19            NaT                NaT     NaN  64180517 2020-10-03 03:51:06         130             1              3      0                 NaN\n",
       "20            NaT                NaT     NaN  60209265 2020-02-13 13:42:55         727             0              3      3                 NaN\n",
       "21            NaT                NaT     NaN  62142122 2020-06-01 22:43:30          33             0              0      0                 NaN\n",
       "22            NaT                NaT     NaN  61888473 2020-05-19 10:11:07          41             0              2      0                 NaN\n",
       "23            NaT                NaT     NaN  63232732 2020-08-03 15:51:49         224             2              8      6          63415069.0\n",
       "24            NaT                NaT     NaN  60062624 2020-02-04 17:28:36         114             1              2      3                 NaN\n",
       "25            NaT                NaT     NaN  62671054 2020-07-01 06:42:21         137             0              2      1                 NaN\n",
       "26            NaT                NaT     NaN  62863374 2020-07-12 16:03:54         378             0              6      2                 NaN\n",
       "27            NaT                NaT     NaN  62472238 2020-06-19 14:17:46         730             2              0      0                 NaN\n",
       "28            NaT                NaT     NaN  64180606 2020-10-03 04:10:57          41             0              1      0                 NaN\n",
       "29            NaT                NaT     NaN  58454157 2019-10-18 15:42:39        5096             1              2      6          58456379.0\n",
       "30            NaT                NaT     NaN  64626722 2020-10-31 21:46:19         101             0              4      0                 NaN\n",
       "31            NaT                NaT     NaN  61680408 2020-05-08 13:35:11         132             2              0      0                 NaN\n",
       "32            NaT                NaT     NaN  64800623 2020-11-12 08:50:15          49             1              3      0          64801036.0\n",
       "33            NaT                NaT     NaN  62327803 2020-06-11 15:23:32         247             1              0      1          62328920.0\n",
       "34            NaT                NaT     NaN  65082609 2020-11-30 23:26:09          14             0              1      0                 NaN\n",
       "35            NaT                NaT     NaN  65140400 2020-12-04 08:42:57          18             0              2      0                 NaN\n",
       "36            NaT                NaT     NaN  63906559 2020-09-15 16:55:29          46             0              0      0                 NaN\n",
       "37            NaT                NaT     NaN  64406166 2020-10-17 18:52:55          90             1              0      1                 NaN\n",
       "38            NaT                NaT     NaN  62772819 2020-07-07 10:06:24         406             0              4      2                 NaN\n",
       "39            NaT                NaT     NaN  64514791 2020-10-24 15:04:55          89             0             11      0                 NaN\n",
       "40            NaT                NaT     NaN  77903161 2024-01-29 23:29:40          16             0              1      0                 NaN\n",
       "41            NaT                NaT     NaN  77866423 2024-01-23 12:47:32          16             0              0      0                 NaN\n",
       "42            NaT                NaT     NaN  77861915 2024-01-22 18:14:55          58             1             17      0          77862615.0\n",
       "43            NaT                NaT     NaN  77836708 2024-01-18 03:21:22          30             0              0      0                 NaN\n",
       "44            NaT                NaT     NaN  77825337 2024-01-16 11:12:17          20             0              0      0                 NaN\n",
       "45            NaT                NaT     NaN  77820479 2024-01-15 14:30:00          33             0              0      0                 NaN\n",
       "46            NaT                NaT     NaN  77814471 2024-01-14 09:18:30          25             1              0      0                 NaN\n",
       "47            NaT                NaT     NaN  77810413 2024-01-13 05:31:57         105             0              4      0                 NaN\n",
       "48            NaT                NaT     NaN  77738486 2023-12-31 07:13:50         241             1              0      0                 NaN\n",
       "49            NaT                NaT     NaN  77729879 2023-12-29 01:30:14         171             1              0      0                 NaN"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_status_w_stack.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "b3732d4c-0d2b-4cfa-9cd3-c1ad1472244a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_w = pd.merge(df_status_w_stack, df_night, how='outer',left_on=['Q_id'], right_on=['ParentId']).reset_index(drop=True)\n",
    "pd_tmp_w[\"Answer_tup\"] = pd_tmp_w.apply(lambda x: (x.Id, x.CreationDate), axis=1)\n",
    "pd_tmp_new_w = pd_tmp_w.groupby([\"Q_id_x\", \"Q_create_time\", \"View_count\", \"Comment_count\", \"Score\", \"Answer_count\", \"Accepted_Answer_id\"], dropna=False)[\"Answer_tup\"].agg(list).reset_index()\n",
    "pd_tmp_new2_w = pd_tmp_new_w[pd_tmp_new_w.Answer_count != 0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "1e42a88a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_status_w_stack[\"Answers\"] = df_status_w_stack[\"Answers\"].apply(lambda answers_list: list(set(answers_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "d6cbd12e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def checker_ans_and_ans_tup(row_indx: int, ans_count: int, ans_list: list) -> None:\n",
    "    if len(ans_list) < ans_count:\n",
    "        print(\"Error!: \", row_indx, ans_count, ans_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "bff2f339",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# df_status_w_stack.apply(lambda row: checker_1(row.name, row.Answers, row.Answer_count), axis=1)\n",
    "_ = pd_tmp_new2_w.apply(lambda row: checker_ans_and_ans_tup(row.name, row.Answer_count, row.Answer_tup), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "0735f1d5-381b-454f-b369-0968480dfa99",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new2_w.insert(len(pd_tmp_new2_w.columns), 'First_ans_time', np.nan)\n",
    "pd_tmp_new2_w.insert(len(pd_tmp_new2_w.columns), 'First_acc_ans_time', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "67d7019b-2ab6-4088-91ac-ad6ae20eceb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new3_w = pd_tmp_new2_w.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "8a596f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index1, row_status in pd_tmp_new3_w.iterrows():   \n",
    "    flag = 0\n",
    "    fr_time = pd.to_datetime(datetime.datetime.now())\n",
    "    acc_time = pd.to_datetime(datetime.datetime.now())\n",
    "        \n",
    "    if not row_status[\"Answer_tup\"]:\n",
    "        pd_tmp_new3_w.at[index1,'First_ans_time'] = np.nan\n",
    "        pd_tmp_new3_w.at[index1,'First_acc_ans_time'] = np.nan\n",
    "        continue\n",
    "\n",
    "    for answer in row_status[\"Answer_tup\"]:\n",
    "        \n",
    "        anwer_time = pd.to_datetime(answer[1])\n",
    "        \n",
    "        if flag == 0:\n",
    "            fr_time = anwer_time\n",
    "            flag = 1\n",
    "        \n",
    "        if fr_time > anwer_time:\n",
    "            fr_time = anwer_time\n",
    "        \n",
    "        if row_status[\"Accepted_Answer_id\"] == answer[0]:\n",
    "            acc_time = anwer_time\n",
    "\n",
    "    pd_tmp_new3_w.at[index1,'First_ans_time'] = fr_time\n",
    "        \n",
    "    if pd.isna(row_status[\"Accepted_Answer_id\"]):\n",
    "        acc_time = np.nan\n",
    "    \n",
    "    pd_tmp_new3_w.at[index1,'First_acc_ans_time'] = acc_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "638a57c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_status_w_stack.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "847107e7",
   "metadata": {},
   "source": [
    "Write df_status_w_stack to the CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "4335bdcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_status_w_stack.to_csv('./status_PT_df_w_stack_with_list_of_answers_col.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4253bbc",
   "metadata": {},
   "source": [
    "Read df_status_w_stack from the CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "29f760c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_status_w_stack = pd.read_csv('./status_df_w_stack_with_list_of_answers_col.csv', encoding=encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "8a424d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_status_w_stack[\"Duration_ans\"] = df_status_w_stack.apply(lambda row: (row.First_ans_time-row.Q_create_time).days, axis=1)\n",
    "# df_status_w_stack[\"Duration_acc_ans\"] = df_status_w_stack.apply(lambda row: (row.First_acc_ans_time-row.Q_create_time).days, axis=1)\n",
    "pd_tmp_new3_w[\"Duration_ans\"] = pd_tmp_new3_w.apply(lambda row: (row.First_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_ans_time) else np.nan, axis=1)\n",
    "pd_tmp_new3_w[\"Duration_acc_ans\"] = pd_tmp_new3_w.apply(lambda row: (row.First_acc_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_acc_ans_time) else np.nan, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "415a4f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd_tmp_new3_w['Duration_ans'] = pd_tmp_new3_w['Duration_ans'].fillna(0)\n",
    "# pd_tmp_new3_w['Duration_acc_ans'] = pd_tmp_new3_w['Duration_acc_ans'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "fecf7b71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_status_w_stack.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "b85e4eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235, 12)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_tmp_new3_w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "e00d09fa",
   "metadata": {
    "scrolled": true
   },
   "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>Q_id_x</th>\n",
       "      <th>View_count</th>\n",
       "      <th>Comment_count</th>\n",
       "      <th>Score</th>\n",
       "      <th>Answer_count</th>\n",
       "      <th>Accepted_Answer_id</th>\n",
       "      <th>Duration_ans</th>\n",
       "      <th>Duration_acc_ans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.340000e+02</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>1.220000e+02</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>122.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.212148e+07</td>\n",
       "      <td>2267.162393</td>\n",
       "      <td>1.431624</td>\n",
       "      <td>1.487179</td>\n",
       "      <td>1.256410</td>\n",
       "      <td>7.174174e+07</td>\n",
       "      <td>640.562460</td>\n",
       "      <td>146.711207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.700887e+06</td>\n",
       "      <td>3702.866391</td>\n",
       "      <td>2.392232</td>\n",
       "      <td>2.303628</td>\n",
       "      <td>0.588208</td>\n",
       "      <td>4.627787e+06</td>\n",
       "      <td>1866.366429</td>\n",
       "      <td>650.821340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.845416e+07</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.845638e+07</td>\n",
       "      <td>0.009167</td>\n",
       "      <td>0.045556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.859033e+07</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.866062e+07</td>\n",
       "      <td>2.106181</td>\n",
       "      <td>1.281042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.338711e+07</td>\n",
       "      <td>832.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.237006e+07</td>\n",
       "      <td>21.820417</td>\n",
       "      <td>13.946667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.614513e+07</td>\n",
       "      <td>2437.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.570437e+07</td>\n",
       "      <td>138.259028</td>\n",
       "      <td>70.811250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.786192e+07</td>\n",
       "      <td>26923.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.786262e+07</td>\n",
       "      <td>11507.728333</td>\n",
       "      <td>6791.945278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Q_id_x    View_count  Comment_count       Score  Answer_count  Accepted_Answer_id  Duration_ans  Duration_acc_ans\n",
       "count  2.340000e+02    234.000000     234.000000  234.000000    234.000000        1.220000e+02    234.000000        122.000000\n",
       "mean   7.212148e+07   2267.162393       1.431624    1.487179      1.256410        7.174174e+07    640.562460        146.711207\n",
       "std    4.700887e+06   3702.866391       2.392232    2.303628      0.588208        4.627787e+06   1866.366429        650.821340\n",
       "min    5.845416e+07     19.000000       0.000000   -2.000000      1.000000        5.845638e+07      0.009167          0.045556\n",
       "25%    6.859033e+07    328.000000       0.000000    0.000000      1.000000        6.866062e+07      2.106181          1.281042\n",
       "50%    7.338711e+07    832.000000       0.000000    1.000000      1.000000        7.237006e+07     21.820417         13.946667\n",
       "75%    7.614513e+07   2437.250000       2.000000    2.000000      1.000000        7.570437e+07    138.259028         70.811250\n",
       "max    7.786192e+07  26923.000000      17.000000   18.000000      5.000000        7.786262e+07  11507.728333       6791.945278"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_tmp_new3_w.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19800ec",
   "metadata": {},
   "source": [
    "## Create DF based on the questions do not have stack traces"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57682814-ce03-41e4-88d5-1ed524f79811",
   "metadata": {
    "tags": []
   },
   "source": [
    "We have to prepare and find answers that has Pytorch parrentID, so first we catch the questions and store as table in DB. Before this job we have to create a table with all answrs and apply inner join to those tables:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ece49f68-9d28-4236-957e-0a98cd01b9f0",
   "metadata": {
    "tags": []
   },
   "source": [
    "SELECT df_hf_wo_stack.Q_id, all_results.Id, all_results.CreationDate, all_results.ParentId\n",
    "FROM df_hf_wo_stack\n",
    "INNER JOIN all_results\n",
    "ON df_hf_wo_stack.Q_id = all_results.ParentId;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "45f26363",
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_data = working_directory_path + \"db_results/\" + \"hugh_ans_all.csv\"\n",
    "path = Path(pure_data)\n",
    "\n",
    "if path.suffix == \".csv\":\n",
    "    df_night_wo = pd.read_csv(path, encoding=encoding)\n",
    "else:\n",
    "    raise ValueError(\"{data_file_path.suffix} extensions are not supported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "e9c567d8-93cd-488a-9715-2c3b6a011077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1621, 4)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_night_wo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "9f2258f0-f3e4-4a6a-99a1-9413d666c046",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Q_id</th>\n",
       "      <th>ParentId</th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>54873721</td>\n",
       "      <td>54873721</td>\n",
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       "      <td>2019-03-01 20:58:35</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>58454157</td>\n",
       "      <td>58456379</td>\n",
       "      <td>2019-10-18 18:28:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60192523</td>\n",
       "      <td>60192523</td>\n",
       "      <td>60205849</td>\n",
       "      <td>2020-02-13 10:39:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60187634</td>\n",
       "      <td>60187634</td>\n",
       "      <td>60213244</td>\n",
       "      <td>2020-02-13 17:13:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60220842</td>\n",
       "      <td>60220842</td>\n",
       "      <td>60224015</td>\n",
       "      <td>2020-02-14 09:59:27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Q_id  ParentId        Id         CreationDate\n",
       "0  54873721  54873721  54952215  2019-03-01 20:58:35\n",
       "1  58454157  58454157  58456379  2019-10-18 18:28:42\n",
       "2  60192523  60192523  60205849  2020-02-13 10:39:28\n",
       "3  60187634  60187634  60213244  2020-02-13 17:13:30\n",
       "4  60220842  60220842  60224015  2020-02-14 09:59:27"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_night_wo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "6195bf92-32fc-45a2-8560-d3e654b35132",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df_night_wo.drop_duplicates([\"Q_id\", \"Id\", \"ParentId\", \"CreationDate\"], ignore_index=False, inplace=True)\n",
