WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "fuzzy-trance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from citipy import citipy \n",
    "from datetime import datetime "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "sized-spread",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the request library.\n",
    "import requests\n",
    "\n",
    "#Import the API Key.\n",
    "from config import weather_api_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "stuffed-quick",
   "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\n",
    "#print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "formed-administration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x7ff8e67d0e80>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a set of random latitude and longitude combinations.\n",
    "lats = np.random.uniform(low = -90,high = 90,size=1500)\n",
    "lngs = np.random.uniform(low = -90,high = 90,size=1500)\n",
    "lat_lngs = zip(lats,lngs)\n",
    "lat_lngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "viral-administrator",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "seasonal-subsection",
   "metadata": {},
   "outputs": [],
   "source": [
    "coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "assumed-recall",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "590"
      ]
     },
     "execution_count": 135,
     "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": 136,
   "id": "southeast-evanescence",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Create an empty list to hold 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "disturbed-payroll",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(len(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=\" + cities[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "visible-tennis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #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 the Set {set_count} | {city}\")\n",
    "#     #Add 1 to the record count.\n",
    "#     record_count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "instructional-ghost",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval    \n",
      "----------------------------\n",
      "Processing Record1 of the Set 1 | olafsvik\n",
      "Processing Record2 of the Set 1 | bambous virieux\n",
      "Processing Record3 of the Set 1 | ushuaia\n",
      "Processing Record4 of the Set 1 | hofn\n",
      "Processing Record5 of the Set 1 | akdepe\n",
      "Processing Record6 of the Set 1 | lebu\n",
      "Processing Record7 of the Set 1 | arraial do cabo\n",
      "Processing Record8 of the Set 1 | port hawkesbury\n",
      "Processing Record9 of the Set 1 | ribeira grande\n",
      "Processing Record10 of the Set 1 | oranjemund\n",
      "Processing Record11 of the Set 1 | harper\n",
      "Processing Record12 of the Set 1 | minab\n",
      "Processing Record13 of the Set 1 | starozhilovo\n",
      "Processing Record14 of the Set 1 | tres arroyos\n",
      "Processing Record15 of the Set 1 | guabito\n",
      "Processing Record16 of the Set 1 | moose factory\n",
      "Processing Record17 of the Set 1 | azimur\n",
      "City not found. Skipping...\n",
      "Processing Record18 of the Set 1 | jamestown\n",
      "Processing Record19 of the Set 1 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record20 of the Set 1 | nanortalik\n",
      "Processing Record21 of the Set 1 | atar\n",
      "Processing Record22 of the Set 1 | pisco\n",
      "Processing Record23 of the Set 1 | brufut\n",
      "Processing Record24 of the Set 1 | longyearbyen\n",
      "Processing Record25 of the Set 1 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record26 of the Set 1 | hermanus\n",
      "Processing Record27 of the Set 1 | marsh harbour\n",
      "Processing Record28 of the Set 1 | geraldton\n",
      "Processing Record29 of the Set 1 | bargarh\n",
      "Processing Record30 of the Set 1 | castro\n",
      "Processing Record31 of the Set 1 | lagoa\n",
      "Processing Record32 of the Set 1 | bandarbeyla\n",
      "Processing Record33 of the Set 1 | touros\n",
      "Processing Record34 of the Set 1 | busselton\n",
      "Processing Record35 of the Set 1 | marzuq\n",
      "Processing Record36 of the Set 1 | dingle\n",
      "Processing Record37 of the Set 1 | vila velha\n",
      "Processing Record38 of the Set 1 | namibe\n",
      "Processing Record39 of the Set 1 | dikson\n",
      "Processing Record40 of the Set 1 | bognor regis\n",
      "Processing Record41 of the Set 1 | cape town\n",
      "Processing Record42 of the Set 1 | saint-philippe\n",
      "Processing Record43 of the Set 1 | kiruna\n",
      "Processing Record44 of the Set 1 | upernavik\n",
      "Processing Record45 of the Set 1 | ormoz\n",
      "Processing Record46 of the Set 1 | mar del plata\n",
      "Processing Record47 of the Set 1 | khormuj\n",
      "City not found. Skipping...\n",
      "Processing Record48 of the Set 1 | terrasini\n",
      "Processing Record49 of the Set 1 | grootfontein\n",
      "Processing Record50 of the Set 1 | vikevag\n",
      "Processing Record51 of the Set 1 | zachagansk\n",
      "City not found. Skipping...\n",
      "Processing Record52 of the Set 1 | kruisfontein\n",
      "Processing Record53 of the Set 1 | tasiilaq\n",
      "Processing Record54 of the Set 1 | qaanaaq\n",
      "Processing Record55 of the Set 1 | paradwip\n",
      "City not found. Skipping...\n",
      "Processing Record56 of the Set 1 | coquimbo\n",
      "Processing Record57 of the Set 1 | guarapari\n",
      "Processing Record58 of the Set 1 | bredasdorp\n",
      "Processing Record59 of the Set 1 | port elizabeth\n",
      "Processing Record60 of the Set 1 | bathurst\n",
      "Processing Record61 of the Set 1 | teguise\n",
      "Processing Record62 of the Set 1 | saint george\n",
      "Processing Record63 of the Set 1 | mahebourg\n",
      "Processing Record64 of the Set 1 | los llanos de aridane\n",
      "Processing Record65 of the Set 1 | chuy\n",
      "Processing Record66 of the Set 1 | torbay\n",
      "Processing Record67 of the Set 1 | kavaratti\n",
      "Processing Record68 of the Set 1 | albany\n",
      "Processing Record69 of the Set 1 | ilhabela\n",
      "Processing Record70 of the Set 1 | porto novo\n",
      "Processing Record71 of the Set 1 | hammerfest\n",
      "Processing Record72 of the Set 1 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record73 of the Set 1 | stornoway\n",
      "Processing Record74 of the Set 1 | sidi ali\n",
      "Processing Record75 of the Set 1 | ouahigouya\n",
      "Processing Record76 of the Set 1 | port alfred\n",
      "Processing Record77 of the Set 1 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record78 of the Set 1 | adrar\n",
      "Processing Record79 of the Set 1 | luderitz\n",
      "Processing Record80 of the Set 1 | olavarria\n",
      "Processing Record81 of the Set 1 | marcona\n",
      "City not found. Skipping...\n",
      "Processing Record82 of the Set 1 | taltal\n",
      "Processing Record83 of the Set 1 | san francisco de macoris\n",
      "Processing Record84 of the Set 1 | kidal\n",
      "Processing Record85 of the Set 1 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record86 of the Set 1 | iqaluit\n",
      "Processing Record87 of the Set 1 | strezhevoy\n",
      "Processing Record88 of the Set 1 | kosa\n",
      "Processing Record89 of the Set 1 | pelaya\n",
      "Processing Record90 of the Set 1 | georgetown\n",
      "Processing Record91 of the Set 1 | constantine\n",
      "Processing Record92 of the Set 1 | klaksvik\n",
      "Processing Record93 of the Set 1 | hargeysa\n",
      "Processing Record94 of the Set 1 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record95 of the Set 1 | mehamn\n",
      "Processing Record96 of the Set 1 | saint-francois\n",
      "Processing Record97 of the Set 1 | prattville\n",
      "Processing Record98 of the Set 1 | vardo\n",
      "Processing Record99 of the Set 1 | karatau\n",
      "Processing Record100 of the Set 1 | onega\n",
      "Processing Record101 of the Set 1 | tucurui\n",
      "Processing Record102 of the Set 1 | chichester\n",
      "Processing Record103 of the Set 1 | victoria\n",
      "Processing Record104 of the Set 1 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record105 of the Set 1 | bonavista\n",
      "Processing Record106 of the Set 1 | asmara\n",
      "Processing Record107 of the Set 1 | itacoatiara\n",
      "Processing Record108 of the Set 1 | ati\n",
      "Processing Record109 of the Set 1 | kukmor\n",
      "Processing Record110 of the Set 1 | hithadhoo\n",
      "Processing Record111 of the Set 1 | raudeberg\n",
      "Processing Record112 of the Set 1 | bafq\n",
      "Processing Record113 of the Set 1 | panaba\n",
      "Processing Record114 of the Set 1 | laje\n",
      "Processing Record115 of the Set 1 | barentu\n",
      "Processing Record116 of the Set 1 | litoral del san juan\n",
      "City not found. Skipping...\n",
      "Processing Record117 of the Set 1 | merke\n",
      "Processing Record118 of the Set 1 | ylivieska\n",
      "Processing Record119 of the Set 1 | boa vista\n",
      "Processing Record120 of the Set 1 | vestmannaeyjar\n",
      "Processing Record121 of the Set 1 | ngaoundere\n",
      "Processing Record122 of the Set 1 | tuttlingen\n",
      "Processing Record123 of the Set 1 | beaverton\n",
      "Processing Record124 of the Set 1 | sencha\n",
      "Processing Record125 of the Set 1 | urubicha\n",
      "Processing Record126 of the Set 1 | mecca\n",
      "Processing Record127 of the Set 1 | oktyabrskoye\n",
      "Processing Record128 of the Set 1 | esmeraldas\n",
      "Processing Record129 of the Set 1 | bathsheba\n",
      "Processing Record130 of the Set 1 | santa rosa\n",
      "Processing Record131 of the Set 1 | collierville\n",
      "Processing Record132 of the Set 1 | marawi\n",
      "Processing Record133 of the Set 1 | karkaralinsk\n",
      "City not found. Skipping...\n",
      "Processing Record134 of the Set 1 | mrirt\n",
      "City not found. Skipping...\n",
      "Processing Record135 of the Set 1 | grand gaube\n",
      "Processing Record136 of the Set 1 | saldanha\n",
      "Processing Record137 of the Set 1 | coihaique\n",
      "Processing Record138 of the Set 1 | itarema\n",
      "Processing Record139 of the Set 1 | pita\n",
      "Processing Record140 of the Set 1 | krutinka\n",
      "Processing Record141 of the Set 1 | hit\n",
      "Processing Record142 of the Set 1 | punta arenas\n",
      "Processing Record143 of the Set 1 | lucea\n",
      "Processing Record144 of the Set 1 | bara\n",
      "Processing Record145 of the Set 1 | mutis\n",
      "Processing Record146 of the Set 1 | caravelas\n",
      "Processing Record147 of the Set 1 | liepaja\n",
      "Processing Record148 of the Set 1 | beloha\n",
      "Processing Record149 of the Set 1 | somerset\n",
      "Processing Record150 of the Set 1 | bad hall\n",
      "Processing Record151 of the Set 1 | east london\n",
      "Processing Record152 of the Set 1 | cayenne\n",
      "Processing Record153 of the Set 1 | gardan diwal\n",
      "City not found. Skipping...\n",
      "Processing Record154 of the Set 1 | tirumullaivasal\n",
      "Processing Record155 of the Set 1 | sao filipe\n",
      "Processing Record156 of the Set 1 | louisbourg\n",
      "City not found. Skipping...\n",
      "Processing Record157 of the Set 1 | tabas\n",
      "Processing Record158 of the Set 1 | alingsas\n",
      "Processing Record159 of the Set 1 | warrenton\n",
      "Processing Record160 of the Set 1 | hambantota\n",
      "Processing Record161 of the Set 1 | garbolovo\n",
      "Processing Record162 of the Set 1 | mitsamiouli\n",
      "Processing Record163 of the Set 1 | tougan\n",
      "Processing Record164 of the Set 1 | aracaju\n",
      "Processing Record165 of the Set 1 | zhezkazgan\n",
      "Processing Record166 of the Set 1 | floro\n",
      "Processing Record167 of the Set 1 | asfi\n",
      "Processing Record168 of the Set 1 | namwala\n",
      "Processing Record169 of the Set 1 | shieli\n",
      "Processing Record170 of the Set 1 | souillac\n",
      "Processing Record171 of the Set 1 | felidhoo\n",
