WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x1b1a8776300>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a set of random latitude and longitude combinations.\n",
    "lats = np.random.uniform(low=-90.000, high=90.000, size=1500)\n",
    "lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "lat_lngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the latitudes and longitudes to a list.\n",
    "coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the citipy module to determine city based on latitude and longitude.\n",
    "from citipy import citipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "614"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list for holding the cities.\n",
    "cities = []\n",
    "# Identify the nearest city for each latitude and longitude combination.\n",
    "for coordinate in coordinates:\n",
    "    city = citipy.nearest_city(coordinate[0], coordinate[1]).city_name\n",
    "\n",
    "    # If the city is unique, then we will add it to the cities list.\n",
    "    if city not in cities:\n",
    "        cities.append(city)\n",
    "# Print the city count to confirm sufficient count.\n",
    "len(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the requests library.\n",
    "import requests\n",
    "\n",
    "# Import the API key.\n",
    "from config import weather_api_key\n",
    "\n",
    "# Import the datetime module from the datetime library.\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Starting URL for Weather Map API Call.\n",
    "url = \"http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=\"+ weather_api_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval     \n",
      "-----------------------------\n",
      "Processing Record 1 of Set 1 | san quintin\n",
      "Processing Record 2 of Set 1 | awbari\n",
      "Processing Record 3 of Set 1 | nova olimpia\n",
      "Processing Record 4 of Set 1 | ushuaia\n",
      "Processing Record 5 of Set 1 | mataura\n",
      "Processing Record 6 of Set 1 | karasjok\n",
      "Processing Record 7 of Set 1 | vaini\n",
      "Processing Record 8 of Set 1 | fort nelson\n",
      "Processing Record 9 of Set 1 | yankton\n",
      "Processing Record 10 of Set 1 | puerto ayora\n",
      "Processing Record 11 of Set 1 | arraial do cabo\n",
      "Processing Record 12 of Set 1 | port elizabeth\n",
      "Processing Record 13 of Set 1 | bambanglipuro\n",
      "Processing Record 14 of Set 1 | vestmannaeyjar\n",
      "Processing Record 15 of Set 1 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 1 | meyungs\n",
      "City not found. Skipping...\n",
      "Processing Record 17 of Set 1 | atuona\n",
      "Processing Record 18 of Set 1 | cabo san lucas\n",
      "Processing Record 19 of Set 1 | hofn\n",
      "Processing Record 20 of Set 1 | albany\n",
      "Processing Record 21 of Set 1 | ekhabi\n",
      "Processing Record 22 of Set 1 | payo\n",
      "Processing Record 23 of Set 1 | hobart\n",
      "Processing Record 24 of Set 1 | rikitea\n",
      "Processing Record 25 of Set 1 | kirakira\n",
      "Processing Record 26 of Set 1 | sioux lookout\n",
      "Processing Record 27 of Set 1 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 1 | katsuura\n",
      "Processing Record 29 of Set 1 | provideniya\n",
      "Processing Record 30 of Set 1 | yellowknife\n",
      "Processing Record 31 of Set 1 | souillac\n",
      "Processing Record 32 of Set 1 | cape town\n",
      "Processing Record 33 of Set 1 | umm kaddadah\n",
      "Processing Record 34 of Set 1 | ahipara\n",
      "Processing Record 35 of Set 1 | cockburn town\n",
      "Processing Record 36 of Set 1 | haines junction\n",
      "Processing Record 37 of Set 1 | udachnyy\n",
      "Processing Record 38 of Set 1 | plotnikovo\n",
      "Processing Record 39 of Set 1 | hilo\n",
      "Processing Record 40 of Set 1 | ilulissat\n",
      "Processing Record 41 of Set 1 | saint george\n",
      "Processing Record 42 of Set 1 | chokurdakh\n",
      "Processing Record 43 of Set 1 | georgetown\n",
      "Processing Record 44 of Set 1 | qaanaaq\n",
      "Processing Record 45 of Set 1 | te anau\n",
      "Processing Record 46 of Set 1 | periban\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 1 | saskylakh\n",
      "Processing Record 48 of Set 1 | tasbuget\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 1 | camana\n",
      "Processing Record 50 of Set 1 | taltal\n",
      "Processing Record 1 of Set 2 | upernavik\n",
      "Processing Record 2 of Set 2 | honiara\n",
      "Processing Record 3 of Set 2 | tiksi\n",
      "Processing Record 4 of Set 2 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record 5 of Set 2 | mezen\n",
      "Processing Record 6 of Set 2 | new norfolk\n",
      "Processing Record 7 of Set 2 | abeche\n",
      "Processing Record 8 of Set 2 | mehamn\n",
      "Processing Record 9 of Set 2 | karratha\n",
      "Processing Record 10 of Set 2 | butaritari\n",
      "Processing Record 11 of Set 2 | caravelas\n",
      "Processing Record 12 of Set 2 | aklavik\n",
      "Processing Record 13 of Set 2 | wilmington\n",
      "Processing Record 14 of Set 2 | carnarvon\n",
      "Processing Record 15 of Set 2 | candolim\n",
      "Processing Record 16 of Set 2 | cidreira\n",
      "Processing Record 17 of Set 2 | lompoc\n",
      "Processing Record 18 of Set 2 | nacunday\n",
      "City not found. Skipping...\n",
      "Processing Record 19 of Set 2 | verkhniy mamon\n",
      "Processing Record 20 of Set 2 | ballina\n",
      "Processing Record 21 of Set 2 | lebu\n",
      "Processing Record 22 of Set 2 | san policarpo\n",
      "Processing Record 23 of Set 2 | necochea\n",
      "Processing Record 24 of Set 2 | nampula\n",
      "Processing Record 25 of Set 2 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 2 | bluff\n",
      "Processing Record 27 of Set 2 | sao felix do xingu\n",
      "Processing Record 28 of Set 2 | mogadishu\n",
      "Processing Record 29 of Set 2 | flinders\n",
      "Processing Record 30 of Set 2 | vizianagaram\n",
      "Processing Record 31 of Set 2 | saint-philippe\n",
      "Processing Record 32 of Set 2 | kapaa\n",
      "Processing Record 33 of Set 2 | rizhao\n",
      "Processing Record 34 of Set 2 | wahran\n",
      "City not found. Skipping...\n",
      "Processing Record 35 of Set 2 | vostok\n",
      "Processing Record 36 of Set 2 | inirida\n",
      "Processing Record 37 of Set 2 | orlik\n",
      "Processing Record 38 of Set 2 | mayo\n",
      "Processing Record 39 of Set 2 | el vigia\n",
      "Processing Record 40 of Set 2 | privodino\n",
      "Processing Record 41 of Set 2 | kaitangata\n",
      "Processing Record 42 of Set 2 | poum\n",
      "Processing Record 43 of Set 2 | esperance\n",
      "Processing Record 44 of Set 2 | umtata\n",
      "Processing Record 45 of Set 2 | mangrol\n",
      "Processing Record 46 of Set 2 | balkhash\n",
      "Processing Record 47 of Set 2 | kroderen\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 2 | sentyabrskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 2 | vanino\n",
      "Processing Record 50 of Set 2 | tuktoyaktuk\n",
      "Processing Record 1 of Set 3 | ulaanbaatar\n",
      "Processing Record 2 of Set 3 | komsomolskiy\n",
      "Processing Record 3 of Set 3 | jacareacanga\n",
      "Processing Record 4 of Set 3 | mahebourg\n",
      "Processing Record 5 of Set 3 | biharamulo\n",
      "Processing Record 6 of Set 3 | namtsy\n",
      "Processing Record 7 of Set 3 | castro\n",
      "Processing Record 8 of Set 3 | opuwo\n",
      "Processing Record 9 of Set 3 | mirnyy\n",
      "Processing Record 10 of Set 3 | jiayuguan\n",
      "Processing Record 11 of Set 3 | punta arenas\n",
      "Processing Record 12 of Set 3 | adrar\n",
      "Processing Record 13 of Set 3 | korla\n",
      "Processing Record 14 of Set 3 | port said\n",
      "Processing Record 15 of Set 3 | port hawkesbury\n",
      "Processing Record 16 of Set 3 | mount gambier\n",
      "Processing Record 17 of Set 3 | progreso\n",
      "Processing Record 18 of Set 3 | salalah\n",
      "Processing Record 19 of Set 3 | guiyang\n",
      "Processing Record 20 of Set 3 | praxedis guerrero\n",
      "Processing Record 21 of Set 3 | artur nogueira\n",
      "Processing Record 22 of Set 3 | granard\n",
      "Processing Record 23 of Set 3 | madhogarh\n",
      "Processing Record 24 of Set 3 | narsaq\n",
      "Processing Record 25 of Set 3 | durant\n",
      "Processing Record 26 of Set 3 | san felipe\n",
      "Processing Record 27 of Set 3 | srednekolymsk\n",
      "Processing Record 28 of Set 3 | royal tunbridge wells\n",
      "Processing Record 29 of Set 3 | hermanus\n",
      "Processing Record 30 of Set 3 | hithadhoo\n",
      "Processing Record 31 of Set 3 | kuito\n",
      "Processing Record 32 of Set 3 | agirish\n",
      "Processing Record 33 of Set 3 | alihe\n",
      "Processing Record 34 of Set 3 | dikson\n",
      "Processing Record 35 of Set 3 | najran\n",
      "Processing Record 36 of Set 3 | haibowan\n",
      "City not found. Skipping...\n",
      "Processing Record 37 of Set 3 | carnduff\n",
      "Processing Record 38 of Set 3 | grindavik\n",
      "Processing Record 39 of Set 3 | catuday\n",
      "Processing Record 40 of Set 3 | kinsale\n",
      "Processing Record 41 of Set 3 | mae hong son\n",
      "Processing Record 42 of Set 3 | norman wells\n",
      "Processing Record 43 of Set 3 | ponta do sol\n",
      "Processing Record 44 of Set 3 | bafia\n",
      "Processing Record 45 of Set 3 | barrow\n",
      "Processing Record 46 of Set 3 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 3 | tiruvottiyur\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 3 | arecibo\n",
      "Processing Record 49 of Set 3 | tasiilaq\n",
      "Processing Record 50 of Set 3 | avarua\n",
      "Processing Record 1 of Set 4 | lorengau\n",
      "Processing Record 2 of Set 4 | severo-kurilsk\n",
      "Processing Record 3 of Set 4 | ayan\n",
      "Processing Record 4 of Set 4 | sungairaya\n",
      "Processing Record 5 of Set 4 | ribeira grande\n",
      "Processing Record 6 of Set 4 | chapais\n",
      "Processing Record 7 of Set 4 | kajaani\n",
      "Processing Record 8 of Set 4 | bilibino\n",
      "Processing Record 9 of Set 4 | panaba\n",
      "Processing Record 10 of Set 4 | bethel\n",
      "Processing Record 11 of Set 4 | bredasdorp\n",
      "Processing Record 12 of Set 4 | rawson\n",
      "Processing Record 13 of Set 4 | fukue\n",
      "Processing Record 14 of Set 4 | busselton\n",
      "Processing Record 15 of Set 4 | staunton\n",
      "Processing Record 16 of Set 4 | clyde river\n",
      "Processing Record 17 of Set 4 | ixtapa\n",
      "Processing Record 18 of Set 4 | san jeronimo\n",
      "Processing Record 19 of Set 4 | west wendover\n",
      "Processing Record 20 of Set 4 | smiltene\n",
      "Processing Record 21 of Set 4 | thompson\n",
      "Processing Record 22 of Set 4 | zyukayka\n",
      "Processing Record 23 of Set 4 | mar del plata\n",
      "Processing Record 24 of Set 4 | pevek\n",
      "Processing Record 25 of Set 4 | yeppoon\n",
      "Processing Record 26 of Set 4 | moron\n",
      "Processing Record 27 of Set 4 | zeya\n",
      "Processing Record 28 of Set 4 | abu jubayhah\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 4 | klaksvik\n",
      "Processing Record 30 of Set 4 | winnemucca\n",
      "Processing Record 31 of Set 4 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record 32 of Set 4 | bonavista\n",
      "Processing Record 33 of Set 4 | esmeralda\n",
      "Processing Record 34 of Set 4 | half moon bay\n",
      "Processing Record 35 of Set 4 | kautokeino\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 36 of Set 4 | kampot\n",
      "Processing Record 37 of Set 4 | koslan\n",
      "Processing Record 38 of Set 4 | tabuk\n",
      "Processing Record 39 of Set 4 | yakeshi\n",
      "Processing Record 40 of Set 4 | nyzhni sirohozy\n",
      "Processing Record 41 of Set 4 | quatre cocos\n",
      "Processing Record 42 of Set 4 | codrington\n",
      "Processing Record 43 of Set 4 | kasongo-lunda\n",
      "Processing Record 44 of Set 4 | rorvik\n",
      "Processing Record 45 of Set 4 | batemans bay\n",
      "Processing Record 46 of Set 4 | cayenne\n",
      "Processing Record 47 of Set 4 | bengkulu\n",
      "Processing Record 48 of Set 4 | maxixe\n",
      "Processing Record 49 of Set 4 | cozumel\n",
      "Processing Record 50 of Set 4 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record 1 of Set 5 | krasyliv\n",
      "Processing Record 2 of Set 5 | klyuchi\n",
      "Processing Record 3 of Set 5 | nizhyn\n",
      "Processing Record 4 of Set 5 | kodiak\n",
      "Processing Record 5 of Set 5 | jaque\n",
      "Processing Record 6 of Set 5 | jabiru\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 5 | aden\n",
      "Processing Record 8 of Set 5 | vung tau\n",
      "Processing Record 9 of Set 5 | beringovskiy\n",
      "Processing Record 10 of Set 5 | minas\n",
      "Processing Record 11 of Set 5 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record 12 of Set 5 | zhanatas\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 5 | hirara\n",
      "Processing Record 14 of Set 5 | vanimo\n",
      "Processing Record 15 of Set 5 | miranda de ebro\n",
      "Processing Record 16 of Set 5 | beyneu\n",
      "Processing Record 17 of Set 5 | port shepstone\n",
      "Processing Record 18 of Set 5 | sitka\n",
      "Processing Record 19 of Set 5 | pokhara\n",
      "Processing Record 20 of Set 5 | cherskiy\n",
      "Processing Record 21 of Set 5 | javanrud\n",
      "Processing Record 22 of Set 5 | takapau\n",
      "Processing Record 23 of Set 5 | yazman\n",
      "Processing Record 24 of Set 5 | meadow lake\n",
      "Processing Record 25 of Set 5 | sunrise manor\n",
      "Processing Record 26 of Set 5 | liberty\n",
      "Processing Record 27 of Set 5 | nanortalik\n",
      "Processing Record 28 of Set 5 | krasnokamensk\n",
      "Processing Record 29 of Set 5 | jamestown\n",
      "Processing Record 30 of Set 5 | jorpeland\n",
      "Processing Record 31 of Set 5 | kungurtug\n",
      "Processing Record 32 of Set 5 | yumen\n",
      "Processing Record 33 of Set 5 | roma\n",
      "Processing Record 34 of Set 5 | surgut\n",
      "Processing Record 35 of Set 5 | sao filipe\n",
      "Processing Record 36 of Set 5 | adana\n",
      "Processing Record 37 of Set 5 | filingue\n",
      "Processing Record 38 of Set 5 | gorom-gorom\n",
      "Processing Record 39 of Set 5 | metlaltoyuca\n",
      "Processing Record 40 of Set 5 | yerbogachen\n",
      "Processing Record 41 of Set 5 | paita\n",
      "Processing Record 42 of Set 5 | egvekinot\n",
      "Processing Record 43 of Set 5 | gua musang\n",
      "Processing Record 44 of Set 5 | ust-uda\n",
