northamerica.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the dependencies\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_PATH = os.path.join(\"static\", \"data\", \"csv\")\n",
    "IMG_PATH = os.path.join(\"static\", \"img\")\n",
    "FILE_NAME = \"full_inventory.csv\"\n",
    "FILE_PATH = os.path.join(CSV_PATH, FILE_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dadra\\anaconda\\Anaconda3\\envs\\PythonData\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3020: DtypeWarning: Columns (10,11,13,18) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# Read in the inventory file\n",
    "# because 'NA' is the code for North America, we need to change pandas.read_csv default behavior\n",
    "# for reading missing values (na's)...\n",
    "\n",
    "df = pd.read_csv(FILE_PATH, keep_default_na=False, na_values=['', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
    "    '1.#IND', '1.#QNAN', 'N/A', 'NULL', 'NaN', 'n/a', 'nan',\n",
    "    'null'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North America    571534\n",
       "Oceania           62309\n",
       "Europe            23427\n",
       "Asia              12709\n",
       "South America      8525\n",
       "Africa             5109\n",
       "Antartica           500\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that these are not unique station counts - these are unique station + element counts.   \n",
    "# If a station records 10 meterological elements, it will be counted 10 times.\n",
    "df.continent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count            684293\n",
       "unique              219\n",
       "top       United States\n",
       "freq             503430\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "df.country.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North America    76073\n",
       "Oceania          17283\n",
       "Europe            6964\n",
       "South America     6506\n",
       "Asia              5717\n",
       "Africa            2116\n",
       "Antartica          107\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Removing the duplicated station ids gives us the number of distinct stations per continent in the archive\n",
    "df.drop_duplicates('station_id').continent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States    61613\n",
       "Australia        17088\n",
       "Canada            8780\n",
       "Brazil            5989\n",
       "Mexico            5249\n",
       "India             3807\n",
       "Sweden            1721\n",
       "South Africa      1166\n",
       "Germany           1123\n",
       "Russia            1123\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates('station_id').country.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine the Stations in North America"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(571534, 19)\n"
     ]
    },
    {
     "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>station_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>element</th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "      <th>years</th>\n",
       "      <th>elevation</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>gsn_flag</th>\n",
       "      <th>hcn_crn_flag</th>\n",
       "      <th>wmo_id</th>\n",
       "      <th>state_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>continent_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>PRCP</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>SNWD</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id  latitude  longitude element  first_year  last_year  years  \\\n",
       "0  ACW00011604   17.1167   -61.7833    TMAX        1949       1949      1   \n",
       "1  ACW00011604   17.1167   -61.7833    TMIN        1949       1949      1   \n",
       "2  ACW00011604   17.1167   -61.7833    PRCP        1949       1949      1   \n",
       "3  ACW00011604   17.1167   -61.7833    SNOW        1949       1949      1   \n",
       "4  ACW00011604   17.1167   -61.7833    SNWD        1949       1949      1   \n",
       "\n",
       "   elevation state                   name gsn_flag hcn_crn_flag  wmo_id  \\\n",
       "0       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "1       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "2       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "3       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "4       10.1   NaN  ST JOHNS COOLIDGE FLD      NaN          NaN     NaN   \n",
       "\n",
       "  state_name country_code              country continent_code      continent  \\\n",
       "0        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "1        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "2        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "3        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "4        NaN           AC  Antigua and Barbuda             NA  North America   \n",
       "\n",
       "  type  \n",
       "0    W  \n",
       "1    W  \n",
       "2    W  \n",
       "3    W  \n",
       "4    W  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_df = df.query(\"continent == 'North America'\")\n",
    "print(na_df.shape)\n",
    "na_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States                         0.809919\n",
       "Canada                                0.115415\n",
       "Mexico                                0.069000\n",
       "Puerto Rico [United States]           0.002774\n",
       "Greenland [Denmark]                   0.000907\n",
       "Virgin Islands [United States]        0.000565\n",
       "Bahamas, The                          0.000500\n",
       "Panama                                0.000131\n",
       "Honduras                              0.000105\n",
       "Nicaragua                             0.000079\n",
       "Cuba                                  0.000079\n",
       "Costa Rica                            0.000079\n",
       "Dominican Republic                    0.000066\n",
       "Trinidad and Tobago                   0.000053\n",
       "El Salvador                           0.000039\n",
       "Jamaica                               0.000039\n",
       "Guadeloupe [France]                   0.000039\n",
       "Antigua and Barbuda                   0.000026\n",
       "Saint Lucia                           0.000026\n",
       "Martinique [France]                   0.000026\n",
       "Bermuda [United Kingdom]              0.000026\n",
       "Guatemala                             0.000013\n",
       "Dominica                              0.000013\n",
       "Cayman Islands [United Kingdom]       0.000013\n",
       "Saint Pierre and Miquelon [France]    0.000013\n",
       "Barbados                              0.000013\n",
       "Belize                                0.000013\n",
       "Sint Maarten                          0.000013\n",
       "Curacao                               0.000013\n",
       "Name: country, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the distribution of stations among countries in North America?\n",
    "na_df.drop_duplicates('station_id').country.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_stations = na_df.drop_duplicates('station_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76073, 19)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_stations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>station_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>element</th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "      <th>years</th>\n",
       "      <th>elevation</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>gsn_flag</th>\n",
       "      <th>hcn_crn_flag</th>\n",
       "      <th>wmo_id</th>\n",
       "      <th>state_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>continent_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACW00011604</td>\n",
       "      <td>17.1167</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>1949</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS COOLIDGE FLD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ACW00011647</td>\n",
       "      <td>17.1333</td>\n",
       "      <td>-61.7833</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST JOHNS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AC</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62844</th>\n",
       "      <td>BB000078954</td>\n",
       "      <td>13.0670</td>\n",
       "      <td>-59.4830</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>1944</td>\n",
       "      <td>2018</td>\n",
       "      <td>75</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GRANTLEY ADAMS</td>\n",
       "      <td>GSN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78954.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BB</td>\n",
       "      <td>Barbados</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62902</th>\n",
       "      <td>BDM00078016</td>\n",
       "      <td>32.3667</td>\n",
       "      <td>-64.6833</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>1949</td>\n",
       "      <td>2019</td>\n",
       "      <td>71</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L F WADE INTL AP KINDLEY FLD</td>\n",
       "      <td>GSN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BD</td>\n",
       "      <td>Bermuda [United Kingdom]</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62932</th>\n",
       "      <td>BDW00013602</td>\n",
       "      <td>32.2667</td>\n",
       "      <td>-64.8500</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>1945</td>\n",
       "      <td>1965</td>\n",
       "      <td>21</td>\n",
       "      <td>13.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BERMUDA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BD</td>\n",
       "      <td>Bermuda [United Kingdom]</td>\n",
       "      <td>NA</td>\n",
       "      <td>North America</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        station_id  latitude  longitude element  first_year  last_year  years  \\\n",
       "0      ACW00011604   17.1167   -61.7833    TMAX        1949       1949      1   \n",
       "11     ACW00011647   17.1333   -61.7833    TMAX        1961       1961      1   \n",
       "62844  BB000078954   13.0670   -59.4830    TMAX        1944       2018     75   \n",
       "62902  BDM00078016   32.3667   -64.6833    TMAX        1949       2019     71   \n",
       "62932  BDW00013602   32.2667   -64.8500    TMAX        1945       1965     21   \n",
       "\n",
       "       elevation state                          name gsn_flag hcn_crn_flag  \\\n",
       "0           10.1   NaN         ST JOHNS COOLIDGE FLD      NaN          NaN   \n",
       "11          19.2   NaN                      ST JOHNS      NaN          NaN   \n",
       "62844       52.0   NaN                GRANTLEY ADAMS      GSN          NaN   \n",
       "62902        6.1   NaN  L F WADE INTL AP KINDLEY FLD      GSN          NaN   \n",
       "62932       13.1   NaN                       BERMUDA      NaN          NaN   \n",
       "\n",
       "        wmo_id state_name country_code                   country  \\\n",
       "0          NaN        NaN           AC       Antigua and Barbuda   \n",
       "11         NaN        NaN           AC       Antigua and Barbuda   \n",
       "62844  78954.0        NaN           BB                  Barbados   \n",