      "City not found. Skipping...\n",
      "Processing Record172 of the Set 1 | les cayes\n",
      "Processing Record173 of the Set 1 | vrede\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record174 of the Set 1 | gat\n",
      "Processing Record175 of the Set 1 | san ramon de la nueva oran\n",
      "Processing Record176 of the Set 1 | kahta\n",
      "Processing Record177 of the Set 1 | astipalaia\n",
      "City not found. Skipping...\n",
      "Processing Record178 of the Set 1 | sao felix do xingu\n",
      "Processing Record179 of the Set 1 | codrington\n",
      "Processing Record180 of the Set 1 | rundu\n",
      "Processing Record181 of the Set 1 | malanje\n",
      "Processing Record182 of the Set 1 | kajiado\n",
      "Processing Record183 of the Set 1 | laguna\n",
      "Processing Record184 of the Set 1 | racale\n",
      "Processing Record185 of the Set 1 | clyde river\n",
      "Processing Record186 of the Set 1 | avila\n",
      "Processing Record187 of the Set 1 | matara\n",
      "Processing Record188 of the Set 1 | sinnamary\n",
      "Processing Record189 of the Set 1 | umzimvubu\n",
      "City not found. Skipping...\n",
      "Processing Record190 of the Set 1 | ilulissat\n",
      "Processing Record191 of the Set 1 | saint anthony\n",
      "Processing Record192 of the Set 1 | port-gentil\n",
      "Processing Record193 of the Set 1 | tabou\n",
      "Processing Record194 of the Set 1 | dakar\n",
      "Processing Record195 of the Set 1 | pithora\n",
      "Processing Record196 of the Set 1 | rio gallegos\n",
      "Processing Record197 of the Set 1 | segou\n",
      "Processing Record198 of the Set 1 | larsnes\n",
      "Processing Record199 of the Set 1 | praia da vitoria\n",
      "Processing Record200 of the Set 1 | san francisco\n",
      "Processing Record201 of the Set 1 | sur\n",
      "Processing Record202 of the Set 1 | takoradi\n",
      "Processing Record203 of the Set 1 | cumaribo\n",
      "City not found. Skipping...\n",
      "Processing Record204 of the Set 1 | cidreira\n",
      "Processing Record205 of the Set 1 | omboue\n",
      "Processing Record206 of the Set 1 | dubrovytsya\n",
      "Processing Record207 of the Set 1 | carnarvon\n",
      "Processing Record208 of the Set 1 | richard toll\n",
      "City not found. Skipping...\n",
      "Processing Record209 of the Set 1 | atasu\n",
      "Processing Record210 of the Set 1 | korla\n",
      "Processing Record211 of the Set 1 | salinas\n",
      "Processing Record212 of the Set 1 | tumaco\n",
      "Processing Record213 of the Set 1 | quatre cocos\n",
      "Processing Record214 of the Set 1 | pangnirtung\n",
      "Processing Record215 of the Set 1 | puerto colombia\n",
      "Processing Record216 of the Set 1 | ponta do sol\n",
      "Processing Record217 of the Set 1 | stokmarknes\n",
      "Processing Record218 of the Set 1 | bekobod\n",
      "Processing Record219 of the Set 1 | butembo\n",
      "Processing Record220 of the Set 1 | faya\n",
      "Processing Record221 of the Set 1 | bonthe\n",
      "Processing Record222 of the Set 1 | aksarka\n",
      "Processing Record223 of the Set 1 | taoudenni\n",
      "Processing Record224 of the Set 1 | puerto leguizamo\n",
      "Processing Record225 of the Set 1 | cherepovets\n",
      "Processing Record226 of the Set 1 | kenitra\n",
      "Processing Record227 of the Set 1 | margate\n",
      "Processing Record228 of the Set 1 | santa margherita ligure\n",
      "Processing Record229 of the Set 1 | yashkul\n",
      "Processing Record230 of the Set 1 | severnoye\n",
      "Processing Record231 of the Set 1 | piacabucu\n",
      "Processing Record232 of the Set 1 | tambura\n",
      "Processing Record233 of the Set 1 | sakakah\n",
      "Processing Record234 of the Set 1 | eslov\n",
      "Processing Record235 of the Set 1 | angoche\n",
      "Processing Record236 of the Set 1 | sinop\n",
      "Processing Record237 of the Set 1 | wagar\n",
      "Processing Record238 of the Set 1 | nador\n",
      "Processing Record239 of the Set 1 | shelburne\n",
      "Processing Record240 of the Set 1 | coroiesti\n",
      "Processing Record241 of the Set 1 | hamilton\n",
      "Processing Record242 of the Set 1 | comodoro rivadavia\n",
      "Processing Record243 of the Set 1 | sarakhs\n",
      "Processing Record244 of the Set 1 | ratodero\n",
      "Processing Record245 of the Set 1 | calbuco\n",
      "Processing Record246 of the Set 1 | calama\n",
      "Processing Record247 of the Set 1 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record248 of the Set 1 | maniitsoq\n",
      "Processing Record249 of the Set 1 | bushehr\n",
      "Processing Record250 of the Set 1 | bom jesus\n",
      "Processing Record251 of the Set 1 | araouane\n",
      "Processing Record252 of the Set 1 | saint-joseph\n",
      "Processing Record253 of the Set 1 | antalaha\n",
      "Processing Record254 of the Set 1 | santa vitoria do palmar\n",
      "Processing Record255 of the Set 1 | igarka\n",
      "Processing Record256 of the Set 1 | lusaka\n",
      "Processing Record257 of the Set 1 | necochea\n",
      "Processing Record258 of the Set 1 | amos\n",
      "Processing Record259 of the Set 1 | sinnar\n",
      "Processing Record260 of the Set 1 | kolosovka\n",
      "Processing Record261 of the Set 1 | rocha\n",
      "Processing Record262 of the Set 1 | skibotn\n",
      "Processing Record263 of the Set 1 | krasnoselkup\n",
      "Processing Record264 of the Set 1 | bud\n",
      "Processing Record265 of the Set 1 | bereda\n",
      "Processing Record266 of the Set 1 | batticaloa\n",
      "Processing Record267 of the Set 1 | jacobina\n",
      "Processing Record268 of the Set 1 | anloga\n",
      "Processing Record269 of the Set 1 | kismayo\n",
      "Processing Record270 of the Set 1 | marfino\n",
      "Processing Record271 of the Set 1 | tapaua\n",
      "City not found. Skipping...\n",
      "Processing Record272 of the Set 1 | kalmeshwar\n",
      "Processing Record273 of the Set 1 | rybinsk\n",
      "Processing Record274 of the Set 1 | westerland\n",
      "Processing Record275 of the Set 1 | mongo\n",
      "Processing Record276 of the Set 1 | nara\n",
      "Processing Record277 of the Set 1 | macapa\n",
      "Processing Record278 of the Set 1 | salalah\n",
      "Processing Record279 of the Set 1 | cabedelo\n",
      "Processing Record280 of the Set 1 | san juan\n",
      "Processing Record281 of the Set 1 | lyubech\n",
      "Processing Record282 of the Set 1 | kushmurun\n",
      "Processing Record283 of the Set 1 | lusambo\n",
      "Processing Record284 of the Set 1 | abhar\n",
      "Processing Record285 of the Set 1 | raga\n",
      "Processing Record286 of the Set 1 | zhanatas\n",
      "City not found. Skipping...\n",
      "Processing Record287 of the Set 1 | gao\n",
      "Processing Record288 of the Set 1 | narsaq\n",
      "Processing Record289 of the Set 1 | burewala\n",
      "Processing Record290 of the Set 1 | ancud\n",
      "Processing Record291 of the Set 1 | san andres\n",
      "Processing Record292 of the Set 1 | salta\n",
      "Processing Record293 of the Set 1 | novosergiyevka\n",
      "Processing Record294 of the Set 1 | havelock\n",
      "Processing Record295 of the Set 1 | harindanga\n",
      "Processing Record296 of the Set 1 | walvis bay\n",
      "Processing Record297 of the Set 1 | diapaga\n",
      "Processing Record298 of the Set 1 | lokomotivnyy\n",
      "Processing Record299 of the Set 1 | muros\n",
      "Processing Record300 of the Set 1 | nurota\n",
      "Processing Record301 of the Set 1 | hualmay\n",
      "Processing Record302 of the Set 1 | wolmaranstad\n",
      "City not found. Skipping...\n",
      "Processing Record303 of the Set 1 | san matias\n",
      "Processing Record304 of the Set 1 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record305 of the Set 1 | coxim\n",
      "Processing Record306 of the Set 1 | karauzyak\n",
      "City not found. Skipping...\n",
      "Processing Record307 of the Set 1 | benghazi\n",
      "Processing Record308 of the Set 1 | xuddur\n",
      "Processing Record309 of the Set 1 | presidencia roque saenz pena\n",
      "Processing Record310 of the Set 1 | honningsvag\n",
      "Processing Record311 of the Set 1 | cavadinesti\n",
      "Processing Record312 of the Set 1 | beasain\n",
      "Processing Record313 of the Set 1 | hoopstad\n",
      "Processing Record314 of the Set 1 | saryshagan\n",
      "City not found. Skipping...\n",
      "Processing Record315 of the Set 1 | owando\n",
      "Processing Record316 of the Set 1 | skjaerhollen\n",
      "City not found. Skipping...\n",
      "Processing Record317 of the Set 1 | mocuba\n",
      "Processing Record318 of the Set 1 | tubruq\n",
      "City not found. Skipping...\n",
      "Processing Record319 of the Set 1 | dianopolis\n",
      "City not found. Skipping...\n",
      "Processing Record320 of the Set 1 | penzance\n",
      "Processing Record321 of the Set 1 | uray\n",
      "Processing Record322 of the Set 1 | waynesville\n",
      "Processing Record323 of the Set 1 | iralaya\n",
      "Processing Record324 of the Set 1 | filotas\n",
      "Processing Record325 of the Set 1 | little current\n",
      "Processing Record326 of the Set 1 | mbigou\n",
      "Processing Record327 of the Set 1 | havre-saint-pierre\n",
      "Processing Record328 of the Set 1 | akkermanovka\n",
      "Processing Record329 of the Set 1 | hollins\n",
      "Processing Record330 of the Set 1 | esil\n",
      "Processing Record331 of the Set 1 | medea\n",
      "Processing Record332 of the Set 1 | nkhotakota\n",
      "Processing Record333 of the Set 1 | najran\n",
      "Processing Record334 of the Set 1 | djenne\n",
      "Processing Record335 of the Set 1 | liverpool\n",
      "Processing Record336 of the Set 1 | dhidhdhoo\n",
      "Processing Record337 of the Set 1 | ciudad bolivar\n",
      "Processing Record338 of the Set 1 | miraflores\n",
      "Processing Record339 of the Set 1 | matosinhos\n",
      "Processing Record340 of the Set 1 | barawe\n",
      "City not found. Skipping...\n",
      "Processing Record341 of the Set 1 | wexford\n",
      "Processing Record342 of the Set 1 | ponta delgada\n",
      "Processing Record343 of the Set 1 | san jose de guanipa\n",
      "Processing Record344 of the Set 1 | rincon\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record345 of the Set 1 | vuktyl\n",
      "Processing Record346 of the Set 1 | zatoka\n",
      "Processing Record347 of the Set 1 | libreville\n",
      "Processing Record348 of the Set 1 | chingirlau\n",
      "Processing Record349 of the Set 1 | lethem\n",
      "Processing Record350 of the Set 1 | saurimo\n",
      "Processing Record351 of the Set 1 | lithakia\n",
      "Processing Record352 of the Set 1 | la palma\n",
      "Processing Record353 of the Set 1 | viedma\n",
      "Processing Record354 of the Set 1 | novyy urengoy\n",
      "Processing Record355 of the Set 1 | rjukan\n",
      "Processing Record356 of the Set 1 | manono\n",
      "Processing Record357 of the Set 1 | shache\n",
      "Processing Record358 of the Set 1 | bushenyi\n",
      "Processing Record359 of the Set 1 | perama\n",
      "Processing Record360 of the Set 1 | rio grande\n",
      "Processing Record361 of the Set 1 | santiago del estero\n",
      "Processing Record362 of the Set 1 | boaco\n",
      "Processing Record363 of the Set 1 | karasjok\n",
      "Processing Record364 of the Set 1 | hobyo\n",
      "Processing Record365 of the Set 1 | saint-georges\n",
      "Processing Record366 of the Set 1 | yirol\n",
      "City not found. Skipping...\n",
      "Processing Record367 of the Set 1 | coromandel\n",
      "Processing Record368 of the Set 1 | tessalit\n",
      "Processing Record369 of the Set 1 | purpe\n",
      "Processing Record370 of the Set 1 | sistranda\n",
      "Processing Record371 of the Set 1 | kuche\n",
      "City not found. Skipping...\n",
      "Processing Record372 of the Set 1 | riberalta\n",
      "Processing Record373 of the Set 1 | hall\n",
      "Processing Record374 of the Set 1 | japura\n",
      "Processing Record375 of the Set 1 | axim\n",
      "Processing Record376 of the Set 1 | vicuna\n",