    "df_night_wo = df_night_wo.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "927a0491",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_wo_stack = pd.DataFrame(columns=['First_ans_time', 'First_acc_ans_time', 'Answers'])\n",
    "\n",
    "df_status_wo_stack[\"Q_info\"] = Question_with_wo_trace_info\n",
    "\n",
    "df_status_wo_stack[\"Q_id\"]          = df_status_wo_stack['Q_info'].apply(lambda q_info: q_info[0])\n",
    "df_status_wo_stack[\"Q_create_time\"] = df_status_wo_stack['Q_info'].apply(lambda q_info: q_info[1])\n",
    "df_status_wo_stack[\"View_count\"]    = df_status_wo_stack['Q_info'].apply(lambda q_info: q_info[2])\n",
    "df_status_wo_stack[\"Answer_count\"]  = df_status_wo_stack['Q_info'].apply(lambda q_info: q_info[3])\n",
    "df_status_wo_stack[\"Comment_count\"] = df_status_wo_stack['Q_info'].apply(lambda q_info: q_info[4])\n",
    "df_status_wo_stack[\"Score\"]         = df_status_wo_stack['Q_info'].apply(lambda q_info: q_info[5])\n",
    "df_status_wo_stack[\"Accepted_Answer_id\"] = df_status_wo_stack['Q_info'].apply(lambda q_info: q_info[6])\n",
    "\n",
    "df_status_wo_stack = df_status_wo_stack.drop(['Q_info'], axis='columns')\n",
    "\n",
    "df_status_wo_stack[\"Q_create_time\"] = pd.to_datetime(df_status_wo_stack[\"Q_create_time\"])\n",
    "df_status_wo_stack[\"First_acc_ans_time\"] = pd.to_datetime(df_status_wo_stack[\"First_acc_ans_time\"])\n",
    "df_status_wo_stack[\"First_ans_time\"]     = pd.to_datetime(df_status_wo_stack[\"First_ans_time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "43a896da-2513-472b-8562-a80b6b4c9ce1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_status_wo_stack[\"Q_id\"].to_csv('../code_output_csv/df_hf_wo_stack.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "bb4a6c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q_id</th>\n",
       "      <th>ParentId</th>\n",
       "      <th>Id</th>\n",
       "      <th>CreationDate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54873721</td>\n",
       "      <td>54873721</td>\n",
       "      <td>54952215</td>\n",
       "      <td>2019-03-01 20:58:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58454157</td>\n",
       "      <td>58454157</td>\n",
       "      <td>58456379</td>\n",
       "      <td>2019-10-18 18:28:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60192523</td>\n",
       "      <td>60192523</td>\n",
       "      <td>60205849</td>\n",
       "      <td>2020-02-13 10:39:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60187634</td>\n",
       "      <td>60187634</td>\n",
       "      <td>60213244</td>\n",
       "      <td>2020-02-13 17:13:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60220842</td>\n",
       "      <td>60220842</td>\n",
       "      <td>60224015</td>\n",
       "      <td>2020-02-14 09:59:27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Q_id  ParentId        Id         CreationDate\n",
       "0  54873721  54873721  54952215  2019-03-01 20:58:35\n",
       "1  58454157  58454157  58456379  2019-10-18 18:28:42\n",
       "2  60192523  60192523  60205849  2020-02-13 10:39:28\n",
       "3  60187634  60187634  60213244  2020-02-13 17:13:30\n",
       "4  60220842  60220842  60224015  2020-02-14 09:59:27"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_night_wo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "afe64a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First_ans_time</th>\n",
       "      <th>First_acc_ans_time</th>\n",
       "      <th>Answers</th>\n",
       "      <th>Q_id</th>\n",
       "      <th>Q_create_time</th>\n",
       "      <th>View_count</th>\n",
       "      <th>Answer_count</th>\n",
       "      <th>Comment_count</th>\n",
       "      <th>Score</th>\n",
       "      <th>Accepted_Answer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61913010</td>\n",
       "      <td>2020-05-20 12:20:43</td>\n",
       "      <td>1792</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>61913108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60940110</td>\n",
       "      <td>2020-03-30 21:50:45</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64313576</td>\n",
       "      <td>2020-10-12 07:44:17</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64727226</td>\n",
       "      <td>2020-11-07 11:34:12</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62757772</td>\n",
       "      <td>2020-07-06 14:04:08</td>\n",
       "      <td>418</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First_ans_time First_acc_ans_time Answers      Q_id       Q_create_time  View_count  Answer_count  Comment_count  Score  Accepted_Answer_id\n",
       "0            NaT                NaT     NaN  61913010 2020-05-20 12:20:43        1792             2              0      2          61913108.0\n",
       "1            NaT                NaT     NaN  60940110 2020-03-30 21:50:45         118             0              0      1                 NaN\n",
       "2            NaT                NaT     NaN  64313576 2020-10-12 07:44:17          41             1              3      0                 NaN\n",
       "3            NaT                NaT     NaN  64727226 2020-11-07 11:34:12          70             0              5      1                 NaN\n",
       "4            NaT                NaT     NaN  62757772 2020-07-06 14:04:08         418             1              0      0                 NaN"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_status_wo_stack.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "02caaef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_wo_stack['Answers'] = df_status_wo_stack.apply(lambda x: [], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "850dca9f-f50d-4844-8636-0c9163cb68dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1621, 4)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_night_wo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "a8006ded-2978-4735-9e8d-1c6f818c5385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1248, 10)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_status_wo_stack.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "73ca48d7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # It is super time-consuming and never run!\n",
    "# for index1, row_status in df_status_wo_stack.iterrows():\n",
    "#     for index2, row_night in df_night_wo.iterrows():\n",
    "#         if row_night[\"ParentId\"] == row_status[\"Q_id\"]:\n",
    "#                 row_status['Answers'].append((row_night[\"Id\"], row_night[\"CreationDate\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "41fd65ef-8327-4d89-b46a-0b6c9e701df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_wo = pd.merge(df_status_wo_stack, df_night_wo, how='left',left_on=['Q_id'],right_on=['ParentId']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "c3a4bb1c-c844-4fb1-b291-4a243d589c2a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd_tmp_wo[\"Answer_tup\"] = pd_tmp_wo.apply(lambda x: (x.Id, x.CreationDate), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "399421d6-ecc5-41bf-872d-52d39610d1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd_tmp_new = pd_tmp.groupby([\"First_ans_time\", \"First_acc_ans_time\", \"Q_id_x\", \"Q_create_time\", \"View_count\", \"Comment_count\", \"Score\", \"Answer_count\", \"Accepted_Answer_id\"])[\"Answer_tup\"].agg(list).reset_index()\n",
    "pd_tmp_new_wo = pd_tmp_wo.groupby([\"Q_id_x\", \"Q_create_time\", \"View_count\", \"Comment_count\", \"Score\", \"Answer_count\", \"Accepted_Answer_id\"], dropna=False)[\"Answer_tup\"].agg(list).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "d0cf8935-4cfb-4a8f-93ea-8a85965cf76d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new2_wo = pd_tmp_new_wo[pd_tmp_new_wo.Answer_count != 0].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "f5d2daa7-d193-4d18-b974-b71b7d697cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def checker_ans_and_ans_tup(row_indx: int, ans_count: int, ans_list: list) -> None:\n",
    "    if len(ans_list) < ans_count:\n",
    "        print(\"Error!: \", row_indx, ans_count, ans_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "b2018458-7fd9-4ca3-9da1-5714b03e3e6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error!:  1 2 [(60574502.0, '2020-03-07 04:51:25')]\n",
      "Error!:  48 3 [(62053791.0, '2020-05-27 22:18:30'), (62130091.0, '2020-06-01 10:24:27')]\n"
     ]
    }
   ],
   "source": [
    "# We checked all Q_id_x are unique\n",
    "_ = pd_tmp_new2_wo.apply(lambda row: checker_ans_and_ans_tup(row.name, row.Answer_count, row.Answer_tup), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "1abeea79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_status_wo_stack[\"Answers\"] = df_status_wo_stack[\"Answers\"].apply(lambda answers_list: list(set(answers_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "21fdae5f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df_status_wo_stack.apply(lambda row: checker_1(row.name, row.Answers, row.Answer_count), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "985c2fbc-1102-4751-bb8f-8d7d484182fd",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First_ans_time</th>\n",
       "      <th>First_acc_ans_time</th>\n",
       "      <th>Answers</th>\n",
       "      <th>Q_id</th>\n",
       "      <th>Q_create_time</th>\n",
       "      <th>View_count</th>\n",
       "      <th>Answer_count</th>\n",
       "      <th>Comment_count</th>\n",
       "      <th>Score</th>\n",
       "      <th>Accepted_Answer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>[]</td>\n",
       "      <td>61913010</td>\n",
       "      <td>2020-05-20 12:20:43</td>\n",
       "      <td>1792</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>61913108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>[]</td>\n",
       "      <td>60940110</td>\n",
       "      <td>2020-03-30 21:50:45</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>[]</td>\n",
       "      <td>64313576</td>\n",
       "      <td>2020-10-12 07:44:17</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>[]</td>\n",
       "      <td>64727226</td>\n",
       "      <td>2020-11-07 11:34:12</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>[]</td>\n",
       "      <td>62757772</td>\n",
       "      <td>2020-07-06 14:04:08</td>\n",
       "      <td>418</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First_ans_time First_acc_ans_time Answers      Q_id       Q_create_time  View_count  Answer_count  Comment_count  Score  Accepted_Answer_id\n",
       "0            NaT                NaT      []  61913010 2020-05-20 12:20:43        1792             2              0      2          61913108.0\n",
       "1            NaT                NaT      []  60940110 2020-03-30 21:50:45         118             0              0      1                 NaN\n",
       "2            NaT                NaT      []  64313576 2020-10-12 07:44:17          41             1              3      0                 NaN\n",
       "3            NaT                NaT      []  64727226 2020-11-07 11:34:12          70             0              5      1                 NaN\n",
       "4            NaT                NaT      []  62757772 2020-07-06 14:04:08         418             1              0      0                 NaN"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_status_wo_stack.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "6f892ae5-e359-4a30-ae5e-255cc23ae8a5",
   "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",
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       "    }\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>Q_id_x</th>\n",
       "      <th>Q_create_time</th>\n",
       "      <th>View_count</th>\n",
       "      <th>Comment_count</th>\n",
       "      <th>Score</th>\n",
       "      <th>Answer_count</th>\n",
       "      <th>Accepted_Answer_id</th>\n",
       "      <th>Answer_tup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54873721</td>\n",
       "      <td>2019-02-25 19:55:37</td>\n",
       "      <td>2123</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[(54952215.0, 2019-03-01 20:58:35), (60359949.0, 2020-02-23 07:10:23)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57248098</td>\n",
       "      <td>2019-07-29 06:16:27</td>\n",
       "      <td>442</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>60574502.0</td>\n",
       "      <td>[(60574502.0, 2020-03-07 04:51:25)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59435020</td>\n",
       "      <td>2019-12-21 09:24:46</td>\n",
       "      <td>1356</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>59520904.0</td>\n",
       "      <td>[(59520904.0, 2019-12-29 15:52:57), (60146344.0, 2020-02-10 08:09:05)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59701981</td>\n",
       "      <td>2020-01-12 07:56:33</td>\n",
       "      <td>6759</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>59703711.0</td>\n",
       "      <td>[(59703711.0, 2020-01-12 12:18:03)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59962851</td>\n",
       "      <td>2020-01-29 08:10:59</td>\n",
       "      <td>438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[(59992623.0, 2020-01-30 18:49:29)]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q_id_x       Q_create_time  View_count  Comment_count  Score  Answer_count  Accepted_Answer_id                                                              Answer_tup\n",
       "0  54873721 2019-02-25 19:55:37        2123              0      5             2                 NaN  [(54952215.0, 2019-03-01 20:58:35), (60359949.0, 2020-02-23 07:10:23)]\n",
       "1  57248098 2019-07-29 06:16:27         442              2      1             2          60574502.0                                     [(60574502.0, 2020-03-07 04:51:25)]\n",
       "2  59435020 2019-12-21 09:24:46        1356              0      4             2          59520904.0  [(59520904.0, 2019-12-29 15:52:57), (60146344.0, 2020-02-10 08:09:05)]\n",
       "3  59701981 2020-01-12 07:56:33        6759              0      3             1          59703711.0                                     [(59703711.0, 2020-01-12 12:18:03)]\n",
       "4  59962851 2020-01-29 08:10:59         438              0      0             1                 NaN                                     [(59992623.0, 2020-01-30 18:49:29)]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_tmp_new2_wo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "dcb04af7-e1a0-459f-a6a9-220ec4df1038",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new2_wo.insert(len(pd_tmp_new2_wo.columns), 'First_ans_time', np.nan)\n",
    "pd_tmp_new2_wo.insert(len(pd_tmp_new2_wo.columns), 'First_acc_ans_time', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "28955ecd-1434-4f76-bac1-6da683f872f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new3_wo = pd_tmp_new2_wo.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "ad26bd44",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index1, row_status in pd_tmp_new3_wo.iterrows():   \n",
    "    flag = 0\n",
    "    fr_time = pd.to_datetime(datetime.datetime.now())\n",
    "    acc_time = pd.to_datetime(datetime.datetime.now())\n",
    "        \n",
    "    if not row_status[\"Answer_tup\"]:\n",
    "        pd_tmp_new3_wo.at[index1,'First_ans_time'] = np.nan\n",
    "        pd_tmp_new3_wo.at[index1,'First_acc_ans_time'] = np.nan\n",
    "        continue\n",
    "\n",
    "    for answer in row_status[\"Answer_tup\"]:\n",
    "        \n",
    "        anwer_time = pd.to_datetime(answer[1])\n",
    "        \n",
    "        if flag == 0:\n",
    "            fr_time = anwer_time\n",
    "            flag = 1\n",
    "        \n",
    "        if fr_time > anwer_time:\n",
    "            fr_time = anwer_time\n",
    "        \n",
    "        if row_status[\"Accepted_Answer_id\"] == answer[0]:\n",
    "            acc_time = anwer_time\n",
    "\n",
    "    pd_tmp_new3_wo.at[index1,'First_ans_time'] = fr_time\n",
    "        \n",
    "    if pd.isna(row_status[\"Accepted_Answer_id\"]):\n",
    "        acc_time = np.nan\n",
    "    \n",
    "    pd_tmp_new3_wo.at[index1,'First_acc_ans_time'] = acc_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "59f34b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new3_wo[\"Duration_ans\"] = pd_tmp_new3_wo.apply(lambda row: (row.First_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_ans_time) else np.nan, axis=1)\n",
    "pd_tmp_new3_wo[\"Duration_acc_ans\"] = pd_tmp_new3_wo.apply(lambda row: (row.First_acc_ans_time-row.Q_create_time).total_seconds()/3600 if not pd.isna(row.First_acc_ans_time) else np.nan, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "4f1467f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd_tmp_new3_wo['Duration_ans'] = pd_tmp_new3_wo['Duration_ans'].fillna(0)\n",
    "# pd_tmp_new3_wo['Duration_acc_ans'] = pd_tmp_new3_wo['Duration_acc_ans'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daac7769",
   "metadata": {},
   "source": [
    "### Find the durarion of answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "6d6e7aaf-49d8-434f-93c2-1a5874830b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have to delete rows or posts that doesn't have answer, because their zero values affect the meadian value (or the box plot values):\n",
    "# df_status_w_stack_filtered = pd_tmp_new3_wo[pd_tmp_new3_wo[\"Answer_count\"] > 0]\n",