       "62902  78016.0        NaN           BD  Bermuda [United Kingdom]   \n",
       "62932      NaN        NaN           BD  Bermuda [United Kingdom]   \n",
       "\n",
       "      continent_code      continent type  \n",
       "0                 NA  North America    W  \n",
       "11                NA  North America    W  \n",
       "62844             NA  North America    0  \n",
       "62902             NA  North America    M  \n",
       "62932             NA  North America    W  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_prcp = na_df.query('element == \"PRCP\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74440, 19)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_prcp.shape  # Number of stations in North America reporting PRCP (at some point in time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74440, 19)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_prcp.drop_duplicates('station_id').shape  # sanity check!  no duplicate stations after filtering on elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consider the Different Types of Stations in North America"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    35986\n",
       "C    22667\n",
       "0     7931\n",
       "N     5170\n",
       "W     1794\n",
       "R     1509\n",
       "S      859\n",
       "M      107\n",
       "E       47\n",
       "P        3\n",
       "Name: ctype, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ctype'] = df.type.map(lambda x: str(x)) # type type column had issues with 1 and '1' being different - fix here\n",
    "na_df = df.query(\"continent == 'North America'\")\n",
    "na_stations = na_df.drop_duplicates('station_id')\n",
    "na_stations.ctype.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_station_types_by_year(inventory, element):\n",
    "    \n",
    "    def initialize_dict_from_array(arr):\n",
    "        return {item : 0 for item in arr}\n",
    "    \n",
    "    # range of years\n",
    "    min_year = inventory[inventory['element'] == element].first_year.min()\n",
    "    max_year = inventory[inventory['element'] == element].last_year.max()\n",
    "    sub_inventory = inventory[inventory['element'] == element]\n",
    "    \n",
    "    types = inventory.station_id.apply(lambda x: x[2]).unique()\n",
    "    \n",
    "    results_list = []\n",
    "    # Not the most efficient - but need to figure out why the above failed...\n",
    "    for year in range(min_year, max_year + 1):\n",
    "        results_dict = initialize_dict_from_array(types)\n",
    "        results_dict['year'] = year\n",
    "        \n",
    "        for row in sub_inventory.itertuples():\n",
    "            if ((row.first_year <= year) and (year <= row.last_year)):\n",
    "                # station was active in the given year:\n",
    "                station = row.station_id\n",
    "                \n",
    "                # get the station type:\n",
    "                station_type = row.station_id[2]\n",
    "                results_dict[station_type] += 1\n",
    "                \n",
    "        results_list.append(results_dict)\n",
    "        \n",
    "    return pd.DataFrame(results_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>C</th>\n",
       "      <th>E</th>\n",
       "      <th>M</th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>W</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1291</td>\n",
       "      <td>19259</td>\n",
       "      <td>6968</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>853</td>\n",
       "      <td>1165</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1227</td>\n",
       "      <td>19623</td>\n",
       "      <td>6816</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>850</td>\n",
       "      <td>1164</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1168</td>\n",
       "      <td>19798</td>\n",
       "      <td>6697</td>\n",
       "      <td>3</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>832</td>\n",
       "      <td>1159</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1125</td>\n",
       "      <td>19748</td>\n",
       "      <td>6498</td>\n",
       "      <td>3</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>770</td>\n",
       "      <td>1155</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1066</td>\n",
       "      <td>18134</td>\n",
       "      <td>6219</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>764</td>\n",
       "      <td>1150</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1     C  E   M  N  P  R    S     W  year\n",
       "179  1291  19259  6968  3  94  0  0  0  853  1165  2015\n",
       "180  1227  19623  6816  3  94  0  0  0  850  1164  2016\n",
       "181  1168  19798  6697  3  89  0  0  0  832  1159  2017\n",
       "182  1125  19748  6498  3  89  0  0  0  770  1155  2018\n",
       "183  1066  18134  6219  0  88  0  0  0  764  1150  2019"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_prcp_by_type = count_station_types_by_year(na_df, 'PRCP')\n",
    "na_prcp_by_type.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_station_counts (summary_df, xlabel, ylabel, types, colors, title, filename, img_path=IMG_PATH):\n",
    "    \"\"\" Plot a line plot for the number of active stations each year for each type:\n",
    "    \n",
    "    Args:\n",
    "        summmary_df: a DataFrame of counts versus year for each continent\n",
    "        types: a list of type names to be used in the plot.\n",
    "        colors: a dict of the form {\"continent\" : \"color\"} for plotting\n",
    "        xlabel: a string for the xlabel\n",
    "        ylabel: a string for the ylabel\n",
    "        title: a string for the title\n",
    "        filename: a file for saving the figure\n",
    "    \n",
    "    Returns:\n",
    "        the return values of the plt.subplots() function\n",
    "    \"\"\"\n",
    "    #sns.set(style=\"ticks\")\n",
    "    fig, ax = plt.subplots(figsize=(10,8))\n",
    "    plt.rcParams.update({'font.size': 14})\n",
    "\n",
    "    for stype in types:\n",
    "        counts = summary_df[stype]\n",
    "        \n",
    "        x_axis = summary_df.year\n",
    "        ax.plot(x_axis, counts,\\\n",
    "                #linestyle=\"--\", linewidth=0.5,\\\n",
    "                color=colors[stype],\\\n",
    "                #alpha=0.7,\\\n",
    "                label=stype)\n",
    "        \n",
    "    #ax.set_xlim(-5, len(counts)+10)\n",
    "    #ax.set_xlim(1781,2019)\n",
    "    \n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_title(title)\n",
    "    ax.grid(which='major')\n",
    "    ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "    \n",
    "    plt.legend(loc=\"best\", fontsize=\"medium\", fancybox=True)\n",
    "\n",
    "    #plt.show()\n",
    "    #labels =  [item.get_text() for item in ax.get_xticklabels()]\n",
    "    #print(labels)\n",
    "    #labels[-2] = '2021'\n",
    "    #print(labels)\n",
    "    #ax.set_xticklabels(labels)\n",
    "    \n",
    "    output_file = os.path.join(img_path, filename)\n",
    "    fig.savefig(output_file)\n",
    "    plt.show()\n",
    "\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_color_dict = {'W': 'blue', '0': 'orange', 'M': 'green', '1': 'red', 'E': 'purple', 'N': 'brown', 'C': 'black', 'P': 'pink', 'R': 'yellow', 'S': 'magenta'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_types = na_df.ctype.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_station_counts(na_prcp_by_type,\\\n",
    "                    \"Year\", \"Number of Stations\",\\\n",
    "                    station_types, station_color_dict,\\\n",
    "                    \"Number of Active Stations Measuring Daily Precipitation By Year\",\\\n",
    "                    \"type_prcp_by_year.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "na_tmin = count_station_types_by_year(na_df, \"TMIN\")\n",
    "fig, ax = plot_station_counts(na_tmin,\\\n",
    "                    \"Year\", \"Number of Stations\",\\\n",
    "                    station_types, station_color_dict,\\\n",
    "                    \"Number of Active Stations Measuring Daily Min Temp By Year\",\\\n",
    "                    \"type_tmin_by_year.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>C</th>\n",
       "      <th>E</th>\n",
       "      <th>M</th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>W</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1287</td>\n",
       "      <td>0</td>\n",
       "      <td>5231</td>\n",
       "      <td>17</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1292</td>\n",
       "      <td>853</td>\n",
       "      <td>1156</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1237</td>\n",
       "      <td>0</td>\n",
       "      <td>5107</td>\n",
       "      <td>17</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1281</td>\n",
       "      <td>849</td>\n",
       "      <td>1156</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1185</td>\n",
       "      <td>0</td>\n",
       "      <td>5011</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1272</td>\n",
       "      <td>832</td>\n",
       "      <td>1152</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1145</td>\n",
       "      <td>0</td>\n",
       "      <td>4873</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1259</td>\n",
       "      <td>770</td>\n",
       "      <td>1149</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1091</td>\n",
       "      <td>0</td>\n",
       "      <td>4706</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1234</td>\n",
       "      <td>764</td>\n",
       "      <td>1144</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  1     C   E   M  N  P     R    S     W  year\n",
       "175  1287  0  5231  17  96  0  0  1292  853  1156  2015\n",
       "176  1237  0  5107  17  95  0  0  1281  849  1156  2016\n",
       "177  1185  0  5011  17  90  0  0  1272  832  1152  2017\n",
       "178  1145  0  4873  17  90  0  0  1259  770  1149  2018\n",
       "179  1091  0  4706   2  88  0  0  1234  764  1144  2019"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_tmin.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "na_tmax = count_station_types_by_year(na_df, \"TMAX\")\n",
    "fig, ax = plot_station_counts(na_tmax,\\\n",
    "                    \"Year\", \"Number of Stations\",\\\n",
    "                    station_types, station_color_dict,\\\n",
    "                    \"Number of Active Stations Measuring Daily Max Temp By Year\",\\\n",
    "                    \"type_tmax_by_year.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>C</th>\n",
       "      <th>E</th>\n",
       "      <th>M</th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>W</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1288</td>\n",
       "      <td>0</td>\n",
       "      <td>5231</td>\n",
       "      <td>17</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1292</td>\n",
       "      <td>853</td>\n",
       "      <td>1156</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1236</td>\n",
       "      <td>0</td>\n",
       "      <td>5108</td>\n",
       "      <td>17</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1281</td>\n",
       "      <td>849</td>\n",
       "      <td>1156</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1186</td>\n",