      "Processing Record377 of the Set 1 | puerto cabezas\n",
      "Processing Record378 of the Set 1 | yazman\n",
      "Processing Record379 of the Set 1 | meybod\n",
      "Processing Record380 of the Set 1 | qaqortoq\n",
      "Processing Record381 of the Set 1 | xai-xai\n",
      "Processing Record382 of the Set 1 | lebane\n",
      "Processing Record383 of the Set 1 | aripuana\n",
      "Processing Record384 of the Set 1 | klyuchi\n",
      "Processing Record385 of the Set 1 | guhagar\n",
      "Processing Record386 of the Set 1 | michigan city\n",
      "Processing Record387 of the Set 1 | plouzane\n",
      "Processing Record388 of the Set 1 | nampula\n",
      "Processing Record389 of the Set 1 | rawson\n",
      "Processing Record390 of the Set 1 | tasbuget\n",
      "City not found. Skipping...\n",
      "Processing Record391 of the Set 1 | bilma\n",
      "Processing Record392 of the Set 1 | matagami\n",
      "Processing Record393 of the Set 1 | khandbari\n",
      "Processing Record394 of the Set 1 | khash\n",
      "Processing Record395 of the Set 1 | kulu\n",
      "Processing Record396 of the Set 1 | qasigiannguit\n",
      "Processing Record397 of the Set 1 | soyo\n",
      "Processing Record398 of the Set 1 | horki\n",
      "Processing Record399 of the Set 1 | guelengdeng\n",
      "City not found. Skipping...\n",
      "Processing Record400 of the Set 1 | ginir\n",
      "Processing Record401 of the Set 1 | bugiri\n",
      "Processing Record402 of the Set 1 | berlevag\n",
      "Processing Record403 of the Set 1 | sete\n",
      "Processing Record404 of the Set 1 | santander\n",
      "Processing Record405 of the Set 1 | utrecht\n",
      "Processing Record406 of the Set 1 | yar-sale\n",
      "Processing Record407 of the Set 1 | kafanchan\n",
      "Processing Record408 of the Set 1 | batsfjord\n",
      "Processing Record409 of the Set 1 | kuvshinovo\n",
      "Processing Record410 of the Set 1 | kargasok\n",
      "Processing Record411 of the Set 1 | grindavik\n",
      "Processing Record412 of the Set 1 | bollnas\n",
      "Processing Record413 of the Set 1 | kaputa\n",
      "Processing Record414 of the Set 1 | duz\n",
      "City not found. Skipping...\n",
      "Processing Record415 of the Set 1 | zhitikara\n",
      "City not found. Skipping...\n",
      "Processing Record416 of the Set 1 | malinovskiy\n",
      "Processing Record417 of the Set 1 | malwan\n",
      "City not found. Skipping...\n",
      "Processing Record418 of the Set 1 | baghdad\n",
      "Processing Record419 of the Set 1 | saint-pierre\n",
      "Processing Record420 of the Set 1 | san cristobal\n",
      "Processing Record421 of the Set 1 | kuloy\n",
      "Processing Record422 of the Set 1 | sandane\n",
      "Processing Record423 of the Set 1 | tombouctou\n",
      "Processing Record424 of the Set 1 | concordia\n",
      "Processing Record425 of the Set 1 | sechura\n",
      "Processing Record426 of the Set 1 | angra\n",
      "City not found. Skipping...\n",
      "Processing Record427 of the Set 1 | verkhnyaya maksakovka\n",
      "Processing Record428 of the Set 1 | iisaku\n",
      "Processing Record429 of the Set 1 | goderich\n",
      "Processing Record430 of the Set 1 | tacna\n",
      "Processing Record431 of the Set 1 | lima\n",
      "Processing Record432 of the Set 1 | verin\n",
      "Processing Record433 of the Set 1 | asyut\n",
      "Processing Record434 of the Set 1 | turukhansk\n",
      "Processing Record435 of the Set 1 | voloshka\n",
      "Processing Record436 of the Set 1 | marks\n",
      "Processing Record437 of the Set 1 | chapada dos guimaraes\n",
      "Processing Record438 of the Set 1 | mugla\n",
      "Processing Record439 of the Set 1 | qazanbulaq\n",
      "Processing Record440 of the Set 1 | verkhnyaya inta\n",
      "Processing Record441 of the Set 1 | salamiyah\n",
      "Processing Record442 of the Set 1 | ostrovnoy\n",
      "Processing Record443 of the Set 1 | bolungarvik\n",
      "City not found. Skipping...\n",
      "Processing Record444 of the Set 1 | darab\n",
      "Processing Record445 of the Set 1 | abensberg\n",
      "Processing Record446 of the Set 1 | umm kaddadah\n",
      "Processing Record447 of the Set 1 | eregli\n",
      "Processing Record448 of the Set 1 | isla mujeres\n",
      "Processing Record449 of the Set 1 | puerto baquerizo moreno\n",
      "Processing Record450 of the Set 1 | lana\n",
      "Processing Record451 of the Set 1 | podor\n",
      "Processing Record452 of the Set 1 | melfi\n",
      "Processing Record453 of the Set 1 | coruripe\n",
      "Processing Record454 of the Set 1 | kyabe\n",
      "Processing Record455 of the Set 1 | leskovik\n",
      "Processing Record456 of the Set 1 | union city\n",
      "Processing Record457 of the Set 1 | tazovskiy\n",
      "Processing Record458 of the Set 1 | tangermunde\n",
      "Processing Record459 of the Set 1 | sorland\n",
      "Processing Record460 of the Set 1 | concarneau\n",
      "Processing Record461 of the Set 1 | micheweni\n",
      "Processing Record462 of the Set 1 | richards bay\n",
      "Processing Record463 of the Set 1 | aguimes\n",
      "Processing Record464 of the Set 1 | mafeteng\n",
      "Processing Record465 of the Set 1 | sao francisco\n",
      "Processing Record466 of the Set 1 | remontnoye\n",
      "Processing Record467 of the Set 1 | betare oya\n",
      "Processing Record468 of the Set 1 | mandera\n",
      "Processing Record469 of the Set 1 | lamu\n",
      "Processing Record470 of the Set 1 | andenes\n",
      "Processing Record471 of the Set 1 | ejura\n",
      "Processing Record472 of the Set 1 | sigulda\n",
      "Processing Record473 of the Set 1 | vega baja\n",
      "Processing Record474 of the Set 1 | jardim\n",
      "Processing Record475 of the Set 1 | roncesvalles\n",
      "Processing Record476 of the Set 1 | strenci\n",
      "Processing Record477 of the Set 1 | road town\n",
      "Processing Record478 of the Set 1 | zabol\n",
      "Processing Record479 of the Set 1 | simdega\n",
      "Processing Record480 of the Set 1 | estepona\n",
      "Processing Record481 of the Set 1 | gamba\n",
      "Processing Record482 of the Set 1 | itaueira\n",
      "Processing Record483 of the Set 1 | marivan\n",
      "Processing Record484 of the Set 1 | shakawe\n",
      "Processing Record485 of the Set 1 | nata\n",
      "Processing Record486 of the Set 1 | alta floresta\n",
      "Processing Record487 of the Set 1 | mungaoli\n",
      "Processing Record488 of the Set 1 | baoro\n",
      "Processing Record489 of the Set 1 | kegayli\n",
      "City not found. Skipping...\n",
      "Processing Record490 of the Set 1 | inta\n",
      "Processing Record491 of the Set 1 | bergen\n",
      "Processing Record492 of the Set 1 | brae\n",
      "Processing Record493 of the Set 1 | quelimane\n",
      "Processing Record494 of the Set 1 | fianarantsoa\n",
      "Processing Record495 of the Set 1 | riyadh\n",
      "Processing Record496 of the Set 1 | tarsus\n",
      "Processing Record497 of the Set 1 | sibiti\n",
      "Processing Record498 of the Set 1 | karpogory\n",
      "Processing Record499 of the Set 1 | palm beach\n",
      "Processing Record500 of the Set 1 | scalloway\n",
      "City not found. Skipping...\n",
      "Processing Record501 of the Set 1 | epe\n",
      "Processing Record502 of the Set 1 | krasnyy kut\n",
      "Processing Record503 of the Set 1 | pokaran\n",
      "Processing Record504 of the Set 1 | horqueta\n",
      "Processing Record505 of the Set 1 | dvinskoy\n",
      "Processing Record506 of the Set 1 | karaul\n",
      "City not found. Skipping...\n",
      "Processing Record507 of the Set 1 | machaneng\n",
      "Processing Record508 of the Set 1 | gornopravdinsk\n",
      "Processing Record509 of the Set 1 | mweka\n",
      "Processing Record510 of the Set 1 | brokopondo\n",
      "Processing Record511 of the Set 1 | vredendal\n",
      "Processing Record512 of the Set 1 | dharchula\n",
      "Processing Record513 of the Set 1 | revin\n",
      "Processing Record514 of the Set 1 | marsa matruh\n",
      "Processing Record515 of the Set 1 | langen\n",
      "Processing Record516 of the Set 1 | talalayivka\n",
      "Processing Record517 of the Set 1 | cockburn town\n",
      "Processing Record518 of the Set 1 | meulaboh\n",
      "Processing Record519 of the Set 1 | aleksandrov gay\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record520 of the Set 1 | broadstairs\n",
      "Processing Record521 of the Set 1 | camacha\n",
      "Processing Record522 of the Set 1 | tocopilla\n",
      "Processing Record523 of the Set 1 | uhlove\n",
      "Processing Record524 of the Set 1 | paucartambo\n",
      "City not found. Skipping...\n",
      "Processing Record525 of the Set 1 | ambodifototra\n",
      "City not found. Skipping...\n",
      "Processing Record526 of the Set 1 | olinda\n",
      "Processing Record527 of the Set 1 | olmos\n",
      "Processing Record528 of the Set 1 | altamira\n",
      "Processing Record529 of the Set 1 | doha\n",
      "Processing Record530 of the Set 1 | ardistan\n",
      "City not found. Skipping...\n",
      "Processing Record531 of the Set 1 | avanigadda\n",
      "Processing Record532 of the Set 1 | opuwo\n",
      "Processing Record533 of the Set 1 | correntina\n",
      "Processing Record534 of the Set 1 | kondopoga\n",
      "Processing Record535 of the Set 1 | luanda\n",
      "Processing Record536 of the Set 1 | santa maria\n",
      "Processing Record537 of the Set 1 | maceio\n",
      "Processing Record538 of the Set 1 | hirson\n",
      "Processing Record539 of the Set 1 | mana\n",
      "Processing Record540 of the Set 1 | araruama\n",
      "Processing Record541 of the Set 1 | sao joao da barra\n",
      "Processing Record542 of the Set 1 | solwezi\n",
      "Processing Record543 of the Set 1 | zelenchukskaya\n",
      "Processing Record544 of the Set 1 | aswan\n",
      "Processing Record545 of the Set 1 | safwah\n",
      "City not found. Skipping...\n",
      "Processing Record546 of the Set 1 | guajara\n",
      "Processing Record547 of the Set 1 | ambilobe\n",
      "Processing Record548 of the Set 1 | umtata\n",
      "Processing Record549 of the Set 1 | cotonou\n",
      "Processing Record550 of the Set 1 | ingraj bazar\n",
      "Processing Record551 of the Set 1 | aden\n",
      "Processing Record552 of the Set 1 | sheffield\n",
      "Processing Record553 of the Set 1 | sabha\n",
      "Processing Record554 of the Set 1 | gezing\n",
      "Processing Record555 of the Set 1 | yanam\n",
      "Processing Record556 of the Set 1 | sighetu marmatiei\n",
      "Processing Record557 of the Set 1 | paraopeba\n",
      "Processing Record558 of the Set 1 | havoysund\n",
      "Processing Record559 of the Set 1 | osa\n",
      "Processing Record560 of the Set 1 | trincomalee\n",
      "Processing Record561 of the Set 1 | barmer\n",
      "Processing Record562 of the Set 1 | puerto madryn\n",
      "Processing Record563 of the Set 1 | barra do garcas\n",
      "Processing Record564 of the Set 1 | visnes\n",
      "Processing Record565 of the Set 1 | mercedes\n",
      "Processing Record566 of the Set 1 | jacqueville\n",
      "Processing Record567 of the Set 1 | valea viilor\n",
      "Processing Record568 of the Set 1 | santa marta\n",
      "Processing Record569 of the Set 1 | hwange\n",
      "Processing Record570 of the Set 1 | sandwick\n",
      "Processing Record571 of the Set 1 | vengerovo\n",
      "Processing Record572 of the Set 1 | manbij\n",
      "Processing Record573 of the Set 1 | baiima\n",
      "Processing Record574 of the Set 1 | tarboro\n",
      "Processing Record575 of the Set 1 | caucaia\n",
      "Processing Record576 of the Set 1 | pedernales\n",
      "Processing Record577 of the Set 1 | curuca\n",
      "Processing Record578 of the Set 1 | elizabethton\n",
      "Processing Record579 of the Set 1 | abalak\n",
      "Processing Record580 of the Set 1 | nouadhibou\n",
      "Processing Record581 of the Set 1 | nyzhni sirohozy\n",
      "Processing Record582 of the Set 1 | plettenberg bay\n",
      "Processing Record583 of the Set 1 | morant bay\n",
      "Processing Record584 of the Set 1 | chiredzi\n",