      "Processing Record 45 of Set 5 | cabinda\n",
      "Processing Record 46 of Set 5 | palmer\n",
      "Processing Record 47 of Set 5 | jalu\n",
      "Processing Record 48 of Set 5 | alice springs\n",
      "Processing Record 49 of Set 5 | togul\n",
      "Processing Record 50 of Set 5 | los llanos de aridane\n",
      "Processing Record 1 of Set 6 | sabha\n",
      "Processing Record 2 of Set 6 | zhanaozen\n",
      "Processing Record 3 of Set 6 | acarau\n",
      "Processing Record 4 of Set 6 | sansai\n",
      "City not found. Skipping...\n",
      "Processing Record 5 of Set 6 | bykovo\n",
      "Processing Record 6 of Set 6 | louisbourg\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 6 | waddan\n",
      "Processing Record 8 of Set 6 | vestbygda\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 6 | teguise\n",
      "Processing Record 10 of Set 6 | mys shmidta\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 6 | dubbo\n",
      "Processing Record 12 of Set 6 | torbay\n",
      "Processing Record 13 of Set 6 | hwange\n",
      "Processing Record 14 of Set 6 | moletai\n",
      "Processing Record 15 of Set 6 | orikhiv\n",
      "Processing Record 16 of Set 6 | mubi\n",
      "Processing Record 17 of Set 6 | upanema\n",
      "Processing Record 18 of Set 6 | sobreira\n",
      "Processing Record 19 of Set 6 | markova\n",
      "Processing Record 20 of Set 6 | udimskiy\n",
      "Processing Record 21 of Set 6 | ancud\n",
      "Processing Record 22 of Set 6 | khandyga\n",
      "Processing Record 23 of Set 6 | araripe\n",
      "Processing Record 24 of Set 6 | eureka\n",
      "Processing Record 25 of Set 6 | nisia floresta\n",
      "Processing Record 26 of Set 6 | chuy\n",
      "Processing Record 27 of Set 6 | hammerfest\n",
      "Processing Record 28 of Set 6 | pagudpud\n",
      "Processing Record 29 of Set 6 | bac lieu\n",
      "Processing Record 30 of Set 6 | tahta\n",
      "Processing Record 31 of Set 6 | uyuni\n",
      "Processing Record 32 of Set 6 | cascais\n",
      "Processing Record 33 of Set 6 | airai\n",
      "Processing Record 34 of Set 6 | nikolskoye\n",
      "Processing Record 35 of Set 6 | bambous virieux\n",
      "Processing Record 36 of Set 6 | farmington\n",
      "Processing Record 37 of Set 6 | dubrovytsya\n",
      "Processing Record 38 of Set 6 | hasaki\n",
      "Processing Record 39 of Set 6 | talnakh\n",
      "Processing Record 40 of Set 6 | iquitos\n",
      "Processing Record 41 of Set 6 | pisco\n",
      "Processing Record 42 of Set 6 | lodja\n",
      "Processing Record 43 of Set 6 | santa cruz\n",
      "Processing Record 44 of Set 6 | brufut\n",
      "Processing Record 45 of Set 6 | mapastepec\n",
      "Processing Record 46 of Set 6 | khatanga\n",
      "Processing Record 47 of Set 6 | atagay\n",
      "Processing Record 48 of Set 6 | sorland\n",
      "Processing Record 49 of Set 6 | atar\n",
      "Processing Record 50 of Set 6 | garhi khairo\n",
      "Processing Record 1 of Set 7 | hualmay\n",
      "Processing Record 2 of Set 7 | verkhniy avzyan\n",
      "Processing Record 3 of Set 7 | kahului\n",
      "Processing Record 4 of Set 7 | alexandria\n",
      "Processing Record 5 of Set 7 | kokubu\n",
      "Processing Record 6 of Set 7 | marcona\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 7 | paamiut\n",
      "Processing Record 8 of Set 7 | collecchio\n",
      "Processing Record 9 of Set 7 | karaul\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 7 | lolua\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 7 | hearst\n",
      "Processing Record 12 of Set 7 | buin\n",
      "Processing Record 13 of Set 7 | zgierz\n",
      "Processing Record 14 of Set 7 | santiago nonualco\n",
      "Processing Record 15 of Set 7 | devonport\n",
      "Processing Record 16 of Set 7 | kruisfontein\n",
      "Processing Record 17 of Set 7 | cooma\n",
      "Processing Record 18 of Set 7 | goderich\n",
      "Processing Record 19 of Set 7 | fortuna\n",
      "Processing Record 20 of Set 7 | tomatlan\n",
      "Processing Record 21 of Set 7 | geraldton\n",
      "Processing Record 22 of Set 7 | igrim\n",
      "Processing Record 23 of Set 7 | vao\n",
      "Processing Record 24 of Set 7 | saint-pierre\n",
      "Processing Record 25 of Set 7 | champerico\n",
      "Processing Record 26 of Set 7 | kamenka\n",
      "Processing Record 27 of Set 7 | khonuu\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 7 | abadan\n",
      "Processing Record 29 of Set 7 | zhangye\n",
      "Processing Record 30 of Set 7 | cabo rojo\n",
      "Processing Record 31 of Set 7 | virginia beach\n",
      "Processing Record 32 of Set 7 | vaitupu\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 7 | sorvag\n",
      "City not found. Skipping...\n",
      "Processing Record 34 of Set 7 | boueni\n",
      "Processing Record 35 of Set 7 | launceston\n",
      "Processing Record 36 of Set 7 | brae\n",
      "Processing Record 37 of Set 7 | tiarei\n",
      "Processing Record 38 of Set 7 | pitimbu\n",
      "Processing Record 39 of Set 7 | waterloo\n",
      "Processing Record 40 of Set 7 | labuan\n",
      "Processing Record 41 of Set 7 | qandala\n",
      "Processing Record 42 of Set 7 | pangai\n",
      "Processing Record 43 of Set 7 | ostrovnoy\n",
      "Processing Record 44 of Set 7 | karangampel\n",
      "Processing Record 45 of Set 7 | tuatapere\n",
      "Processing Record 46 of Set 7 | wanning\n",
      "Processing Record 47 of Set 7 | flin flon\n",
      "Processing Record 48 of Set 7 | kuche\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 7 | rabo de peixe\n",
      "Processing Record 50 of Set 7 | victoria\n",
      "Processing Record 1 of Set 8 | mundelein\n",
      "Processing Record 2 of Set 8 | nome\n",
      "Processing Record 3 of Set 8 | macaboboni\n",
      "City not found. Skipping...\n",
      "Processing Record 4 of Set 8 | atasu\n",
      "Processing Record 5 of Set 8 | sambava\n",
      "Processing Record 6 of Set 8 | non sang\n",
      "Processing Record 7 of Set 8 | faya\n",
      "Processing Record 8 of Set 8 | east london\n",
      "Processing Record 9 of Set 8 | lugovoy\n",
      "Processing Record 10 of Set 8 | arman\n",
      "Processing Record 11 of Set 8 | lavrentiya\n",
      "Processing Record 12 of Set 8 | yinchuan\n",
      "Processing Record 13 of Set 8 | port alfred\n",
      "Processing Record 14 of Set 8 | loralai\n",
      "Processing Record 15 of Set 8 | longyearbyen\n",
      "Processing Record 16 of Set 8 | loei\n",
      "Processing Record 17 of Set 8 | tukan\n",
      "Processing Record 18 of Set 8 | wichian buri\n",
      "Processing Record 19 of Set 8 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 8 | shenjiamen\n",
      "Processing Record 21 of Set 8 | verkhoyansk\n",
      "Processing Record 22 of Set 8 | inhambane\n",
      "Processing Record 23 of Set 8 | joacaba\n",
      "Processing Record 24 of Set 8 | san cristobal\n",
      "Processing Record 25 of Set 8 | avera\n",
      "Processing Record 26 of Set 8 | borogontsy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 27 of Set 8 | evensk\n",
      "Processing Record 28 of Set 8 | amudalavalasa\n",
      "Processing Record 29 of Set 8 | borovskoy\n",
      "Processing Record 30 of Set 8 | olafsvik\n",
      "Processing Record 31 of Set 8 | stornoway\n",
      "Processing Record 32 of Set 8 | yenagoa\n",
      "Processing Record 33 of Set 8 | oktyabrskiy\n",
      "Processing Record 34 of Set 8 | itarantim\n",
      "Processing Record 35 of Set 8 | potenza\n",
      "Processing Record 36 of Set 8 | rosetta\n",
      "Processing Record 37 of Set 8 | fredericksburg\n",
      "Processing Record 38 of Set 8 | nizhneyansk\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 8 | kilimatinde\n",
      "Processing Record 40 of Set 8 | khon kaen\n",
      "Processing Record 41 of Set 8 | rapid city\n",
      "Processing Record 42 of Set 8 | umm durman\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 8 | adamovka\n",
      "Processing Record 44 of Set 8 | assiniboia\n",
      "Processing Record 45 of Set 8 | nantucket\n",
      "Processing Record 46 of Set 8 | reconquista\n",
      "Processing Record 47 of Set 8 | charters towers\n",
      "Processing Record 48 of Set 8 | ponta delgada\n",
      "Processing Record 49 of Set 8 | suntar\n",
      "Processing Record 50 of Set 8 | mao\n",
      "Processing Record 1 of Set 9 | lima\n",
      "Processing Record 2 of Set 9 | conde\n",
      "Processing Record 3 of Set 9 | palabuhanratu\n",
      "City not found. Skipping...\n",
      "Processing Record 4 of Set 9 | portland\n",
      "Processing Record 5 of Set 9 | rokupr\n",
      "Processing Record 6 of Set 9 | marsh harbour\n",
      "Processing Record 7 of Set 9 | samtredia\n",
      "Processing Record 8 of Set 9 | topchikha\n",
      "Processing Record 9 of Set 9 | puerto rico\n",
      "Processing Record 10 of Set 9 | rincon\n",
      "Processing Record 11 of Set 9 | leningradskiy\n",
      "Processing Record 12 of Set 9 | padang\n",
      "Processing Record 13 of Set 9 | chaa-khol\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 9 | cap malheureux\n",
      "Processing Record 15 of Set 9 | port hardy\n",
      "Processing Record 16 of Set 9 | rocha\n",
      "Processing Record 17 of Set 9 | izumo\n",
      "Processing Record 18 of Set 9 | faanui\n",
      "Processing Record 19 of Set 9 | broome\n",
      "Processing Record 20 of Set 9 | casselman\n",
      "Processing Record 21 of Set 9 | touros\n",
      "Processing Record 22 of Set 9 | grand gaube\n",
      "Processing Record 23 of Set 9 | issoire\n",
      "Processing Record 24 of Set 9 | mpongwe\n",
      "Processing Record 25 of Set 9 | bara\n",
      "Processing Record 26 of Set 9 | sao gabriel da cachoeira\n",
      "Processing Record 27 of Set 9 | hambantota\n",
      "Processing Record 28 of Set 9 | kagalnitskaya\n",
      "Processing Record 29 of Set 9 | saldanha\n",
      "Processing Record 30 of Set 9 | maromitsa\n",
      "Processing Record 31 of Set 9 | sulangan\n",
      "Processing Record 32 of Set 9 | vigrestad\n",
      "Processing Record 33 of Set 9 | coihaique\n",
      "Processing Record 34 of Set 9 | olinda\n",
      "Processing Record 35 of Set 9 | isla mujeres\n",
      "Processing Record 36 of Set 9 | agadez\n",
      "Processing Record 37 of Set 9 | romny\n",
      "Processing Record 38 of Set 9 | zabol\n",
      "Processing Record 39 of Set 9 | cabedelo\n",
      "Processing Record 40 of Set 9 | warmbad\n",
      "Processing Record 41 of Set 9 | constitucion\n",
      "Processing Record 42 of Set 9 | lata\n",
      "Processing Record 43 of Set 9 | kristiansund\n",
      "Processing Record 44 of Set 9 | california city\n",
      "Processing Record 45 of Set 9 | mujiayingzi\n",
      "Processing Record 46 of Set 9 | vila franca do campo\n",
      "Processing Record 47 of Set 9 | gladstone\n",
      "Processing Record 48 of Set 9 | riobamba\n",
      "Processing Record 49 of Set 9 | dawei\n",
      "Processing Record 50 of Set 9 | turkistan\n",
      "Processing Record 1 of Set 10 | hvide sande\n",
      "Processing Record 2 of Set 10 | barsi\n",
      "Processing Record 3 of Set 10 | svetlogorsk\n",
      "Processing Record 4 of Set 10 | umzimvubu\n",
      "City not found. Skipping...\n",
      "Processing Record 5 of Set 10 | jaramana\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 10 | sibolga\n",
      "Processing Record 7 of Set 10 | wajir\n",
      "Processing Record 8 of Set 10 | khasan\n",
      "Processing Record 9 of Set 10 | san ignacio\n",
      "Processing Record 10 of Set 10 | kushiro\n",
      "Processing Record 11 of Set 10 | aswan\n",
      "Processing Record 12 of Set 10 | turochak\n",
      "Processing Record 13 of Set 10 | nautla\n",
      "Processing Record 14 of Set 10 | san andres\n",
      "Processing Record 15 of Set 10 | malwan\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 10 | plettenberg bay\n",
      "Processing Record 17 of Set 10 | impfondo\n",
      "Processing Record 18 of Set 10 | bailesti\n",
      "Processing Record 19 of Set 10 | rawlins\n",
      "Processing Record 20 of Set 10 | bogovarovo\n",
      "Processing Record 21 of Set 10 | tabou\n",
      "Processing Record 22 of Set 10 | yermakovskoye\n",
      "Processing Record 23 of Set 10 | jambi\n",
      "Processing Record 24 of Set 10 | marawi\n",
      "Processing Record 25 of Set 10 | newport\n",
      "Processing Record 26 of Set 10 | guadalupe y calvo\n",
      "Processing Record 27 of Set 10 | sapa\n",
      "Processing Record 28 of Set 10 | kisangani\n",
      "Processing Record 29 of Set 10 | santa marta\n",
      "Processing Record 30 of Set 10 | aporawan\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 10 | broken hill\n",
      "Processing Record 32 of Set 10 | guerrero negro\n",
      "Processing Record 33 of Set 10 | labytnangi\n",
      "Processing Record 34 of Set 10 | cadillac\n",
      "Processing Record 35 of Set 10 | denau\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 10 | kieta\n",
      "Processing Record 37 of Set 10 | gilbues\n",
      "Processing Record 38 of Set 10 | moreira sales\n",
      "Processing Record 39 of Set 10 | kitui\n",
      "Processing Record 40 of Set 10 | darhan\n",
      "Processing Record 41 of Set 10 | coquimbo\n",
      "Processing Record 42 of Set 10 | sumbe\n",
      "Processing Record 43 of Set 10 | alofi\n",
      "Processing Record 44 of Set 10 | mbigou\n",
      "Processing Record 45 of Set 10 | ijaki\n",
      "City not found. Skipping...\n",
      "Processing Record 46 of Set 10 | guangyuan\n",
      "Processing Record 47 of Set 10 | soyaltepec\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 10 | noumea\n",
      "Processing Record 49 of Set 10 | morondava\n",
      "Processing Record 50 of Set 10 | shache\n",
      "Processing Record 1 of Set 11 | genhe\n",
      "Processing Record 2 of Set 11 | fenoarivo\n",
      "Processing Record 3 of Set 11 | vanavara\n",
      "Processing Record 4 of Set 11 | gorgan\n",
      "Processing Record 5 of Set 11 | ihosy\n",
      "Processing Record 6 of Set 11 | yining\n",
      "Processing Record 7 of Set 11 | vila velha\n",
      "Processing Record 8 of Set 11 | beaufort\n",
      "Processing Record 9 of Set 11 | scottsburgh\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 11 | thomaston\n",
      "Processing Record 11 of Set 11 | kamaishi\n",
      "Processing Record 12 of Set 11 | xiaoweizhai\n",
      "Processing Record 13 of Set 11 | la ronge\n",
      "Processing Record 14 of Set 11 | naze\n",
      "Processing Record 15 of Set 11 | tessalit\n",
      "Processing Record 16 of Set 11 | mackenzie\n",
      "Processing Record 17 of Set 11 | samfya\n",
      "Processing Record 18 of Set 11 | malakal\n",