    "# pd_tmp_new3_filtered = pd_tmp_new3_w[pd_tmp_new3_w[\"Answer_count\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "bf33ac9d-d93f-423f-bec9-f1c38644a4c2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pd_tmp_new3_w[[\"Q_create_time\", \"First_ans_time\", \"Answer_count\", \"Duration_ans\"]].head(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "d355b03d-d61b-4124-9ce2-7dddc08b253e",
   "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>Answer_count</th>\n",
       "      <th>Duration_ans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>234.000000</td>\n",
       "      <td>234.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.256410</td>\n",
       "      <td>640.562460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.588208</td>\n",
       "      <td>1866.366429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.106181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.820417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>138.259028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>11507.728333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Answer_count  Duration_ans\n",
       "count    234.000000    234.000000\n",
       "mean       1.256410    640.562460\n",
       "std        0.588208   1866.366429\n",
       "min        1.000000      0.009167\n",
       "25%        1.000000      2.106181\n",
       "50%        1.000000     21.820417\n",
       "75%        1.000000    138.259028\n",
       "max        5.000000  11507.728333"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_tmp_new3_w[[\"Q_create_time\", \"First_ans_time\", \"Answer_count\", \"Duration_ans\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "7d041705-edf2-43bf-a88e-45f9d3036f4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_tmp_new3_w.to_csv('./CSV_data/plt_df_hf_w_ans_stack.csv', encoding='utf-8')\n",
    "pd_tmp_new3_wo.to_csv('./CSV_data/plt_df_hf_wo_ans_stack.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "c005c133-bf61-4d79-acc3-19f170740a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example:\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# ###################################################################################\n",
    "df_1 = pd.DataFrame(columns=['Question Type', 'Hours'])\n",
    "df_1['Hours'] = pd_tmp_new3_w['Duration_ans']\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Hours'])\n",
    "df_2['Hours'] = pd_tmp_new3_wo['Duration_ans']\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "resultComment = pd.concat([df_1, df_2])\n",
    "resultComment['Type'] = \"First Answer\"\n",
    "# ###################################################################################\n",
    "df_1 = pd.DataFrame(columns=['Question Type', 'Hours'])\n",
    "df_1['Hours'] = pd_tmp_new3_w['Duration_acc_ans']\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Hours'])\n",
    "df_2['Hours'] = pd_tmp_new3_wo['Duration_acc_ans']\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "resultAnswer = pd.concat([df_1, df_2])\n",
    "resultAnswer['Type'] = \"First Accepted Answer\"\n",
    "# ###################################################################################\n",
    "\n",
    "result = pd.concat([resultComment, resultAnswer], ignore_index=True)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=\"Type\", \n",
    "               y=\"Hours\", \n",
    "               data=result, \n",
    "               hue=\"Question Type\", \n",
    "               palette=\"Set2\", #colorblind\n",
    "               split=True, \n",
    "               # scale=\"count\",\n",
    "               # inner=\"quartile\",\n",
    "               scale_hue=False,\n",
    "               width=.5)\n",
    "\n",
    "plt.ylim(-5000,10000)\n",
    "# plt.xlim(-1,1)\n",
    "\n",
    "fig.suptitle('Answers Duration (hug Questions)')\n",
    "# fig.suptitle('TensorFlow Questions', prop={\"size\":10})\n",
    "# plt.gca().axes.get_xaxis().set_visible(False)\n",
    "# plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "2cb65388",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ICDVv3lxr166VYRhyu93asGGD7rjjDplMJk2fPl3x8fGKjo7Wjz/+qJiYGNWoUaPQjzOnnIHVw1NPenp6nto8ypcvr759+2rRokXq1auXWrZsqU6dOqlbt27eIennU7VqVW+o3LZtm/bt26cDBw5ox44d3qkSnpXH//jjD0lSs2bN8uzHM70jp6NHj+qRRx6R0+lUmzZtfN53RXWxr8HzPYa77rrLe94TfFevXq1du3b5bOf5EcHzGizKe9jz/9Pz2gMAnBsBGwAKKTU11bs4kie45vbHH39o69atPl+I85u/6Tlusac3KTY2Vp9//rn++9//auXKlZo3b57mzZunkJAQ3XvvvXrsscdkMplUp04d1a1b19tLuXbtWlWqVEkNGjRQmzZt9OOPP8rpdGrVqlWSzvZQpqSkSJK2bNmiLVu2nPMxJiUl+QTskJAQn+ujo6P1xRdfaOrUqVq+fLkWL16sxYsXKygoSLfccouee+45b8/+xfAsyJU74F+I3LVL2eHrvffe08yZM729hFdccYVatmypWrVq6c8//7zgeaapqakKDg7O9/9zdHS0pOx50zmd7zVRmPuT8n98Xbt21aZNm/THH38oKytLqampat++vSRp+vTp2rhxo9q1a6edO3fmma98Ic73/7ig5++VV15RkyZNNHfuXG3YsEEbNmzQhAkT1KhRI7300kt5DheW26xZs/TOO+94F+SKjIxU06ZN1aBBA23evNl7/57XfGGDcnJysurXry+Xy6X/+7//080331xgLQW52NdgYR+DZyHEWbNmnXMbz30V5T3s6cm/0NEdAHA5ImADQCEtWbJEmZmZatasWb5fvPft26f169friy++yLfHqSCxsbF6++23ZbfbtXnzZq1atUrz5s3T+++/r2rVqulvf/ubJKlz58765JNPlJiYqPXr16tt27YymUxq3769li5dqu3bt+unn35S7dq1vcOIPV+QH374Ye9q4EVVs2ZNvfTSS3K5XPr999/1448/eldKj4yM1KhRoy5q/06nU1u2bJHZbFbz5s0l5f1BIqfcq7Gfz4wZM/T222+rXbt2euCBB9S0aVNVqlRJkvTggw/qzz//vOB6y5Ur5z0WsqcX2sMTkHIO271YERERkpTvsZe7dOmiCRMmaM2aNXI4HDKbzWrdurVMJpMsFovWr18vu90uwzDyDF0uLkFBQRo8eLAGDx6sI0eO6Oeff9aSJUv0008/6cEHH9Ty5csVFBSU722/+eYbPf/884qJidFzzz2nZs2aeX8QevHFF7V582bvtp7XfH7TDOx2uywWi0/vcqVKlfTRRx9p165dGjJkiJ599lnNmTNHZrN/Z9NdyGsw52PIPSogMzNTNptNZrPZu913332nWrVqFVjDhb6HPa/j/H4wAAD4Yg42ABSSZ3j4008/rRdffDHP30svvSSTyaRFixblmdtZkLlz5+rFF1+UYRiy2Wxq3769Ro0a5R3OnXNudteuXWUYhr755hvt3btXbdu2lSRvT+UPP/ygTZs2+cyv9Qy/9czfzW3SpEl6//33Cxye/N133+n5559XamqqLBaLmjdvruHDh+vTTz/NU2dRLV26VCdPnlTHjh0VFRUlSd7Ald/zmnPV7IIsXLhQFotF7777rrp27eoNNoZhaM+ePd7zF8Lz3G7atCnPdZ6Vuz1zWP3BMxfXszp4To0aNVJ0dLTWrl2rX375RXFxcSpfvrzCw8PVqFEjbdiwQT/++KMiIyPzHdKeU2F71C/EwYMH9cYbb+j777+XJFWvXl233367pk+frg4dOuj48ePew3Hld/8LFy6UJL311lvq1auXz2gLTzD1/P/zrFuwdevWPPt5//331axZM61fv97bFh0drcqVK+uaa67RDTfcoN9//10ff/xxgY/pQp+nC3kNnu8xvPDCC2revLkOHjzofQ3mN0d6//79evXVV7VixQpJRXsPe15r1apVu6DHCgCXIwI2ABTC4cOHtWHDBtWqVeucwaRWrVpq27at0tLS9M0331zQ/n///Xd9+umneW7nCRvVq1f3tnkOrfTBBx9Iktq1aycpO8RdccUV+r//+z85HA6fxYpq1qypdu3aaeXKlVq2bJnPfcyfP1/vvPOOVq5cec7DEXns379fs2bN0meffebT7hnSnbPOoti5c6fGjRsni8Xi09MeFRWliIgIbd261Wce6LZt2/TDDz8Uev8hISFyuVx5jjc9depUHTx4UJJ8juVstVoLPLbzrbfeKkl68803fULv8ePHNWHCBJnNZt18882FrrEgDRs2lKRz9rZ37txZGzZs0KZNm7yvDSn7dbN792798MMP6ty5c565wbl5rvesE+APISEhmj59uiZOnOjzY47dbldCQoJsNpt3HrDn/nM+/54e1NzHu54/f743LHu279mzp8LCwvTxxx/7HAM8MTFRc+bMUbly5dSiRYt863zqqadUrlw5vf32296FCM/FarXmqfN8LuQ1eOONN8psNuu///2vz2vrr7/+0tKlS1WrVi3VqlVLN998sywWi95++22f58bpdOo///mPZsyY4b2/oryHPa+1wsyTB4DLHUPEAaAQFixYIMMw8ixultuAAQO0fv16zZkzR48++mih93///ffr66+/1hNPPKElS5aoTp06Onz4sL799ltVrlxZAwcO9G5rs9nUoUMHff/994qMjPT2cknZK5Z/8803KleunHdVZY8XX3xRf/vb3zRixAh16dJFDRo00L59+/TDDz8oIiJCzz//fIF13nHHHZo9e7beeOMNrV+/Xg0bNtTJkye1ZMkShYWF6cEHHyzU412/fr23d94wDKWnp+vPP//0zi1/4YUXfIbZWywWDRgwQDNmzNDtt9+u66+/XqdOndKSJUvUvHlznxWyz+fmm2/Wli1bdPfdd+uGG25QUFCQ1q1bp23btikqKkonT55UUlKSd/sqVarowIEDGj16tDp27OgN0zm1b99egwYN0scff6ybb75Z3bt3l8vl0vLly5WYmKhHH330oufy5tS6dWuVK1cu3x5zKXuEg2fVcs/oBin7h5hp06YpNTW1UKuHV61aVZL02muvqUOHDho+fPhF1165cmX9/e9/14wZM3TjjTeqa9euMpvN+vHHH7Vnzx4NGzbMO9/Yc/+fffaZkpOTNXDgQN18881avHixhg8frr59+yo8PFy//fab1q9fn+f/X2RkpJ577jk99dRT6t+/v6699lqFhYVpyZIlSkhI0JQpU875g1KVKlU0YsQIvfLKK3rxxRf17rvvnvMxVaxYUTabTevWrdMrr7yinj175nnv5XQhr8H69etr+PDhmjRpkm655RZ1795dhmHo66+/lt1u18svvywpe9G50aNH6+WXX1bfvn117bXXqkKFClq1apX27NmjLl266JZbbpFUtPfwpk2bFBERoUaNGp3/fzAAgIANAIXhOfzWTTfddN7trr/+eu9c0N27dxd6/zVr1tRnn32mqVOnatOmTVqxYoUqVqyom2++WSNGjPAZCitlz7X9/vvv1aZNG58hqu3bt9c333yja665Jk94qFevnubNm6epU6dq5cqVWr16taKjo3XLLbfon//8Z74rQ+dWoUIFffLJJ3r33Xf1888/a+3atQoPD1eXLl00YsSIQg+FXr9+vc/w3ODgYFWtWlW33HKL7r333nwD6WOPPabQ0FDNnz9fH3/8serWrat///vfioyMLHTA9sxj/9///qc5c+aofPnyqlevnt566y0FBwdr2LBhWrlypXeUwqhRo/T000/r66+/VkJCQr4BW5KeffZZNW7cWP/73/+0YMECBQUFqXHjxho8eHCRD4V1LjabTZ07d9YPP/yg1NTUPAtgXXPNNQoKCvKuhu3Rpk0bWa1WGYahzp07F3g/f/vb37Rp0yZt3LhRf/75pwYPHuyX+p944gnVrl1bc+bM0ZdffimXy6UGDRrolVdeUb9+/bzbtW3bVvfcc48WLFigTz75RFdffbW6deumCRMmaNq0aVq4cKFCQkJUq1YtPffcc2rZsqX69eunlStXen8I69evn6pUqaL33nvPe8irRo0aafz48QXOQR80aJC+/PJLrVixQkuXLtX111+f73Y2m03PPfecJk2apE8//VTh4eHnDdgX+hocNmyY6tWrp48++kgLFiyQyWRSy5YtNXLkSO8aBZL097//XfXq1dOMGTO0dOlSud1u1axZU2PGjNE999zjnWZxoe/hhIQE7dq1S7fddpu3tx4AcG4m40InmwEAgIDavHmz7rrrLo0bN0633357oMtBGfbBBx/orbfe0qJFi3yOfQ4AyB9zsAEAKGVatmypDh06aM6cOYEuBWWY2+3WF198oeuvv55wDQCFRMAGAKAUeuqpp7R9+3atXLky0KWgjPrqq68UHx+vxx9/PNClAECpQcAGAKAUio2N1UMPPaS33npLbrc70OWgjLHb7Zo0aZIee+yxQh1bGwCQjTnYAAAAAAD4AT3YAAAAAAD4AQEbAAAAAAA/IGADAAAAAOAHBGwAAAAAAPyAgA0AAAAAgB8QsAEAAAAA8AMCNgAAAAAAfkDABgAAAADADwjYAAAAAAD4AQEbAAAAAAA/IGADAAAAAOAHBGwAAAAAAPyAgA0AAAAAgB8QsAEAAAAA8AMCNgAAAAAAfkDABgAAAADADwjYAAAAAAD4AQEbAAAAAAA/IGADAAAAAOAHBGwAAAAAAPyAgA0AAAAAgB8QsAEAAAAA8AMCNgAAAAAAfkDABgAAAADAD6yBLqAs2rx5swzDUFBQUKBLAQAAAABcBIfDIZPJpJYtWxa4LQH7EjAMQ4ZhBLoMAAAAAMBFupBsR8C+BDw9102bNg1wJQAAAACAi/Hbb78VelvmYAMAAAAA4AcEbAAAAAAA/ICADQAAAACAHxCwAQAAAADwAwI2AAAAAAB+QMAGAAAAAMAPCNgAAAAAAPgBARsAAAAAAD8gYAMAAAAA4AcEbAAAAAAA/ICADQAAAACAHxCwAQAAAADwAwI2AAAAAAB+QMAGAAAAAMAPCNgAygS3262dO3cqPT090KUAAADgMkXABlAmrFq1SqNGjdLLL78c6FIAAABwmSJgAygTfvjhB0nSli1bAloHAAAALl8EbAAAAAAA/ICADQAAAACAHxCwAQAAAADwAwI2AAAAAAB+QMAGAAAAAMAPCNgAAAAAAPgBARsAAAAAAD8gYAMoEwzDCHQJAAAAuMwRsAEAAAAA8AMCNoAyx+12B7oEAAAAXIYI2ADKHIfDEegSAAAAcBkqUQF76tSpGjRokE/bihUrNGDAALVs2VI9evTQq6++qszMTO/1brdbkyZNUufOndW8eXMNGTJEBw4c8NnHjh07NHDgQLVo0ULdunXT9OnTfa4vzD4AlB4EbAAAAARCiQnYH374oSZNmuTTtnHjRg0fPlzXX3+95s+fr7Fjx+qbb77RCy+84N1m6tSpmjVrlsaNG6fZs2fLZDJp6NChstvtkqTExEQNHjxYdevW1dy5czVixAhNnDhRc+fOLfQ+AJR8ORc5I2ADAAAgEAIesI8fP64HHnhAEydOVL169XyumzVrljp06KB//OMfqlOnjrp06aJHH31UX331lex2u+x2u2bMmKERI0aoa9euio2N1YQJE3T8+HEtW7ZMkvT555/LZrNp7Nixql+/vgYMGKC///3v+uCDDySpUPsAUPLl/EGMH8cAAAAQCAEP2Nu2bVNERIS++uorNW/e3Oe6IUOGaPTo0Xlu43Q6lZqaqp07dyotLU0dOnTwXlehQgU1atRIGzZskJTdC962bVtZrVbvNh06dNC+fft08uTJQu0DQMmXs9eagA0AAIBAsBa8yaXVo0cP9ejRI9/rGjVq5HPZbrdr5syZaty4sSpVqqSNGzdKkqpVq+azXXR0tI4ePSpJOnbsmGJiYvJcL0lHjhzRsWPHCtwHgJKPHmwAAAAEWsADdmE5nU6NHj1au3fv1qeffipJysjIkCTZbDafbYODg5WcnCxJyszMzPd6ScrKyirUPorCMAylp6cX+fYALkzOxQ9TUlJ4/wEAAMAvDMOQyWQq1LalImCnpqbqkUce0bp16zRp0iTvUPKQkBBJ2b1VnvNSdnAODQ31bpO7NysrK0uSFBYWVqh9FIXD4dCOHTuKfHsAFyYtLc17fvfu3RwLGwAAAH6Tu0P2XEp8wI6Pj9fQoUN16NAhffDBBz5zpT3DuuPj41W7dm2f28TGxkqSqlatqvj4+Dz7lKQqVarI6XQWuI+iCAoKUoMGDYp8ewAXJucq4lWrVlVcXFwAqwEAAEBZsXv37kJvW6IDdnJysu677z6lpqbqf//7nxo2bOhzfWxsrMLDw7Vu3TpvOE5JSdH27ds1cOBASVLbtm01a9YsuVwuWSwWSdKaNWtUr149RUVFqXz58gXuoyhMJpPCwsKKfHsAFybnSBXefwAAAPCXwg4Pl0p4wH755Zd18OBBTZs2TZUqVVJCQoL3ukqVKslms2ngwIF64403VKlSJdWoUUOvv/66qlatql69ekmSBgwYoGnTpumZZ57RAw88oK1bt+qjjz7yHku7MPsAULIZhsEiZwAAAAi4Ehuw3W63vv76azkcDt133315rl++fLlq1qypkSNHyul06tlnn1VmZqbatm2r6dOne8fIR0VFadq0aRo/frz69eunypUra/To0erXr593XwXtA0DJ5pnq4UHABgAAQCCYjJwTF+EXv/32mySpadOmAa4EuDykpqbq7rvv9l4eOnSobr755gBWBAAAgLLiQvKd+VIXAwCXmsPh8LnsOVIAAAAAUJwI2ABKvdxDwnMPGQcAAACKAwEbQKmXuwc792UAAACgOBCwAZR6DBEHAABASUDABlDq5Q7YLpcrQJUAAADgckbABlDq5Q7UzMEGAABAIBCwAZR6uQM1ARsAAACBQMAGUOrl7sFmiDgAAAACgYANoNRzu93nvQwAAAAUBwI2gFKPgA0AAICSgIANoNQjYAMAAKAkIGADKHNMJlOgSwAAAMBliIANAAAAAIAfELABlHr0WAMAAKAkIGADKPVyB2wCNwAAAAKBgA2g1DObff8ps1gsAaoEAAAAlzMCNoBSL3egzh24AQAAgOLAt1AApV7uQE3ABgAAQCDwLRRAqWe1Ws97GQAAACgOBGwApV7uQB0UFBSgSgAAAHA5I2ADKPXowQYAAEBJQMAGUOrl7rEmYAMAACAQCNgASj2bzXbeywAAAEBxIGADKPVy92ATsAEAABAIBGwApV7uQM0iZwAAAAgEAjaAUi93wA4ODg5QJQAAALicEbABlHpWq1Umk8l7mR5sAAAABAIBG0CpZzKZfHqx6cEGAABAIBCwAZQJOQM2PdgAAAAIBAI2gDKBHmwAAAAEGgEbQJmQM2BzmC4AAAAEAgEbQJnAEHEAAAAEGgEbQJnAEHEAAAAEGgEbQJlADzYAAAACjYANoEzIGaoJ2AAAAAgEAjaAMsFisXjPs8gZAAAAAoGADaDMoQcbAAAAgUDABlDmELABAAAQCCUqYE+dOlWDBg3yaduxY4cGDhyoFi1aqFu3bpo+fbrP9W63W5MmTVLnzp3VvHlzDRkyRAcOHPD7PgCUHjmHiwMAAADFpcQE7A8//FCTJk3yaUtMTNTgwYNVt25dzZ07VyNGjNDEiRM1d+5c7zZTp07VrFmzNG7cOM2ePVsmk0lDhw6V3W732z4AlHyGYQS6BAAAAFzmAh6wjx8/rgceeEATJ05UvXr1fK77/PPPZbPZNHbsWNWvX18DBgzQ3//+d33wwQeSJLvdrhkzZmjEiBHq2rWrYmNjNWHCBB0/flzLli3z2z4AAAAAAChIwAP2tm3bFBERoa+++krNmzf3uW7jxo1q27atrFart61Dhw7at2+fTp48qZ07dyotLU0dOnTwXl+hQgU1atRIGzZs8Ns+AAAAAAAoiLXgTS6tHj16qEePHvled+zYMcXExPi0RUdHS5KOHDmiY8eOSZKqVauWZ5ujR4/6bR9FYRiG0tPTi3x7ABfG7XZ7z/PeAwAAgL8YhiGTyVSobQMesM8nMzMzz/Fsg4ODJUlZWVnKyMiQlPeYt8HBwUpOTvbbPorC4XBox44dRb49gAuTmprqPc97DwAAAP6UOy+eS4kO2CEhIXkWGsvKypIkhYWFKSQkRFL2PGrPec82oaGhfttHUQQFBalBgwZFvj2ACxMeHu49HxcXF8BKAAAAUJbs3r270NuW6IBdtWpVxcfH+7R5LlepUkVOp9PbVrt2bZ9tYmNj/baPojCZTAoLCyvy7QFcmJyH5uK9BwAAAH8p7PBwqQQscnY+bdu21S+//CKXy+VtW7NmjerVq6eoqCjFxsYqPDxc69at816fkpKi7du3q02bNn7bBwAAAAAABSnRAXvAgAFKTU3VM888o927d2vevHn66KOP9OCDD0rKHgc/cOBAvfHGG1q+fLl27typRx99VFWrVlWvXr38tg8AAAAAAApSooeIR0VFadq0aRo/frz69eunypUra/To0erXr593m5EjR8rpdOrZZ59VZmam2rZtq+nTp3snoftjHwAAAAAAFMRkGIYR6CLKmt9++02S1LRp0wBXAlw+xo4dq19++UWStHDhwgBXAwAAgLLiQvJdiR4iDgAAAABAaUHABgAAAADADwjYAMoEZrsAAAAg0AjYAAAAAAD4AQEbAAAAAAA/IGADKBNMJlOgSwAAAMBljoANAAAAAIAfELABlAkscgYAAIBAI2ADAAAAAOAHBGwAAAAAAPyAgA0AAAAAgB8QsAEAAAAA8AMCNgAAAAAAfkDABgAAAADADwjYAAAAAAD4AQEbAAAAAAA/IGADAAAAAOAHBGwAZY5hGIEuAQAAAJchAjaAMoeADQAAgEAgYAMoE0wmk/e82+0OYCUAAAC4XBGwAZQ5BGwAAAAEAgEbQJmQc1g4ARsAAACBQMAGUOYQsAEAABAIBGwAZQ4BGwAAAIFAwAZQ5rCKOAAAAAKBgA2gTGAVcQAAAAQaARtAmcAiZwAAAAg0AjaAMoFh4QAAAAg0AjaAMoEebAAAAAQaARtAmUAPNgAAAAKNgA2gTKAHGwAAAIFGwAZQJuQM2PRmAwAAIBAI2ADKhJy91gRsAAAABAIBG0CZQ8AGAABAIBCwAZQJhGoAAAAEGgEbQJnAImcAAAAINAI2gDKBOdgAAAAItFIRsB0OhyZMmKBu3bqpZcuW+tvf/qZNmzZ5r9+xY4cGDhyoFi1aqFu3bpo+fbrP7d1utyZNmqTOnTurefPmGjJkiA4cOOCzTUH7AFCy0YMNAACAQCsVAfvdd9/V3LlzNW7cOM2fP19XXnmlhg4dquPHjysxMVGDBw9W3bp1NXfuXI0YMUITJ07U3LlzvbefOnWqZs2apXHjxmn27NkymUwaOnSo7Ha7JBVqHwBKNnqwAQAAEGjWQBdQGMuXL9eNN96oTp06SZKefPJJzZkzR1u2bNH+/ftls9k0duxYWa1W1a9fXwcOHNAHH3ygAQMGyG63a8aMGRo1apS6du0qSZowYYI6d+6sZcuWqW/fvvr888/Puw8AJV/OgE0PNgAAAAKhVPRgR0ZG6vvvv9ehQ4fkcrk0e/Zs2Ww2xcXFaePGjWrbtq2s1rO/FXTo0EH79u3TyZMntXPnTqWlpalDhw7e6ytUqKBGjRppw4YNklTgPgCUfARsAAAABFqp6MF+5pln9Oijj+raa6+VxWKR2WzWxIkTVbt2bR07dkwxMTE+20dHR0uSjhw5omPHjkmSqlWrlmebo0ePSlKB+4iKirrgmg3DUHp6+gXfDkDROJ1O7/n09HTefwAAAPALwzBkMpkKtW2pCNh79uxRhQoV9M4776hKlSqaM2eOxowZo08++USZmZmy2Ww+2wcHB0uSsrKylJGRIUn5bpOcnCxJBe6jKBwOh3bs2FGk2wK4cJ73upT9b0ZR37sAAABAbrnz4rmU+IB9+PBhjRo1Sh9++KHatGkjSWratKl2796tyZMnKyQkxLtYmYfni3VYWJhCQkIkSXa73Xves01oaKgkFbiPoggKClKDBg2KdFsAFy7nFI9atWopNjY2gNUAAACgrNi9e3ehty3xAXvr1q1yOBxq2rSpT3vz5s21atUqVa9eXfHx8T7XeS5XqVLFO2w0Pj5etWvX9tnG8wW8atWq591HUZhMpiKHcwAXLue8a6vVyvsPAAAAflHY4eFSKVjkzDN3+o8//vBp37Vrl+rUqaO2bdvql19+kcvl8l63Zs0a1atXT1FRUYqNjVV4eLjWrVvnvT4lJUXbt2/39ogXtA8AJV/OOdg5zwMAAADFpcQH7GbNmqlNmzYaM2aM1q5dq/379+vtt9/WmjVr9I9//EMDBgxQamqqnnnmGe3evVvz5s3TRx99pAcffFBS9lj5gQMH6o033tDy5cu1c+dOPfroo6patap69eolSQXuA0DJR8AGAABAoJX4IeJms1lTp07V22+/raeeekrJycmKiYnRhx9+qBYtWkiSpk2bpvHjx6tfv36qXLmyRo8erX79+nn3MXLkSDmdTj377LPKzMxU27ZtNX36dO9E9aioqAL3AaBkczgc+Z4HAAAAiovJMAwj0EWUNb/99psk5Zk3DuDSufXWW73TPB5//HF169YtsAUBAACgTLiQfFfih4gDQEFcLpfPGgq5jwoAAAAAFAcCNoBSL3egZog4AAAAAoGADaDUO9dx7AEAAIDiRMAGUOrlDtQEbAAAAAQCARtAqUfABgAAQElAwAZQ6hGwAQAAUBIQsAGUepmZmT6XCdgAAAAIBAI2gFIvd8DOfRkAAAAoDgRsAKVe7h5rAjYAAAACgYANoNRjDjYAAABKAgI2gFIvIyPjzLnsf9LowQYAlFW//fabXnjhBf3888+BLgVAPqyBLgAALpanx9pkDZbhzCBgAwDKrPfff1/79+/X5s2bdc011wS6HAC50IMNoNTzBGqTJcTnMgAAZc2hQ4ckSS6XK8CVAMgPARtAqecN2NZgSczBBgBcHvhBGSh5CNgASj3vEHELARsAUHZlZmbK6XR6LyckJASwGgD5IWADKPXO9mBnDxHPysqSYRiBLAkAAL87fvy4z+Vjx44FqBIA50LABlDqne3BtkmSDMOQ3W4PZEkAAPjd4cOHz3sZQOARsAGUep4w7RkinrMNAICygoANlHwEbAClnjdgm62STD5tAACUFQcPHpQkVQ6z+FwGUHIQsAGUet4wbbJI5uwvHSx0BgAoazxzrq+qGOxzGUDJQcAGUOqd7cG2yGTKDtgOhyOQJQEA4HeeRc7qRWavOXLq1Ck+74AShoANoNTzfrkwWSST2bcNAIAywDAMJSUlSZKiy1llNmW3paSkBLYwAD4I2ABKvbMB25wdssUcbABA2ZKRkSG32y1JCrWaFWLJXnMkNTU1kGUByIWADaDU8wRsk8ki05kebKfTGciSAADwK5fL5T1vMUlmc3bA5vMOKFkI2ABKPe+XDpPJO0ScLxwAgLLEYrF4z7sNyTCyz5vNfJ0HShLekQBKPW+YNpkJ2ACAMik4ONgbpjOcbmU6s4eLlytXLpBlAciFgA2g1POEaVOOgJ1zKB0AAKWdxWJRhQoVJEknMlxynenBjoyMDFxRAPIgYAMo9c6GaZNMJlOuNgAAyobo6GhJ0r6k7IU8IyMjZbPZAlkSgFwI2ABKPc+qqtm91ybfNgAAyohq1apJknYnZvlcBlByELABlHpnwzSLnAEAyq4aNWpIko6mZn/G1axZM5DlAMhHkQP2/PnztXLlSknSjh071LdvX7Vq1UpPP/00x58FUGwMw8jRg22Spwfb8CyvCgBAGZE7UHsCN4CSo0gBe+bMmXrqqae0fft2SdILL7yg5ORk3X777fruu+80adIkvxYJAOeSM0ibZDoTsgnYAICyJ3egrl69eoAqAXAuRQrYn3/+uR544AH985//1JEjR7RlyxY9/PDDeuqpp/T4449r8eLF/q4TAPLlM9f6TLiWWOQMAFD2VK1a1ecyARsoeYoUsA8dOqQuXbpIklauXCmTyaQePXpIkq688kqdPHnSfxUCwHnk7qk2MUQcAFBGhYWFyWq1ei97VhUHUHIUKWBXqlRJJ06ckCR9//33uvLKK72/qP3xxx+64oor/FchAJzHuYI0ARsAUNaFhoYGugQAuVgL3iSvHj166M0339SaNWu0atUqPfroo5Ky52a/88476t+/v1+LBIBz8Q3SpnNuBwAAAFxqRQrYTz31lFwulzZs2KC77rpLQ4YMkSTNmjVLXbt21SOPPOLPGgHgnPL0VJs4DjYAoOwymfgxGSjJihSwv/76az388MN5Flr46quvFBwc7JfCcps/f77ef/99HTx4ULVr19bw4cN1ww03SMo+TNj48eP1+++/KzIyUoMGDdL999/vva3b7daUKVM0Z84cpaSkqHXr1nr++edVp04d7zYF7QNAKcCXDgAAAARQkeZgv/zyy/r999/ztF+qcL1gwQI9/fTTuvPOO7Vo0SL16dNHjz32mDZv3qzExEQNHjxYdevW1dy5czVixAhNnDhRc+fO9d5+6tSpmjVrlsaNG6fZs2fLZDJp6NCh3uN1F2YfAAAAQKDRgw2UbEXqwY6KilJKSoq/a8mXYRiaOHGi7rvvPt13332SpGHDhmnTpk1av3691q9fL5vNprFjx8pqtap+/fo6cOCAPvjgAw0YMEB2u10zZszQqFGj1LVrV0nShAkT1LlzZy1btkx9+/bV559/ft59AAAAAABQkCIF7DvuuEMvvvii1q1bp6uuuirfVcNvvfXWi61NkrR3714dPnxYN910k0/79OnTJUlDhw5V27ZtfQ5Z0KFDB7333ns6efKkDh8+rLS0NHXo0MF7fYUKFdSoUSNt2LBBffv21caNG8+7j6ioKL88FgAAAABA2VWkgP3KK69Iyh66nR+TyeS3gL1//35JUnp6uu6//35t375dNWvW1D//+U/16NFDx44dU0xMjM9tPMcEPHLkiI4dOyZJqlatWp5tjh49KkkF7oOADZQmDJ0DAABAYBQpYC9fvtzfdZxTamqqJGnMmDEaPny4nnjiCS1dulQPP/ywZs6cqczMTNlsNp/beOaCZ2VlKSMjQ5Ly3SY5OVmSCtxHURiGofT09CLdFkDhZWZm5ttut9t5DwIAypycR8/gcw4oHoZhFHr9gyIF7Bo1ahTlZkUSFBQkSbr//vvVr18/SVJcXJy2b9+umTNnKiQkxLtYmYcnFIeFhSkkJERS9pdtz3nPNqGhoZJU4D6KwuFwaMeOHUW6LYDCy/3e9Th27BjvQQBAmZMzYPM5BxSf3B2y51KkgD1lypQCtxk+fHhRdp2H51BguYdwN2jQQD/88INq1Kih+Ph4n+s8l6tUqSKn0+ltq127ts82sbGx3vs43z6KIigoSA0aNCjSbQEU3rl6sKtWraq4uLhirgYAgEvLbD57ECA+54DisXv37kJv6/eAHR4erujoaL8F7EaNGqlcuXL69ddf1aZNG2/7rl27VLt2bbVq1UqzZs2Sy+WSxWKRJK1Zs0b16tVTVFSUypcvr/DwcK1bt84bsFNSUrR9+3YNHDhQktS2bdvz7qMoTCZTkXu/ARRezi8aOdlsNt6DAIAyjc85oHhcyOHxihSwd+7cmactPT1dv/zyi8aOHat///vfRdltvkJCQvTAAw/onXfeUZUqVdSsWTMtXrxYP//8sz788EM1aNBA06ZN0zPPPKMHHnhAW7du1UcffaQXXnhBUvaX7IEDB+qNN95QpUqVVKNGDb3++uuqWrWqevXqJUkaMGDAefcBAAAAAEBBihSw8xMWFqbOnTtr2LBheu211/Tll1/6a9d6+OGHFRoaqgkTJuj48eOqX7++Jk+erPbt20uSpk2bpvHjx6tfv36qXLmyRo8e7Z2vLUkjR46U0+nUs88+q8zMTLVt21bTp0/3jqOPiooqcB8ASqacc9EAAACAQPJbwPaoVq2a9uzZ4+/davDgwRo8eHC+1zVr1kyzZ88+520tFotGjRqlUaNGnXObgvYBAAAAAMD5+C1gG4aho0eP6oMPPijWVcYBAAAAACgJihSwY2NjzznR2zAMvfbaaxdVFAAU1rmGiDN0HAAAAMWtSAF72LBh+Qbs8PBwdevWTXXr1r3YugCgCAq/wiMAAADgb0UK2CNGjPB3HQDgV/RgAwAAoLgVeQ623W7XvHnztG7dOqWkpKhixYpq06aN+vXrp+DgYH/WCACFdwHHKQQAAAD8qUgBOyUlRffee6927typ6tWrq3Llytq3b58WLVqkTz/9VP/73/9Uvnx5f9cKAHkwBxsAAAAlhbkoN3rzzTd17NgxffLJJ1qxYoVmz56tFStW6JNPPtHJkyc1ceJEf9cJAPnyCdJ0XgMAACCAihSwly9frkceeURt2rTxaW/Tpo1Gjhypb7/91i/FAcCFOZuw6cEGAABAcStSwE5LS1OtWrXyva5WrVpKSkq6mJoAoNDcbneuluyQTcAGAABAcStSwL7yyiv1/fff53vd8uXLVadOnYsqCgAKyzdI04MNAACAwCnSImf333+/HnvsMdntdt1000264oordOLECS1cuFBz5szR2LFj/VwmABSSiR5sAAAABEaRAnafPn20f/9+/fe//9Xnn38uk8kkwzBks9k0bNgw3Xnnnf6uEwDylXOIuMlEDzYAAAACp8jHwX744Yc1cOBAbdmyRcnJyYqIiFDz5s0VERHhz/oA4AJlh+y8c7MBAACAS6vQAfvee+8t9E5NJpM++uijIhUEABfibJA25fgvAAAAUPwKvciZYRh5/tavX6/U1NQ87fQcASh+Z6I1c7ABAAAQIIXuwf744499LjudTjVp0kT/+c9/1LhxY78XBgCF4f1Bz9t1zRBxAAAABEaRDtMl+S4mBACBcran2uRzSg82AAAAiluRAzYAlATn6qkmYAMAAKC4EbABlA0m5mADAAAgsAjYAEq1c/VgMwcbAAAAxe2iAzZzsQEEEnOwAQAAUFIUehXxHj165BumH3roIQUFBfm0mUwmfffddxdfHQAUgCANAACAkqLQAbtdu3b0VgMocfL0YJ85YYg4AAAAiluhA/Yrr7xyKesAgItjynMGAAAAKFYscgYAAAAAgB8QsAEAAAAA8AMCNgAAAAAAfkDABlA2GL5nWJQRAAAAxY2ADaBUM5s9/4wZPicEbAAAABQ3AjaAUi1vkKYHGwAAAIFBwAZQqnl7sL3HwzZ82wEAAIBiwjdQAKVa3iHiBGwAAAAEBt9AAZRq9GADAACgpOAbKIBSzWKxnDnnliQZZ4L22XYAAACgeBCwAZRqOYO0YbglIztoW63WQJUEAACAyxQBG0Cp5hOkCdgAAAAIIAI2gFLtXAGbIeIAAAAobqUqYO/bt08tW7bUvHnzvG07duzQwIED1aJFC3Xr1k3Tp0/3uY3b7dakSZPUuXNnNW/eXEOGDNGBAwd8tiloHwBKrpwB2zDc2cPEJQUFBQWqJAAAAFymSk3AdjgceuKJJ5Senu5tS0xM1ODBg1W3bl3NnTtXI0aM0MSJEzV37lzvNlOnTtWsWbM0btw4zZ49WyaTSUOHDpXdbi/0PgCUXGaz+WzINlzZf5JsNlsAqwIAAMDlqNRMUpw8ebLKlSvn0/b555/LZrNp7Nixslqtql+/vg4cOKAPPvhAAwYMkN1u14wZMzRq1Ch17dpVkjRhwgR17txZy5YtU9++fQvcB4CSLygoSE6nU6IHGwAAAAFUKnqwN2zYoNmzZ+vVV1/1ad+4caPatm3rM0S0Q4cO2rdvn06ePKmdO3cqLS1NHTp08F5foUIFNWrUSBs2bCjUPgCUfJ7easPtktzZPdgEbAAAABS3Eh+wU1JSNHr0aD377LOqVq2az3XHjh1T1apVfdqio6MlSUeOHNGxY8ckKc/toqOjdfTo0ULtA0DJ5x0OnmOIeHBwcAArAgAAwOWoxA8RHzt2rFq0aKGbbropz3WZmZl55ll6vlRnZWUpIyNDUt65mMHBwUpOTi7UPorKMAyf+eIALh1Pb7XhdskwnNnneQ8CAMo4PueA4mEYhkwmU6G2LdEBe/78+dq4caMWLlyY7/UhISHexco8PKE4LCxMISEhkiS73e4979kmNDS0UPsoKofDoR07dhT59gAKz+3Onnctw+kdIn7o0CE5HI4AVgUAgP95P/MkvmsCxaiwC+iW6IA9d+5cnTx5Ut26dfNpf/755zV9+nRVr15d8fHxPtd5LlepUiV70aMzbbVr1/bZJjY2VpJUtWrV8+6jqIKCgtSgQYMi3x5A4UVEROjYsWNnerCzA3ZsbGye6R8AAJR2ZvPZGZ5xcXEBrAS4fOzevbvQ25bogP3GG28oMzPTp+26667TyJEj1adPHy1evFizZs2Sy+WSxWKRJK1Zs0b16tVTVFSUypcvr/DwcK1bt84bsFNSUrR9+3YNHDhQktS2bdvz7qOoTCbTRfWAAyg8z4gUGWcXOYuIiOA9CAAo0/icA4pHYYeHSyV8kbMqVaqoTp06Pn+SFBUVpRo1amjAgAFKTU3VM888o927d2vevHn66KOP9OCDD0rK7sYfOHCg3njjDS1fvlw7d+7Uo48+qqpVq6pXr16SVOA+AJR8nnUTDJdDkuHTBgAAABSXEt2DXZCoqChNmzZN48ePV79+/VS5cmWNHj1a/fr1824zcuRIOZ1OPfvss8rMzFTbtm01ffp07xj6wuwDQMl2NmBn5WkDAAAAikupC9h//PGHz+VmzZpp9uzZ59zeYrFo1KhRGjVq1Dm3KWgfAEo273GwXdkLFprNZp9j2wMAAADFoUQPEQeAwjgbsLO8ly9krgwAAADgDwRsAKVe7h7swh5GAQAAAPAnAjaAUs8bsN0EbAAAAAQOARtAqRcUFJR9xu30vQwAAAAUIwI2gFLv7BBxhyQCNgAAAAKDgA2g1POsGG64HT6XAQAAgOJEwAZQ6lksluwzhtv3MgAAAFCMCNgASr2zAdsliR5sAAAABAYBG0CpZzabz3sZAAAAKA58CwVQ6plMpkCXAAAAABCwAQAAAADwBwI2AAAAAAB+QMAGUOq53e7zXgYAAACKAwEbQKnndDqzz5iy/0lzuVwBrAYAAACXKwI2gFLPG6hN2Yfn8gZuAAAAoBgRsAGUep5AbTJbfC4DAAAAxYmADaDUy8rKkiSZLDZJkt1uD2Q5AAAAuEwRsAGUep5AbbIE+1wGAAAAihMBG0Cpl7sH23MZAAAAKE4EbAClXmZmpiTJZA3xXjYMI5AlAQAA4DJEwAZQ6qWnp0uSTNZQSdmrijNMHAAAAMWNgA2g1MvIyJAkmc8E7JxtAAAAQHEhYAMo9bxh2hwkmbOPhe3p1QYAAACKCwEbQKmXmpoqKXuRM5PZ5tMGAAAAFBcCNoBSzydgW4J82gAAAIDiQsAGUOp5A7Y5yHuorrS0tECWBAAAgMsQARtAqZaZmSmHwyFJMlmCvUPET58+HciyAAAAcBkiYAMo1c4OBTdJZqtMlmBJBGwAAAAUPwI2gFLNE6RNFptMJpNkoQcbAAAAgUHABlCqpaSkSJK359pz6mkHAAAAigsBG0Cp5g3Y1uxgbbYSsAEAABAYBGwApRo92AAAACgpCNgASrXk5GRJkskSkn16pgfb0w4AAAAUFwI2gFItKSlJ0tlg7QnaSUlJMgwjUGUBAADgMkTABlCqJSYmSpJM1hCfU7vdroyMjIDVBQAAgMsPARtAqeYN2JbQ7FOzVTJbfa4DAAAAigMBG0CpdurUKUmS+UzPtSSZrKE+1wEAUFYw/Qko2Up8wE5KStJzzz2nLl26qFWrVrr77ru1ceNG7/U7duzQwIED1aJFC3Xr1k3Tp0/3ub3b7dakSZPUuXNnNW/eXEOGDNGBAwd8tiloHwBKJpfLpZMnT0qSTEFh3nbzmYCdkJAQkLoAALhUcgZswjZQ8pT4gP3YY4/p119/1VtvvaUvvvhCjRs31v333689e/YoMTFRgwcPVt26dTV37lyNGDFCEydO1Ny5c723nzp1qmbNmqVx48Zp9uzZMplMGjp0qOx2uyQVah8ASqakpCS53W5JJu/ca0kyBZWTJJ04cSJAlQEA4H+GYfiEag5JCZQ81kAXcD4HDhzQzz//rM8++0ytWrWSJD3zzDNatWqVFi1apJCQENlsNo0dO1ZWq1X169fXgQMH9MEHH2jAgAGy2+2aMWOGRo0apa5du0qSJkyYoM6dO2vZsmXq27evPv/88/PuA0DJ5emhNgWFymQ6+3uh+Uxvdnx8fEDqAgDgUkhJSZHL5fJejo+PV0RERAArApBbie7Brlixot5//301adLE22YymWQYhpKTk7Vx40a1bdtWVuvZ3wk6dOigffv26eTJk9q5c6fS0tLUoUMH7/UVKlRQo0aNtGHDBkkqcB8ASq7Dhw9LksxB4T7tnstHjx4t9poAALhUjhw5ct7LAAKvRAfsChUqqGvXrrLZbN62b775Rn/99Zc6deqkY8eOqWrVqj63iY6OlpT9D86xY8ckSdWqVcuzjeeLd0H7AFByHTx4UJJkDvb99d4cXMHnegAAyoJDhw6d9zKAwCvRQ8Rz++WXX/T000/r2muvVY8ePfTyyy/7hG9JCg4OliRlZWV5j4Gb3zbJycmSpMzMzPPuo6gMw1B6enqRbw+gYJ4FC822Cj7tnsuJiYlKSEhQuXLlir02AAD8bd++fT6X9+/fz/dNoBgYhiGTyVSobUtNwP7uu+/0xBNPqHnz5nrrrbckSSEhId7Fyjw8oTgsLEwhIdmLHtntdu95zzahoaGF2kdRORwO7dixo8i3B1CwP//8U9LZHmsPkyVIJmuYDGe6Vq5cqXr16gWiPAAA/GrXrl2SpPoVbdqTaNe+ffv4vgkUk9ydsudSKgL2J598ovHjx6tXr1564403vA+uatWqeRYx8lyuUqWKnE6nt6127do+28TGxhZqH0UVFBSkBg0aFPn2AM7vxIkTZ0aimGQJrZTnekvYFXKm/KWMjAzFxcUVf4EAAPhZWlqaJOmqisHak2hXUlKSYmNjC92zBqBodu/eXehtS3zA/t///qf//Oc/GjRokJ5++mmZzWenjbdt21azZs2Sy+WSxWKRJK1Zs0b16tVTVFSUypcvr/DwcK1bt84bsFNSUrR9+3YNHDiwUPsoKpPJdFE94ADOb+/evZIkc0hFmcxBea63hFaWM+Uv/fnnn7wXAQBlgmcB3roR2Z97WVlZMgyDqVDAJXYhP2KV6EXO9u3bp5deekm9evXSgw8+qJMnTyohIUEJCQk6ffq0BgwYoNTUVD3zzDPavXu35s2bp48++kgPPvigpOxu/IEDB+qNN97Q8uXLtXPnTj366KOqWrWqevXqJUkF7gNAyfT7779LkixhlfO93hJ2hSRp586dcjgcxVYXAACXQmZmpnd9oYohFoVYs7/wc9QboGQp0T3YS5culcPh0LJly7Rs2TKf6/r166dXXnlF06ZN0/jx49WvXz9VrlxZo0ePVr9+/bzbjRw5Uk6nU88++6wyMzPVtm1bTZ8+3TvMPCoqqsB9AChZnE6nVq9eLUmylqua7zbm4AiZrCHKyMjQxo0bdfXVVxdniQAA+JVnMTOTJJvFpBCrWZlOlzd0AygZSnTAfuihh/TQQw+dd5tmzZpp9uzZ57zeYrFo1KhRGjVqVJH3AaBk2bRpk06fPi2TJUSWcvmvlWAymWWtUEeOU39o5cqVBGwAQKnmWYTXajbJZDIpyOzbDqBkKNFDxAEgPytXrpQkWSvUlsl07n/GgiLqSpLWr1/vXRgGAIDS6FxzQFngDChZCNgASpUjR47o559/lnQ2QJ+LOThS5uAIORwOLVy4sBiqAwDg0vAs9Os2DEmSK/vEu0gvgJKBgA2gVPn000+zV/0vVy3fw3PlZDKZZLuisSRp3rx5Zw7rBQBA6RMcHCwpO1i7DUNOd3bCLuyxeQEUDwI2gFJj9+7dWrVqlSQpOLpZoW5jLV9L5pCKysjI0Oeff34pywMA4JLJGaSdbsnpImADJREBG0Cp4HA49N5770mSrBXqyBJSsVC3M5lMCq6cHca//vpr7d69+5LVCADApZJzKLhhGHLn0w4g8AjYAEqFmTNnaufOnZI5SMGVm17QbS3lqsoaXkNOp1Mvv/yyTp8+fYmqBADg0jNynjeMc24HoPgRsAGUeCtXrvQuUhZavYPMtvALur3JZFJI9fYyBZVTfHy83nrrLbnd7oJvCABACZGamirp7HGwgy3Zq4dzlAygZCFgAyjRdu/ercmTJ0uSbFGNZC1fo0j7MVlsCq3ZSTJZtHHjRn3yySf86g8AKDVOnTolSQoNMslsMqncmQNhJyYmBrIsALkQsAGUWL///rueeeYZZWVlyVKuimyVm1zU/iwhFRVStY0kac6cOZoxYwY92QCAUuGvv/6SJFUOs/qcHjhwIGA1AciLgA2gRFq3bp2ef/55paenyxJWWaE1rpHJdPH/ZAVF1lNwdEtJ0vz58zVx4kS5XK6L3i8AAJfSH3/8IUmqWi47WFcNt/q0AygZrIEuAAByW758uSZNmiS32y1reA2F1LhaJrP//rmyRTWUyWJT5tH1WrFihdLS0jRq1CjvMUYBAChJDMPQL7/8IkmqF5l9WK56EdmnW7duld1u53BdQAlBDzaAEiMjI0PvvPOO3n777exwHVFXITWv8Wu49giKrHdmTrZZ69at0+OPP659+/b5/X4AALhY+/fv1/Hjx2UxnQ3YVcpZVd5mlt1u15YtWwJbIAAvAjaAEmHnzp3617/+pSVLlkiSbFFxCqnW3i/Dws/FWr6GQmt3k8kSrAMHDujRxx7TF198wZBxAECJsmrVKknSVZWCFWzJ/lw0mUxqfEWIpOyjbQAoGQjYAALK4XDo448/1ujRY3T06FGZrGEKrd1dwdHNZTKZLvn9W8OiFXblDbKG15DL6dRHH32kp59+WseOHbvk9w0AQEHcbrc3QDetHOJzXdPo7Mvr1q1TRkZGsdcGIC8CNoCA2bZtm5544gl9/vnnMgy3rBXqqNyVvWUtV6VY6zBbQxRSs5NCqrWTzFZt375dI0aM0IIFC+RwOIq1FgAActq2bZsSEhIUbDEpppLvWiHVw62qFGpRVlaW1qxZE6AKAeREwAZQ7A4fPqzx48frySef1N69eyWLTSE1Oiq0xtUyWQKzSIvJZFJQ5JUqV6+3LKGVlZmZqWnTpunhhx/Wjz/+yDGzAQAB8eOPP0qS4q4IVpDFd2SXyWRSszO92p5h5AACi1XEARSb5ORkffbZZ1qyZMmZec7ZodZWuYnM1tBAlydJMtvCFVqnuxxJ+2RP+E3Hjh3Ta6+9pgULFmjw4MFq3LhxoEsEAFwmXC6XVq9eLUlqkmt4uEeTyiH64a80bdmyRadPn1b58uWLs0QAuRCwAVxymZmZWrhwoebMmeOdI2YJr67g6OayBEcEuLq8TCazbBXrKyiituwn/5D91E798ccfevLJJ3X11Vdr0KBBqlWrVqDLBACUcdu2bVNycrJCrSbv6uG5XRFmVZVyVh1Pc2rdunXq2bNnMVcJICcCNoBLJjk5WYsXL9aiRYt0+vRpSZI5pKKCo1sU+zzrojCZgxRcuYmCIuvLfuI3OZL2ac2aNVq7dq2uvvpqDRgwQDExMYEuEwBQRnnmVcdGBctynoU/46KCdTzNqTVr1hCwgQAjYAPwu/j4eC1YsEBLly5VVlaWJMkUFK7gyk1krVCnWFYH9ydzUKhCqrVTUKWGssdvlTP1sFavXq3Vq1erWbNmuu2229SiRYtS97gAACWXw+HwzqtudEX+w8M9Gl2RPUz8l19+UXJysiIiSt7oMOByQcAG4DcHDhzQvHnztHLlSu+xpM0hFWWLipO1fM1Lekzr4mAJjlBorc5yZSbJfmqnnMkHtHXrVm3dulX169fXgAED1LFjR1kslkCXCgAo5VavXq2UlBSVt5l1ZcXzLwAaXc6qGuWtOnzaqW+//Va33357MVUJIDcCNoCL4na7tWXLFi1cuFAbN270tlvCqsgWFSdLuSplrmfXEhKp0Ood5K7cVPaTf8iRtEd79uzRa6+9pmrVqunGG2/Utddeq3LlygW6VABAKZSenq6ZM2dKklpXDT3v8HCPttXCdPh0ij7//HN17dpV0dHRl7pMAPkwGRx7xu9+++03SVLTpk0DXAlw6aSnp+u7777T119/rcOHD3vbreVryRYVK0toVACrK16GM0v2xD/lSNwlw2WXJIWEhKh79+668cYbVbt27QBXCAAoLTIyMvTGG29o/fr1qhhi0cOtovIcnis/bsPQzK2JOpjiUMOGDfXss88qMjLy0hcMXAYuJN8RsC8BAjbKsoMHD2rRokVasWKFMjMzsxvNQQqKqCdbpatktl2+hwcx3E45kvfLkfin3FnJ3vZmzZrpxhtvVLt27Rg+DgA4p2PHjmncuHE6cOCALCZpYJOK51w9PD8J6U5N23JKWS5DV1xxhZ555hk1aNDgElYMXB4I2AFGwEZZ43K5tGHDBi1atEi//vqrt91sq6CgSlcpqEJdmSxBAaywZDEMQ670eDkS/5Tz9GFJ2f/MVq5cWTfccIOuu+46FqABAHj9+eefWrx4sX788UfZ7XaFB5l1Z6MI1apQ+HDtkZDu1KztSTqZ4ZLZbFaHDh3Ut29fNW3atMxN2QKKCwE7wAjYKCuSk5P17bff6ptvvlFCQsKZVpOs4dUVVClGlrBoPqwL4HakyZG4W46kPd7h40FBQercubP69u3LYb4A4DKVlpamdevW6euvv9Yff/zhba9dIUi3xUaoQnDRRzxlON1asCtFO09medtq1aqlvn37qlOnTvzIC1wgAnaAEbBRmhmGoV27dmnRokX66aef5HQ6JUkmi01BkVcqKLKBzLbwAFdZ+hhul5wpB2RP/FPuzERv+1VXXaW+ffuqc+fOstkuvKcCAFA6xMfHa/v27dqxY4e2b9+uAwcOyPM13GLKPtRW2+qhqlU+yG8/Xh9Pc2j9kQxtjc+Uw332K3+NGjXUqFEjNWrUSHFxcapevTo/mAPnQcAOMAI2SqOsrCz9+OOPWrx4sXbv3u1tN4dUkq3iVbJWqCWTmQMPXCzDMOTOPCn7qT/lPH1QMtySpPLly+u6667TDTfcoCpVqgS4SgBAURmGoZSUFB09elS7d+/2huoTJ07k2bZSiEUtqoSoVdVQhdsu3RodmU63thzP1ObjGTqe5sxzfUREhOLi4hQXF6eYmBjVqFFDkZGRhG7gDAJ2gBGwUZokJiZq0aJF+uabb3T69OnsRpNZ1gq1Zat41WW1Gnhxczsz5UjaK0fibhnOdEmSyWRSu3btNGDAAMXFxQW4QgBAfgzDUFJSko4ePaqjR4/qyJEj3vNHjx5VWlpantuYJFULt6p2BZtqVQhS7Ygglb+Eofpc0h1uHUxx6K8Uuw6mOHT4tEOufNJASEiIqlWr5vNXvXp1VatWTZUqVZLZbC722oFAIWAHGAEbpcHhw4f15ZdfasWKFXI4HJIkU1CYgiKvUlDklTJbgwNc4eXDMNxyph6RI/FPudKOe9vj4uLUv39/tWvXji8yAFCM3G63UlJSlJiYqMTERJ04ccInQB89elQZGRnn3UcFm1mVy1lVu0KQalewqUb5INkKcbit4uZ0GzqS6sgO3ckOHU93KjnTpfMFBJvNlid8V65cWRUrVlTFihUVERHBUTNQphCwA4yAjZJs586dmjdvntauXeud+2UOiZItKlbW8jVkMhHkAsmVlSLHqZ1yJO/3Dh+vUaOG+vfvr+7duysoiNXaAaCoMjMzlZSUpMTERJ06dUpJSUk6deqUN0h7/pKSkuR2u8+7L5OkiGCzKoVaVSnEokqhZ/5CrKoYYinUsatLKqfbUFKmS6cyXTqV4dKpTGf2aYZLSVkuuQtID2azWRUqVPAG7ooVK6pSpUqKjIz0nq9YsaIiIyMVFhZWPA8KuAgE7AAjYKOkMQxDv/zyi7744gtt27bN224Jry5bVKwsoZWZZ1XCuB0ZciTukj1xt+TOHmFQsWJF3XTTTbrxxhsVGhoa4AoBIPAcDodSU1N1+vRp719qaqo3ROf+S09Pv6D9lwsyK9xmVnmb+UyItp4J0RZFhlhkNV9+n50ut6HkrBzh+0wAT8lyK9XhVprdfd7e79xCQkJ8gnjOXvAKFSooPDzc55QfmhEIBOwAI2CjJDl8+LDef/99bdq0KbvBZJa1Qp3sYB3MYTpKOsPlkCNpj+yn/pDhzB6OGBUVpSFDhqhz5878MAKgTHC5XN6gnJqaqpSUlHxPcwbp06dPFzhMOz9WsxRus6i8zazwMwE6O0Rbss+faSsXZJblMgzQF8ttGEp3uHXa7laq58/hVqrdlafNnt/k7wIEBwerfPny3tBdvnz5Av/Cw8NltbJQK4qOgB1gBGyUBOnp6fr888+1YMGC7ENtmcwKqniVbJUayhxU9oZjef4pK6uB0zBccib/pawTv8twZC+e06RJE/3jH/9QvXr1AlwdAGTPW87MzFRqaqrS0tK8p56//AKyJzzntyjYhQixmhRmNSs0yKRQq1lhQdm9ztmB2XL2vM2sYIupzH5WlDZZrhyB+8zfaYdLqXa30hxuZTgMZTjdSne4lek0LqhnPLewsLA8veGegB4eHq7w8HCVK1fO++e5HBYWxjooIGAHGgEbgWQYhlauXKmZM2fq1KlTkiRLuWoKqdJS5uAKAa7u0jAMQ+kHlsskKbTOtWX6i5Phdsp+cqfsJ3dIhksmk1l9+/bRPffco/Bwjk8OoOgMw1BGRkaeYJxfWM6vPT09vcB5ywUJtpgUGmRWaK7A7AnNodYc1wdlt4dYTTKX4X/3kc1tGMpyngncTkMZDrcynDlCeI7z2aE8+3ym8+KijslkUlhYmE/4zh3Cz9dGQC8bLiTfMVYCKEMyMjL00ksvacuWLZIkU1A5hVRpJUt49TIdOmW45M444T0vU9n9p81ktiq4chMFRdZT1vHNcp4+pEWLFmnVqlV65pln1KhRo0CXCCBADMNQVlaWT/DNGYDPdT7n38UGZEmymKSQM8HXe2rJPp8zJIdZcwbm7PMMyca5mE2m7B9cgsyqdAG3cxuGMp3Zw9azw3d2OPcN5IYyz4TxTKdbma7sU6c7+33leX8URc6Afr5Afq7TkJCQsv0drgwqu99CgctMZmamXnzxRf3++++SySLbFY1kqxQrk5nDZJRF5qByCq3ZSc60Y8o6tkkpKSkaO3asXnzxRcXGxga6PABF5HK58oTgCzl1Op0XXYPZJG/PcHY4zhWWrWZvYPZuk+O6IEIyShCzyaSwoOwRDxfK6TbyhO7M3GHcaSjT5bns25Y7oMfHx19wDRaL5bwB3HM+Z3vO65h7XvwYIn4JMEQcxS0rK0svvviitm7dKpmDFFa7myyhUYEuq1gYbqcyj6yV8/QhSZK1fE2FVO8gk/ny+UAx3E5lHFwlV3q8wsLCNG7cOF111VWBLgu47GVmZio5OTnPX1JSknf+ce6AXJRFu3IzSXlCb2hBwdiSPRw7xGKW1Vx217MoCxyu7FEGQRaGHZd0Dnd+oTxnID97mpEzyDuyL/sjpIWEhOQbyitUqKCIiIh8/0JDQ/k3IBfmYBeB2+3WlClTNGfOHKWkpKh169Z6/vnnVadOnQveFwEbxcnhcOg///mPNm/eLJmtCqvVTZawKwJdVrHJOLxazpS/fNqsFWortEbHAFUUGIbbqYy/VsqVkaBy5cpp/Pjxql+/fqDLAsqUzMxMpaSknDM0p6SkeE+Tk5OVlZVV5PsKMmcH4NBcYTg0n57k0FzDsVnEq2yyuwwt2JWs7SeyX1eNrgjWLTERspXi423j3AzDkN3t2yue4czbU56RI5Rnus5uU5QV2j2CgoLOGb7z+7schrEzB7sIpk6dqlmzZunll19WlSpV9Prrr2vo0KFatGiRbDZboMsDzmnlypXZ4dpkUWitLpdVuDbcLjlPH87T7jx9WIbbdVkNjzeZrQqt1UXpB1cqLe2EPvjgA73yyiuBLgsoVZxOpw4dOqS9e/dq7969OnLkiE94zszMvOB9WkxSuTOHfCoXlD0H2XOaOyyH5hiOzVxk5LZgV7K2nTj7o032+WTdHhcZsJpw6ZhM2T+WBVukiOAL/z7jOrMoXGY+c8wznIbSz6zOnnZmxXbPn9Od3Xlz4sQJnThxolD3ZbPZfAJ31apVVa9ePdWvX1+1a9e+7LIUAVuS3W7XjBkzNGrUKHXt2lWSNGHCBHXu3FnLli1T3759A1whcG5//ZXdexsUWV/WsOgAV1O8DGdG9qJmea5wyXBmyGS7vFbVNlmCFFK1tdL3LdWhQ4cCXQ5QomVkZGjfvn3at2+f9uzZo3379unAgQNyOBznvZ3FJG9ILhdkVjmb2edyWJDp7HVBZtnoTYYfONyG/jiVd0TEH6ey5HAbzHtHHpYizj23uwxv2E73BG97jvO5rnO6s7NUQkKCEhIS8tZhsahWrVqqV6+errzySu9fWT7yCQFb0s6dO5WWlqYOHTp42ypUqKBGjRppw4YNBGyUaJ5fF81B5QJcCUoCzzHOPcNTg4ODA1wRULJ88cUXWrZsmY4ePar8ZsnZLCZVLWdVtXCrKodZFZ4rQDP8GoGQas8OMrk53dnXVQy5fEZs4dKyWUyyWSyFek15hrFnB/CzwfxEulNH05w6lupQhtOl/fv3a//+/fr++++9t42OjtbVV1+tIUOGlLnDmBGwJR07dkySVK1aNZ/26OhoHT16tEj7NAxD6enpF10bUJDjx49LkkzWkABXEljXX3+9br31Vs2fP1/ffvut3M4MmS+zHmxJktkmmcyS4dbBgwdVvXr1QFcElChfffWVEhMTfdpqlg9Shxphqh5uVWSIhWMqo0TL/Xl32u4iYCMgvMPYQ82qFJr3esMwlGJ362iqQxuPZmh3ot17XXx8vBYvXqz+/fsrJKTkf4c1DKPQP64SsCXvip255wcEBwcrOTm5SPt0OBzasWPHRdcGFMRz+AX7yR2ylq9xWa2ebbKGSjJLcuvWW29VzZo1deutt2rp0qUy3Bd/qJrSyH5yh2S4ZbFYdPTo0SL/GwaUVX/729+0YcMGbdu2zXtc20OnHfpmz2lVKZfdYx1uM6u8zazwILPCbRZvWwi91wiQcJtZZpPkNpTn8+5iFrMC/CHL6Vaqw61Uu1un7dmnqXaXty0h3ankrLNDMIKDgxUXF6c2bdpo3759Aaz8whR2Lvnl8038PDy/mtjtdp9fULKyshQams/PMYUQFBSkBg0a+KU+4HyGDRumJ598UqdPJynz2C8KqdbusvkCaDJbZAmvKlfqEc2fP9/7i77JZJIlOCLQ5RU7Z9ox2ROyV7kcMmSIWrduHeCKgJInLi5OXbp0kdvt1vbt27V69WqtW7dOqamp2ptkP+9trWb5hO7yZ4J37rZyQWYWKYNfBZlNahBp065Ee57Pu8phfJ2H/7mN7CHfqfZcwdnhytPmcBf8I09wcLDatGmja665Rs2aNVNQUFAxPAr/2b17d6G35R2ps0PD4+PjVbt2bW97fHy8YmNji7RPk8mksLAwv9QHnE/t2rU1ZswY/fvfz8mZvE+OkEqyVbp8joEcWqOj0g+s0LfffqulS5fKZDLJFt3KOxf5cuG2pyrz8BpJhnr27KmbbrrpsvmhBSiqdu3aqV27dnI4HNq5c6cSEhKUmJioU6dOKSkpyed8WlqanG4pKcutpKx8JsPmEhZkUpjVrNAg32NP5z6kVmiuVcRtFhND1JGv2+Ii9eHWUz6fdzdcGV6kFaZxeTCM7MN1ZXiOve3IcVivPIf+Ons548x86gsZGxEaGqrIyEhVqlRJkZGRqlixoipVqqSKFSsqKipKcXFxpWIo+LlcyHcqArak2NhYhYeHa926dd6AnZKSou3bt2vgwIEBrg4oWPPmzTVo0ED93//9n7KO/yJXRoKCq7SS+TKYl20yW1Wu3nVyZZ2W25EqS3DEZRWuDcOQI3G3shJ+ldxOXXnllXrooYcI18AFCAoKKvDYppmZmd7QnfvPE8I9p2539oI/6Q6XlJHPkQ7OwyQpOJ/DdhUmnIdYzbLSc15m2Swm/aNllE5mOJWY6VLlMCvh+jLgOnMs7JzBOMNxJjCf5zBcnssXM4HAZDIpIiLCG5o9gTm/v6KO+i2LCNjKHk8/cOBAvfHGG6pUqZJq1Kih119/XVWrVlWvXr0CXR5QKLfddpvS0tI0b96Xcqb8JWfqMYVUaSFrRL3LImxZgsvLElw+0GUUK1dmkjKPbZA746QkqWHDhho9ejQrhwOXQEhIiKpWraqqVauedzu3262UlBQlJibq9OnTSk1NVVpa2nlPPeftdrsM6cyXZJeSilCn1SyFWMxnQrpJwWcCevCZXnTvqdXk7Vn3nA+2Zve2M7y9ZIsKtSoqlK/wpUHOY1FnubKDctaZYJx1plc5+9RQ1pkAnXv7wgy/LojValV4eLjKlSuX72nu8+Hh4apYsaIiIiJksfAjzoUyGfkdo+Iy5HK59NZbb2nevHnKzMxU27Zt9dxzz6lmzZoXvK/ffsueA1nQr+HApbB7925NnjxZe/fulSRZwqIVUrWNzMEVAlwZ/MVwu2Q/sS17QTMZCg0N1X333afevXvzQQiUYna7/ZzhO/f53Kfp6en5HnasKC40pHuCefCZHvdgq4medJR6JSUce4SFhZ03JJ/vNDg4+LLobLmULiTfEbAvAQI2As3lcumrr77Sp59+qqysLMlkVlDElbJFNZTZdnn18pYlhtspR9I+2U/tlOHIXv24ffv2euihh3TFFVcEuDoAgeR2u5Wenu4N22lpaec8Pdd1mZmZfqvHala+Q9hDzoTw0KCcw999r+dY4/AHzzGa8x9Gnf/c45zb+jMc22w2lStXTmFhYSpXrpzP+YJOw8PDFRYWxg/oAUbADjACNkqKY8eO6d1339WmTZvOtJhkLV9TtqhYWUKjAlobCs/tzJIj8U85Ev+U4cqSJFWqVEkPPvigOnbsGODqAJQVLperwHBeHCHdJJ1zrrn38ple9DxBPcgkC+G8zHAbxnnnF+cbkl2GMh3Zp/7IyBcTjj2nnkOqovQiYAcYARsliWEY+v333zVv3jxt3LjR224Ji84O2uWq0VNQQrntqbKf+kOOpL2Skb1QUpUqVXTrrbeqZ8+epXo1TgBlU86QXpSh7k6n86JrCLWaFBaUfbi03KflgnyvCwtiYbji5DIMZTjcSnO4leYwlH7mfO7TNHv2+YyLXKRLkiwWS5GHVhOO4XEh+Y5XDFDGmUwmNW3aVE2bNtX+/fv15ZdfauXKlXKlxysjPV7m4AjZKjWUtUJtmcz8kxBohmHInXFS9sRdcqYclM58tahfv74GDBigjh07MkwMQIllsVhUvnx5lS9/4dORDMOQ3W4/70JwBc1Dl6QMp6EMp0snC7mCe7DFlB2+bTkDuSlPQM8+3rmZw6jlYOQ4VvLZgHw2OOcOzxnOosXl0NDQQi3QlV8b849R3OjBvgTowUZJl5CQoK+++kpLliw5O5zPHKSgiLoKiqwvS0hkQOu7HBkuuxzJ++VI2iN3VrK3vVWrVurfv7+aNWvGFwQAOA+Xy6XTp08rOTlZKSkpSk5O9jmflJTkPZ+SkqKUlBS53QUf0zwns0mKCLYoItisyBDLmfMWRQabFXHmclnqEXe5DaXY3UrOdCkpy6XkLJeSMl1KznIr+cxl54U9hTKZTAoPD1dkZKQqVKigiIgIRURE+JzP+Ve+fHl6kRFwDBEPMAI2SovU1FQtWbJES5Ys0fHjx73t5tAo2SIbyFqhFr3al5C3tzppd3Zv9Zlh4DabTZ06ddKtt96qevXqBbhKACib3G63UlNTvUH8XME8519hAnm4zewTuiPPhPDyNrNK4u+khqQ0e3ZgTspyKTnTfebUpdN2d4FDtD3HSvYE5HMFZU97+fLlGYmFUoeAHWAEbJQ2brdbW7Zs0dKlS7Vu3Tq5XGeG1dGrfUmcq7e6Tp066t27t7p166bw8PAAVggAyM3lcunkyZNKSEhQfHy84uPjlZCQ4HPZbrcHuky/s1qtqly5sqKjoxUdHa3KlSv7XI6KilJQUFCgywQuKQJ2gBGwUZolJibqu+++09KlS/P0agdFXqmgCrVlMvNBeqEMw5ArI0GOpL15eqs7d+6s3r17q2HDhgwDB4BSyjAMpaSk5AngntOkpKRAl3hO5cuX94bnnKfR0dGKiIiQ2WwOdIlAQBGwA4yAjbLA7Xbr119/1ZIlS3L1alsVVKG2giLryxxSiUBYALczU47kfXIk7ZVhP+1tp7caAACgdGAVcQAXzWw2q2XLlmrZsqUSExO1YsUKffvttzpy5IgcSXvlSNorc3CEgiLrKyiijkyW4ECXXGIYhluutGPZvdWnD8uzEnhISIi6dOmi6667TjExMfw4AQAAUMbQg30J0IONssowDG3btk1Lly7V6tWrz841M5llLV9TQRUbyBJa+bINjm5HuhxJe7J7q50Z3vaGDRuqV69e6ty5s8LCwgJYIQAAAC4UQ8QDjICNy0FqaqpWrlyppUuXat++fd52c3CEgipepaCIupfFCuTeudWn/pTz9CF5eqvDw8PVo0cP9erVS3Xr1g1ojQAAACg6AnaAEbBxOTEMQ7t379aSJUv0ww8/nO3VNgcpKLKebBWvktlWPrBFXgKG2yFH8gE5Ev/0WQm8cePG6t27tzp27CibzRbACgEAAOAPBOwAI2DjcpWamqrvvvtOX3/9tY4ePeptt5SrKlvFq2QJryaTqXSvROrOSpE9cbccyfskt0OSFBwcrG7duqlv374ctxoAAKCMIWAHGAEblzu3261NmzZp8eLF+uWXX+T5Z8ZkK6/gqDhZI+qWuqDtyjgl+4ltcqYe9rZVq1ZNffr0Uc+ePVkJHAAAoIxiFXEAAWU2m9WmTRu1adNGR48e1ddff63vvvtOqamnlXl0vUwntskW1UhBkXVlMlkCXe55uTJOKuvENrlSj0iSTCaTWrdurRtvvFEtW7bk2KAAAADwogf7EqAHG8grPT1d33zzjb788kslJ2fPWTZZw2SLilNQ5JUymUtW0HamJ8h+YptcacckSSazWV27dNHtt9+u2rVrB7g6AAAAFBeGiAcYARs4t8zMTC1dulTz5s3TqVOnJEkma0h2j3bFBgEfOu7KOKms+F/lSo+XlN0b36NHD91+++2qXr16QGsDAABA8SNgBxgBGyiY3W7XsmXL9MUXX+jEiROSJHNwpEKqtpEl7Ipir8dwZSkrfqscSXskSVarVddee61uu+02Va1atdjrAQAAQMlAwA4wAjZQeA6HQ8uWLdPHH3+s1NRUSVJQ5JUKrtxcJmvwJb9/wzDkTN6nrPhfZbiyJEk9evTQwIEDVbly5Ut+/wAAACjZWOQMQKkRFBSkPn366JprrtGHH36o7777To6kvXKePqzg6OayRtSTyWS6JPftykxS1rGNcmVk96DXrl1bDz/8sBo3bnxJ7g8AAABlGz3YlwA92EDRbdu2Te+++64OHDggSbKUq6bQGlfLZLH57T4Mw5Aj8U9lHd8syVBISIjuvvtu3XzzzbJa+d0RAAAAZ11IvuP4MgBKlMaNG+vtt9/WkCFDZLPZ5Eo7qrT9y+TOSvHL/g3DpaxjG5R1fJMkQ+3bt9fUqVPVv39/wjUAAAAuCgEbQIljtVrVr18/vfbaa7riiitk2E8rbf8yOc8ci7qo3M5MZRz4Xo6kvTKZzBoyZIieeeYZ5loDAADAL+iuAVBi1a9fXxMmTNDLL7+s7du3K+PgKlkr1JbJXLR/upypx2Q40xUWVk6jR49S69at/VwxAAAALmcEbAAlWmRkpMaNG6f33ntPS5culTPlr4vaX40aNfTss8+qZs2afqoQAAAAyEbABlDiBQUFadiwYWrXrp32799f5P2EhYWpW7duCg8P919xAAAAwBkEbAClgslkUrt27dSuXbtAlwIAAADki0XOAAAAAADwAwI2AAAAAAB+QMAGAAAAAMAPCNgAAAAAAPgBARsAAAAAAD8gYAMAAAAA4AcEbAAAAAAA/ICADQAAAACAHxCwAQAAAADwAwI2AAAAAAB+QMAGAAAAAMAPCNgAAAAAAPgBARsAAAAAAD+wBrqAssjhcMgwDP3222+BLgUAAAAAcBHsdrtMJlOhtiVgXwKFffIBAAAAACWbyWQqdMYzGYZhXOJ6AAAAAAAo85iDDQAAAACAHxCwAQAAAADwAwI2AAAAAAB+QMAGAAAAAMAPCNgAAAAAAPgBARsAAAAAAD8gYAMAAAAA4AcEbAAAAAAA/ICADQAAAACAHxCwAQAAAADwAwI2AAAAAAB+QMAGUCiDBg1Sw4YN8/0bP368d5snn3yyyPdhGIa+/PJLnTx58rzbpaamqnnz5urYsaPsdnuR7w8AUHLwOVN4hX0cBenRo4cmT5583m1K+nMBlDTWQBcAoPS44YYb9Mwzz+RpDw0NlSRNnjxZFoulyPvfsGGDnnzySS1fvvy82y1evFhRUVE6ceKEli1bpr59+xb5PgEAJQefM4VT2MfhDyX9uQBKGgI2gEILCQlR5cqVz3l9ZGTkRe3fMIxCbTd37lx16tRJx48f16xZs/iwB4Aygs+Zwins4/CHkv5cACUNQ8QB+E3OoXvz5s1Tjx49NH78eLVp00YPPfSQJGn69Onq2bOnmjRpoh49euidd96RYRhat26d7r33XknStddeq3nz5uV7H3v27NGvv/6qa665Rr1799b69eu1Z8+ePHW8+uqrevrpp9WmTRu1atVKY8aMUVpamnebc9WxfPlyxcbG6tSpU95tb731VvXu3dt7+fTp02rSpInWrFkjSfr+++/Vv39/NWvWTL169dLbb7/tM4yuYcOGmjBhgrp3765rrrlGe/fuvZinGQAuWyXlc0aSPv74Y11//fVq1qyZ+vTpowULFnivO3XqlMaMGaP27durdevWGjp0qPbv3++9vjCfG5999pnuvvtuNWvWTDfddJO3t/pcj2PTpk2655571KxZM3Xr1k0vvPCCUlNTvfs8ffq0xowZozZt2ujqq6/Whx9+WODzzWcuUAQGABTCwIEDjTFjxhR6m7lz5xoxMTHGiBEjjL/++svYtWuXsXz5cqNNmzbGTz/9ZBw+fNhYvHix0bhxY2P+/PlGVlaWsXTpUiMmJsb49ddfjYyMjHzv45VXXjFatGhhpKenGykpKUaTJk2M//znP3nqaNy4sfHmm28ae/fuNRYvXmw0adLEmDJlimEYxnnryMjIMJo1a2YsXrzYMAzDOHnypBEXF2fExMQYx44dMwzDML7++mujTZs2ht1uN1auXGk0bdrU+N///mccOHDA+PHHH43rrrvOGDlypLeemJgYo3379sbWrVuNzZs3F+n5B4CyrjR9zkybNs1o1qyZMXv2bOPAgQPGJ598YsTFxRk//fST4XA4jFtuucW49dZbjQ0bNhi7d+82HnzwQaN79+6Gw+Eo9OdGixYtjE8++cTYs2eP8frrrxuxsbHGL7/8ku/j2LFjh9G0aVPjnXfeMfbt22ds2LDBuP32243bb7/dcLvdhmEYxpAhQ4zevXsbGzZsMLZv327ce++9RkxMjDFp0qRzPt985gIXjiHiAApt4cKFWrp0qU9by5YtNWPGjHPe5uGHH1atWrUkST///LOCg4NVs2ZNVa9eXdWrV1d0dLSqV68um82miIgISVKlSpUUEhKSZ19Op1MLFy5U9+7dvfPxunbtqgULFujxxx/3tklS/fr19dhjj0mS6tWrp8WLF2vTpk2SpL/++uucdYSEhOjqq6/WTz/9pD59+mjt2rWKjY1VYmKi1q1bp5tvvlkrV65U165dFRQUpP/+97+67bbbdPfdd0uSateurRdeeEH33XefDh06pJo1a0qSbrnlFjVt2rRIzzsAXC5Ky+fMhx9+qHvvvVd33HGHJOmee+5RZmamXC6X1q5dqx07duibb77RlVdeKUn6z3/+o+nTpyspKanQnxsDBgzQPffcI0l64okntGHDBn3yySd666238jyO6dOn6+qrr9bDDz8sSapbt67efPNN9ezZU+vXr1flypX1008/6cMPP1SbNm0kSW+++aa6d+9+zueVz1ygaAjYAAqtR48eeuKJJ3za8vuCklPdunW952+++WbNnTtX1113nRo2bKhrrrlGvXr1UvXq1Qt1/ytXrlRCQoL69OnjbevTp4+WLVumxYsX67bbbvO2169f3+e25cuXV0pKSqHq6NGjh6ZOnSpJWr16tTp06KD4+HitXbtWN910k1atWqV///vfkqTt27dr69at+vLLL733ZZyZG7dnzx7vh32dOnUK9RgB4HJWGj5nTp06pfj4eDVv3tzntvfff7+k7OHQFSpU8IZrSapcubJ3aHthPzfatWvns//mzZtr9erV+da9fft2HThwQC1btsxz3Z49e5SYmChJPqHziiuu8P4wUdTnwoPPXOAsAjaAQitXrtwFf2jl/GJUqVIlLViwQJs3b9bPP/+sn376STNmzNCIESM0fPjwAvflmWc2cuTIPNfNmjXL58PeZrOdcz8F1dGtWzc999xz2rNnj1avXq0XXnhB8fHxmjp1qn777TedPn1anTt3liS53W498MAD6tevX577yblQT0FfEAEApeNzxvP5YjKZ8t2H1Wo953VS4T83rFbfr+lut1tmc/7LJ7ndbt10003eeeg5VapUST///LN3u9y1ngufuUDRsMgZgGKzYMECffbZZ2rdurVGjhypzz//XLfffru+/vprSef+siJlLxizcuVK9e/fX/Pnz/f5u+222/Tbb79p27ZtfqkjOjpaTZo00ezZsxUfH6/WrVurY8eOOnTokD755BN16NBB4eHhkqSrrrpKe/fuVZ06dbx/x48f12uvveazwAsA4NIrjs+Z8PBwRUdH67fffvO5/ciRIzVu3Dg1aNBAycnJOnDggM++27Ztq19++aXQnxu5979lyxY1btw438dx1VVX6c8///TZp8vl0ssvv6yjR4+qUaNGkuQdti1JKSkp+uuvvy7quSgMPnNxuSFgAyg2WVlZevXVVzV//nwdOnRIGzdu1Pr1671D2sLCwiRJO3fuzPNBuWDBAjmdTj3wwAOKiYnx+XvooYdksVj02Wef+aUOSerevbs+++wzNWvWTGFhYapWrZrq1q2rhQsXqmfPnt7thg4dqm+//VaTJ0/Wvn37tGbNGj311FNKSUk576FmAAD+V1yfM//4xz/00Ucfaf78+frrr7/06aefavny5erZs6euvvpqNWnSRKNHj9avv/6qP//8U0899ZSioqLUtGnTQn9ufPTRR1q4cKH27dunV199VTt37tR9992X7+MYMmSIduzYoeeee067d+/Wr7/+qieeeEL79u1T3bp1Vbt2bfXu3VsvvviiVq9erV27dmn06NE+q28X9bm42P8nEp+5KFsYIg6g2Nxxxx1KTk7W1KlTdfToUUVEROj666/3zreLiYlR165d9cgjj+ixxx7TkCFDvLedN2+eOnbsmGeelyTVqlVLvXr10uLFi71z3C6mDin70CeTJk1Shw4dvG1XX321Dhw44LMoTO/evTVhwgS99957eu+99xQREaHu3btr1KhRRXqOAABFV1yfMwMHDlRWVpYmTZqkhIQE1a1bVxMmTPB+ZkydOlWvvPKKd152+/btNX36dNlstkJ/btx5552aOXOm/vzzT8XGxmr69OmKjY095+OYNm2aJk6cqP79+ys0NFQdOnTQmDFjvMO3X331Vb322mt69NFH5Xa7deedd/ocHisnPnOBojMZRjEeqR4AAADAeTVs2FAvv/yy+vfvH+hSAFwghogDAAAAAOAHBGwAAAAAAPyAIeIAAAAAAPgBPdgAAAAAAPgBARsAAAAAAD8gYAMAAAAA4AcEbAAAAAAA/ICADQBAEV2O64QW52MuSc9vSaoFAFByEbABACXali1b9Nhjj6lbt25q1qyZrr32Wj377LPas2dPQOuaM2eOXn31Ve/lefPmqWHDhjp06FAAq/LldDrVv39/rV69uki3HzRokAYNGuS9XFyP2W636+WXX9bChQv9ut9Dhw6pYcOGmjdv3gXd7t1339X06dP9Wsul9vjjj2vatGmBLgMALjsEbABAiTV9+nTdfffdSklJ0ahRozRt2jQ99NBD2r59u/r166fFixcHrLZ3331XSUlJ3svdunXT7NmzFR0dHbCacnv33XcVHR2tjh07Fun2zz//vJ5//nmf/eV8zJdKfHy8PvzwQzmdzkt+X4Xx9ttvKyMjI9BlXJDRo0fr/fffD/gPUQBwubEGugAAAPLz008/6fXXX9c///lP/etf//K2t2vXTrfeeqsef/xxPfnkk4qJidFVV10VwEqzVapUSZUqVQp0GV7x8fF6//339emnnxZ5Hw0aNPBjRShOVapUUZ8+ffTGG2/o3XffDXQ5AHDZoAcbAFAivfPOO6pXr55GjhyZ57qgoCC98MILslgs+uCDD7ztPXr00JNPPumzbX7DmHft2qUHH3xQrVq1UqtWrTRs2DAdPHjQ53Yff/yxevfuraZNm6pz584aO3asUlNTvfdz+PBhffnll95953c/P//8s/72t7+pdevWat++vR5//HEdPXrUp7ZGjRrp119/1Z133qmmTZuqW7duPo9Jkr7++mvdfPPNatasmTp06KAnnnhC8fHx533+Zs6cqWrVqqlZs2aSpGHDhunmm2/22WbIkCFq3Lix0tLSvG2vv/66unfvLsl3iHh+j9nj119/1V133eWtv6Dh1FlZWXrhhRfUpUsXNWnSRL1799aMGTMkZQ/jvvbaayVJTz31lHr06OG93Zw5c9S/f3+1aNFCzZo10y233KKvv/7aZ99//fWXRo4cqXbt2qlt27YaOnSo/vzzz3zrMAxDTz75pJo2bapVq1blu03Dhg0lSVOmTPGenzx5snr16qUpU6aoffv26tmzpxITE5WZmak333xT1113nZo0aaJWrVpp8ODB2rFjh88+f/75Z91zzz1q2bKlOnXqpOeee07Jycne648cOaLHHntM7dq1U/PmzXXfffdp+/btPvsozGvi5ptv1vfff69du3bl/z8CAOB3BGwAQImTmJiozZs369prr5XJZMp3m4oVK6pjx45avnz5Be173759uuuuu3Ty5Em98sorGj9+vA4ePKi7775bJ0+elCQtXrxYr776qu655x5Nnz5dw4YN04IFCzRu3DhJ2WGrcuXK6tq16zmHhS9YsEBDhgxRlSpV9NZbb+mpp57S5s2bdeedd3rvR5LcbrceeeQR9enTR++//75at26tN954Qz/++KMk6ZdfftETTzyh6667Th988IGeeuoprV27Vo8//vh5H+fChQvVu3dv7+Vu3bpp165d3vu22+3atGmTnE6nNm/e7N1u1apV3oCd0/ke89ixY3XjjTfqvffeU7NmzfTaa6/p+++/P2dt48eP18qVKzVmzBhNnz5d1157rV599VXNmzdP0dHRmjJliiTpn//8p/f8p59+queee07XXnut3nvvPb3++usKCgrSqFGjdOTIEUnZvfa333679u7dq+eff15vvPGGkpOT9fe//12nTp3KU8e4ceO0aNEiTZkyRV26dMm31tmzZ0uSbrvtNu95KTsEL1u2TG+99ZYeeeQRVaxYUaNHj9YXX3yhf/zjH5oxY4aefPJJ7dq1S48++qh3kbSVK1fqgQceUGRkpCZMmKBRo0ZpxYoV3h+STp06pbvuukvbtm3Tv//9b7355ptyu9265557vMO9C/uaaNmypapUqaJFixad8/8FAMC/GCIOAChxjhw5IsMwVKNGjfNuV6dOHS1fvlxJSUmKjIws1L6nTJmikJAQffjhhwoPD5ckXX311erZs6emTZumMWPGaN26dapRo4buuecemc1mtWvXTmFhYUpMTJQkNWrUSDabTZUqVVKLFi3y3Ifb7dbrr7+ujh07asKECd72Vq1aqU+fPpoxY4ZGjRolKbsX9eGHH9btt98uSWrdurWWLVumH374QZ07d9Yvv/yi4OBgDR06VMHBwZKkyMhI/fbbbzIMI98fIPbs2aOEhARv77Ukde3aVZK0Zs0a3XjjjdqyZYsMw1D9+vW1fv16derUScePH9euXbs0evToPPs832N+7LHHdPfdd0uSWrRooRUrVmjt2rX5BnVJWr9+vTp27Ki+fftKktq3b6+wsDBVrFhRNptNcXFxkqTatWurUaNGkqSDBw9qyJAhGjZsmHc/NWvWVP/+/bVp0yZVr15dM2fOVGZmpmbOnKnKlStLkuLi4nTnnXdqy5YtiomJ8d72zTff1OzZszV58mTvc5Mfz2OtWrWqz+N2Op0aM2aMd3673W5XWlqa/v3vf6tPnz6SsqczpKWl6ZVXXlFCQoKio6M1adIkxcbG6p133vHuKyQkRG+99ZaOHz+u//3vf0pKStJnn33mff136dJFffr00cSJEzVp0qRCvyZMJpOaNGmiNWvWnPPxAQD8i4ANAChxPL195+q99vBc73a7C73vtWvXqn379goJCfEuohUeHq42bdp4V9vu0KGDZs+erf79++u6665Tt27ddNNNNxVYj8e+ffuUkJCgxx57zKe9du3aatmypdatW+fT3rJlS+95T4hNT0+XJLVt21YTJkzQTTfdpBtuuEFdunRRp06dzhsKPcPda9as6W2Ljo5Wo0aNtHr1at14441as2aNWrVqpTp16mj9+vWSsntXw8LC1L59+0I9To82bdp4z4eFhemKK65QSkrKObdv3769Zs2apePHj6t79+7q2rWrT3DOj2fo/+nTp7V//37t37/fGxwdDoek7J7dFi1aeMO153F7etM9w9o//fRT/f7777rlllvO+SNAYeQM7DabzTs0Pj4+XgcOHNDevXu99+1wOJSZmalt27ZpxIgRPvu5/vrrdf3110vK/gEkLi5OVapU8b4+zWazunTpoq+++krShb0matSooU2bNhX5MQIALgwBGwBQ4lSv/v/t3X9IVfcfx/GnppYz10qdpf00YpndyghpQ0v/qHAziHAQBNNb+VubJStbdWujwigDw+6cMWlG0i9I+mFrCpZZV2wsK9c2ipoWlhSbJswW5f3+IZ51vWnW17ZYr8df13PuPef9OecD8j6fz+d9AgCc1kV3d/v2bd56660+j14DtLS0UFZW5rR2FzCKlH344Yd0dHRQUlJCfn4+eXl5BAYGkpWVZYy6Pu8cAL6+vk77fH19ndbTDho0yOFvV1dX4yFDaGgohYWF7Nmzh2+++YaCggL8/PxISEggLi7umedva2sDwNPT02H77NmzKS0tBToTucjISEaPHs3hw4dpb2+nqqqK8PBwPDw8ntvGp3U/z9PxP8vatWsZPnw4R48e5YsvvjDaabFYjBHr7hobG7FYLNTU1ODm5kZQUJCxJrrrXC0tLQ4PFXryyy+/EBERwfHjx4mLiyMkJKRP7eyu+/09e/YsW7Zs4caNG3h5efHee+/h5eVlxNja2ordbsfHx6fHY7a0tNDQ0NBjTO3t7S/UJzw9PY3+ICIir54SbBERee0MGzaM0NBQKioqyMrKwtW1s2RIa2srDx48YNSoUbS1tXH+/HnCw8ON/QBPnjxxOFbXSHAXb29vPvjgA8xms9N53dz+/rcYExNDTEwMbW1tVFdXs3v3bj777DNmzJiBv79/r/F3Jfz379932nfv3j2GDh3a+wXoJiIigoiICNrb26mpqaG4uJgtW7Ywbdo0pk6d6vT9ruN3H0WOjIzEarXy888/c+XKFVatWsWYMWN4/PgxtbW12Gw21q5d+0KxvQwPDw9SUlJISUmhqamJyspKrFYrWVlZnDx50un7HR0dJCYm4u7uzsGDB5k0aRJubm5cv37dGNWFznv7rLXWNpuNkSNHGjMQPv30U+Li4oiJiWHdunUcOnTI4d6/jMbGRtLS0ow14qNHjwY6R8u71tMPHjwYFxcXpxgfPXqEzWZjypQpeHt7ExYW9sxp+oDx8KOvfeLBgwcv3N9EROTlqciZiIi8ltLT02loaCAvL8/YVl1dzdy5c8nOzsZisdDe3k5ycrKxf/Dgwdy9e9fhON2nx4aFhXH9+nWCg4MxmUyYTCYmT57Mnj17KC8vByAzM5P09HSgM2mLjo4mNTWVJ0+eGJWan07quxs3bhx+fn4cO3bMYfutW7eoq6tj+vTpfb4OW7duJTY2FrvdjqenJ1FRUaxevRrAoSL507pmAHS/FiaTiWHDhrFr1y48PDwwmUz4+PgwYcIECgoK+PPPP3udet5bm/vq4cOHzJs3z6gaHhAQwOLFi/noo4+MeAcMGODwmz/++IObN28SGxvLlClTjGS4q/J31xKBGTNmUFdX51BE7vfffychIcGhGJ6vry8DBw7EYrFw9epVI5ae9KXd9fX1/PXXXyQlJRnJNWAk13a7HS8vL4KDg50K81VXV5OYmMjdu3cJCwvj5s2bjBs3zuifJpOJo0ePcujQIQYMGPBCfeLOnTvPrWUgIiL9Rwm2iIi8lsLDw1m9ejWFhYUkJCRQVlaGv78/n3zyCUeOHDFeU/T0VNqoqCguXLhAQUEBNTU15OTkOBV4Sk1NpbGxkaSkJCoqKjh79iwZGRmcOHGCiRMnAp1rsMvLy9m6dSs2m41Tp06Rl5fH2LFjje+8/fbbXL16ldraWh4+fOhwDldXV1auXMn58+dZsWIFZ86cobS0FLPZzJAhQ545et6T999/n/r6erKzszl37hynT59m06ZNvPPOO8ycOfOZvwkKCiIgIMDp4ULXWt7y8nKmT5+Ou7s70Lkm+scff2Tq1Km9Tl/urc19NWjQIEJCQsjPz2fv3r3U1tZy4MABjhw5YqxD9vb2BjpHni9duoSPjw+BgYHs27ePU6dOYbPZ2LZtGzt27AA6p00DxMfHM3DgQJYuXcp3331HZWUlqampvPvuuyxYsMAploiICKKjo8nPz+e3337rtd0XL17kwoULPU59DwkJwc3NjW3btnHu3DkqKyvJyMjg9OnTwN8zKZYvX85PP/1EZmYmVVVVlJaWsmHDBqKioggODiY+Pp6Ojg7i4+MpKyvDZrOxfv16iouLCQoKAvreJ+x2OxcvXiQ8PLzvN0hERP4vSrBFROS1ZTabKSkpwcvLi5ycHMxmM99//z0LFiwgIyODkydPEhcXZxSvSkpK4uOPP6aoqIiUlBSam5vZvHmzwzEnTpzIvn37cHFxYdWqVSxfvpx79+6xa9cu5s6dC8CiRYtYt24dVVVVJCcnY7FYGD9+PEVFRUZSumTJEu7fv8/SpUupr693in3hwoXs3LmThoYG0tLSyMnJITQ0lMOHDzsU4XqeWbNmsX37dq5du0Z6ejorV67E09OT4uLiXteez5s3jzNnzjht7xqhfrqQWdfnyMjIXmN5Xpv76ssvv2ThwoUUFRWxZMkSrFYrsbGxbNy4EeiciWA2m6moqGDZsmU8evQIq9WKv78/2dnZZGZmUldXx1dffUVQUBA//PADACNGjKCkpIThw4ezZs0asrOz8fPz49tvv+3xWn3++ee4u7uzfv36HpPn5ORkrly5QkJCQo+zBsaMGUNubi7Nzc2kpKRgsViAzvepu7i4GDFGRUXx9ddfc/v2bdLS0tixYwfR0dHk5uYC4O/vz/79+wkMDGTjxo0kJydz+fJlNm/eTHx8PND3PnH58mVaWlocXtcmIiKvlou9tyokIiIir7Gmpib27t1Lenq6UUxKOjU3NzNnzhyKioocqnzLm2PNmjW0trZitVr/7VBERN4YSrBFRET+o3Jzc/n1118pLCz8t0ORf1hTUxPz58+npKTEqLYuIiKvnqaIi4iI/EdlZGTQ3NxsFNqSN8f27dtJTExUci0i8g/TCLaIiIiIiIhIP9AItoiIiIiIiEg/UIItIiIiIiIi0g+UYIuIiIiIiIj0AyXYIiIiIiIiIv1ACbaIiIiIiIhIP1CCLSIiIiIiItIPlGCLiIiIiIiI9AMl2CIiIiIiIiL9QAm2iIiIiIiISD/4H6wdoFFJRC9yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1 = pd.DataFrame(columns=['Questions (with stack traces)', 'Hours'])\n",
    "df_1['Hours'] = pd_tmp_new3_w['Duration_ans']\n",
    "df_1['Questions (with stack traces)'] = df_1['Questions (with stack traces)'].apply(lambda x: \"First Answer\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Questions (with stack traces)', 'Hours'])\n",
    "df_2['Hours'] = pd_tmp_new3_w['Duration_acc_ans']\n",
    "df_2['Questions (with stack traces)'] = df_2['Questions (with stack traces)'].apply(lambda x: \"First Accepted Answer\")\n",
    "\n",
    "result = pd.concat([df_1, df_2])\n",
    "\n",
    "# Reset index\n",
    "result.reset_index(inplace=True)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=result[\"Questions (with stack traces)\"], y=result[\"Hours\"])\n",
    "\n",
    "# plt.ylim(-250,500)\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 5))\n",
    "# gs = fig.add_gridspec(1, 2)\n",
    "\n",
    "# # min_ = min(min(df_status_w_stack['Duration_ans']), min(df_status_w_stack['Duration_acc_ans']))\n",
    "# # max_ = max(max(df_status_w_stack['Duration_ans']), max(df_status_w_stack['Duration_acc_ans']))\n",
    "\n",
    "# ax1 = fig.add_subplot(gs[0, 0])\n",
    "# sns.violinplot(data=df_status_w_stack['Duration_ans'])\n",
    "# ax1.set_xlabel(\"First Answer\")\n",
    "# ax1.set_ylabel(\"Day\")\n",
    "# # ax1 = plt.ylim(min_,max_)\n",
    "\n",
    "# ax2 = fig.add_subplot(gs[0, 1])\n",
    "# sns.violinplot(data=df_status_w_stack['Duration_acc_ans'])\n",
    "# ax2.set_xlabel(\"First Accepted Answer\")\n",
    "# ax2.set_ylabel(\"Day\")\n",
    "# # ax2 = plt.ylim(min_,max_)\n",
    "\n",
    "fig.suptitle('Answers Duration (with stack traces)')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "e5b540d9-50f5-4867-be9a-e13e9bd585ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1 = pd.DataFrame(columns=['Questions (W/O stack traces)', 'Hours'])\n",
    "df_1['Hours'] = pd_tmp_new3_wo['Duration_ans']\n",
    "df_1['Questions (W/O stack traces)'] = df_1['Questions (W/O stack traces)'].apply(lambda x: \"First Answer\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Questions (W/O stack traces)', 'Hours'])\n",
    "df_2['Hours'] = pd_tmp_new3_wo['Duration_acc_ans']\n",
    "df_2['Questions (W/O stack traces)'] = df_2['Questions (W/O stack traces)'].apply(lambda x: \"First Accepted Answer\")\n",
    "\n",
    "result = pd.concat([df_1, df_2])\n",
    "\n",
    "# Reset index\n",
    "result.reset_index(inplace=True)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=result[\"Questions (W/O stack traces)\"], y=result[\"Hours\"])\n",
    "\n",
    "# plt.ylim(-250,500)\n",
    "\n",
    "fig.suptitle('Answers Duration (W/O stack traces)')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fd473e",
   "metadata": {},
   "source": [
    "## Statistics Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "865c91d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question Numbers with all ML tags:  171114\n"
     ]
    }
   ],
   "source": [
    "print(\"Question Numbers with all ML tags: \", df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "1ac51430",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question Numbers (with hug tags):  1848\n"
     ]
    }
   ],
   "source": [
    "print(\"Question Numbers (with hug tags): \", df_w_hug_tags.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "817104d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question Numbers (without hug tags):  169266\n"
     ]
    }
   ],
   "source": [
    "print(\"Question Numbers (without hug tags): \", df.shape[0] - df_w_hug_tags.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "0ada499a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hug Question Numbers (with code):  1629\n",
      "hug Question Numbers (without code):  219\n"
     ]
    }
   ],
   "source": [
    "print(\"hug Question Numbers (with code): \", count__question_w_code)\n",
    "print(\"hug Question Numbers (without code): \", count__question_wo_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "356dcb8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hug Question Numbers (with code) have stack trace:  381\n",
      "hug Question Numbers (with code) that doesn't have stack trace:  1248\n"
     ]
    }
   ],
   "source": [
    "print(\"hug Question Numbers (with code) have stack trace: \", count_w_t)  # 40 disappeared ?!\n",
    "print(\"hug Question Numbers (with code) that doesn't have stack trace: \",count_wo_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "1cebaf5d",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hug Question Numbers (with stack trace:) on Unix based systems:  313\n",
      "hug Question Numbers (with stack trace:) on Windows based systems:  68\n"
     ]
    }
   ],
   "source": [
    "print(\"hug Question Numbers (with stack trace:) on Unix based systems: \", count_unix)\n",
    "print(\"hug Question Numbers (with stack trace:) on Windows based systems: \", count_win)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "3777daa8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of line codes in the body part (w/o stack trace) is 50786.\n",
      "The total LOC amongst stack traces is 11121 (Unix-based reports).\n",
      "The total LOC amongst stack traces is 2085 (Windows-based reports).\n",
      "The total LOC amongst stack traces is 13206.\n"
     ]
    }
   ],
   "source": [
    "line_count_unix   = 0\n",
    "line_count_win    = 0\n",
    "line_count_simple = 0\n",
    "\n",
    "for tuple in df_w_hug_tags[\"Line_code_u_w_s\"]:\n",
    "    line_count_unix   += tuple[0]\n",
    "    line_count_win    += tuple[1]\n",
    "    line_count_simple += tuple[2]\n",
    "        \n",
    "print(f\"The total number of line codes in the body part (w/o stack trace) is {line_count_simple}.\")\n",
    "print(f\"The total LOC amongst stack traces is {line_count_unix} (Unix-based reports).\")\n",