      "Processing Record585 of the Set 1 | tabuk\n",
      "Processing Record586 of the Set 1 | portobelo\n",
      "Processing Record587 of the Set 1 | kyshtovka\n",
      "Processing Record588 of the Set 1 | krasnoborsk\n",
      "Processing Record589 of the Set 1 | reconquista\n",
      "Processing Record590 of the Set 1 | tabatinga\n",
      "-----------------------------\n",
      "Data Retrieval Complete      \n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "#Create an empty list to hold weather data.\n",
    "city_data = []\n",
    "\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 the 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": 140,
   "id": "mysterious-intervention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "541"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(city_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "confirmed-conspiracy",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Olafsvik</td>\n",
       "      <td>64.8945</td>\n",
       "      <td>-23.7142</td>\n",
       "      <td>37.26</td>\n",
       "      <td>69</td>\n",
       "      <td>83</td>\n",
       "      <td>19.55</td>\n",
       "      <td>IS</td>\n",
       "      <td>2021-03-20 05:11:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bambous Virieux</td>\n",
       "      <td>-20.3428</td>\n",
       "      <td>57.7575</td>\n",
       "      <td>82.99</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>12.66</td>\n",
       "      <td>MU</td>\n",
       "      <td>2021-03-20 05:11:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>-54.8000</td>\n",
       "      <td>-68.3000</td>\n",
       "      <td>39.20</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>20.71</td>\n",
       "      <td>AR</td>\n",
       "      <td>2021-03-20 05:11:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hofn</td>\n",
       "      <td>64.2539</td>\n",
       "      <td>-15.2082</td>\n",
       "      <td>38.79</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>16.93</td>\n",
       "      <td>IS</td>\n",
       "      <td>2021-03-20 05:11:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akdepe</td>\n",
       "      <td>42.0551</td>\n",
       "      <td>59.3788</td>\n",
       "      <td>44.60</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>TM</td>\n",
       "      <td>2021-03-20 05:11:28</td>\n",
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       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Portobelo</td>\n",
       "      <td>9.5500</td>\n",
       "      <td>-79.6500</td>\n",
       "      <td>77.00</td>\n",
       "      <td>86</td>\n",
       "      <td>97</td>\n",
       "      <td>17.00</td>\n",
       "      <td>PA</td>\n",
       "      <td>2021-03-20 05:13:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Kyshtovka</td>\n",
       "      <td>56.5550</td>\n",
       "      <td>76.6271</td>\n",
       "      <td>21.76</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>14.76</td>\n",
       "      <td>RU</td>\n",
       "      <td>2021-03-20 05:13:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Krasnoborsk</td>\n",
       "      <td>61.5598</td>\n",
       "      <td>45.9340</td>\n",
       "      <td>9.00</td>\n",
       "      <td>95</td>\n",
       "      <td>97</td>\n",
       "      <td>5.08</td>\n",
       "      <td>RU</td>\n",
       "      <td>2021-03-20 05:13:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>Reconquista</td>\n",
       "      <td>-29.1500</td>\n",
       "      <td>-59.6500</td>\n",
       "      <td>68.63</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>2.64</td>\n",
       "      <td>AR</td>\n",
       "      <td>2021-03-20 05:13:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>Tabatinga</td>\n",
       "      <td>-4.2525</td>\n",
       "      <td>-69.9381</td>\n",
       "      <td>75.20</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>2.30</td>\n",
       "      <td>BR</td>\n",
       "      <td>2021-03-20 05:13:46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City      Lat      Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0           Olafsvik  64.8945 -23.7142     37.26        69          83   \n",
       "1    Bambous Virieux -20.3428  57.7575     82.99        74          40   \n",
       "2            Ushuaia -54.8000 -68.3000     39.20        93          90   \n",
       "3               Hofn  64.2539 -15.2082     38.79        55           2   \n",
       "4             Akdepe  42.0551  59.3788     44.60        61           0   \n",
       "..               ...      ...      ...       ...       ...         ...   \n",
       "536        Portobelo   9.5500 -79.6500     77.00        86          97   \n",
       "537        Kyshtovka  56.5550  76.6271     21.76        85         100   \n",
       "538      Krasnoborsk  61.5598  45.9340      9.00        95          97   \n",
       "539      Reconquista -29.1500 -59.6500     68.63        72           1   \n",
       "540        Tabatinga  -4.2525 -69.9381     75.20       100          40   \n",
       "\n",
       "     Wind Speed Country                 Date  \n",
       "0         19.55      IS  2021-03-20 05:11:26  \n",
       "1         12.66      MU  2021-03-20 05:11:27  \n",
       "2         20.71      AR  2021-03-20 05:11:27  \n",
       "3         16.93      IS  2021-03-20 05:11:27  \n",
       "4         11.50      TM  2021-03-20 05:11:28  \n",
       "..          ...     ...                  ...  \n",
       "536       17.00      PA  2021-03-20 05:13:45  \n",
       "537       14.76      RU  2021-03-20 05:13:45  \n",
       "538        5.08      RU  2021-03-20 05:13:45  \n",
       "539        2.64      AR  2021-03-20 05:13:45  \n",
       "540        2.30      BR  2021-03-20 05:13:46  \n",
       "\n",
       "[541 rows x 9 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert array of dictionaries to a Pandas Dataframe.\n",
    "city_data_df = pd.DataFrame(city_data)\n",
    "city_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "dietary-insert",
   "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>Olafsvik</td>\n",
       "      <td>IS</td>\n",
       "      <td>2021-03-20 05:11:26</td>\n",
       "      <td>64.8945</td>\n",
       "      <td>-23.7142</td>\n",
       "      <td>37.26</td>\n",
       "      <td>69</td>\n",
       "      <td>83</td>\n",
       "      <td>19.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bambous Virieux</td>\n",
       "      <td>MU</td>\n",
       "      <td>2021-03-20 05:11:27</td>\n",
       "      <td>-20.3428</td>\n",
       "      <td>57.7575</td>\n",
       "      <td>82.99</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>12.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>AR</td>\n",
       "      <td>2021-03-20 05:11:27</td>\n",
       "      <td>-54.8000</td>\n",
       "      <td>-68.3000</td>\n",
       "      <td>39.20</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>20.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hofn</td>\n",
       "      <td>IS</td>\n",
       "      <td>2021-03-20 05:11:27</td>\n",
       "      <td>64.2539</td>\n",
       "      <td>-15.2082</td>\n",
       "      <td>38.79</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akdepe</td>\n",
       "      <td>TM</td>\n",
       "      <td>2021-03-20 05:11:28</td>\n",
       "      <td>42.0551</td>\n",
       "      <td>59.3788</td>\n",
       "      <td>44.60</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Portobelo</td>\n",
       "      <td>PA</td>\n",
       "      <td>2021-03-20 05:13:45</td>\n",
       "      <td>9.5500</td>\n",
       "      <td>-79.6500</td>\n",
       "      <td>77.00</td>\n",
       "      <td>86</td>\n",
       "      <td>97</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Kyshtovka</td>\n",
       "      <td>RU</td>\n",
       "      <td>2021-03-20 05:13:45</td>\n",
       "      <td>56.5550</td>\n",
       "      <td>76.6271</td>\n",
       "      <td>21.76</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>14.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Krasnoborsk</td>\n",
       "      <td>RU</td>\n",
       "      <td>2021-03-20 05:13:45</td>\n",
       "      <td>61.5598</td>\n",
       "      <td>45.9340</td>\n",
       "      <td>9.00</td>\n",
       "      <td>95</td>\n",
       "      <td>97</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>Reconquista</td>\n",
       "      <td>AR</td>\n",
       "      <td>2021-03-20 05:13:45</td>\n",
       "      <td>-29.1500</td>\n",
       "      <td>-59.6500</td>\n",
       "      <td>68.63</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>Tabatinga</td>\n",
       "      <td>BR</td>\n",
       "      <td>2021-03-20 05:13:46</td>\n",
       "      <td>-4.2525</td>\n",
       "      <td>-69.9381</td>\n",
       "      <td>75.20</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>541 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City Country                 Date      Lat      Lng  Max Temp  \\\n",
       "0           Olafsvik      IS  2021-03-20 05:11:26  64.8945 -23.7142     37.26   \n",
       "1    Bambous Virieux      MU  2021-03-20 05:11:27 -20.3428  57.7575     82.99   \n",
       "2            Ushuaia      AR  2021-03-20 05:11:27 -54.8000 -68.3000     39.20   \n",
       "3               Hofn      IS  2021-03-20 05:11:27  64.2539 -15.2082     38.79   \n",
       "4             Akdepe      TM  2021-03-20 05:11:28  42.0551  59.3788     44.60   \n",
       "..               ...     ...                  ...      ...      ...       ...   \n",
       "536        Portobelo      PA  2021-03-20 05:13:45   9.5500 -79.6500     77.00   \n",
       "537        Kyshtovka      RU  2021-03-20 05:13:45  56.5550  76.6271     21.76   \n",
       "538      Krasnoborsk      RU  2021-03-20 05:13:45  61.5598  45.9340      9.00   \n",
       "539      Reconquista      AR  2021-03-20 05:13:45 -29.1500 -59.6500     68.63   \n",
       "540        Tabatinga      BR  2021-03-20 05:13:46  -4.2525 -69.9381     75.20   \n",
       "\n",
       "     Humidity  Cloudiness  Wind Speed  \n",
       "0          69          83       19.55  \n",
       "1          74          40       12.66  \n",
       "2          93          90       20.71  \n",
       "3          55           2       16.93  \n",
       "4          61           0       11.50  \n",
       "..        ...         ...         ...  \n",
       "536        86          97       17.00  \n",
       "537        85         100       14.76  \n",
       "538        95          97        5.08  \n",
       "539        72           1        2.64  \n",
       "540       100          40        2.30  \n",
       "\n",
       "[541 rows x 9 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reorder the columns \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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "aquatic-poster",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an output csv File.\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": 144,
   "id": "handy-shanghai",
   "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": 145,