      "Processing Record 19 of Set 11 | kavieng\n",
      "Processing Record 20 of Set 11 | dunda\n",
      "Processing Record 21 of Set 11 | aykhal\n",
      "Processing Record 22 of Set 11 | san carlos del zulia\n",
      "Processing Record 23 of Set 11 | dir\n",
      "Processing Record 24 of Set 11 | trelew\n",
      "Processing Record 25 of Set 11 | kailua\n",
      "Processing Record 26 of Set 11 | fairbanks\n",
      "Processing Record 27 of Set 11 | moerai\n",
      "Processing Record 28 of Set 11 | burica\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 11 | bom jesus\n",
      "Processing Record 30 of Set 11 | natal\n",
      "Processing Record 31 of Set 11 | boende\n",
      "Processing Record 32 of Set 11 | kalmar\n",
      "Processing Record 33 of Set 11 | pak chong\n",
      "Processing Record 34 of Set 11 | greifswald\n",
      "Processing Record 35 of Set 11 | viligili\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 11 | nauta\n",
      "Processing Record 37 of Set 11 | urdzhar\n",
      "City not found. Skipping...\n",
      "Processing Record 38 of Set 11 | pangnirtung\n",
      "Processing Record 39 of Set 11 | luzhou\n",
      "Processing Record 40 of Set 11 | katherine\n",
      "Processing Record 41 of Set 11 | sompeta\n",
      "Processing Record 42 of Set 11 | shima\n",
      "Processing Record 43 of Set 11 | ayagoz\n",
      "Processing Record 44 of Set 11 | asau\n",
      "Processing Record 45 of Set 11 | hobyo\n",
      "Processing Record 46 of Set 11 | kytlym\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 11 | san patricio\n",
      "Processing Record 48 of Set 11 | birao\n",
      "Processing Record 49 of Set 11 | kropotkin\n",
      "Processing Record 50 of Set 11 | urucara\n",
      "Processing Record 1 of Set 12 | slawno\n",
      "Processing Record 2 of Set 12 | saleaula\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 12 | denpasar\n",
      "Processing Record 4 of Set 12 | macau\n",
      "Processing Record 5 of Set 12 | kuytun\n",
      "Processing Record 6 of Set 12 | sinnamary\n",
      "Processing Record 7 of Set 12 | northam\n",
      "Processing Record 8 of Set 12 | maceio\n",
      "Processing Record 9 of Set 12 | kudahuvadhoo\n",
      "Processing Record 10 of Set 12 | vizinga\n",
      "Processing Record 11 of Set 12 | ville-marie\n",
      "Processing Record 12 of Set 12 | yar-sale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 13 of Set 12 | lagoa\n",
      "Processing Record 14 of Set 12 | kununurra\n",
      "Processing Record 15 of Set 12 | aksu\n",
      "Processing Record 16 of Set 12 | leh\n",
      "Processing Record 17 of Set 12 | adre\n",
      "Processing Record 18 of Set 12 | metu\n",
      "Processing Record 19 of Set 12 | xining\n",
      "Processing Record 20 of Set 12 | abrego\n",
      "Processing Record 21 of Set 12 | kloulklubed\n",
      "Processing Record 22 of Set 12 | vammala\n",
      "Processing Record 23 of Set 12 | nishihara\n",
      "Processing Record 24 of Set 12 | siyabuswa\n",
      "Processing Record 25 of Set 12 | negombo\n",
      "Processing Record 26 of Set 12 | yuzhno-kurilsk\n",
      "Processing Record 27 of Set 12 | torres\n",
      "Processing Record 28 of Set 12 | yishui\n",
      "Processing Record 29 of Set 12 | paradwip\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 12 | okha\n",
      "Processing Record 31 of Set 12 | la macarena\n",
      "Processing Record 32 of Set 12 | thunder bay\n",
      "Processing Record 33 of Set 12 | ust-nera\n",
      "Processing Record 34 of Set 12 | york\n",
      "Processing Record 35 of Set 12 | karatau\n",
      "Processing Record 36 of Set 12 | aloleng\n",
      "Processing Record 37 of Set 12 | santa maria\n",
      "Processing Record 38 of Set 12 | marzuq\n",
      "Processing Record 39 of Set 12 | caucaia\n",
      "Processing Record 40 of Set 12 | la oroya\n",
      "Processing Record 41 of Set 12 | zhigansk\n",
      "Processing Record 42 of Set 12 | pospelikha\n",
      "Processing Record 43 of Set 12 | luderitz\n",
      "Processing Record 44 of Set 12 | port lincoln\n",
      "Processing Record 45 of Set 12 | wamba\n",
      "Processing Record 46 of Set 12 | tahoua\n",
      "Processing Record 47 of Set 12 | ambon\n",
      "Processing Record 48 of Set 12 | elliot\n",
      "Processing Record 49 of Set 12 | inyonga\n",
      "Processing Record 50 of Set 12 | berlevag\n",
      "Processing Record 1 of Set 13 | gander\n",
      "Processing Record 2 of Set 13 | hamilton\n",
      "Processing Record 3 of Set 13 | presidencia roque saenz pena\n",
      "Processing Record 4 of Set 13 | oriximina\n",
      "Processing Record 5 of Set 13 | cabanas\n",
      "Processing Record 6 of Set 13 | namibe\n",
      "Processing Record 7 of Set 13 | uttaradit\n",
      "Processing Record 8 of Set 13 | vangaindrano\n",
      "Processing Record 9 of Set 13 | shingu\n",
      "Processing Record 10 of Set 13 | prado\n",
      "Processing Record 11 of Set 13 | ardahan\n",
      "Processing Record 12 of Set 13 | barentu\n",
      "Processing Record 13 of Set 13 | umm lajj\n",
      "Processing Record 14 of Set 13 | umm ruwabah\n",
      "City not found. Skipping...\n",
      "-----------------------------\n",
      "Data Retrieval Complete      \n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "# Create an empty list to hold the weather data.\n",
    "city_data = []\n",
    "# Print the beginning of the logging.\n",
    "print(\"Beginning Data Retrieval     \")\n",
    "print(\"-----------------------------\")\n",
    "\n",
    "# Create counters.\n",
    "record_count = 1\n",
    "set_count = 1\n",
    "\n",
    "# Loop through all the cities in the list.\n",
    "for i, city in enumerate(cities):\n",
    "\n",
    "    # Group cities in sets of 50 for logging purposes.\n",
    "    if (i % 50 == 0 and i >= 50):\n",
    "        set_count += 1\n",
    "        record_count = 1\n",
    "    # Create endpoint URL with each city.\n",
    "    city_url = url + \"&q=\" + city.replace(\" \",\"+\")\n",
    "\n",
    "    # Log the URL, record, and set numbers and the city.\n",
    "    print(f\"Processing Record {record_count} of Set {set_count} | {city}\")\n",
    "    # Add 1 to the record count.\n",
    "    record_count += 1\n",
    "    \n",
    "    # Run an API request for each of the cities.\n",
    "    try:\n",
    "        # Parse the JSON and retrieve data.\n",
    "        city_weather = requests.get(city_url).json()\n",
    "        # Parse out the needed data.\n",
    "        city_lat = city_weather[\"coord\"][\"lat\"]\n",
    "        city_lng = city_weather[\"coord\"][\"lon\"]\n",
    "        city_max_temp = city_weather[\"main\"][\"temp_max\"]\n",
    "        city_humidity = city_weather[\"main\"][\"humidity\"]\n",
    "        city_clouds = city_weather[\"clouds\"][\"all\"]\n",
    "        city_wind = city_weather[\"wind\"][\"speed\"]\n",
    "        city_country = city_weather[\"sys\"][\"country\"]\n",
    "        # Convert the date to ISO standard.\n",
    "        city_date = datetime.utcfromtimestamp(city_weather[\"dt\"]).strftime('%Y-%m-%d %H:%M:%S')\n",
    "        # Append the city information into city_data list.\n",
    "        city_data.append({\"City\": city.title(),\n",
    "                          \"Lat\": city_lat,\n",
    "                          \"Lng\": city_lng,\n",
    "                          \"Max Temp\": city_max_temp,\n",
    "                          \"Humidity\": city_humidity,\n",
    "                          \"Cloudiness\": city_clouds,\n",
    "                          \"Wind Speed\": city_wind,\n",
    "                          \"Country\": city_country,\n",
    "                          \"Date\": city_date})\n",
    "\n",
    "# If an error is experienced, skip the city.\n",
    "    except:\n",
    "        print(\"City not found. Skipping...\")\n",
    "        pass\n",
    "\n",
    "# Indicate that Data Loading is complete.\n",
    "print(\"-----------------------------\")\n",
    "print(\"Data Retrieval Complete      \")\n",
    "print(\"-----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "562\n"
     ]
    }
   ],
   "source": [
    "# Check that over 500 cities are in city_data\n",
    "print(len(city_data))"
   ]
  },
  {
   "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></th>\n",
       "      <th>City</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Quintin</td>\n",
       "      <td>30.4833</td>\n",
       "      <td>-115.9500</td>\n",
       "      <td>66.45</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>15.95</td>\n",
       "      <td>MX</td>\n",
       "      <td>2021-03-19 22:00:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Awbari</td>\n",
       "      <td>26.5921</td>\n",
       "      <td>12.7805</td>\n",
       "      <td>66.47</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>8.95</td>\n",
       "      <td>LY</td>\n",
       "      <td>2021-03-19 22:02:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nova Olimpia</td>\n",
       "      <td>-14.7972</td>\n",
       "      <td>-57.2881</td>\n",
       "      <td>73.36</td>\n",
       "      <td>92</td>\n",
       "      <td>100</td>\n",
       "      <td>4.81</td>\n",
       "      <td>BR</td>\n",
       "      <td>2021-03-19 22:02:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>-54.8000</td>\n",
       "      <td>-68.3000</td>\n",
       "      <td>50.00</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>10.36</td>\n",
       "      <td>AR</td>\n",
       "      <td>2021-03-19 21:54:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mataura</td>\n",
       "      <td>-46.1927</td>\n",
       "      <td>168.8643</td>\n",
       "      <td>55.00</td>\n",
       "      <td>85</td>\n",
       "      <td>98</td>\n",
       "      <td>5.99</td>\n",
       "      <td>NZ</td>\n",
       "      <td>2021-03-19 21:52:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Karasjok</td>\n",
       "      <td>69.4719</td>\n",
       "      <td>25.5112</td>\n",
       "      <td>37.40</td>\n",
       "      <td>75</td>\n",
       "      <td>20</td>\n",
       "      <td>6.91</td>\n",
       "      <td>NO</td>\n",
       "      <td>2021-03-19 22:02:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vaini</td>\n",
       "      <td>-21.2000</td>\n",
       "      <td>-175.2000</td>\n",
       "      <td>84.20</td>\n",
       "      <td>79</td>\n",
       "      <td>40</td>\n",
       "      <td>8.05</td>\n",
       "      <td>TO</td>\n",
       "      <td>2021-03-19 21:57:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fort Nelson</td>\n",
       "      <td>58.8053</td>\n",
       "      <td>-122.7002</td>\n",
       "      <td>33.80</td>\n",
       "      <td>51</td>\n",
       "      <td>75</td>\n",
       "      <td>12.66</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-19 21:56:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yankton</td>\n",
       "      <td>42.8711</td>\n",
       "      <td>-97.3973</td>\n",
       "      <td>55.40</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>19.57</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-03-19 22:02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Puerto Ayora</td>\n",
       "      <td>-0.7393</td>\n",
       "      <td>-90.3518</td>\n",
       "      <td>84.20</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>9.22</td>\n",
       "      <td>EC</td>\n",
       "      <td>2021-03-19 21:54:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0   San Quintin  30.4833 -115.9500     66.45        55           0   \n",
       "1        Awbari  26.5921   12.7805     66.47        19           0   \n",
       "2  Nova Olimpia -14.7972  -57.2881     73.36        92         100   \n",
       "3       Ushuaia -54.8000  -68.3000     50.00        66          75   \n",
       "4       Mataura -46.1927  168.8643     55.00        85          98   \n",
       "5      Karasjok  69.4719   25.5112     37.40        75          20   \n",
       "6         Vaini -21.2000 -175.2000     84.20        79          40   \n",
       "7   Fort Nelson  58.8053 -122.7002     33.80        51          75   \n",
       "8       Yankton  42.8711  -97.3973     55.40        33           1   \n",
       "9  Puerto Ayora  -0.7393  -90.3518     84.20        74          40   \n",
       "\n",
       "   Wind Speed Country                 Date  \n",
       "0       15.95      MX  2021-03-19 22:00:34  \n",
       "1        8.95      LY  2021-03-19 22:02:58  \n",
       "2        4.81      BR  2021-03-19 22:02:58  \n",
       "3       10.36      AR  2021-03-19 21:54:36  \n",
       "4        5.99      NZ  2021-03-19 21:52:21  \n",
       "5        6.91      NO  2021-03-19 22:02:58  \n",
       "6        8.05      TO  2021-03-19 21:57:11  \n",
       "7       12.66      CA  2021-03-19 21:56:22  \n",
       "8       19.57      US  2021-03-19 22:02:59  \n",
       "9        9.22      EC  2021-03-19 21:54:39  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the array of dictionaries to a Pandas DataFrame.\n",
    "city_data_df = pd.DataFrame(city_data)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Quintin</td>\n",
       "      <td>MX</td>\n",
       "      <td>2021-03-19 22:00:34</td>\n",
       "      <td>30.4833</td>\n",
       "      <td>-115.9500</td>\n",
       "      <td>66.45</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>15.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Awbari</td>\n",
       "      <td>LY</td>\n",
       "      <td>2021-03-19 22:02:58</td>\n",
       "      <td>26.5921</td>\n",
       "      <td>12.7805</td>\n",
       "      <td>66.47</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>8.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nova Olimpia</td>\n",
       "      <td>BR</td>\n",
       "      <td>2021-03-19 22:02:58</td>\n",
       "      <td>-14.7972</td>\n",
       "      <td>-57.2881</td>\n",
       "      <td>73.36</td>\n",
       "      <td>92</td>\n",
       "      <td>100</td>\n",
       "      <td>4.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>AR</td>\n",
       "      <td>2021-03-19 21:54:36</td>\n",
       "      <td>-54.8000</td>\n",
       "      <td>-68.3000</td>\n",
       "      <td>50.00</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mataura</td>\n",
       "      <td>NZ</td>\n",
       "      <td>2021-03-19 21:52:21</td>\n",
       "      <td>-46.1927</td>\n",
       "      <td>168.8643</td>\n",
       "      <td>55.00</td>\n",
       "      <td>85</td>\n",
       "      <td>98</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Karasjok</td>\n",
       "      <td>NO</td>\n",
       "      <td>2021-03-19 22:02:58</td>\n",
       "      <td>69.4719</td>\n",
       "      <td>25.5112</td>\n",
       "      <td>37.40</td>\n",