       "      <td>0</td>\n",
       "      <td>5012</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1272</td>\n",
       "      <td>832</td>\n",
       "      <td>1152</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1145</td>\n",
       "      <td>0</td>\n",
       "      <td>4875</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1259</td>\n",
       "      <td>769</td>\n",
       "      <td>1149</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1089</td>\n",
       "      <td>0</td>\n",
       "      <td>4708</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1234</td>\n",
       "      <td>763</td>\n",
       "      <td>1144</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  1     C   E   M  N  P     R    S     W  year\n",
       "175  1288  0  5231  17  96  0  0  1292  853  1156  2015\n",
       "176  1236  0  5108  17  95  0  0  1281  849  1156  2016\n",
       "177  1186  0  5012  17  90  0  0  1272  832  1152  2017\n",
       "178  1145  0  4875  17  90  0  0  1259  769  1149  2018\n",
       "179  1089  0  4708   2  88  0  0  1234  763  1144  2019"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_tmax.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "na_snow = count_station_types_by_year(na_df, \"SNOW\")\n",
    "fig, ax = plot_station_counts(na_snow,\\\n",
    "                    \"Year\", \"Number of Stations\",\\\n",
    "                    station_types, station_color_dict,\\\n",
    "                    \"Number of Active Stations Measuring Daily Snowfall By Year\",\\\n",
    "                    \"type_snow_by_year.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoQAAAH8CAYAAABILr5+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xl4E+X2wPHvaaELlNWyWUEEN5RNFpVFKCAqLr2gqAgqoOB1QUVEFvUqKioqKIgiKCK4sekVAf2BbBUXEAFBlEUUuSirIAJtgZb2/f3xTkqapm3aJk1ozud5+qSZmcycSSaTM+82YoxBKaWUUkqFr4hgB6CUUkoppYJLE0KllFJKqTCnCaFSSimlVJjThFAppZRSKsxpQqiUUkopFeY0IVRKKaWUCnOaEIYxETEiMjHYcfhKROqLyP+JyEEn9j7BjglARKaKyPZgxxFMzucxIthxqJNEJFlEkoMdh7+JSKJzvCW6TRshIjqG2ilIRPo4n+elwY4l3GlCGEBuB/pxEantZf6CcE8kCmky0BIYAdwGLPflRSLym/M53FbUDYtIQ+dHp25R1xEIIlJNRF4Rkc0ikiYi+0VkrYiME5HT3Za7trgJm4jcKiIDix10CXNLIIyI9M1jmVnO/GMlHV9p5Hbuc/0dE5FdIrJQRB4QkQrBjrEgItLIOS62u8X/5al24eNcGLg+hywROSwiW0TkPRHpXIJxPC4iXQOw3kSPY804hQarRaS/iIgftiHOZ39ARKp7mV/eOU5+EpGyxd1esJQJdgBhIgp4FLgn2IGcqkQkErgMeM0YM64Qr2sD1AO2A7cC7xUxhIbAk0Cysy53/QnCxZWIVAXWAJWBqcBPQCWgMXAH8Amwy1n8WuDf2GS6qG4FzgfGepkXC5woxrpLwjGgF/CO+0QRqQhc58wv9o9HCLki2AFgj7ffgLJATSARe/wMEpEkY8yPRVjncuzxlu6nGHNxzhtLgd3Y79afwOnApcDjFO97FAy7gSHO/+WBs4HrgVtFZBZwqzEmI8AxPA7MAOYEaP2vAyud/6sC3YE3gXjg+eKs2BhjROQuYD3wMvZc6G4EUAdoWwLvY8BoQlgy1gF3iMhzxpg/gh1MSXKuzqKNMcUteTkNiAT+KeTrbgW2Yk8Ib4lITWPMnmLGkkMQTwB3ArWBTsaYpe4zRKQ8Jfj99sPnWxI+A7qJyOnGmF1u02/AJvQLCY0kqlhEJNYYc9QYE7CEqRAWGmNWuj1/XkQ6AvOBuSLSwBhztDArNMZkYZP3QHoUSAVaGGP2u89wL3k/hRw2xrzvPkFEhgGvAvcC/+Nkwniq+toYM8P1REQmAL8At1DMhBDAGLNFRJ4DnhKRd40xXzjbaQIMBN4wxnxb3O0Uhuu77q/1aZVxyXAdjI/mt5CI1M2rbZxTHD3V7bmrSqa9iLwsIvtE5JCITBGRGKcIe5JThXhIRF4XEa8JgojcLCIbnWqRH0XkGi/LVBSR0U4cx0Vkh4iMEpFoj+WMiEwUkRtF5EfgONCjgP1uKyJLRSRFRI6IyCIRucRt/ghgr/P0SVe1QH7rdF4XBdwETAf+iy3BuiWPZWs579efzv5tF5G3RKSC83lMdxZd5lYt0cd5bY42hE61wVd5bCfXPBHpISLfia3yPSQi80TkgoL2D6gPGLxUnRtjUo0xh1zxYUsHkZzVKnWdaX1EZLGI7HH2/RcRGSoi2ecHsW3RrgTOdF+H2/xcbQhFpI6IfOAcg8dEZJ2I3O6xjOuYHyYit4ut+j7uHIeXeywb5xyDvzvr2+scN4k+vFdgk5AUch8DvYD/A/729iIR6Swiy5xjM9X5v7XHMmeKyAQ5WXX/j/M5XuhlffeKyAZnXX+LyBoRudttvtc2qW7f+bpu07aLbXrSQURWiq3yHurMy9GGsDDvtbN8W+e4PCYi/xORISLS1zOGwnIuXp4BzsStpEVEGjv7/puzzb9EZLp4NLcRL20IvcTuOu5yVd+JyGznWM/vgqk+8LNnMujE734x4f4ZXCwiX4vIURH5Q7w0rxCRqiLyhojsdt77TSIySORktaaI/FdENnq87j1nn+9wm1bDmXY3RWCMyQQeADYBA0Skksc2CzwvOZ/XMef4/0zsOXyviLzo/t4754pooLecPH8ke4RUVkSec96boyLyhYicVZR9c/bvBHAAyL5g98NxMQrYCLwhIrFiz5GTsL9Pwz3WV0VExor9rUx3jpORntsWW629zHnfjjvfyxzHhLPcSrHn0GZiq69TgVcK+bbkS0sIS8YObLVDIEoJxwJ/AU8BzYG+2Cvb+kAa8B+gI/YqcCu5q/vaYJOmV4EjwF3AHBHpZIxZDvYqBFtVeha2CP43oCnwMLYK0bNdyGXY4vrXgD3A5ryCF5F2wCJslcxI7EXK3cCXItLeGPMdNpn7y1nfJ85zX1yNrTqYYYw5JCKfY3+AcnyJRKQmsAqoBryFrXqtBXTDlkwux1ZH3Ac8hz2BAuR1NTgDeFpEzjDG/Om2nQuBC4EBbtOGYk8y/wXeBeKwn9U3ItLMGPN7Pvu3HVvF2Rt4O5/lJmFLEjti2166/OU8DgC2AAuwx0xnJ6ZKnLyIeRaogq02eyifbbn2Kx74Bvv+j8dWXfcApolIFS/V/t2xVTuTgKPYK+5PRORMY4wrUXsDe6y+DvzsxHMJ9lhMLigmbKnSx9gEcIwTZy2ggxObtwuhW4D3gWXYKq8IbHX8UrfjE2zb1vbYz3E79n26G1guIhe6SqVF5E4n/o+xx3NZ7DHRFihqB6+znfVNBqZgzzf5KfC9Flvq8QX2B/UZbPVsf+y5xR/ew36XrsB+58Aed+dj3+8/nf26G2gpIo0KWRIyFegJdAHmuiY6Sc+1wEQnYcjLdqCNiDQxxqz3YXtnAfOc7X4A3Ay8IiIb3UqSorHV0A2xx/Jm7DE3Bvv9dH2vlmNLsqsZY1zf0XZAlvM4xZnW3nn0evHpC2NMpoh8iP2M22JL0Qt7XorAnjvWYS9GEoFHsE1Z7nKWuc2JeyX2NwROXuS7vII9Hp/HHp+Dse9la3xTwTnvgD033AS0AB50W2YqxTgujDHpItIf+Bp4AvgDew7qZow57La+OOzneDr2e7Yd+/s8HHtcuxeS3I+tip6PPUddhT0m4oCnPUKohn2vZ2Lfm1wXLMVijNG/AP0BfbAlOJdi2xccxxYru+YvALa7Pa/rLN/Hy7q2A1O9rHsRIG7Tl2JPHO95vH4jsMFjmnH+WrtNOw1bUvKt27RHsYlCA4/X3+u8/jKPdWYBTX18j1Zjf3SquU1LwCanX7tNq+mse0Qh3v+PgPVuz2901uG5H1OdmC/zsg5xHns4r030ssxUj8/RVXL3sMdyI7GllDWc53WwV68jPJarha0af7uA/asG7HO29Qv2RHs7EO9l2Yn26+51PeW8TJuMLU2Lzut49XIsjXB7PtqZ1sltWhTwnbPeSh7H/EGPY6CpM/0+t2kHsW1IC/s9THTW1QPo5Px/gTPvYeAQEON8jsfcXlfe+S5M9VhfOeB3YEkB7+HZ2BP8Y27TPgF+KiDeHMeT2/Q+Tux13aZtd6YleVk+GUh2e16Y9/pTJ/Yz3abFO+9Hjhjy2AdXrJfms8w/wNoC3sO2znp6efk8E92mjXA/vrFJyh/ALI/13em8tlkB8XcEMp2/75zj+Rogxsuyrs/gcrdp0diEZ7bbtAHOcne6TRNs0pUFnONMa+Ysd4PzvI7zfCbwm9trX8MmBFLAviQDm/OZ39VZ/wNu2/PpvOQcqwaY5LHsNGefznObdgyP75LHsfIlEOE2faAz/UIfv9+ef1nAUx7LFuu4cFt+gvMeHQY+9jL/aexv2Nke0wc522lZwHH/PvZ7Guk2baXz2rt8ibEof1plXEKMMe6lhLl6HBfDFOMcLY7vsCcZzxKj77CJiqfVxq3dgzHmAPAh0EpEqjiTb8KW9vwlIvGuP2CxM7+jxzq/NcasKyhwp2SuOTDNnLwSxhiz04mhtVsMhSIilbFXfDPcJs/Dfkndq6kisCWBC4wxua60Pd5bnxhjfsMmujd7zLoZWGaMcV0ZX48tpZ/h8b5mYD8vz/fVczt/Ya+AJwIVsCU404A9YnseR/kYbxrYjjtONUc89kekPHCeL+vw4lrgB2PMErftpGNLqMtjT+LuZnscA+uwJ9t6bsscBi4WkYQixgS2pG8XtpQQ5/G/xnsbyM7YkoYPPD6fcthj/zJX9Y/rPQQQkXIichr2x/MX7DHuvg9niMjFxdgHT38aY+YWvFi2fN9rsR24OgNzjTH/c1tuP/Z76S8p2OPWtX739zDOeQ83Y9/H5rlfnjdj2xm+D1znURV6G7DRGLO2gNcvxdZ0zAMuwF44zAf2ivee6luNMYvdXn8c+wPufvxei03gprotZ4CXsOfsq53J67GfRzvneTvseetVoJ7b8d8Oe9Fc6HOUhxTn0fVZFOW89KrH8/HYfepSiDgmOZ+by5fOYz1vC3vxLPa47Yw9174PPCFuVffFPS7cDMNJxrElfJ5uwp5D//F4Dxc587PfwzzOv8uwJayev9lH8egU50+aEJasZ53HfNsSFpJn9dAh59GzWvoQECsebf6w1ciefnEe6ziP5wKXY6sY3f+2OPM9u+H/VnDYgC2xAO9VyhuxX7Y6Xub54kbsVfpKsW2n6mJLGZOBXm7tM6oBFYENRdxOXmZgq7pcP7LNsSVG7gnquc7jJnK/t1eQ+33NxRizwxhzjzGmFvbk8W/s+z8QeMyXQMW2FVuOLQX+29m+qzd2ZV/W4UVd8v5cXfPd/Y/cDmKrnF2GAo2AHWKHlBgpIg0KE5TzgzAD6Cm2PdRF2KoXb1yfzxfk/nz6Yat7KwOIbbf7oojswlar7neWa0TO9/AF7A/wd2Lbyk0U28miOLYVcvmC3uvq2F68+Z0b/CEOm+gA2W2uJonIAWe66z2sTNGOw2nYkt/uzvprY5Mon0YaMMZ8a4zp6my7GbaK0ABTvHxmvhy/dbGJY6bHcjm+E878bzmZEF7mPF+F/Y5e5lwoN8THobcKEOc8uj6Lwp6XDPCrxzTXcXJmIeLwfA8POo9VPRfMw0/GmMXO3yxjzO3YauHnJedQMcU6LgCMrR7eAuw0uduUCvZcfy253z9Xr/rqbst3EJGvscme6/w72Zntedz/YQLYiVHbEJYgY8wOsQ387xDbWynXIvm8PDKP6Z4nl4Kmew6r4W2bnstEYKui8+qp9afHc3/0eiru8B+uUsClecxvi21749pOca+yPc3EXvn3wLaV6oG9wnZv/+i6IOuC9yFb8voMvTLGbAPeFJGPsEnhbdihcvLkJKyLsD/+A7EXGMexP4Av4P+Lxrw+1wKPV2PMDBH5EkjC/jA9AAwVkb7GowdlAT7AVt28hh2OY1key7n2vQ+wM49lXBdg47BJ4nhsafohbJXVWLf1YIzZKCLnYT/zK3GGAxKRScYYV+eAvI7FvM4Bhf2++XpuKOoyBa9E5AxsG1X3RGIG9od5NPADNkExzvRCH4fGmM0isgp7Lnibk6XCeV0A5LWeTCeeH0TkG2CJs073c0tx3lNvvgKecUqx2mGbAGWIyErneZqzbn8khA2dR9dn4dfzUiH4+z0E+xklYdv4fgb+Oy7yIZxsVzkmj2V2ADjnggXYNtEPYAtyjmObmT1D7uPebz2KvdGEsOQ9i/2B8VZK6Go8n+OqwCnVqxWgeM71Mu0c59FV+vgbUMG9SsRPtjuP53uZdz72x6CgBvK5iEgd7FX1BE4W0bt7C3sy+ArbBu8wtiQnP4VKGI0xfzpXfTeLyPPYKoSFxpiDbou5SlJ3GGM25lpJERlj/haR3zh5ooe840/CXi1f5149KN579xXmPdhO3p+ra36hGWN2YxtpT3KaBazEJr0+J4TGmLUishnbmeQVLyU2Lq7P5y8fjv2bgXeNMTl6ljolOTkafhtjUrHtWz8S26NxGjYpfMZpLnEQ7yVidQuIwV/2YX94zvEyz9u0onB1bnJ1uKiMTfJHGGOeci0kIjHYavuimga85pQC3YptU1mcTn2rnMeiDD2zHWgmIpEex1wDt/kuy7HJQDfsd2a52/Tu2IQwBduRo8ic5gE9nfV940wu7HnJVSL2s9s01++Ke6mfvy+6feHq0RvnMd3fx0U2Y0yWiPwOlPfhvNEN27a6i1tTIgpb8+EvWmVcwtzbEmJ7lrnPO4ItLu7g8bK7ybt0oLhaiEgr1xOn3U5PYKVb8uKq/kzyfLHYrveeXzafGNvzcg1wu1vvMNc4X72wbREP5vX6fNyKPUm9bIyZ4/kHfA7cKCJRThXiJ0AX8RhKxInFdXXq6l1ZmB+n6dhBou/EVn3P8Jj/EfYK/ClxG+LFbdvV8lu5iFwqXu74ICJnYts9uVfZpjrzPON3/TC5D3sRjVtPaI91+Fp1Nx+4SESyj2Wnvd2D2B+fZB/X43ptpEebH4wx/2A7dxQlYRiE7Zn/Rj7LLMC2X3vcS1MLz88nE4+SDLE9lE/3mHaa+3NjezS6qpFc+/ErUElELnJ7XRy2N3nAOcnKIiDJOZZcMcRjzw3F4lS3/gf72bkSeVfbMc/SoIco3u/UDGzJ/Ghsb+53fY3R23eSk+388hw5IR/zsU1Usodecs4vg7HJ0uduy67CdsIY5jx+70z/CrsfSdjzY5EHg3eSwVexCemrxhmmiqKdlx7weO5qV7fAbVoqxUvui8L1eXkOgF6k46IQZmCr9nONa+q0MS7nPHWdf92H+IrFdtgscVpCGByuUsILyN1uYhL2B+gdbOlHC2zPSP92Lz/pJ2C+iIzn5LAzFck5ppKrh91/ReR97MkqGtvh4Cbsl8598NnCGIRtoL9SRN7C/iDcg72yG1zEdfbCNhDOqy3jPOxJ+RpsMjgc2xB5iYi8ib3SrYFtXN0Ne+W+FvujNdwpzTgKfGfyHxbmI+wJ92Vn+U/dZxpjfheRIc78lSLyX2wp8ZnY9/Q77MVAXm4FbhORT7CJdRq2HWFf7OfjXl282nl8TUT+D3vCn4cdjDkdewxMcl53Gyd/oPFYxw0iMs6JLcu4DQTrYRS2mnyeiLyKrZq9GVsV8pDbj4+vKgA7ReRjTja6b4MdouH1Qq4LY8z/YccezG+ZI2LvTjAdWC8iH2CHUToDe9GWyslG83OxFzaHsd+pptj99Wzf94WI7MMOW7EHW7JyP7YNq6s0Zjr2/fvEea/LYi8g9+FxERlAT2KrtL8WkTewP579sd+FKvhe2nOliJyN/a2pgW1M3xl73ktydeYxxhwWOy7dELGdof6HbdbRHjsKQZE4peXzsOepo9jheXzxKhDnfLc2YX+wm2G/GwfwfreegkzGNit4U0SaYtvZXe38jTPGZLfZNHZ4k1XY6uEvnU4qACuw391zKFwSU1FEXM1oynHyTiX1scnLf9y2XdjzUgbQTuzwNV9jvxvdsR0e3RPn1cDlIjIY28xon/EYUL+Y2srJMQSrYO8+1AE77Ngm9wWLcVz46jnsuekzEZmGPT/HYkt7b3TiWoc9Bz0H/J/z+1cOmxsEZ6D//Log61/x/shn6AWcYUDwGF4CW333BvYLmIpt9+C69drUgtaNvaLMNSwE9gRmcBs2wXk+EfvDtQnbduEnbPWhZ7zlsEM7bHaWO4C9an0CqOy5zkK+T5dh23GlYqtBFnvZL5+GneHkkA3P57NMBWcfPnabdga299ZeZ97v2OQ8zm2ZO7Ft7U7gNjwQeQwT4sxb4Cw7O594rnH2/zA2qdvqxNKygH1thG3ntxpbspzhxP8p0M5j2UhsO7c92GQv+xjBJjU/YE+Mf3Cyt57n0B7lsT9CB1zr8PjcR3hssw62V+oB7AluPXC7xzJ1ndcO87J/23GOeWy1yotOnIecY+UnbO/PMgW8T4nONnoUsNxU3IadcZveGlu687ezH79jf0TdhxmpiB32Z68T25fYdkvJ5Bz65S5n2l9ux9mrQHWPbXbE/mCkO8s8SN7DzizIY388t+3Te+02rR324u84J+9kcb+zjhoFvJeuWF1/x7EXBV9gS5MqeHlNLWCWc7wcdt7zsz1jw4dhZzzWe52z/If5xezxmquwTUs2OrG4PqvJwFle3rtcnwFezgvYYb0mYr+H6diOCQ/jZegYbBsyAzztMX2FM72dj/uS7PFZHMEmo+8BnfN5XYHnJWcfj2GTxc+w5++/sIUIUR7rOw/bpi/FiSPZ41jxPOe7jtc+Pn6/PY+3TdimWWX9dVx4eV/zG84nDjvU2C9OPPuxyfTj7sc/trR3Pfb8+z/nWL7W8z3BFrqsK0qsvv65xlhTSiml8uWUWN6FvVAKVOcCvxKRK7EXZlcbWzKs/MTpJNnDGBMT7FgKS4+L3LQNoVJKqVyctkzuz6thq0yXnyrJoKM/J0snlXLR48KDtiFUSinlzXanzfAWbOeYfni/nVZIEpEe2Hba1wPDT7EkVgWIHhd504RQKaWUN59hfzRrYtvNrsbeQu6bfF8VOqZj23O+h8f9y1VY0+MiD9qGUCmllFIqzGkbQqWUUkqpMKdVxoUUHx9v6tatG+wwAEhNTaV8+fLBDqPEhet+g+57OO57uO436L6H476H635D4PZ9zZo1+40x+d7oADQhLLS6deuyevXqghcsAcnJySQmJgY7jBIXrvsNuu/huO/hut+g+x6O+x6u+w2B23cR8bwBhldaZayUUkopFeY0IVRKKaWUCnOaECqllFJKhTlNCJVSSimlwpwmhEoppZRSYU57GfvZ4cOH2bdvHxkZGQHfVqVKldi0aVPAt+OL8uXLc8YZZxARodcYSiml1KlGE0I/Onz4MHv37iUhIYHY2FhEJKDbO3LkCBUqVAjoNnyRlZXFzp072b9/P9WrVw92OEoppZQqpBItzhGRdiIyV0R2iogRkT4e80VERojILhE5KiLJInKhxzJVROQ9ETnk/L0nIpU9lmkkIl8669gpIk+IR3YmIjeIyEYROe48divu/u3bt4+EhATKlSsX8GQwlERERFCjRg0OHToU7FCUUkopVQQlXb8XB/wEPAgc9TJ/CPAwcD/QEtgHLBIR92KwD4FmQBfgKuf/91wzRaQisAjY66zjAeARYJDbMq2AmcAHQFPncbaIXFKcncvIyCA2NrY4qzhllS1blhMnTgQ7DKWUUkoVQYlWGRtjPgc+BxCRqe7znBK8gcAoY8zHzrTe2KSwJzBJRBpgk8C2xphvnWX+DXwlIucZY7YAvYByQG9jzFHgJ+d1g0TkZWOMcbazzBjzrLP5Z0WkgzP9luLsYziVDLoL1/1WSimlSoNQ6gFwFlAT+MI1wUnolgOtnUmtgBTgW7fXfQOkeizzlfNal4XA6UBdt2W+IKeFbutQSimllAobodSppKbzuNdj+l4gwW2Zv5xSPgCMMUZE9rm9vibwp5d1uOb97jx6205NvBCRu4C7AGrUqEFycrLXHahUqRJHjhzxOi8QMjMzS3R7BTl27Fie740/paSklMh2QpHue3Kwwyhx4brfoPsejvservsNwd/3UEoIXYzHc/GY5jnfl2XEy/SCtnNyQWPeBN4EaNGihcnr5tObNm0q0V6//uhlPHHiRB5++GEOHjxIVFQUAOnp6VSuXJn69euzYcOG7GW3bt3Kueeey5IlS+jYsWOudcXExHDRRRcVKx5f6M3PE4MdRlCE676H636D7ns47nu47jcEf99Dqcp4j/PoWUpXnZOleXuA6u49hp3/q3ks420d+LCMZ6lhqdexY0fS0tJYtWpV9rTvvvuOSpUq8csvv/DXX39lT09OTiY6OprWrbVmXSmllCpNQikh/B2bqHV2TRCRGOAyTrYZXIHtqdzK7XWtgPIey1zmvNalM7AL2O62