   "id": "flying-encyclopedia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tXkL3I7v9bX8gbSkodIWSIX2ccqBviNOmTeWrzz9m15YNfPX5x4piOIY40JFdd9sro0SFQ8WxFq2k0EMO9G1T4djGKH2sf/l+QNK/b1+eevCeLhV4T0aCyihR4WBRlMNxyh233BSJQpl4FdacSFRK/ZI3eerBu462aAoHQCzy6PRfc/qVP9zH/e3TFqpqSTDR2NjIrX/8M72emxkLW1WUgcLBoiiH4xTFj3BicCC+I4gkNN752FOkTbyKpNwmihe+i3nYNPoMGYXXVaeErSocMhSfw3GM4kc4/jlQ31FtXX1Mmexd9QXJp/+ShD7DqWts6lHYqlJGRaGnKMpBQeEIE99BtzicVBVvaLe+O9+Rz+fD62ji2xfvpalkM0GNARkK4fP7ge4Vy4GEPysoKMpBQeEI0rGDzhhzHjs++w8VW9f0KLpIrVZRvPBdTCMvwpgzkGBLLe7mWgIBPzt27qKqeEOXikVJkFM4EBTloKBwBOnYQfc+/RIKx06jfO6zFM++DcPWufvxHQnMw6ahTc1Dn9GL5iWvE/Y40dgycbfUs+Oz/zBhbKSOWkcT0ubNmzo1YW3evEkxNSnsg+KQVohxrBVsO9TEn99dt9+Kx+M94ufXWZ2rwtOm49u2mF1bNux3/1AoRJ8ho6hrbMJTsR3zoNNxrZtLc3M1tsw8CsdOY+mqNfvUX6oq3kDz2k0sfPhKrHn96TXuPAC2ffESnmY7a3dV0WfSlXityYpTWwFQlINClAMt/na80fH8woaGw3Z+3SnZg81P0ev14Kqjf78BVPkcpI2dTij4c1QeO/379SUcClI8+7Z2IxS73U6LsJD6sxtoXvUB+qHnsPXLNwh5WzGPupDUPiMI22vY+c1bFE2+JGZqOhHuu8KPRzErKQAnvj264/mpdYbDcn77c/oebAZzRnpabH9jUgat5VsJOpvISE8DoHTZx7Q4nCxZtpxaj8But1NbV4/Gkoy5YDB4WjAlpmEYMgWhM2LI6IXOZMGY3Z/E8VdQsuIzpRaTAqAoB4UoJ3rBth97fm12+6yCPqTm9SG7d1G3dvn9Kdlp06by1IN3Ydg6t1Mfw/5CTa1Wa2x/tb0CxzcvkyidWMwJ7PnmfUpXzSPv3FuxFQzC42iivKoGj9eLWmvAW7ObhNRs+vfriymjFyLow2gwEAp4ATBk9sHTVKNk2isAillJIcqJXo7jx5xf2yhAnTcUj7EGy+iLUZtsNAh3lyapnsyd0VUGc09Ne/H7t5mwipe8QovDSf/zbiV30Eh0hChe9D6W0RchtUZayzbjXP0hRZMviTRir0GXYCUjPY3yqhqwJBOoL0NrsiqZ9gqAMnJQiHKiF2zreH4hv3e/59c2CqjfuYHE8VdiLhiC1paGW5vYpUnqYObO+DGmvfhEyESrheyiYQCkDxpD0eRL8G2YS917D9Cy4DlyBo8htWgELWXbkVvnY8KHtFeTm5GGv3QDdfOfo8CmUTLtFQBl5KAQ5UQvx9Hx/FS337rf82sbBXiaakjO7AMQMc/Y67H27t/pTHoHU/OqpLSUpNxIgpunqQZjciYFY39OdQ9Nex1HR+mDxqAz2zBsncsdt9wUOffZt9GrsJAX/v5Q7HqUlJbSv7CQmf999oS53woHj6IcFGKc6AXb4s+vJ/PstnW2xuRMvDW7MWb3JxTwotfpuhwNtLV/9wMPs35PKW1VVnuCJcFE8cJ3ST79lyRn9sFbs5viha/RJ9nUo/27U0xd3dsT+X4rHByKclBQ6IK2zjat3zAql88hFPU5JAk39d9+0u1owCP0nHLdw7FOuidhs0KljiW4IVRoU/MwD5uGqFjcI3lP9NGfwpFF8Tko9JjDWbTtWCwI1xZZlO0pxeipx7NoJoEF/yS1bDFXnnsmTz43cx95582bzzU33Iw991TqfBqcrtYehwXX1NVhSM3F3VCFs3oPYVcjfYaMwuF0HpDMSjFGhUOBohwUekRX8fsOh+Owtd1TBXGoFUt8e09G50io3rubhvLdvPj8v2hsbOSvTz7L2l1VJJ12ZUzehx95lNsffAJPEKxF4wgbbZRX1WC32/cbNjtv3nycbi8BtwNLVm9MqdmEpcBVs+eQRYwdiwpY4dhFUQ4KPSI+ksbpaqXcEaQhYxQ7d5dgy8zn4UcePSRtH2gC3oEolvjOceeu3V1uE99eQ/4kLr/pDrIK+jB87ARm3HYXjQVnkHv981hPu5qd33yE32UnbeJVPDPrZdImXkVCeh6+2j1odEY0lmRq6+r3G7H05HMzyT/tQpyrP8RbvRO1RkfQ1cSeL2YdkogxpSKrwoGiKAeFHlFSWkpTdQWLnrmDVc/cSu3//otQaRBqNcbJN/H4f1770QriYBLweqpYOnaOYYOt086xoxJsERasp1+HLyGLyszxNHrB1dJAyOclpEtAN2ASG99/Bq+jCafDgTWnL73GnUfL8rfwVO1ApdbiqNyx37DZktJSCk+bTtHkS3Cv+ZDK1/6Ab8NcTPi6NA0dyEjgRM+AVzj0KMpBAdh/RyNDQfYs+xjzqb8g46onsI2/AtfG+SAExvwhWCddxzOzXv5Rxz6Y3ICeKpYnn5uJOm8oxQveYMk/b8LraEKdN3SfzjG+vbayE4bsIlqbqtGkFpA06Vd49qzF53ahMtoQWj1Bv5f1b/8DSaR8RVuOgXvNh1S8/HsCq9/Zr2O47RqkDxrDmOsfZdKdL1J09jUMHjyk0+0PdCRwomfAKxx6lGglhR5l5jY027GMuwZdagEIgS41H/OwqSAh5HOjy+xLY1MjU86ZfsBVXduigpryhlK/Yz2t9RWoZJA/3Txjv/vGx/bXbf2WkhWf0VpXjlEDDz/yKEtXraGktJTKqmpU5u2knfVbkjP7oEpopXLzKpo89fu0V1W8Abc2EafLhToQItBcg9qcgpRh1LYMgo56NJYUPGXf41w7l6QzZ2DIHUSwcjOlqz4AwJSSTdDrJuR1kV/UP3aduyrId6D5EQc6veiJngGvcOg5qspBCHE7cD0ggU3AdYAJeAcoBEqBy6SUzUdJxJ8EPelovF4vqb2G4nXUI8NBhEaPPn8I0EjIbad121LUlpTIm2wnCqa7jnHatKl8t2YN/3xxDqaR08mZ2IeEkJM5cz9h1MiR3SqYeMVSuXkVltEXYzLZMPiaeOKFlykcO42iGbfhXDwfx8YvCbkdCJUaldaAZfTFeBa1HzlMGDuSJ174D8lnXI8urTfe6l00f/MqmuQc7EvfJNBUiQwF8JSsxbXxS8zDpmHMOwm9yUIgrZD+5/0/dr//d4LaBNImXMlJg0aAq44b73yAsN9Lr/Nv7VIBm0SQ9S/fBwj69S7kqYfu7/Lce1Kmo7Pr9GOS8xR+mhw15SCEyAF+BwySUnqEEO8ClwODgIVSyseFEH8G/gwoT/BhpCcdjcVqxd9QBtoEVKZEpN9DsL4M0T+RkKsJ1/cL6HvR7Z0qGGC/I5Olq9Zw8mV3tHuzbTGb91s6um3dNTfcjHH0LzAlppGRnkZtnY7kM66nfuPn9D79EvTphSSOv5LmNR+T0GdEZBRgsqHXtv8JLF21hsKx06jf+DmtDVVIgw1tei8C9SUkTfoNKpMVf/1e7CveIexxYu01FF2CjaDfg16nI7t3f3ZKGHHVXT+cS3IyZYOm4lw7lzqfhvLtO9DrdJiGX9Dh+lzL6Zf/0HF3xbx582lxOFn8xG9ISM+j17jzSB80ptuRgJIDoXCgHG2zkgYwCiECREYMVcBfgEnR9a8Ci1GUw2GlJyaH391wHU+88CLG4RdgKhqPr7GclsWvEB59C47VHyCDfvoMHduu3TYF05ORSVcKat27kVnKtmzbhj8QRKuCwYOHMGHsyJjJqFdhITqthlMmnIVKHXmkyyoqMeUOpGXRfwHQajSEDGY8FdvZ/Z8Z+O+7k+b/vUrv1BTgB5PPkmXLKbjmPAYMmwLA7j0lNH3zKpaR01FbklGbbGiTskGGsS99A39TFSqtDr+9AY1KsG7pAnx+H5jTY+dht9sJmFJw1ZSy+5U/AQKEQGtNxRRw7HN9/C47dS2tXHrNdZw6Zky7UZbD4eDOx54i79xbaZYmQm47xYvew1VfTqh8Y7cjgRM9A17h0CKklEfv4EL8HngM8AALpJRXCSFapJSJcds0SymTOtn3BuAGgIyMjBFvv/12bJ3L5cJsNh9u8Q8pR1Nmh8NBeVUNGnMyap2BkN9L0NVEXnYmVqs1tl11dTXVNXWgUiE0OtRGK1mJJmocXgItNSSk5qDWGWLbB7xu/PY6QqEQupQctBotarU6uq4Vb3MtWo0GvV5PMBhAZU5tt7/P1Uyg1YHelkYQFYRDhFqb0ehNBNxOdGYrenMSIb8Xd1M1hsQMtIZIqQmfz0c4FCTstmNIysAfCCBDQUKtzagTksg0q6lucqEK+UhNstFkd6IxJ+N1NKIyJYJK/cN5NFejScxEqLWRBVIiw0ECTZUIlQaNJTmyToYJtTYjwyHUCUnojQkA+AMBwgEfQUcDQghUBgsqkxUZ8BJyNaNRgSmjEICgtxWvsxm1ORkQ6NSi3b1oaGig0UvkPoVCBIORIoLS3Uyvgvx29+tY4Xj8PcJPQ+7JkyevlVKO7Gzd0TQrJQEXAL2AFuA9IcTVPd1fSjkLmAUwcuRIGV8npyd1c441jrbMPZ0i9OFHHuWJF14n+YzrMeXmcEv/Vv587/1cdMZYviv+mLSoTbuqeAM7PvtPxESzYz36YeeiMSeTl52Jr3I72756i6QR5zD41Mk4Snexd/7LMZt8m018w+uP0PfC23Fr3YSNNjQ6M56qWhqXvkzi2EvwbXyPMddHwmf3LFlGxXf/4eTL7ogef1fs+IFMNe6WBhpXvIv55LMw9sngj4MDPPzMSwz6+bVs+9u9DLz8LyTmZ7LjywXsXf8N5hEXoEsrJGivpel/L5B81m8xFgwl7PcQdrcgnfUEt/4Ptb2CQBg8QWImHoBtXz1J0ohzwJpOa10Frg1fYCoajy41H8fqNzD2H4ep/6l4S8qQ6z6IHn8A3774D0wjL0Kbqo3N7tZS1oLhs5f46vOPee75fzMvMCg2QgKis7/9X4+mGf2x9/1gONrP9o/lpy730TQr/QwokVLWAwghPgTGAbVCiCwpZbUQIguoO4oyHnGO1jzOPTU53H/fvQA8M2smdQ4H4Ucf4K4br+H+++79YW6BL0ppcTgpHHsevU+/BHNaXmxugZoaaPj6HczDplEwZHQs5p6pM2hd+iqub16KFawLhcKYM3vTXF2DwRoZURgy++C3N2DKHUjjl/9mxcy/0NpYjdqcRKC+gtalr1LtdNKrsJALb7yGpavW8M1rb6BN742p/zjMA8aDlAitCm9LLZjTaWluZv1b/0Cj0xESGiwnTca15WscjgZCbgcgaZr3LzSJWZgGnIbKZMOx6n2MBj3aMDgdDozJmQRaHWydOxuN3kTI1Uzl588iAV1qAdbRF6KxZaCxZWId9wvsy98mYeBEtMm5CK0m4mOYeBXuxhqsydl4m2uR4TBr129AJcC7YQMQmSZ001v/oq54HUFfKxp9AulFwynaT9RRZ88V7N8XpPDT5WgqhzJgrBDCRMSsdCawBmgFrgUej/7/5KhJeIQ5XuZxvv++e2NKIv4tJV7B9D1pGIWnTQcipaMBSpZ/St3erWi1Gk4aMgqbzRZr05rTlz11dVjSsimYdBn1O9bTXLad4nXLMWX1IRTwotEZ8dbsRmdLxVWykWAwgG74BVgy++KrKqZ50X+pa3Yw++kn2l2v1Lw+GMddCgnJyIAPoTMgg26EKZHdm75Dn5pH5qX3Uf3h4yQMPRtD3mC0eUNwbvwSf+U2LKMuRJucS6CpAse3H6DSGTAVjcddvBx1v1EkGBNx71iJ+eSzSEgvwFdfhmf9F6iFFhnwkXbJ/Qi1hmBLLYRDaFMLCLmaCDnqCTRVIANBbjz3TJaumgteB62716FKzkNtSUOt1uCt2EpLq4drfnktZ5wxmdpdm0ia8lsMuQPwVmyn9uvZnH7W+C7vV1fPlUkESZt4bY/DYRV+Why1JDgp5bfA+8A6ImGsKiJmoseBKUKIncCU6PefBMdaFuvB1OLpmNiWPmgMRWdfw8TTxnPqmDHgaj8gdFTuwhcIos4bSuXmbzGNupiMqTfj/H4BrdW78TXX4tq7iZblc0jrfwoNi14i4eQp6LOLIBxCbU4m9Yzf4EbP3Q883E5uGfDjXP0B0tlAwFGPt2wzIVcz6tQCXBvm0WvixYRa7YTcDowFQwm6mvBVbMW7Zy1Jk6/H1G8sKp0BY68RJP/sRtSmRBKKxpM0+dd46/biK9+Mbdwv0Kf3IhwMokvvReK4XxDyOFAZLXh2ryHQWIEMBQjaa/Hs/g4Z8NG6YyW+bd+QMeY85sxdyB233MR7r79E0/K38TeWE7DX4t6zFseq97Ccci7vfLYAu92JZdDpONd+SvXrf8K59lMsg05n3qJlXd6Lrp6rHbt2KYlxCl1yVKOVpJQPAA90WOwjMor4yXGgseuHk4cfeZT/e342YaEhIS2XYrvgsutuxGIycNLAgfs1d+0vrr6zdVoV1O9YT+L4KzBmRxLHEILGFe/ictSSYLagDvhxbKwn5KjHUDCMQGMFKq2OUONeXFu+wVO1m821Wvrnj6Zoxm04KnfhX7uetL7DcG2ah6tmL6GAj/DIO2ndtBBzZgEJCQkkZ2fSYEnCW7UDtSUFT8k6hEaHLrsI6XcjgwFCrU2ojBYCjeXIUAB9zkBavnkVGfSjNiejtqSAEBGHtQwjgxFHtGvDPKxjL0GdkETI3ULr919hLBqHZ8cKzIVDSBk2BdlrcKzIX8jnxrVhPmGvC5XRQtjvQZeaR6tGRygUxFO+Gdv4K9Bl9MZfuwf78rfwNzW2u/7xZqSa2joG5o6Lravb+i0lyz/F6/OzeeUiCoaMjo3ilMQ4hTaOdiirQhxHO4u1rUPZsm0bDc0OjIVDwdtKc9kOhN5E8sRfYc7Ix2uUMXOX0WjYZ/82u/aVUVNJV3H1HWPun3xuJivWbYrNugZgzB9MXko24UXP8uw/Ho+ZR4q/fB3pdyN0RoL1JTg2foV19EWYx16G9LmoXPcp5rQ80geNIf+0C6n4bgEnX3YHXkcTxQvfRWtLJ/+avxFodVC86H2KJl/CoDMvY+MnL2CbeC0hZwOaxCx85VvQ2NJRW1NR6Yz4KrYhwyF8ldvwN5RFo5PCBOrLUButCK0BodERdtsRKhW2Uy8FCU0L/oNQa9DY0jH1PxV93mD0mf1wrfuM8qoacjPSKSmNhP2mTPoVuoJhqI0WAHzVO2lZ+jq6xAwQKiyjLkKf1Q8AfVY/LKMuomXek+3uQ7wZKbByEcUL30UVjcBq8/+kFw6nee3nBEJh+gwZBa46JTFOIYaiHI4hjnQWa3xnbkkw0egJUTB1BuZe5+HZvAx38XKSJv+asJyLZfh5qBNseH1+EouGxGzT9/zptlhbbR1SUm4Taxe/z9IVzzKoqD/P/uPxfUYZXTnAV113I63lW0nIH0wo4CXobCJROkktLGxnHuk1/ny2LXwX04gLcKz/Elv0zTwc9GEuGIJGpWLj+8+gNRgxJmWg9rTg+uYlthTvxDbpOlQaHUlJyTQJDZbRF1Gy/FOKzr6GsKMe15qPCXkcGHqNpGXJqyRN/g1qSwq+qmLs376PeehUWrcvQ/o9mIefi0pvwrVhHgiBPmcAweZqHKs+IOz3oUnKRqXWIjRa0i99COn3oDJaUemMqM3JNH89G1/DXsoay+hfWEhJaSk5511OTUMzQqVBpTOgScom0FBO1sSLEAJURjNhnweVzkDY70VlNGM2GWPXsGPeRMGQ0QRCYXZ+/Q4avRHL6IvQmJPp1X8Qvpxcdn79DusXv8ipY8YoiXEKMRTlcAxxqLNYu4t86vh2ufI/d2IYcjbCloXfWYm/egdJk3+DxppC0FGHPncgYZ8b6W6mqrqa2iYvNd8s4dypP2PJ0mUsXbWGtIlX4XfZ2fnNRySedjXJydk0lm1ixm13kf7QYziiUURdmaSmTZvKn26ewT9ffJnWvJPx1+4h5GigQYS48OYZvPb2+zGzW5uTe+c3H+Gr2YXQGdFrVKA1EvI4kUYbUqUm55f/pLV8K+6vZ1Nvb0VrspJ80kRQB2lqaSQ50YpTBXV7t2LYOpfBgwcjT7mYqvK9OLYtJeRx4Fj7KSF7LaqEREx9x6DLHoDju4+xjpqOddQFhP0ehEqDa+N8mr+ejS61AG1GX/x1uwm77ZgKh6BLysZXVYyxYCio1EgZJtBUiTY5l6ZVHyF9rfzmll8D4A25SLaaabLXAoJAUwVqk5XGTUvQTTsZa9hBq1NFIBRCrVZjDTvJGzYsdh07midtNht9hoxi/eIXaQ0EKZj4SzIzMyOmJNsYUotGUDz7Nr76/OMf9ZwpnJgoyuEY41Blse4v8qnj22XA7SC511Bq6+rR63QEHXXosoto3fw1Yb+X1uIV6DN6QyhETX0jIU8rxrxBqCxpPPHEsxiljzFTbuO7lx+M+QyklDhdLbh8kkDmeAwnF7DDXsONdz7A9WvWsHTVGjZv3kQgDDqtJubL+APwzxfnYBk5HUvWD3WWLAmmdma39EFj0JltbHv7b/RJNZGYPwC73c7ukr2EPA40iVm02hsJaxMwjphO88YvMSRn46vdgxhciMaSjMtlJ8MoKThtPF99/nHsumUPvwBBiMbl72EqGo8+vTdqcxJBZyNhrwuh0UYKDwqBSmtAn9UPU9E46t65F9NJk3Gu/oCw34tn3acYEywkDjuLukWvojZa0ab3IthYhn3ZHIz9T8Wx6j2Sh01hztyFXHnumcyZ+ybG4RegVelorS3Fsep9tBoVuaPOIgy413xE4dQZWHMGRkeXX3JH3OiyM/MkrrpIIADgNcp2kWKKn0GhMxTlcAIRP1JocTjJGHNelyUZNm/exPApt8X2NSZnEmysJKgzkp+bQ4U5Cefaz/Du3Yh52FTcW75Gk5CIMJgJ2+twrf+c5JHnodIZIzWMPvsHjspdeJpqYj6DUMCLc9NCzCMvQJc3BLQ6SEjBHwjw+NP/ptfky/AYa7BE52ZuEO5YiGVndZZal74aywfwOprYvfh93HV7yclIY+/82TB1BpjT8dWV4FzzCSGPk8Z5z5Jw8hS0qQUEfW40g8+gaembhCf+AZXahKNyB6JiZcx0Fz96q9m8Cen34Pj2fWzjrsBgNBNsqcK59nOEWkvDx38j7HWhtqZi7D0Stb2GkNuBa+U7nHzRzWz74GnyTj6V+jUf4mmqAa8L+/I5EcVlScV6ys9RJyThSy9kwJQrkfZqlq6ay5Xnnsn/Pf8U/hBok3NIOuVsfHs3YE7LQ29LJ8WoxrC1a19OR/NkVfEG9nwxCxM+MjMyaIxeK6UAn0J3KMrhBKHjSGHd0gVUbv4ac1oeEHFCJo6/ipBQs8NVT5P3e5b840YGnXcD6YPG0GvceWz76nWSRpyDZUB/MoqGU7bsI1Kn/g5j3kl4EjNwrv4Qf0sN0u9Ga8uMHduUO5CAp5U1L95DIBRiz6zfok3MxNhnFP6WOvR5Q1Bp9ag0OlBr0eScRCAYoH7nBhLHX4kxuz9BvweHs5Fg4UR2ff4svUcKhN0ee8O15vSl2unk2X88zt0PPExxRX27qqclnz5L9RfPUbq3DDQ6LKecg+WUaQQayrCveAehMxL2ubGv/TwS3eRsoOLlf2LUwFOznm/XubZ9vv3BJ2DkL1BpDLSs+ZSWr2djsCYTdreiMlowD5uKIf/kyDGWv0XY60JvTWbwudejM9vo17sQT/lGis66GmtOX0qXfUzpqnkU/PwGHJgIB7zYl88htdcgtr/3f7gba8DroKG+jmHX3Ee5M4whNQ8hBE3AxvefwT3wTnbuKeWvnVRsnTdvPvc89Bg7du0iFApTted+dDodvpAk/7QLKTxtekQZfPpsu2RBxc+g0BmKcjhBaDMT+V12vnv5QRy1FahMVoq/fBWdJZnE8VegsqQRbLWjKxxG+rREGha9zLav3iIcDmGwJmNVB0jZ+zXFmz+hT2EhjgQj1tRM/M46EjIK0J/5G8I6M3XvPUjSmdfTtPwtwmf1oWn1QtRGK+rkTGyjLkaVkESotRnndx8BYQINZeiy+iFlGKFSEWyqBKFqN8qQoRBefwBj/hBUWh0eRxPl4ci52Ww2HJW7sFosPPncTHbuKSXtzBk/hGAmJ5N2xnXs+ugphEZLys9/j9qcigwF0Wf1R583mNati0ic+EtMvU7BV1+KRJBkNe+TMNfxekqfhrDRhrX/aDxVO3Cv+RChqsd4ynnoU/MItzah0ptIOHkKjqWvMeiCO9CZbZG38Yfuj7XV9pb/ixuvYemqVSxbsYKwSocM+qj1Okmd/GusWX0IlG2ieOkccraspHHXZgLOZlR6E2G/B+v4K9EmZ5Ny5ox9kiPnzZvPjXc+QLDoLDIn/J5gYyVNS17H46incOKF9D79EiCS6Nbr/FsxbJ3LulVLD/NTqXA8oyiH45j2sey1FCQNo3LNlySOvwJbWiGuimKa/jcTf6szWpKhDn1iBhqdkYSc/jQFvSSNvZBtHzzNqWNG79NRTjlnOl6jJDF/EDt27kJlTsFTswd1QiLa5Fxs4y4n2NpCy9q5GJKySJn0K4zZ/Wl1NCPUGkwnnYH3y3/TOO9fCI0OTVIWxvyTce9YhdCZMCZn4q3ZjTG7Pz5nMxpzMu6aUlRGG03L38Ey4nx2eVrxN1bg/PZ9jBoVCROuhY3b0eYPobyqJiZrvV+Dp9UFREwxKq2RgKOeUGsz3rLvsY69FFvfEficzaj1CWhMNtKTrPudL0G4WiPHsSSjz+hNfV05Ia+LoaeMIzE5ObZ9S1M+61e8TvOyOfu8jXf2hl9cVk2w6CwcmxZiGXkBmJIIOBopGDIaT80uytYvJflnM9CkFuIt3YBr/RdIvwcZDlEwZDQyP69dJvOTz81EDJpKQuEwNDoj2pwikk//JbX/m039jvUx5QDtq90e6TItCscPyjShxykdp4nUpeSxZ+mHWE/9Bcbs/mi0OkyZvUkceylBr4tA2SZUKlWsaqm3ZjcJqdkMPnUymRnpfPX5x/t0DhPGjuT7d59kzeIvcDodeGtLaf76RVQhHzVv3knz0jcI+73IYABPfRkNy9/FtWsNKq0BXXI20teKJjGDxAm/JPX8OzENmIhr89cE7TUQ8BG0ZFK/+DWcJRsJ+tz4GvZiX/ku5v5jCDibaJj3DHUfPIL7+y9BqNGOvIRyR4iwwYKreg8hbQJ7Sveyt6KKoNuBMXcgutR8POXbkCE/GmsqQqgIOesxZPQmHAwgQwFUQiDVWjZv3dZl5nebU9dms5GXnYnKY8dRvAKjBgYV9d8nw7vN4btrywbuuOUmnnxuZpeZ5U8+N5OCqTPoO2wsQXsduow+qBMS0WjU2Gw2fLV7sIyaji6tF2qdEW1KDrbxV+DesRydVovNZtsnk7mktBRsmai1P+SdGDL7IH1uWusr2h2/dNnHuNF3OsVoZ1nxbcuyCvqQmteH7N5FB5wxr3D8oYwcjlOefG4mxuEXxCaPMQ6YgGPRa2DNQEpJKOAl7G6h/ynj2LX+PYylS/AUTiRoSyXYVEXL8rcomnxJl5Eq8+bNZ9Yb7xEOhWj8+iXCAT8qnRGtLY3UM36NNq0Qx+71kaidoWdjGjCBsNtB03cfYg4EMOQPwb19KUmnXYEuZxBhjxNdSg6W0dNp+fq/ZA2diNdRjbu5irq5/4yWnSgkIX8I7srt2E67EkPeEAINeyORP44G9AUnE/A4sQybhmPV+1jHXorQ6Ai2tuBY/SGJI36OWq2jftnbIENok3MiM7f5vQRqd0N6bzQJNoJed6TGUXpvGvIndVq/qqNTV9qrI47rWc8DnWd4P/XgXT2qj9U2KlGpNVgzchH2KgxZ/fB6WgDwNlZhTisk7LEjnQ0ICdrU/EjJj2jJ8473rVdhITvsNYSSMtHoIjkP3prd6M2JhF0NtJRtj8latuwj+l6478RM9zz0GG6paSf7jNvuQqUzYCk6tdPggY7XTeHEQVEOxylbtm1Dl/dztEYbBqsBrS0N7br5eErXo8rqi16nIy87E2mvJjMjAyGC7F0wk0AohMZoxTr8HGqaHMhl7/LC3x/ap/27H3gYR0hLytm/wZDZB9fezdTMfQrzqAsxZPXD0+pEbU1FY0khWF+KGDgRtTkJy6iLcK79hHDQT6C5GlPeSYRQgRCozSkYE5JpESqyhpwWy1VoKdvOdy/chU6nw12+BdtpV0E001iX2RfbuCuon/sPgi3VqHQmTH1GgUqDffkc/PWl6FLykAEfjUveRGj1hP2RkhXS58ZgScRmNtG69mOSzphBUKUi5Gom1BrCNnwqbm0i6Z0Um+tJzkln66acM32/E/fEh5r2GncexYvewjL6IozWZFrKthMO+BA+J/qkdHytdqTfh3fvRkS0028p275PhNEdt9zEjXc+QGsojKnX0JjPwaoOcsPNM9plqltMBrKLhrW739acvqzftYtTrnukndJwo8cyaCr1279pFzzg9tg7vW4KJw6KcjhOaXV7CDsawZxGMOxDq9VhLjoV57cfMOCav2DNiXRAJZ8+i0pnoGDqrxk5LZ2dG1fRsup9HN99TDAlCxO+TtvfuaeU1PPvitU4svQaSp3OiMqUiLehnGAggBCqSPkKZyNGWyqelnqEzoC3YhuisRS9VoOrZD2GzH4YrclojWZaSjahT8unZMVnMeVgzemLUGvQZfTBteErpFqLkBLCAVBrUdsykH4vTV/+G+vYi9EkJKI2WSPRT8EgQq0hccI1mHOLqHjnQWwjpqNPyaF3r0JsNltM+fg2zKWpdAvGnIFofvZbrP0L8DaUY+3dv9P6VW05Jw8/8ijPzHqZ6Zdfg8VqZdrk06hptO9T/nrKOdNZsmw5tvJmeo0/H/ghSkxqdHhtWm5/8IlYLgMTryK1aASu+nL2fPYPhBBUqQQ6QjQtfhXz6AsxFgwl5HbiXPsZweZq3HV7MWz9ah9FNW3aVF6ASLTS4hdpm4f6rw89uk/nPeWc6VQVb8CtTcTn90emLA20AGKfQnz+VgfYMtsFD6i1Brz2+i6vm8KJgaIcjkPmzZuPx+UksPojVOOvRJdeiLN8G76yTZjCnnYx8OlJVhImRMoy79i5C0u/URiSMnGv+ZAx1z9KS9n2Lt7+JJqUnHZLdElZBJsrGfmz89mxcxduXwDp96C1RabEFCo10u8mpc/JFJ19Ddvf+z8cK9+DcZcj6YW3ZjeOle+hz+pPy5ZFLP779