       "      <td>75</td>\n",
       "      <td>20</td>\n",
       "      <td>6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vaini</td>\n",
       "      <td>TO</td>\n",
       "      <td>2021-03-19 21:57:11</td>\n",
       "      <td>-21.2000</td>\n",
       "      <td>-175.2000</td>\n",
       "      <td>84.20</td>\n",
       "      <td>79</td>\n",
       "      <td>40</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fort Nelson</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-03-19 21:56:22</td>\n",
       "      <td>58.8053</td>\n",
       "      <td>-122.7002</td>\n",
       "      <td>33.80</td>\n",
       "      <td>51</td>\n",
       "      <td>75</td>\n",
       "      <td>12.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yankton</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-03-19 22:02:59</td>\n",
       "      <td>42.8711</td>\n",
       "      <td>-97.3973</td>\n",
       "      <td>55.40</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>19.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Puerto Ayora</td>\n",
       "      <td>EC</td>\n",
       "      <td>2021-03-19 21:54:39</td>\n",
       "      <td>-0.7393</td>\n",
       "      <td>-90.3518</td>\n",
       "      <td>84.20</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Country                 Date      Lat       Lng  Max Temp  \\\n",
       "0   San Quintin      MX  2021-03-19 22:00:34  30.4833 -115.9500     66.45   \n",
       "1        Awbari      LY  2021-03-19 22:02:58  26.5921   12.7805     66.47   \n",
       "2  Nova Olimpia      BR  2021-03-19 22:02:58 -14.7972  -57.2881     73.36   \n",
       "3       Ushuaia      AR  2021-03-19 21:54:36 -54.8000  -68.3000     50.00   \n",
       "4       Mataura      NZ  2021-03-19 21:52:21 -46.1927  168.8643     55.00   \n",
       "5      Karasjok      NO  2021-03-19 22:02:58  69.4719   25.5112     37.40   \n",
       "6         Vaini      TO  2021-03-19 21:57:11 -21.2000 -175.2000     84.20   \n",
       "7   Fort Nelson      CA  2021-03-19 21:56:22  58.8053 -122.7002     33.80   \n",
       "8       Yankton      US  2021-03-19 22:02:59  42.8711  -97.3973     55.40   \n",
       "9  Puerto Ayora      EC  2021-03-19 21:54:39  -0.7393  -90.3518     84.20   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        55           0       15.95  \n",
       "1        19           0        8.95  \n",
       "2        92         100        4.81  \n",
       "3        66          75       10.36  \n",
       "4        85          98        5.99  \n",
       "5        75          20        6.91  \n",
       "6        79          40        8.05  \n",
       "7        51          75       12.66  \n",
       "8        33           1       19.57  \n",
       "9        74          40        9.22  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reorder the columns of the city data df.\n",
    "new_column_order = [\"City\", \"Country\", \"Date\",\"Lat\",\"Lng\",\"Max Temp\",\"Humidity\",\"Cloudiness\",\"Wind Speed\"]\n",
    "city_data_df=city_data_df[new_column_order]\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the output file (CSV).\n",
    "output_data_file = \"weather_data/cities.csv\"\n",
    "# Export the City_Data into a CSV.\n",
    "city_data_df.to_csv(output_data_file, index_label=\"City_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract relevant fields from the DataFrame for plotting.\n",
    "lats = city_data_df[\"Lat\"]\n",
    "max_temps = city_data_df[\"Max Temp\"]\n",
    "humidity = city_data_df[\"Humidity\"]\n",
    "cloudiness = city_data_df[\"Cloudiness\"]\n",
    "wind_speed = city_data_df[\"Wind Speed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import time module\n",
    "import time\n",
    "\n",
    "# Build the scatter plot for latitude vs. max temperature.\n",
    "plt.scatter(lats,\n",
    "            max_temps,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Max Temperature \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Max Temperature (F)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig1.png\")\n",
    "\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for latitude vs. humidity.\n",
    "plt.scatter(lats,\n",
    "            humidity,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Humidity \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Humidity (%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig2.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for latitude vs. cloudiness.\n",
    "plt.scatter(lats,\n",
    "            cloudiness,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Cloudiness (%) \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Cloudiness (%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig3.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for latitude vs. wind speed.\n",
    "plt.scatter(lats,\n",
    "            wind_speed,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Wind Speed \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Wind Speed (mph)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig4.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linregress\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Create a function to create perform linear regression on the weather data\n",
    "# and plot a regression line and the equation with the data.\n",
    "def plot_linear_regression(x_values, y_values, title, y_label, text_coordinates):\n",
    "\n",
    "    # Run regression on hemisphere weather data.\n",
    "    (slope, intercept, r_value, p_value, std_err) = linregress(x_values, y_values)\n",
    "\n",
    "    # Calculate the regression line \"y values\" from the slope and intercept.\n",
    "    regress_values = x_values * slope + intercept\n",
    "    # Get the equation of the line.\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    # Create a scatter plot and plot the regression line.\n",
    "    plt.scatter(x_values,y_values)\n",
    "    plt.plot(x_values,regress_values,\"r\")\n",
    "    # Annotate the text for the line equation.\n",
    "    plt.annotate(line_eq, text_coordinates, fontsize=15, color=\"red\")\n",
    "    plt.xlabel('Latitude')\n",
    "    plt.ylabel(y_label)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Northern and Southern Hemisphere DataFrames.\n",
    "northern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] >= 0)]\n",
    "southern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] < 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5OQEEa9WULRL2GZQbw+ylReFnES1b9Fjv5D4oKi7hhpkrYmZll5SWM35uZf5IvKu0VK/uMoV4fGxK/aLe5jk4xapni1BhTEw7sls+QibEu3utPdR5wkJfIasXfPkmU/71YPj9nqwGvDPvY+5Y8avn2P9kbclqxwNDO9e5gTwVaK6GAhlcWymdOJkP7h1yjCeTj5vt2m1VUlNLea8z4J8dFIOArelo1tFn0PrmeYw++zoAGlaU0fec7iy67Sx+98sP1e5j9yxSmQ9ww8wVKX22dcUUo7kaSizqrXJI1Hwwum97x7pKsfwUNbWUL+iSz+i+7WkezLmYsmCdL8etWw2pWUefSeub53HTWZaSaFReyuLHLmPtvYM46NcfqtzH7t6potyYlD3bumSK0Q2MlFjUW7NSMritcCUvLd5UxbzkVsIhVUt5p0xgL6YlP6Wv3eR/+LcldLmrMldiZ6ARZ//lKUZd1rvafez6zAIq4n4CztiVXLfDSzZ1XTLFaMlzBdSslDLuKujE/UM7e159pGIp7zab9ZII5mcF5RbJ0+XOmyj8YguTz7Mc17mlu3nvoUsoOLk9bN1a5Rq7Pu8b2pnf92hlG5UEkJ1lldbwy/adpdxWuDJmhVUvK4K6ZIqpL453JX505VCDpGLm6XbPrcHBLhoB1k86N67+PNcrevxx+MtfKt/vsw+sWwcHH+x6f6fPEypOGNl37w7NeGftNsckxhDR5TCiZ8hev5e6tHJQFNCVQ8aQihh6t9lsKuzKnvM0rrrKik995BHr/a+/QvPmlO6zHwNumeU4i3f6PD+XlIb7vn9oZ37bXcaLizdRVFxCXm7AMVEOqtdJil49OfVZFKwJFSIZ319dcWgrdR9VDjVIKpbybgogIxK6rr7aUhIPPwxAYMcvzL17CMsevJA9W7ZWM9/s57ARUqi9cFkRo2etqBJ+u31nKTM+30xOwPufc6RCcFOWkfIl+v3VJYe2Uvep17WV0kG8dZOccKvXlFE1gEaMoOevR9Dn7Znc8dYT5O3aweePXALAkF0vUNDl94BzpnSofcqCdVVKeocoLTfs3agBIFWeRawKq2D/DENE19dK5Ptz8wGprV/JNFQ51HJiKYB4BrN490GIxdbiEp7v2p/nu/bnsiVzGf/2VABm3nMJ3HMJfPut7Z7dULmXt5vzt3hnKfcP7VzNL2G3edHOPWUULiuq8nxGeiipngh1yaGt1H1UOdQBkrkaSeUWmJH7IDzXbQDPdRvAQ3P/xoA171sntG3LeuCkPz/Flqj9JGLtpRA6Zvcs7DYv2r6ztMrnKuiSz5QF63zt0+BXieo+EEptQn0OdZBEnJ7x7oPgBTsfyM2Dx1L4xZYqW5d+GNpP4uf/AVX9JKP7trcNaQ1kSzVfym2FK2kzZj4jZyy3LRFSUlrODTNXhJ+PHx+Nnf9g9Csr6DxhoeNzzwgfkKJ4JK0rBxHJA54COmKZhi8H1gEzgNbABmCIMWZ7eiSsfSQ680+l6cPVBDZjhvVz/vkwezZglQoHWDBvMX2j9lKIXAmEdoyL/Hy3Fa6ssrWoE+XGMPqVFbHli8JOiZaWm7BMds89o3xAihKDtOY5iMg04ANjzFMi0hDIBW4BfjLGTBKRMUATY8zNbvepLXkONUGisfgZE8s/eDDMmVO1beNGaNXK8ZJIM4/fv2qvmdQh3AovRqI5EEomk5F5DiKyL3AK8DSAMWaPMaYYGAhMC542DShIh3y1lURn/hlj+pg92wqBHTiwsu2QQ6ywpc2bq50ebebxy3YHR7gTXv0E0bkS8VCbciNqk6yKO+n0ORwKbAOeFZFlIvKUiOwFHGSM+Q4g+HpgGmVMCan8B0o08S3jyioUFlpKon//yrZWrSwlsWVLuMnr5k3JYnTf9p7/eRLJZUhFbkSq/v40j6NukU7l0AA4FnjMGNMF+A0Y4/ViERkuIktEZMm2bdtSJWPSSfU/UDJm/hm5BebcuZaSOOecyraWLS0lUVSUsE8kzyH5zomCLvnVdyxyIBGHfrIDBFL595fKYAal5kmnctgCbDHGfBp8/wqWsvifiBwMEHz93u5iY8xUY0w3Y0y3Zs2a1YjAySDV/0AZN/NPNvPnW0qib9/KthYtWD+5Hwf++mPMyxtm24/oIvgeIGPtThdJUXFJXLP1ZAcIpPLvT/M46hZpi1YyxvxXRDaLSHtjzDqgD/BV8OdSYFLw9bV0yZgKauIfKNlZ2BnJG29Yr2eeCW++CcBnj14KwHEjnmfb3k2rFdgLlVi3IzrvwQvZLsX+ohEIO/r9RJAlOzcilX9/msdRt0h3nsO1wEsi8iXQGfgrllI4Q0S+Bs4Ivq8z6CYrSWbhQmsl0adPuOnzR/7Ahsn9uOeMVlX2rYjeeyMavzNot2J/0cQq/udEsgMEUvn3lzHBDEpSSKtyMMYsD5qGjjbGFBhjthtjfjTG9DHGtAu+/pROGZON/gMlh2pO1SnTLCXRq1f4nHNPPQoOPxyKi5myYJ2nKCY/M+i7Cjq57kERi5CpKdLEFP25gKSYCUP3LSouqeYqSdbfX503adYzdD+HNJCq2kX1BU+7mN1yC9x9d/j4f5rmM/AP9/Fro71c752KvTVimZ9CsgMp2Z3N7nmFChLm699fvcYtz0GVg1Lr8JyoZwyMGwd33hlu+qZpC877w72OSiKUbQ3+M5mdlNbgrvm2xf+icVIiTgrL6yQjYxIblYwjI5PgFCVePDtVReCOO6CignV/ug6Aw37awsoHhrLwqavZe/dOoreA2L6zlNGvrGD0rBW+wz2dzCp3FXTi7kGdaJLrHi7rtLqw+7x+QlI1ikiJB1UOSsbgNTnLyXmaJWJ/rQjtn3qQwqWbeb7XMAAO/3ETqx4YwhvPXMveu3dWuU9puam2Z4RXB7JTjkiyN3TyE5KqQRBKPKhyUDICPzNhO6c+WDNvt2sLjm3BH975B1RUwM1Wua62/1vPqgeG8K9nrmGvKCURjVOpcK/4LdEBlm+gd4fqeTx+VgOxgiC05IVihyoHJSPwMxOONt9k22wf5zrTF4FJk6CighdPGQrAEds2sPqBIfzz2WsdlYRdP05ED7i3Fa70fG0kBnhp8aZq1/tZDbhFEWnJC8UJdUgrGYFTlVMB1k86N2XXFi4rYuzsL7n+zae46rPKKrCrDzyUCy6ezM6GVQfbDTHuF76nQ3RQLJzOE+D+oZ2r5G0kI7JJndX1G3VIKxmPmx8h1izWzyzaNo9g8NG8cN4I2tz0Ov84+QIAjvr+P3x1/wW8/tz15O6xBs/84P1imWHsVkFeFEOT3IDjeQa4YeYKz/kPXkxFhcuKHE1l9a2arFIdXTkoGYHdTDhErBmx11m05/O+2ELx1ddx2aevhttWHtyODa/MpzwnN+Y9vO71YPc5swR+2xO7uqzbM/HyOd2edwgBLu7RirsKOvn+LMla2SipRfMclFpB4bIibpi5wlOsf3SMf+8OzXhn7TbXmH8/JpTCZUVMmLuKa19/lMuXzg23r8xvz5AhEylp2LjK+U1yA+Q2bMDW4hKyfNRcisar+clJbvD2OZ3OsZMn0pwVidt34PQM1FyVWahZSakVFHTJp8JDrL+dE3X20iJG923vWmbcb7z/jj3l3HH6cFrf9DrPdrX2k+hUtI4195/Pq8/fQOPSXeFzt+8sDcsTr2IA74rBTW4v7V5zHAwwcsZy2zIf0d/Bi4s3xXwGmltRe1DloGQUXvwH8Zad9uObmLJgHaXlwQFOhAmn/5nWN73Oc8f2A6DLd+tYe9/5zH7hxipKIlH8RET5zV+IbPeb41BUXMLoWSvCCiLezZW8+IHUN5EZqHJQMgovhQnjzfj1U/TQ9l4ijD/jKlrf9DrTjrWilrpuXcva+87nlRdH06h0t2v/scgJZDPs+JbVZAxkCYGofSjciuV5+ZxOuSJulFYYxs9dDcS3ArCTWUNpMxdVDoo/Hn0Uzj0X9t/fyhd4911v140bB506wb77wj77QLduMGNGtdO8VPaMN+M31r3ffmURn7XrRkmgMUsfuoi7FjwSjlSKJG/XrwTKy9m2Vx5lYv0LdStaw7r7BjPzpZsclUR2RTl/WTyLd6Zeybp7Cvj0scuY/OGzCNZOdI0DWcijjzF1xjiWPzSMDZP70e+ndUy54BimnH+M52qnXp6h3Tk92zaNubldcYmVyOd15ZEt4iqz7h6XuahDWvFHjx6WUmjTBqZPh3feqVIm25FRo6BFCzjySMjOhldegSefhFmz4PzzfYmQikiYee+vocc5J/KfJs154vjBNCn5lTHvPssX+UcwfNBt4fP23r2TOS/cyM6GjZnafRDbc/al3Q8bOWfdh/TYvDp83qctO/KHIXewu0FDwHLs3jvvXnpuXMGDPYex5cBWXHd4Y7rt/C+FF4wIf55Xn78BI7C1ycH0W/2u9+ebJEJOZjdn9YZJ53qKdvLynSSSo+IFrYDsjptD2tNOcCJyLHASln/qI2PMF0mUT6lNfPwxZGXBqlWWcvDK/fdXfX/mmbB6NTz/vG/lEPrndvynf+45GD8eNmzwfM8tf72XRqW7uWLw7fzSeG8Aihvvw1Nz7qTTd1+z8uB25OUE+PO7M2lYXkr/YfezO9AIgE8OOZrnu/Yny1Rw14JHuGjFAo7fvIp19w7ik1aduGLoHfTcspL+a97n7D/+nR9btWVc/6PoFvockxaFB9lBl0zBSBaHb9tgKYcaJrSLYJc7FtqW+wgVD7T7DrxEjEXjZfc4twE+1rFIBeZnBz7Fg3IQkduBC4BQ+uizIjLLGHNXSiWr78yfD/37w7ffWrP0EOvXQ9u2UFgIAwbUvFxZSbRE7r8/7NlT+b5fP/jmG1i2DHKCg8O998Ktt8LSpXDUUeFTk70VaouN/2bl79qFFQPAB226UIHw+qE/wzhrFvvjlKE83XVAWDGECJX6ntLkJm7tO4JJCx5h6IoFnLBpJaunnMcPufuxuGUnvjmgFTmlFVWujbTfG6n6fK/5xxfMf+O3Gp/1jut/FKNfWVHplAcC2RIuZw7J+Q5G921vuwqMrPvkNMADroO/m8lKlUNsvKwchgFdjDG7AERkEvAFoMohlZx1FjRvDtOmWbPgEM89B82awTnnOF9bXm7tZeBGVlZyB3qvlJXBjh2W8lu4EF5+ufLYk09Cx44wdiw88ACsWQO33QYTJlRRDKlgPymnNLvqv0NZVjYVWULWmjVWw/r17P9bMb803ptnZ42j54YV/Nool1eP6s3jZ11ZdbCc3B8qKnj9+HPpv+QNDtj5MydvXM7Hj17Kvrt30mByBfQ7Bx5+2HH2DPDDjt2YpsmZ9foxscRcnSWJWP3E8km4Df5aqjwxvCiHDUBjIBSv1wj4NlUCKUGys+GyyyzlMG6cZec3xnp/ySXQwOWr69MH3nvP/f6XXmopmppk8WI44QTr9wYN4OGHoaCg8vjBB1ttF19srZrGjoUuXeDGG93vW1Fh/US+B0sRhRCxnqkDB3ftyH6Fs2hQXkZZUEl0+d83NKio4INP/82YSYv46+92cCow5t1neb3DyVw6ZAJHfr+e0e8/T3lWNtwZZSPPyuK6Ptdw3WlX882UgWQbQ/NffwRg7QGH0GHJEjjvPEY/Npuxr66qVospmkRmvfGYWJK9OnPCrZ94Bvii4hJaj5nveFxLlXvDi3LYDawWkTexfA5nAB+KyEMAxpjrUihf/ebyy+Gvf7Uignr3tpyTGzfCH//oft0TT8Cvv7qfc8ABzseMsVYfIWIMrJ7p1Ak+/xyKi62VwzXXWNFLw4ZVnjNsGMyZY0VEZWXBihWx+77jDmt1EU0gYnOdQw5x9UEcdusoKqY/zT3vP8XE44bSyuxk/MJHKZMsyiWLouISHlq1jlOBr/dvxdizrT/7Tw45hr32lDBi8UzYuRNyc6vcN7QqKJdsdjVowNttuzNg7ft0+GGjdcLmzRR8vwoGdaoyezbb7OWMTgZ0m9lHHrfLWK4NJpZYPgm/ZdS97petjmxvyuHV4E+Id1MjilKNQw+1IlWefdZSDs8+C927xzaxHHaYN7OSE++9Z/UX4tRTvYesurHXXlYIK8Dpp8PPP1v7KkQqB7Dev/KK5VNp1y72fYcPt/wVIebNg6lTYW5l2QsaNap+XSQdOpA1dSoFo0ZR8Nk8yiWL6cf0pQLhh73yAMtBDfDJIVVrDS09tDONPnzJ8g91qnosZFP/ufHebM47iOsG3sTYQTfxrxXP0mr+bOuks8/mhBN70eDsMeHr9m1sv2tcaFCMtRKIPl6TGcvJHFhj+SRiRUxFkpcTQARGzVjOlAXrHOVSR7ZFTOVgjJmWSgFEJBtYAhQZY/qJSFNgBtAay6Q1xBizPZUyZDRXXAFXXgl3323NqO+9N/Y1iZqVuna1Zvgh9tnHs7i+OPZYS+GVllbO8n/5xQp77dLFGtwXLIC+fd3v07y59RNi1Spo2LBSEXnl8svhoovg66/pMXUFP+bsy/KHLmLG0WcCsKnJ79gdNDlli1BhDM3zcriq3aHwPLy9bhu3z19kOyhufukQ2L2L/GB7q4mvWJ+7cWOoqOCgj9/lvY/f5Z1DuzJ80G3st6esmniRg+KE11e72tu9ZjAn28SS7IHVi+8jdCxWUP7usgpPcqkj28JLtFI/4E7gkOD5AhhjzL5JkuF6YA0Qut8Y4G1jzCQRGRN8f3OS+qp9DBoEI0bAhRdatvQLL4x9TaJmpVCSWqr56CMr9yHS/DNypGXSWrTI+txXXGEN9vvtl3p5wBqsO3WiYf42zvtgHmIqmNfhZABKswN82LoLJ278kinGVMbhjx9PWeMcRi3dwS/GMoEVFZcwasZyRs5YTn5eDk8M7E/Hqffx0RVHh5/9h88VclJFBUOG3c1FK96g4Kv36P2fpXx9z3ksbmGtDvdu1ACBKoNi4bIix13lQisBLysCryYWP6RiYHXzSUQecysmmC3iWS51ZFt4MSs9AAwCVpokZ8yJSAvgXGAi8H/B5oFAr+Dv07DMWPVXOTRubDloH3nEMrfk5cW+pn1y/+GrsGSJZbvfvNl6/9578MMP0Lp1pUJ5/nlrFv7tt5atP+Qnuegiy1S2Ywe8+qoVqfTYY5X3njfPWkn861/W5/z7363opeuvT73z/JdfYOJEOOUUaNCAZ9fNpc0bTzD2rGv5Oady5fTQiRcy66Wbefitv8PCAHz5JUyaxNOnXhxWDPk/f897T1zBTedcz5yOfSgqLuGyQGfe3zeP3P794ZZbWLJyI20nTeCDQzrzWatOfNaqE/937v/x7KwJnLrhC3pssRLqbpn7IMNvvIXup3eHiFm0E6GVgJOtPnLF42ZWidcslOjAmkjfo/u2rxZ+C1b5keg9wd3k8pJ7UR/wohw2A6uSrRiCPADcBETaLQ4yxnwHYIz5TkQOTEG/tYuCAks5XH55uiWxoommRVgaQ2G2kWaqioqq4bR5eZbZ56674L//td4feaTllA6F5P70k+U7uPJKK4wXoGlTK7y1Xz8YPNiKYEoV2dlWfsWTT0JJCYd37MinU57grV9bQknlLH1F8/ZcPXQ89yx72ZLnwAPh1luZvKNz+BzB0MBUkBXxL/NDVmMuu3gSM7+aDhdeyOEVWSw87Hju6HNl+JyKrGy27d2kiliH/lTEobeMYOeDzcndvAECAdeBNrQScLLVx8pYTtQslMjAmmjfoXMmvL46vLLKywkwfsBRjlnfdnLF8nPUF2KWzxCR47DMSu9hRS4BYIy5L6GOLXPVOcaYq0WkF3Bj0OdQbIzJizhvuzGmic31w4HhAK1ateq6cePGRMTJbG66yapDtH59enIT6hnJ3CsiksiSEF42BMquKOfBuVPot+7DcNt7R/bk/wbfyo97Kqqdn5cTYPm4Mx0/h5dZeKLbhsZb2sTPXh7xULisiNGzVlRZQQSyhCkXHJP01VNtItHyGROBHVi5Dg2TKFdPYICInBO8974i8iLwPxE5OLhqOBj43u5iY8xUYCpYtZWSKFfmsG4dfPWVZXoZN04VQw1gN3udvbQo5uBmN9uMJrpkdixlUp6VzTUFY7i+opy/vzaZc/79Mad+9RFLvzqHfx1+ItcMvNnKr8BSPP2OObjK9fHkKSRqFooneS70zJMdURU5wO+XE6CaOnWpMuj27JwmD0XFJWQHQ4bz64BC8bJyWOKkWZImRNWVwxTgxwiHdFNjzE1u19fZwnu9esGnn1ohnS+8YEXgKCklkZlzZNG66B3d4tmmM5rsinIefm0yZ//743DbPw8/kWuDSiIZ23AmunJIZp+J9O31+fq9t5/vrTZsi5roTnBviciZsU9LGpOAM0Tka6yEu0k12Hdm8e67UFJimZRUMdQIicycC7rk89GY09gw6VzuH9o5ZontxgF/K8HyrGz+ct4tHHZjIQva9QDgnH9/zLdTBvJw4ST27N7DLXO+pO3Yf9J6zHzajv0ntxWujHHXqvjZ8yJZuD3bePv2Gsrrd1XiZ5Oj2l563MvK4VdgL2BP8CfZoawJU2dXDkqNUxMz53hWDXY0KC/j0dcmcebXi8Ntr3c4mZH9bwybm0L4MXOk0t5ud28nZ3G2CPcOsfcJxMKLTwf8f69e7xsiWaXHU0VCPgdjTIoyoBQl86iJSJV4t9iMpiy7AcMH3UaD8jIeK/wrZ3zzGf3XfkD/tR8w94hTGNnvBiqyKvMuvEb+pKqmklM00uCu+cxeWpTU/Tm8+HTi+V693Df6/NpKzHWtWPxeRP5f8H1LEemeetEUpebxsotaovg1ZcTana0suwFXDr6ddje+ytttjwNgwJr3+c+UgTzw+hSyKqxBN91mDqcEuXfWbuPuQZ3Ce0UANGqQWPCFnXkskCU0yQ0k9L362V61toe/eolWehSoAE7DCmndATwCHJdCuRQlbaSyGmnhsiKqeasdCGQLGKqGX2ZLtSSvEKXZAf50/jgC5aU8MWcip/1nCQVfvUfBV+/x6pG9uOHcUWnN8o3lz9kVsc9FcUlpystuxIPbJkf1MVrpC2PMsSKyzBjTJdi2whhzTI1I6AH1OSiZgJcqqbF8DSG9kZ+Xw2+7y8J7NkeSbVNhFSA3kMXOiAE2UF7Kk7Pvotf6peG2f3U5g7M//1dyquz6xM2fA/YVVlMZJaUknudQGiyOZ4I3awbVQ4YVpT7jJbvXi6/h/qGdw+e3cdiToNwYcgLZVe4VyBIaBbKrKIfS7ACXDZlAw7JSnpxzJ6eu/4Kzl71p7aVx6aXwzDMUrvgu7tl14bIixs9dHVZgod3wnK538+eMmrHc9hq3EuV1dcaeKTga9kTkueCvD2GV7D5QRCYCHwJ/Tb1oilJ7iLVjGcT2NeTn5VQZ1JycmSF7ecgvkpcTAKFKMb6cQDa/79GK/LwcShsEuGX4FOYu/hbOOMM6Ydo0yM6m/I9/ZOv23zBYCm3kjOW0HjOf1mPm03nCQssMZkMo4zhyZbN9ZymjX1nheI2bP8fps+6XE6DnpEW0HjOfUTOWUxSsvlpUXMKLizeFVxuhlVRIKTvJoHjH0awUMicFf+8A9MFa9b5tjFlTcyLGRs1KSrpxCnGMDGV0S/ayi87xWorCd/jtrl1Wvaq33w43zeh0BmPOvrbaHtZOJSbcPkuyktYCWQKCo4/FjVSYo+piSY14zUq5ItKFymCJT4KvOSJyrDHmi2QKqSi1GS8F55xKbOQGsmgUyApvQhNZyykvN0CjBln8XFLqOCB5TdyrYgbqNopGnUfw7KzxnLjpS4aufJOhK99k+tFncstZ14SVRGmF8VXW2umY3cAKVR27g7vmV6lhtXNPmWNp8lgk2/HuZXOluqY43JRDPnAv9pF0Bit6SVEUvOVHOEW6zF5auT9DyFwSYvvOUnIC2VV8EdF4UUx2hed2N2jIRcP+SqOyPTw3axwnbFrJsC8XMuzLhfzjmL7c2ncERrJ8lbWO7jfUd/TAOvqVFVUisexqWDn5XLyQ7PyCWGbDurhznJtZKRydlOmoWUnJBJJZBTUaNzOJF/OTl34ale7m+VnjOH7zqnDbS53P4tEhN/LR2D7V+oxWNpV9ZzG4a4vwKsBu/2ovn9Prs6nef/JrGrmZDZ0UZW2ItEo0WklRFA8kswqqn/O8xPR76Wd3oBFDL5pkKYmZt3P8ltVcvPwNLl7+BnNeG0DWo49QcGyLKn2OnfMlJaVVgxdLSiuqrH68KoZoOe1WY5GhvjUZreS2OqurO8e5KYf6u/uaoiSAnxWE13IMscwkIcUU6jvkvwj17afsw+5AI4ZePJnGpbt4YcbtHFf0FYM+nQtd5zLjuP6M6T2c5k1yGd23PU33ahTX7N6JyM+ZqkS2eHAzG/rZSKg2ETMJrjagZiUlU/C72Y2XxDivZhK3vgFHM1AsGpfu4qWXb6Pr1rXhtueO7cfks6+mpCy+lCe77O9ML3HtpPTj3eAoVfL4wc2spMpBUZJIPFVd49l5Lp6+o5PW/NK4dBfTp99Kl+8qczee6zqA8X2uBIlVAaqSULIcEJ511/YktpqOVkqWQkpIOYhIY2PMrqi2A4wxP3iWIMWoclAyBS/5Dn7xOvD46TuRsuE5e3Yx/eVb6Pzdv8Ntz3QdYO2H7UFJRCrKdM26azvJKi2f6GY/n4tIj4ibDQY+djlfUeotTnbmeO3PocEzMjPYKQM4Vt+Fy4roOWkRbcbMZ8qCdQzumh/OVm6SG7CSzjxQ0rAxBX+4jyNGvcLyg9sBcPnSuWz4W39ue/tJiDHhjHTUesksTwWRz6LnpEW1LqO6JpzgXpTDRcDfRWSKiLwEXInmOCiKLcneSc3P4OnWt52Smb20iNF927N+0rksu/1MplxwTLgInhcsJXE/R46axZe/OwyAK5a8xoa/9eeWRU87KolIJZaOSB8/CjdTSfYkxI6YysEYsxKYCFwF9AauMcZsSZoEilKHcKofBMQ1U/UzeLrVLvKiZELbnLqtH+yO7WyYw4BLH+CoUbMo7tARgOGfv8qGv/Vn7DvPVFES0YqyJga5aNK1WkkmNbGda8w8BxF5GmgLHA0cDrwuIg8bYx5JmhSKkmaS6VCMznfwUrHVCS/Zz259h3BSMkXFJRQuK6pW8M9t206n0M28A5uSt2Yl8z5cR/sh59Luu2/582dz+PNnc3j8+MG8MPAvjD6rQ5W+/O68l4zvqS7kJdREmK8Xs9IqoLcxZr0xZgHQAzg2aRIoSppJpZmhcFkRN8xcEfdMNVkzRLeZePRndeoztJ9zLJn6ndSedlu/gV9+gaOPBuCqT2fz0S2nUzDj71VWEn523kvW95SO1UoqCK301k86l4/GnJZ0B76Gsir1Hj+RH35mrrEigrxGMCVjthxLlujPGqtQXl5uAGNwLQgY5pdfoGdPWFVZloOxY2HiRF8hsMmK0EkkQqquFdhLqHyGiLQD7gaOBBqH2o0xhyYoVEvgeeB3WJsHTTXGPCgiTYEZQGtgAzDEGLM9kb4UxQ0/VU39mIdibe7jdaaajG1LQ9eP9LCpTnSfdvkRXgoChtl3X1i50lISJ54Iq1fD3XdbP7feCnfe6aokQgOyUya2X3NQvCaZRMyDtREvZqVngceAMiyH9PPAC0nouwy4wRhzBJapaoSIHAmMwdozoh3wdvC9oqQMr2YGv45Mt0ErHZvPF3TJd4xGcnoGoQHRLnHOtxN3331h1SrmvfcV6w9sZbVNnAhZWXD77bbRTZGmJCfiMQfFY5KpC45sP3hRDjnGmLexTFAbjTHjSUIoqzHmu9CeEMaYX4E1WGXCBwLTgqdNAwoS7UtR3PBq1/fryHQatLJF0pbk5deHEWv1U1Rc4isCq3BZEaPf3EjvPz7K0de/zLdNrUJ+3HmnpSTGj/fVfywlm8x8hrrgyPaDF+WwS0SygK9F5BoROQ84MJlCiEhroAvwKXCQMeY7sBRIsvtSlGi8OkX9OjJjOXYTJZ6Bz+2z2t3Py8DnxzkcOdj/0nhv+lz5OEdf/zKb9g8+jwkTLBPTHXcA7gOvm/Makh9oUFcc2V7xUj7jOKxZfR5wJ7Af8DdjzOKkCCCyN/AeMNEYM0dEio0xeRHHtxtjmthcNxwYDtCqVauuGzduTIY4iuJIPI7MVDkwk112wul+jQNZvnZji+Ucdi3xMaYnHHccfPNNuP3eky7m7z2HOfbl9jyT5cAOURdLfWRs4T0RCQDzgAXGmPuCbeuAXsaY70TkYOBdY4yrcVajlZSaIlOiVZI98DndLy8nwO6yCs81mGJFYHmRe/57q+lYcAaHFH8XPj7l5Et45MShtv1d3KMVdxV0qnYsnXWuagtxRSuJyFy3mxpjBiQolABPA2tCiiHIXOBSYFLw9bVE+lGUZJKMyKFkkEz7d+GyIkeHb3FJKU1yA2HlkJcTYPyAo5jw+mrbFUUsE0uspLfCZUXc8MZGyv/8JPuV/Mrr00bS6uf/MfqDFxj9wQv87ZQ/8OgJQ8LXGuClxZvodkhTWzNgsvdZyJTvvyZw8zmcALQAPgDuwdpPOvInUXoClwCnicjy4M85WErhDBH5Gjgj+F5RlAiSZf8OmUqcEKiiBHaXVbBk40/s2FVW7dxAttC7QzNXP0gsn8fYOSvDO8f9nLMPp1z1NMdcN50t+1qux5vef54Nk/vxl8Wzwvc04LvWlBIbtz2ks7EG52FYpTPmA9ONMatrTjxvqFlJqW/Y2b8D2cJeDRt4S0wLEs8+zdkOe0LnBrIwSNw2+Viy5JX8wvxnryf/123htrt7XcYTx58PwAM2ORd1zQyUbBL2OYhIIywlMQW4wxjz9+SKmBiqHJT6SOTAl5cbYMeuMt87qznZ5ZOJVz9ILFmyBCqMpST++ex1NP+1ckuZib0u58WTLqjVzuF0EPd+DiLSSEQGAS8CI4CHgDnJF1FRFL9EJnLlNmxQbQtQLwla8djfs32UvADvfhA3WfZqmM2+jQMAFOfsy4lXP0eXa1/iv3s3BeDWd59hzV1ns/m2O33J5kZt3/MhURyVg4hMw9rU51hggjHmOGPMncaY+vWEFKUWEK+D2s4uH0m0GsgJZDPs+Ja2tvy8nIDtPbwqICcfwe97tKLCUC1Le3vufvQY8TzHXvsS/wsqiWv/+YSVJ3HffSRCXdjzIVHcfA4VwG/Bt5EnCWCMMfumWDbPqFlJqW0k2xbuFoq6V6MG1fqJ7H+/nAB7ysrZWVpR5dqcQDaDu+ZX288aqBKtFIpgAhLOA7B7Lm51lSLZ/7diFj53LfvviCjFdv/9MHKk7+ed7FDhTCVj8xyShSoHpTaRimQqWwd1loBAaXlVP8TgrvnMXlpUrX87RWCnSH7bU1btnpERR8l2AHv1i4TlyA9Ax46wrdJxPfHMq3iySz9bmf30mUiORCaS6B7SiqIkkVQUcLMLEd27cYMqg3ion+mfbrbt/52126oVo4s2rxSXlNreM5XF57yYpfJyAjQOZDFqxnJ6PrOKwgXL4L//hf33B+DWhY+zYXI/Ll36uieZ61upDDtUOShKDZOqAm7RlUaLHcpe2IWhOvUfq/BdiKLiEm4rXJkSO30sv0jPtk3ZXVbB9p2lVfvdWgY//MBx17zA9sb7ADDhrSfYMLkfl3wxz/V5a46EKgdFqXFqalbqVhXW6/l+FNZLizc5rogSifwJrYqc5F78n+2uK7GG+c3pcv10jhvxAj832guAO998nPWT+8Gjj7r26WWHurqKKgdFqWFqalbq1M+w41ta/ogIAlli278fheXkFwjN5BNZURR0yafCYcUTayUUeg7b9m7CMSNncNyI5/klqCQYMcKKbnrsMds+U7kNZ6ajykFRapiampU69dPtkKbVY1QdUhfsFIzfQSNbJCk+Fr8rq9D50c+hYYt8Fn2yDoqKYB/L3MTVV1tK4oknfPVRl9FoJUWpZ/gN04yOQCreuYff9nir0poTyE54D+1IOaIjsgT7FYuAty1MwVIS7dvDb79Vtk2dClde6Vm2WGRqGQ+NVlIUJYxfh3i0ecWrYgitVPxuTeqE3UrIaWpr8LGvc34+7NgBW7ZAbq7VNny4tZJ46ilfMtpRWxPqVDkoSj2jphziITt9Mn0s0YrKSfE0ybXP1nYlP99aPWzeDI0bW21XXmkpiWee8X+/ILV172lVDopSz0h0sHYqkxFJZGRRKn0so/u2J5Bd3WGyY1dZ/DPzFi2gpAQ2bYJGjay2P/3JUhLPPuv7drV172lVDopSz0h0sB4/4Khq0U7RREcQpTLyp6yiunGptMIkPjNv2RJ27YKNGyEQVIiXX24pieee83yb2ppQ57gTnKIodZdEdjQLXedW88jJ3JNMQrZ8p5iapM3MW7WCPXssJdG2LZSXwx//aP1MmwZ/+IPr5bF2v8tUdOWgKIpvQiuBB4Z2Tlsmcazs7f08mL98ccghUFYG69dbqweASy+1fn/hBcfLamtCnYayKoqSEOkK0/RSkC9UMTYl8mzYAG3aVG178UW4+OLk95UitCqrotRTMjW+Phl43eI0kCVMueCY1H3u9evh0EOrtr30Elx0UWr6SyKa56Ao9ZDaGl/vld4dmjkldlchKc5pN9q0AWPg228r2y6+2DI3TZ+eun5TjCoHRamj1Nb4ei8ULiti9tIiz/tf+3FOx10k8NBDLSXxzTeVbRddZCmJGTM8958pZKxyEJGzRGSdiHwjImPSLY+i1DZqa3y9F7yWEg/hNWw0Kauttm0tJfH115VtF15oKYmZM73fJ81kpHIQkWzgEeBs4EhgmIgcmV6pFKV2UVvj673gR8E5VZy1I6mrrcMOq64khg61lMSsWf7vV8NkpHIAugPfGGP+Y4zZA7wMDEyzTIpSq6jLG9Y4KbgmuYEqpTPycgIM7d6SKQvWeTITpWS1FVIS//53ZduQIZaSmD07/vummExNgssHNke83wIcnyZZFKVWEpms5iVaKZMim2LJ4pRYNq5/1bDV6EquITMR2Bfma56XYxsBlZTVVrt2lUqifVBBn3++9Tp7NgwalHgfSSRTlYNdEEIV35OIDAeGA7Rq1aomZFKUWofXTGi/g2gq8SKLV8XnZiay+1w1ks18+OGWkli3Djp0sNoGD7Ze58yB885LXl8JkJF5DiJyAjDeGNM3+H4sgDHmbrvzNc9BURLD7x4PtUUWt0S5fAeFUuMrqLVr4YgjqrYVFsLA1FvS3fIcMnXl8DnQTkTaAEXAhUDmZ5QoSi0lkyKb4pXFblB3MhOB8+ookbpTcdGhg7WSWLMGjgzG3RQUWK+vvQYDBtScLBFkpEPaGFMGXAMsANYAM40xq9MrlaLUXTIpsikeWZxCUHt3aFbNKR9JSWk5I2csd3RUx53zEA9HHGEpia++qmwbONByXL/+eur6dSAjlQOAMeafxpjDjTFtjTET0y2PotRlMimyKR5ZnHwL76zd5robXQi7fIa0ZZiHlMTqiPnwgAGWkpg/P7V9R5CxykFRlJojkyqHxiOLmykqVEE2loKIzmdIe4b5kUdaSmLlysq2fv1qTElkqs9BUZQapsZt7S74lcUtBDXkiygqLkHAteRGUXEJhcuKKOiSH5fvIyXO7I4dLSWxahV06mS19etnvf7zn3D22Ynd3wFdOSiKUutxMkX17tAsbBoCd8UQImQ68uv7SLkZqmNHCr/Ywh+um1rZds45cO+9ybl/FKocFEWJSY06ZuPAyRT1ztptvmowQaXpyK/vI9VmqJDyeT+nOa1vnsfZf3yIb/dvyYf7pibPS81KiqK4kkkJcm7YmaJGzVge171CvgrwnmGe6nDgaOWz5sBD6XPFY+T/mMNHSemhKqocFEVxxW+WcSbh5IvIywmwV6MGjjkQIdORH99HSktvUPO5KGpWUhTFlUxKkPOLnWkokCWI4KgY/FRxjdVXMsOBazoXRZWDoiiuZFKCnF+ifRF5OQEQ2L6z1PGavRs3iGtFlOpw4JrORVGzkqIortRIMboa4tddZZTHqCdX7KI4YpHKcGC/PpBEUeWgKIorNT0oJZNoZ3osxQCZvSKqyVwUVQ6KosQkkxLk/OB3O9HauiJKBaocFEWps3hxmoeypp1KeNdXVDkoilJncQovzRahwhj2ywkg4s/PEF0io3eHZryzdlutM7nFQpWDoih1Fidn+t2DrBpFbsl9dnWS7K55cfGm8L0zNUEwHlQ5KIpSZ3FzpvectMi13IWd4mjUICumD6O2JAjGQpWDoih1Gidnultyn1NWuFfntlOCXW1Ck+AURamXuCX3JZr9nS2S0PWZgCoHRVHqJW4Zx4nmOnjJp8h01KykKEq9JFZyX7Qj2w+xdp2rDahyUBSl3mLnjwhFKZWUlpMtQrkx5OflsHNPmWtNphB1JZFOlYOiKEoQu3IbkYN9rNVEXUqkU+WgKIoSxG3vio/GnAbADTNX2PoU8vNywufUBdLikBaRKSKyVkS+FJFXRSQv4thYEflGRNaJSN90yKcoSv0k1t4VBV3yuXfIMTVaOjtdpCta6U2gozHmaODfwFgAETkSuBA4CjgLeFREsh3voiiKkkS87F2R6n0bMoW0mJWMMQsj3i4Gzg/+PhB42RizG1gvIt8A3YFPalhERVHqEbcVrmT6p5ttzUV2q4LaWqXWD5ngc7gcmBH8PR9LWYTYEmxTFEVJCbcVrqxSHymSeBzMdjWZaqMiSZlyEJG3gN/ZHLrVGPNa8JxbgTLgpdBlNufbZpOIyHBgOECrVq0SlldRlPrJ9E8327Zni/h2MEdHO9XmQnwpUw7GmNPdjovIpUA/oI8x4bXcFqBlxGktgK0O958KTAXo1q1b7U9HVBQlLThlM8eT5ewW7VTblEO6opXOAm4GBhhjdkYcmgtcKCKNRKQN0A74LB0yKoqi+CVWtFNtIl0+h4eBRsCbYhWoWmyMucoYs1pEZgJfYZmbRhhj4stfVxRFqWGcNhfK5H2pnUjLysEYc5gxpqUxpnPw56qIYxONMW2NMe2NMf9Kh3yKotQfnOogxVMfya2YX21Dq7IqilKv8TKgFy4rouekRbQZM5+ekxZRuKzI9l51KQciE0JZFUVR0kas6qx+I5DqSg6EKgdFUeo9bgN6XYpA8oOalRRFUVyoSxFIftCVg6IoigvJjECqTdnTqhwURVFcGN23fbV9HGJFINkpAaBWZU+rclAURXEhlsM6GicHdqMGWbXKd6HKQVEUJQZ+IpCcHNhOO8hlqu9CHdKKoihJxO9gn6nZ07pyUBRF8UEsp7KTA7tJboBdpRW+fBfpRFcOiqIoHgn5E4qKSzBU+hMiM6adMq7H9T+qVmVP68pBURTFI14S4mI5sDNVGUSjykFRFMUjXhPi6kIJDTUrKYqieMTJeZypTuVEUOWgKIriEa8lub1Wcc1k1KykKIriES8JcXVlH2lVDoqiKD6I5U+oK1Vc1aykKIqSROpKFVdVDoqiKEmkrjitVTkoiqIkkbqyj7T6HBRFUZKI3yqumUpalYOI3AhMAZoZY34Ito0F/gSUA9cZYxakUURFURTf1IUkuLQpBxFpCZwBbIpoOxK4EDgKaA68JSKHG2Psa90qiqIoKSGdPof7gZsAE9E2EHjZGLPbGLMe+Abong7hFEVR6jNpUQ4iMgAoMsasiDqUD2yOeL8l2KYoiqLUICkzK4nIW8DvbA7dCtwCnGl3mU2bsWlDRIYDwwFatWoVp5SKoiiKHSlTDsaY0+3aRaQT0AZYISIALYAvRKQ71kqhZcTpLYCtDvefCkwF6Natm60CURRFUeJDjEnvuCoiG4BuxpgfROQo4B9YfobmwNtAu1gOaRHZBmyMo/sDgB/iuK4mUNniQ2WLj0yWDTJbvtos2yHGmGZ2BzIqz8EYs1pEZgJfAWXACC+RSk4fLhYissQY0y2ea1ONyhYfKlt8ZLJskNny1VXZ0q4cjDGto95PBCamRxpFURQFtHyGoiiKYkN9Vw5T0y2ACypbfKhs8ZHJskFmy1cnZUu7Q1pRFEXJPOr7ykFRFEWxoV4qBxE5S0TWicg3IjImzbI8IyLfi8iqiLamIvKmiHwdfG2SJtlaisg7IrJGRFaLyPWZIp+INBaRz0RkRVC2CZkiW4SM2SKyTETmZaBsG0RkpYgsF5ElmSSfiOSJyCsisjb4t3dCJsgmIu2Dzyv084uIjMwE2YLyjQr+L6wSkenB/5G4Zat3ykFEsoFHgLOBI4FhwYJ/6eI54KyotjHA28aYdli5HulSYGXADcaYI4AewIjgs8oE+XYDpxljjgE6A2eJSI8MkS3E9cCaiPeZJBtAb2NM54hQx0yR70HgDWNMB+AYrGeYdtmMMeuCz6sz0BXYCbyaCbKJSD5wHVbOWEcgG6uIafyyGWPq1Q9wArAg4v1YYGyaZWoNrIp4vw44OPj7wcC6dD+3oCyvYVXSzSj5gFzgC+D4TJENK7v/beA0YF6mfa/ABuCAqLa0ywfsC6wn6A/NJNmi5DkT+ChTZKOyLl1TrBSFeUEZ45at3q0cqB3F/Q4yxnwHEHw9MM3yICKtgS7Ap2SIfEGzzXLge+BNY0zGyAY8gFV1uCKiLVNkA6tm2UIRWRqsUwaZId+hwDbg2aBJ7ikR2StDZIvkQmB68Pe0y2aMKQLuwdoC4TvgZ2PMwkRkq4/KwXNxP8VCRPYGZgMjjTG/pFueEMaYcmMt8VsA3UWkY5pFAkBE+gHfG2OWplsWF3oaY47FMq+OEJFT0i1QkAbAscBjxpguwG+k3/xWBRFpCAwAZqVblhBBX8JArLp1zYG9ROT3idyzPioHz8X90sj/RORggODr9+kSREQCWIrhJWPMnEyTD8AYUwy8i+W7yQTZegIDgnXDXgZOE5EXM0Q2AIwxW4Ov32PZzbtniHxbgC3BVSDAK1jKIhNkC3E28IUx5n/B95kg2+nAemPMNmNMKTAHODER2eqjcvgcaCcibYIzgAuBuWmWKZq5wKXB3y/FsvXXOCIiwNPAGmPMfRGH0i6fiDQTkbzg7zlY/xxrM0E2Y8xYY0wLY5WGuRBYZIz5fSbIBiAie4nIPqHfsWzTqzJBPmPMf4HNItI+2NQHq9Za2mWLYBiVJiXIDNk2AT1EJDf4f9sHy5Efv2zpdOqk6wc4B/g38C1wa5plmY5lIyzFmjX9Cdgfy5n5dfC1aZpkOwnL5PYlsDz4c04myAccDSwLyrYKuD3YnnbZouTsRaVDOiNkw7Lrrwj+rA79D2SQfJ2BJcHvthBokkGy5QI/AvtFtGWKbBOwJkirgBeARonIphnSiqIoSjXqo1lJURRFiYEqB0VRFKUaqhwURVGUaqhyUBRFUaqhykFRFEWphioHRYlCRHb4OLeXiJwY8f4qEflD8PfLRKR5HP1vEJED/F6nKMkk7XtIK0otpxewA/gYwBjzeMSxy7BizjMtA19RYqLKQVE8ICL9gduAhlhJUBcDOcBVQHmwjs21WJmpO7CqnnYDXhKREqxqwGuwSir/ICLdgHuMMb1EZH+sZMhmwGdE1P8K3ve6YL+fAlcbY8pT/4mV+o6alRTFGx8CPYxVDO5l4CZjzAbgceB+Y9X5/yB0sjHmFaws34uDx0pc7j0O+DB477lAKwAROQIYilUkrzNQjqWUFCXl6MpBUbzRApgRLF7WEGvPgWRxCjAIwBgzX0S2B9v7YG0q87lVLocc0lzkUKk/qHJQFG/8HbjPGDNXRHoB4+O4RxmVq/XGUcfs6tgIMM0YMzaOvhQlIdSspCje2A8oCv5+aUT7r8A+DtdEH9uAtRIAGBzR/j5Bc5GInI1VaA6sQmnni8iBwWNNReSQOOVXFF+oclCU6uSKyJaIn//DWinMEpEPgB8izn0dOC+44fzJUfd5Dng8eCwHq2rmg8F7RDqVJwCniMgXWOWzNwEYY77CcoIvFJEvgTextnpUlJSjVVkVRVGUaujKQVEURamGKgdFURSlGqocFEVRlGqoclAURVGqocpBURRFqYYqB0VRFKUaqhwURVGUaqhyUBRFUarx/wHUpKf2hlgSagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere for Maximum Temperature', 'Max Temp',(10,40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for Maximum Temperature', 'Max Temp',(-50,90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere for % Humidity', '% Humidity',(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for % Humidity', '% Humidity',(-50,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MORcBd8eRea1shuYhYjQS1p4bl1rMKawUkduBJ4C9wBocc9D+wK0i8kmcjuP8uGSohc3QPESMRsLac+NSk8VrqnolcGXR4TdwRg0NSS3nMgwjaqw9Ny5NH+aiWiQ1uqRhlIO158bFtuOsIsXeGqccNYKHN25PjPdGGG+SK7rWs2TlVvpUSYkwd+porp49MXQ6ccgWxTXdu3pIidCnSkfm2lXP73AtcyXlrVa5wqQXtF3mX3dQaxoR2LmnlxaB7E617a1pFp59DODtHeVVHr97AXDVPRvYucfZ+bA13cJ+6RS79vTS3pZGFXb39ObKs+zJbblzszKFqbOuNd2Ddlo8450juXfdttyxctMOSxT33287TusUakTS9roNI88VXeu56bEtg9K4cNoYOscOj7xc5dRVVNdkyX/R5TP9iOE8sWV3WeWtVrnCylBMJfUGjjkilRJ6+wYqMJsm4FqeOVM6uGN1t2ua6RahH+hzuyEBSbcIi88Ptp+6157sUaQdlqjuv1+nYOajGhGn90Y5ETO95Ln0lrWD0liycqtrGktWbo2lXOWkGdU1WbzeB4/+cUfZ5a1WucLKUEwl9QbQDwUdQn6aXuVZsnKrZ5q9/VpRh5BNI2idLV6+KXCHEDbtsFTD66vpo6TWiri8N8r1H/fLtzgNt3hR4MSRiqNc5aQZ5TXlECStapWrHBmikKmSNL3aWJREUfYor6kk3Sjzs5FCjYgrXk25msRBrWnf3/PT8IpVnxKJpVzlpBnlNeUQJK1qlascGaKQKUiaXtd6tbEoiaLsUV5TSbpR5medQo2Iy3ujHE2ia003r725N3Dac6eOdv197tTRsZSrnDTLueaUo0Z4/tbi8Y6afsTwsstbrXKFlaGYcmTKpwVIpworMJumV3nmTh3tmWa6RUh53ZCApFskcJ3NmzWBdIj8wqQdlmp4fZn5qEbEFa+mHP/xxcs3DbL5eqUN5LyMvLyPsmlGVa5y6qqcax7euN31eEqEaz80KXLvo2qVK6wMYb3i8tPo3tUT2vvI67fOscMT4X2UPS8J3kfViHNl3kcNRjneCePnL6NUK6ilZ1S18KoHAYugazQU5n3URJQT28nPrttMMeVtXwLDMPNRQxI2tpNX5Nhm6AjysX0JDMM6hbohztj1tYrHnzSsHpoP2xNiMDanUAckbfWzYTQCzfxc2ZxCnWOx6w0jeuy5csfMR3WAxa43kk49mmHsuXLHRgp1gHnFGEkma4bp3tWDMhAWJUjMrVpiz5U71inUARa73shSTrDDuKlXM4w9V+6Y+agOMK8YA8oPdhg39WqGsefKHesU6oRa7CttJAs/jbyWbaOet+a052owZj4yjDohqRq5mWEaC+sUDKNOSOrEaDmhVYzkUtJ8JCJDgR5V7ReRtwNHAferam+JSw3DiJAkh+EwM0zjEGRO4VfA34jIMOAhYBXwYeAjcQpmGEYhNjE6QD2ui6gXgnQKoqp7ROSTwHWq+nURWRO3YIZhDMY08uR6YTUKQeYUREROxBkZLMscM68lwyiDJK4zqDfqdV1EvRDk5X4psAC4S1U3iMjhwMOxSmUYDYhpuNGQVC+sRqFkp6CqvwR+CSAiLcArqvrZuAUzjHohqH07qesM6o16XhdRD5Q0H4nIz0TkwIwX0tPAJhGZF79ohpF8wsT9MQ03GmxdRLwEmVM4WlVfBWYD9wFjgI/GKZRh1Ath7NtJXWdQbzTVughVePJJ+OpXYfJkEBn4u/76WLIMMqeQFpE0TqfwLVXtFZH63ZnHMCIkjPaf5HUG9UbDeWG98QasWAF33QVdXbB9e+lrjj8+FlGCdAr/BWwG1gG/EpGxwKuVZCoi7cAPgGMBBT4BbAJuAcZl8vuQqu6sJB/DiJsw9u2o1hmYj34d8/LLsHSp8/K/775g17ztbXDOOTB7NkydCqlUyUsqoaztOEVkiKruLTtTkRuBX6vqD0RkH6AN+Cdgh6ouEpH5wDBVvdwvnWbZjtNILtXe0rGZt5CsG1Rh/XrnxX/XXbBuXbDrTjrJefl/8IMwblysIvptx1myUxCRQ4F/BUap6ukicjRwoqr+sExhDsQZdRyueZmLyCbgZFXdJiIjgUdU1XdcXW6n4KVp1asGVq9yNwrVrP/pi1a4jkxSIvSr2v0PSCT37I034KGHHHPPXXfBK6+Uvqa11Xnxn3MOzJoFBxxQlvyVUmmncD/wY+CfVXWSiAwB1qjqxDKFOQ64HseTaRKwGvgc0K2q7Xnn7VTVYS7XXwxcDDBmzJgpzz//fKj8vTStOVM6uGN1d91pYKY5Nhfj5y+j1Nje7r8/oZ+Zl1+Gu+92Xvz33x8sk6zJ55xzHJNPS7Jij/p1CkEkPURVbwX6ATJmoz7/S3wZAhwPfFdVJwOvAfODXqyq16tqp6p2jhgxInTmXt4iS1ZurctVkra6s7kI4qlk998f12fmzb3cdcN9sHAhTJpU6OVz6KFw8cXuHcJJJ8E3vwmbNztmo+zfs8/C178OJ56YuA6hFEEmml8TkYNxJoQRkWnA7gryfAF4QVVXZr7fjtMpvCQiI/PMRy9XkIcnXt4ifR4jpqT7kJvve3Ph5sHkht1/D954g7ev/hX/+IffMevZxxjeE8Bnpq1tYKK3hiafahGkU/g8sBQ4QkQeBUYA55Wboar+r4hsFZEJqroJmIljSnoauAhYlPl/d7l5+OHlLZISce0Yku5Dbqs7m4tiD6aWGrbbRM9lvfTSgJdPkYb/Y49LthzcwZhPfsTpAE44oe40/KgIEubiCRE5CZgACLApgr0UPgP8NON59Cfg4zimrFsz0Vi3AOdXmIcrXr7iXnMKSfchN9/35iPfR9/LPh73/U