TGdy6kzOtolh4dxzz+X0009n2bJl2dOWLVvG5ZdfTosWLXIUXy9btoxWrVoRExPjZU1KKaWUOlWV9DiEcSLSVESaOtuu4zyv47QLHAsME5HrRaQhMBXbieRDAGPMJmABtsfxpc7wMZOA+U4PY5xl04CpItJQRK4HhgEvu7U9HAd0FJHhInK+iAwHOjjbDzsdOnTIlRAmJiaSmJiYY3pycjIdOnQIRohKKaWUCqCSbkPYAljm9vwp528a0Ad4EYgFXgeqAN8BVxhj3HtO9AJe5WQv4bnAANdMY8whEensrGM1cBAYA7zstsy3ItIDGOls/zfgZmPMd/7aUZeBA2HdOn+v1crMjCUyMvf0pk1hbCFS2w4dOjBgwACOHz+OMYaVK1cyefJkateuzYMPPgjA5s2b2b17t9e2g0oppVRYMMb+lcLbtJb0OITJnOzg4W2+AUY4f3kt8zdwawHb2QC0K2CZj4CP8lsmXHTo0IFjx46xYsUKjDHEx8dTv359atasyW+//caePXtYtmwZ5cqV45JLijV2t1JKKXXquu46WLUKrr0WunaFzp2hlNyQIhR7GZcqhSmpK6wjR476pVdzvXr1OPPMM0lOTsYYk93LqXz58jRv3pzk5GSSk5Np27YtZcuWLfb2lFJKqVPSDz9A2bLw3//CO+9Ax46wZEmwo/KL0lfmqYrE1Y7Q1X7QJTExkaVLl5KcnKzVxUoppcKXMbB/P9x2G+zbB337wrffQim5basmhAqwCeHKlSv57rvvciSE7du3Z8aMGezbt087lCillApfqamQng6nnQZRUZCYCMeOwdatwY7MLzQhVIBNCNPT06levTr169fPnt62bVuOHj1KxYoVad68eRAjVEqVtJ07d+J2Yyilwtv+/fYxPt4+NmliHwPVc7SEaUKoAKhduzbGGP74448c0+Pi4sjIyODQoUNEeuvSrJQqdXbt2kWvXr0444wzePLJJ4MdjlKhwTMhbNDAtidcvz54MfmRJoRKKaUAMMYwevRozjvvPD766COaNm3Kc889xw8//BDs0JQKPldCeNpp9jEqCi64QEsIlVJKlS7Lly/nkUceoV27dmzcuJElS5ZQrVo1+vbtS3p6erDDUyq4Dhywj64SQrAD/2oJoVJKqdJk7dq1AEydOpX69etTtWpVJk2axPr16xk1alSQo1MqyDyrjMEmhHv2wN69wYnJjzQhVEopBcCPP/5IzZo1qVatWvY8/+QzAAAgAElEQVS0pKQkevbsyciRI9mwYUMQo1MqyPbvt3coqVz55DRXx5JSUEqoCaFSSinAJoSNGzfONf3VV1+lQoUKPPPMM0GISqkQceAAVK2a87Z1painsSaESimlOHHiBD///LPXhPC0007j9ttvZ86cOex3VZspFW72789ZXQw2QaxdWxNCpZRSpcPWrVs5fvy414QQ4I477iAjI4MPPvig0OvOyMjgo48+Yvr06cyePZsFCxaQkZFR3JCVKlneEkLIv2PJ0aMwadIpcTcTTQiVUkrx448/AuSZEDZq1IiWLVvy9ttvF3qw6pdffpkbb7yRnj17ctNNN9GlSxd69uzJiVPgR1KpbPklhJs32+TP04cfwt13w//9X+DjKyZNCJVSSrF+/XrKlCnD+eefn+cyd955Jxs2bGD16tU+r/fgwYOMGjWKq666io0bN7Jhwwaef/55PvroI3r37k1mZqY/wlcq8A4cODkGobsmTSArC37+Ofe85cvt48KFgY3ND8oEOwCllFLB9+OPP3L++ecTHR2d5zI9evTgoYceYsqUKbRs2dKn9b744oscOnSIF154gQYNGgDQsGFDAIYPH05UVBRvv/02ERFaPqFCmDH5lxCCbUfYokXOea6EcMGCwMbnB/oNVNkmTJjAWWedRUxMDM2bN+err74KdkhKqRKSVw9jd5UqVaJ79+58+OGHpKWlFbjO3bt3M27cOHr27Jlr3cOGDWPEiBFMnTqVxo0b89prr3Ho0KFi7YNSAZOSAunp3hPCs86CuLjc7Qj//BO2b4dzz4XffrN/IUwTQgXAzJkzefDBB3n00Uf54YcfaN26NV26dGHHjh3BDk0pFWAHDx7kjz/+KDAhBFttfPjwYT7++OMCl33mmWfIyMjg6aef9jr/iSee4P333yc2Npb777+fhIQEJk2aVOj4lQo4b4NSu0RE2Gpjz57GrkKVp56yjyFebawJoQJso+8+ffrQv39/GjRowPjx46lVqxZvvPFGsENTSgWYa8BpXxLCdu3acd555zF06FC2bt2a53K//vorb731FnfddRf16tXzuoyI0KtXL77//ntWrVrFpZdeyr333svCEP/hVGHIdds6b20IAZo1g7VrbUmiy1dfQYUK0L27LUUM8eNaE0JFeno6a9as4Yorrsgx/YorruDbb78NUlRKqZJSUA9jdyLCRx99REZGBh07duT333/PtUx6ejq33347MTExPP744z7F0LJlS+bMmcOFF15Ijx49+OWXXwp8zfHjx1mzZo1P61eqWPIrIQTo0QPS0mD69JPTli+H1q2hTBm48kpYutRWO4co7VQSaGsGwsHADFgZm5kJkZG5Z1RpCs3H+rye/fv3k5mZSY0aNXJMr1GjBosXLy5umEqpEPfjjz9StWpVTj/9dJ+Wb9iwIYsXL6ZDhw507NiRL7/8kjp16mTPHzJkCCtWrGDmzJnUqlXL5zji4uKYO3cuLVu2JCkpidGjR+e57IkTJ+jevTvz58/n2Wef5dFHH/V5O0oVWkEJYatW0LChHXOwf39bovjzz3DLLXb+lVfCxImwYgW0b18yMReSlhCqbCKS47kxJtc0pVTp4+pQUpjve5MmTVi0aBEHDx6kefPmvP7662RkZDBr1izGjRvHgw8+yE033VToWOrWrctHH33Eb7/9xlNPPcXx48dzLWOM4Z577mH+/Pm0aNGCxx57jBdeeKHQ21LKZ66EMK8qYxH4979hzRr79803dnq7dvaxY0dbUhjC1cZaQhhohSipK6yjR45QoUKFYq8nPj6eyMhI9uzZk2P6vn37cpUaKqVKl6ysLDZs2EC/fv0K/VrXaAQPPPAAAwYM4NVXX2XXrl20bt2aF198scgxtW/fnrfeeou+ffvSq1cvZsyYQZkyJ3+unnrqKSZPnszjjz/OiBEjuO222xg2bBjGGLp27UpmZiYiwvnnn6/D2Sj/OHDAdh6pXDnvZW69FYYMgTfftG0Ho6LANTxTxYq2FHHhQnjuuZKJuZD0m6KIioqiefPmLFq0KMf0RYsW0bp16yBFpZQqCdu2bSMtLc2n9oPeNGrUiKVLlzJ37lwiIiKIi4tj1qxZREVFFSuuPn36cN999/Hxxx/Tv39/srKy+Oabb7jjjjt46qmnuOOOO3j66aeJjIzk3Xff5cYbb2T48OE0aNCAhg0bcuGFF9KgQQMmT57stZRRqULZv9+WDuZ3gVG5Mtx8s707yYIFcMklEBNzcv6VV9qOJ3v3Bj7eItCEUAEwaNAgpk6dyuTJk9m0aRMPPvggu3bt4u677w52aEqpAHL1MG7UqFGR1yEiXHfddfz8889s27aNhIQEv8TWvXv37LEKa9asSdu2bZk1axb33XcfEydOzK7iLlOmDB988AFz5sxh+vTpzJo1i7feeou4uDj69+9P3bp1mTZtWqFvuadUtrwGpfb073/bnsY//wyXXZZzXqdO9nHFCv/H5wdaZawAuPnmmzlw4AAjR45k9+7dNGzYkM8//5wzzzwz2KEppQLogDOcRmE6f+QlIiKC2NjYYq/H3RNPPEFmZiYrV66kV69e3HDDDcTFxeVarmzZsvzrX//KMe3OO+9k6dKlPPnkk/Tp04f58+czceJETsurHZhSecnrtnWeLrkEGjeGH3/MnRC6EsojR/wfnx9oCaHKdu+997J9+/bsoRzauRrDKqVKLdcdR8qVKxfkSLwTEZ5++mm++OILevfu7TUZzO+1nTp14ssvv2TUqFF8+umnNG7cmDlz5mhpoSocX0sIReCRR6B6dWjTJuc818XS0aP+j88PNCFUSqkwdtT5cQrVhNAfIiMjGTp0KCtXrqRSpUp069aNFi1aMG/ePE0MlW98TQjBdi7Zs8d2LHGnCaFSSqlQ5SohjHFv/F5KNWvWjPXr1/POO+/wzz//kJSUpImhKpgxhUsIwZYUenJ9xzQhVEopFWrS0tIoV65c2Iw5WrZsWfr06cPmzZuZMmVKdmLYsmVLPvnkE06cOBHsEFWoSUmBjAzf2hDmx5UQHjtW/JgCQBNCpZQKY66EMNyULVuWvn37ZieGBw8e5Prrr6d27doMHTrUp1vnqTBR0F1KfBURAdHRWkKolFIq9IRrQujinhh++umnXHzxxYwZM4bzzjuPK664grlz55KZmRnsMFUw+SshBNuOMEQTQh12Rimlwli4J4QuZcuWJSkpiaSkJPbs2cPbb7/NG2+8wb/+9S8SEhJo2bIlF154IU2bNqVr16457pyiSrkwSQi1hFAppcKYJoS51axZk8cee4zt27cze/ZsWrVqxebNmxk1ahQ33ngjPXr0ID09PdhhqpLijNVZ7DaEoAmhUkqp0KQJYd7KlClD9+7dmT17Nps2bSI1NZXRo0fz8ccfc9NNN2lSGC78WUIYE6MJoVJKqdCjCaHvoqOjefjhhxk/fjyffvopN9xwg94nORzs3w+RkVCpUvHXFRurvYyVUkqFHk0IC2/AgAG88cYbzJ8/n65du3IsRH/glZ/s3w9Vq9pewsWlVcYqlPXp0wcRoV+/frnmDRkyBBHh2muvDUJkSqlAS0tL8/v9h8PB3XffzVtvvcXChQtJSkrKHuBblUIHDvinuhg0IVShr3bt2sycOZPU1NTsaSdOnOC9996jTp06QYxMKRVIWkJYdP369WPKlCksXryY6667Lsf5U5Uihb1LSX40IVShrnHjxpxzzjnMmjUre9pnn31GTEwMiYmJwQtMKRVQmhAWT58+fXj33XdJTk4mMTGRnTt3Bjsk5W+aEKpwc+eddzJlypTs51OmTKFv375hc0srpcKRJoTFd+utt/LJJ5+wefNmWrZsyapVq4IdkvKn/fv9M+QMhHQvYx1ZM8AGLhjIuj3rArLuzMxMIiMjc01vWrMpY68aW+j19ezZk8GDB7N161YqVKjAggULGD9+PE888YQ/wlVKhZjMzEzS09M1IfSDpKQkVqxYQVJSEu3atWP69Ol069Yt2GGp4vr7b9i3D2rX9s/6tJexOhVUqVKFbt26MWXKFKZNm0ZiYqK2H1SqFDvqlFRoQugfDRs2ZNWqVTRq1Ij+/ftz6NChYIekiuuzzyArC7p08c/6QrjKWEsIA6woJXW+OnLkCBUqVPDrOu+44w569+5NXFwcTz/9tF/XrZQKLa6esZoQ+k98fDwTJ06kRYsWvPTSS4wcOTLYIanimDsXatWC5s39s74QTgi1hFDl0KlTJ6Kioti/fz9du3YNdjhKqQDShDAwmjdvzi233MLLL7/Mrl27gh2OKqrjx2HBAkhK8s8YhGATwsxMyMjwz/r8SBNClYOI8OOPP/L7778THR0d7HCUUgGkCWHgjBw5khMnTvDUU08FOxRVVMuWQUqKTQj9xTXmZwiWEmpCqHKpUKECFStWDHYYSqkA04QwcOrVq8c999zD22+/zebNm4MdjiqKuXOhfHno2NF/64yJsY+aEKpQNHXqVObPn1/k+UqpU5MmhIH1+OOPU65cOQYOHEhmZmaww1GFYYxNCK+88mQS5w+uEsIQ7GmsCaFSSoUpTQgDq1q1aowaNYqFCxcycOBAjDHBDkn5au1a2LnTv9XFENJVxtrLWCmlwpQmhIF37733sm3bNsaMGUNCQgLDhg0LdkjKF59+ajuSXHONf9erCaFSSqlQ40oIY10/UiogXnzxRXbv3s3w4cOpUqUK/fr183pTARVC5s6FNm38d8s6lxBOCLXKWCmlwpSWEJaMiIgI3nnnHTp16sTdd99N7dq1eeihh1i3LjB3sVLFtG0brF8P//qX/9etCaFSSqlQowlhyYmKiuKzzz5j5syZXHLJJUyYMIFmzZoxb968YIemPM2YYR9vvNH/69ZexkoppUKNJoQlKzo6mptuuolPPvmEPXv2ZA9grSWFIWb6dFtdHIhbt2ovY6WUUqEmLS2NiIgIoqKigh1K2KlSpQpz586lSpUqXHfddezevTvYISmAn36yf7fcEpj1a5WxUkqpUJOWlka5cuUQkWCHEpZq1arFvHnzOHjwINdddx3btm0Ldkhq+nSIjAxMdTFoQqiUUir0uBJCFTxNmzZl+vTpbNiwgXPPPZdbb72Vn376KdhhhSdjbPvBTp2gevXAbEMTQqWUUqHm6NGjmhCGAFfp4MCBA5kzZw6NGjXitttuo2/fvrz99tscOXIk2CGGh1WrbA/jQFUXgyaEKrQtX76cpKQkEhISEBGmTp0a7JCUUiVASwhDR0JCAqNHj+Z///sfY8aMoU6dOsybN49+/frRpk0b/vzzz2CHWPpNnw7R0dCtW+C2ER1tHzUhVKEoJSWFhg0bMm7cOB2gVqkwoglh6DnttNMYNGgQzz77LH/99Reff/4527dvp1WrVlqVHEiZmTBzJlx9NVSqFLjtRETYpFB7GatQdPXVV/Pcc8/RvXt3IiL0kFAqXGhCGNpEhC5durB8+XIyMzNp27YtM2bM4MSJE8EOrfRZuxb27IGbbgr8tmJjtYRQKaVU6NCE8NTQtGlTVq5cSZ06dbjllluoX78+o0eP5u+//w52aKXH//5nHy+4IPDbCtGEUO9lHGgDB0KABh2Nzcy03eM9NW0KY8cGZJtKqdIjLS2NM844I9hhKB/UqVOHH374gXnz5jF27FgeeeQRHn30Ua666ipuueUWkpKSKF++fLDDPHXt3GkfExICv60QTQi1hFAppcJUWlqaths+hURGRtK1a1eSk5NZt24d999/P2vXrqVnz54kJCQwZMgQ7XxSVDt32rZ9VasGflshmhBqCWGgBbCk7uiRI1SoUCFg61dKlW5aZXzqatKkCWPGjOGll17i66+/ZsKECbz88su88sor3HbbbYwdO5aKFSsGO0y/M8awdu1a6tWrR5UqVfy34p074fTToSQGaY+JCcmEUEsIlVIqTGlCeOqLiIigXbt2zJgxg19//ZX77ruPd999l0suuYQtW7YEOzy/McYwZ84cmjVrRosWLahWrRrt27fnpZde4vvvvyc9Pb14G9i5s2Sqi8GWEGovYxWKUlJSWLduHevWrSMrK4sdO3awbt06duzYEezQlFIBpAlh6VK3bl3Gjh3L4sWLOXDgABdffDGffvppsMMqtBMnTrBv3z5++OEH3n33XQYPHkyTJk3o1q0bKSkpvPHGGwwbNozDhw8zZMgQLr74YipWrEibNm14++23McYUfqMlnRCGYAmhVhkrVq9eTYcOHbKfP/nkkzz55JP07t1bB6lWqpTKyMjgxIkTmhCWQomJiaxZs4brr7+erl27kpiYyNChQ7nyyitD7r7VmZmZ/PDDDyxatIhFixaxevXqXHdmiYmJoXHjxkybNo2ePXtSpoxNXUaOHMnOnTtZsWIFK1euZMmSJfTr149PPvmEyZMnU7NmTd+CMMYmhElJ/t4972JjYd++ktlWIWhCqEhMTCzaFZVS6pSVlpYGoAlhKVW7dm2++uorXn/9dV555RW6dOlC48aNee2117jsssuCElNmZiarV69m8eLFrF27li1btrB169bs6t4mTZrQoUMHmjVrRnx8PDVq1KBhw4acffbZ2Umgp4SEBLp370737t3JysritddeY+jQoTRq1IjJkyfzr3/9q+DA/vnHltiFeQmhVhkrpVQY0oSw9IuJieHhhx9m27ZtTJ06lSNHjtC+fXsGDhyY/fkXVlZWFkePHuX48eM+v2bNmjX06tWL+Ph4Lr30Uv7zn//w008/Ub9+fQYOHMgHH3zAnj17WLduHQ899BBPPvkk9913H927d+f888/PMxn0FBERwQMPPMDatWupU6cOXbt25ZFHHil4IO+SHHIGQjYhDKkSQhGJBEYAtwK1gN3AB8AIY8wJZxkBngTuAqoA3wH3GWN+dltPFeBVwFX+Oxe43xjzj9syjYDXgIuBv4FJwDNGi8qUUmFAE8LwERUVRe/evbnhhhsYNmwY48aNY/78+Vx99dXUqlWLmjVr5nisXr16jqrltWvX8thjj7FkyRIyMjKyp8fHx1O7dm3OPfdchg8fTpMmTbLnGWNYtmwZo0aNYtGiRVSsWJEbb7yRzp0706lTJ+Lj4wO2vw0aNODbb79l0KBBjB49mu+++46ZM2dSq1Yt7y8o6YQwRHsZh1RCCAwF7gN6AxuAxsA04DjwjLPMEOBhoA+wBXgCWCQi5xljXA0PPgTqAF0AA0wG3gOuAxCRisAiYDnQEjgPmAqkAmMCuH9KKRUSNCEMP3Fxcbz22mvccMMNDBkyhHfffZdDhw7lWq5q1aq0bt2aNm3asHbtWmbPnk3VqlW57777qFy5MlFRUWRmZvLnn3+yY8cOFi1axOzZsxkwYAAjRozITgS///57atasyQsvvMC///1vKgXyHsEeoqOjef3112nTpg39+/enadOmTJkyhWuuuSb3wlpCCIReQtgamGeMmec83y4ic4FLILt0cCAwyhjzsTOtN7AP6AlMEpEGwFVAW2PMt84y/wa+cpLGLUAvoBzQ2xhzFPjJed0gEXlZSwmVUqXdUecHSRPC8NOhQwe+//57wF4Y7Nmzhz179rB792527drFunXr+Oabb5g/fz5xcXE88cQTDBo0KM+E7u+//+bxxx9n/PjxTJgwgRMnTlC/fn0mTZrE7bffTkxMTEnuXg49e/akadOm3HLLLVx77bXcc889jB49mqNHj7J582ZSU1PpvHMnApBXCaK/heiwM6GWEH4N3Csi5xtjNovIBUBH4Hln/llATeAL1wuMMUdFZDk2mZwEtAJSgG/d1vsNtvSvNbZUsRXwlZMMuizElkLWBX73/64ppVTo0BJCBfbzr1evHvXq1cs1b//+/URFRRU4wHXVqlWZMGECd9xxB2+++SadOnXihhtu8LntX6BdcMEFrFq1iscee4wxY8YwZcqUHG0gP7/6arqcdpqtyi0JsbGQmQkZGVC2bMls0weh8Wmd9AJQAdgoIpnY+J41xkxw5rv6kO/1eN1eIMFtmb/cS/mMMUZE9rm9vibgeX+fvW7zciSEInIXts0iNWrUIDk52WvwlSpVytVdPpAyMzNLdHsFOXbsWJ7vjT+lpKSUyHZCke57crDDKHGB2u+VK1cCsGnTJr+v21/C9TOHU3ffe/bsCcDXX39dpNcHcr+vvfZaEhIS+Oqrr6hZsya1a9dm3LhxPPnVV7SNj2dNCb3fZ+zaxdnAV198Qabb/aeD/ZmHWkJ4M3A7tvr3Z6ApME5EfjfGvO22nGeVrnhM81blW9Ayksd0jDFvAm8CtGjRwiQmJnoNftOmTSV6K7kjIXbrupiYGC666KKAbyc5OZm8PoPSTvc9MdhhlLhA7ff+/fsBaNu2LY0aNfL7+v0hXD9zCN99D/R+JyYm8tBDD2U/r1KlCvfccw+rzj+fTiX1fm/cCMBlLVpAjRrZk4P9mYfasDMvAaONMTOMMRuMMe8BLwPDnfl7nEfP0Sarc7KEbw9QXdy6SDn/V/NYxts6IHfpo1JKlTpaZawU9O3bl4SICJ7+44+S26irajrEOpaEWkJYDsj0mJbJyTh/xyZznV0zRSQGuIyTbQZXAHHYdoIurYDyHstc5rzWpTOwC9he3J1QSqlQpwmhUhAdEcGQrCyW79nDl19+WTIbjY21j5oQ5mseMExErhGRuiLSDRgEfAK2LSAw1lnmehFpiB0uJgU71AzGmE3AAmyP40tFpBW2s8l8p4cxzrJpwFQRaSgi1wPDAO1hrJQKC5oQKgXs3k1/oEbFijzzzDMFLu4XroQwxHoah1pCeD/wETAB2IQdE/At4DG3ZV7EViO/DqzGDmB9hdsYhGCHlVmP7Y280Pn/NtdMY8whbIng6c46Xne29XIgdirU9enTBxHJ9XfppZcGOzSlVIBoQqgUsHMnscDg7t1ZsmQJ69atC/w2Q7SEMKQ6lThJ3UDnL69lDPZuJiPyWeZv7N1O8tvWBqBdUeIsjS6//HLee++9HNOioqKCFI1SKtDS0tIoU6YMZUNo2AulSpwzKHWXq6/mkSlT2LJlC02bNg3sNjUhVKEsOjqamjU9+9kopUqrtLQ0LR1UykkIqzVoAMBff/0V+G2GaEIYalXGSimlSoAmhEphE8KoKKqecw4iUjIJYYj2MtYSwgBbMHABe9btKXjBIsjMzCQyMjLX9JpNa3LV2KsKta4FCxYQFxeXY9p9993HCy+8UKwYlVKhSRNCpbAJ4emnU6ZsWapUqRLWJYSaECoA2rVrx5tvvpljWuXKlYMUjVIq0DQhVAqbECbYG51Vq1atZBPCEOtlrAlhgBW2pK4w/HmnknLlynH22Wf7ZV1KqdCnCaFS2ISwWTMgCAlhiJUQahtCpZQKQ5oQqrBnDOzaFbwSwhBLCLWEUAFw/Phx9uzJ2dYxMjKSatWqBSkipVQgHT16lOrVqxe8oFKl1aFDkJaWIyH8+uuvA79d7VSiQtnixYupVatWjmkJCQn8+eefQYpIKRVIaWlpxLpKKpQKR86QM+4J4YEDB8jKyiIiIoAVqCIQHR1yCaFWGSumTp2KMSbXnyaDSpVeWmWswp4rITz9dMAmhFlZWRw8eDDw246N1YRQKaVU8GlCqMLe3r320bkpg6uJVIm1IwyxXsaaECqlVBjShFCFvZQU++iM1lHiCaGWECqllAomY4wmhEqlptrH8uUBTQg1IVRKqTCTnp5OVlaWJoQqvGlCmIMmhEopFWbS0tIANCFU4S0lxQ4B49wCNj4+HiihhDAmRhNCpZRSwaUJoVLYEkKndBAgOjqaihUragmhUkqp8KAJoVLkSgihhO9Wor2MlVJKBZMmhEphE8K4uByT4uPjtYRQKaVUeNCEUClsG8JglhBqQqiUUiqYNCFUijyrjPfv3x/4bWtCqEJRnz59EBFGjhyZY3pycjIiUjJfDqVUiXElhHovYxXW8mlDaIwJ7LY1IVShKiYmhhdffLFkisqVUkGlJYRKYauMPdoQVqtWjfT0dI4cORLYbbuGnQl04lkImhAqADp06EDdunV55plngh2KUirAjjolE5oQqrCWRwkhlMBYhLGxkJUFJ04EdjuFoAmhAiAiIoJRo0YxceJEfvvtt2CHo5QKIC0hVIrgJ4QQUtXGZYIdQGm35vnnObhlS0DWnXniBJFlcn+EVc47j+bDhxd6fVdffTVt2rThscceY8aMGf4IUSkVgjQhVGHPGK/DzgQlIaxYMbDb8pGWEKocXnzxRWbPns3q1auDHYpSKkB27txJRESEdipR4Ss93VbXaglhNi0hDLCilNT56siRI1SoUMGv62zZsiU33HADQ4cO5T//+Y9f162UCr7U1FSmTZtG165dKeOlhkGpsJCaah81IcymZwOVy3PPPccFF1zAggULgh2KUsrP3nnnHQ4ePMjDDz8c7FCUCh5XQuhRZVy+fHliY2MDnxDGxNjHEEoItcpY5XL22Wdz1113MW7cuGCHopTyo8zMTF555RUuvfRSWrduHexwlAqelBT76FFCCCV0txJXCWEI3c9YE0Ll1RNPPKHVSUqVMnPmzGHbtm0MHjw42KEoFVx5VBlDCSeEIVRCqL/4iqlTp+aaVr169cAPzKmUKlFjxoyhXr16dO3aNdihKBVc+SSE8fHxgb9DVwgmhFpCqJRSYWDFihWsWLGCgQMHEhkZGexwlAouV5WxRxtCCN8SQk0IlVKqlMvMzGTYsGFUqVKFvn37BjscpYJPq4xz0SpjpZQq5V544QWWL1/OO++8Q5yXEhGlwk4BCWFqaipHjx4N3Fid2stYKaVUSVq5ciVPPPEEPXr0oHfv3sEOR6nQkMewM1BCYxFqL2OllFIl5fDhw/Ts2ZPatWszceJERCTYISkVGgoYdgZKKCEMoRJCrTJWSqlSKCsri379+rFjxw6WL19OpUqVgh2SUqEjNRVETiZmbkokIYyJgUOHIITuJ64JoVJKlUJDhgxh9uzZvPjii1H4TbcAACAASURBVDoItVKeUlNt6aCXUvMSSQhFoGLFwK2/CLTKWCmlSplXXnmFMWPGMGDAAB2EWilvUlK8VhcDnHHGGURFRbFhw4YSDiq4NCFUSqlSZMaMGQwaNIju3bszduxYbTeolDeuEkIvYmNjad26NYsWLSrhoIJLE0KllColdu7cyZ133slll13Ge++9pwNQK5WXfBJCgM6dO7Nu3brAj0cYQjQhVNn27t3Lgw8+SP369YmOjiYhIYEuXbrw+eefBzs0pZQPhg0bRmZmJtOmTSPGNc6ZUiq31FSvQ864XH755QAsXbq0pCIKOk0IFQDbt2+nWbNmLFy4kOeff54ff/yRxYsXc80113D33XcHOzylVAFWrlzJ+++/z8MPP8xZZ50V7HCUCm35tCEEaN68OZUrVw6ramPtZawAuPfeezHGsHr16hx3MmjQoAG9evUKYmRKqYJkZWXx4IMPUqtWLYYPHx7scJQKfampUKNGnrMjIyPp2LEjixYtwhgTFm1xtYRQ8ffff7NgwQIGDBjg9bZWVapUCUJUSilfvf/++6xatYpRo0bpremU8kUBVcZgq4137NjBr7/+WkJBBZeWEAbYwIEDWbduXUDWnZmZ6bXReNOmTRk7dqzP6/n1118xxtCgQQN/hqeUKgFffvklgwcP5uKLL+bWW28NdjhKnRoKqDIG27EEYPHixZxzzjklEVVQaQmhwhgT7BCUUoV09OhRHnroIRITE6lUqRJTpkwhIkJP6Ur5pIBexgD169fnzDPPDJt2hFpCGGCFKakrrCNHjlChQoVir+ecc85BRNi0aRPdunXzQ2RKqUA6fPgwrVq1YuPGjQwYMIBRo0ZRvoAfN6WUwxhISyuwylhE6Ny5M7Nnz86zRq400ctJRdWqVbnyyit57bXXSHHd8NvNP//8E4SolFJ5mT17Nhs3buTjjz9m/PjxmgwqVRhHj9qk0IfvzeWXX86hQ4dYvXp1CQQWXJoQKgAmTJiAMYYWLVowe/ZstmzZwubNm3njjTdo3LhxsMNTSrn58MMPOfvss7VEX6micBV8+JAQdurUCYAvvvgikBGFBE0IFQBnnXUWa9eupXPnzgwdOpTGjRvTsWNH5s6dy6RJk4IdnlLKsXv3bpYtW0bPnj3DYigMpfwuNdU++pAQxsfHc/HFF/PZZ58FOKjg0zaEKlutWrUYP34848ePD3YoSqk8zJw5E2MMt9xyS7BDUerU5EoIfRyiKSkpiccff5w9e/ZQs2bNAAYWXD6VEIpIhIhEuD2vKSL9RKRN4EJTSinl6cMPP+Siiy7i/PPPD3YoSp2aClFlDDYhBJg/f36gIgoJvlYZfwbcDyAiccBq4CUgWURuD1BsSiml3GzdupXvv/+enj17BjsUpU5dhagyBmjYsCF169Zl7ty5AQwq+HxNCJsDrjs8Xw8cBqoD/YHBAYhLKaWUhxkzZiAi9OjRI9ihKHXqKmSVsYiQlJTEokWLSEtLC2BgweVrQlgBcI09cgXwiTEmA5sk1g9EYEoppU4yxvDBBx/Qrl07zjjjjGCHo9Spq5AlhADXXXcdx44dY8mSJQEKKvh8TQh3AG1EpDxwJeAatrsqUHrTZaWUChHr169ny5YtWl2sVHEVsg0hQLt27ahYsWKprjb2tZfxy8B7QArwP2C5M70dsCEAcSmllHKzcOFC4GQDd6VUERWhhDAqKoouXbowb948srKySuVtIn3aI2PMJOBS4A6grTEmy5n1G/CfAMWmlFLKsWzZMi644IJSPeyFUiWikG0IXZKSkti7dy/ff/99AIIKPp9TXGPMGmPMJ8aYFLdpnxljvglMaEoppQDS09P5+uuv6dixY7BDUerUl5ICZcpAVFShXtalSxciIyP59NNPAxRYcPk8MLWIXAJ0wvYuzpFIGmMe8HNcSimlHN9//z2pqal06NAh2KEodepLTS1UdbFLlSpVaN++Pf/973959tlnS92dgnwdmHowsALoAzQFGrn9NQxUcEoppWx1sYjQvn37YIei1KkvNbXQ1cUuN910E1u2bGHDhtLXfcLXKuMHgQeMMecaYxKNMR3c/rQO4xTXp08fRAQRoWzZstSrV4/BgweT6mpnoZQKqqVLl9KkSRNOO+20YIei1KmviCWEANdffz2RkZHMnDnTz0EFn68JYUXg80AGooLr8ssvZ/fu3Wzbto2RI0cyYcIEBg/WMceVCrb09HS+/fZbrS5Wyl9SUoqcEFarVo2OHTsya9YsjDF+Diy4fE0IpwNXBTIQFVzR0dHUrFmT2rVr07NnT3r16sWcOXOCHZZSYW/jxo0cP35cE0Kl/KUYVcZgq41//fVX1q1b58eggs/XhPAP4CkR+UBEhorIIPe/QAaogiM2NpaMjIxgh6FU2Fu7di0RERG0a9cu2KEoVToUo8oYoFu3bpQpU6bUVRv72su4H3ZQ6tbOnzuDHbhaefPrDkgJzM1cYjMzITIy94y4cnB2nSKvd9WqVXz44Yd06tSpGNEppfxh3bp1NG/enEqVKgU7FKVKh5QUqFP038jTTjuNyy+/nFmzZvH888+Xmt7Gvg5MfVY+f/X+n737Do+qTPs4/n1IDyUgUgQBXREQsKNUKQrY0LWxFtQVkaK7ay/YAMVFcRF7gRVREX0tKEWQohL62hAUxQIISJGaQBISSHneP54ZiTEkQ5iZMzP5fa7rXGdy5sw590mA3NxPC3WQEnozZ86kWrVqJCcn0759ezp37syzzz7rdVgilVpOTg4rV67U/IMiwXSIFUJwzca//PILX331VZCC8l7A8xD6GWOqAdZaqyGogTiESl15crOyqF69elCu1blzZ8aOHUtCQgINGjQgISEhKNcVkYpbtGgRBQUF6j8oEkyH2IcQ4KKLLmLgwIG88847tGnTJkiBeSvglUqMMf8wxqwHdgG7jTHrjDE3hS40CafU1FSaNm1KkyZNlAyKRIiZM2eSkJBAp06dvA5FJHYEoUJYq1YtevbsyZtvvklBQUGQAvNWoBNT3wc8BowDevq28cBjxpjBoQtPRKRystYydepUTjnlFKoe4i8vEfEpLIS8vENOCAH69evHxo0b+eijj4IQmPcCrRAOAgZYax+y1n7i24YBN/q2oDHGHGGMec0Ys80Yk2eM+d4Y06XY+8YYM8wYs8kYk2uMSTfGtCpxjVrGmAnGmF2+bYIxpmaJc443xszzXWOjMWaIiZWeoSIS9X744QdWr15N+/btvQ5FJHb4F1w4xCZjgF69elG/fn3Gjh17yNeKBIEmhHWBL0o5/jlQL1jB+JK2RYABzgeOA/4FbC122t3AHb7jp/nem2OMKd6Z7k3gFOBc3PyJpwATit2nBjAH2OK7xs3AXUClnELn1Vdf5cMPP/Q6DBEpZurUqQB06FByYgcRqTB/QhiECmFCQgL9+vVjxowZ/Prrr4d8Pa8FmhD+BFxVyvGrgB+DFw53A5uttddaaz+31v7iq0auBFcdBG4FHrPWTrLWrgD+DlT3x2eMOQ6XBA6w1i621i4BBgK9jDHNfffpA6QCf7fWrrDWTgJGArerSigikWDatGmcfPLJ1KlTx+tQRGJHdrbbB6kbRr9+/bDWMm7cuKBcz0uBJoTDgCHGmI+NMQ/5mmw/Bh4AhgYxnouAz4wxbxtjthpjlhlj/lksSTsaqA/M9n/AWpsLzGf//IjtcXMmLi523UVATolzFvg+6zcLaAAcFcTnERE5aNu2bWPx4sVceOGFXociEluC2GQMcPTRR9OzZ0/GjRsX9YNLApp2xlr7vjGmLXAb0AvXpPs9cLq19usgxvMX4CbgSdwglpMA/2R4z+GSQXBNvcVtARr6XtcHttliiwxaa60xZmuxz9cHNpRyDf97vxR/wxgzABgAUK9ePdLT00sNPi0tjaysrDIfMJgKCwvDer/y5OXlHfB7E0zZ2dlhuU8k0rOnex1GWMycORNrLUcccUSleu6S9OzpXocRdqF+7hrffsspwPJVq8gI0n06dOjArFmzePzxxw+pi4fnP3NrbcRswD5gcYljI4CVvtcdcCujNCpxznhgpu/1fcDqUq79CzDY93o2MK7E+018125XVoynnnqqPZDvv//+gO+Fwu7du8N6v/KE6/nnzp0blvtEIj175XDJJZfYBg0a2KKiokr13CXp2SufkD/3rFnWgrULFwbtkvv27bP16tWz559//iFdJ1TPDnxpA8jBDthkbIw5rPjrsrYg5qebcZXH4lYC/tmdf/Pt65c4py77K3y/AXWL9wX0va5T4pzSrgF/rj6KiIRNXl4es2bN4oILLoiZJbFEIkaQ+xCCG1wyYMAApk+fztKlS4N23XArqw/hNmOMP0naDmwrZfMfD5ZFQPMSx5oB63yvf8Elcz38bxpjkoEz2N9ncAlQDddP0K89ULXEOWf4PuvXA9gErD3UhxARqaj09HRycnK44IILvA5FJPYEuQ+h3x133EHt2rW58847/a2OUaeshPBMYGex16Vt3Xz7YHkSaGeMud8Y09QY0xs3Jczz4PoCAk8Bg40xlxhjWgOv4gaRvOk7ZyUwExhjjGlnjGkPjAE+tNb6R0S/CewBXjXGtDbGXAIMBkbbaP1JikjUs9by7LPPUq1aNa1fLBIKQZx2pri0tDSGDh3K3LlzmTFjRlCvHS4HHFRirZ1X7HV6OIKx1n5hjLkI12/wQWC9b/9CsdMeB1JwSWIt4DOgp7W2+OiKPsAz7B+NPBX4Z7H77DLG9PBd40sgA3gCGB2CxxIRCcjrr7/OjBkzePLJJ0lJSfE6HJHYE4ImY7+BAwfyzDPPcPfdd3P22WcTHx/QuN2IEVC0xphC4Ahr7dYSx2sDW621ccEKyFo7HZhexvsWNw3OsDLO2QlcXc59vgU6VyhIEZEg27hxI7fccgsdO3bkX//6l9fhiMSmEFUIARITExk5ciSXXnop48ePp3///kG/RygFOg/hgXo2J+FGBouISAVZaxk4cCD79u1j/PjxxMUF7f/YIlJcTg4kJ0OI/o5dfPHFdOzYkQcffJAcf/IZJcpMCI0xtxtjbsdNxzLI/7Vvuwt4CfghHIFK6Fx33XUYYzDGEB8fT+PGjbnxxhvJyMjwOjSRSuGNN95g+vTpjBgxgmOPPdbrcERiV05OSKqDfsYYHn/8cbZs2cJzzz0XsvuEQnlNxv52CwPcABQWe28fbkTuoOCHJeHWvXt3JkyYQEFBAd9//z3XX389mZmZvPXWW16HJhLTtm7dyq233kqHDh3UVCwSatnZQR9hXFKHDh0499xzGTlyJIMGDSItLS2k9wuWMiuE1tqjrbVHA/OAE/1f+7bm1tqzrbWfhSdUCaWkpCTq16/PkUceSc+ePbn88suZPXt2+R8UkUNy++23k5WVxX//+181FYuEWogrhH7Dhw8nIyODp556KuT3CpaA+hBaa7tZa9V+WEmsWbOGmTNnkpCQ4HUoIjFt1qxZTJw4kXvvvZeWLVt6HY5I7AtTQnjqqady8cUXM3r0aHbs2BHy+wVDwGOijTHNgMtwq4YkFn/PWnt9kOOKIbcCy0Jy5ZSUQqC0isJJuOkaAzdz5kyqVatGYWEheXl5AIwerVl4REIlJyeHQYMG0bx5c+69916vwxGpHLKzw5IQAjz00ENMnjyZUaNG8eijj4blnocioAqhMeZ84BvgAuB63Goi5wEXA4eHLDoJm86dO7Ns2TI+//xz/vWvf3Heeedx8803ex2WSMwaNmwYa9euZcyYMSQnJ5f/ARE5dDk5Ie9D6Hf88cdzxRVX8Mwzz7BtWzAXdQuNQCuEDwMPWWsfNcZkAdfglnmbgFsGTg4odP0HcnOzqF69elCulZqaStOmTQF45pln6NatG8OHD2fYsGFBub6I7DdnzhyeeOIJBgwYQJcuXbwOR6TyCFOTsd8999zDW2+9xfvvv8/AgQPDdt+KCHQewubA277X+UCqtTYPlyjeGorAxFtDhw5l5MiRbNq0yetQRGLKb7/9xtVXX81xxx2nbhki4RbmhPCEE07g2GOPZdKkSWG7Z0UFmhBmAf42jc1AU9/reNzycRJjunbtSqtWrXjkkUe8DkUkZhQWFtKnTx+ysrJ45513qBrGX0wiQlimnSnOGMMll1zC3Llz2blzZ9juWxGBJoSfAZ18r6cDTxhjhgLjUZNxzLr99tsZN24c69at8zoUkZgwYsQIPv30U5599llatWrldTgilU+YK4QAl156KQUFBUybNi2s9z1YgSaEtwP/870eBswGLgVW4Saslij26quv8uGHH/7p+FVXXcXevXtp0qSJB1GJxJbRo0czZMgQrrrqKq6/XhMziITdvn1QUBD2hLBNmzY0btw44puNAxpUYq1dU+z1HuDGkEUkIhJDrLXcf//9PProo/Tu3ZtXXnkFYw60PLyIhEx2ttuHOSH0Nxu/+OKLZGUFbzBosAU67cwaY0ztUo7XNMasKe0zIiKVXVFRETfeeCOPPvooAwYM4K233iIpKcnrsEQqp5wctw9jH0K/Sy65hL179zJjxoyw3ztQgTYZH0XpMyAnAQ2DFo2ISIwoLCykX79+jBkzhsGDB/PSSy9paToRL/kTQg8Gc3Xo0IF69epFdLNxmU3GxphLin15vjFmV7Gv44CzgLUhiEtEJGoVFhbSt29fJkyYwLBhwxg6dKjXIYmIhwlhXFwcF198MRMmTCA3N5eUlJSwx1Ce8iqE7/k2C4wr9vV7wBtAN+COUAYoIhItMjMzWbx4MVdffTUTJkxg+PDhSgZFIoW/D6EHTcbgmo1zcnL45JNPPLl/ecqsEFprqwAYY34BTrPWbg9LVCIiES4jI4NJkyaxYsUKvv/+e7777rs/TOQ+YsQIrVEsEkk8rBACdOrUifj4eBYvXkyvXr08iaEsgY4yPjrUgYiIRIv09HSuvvpqNm7cSGpqKscddxzdu3enVatWtGrVihNOOIFGjRp5HaaIFOdxQpiSksIJJ5zA559/7sn9y1NeH8ITgcOstXOLHesDDAeqAe8DN1tr94U0ShGRCJCfn8+wYcN49NFHadq0KYsWLaJdu3ZUqRLo+DwR8YzHTcYAbdu2ZeLEiRQVFUXcvxvlRfMI+1cowRjTErc6yc/AW0Af4J6QRSciEiEyMjLo2bMnI0aM4Prrr2fp0qV06NAh4v5RF5ED8LhCCC4h3L17Nz/88INnMRxIef+SnQLMKfb1FcD31tqzrbW3ALcCl4cqOBGRSLB27Vo6duzIokWLeP3113n55Zep5mGVQUQqIEISQoDPPvvMsxgOpLyEsDawsdjXnYHii/GlA42DHJN4YNu2bdx0000cddRRJCUlUa9ePc466yzmzJlT/odFYtjSpUtp164dmzdvZvbs2VxzzTVehyQiFZGdDcaAh1O+NGvWjLS0tIjsR1jeoJJtuImnfzXGxAGnAqOKvZ8IFIUoNgmjSy+9lD179jBu3DiaNm3K1q1bmTdvHjt27PA6NBHPrF69mp49e1K1alU+/fRTWrZs6XVIIlJROTmuOujh0pFVqlThtNNOi8gKYXkJYTow1BjzD+Ay37G5xd5viSamjnqZmZksWLCAOXPmcNZZZwHQpEkTTjvtNI8jE/FOZmYmvXr1wlrLJ598QtOmTb0OSUQOhT8h9Fjbtm157LHH2LNnD6mpqV6H87vymowfBI4FVgH/Bu621uYUe/8aIDJnWJSAVatWjWrVqjF16lTy8vK8DkfEc/n5+fTu3ZvVq1fz/vvvKxkUiQURlBAWFhaydOlSr0P5g/Impl5rjGkBtAK2WWs3lThlKLAhVMHFhFuBZaG5dEphSukrTJ8EPBX4deLj43n11Vfp378/Y8eO5eSTT6Zjx4707t379w6wIpVFXl4eAwcO5OOPP2b8+PF06dLF65BEJBiysz2dcsbv9NNPB9zAkk6dOpVzdviUO1+CtbbAWru8lGQQ33F1MosBl156KZs2bWLatGmce+65LF68mHbt2jFixAivQxMJmxUrVnD66afz+uuvM2zYMK677jqvQxKRYImQCmG9evVo0qRJxA0sCWilEjkEB1GpO1i5WblUr149aNdLTk6mR48e9OjRgyFDhnDDDTcwbNgw7rzzThITE4N2H5FIs2/fPp577jnuu+8+0tLSmD59Ouedd57XYYlIMEVIQgiu2TjSBpZoRlU5oJYtW1JQUKB+hRKzCgoKeOWVV2jWrBl33HEHPXv25Ntvv1UyKBKLIqTJGFxCuG7dOrZs2eJ1KL9TQijs2LGDM888kzfeeINvvvmGX375hXfffZfHH3+cs846ixo1angdokjQrVy5ktatW9OvXz/q1KnDzJkzmTJlCnXr1vU6NBEJhQirEEJkTVB9wCZjY8wrwC3W2ixjTGdgsbW2IHyhSbhUq1aNdu3a8fTTT7Nq1Sr27t1Lw4YNueqqq3jggQe8Dk8k6BYvXkyvXr1ITExk8uTJXHjhhRgP5yYTkTCIoITwlFNOIS4ujuXLl3PhhRd6HQ5Qdh/Cq4H7gCzc3INHAFvDEZSEV1JSEiNGjNAAEqkUpk6dyuWXX06jRo2YOXMmf/nLX7wOSUTCIYISwpSUFNavX88RRxzhdSi/KyshXAv8yxgzGzBAe2NMRmknWmvnhyA2EZGgeu+997j88ss59dRTmT59OnXq1PE6JBEJB2tdQhghfQgBGjRo4HUIf1BWQngX8F/gXsACHxzgPEvps+GJiESMTz75hD59+tCuXTtmzZpFtQj6xSAiIZab65LCCKkQRqIDJoTW2inAFGNMTWAnbnJqNRmLSNT58ssvueiii2jWrBkffvihkkGRyibHt8iaEsIDKnceQmttpjGmG/CzBpWISLRZvnw55557LrVr12bWrFnUqlXL65BEJNyys91e/xk8oICmnbHWzgPijDHXG2NGGWP+Y4zpa4xJCnF8Ucda63UInqiszy2Ry1rLuHHjaNeuHYmJicyePTvi+uyISJioQliugBJCY0xL4CdgNNAWaAc8CfxkjDkudOFFl4SEBHJzc70OwxP5+fnEx2vhG4kMOTk5XHfdddxwww106tSJr7/+mmbNmnkdloh4RQlhuQKdmPppYBnQ2Fp7hrX2DKAxsJyQLs4WXerWrcvGjRvZs2dPpaqYFRUVsWXLFtLS0rwORYRffvmFDh06MGHCBIYNG8bMmTM12bRIZedPCNVkfECBlnQ6AqdZa3f7D1hrdxtj7gf+F5LIopB/RY9NmzaRn58f8vvl5eWRnJwc8vsEomrVqhx++OFehyGV3Ny5c+nduzeFhYXMmDGDc845x+uQRCQS+PsQqkJ4QIEmhHlAzVKOp/neE58aNWqEbam39PR0Tj755LDcSyTSvfzyywwaNIhmzZoxZcoUjj32WK9DEpFIoSbjcgXaZDwN+K8xpqMxJs63dQLGAFNDF56ISPkmTJhA//796dGjB//73/+UDIrIHykhLFegCeEtwM/AAlxFMA+YhxtocmtoQhMRKd8HH3xA3759OfPMM/nggw/CVqEXkSiiaWfKFVCTsbU2E/irMaYpcBxuKbvvrbWrQhmciEhZ5syZwxVXXMFpp53GlClTIqZPrYhEGFUIy3VQ84T4EkAlgSLiuUWLFnHRRRfRokULZsyYodVHROTAcnIgPh4SE72OJGIF2mQsIhIxvv76a8477zyOPPJIZs+erdVHRKRsOTlqLi6HEkIRiSorV66kZ8+e1KxZk48//ph69ep5HZKIRLrsbDUXl0MJoYhEvD179jBv3jwuv/xy2rRpQ1xcHB9//DGNGjXyOjQRiQY5OUoIy1FuH0JjTDwwAJhsrd0U+pBERJz169fz9NNP89///pesrCzq1KnDtddey+23366pZUQkcEoIy1VuQmitLTDG/AeYHoZ4RKSSKyoqYt68eYwdO5Z3330XgL/97W+0adOGm2++WWtmi8jBy85WH8JyBPov6/+AU4B1IYxFRCqxrVu3MnbsWMaPH8+aNWtIS0vj1ltv5eabb6Zx48akp6crGRSRisnJgdq1vY4iogX6r+t/gSeMMU2Ar4Cc4m9aa5cGOzARqRzWrFnDqFGjGD9+PHl5eXTr1o2HH36Yiy++mNTUVK/DE5FYkJMDjRt7HUVECzQhfNO3H13KexaIC044IlKZjBo1invuuYf4+HiuvfZa7rjjDlq0aOF1WCISazTtTLkCTQiPDmkUIlLpPPLIIzz44INcdtllPP300zRo0MDrkEQkVmnamXIFunSd+g6KSFBYaxk6dCjDhw/n2muv5ZVXXiEuTo0MIhJCGmVcroDnITTGnGuM+dAY870xppHv2A3GmLNCF56IxJpHHnmE4cOHc8MNNzB+/HglgyISWoWFkJenJuNyBJQQGmP6AO8AP+OajxN8b8UBd4cmNBGJNZMmTWLIkCFce+21jBkzhipVNDe+iIRYjm8crCqEZQr0X+O7gf7W2tuAgmLH/wecFPSoRCTmLF++nGuvvZb27dszduxYJYMiEh5KCAMS6L/IxwJLSjmeDdQIXjgiEou2bt3KhRdeyGGHHcb7779PUlKS1yGJSGWhhDAggY4y3gQ0488TU3cGVgc1IhGJKdZarrnmGrZu3crChQupX7++1yGJSGXiTwjVh7BMgVYIxwLPGGM6+r5uZIz5O/A48GJIIhORmDBx4kRmz57NqFGjOPXUU70OR0Qqm+xst1eFsEyBTjvzuDEmDZgDJANzgb