RiTMzFYklBpDXgri9Em5yKDPtSWNILORlQGM77K7WhTcjENmEDrxgW0LH4FXVI2lqJTCTZVYB46FV1aAWqNFul1YcgfjC7kbhcGq9HpKDr7GooXvIFp5EWo9AmEAl70Ol23SWA/TG50Eym5A2kt28K7C2aT0XcIQ2Y83c70UjB1BgW9zsPjaKJ40fvgbyVx8vVoU/NQeewk5veFiVexdNXcWC2p4i8ipUvScgspiOYeVBVvYPPbf8fx7Qc4Vr2PxpqKbegUsvMKMBoMXWYy9zSBcsLYkTzxwn+iEzYNxF2xjYqvXyQjJXGf5DldghXsNe2CB3py3RSOfxSH9BHiYMpfd+TJ52ZizumHpc8InKs/oObNu3B+9xGGrL6MGDWarz7/mF1bNvDV5x/jcDpjb4M+vx+11oAhs08kKYuuSzT379sXd8lGgn4PUkqCfg/6tEI8az+hpWw76SnJBOpLCDobMOWfhNfeQNDZgHvNx/Qafz7CloV25GUIIfBs+4b6jx+nfv5zJFgT8VRsI2nyb8i+5h/oh55D7a7vyRg9jYFTrkD6Wmle8B98VdshFMBbtgnnurmYisZjyj+J7EvuI+28P4FKhW/veoRKhSE1F43fgbehHK0lCRy1hELh2Lk4KnfRv29f6pe8SVq/YbQsn0PY7yFgr8cUaKF+yZuxt/+OPDPrZZLPuB5zwRBUag3arCKSzphBXfG6WPlrN3rEoKkk5g8gMzMTjTkZy+iLcDfXoUnOJuhsIiM9rd31njZtauw+paalUzB1Rqykdu6gkaRMuhaNyUrm+X8g56wZFI39GdlFw/D5fO2epVNGj2P42AkH9FwtXbWGwrHT8G38nKrX/4hv4+cUjp1GYlIK9UvejJi2QkFayrZjwofcOj923Vx7N/Xouikc/+x35CCESAfGA9lEJuXZDKyRUoa73VEhRldOyu+iJSRKSku56/Zb8Xi8PXrzKyktpc+kK9n5zUeYCobhLtuMv6mS5prdTPjDLe22tVosbF65CJGUQygUwu9qIeysx5gcSWLr6u3vsQfu4cY7H8CvVuFxNNG6bQnBlmpyM9NpXfoqDqeTbIsFnfpyWtd+iuebN1El2BBCsHXhuyT0PxXzwNOwr3yHtDN/g8aSTKC+jOpP/o+kSdeSkNELX1MVpsQ0kk69DNfuFZx09tUUaFNw1uzBuWkRzZXbUKk1pJx5PdbsvmSkp1FbV4/GYCLQsBdbUiIag44UjZ/cAScDUCcuY9tXb5I04hzCof5xzuJ7gIhibfLUE3bWE1jwLKkDB3JHN0lgToeDlNyBse9SSgy5Awh6XXz74r14mmrwuVpQ2xvYsXNXRAGrVGgTrIQDfgJlm/ZbErurUu3OVe8y5KRBsWUVW9cQHKDm4t/cStqEKzGfMozdq+ZiHjaNPkNG4XXV9chJXFJaStGM29qFt0amGl3Ms/94vJ0vZfbTT7S7bp5FM9FrNfu9bgrHP10qByHEZODPQDKwnsh0nQZgOtBHCPE+8E8ppeMIyHlcc89Dj+EpPINyZxj9nlIy0rMwDr+A/3v+KYZdcx9FU/oSNjT0OPqjV2EhXmsyGX2HsHf911hHXYQtrQCaK5gzdyGjRo5k2rSpzJs3n7pmB46Gz0meeA3axCyce9bj3vA5g86+ulPHZhttNuz4bOT8aDZy/ZI3Y5VWFy9ezJ2//y2PPf8ySWfcgCF3IP6aXTQve5Og20lCajZ52ZnU1tUjNTpCPjf9Th7TLkegOByictW7AGSkp+EPhlCPOh+jVkXQ50EgyEhPw2azxXwIAyZMaFcLqcVsxprTFz9qQo566hbM5JuFs8jMyMCSYOLWP/65XTz/4sWLqdxTvN97Z7FacVdsw1wwBCAyEqrYBio1ppEXkZzZh6aty3Gs+xytNQVr/1MJBby07l5HYUE+mtIlyPw8wuaELktVdFYLKSHkRCWDsSijtjm55cDfkT71FrRp+ZR++DdSJlyNPr2AusYm+vfr2Uxu3ZWG78o0pSiBnx7djRx+DsyQUpZ1XCGE0ADnEpmp7YPDJNsJwbx589lavIPcCb9HE7Vzl1fVIKSRsNDEfqBqnSE269v+fohtYZb1La1knv1btGn5BJ1N5A07lUZcXHPDzSRaLbQ4nOSdeys5hChZ8VHElKQ1Emgo5/u3/w+L1crvbriuy+O1Fegzn/7rHzqS5OR9OqBnZr1M6uRfIyzJEAqgzy7CNvZSGr94muFX/Kldp96alBTJEYhTDh07wkTpZM83L2PCR05GBo3FC5C9CjrtYOOjijZ8sA2H20vhxEipiLYOVT9wEkXR0hGdzUPRHb+74TqeeOFFiNrnA9XFNH09m4TewzFk9SMU8KJNycUy/Bzsa7/A2m8MgYZyXBvmkZFs4q9xvoWuSlV0Vqrds+4T/hQXZdTicJJSeDIyHCQgNIQcTQSdTZHpRKMOYujZ5FBHujS8wvFJlz4HKeWfOlMM0XVBKeXHUkpFMeyHJ5+biSm9gGBTFUIINDojGksyzurdJKTlttu2zR69P//EtGlTGVWUH5laMyxxN1SRYNDhq9xO5eZVaEf/gqIZT+MJQrM0oc8ZwJjrH+WUG/9Jyln/D60lmZ899B4DL/8Lc+Yu7NZOXVJaGvNZ2O12duzcRZk9wMpvv43t53Q4MPcaisESmeks0FiOxpxEOOBDZ7bF7Nf1S97kdzdct49du60jNGydS/Hs20gtW8zbM5+keu9u1q9ewQt/fyi2zrB1bqeROl99/jGDBg5k2DX30fv0S1CpNbi1iSRN+jVl369ky/Yd1Pk0GIdfcEDTrt5/373cdeM1eBbNpPT563D+73nCLXUkDPs5zuo9hJ2NqIXA2msogcZyKl/7A+41H1J05mU4W93tfAvxyXHxvoInn5vJleee2e4cIxMlrYlFRIV9HhwN1QiNDhkMoDYlojYn463dHXMQQ+dmq47PE8BTD97V5TWdN28+p4wehzk1G3NqFsPHTlAS3n6CdGdWekVK+avo52ullK8eMalOIH7wD7wF46/AkNmHQH0ZjlXv03fC+e22basftL8kqocfeZT3vvgaVBrqP3wMhIp6kw2tVkfypGsxJaahUmtISM8j5LZTW6fDZrNRW1dPyG0nIT0v5kzdnxmizQQhbFmUV9WgsSQjXHZ0KXnc/uAT/P2e27FYrbSWbUGT1R9hSkIA/qrtGI3GTquHjho5cr9v0w8/8ijX3HAzTocjNsLZ33wDHW33Hq8XdXofAs5mDKl5hAJemv1+6rZtO6B7eP9993L/fffGTFh1tlZ0QqJNjTibtVot/oYyEgsGMub6R4FILkJ8J91xMqTihe+28xXMmftm7Dp05qNyr92EJXcwmoRE7Cuexjb+ChKG/Az70jdRjTqfPkNGdWom7Mrf9dSDd3V6PefNm8+M2+7CEdKSev5daFJyqCnZyI13PsALKOalnxLdmZWGxn3+PaAohx9Bm3+gaPIllKz4kMamGrQmK7lJJkLlG2kpG4w1py8hv5f6JW9ilCHSTr92n+zV+A788X88RdiQSOKkK9ClFRJyt+BY9T7e2j1InSkWGRNJsHqP4ElnEO5diKNyB54tX1M0+VIgMhKo9Qj2LlvOlHOmd1pfp80E4SmciDZ/CIGGclpWvE3RGb9AZ44onGmTT+PdBbNJOuN6dDkD8FcW07ToJQzITtvcX8hlx/BRd8W2iGmHSEfd3bWOt6VLKfHX7EabmIEQAm/ZFhq//ZBAQwM7d+3ucQBAG23F+GwuO8WL3iZx/BVokrPxlm6geeV7FI6dRjgU7NRMEz9/xrcv3kvy6b9Em5rXqa+g41wbifkDMPYeScv6+QTOH4YM+GhaOAvp94G7mcyKxVRv+6zLRL2ObXX3QvDkczNxoyf59KtjOS4JfYbjV6uUhLefGN0pB3nEpDiBaetc0yZexajrHozrOB4GfsiyVd1+K089eBe/ufn3tHoE5Vu2otfpyEhPa2dHnjdvPn40pEz+NYb8IcigH6HWYB19EU0L/o2/vgzb0IheTx80Bld9ObXfvkPx9i9wNzahL5pArbTQsG07wbAk7G7BVjCIKmMhl113IxaTITbpTnwnfuk1vwaDFVNKJkWTLyF90BjCoSA+31ZqGu0Y0/JoWvB8ZPpKvQlTwcmoXHXc89BjXc5G1xWR8NGbYk5gc8EQOON6npk1s1vl0NGWHqjfS/PqD0kccibOnatpWv0x5mHT0CdlETbYDrj8Q3zZC4CSFR/Gymzf9fvfsnTVGopn39ZpJx0/qokllAnVPr6CefPms/Lb1bBxO6aUTHqNOw8Af+1uEk+7OlLw8LSrcKx8h4yhE+kja7odUXUcTbW9EJQsWYohKYOQ34ctOYXf3XAd9993LyWlpfhbvRji5vFWaw1gy6Rk8yc9uk4KJwbdKYdcIcQzgIj7HENK+bvDKtkJwv7KMLT9X7x4MR6Pl+bmFlq+eJ6wtxWNNZVaWzqysYxQS22siqbQ6tFl9UeGQqBSo05IQkqJUGtpWfkuLf37xRyNofKNvB6tBzTjtrtwVBZj6jMan1pP0NWEe+0nZPUfSuXmb0k86xaM1mS8Rtmu45w2bSqnjhmNd9C5+0S46Iv0lJSWYht/I5nphQghAJDhEOWv3M7WOjt9zrwCh7qZFes2seq6G/nTzTNinXxnU5l2DB+FyJwRdY7uA+M6Xuugw0lG75Px1myjbvVHJJ0xg4S8QWjDvgMKAGgjfmSSPmgM6YPG0FK2HcPWud0qrY77tiWUaVPz2vkKLAkmbn/wCVLOnIE2fwjBpiqKF72FCPpIOvUXYLAgBNgKB6NRQf2C53j+5Rd6fFy73U55VQ0+ez2a9F7YxlyK87sPEQVDeOKF12PbN+2qiiW8AQS8brx1e2msretyhKlw4tFdEtyfgLXAmrjP8X8KPSTeKdlZ9dM27n7gYVS2DMzDppHxi0cwDz8XT9lmRHpfTrn1BbyDzqV0bzkaWzqBxjKERotQaUClwV+3B7UlGYP0depofPK5mfQ6/1YGTrkC99qPqHnnXlzr5mIZeBrN5TtIHH8FCfmD8QdDJOYPIG3iVdz9wMMxR2ZDfR17589u50jeO382wWCAmto6vHV7CXjdsXPx1uwGrRFtQiKVm7/FNOpi8n79DIln3cI/X5wTq/YZX1m2rTqoXqfFXdHeL+Cu2IbFaj2ga/36rOcxBVooOutqdAmJWHoPRfrd+ySk9ZQ7brlpH2d6TxPB4vctGPtzmr55jdbd60hPSY61I1Rq0iZeRcGQ0YRa7WhT80gcdzmu+go05iQy0tMQ4RDehnKM1mSsJkOPItvajltTU0PQ1UTzyvewDJ2KsWAwtvFX4K0sJvmM63lm1svccctNmPDR9M1reCqL8bU6cOxag2vDFwy8+LZ2FVzbOJQJngrHDl2OHBQH9IHT2Vvwgbxh7dxTStr5d6FJzsHXakdltJI06Tocqz+K5AUkJyO0OvR5Q3CseAfruMvRZfTBX70Dx6r3UIUC/PH3v+30LTbeJJI+aAzfvngvphEXgs6IY93nJGf2oWX7Chzr57PgoyaEWgPhIKNO/3XMkRk/g5jVYiHs96IypzLw4tvY+uUbhBFYep9CuKWapiWvE3Q2YExKj80pDZCQP5jgyOmxiKHO7OFB13NUf/1D+Ki7YhtNX7/IXTded0D3I34kgdfRISGt5YDLP/SkGF9P9q0uLaVPsgnRwVdw6x//TFbOD2ar2rp63NW7kOEQ3toSXDodIqTHvXIO9XXlGDWRZ66748cfd++y5dgKBmHqN5aE/mMB0GX0JmCvazcyS0+y0lhcTNUHjxLyeVEbzag1Gvau+oJe485rN+LaXxXaefPmU1FZyfU336bMG3Gc0V200izgGSnl5k7WJQC/AHxSyjd/7MGFEInAi8BgIj6OXwPFwDtAIVAKXCalbP6xxzhS9KRU8/6J1DPSGsxojWacNaVocwcRXvTSD1tI8JV9j7H3KBzffkCwuQoZ9BN0NfPQfXd3ad7o6KyNnxbUmJRBw8oPaS3dQOJpV6LP7Eftuw+QMPRs3FobidHIpvgZxKacM52ECdei1hnIHDwOlUrNti9eouXrWeh1evr1LoTkAnaU17azX4cCXixZfShZ9AVAp5nB1cBdN17DM7NmUheNVrrrxuv2a7rpjDazWNv9aUtIawsAONDY/p7WL/ox+/Z6bmbsHtlsNnyV26neuZLk4VPxbltEyFmPr/8IdEOmYTInkdTDstltx51yzvTI6LMlEFHseiP+2j1obem4K7ah12mjz/C1nHF5JPFu60fPkTj0ZySNPA9vzW6KF71Fv9MvpDo64uro8Ba2LDyFE7n0ml/Tr3chjZ4Q99z1J4qidaiUMt/HD92Zlf4N3C+E2CaEeE8I8W8hxEtCiKXACsACvH+Qx/8XMF9KOYBIdNQ2IlnZC6WU/YCF0e/HPPE/krYw0bY3rJ7SsZ6RUKnxVWwlISUrto3OloY2oze+8u8JOevRJmWRMGAC1qSU/Tpr400iOrMtNi2o2l6Bfd1cbGMvxZA9AEIBQh4HxsJh1NU3xNqIN8PE5z9AxPk94fbnKcjLw9VQxfrVK/jrQ/ejkkFay7fG6jMFnU0khCJzF7cprHja3ubvv+9eGsp347PX01C++0cphnimTZvKUw/eRevSV/nm0StwN1ZhlJ1XpD1adLxHO79+B/OwaQyYciVFky/FufErVEYrwmglPzeH3EEjD+gZa2s/IWgnYK/Fs3cz9uVvYcgpounrF0lPT2/3DLu1iSSd8Rta925CqNQYs/uTOP4Kdi9+Pzbi6pgHU15VgzZ/CBis7LUHCRadBWrtj/5NKBw9ujMrbQAuE0KYgZFAFpHaStuklPuvO7AfhBBWYCLwq+jx/IBfCHEBMCm62avAYuCYT93sqj7OgZQ0jq9nhC0T2VSJY8W79Br381iIZILw4SxZh23c5aiTsgk1V+FY9S5/ueVGoGvTVmcmkfhpQXXWlEj9o8ZyhEaLLjGDUEs1qLUx+eLNMLGOPTuz0/UQ6ZD/dPMM/vniywRHTseS1YdE6cSz7hPuiL6xH+lMXbfUcMp1j2DJysR8+q+PqTfZjvfI31jLSUNGRcxgtjFoDUY0BjNqdWu7irM9ecbangtHfRX+/83G727FH5aE/D6M3gbuuvE6Xnv7/XYdvau1lZDHhbd6F7tn3YzGnIyp/6m4akq44+lItF38iLS2rj7yDDWUY0qJlvnuNZRgMBiT40B/EwpHDyHl0YlYFUIMA2YBW4mMGtYSyaeolFImxm3XLKVM6mT/G4AbADIyMka8/fbbsXUulwuz2Xw4xd+Hnbt2EzbYUOt+KMsQ8ntRee3069unmz0jtMnscDioravH5/Oh1+sxJ5hwtbpj3zPS02htbaWuoZFQKIRarSY9NYWsrCwcDkckUc2cjFpnIOT34nc0oFFBKBSO7W/txLG7cdNmVJY0VNEJZcK+VgKuFtQmKwkWGyG/l6CribzsTKxWa+xYGelptAS1+6yPp+M5xcvQ3bpDTfw9StSGaAmoD+geHWk6PlOtDVVkZaRR747cS+jZM9bZc9HZvWo7Hmot/kCAsN9DyONCbbKh0huRAT9BVxOE/PTt3avdc6AxJ+MPSUASsNdBOIyUEo01laxEE46QtsfyHiscjX7kUHAgck+ePHmtlHJkZ+uO5nwOGmA4cKuU8lshxL84ABOSlHIWEeXCyJEj5aRJk2LrFi9eTPz3I4HH4435HDq+BfdElh8rc/xIob6hEcvEa9GkgF6nQ8owzWWlONfNJfWsmzCH62HrG506UZcsXcYTTzwbV+O/gvovniE3IwWgU2dim7PxiaeePS6cjdfffBtFM55GpdZwQUYLn9QmRquRPsquLRuOtnj70PGZKl22lesn9uO1bQGyi4b1+Blr8zUk5v8wymspa8Hw2UvtciTajteW8Fj1/pOYTp6KMX8wKqMa6QdvWQXetR8zZnC/2L5tz+DKb78lrNITUmlIPnMGYb8Hx3ev8Ph9f+TVnWoSQq4D+k0cbY5GP3IoOFRyH03lUAFUSCm/jX5/n4hyqBVCZEkpq4UQWUSqwR7zHEwky4+lY0mG0rf/gTVrENoEK36/B5+9AV3OQOR3H6K2ZeJ0NmAZdHansf1tNv14J/Ddv7+xW1t/W3XTY7Fj7YzuqpEei3T2TGWmjSH1y3coXvJKj5+xnpo8OyY8Bl3N6LP6IgMegh4nUoawFJ6Me/UH7UKA453+F1zxS5J//gcMeYNBhkFKwm4Hm/59L5MmTDjsvwmFQ0ePlYMQIkFK2XqoDiylrBFClAshiqI+jDOJmJi2AtcCj0f/HzdpmQcTyfJj6FiSQZ+aT6ilhqDeRFhEkuO85ZvRWDNQ6Y1oSMVlD3YZ299WQ+hEJT6DmozMbkuWHwl6Evrc8ZlavHjxfmtMdeRAlGJ8wuPWL14l5GpEnz0AGfQj3S2E7LXoEqz0KszudF/CYQxZfSOZ+0Jg6z8aXXIQlVAdsNwKR5f9zgQnhBgnhNhKJJIIIcRQIcS/D9HxbwXeFEJ8DwwD/kpEKUwRQuwkUhL88UN0rBOO+EgRT1MNSaPOx77yHbzVOwmHQgQay7EvfwvzyVMAUOkMeOvLjtk35cNNW8SSYetcvPVlnVZ4PVJ0lQB4OBLI9pe81zGJbcLYkeydPxuS8nCsfA9v+WaCjnoCzTU0LH4FE759Ev/a2pBI6j56jFDlFgwGA2q1mnDAu08Co5I4d+zTk5HDU8DZwKcAUsqNQoiJh+Lg0YiozpwhZx6K9k90OpZk0CQkYjvlbJyr38dvr0dlsBByO1CbrJFEqvJtuFZ/wISbr4uV4jgefAWHkrY38cWLF3PLzb89anIcaEG8g6E7k2fH/Jyq4g3888VZeOor0KZ7CTsbaVnwPOGAH1QqjFo1s197aR/f0+0PPoE6byim7Fo8DVXUfPkfrFVnYioYQjDNxO9uuG6f7Q8uJ0jhcNMjs5KUsrytZk6U0OERR+FAiDeTFIz9OcULX8M8bBrDrrqH+tKtlMx7EXPfETi/fR9/YyXhgIcJIwYzZ+5C5Yd5lDkUoc8HQlcmz3glZbfbaREWrKdfh+ezf5J16X2EWu3kZWdis9mizvvb9mnnyedmos6L1OdKGn8FKSl5uMu20vT1iwS2/o/Maf/k9t/d3Okx4fAqRoUfz37NSkC5EGIcIIUQOiHEH4mamBQOHz0ZdsebSZqXzaFPsi5Svvnd+8mrW8VlZ41H27ATf1UxFr2K++64BbXefNDJegoHT3cJgIeDrp6neNNkW55CQt4gZDiMq3oPQW0Cu0tKqaqu3ke+tkmBFi1Zyvb5r+Gq2kXtglkEyreQ2Hc4KROuYMzo0WRlZbWTpWMCJRx4nSuFw09PRg43EclkziESYbQAuLnbPRS6pCdOyAMZdh+oE7zvScOO6BurQuccyak6u3ue4k2TPr8fg9WAq3wraksqjlXvYxt3BWi0lG1dh2rrfF75199ibc647S5agmpSz78LbWo+3rJNONd+Sv03rxMK+duVSYnneIsa+6nS7chBCKEGnpZSXiWlzJBSpkspr5ZSNh4h+U4oeuqEPBSlOLriSL+xHo8cCWdp/Kivq+lPDxXdPU/xzmqdRk1r2WYal87BOvoiLMPPpWXZG9R/8Ait33+Jp7m2nf/CjR7ruCsx5A5EpU/A2Gs4lpEXIAxm7Ovnx8qkdORgqtsqHDm6VQ5SyhCQJoTQHSF5Tmh62ukfzmH3j/lh/pQiS45kFFFPS7kfLN09T/FKqvWLv9Oy4Dm06b0Rag2ONZ8QaCxHl5RF0qjp+PyB2LOwZNly3PZGNKkFtBVZEBotmsRMwm4HgeYa9nwxiwljR+JwODqdw7qtztX6l+8/5upcKfTMrFQKLBdCfArE8hyklE8eLqFOVHrqhDycw+4DTdb7qUWWnIjO0v09T/GmyXnz5nPBpVfgLVlH4mlXYMgdTNBeQ9M3r6ImHHsWbOXNOB12/HUl6HMGgFAhAz6CzVUInYFwwEPKkIm8+O5n/DE5OaJs456fK889M1bnqs2sdiI/V8cjPXFIVwFzo9ta4v4UDpCemnQO97D7QN5YD6eJ61jkRHSWHsjzNG3aVPILCrCdehnGgmGojWa0ybmYh05FpdWjzhtK8YI3cFbvIdBSR8uyOfgqtxP2tuIpWYdzzaeE/V7Szvw12t5jEYOmEka1z/PzzKyXf1LP1fHIfkcOUsqHjoQgPwXuuOUmbrzzAcoGTQVbJthrkFvn88Lf21/io1GKoyuOdMjl0eZEdJb+mOcpb8BQ6hvrCbQVdywsYvt8JyXrlpI4/gpyp/WndvHrtG5aSP0nj0cmGkGADKNNzibg9eFqbUVlSY9MZxsXCW/N6YvT4ehUCZ+oz9XxyH6VgxBiEZGJeNohpTzjsEh0ghP2e3FvmIe/1YEuwYqJzm2tR7oUR1eciJ1ldxzJKKIjyYE8T70KC/GGXOScPASIlO/etWEVQmfAdupl6LOL8HlcmAefian3SJq+egGhUWMdcymGwqEEmiqwL3sLU/9TUelNCFVhux7EUbkLi9X6k3qujkd64nP4Y9xnA3AxEOxiW4VuaJvHOf4H0VK2nSefm8k9f7rt6AnWDSdqZ9kVx9Ko7WjR8Z7v3bQa14Z5aHR6VCYboYCPkNeF2pyM2pJC2Osg9bw/oU0vJOy2o0stwDb+Clq+eQVkGHnGTbRUbG/3/PzuhuuY9cazuNG3e1Ga/fQTR/v0FaL0xKy0tsOi5UKIbw6TPMc93eUxHI8mmp9iZ3msjNqOFh3veWNtHQMvvo09yz4j5GwElQYZ8CHMavw1u5EyjC67P0IVmXc85GpGaPUEmipJmXAVAsn3rz1Ea2srKrWavKwMIFLry9LBxHqoONj53BV6ZlZKjvuqAkYAmV1s/pNmf5E9x6uJ5qfeWf4Uib/nU86ZjteajKHvaFrWfILttCtBo8dfVYzzu49RaXT4q3agzx2I0JnQmhLxlm9GbUgAdxNSqEiY8CvSeg0l2FhJ/ZLX+duTzzLg8r+QO6ittNoQWvLzDklU2E8twu5w0ZNopbXAmuj/lcAfgN8cTqGOV/YX2aMk/ygcj7Q9t2GNnsQR52Jf+gZ1795H05fPY+4znIS+o2le9CLesk3IUBBv2Waav/4v6oRkmjcsQJWQhDZvCCqtEWNOEcmn/5KghFZ1+6DHQxUV9lOLsDtc9MTnMFBK6Y1fIITQHyZ5jmv2ZzbqzkSzePHiIyytgkLPmDZtKt+tWcNfn3yWYDCIxmCmz8SLSMwrYufX7xBsLCcnJYm6/z1Po9cLQoDfizYtFwwWhEaH0BnwBwLoAENmHwTgrN4NRUWx4zgqd2G1WA66YvDhMN/+FM1UPVEOK4hM5xnPyk6W/eTpidlIMdEoHG/MmzefOXMXMvDyv9AsTYTcdmpXf0AAFSGvC0uCiT59+/P80ze1N0UNOpfiBW8ggwEIqhAaHYFAANlUhtaSTPPyd1gDWLL6kBBy0rjsLcJ+LwkTrj0oc9ChNt/+VM1UXZqVhBCZQogRgFEIcYoQYnj0bxJgOlICHk8oZiOFE5E2M03uoJHk5+ZgSkzDMHAyFcs+JHfUWQy//b/7lBlpSybsNe48wh477pJ1hP0ePNU7aFj8CiGPi/S+JxPauZzKD/7Kro+eQuN3xqL5emIO6qqsy6H+Hf5UzVTdjRzOBn4F5ALxpTKcwN2HUaZjgh8zjPwpRvYonPjEm2lsNhs2m43icAin0ULv0y8B9i0z0vb2nj5oDEZrJYHvZ9O8cDbIMCIcwjzsbIzDp1KYngbA3k2rKfviOVxfvk6v8eeTPmgM0LU5qCdv8wdSIib+tz5h7Eg+/vxLrrniUs695EqCfh9Dcse12+dYjzI8FHSpHKSUrwKvCiEullJ+cARlOuoczDBSMRspnGjEm2nsdju7v/uaxnVfEnS1sOxfv6P/lKtIHzSmXYcZnyshh1gQag1ChpEIQqEQppPOIKDSs7e8AoQKbf4QNAmJ6IedS/Gi9wFIHzSmS3NQVzWw7n7gYZ58biabN28iEAadtnvLecffeumyj/nbf17FduplaFPySD3/Luq+fpktn72ISqWOKa3jIcrwYOlJnsMHQohzgJOIJMG1LX/4cAp2NDkRi68pKPxY2jp61/ALqCorwbF1CZaR56PPLsJfvYOtC94EQGe2tSvmB3D3Aw/jzbsS05CzMJyWRdjjonnxS7h3fAv9x0JYojbbCDZVkZCSjcacjGX0RZQs/xSd2dZlwmVnTmevo4niinr8+aPxGGuwjL4YtclGg3B3+XLX8bdev3MD1rGXoc8filCpMeYUkTr5V9Qv/C9r33wckKi1BixGHa/P/vehvMzHHPsNZRVCzAR+AdxKpELKpUDBYZbrqHIiFl9TUPixtJX1Lp/7LE3L38Y6/FwsvYahMdkwFp6Cadg5bPr433w7+26+WbqM1Lw+PPzIo0ybNpXUtHRMyZkkZPdFY0nFmD+YpEnX4fz+S0KuJkJBP776UlqWv0XfidPJy87EaE3Gvndrt3NcdFbEcvfi90mbcCX1OzeQOP5KzAVD0NrScGsTu/QRdPyte5pqUCdlg/qH92Z1Sj4hb2sk2S+rP6aTp+D0+Ln1j38+ocvY9yRaaZyU8mQhxPdSyoeEEP8EPjzcgh1NjtdkNQWFw8W0aVNJtFrwSi1JA8YiVGr8AT/BsAphTsbnaiH9nNuw9j4Fd8U2nnjhRSDS+ap1Bnz+JtSWRJBhDHmDEFLS+OXzhDxOVHojvcedGzPZSHs1BaeN56vPP+5Sns7Kurjr9nLSoBFsWPEOyZl9AFBrDXjt9Vh79+/UR9Dxt25MziTUXIXWmgZoCIVCeKp3oTbZ0CZnYxt7Cc2LX0VqDTj7TWXkpKknbPRST5Lg2nIc3EKIbCAA9Dp8Ih19lKgjBYV96VVYiC7BirdmN6FQiFAoDDJMsKUGXUo+alsmIb8XfXovzKMv4dF//IsWhxOfqxm9TodKhpDhEN7yzUghSD7rZjIue4iUyddRvX0dNZtX9Pi31tlMeoOK+oOrDmNyJt6a3QCEAl70Ol2XL3cdf+tp/YbhWPUuvrKNyHAIV/k27MvfIux3Yz75LPRZ/bCMOAe1yYY6OfeEjl7qiXL4TAiRCPwfsI7I5D9vHUaZjgrxYXFPPjeTK88984hM4aigcLxwxy03YcJH0zev4SrfRjjgw12yDseq97GMPB+1ORmvoxGvsxlDwVCEwULeubfidzkI7FlFyNVIsLqY5kWvYB78M1RGM0KoUNmy0A06g/VvPs62t//Gleee2aPfWtu8JL+8/BLWf7+JzVu38u2L96EyJNCyfA6uvZsI2OsxBVq6nb8iXslke0r5y/+7luzKJQQay6n7+G+Efa1Yx1yMIX8I4UAAtS0y251arY6105nZ+XifQbFbs5IQQgUslFK2AB8IIeYCBiml/VAJEJ2neg1QKaU8N1rL6R2gkIgiukxK2XyojtcZnUUnzZn7pqIQFBTimDZtKrOJOJk3ffw3UKnRJmYi1BpUJhuodYSDQXRJmfjr96KzpZE7aCQGWxWOjV+h37oIXyBIyN6AsfdwhFqDxpyCFCrUllRa1BoyxpzHnLkLGTVyZI9+ew8/8ihPvPA6yWfcREruQFwlG2lY9BI6nwPPopnotRpSBw7kjm5+y20RhvPmzefuBx7m7//6N6FQECEEKhlGpdUT9rYiw2GERkuwKTLbXXpaaqyNjiOT+D4la3Q6O7au5eLf3EpRbhp/fej+46Jf6VY5SCnDUR/DqdHvPuhiAoIfz++BbYA1+v3PRBTS40KIP0e/H9b60Ep0koJCz2jrSFPz+mCcfBPmgiG0bFtGy7I52MZeitAZ8NXvxb58Dr3Hnw+A1mAi0Wph15YNAKTm9SFkr8XYazhSqFGp1Pjq96JNzqV+5waKzrq6x7+9Z2a9TPIZETkArH1HoNLq8CyaSUP57h6f17x585lx2104QloSp/4eabCisiShSe+NLrMf7m1LAInQmXCueg+CXkwBO+FQWqdl7Nv6FGHLoryqBl3hMNITktm7/M3jxj/RE7PSAiHExUIIsf9NDwwhRC5wDvBi3OILgFejn18Fph/q47YRP1l6rUdgt/8wIFKikxQUukargqZF/6X05dup/2o2gYa91H/8V2rfvhfHklfpPf58eo2KzAcW8nvbvVVrVdCy4h38DeXIcBBf7W6a/jcTGQrStGcz6z56gaVLl/bIDON0ODDlDmy3zJQ7EKfDcUDn8+RzM3GjJ/n0XyIsqWht6QidEeupvyDYWIZ1xLk4V72LqnQVfadcgwh4WfPfe1lw/8WsefEejLL9O3NbFFRtXT0aSzIanRFDVl8Cbsdx458QUu4zyVv7DYRwAglACPAQCWeVUkprtzv25OBCvA/8jcic1H+MmpVapJSJcds0SymTOtn3BuAGgIyMjBFvv/12bJ3L5cJsNnd7bIfDQXlVDZqonVRlSgSVGp1Wi1qtJuT3ovLa6de3z8GeZo/oiczHIorcR5ZjRe5t27fj9YdQJSQidEYIBgi6miAcQKPRoLelo9YZCPm9JOtBr9dhtVpxOByU7C0jGAwiNDoIhyOF+oRAY06G6JwQQWcDGrWKwrxcrNauu5qNmzajsqSh0hljy8J+D2FnPUOHDO7x+WzespVASKJLzSUU8CM0OjKMUOsOE2iqRJ+ah7+xAlNqLj5PK2G3HbU1DcIhQq3NaI0J4PeQl52J1Wpl567dhA02/GFQaXQRuQJewq0tJKRm460vY/BJg378DeiGA3lGJk+evFZKObKzdT1JgrPsb5sfgxDiXKBOSrk2Wq/pgJBSzgJmAYwcOVJOmvRDE4sXLyb+e2e0FQZLzM+kbuteihe9gmX0RRityWQYZWyYuL92DhU9kflYRJH7yHIsyD1v3nz+/OBf0Y6+HFPvVFR6LQIt3qpy3Cvfok+qiZSUlFg5iltv/DXnn38+8+bN587HnsIYn0w3bBrO7xdgG3MJarMWtcFE2OeG1lbc333AiL7Z3Ya0Llm6jCeeeJbkM67HlDsQd8U2mr5+kbtuvOaArtNj//c0a3dVYT3takK6BNSmRP4wXMNfv9yFY9V7JJ96Kb4NczEOnETz2s+xnHQ6pgGZaHRmPFW1uNe8TdFZV2P47CW++vxjPB4vtz/4BJ7CiWjzhxBsqqJl+VsUTb4EnbkGw9YF3HLzbw/+ZnTCoXpGejLZjwCuAnpJKR8RQuQBWVLK1Qd57PHA+UKInxPJvLYKId4AaoUQWVLKaiFEFlB3kMfplPgMy7b46pLln1K3dysFp41XnNEKCp3Q5mgNCi0JaQUInZFwMIAAtGmF+Fwt1IZdrFu1NLZ9RWUlfU8aRovDScaY88gdNBJLTj+2NFXQvPgVwn4PQmdEBn3IgAqDJQlNajYti1+mpNTf7tgd652NGjmSjDfmUPHJ3wmHQiSYzdx18w3cf9+9+8jdXa20O265iRm33UXTN69hHXspgYAf6U+i9dv3ySgaTuM3L2PCR+PC2Qy8+DbKKiqp+fRJAvZa1JZU/LWl1HoErdu2AT/4E+556DG2LpiJKb2AfpMu6Tbr+1ijJ0lw/wbCwBnAI4ALeB4YdTAHllL+BfgLQHTk8Ecp5dVCiP8DrgUej/7/5GCO0xUdk1/SB41BZ7Zh2Dq32zcVBYWfMm2O1qYFbxBqqUFry0Co1KBSE6zdg8pkw+FujPkLbn/wCW6/9WaKZjzNuqULqNz8Nea0PABCzVXkXHgX9UveQPrdqM0p6BNsaI1mnCUbCXhc1HjtTDlnOhPGjmTO3IXtIgpvvPMBwn4vvc6/g4Fx81OPGtneStKTWmnxkVg75/+LUChI+OT7SPA10EfW8PzMJ5k2bSpTzplOVWMVru3LsY6/El1Gb3yVxTQvfonm7Svxur3Mmzc/5rhvi4J68rmZlCybc1wV4+yJQ3qMlPJmoslw0bBS3WGU6XFgihBiJzAl+v2QoyS6KSgcOG2O1qS8/thXvE3rrtUEWmrwlm+mZelriICH/NMu5J6HHuOaG27GnnsqQSlwulqx5vTHMvpiSlZ8RsmKz0gcfwV+VyPS76Hh86doXPBvWrYswVmykfr/zUaqdUi9hW837+Thx//B3tK9FC94g4bitSTmD0AMmorDG2TrF6+y8PHrWffRLJoM2dzz0GPtZO5pye1p06ayfvUKXA1VeJrrGHbyEKr37uarzz+OdeZ33HITZcs+IunUS9GmFSKDQdSWZJImXI1jw5fkn3Zhp+1+9fnHPPuPSFd26x//fFzkPfRk5BCI5iJIACFEGpGRxCFDSrkYWBz93AiceSjb7wylvLaCwoHTq7CQ0mUfU7trE7aTf4Zj89c4HPWEg170hgQGX3gzusx+fLfwLdQGMylF45CqIOVVNSQnWvH5/bTWlSPUWtT2Ouzr55N65vVok7Jw7d1E89I3aXY2orGmkXnO79AmZeEs/R7/2rlYR56PMTGN4kVv0VJeTP22dfgaq9Gp9NhOuwp1QhIty+ewuWZn7O0dDu3McNOmTcViMmBKTMXVXEUkFQw0iRmo1RoKT5tO8ezb9tnveJwwqCfK4RngIyBDCPEYcAlwb/e7HB8o5bUVFA6MO265icuuu5HEs24hIX8wusLhCKMV7NV4131M+qAxfL/sK0zpBWgMJny1exCDC9FYknG57CQJNy4NeD0OHGs+I3XydSTkRky71r4jMGjUVM//N1nn/A5jThGuhkpMvYajMiXhWP0Byb94AE/BMPau/5rECb9EZU5G+r3YV76N5ZRzsI25mKb5z7bLkzjUtdJOGjgQr1EiVGbCRhsanRFP1Q7MmQUHXGL8WM6l2q9ZSUr5JnAn8FegCpgupXzvcAumoKBw7NH25my0JuNtKEenFgivA11qLu7Gmoh5dukc+ky6hF7jzqNl+VuEA15Uai2Oyh141n3C67Oe587f/xZ/UyXSko7X68XvaSXobCJ/0AjCoRCalBxcu9bQuGAm1W/9Bcd3H+GvK8XtctC693vMIy6IZGWHQwitHvMpP8e1cT4qkw21RtsuR6k7E/KPKXHR1p4p0ELAXo9r7yZals8hrd+wdqbp+LZXfvstmNPbtXOs51L1ZOQAkWlB20xLxv1sq6CgcALT9uacmB+J07fb7ezdtBq8Dgxb51KUm4bBmhx7Sw63tlDx8l8wauCpWc8DMGfuQoxJGfgrtxFOykYgyUhPA1cdCWYzzSs/xFuzC+upl6HL7Iu/agfNi/6Lu3gF3toSLOOuQGPLQGgNyIAPKSX+uhKEx44hMY1ehdkxebsyIQNdmnoAKioruf7m2/aJbopvr27bNnyBIEYVZHtKY2U6OpqRmv5zJ7s3fUffYWOx2WzAsV/puSfzOdxPJFM5GUgFXhZCnBBmJQUFhQOn45u4tFdjLF3Ce69HYvz/+tD9lHz6LMufu4Mtn81GhkMQCqJVRaKBLr/uBsrLylCp1bg3fI5WBjAkpdOwezP1S97k/LMm49zyNeaRF6BL740M+FCbk0iafB3u4mUQ8iO9TtR6EwgQOgPS60KGgjhWvYcJ3z6BJZ05ha+54WbUeUP3cVTf89Bj3P7gE4QNNpJOu5K1u6q48MprGT52Qmxk0dZe5Z5iGsp37+O47ugE73fGL3BtmMfeTasJh4LsXLeM717/G4sXL47Nf3Gs0ZORwxXAKVJKL4AQ4nEi1VmPvbNRUFA47PQkmEOlM2AZNJWwJT3ikzDZEGlZbC0vwTL6FySk9ybsasKx9DXs37xCwOsGr4M7f/9bnnnhJUIBHyqDmWBLVVuLCL0Zb+0eZDBAy/K3CXlb0SRmEWypxrn2M8J+LyZvPc6gZPrl12CxWvndDdfFch46vs3Hh9bGz1m9ftcuTrnuEXyuZr7/+AUsYy4maVwe1faqHjuROzrB0weNoXzNAiq/eI6Kuc8AoLGmobKk0apO4LGnIxFOHfMzjiY9UQ6lRJLU2uZ10AM9r2iloKBwwtFdMMeTz82kYOoMEvMHsGPnLtSGBMxF46n/9iNSfn47uqx+kbITKg3WCb8ksGk+p1z4Wwxb5/LKG3NwhtToUguR4RDapByCznpkKECwuRptci6B5irC3lZcG+YR9jhRGS2Eva0IIWgOaLCOvRRLZm+ko54nXngNiHS6HZ3C1pz+aBKSKFnxWbu5oUFQs2UlgZyhJJ15A4a8kwgHvATVGrDZeuRE7ugE3/HlazRWlJB67h9oWvwqar0J22lXRkxmdSXYl7zOE08+c0wph57kOfiALUKIV4QQLwObAZcQ4hkhxDOHVzwFBYXjjfipN31+P0KocJdtRmh06HMGINQaUKlRm5NQWVJx1ZXFHLkVNfUkTb4ey+gLsS95Dc/eDchwiEBjBY41n0TnjUjCOvZS0i74M5nX/IO06X/BOvYSUKlRGa04vvuQ5sWv4LPXkzjhlzwz6+V95ALISE9DbbLRWlfezlHdr3chZd9+iRACfe5AEAKV1oDGkopLZY45kbtzZnc0vZWu/IKkSb/CkDuQsLsF66mXoU5IIthSg9poxXrqpfgCoSN3k3pAT0YOH0X/2lh8eERRUFA4EYh/a9brdEgZxt9UidqWgb+uBH1Gn2ixPTX+2t1IvztmlgqHQuiy+uEr30zY74mNDoRGhwwGQAgEAn12f4KOegiH8FUV4965KlISVKXCNv5K1KZE7MvnQP9TYxVaO77N22w2kip34tJA8ezb2jmqz7v0SoRGh792D/rMvoBEpTPiri9jYGHhfvMWOpreQj435vyTCIbCEA6hTkhEnZCE0GiRwQAyFEDKQ5o+dtD0pPDeq/vbRkFBQaGN+Pmd01PSCXudhH1u9L1H0LL0DWynXhYpO1FVjOPbD8jNTIt1pglmM77Kbbi+X0DSz25Cn9UP6ffQunUx7uLlNP9vFkKrw71zJZaTz6J1yyK8e9ZgHX0h6oQkwgEvjpXvRHIexl9J86L/YolWde1s3um20Np4M9G8efNRaTSg1tKydDa2067AkNkPb8VWXKs/4I6Xn+9R3kK8kkjN64OvegeajH4IjS5SyTU5FyDy3dUUS6g7VuhJ4b1zidRUKohuf8hKdisoKJx4xL81b922jdDvbyYhbyC+2j3o84ZgX/UuIUcDMhzEkNkPZ2sVw8dOwOF0kmIzU7n4ZWQoiC69MKIYti3BvWMltvFXIvQmwh4nLYtfIux2EKjdFZlkyGhFnZCIRmvAeuovcK7+kOSzbyXYXMXv7rw9Jtd3a9bwzKy/4XQ4Yg7rjorh9gefIPe0iyDow1AwFMfyt2ly1CODXi4/72ymTZvKrX/88wFlXf/uhut44oUXSZr0a0DgWP0hQqVBm96LQF0JjtUfojq0hScOmp6YlZ4GLgI2yf1N/qCgoKDAD2/NU86Zjiklm6LTzmXHV2/i3PAFADIsyTzjlyTZbJSt+JTGgjMYfOpkHJW7aHj5Ptw+P9Wv/B6NLZ1Qq4OkSdeiNtmQIT9qowXLyAtoWfwyyDBoDRAKAgKkRJuaT6C5Ck/pWtLTUtpFK82Zu5CBl/8lNnKYM/fNdlOSxo8I9OZygpWbCTrqUAW93H3HrbG2DjTrum2/Z2bNQga9aNN6YV/5LqHWZjTWNAzZ/emVbjpMd+PH0ZNxTDmwWVEMCgoKB0pJaSnhcIjiRe9jO/1X5Pzm36RP/wuaBBu52dnUbfya5InXIJJyUKk1+F12SMwh8+z/R84l95I6/hcgQwRbalCZEtEk5aCxZaDL6IPQ6BEaHYHmKlQJiYQ9TsK+Vrwl6wh7XbQue5ObrvtlTJaeFOCLd1rrTBYm3fpPptzzKrnZWe0iiX5M4c7777uXhvLdfPbumyS2lpE+6hyyLvgjacPPxmLfw2MP3HMY7sCPpycjhzuBL4QQ3xA3f7SU8snDJpWCgsIJQa/CQnzOZhLHX4Exuz9erxdtSh6J46+gZMVneJpqsKbkoPa5AChZ8RnJE68BnZH83Bxq63RYx1xM6/cLSDhpMkKtRWh0hN0OhEqNod94nKs/RGvLRGVOwlu+FdeGL7COvgiTLZU5cxfGRgY9KcDX0xHBwRTunDZtKi9E9y3Z/EkkA/vvDx1zNZZ6ohweIzKHg4HDW6pbQUHhBOOOW25iW3ExmuQcpJSoZAi/swFTThH1376HQFL57sOogl6aU7Nx1eyNKQubzYbNZmNDq4uWpkr8NbvQZfXHV7cHx+oPQEoSBozHU7wCx+oP8FXvRJuSi3X0hWgsKQiVmrRePziJO3b8dVu/ZefX7+BvrGXKOdO545ab2jmtQ6lpfL/sK+qXzqEoN61dpVc4uMKdx0PRz54oh2Qp5VmHXRIFBYUTjmnTplK6dy+Bsk0Ek3LQ63RkpqXQsHszodZm1Ak2bCPPw9z7FIKNlbR88ndcu9fRf/j4WBsqVz16WyqOVe8RcregsWViLByGV0CouRqNLY3U8/5EzRt/Iu3i+yPzWTtqCSPAnB7LS4jv+L2OJooXvot52DSyDGrWfvcF06/4JQa9Ho2A6tcfpjX7Dpwb5nHSeTMwWJOP+RLbh5qeKIf/CSHOklIuOOzSKCgonHDkZGdhLH2btPyrsOZE3t6NpUsY0K8P5tN/jbBlUVtXT1BnJHHwJFyrP0D26U3YnICjchdy63ySTDrM4y7GqbIQcttxrv6QjIEjad22gKC9Dm/5ZtTWNPyV21GZbKj0Cai0Bsq2rqV/1CQUbwpa/+1qUs6cgUWvpmL1PKzjriTBko6/Ygu+rQvRpvYCtQZPUzV7V31Br3HnxfwTPxXl0BOH9M3AfCGEVwjhEEI4hRCOwy2YgoLCiYHVauWpB+/CsHUuxbNvw7B1Lk89eBfOVjfWnL7YbDb69+vLkJMGMerCGVjUwXbbvvD3h5j99BPk1a2CRc8QXPZfrMFm+sgaXvnX38hLT6Tx86fwlm2iaeEsAk0VhANePKUbqFn8OhPG/jBtaFvBvMyMdAafOpm6jV+TOP4K1KmFqI0WtCl5JAy/APvWpSAlISkQmQMpXvQ+XkfTMV1i+1DTkyQ4y5EQREFB4cSlMxt7r+dmdur8HTx4yD7zuLeVpjAajQwa2L6E9vPARVdei0jri7+piub/vYDQ6tGl5mMbcGo7p3RbWy0OJ+uWLsBRW4EtrZBAMARBPwgV0pSI0GjRpuRhG38Vrq1fY+49nN2L32dE38LDdo2ONXqSBCeAq4BeUspHhBB5QJaUcvVhl05BQeGEpbOM5folb8ZKWLTRVamK79asYemqNazfsIFgKIxsqESblo8h/2R85Zsw9xtD0difIe3VMXNQW1sZY86jcvPXqExWXBXF6FLzCbntgCTsdqBNzgEkusw+aBMzcKx6l2DdXu54+uGjcq2OBj3xOfybyJzRZxDJlHYRUdajDqNcCgoKJzg9DQftrFRFU95Q/vniHHr//Ab0mVNI8bhwfvchutzB+Mo3YSwYSrBkDb68XEqWf4p971amnDOdhvo6jCMuw61NRO+R+DbMp+mrmdjGXoIhvQBPXRmtW77GfPJZyHAItSEBkZBIoKmSk4r6d+lvmDdvfiQ0tbR0n8mBDheH+5g9UQ5jpJTDhRDrAaSUzUIIJaRVQUHhoOlJSGdn+Qn1O9ZjGjkdtzYRtd6ILq0QlU6PfeV7JI67HOd3HxFqqqB40ftYRl9EwcRf4jVKtr/xONa0EqyDJ5F0ylSsgyfh2LwY59JX0SbacDa3YBh4OpqkLAQCtc5IoLoYnZouk9TmzZvPjNvuwo0ef6uXpl1VzLjtLmZz+CKbuiv8ZzQaDskxeuKQDggh2qYIRQiRBsdYERAFBYUTlrb8hHha6yuwZPXB5/ej1upBhtFl9CbkrEebVoC/sYJwOIRl9EVozMlkZmaSmD8A65hLaFn7BTWfPknpS7dR8+mThH2tmBISqNxTzN133EqgbCPC3YJE4tqxivr/zWL6WZO67OjvfuBhHCEt1tOuJvfaf2I97WocIS13P3D4TFA9yfY+WLpUDkKIV6IfnyFSsjtdCPEYsAz46yGTQEFBQaEbOitVoZJBEkJO9DodKhlChkP4q3ehtWUQqt2FDHgJuR0YrcnkZWdis9ko+e5rmld9iAwH0fc/ldTpf8E8/FwcW5cQ9nkAWLpqDYVjpxHY+hUhey2idBV9TzuPmkZ7l/Lt3FNK8sRrMGb3R6jUGLP7kzzxGnbuKT1s16Tj3BQQyfY+lNFU3ZmVTgaQUr4phFgLnEmkIut0KeW2QyaBgoKCQjd05pv4080zmDP3E0zDL6DZ7yfoasa+8j30WX1xLn2Ve++4maWr1uA1yphi2LP8U9SGhMgMbGmFBJ0NqIwWUk+/Ft+yl4CoCWvGbfQ+/RIsGS2Muf5RwqEgxbNv60ZCiSYlp92SyPfDV47uQAv//Ri6MyuZhBCnCCGGAyZgJbACMEaXHRRCiDwhxCIhxDYhxBYhxO+jy5OFEF8JIXZG/ycd7LEUFBSOD7qaXa0tP2HXlg189fnH3H/fvTz14F2kli0msOCfeL+ZhTXYzAibl7dnPsn9993bbsRRunIu1jGXEvI40GX0QaUzorGkIaVEbUlBr428J3dmwtpfp9u/b1/cJRsJ+j1IKQn6PbhLNtK/b98u9zlYfkzhvwOlu5FDDvBPIqOFjkgi0UsHQxD4g5RynRDCAqwVQnwF/ApYKKV8XAjxZ+DPwF3dtKOgoHACsL/Z1TqyP2d2/IjDW70La4INjS2dQP1edJl9EVodhMMEmisYPHAg0D68loxM9nzzPnsWvgVCYE7Non/fvjz2wD3tjvvYA/dw450P4FerwJYJ9ho0xQt47O8Pxc7rnoceY8euyPzU/XoX8teH7j8oZ3V3kV6LFy/+0e3G051y2CWlPFgF0CVSymqgOvrZKYTYRkQhXQBMim72KpFpSRXloKBwgtOT2dUOlDYFkprXh7CrCcuwqdhXvIVt3BVokrLxN5Xj/PZDJtz8q9j2bbK4839G6ZKPUCfnkHr6tWhScqgp2ciNdz7AC3Hbdldldd68+dx45wMEi84ic8LvCTZWsnvJ64ckmulwF+8TXU3TIIRYL6U85bAduf2xCoElwGCgTEqZGLeuWUq5j2lJCHEDcANARkbGiLfffju2zuVyYTabD7PUh5bjUWZQ5D7SHI9y91TmzVu2YkjL32e5t76MwScN+lHHdjgc1NbV09raSlioUFtSQYYJtdqRQT9CrUatT8CkDtOvb592+3m9Pipq69GYU1DpDAghkDKM9LkxhD3ttu+Knbt241UZEXpTbBrQcMBLyNVEgl7bozYOlAN5RiZPnrxWSjmys3XdKYcjUmxPCGEGvgEek1J+KIRo6YlyiGfkyJFyzZo1se+LFy9m0qRJh0niw8PxKDMoch9pjke5eyrzlHOm4x10bjsna0vZdgxb5+5TTqMnxJuprDl9WT37bprLigGBSm/CMmgC1sFnkJuRRvW797Nry4Z2+9141cX85fFnyLzq7wDotFpUKhWeulK0y1+Ibd8dWQV9cGiTCbkdaG3pJA47m4Tep1Dx6h/ItBl61MaBciDPiBCiS+XQpVnpCCkGLfAB8KaU8sPo4lohRJaUsloIkQXUHW45FBQUjj49LafRUzqaqcbe9He++ceNGE85D2Nmb/Q6HRnpaUh7dTuHc9t+Kk0YtdGKv74UfUYfAoEAGpUEe02PooLmzZuPGz2W0RdhyBmIv76UpuVv4W+qRJdgpVdh9o86ryNFT5LgDgvRmk3/BbZ1mFXuU+Da6OdrgU+OtGwKCgpHnmnTpnZavfXH2tU7ywUomnotztUfkGdR0bd3IdJevU+UT9t+erMN/K20LHkNX80ugj43rbvXIbfOZ8LYkZ1GVcXz5HMzI+U9rKmEAz50GX2wjr6I5jWfYsJ3SCOLDgc9KbxnkFJ6OyxLlVI2HOSxxwPXAJuEEBuiy+4GHgfeFUL8BigDLj3I4ygoKBwnHEona2e5AAZrMkW5abi+eYn1e0oBuU/Iadt+muxMBp97PcVfvkr9J49DOMiQQYOYftl5zJm7cL9RVZGyH8OwuFqprK7G62xA6IyowwFmP/3EMT8vRE9GDt8JIca2fRFCXEwk3+GgkFIuk1IKKeXJUsph0b8vpJSNUsozpZT9ov+bDvZYCgoKPz26ygW48Lyf4xF6CiZdhsaSyqbtOzjvsqvI7t2fU0aPY8u2bWx4/RF8rmZSi0Yw5OLf0adoEJ++8ybrVi1l6ao1PSpd0aZkbDYbgwYMYPiwofRJNXHauHHHvGKAnhXeuxJ4SQixGMgGUjj4HAcFBQWFw0pXuQBPPjcTdd5QSr/9Eqk1knbBX1AnZuIp3cCOjV8w6OzfEFTpCLQ6WPfUbxg8eEg781ZnhQCtOX0p/qK03bJD7UM50vRksp9N0ZpKrwNOYKKUsuKwS6agoKBwkHRmprr1j3/GoW4GXQKJE65Gn9WPcCCAqfcI1Bote1d9wZjrH8WYVNPpxEM9LV3R05Lkxyo98Tn8F+hDpNZSf+AzIcRzUsrnD7dwCgoKCoeaXoWFrFi3iZBQo8voDYCUYVRaA5rETBxNNQCodYZOC9kdyIjgcCeqHU564nPYDEyWUpZIKb8ExgIHXVtJQUFB4Whwxy03oZJBVAYz/po9EA4jABn0