9EHCdVx7MnO9H75JOBLtveeSLXD5/EfYefQPdBbwEG5hTGJKVTqyFBo52egLN+YAhwvIigqj8pN1NVXQu4TXLMLDfNoPj5ineOHV41zScqLSu/PN27ekiJFNiUE6O5GbFQqz0WqhrH6fXXHS+f7MKuP/+59DVZk0/Wy2f//XM/jQCOWdPNfcs3IUkc5dSYIN5H/w0cAaxlYIJZkxAUr17XKcThMWReSEY18fKCEuC5RWeUl+j//u+AyefnPw92zZFHDrz8m9jkExY/76MgI4VOnPhHFtoiIuLQsiwCp1FNyp7Lypp8sgu71q8PluHJJw8s7Bo7NrzARmCCdApPAW8FtsUsS9MQh8eQeSEZ1aTkXNbrr8MvfjFg79+xo3SiQ4c6Hj4uJh+jegTpFA4BnhaR3wNvZA+q6tmxSdXgxOExZF5IRjXJjj5/ePvvmLj6Ec7+0++Z9uzjcHWAi9/+9gEXTzP5JI4gncLCuIVoNuLwGDIvJCM2VGHt2gGTz1NPAU7Y5Nl+151yyoDJZ8yY2MU0oiHozmtGhAT1GAnjoeSW5ilHjWDx8k1cdsta3+sT7W9uVI+sySf78t8ZIEjx0KEDE72nnmomnwbA0/tIRH6jqu8Rkb9AgaOB4HgfHVgNAf2oV++jIFTqTRT0evNaakK2bXNi+XR1wfLlwa7JmnzOOQfe9S4z+dQ5ZXkfqep7Mv8be013jSilnVfqTRT0evNaalBUYc2aAa1/w4Zg15nJp+nx7BREZLjfhaoawJ3AcCPIatBKvYmCXm9eS3XO66/Dgw8OvPx37Sp9zf77F8byGTo0bimNOsJvTmE1jtlIcPZl3pn53I4ThmJ83MI1KkG080q9iYJeb15LdULW5HPXXfDAA8GumTBhwOTT2RmpycfmoRoXz1aiquNV9XBgOXCWqh6iqgcDZwJ3VkvARiSIdj5v1gRa04Xb7oXxJgp6faX5GBGiCk88AV/+Mhx7bGH45lGj4B/+wb1DmDEDrrsOtmwpDN+8cSNcc03kbp/ZkW73rh6UgZFu15ruyPIwakcQl9R3qeol2S+qer+I/EuMMjU8QbTzSmPaBL2+VrFzmpqeHsfkk13YFcbkk/XyqaHJx+ahGpsgncIrInIFcBOOOelCIEBEKsOLoGsK8qNhlkPQ6yvNx/Bg27aBF/+DDwa7JkaTT1TYPFRjE6RTmAtcCdyV+f6rzDGjTEw7byCyJp/sRO/TTwe7bsaMAS+f0aPjlTFibB6qsQmyeG0Hzh7KRoSYdl5nZE0+2Zf/7gCbDx5wwEAsnxqbfKLEVs83NiU7BRF5GAZHyVXVGbFIVEeYB0YD8uKLjsmnqyu4yeeoowZcPBNq8okSG+k2NkHMR1/M+7wfMAfYG4849UMidp4yykMVVq8e2LQlqMln5swBk89hh8UqYtKxkW7jEsR8tLro0KMi0vTxkMwDow7o6XFcOLMv/6Amn3wvn7a22MU0jCQRxHyUv7K5BZiCs79CU2MeGAli0yZYtAhuuCH4NVmTT9bLRyQ28QyjnghiPspf2bwXeA74ZJxC1QNxeGDYHIUP/f1w773OYqzHHgt+nZl8DCMUQcxHFs7Chag9MGyOIsOrr8J3v+u8/IOYe7IcdBAsWACXXOJ8NgyjLIKYj9LAPwDvzRx6BPgvVe2NUa7EE7UHRhRzFHU10ti40TH53HhjuOumTXNe/mee2fBePoZRC4KYj74LpIHvZL5/NHPsU3EJVS9E6YFR6RxFIkca+V4+d90FzzwT/NqPfxy+9CXH9m8YRtUIGvtoUt73FSKyLi6BmpVK5yhq6g21Z4/j5ZMN6fDqq8Gua28fMPkcWPM9m4wGoq5GzQkjSKfQJyJHqOofAUTkcKCvxDVGSCqdo6iKN1R398CL/6GHgl1z9NEDXj7HH29ePkbsJHLUXEcE6RTmAQ+LyJ9wPJDGAh+PVaompNI5isi8oVRh1aoBk8/GjcGue//7B7x8Ro0Kl6dhRIitIaqMIN5HD4nIkcAEnE5ho6q+EbtkTUglcxShRxpZk0/25f+Xv5TOpL19IJzD+98PrRYAzUgetoaoMvy24zzX46cjRARVtY12EoTnSOMtwLe/Hc7kc8wxAyafyZPN5GPUFRbFtTL8Rgpn+fym2O5ryUEVHn+c2V1dzM43+Swocd2ppzov/rPPNpOP0TBYFNfK8OwUVNXmDZJGOSafYcMGwje/731m8jEaHoviWhl+5qPPA7tV9YdFxz8DpFT1mzHL1ry88MJA+GYz+RhGaCyKa/n4mY8+ARzvcvx64HHgm5VkLCIpYBXQrapnZgLv3QKMAzYDH1LVnZXkETdXdK1nycqt9KmSEmHu1NFcPXvioPNcfaYnjSz08tm0KVCeL007iZ+8dTK3j5rMkNGHla0BVerH3bWmm6vu2cDOPc7C9vbWNAvPPib2B7Ea/udB8riiaz0/W7mF/sxOI63pFq4595119SKKqi5reU/iyjtIuo26FkJUB+2f4/wgsl5VB7/hSvwWOGNnJNIJHJjpFL4O7FDVRSIyHximqpf7pdHZ2amrVq2qRIyyuaJrPTc9tmXQ8QunjSnoGO757f/wwH/cyMnPPMqsZx9j/zcDeEB4mHyK/a/BsZVec+7E0C/0StLpWtPNvNvX0dtX2HbSLcLi8yfF9mBEVf5K8/C69y3ANz58XF28GJLSlirJY86UDu5Y3R153kHKVI1yx4mIrFbVTrfffIPHiMihQY6VIdBhwBnAD/IOfxDIBsK5EZhdaT5xsmTl1oLvI1/dzt+tvofTL73QMd1k/s6afiTX3X41czY8PLhDOPZY+PKXnVAQ/f3OhLEq7NgBP/oRnHVWwRyAn/91lq413UxftILx85cxfdEKutZ0D5I9SDp+LF6+aVCHANDbr4HTKIdK5Y4qj+J7n6U/c309EFVd1vKeLFm5NZa8g5SpGuWuFX7mo8XAMhH5AvBE5tgU4OvAv1eY7zeBLwEH5B07VFW3AajqNhF5i9uFInIxcDHAmDFjKhQjJP398PjjcNddPPD9n3LEjhcCXfbL8cez/O0n8uDbprJ9f2d7CgGeW3RGqOxL+V8HXclZqR+333lx+oJXw/88SB59HqPrqGWJk6jqspb3xOs+VJp3kDI18loIP++jn4jIduCrwLE4bqgbgCtV9f5yMxSRM4GXVXW1iJwc9npVvR5nXoPOzk7vp7MSXnsNli8fsPe/9tqgU44o+r5rv/1ZfuSJLH/7iTw3aRoPf/l0AKYvWhGZz3Qp/+ugKzkr9eP2uj5MGuVQDf/zIHmkRDxfSPXiCx9VXZZKJwq7u1ceXveh0nsQpG4aeS2Er/lIVe9X1ZNU9WBVPSTzuewOIcN04GwR2QzcDMwQkZuAl0RkJEDm/8sV5lOarVvhuutgxowCkw/77w9z5sBNNw3uECZOhC9/mW9fewvjvnQP4y6/l3GX38txn7uZyz/wOVa87QQ2v9afO33erAm0plMFSZTrM10qraDaS6UyzZs1gXRqsHdTukVi9QWPsi4ryWPu1NGu17Zkrq8HoqpLv3SyI9fuXT0oAyNXN5NmOXnMnTo6lvYQpG6q0RZrRZDYR5GiqgvILKvKjBS+qKoXishi4CJgUeb/3bEJsWuXM5nrx2mnORO9Z50FI0cO+vnTwHe+8nNee3NwbMB8bSFKn+lSaQXVXrLn53sP7Tsk+N4EbtdXw/uoGv7nQfLIOhLUs/dRkHIG0fL90pm+aEUkMYj88ugcOzzy9hCkbhp5LYSn91FVMh/oFM4UkYOBW4ExwBbgfFXd4Xd92d5Hb77pbNKyevVALJ+ZM0Mv7EqaB0IYeZImu5Esomgf4+cvw+3tUs58mhEtft5HVR8p5KOqj+Ds5Iaq/hmYWZWM99nHWRlcIUnQFoq1uTlTOnh44/aS8lgkyXhoFN/1IO2jVFkb2e7eyATuFERkGvCvwL7AYlXtikuoeqKWKyfdvI3uWN0dSJtrZO+JWtFIcfyj8HSzGET1iachWUTeWnTo88DZwGnAv8QpVD0QZD1A3FTiK+2lrZWrxSWhPmpNI/mul2ofQco6e3IH15w7kY72VgToaG8182Qd4DdS+J6IrMYZFbwO7AL+FmeNTsD9FhuTpGiElWj7UWpxSamPWtNIo69S7SNoWS0GUf3hOVJQ1dnAWuBeEfkocClOh9BGwlcbx01SNMJKtP0otbik1EetiXr0VUtKtY9GKauNcAfjO6egqveIyH3A/8XZP+FrqvrrqkiWYJKiEVaq7UelxSWlPmpNo9nQ/dpHI5TVRrju+M0pnC0ivwFWAE8BFwDniMgSESle0NtUJEVLSorNNin1UWuScj+ixk2bboSy2gjXHb8oqU8CJwKtwH2qekLm+JHAv6jqBVWT0oNaRUk1H/9CrD4al0a+t828jqLcdQq7cUYHreSFnFDVZzPHm5Ksb3ZPb18u9kpHHfujl6LSVa1GfdPI61lsHYU7fp3COcBcoBfH66jpKdaa+lRzdtR6f0DcCGNzNS+TxqSR54saYV4kDvy8j15R1etU9Xuq2tQuqFmazQbZbOU1BtPI80WNMC8SBzUNc1FvNLLW5EazldcYTKNr0zbCHYx1CiFoNhtks5W3GQgbm6nc+aJGiQHVjFinEIJG15qKabbyNjrl+uWH1abN/7++CR5E32g6G2SzlbfRueqeDVWZI7K5qPrGRgohaTYbZLOVt1HpWtOd2xCpmKjniGwuqr6xTsEHs4sajYKflh71HJHNRdU3Zj7yIKr9ZQ0jCfhp6VHPETXy/sXNgI0UPGjklZw2Amo+vLT39tY0syd3RNombIV7fWOdggeNahc1z5DmxMuTbOHZx8TSJmwuqn6xTsGDRrWLNvIIKEkkbTTmp71PX7TCtU1cdc+GRJXBqA42p+BBo9pFG3UElCSSOh81e3IHj86fwX98+DgALrtlLdMXrXBVfgB27ulNXBmM+LFOwYNG9dFv5Fg2SSHJfvpuHZYEvDYpZTDixcxHPjSiXbTUKuWkmT3qkSSPxtw6LMXZQ8B9Z5VCklCGJNMIz4+NFJoMvxFQUs0e9UaSR2NeL3WFgjbR3pp2PS8JZUgqjfL82EihCfEaAdkkdDQkOWaUlwNFR3srj86fkfvuteNaEsqQVBrl+bGRgpEjyWaPeiLJ81FBHSiSXIak0ijPj40UGpwwNs5GdcOtBUmdjwqzsCypZUgqjfL8WKfQwIRdlJRks4cRHfayj4dGeX6sU2hgwto4LTyB4UfXmm6uumdDLtpqe2uahWcfY+0jQ6M8P9YpNDDl2DhNizTc6FrTzbzb19HbN+C4uqunl3m3rQMsREqWRnh+qt4piMho4CfAW4F+4HpV/U8RGQ7cAowDNgMfUtWd1ZYvLLX0Sy7O+5SjRvDwxu257+1tadcY+pXYOK/oWs+SlVvp04GXQ76P+7C2NFee5a09utUXeGtX1arfrjXdLFy6gV09vYHK4ZdOVt72tjSqsLunt+Bzfjnyzz+oNY0I7NrTG3tbCluvi5dvKugQsvT2a6K9a7zKWU67aoQ1CEEQ1SBLViLMUGQkMFJVnxCRA4DVwGzgY8AOVV0kIvOBYap6uV9anZ2dumrVqrhF9sTLba8aXhpueReTbhEQCh7mSuS7oms9Nz22peR56ZSw+LxJg/Jwk9lPRqAq9du1ppt5t62jt7/wWfAqh186pe5JltZ0ijlTOrhjdbfn+XG1pXLa7fj5yzwXtwnw3KIzIpUxCrzK6Vbvpcpfy2c9DkRktap2uv1WdZdUVd2mqk9kPv8FeAboAD4I3Jg57UacjiLR1DKcgVvexfT2K0P3GRKZW+GSlVsDndfbp6514CZzb78O0kCzdVit+l28fNOgDgG8y+GXTpAOAZxyLFm51ff8uNpSOfXqN7pMqneNVznd6r1U+ZMcuiRqajqnICLjgMnASuBQVd0GTschIm/xuOZi4GKAMWPGVElSd2rplxw0j909vay98tRI8uwLMap0ky9MvfidW63tI8PmFVauIPUZR1sqp93OmzVh0JwCOCO9pHrXeJXHq97LaQf1tgYhCDVbvCYi+wN3AJeq6qtBr1PV61W1U1U7R4wYEZ+AAahlOIOgeeSf17Wmm+mLVjB+/jKmL1oRevl9SoKGTnOXL0y9jGpvrVr9RqUFh5UrSH3G0ZZK1atbO5k9uYPF501iWNtA+Iv21jSLzw9uXqs2XuX0qvdy2kFSR0mVUJNOQUTSOB3CT1X1zszhlzLzDdl5h5drIVsYahle2y3vYooD3VUal2Xu1NGBzkun3LVHN5nTLUI6VfiQZuWuVv3OmzXBmdsowqscfumUuidZWtMp5k4d7Xt+XG3Jr1792snsyR2s+cqpbF50BpsXncHaK09NbIcA3uV0q/dSdd2oofTdqIX3kQA/BJ5R1W/k/bQUuAhYlPl/d7VlC0st/ZLd8i72PsqXJYq4LFfPdiZ/y/U+8qovt2P518ddv9n0KvU+Ki5fEO+jzrHDq+59VM6GO0n2MPLCr5z59R6krhtlDUIQauF99B7g18B6HJdUgH/CmVe4FRgDbAHOV9UdfmnV2vuonvDyHkmq54hRG6ydNAd+3kdVHymo6m/Ac1+PmdWUpZmIY82C0Xg0Svweo3wsSmoT0LWmm7++vnfQ8bA2c6PxaSbbueGOhbmoQ8pajerihz90nyENaRMNQ7OsUvWjuA7mTOnwnJsyGh/rFOqMsJFPwduXenfPYHNSM1FOXTYabnVwx+ruul2pa1SOmY8SRJB1BFGuRi0+Xuk6hnqjmVap8vTTMHMmtLXBqFHwla9AX59/HWzYAKed5py/774wZgx86lOwbVth2g8+CHPnwrhxIAILF1atWIEIWg4R97999y2dx803w/HHw/77Q0cH/N3fwYsvFp7zne/AGWfAwQc76T7ySGRFjBIbKSSEoFpruatRS8V5b0atuWlWqe7cCe97Hxx9NNx9N/zxj/CFL0B/Py/uPdH1khd39cDu3TB+vPOCGzUKnnsOrroKVq+Gxx+HIZnXx89/Dk8+6XQ6N99cxYIFJGg5fve7wdeedRZMn+6f/tKlTqf46U/D4sVOZ3PFFXDmmbBqFbRkdO+f/MTpDGbNgiVLoi1jhFinkBCCriMoxztk9uQOVj2/I7e+ICXCnCkdg9YCNIp/elCaxtPme9+Dnh6480448EB4//vh1Vdh4ULe9qVbePb1wQaDUe2t8O53O39ZTj4ZDjsMTj3V6QSOP945vngxXHut8/numJcX3XCDMxLZvDn4NUHLMW1a4XW//z288orzwvfjZz9z0vjWtwaOHXggfPCDsGkTvOMdzrHf/tbpIJ56KtGdgpmPEkJQrbUc75CuNd3csbo7t+CsT5U7VncXmIeaRmvOo2qeNsuWOS+D554rPP7cc87xpUujza+Y++93tNMDDxw4dsEF0NPDv7T/OVwdHHyw8//NNweOtQR4jZx5Jhx1lNM5Zbn2WthvP8e8U23cylHMzTfD0KHOaMGP3l446KDCY+3tzv/8dWBB6ikB1IeUTUBQu385G6oHsZ03U2yXLFXbnD5rz77xxsLjN9wAI0bABz7gfW1fH+zd6//X3+99PcDGjc4LOZ8xY6CtjWm920vXQX+/8/LctAnmz4d3vQtOOCFMDcD3vw/bt8OCBc73Z55xTCxXXQXHHBMurXIJUw5VuO02R9tva/NP9xOfgF//2jEPvfoq/OEPTtlOOcUx2dUZZj5KCGH2dw27u1OQUUCj7C8blqrslJVKwcc+5nQKV17p2JVVne8f/eiATduNmTPhl7/0T/+ii5wOxoudOwc013yGDYOdO0vXwQc+AMuXO5+nTIH77guv9Y4c6ZhXPvIRR/NesAAmT4YvftH/uv7+wk4v+3lv3robEaeOSxGmHL/+NbzwgjOiKsUZZzj1/8lPOvcCHHNV3CPAmLBOISHEGVsliO28nmK71OXagk98Av71X+GRR+hqfzsPfedmrnv+eS7sO5rzMsHmXPmv/4K//MU/7UMOKZ2/W2RQVffjxVx3HezYAc8+C1dfDaefDo8+6ph+wjB3rjOvccYZzst43brSL/OvftUZTRSTHojWytixweYYwpRjyRKn05w1q3S6Dz8Ml1wCn/uck+ZLLznzHuecA7/4RbAOK0FYp5Ag4tJag44C6mF/2br1kjr8cDj5ZLZc+20WTP4UX/vdMtaOfDu/2fdQVvvJ/7a3Fdql3SiltQ8bBrt2DT6+e7f7CKKYI490/k+dCn/zN44nz89+5nR0YZk7F26/Hc4+eyBdPy6+2JmPyHLvvXD99YVaeBCXUQhejr174Y47YM4c2Gef0ul+4QtOef7t3waOHXecY7K7+24499xg8iUEm1NoAqpmO6+AoGsk6nptwac+xYgHlnHgjpc47Q+/5baJ7wNKyD9zpqMV+/2VejkfdZQzr5DP1q3w2muD5xpKMXYsDB8Of/pTuOvAsbdfdpljNlq6dMCU48eoUdDZOfA3bpzzos4/NnFieFn8yvHQQ878RymvoywbNzqdQD4TJkBrq+P+W2fYSKFJSPIoIIz2X9deUueey5ufuJjrln6dFlXuecd7cz95yh+F+ej00x230b/8BQ44wDl2yy3OS+ukk0IUAGeS9s9/drTssFx6qTNxvmKF49P/qU857pnFnjvVwK8cS5bAW9/quK4GYexYeOKJwmPPPON4Wo0bV6mkVcc6hSpTl/bwmAmzRqKu1xbstx+/mPw+5jx2N3e/4yRe3W//3E+e8k+IYKL/kkvg//0/x4xx+eWOdrxwIXz+84Vuqm97m9NJ/PCHzvcvftGZBJ861TEzPfMMfP3rcMQRhROwzz/vLAIDx7vn6acdE9HQoU6HBI7Z58c/dtxj29sd+/6xxzp2eL9J8igIWg6AN96Ari7HMaDILNe1ppuf3Pwrbl18IdecN4+JCz7jtM9LLnFGQKNGDcwpfPWrToeQ71m2apUz97E1s9f5L3/prIMYN84Z8SQE6xSqSN3aw2MmjPZf715Sb73oAnjsbm595/tzx2KXf9gwxyTyj//oeP60tzsvseJwFHv3Opp8ls5O5+V9/fXw+uuOG+ucOY7n0NChA+c9/DB8/OMD32+7zfnLTgDv2OHMDfz93zvuueCYbr7/fWe+YM6c0msBKiFoOcDptHbvHtRZZJ/dg1/tYYj2s/u1Nwae3c9+1jFpffe7zkLB9nZ4z3vgmmsK0//WtwrdkrP1X8p7rMpUfZOdKKm3TXamL1rhquV2tLfy6PwZNZAoGYStl7oebX3pS+z5759x6mduoPvVN+pO/iu61hesjJ87dXRuR75GptGe3URtstPM1LU9PEbCav9Jnh/xZNMmx6zy3e/SduWV/OaL76u1RKG5oms9Nz22Jfe9TzX3vdE7hmZ6ds37qIo046rhIFTqHVUX0V3/z/+Bv/1bx8b82c/WWpqyWLJya6jjjUQzPbs2Uqgi9W4Pj5Nytf+6madJaJjkMPR5mJq9jjcSzfTsWqdQRaq1ariube4hacbortXArQ2lRFw7gFSQVdEJodxno55W/FeKdQpVJm57eN1ozhHRTLbeauHVhqYdPoxH/7hj0Plzp46utohlUemzUZdzWWVgcwoNRl2v+HWh1HxBM9l6q4VXG9r85x4unDYmNzJIiXDhtDF1M8ncaM9GXNhIocFoJM05iGbXTLbeauHXhq6ePbFuOoFiGunZiBPrFDI0gh2+a003LR5233rUnIPMFzSyrbdWbbKuV4370KjlihrrFGgMO3y2DG4dQr1qzkE1u0a09dayTTbq6KtRyxU11inQGB4sbmUAx+4bRUTUWmitjarZBanLWrbJRh19lVuuRrAihME6BRrD1ugla79qJB1CLbTWRtTsgtZlrdtkI46+IHy5GsGKEBbrFGgMjTTOMtRKa603jdVNo4RC+V97Y2+guqxFm2w2jTgIjWBFCIt1CjSGRhpnGWqptdaLxuqmUc67bR0I9PZp7pgXxXVZ7TbZjBpxEGo9YqsF1ilQfxqpG7XY47lFhK6i/YWToG0G0dizchWfe8pRI3h44/ZB37t39eRW9IoM7JA5dJ8U6VQLu3p6B8nR2x88/IMC4+YvA0Ay34sR16P+ZS8uj9f98NKIr7pnQ0X3M8y9iJoo2mJUI7YkPBdBsdDZRkmKtch8WtOp3ES223n5v9dK1nSLFGjsWbnmTOngjtXdruVKKumUsPi8Sa716Xefsnjdj/HzlwXocsLdzzD3Iuo2ElVbjCKdJDwXxfiFzk5cpyAipwH/CaSAH6jqIq9zG7FTyNcoDmpNIwK79vS6ahd+555y1AiWPbmNnXscDba9Nc3Cs48p0I67d/V4aqVZOjJp3bH6BXp6+13PGdaWZs1XTvWMOd/emmbovkMCaeP5tKVbOHfKYblz29vSvNHbx548OYa1pTnjnSMLyhoUr1g+9UJHUZvwqn83smXPppFtD0Fob02z9spTC465acJh0szuS+C3X0MYbXvyVx9wbQ8tAtkBXP4z4ZU+wFX3bHB9joKSxL0Y6qZTEJEU8Afg/cALwOPAXFV92u38RusUSml6pbTyUqRbhA+fMDoW7fibHz6Oy25ZG0jbNKIjv00E1fbd0gg7avrmh48reJm6acJh2pgAH5k2pmC/hiwXThtD59jhgbXtrjXdXHrL2kD5pluExedPAohtVON1XwR4btEZgdOJEr9OIWmxj04A/kdV/6SqbwI3Ax+ssUxVw2utQZb8OC2lznWjt19ZsnJrLOaSxcs31ZW3FtRXdE8v8ttEufXf09vHwxu35/a0CEJ+vCCv+Ygw9TuqvdV3v4YwcYvCxDLq7VcWL9/kmn5vvxZ0CH55+lFv8bmS1il0APkt44XMsRwicrGIrBKRVdu3b6+qcHETxKMhe0653g9xmUte3NXDvFkTaE2nYkk/alrTKeZOHR27vOlU/B1Pti1U4pn04q4eZk/u4NH5MwgicX7782qLfaqB6jfrVeW3X0MYL6Cwz8aLu3pCXRM2fbfnIsnejUnrFNzaY0FLUdXrVbVTVTtHjBhRJbGqQxDNIXtOuVpGXNrxqPZW1x3UhrWlY8mvHFIiBTu7XT17YkntuDXdElh7LmZYW5rF503K1UecdQ+OB1p7a3n1nd+ewrRDv/Oz9exXf/kr7r3qJyUSStsO+2yMam8NdU3Y9CvdWbDaJK1TeAHID85+GPBijWSpOqU07XztohytPN0isWjH+XJltc3nFp3Bo/NncOVZxyRi9JBqEa790KScXPkB9R6dP4Nvfvg4V23umnPf6fv79COGu+bXAlx51jEF9XHthybFWvcAC88+hhaPvsfreHEaYdqh1/nZc0rV77UfGvCk8tqXYe7U0aG0bbdz0y1CyqUC0i3CvFkTPK8pHumVq+EXPxdJ7RAgeesUHgeOFJHxQDdwAfC3tRWpehSvNfDzPip1rp/3UefY4aG9j+5dt83VF7/YA6ZUmarhfZRugXxHqaH7pPjaOf6aWal1Hn6/X9G1np+t3JLzamlNt3DNue8clF9+GvnrHoa1pVGF3T29ufu4c09vwb3JXw9R7DmUn0/28z/d+WSunkTgI1PHFNz3IGkE9YILskYmyDlZLyMv76NS15fKC0p7EtVqTUWSSJT3EYCIfAD4Jo5L6o9U9Wte5zaa95FhGEY18PM+StpIAVW9D7iv1nIYhmE0I0mbUzAMwzBqiHUKhmEYRg7rFAzDMIwc1ikYhmEYORLnfRQGEdkOPF/GpYcAr0QsTlSYbOVhspVHkmWDZMtXz7KNVVXX1b913SmUi4is8nLHqjUmW3mYbOWRZNkg2fI1qmxmPjIMwzByWKdgGIZh5GjWTuH6Wgvgg8lWHiZbeSRZNki2fA0pW1POKRiGYRjuNOtIwTAMw3DBOgXDMAwjR1N1CiJymohsEpH/EZH5NZblRyLysog8lXdsuIg8KCLPZv4Pq5Fso0XkYRF5RkQ2iMjnkiKfiOwnIr8XkXUZ2a5Kimx5MqZEZI2I3JtA2TaLyHoRWSsiq5Ikn4i0i8jtIrIx0/ZOTIJsIjIhU1/Zv1dF5NIkyJaR77LMs/CUiCzJPCNly9Y0nYKIpIBvA6cDRwNzReToGop0A3Ba0bH5wEOqeiTwUOZ7LdgLfEFV3wFMAz6dqaskyPcGMENVJwHHAaeJyLSEyJblc8Azed+TJBvAKap6XJ4fe1Lk+0/g56p6FDAJpw5rLpuqbsrU13HAFGAPcFcSZBORDuCzQKeqHouz5cAFFcmmqk3xB5wILM/7vgBYUGOZxgFP5X3fBIzMfB4JbKp1vWVkuRt4f9LkA9qAJ4CpSZENZ7fAh4AZwL1Ju6/AZuCQomM1lw84EHiOjPNLkmQrkudU4NGkyMbAvvbDcbZCuDcjY9myNc1IgYHKy/JC5liSOFRVtwFk/r+lxvIgIuOAycBKEiJfxjyzFngZeFBVEyMbzgZRXwLy9n5LjGzgbOb2gIisFpGLM8eSIN/hwHbgxxnT2w9EZGhCZMvnAmBJ5nPNZVPVbuDfgS3ANmC3qj5QiWzN1Cm47VBr/rg+iMj+wB3Apar6aq3lyaKqfeoM5Q8DThCRY2ssEgAicibwsqqurrUsPkxX1eNxzKifFpH31lqgDEOA44Hvqupk4DVqb2YrQET2Ac4Gbqu1LFkycwUfBMYDo4ChInJhJWk2U6fwApC/M/hhwIs1ksWLl0RkJEDm/8u1EkRE0jgdwk9V9c6kyQegqruAR3DmZpIg23TgbBHZDNwMzBCRmxIiGwCq+mLm/8s4dvETEiLfC8ALmVEfwO04nUQSZMtyOvCEqr6U+Z4E2d4HPKeq21W1F7gTeHclsjVTp/A4cKSIjM/0+BcAS2ssUzFLgYsyny/CseVXHRER4IfAM6r6jbyfai6fiIwQkfbM51ach2JjEmRT1QWqepiqjsNpXytU9cIkyAYgIkNF5IDsZxzb81NJkE9V/xfYKiITModmAk8nQbY85jJgOoJkyLYFmCYibZnndibOBH35stVy0qYGkzIfAP4A/BH45xrLsgTHBtiLoyV9EjgYZ5Ly2cz/4TWS7T04prUngbWZvw8kQT7gncCajGxPAV/JHK+5bEVynszARHMiZMOx26/L/G3IPgMJku84YFXm3nYBwxIkWxvwZ+CgvGNJke0qHMXoKeC/gX0rkc3CXBiGYRg5msl8ZBiGYZTAOgXDMAwjh3UKhmEYRg7rFAzDMIwc1ikYhmEYOaxTMIwMIvLXEOeeLCLvzvt+iYj8Xebzx0RkVBn5bxaRQ8JeZxhRMqTWAhhGnXIy8FfgtwCq+r283z6G4zOetBXzhlES6xQMwwcROQu4AtgHZ/HSR4BW4BKgLxNn5jM4K0n/ihOFtBP4qYj04ETnfQYntPErItIJ/LuqniwiB+MsYhwB/J68+FyZdD+byXcl8H9VtS/+EhvNjpmPDMOf3wDT1AnSdjPwJVXdDHwP+A914uz/Onuyqt6Osyr3I5nfenzSvhL4TSbtpcAYABF5B/BhnOB1xwF9OJ2RYcSOjRQMw5/DgFsyQcX2wYn5HxXvBc4FUNVlIrIzc3wmzmYujzvhbGilxsEHjebBOgXD8Oc64BuqulRETgYWlpHGXgZG5fsV/eYWZ0aAG1V1QRl5GUZFmPnIMPw5COjOfL4o7/hfgAM8rin+bTOO5g8wJ+/4r8iYhUTkdJwAcOAEMDtPRN6S+W24iIwtU37DCIV1CoYxQJuIvJD393mckcFtIvJr4JW8c+8Bzsls5P43RencAHwv81srThTL/8ykkT9ZfBXwXhF5AieM9RYAVX0aZ3L7ARF5EngQZ0tFw4gdi5JqGIZh5LCRgmEYhpHDOgXDMAwjh3UKhmEYRg7rFAzDMIwc1ikYhmEYOaxTMAzDMHJYp2AYhmHk+P8JAkPiQSj9tAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for % Cloudiness', '% Cloudiness',(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for % Cloudiness', '% Cloudiness',(-50,60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oirr3OhO4RgE4GcBB8/tJ5jahwijHqAc7sjBIulG1Py/fjPPeed3jpjFDKyfaxy+qh4g+A2AxgHXmpo8AWGyOBoqCRPUkj0S8CEni1f5uWrHR1RlJAHYsnRNbuaXQ7kNH9TDzLwFMA/CQ+flQMZW+kA4k4kVIEq/2F+eM5XJt976K3wzLvBDAJGZ+GEB/IjondsmEVCERL0KSqNpZtqMTMyYMj80+X67tXieO//8B+BCAeeb3twH8e2wSCalE8sAISeLVzlZuyOKqqQ1oqKsBAWioq4nMFFOu7V5H8U9j5i8AeBcAmPkQgP6xSiWkDol4EZLErf1ZdHb1YN3WdjyzcCZ2LJ2DZxbOjMz+Xq7tXieqp4uIqmFO5iKi4QBOxCqVkDok4kVIEqud3bhio+vvcZleyrXd60T1XAvgagBTAdwN4OMAbmPm38QunYlE9QiVSDmGERbK9KVrXSdg1dVksHHRRxOQKN0UEtVzL4BbANwBYB+A5mIqfUGoRIJMSqokFswej0xVfkKAI8e7K75ugqCbpG0YgKPM/EMAbxDRuBhlEoSKp1zDCAuleUoDThqYb6Hu6uGKr5sg6IRzLgLwNQC3mpsyAH4Vp1CCUOmUaxhhFHQc7XLdLnWjj06P/0oAlwM4AgDMvBdGCgdBEGKiXMMIo0DqpnB0FP9xNjzAVlTPoHhFEoT00tKWxfSlazFu4aOYvnRtbHblcg0jjAKpm8LRCee8n4h+AqCOiD4H4HoAP41XLEFIH868LZbDFUDk0TblGkYYFK/Ipkqvm0LQWnqRiC4CYMVKPc7MT8QqlQMJ5xTSgCqUsKGuBs8snJmAROVNqSdISwOFLr24CcAfADxt/i8IFYc4XIvL4lWbJbIpJnSiej4L4DkAc2FM3vozEV2vcdxoIlpHRFuIaDMRfcXcPpSIniCiV8y/9YVehCAUA3EqFo+Wtiw6OiV6Jy50evwLAExh5uuYeT6MGbxf0ziuG8DNzHwGgHMBfIGIzgSwEMCTzPw+AE+a3wUh9YhTsXh49erjeNEWy2mfFnScu6/ByMhp8TaAPX4HMfM+GDN9wcxvE9EWAA0ArgBwgbnbPQCegt6LRBASJYhTUeWUlDQMenj16qN+0RbTaZ8WdHL1/AeAswE8DCOk8woYpp//AQBm/r5vIURjYfgHzgKwm5nrbL8dYuY8cw8R3QDgBgBobGycumvXLq0LEoSkUTklr5ragJUbsuKs1EDlSK+vzaDtG9Hm5Clnp30hzt3tAFqA3tXNHobRkz8ZGhO5iOgkACsB3MjMb+kKzMzLmbmJmZuGDx+ue5gnlTacE5JBlW7hvvV7xFmpicqstuiyiZGXVYlOe19TDzMvsf43HbEdrBMDauyfgaH072XmB83NB4hoJDPvI6KRAF4PIXdgKnE4JySDSmH0KB6bclYwYSlmrP6ouhrXHn85O+2Vip+IvgHgfmbeSkQDADwGYDKAbiL6W2b+ndeJzSUbfw5gi8MctArAfABLzb8PF3YJenglvUq74he7cGmhUiTVRK7Kv5wVTCE0T2koSjtfMHu8q2munJ32XqaeqwFYY9D55r7DAXwERopmP6YD+DSAmUS00fxcAkPhX0RErwC4yPweO6U6nJP0vKWHykwxb9poiQpKIc1TGnDn3LNjWboxrXiZeo7bTDqzAdzHzD0AthCRjonojwDyE2cbzAomZuGU6nCulEcqlYqXmaJpzNDERm8SaaSmWKOLtOClwI8R0VkADgCYAeCrtt9qY5UqBkp1OJfUSKVUlUGccgc5t0qRJKVgVD6u1l0HcyKNkvB9RXHPrHNkOzp7TWoNJdRui42X4v8KgAdgmHfuYuYdAGCaa9qKIFuklGpipyRGKqXqCI9T7ijPncRL1SvSyOl3KOaIMop6dZ7Dup5SabdJoLTxM/N6Zp7AzKcw8zdt23/LzPOKI160NE9pwDMLZ2LH0jl4ZuHMkmgMScwWLdXVn+KUO6pzJ+WzSWukURT16naOsOeqFHSTtAkJkYTjqVQd4XHKHdW5k3qpqkaI1eTuhiuW76vQem1py7qOiMOcq5LQSdkgJEyx7cKl6giPU+6ozp3US1Xl41LNJi6W76uQerVGTzplCLlIj1/Iw828lKkmHDnWnepZz3GaxdzOTTBMNUHqo5gZPu0z1Zet2YYPNA7p7eFXE+GqqQ24vfnswCPKKGfAF3LPvEw8Qc+VRuLMNOA1gWuu14G2mbhCmeF0hNfVZvDOu929aXLT6jSL04FvP7fVQ7Ws40Hqo1jRZW5OU3vPuocZKzdk0TRmaKARZdQO9ELumd8oqZSjeuIOsFAmaSOiX5r/vgfAeQDWmt9nAHiKmT1fDFEiK3AlSzknsQpKS1sWN63YCLenRrc+ihHVo7pnToLewzS1hTTJEjVRXZsqSZuyx8/MnzEPXA3gTDPNMsz8Ov+uXbIQCUnG1ZeqszcOlq3Z5qr0Af36KIbPRleWoPcwTW1hxoTh+NWfd7tuL3XirmcdG/9YS+mbHADw/khKF7RIOm2DrDzVh9eDl6b60JUlqMxpagvrtrYH2l5KxF3POor/KSJaQ0TXEdF8AI8CWBdJ6YIWScfVy8pTfagePEL4BULicOK53TMnYe5hmtpCmkYfURN3Pevk3Pmi6ej9G3PTcmZ+KJLSBS2SbuClOus5DtycswTg2nMbQ9VHUCeersnP7Z7NmDAc67a2F3QP09QWSjXsWIe469l3Ba40UOnO3SSdWMX0LeiUpSuPcz83pQeEe7CC1InfvkHurWplr3LIJBmmnZVzfUSFyrmrs/TiXADfhhHdQ+aHmXlwHIK6UemKP6kGXsxydcrSlcdtPyeZKgII6Orpa/9RX5uOvOMWPurqLCYAO5bOydlWrlEshbSzUk0mWCwCR/XY+A6Ay5h5S/RiCTokNbwuZkponbJ05dGZ2NN1Il/dRn1tOvIGMVckbfKLC937qlLyouiDo6P4D4jST54kGngxFY0q5txelq48hcgX5bXpyKua0DVjwnBMX7o2R8mVq01bp55KNWNsWtGJ6mklohVENI+I5lqf2CUTEqdYoXstbVnlij32snTlKUS+KK9NR163JHxW/hxn+O6MCcNTE1ETJTr1lHRkW7mho/gHAzgK4KMALjM/l/odRES/IKLXiegl27bFRJR1LMUopJRihe6pJkU5QyR15dEJZcxUETLVua+bqK9NV15nuvB1W9tdldy6re1luUSgTj2Vq5krKXTCOT8T8tx3A/ghgP9wbL+Lmb8b8pxCEWme0oDWXQd7F+uwEntFrWhUD6/zZaDr69ANZbTvU1ebATNw04qNWLZmWyQ+lLC+GS8lF9TkVwrOT796amnLokoWqo8UryRttzDzd4joB8h/BsHMX/Y6MTM/TURjCxdRSOrhbWnLYuWGbO8D50zsFfaczmtR2a4B5NlxdRVfkP3itB+H8c1EZcsvFbu4V/u2rsFN6ZeDmSspvJK0XcbMj5izdfNg5nt8T24o/tXMfJb5fTGA6wC8BaAVwM3MfEhx7A0AbgCAxsbGqbt27fIrTgud+O60PRTFDOW014+qlxU2fFB1LW454aMoT5e0hUlGdc/Tdl1uuF0rwehpNtTV4MixvqywdqqJ8L1PTkrVs5pGVOGcXjb+04nogwDuZeZ7nJ+QcvwIwOkAJgPYB+B7qh2ZeTkzNzFz0/Dh0SRdcst586s/704sB44OxXRqOesn6mX5VNdi2a5VxG3HTZv9OKpV19J2XW64tQl7ums3pQ8AJ5hF6ReAl43/NAD/CmACEb0I4FkAzwD4EzMfDFMYMx+w/ieinwJYHeY8YdGJ7y7mQtM66Ia6RWEK0qkfILxd1c92bc91H0V5uqQxTLKQ8F2rPaimZo6qq0mN7T/sS0hs+4Xhtdj6V5n5PAAjAPwTgIMArgfwEhG9HKYwM6WzxZUAXlLtGwdxpaqNAytxl9fDa+0XVeZOnesuxK7qF7aXVAKwNCUeKxR7e3DDmiOQZLZXO2EUeKnemzShM4GrBkZI5xDzsxeA70KXRHQfgAsADCOi1wAsAnABEU2GMZrbCeDzYYQOi5cT0bmfLnH0nG5r2YR7/7xbqfTtDT/K2bWq+qkmwgnmgq/Pb/WpsFEw1j3IdnSi2vRLqFZfUu171dSGyHw9bn6k1S/s6zVb1NdmsOiyiQW1E1W78xq11dVksPjyiUWdke2HW5twUl+bQW3/fomOTtIyQooKr6ie5QAmAngbwHoYpp7vq5yxTph5nsvmn4cRMip0GlmQ3kQcURMtbVlPpe9UaFHacVWKOSpHso5iDxOuaJfZ8ku43QuvfVduyEZynW5twrlYyKGjXVjwwAs5shVahnWtXvf9WPcJz32SGOk6l7S0HLsWNZnqgl+ShVIq0VFB8OrxNwIYAOAVAFkArwHoKIJMsRF1qto4ek5etlkC8qIxorRPFyMnUNSpJ7x6uDq5flT7xiGPna4eDl2eV7vzGtX67ePVZuLs8drbRBp71mkaIUWF19KLFxMRwej1nwfgZgBnEdFBGA7eRUWSMVKiVDxx9JyCrvAU9eLdpZb0yq+udXL96P4ehTxRlOfV7u66erLnqFa1j1ebKWaPN43tL00jpKjwTNnABi8B+C2Ax2BE9ZwO4CtFkC31xJHLJugKT1GF/pUqfnWtk+tH9/co5ImiPK92Z7WHanLPfmTfR7fNVHqenDQtNxkVSsVPRF8mol8T0R4AT8PIz7MNwFwAQ4skX6qJIxrE7Zx+Kzw5c71UitIHvPPyOO9FkH3jkMdOpppCl+fX7pqnNOB7n5zku49umynHHm8Qyinqy8LLxj8WwAMAbnIsti6YxGETTyr3fqnidA56RfUE2VeFnw1a5UcKE9Xjl3/eT46o8iwF8QmoZE7bSm5hji2nZ1KWXhQETdK2IlmxZC1k5TNVSo401ls5LuUYJmWDYGJNphq38FFMX7o2VSkdSoVyqMOobN06dVFoWVHa5XV9Aqoy71u/p2g+gkKuu5J8GToTuCqauGL1y2nY6Ee5xEFHYevWrYswZdnblWocH9YurxNtozp31DmfwsigU1Yl+TKkx+9D1L2AKFMslArl0pOKIrpDty6CluVsVyrijERRndsrwqhYMuiUVY7ROypE8fsQdS+gXJRgEMqlJxVFdIduXQQtS2fiWNyRKCqZ500bHVlUjJ+ZrJB7VI7ROyrE1OND1JkbS0EJ3tayKScaZN600bi9OTdtchBzVZwzRcOazcIcV0h0h07GTCcD+lX1KnO/SCCv9kPm+eM2KXrVT9OYoQWbN3XMZIXco3KM3lEhit+HqGfGpjEFsJ3bWjbl5JbpYe79bin/oDZ7VY6kI8e60dKWdU2kpnP+sL6DQnwOYWaWukWL2HG2J7f93+064VmGql0Ve9EVVf3o1ptfOKVO6oRCZv+mceZwHIipx4eoZ8ameTjZ0pbNSyhmcd/6Pb3/65ir7EPyZWu24aqpDaivzeQc09HZ5erf0DWHhTWbFcPcZr/+m+9/wXN1MWd7CiNfmtuVLn7+r1IYLZcK0uPXIMpeQFqHk9ZDp8IemeH3ALr1qFduyGJAv/x+hluPTXX+bEcnpi9d21tfYRVB3ApElQXUiVvSvbDypbVdBcGvR5/UaLkco/BE8SdAGoeTfs5Be2SG3wOoeoC9Eoc5z6PKMGk3y4RVBHErkEJXMgsrXxrbVRD8XnhRm111KJdQZCdi6hEA+Pd2500b3fu/n1khaM/ZqdD88t1YvcCw5o24zSKFrmS2YPZ4ZKpyQyAzVeFz+5QKfuGUSSQkLNcoPOnxJ0Daho4tbVlUmXlr3MhUAff+eTfWbW3Xyl2i6rHW12bwbtcJ1x6bs06sFbFUPX9rnV4vOezX59znzrlnY/Gqzb35cwZmousD+a1kVlebATNw04qNvS+wvPvvDH13D4UvOkFWOwvKjAnD8xYhcr4gnaMay5cS17NUVL8CM/D668Crr/Z9+vUDvvY1oCraPnpsuXqI6BcwMnq+zsxnmduGAlgBIwHcTgCf1FnRq5xy9bhFbFirDkX1ABUqjxc6uUu8cp4A+YoagHJ/1QLsutEqSeSP8bt+v3ww05euTUWEjhOvtlJo3anOPah/NY4e73FV6n65daLoYAW+F8zAG2/kKm/rs307sGdP/jF+PPwwcPnlwY+DOldPnIr/fADvAPgPm+L/DoCDzLyUiBYCqGfmr/mdq5wUv6ohWRQ7KZSfPG7oKKAgD53Xw1XocpCqc1crRjhRKVfV9esoknELH3WN9ycAO5bOKVi2sPi1lULqTrcdWusG+9VlwcuIHjoEbN+O53/3PP7w+HMY+eZeNB7ej8aOAxh9+ECgawvEsGHAe99rfE4/HfjQh4A54e+5SvHHZuph5qeJaKxj8xUwFmAHgHsAPAXAV/GXE35DRJ0l3aI0FYUZsuoc4+do1M0rU2i0SlL5Y1TXr2M6SOtcjzhXMNM91goB9jpmb0cnlq3Zhn7vvIWJHfvReGgfxnTsR2PHfoz4rwMADhs9cA0+aH4CMXRon/K2FPjppxv/NzQY5puEKbYEp1q5/Zl5HxG9R7UjEd0A4AYAaGxsLJJ48eMVsWLhl4gryigDHXncjikEXfMSA73hm2F7kl72djflH7dy1VHqQaJXiukv8msrQfLz65570LGjZk97P0Z37MeYjv0Yc2gfxv3odfzvwX2oZu+JbaGpq8tV3jYl/vAbVfjO2ldT46MLQ/KvHgXMvBzAcsAw9SQsTmSoZrHa8VI+US/8vGD2eNy0YqNnYi87UUS/6IY7AoW/2FRKVGXjjztyRkepB3FaFzPU0KvtutVd79yQI0fw/sP70fjKfmxduwKvDj2O9751wLB5v/oq0N2NZyKXFninfw12143ArrqR2F03AnvqRmD3kFNxrHEsVtxxDdC/f+BztrRlcesjpR/eWWzFf4CIRpq9/ZEAXi9y+YnjXAXKcuxa+CkfryiXsPLcuGKj8vd6MwLlcGdXZL2boLLaw+eizK/TNGaoZ2RP1PmCrP06u3p6Rxx1NRkc7+7BjSs24sYVG3Ny8vhdW9SdAK9rWnh+I5qHHMPbJ17BztaXcFrHvr4eeMd+9O/pAm7PPUez+SmEo5kB2D1kBHbXj8SuuhGGAh8yAscax+K+b12Nli1vKlf8UrVrAkIpfSDeOi8mxVb8qwDMB7DU/PtwkctPBfaH2pkQzWt5vJa2bN6LwqIQE0VDEfO8eIWONtTVKG3+Vs9KJ3+P13KFTo5195kKDh3tsx8D0C5vwQMvoKuHe/db8MALrvs5Z/NmqghvvduFE7YLPnS0y/V4NwKHGh47BuzY0dfTdn6OHu3dtRnuSvvTnhK5826//obyrjvV6HXXjcSimy43TCdjxwI16lTTSx7ZjENHu3K290ZIDRzomRvI/lK3U8izUi5pI2JT/ER0HwxH7jAieg3AIhgK/34i+nsAuwF8Iq7yoyQuO2pLWxYrN2R7lWAPM1ZuyKJpzFDX86uyOxJQ8ALvUcyI9KsnS/m5KX2rPFX4ZjWRb08rqOnDb3KOTs9uySObe5W+RVcP4+sPbcqR6+b7X8i77q4T7ga2rh5W9yCPHwd27QK2b8cXt6zB4H170Gg6LkcfPoCTjpt1923XU4fmWHU/7Bli9Lgts8muOqMX/tqQU4FBg/IiZryibhbN8e9QWEo97PO3+PKJkc/0TavjPShxRvXMU/w0K64y48BNmdxkDssLjbsPOmxU9SoYhdkXo8jzoqN0Vbb9aqIcpeH2sOqke4iqPr16b87fnL1RiyPHe3qTizlfdv16ujHqrXZDYR/OjToZ3bEfg4+bPe9blWIAAG72/jmf6uq+6BLH56KWPXils4BZYi71HFWHImwqijjyFyWRNiIOUuvcTQtuysR6hAt17ARVPF6pdwul0DwvOkpXdV0nmHv3UT2sqpGAvacVVX1a5/Qsr7sb2LMH5+3c2KfAOw6gsWMfxhzahyHHjvT2uptdSy8AW3jgSwNOwX1v9MPGzFB0N47F/2meGuo+/u99rxUsVrajMyfNdqGKN4qRtnPU4DlbWvN8hVxTWhDF70MUcfdOwizKAcTb2yj0IYsyPl31EvK79iDD8Ja2LN599xgaDr/e29NuPLwf4w4fwIeqDmPQa7swoEMxqdzWE/8v9z182TPkVOyuO9WMOBnZa0LZXTcCnYNOxrKPT9Kq/7MAfCukDHbChPW64bYwShilGFXEUktbNs/W73Uuneeg1JPhAaL4fSk07t5J0EU5rGOsxliTqQKRMTPczxkcVqYwD1nU8elO3HpaMyYMx3cf24KlP/sdPtB9CDf3fxsHNm7ByIN7TWV+AMOPdhgncJhNmlF4T/y1wcPNEMER2FWfq7w7Bp6MhvpaAOpILDfqazNY5rHSlheFvLx1wox1iCrCJYjZTnXdXs9aZ1cPljyyOa892UN8SzVUUwdR/D64JY5yUuhi2xYNZuOzhqRujfGobSUmP2dwITIFfYALik+fNBLYtg3YsMH4tLYaH1uUCRCNsnay9+Rhvcq7Y8Rp+Nx1F/aZUoYNA4gC5zMCcq9d91gC0PaNj4a6jiAvb79VrvZ2dKK2fzWOHA/3EogiwkXXbOd13X7zRQ4d7er10WQ7Ol2f81IM1dShohW/ThTKyg1ZT6Uf1WLbVmSOsxH7vXSiaJhRhKg1Tx6FAW+2o2XlH1D72i5MfPcNfHL7sxh8+yu5+8GhvH0cmDrsP2lob6TJoVNH47PzZ/VNlT/1VIBynZZeuXA+d21+XhTdCWdWOKpbW1L5KOwUEhmi+/L2e0E493WLRvJDdR1xrNPsdd1BX0BeqUN0SVvmXRUVq/gLjUI5wRzqxno1aC9HsheF9rB6ZWLG0M63esMDz3r3DeD6lX1x3695OwA/Zn4KorYWb4w/G6v7j0Lb8PfipRF/hR31ozBgQP+cyB8v5f3ZT3sntQoakqdTv15zHiyFqpIZKNxXo/vyDjK6U0VZeeGVWqLQdZrdzu113VH5LXRfyKW0aEvFKv5Co1DCZkn0atA3ecyg9SKnYTIDBw+6p4Tdvh3Ynb+mbmTT5YcN6wsXPP10Y2JOQwPQ1ASMH6+dnOoKl/hv570pJJ46qK/BT4F4HWvvAaomrjnDWb1Q9Sh16yPo6M6SSdXzr6vJYNCAfr493KDmRN3oGa/rVvktrNnobpO7gs6kL+Qak6RiFX8xsySqFhlxNmi7OWDwu+/09rwbbTHejYeNKfJ5RGAyAYBDA0/G/lNGoWvcOPzpxBBsH2zNthyBw0Pfg9s/Pjn2Rqxzb6J2FHuN3NzK0llDQWft3SCpgr16lLr1EaZNe82vsFIk+xF2HWG/c3tdt9d99lqnwe3Z1KGUZvVWrOLXeQDGnpK/j27vblRdDW79cAMGvbYLax94GnPe3NurwMccPoBvHt4POmE6ak2lHVnP28wsmB06EuuOn4SXa4bjyGmNmNP8YXz04g969rzrzY/rrMseYPGqzaEVf6GZGu33ptB4apVScabQGHZSBgfePp6zj0rZO1MMqNJr6JgK3erKq0dpmZn86kP1EpsxYbiqqgAUXt9xzXj1k8v5uzUrW3UcAKzb2h5KllKa1Vuxit+rp9DSlsXXH9qEI8d7UHu8E6MPHzCU9qF9OJcP4aLWo30mlO7u3uOb4R51MiOEfG/1r8X+U0bhpDPeh1EfmJib27ux0TfJlFuP5vH1h3Fnw4GCemgdnV05k3R0CWL/nDFhOH7153yTlFM5RR1PfVvLppxye5jzlD5gdAjclL49Zw+g9s/4mQpVdeU3e1mnPpqnNKB118GcoAEGtKLD/JSoF27PW6aacORYN8YtfDQ2R2hQZ3YhNvpSmtVbGYr/6FEjOZXN5t28fTtmbd6GAXt2GZkFLcwMg81RFJsZ0JsSdrcts+DuuhHIDjkV276rLmWw+dEhaO9QpxF72bXD2CyDyKPqcYXtiely33q9ZfGe2X4wb9uyNdvycvao8OsBquoqqjUE1m1tDxW2WIhidL406mozeOfd7l47u9+5dGP1rSR5i1dtxuHOLle/iupaC31mSmlWb3kr/t/8BvjkJ5U/n6xxCiuz4K76Eb0zLJfceFleZkGviA037GkWCgkBC9s79GPB7PHKtLZRrtrltj0pW2nQsEU7urLp9AC9Vg1z5i0K06MMW79RKEZrv+lL1+blOers6sHN97/QO4fFS7l7xep39XDvCyXISmuRhDWXyKze8lb8Eyca9uyqKvdl0CzlPWhQ7yF+CryaCEvmXJK3XdVDriLAmYTR/rCG7UVZLwu3MqPoHTZPaXBNiQvkrowVh403KVupqs508BohWTb9OjOaxC9fjFdOJms0V0iPMmz9RvlC9nq5Af7KPWysvkWa2l0SlLfiP/NMoCtfcXnhF7o3MFPlauN2cwQD+Uq/NlOF/v2qeh/+o8e7A/eidGaSRtE7XHRZflpbi6jtn/ZRT11tBpkqyklbXAxb6bxpo119C06mnz40b9uC2ePzbPwAkKkiLPvEJAB6+f2tc3lFqhTaowxri1Y9G0NqMpi+dG2gl5FOjL2fcg8bq6+61lKy0RdKeSv+EPjlLDlyvCfvgW1py+JZF7uvG0e7TvSmXfBqsPbG7jQFHTmW/7JwElXvcGCmSllW0NwpgF5o3aGjXchUE+pqMsqVv1rasvinB1/srUsi4Nppjbi9+WzP6/GSzTrWK6pn+ulDce/nPpR33l6n6frdsAYNtZkq3DH3rwEAN92/Ec7BhFV/bvVy59yzY7MVW+exj+gG9PNffczVQVtFOHJcbatX1bdOKhTrfKqFgrxi9Z3oRFKVko2+UIgLsGsWi6amJm5tbS1aeTqTbuwzNVULThSCdf4weWJ0Ysz90C2XgJwIFVV89J1zDaXq9lB5LdjhNhu2pS2Lf7x/Y95oCgA+da5a+bvJlqkinDSwHzqOFra0pFdc+Irn93g6ft1GZrqx/WHktEyEbpOVrPukuoetuw7mvBgH9KOc/FEWDQqlrFrv2ItPndvouj6yVUfO0eI773bnjRbjqs+0Q0QbmLkpb3s5K/4o8mZ4pQawFF5Qx64f9oYa9KWiepiD9MoB/ZeZUzmrjquvzeDdrhOuD69qsXfnS0VHtmoibL8z3wfjd5xTpqDtRHXusH6DuJa9XPCbF5Qrf1nlAu6j0bqaDI51n9BS2IToUj1b6xDrPss6ObgqoVcPqBV/IqYeItoJ4G0APQC63QQrlKjyZug4fKJo4KoEX0GiRQb0q8qbhh4mUZduuUFyp7g5iS3Zosyd41Sy9odcR/2GnWLv56wMShQRTE4Fd/DIMU+l71euW4oDFaPM9hwFh4529Ub63HW1/8xxLz9IKeXTiZMq/11iYwYzT45D6QP+66nqsmD2eNRkqnO2ORWe2z4AkLHVbpXHqnb1tRk8s3Amdiydg2cWzsxpgCrlV1+bQUNdDQjGS+POuWfjsOLBdD6ASx7Z7Fs3qnKriXLKdMudEoS9HZ2u9ec1m9SrjGpbJk7rIc9qKn27TEHxqq8wFBpJ4rz2bEcnOl1MMm7lFlq29XxEGQ1jXcOtD27qXdIyDFHphZa2LKYvXYtxCx/F9KVrC5IpCZJU/LHiFQkQ5KY1T2nAnXPPzlOydoXn3KeuJoNMNcH+nKk6WplqwqLLJirLV714Fl02Me9loXrQ7Ntb2rLKdWKduXDcyv3eJydhx9I5vY5jZx2qjquryShla57SgKumNsCuIq3ZpPZ7Y903r9HVvGmje//XTafsJlNQVNc9b9poZKq9lb/z1ygiScJcu1WuqiOjwq0T0jylIdB5dF+PYZS0nShCUt1eqoW+kIpNUlE9DOBxImIAP2Hm5VEX4BV6FnSopzsV3nI06eYw13G+Bok0cIuUcCoRr4dmSE0mxzwwpCaDgZmqPMenbkprZw4UN/vy0ePdaGnL+s4m9XM226N6vOY49O5vXu+R4905jtewStfrPjWNGaqcE2FPDJbt6EQ1UY5yCzKRz152UNOjW1vUWUPA6oRY+9tTOQBGtJBOxM25763HczsO+ZqigMLMYIXE6vvNnUljFk4ViTh3iWgUM+8lovcAeALAl5j5acc+NwC4AQAaGxun7tq1K1AZqiiLgZkq1wcwCmdakAgcleNStxyrAVrOw3qXaAYCcK0jymXswkeV5x3UvxonGL4RJkGjcCyZ3eLcrTK86qyhrgZHj3dr3Tede2A/phiOPmfUCTPywlS9oqH85HE7VpUgzkkVGS9AVVSTV+BCg+2l7paHBwwtRQ70vQBXv7Cv15fgNvnRKjfss+oa2VVNGNS/nzJ0WHWck0Ke6bhIlXOXmfeaf18noocAnAPgacc+ywEsB4yonqBlqHpgqpz3UTiiggyvw9o/Val+VTNsnfltvKJM3Jbac+vJhBkue+Wy8ZppDOjPd7DK8boHbstBhg3f1HlhuM1RqMlU5zkpC0mHoFrAxy3Cy5522Brx2JcfdI7cvGYR28OZ3dImqHC7151dPVi3tR0bF/UtPal6GRZiBgubM0jn2S6lGb5Ft/ET0SAiOtn6H8BHAbwUR1nNUxpy7OAAUKVwtkVx06LM16IiqO0229GZ488IE2XivC4dX4LfOZxYM42DYpWpY/9XOaSDEsTGq+tM1H2ZuvmnVMdaczkA9JqQ1m1tx4LZ47Fj6RwMGtAvT0F3dhmLkFvoBDcE6TQRjOykbjjPo+NfC4NdL9T275c3Kglyf+xYz1op2PqT6PGfCuAhMhRwPwD/xcz/HXeh1sOqWgwjimnZqt4RkeHwLXSSEBB8ZELo6zGHDTl1KvQwU9v97M72mca6ctrTaAcx7xSKSpkveWRznglOhdvL1M/2rPKt1NVmXEd81US9E7XccuB4hd7e1rIJtzefreVjCuJTsK5H187uNiKL0jyn+8LVvcZSCQ8teo+fmV9l5knmZyIzf6sY5ap6ykGWvfND1Tu665OT0faNj7qGawYlyMhE184LGOkFVOdwKvQwPTGvCA97HppnFs5URgDVZKpcywxq3gmL36ji0NGu3t/8RlZuL1O/nrXqhcMM17q1ZFA5zb3a0r1/3t3bc3WOnN1yCznLz1QTMo4YZq+oId17FHVEje7oNUiEUqGRR8WgYnL1qN7sJ5i1nGc6PQydHCiFEiQ3SRCzjirGm5Gbk6iQFa8A5PWI3aJJVKHvAzPVrr12r1FQmLQVbtcJBFt03AunkrPKs/s63ORWXefhzi7cdfVk3zQjdrIdnfjUuY3KpHQM/XUXvCK57NtmTBjuGzHmR6GpoZ0ETR7oNknSjTQut2inYhR/2DCuMDP93rUp0Y7OrkiHfk4FquKEqTx0hqfVRBgxZKDSiQdEM+NR15HaoZhnoNqu44DURXWdXsnqdLHSGNiVnJuz3rlmrLWfSqFbcyGs/cd5RG7ZudcnE6lls9ZRyqp7q7rOjk53J7cfUa/VEDR5oDUnxU/5p93RW7YTuJyoZoH6rTUadKZfVDMDvbCG3juXzslZ0MWO1YB1hqc9zL7D72Jcl0VQ53EhpgMnqutUTXrTpaGuxtVUolOvQf1TukpHZzwY1eSkqNpPmMACP1SmLJXMRO6mNYtSSOVctj1+t1TGbvgt5xe0h1Gs1aP8siy6pUL2yjTq58Qr5qpYQZ3HQSa5+Zmr4rieTDUpZdepV13/lHOx96iIYnJSVO1Ht21E4QBWydZxNNe0NqQmAyJEErxRLMpS8bsN11X4NbygJqJirOLjvD57zHaDzZbqXMLOuZi4hTXq8TLFFHN1oiCK3H5M0IlOQeLW61xm+erSr4oKqlcd/5TXBLkoKPSFGFX70WkbxUjQGHb+R1ooS1NPlBOpFswenxedkKlS9+BU5hUrNUEUqCbsWA7BlRuyrlEPhSxiHqU5RQe/SJIw6JgbVNd56aSR+iFSDjq7TijvvU696pg3vCbIRYG13GbYNlxo+7HPX1i2ZlvvXAS3tqG6z4tXbUYQit3mi0lZ9vgjn0jljDKxfXcbUt4592wsXrU5xwF06Giuk7eQoajXsFnV6N1WgPI7nx3dXniac53rmBuapzTkLTZizXbVTT/ghltqbN0IF9XKV0ePd2PswkcLWis4CIXEqIcZxVk4RzPZjk4seOAFpRxKE01nV059+UV8FSJz2ilLxa8aotXXZlDbv1+gm+jWk+rq4d5eotuQ8s65Z2PQgH7K3Piq4wC9B2qIIqpgSE1GPYvTQy/oDrf9hrdpz3WuO0lq5YZsryLtYfZdLcpaS0F1XwCjLqy1moNGuDgVkDPVgp/S143u0qEQe39Y88iSRza7PoNLHtnsej6/yVZuk9m8QpajXhAnDZSlqSdIKmMvWtqyygbk1bu2Gk6Y4/zkmb50rVK5EIVPKRwFxYz8CUMhk6RUWCGjO5bOwcZFH0V9rfvkMwC9Jrcw9WQ3fbmlWvBi7Ck12mmPdYh6mVE/VM5q1fYg7dle7+WQblmXslT8UeT4sBqBCq8VhqzeQpjj/OTxeug6jnYVlFK4UIoZ+RMGnXYRNO+Ms74XXTZRGeqn0ynQIWh9Prv9YKRLg4ZdXKZYNE9p8HwBO7Hq0ysVR7lRlqYeIPyw0sLLQWz1ElWTqKxZiqrc+F7HhZHHfnzzlIY8/0KxKGbkT1j82kWQvDP2Wc328wPAjR5ZYAutp6D59qO2/hfDn2BHNWFKldoDMF7AujOtrXr3yl1kmeniplg+srLs8YfFHjng9WB5rTBUk6nGjAnDsXJDNueBIwBXTW3wPM6rt+7Xy7Mfv/hyda/TSZCekR/lEAXhdg2q/q1q8lzzlIbAE+uC1FPQFbJ0qclU41+unqyU3cLv96hZfPlE18i6xZdPVB5jH90B6ntor3e/jlfcFNPUVLY9/qDoLqJin+yk8vqrwi2tsMkw0QJevTxndIJbziA3MtWEOX89EtOXro2kh5F0FEQUvSW3a7Be5EGykXpNNCq0nrzyHqkWrHGjJlOFoYMGuMqgGrFY1+ZF1L1W3dh9t9+dTluvPFELZo/3HKnFTdR5iLxIZAWuoDQ1NXFra2usZfjlcgf0V0RSrVpU6KpbQVZoamnLui51aOdT5za6KrSospUWE7/6CbJwiipvS1BllkRoa1SrwE1e8rjSvGJfLEWn/LjbVJRlqq47yrTeKuLQG6lagSuNeL3R3ZJreRGHrTtoL3HZmm2eSr+hrgbrtrYXrYcRN36RMjphpn7hqGFGD8WuR7d2cuRYt6sy82qPiy/Pt5HXZKo9zStWucVuU1GWqbruYpgri+kjE8VvEmWGxzALlegQRJH4DU0XzI53GcpiEzZ8VjdhWtIvwiCjB2c7CbOEYVhzVBKRXVGWmaS5Mi694YYofpMoKz1pWzfg7ROoq8mgeUpDqOiitOLVW9JVDGkNRy10YlzY9hhmxJJEZFchKddVfoEkXvTF1Bui+E1UTj23ZGe650uyl7hg9nhXG3+mui8aopg9jLjxuhbdF1xaw1GjGIkUqz0m0abClJnWWebFuk+JhHMS0cVEtI2I/peIFiYhgxv22ZFeyc5KgeYpDVj2iUk5sc71tRks+/iknOifOBazTgKva9ENn0xrOGpaRyJuJNGmwpSZ9lnmcVP0qB4iqgbwPwAuAvAagOcBzGPml1XHFCOqx4kqyqcY3n0hegqN6kkSaYvRE0cETRpJU1TPOQD+l5lfBQAi+jWAKwAoFX8SlFIvS/BHdwidtInOjXIyyaWFtJr1ikUSpp4GAHts318zt+VARDcQUSsRtba3txdNOIs4lngThDCUk0kuLaTVrFcskujxu82ezht1MfNyAMsBw9QTt1BOpJclpIk0jkRKmTRE3iVJEor/NQCjbd9PA7A3ATk8qfSGIQjlTiW/TJNQ/M8DeB8RjQOQBXANgL9NQA5fKrlhCIJQvhRd8TNzNxF9EcAaANUAfsHM5ZfwWhAEIaUkMoGLmX8L4LdJlC0IglDpSD5+QRCECkMUvyAIQoUhil8QBKHCKImFWIioHcCugIcNA/BGDOJEgcgWnjTLJ7KFQ2QLj598Y5h5uHNjSSj+MBBRq1uOijQgsoUnzfKJbOEQ2cITVj4x9QiCIFQYovgFQRAqjHJW/MuTFsADkS08aZZPZAuHyBaeUPKVrY1fEARBcKece/yCIAiCC6L4BUEQKoyyVPxpWtOXiH5BRK8T0Uu2bUOJ6AkiesX8W5+QbKOJaB0RbSGizUT0lbTIR0QDieg5InrBlG1JWmSzyVhNRG1EtDpNshHRTiLaREQbiag1TbKZstQR0QNEtNVsex9Kg3xENN6sM+vzFhHdmAbZTPluMp+Fl4joPvMZCSVb2Sl+c03ffwfwMQBnAphHRGcmKNLdAC52bFsI4Elmfh+AJ83vSdAN4GZmPgPAuQC+YNZVGuQ7BmAmM08CMBnAxUR0bkpks/gKgC2272mSbQYzT7bFeKdJtn8F8N/MPAHAJBh1mLh8zLzNrLPJAKYCOArgoTTIRkQNAL4MoImZz4KR2fia0LIxc1l9AHwIwBrb91sB3JqwTGMBvGT7vg3ASPP/kQC2JV1vpiwPA7gobfIBqAXwFwDT0iIbjAWEngQwE8DqNN1XADsBDHNsS4tsgwHsgBlYkjb5bPJ8FMAzaZENfUvWDoWRVXm1KWMo2cquxw/NNX0T5lRm3gcA5t/3JCwPiGgsgCkA1iMl8pmmlI0AXgfwBDOnRjYA/wLgFgAnbNvSIhsDeJyINhDRDSmT7b0A2gH80jST/YyIBqVIPotrANxn/p+4bMycBfBdALsB7ANwmJkfDytbOSp+rTV9hT6I6CQAKwHcyMxvJS2PBTP3sDHsPg3AOUR0VsIiAQCI6FIArzPzhqRlUTCdmT8Aw9z5BSI6P2mBbPQD8AEAP2LmKQCOIFmzUx5E1B/A5QB+k7QsFqbt/goA4wCMAjCIiD4V9nzlqPhLYU3fA0Q0EgDMv68nJQgRZWAo/XuZ+cG0yQcAzNwB4CkYvpI0yDYdwOVEtBPArwHMJKJfpUQ2MPNe8+/rMGzU56RFNhjP52vm6A0AHoDxIkiLfIDxwvwLMx8wv6dBtgsB7GDmdmbuAvAggPPCylaOir93TV/zzX0NgFUJy+RkFYD55v/zYdjWiw4REYCfA9jCzN+3/ZS4fEQ0nIjqzP9rYDT8rWmQjZlvZebTmHksjPa1lpk/lQbZiGgQEZ1s/Q/DDvxSGmQDAGbeD2APEY03N80C8DJSIp/JPPSZeYB0yLYbwLlEVGs+t7NgOMXDyZakAyVGR8glAP4HwHYAX09Ylvtg2OS6YPR2/h7AKTAcg6+Yf4cmJNuHYZjBXgSw0fxckgb5APw1gDZTtpcAfMPcnrhsDjkvQJ9zN3HZYNjQXzA/m632nwbZbDJOBtBq3tsWAPVpkQ9GIMGbAIbYtqVFtiUwOj8vAfhPAAPCyiYpGwRBECqMcjT1CIIgCB6I4hcEQagwRPELgiBUGKL4BUEQKgxR/IIgCBWGKH6h4iCidwLsewERnWf7/g9E9Hfm/9cR0agQ5e8komFBjxOEqOiXtACCkHIuAPAOgGcBgJl/bPvtOhgx1WmbGS4InojiFwQARHQZgNsA9IcxgedaADUA/gFAj5kX5UswZky+AyMDZhOAe4moE0ZW2C0w0ua+QURNAL7LzBcQ0SkwJvINB/AcbPmkzPN+2Sx3PYD/y8w98V+xUMmIqUcQDP4I4Fw2Eof9GsAtzLwTwI8B3MVGnvY/WDsz8wMwZp9ea/7W6XHuRQD+aJ57FYBGACCiMwBcDSOp2mQAPTBeOIIQK9LjFwSD0wCsMBNd9YeRMz4qzgcwFwCY+VEiOmRunwVjwY/njfQrqEHCCfGEykAUvyAY/ADA95l5FRFdAGBxiHN0o28UPdDxm1tuFAJwDzPfGqIsQQiNmHoEwWAIgKz5/3zb9rcBnKw4xvnbThg9eAC4yrb9aZgmHCL6GIykZICRVOvjRPQe87ehRDQmpPyCoI0ofqESqSWi12yff4TRw/8NEf0BwBu2fR8BcKW5+PbfOM5zN4Afm7/VwMie+K/mOewO2iUAzieiv8BIk7wbAJj5ZRgO5ceJ6EUAT8BYPk8QYkWycwqCIFQY0uMXBEGoMETxC4IgVBii+AVBECoMUfyCIAgVhih+QRCECkMUvyAIQoUhil8QBKHC+P+uk+lPFD0yKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere for Wind Speed', 'Wind Speed',(40,35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ycqZ4F3g1xHl7ga+r6lIR2Q9YIiILgc8Cj6vqLBGZDkwHvpWb2IZhhOHaeSu565lXaVOlToSpJwzlxsljyy2WUWbCKP5m4BkReRBn4/8Y8KyIfA1AVX/kdZKqbsGleEBV3xWRNcDg5PmnJg+7E1iMKX7DKDrXzlvJ/zz9SsfnNtWOz6b8c6eWFr0PE865HphHMo4feBCn0Pcj5EQuERkBjAeeAQ5JNgqpxuFgn3OuEJEmEWnavn17mMsYhpHGXc94D8z99hv+pNZbaN7RgtK53kIxFtspB2HCOa9P/S8i/YAdyQldoRCRfXELt1ytqjvdgl7ZUdU5wByAxsbG0NczDMPR5vOa+u03/IlyvYVy4NvjF5HvisiRyf97i8giXO9/q4h8NEzhIpLAKf3fqur9yd1bRWRg8vuBwLZCKmAYhjd1Pp0sv/2GP7W23kKQqecSYG3y/8uSxw4ATiFEPv7kWr2/BtZk+AHmJ8tLlftgjjIbhhGCqScMzWm/4U+trbcQpPj3pJl0JgF3qWqbqq4hnFN4IvAZ4HQRWZ7czgVmAWeKyEvAmcnPhmEUmRsnj+XTJw7r6OHXifDpE4eZYzcPam29BfEz14vI08DlwFZcz39CKge/iLyoqkeWSsjGxkZtamoq1eWMCqSWIiqM6qQan0ERWaKqjZn7g3ruXwXm4sw7t6Qp/XOBZZFIaRgepCIqUs61VEQFUPEvnlE71NJ6C76KX1WfAbr16lX1EeCRKIUyjHRqLaLCMMpN2CRthlE2ai2iolRUo2nCKA1hJnAZRlmptYiKUlBrE46M4mKK36h4ai2iohQEmccMw9fUIyJTgk5Mm5BlGJGSMk+Y2SI8Zh4zggiy8V+Q/HswcBKwKPn5NFxiNVP8RsmopYiKKEnZ9f2SMph5zIDgqJ7PAYjIw8CHUonVkmkWflYa8QzDCEtm2GsmZh4zUoSJ6hmRUvpJtuLW3jUMo4LwsuunGGzmMSONMIp/sYg8BtyFS838KeCJSKUyDCNn/Oz3Ajw1/fTSCmNUNGHSMl+VdPR+JLlrjqo+EK1YhmHkyqCGepo9lL/Z9Y1MQoVzqur9qnpNcjOlbxgViIW9GmHJqvhFZIqIvCQi74jIThF5V0R2lkI4wzDCM3n8YG6aMpbBDfUIzq5/05SxZtc3uhHGxv8D4IJkOmbDMCoYC3s1whDG1LPVlL5hGEbtEKbH3yQi9+AWXN+d2mkzdw3DMKqTMIp/f2AXcFbaPsVm7hqGYVQlYcI5P5dPwSJyG3A+sE1VxyT3zQS+CGxPHvbtZH5/wygalo7YMIIJStL2TVX9gYj8BLqn/lDVr2Qp+w7gp8BvMvbfoqo35yqoYYTBVusyjOwE9fhTDt28FrtV1SdFZEQ+5xpGvuSyWpeNDIy4EqT4R4rIccBvVXVvEa95lYj8M65B+bqqvu11kIhcAVwBMGzYsCJe3qhlwqYjruWRgTVoRjaCwjmHAD8GtonIYhH5voicJyIHFnC9W4GRwDhgC/BDvwNVdY6qNqpq44ABAwq4pBEnwq7WVasLldjKW0YYfBW/qn5DVU8CDgW+DbwFfB5YJSIv5HMxVd2qqm2q2g78Ejg+n3IMw4+waQtqdaGSWm3QjOISJpyzHhfSeUBy2wyszOdiIjIwLcXzRcCqfMox4sm8Zc3MnL+aHS2tAPTrm+C6C47qYsYIu1pXNSQ0y8dk49dwNe9oYd6yZjP5GEBwVM8c4CjgXeAZ4K/Aj/xs8h7n3wWcCvQXkdeA64BTRWQcLkpoI3BlAbIbMWLesmam3fc8re2dAWZv72pl2tznAbop/2wKbtqkUd0WLamkhGb5+iAa+iZ4e1er53dfu3d51vONeBDU4x8G9AZeApqB14AdYQtW1akeu3+di3BGfMns7f5j994uSj9Fa5t6Ruxko9LX8c0lOikd9VtzEWhX8rpXRu0RtPTi2SIiuF7/ScDXgTEi8hbwN1W9rkQyGjHDq7cbRL52+UpOaJavD+KdFu/eftjzjXgQmKRNHauAR4BHgadwUTlfLYFsRkwJWkLQi0qyyxeLsNFJxf7eiAe+il9EviIid4vIq8CTuPQLa4EpQCEhnYYRSC690kSdVIxdvpjku6jKtEmjfF/qHkJN3isjd4Js/COAucA1GYutG2UgTpNy/CJu+vVNoEpgVE+tMHn8YJo2vcVdz7xKmyp1Inx8QnbTVOr7GfevoKW1vWN/30QPvj/l6Jq8V37E6Z3JFdEgb1CF0NjYqE1NeWWOqAkybd7gen+1urpS3Orrhd2DwrD75xCRJaramLk/1Jq7RnmJ26Sc9CUEAepEOuoblxmocfvNC2HesmYmzlrEYdMXMHHWoo6evt0/f8JM4DLKTK3OMg2i02RRm/l0shHH3zwf/OY7+AUH2P1zWI+/Csg3wqPaiXOvLa6/ea74PSN1Ip7HV/r98xq9RIEp/iog3wiPaifOvd64/ua54vcstKmW/f7lqsRLmWDPTD1VQKXPMo2KfPLpFDOSo5xRIbX4m0dxP/2ekcHJ8st1//JJuZHvbO18sKgeo2LJNTKjmJEcFhVSXKK6n5X6O02ctci3QXpq+ume5xw2fUH3pQ4BATbMOi8vOfyieqzHb1Qs2Xq9Xvl8itVjKmXvKw5EdT8rdWSUj5mylBljTfHXKLUyecUvn04u+Xzy8QnUgn+hkp6BQu5ntnpUYs6lfJR4KTPGmnO3BonDKky55PPJp8dU7VE1lfYM5Hs/K60eYcnHOZ8+f0VwZqGoTFa1rfjffBO+9CW44QZYsAC2xCPzRBzCIMP2vPPtMVV7VE2lPQP53s9Kq0dY8lXik8cP5qnpp7Nh1nk8Nf30yEYyNWvqmbesmQ3XzeKah27NfvCBB8Kxx8KECW479lj4wAfAJxa40imHmSIqs4JfuUH5fPr26pmzHF7XuWnK2IoxlQTdX6/vKs1Ula8tPqp6ZK7mBt65nwp5rivRBJWiJqN6Ouy/e/Zy1ktPc/TrL3H0tpc54a0N9H77zfwF6dPHNQrpjcTo0dCzstrPfCIKCqGUERvgXtDzjh7I75Y0xyKCJ0g+wPO7PokenitxRfUMREUUz7LXam4pEnXC7IuPYfL4wRX/XITBL6onMsUvIrfhUjlvU9UxyX0HAvfgMn9uBD4ZZinHXBV/zg/L66/DsmWwdGnntnFj6Ot5Mm5c10bimGOgvjT24Xwe2EJ6Nn73u06EdtW8e8t+5YKrz8cnDOaJF7cX3CMvdUOZK0Hygbdju6E+we697VWttCCaRjnouYLO371Yz0U5nezlCOe8A/gp8Ju0fdOBx1V1lohMT37+VrEvnPPw8NBD4Zxz3BbEO+/A8uVdG4gXXvA+dvlyt912W3CZH/xgZwOR2vr1Cz4nC7mm9M13fdcUQbMn8ykvW7ng7LxPvLi9KIq50swimeQj3zstrdxyybiKMVXlSxThmtl+19T3xXguCn23oiIyxa+qT4rIiIzdH8MtwA5wJ7CYCBR/ZPGwBxwAp5zitiDefx9WruzaQCxdCu3t3Y/9+9/ddvfdgUXuOmQgfU84rmsDMWiQpx9i3rJmfrekuUPxtqnyuyXNNA4/0PNhKzTG2u9+51te2HKLpZhLGT+dD9nk8/uukm3MuVDsemR7rlL3tRjPRaXOByl1VM8hqUVdkn8P9jtQRK4QkSYRadq+fXtOFyl7REafPnDccXDllfCLX8Bzz0Fbm1sJO31rbYVVq+A3v4Grr4aTT4Z99/Ussu/WLTB/PsycCRdeCEOGQI8eTvGnbwcdxOBPXshXF/6K89c8yYi3mhFtD4yEKLRn43W/CykvbLnFUsxlf16yECRfpcteiUybNIpED+/AjfQV3Ypxbyt1NFlZXsk0VHUOMAecjT+Xcyt1Nl83evaEo45y22c+07G7m21RlSHvbOWU917l34e3do4gtm3rXuZbb3HcW29x3Lql3teckfyb5qi+fEs9f9l/KC8dNIy9dZ2PRFjFmnm/e4h0jDbSyVVRp8rNjL6A4im39NztdUm5B1fY8xLmea74Zz0kpbCH+z1XmVE9xdAjuYwaSukLiDSqJ2nqeTjNubsWOFVVt4jIQGCxqmZ9e+OWq6egnB2vv87XZ9zOoevXMGbresZsXc/Qd7YWJlC6o/rYY52jum9f38OjcMhF8VLUQtRGLVGLv0fYOkVV95JH9SQvOoKuin828Gaac/dAVf1mtnLipvgLjSYI9RDt3NklkmnnX59l/5f/XpjgRxzRMQ/iL/sN48bNvVm7J1GxvdCoopGM/Kj06Kp8CdNpiaruJY/qEZG7cI7c/iLyGnAdMAu4V0S+ALwCfCKq61czhebsCDVE3X//Lo7q/f0Ke/99WL0alixxW5Cj+qWX3Hb33XwY+H36dzPS/h80qOtkuQBHdZREFY2UjUrKoRM1udS1Uu3hhRLGOV3qukcZ1TPV56szorpmrVAM22LRIiH69OlU0kG0tbnopFTDkGoo3nuv+7GbN7vtoYeCy2xo6D6jeuRI59QuAlFFIwWRb3hfNTYWuda10qOroqTUda/JmbtGBaMKr7zSNcx1yRLYWoAfolevrj6ICROcwzyRCDzNb2ZwJoXkQ88knyF9tdq+c61rtdazGJTaxl+xUT1GjSICw4e77aKLunyV2av9znEHce7e17s2Ei+/3L3MPXvg6afdlo2jj+5oICYfeyx154xk1pOvFjUayY95y5p9RxhBQ/pKjQXPRq7mi6qJxouAUtfdFH/MiSpaJtcyvcwCX1+8hT1TxjJ5xqTgC+7cCc8/37WBWLXK+9gVK9x2xx0AXJDcMtnQbyCrDzmctYMO58SLz3SZXg86KFiOEPXzI6hx8VOUqUYk1/ud72+e63n5mC+qddJZMd6jUtbdTD0xJqqwy3zKDGsWKOgF273bNQiZM6r37g1fwUwGDeqecmPIkG6O6mz5YT594jBunDzW8zu/cwW49MRhOSWry/f3yTf/UxxMN5Vcz7KEcxYLU/zREEUIWb5lhpm7ULIXLNNRndp27sy7yHd678OqQ0ey6pDDWX3ISFYdMpINBw5CxTmqs9n4r7lnuef9qfMxT/mVl+/vk+951eiUzpVKDkM1G7/RjShCyPItM4xZoGS27ro6l2579Gi49FL/47wc1UuXumyvGRyw+x9M3LSCiZtW+Jc3g+6O6mOPZfJRR3G1zyleSh9y/x3CJi7L9bxqNd3kQjWGoZrijzFRhJDlW+a0SaO65UhP9JAucxcq7gULcFRn8siiFdz/q4f4YPPfOSo5o3r4ju4NhJ+jeqNHmWsGjGD1oYezMjmCWHPwYezq1ZlgzIt8f584h1pmoxrvjSn+GBPF4s4FlZk5fyvjczW+YCnOPf1o9vQ7iOsfWt1tgZT0tQXe2fomH2nZzP/dbwdHb3s50FE9evtGRm/fyMUr/+h90dSkuZEjO+ZB/OCAYXxjex1bEvt0uX6236eUC4GnqBYzUTnuTaGYjT+mpF6q5h0tRU9OFuaFzTxm1569nitGpdInNPRN8H5rGy2tXWcMC6BQcYnVgsis+4iD6vnr+re62PC9fBfXzlvZscZCfdterhq4m0HrX2DX357lqNfXc9TW9STawy1A78mhh3ZOlEv9TXNUl1IR5+LPCStXIfJnO7dUkVW5Ys5do4NyRyGEnTiVK5USSZELQY7bdOeg1z2r6yG0eSwfOHHkgSx95R1aWtvo0d7GiLe3uIR9r69jzNb1jN26jv127ypc+DFj4Pzz4bzz4MQTi7oEaS5RXlEnQSv2+1LK988Uv9FBuaMQsoU2FkIlRFLkQtC9SI9oKvY963afVOG117rMpm7905Mk3nu3sAsdf3zXGdVjxjgHdhbCZqgN+ywX8swX+30p5ftnUT1GB+V2kkZ5nUqOpPAiSN5030Wx69WtPBEYOtRtH/tYcK/0oDZ45BFYsMBtQZ3HZ591WzbGjOloIP6871D2bVXeTfTpdlimPyfss1zIM1/s96Xc7x+Y4q840m3vKepEmHrCUN8JPrlSbiep3/Ub6hPs07snm3e0IAIeVoxQZVcTQYni0p2DYRLK5XrdIPxCZ79+7/Nco8qghg8y7XsXMPmhANPEu++6WdLpoa4rfMJZV63qWI3uI4DfHOd/DBkOSzqXIP1Qz92s3ts7a/0KeeaL/b6U+/0DU/wVhZ/tu02V/3n6FYCiKP9yRyH4XX/mhW71o3nLmpl23/O052iGrPRICi+mTRrFtLnP09rWta6Z+Ue97lkYG78XYe5TUVJW77cfTJzotiD27HGpv5cu5Xe/ms9hr6zlqK3r6d3WfUb1Pq9tgrmbYO5cABb4FPl+/4Phz40dDcR3xw7i6r/tpmVvZ3BA2Oel2O9Lud8/MBt/RZHNjlsnwvqbzs25XK8IAogmIVQxIizysWdXU1RPJuOu/0O3pSWhM5o16Ddr2vRWR6RP+sgw/f429E2gCu+0tGb9recta/YMOfUj24zjXJ+xQNv+98+Bl17iufv/yEuPPsnwV15k7NaX2f99j9TfYdl//24T5ub9Yx9m/3Fdl4i3flnu4bxlzV2WcsxcxjETi+oJQVwUv99Dn87GHNMDlzKCoFjXCnMf0ilm2uRyMGK6X7+1k1JELM1b1uw5+gjC795HlbMpVLmZjurUtnlz6Hpl0tqjjhcOPZz9Tzqew876iGskxoyB3r07RqitGaOvRJ0w++JjytoZMeduFZDNjluXxwpVpUzpW6xr+d0Hv7w01WbXz8SvXumUIg3z7MfW5qT0wf/e5/ssZDODhCo3w1EN/g3RzacP4by9Wzoah1cff4qhbzZ3kyvR3sYxm9fC3LUw97+7fDc5uaV4sf9wVh/qZlP/4Y2/M/nmLzizVwVRFsUvIhuBd4E2YK9XixRHvB76dKaeMDTnMksZQVCsa/m9/B+fMNgzE2W12fUzyab0U0Qd9ZGt/PpEXbd7f9qRA5g4a1E3k0VQKmmv41Nky0uf7zPm12B8/5ltnDf9TDjzTABO9hlt9t3TwpHbNjJ26zquH7I70FF95BubOPKNTXx81SJ4HJhzjedxrxxwCOuGjuKQU0/iqPNPdaOIAQMC61EsytnjP01V3yjj9SuO9Ie+WFE9pYggSNkr/dRX0LWCbJ1e+xuHH1gV0/hzYXDIiJ2oRzZBI86UDyX93p925IAuDXHzjhauuWc5V9+z3HcUI3SuI+DnIA5K7Jbv8xy2wfArf1evepYOGc2yIaMZf8m4LvKlm6cSba0c/uarjHl9HWNfX8+xb7zMmK3rnQM7g2HvbGXYO1th1ZPw01nego8eDU1N0LdvYP1ypSw2/mSPvzGs4o+LjT8KorbxZ5uFG0Vu+EqiGE66MLb1ctr4Ez2E2Z/obqvO1QmfSq+RSS4Tl0qx3kO2WeVe54S18afkEG1neNqM6rFb1/k7qjdtgmHDfOUJotJs/Ar8QUQU+IWqzimTHDVP1Eu6eQ2hU2SLtMnVDlzqpF1h8rPks3B6Jqlj06Np+iZ60KtnXahInHwIqlu6HA31iY4w20xyMT3165vwjRQKKsdLzpumjM35OQgbQuk36g6SN3VOmKie1LkqPdh44GA2HjiYh0ef3EWmUnR8ytXjH6Sqm0XkYGAh8GVVfTLjmCuAKwCGDRs2YdOmTSWX08hO2Kn1hZ5b6tGBX88v/YUud+qLfCnWvcylxz84aYop5+LruXYcivH7hk1GmG/52fDr8WfOEykJqro5+Xcb8ABwvMcxc1S1UVUbB5TI4WHkTlDe92KeGzQ6iAK/kczbu1qZcf9K5i1rroip9/lQrHs5bdIo6hN1oY7dvKPF8/gg53yxf/PJ4wfz1PTT2TDrPJ6afnqoUUJYeecta2birEUcNn0BE2ctYt6y5o6Gq3lHC4pr9N57fy+JuuDovFI8PyU39YjIPkAPVX03+f9ZwPdKLYcRjmy9pEJmIWY7N/3afuPSqF6SoHJTyicqx3nUJq1iNViZZhE/Gz64e5Kr2THbIvPFJHPiWsrEFcas5Gfy65Po0a3ham3XjtQkfvUoRXhyOWz8hwAPiItJ7wn8r6r+vgxyGFkIY8MuxIcQdG7Y1M1RvSTZ5lRs3tHCLZeMC93o5TKjuRh+g6DrFbPBSo/A8Zv1m35PclmK0U9OSV6rWI2hl1N7R0sr0+57ntmfOCar2cVvZOL37L7T0sry687yNWWVIjzZZu4avpTThh3GfpxLPvVc0xdka3jSZ5Lm2iMMkr1YduWg65Ui0qsYI5awaxUUStCzFuY6uc40z1xnIcrRXaVF9RhVQDlt2EHXEAj9kmQqufTeaFBv2itSI0WuPdhcopeKcc+zXS/MKC2fVdROO3IAT7y4vePzLRnx7rkyefxgrr5nued3xXwGg8oKc52gbLO797YH9ujLtRi9KX7Dl3Kmj/W7dq49vaBwUwgOH029lIX2ynJR5sW452GuF6RwwpibvI5JZZD1Oycf/Ca3pd+PQn+fILNemPselG0WogulLgRT/IYv5UwfW6xrF7LQRopCe2W5KPNi1LvQxiPMCCVbg+p1TiZhTHBhAgDC+ESCGge/1NiJHhLqvmcbQWXKEZSyolSY4jd8iXryVymuHWYBk6hHMLko82LUu9DGI8yIIaypxe+4XE1wQWk9sjVS2RqHXCeueRGmc+AlxzX3LKdp01tFW2QpLObcNWqaQlJKFFuOSpp1HEQYB3PYyVt+prkw5xfiWA2zXnHQxLFc/Rth7q+fHAKe/pBiPDPm3DViSWaPMZdFSYotRymH9IVcL8yIIVsmWa9z0imGCQ7CmbVy8bHk698I48/wk0Ohm0msWGG9fpjiN2qWzB5ToVEmcSGMucnrmMyonqBGtVgmuDCNlG/UTd9EN3t7vv6NMDmmgmwrmemqd+3ZG+k6Gqb4jZok6h5TrRNmxFDsUUU6YX0Sfo0U0KFID6hPkKiTLs7bRJ3w3vudeXNSz4efPGH8G34jiDCrmmWmq/ajWGGspviNmqSUK49VO6X2P0BxTXCZYbdX37O8S/qIHS2tJHoI/fom2LHLlf+P3Xu7zc9oaW0LtcpbLlFT1z+0OpTSD+tpLVYggin+GFCOF7vcVGsCtVLjNTK6+p7lzJy/Oqeolnwopt8jsx6ZirS1XenbqyfLvnsW4JzCXrSpeq40ls2/4TdCCcrEmZqIGDb3UDFDqcuSndMoHV4ZAlPZJWuZQrKGxgm/ePwdLa1V9ZyEmVeQ3uj7PQeDG+q5acpYBjfUI2mfM/0b2Y4JQypL6GAfWRrqEwVfww9T/DVOqdMZVwq5pgCOK2GykFYDYSOAUvillE6NeHbt2culJ7pVr665Z3lHquUUmSmewfkURkxfwMgZjzAimZ65PuGtYhvqE4GyJHoIIkQ2SjdTT41TzSaPQkxUhUyEipNpLEwW0mogWz28cuSA/0pbb+9qDZ2CItPMlPIRNO9oIVEn9ADa045P9JCOdA6ZsqSc0f/Y093x7HXtfLEJXDVOLa0SlXKCZVvSsdjXDTPJ69p5K7nrmVdpU6VOhKknDC35bMx8CJuFtNIJqke2Wbi5riSWeT+ynd+vb4K+vXqWdOWvFDaBK6aUM99OIXiZqFJdlChDM/OJBrp23souvcM21Y7Pla78vdIVpKiG5yRFUDbV3XvbvU7pIJdRjdex2c7fsau1w6mcImhUWYpRutn4a5xiOaJKTbaHPCr7cz4v3V3PvJrT/kpj8vjBLPvuWfy/S8ZV3XOSzuTxg9mnd/e+bLZnJReHv9ex2c7P/D5bwEUpAhOsx18lFGrvrqYXGMKFuUVhf84ns6VX3HfQ/kqlGp+TTPJpuKdNGsW0+56ntT349/IbAQVNRvM6J9uoshSjdOvxVwFxDMkMs5B3FKGZ+UQD1Yn34tl++43oyKe3PHn8YGZ/4pgukTaZNNQnfEdA6aNq6Pzd/UZN2RqnUozSy9LjF5GzgR8DdcCvVHVWOeSoFuI4CzUz6iJzdmNU9ud8ooGmnjC0i40/fb9RWvLtLadGO36O1X169wx8BnIZLYUZVUY9+iq54heROuBnwJnAa8BzIjJfVV8otSzVQjWHZBZC+sNfyhDLXF+6lAO3GqN6ao1C1zMoxbtWCQEX5ejxHw+sU9WXAUTkbuBjgCl+H8q5BGKlUOn25xsnjzVFXyEU8qyU4l0r5wJHKcqh+AcD6eEOrwEnZB4kIlcAVwAMGzasNJJVKJXQQzCMOFCqd63cHZlyKH4vj1c3d7qqzgHmgJvAFbVQlUwl9BAMIw7E5V0rh+J/DUj3eg0BNpdBjqqi3D0Ew4gLcXjXyhHO+RxwhIgcJiK9gE8B88sgh2EYRiwpeY9fVfeKyFXAY7hwzttUdXWp5TAMw4grZYnjV9VHgEfKcW3DMIy4YzN3DcMwYoYpfsMwjJhRFfn4RWQ7sKnccoSgP/BGuYWIkFquXy3XDax+1UwhdRuuqgMyd1aF4q8WRKTJa9GDWqGW61fLdQOrXzUTRd3M1GMYhhEzTPEbhmHEDFP8xWVOuQWImFquXy3XDax+1UzR62Y2fsMwjJhhPX7DMIyYYYrfMAwjZpjiLxARmSkizSKyPLmdm/bdDBFZJyJrRWRSOeUsFBH5hoioiPRP21f19RORG0RkRfK3+4OIDEr7rqrrJyKzReTFZP0eEJGGtO+qum4AIvIJEVktIu0i0pjxXdXXD9wytck6rBOR6UUrWFVtK2ADZgLf8Nj/IeB5oDdwGLAeqCu3vHnWcSguqd4moH8t1Q/YP+3/rwA/r5X6AWcBPZP//wfwH7VSt2Q9RgOjgMVAY9r+WqlfXVL2DwC9knX6UDHKth5/dHwMuFtVd6vqBmAdbtnJauQW4Jt0XTCnJuqnqjvTPu5DZx2rvn6q+gdV3Zv8+DRu7QuogboBqOoaVV3r8VVN1I+0ZWpVdQ+QWqa2YEzxF4erksPp20SkX3Kf1xKTVbe6g4hcCDSr6vMZX9VE/QBE5N9F5FXgUuC7yd01U78knwceTf5fa3XLpFbqF1k9ypKWudoQkT8Ch3p89R3gVuAGXE/xBuCHuJcs1BKTlUCW+n0bZzLodprHvqqrn6o+qKrfAb4jIjOAq4DrqJL6Zatb8pjvAHuB36ZO8zi+4uoG4erndZrHvoqsXxYiq4cp/hCo6kfDHCcivwQeTn6smiUm/eonImNxNtLnRQRcHZaKyPHUQP08+F9gAU7xV0X9stVNRC4DzgfO0KThmCqpG+T026VTNfXLQmT1MFNPgYjIwLSPFwGrkv/PBz4lIr1F5DDgCODZUstXCKq6UlUPVtURqjoC9yAeq6qvUwP1AxCRI9I+Xgi8mPy/6usnImcD3wIuVNVdaV9Vfd2yUCv1i2yZWuvxF84PRGQcbgi2EbgSQFVXi8i9wAu4Yfa/qmpbuYQsNjVUv1kiMgpox0Ut/QvUTP1+iotsWZgcsT2tqv9SI3VDRC4CfgIMABaIyHJVnVQr9dMIl6m1lA2GYRgxw0w9hmEYMcMUv2EYRswwxW8YhhEzTPEbhmHEDFP8hmEYMcMUvxE7ROS9HI49VUROSvv8LyLyz8n/P5uezTOHMjemZzk1jFJjcfyGEcypwHvAXwFU9edp330WN2GvGmeFGjHGFL9hACJyAXAtLv3tm7iEbfW4CV1tIvJp4MvAGbiGYCPQCPxWRFqA/wOswaUHfiOZH/5mVT1VRA4C7sJNNHqWtBwsyXK/krzuM8CXqnGykVFdmKnHMBx/AU5U1fG49LffVNWNwM+BW1R1nKr+OXWwqs4FmoBLk9+1BJR9HfCXZNnzgWEAIjIauASYqKrjgDZcg2MYkWI9fsNwDAHuSeZe6gVsKGLZJwNTAFR1gYi8ndx/BjABeC6ZUqEe2FbE6xqGJ6b4DcPxE+BHqjpfRE7FrayWK3vpHEX3yfjOKzeKAHeq6ow8rmUYeWOmHsNwHAA0J/+/LG3/u8B+PudkfrcR14MH+Hja/idJmnBE5BwgtVjP48DFInJw8rsDRWR4nvIbRmhM8RtxpK+IvJa2fQ3Xw79PRP4MvJF27EPARcnF2D+SUc4dwM+T39UD1wM/TpaR7qC9HjhZRJbiFrV5BUBVX8A5lP8gIiuAhUB6mm/DiATLzmkYhhEzrMdvGIYRM0zxG4ZhxAxT/IZhGDHDFL9hGEbMMMVvGIYRM0zxG4ZhxAxT/IZhGDHj/wO4geExoaDNkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for Wind Speed', 'Wind Speed',(-50,35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}