3AKGvt8yGMT0Si2Pbt27ntttto3749N954o9fhiEhlpCbjgAS8MKi19n5jzL+BlrjK4vfW2uyQRSYiUe/OO+8kMzNTg0hExDtqMg7Iwf4LbYE8YA9QGPxwRCRWfPLJJ7z22mvcc889tG7d2utwRKSyUpNxQAKdhzDJGPMUsBNYDnwD7DTGPG2MSQ5lgCISfXJzcxk4cCDHHnssDzzwgNfhiEhlpibjgATaZPwi0BO4gf3Tz7QHHgWqVGMVSAAAIABJREFUA9cHPzQRiVbDhw9n9erVfPrppyQn6/+MIuIhNRkHJNCEsDdwibV2TrFja4wxW4FJKCEUEZ9vvvmG//znP/Tt25du3bp5HY6IVHb+hDA11ds4IlygfQhzgI2lHN8I5AYvHBGJZoWFhfTv359atWrxn//8x+twRERcH8LkZNC66WUKNCF8FhhqjEnxH/C9ftD3XkgYY+4zxlhjzHPFjhljzDBjzCZjTK4xJt0Y06rE52oZYyYYY3b5tgnGmJolzjneGDPPd42NxpghxhgTqmcRqQxeeOEFPv/8c5566ilq167tdTgiIq5CqP6D5Tpgk7ExZmqJQ12BjcaYb3xfH+/7fEi+y8aYdkB/3ACW4u4G7gCuA34EhgBzjDHNrbVZvnPeBBoD5+JGRr8MTAAu8F27Bm5OxfnAaUBz4FVcJfSJUDyPSKz78ssvGTx4MGeffTZXXnml1+GIiDjZ2eo/GICy+hDuKPH1pBJf/xLkWH7nmwR7ItAPl/D5jxvgVuAxa+0k37G/A1uBq4AxxpjjgHOATtbaxb5zBgILfEnjj0AfIBX4u7U2F1jh+9ztxpjR1lobqmcTiUVr1qzh/PPPp27durz66quo2C4iESMjA2rV8jqKiHfAhNBa2zecgZQwFnjPWvupMWZIseNHA/WB2f4D1tpcY8x8oAMwBjf6ORtYXOxzi3DVvw64qmJ7YIEvGfSbBQwHjiKEya5IrNm+fTvnnHMOBQUFzJw5U2sVi0hkycyEmjXLP6+SC3ilknAxxvQHmgLXlPK2/zfNlhLHtwANi52zrXiVz1prfSOi6xc7Z0Mp1/C/94eE0BgzABgAUK9ePdLT0wN9nJDKzs6OmFjCqbI+N0Tes+fl5XHHHXewbt06Ro0axebNm9m8eXNI7hVpzx4ulfW5Qc9eGZ89FM/dZuNGchs25LsI/356/TMPKCE0xtQChgHdgLqUGIxira0bjGCMMc2BEcAZ1tp9ZZxasknXlDhWWpNveeeYAxzHWjsWV7WkTZs2tmvXrmWEFj7p6elESizhVFmfGyLr2QsLC7nssstYuXIlkyZN4uKLLw7p/SLp2cOpsj436Nkr47OH5Lnz86nWtGnEfz+9/pkHWiF8HWgFvIarpIWqj1174HBcnz7/sTigszFmkC8GcFW8X4t9ri77K3y/AXWNMcZfJfT1PaxT4pyS7Vr+pLZk9VFESrDWcvPNNzN58mSeffbZkCeDIiIVlpGhJuMABJoQdgW6WGuXhjAWgMnAlyWOjQd+xlUOf8Ilcz2ALwB8S+edAdzlO38JUA2XXPr7EbbHjYZeXOyckcaYZGttnu9YD2ATsDaoTyQSgx5//HFeeOEF7rrrLv75z396HY6ISOkKCtwoYyWE5Qo0IVxN4HMWVpi1NhPILH7MGJMD7LTWrvB9/RRwvzHmB1yC+ABuEMmbvmusNMbMxI047o9rCh4DfOgbYYzv3KHAq8aYR4BmwGDgIY0wFilbeno6gwcP5sorr+Sxxx7zOhwRkQPbtcvtNcq4XIEmebcAjxpjTjTGeD3V9+PAaOB5XDXxCKBnsTkIwU0rsxw3GnmW7/Xvg1SstbtwFcEGvms8j5t/cHQY4heJWkVFRdx+++00btyYcePGUaVKyP+fKCJScRkZbq8KYbkCrRCuAlKApcCf5hiz1oYsSbTWdi3xtcUNcBlWxmd2AleXc91vgc6HHKBIJfLGG2/w9ddfM3HiRFJSUsr/gIiIlzJ9jY5KCMsVaEL4FpAG3ExoB5WISITas2cP999/P23atOGKK67wOhwRkfL5E0I1GZcr0ISwDXC6vx+fiFQ+Tz75JBs2bGDixIlqKhaR6KAm44AF+q/690CNUAYiIpFry5YtPPbYY1x00UV07qyeFiISJdRkHLBAE8IHgNHGmO7GmHrGmMOKb6EMUES8N3z4cHJzczWqWESii5qMAxZok/EM3342f+w/6F/9w+uRxyISImvWrGHs2LH069eP5s2bex2OiEjgMjIgPh5SU72OJOIFmhB2C2kUIhKxhg4dSlxcHEOGDPE6FBGRg5OZ6ZqLS8yOIn8WUEJorZ0X6kBEJPJ8++23TJw4kbvuuouGDRt6HY6IyMHJzFRzcYACSgiNMaeU9X4YlrQTEQ888MAD1KhRg3vuucfrUEREDp6/QijlCrTJ+EtcX8HiNdfifQnVh1AkxixZsoSpU6fy73//m8MO09gxEYlCGRlKCAMUaEJ4dImvE4CTgfuBe4MakYhEhDfeeIOqVatyyy23eB2KiEjFZGZC48ZeRxEVAu1DuK6Uw6uMMbuAocBHQY1KRDy3cOFCOnToQNWqVb0ORUSkYtRkHLBDXW7gF+CkYAQiIpFj165dfPvtt3Ts2NHrUEREKk5NxgELdFBJyQ5EBjgCGAb8GOSYRMRjS5YswVpLp06dvA5FRKRi8vJg714lhAEKtA/hdv44iARcUvgrcHlQIxIRzy1cuJC4uDjatm3rdSgiIhWjVUoOSkUnpi4CtgGrrLUFwQ1JRLy2cOFCTj75ZKpVq+Z1KCIiFZOR4faqEAZEE1OLyB/s27ePzz77jEGDBnkdiohIxfkrhEoIA1JmQlhK38FSWWt3BiccEfHa0qVLycvLU/9BEYluajI+KOVVCEvrO1iSDeA6IhIlFi5cCKARxiIS3dRkfFDKS+RK9h0s7hzgFkB9CEViyKJFizjmmGOoX7++16GIiFScmowPSpkJYWl9B33rGo8EOgNjgOGhCU1Ews1ay8KFCzn//PO9DkVE5NAoITwoAU9MbYw52hjzJvAZsBNoaa292Vq7LWTRiUhY/fTTT2zfvl39B0Uk+mVmQkoKJCV5HUlUKDchNMbUNsY8DfwA1AfaW2svt9auDnl0IhJW/v6DSghFJOpplZKDUmZCaIy5D1gNdAH+aq0901r7ZVgiE5GwKioqYuLEidSpU4fmzZt7HY6IyKHJzNQI44NQ3qCSR4BcYANwkzHmptJOstZeGOzARCS8nn/+eebOnctLL72EMcbrcEREDk1mpiqEB6G8hPB1yp92RkSi3MqVK7n77rs577zzGDBggNfhiIgcuowMqFfP6yiiRnmjjK8LUxwi4pF9+/Zx9dVXU7VqVcaNG6fqoIjEhsxMUPeXgGlCaZFKbujQoSxdupRJkyZp7kERiR3qQ3hQAp52RkRiz0svvcRjjz1G//79ueSSS7wOR0QkOKxVH8KDpIRQpJJ69913uemmm+jVqxfPP/+81+GIiARPdjYUFiohPAhKCEUqoU8++YQ+ffrQsWNH3n77bRISErwOSUQkePyrlKjJOGBKCEUqmW3bttG7d29atGjB1KlTSU1N9TokEZHgyshwe1UIA6aEUKSSGTx4MFlZWfzf//0ftfS/ZxGJRVrH+KApIRSpRBYvXswrr7zCbbfdRsuWLb0OR0QkNNRkfNCUEIpUEgUFBdx00000bNiQIUOGeB2OiEjoqMn4oGkeQpFK4sUXX2T58uW88847VKtWzetwRERCR03GB00VQpFKYPfu3Tz44IN0796dyy67zOtwRERCy58QpqV5G0cUUUIoUgm88cYb7Nq1i+HDh2tpOhGJfZmZUKMGxMV5HUnUUEIoEuOstbzwwguccsoptG3b1utwRERCLyNDzcUHSX0IRWLcggUL+O6773j55ZdVHRSRykHrGB80VQhFYtwLL7xAzZo1ufLKK70ORUQkPHbsUEJ4kJQQisSw3377jUmTJtG3b1+tSCIilcf69dCokddRRBUlhCIx7OWXX6agoIBBgwZ5HYqISHgUFMDGjdCkideRRBUlhCIxqqCggDFjxtCjRw+aNWvmdTgiIuGxcSMUFiohPEhKCEVi1KRJk9iwYQP/+Mc/vA5FRCR81q1zeyWEB0UJoUgMstby2GOP0bx5cy644AKvwxERCR8lhBWiaWdEYtCcOXNYtmwZ48aNo0oV/b9PRCoRf0KoQSUHRb8pRGLQyJEjadCgAX369PE6FBGR8Fq/HurWhZQUryOJKkoIRWLMF198waeffsptt91GUlKS1+GIiITXunVqLq4AJYQiMWbkyJHUrFmTAQMGeB2KiESDPRshey0U7nVfFxXA7h/h1w8g81tPQ6sQJYQVoj6EIjHkxx9/5P333+fee++lRo0aXocjIpEs4xv47hFY/x5g3bGk2pCfBUX73NdVEqDdq3DUVV5FeXCsdU3GvXp5HUnUUUIoEkPuu+8+qlatyi233OJ1KCISLvnZkPUz2EKwBZBcF6r95cDn7/wKVgyHDVMgvjq0vAeqH+sqhbmbIKE6pLWC6s1g+X2wuA/s+RWOuxsifT30bdsgN1cVwgpQQigSI5YsWcL777/Pww8/TN26db0OR0RCyRbBlrmw5lX49X0o3FPsTQPH9IMTR0ByHd/5FrYvhhX/hs0fQUJNaD0Umt8MSYcd+D7dZsKSv8OywbBnA5z6NJgI7m2mKWcqTAmhSAyw1nLXXXdRv359br/9dq/DEZFQKiqAuWfDlk8hIQ2Ovhrq94QqiWDiYMsn8OMzrin42IGQtQq2zoe92yDpcJcoNvsHJATQrSQuCTq+CakN4YfRUJgHp4+J3KRQCWGFKSEUiQFTpkxh0aJFjBkzhqpVq3odjoiE0rcPuWTw5Cfg2BshvsT0Kg3Pg2NugK9uhu9HQtUm0OBcqNsVmvwN4g/y3whTBU4eBXEp8N2/XXWy7X8jMylUQlhhSghFolx+fj733HMPLVq04Prrr/c6HBEJpd8+dUnZX/rCcWW0BqQdB91mQ/5uSEw79PsaAycMdxXIFQ8DFtqOi7w+hevWQfXqkBaEZ65klBCKRLlx48bx008/MWXKFOLj9VdaJGblbXUDPGo0gzbPln++McFJBotf74SHAAMrHnIDV1o/ELzrB8P69a46GGmJahSIwHqviAQqKyuLoUOHcsYZZ2jNYpFYVVQIG2fAvAthXwZ0fPvgm32D6fihcNTV8M2DsGGqd3GURnMQVpjKCSJR7IknnmDr1q1MnToVo/8Ri8SeH56ClaMgdyMk1YF2r0CtE72NyRg4fSzs/gEWXw1n/w/SWnobk9+6ddChg9dRRCVVCEWi1ObNmxk1ahS9e/embdu2XocjIsG2ZwMsvQ2qHQVnTIKLNkTOBNHxKdD5A4hPhXl/dX0VvZaVBRkZqhBWkBJCkSj10EMPsXfvXkaMGOF1KCISCjs+d/uTR0OjSyAu0dt4Sko9Ejq9C9mr4LtHvY5GI4wPkRJCkSj0ww8/8PLLLzNo0CCaNm3qdTgiEgrbP3NLx3ndRFyWumfAUdfAD09C9i/exqKE8JAoIRSJQs8//zyJiYk8+OCDXociIqGy43OoeZKbHDqSnTTCzUm47B5v41BCeEiUEIpEoblz59K5c2ctUScSq2wh7PwSap/udSTlSz3SrXO8/l3YutC7ONatg8REqFfPuxiimBJCkSizbds2vvvuO7p06eJ1KCISIqkF66EgOzoSQoCWd0FKQzcIxhZ5E8O6ddC4MVRRalMR+q6JRJn58+cD0LVrV28DEZGQqZH/g3sRLQlhfFU46VFX1VzYG/K2hT8Gf0IoFaKEUCTKpKenk5qaSps2bbwORURCpPq+HyAhza1KEi2OuhpOGgkbP4QZrWHDlPDdu7AQfvwRjjkmfPeMMUoIRaJMeno6HTt2JCEhwetQRCREauSvhNqnucEa0cIYaHk3nPOVaz6efxF8datbaSXUli1zcxCqK02FRdGfNBHZtWsXK1asUHOxSCwryKVa/uroaS4uqWZr6Pk/aH4r/Pg0LLgY8rNDe885c9y+e/fQ3ieGaek6kSiyfPlyQP0HRWJaxtcYiqI3IQQ3ifapT0L1pvDVzfBxFzjhYUg8DBJrQmEu5KyHPethz6++179yfFYR5LwDVQ+yL+CcOXDCCRphfAgiqkJojLnXGPOFMWa3MWabMWaaMaZ1iXOMMWaYMWaTMSbXGJNujGlV4pxaxpgJxphdvm2CMaZmiXOON8bM811jozFmiNFisBLhli1bRkpKivoPisQy/wol0ZwQ+jX7B3SeBlk/wrxeMKcDTG8JM091lcOvboGfnoOMryEuhbR9K2BmG9g6P/B77NkDCxdCjx6he45KINIqhF2BF4AvAAM8DHxsjGlprd3pO+du4A7gOuBHYAgwxxjT3Fqb5TvnTaAxcC5ggZeBCcAFAMaYGsAcYD5wGtAceBXIAZ4I5QOKHIrly5fTsWNHEhMjbAkrEQmeHZ+TV6UOySlHeB1JcDQ8Dy5cC9mrYV+G2+KSoWoTSG0ESYe7/ofA0jmvc3rev+GTs+DUp+DYm35/74AWLIB9+9RcfIgiKiG01p5d/GtjzDXALqAjMM1XwbsVeMxaO8l3zt+BrcBVwBhjzHHAOUAna+1i3zkDgQW+pPFHoA+QCvzdWpsLrPB97nZjzGhrrQ3H84ocjB07drBmzRquv/56r0MRkVDa8RlZiceR7HUcwZR8uNvKsSehMXT5DBb3gS//CRunw+ljoGqj0j9QuNc1FycmQufOQQ66comohLAU1XHN2hm+r48G6gOz/SdYa3ONMfOBDsAYoD2QDSwudp1FuOpfB1xVsT2wwJcM+s0ChgNHAR4vyCjyZ/75BzUhtUgM27sDstewu3p36ngdi1cSa0KXafDT87BsMExvBccPBRMPu3+ArJ9dv8PcTW7y7ml1oGNHSE31OvKoFukJ4dPAMmCJ7+v6vv2WEudtARoWO2db8SqftdYaY7YW+3x9YEMp1/C/p4RQIk56ejpJSUmcdtppXociIqGSsx6A3PgjPQ7EY6YKNP8XNDwfPusPX9/pjifUhBrNodaJ0OA82PAz/DQdetUs+3pSrohNCI0xo4FOuKbfkpMYlWzSNSWOldbkW9455gDHMcYMAAYA1KtXj/T09DJjD5fs7OyIiSWcKutzT58+nRYtWrBkyZLyT45BlfXnXlmfGyrns6ftXcbJQNbeKpXu2eEAP/MqD1C1Th/2ValFfpWark9hAZAF9b6YzXFMp6jmVJbNepbdScd7EXZQeP3nPSITQmPMk8AVQDdr7Zpib/3m29cHfi12vC77K3y/AXWNMcZfJfT1PaxT4pz6/FFd375k9RFr7VhgLECbNm1spEz5kZ6eXimnH6mMz71z507WrFlD3759K92z+1XGnztU3ueGSvrsG7JgPiSmHk77yvbslPUz71b6B157DWrVpErr2pyS8wh0+twNVolCXv95j6hpZwCMMU/jBoicaa39ocTbv+CSuR7Fzk8GzmB/n8ElQDVcP0G/9kDVEuec4fusXw9gE7A2KA8iEkTz58/HWsuJJ57odSgiEkr5uwEoqKL+cOWyFj7+GM7qDmdOg8I8N53N6vFgi7yOLupEVEJojHke6AtcCWQYY+r7tmrg+gICTwGDjTGX+OYofBU3iORN3zkrgZm4EcftjDHtcYNNPvSNMMZ37h7gVWNMa2PMJcBgQCOMJSLNmzeP5ORkWrRo4XUoIhJKBS4hLDRVPQ4kCmRnw4YN0KYNpB0HPRZDjRbw2fXwcVfYtdLrCKNKRCWEwE24kcWfAJuLbXcWO+dxYDTwPPAlcATQs9gchOCmlVmOG408y/f6Gv+b1tpduIpgA981nsfNPzg6FA8lcqjS09Pp0KGD5h8UiXX+CqFRhbBc27e7fV1fj6+araD7fGg7DnZ9B7PbwbbFB/68/EFE9SG01pa7UoivgjfMtx3onJ3A1eVc51tAkxZJxMvIyGD58uUMGzbM61BEJNTys8DEUWSSvI4k8m3b5vZ1ik3QY6rAMddD/R7waXf4tAd0mQL1NWl1eSKtQigiJSxYsABrbeXrXC9SGeXvhvjq5a/OIfsrhIeXMuF11UauWlj9GEg/HzZ+GN7YopASQpEIl56eTnJyMqefHgPrmopI2fJ3Q0INr6OIDqVVCItLqQdnpUNaS/h8QNjCilZKCEUiXHp6Ou3atSM5OaYWshKR0ighDFxZFUK/pMPg6GshdzPkbQ1PXFFKCaFIBMvMzGTZsmVqLhapLJQQBm7bNkhIgBrlfL/SWrv9ru9CH1MUU0IoEsHUf1CkklFCGLjt2111sLz+lmmt3D5TCWFZlBCKRDD/+sVt27b1OhQRCYcCJYQB27btwP0Hi0s5AhJrwa4VoY8piikhFIlg6j8oUsmoQhg4f4WwPMa4ZmMlhGVSQigSodR/UKQSUkIYuEArhAA1W0PmCrfcnZRKCaFIhFq4cCFFRUV06dLF61BEJByKCqEgRwlhoAKtEILrR5i/C3I3hTamKKaEUCRCpaenk5iYSLt27bwORUTCocC3AqsSwvLl50NGRuAVQv9I40w1Gx+IEkKRCDVv3jzatWtHSkqK16GISDjkKyEM2M6dbn8wFUJQP8IyKCEUiUC7du1i6dKl6j8oUpnk73Z7JYTlK2+VkpKSD4fkepqLsAxKCEUikPoPilRC/oQwXglhuQJZpaSktNZqMi6DEkKRCDRv3jz1HxSpbH6vEFb3No5ocLAVQnAjjXd9B7YoNDFFOSWEIhEoPT2dtm3bkpqa6nUoIhIuBWoyDlhFK4SFeyBnbUhCinZKCEUizO7du/nqq6/UXCxS2agPYeD8FcKDSgi1hF1ZlBCKRJhFixZRVFSkASUilY0SwsBt3w5paZCQEPhnamqkcVmUEIpEmPT0dBISEmjfvr3XoYhIOP0+qER9CMt1MKuU+CXUgNTGGlhyAEoIRSJMeno6p59+uvoPilQ2+bshvipUifM6ksh3MKuUFJfWShXCA1BCKBJBNm/ezBdffEH37t29DkVEwk3rGAeuIhVCgJrHw+6VsOuH4McU5ZQQikSQ9957D2stf/vb37wORUTCTQlh4CpaITx2ICTWgrk9IGdd8OOKYkoIRSLI22+/TevWrWnZsqXXoYhIuOVnaVLqQFhb8Qphtb9At9nue/1Jd8jdEvz4opQSQpEI8euvv7Jo0SKuuOIKr0MRES8UqEIYkOxs2LevYhVCgFonQtcZkLsJPu0OGcuCG1+UUkIoEiHeeecdAC6//HKPIxERT6jJODAVWaWkpDodoMsUyN0IH50Ci6+B7LVBCS9aKSEUiRBvv/02p556Kk2bNvU6FBHxghLCwFRklZLS1O8OF66BlvfAr+/Bh81cYrj980OPMQrFex2AiMCaNWv44osvePzxx70ORUS8ooQwMMGoEPol1oSTHoVm/4TvR8KaV2HtG3BYG6hzBlQ72m11u0JCtUO/XwRTQigSAd5++20AjS4Wqays9SWEmpS6XMGqEBaX2hDaPAMnPgJrXoc142HVS1CY696vehS0fw3qdg7ePSOMmoxFIsDbb79N+/btadKkidehiIgXCnPBFqpCGIhgVghLSqgBzf8J534Ff8uBi39zA1BMHHzcFb6+Bwr3Bv++EUAJoYjHfvrpJ5YvX67BJCKVmdYxDty2bW4N4+ohrqYaAyn1oMG5cO4yaNofVj4OH3eBvO2hvbcHlBCKeGzSpEkAXHrppR5HIiKe+X0dYyWE5dq+3VUHjQnfPROqweljoNO7bpqajzvF3KhkJYQiHps0aRJt27blyCOP9DoUEfGKKoSB27YtuP0HD0bjy+DMj92E1nM6QMZyb+IIASWEIh5au3YtX331laqDIpWdEsLA+SuEXqnbCXosdP0K53SCDVO9iyWIlBCKeEjNxSICQEGW2yshLJ+XFUK/mq2g5/+gRguYfxF896gbKR7FlBCKeGjSpEmcdNJJ/OUvf/E6FBHxkiqEgfO6QuiX2hC6z4cmV8Ly+2DRlW6N5CilhFDEIxs3bmTJkiWqDopIpUwIrYX/+z947bWD+FB+PmRmel8h9ItPgQ5vwEmPwa/vumXwdn7ldVQVooRQxCMffPABAJdddpnHkYiI5ypZQrhpE1x4IVx5JVx3HcyeHeAHd+xw+0ioEPoZ45a/OysdivJgdnv44cmoa0JWQijikUmTJtGyZUtatGjhdSgi4rX83VAlEeKSvI4k5KZMgVat4JNPYNQoaNkSrr0Wtm4N4MMrVrj9UUeFMsSKqXsGnLscGpwPS2+Hz/pB4T6vowqYEkIRD2zbto358+eruVhEnEqyjnFREQwcCI0bw/LlcMcd8NZbrhW4b98Aimpz5rhJqTtH6BJySYfBGe9D66Fu+bv0c2FfptdRBURrGYt4YOrUqRQVFXHxxRd7HYpIUBQVQVYW7NsH1apBcrJrSdu3z/2y373bdf8qKDjwVvL95cvrsWoV5OZCXp7bcnPdvapVcwtVHHYYHHssNG8OtWp5/V04BJUkIfziC9iyBZ54wv3cAE44Af7zH7j5ZmjS5Ei6di1jzuk5c6B9e/cHIFIZAycMg+rHuCrh7PbQbjwc3s7ryMqkhFDEA1OmTKFJkyacdNJJXociUq5t2+DLL133rX37YO9e2LABfvwRfvgBNm6EXbv+WN2Ji4PERJfAVdxxfzqSkOD2+fl/Prt2bahRA1JSAt/i413yWVjoktijjoKjj4YWLUK/MtofVJKEcNo092fj3HP/ePyf/3T9CF98sSkLFkC/ftCnT4muglu3wtdfw/DhYY35D7KA5cCXvm2t71gWsBfX7hoHVAeOvwaadoCkO2BXezj6ajf4JLWhN7GXQwmhSJhlZ2cze/ZsBg0ahAnn0ksiAfj8c/j2W1i7FlavdhWdVav+fF5cHBxzjEucunVz1bmaNV0SmJ29v1qYluaOp6W59+Lj/7wlJJR+/Ouv/0fXru1ITnbJWlKSOw7u2llZbgaSn35yienq1ZCT45JQ/5aV5fKI4sf8W1HRgb8Phx3mkuCjjw7Jt/nP8ndDfDgzUG9MmwYdO7rvb3HGwDvvwP33/8iiRc257Ta47Tb356pRI7f1yvqEQcDrv/XETHDHjjwSGjZ0yX1IrAReBL4Gfga2FHuvIdAMOBqXACYDhb4tA1gIvHUMMBnO/xp6nwm/vg9tx8FRV4Qo4IqzEn7yAAAgAElEQVRTQigSZrNnz2bv3r389a9/9ToUkT94/XX4+9/d67g49wv3lFNgwABo2xaOOMIlZYmJ7hd6YmJo49m6NY+GByimJCa6imDt2q65+IILDu7a1u5voo6Pd8+bne0S4Z9/huuvhxtugI8/DtOSufm7IeWIMNzIO+vWwTffuObh0qSkwIUXbmb06OZ8+y3MmAHr17ttwwao9dMcdlKLvs+fStHzf/xs7douOeza1VUbmzY