    "print(f\"The total LOC amongst stack traces is {line_count_win} (Windows-based reports).\")\n",
    "print(f\"The total LOC amongst stack traces is {line_count_unix+line_count_win}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "13260028-8d85-4f82-8d22-ca47a7e5b65b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 1093 unique pairs in stack traces posts related to the hug frameworks.\n"
     ]
    }
   ],
   "source": [
    "print(f\"We found {max(dic.values())} unique pairs in stack traces posts related to the hug frameworks.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "ef2d46d5-b096-40ca-83c2-f72897d7114e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pst:  1,848 \n",
      "Pst_wc:  88.15 \n",
      "Pst_woc: 11.85 \n",
      "CB: 3,752 \n",
      "CB_ws: 23.39 \n",
      "CB_wos: 76.61 \n",
      "ST: 381 \n",
      "U: 82.15 \n",
      "W: 17.85\n",
      "1,848 & 88.1\\% & 11.9\\% & 3,752 & 23.4\\% & 76.6\\% & \\multicolumn{1}{r:}{\\textbf{ 381 }} & 20.6\\% & 82.2\\% & 17.8\\%\n",
      "1,848 & 88.1\\% & 3,752 & 23.4\\% & \\multicolumn{1}{r:}{\\textbf{ 381 }} & 20.6\\% & 82.2\\% & 17.8\\%\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# print(\"Pst: \", '{:,}'.format(df_w_hug_tags.shape[0]), \"\\nPst_wc: \", math.ceil(count__question_w_code*100/(count__question_w_code+count__question_wo_code)), \"\\nPst_woc:\", 100-math.ceil(count__question_w_code*100/(count__question_w_code+count__question_wo_code)), \"\\nCB:\", '{:,}'.format(count__num_codes), \"\\nCB_ws:\", math.ceil(count_w_t*100/(count_w_t+count_wo_t)), \"\\nCB_wos:\", 100-(math.ceil(count_w_t*100/(count_w_t+count_wo_t))) , \"\\nST:\", '{:,}'.format(count_w_t), \"\\nU:\", math.ceil(count_unix*100/count_w_t), \"\\nW:\",100-math.ceil(count_unix*100/count_w_t))\n",
    "print(\"Pst: \", '{:,}'.format(df_w_hug_tags.shape[0]), \"\\nPst_wc: \", \n",
    "      '{0:0.2f}'.format(count__question_w_code*100/(count__question_w_code+count__question_wo_code)), \n",
    "      \"\\nPst_woc:\", '{0:0.2f}'.format(100-(count__question_w_code*100/(count__question_w_code+count__question_wo_code))), \n",
    "      \"\\nCB:\", '{:,}'.format(count__num_codes), \n",
    "      \"\\nCB_ws:\", '{0:0.2f}'.format(count_w_t*100/(count_w_t+count_wo_t)), \n",
    "      \"\\nCB_wos:\", '{0:0.2f}'.format(100-(count_w_t*100/(count_w_t+count_wo_t))) , \n",
    "      \"\\nST:\", '{:,}'.format(count_w_t), \n",
    "      \"\\nU:\", '{0:0.2f}'.format(count_unix*100/count_w_t), \n",
    "      \"\\nW:\",'{0:0.2f}'.format(100-(count_unix*100/count_w_t)))\n",
    "\n",
    "print( '{:,}'.format(df_w_hug_tags.shape[0]), \n",
    "      '& {0:0.1f}\\%'.format(count__question_w_code*100/(count__question_w_code+count__question_wo_code)), \n",
    "      '& {0:0.1f}\\%'.format(100-(count__question_w_code*100/(count__question_w_code+count__question_wo_code))), \n",
    "      '& {:,}'.format(count__num_codes), \n",
    "      '& {0:0.1f}\\%'.format(count_w_t*100/(count_w_t+count_wo_t)), \n",
    "      '& {0:0.1f}\\%'.format(100-(count_w_t*100/(count_w_t+count_wo_t))) , \n",
    "      '& \\multicolumn{1}{r:}{\\\\textbf{', count_w_t, '}}',\n",
    "      '& {0:0.1f}\\%'.format(count_w_t*100/df_w_hug_tags.shape[0]), \n",
    "      '& {0:0.1f}\\%'.format(count_unix*100/count_w_t), \n",
    "      '& {0:0.1f}\\%'.format(100-(count_unix*100/count_w_t)))\n",
    "\n",
    "print( '{:,}'.format(df_w_hug_tags.shape[0]), \n",
    "      '& {0:0.1f}\\%'.format(count__question_w_code*100/(count__question_w_code+count__question_wo_code)), \n",
    "      '& {:,}'.format(count__num_codes), \n",
    "      '& {0:0.1f}\\%'.format(count_w_t*100/(count_w_t+count_wo_t)), \n",
    "      '& \\multicolumn{1}{r:}{\\\\textbf{', count_w_t, '}}',\n",
    "      '& {0:0.1f}\\%'.format(count_w_t*100/df_w_hug_tags.shape[0]), \n",
    "      '& {0:0.1f}\\%'.format(count_unix*100/count_w_t), \n",
    "      '& {0:0.1f}\\%'.format(100-(count_unix*100/count_w_t)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "3808f478-1be0-46aa-b0e3-81e90bbb5718",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w-ST: 381 wo-ST: 1,248\n",
      "P1: 0.0289\n",
      "N1: 381\n",
      "P2: 0.0553\n",
      "N2: 1248\n"
     ]
    }
   ],
   "source": [
    "print(\"w-ST:\", '{:,}'.format(count_w_t), \"wo-ST:\", '{:,}'.format(count_wo_t))\n",
    "ACC_W, ACC_WO = 11 , 69\n",
    "print('P1: {0:0.4f}'.format(ACC_W/count_w_t))\n",
    "print('N1: {}'.format(count_w_t))\n",
    "print('P2: {0:0.4f}'.format(ACC_WO/count_wo_t))\n",
    "print('N2: {}'.format(count_wo_t))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f01a48e",
   "metadata": {},
   "source": [
    "## Graphical Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "034f3688",
   "metadata": {},
   "source": [
    "### LOC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "d2cdecdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "code_wo_trace  = df_w_hug_tags[(df_w_hug_tags.Has_code == True) & (df_w_hug_tags.Has_trace == False)].Line_code_simple_code\n",
    "code_w_trace   = df_w_hug_tags[df_w_hug_tags.Has_trace == True].Line_code_win + df_w_hug_tags[df_w_hug_tags.Has_trace == True].Line_code_uix\n",
    "\n",
    "df_1 = pd.DataFrame(columns=['Question Type', 'LOC (in each question post)'])\n",
    "df_1['LOC (in each question post)'] = code_wo_trace\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"code blocks w/o stack trace\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'LOC (in each question post)'])\n",
    "df_2['LOC (in each question post)'] = code_w_trace\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"code blocks with stack trace\")\n",
    "\n",
    "result = pd.concat([df_1, df_2])\n",
    "result['Type'] = \"Question Type\"\n",
    "\n",
    "result.to_csv('./CSV_data/plt_df_loc_hugh.csv', encoding='utf-8', index=False)\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=\"Type\", \n",
    "               y=\"LOC (in each question post)\", \n",
    "               data=result, \n",
    "               hue=\"Question Type\", \n",
    "               palette=\"Set2\", #colorblind\n",
    "               split=True, \n",
    "               # scale=\"count\",\n",
    "               inner=\"quartile\",\n",
    "               scale_hue=False,\n",
    "               width=.7)\n",
    "\n",
    "plt.ylim(-15, 100)\n",
    "# plt.xlim(-1, 1)\n",
    "\n",
    "fig.suptitle('Comparing the LOC between regular code and stack trace')\n",
    "\n",
    "plt.gca().axes.get_xaxis().set_visible(False)\n",
    "# plt.plot(legend=None)\n",
    "plt.legend(loc='upper right', prop={\"size\":8})\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf06607",
   "metadata": {},
   "source": [
    "### Question Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "f198cd0c-00e6-4673-96e0-300b3dc0492c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.DataFrame(columns=['Question Type', 'Words (in each question post)'])\n",
    "df_1['Words (in each question post)'] = list_num_words_wo_tra\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"w/o stack trace\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Words (in each question post)'])\n",
    "df_2['Words (in each question post)'] = list_num_words_w_tra\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"with stack trace\")\n",
    "\n",
    "result = pd.concat([df_1, df_2])\n",
    "result['Type'] = \"Question Type\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "27aebb4b-37f8-4817-a1d3-a0d462cbad59",
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('./CSV_data/plt_df_ques_len_hugh.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "92381996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=\"Type\", \n",
    "               y=\"Words (in each question post)\", \n",
    "               data=result, \n",
    "               hue=\"Question Type\", \n",
    "               palette=\"Set2\", #colorblind\n",
    "               split=True, \n",
    "               scale=\"count\",\n",
    "               inner=\"quartile\",\n",
    "               scale_hue=False,\n",
    "               width=.7)\n",
    "\n",
    "plt.ylim(0, 300)\n",
    "# plt.xlim(-1, 1)\n",
    "\n",
    "fig.suptitle('Comparing the Question Length')\n",
    "\n",
    "plt.gca().axes.get_xaxis().set_visible(False)\n",
    "# plt.plot(legend=None)\n",
    "plt.legend(loc='upper right', prop={\"size\":8})\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb4fb36",
   "metadata": {},
   "source": [
    "### Plotting Score, View, Answer, Comment Counts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "9593359f-60ab-48a0-b48c-a93d7f61679a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_status_w_stack.to_csv('./CSV_data/plt_df_hf_w_table3_stack.csv', encoding='utf-8')\n",
    "df_status_wo_stack.to_csv('./CSV_data/plt_df_hf_wo_table3_stack.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "8a03529c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sturges_rule_number(n_obser: int) -> int:\n",
    "    '''\n",
    "    Sturges’ Rule is the most common method for determining the optimal number of bins.\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    n_obser : int\n",
    "        The total number of observations in the dataset.\n",
    "\n",
    "    Returns:\n",
    "    ----------\n",
    "    Use ⌈ ⌉ for returning a value: Symbols that mean “ceiling” – i.e. round the answer up to\n",
    "    the nearest integer.\n",
    "    '''\n",
    "    # return round(1 + math.log2(n_obser))\n",
    "    return round(np.ceil(1 + (3.322 * np.log10(n_obser))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "53a4e98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[25,15])\n",
    "plt.subplot(211)\n",
    "\n",
    "plt.title('View Count (With Stack Traces)', fontsize=25)\n",
    "plt.xlabel('\\nView Range', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "\n",
    "n_bins = get_sturges_rule_number(len(df_status_w_stack['View_count']))\n",
    "\n",
    "n, bins, patches = plt.hist(df_status_w_stack['View_count'], \n",
    "                             bins=n_bins, \n",
    "                             log=True, \n",
    "                             align='right', \n",
    "                             color='purple', \n",
    "                             edgecolor='black')\n",
    "\n",
    "# define minor ticks and draw a grid with them\n",
    "minor_locator = AutoMinorLocator(2)\n",
    "plt.gca().xaxis.set_minor_locator(minor_locator)\n",
    "# plt.grid(False)\n",
    "# plt.grid(which='minor', color='white', lw = 0.5)\n",
    "# x ticks\n",
    "xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]\n",
    "dist = (xticks[1] - xticks[0])/2\n",
    "xticks = [e+dist for e in xticks]\n",
    "xticks_labels = [ \"{:.1f}\\nto\\n{:.1f}\".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]\n",
    "plt.xticks(xticks, labels = xticks_labels, fontsize=18)\n",
    "\n",
    "\n",
    "plt.subplot(212)\n",
    "\n",
    "plt.title('View Count (W/O Stack Traces)', fontsize=25)\n",
    "plt.xlabel('\\nView Range', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "\n",
    "n_bins = get_sturges_rule_number(len(df_status_wo_stack['View_count']))\n",
    "\n",
    "n, bins, patches = plt.hist(df_status_wo_stack['View_count'], \n",
    "                             bins=n_bins, \n",
    "                             log=True, \n",
    "                             align='right', \n",
    "                             color='purple', \n",
    "                             edgecolor='black')\n",
    "\n",
    "# define minor ticks and draw a grid with them\n",
    "minor_locator = AutoMinorLocator(2)\n",
    "plt.gca().xaxis.set_minor_locator(minor_locator)\n",
    "# plt.grid(which='minor', color='white', lw = 0.5)\n",
    "# x ticks\n",
    "xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]\n",
    "dist = (xticks[1] - xticks[0])/2\n",
    "xticks = [e+dist for e in xticks]\n",
    "xticks_labels = [ \"{:.1f}\\nto\\n{:.1f}\".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]\n",
    "plt.xticks(xticks, labels = xticks_labels, fontsize=15)\n",
    "\n",
    "plt.tight_layout(pad=4.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "cb5a9b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_1['Frequency'] = df_status_wo_stack['View_count']\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_2['Frequency'] = df_status_w_stack['View_count']\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "result = pd.concat([df_1, df_2])\n",
    "result['Type'] = \"Question Type\"\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=\"Type\", \n",
    "               y=\"Frequency\", \n",
    "               data=result, \n",
    "               hue=\"Question Type\", \n",
    "               palette=\"Set2\", #colorblind\n",
    "               split=True, \n",
    "               scale=\"count\",\n",
    "               inner=\"quartile\",\n",
    "               scale_hue=False,\n",
    "               width=.7)\n",
    "\n",
    "plt.ylim(-2500,13000)\n",
    "# plt.xlim(-1,1)\n",
    "\n",
    "fig.suptitle('View Count')\n",
    "plt.gca().axes.get_xaxis().set_visible(False)\n",
    "# plt.plot(legend=None)\n",
    "plt.legend(loc='upper right', prop={\"size\":10})\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "2e8319c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Sp59+Kg8Pj4euGxAQYHns58yZo5SUlJwq0+4SExP11VdfycXFRV26dEl1mznY+vvvv3Xu3Lk0227ZskXSvcfvueeekyRt375dJpMpzbrmYOvZZ59Nc9tff/2lEydOqEmTJnJxcbEsnzRpkhISElSzZk2NHTtWzs4PPzSsUqWKPvroI0tdK1aseNTdTuV///ufJKlq1apyd3d/6HrdunWTJN25c8cyrSJg1rVrV7m6umrmzJlKTEy0dzkAAACwM4ItAAAA5Em7du3ShQsX5OzsrBYtWqh8+fKqVauWJGnBggXpbtOzZ0/5+vpqy5YtCg8P15tvvqmAgAD5+fmpRYsW+ve//63o6Oh0t127dq369u2r559/Xn5+fmrcuLH69u2rVatWpQpzQkJC5Ovrq5kzZ6b5Hfv27ZOvr698fX21cePGNLfPnTtXvr6+lintzK5du6ZJkyapffv2ql27tvz9/fXSSy9p9uzZio+PT/N7pk+fLl9fX02ePFnr169XmzZt5Ofnp8DAQK1du/bhD6ruTR23atUqSVKXLl3SvebTg4YMGaJp06Zp6dKlaQKU5ORkLV68WD179lT9+vXl5+en5s2ba9SoUTpy5Eia33XhwgXLY5ReICTdm9bN19dXy5YtsyzbvXu3fH191a1bNyUmJmr27Nnq2LGjateurXr16qlXr15av359uo/TxYsXJUljxoyRr6+vpk+f/tj7LEnr1q3TlStX1Lhx4zQjlcqWLaty5cpJSn/Ulnl0VpMmTVSjRg098cQTio6O1uHDh1Otd+fOHR09elRS+sFWetMQnjx5Urt27ZIk9e3bN1Xg9TDPP/+8/P39JSlTI+/M08YdPHhQ169ff+h6JUqU0IoVK7R+/Xr5+flJ+r+emQPq1atXy9fXVz179ky17blz5zR+/Hh17NhR9erVU40aNdSwYUOFhoZq0aJFSk5OTvdvxsbG6ttvv9WLL76oevXqqVatWnrhhRf0+eefZ3jUWFxcnOV1o1mzZjp79myGtrPGsmXL5Ovrq+HDh2vfvn0KDg6Wn5+fmjRpou+//96y3tWrVzV16lR16dJFDRo0UI0aNdSgQQOFhIRo9uzZunv3brq/PyEhQT/99JO6d++uhg0bys/PT61bt9bHH3+sqKiodLcJDw/X22+/rebNm8vPz08NGzZU37599fvvvz/0fmzdulWvv/66WrZsqZo1a6phw4bq2bOn5s+fr4SEhHS3KVGihBo3bqwrV67ov//9b8YfNAAAAORJBFsAAADIk5YuXSpJatCggSVUME/p9uuvv+rmzZsP3XbLli3q0qWL1q9fryJFiuipp57ShQsX9MMPPygkJCTNSe8JEyZoxIgR2rZtm5ycnOTr6ytXV1dt27ZNo0aN0jvvvGNZ1zwlovn6SffbsWOH5f/NwcP9Nm3aJElq0aKFZdm+ffv0wgsvaNasWYqMjFS5cuVUunRpHTlyRBMnTlS3bt0eelJ6z549Gjp0qG7duiVvb29dvXpV1apVe+jjIkkHDhzQnTt3JKUfpKSncuXKatOmjZ588slUy2NjY9WjRw+NGTNGYWFhKliwoHx9fRUTE6NVq1apS5cumjNnTob+RkYlJiaqf//+mjhxoq5evSpvb28lJydr165dGjJkSKrQ86mnnlKdOnUsI40qVKigOnXq6KmnnsrQ3/rll18kKc00jWYBAQGS0gZbt2/f1oEDB+Tm5qaGDRvK2dnZsu6D0xEeOHBASUlJevLJJ1W1atU0f2Pz5s1ycnKyjPqS/u959uDyx2nVqpUk6fDhw/r7778ztI359//999/q2rWr5s+f/9DnY7Vq1VSuXDnL412wYEHVqVNHxYoVkyQVLVpUderUkY+Pj2Wb9evXq0OHDpo7d67Onz+v0qVL6+mnn1ZCQoJ2796t999/X6NHj07ztyIiItS5c2dNnjxZR48eVcmSJVWhQgWdPXtWX331lV5++WXdunXrkfctPj5er7/+usLCwlSmTBnNmzdPFStWzNDjkhWnT59Wv379dPHiRVWpUkW3bt1S5cqVJUl//vmnXnjhBc2cOVMnTpxQiRIlVKVKFaWkpOjAgQOaOHGi+vbtmybsu3r1qrp3764PP/xQ+/fvV+HCheXt7a3Lly/rxx9/VOfOnS0Br9n8+fP14osvasWKFbp586aqVKkiLy8vbdu2TUOHDtXIkSPT/J25c+eqX79+2rBhg+Lj4+Xj46P8+fMrLCxMH330Ubq1mZmfS48L3wEAAJD3EWwBAAAgz4mJibF8q79jx46W5R06dJCrq6vi4+Mto0DS8+OPP+rZZ5/Vxo0btXbtWv33v//Vl19+KRcXF507d05LliyxrBsREaHvv/9eHh4emjt3rjZs2KClS5dq69atmjhxopydnbVy5UrL9IfmUOrPP/9ME5Bt377d8v8PBlu3b99WWFiYnJ2dLeHYlStXNHjwYN24cUPdunXTjh07tGbNGq1du1br1q1T7dq1FR4ermHDhqV7P//8808FBgZq8+bNWrlypbZs2aKnn376kY/t6dOnLf+fXpCSGW+99ZYOHDig4sWLp3rsdu7cqcGDByslJUWffPKJ1q1bl6W/c7+jR4/q4MGDmjx5snbt2qVly5Zpy5YtlqkBP//8cyUlJUm6NyJtwYIFKl68uCSpf//+WrBgQZppBdOTnJyssLAwSfeufZWeRo0aSUobbO3atUuJiYny9/dX/vz5Jf1fiPhgsGWehrBx48apro8l3RuBs3PnTvn5+VnCIeneKBvp3qixAgUKPPa+mFWvXl3SvWk+MzpdYEBAgIKCgiRJ58+f10cffaTnnntOHTp00L/+9S+tXbv2oaMgq1evrgULFlhGmz377LNasGCB3n//fUnSzZs39d577ykhIUHdu3fXjh07tGrVKq1evVrbt2+3jOxas2aNTp48mepxGT58uCIjI+Xn56fff/9da9eu1erVq/XLL7+oYsWKOnXqlD788MOH3q+EhAQNHjxYO3fuVPny5TVv3jzLCLzsFh4eLh8fH23cuFHLly/X5s2b9eyzzyo5OVmjRo3SrVu31LJlS23dulVr1qzRihUrtHPnTo0cOVKStHfv3jTPo/fee0+HDx9W+fLltWzZMq1bt04rV67UH3/8IX9/f0VFRVm2l+6F/+PGjZOzs7P++c9/au/evVq+fLk2btyo77//XsWKFdOaNWtSjW68deuWJk+eLEmaMmWKtm7dqqVLl2rDhg2aNWuWPD09FRYWpt9++y3d+23ej3bv3v3Q8AsAAACOgWALAAAAec6aNWt09+5deXh4qE2bNpblxYoVswQYCxcufOj2xYoV07Rp01SiRAnLshYtWlhOsN8fRBw/flySVKlSJTVs2DDV7+nUqZO6d++uDh06WKbY8vb2VsWKFZWYmKjdu3db1o2JidGhQ4fk7e2tokWL6vjx46mmbtuxY4cSExP1zDPPWEKKWbNm6caNGwoMDNS4ceNUqFAhy/rly5fXl19+qQIFCmjv3r2WKeke9Pbbb1tGyGRkWsH7R7plZP2H+fPPPy3TLU6bNi3VY+fu7q4333xTL7/8siRZTobbytChQy1hi3RvZNCoUaMkSTdu3NCZM2ey/DeOHj2qW7duydnZWd7e3umuYw6jIiIidOPGDctyc+hw/2iqJk2aSLo3pd/9I4nMwZb59vvt3btXd+7cSTNizPy8euKJJzJ1n+4Pxx4WRqVn4sSJGjlypCWkM5lMOnnypBYsWKARI0bo2WefVc+ePbVv375M1bN3714lJiaqePHiGjNmjPLly2e5zcvLS++8845lKsT7g7j169fr+PHjyp8/v77++mtVqFDBcluFChU0YcIESfemkoyJiUnzdxMSEvTGG29o27ZtqlixoubNm6fSpUtnqvasGjZsmAoWLChJKlKkiJycnBQeHq4bN27I3d1dH3/8sQoXLmxZ383NTQMGDLCEb/c/HgcPHtTWrVvl7OysL7/8UjVq1LDcVqJECU2ZMkUuLi46cOCAJdieMmWKTCaT3nrrLYWGhqaazrJx48aWx3DOnDmW59uZM2cUHx+vwoULq3379qnuT5MmTTRgwAC1adPG0rMHValSRS4uLoqJiUl3mlIAAAA4DoItAAAA5DnmaysFBgamGZFiHsF15syZdKf7k+6dmPXw8Eiz3BxQ3H+y23xSPDw8XBMnTkxzjZ2xY8fqs88+U4MGDSzLnn/+eUmpR2iZRyEEBATomWeekclksoz4kdKfhtB8Taj7R6Xd78knn7SM9Envml3FixfP9CiT+8ODxMTETG17P3M9tWrVUp06ddJdp0+fPpLuXUMpoyOEMsL8+N/v/vDpcVPQZcSFCxckSSVLlpSnp2e66xQpUkRVq1aVyWSyjOiT/m+ayvvDqlKlSlmmTTQ/LxISEnTo0CFJ/zet4f22bNkiKe1UiObrrj0sQHiY+8MLk8mUqe0GDBigrVu3avLkyQoKCkoVGqekpCgsLEw9evRI99pzD9OiRQsdOHBA69evl6ura5rb4+PjLeFdXFycZfmGDRskSS1btkwzPaYk1alTR8uWLdOOHTss4ZFZUlKS3nzzTW3evNkyUuvB66dlN2dnZ8v1zu5Xo0YN7dmzR3v27FGRIkXS3J6QkGAJu9J7POrUqaMqVaqk2a506dJatmyZdu7cqaeffloXLlzQsWPHJD38tadZs2YqUqSI7t69q507d0q6N0LQ1dVVN2/e1DvvvGMZOWhmvhZf69at0/2dHh4eKlWqlKR7o/8AAADguNJ++gcAAAAM7NSpU5aT/emddG3VqpW8vLx0584dLViwwDId3P0edqLaHFCYp6qT7p1MDgoK0urVqzV79mzNnj1bZcqUUePGjdWkSRM999xzacK1wMBAzZkzJ1WwZQ4zGjVqpHPnzmnDhg3atWuX2rRpI5PJZBlx1bJlS0n3piY0X/Pmyy+/1Ny5c9Ot2bzO/VMImt0fLmTU/dtcv37dMgons8z13D865EEVK1ZUgQIFFBsbqzNnzqS6tlJWpNff+8MnW0xzZh7R9GAw8qDGjRvr2LFj2r9/v5o3b64zZ87o/PnzKlasWJrrnTVp0kQRERHatWuXWrZsqYMHDyohIUE+Pj7p9nLz5s0qUqSI/Pz8Ui03hz2Pus5ceu4fQWjNaL38+fMrKCjIMlru3Llz2rlzp/773/9q27ZtMplMmjp1qqpXr24ZHZkRnp6eCg8PV3h4uM6fP6/IyEidOnVKJ0+etISv9wdxkZGRkh49lebDnpfz5s3T3bt3Jd0b3ZeSkpLhOm2lUKFCDw1LpXuPx9mzZ3X48GFFRkbq/PnzOnXqlI4fP24JNe+vOyOPx/233T+t45AhQx66jflvmff1YsWKqV+/fpo5c6ZWrFihFStWqHjx4mrUqJGaNGmipk2bPvZ5VahQIV28eDHD13gDAABA3kSwBQAAgDzl/utfvf76649c948//tC1a9fSjNrI7EiWTz/9VI0aNdLixYt18OBBXbx4UUuWLNGSJUvk4eGhbt26afTo0ZYp/+rWrasnnnhCZ8+e1fnz51WuXDnt2LFDLi4uatiwoWVaM/OIssOHDysqKkqVK1dWxYoVJSnV9bkyMpopvSnV0huV9jiVKlWy/P/JkydVtmzZx26TkpKi48ePy9fXV87O9yaNMNf/uOAnf/78io2N1e3btzNd68M8rr+ZGY30MObQ6P4RbukJCAjQ7NmzLdNbmgPOgICANNfMevbZZ/XDDz9YprA0T0NoHpV3vwsXLuj06dPq2LGj5TE3q1q1qn755RdFRkYqLi7usTWamUfpSLJJyFihQgVVqFBBISEhCgsL0+uvv67Y2Fj99NNPGQ62Nm/erM8//1xHjx5NtbxEiRJq27attmzZkibAM0/76OXllema7969Kx8fH8t0iu+//76++eabTP+erHjUfmu+ftz9oz2le6MDmzVrpqNHj1pGE5pl9vG4/7XkwevDPW794cOHy8/PT/PmzdPevXsVFRWl1atXa/Xq1XJ1dVX79u01duzYh74umJ+rthhVCQAAAOMi2AIAAECekZiYqFWrVkm6983+h52wN5lMunr1qhITE7VkyRINGjQoS3/XyclJXbp0UZcuXRQdHa3du3crLCxMmzdv1sWLF/Xjjz9KksaMGSPp3tRszZo108qVK7V9+3Y1adJE586dU61atVSwYEFVq1ZNRYoU0enTp3X16lXLaK37pyG8/76tXr3aZqOZHqdatWoqU6aMLl68qO3bt6c7rd+DDh48qJCQEBUuXFhz5sxRjRo1LCO90gvc7me+Pb2RYQ8LoO7cufPYmrKbOXx43An4evXqyc3NTYcPH1ZycrJlFF9618xq0KCB3N3ddfLkSd28edNyTar0gi3zc+bBaQglqXnz5poyZYoSExO1adMmtWvXLkP3yTz1ZY0aNdKdwu9BR44c0bvvvqubN29q3bp1jwxkGjRooB49eujrr7/O8DXOdu3apUGDBiklJUXPPPOMgoKC5OPjI29vb8v1wO6/TpmZed+xJiytWrWqvv/+e0VGRiokJESbN2/WsmXL9OKLL2b6d9laRESEQkNDdffuXVWuXFkvvfSSqlatKm9vb8soxZCQkDTBVmYfD3MA9sQTT6S6TmBGtWrVSq1atVJsbKzCwsIsr5WnT5/WqlWrFBMT89ApKc37kzWhPAAAAPIOrrEFAACAPGPz5s2WKapmz56tLVu2pPtv69atliBo0aJFWZpOLDY2VocPH7ZMt1W0aFG1a9dOH3zwgdavX6/u3btLklauXJlqu8DAQEn3RuiYTw6br5Pk5ORkmSJx9+7dlutRmachlO4Fd+Zw4dSpUw+t7/jx4zp27Fimp517lPbt20uSli9fnqEpwebPny/p3rWBzNfwefrppyXdCz8eJiIiwhJSma9ldv+1lBISEtJsc/fu3ceGZTnB3Jv7p+9LT758+eTv76+4uDidPHlS+/fvl5OTU7rBVr58+VSnTh2ZTCYdPXpUBw4ckLu7u+rXr59m3S1btsjZ2Tnd3+Pr66uGDRtKkmbOnJnu4/igPXv2WEYQvvrqq49dX7r3HD1+/Lj++usvy3WWHqV48eKSMj7N4bfffquUlBQ1atRIP/30k1599VU1aNDAEmolJCSk+/ibRz3eP6XegwYNGqTBgwdbpjU1a968uYoUKaLatWurZ8+ekqQJEyboypUrGao5O/3www+6e/eunn76aS1ZskR9+vRRQEBAqqk306szI4/H+++/r4EDB2rz5s2WUZs3btxQVFTUQ7fZu3evIiIiLFM33r171zJlpCQVKFBAgYGBeuedd/Trr79q5MiRku5df+9h+7C5nxkJVgEAAJB3EWwBAAAgzzBPQ+jj46OaNWs+ct2QkBBJ965BtWXLFqv/5rRp0/TSSy9p4sSJaW5zdnZW48aNJaW9btNzzz0nd3d37dq1Szt27JCkVNf7Modca9as0ZEjR1SiRIk096l58+aS7l33J71wLiYmRr169VKnTp30ww8/WH0fHzRw4EAVL15csbGx+uc//2m5lk561q9frzVr1li2M0/HaB7pdejQoYdOZ/b9999LkkqVKiVfX19J90aJmKfoS++6YRs2bEh1DTRbMP+9zExRaD75f+vWLcXFxT1yXXPfly5dqps3b6pq1aoPPXFvDqrWrFmj27dvq27dummutxQfH6/du3erdu3alutpPWjcuHHy8vJSeHi4Pvjgg0deVywyMlKjRo2SdG90WOfOnR95f8zKlSsnf39/SdJnn32WavrMB6WkpOjXX3+V9H/Pa7OHPf7mkUdVq1aVi4tLmt+5YsUKyzW27n9OmEex/fHHH+kGX+Hh4dq4caM2bNigIkWKPLTmYcOGqUyZMrp165bef//9h66XU8zX0/P29k53tOr27dt16dIlSalfj8yPx759+9IdLff3339r5cqV2rRpkzw9PeXt7W0JmufNm5duLfv27VOPHj3Uvn17/fnnn5Kkn3/+WcHBwRo1alS6+5L5NU9Suvvw3bt3LdMmmoNxAAAAOCaCLQAAAOQJUVFR2rp1qyTppZdeeuz6wcHBlim1Fi5caPXf7dixo5ycnLRp0yZ9++23lhPpknTp0iXLlFoPTgmXP39+NWjQQDExMfr999/l4eGhOnXqWG43B2KbNm2SyWRSYGBgmmsuDRgwQF5eXtq3b59GjRql6Ohoy20XL17UgAEDdP36dRUsWFA9evSw+j4+qGDBgho3bpzc3Ny0ceNG9ejRQ1u3bk11sjo2NlZffvmlhg0bJpPJpICAAIWGhlpu9/f3tzwmQ4cOTTWlWUJCgqZNm6ZFixZJkkaPHm25756enqpevbokafr06alGoGzbtk0fffSRze6nmfl5Yg4OMqJatWry8vJSSkqK5cT+w5hP6C9dulRS+tMQmplv++WXXySlPw3h7t27FRcX98jrVFWoUEHjx4+Xu7u7li1bpl69eunAgQOp1rlz545+/vlndevWTZcvX1b58uU1ceLENM/DR3n33XeVL18+nThxQl27dtX69evTjBCLiIjQ4MGDtW/fPlWsWDHNc9U8DaU5lDEzhxtr165VRESEZXl8fLzmzZunjz/+2LLMPGpIkjp06KCKFSvq1q1beuONN/TXX39Zbjt9+rTeeustSVKbNm1Urly5h943Ly8vjRs3TpIsUxLakzlM3b59u/bu3WtZnpSUpDVr1mj48OGWZfc/Hg0bNlT9+vWVnJysN954I9VjeeXKFb355puKj49XrVq1LCP93nzzTUnSN998o2+//TZVT/fu3Wu5/ZlnnrEEt+3atZObm5tOnDihf//736mmDI2OjtbUqVMlSbVr1043UDx48KCSk5OVL18+VatWzcpHCQAAAHkB19gCAABAnrBixQolJSXJzc1NHTt2fOz6BQoUUFBQkH7++Wdt3rw5zUnzjPLz89OwYcM0depUTZ48Wd98843Kli2ruLg4nT9/XklJSSpfvrzeeeedNNsGBgZq27ZtSkxMVOPGjVNdN6ZcuXIqW7asZVTK/dMQmlWoUEGff/65hg8frjVr1uj3339X5cqVlZiYqLNnzyopKUleXl765ptvLNOz2crzzz+vWbNmaejQofrf//6nfv36qVChQipbtqySkpJ05swZS8jXoUMHjR8/Ps2omkmTJmnQoEE6cOCAQkNDVaZMGRUtWlRnzpxRbGysXFxcNGzYML3wwgupths2bJhef/11nTp1Si1btlTlypV18+ZNXbx4UTVr1lSdOnX0xx9/2Oy+Vq9eXSdOnNB3332nLVu2qFWrVho8ePAjt3Fzc1OjRo20YcMG7du3zxJUpqdmzZoqUKCAZUTTo4It82iua9euPXRd8wjE9K6vdb/27durTJkyGjZsmPbs2aOQkBAVK1ZMTz31lOLj43Xu3DlLYNGuXTt9/PHHKlCgwCN/54Nq166tr776Su+8845Onz6tIUOGyMvLS2XKlJGnp6euXr1qCSerVaum6dOnp/kb5hBj//79atu2rSpXrqz//Oc/GjJkiHbs2KGoqCgFBQWpYsWKcnd317lz53Tnzh0VLVpUlSpVUnh4eKrwyt3dXTNmzFC/fv20d+9eBQYGqkqVKoqPj1dkZKSSk5NVvXp1ffjhh4+9f+YRbMuXL9eECRP07LPPppr6Lyf16dNHa9as0fXr19WjRw9VrFhR+fPn14ULF3Tz5k15eXnJ399fBw4cSPV4SPdG1PXr108nTpzQCy+8IG9vbzk7O1v24zJlymjKlCmW9V944QWdPXtW06dP1+TJk/X111+rYsWKio6OtgTAlSpV0pdffmnZpkSJEvr3v/+tUaNGae7cuVqyZInKly+v5ORkRUZGKj4+XkWKFNH48ePTvX/ma8o1btzYMvITAAAAjokRWwAAAMgTli9fLuleWJTRa/SYr3+VkpJiGR1kjUGDBmnGjBlq1qyZ3N3ddeLECUVFRalatWoaMWKEVq5cme7J7hYtWlj+P73gwzySp0CBApaREg9q1qy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      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[25,15])\n",
    "\n",
    "plt.subplot(211)\n",
    "\n",
    "plt.title('Answer Count (With Stack Traces)', fontsize=25)\n",
    "plt.xlabel('\\nAnswer Range', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "\n",
    "n_bins = get_sturges_rule_number(len(df_status_w_stack['Answer_count']))\n",
    "\n",
    "n, bins, patches = plt.hist(df_status_w_stack['Answer_count'], \n",
    "                             bins=n_bins, \n",
    "                             log=True, \n",
    "                             align='right', \n",
    "                             color='g', \n",
    "                             edgecolor='black')\n",
    "\n",
    "# define minor ticks and draw a grid with them\n",
    "minor_locator = AutoMinorLocator(2)\n",
    "plt.gca().xaxis.set_minor_locator(minor_locator)\n",
    "# plt.grid(which='minor', color='white', lw = 0.5)\n",
    "# x ticks\n",
    "xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]\n",
    "dist = (xticks[1] - xticks[0])/2\n",
    "xticks = [e+dist for e in xticks]\n",
    "xticks_labels = [ \"{:.1f}\\nto\\n{:.1f}\".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]\n",
    "plt.xticks(xticks, labels = xticks_labels, fontsize=18)\n",
    "\n",
    "plt.subplot(212)\n",
    "\n",
    "plt.title('Answer Count (W/O Stack Traces)', fontsize=25)\n",
    "plt.xlabel('\\nAnswer Range', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "\n",
    "n_bins = get_sturges_rule_number(len(df_status_wo_stack['Answer_count']))\n",
    "\n",
    "n, bins, patches = plt.hist(df_status_wo_stack['Answer_count'], \n",
    "                             bins=n_bins, \n",
    "                             log=True, \n",
    "                             align='right', \n",
    "                             color='g', \n",
    "                             edgecolor='black')\n",
    "\n",
    "# define minor ticks and draw a grid with them\n",
    "minor_locator = AutoMinorLocator(2)\n",
    "plt.gca().xaxis.set_minor_locator(minor_locator)\n",
    "# plt.grid(which='minor', color='white', lw = 0.5)\n",
    "# x ticks\n",
    "xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]\n",
    "dist = (xticks[1] - xticks[0])/2\n",
    "xticks = [e+dist for e in xticks]\n",
    "xticks_labels = [ \"{:.1f}\\nto\\n{:.1f}\".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]\n",
    "plt.xticks(xticks, labels = xticks_labels, fontsize=18)\n",
    "\n",
    "plt.tight_layout(pad=4.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "0f44e9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "df_1 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_1['Frequency'] = df_status_wo_stack['Answer_count']\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_2['Frequency'] = df_status_w_stack['Answer_count']\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "result = pd.concat([df_1, df_2])\n",
    "result['Type'] = \"Question Type\"\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=\"Type\", \n",
    "               y=\"Frequency\", \n",
    "               data=result, \n",
    "               hue=\"Question Type\", \n",
    "               palette=\"Set2\", #colorblind\n",
    "               split=True, \n",
    "               scale=\"count\",\n",
    "               inner=\"quartile\",\n",
    "               scale_hue=False,\n",
    "               width=.5)\n",
    "\n",
    "plt.ylim(-.5,4.1)\n",
    "# plt.xlim(-1,1)\n",
    "\n",
    "fig.suptitle('Answer Count')\n",
    "plt.gca().axes.get_xaxis().set_visible(False)\n",
    "# plt.plot(legend=None)\n",
    "plt.legend(loc='upper right', prop={\"size\":10})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "a2f7989a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[25,15])\n",
    "\n",
    "plt.subplot(211)\n",
    "\n",
    "plt.title('Comment Count (With Stack Traces)', fontsize=25)\n",
    "plt.xlabel('\\nComment Range', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "\n",
    "n_bins = get_sturges_rule_number(len(df_status_w_stack['Comment_count']))\n",
    "\n",
    "n, bins, patches = plt.hist(df_status_w_stack['Comment_count'], \n",
    "                             bins=n_bins, \n",
    "                             log=True, \n",
    "                             align='right', \n",
    "                             color='b', \n",
    "                             edgecolor='black')\n",
    "\n",
    "# define minor ticks and draw a grid with them\n",
    "minor_locator = AutoMinorLocator(2)\n",
    "plt.gca().xaxis.set_minor_locator(minor_locator)\n",
    "# plt.grid(which='minor', color='white', lw = 0.5)\n",
    "# x ticks\n",
    "xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]\n",
    "dist = (xticks[1] - xticks[0])/2\n",
    "xticks = [e+dist for e in xticks]\n",
    "xticks_labels = [ \"{:.1f}\\nto\\n{:.1f}\".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]\n",
    "plt.xticks(xticks, labels = xticks_labels, fontsize=18)\n",
    "\n",
    "plt.subplot(212)\n",
    "\n",
    "plt.title('Comment Count (W/O Stack Traces)', fontsize=25)\n",
    "plt.xlabel('\\nComment Range', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "\n",
    "n_bins = get_sturges_rule_number(len(df_status_wo_stack['Comment_count']))\n",
    "\n",
    "n, bins, patches = plt.hist(df_status_wo_stack['Comment_count'], \n",
    "                             bins=n_bins, \n",
    "                             log=True, \n",
    "                             align='right', \n",
    "                             color='b', \n",
    "                             edgecolor='black')\n",
    "\n",
    "# define minor ticks and draw a grid with them\n",
    "minor_locator = AutoMinorLocator(2)\n",
    "plt.gca().xaxis.set_minor_locator(minor_locator)\n",
    "# plt.grid(which='minor', color='white', lw = 0.5)\n",
    "# x ticks\n",
    "xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]\n",
    "dist = (xticks[1] - xticks[0])/2\n",
    "xticks = [e+dist for e in xticks]\n",
    "xticks_labels = [ \"{:.1f}\\nto\\n{:.1f}\".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]\n",
    "plt.xticks(xticks, labels = xticks_labels, fontsize=18)\n",
    "\n",
    "plt.tight_layout(pad=4.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "46a20d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_1['Frequency'] = df_status_wo_stack['Comment_count']\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_2['Frequency'] = df_status_w_stack['Comment_count']\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "result = pd.concat([df_1, df_2])\n",
    "result['Type'] = \"Question Type\"\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=\"Type\", \n",
    "               y=\"Frequency\", \n",
    "               data=result, \n",
    "               hue=\"Question Type\", \n",
    "               palette=\"Set2\", #colorblind\n",
    "               split=True, \n",
    "               scale=\"count\",\n",
    "               inner=\"quartile\",\n",
    "               scale_hue=False,\n",
    "               width=.5)\n",
    "\n",
    "plt.ylim(-1.1,10)\n",
    "# plt.xlim(-1,1)\n",
    "\n",
    "fig.suptitle('Comment Count')\n",
    "plt.gca().axes.get_xaxis().set_visible(False)\n",
    "# plt.plot(legend=None)\n",
    "plt.legend(loc='upper right', prop={\"size\":10})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "5d464c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example:\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "# ###################################################################################\n",
    "df_1 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_1['Frequency'] = df_status_wo_stack['Comment_count']\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_2['Frequency'] = df_status_w_stack['Comment_count']\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "resultComment = pd.concat([df_1, df_2])\n",
    "resultComment['Type'] = \"Comment Count\"\n",
    "# ###################################################################################\n",
    "df_1 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_1['Frequency'] = df_status_wo_stack['Answer_count']\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_2['Frequency'] = df_status_w_stack['Answer_count']\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "resultAnswer = pd.concat([df_1, df_2])\n",
    "resultAnswer['Type'] = \"Answer Count\"\n",
    "# ###################################################################################\n",
    "# df_1 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "# df_1['Frequency'] = df_status_wo_stack['View_count']\n",
    "# df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "# df_2 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "# df_2['Frequency'] = df_status_w_stack['View_count']\n",
    "# df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "# resultView = pd.concat([df_1, df_2])\n",
    "# resultView['Type'] = \"View Count\"\n",
    "# ###################################################################################\n",
    "# df_1 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "# df_1['Frequency'] = df_status_wo_stack['Score']\n",
    "# df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "# df_2 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "# df_2['Frequency'] = df_status_w_stack['Score']\n",
    "# df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "# resultScore = pd.concat([df_1, df_2])\n",
    "# resultScore['Type'] = \"Score Count\"\n",
    "# ###################################################################################\n",
    "result = pd.concat([resultComment, resultAnswer], ignore_index=True)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=\"Type\", \n",
    "               y=\"Frequency\", \n",
    "               data=result, \n",
    "               hue=\"Question Type\", \n",
    "               palette=\"Set2\", #colorblind\n",
    "               split=True, \n",
    "               scale=\"count\",\n",
    "               inner=\"quartile\",\n",
    "               scale_hue=False,\n",
    "               width=.5)\n",
    "\n",
    "plt.ylim(-1,6)\n",
    "# plt.xlim(-1,1)\n",
    "\n",
    "fig.suptitle('hug Questions')\n",
    "# fig.suptitle('TensorFlow Questions', prop={\"size\":10})\n",
    "# plt.gca().axes.get_xaxis().set_visible(False)\n",