9hEAt8DHwJPARkAS0BXoBTYGWwGlAID+u7cALwLCTYd1vcOe1sPhK2LMOjrs7vOsxl0MJoUiYTZ48mVq1anHGGWd4HYrI77Zvh9tvd92zJk50v1z9zbOxyBiXVBZPatPS4MQT3bZzpxv8MGYMDBoUhoDyd0ON5mG4kXc+/NDtA0nejz/ebb+zFhrPwZ57JrtejWPTJpckFt9++QVeeAGeecbd44ILXFfD1FSoWtXtS3v9+5/zPOBNXCK4AqgHPAwMAio6y83hwBDgdOCqJLj5/+CYIRD3E9RfAje2hfPiKnjx4FJCKBJGBQUFfPjhh1xwwQXEx+uvn0SOO+90/QDHjg1jM2kE698f3n0X7roLzjknDLOcFGTFfB/CadP2DwA6aD/+CBs2YB54gGrVoFkzt5W0eTO8+KLbpga4xPDJcdA3Dq4sgMOL4MdkeOcvsKgxJHwOVb/7YwJZo4arkJfc0tJcBT0pyVWc/f1tAaqfDeYr4H4DG1rCb4fBmgI4YTqcd2EFviHBp99IImG0YMECMjIy1FwsEeXTT91qEffdB61bex1NZDAGXn7ZfT/+/neXGNarB3Xrul/81au7X/pBE+ODSrKyYO5c15xbIf6Zq3v2LPO0I46Ahx+GBx6A335zfUr37HGb/3VeJlRfAXVWwFEroP5mKLTwTSMYewx8UR1yfJ/Zs/HP1/AneQejZk3XjN28OQx+wdC69RHwyxvQ4LwKfDNCQwmhSBhNnjyZ5ORkzj77bK9DEQHcVC6DBrlfVg884HU0kaVJE3j6adeXcP78P7+fkuISwxo13L74llRsfumEhP3HU1Pd18W3xIR8ronPZcVPNVi93h37/vtav382JWV/hSohwSWi/n6P/s0/ECeCuqT9wZw5rmJ2sH09/3CBY44JuHydmOjmOgRgD/A/YAkw3/c6Dzci+DTgXoi7Ek4+HE4O4Np5eZCR4boVFN9273bJ4r59rm+qv0uCta5v6qpVrl/kpEnw7LPQr9/VEfXzUkIoEibWWiZPnkyPHj2oWrWq1+GIAG4gyc8/uwJMyEZqRrHrr3dNxhs2uPnztm51v/izsvbvi2+bN7vWzeLT4vhHRGdnl36PWlWzuGYsjB1fg2dn+Y+eWKF4/clh8ZHb/tcl9xU55n8dF1d68lmlipsqpkEDV61LS3OJ7LvvuipZx44VeKj8fEhPh6uvDvwzXwPv4BLAL4B8XAJ4Eq5P4JlAZyDt4MNJTnbPdkQFxgD99pt7jP79XcX0pZfCPL1RGZQQioTJsmXLWL9+PUOHDvU6FJHfffCBqw527+51JJGrQQO3HaqiIjfdTX7+/hHO+flgs3fDMrjrvhpcN8Id+/zzr2nd+mTy811Fyt9cmZ/v5kwsLNw/f6L/dckJvotP9B3IMX9shYXlf7agoPRnLCx0CXBprrqqggOV5s932XSPHuWfuwcYCozGJYBtgNuALkBHKpQABlP9+jBrFjz6KAwd6vqm/vvf3sbkp4RQJEwmTJhAQkICF1S4zUQkuHbvduvJ3nJL5DY1xpIqVdyghD/JcAlho6Nr0KiRO5Sbu4uuXcMZXXBY6/5cbdwImza5PG7PHpdslpyMOmDPPefmlTnnnDJuDMwFBgKrgAHASKBmBe8ZQnFxrnvGmWdCJK1NoIRQJAz27t3L66+/zl//+lfq1Kno/AUiwfXRR67qc9FFXkcSA2wR5G6CrJ8haxXs3Qb5Wb5ttxtFnJ8FWEiu5+YcTDkCkuvD3u3uGjEwqMQY10yclgYtWwbhgqtWwZQpbsRTauqf3y8CpgKP4/oIHg18gmsSjnAdOngdwR8pIRQJg6lTp7Jjxw769evndSgiv5s82fX3ahfZS6xGjsI8l/BtWwzbFsGOz2BfBhTugYI9uDJVMVUS3OojCdX378F9LnczFJZY1y9J/1n8k2eecR0X//GPP7+3DLgGN2fgUcBzQF+glLxRyqeEUCQMXn75ZRo1akSPQPrAiITBvn1uxGPv3kGePiWWFBXAuv+DX16D3T/Bnl/5PelLrguHd3BVvrgUiEt1a9RWbwrVmkJKfYhLPvC1rXWVw7zfXHJYlA81TwjLY0WNzEx45RW44oo/juCwwPPAHUBt3GTSvVFGc4j07RMJsXXr1jFnzhyGDBlCnH7zSoRIT3d9vdRcXIK1kLcVNk2H70ZA9mq3gkjdzi7Zq34s1D4dqh1zaB0vjYHENLfF+AolFfbyy24kzW237T+2D7gcmAycB7xKxVcRkT9QQigSYuPHjwegb9++Hkcist/kya5L1llneR1JGFgL+3ZCznpX5cvfDUV7oXCva/LN3eS2nHWu/19BlvtcrVPgjA/gyAvBVPH2GSqbggLXXNy1K5xcbHbAp3HJ4OPAnYAGQwWNEkKRECosLOSVV16hR48eNGnSxOtwRAA3/cmUKW7QZszNPViU7/r5Za6AnV/Bzi/cPn/3gT+TWAtSGkLqkVCnk6sE1jzRVQU1/Nobb7wBv/7qRhj7bcatLXwBcJdHccUwJYQiITRlyhR+/fVXnnjiCa9DEfndZ5+5KUGirrk4bzvs/BJ2fb+/qpe3FYryXLWvINs18Rb5ZoWukuASuyZXuWbZqo0htZFLAKskQpUkN7I3Ptay4ii3aZNrJm7XDnr12n98MK7JeLRXgcU2JYQiITJp0iT69OnDcccdx4UXRsbi5VI57doFCxa4foPp6fD11261hfPP9zqyAK36r+vPl7N2/7G4ZFfVS67nXsfXcAnfkRdBWitIa+n2cUkHvKxEIGvdMh5797oFtqv4muqXAK8D9wJNPYwvhikhFAmBZ555hltvvZV27doxbdo0kpL0S0nCy1rX2vbaay4BLCpy66q2bw8PPgiXXAKHHeZ1lAHY/hl8cSMcdhocexPUbuOqfom11Jwbg+p/9JEb/v7UU9CsmTtYBPwLaADc52FwMU4JoUiQPffcc9xyyy1cdNFFvPnmm6TEXCctiXS7d8N117ll6U4/3a2K0K0btG0bXX0G44pyYXF/Vwns9hEkRuCyExI869bR9PnnoUsX+Ne/9h//D/AV8AZQzaPYKgElhCJBVFBQwMiRI+nSpQvvvfeeppmRsCoqctXAPn3cAg9PPOG6YkVrIa3p7udgz2ronq5ksDIYPBhTVATjx+9vKk7HVQX/BlzlXWiVgRJCkSD66KOP2LBhA08//bSSQQmp3FxYsQKWLYPly/fvs7Ohbl23RnGXLl5HWUEFe2DtRI7YMwNa3utG+0ps+/57ePttNvx/e/ceJUV55nH8+8yVQYaLEBgQEFRkVFx0JV5QWELwqInEXaMYk5g1OdEkumaVGCWJR02IZvcYk7iaG1E3G00MZj3ZRBd1cc0YLhIiCgmIEqMIIgwzrMBcmZ7ud/94q5maZi4N0z3dPfX7nFOnqqvefud9pnreefqt25VXcuzkyX7du/h7Dk4BHkS3mMkyJYQiGbRkyRKqqqqYP39+rpsiA0QsZqxdC2vWwLp18Oab8NZb/kJMFzw0o7ISpk/3h4mnT4f582HMmJw2+/Ak4rBnLby7DGqf97eKScTYX1rN0FPvzHXrpD8sXgxHHcU7CxZwLEAMnww2As8DlblsXDQoIRTJkO3bt7Ns2TIWLVpEaWlprpsjBco5eP11SJ5b/8ILs4gFd1EZO9afZz9vHkyeDKeeCqedBpMmdRxhKwht++C9V/y054+wazkcqAcrhqNnwNSbYPTfsX5LMbOLy3LdWsm2YHSQRYuIDRsGceAzwErg58ApuW1eVCghFMmQBx98EOcc11xzTa6bIgWmqQl+97uOJHDrVr/+5JPhkkt28LGPTeCss2D8+Jw2s28Scdj5LPx1Cex4Clzcr68YC2MvhHEfhnEX+KuHk2/5S01u2ir9KxgdZOFC2LARrsZfQPJNdN5gP1JCKJIB7e3tPPTQQ1xwwQVMmjQp182RPBaPw+7d8M47sHp1chTQ33btqKP8o+QWLfJPETn2WKip+Stz5kzIdbMP3/+tg/VfhXiLf0xc8zv+RtKDRkP1QhgzF0acDhWFdGxbMi40OsiIUVT/azUsBxYDX8t146JFCaFIBixbtowdO3bwQPgxS1LQEgnYscNfrVtXB62tHVNLS+fXXa3rrsyePb7upOpquP56uOgimDULBswtKzfeBfWrYeT7oWQwHDUJJl4Ox3wEdBhYku64w38TuvRmuAiqllf5ZPC2XDcsepQQivTRmjVrWLhwIWPHjuXi8GOWpN85B+3tEIt1npqa/Ijctm2wcye0tfmRulgM6uuhttaP2jU1+ZG6Awd8uQMHev55ZWX+iR/hqaLCJ3UVFTBihJ+Ht48aBePGwTHH+HMAkxdUDiitu2HHk1B9I5x+T65bI/lq1Sr4z1/DvGUw+2gogdcXvs7U26bmumWRpIRQ5Ai1trZy++23c++99zJ+/HiWLl1KSYn+pLrinE/CkiNlzc3w3nuwd6+/ifKBA357MhlLLifnra3w5z9Xs3ixv7q2sfHQpC8W80ne4SguhpEj/RW5o0f7eVmZn8aMgSlT4IQToKrq0MRu0KACu5CjP731CLh2OO7TuW6J5KtEAj63BErWw3PT4BLgAdj5xk6mooQwF/TfSyQNjY2NrFmzhlWrVrF582beeOMNtmzZQkNDA9deey333HMPQ4cOzXUzj5hz/nBmMknrar5/f+fDn8lELZ3l3kbaelJS4kfcKiuHcdxxcMopMHQolJamP1VU+BG5CRP8PJnMFeoNm/Oac/DmwzDybP88YZFUTcDlr8Omh2H4AXgIuDTY9kYO2xVxSghFulBXV8fKlStZsWIFK1eu5OWXXyYej1NUVMTkyZM54YQTOPvss7n00kuZO3durpsL+EOle/d2n9D1ti15a5PuVFTA4ME+ORs0qGOeXB45suv1XS0nD6eOGOGTu0GD/KhcebmfkstlZaEHFtT8gTlz5mT99yh9tGct7HsVzvxJrlsi+SYBPAYsSsA7J8GoJ+D1f4BCeKZ2BEQ6ITSz64AvA2OBTcCNzrkVuW2V9Ifm5ma2bNnCtm3b2L9/Pw0NDdTW1rJ+/XpeeeUVtm3bBkB5eTlnnXUWt956K7NmzWLmzJn9OhLY1uYvQtizx5/rtnz5GGpq/IUO27Z1TugaG3uuq6SkIwkbPtxPkyf7eXhdcjk8HzbMj7SJ9OrNh6F4MBy7INctkXyRAF4AFgFrgbG7gAXwxN1wtM67yBeRTQjN7ArgPuA6/O0vrwOeNrOTnXPbcto4yZh4PM6ePXvYuHEja9asYc2aNWzYsOFgwhdmZkyZMoWZM2dy/fXXc+655zJjxgzKM3TZZyLhE7e6uo6pvr7zcjLxSy7v359ay0mY+duRTJwIxx/fczIXXh48WIdIJcvam2HrY/5q4tLCPYVCMsABfwCWAr8CdgDjHCxYBo/PhysWwGw9kjCfRDYhBBYCP3XOJY9r3GBmFwJfAL6Su2YNfM45EokE8Xic9vZ22traaG5uPmRqaWmhra2NAwcO0NLSwt69e9m7dy+bNm3i8ccfJxaLHTI1NTXR0NBAQ0MD9fX11NXVkQjd42Pq1Kmcd955VFdXU11dzeTJkxk2bBiVlZUMHz6cQYMGHXFctbX+orlVq2DLFn/hREsLNDR0JHzdXfQwZIi/+jQ5nXhi59cjR/r59u1rueKKMwfOrUmkfzkHLgEk5wm/LtHmrwxurfVPDHExv90l/A2kD5YNrUvE/PsSbf4+g4k2aHgD2hvg+M+k3aR4W5x4WxyXcDjncAkXNM/R9FYTr9a/Sv1r9TTVNeHifnunqat1CUcinui2LAAGVmSY2WEvF5UUUVRa1HleUkRxafHB5fD6dKbismLKhpRRXllOWWUZsX0x4m1xisuKaYm1UNtUy+6m3bS2t/rfEy7Ypf233BPDKLIiyvaVMfG3E5n4+EQq36gkUZqgfnY9dTfWMuzluxj/2FJ2XzyXLd+4BratxPC/0+T81f2vMnjH4EPWJ+dFVtTtuiIrOqRMwiUOxtDVHKCsuIzyknI/Ly4/uFxk0Rq9tOQvJErMrAxoBq50zv0qtP77wDTnXLePhJ8xY4Z76aWXsta2+p98geGPZv/W7Ee8110+DzFZl88+tx629at8/t3lc9ugDx/YfpDvv7vcf/K7l++/u3xunzOIF0F7SccUK8GSywfLhcofZJ3r6Wk5/LfXXdlEEcRKoa0Mi/lzSxKn/4n4x39F4uJnYWjjwZ+az3/KudBYO5aRp/8vADU1NVk5T9rM1jnnZvRWLqojhKOAYqA2ZX0tMC+1sJldC1wLMGbMGGpqarLWsGmxYmgckrH6uuvO+tTNWeqfdPY6zcOu+ZC25Zl8bl8e/+8D8vx3l8dtg/xtnznyOkUw8vd3B1CegJI4lMSgtB1X4idK4p3bnVzual3qcli67zMHpTEoj+EqmnHzXsBNe81vOqTOtKPLjDzefQCJeOJgTtHY2JjV/KI3UR0hHIc/o2F2+CISM7sDP2pY3d17sz1CeDiy9W0i30U1blDsUYw9qnGDYo9i7FGNG3I/QhitA+Qd6oE4UJWyfjSHjhqKiIiIDGiRTAidc23AOuD8lE3nA6v7v0UiIiIiuRPVcwgBvgM8YmZrgVXA54FxwI9y2ioRERGRfhbZhNA5t9TMRgK34W9MvRH4kHPu7dy2TERERKR/RTYhBHDO/QD4Qa7bISIiIpJLkTyHUEREREQ6KCEUERERiTglhCIiIiIRp4RQREREJOKUEIqIiIhEnBJCERERkYhTQigiIiIScUoIRURERCJOCaGIiIhIxCkhFBEREYk4JYQiIiIiEaeEUERERCTilBCKiIiIRJw553LdhoJiZnXA27luR2AUUJ/rRuRAVOMGxR7F2KMaNyj2KMYe1bghe7Ef65x7X2+FlBAWMDN7yTk3I9ft6G9RjRsUexRjj2rcoNijGHtU44bcx65DxiIiIiIRp4RQREREJOKUEBa2JbluQI5ENW5Q7FEU1bhBsUdRVOOGHMeucwhFREREIk4jhCIiIiIRp4RQREREJOKUEOaQmc02s9+a2Q4zc2Z2dcr2IWZ2v5m9Y2YtZva6md3URT1nmtlyM2s0swYzW21mo0LbR5jZI2a2L5geMbPh/RBilzIRt5lVBXHsMrMmM9tgZp9IKZNXcQdt6i32MWb2UzN718yazewZM5uSUqY8+P3UB7H/1szGp5SZaGZPBtvrzezfzKysH0LsVl9jN7Ojg7hfCz4X283sh2Y2MqWevNrvmdjnobIWbHdmdlnKtryKO2hTRmIfgH1cOn/nhdrHfcXM/mhm+82sLuiHpqWUMTO7M4i/xcxqzOyUlDK9xmZmp5rZC0EdO8zsdjOz/ogzVSbiNrNJZvaQmb0ZbH/TzL5lZhUp9WSlf1dCmFtDgI3APwMtXWz/DvBh4CrgJOAu4F/M7KpkATM7C/gfoAY4GzgD+DYQC9XzC+BvgYuAC4PlRzIbymHpc9zAz4JtlwCnBq8fMbPZoTL5Fjf0EHvQkf0XMAX4e+B0/E3QnzOzo0JFvwd8FLgSmAUMBZ4ys+KgnmLgv4HKYPuVwGXAvVmLKj19jX0ccAxwC36ffxKYDTyW8nPybb9nYp8nfQmId/Nz8i1uyEDsA62PO4x9Xqh93BzgB8BMYC7Qjo/t6FCZW/Cf5RuA9wO7geVmVhkq02NsZjYUWA7UBnV8EfgysDAbQaVhDn2PuxooBr4AnBKU+xRwX7KCrPbvzjlNeTABjcDVKes2Al9PWfcC8EDo9Wrgrh7qPQlwwLmhdecF66YWcNyNwKdTyrwN3FwIcXcVO3Bi0L7poXVF+E7js8HrYUAb8IlQmQlAArggeH1R8HpCqMwngVZgaK7jPtLYu6nnQ0GsQwthv/clbmAGsB0YHbznstC2vI67L7EPtD7uMOIu+D4uaNMQ/JeY+cFrA3YCXwuVqQAagM+lGxs+adoPVITK3AbsILhgttDi7qae64A9oddZ6981QpjfVgLzzWwCgJnNBE4DnglejwbOAXaa2UozqzWzFWb2wVAd5+A7ltWhdauAJvw3mXzUY9yhMgvMbKSZFZnZJcD7gOeC7YUYd3kwb02ucM4lgAP4zhD86EgpfsQkWWY7sJmOuM4BNgfrk54N6j8jKy3vu3Ri78rQoExz8LrQ9ntacQcjCI/h/3Hs7qKeQosb0oh9gPZx6X7WB0ofV4lPeN8LXk8Gqujch7UAv6dzH9ZbbOcAK4L3Jj2LP5IwKaMRHJkjibsrQ0N1QBb7dyWE+e2LwHpgm5nF8KNktzrnngq2HxfMvw48jB9WXwE8a2bTg21VQJ0LvkYABMu7g235qLe4ARbgvy3W4zvSnwNXOufWB9sLMe7X8CMAd5s/X67MzG4FxgNjgzJV+G+dqc+7rKUjrqrgdVh98L5Cjr2T4HyixcBPnHPtwepC2+/pxv0j4Bnn3LJu6im0uCG92AdiH5fuPh8ofdx9+P78xeB1sm2pfVRqH9ZbbF31c7Whbbl2JHF3YmYTgZvxh6KTsta/KyHMbzcA5wIfwWf+NwHfNrMLg+3J/fdj59zDzrlXnHNfBdYCnw/V09XNJq2b9fmgt7gBvol/EPg8/KG0e4Cfhf5JQIHF7ZyL4c8NPB7Ygx/1+gDwNN2fN5aUGld3MQ6I2INzrZ7EHx66JbW6Ln5EXu73dOIOzp2djj8/qsfquliXl3FD2vt8wPVxh/FZL/g+zsy+gx/1/KhzLvXvOLWN6fRhvZWxbtb3qz7GnaxjDH7kbznw3V7q6G19Wkr68mbJnuCqom8BlzvnngxW/8nMTsN/Y3gGfz4CwKspb98MTAyWdwGjzcyS37aCk5rfx6HfMnIunbjN7Hh80niac25DUGaDmc0K1n+WAos7yTm3DjjNzIYBZc65OjP7A/BSUGQX/qTjUUBd6K2j8YcekmXOTal6VPC+Qo4d8FehA8mRsoudc62hzQW339OI+4PAyUCjdb6AcqmZveicO48CjBvSin3A9XHQe9wDoY8zs+8CHwM+4Jx7M7RpVzCvwp8TmzSajnanE9suDh0RGx3McxZ/H+NO1lEFPI8/n/6q8EgpWezfNUKYv0qDKfXbRZyO/bYVeBeYmlLmRPwhCfDD1UPw5x0knQMcRefzM/JFOnEPDq3rrkyhxd2Jc25f8E9iCn504DfBpnX4qyvPT5Y1f8uZk+iI60XgJOt8K5rz8Yed1mW77X3VQ+zJc+mewXd+H3LONaa8vWD3ew9xfw34G/x5tMkJ/BekTwXLBRs39Bj7VgZeH3dQD3EXdB9nZvcBHwfmOudeS9n8Fj6pCfdhg/BXzIb7sN5iexGYFbw36Xz852VrRgI5TBmIGzMbi7+ifjP+FIH2ztVksX/vyxUpmjJyFVKyg28Gbg+WJwbba/DfEObgT0i9Gn8LgxtCddwI7AMuB04AvopPGMJXsD0N/Bl/y4ZzguUnCzVufML4F/yI2Jn4Qy9fwl95NT9f404z9svxh4+Ow99uYivwREodP8QfKp2Hv2XF7/DnqhQH24uDWJ8Pts8Lyt9fyLHjT9J+EdiEv2VHVWgqy9f9nol93kWdna4yzse4M/h5H4h9XG+f9ULu476Pv/p3bsrf6JBQmVuDMpcC04Bf4hO5ynRjw99xYVfw3mlBXfuBLxVq3PgLYrbg/wdOSKkn6/17zj40mhz4hMd1Mf002F4F/Huws1vwJyPfTMol9fhzqLbhr8BaC8xL2X408GjwQdwfLA8v5LjxCcET+CHyJmAD8I/5HHeasX8RfzihDT8CsphQshOUGQTcT8f5R08SugVBUGYi8FSwfU9QvryQY+/h/Q6Yk6/7PRP7vIs6u0oI8yruTMbOwOvj0vk7L9Q+rru/0TtDZQy4E39KQCv+wsFphxsb/v6Mvw/q2AncQY5uOZOJuPGDH93VMylULiv9uwWVi4iIiEhE6RxCERERkYhTQigiIiIScUoIRURERCJOCaGIiIhIxCkhFBEREYk4JYQiIiIiEaeEUERERCTilBCKiGSAmT1qZuvNrCxl/QfNLGZmM3PVNhGR3ighFBHJjH8CRuKflgCAmQ0FHgbucc5l5fmyqQmoiMiRUEIoIpIBzrm9wKeBW8zszGD1d4H38I+rwsymmdnTZtZgZrvN7OdmNiZZh5mdZWbLzazezPaZ2YpQXZhZiZk5M/u8mf3GzJqAb/RbkCIyYCkhFBHJEOfcc8APgZ+Z2WXAJ4CrnHNtZnYM/tmlrwDvB84HhgO/NjMLqqgE/gOYBZyNf4j902Y2IuVHfR34Df5Zrj/KblQiEgV6lrGISAaZWQU+6ZsCLHLO3ROsvxs4wzl3QajsKKAuWP9yF3UZsBu4wTn3SzMrAWLA95xzN2U/GhGJCo0QiohkkHOuBfg2cAC4N7TpDOADZtaYnICtwbbjAcxsjJktMbMtZrYPaMCflzgx5ce8lM0YRCR6SnLdABGRAagdSDjnEqF1RcCTwK1dlN8VzB/FH0a+EXgbn1TWAKkXjjRlsrEiIkoIRUT6x8vAJcBW51x7N2XOA651zi0DMLOxQFU/tU9EIkyHjEVE+sf9wCjgMTM708yOM7PzzezB4LxDgC3AVWZ2UnB18S/xo4QiIlmlhFBEpB84594BzgWKgWeBTcADQDP+QhGAq/GHjF8BfgH8GNje320VkejRVcYiIiIiEacRQhEREZGIU0IoIiIiEnFKCEVEREQiTgmhiIiISMQpIRQRERGJOCWEImWTYUkAAAAhSURBVCIiIhGnhFBEREQk4pQQioiIiEScEkIRERGRiPt/9MEskGPI4LkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "na_snwd = count_station_types_by_year(na_df, \"SNWD\")\n",
    "fig, ax = plot_station_counts(na_snwd,\\\n",
    "                    \"Year\", \"Number of Stations\",\\\n",
    "                    station_types, station_color_dict,\\\n",
    "                    \"Number of Active Stations Measuring Daily Snow Depth By Year\",\\\n",
    "                    \"type_snwd_by_year.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Canada                 0.996217\n",
       "Honduras               0.000504\n",
       "Costa Rica             0.000504\n",
       "Greenland [Denmark]    0.000504\n",
       "Panama                 0.000504\n",
       "Nicaragua              0.000504\n",
       "El Salvador            0.000378\n",
       "Barbados               0.000126\n",
       "Guatemala              0.000126\n",
       "Dominica               0.000126\n",
       "Bahamas, The           0.000126\n",
       "Guadeloupe [France]    0.000126\n",
       "Jamaica                0.000126\n",
       "United States          0.000126\n",
       "Name: country, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where are the type '0' stations coming from?\n",
    "na_df.drop_duplicates('station_id').query('ctype == \"0\"').country.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States                     0.991706\n",
       "Puerto Rico [United States]       0.006662\n",
       "Virgin Islands [United States]    0.001632\n",
       "Name: country, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where are the type 'C' stations coming from?\n",
    "na_df.drop_duplicates('station_id').query('ctype == \"C\"').country.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mexico    5170\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where are the type 'N' stations coming from?\n",
    "na_df.drop_duplicates('station_id').query('ctype == \"N\"').country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States                     35020\n",
       "Canada                              876\n",
       "Puerto Rico [United States]          54\n",
       "Bahamas, The                         32\n",
       "Virgin Islands [United States]        4\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where are the type '1' stations coming from?\n",
    "na_df.drop_duplicates('station_id').query('ctype == \"1\"').country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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></th>\n",
       "      <th>station_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>ctype</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Canada</th>\n",
       "      <th>0</th>\n",
       "      <td>7901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mexico</th>\n",
       "      <th>M</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>5170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">United States</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>22479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>1509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>1742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     station_id\n",
       "country       ctype            \n",
       "Canada        0            7901\n",
       "              1             876\n",
       "              W               3\n",
       "Mexico        M              79\n",
       "              N            5170\n",
       "United States 0               1\n",
       "              1           35020\n",
       "              C           22479\n",
       "              P               3\n",
       "              R            1509\n",
       "              S             859\n",
       "              W            1742"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_df.drop_duplicates('station_id')[['station_id', 'country', 'ctype']]\\\n",
    ".query('country == \"United States\" | country == \"Mexico\" | country == \"Canada\"')\\\n",
    ".groupby(['country', 'ctype']).agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>ctype</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>C</th>\n",
       "      <th>M</th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>W</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>7901</td>\n",
       "      <td>876</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>5170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1</td>\n",
       "      <td>35020</td>\n",
       "      <td>22479</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1509</td>\n",
       "      <td>859</td>\n",
       "      <td>1742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ctype             0      1      C   M     N  P     R    S     W\n",
       "country                                                        \n",
       "Canada         7901    876      0   0     0  0     0    0     3\n",
       "Mexico            0      0      0  79  5170  0     0    0     0\n",
       "United States     1  35020  22479   0     0  3  1509  859  1742"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_major_types = na_df.drop_duplicates('station_id')[['country', 'ctype']]\\\n",
    ".query('country == \"United States\" | country == \"Mexico\" | country == \"Canada\"')\n",
    "ct = pd.crosstab(na_major_types.country, na_major_types.ctype)\n",
    "ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 720x576 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x228d8261ac8>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the above cross tab\n",
    "def plot_type_bars (df, colors, filename, img_path=IMG_PATH):\n",
    "    sns.set(style=\"whitegrid\")\n",
    "    #sns.set_context(\"paper\")\n",
    "    sns.set(rc={\"font.size\":20,\"axes.titlesize\":20,\"axes.labelsize\":20})\n",
    "\n",
    "    ct = pd.crosstab(df.country, df.ctype)\n",
    "    stacked = ct.stack().reset_index().rename(columns={0:'value'})\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "    # plot grouped bar chart\n",
    "    sns.barplot(x=stacked.country, y=stacked.value, hue=stacked.ctype, palette=colors)\n",
    "\n",
    "    ax.set_xlabel(\"Station Type\")\n",
    "    \n",
    "    #ax.set_title(\"\")\n",
    "    fig.get_axes()[0].set_yscale('log')\n",
    "    ax.set_ylabel(\"Number of Stations\")\n",
    "    \n",
    "    output_file = os.path.join(img_path, filename)\n",
    "    fig.savefig(output_file)\n",
    "    plt.show()\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "plot_type_bars(na_major_types, station_color_dict, \"type-bars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States                         61613\n",
       "Canada                                 8780\n",
       "Mexico                                 5249\n",
       "Puerto Rico [United States]             211\n",
       "Greenland [Denmark]                      69\n",
       "Virgin Islands [United States]           43\n",
       "Bahamas, The                             38\n",
       "Panama                                   10\n",
       "Honduras                                  8\n",
       "Nicaragua                                 6\n",
       "Cuba                                      6\n",
       "Costa Rica                                6\n",
       "Dominican Republic                        5\n",
       "Trinidad and Tobago                       4\n",
       "El Salvador                               3\n",
       "Jamaica                                   3\n",
       "Guadeloupe [France]                       3\n",
       "Antigua and Barbuda                       2\n",
       "Saint Lucia                               2\n",
       "Martinique [France]                       2\n",
       "Bermuda [United Kingdom]                  2\n",
       "Guatemala                                 1\n",
       "Dominica                                  1\n",
       "Cayman Islands [United Kingdom]           1\n",
       "Saint Pierre and Miquelon [France]        1\n",
       "Barbados                                  1\n",
       "Belize                                    1\n",
       "Sint Maarten                              1\n",
       "Curacao                                   1\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_df.drop_duplicates('station_id').country.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consider Station Types by Continent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>continent</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Antartica</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>element</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRCP</th>\n",
       "      <td>2104</td>\n",
       "      <td>55</td>\n",
       "      <td>5698</td>\n",
       "      <td>6703</td>\n",
       "      <td>74440</td>\n",
       "      <td>17263</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMAX</th>\n",
       "      <td>690</td>\n",
       "      <td>61</td>\n",
       "      <td>1609</td>\n",
       "      <td>4521</td>\n",
       "      <td>30949</td>\n",
       "      <td>1801</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMIN</th>\n",
       "      <td>688</td>\n",
       "      <td>63</td>\n",
       "      <td>1609</td>\n",
       "      <td>4522</td>\n",
       "      <td>30838</td>\n",
       "      <td>1799</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNOW</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>153</td>\n",
       "      <td>91</td>\n",
       "      <td>65842</td>\n",
       "      <td>130</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNWD</th>\n",
       "      <td>86</td>\n",
       "      <td>37</td>\n",
       "      <td>995</td>\n",
       "      <td>4658</td>\n",
       "      <td>52549</td>\n",
       "      <td>130</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "continent  Africa  Antartica  Asia  Europe  North America  Oceania  \\\n",
       "element                                                              \n",
       "PRCP         2104         55  5698    6703          74440    17263   \n",
       "TMAX          690         61  1609    4521          30949     1801   \n",
       "TMIN          688         63  1609    4522          30838     1799   \n",
       "SNOW            8         16   153      91          65842      130   \n",
       "SNWD           86         37   995    4658          52549      130   \n",
       "\n",
       "continent  South America  \n",
       "element                   \n",
       "PRCP                6500  \n",
       "TMAX                 463  \n",
       "TMIN                 467  \n",
       "SNOW                   4  \n",
       "SNWD                 106  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element_by_continent = pd.crosstab(df.element, df.continent).loc[['PRCP', 'TMAX', 'TMIN', 'SNOW', 'SNWD'], :]\n",
    "element_by_continent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 720x576 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x228d97912e8>)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the above cross tab\n",
    "def plot_continent_bars (df, filename, img_path=IMG_PATH):\n",
    "    sns.set(style=\"whitegrid\")\n",
    "    #sns.set_context(\"paper\")\n",
    "    sns.set(rc={\"font.size\":20,\"axes.titlesize\":20,\"axes.labelsize\":20})\n",
    "\n",
    "    ct = pd.crosstab(df.element, df.continent).loc[['PRCP', 'TMAX', 'TMIN', 'SNOW', 'SNWD'], :]\n",
    "    stacked = ct.stack().reset_index().rename(columns={0:'value'})\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "    # plot grouped bar chart\n",
    "    sns.barplot(x=stacked.element, y=stacked.value, hue=stacked.continent)\n",
    "\n",
    "    ax.set_xlabel(\"Element\")\n",
    "    ax.set_ylabel(\"Number of Stations\")\n",
    "    #ax.set_title(\"\")\n",
    "    \n",
    "    output_file = os.path.join(img_path, filename)\n",
    "    fig.savefig(output_file)\n",
    "    plt.show()\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "plot_continent_bars(df, \"continent-bars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}