    "# plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "1599a8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1a1aKfTY7pLW/Hzx4UNKdfapYsWLW9U0mk9q3b6+QkBBJKYPJc+fOad68eZLuBFyWmYTSndfIhx9+qPz58ysmJsZ6+s9p06YpPj5ePXr00Msvv5ziPa5q1aqaOnWqvLy89PPPP+vQoUOS7uwTllM8du7c2br/SVK1atU0bNgwhYWFpXkttkKFCqlo0aIym83asmWLc08cAAAAPAbBFgAAADxOpUqVNHfuXC1evFhDhgxRrVq1rNcLku7MPpk+fbratWunM2fOWJffunXL+qVrly5dbN73yy+/rBUrVlivi2P5Qr9Zs2bWL+Lv9eyzz0q6c82ka9eupbrd1qygdevWKT4+XpUqVUrx5f3dwsPDJUm7d+/WxYsXba5zt4CAAOv/T0xMzHD9tPzxxx9KTExU0aJF9dRTT9lcp0ePHvLx8dG1a9es17nKDE2bNk21rFChQtbg5erVqy7/Dcvp9kwmk8qWLWtznVy5clmDhrtPR2gJtu4OIn19fVW3bl1JSnFtMcv1te4OwSwspyG893SDcXFxkpTi9WyPu68/d/e1yOzRsWNHrVu3Tp9++qk6deqkMmXKpLh9z549eu655zR27Fi777NatWratm2btm3bZvN0oPHx8daw6+4A2rK/1a5d22boWKpUKUVGRmrz5s168MEHU93+9ttva968eSpYsKBmzZqloKAgu2vOLLb29yJFimjLli3atWuXzZqSkpKss/ruvu7YunXrZDabVapUqRSnrLQICAjQDz/8oA0bNqhhw4aKj4/XH3/8IUnWmY73Cg4OVpUqVWQ2m63XKixUqJC1H6+88or+/PPPFDNfO3furE8//VSdO3dO83FbZjCmdTpLAAAAwMI741UAAACA+9NDDz2khx56SC+++KJu3bqlnTt3asOGDVqyZIkuXryoEydO6KWXXtKPP/4oSfr333+t1y9KaxZHkSJFUsx+OXr0qCSlGT7dfVtSUpKOHz+e4tRy0p3Ttt3LMnvjzJkz6tatm837vTugOHLkSIq6bLn77zgya+delpkwDz30UJqzhgICAlShQgUdOHBAR48etRlIOcMy0+Ve/v7+kmTzemqOspwqLSAgIMXMlHuFhobqt99+086dOxUREaGbN29q586dNq+Z1aBBA/3xxx/aunWr+vTpo5iYGJ0+fVr58+e3eRq8devWycvLK9VsLsuMGEcDvLv77cxsPV9fX4WFhSksLEzSndflli1btHr1aq1evVpJSUn68ccfVbVqVXXt2tXu+/X399exY8e0Z88enThxQidPntShQ4e0f/9+a4h3d4BiuUZcerOs0rrt119/tYZCN27cUHx8vN11ZiZb+7uFv7+/YmJitGvXLuvzcfjwYe3bt083b96UlPL5OH78uKT0Ty1asWJF6/8/duyY9XG//fbb8vX1tbnNv//+K+n/93UvLy+98soreuONN7Ru3TqtW7dOBQoUUL169VS/fn01adJEJUqUSPdx58+fX5LsCuEBAADg2Qi2AAAAAEm5c+dW/fr1Vb9+fb300kt6/fXXtWzZMv3111/au3evqlWrpsuXL1vXz5Mnj133e/36dUl3TvuVlruvn2TrtHyWUOZulpld169fTzEjKC32BB1ly5aVl5eXkpKSdPDgQdWrVy/DbSRp3759qly5snXWjz2PWfr/x52ZpyLMaKaSo7ORbLGcsjF37tzprme5zpalP1u2bFFCQoJq166d6rmxzMravn27kpOTrbPYHnvssVTh2e3btxUVFaWaNWumOEWf9P+hza1bt3Ts2DGVL1/ersf0zz//WP9/ZsxSKlGihNq3b6/27dvrwIED6t+/v86ePas5c+bYHWzt2rVLEydOTDWjr1ChQmrcuLH++eefVLN7LPvo3bMP7XX79m2VKFFCpUqV0s6dO/Xaa69pwYIFDs9+c5Wt/V26EyJ98MEHWrduXYrwKm/evKpTp47OnTtnvcaahaPPx90zRvfs2ePQ+p07d1a5cuU0c+ZMbdq0SVeuXNGqVau0atUqmUwmNWnSRG+99VaaAZdlf8qMWZUAAAC4vxFsAQAAwGOMHTtWW7ZsUYcOHfTcc8+luZ6/v7/eeecdrVq1SgkJCTp69KiqVauW4svh69ev2zWzxRKA2TrFoMXdX+TaG5hZvgRu0aKFpk6datc2GSlUqJBq166tbdu2aePGjerRo0eG25w9e1YdOnRQ7ty5NWnSJDVr1syuxyz9/+O29ZjTCqAss1LcyXLNoYwe34MPPqgSJUro33//1fnz562nGbR1asGKFSuqZMmSiomJ0YEDB7Rjxw5Jtq+vtWXLFsXFxdk8tVz9+vXl4+OjhIQE/fbbb+rfv79dj+m3336TdGe2lq0ZYvc6e/asBg8erNjYWH333XdpnpJRuhOUvfDCCxo7dqyOHTtmVz2HDx9Wr169dPv2bVWqVEnPPPOMqlSpoooVK1pn5XXt2jVVsGXZL5wJS0uVKqVZs2YpV65catOmjaKjozV9+nS9+OKLDt9XZrt48aJ69OihixcvqlSpUurcubOqVq2qBx98UGXKlJHJZNKIESNSBVuOPh93v8ft3LnT7vcji3r16qlevXq6ffu2tm/frm3btmn9+vXau3ev1q5dq5iYGC1evDjFdeEsLO8Hd1/TCwAAALCFa2wBAADAY8TFxen48ePWL/HTkzdvXuuXupYAKzAw0Dp7xnIqwHv9/fff6tatm0aPHi2z2Wy9js/evXvT/Ft///23pPSv2XQvy/Vo0qpDujNrJyoqSidPnrT7FHytW7eWdOdUdwcOHMhw/blz58psNishIUE1atSQJOtj3rdvX4qZJXe7fv26NeQoV66cdbnl+U3rNHDnzp2z63FkpQceeEDSnddTRoGBZdbW7t27tW3bNkkpr691t9DQUEl3nrf0gi3LNZBsBVuFChVS27ZtJUnffvutdXZZeo4dO6alS5dKkrp3757m6SPvVrhwYR04cEBnzpyxXjcsPZbT69m6XpYt3377rW7fvq0HH3xQCxYsUN++fRUaGpriVJNnz55NtZ1lhlp6+8Ubb7yhQYMGWa9TZvHoo48qMDBQpUuX1ssvvyxJ+uKLL7Rv3z67as5KCxcu1MWLF1WwYEEtXLhQzz33nBo3bqzAwEBrSOTs8zF16lT169dPixYtSvEed+jQoTS32b17t/bv3299/cfHx+vw4cPatWuXpDs/DmjQoIGGDRumyMhITZ48WZIUHR2t/fv327xPy+kwLfsXAAAAkBaCLQAAAHiMdu3aSbpziq3IyMh0192wYYMuX76sggUL6pFHHpF0J+x69NFHJd35otmWpUuXaufOnTp16pRMJpP12lFr1qzRyZMnbW4za9YsSVLNmjWt15nJSOPGjeXl5aUjR45o48aNNtf55ptv1LNnT4WHh+vWrVt23W+nTp0UFBSkpKQkvf766ylOv3iv3bt36+uvv5Z0Z/aM5QvpRo0aydvbW+fPn9fy5cttbjt79mwlJiYqd+7cCgkJsS63BB+Wa/fc+/cyO9iyNXMkI5bgTrpzLan0WK6ltXnzZh04cEAFCxZMdQ01C8tMrm3btunw4cMqW7asAgMDU633xx9/qFixYmleK2rUqFEqWrSozp07p+HDh1uvG2VLbGyshg4dqri4OAUFBWngwIHpPh4LHx8ftWzZUtKd8MdWqHK3ZcuWSZKaNGmSYrnl+b93ht7p06cl3ZnJZuuUjxs3brRe5+nu0NYS9u3YscN6fbu7Xbx4UUuWLNHvv/+e5in/JKlnz56qWbOmEhIS9Nprr1mvreculplppUqVsjlT9NChQ/rrr78kpXw+GjVqpFy5cun06dPavHlzqu1u376tBQsWaMOGDTKbzcqbN691f7S8L93r5MmT6t69u9q1a6cVK1ZIuvOafOqppzRw4ECbobQltL23vrtZ9qW79y8AAADAFoItAAAAeIz69eurRYsWkqQxY8Zo3LhxqU5lFhcXp4ULF1pnbLz00kspTsf1/PPPy2QyacmSJZo+fboSExOtty1evFjfffedJFlPAdeyZUsFBwcrLi5OAwYMSHGqsOvXr+uNN97Qhg0b5O3trVdeecXux1K6dGl16tRJkjR8+HCtWbPGeltycrLmz5+vTz75RJIUERGR4jpe6fH29ta4ceOUL18+/f333+rSpYtWrFiR4svouLg4zZ07V3369FF8fLyCgoI0fPhw6+0lS5ZU586dJd2ZHWP58ttS29y5czVt2jRJd57Pu683ZQkOZ86cqcOHD1uX//333yn+RmaxnHrNEqTYo3DhwtaZMH/++We661q+0I+MjFRycrJCQ0PTnBFlue2XX36R2Wy2OVvr8OHDOnnypBo1apTm3yxYsKCmTJmifPnyacOGDercubPWr1+foofx8fFavny5OnbsqP3796tw4cKaPHmyQ6eBe/nll1W4cGGdPXtWnTp10uLFi1OdKjImJkavv/66li5dqsKFC2vw4MEpbrfsW+fOnUuxL1lmJG7cuFHbt2+3Lk9MTNTSpUs1bNgw67K7g7t69eqpbt26SkpK0pAhQ1K8hs6ePauXXnpJcXFxqlGjRrrXkMuVK5fee+89+fj4WE9J6E6WsCc6OlorV660Ljebzfrjjz/Uv39/a/h2d4gdGBhoncE3cuTIFK/XK1euaOTIkTp79qxKly6tp556SpL04osvysvLS0uXLtX48eNTzEo8cOCABg4cqISEBJUuXdp6340aNVKhQoV0+fJljRo1KkUgfuPGDU2YMEHSnfeGypUrp3p8p06dsobWlvcAAAAAIC1cYwsAAAAeZeLEiQoICNDixYs1a9YszZo1S6VKlVKRIkUUFxenY8eOKT4+Xj4+PhoxYoS6d++eYvvHH39co0eP1vvvv68pU6bo66+/VmBgoM6cOaMLFy5Ikl544QXrzBFvb2999tlnGjBggI4cOaLw8HCVL19eefLk0eHDh3X79m35+/vr7bffVp06dRx6LK+//rrOnj2rtWvX6rnnnlOxYsV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9h6urq0UfBoOBmJgY4N6dWFeuXKFPnz4WO7Lu8/HxYe3atZw7d86ivWPHjhbPRUVFVFVVUV9fz/Llyxv1U1NTQ319PefPn8fHx+d/rnPChAk4OTlhMpkoLi4mKysLW1tbEhISCAkJaVTft29f+vbty82bN/n++++5fPkyRUVFnDx50jz2g7p27dqorU2bNsC9+7vs7e0pLCykbdu2dOnSxaLOysqK3r17U1RUZG4rLCwE7t2x9cMPP1jUV1VVATT6jiIiIiIi8u+mYEtERERERP4VWrdujZ+fH35+fsycOZMtW7Ywd+5cUlNTmTp1Kg4ODlRUVPD0008/sp87d+4AfwQ6D7p/J1Z1dbVFu729vcVzRUUFAD/99BMrVqz4y/Fu3br16IX918SJE+nUqZP5ecKECUycOJFZs2bRvn17Xn755Ub9Llq0iN27d3P37l0MBgMdO3bE19eXs2fPPnSMP+9iu89gMAD37icDKC8vb7Qz674H7wurrKwEYNOmTX+5rsddv4iIiIiI/Dso2BIRERERkRbn9u3bhIWF4ebmRkpKSqPfDQYD4eHh7Nu3j4MHD1JaWoqbmxuOjo7m4OpBVVVVODo60rp1awCuX7/+0Lr7gVXbtm0fOcf7/YwaNYqkpKTHXdpje/HFF5k3bx6zZs1i6tSp7Ny502In2owZM8jPz+eNN95g1KhRuLu7m3eg7dq1q8njGo3Gvzw+8cF2R0dHAHJzc+ncuXOTxxQRERERkX8P3bElIiIiIiItjtFopLKykkOHDlFWVvbIWisrK9q3bw+Au7s7v/zyC7/++mujutGjRzN8+HCMRiOdOnWiqKiI3377rVHdt99+C0CPHj0eOa6bmxt2dnacOXPGvNvpz9avX8/KlSspLy9/ZD+PEhYWxtChQ7l58yZz5swxt1dUVJCfn4+npycffvihxbGKJSUl1NTUPHROj6NXr16UlpY+NPg7deqUxbOHhwcAp0+fblR76dIlEhMT2b9/f5PmISIiIiIiLZOCLRERERERaZEiIyOpra0lOjr6oSFLXl4ehw4dYtiwYeZQJzQ0FJPJxMcff2xxx9SXX35JcXEx/fv3B+D111+nurqahQsXUldXZ647c+YMqampODk5ERAQ8Mj52dvbExISwsWLF1m3bp3Fb0ePHiUpKYmtW7fi7Ozc5G8AEBsbi9Fo5MCBA+zduxcAW1tbrKysqKiooLa21lxbXV1NfHw8AHfv3m3SeGFhYZhMJpKSkiy+4Y4dOxoFWKGhoVhbW5OcnGwRJtbV1REfH8/atWu5efNmk+YhIiIiIiItk44iFBERERGRFundd9/lwoULZGVlERQUxKBBg+jatSt1dXWcOnWK7777jm7duhEXF2d+Z8yYMWRnZ7N9+3bOnz9Pv379uHbtGtnZ2XTs2JGYmBgA3n77bQ4ePMiuXbs4f/48vr6+3Lhxg9zcXEwmE59++qk5LHuUmTNncuLECRITE8nLy8Pb29s8no2NDQsXLsTK6u/9P6KrqyvTpk1jwYIFJCQkMGjQIJycnBg2bBhZWVmEh4czcOBAqqqq+OqrrygrK8PZ2ZnKykoaGhr+7/FHjBjBjh072LVrFxcvXqRfv34UFxfz9ddf065dO8rLy7G2tgaga9euzJgxg48++oiRI0cSEBCAs7MzBw4c4Mcff8Tf35/Q0NC/tX4REREREWlZtGNLRERERERaJGtra5YtW8aKFSsYPHgwp0+f5osvvmDz5s3U1NQwffp0tm3bhouLi8U7q1atYtq0aVRXV5OWlsaRI0d47bXXSE9PN++esre3Z/369URHR3P37l3S09M5cuQI/v7+ZGRkEBgY+FhzdHFxITMzk7feeotr166xYcMGjh07RkBAAJmZmfTr1++JfIvIyEi8vb0pKysz3+e1cOFCJk6cSGVlJampqRQUFODl5UV6ejqjR4+murqao0eP/t9jGQwGli9fzuTJk7l16xZpaWlcuXKFpKQkfH19AWjVqpW5ftKkSaxevZqePXuSnZ1NRkYGNjY2vP/++yxbtgwbG/0/poiIiIiI/MFgaurB6SIiIiIiIiIPuHr1Km3atHnojrWoqCgKCws5ceIEBoPhH5idiIiIiIg0d9qxJSIiIiIiIk/MmjVr8PHx4ZtvvrFoP3nyJMePH+eVV15RqCUiIiIiIk2mHVsiIiIiIiLyxJw5c4axY8dia2tLUFAQrq6ulJSUkJubi52dHRkZGXTv3v2fnqaIiIiIiDRTCrZERERERETkiTp79iwpKSmcPHmSGzdu4OLiwoABA5gyZQpdunT5p6cnIiIiIiLNmIItERERERERERERERERaRZ0x5aIiIiIiIiIiIiIiIg0Cwq2REREREREREREREREpFlQsCUiIiIiIiIiIiIiIiLNgoItERERERERERERERERaRYUbImIiIiIiIiIiIiIiEizoGBLREREREREREREREREmoX/AFwy8R8oLUDIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[25,15])\n",
    "\n",
    "plt.subplot(211)\n",
    "\n",
    "plt.title('Score Count (With Stack Traces)', fontsize=25)\n",
    "plt.xlabel('\\nScore Range', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "\n",
    "n_bins = get_sturges_rule_number(len(df_status_w_stack['Score']))\n",
    "\n",
    "n, bins, patches = plt.hist(df_status_w_stack['Score'], \n",
    "                             bins=n_bins, \n",
    "                             log=True, \n",
    "                             align='right', \n",
    "                             color='orange', \n",
    "                             edgecolor='black')\n",
    "\n",
    "# define minor ticks and draw a grid with them\n",
    "minor_locator = AutoMinorLocator(2)\n",
    "plt.gca().xaxis.set_minor_locator(minor_locator)\n",
    "# plt.grid(which='minor', color='white', lw = 0.5)\n",
    "# x ticks\n",
    "xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]\n",
    "dist = (xticks[1] - xticks[0])/2\n",
    "xticks = [e+dist for e in xticks]\n",
    "xticks_labels = [ \"{:.1f}\\nto\\n{:.1f}\".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]\n",
    "plt.xticks(xticks, labels = xticks_labels, fontsize=18)\n",
    "\n",
    "plt.subplot(212)\n",
    "\n",
    "plt.title('Score Count (W/O Stack Traces)', fontsize=25)\n",
    "plt.xlabel('\\nScore Range', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "\n",
    "n_bins = get_sturges_rule_number(len(df_status_wo_stack['Score']))\n",
    "\n",
    "n, bins, patches = plt.hist(df_status_wo_stack['Score'], \n",
    "                             bins=n_bins, \n",
    "                             log=True, \n",
    "                             align='right', \n",
    "                             color='orange', \n",
    "                             edgecolor='black')\n",
    "\n",
    "# define minor ticks and draw a grid with them\n",
    "minor_locator = AutoMinorLocator(2)\n",
    "plt.gca().xaxis.set_minor_locator(minor_locator)\n",
    "# plt.grid(which='minor', color='white', lw = 0.5)\n",
    "# x ticks\n",
    "xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]\n",
    "dist = (xticks[1] - xticks[0])/2\n",
    "xticks = [e+dist for e in xticks]\n",
    "xticks_labels = [ \"{:.1f}\\nto\\n{:.1f}\".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]\n",
    "plt.xticks(xticks, labels = xticks_labels, fontsize=18)\n",
    "\n",
    "plt.tight_layout(pad=4.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "beea8d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_1['Frequency'] = df_status_wo_stack['Score']\n",
    "df_1['Question Type'] = df_1['Question Type'].apply(lambda x: \"W/O Stack Traces\")\n",
    "\n",
    "df_2 = pd.DataFrame(columns=['Question Type', 'Frequency'])\n",
    "df_2['Frequency'] = df_status_w_stack['Score']\n",
    "df_2['Question Type'] = df_2['Question Type'].apply(lambda x: \"With Stack Traces\")\n",
    "\n",
    "result = pd.concat([df_1, df_2])\n",
    "result['Type'] = \"Question Type\"\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5), dpi=100)\n",
    "\n",
    "sns.violinplot(x=\"Type\", \n",
    "               y=\"Frequency\", \n",
    "               data=result, \n",
    "               hue=\"Question Type\", \n",
    "               palette=\"Set2\", #colorblind\n",
    "               split=True, \n",
    "               scale=\"count\",\n",
    "               inner=\"quartile\",\n",
    "               scale_hue=False,\n",
    "               width=.5)\n",
    "\n",
    "plt.ylim(-7,15)\n",
    "# plt.xlim(-1,1)\n",
    "\n",
    "fig.suptitle('Score Count')\n",
    "plt.gca().axes.get_xaxis().set_visible(False)\n",
    "# plt.plot(legend=None)\n",
    "plt.legend(loc='upper right', prop={\"size\":10})\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "53bc4c67-b180-4602-82e6-3f61eccc7af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result2, result_remove2 = CloConSeqGen_v3(dic_count, threshold=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "6f805fca-8d42-492a-b0c2-79b77ab8d46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x660 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# win_size_list_1 = []\n",
    "# for element in result:\n",
    "#     win_size_list_1.append(len(element[1]))\n",
    "# win_size_set_1 = set(win_size_list_1)\n",
    "# cnt_1=collections.Counter(win_size_list_1)\n",
    "# # print(\"cnt1:\", cnt_1, len(result), sum(cnt_1.values()))\n",
    "\n",
    "win_size_list_2 = []\n",
    "for element in result2:\n",
    "    win_size_list_2.append(len(element[1]))\n",
    "win_size_set_2 = set(win_size_list_2)\n",
    "cnt_2=collections.Counter(win_size_list_2)\n",
    "# print(\"cnt2:\", cnt_2, len(result2), sum(cnt_2.values()))\n",
    "\n",
    "# cnt_2 = dict(sorted(cnt_2.items()))\n",
    "# comulative_y = np.cumsum(list(counter.values()))\n",
    "\n",
    "x = np.array(sorted(cnt_2.keys())) # the label locations\n",
    "# x = [int(e) for e in x]\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "# for length in x:\n",
    "#     if not length in cnt_2.keys():\n",
    "#         cnt_2[length] = 0\n",
    "\n",
    "# y-axis\n",
    "# patters_num_1 = []\n",
    "# for key in sorted(cnt_1.keys()) :\n",
    "#     # print(key , \" :: \" , cnt_1[key])\n",
    "#     patters_num_1.append(cnt_1[key])\n",
    "\n",
    "patters_num_2 = []\n",
    "for key in sorted(cnt_2.keys()) :\n",
    "    # print(key , \" :: \" , cnt_1[key])\n",
    "    patters_num_2.append(cnt_2[key])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6), dpi=110)\n",
    "\n",
    "x = [str(item) for item in x] \n",
    "\n",
    "# rects1 = ax.bar(x - width/2, patters_num_1, width, label='Support Threshold = 1')\n",
    "rects2 = ax.bar(x, patters_num_2, width, label='Support Threshold = 2')\n",
    "\n",
    "ax.set_title('Hugging Face', fontname=\"Times New Roman\", fontsize=16)\n",
    "ax.legend(prop={'family':'Times New Roman', 'size':10}, facecolor='w', framealpha=1)\n",
    "\n",
    "ax.set_xlabel('Pattern\\'s length', fontname=\"Times New Roman\", fontsize=14)\n",
    "ax.set_xticks(x)\n",
    "\n",
    "ax.set_ylabel('The number of stack trace patterns', labelpad=10, fontname=\"Times New Roman\", fontsize=14)\n",
    "ax.set_yticks(range(0, max(patters_num_2)+1, 5))\n",
    "\n",
    "# ax.bar_label(rects1, padding=3)\n",
    "ax.bar_label(rects2, padding=3)\n",
    "\n",
    "win_size_list = []\n",
    "for element in result2:\n",
    "    win_size_list.append(len(element[1]))\n",
    "win_size_set = set(win_size_list)\n",
    "counter=collections.Counter(win_size_list)\n",
    "\n",
    "counter = dict(sorted(counter.items()))\n",
    "comulative_y = np.cumsum(list(counter.values()))\n",
    "\n",
    "per_list_patterns = []\n",
    "for element in comulative_y:\n",
    "    per_list_patterns.append((element*100)/comulative_y[-1])\n",
    "\n",
    "x2 = counter.keys() \n",
    "x2 = [str(item) for item in x2] \n",
    "    \n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(x2, per_list_patterns, '-o', color='green', linewidth=2)\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "ax2.set_ylabel('Cumulative % of stack trace patterns', labelpad=10, fontname=\"Times New Roman\", fontsize=14)\n",
    "\n",
    "ax2.grid(axis = 'y', color='green', linestyle='--', linewidth=0.5)\n",
    "# ax2.grid(False)\n",
    "ax.grid(False)\n",
    "\n",
    "fig.tight_layout()\n",
    "# plt.savefig('hug_le_rq1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "940b3043-8e11-4942-83e1-e7b3a6093a1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt_dic_hf_plength_rq1 = {\"x\":x, \"y2\":per_list_patterns, \"y1\": patters_num_2}\n",
    "pickle.dump(plt_dic_hf_plength_rq1, open(\"./Pickle_data/plt_dic_hf_plength_rq1.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "d103cadc-9fc3-40cb-b7a8-8f20235025fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x660 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
    "\n",
    "result2, result_remove2 = CloConSeqGen_v3(dic_count, threshold=2)\n",
    "      \n",
    "sorted_result2 = sorted(result2, key=lambda tup: tup[0])\n",
    "\n",
    "sup_x = sorted(set([element[0] for element in result2]))\n",
    "\n",
    "len_sup_y = []\n",
    "lst_supp = [y[0] for y in sorted_result2]\n",
    "\n",
    "cnt = collections.Counter(lst_supp)\n",
    "for item in sup_x:\n",
    "    len_sup_y.append(cnt[item])\n",
    "    \n",
    "comulative_y = np.cumsum(list(cnt.values()))\n",
    "\n",
    "per_len_sup_y = []\n",
    "for element in comulative_y:\n",
    "    per_len_sup_y.append((element*100)/comulative_y[-1])\n",
    "per_len_sup_y = [100-x for x in per_len_sup_y]\n",
    "per_len_sup_y.insert(0, 100)\n",
    "per_len_sup_y.pop()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6), dpi=110)\n",
    "\n",
    "sup_x = [str(item) for item in sup_x] \n",
    "\n",
    "rects = ax.bar(sup_x, len_sup_y, width, label='Support Threshold = 2')\n",
    "\n",
    "ax.set_title('Hugging Face', fontname=\"Times New Roman\", fontsize=16)\n",
    "ax.legend(prop={'family':'Times New Roman', 'size':10}, facecolor='w', framealpha=1)\n",
    "\n",
    "ax.set_xlabel('Support', fontname=\"Times New Roman\", fontsize=14)\n",
    "ax.set_xticks(sup_x)\n",
    "\n",
    "ax.set_ylabel('The number of stack trace patterns', labelpad=10, fontname=\"Times New Roman\", fontsize=14)\n",
    "ax.set_yticks(range(0, max(len_sup_y)+1, 3))\n",
    "\n",
    "ax.bar_label(rects, padding=3)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "plt.plot(sup_x, per_len_sup_y, '--o', color='orange', linewidth=2)\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "ax2.set_ylabel('Cumulative % of stack trace patterns', labelpad=10, fontname=\"Times New Roman\", fontsize=14)\n",
    "\n",
    "ax2.grid(axis = 'y', color='orange', linestyle='--', linewidth=0.5)\n",
    "# ax2.grid(False)\n",
    "ax.grid(False)\n",
    "\n",
    "fig.tight_layout()\n",
    "# plt.savefig('hug_sup_rq1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "18a0db9b-70d6-454e-a49a-5ada73ad6541",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt_dic_hf_sup_rq1 = {\"x\":sup_x, \"y2\":per_len_sup_y, \"y1\": len_sup_y}\n",
    "pickle.dump(plt_dic_hf_sup_rq1, open(\"./Pickle_data/plt_dic_hf_sup_rq1.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "a6d7a7f1-eb38-42a5-a1d4-5ea9cbe3ab28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dic_hug_win = {\"x\":list(counter.keys()), \"y\":per_list_patterns}\n",
    "# pickle.dump(dic_hug_win, open( \"./Pickle_data/dic_hug_win.p\", \"wb\" ) )"
   ]
  }
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