EWypwZicCUDI7+00ZPVQR1Udq/TErPRUh+924DeHTSIFBQWFw8i0af+/vbuPjqq+8zj+/iaQZ6I8g5AaRKREbVGC2tYH8GEllIpt7altVbqc6qErttbdllK7W7Tt2T6CrrSHKm5rq5VS1yoiuCIVquvBqlStAqlR0oAEEZGESAgmfPePe5MOzCTkYTJzJ3xe5+Qwc++dm8/EON/c3+93f79pfP36a/nhHXfx7rpfMvD8q8kZMpr3tr7E/hcfpezSq9hbs4Xm/ofaHW6ayVcEndWZZqVxwH8CZUDbEkPuflIv5hIR6TX/8e/fZnJ5Od/6zq1UPvoT3m9+nyzLIjenPzvW3E3ZhAmUnDe7zxeAjnRmtNIvge8Ai4CpwD+TeKZWEZGM0frBHzvFRmv/wU1z5yRtuc1M1Zk+h3x3X0swD9Pf3X0BGsoqIn1AKpbbzFSdKQ4HLJhO8DUzm2tmnwSG9XIuEZFed7TlNttbfOhY0JnicCPBSnBfASYRTHkxq6MXiIj0tmR8cMeu/FZXV8ffXqti41OPs7d+H7W1tVx74zw2vLSZmh07Wff0M3z6qtnc+t3vJfutRLIIdWa00nPhwwaC/gYRkbTq6qpx7Wm9Z6HhzJm86wW07K+j8dU/MursT1D71i521+8P1pm+6Mv0H3QCjdUv8qNf/BqApzY8z9bq4Oa2m+bO6XbndbLeS7K1WxzMbEVHL3T3y5IfR0Tk6JK1alzrsVdfdz2NzVA4rITxUz/DsLKzoeUFLG8Ax59/Df0HjqKlsY5+g0vIO/NyfnDbz5k0+9akfJj3xgp4ydDRlcNHgG3A/cCzaISSiEREZ+c36oyKimkcXzyAs6+9jazswz8SDzXW0++4ETTv30t20SCy++WQlV/M3vW/4mBDXVsndk8+zJP5XpKpoz6HEQRTaJ8G3A5cAux29/Xuvj4V4UREEontK2iVaH6jnpzPsvthOfk0bnuF7MJBZPXLBXea97xJ7pAStj7zSNuxsZ3YyfjePXkvydJucXD3Fnd/zN1nEUyZUQWsM7MbUpZORCSB9qbj7u4COonOl51l5LU0Uvd/93NwdzWHWt6naUcl9RuWU3xGBY3hHEwQfJgPKCzoVqdyst9LsnTYIW1mucDHgc8BpcB/AQ929BoRkd6W7BlPE52v9LzZ3L90MZ+9ZjZ163+FNzVSOHgkJ33sMvbsf5/+BcUcammm/s0qtq64g6ycPIrKZnS5HyKqs7d21CF9D0GT0mrgFnd/JWWpRESOItnzGx15vnXr1jFlyhR+9+v/jruLuv6xuzjxuH5U3nUjY0pLGTawmMLzZnW7UzmKczV1dOVwNfAewTTdXwmWfAaCjml39+JeziYiknaJ/rL/xY9uOezD/ORTJ5Jdv4dnl36bxj3BzK4nnjOd2m72Q0RBu8XB3Ttzg5yISJ93tL/sBxQWULl2OYMuuIZBI8ZyYOfrVK79NWMHFaQwZXKpAIiI9JBlZVM0sYL+Q0rAsug/pISiiRVYVna6o3WbioOISBcdOd1Fbe0Oxp4+mazGOg7s3kZWYx1jT59M/b596Y7abZ2ZsltEREKJprvY/8rf2PPXP3LKBVe0Hbe3Zkva71XoCV05iIh0QaJpvk+afh01T/8hcvcq9ERki4OZTTOzSjOrMrNvpjuPiAgknub7hPETKS7I61PrSkeyWcnMsoGfEUzZsR14zsxWuPum9CYTkWNd63QXrfc0QHCHdNmECax59KH0BUuyqF45nAVUufsb7n4QWAbMTHMmEZHITneRbObu6c4Qx8yuAKa5+5fC51cDZ7v73JhjrgOuAxg+fPikZcuWtb2+oaGBoqKi1IbuoUzMDMqdapmYOxMzQ8e56+vreWvX2zQ1NZGbm8vwYUMpLo7GfcFd+XlPnTr1BXcvT7jT3SP3BXwGWBrz/GrgjvaOnzRpksd68sknPdNkYmZ35U61TMydiZndD8+9atVqv3j6TB9b9mG/ePpMX7VqdfqCHUVXft7A897O52ok+xwI+hlKYp6PBnakKYuIHMOiulJbb4tqn8NzwDgzG2NmOcCVQIcr04mI9IZEQ1eHhpPq9WWRLA7u3gzMBf4X2Awsd/dX05tKRI5FiYau9mRxn0wR1WYl3H0VsCrdOUTk2Nbe0NVMvvu5MyJ55SAiEhXHytDVI0X2ykFEJAqiulJbb1NxEBE5iiit1LZ69WMsXLyErdVBobpp7pxeyabiICKSIVI5rFZ9DiIiGSKVw2pVHEREMkQqh9WqOIiIdMORq8GtXv1Yr3/P1mG1sXprWK2Kg4hIF7W2/R8om8H4a2/jQNkMvrbgh71eIFI5rFYd0iIiXRTb9g8E/4Zt/705qimVw2pVHEREumhrdTXjL4lv+69cVd3r3ztVw2rVrCQi0kWpbPtPFxUHEZEuOham1FBxEBHpooqKaSxaMI+8TSupvOtG8jat5PMzLmLh4iUpHb3Um9TnICLSDbFt/31xQSBdOYiI9FBfXBBIxUFEpIf64oJAKg4iIj3UF0cvqc9BRKSHzjunnJ8uXUhB+eUMGDmWwpZ9NG58mEUL5qU7WrepOIiI9MDq1Y/x25VrGXz6+bz10hreWX8vh95vYkhxfrqj9YialUREemDh4iXknzmTllFnMPDCaxl4wSz6Dx7N7rr3+PQ1X+LW734v3RG7RVcOIiI9sLW6mqwxAzh06BDvv1PDvpcf5/hzr8L65XDoQD0/XfobJpeXZ9yQVl05iIj0wJjSUvbVvs7+mr+yZ/09FJRdSFZeIZbdj9wTxlNQfvlhQ1rTMdV3d6g4iIj0wE1z51D/1H3s37Qey8qmYNxZZBUcB2YcOtDAgJFj24a0pmuq7+5QcRAR6YGKimmMHDqQoomXkj1gMAd3VpGVk0924UAONTZQ2LKvbUhrJt0sl5biYGY/NrMtZvaymf3BzI6P2TffzKrMrNLMLk1HPhGRjhzZNLS/sZEPnnkux004l7pnltH05hbMjJY922jc+HDbhHyZdLNcujqk1wDz3b3ZzH4IzAfmmVkZcCVwKnAC8ISZneLuLWnKKSJymETzKO174S807HyDyZdewa6SEqr+9BDv7qrBD+5n5Phxba9tvVmudZEgiO7Ncmm5cnD3x929OXy6ARgdPp4JLHP3JnffClQBZ6Ujo4hIIomahj5w7id5Y9Wd7K3ZQs6IceROmELucUP58JVfp+iC2W39Cpk01be5e3oDmD0C/M7d7zWzxcAGd7833Hc3sNrdH0jwuuuA6wCGDx8+admyZW37GhoaKCoqSkn+ZMnEzKDcqZaJuTMxM7Sf+5VXN5E39ANx2/e/VU1+fj4N772H9cshb8BA+uUVAtBy8ABZB+oYd/JY6uvreWvX2zQ1NZGbm8vwYUMpLi7u9dyJTJ069QV3L0+0r9ealczsCWBEgl03u/vD4TE3A83Afa0vS3B8wurl7ncCdwKUl5f7lClT2vatW7eO2OeZIBMzg3KnWibmzsTM0H7u7/84GGUU2zS0t2YLeZueYM2jD3HyqRMZeO7n+fuGZTTu2Un+oBGceM503n36t1S9+mLacndVrzUrufvF7n5agq/WwjALmAF8wf9x+bIdKIk5zWhgR29lFBHpqqM1DQ0oLKBy7XIKyj/FqGt+SkH5p6hcu5wBhQVHPXeU7oFIS4e0mU0D5gEXuPv+mF0rgN+a2UKCDulxwJ/TEFFEJKHWO50XLl5C5apqxpSWsmjBvLbtlpVN0cQK+g8pAcui/5ASiiZWYNvXdXjeqC0YlK7RSouBXGCNmUHQzzDH3V81s+XAJoLmpus1UklEoiZ2Fbgj1e/bx9jTJ7PrnT0cqHub3Jwcxp4+mdrNj3R4ztiObiD4N7wH4pgpDu5+cgf7vg98P4VxRESSZkxpKQcadnHKuMP7JI42XHVrdTXjL4m/B6JyVXUvpDw63SEtIpJE3R2uGrUFg1QcRESSqKJiGosWzCNv00oq77qRvE0rD+uTaE/U7oHQlN0iIknWUZ9ER6+B9ju6U03FQUQkIrpTVHqLmpVERCSOioOIiMRRcRARkTgqDiIiEkfFQURE4qR9yu5kMLO3gb/HbBoC7E5TnO7KxMyg3KmWibkzMTMcG7lPdPehiXb0ieJwJDN7vr05yqMqEzODcqdaJubOxMyg3GpWEhGROCoOIiISp68WhzvTHaAbMjEzKHeqZWLuTMwMx3juPtnnICIiPdNXrxxERKQHVBxERCROnyoOZnaDmVWa2atm9qOY7fPNrCrcd2k6M7bHzP7NzNzMhsRsi2xuM/uxmW0xs5fN7A9mdnzMvijnnhbmqjKzb6Y7T3vMrMTMnjSzzeHv81fD7YPMbI2ZvRb+OzDdWRMxs2wz+4uZrQyfRz63mR1vZg+Ev9ebzewjUc9tZl8Lfz9eMbP7zSwvWZn7THEws6nATOBD7n4q8JNwexlwJXAqMA34uZllpy1oAmZWAlwC1MRsi3ruNcBp7v4h4G/AfIh27jDHz4AKoAz4XJg3ipqBf3X3CcA5wPVh1m8Ca919HLA2fB5FXwU2xzzPhNy3A4+5+weBDxPkj2xuMxsFfAUod/fTgGyC//eSkrnPFAfgy8AP3L0JwN13hdtnAsvcvcndtwJVwFlpytieRcA3gNjRAZHO7e6Pu3tz+HQDMDp8HOXcZwFV7v6Gux8ElhHkjRx3r3X3jeHjfQQfVKMI8t4THnYPcHlaAnbAzEYDHweWxmyOdG4zKwbOB+4GcPeD7r6XiOcmWJMn38z6AQXADpKUuS8Vh1OA88zsWTNbb2aTw+2jgG0xx20Pt0WCmV0GvOnuLx2xK9K5jzAbWB0+jnLuKGdrl5mVAmcAzwLD3b0WggICDEtjtPbcRvDHzqGYbVHPfRLwNvDLsDlsqZkVEuHc7v4mQQtJDVAL1Ln74yQpc0atBGdmTwAjEuy6meC9DCS4BJ8MLDezkwBLcHxKx+8eJfe3gH9K9LIE2yKT290fDo+5maAJ5L7WlyU4PirjpaOcLSEzKwL+B7jR3evNEr2F6DCzGcAud3/BzKakOU5X9APOBG5w92fN7HYi1ISUSNiXMBMYA+wFfm9mVyXr/BlVHNz94vb2mdmXgQc9uHHjz2Z2iGACqu1AScyhowkuvVKmvdxmdjrBf9iXwv/pRwMbzewsIpy7lZnNAmYAF/k/bphJe+4ORDlbHDPrT1AY7nP3B8PNb5nZSHevNbORwK72z5AWHwMuM7PpQB5QbGb3Ev3c24Ht7v5s+PwBguIQ5dwXA1vd/W0AM3sQ+ChJytyXmpUeAi4EMLNTgByCmQlXAFeaWa6ZjQHGAX9OV8hY7v5Xdx/m7qXuXkrwC3qmu+8kwrkhGPUDzAMuc/f9MbuinPs5YJyZjTGzHILOuxVpzpSQBX8t3A1sdveFMbtWALPCx7OAh1OdrSPuPt/dR4e/z1cCf3T3q4h+7p3ANjMbH266CNhEtHPXAOeYWUH4+3IRQd9UcjK7e5/4IigG9wKvABuBC2P23Qy8DlQCFenO2sF7qAaGZEJugo7mbcCL4deSDMk9nWB01esEzWNpz9ROznMJmrxejvkZTwcGE4xAeS38d1C6s3bwHqYAK8PHkc8NTASeD3/mDxE0U0c6N3ALsCX83PsNkJuszJo+Q0RE4vSlZiUREUkSFQcREYmj4iAiInFUHEREJI6Kg4iIxFFxEDmCmTV04dgpZvbRmOdzzOya8PEXzeyEbnz/aouZnVckHTLqDmmRCJoCNADPALj7kph9XyQYfx7Zu7BF2qPiINIJZvYJ4NsEN1u+A3wByAfmAC3hnDY3ENyl2kBwQ2M5cJ+ZNQIfIbh7tdzdd5tZOfATd59iZoOB+4GhBHeTW8z3vYpgWuYcgon3/sXdW3r/HcuxTs1KIp3zNHCOu59BMNX3N9y9GlgCLHL3ie7+VOvB7v4Awd22Xwj3NXZw7u8AT4fnXgF8AMDMJgCfBT7m7hOBFoKiJNLrdOUg0jmjgd+FE5nlAFuTeO7zgU8BuPujZvZuuP0iYBLwXDgxYz7RmvhN+jAVB5HOuQNY6O4rwqmoF3TjHM3842o974h9ieaxMeAed5/fje8l0iNqVhLpnOOAN8PHs2K27wMGtPOaI/dVE1wJAHw6ZvufCJuLzKyCYMI3CCZNu8LMhoX7BpnZid3ML9IlKg4i8QrMbHvM100EVwq/N7OnCKaCb/UI8Ekze9HMzjviPL8CloT78glm0Lw9PEdsp/ItwPlmtpFg4acaAHffRNAJ/riZvUywbvfIZL9ZkUQ0K6uIiMTRlYOIiMRRcRARkTgqDiIiEkfFQURE4qg4iIhIHBUHERGJo+IgIiJx/h+xxqJW38+OWwAAAABJRU5ErkJggg==\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": 146,
   "id": "infrared-wings",
   "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": 147,
   "id": "exact-batch",
   "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": 148,
   "id": "accomplished-corporation",
   "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": 181,
   "id": "golden-insulation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aXhWpU5XZ4IS2WFZ8yexprSiNCTflENkooAmSyOThVsLbqVy10zGxmpCiMXHW8gjFADB7n94UD59B17UrmfW41anu+tceBnmYZ08fxLH7nkdtDOU5vBbY87KfW1n0gN8mfAUScJADUbO+7fYNf3/7dGvHnGVrVZkoTZom7ZCGxAu2OTlJJ7t0VYvFseomX7SidNEm4+XtiikePoOe1z3DD7tacnZ+pZxvJ55OxdN/Yuetm728BZ6d7V72C0zGbu9PLI2Nou1r9/4+M+97LeCnNHmavHKItYNaOPFE6sRyjJN8Y6YvjarUnCbj8EinLYW7suCtj6G6Gk4/HYDuP37FksmD+HzS2XSs+l/dvuFhrrFEC0ULAQ6cK9r7E0uJ8Gj7einJHsv3QVEaC03erJSMXgSlJUV1E9eoiiXc/PxibipfRK4I5x7WkTtKrSgbL+Yhr13n7GpABWcLgzUZ2xUXHNCziDnL1lJZVU2uSNDk15XSigq6j5nFZW8+znUfPc9O26p5/+HLABh43ngWdj6I1gU+qrbUxGxyCewXHq20oTryXMHvaThO70uhTThxNBNVMk1iitKYaPLKIdrkEQvh5atrjal73atzG0+27/B9vHZ7CxA8iYVPxm4hr8HyjDn9QMq2DeHuoy7k9KVz+PuMewB4/lmrnfedZ5Vx67S7YnlrQmRK1H5f1q+rraN90+/b6/puB+9rpyADqx2vbV810U5pajT5aKVYegZEw6l8da4If2jVIqoT2imaJ1xB5PtyaeHLse29kGjkUHDY7U3+RkQAPSq/5KVnykJ3/vOf4a67qFi0xlEBpSpKqPvYN21XT3b37yaHlzyVeL8PipLtaLSSC25P17HiVL661hjX8tOBp12nJ9hAradg+QDXJ+JoRDOnlZYUheQbfFa0H8XDZ1C04Wdee+pGWlX/BnffDXffTeGePfn1jJEYX4u6Fcj8VetCEtncIoriITxz3O2+3FYrdp+/RispiioHIDmmDnBu4uO2cgDqJk23451WA8kOwQ02n9iZZNa1bc/EZz/k9f98zYNTb+ew1Us5ZsUClt17Fqt3accZF9zD2p3aMPXjHyLuJdwnkgjJNAcm6/NXlMZEk49WSiZO5avPPayja6ROYNJ0W3nYkUgVUi/htE5RQ3OWreXXnOYMGjyBvcpe4ZnuJwHQYeNaPn3gQlZO6E+3/31je91kOXa1zpKipBZdOSSRQFRS4Kk5OFqpYmElLXw5jrbtNVXVFLkkx0XDrdaS3Xg0c1r4cZOCaigF+yJ25OQyqt+1jOp3LUMWvMrYtx8GqOt9fcUZt/LmvvV9p70+2UfzV8RjDqxYWMmY6UvrfBWtC3yMPvUAXTUoig1N3iGdDrw4PQPZ1PE4x52c6gN6FjkWsIvnfIHj3PpGAxz3/SIenToqZGzC0UN48o+DGDfg4KiTcTKDBILPWTZtMTU7QuX25QoTzzpEFYTSJMnaHtJNhWiJVl6Tv2I5f3VNLVM//iGuBL9oiYFuiqGoMJ/+ZReBMbzzwuy68eHvPsWXd55M6b0joMa9T3eiiYlO5wxXDAA1tVHKlytKE0XNSmnAzc4eXn8pHueo0/mdJnG7/YPNOE5T/xp/ZJVT7kV4GGnfAX2skrC//AJHHw1ffAHPPGP9dO8O77wDbdp4vp9AFzq3ct9OpiW3z0AT3BQlEl05pIFoHecSNWk4nT+8pajT/uH1hdyuM3HWcseKqY7O4LZtYelS2LoVBg60xhYtgl13hbw8+OorT/cTaFNqgPVbaup8BwEl6FYHyc3XUVjgc61RpShNEVUOaSDVkTVO5z/3sI6eruulvlDgOLe+21GVXF4elJfDjh0wZow1VlMDXbuCCMye7Xg/XjPFncxPZf264suJVJa5OcKm37fHVWgvWuFDRWnIqHJIA4m00Uzk/HeUHuTpum5mlUAb1Ba+HIaWLyLHYTUCeJ8gRWD0aMvkVF5eP963L4hQ+unMCLnjLSESmMCHli9ipxbNyPfVf+VbF/jIy5UIX4QX/0Y81XxVmSgNiYxGK4lIIfAocCDWg+ElwHKgHCgGVgIDjTHr3c6T7dFK2Y5bKQ27CCo34o4q+vRT6N07dOyGG2DSJMjJiakJUXAJELeop/ASIcEENxayw0me1gU+CvKaRa1lFS6LomSCbI5W+jvwhjGmG3AI8CUwAnjHGNMFeMf/OiXok5yFm9nLyeTk5M+IO6ro0EOtlcQPP8Af/mCN/eMfkJsLxx3HiKMiTWR2xNL3wU3OaPkYTqut9VtqbFcTqYjAUpRUkjHlICK7AEcBjwEYY7YZY6qA04Gn/Ls9BZSm4vqJNvlpTLiZvZwmwR3GRPSFCJBQ9E+HDvDjj7Bpk2VmAnjnHU49cl/emzyYfbdVIVhP6IX5VonugKLy2vehsqqaPUfMdF2J9OnWzlVMr8l8AQWQjNLwipJOMhnKuhewFnhCRA4BFgA3ArsbY34EMMb8KCK72R0sIlcAVwB06tQp5ou7Pck1xWW+UwhttBpGyapvFEHLllRMfIpbXlxM2RsPcfGCV2m3aR1vTjrf2v7JJ9ZqwwW3ctzRjKkvLqikV+c2jt+FWMxtlVXVtC7w2VbR1VLgSraSSbNSM6AH8E9jTAmwmRhMSMaYR4wxvYwxvdq1c3/Ks0Of5LzhZnJKdRTWxFnL2bLdMPa4KykePoNb+l1bv7F3b8ux/fzzMcnulWgmH7vVVmAlY8eGLTUJddFTlHSTSeWwGlhtjPnY//oFLGXxk4i0B/D//jkVF3d6YtMnuVDcTE6pjsIKV9TPdj+J4uEzGDzojvrBQYMsJTF2rOWzcJE90euHE174cMxpBzheZwfQMq9Zyt4rRUk2mY5Weh+4zBizXETGAC39m341xowXkRFAG2PMMLfzxBOt1NSiR1LZeCdVRGtIxFdfwYEHhpbjGDgQ/v1vK6fC4/mc8No4KZjiETMdt0WLgFKUdJPN0UrXA1NE5L9Ad+BvwHjgeBH5Gjje/zrppPqpN5toqM73qGarffeFbdvg11+tchxgmZmaN2fF7sW8NntJ1PM5EYvJJzjqzSmKC2KrSKtRdEqm0aqsTYCoT+BZjNcVT8XCSm6btpC/vHIPZ3wxN2Tb2y/MYfTXO1hTVU2rfB8bf6/BpgZfnUkolpWVl4q7AL4cYeLZ0au/NrUVrZJZ3FYOqhyaAHuOmOlYD6mxmDlCFKAxXDNvGsPeezpknwvPHst7e/V0PEc874cXU1Vhvo8xp3nrG9GQFbnS8NAe0k2cZLbUTDdeVw4hzmMRHjxiIA8eMZB+y//DwxV/A+DpaaMBuO34q/h3j/4R5/DyfgTkqayqJkewXYEEiGdC1yg6JVvItM9BSQMNtaVmLL4Sp4l9Vtf/o3j4DE4ZMrlu7K9vPcTKCf25Y9YD5OywzDe+XIn6fgTLA+6KAeKb0DWKTskWVDk0ARqq8z2WkhNOCjCQe7D0D/tQPHwGva95itW7WHkx5y96ne8mns7zU4bRzmyLq6mSG/FM6A1VkSuNDzUrxUlDCw2Np4lQponFxOLUUxoIcfD+vPOu/PHqJ2hes5WHKv5Gn+8W0Hv1F/xn3BnwcBv47DPo3BmI/IxjCYMF2Lx1OxULK0Ped6+9sce+urQuo7p5M32GU9KPKoc4CI8oCZg7wENPA8UzsfpK3BRgwE8QYKuvORefPRYxOxgx90mu/OQlWLcOiosBePeJVxj5bV7IZ+y1p0SAquqakO9FLN+b32t2OJ5HUdKBPpLEgVbYTA/JMrEEMplbF0SWtzCSw7g+l1Dx2Wp47LG68aMvPp0v7ziJ05fOqd83Rvkhsgqsl++Nfr+UbECVQxxoREl6SLavpMqm8F3wtbjEUhLXXDGpbvzvM+5h5YT+3PzevyPKc3gl8L3w+r3R75eSDXgyK4lID+CPWA9PHxpjPkupVFlOQw4NbWgk01fi9LkV+T+3OrNP6y4UD59Bp/U/8voT19Oy5neu/6ic6z8qZ1aXw7nhtGFsbRZZnsOJQv+Kxev3Rr9fSjYQdeUgIrdj9VXYFWiLVWJ7VKoFy2Y0oqRhEu1zCzfnfN+6PQf86QUOvvE5FrXfF4B+X89j+T1nMueRy9l1c5Wn6wYWHF6/N05lPrZs266lNJS04WXlcC5QYoz5HUBExgOfAXe4HtWIcYqMUWdhdhPtc3My22xssROlF95L7o5axr1xHwOXvM2e639kwf1Wb4l+l9zP8nbFjtfdUF0Tcv0x05dS5R9r4Yt8PrPbD6wuc9nimG5o0XpK7EQtnyEirwPn+ru0Bfo+P2OMiUwxzRBaPkNJBrFUbb3845e4de7jIWMXnzWaOXtHNiAKzpR2qsVkV2IjW0tpaP2nxkOiVVm3AktF5EkReQL4HNgkIv8QkX8kU1BFySSxVG3912FnUjx8BpcNuL1u7IkXxrJyQn8u/bSibiyQeR2otHpT+SLbRLpAuGqw2ShbHdMaTdU08GJWetn/E2BuakRRlMxSWlLE/FXrmPrxD9Q6rKgL8320bN6szpzS/9YrGHX+WXz26nu89vh1ANw2+1Fum/0oFSX9kIcfxoCnyq3hbWqjOaYzZdrJNqWlJq7UEFU5GGOeSocgipJpKhZW8uKCSkfFEGjzGT4JlZYUQelB8Ni18NNP8Mc/wjffULpwFvQu5vOO+9FswBho3tL2vMGsqaoOKe4XnngXcGBnMhEzm6KpNCE1dXiJVuovIgtFZJ2IbBSR30RkYzqEU5R04lY7qXWBD4xl/nEtArj77vD111BdDaedBsCBP3zJksmD+HzS2XSs+p+rDIUFvpDifob6PhPBeR7JNu3E0mAom6L11MSVOryYlSYDZwJLTGNo/qAoDjiZRQQoyGtWV+soQLgZKIQWLeCVV8AYnjr2fIbMfZadtlXz/sOXATDwvPF80vHAiOuEXwMsBRHI7h5aviiiFIiXe3Aj1qfvZEfruZmFgldRuSLUGkNR0D7ZZuJqTHhRDj8An6tiUBo7bsX14p6MRWh1713s99IF9Fv0NpNn3APA88+OAGDYyTfx/EHHAe7lOdZvqalTHG51nuIx7bg9fYdP0oEJvE+3djFfxw43xQShvpqAuS94n2wycTU2vCiHYcBrIvIuVuQSAMaYe1MmlaJkgLJ+XW0dx26TtpdJqO5Ju2UexQf0oefqL3lxShkAd702mbtem8zDvc9k3DEXg0sP6nCZnPwRsRLt6dtuAn9m3vd1+yVi549mFnIy8wX2sfvMNCE1OXjJc3gT2AQsAepKRRpjxqZWNO9onoOSLCoWVnLz84sdndLB5PtyGdCziJn//bHuqT5aS9Dg3IUOG35ixpM3Uvj7prrts/fqxdWlI9nqa+5J3qLC/IRNO9HyKbzmf8STf+HWwhaiFztsXeALMcWJwODDOtGrcxuNYPJAQj2kRWS+08HZgioHJZk4TVjhHLl3Gz5ZuZ6a2tC9fTnCxLMPsZ2M7M7dcusWnnhhDL1Xf1E3tnqXdpxxwT2s3akNIvY1/5KVDBctqc3r+5HMHtyBelex9tAAyBHIzZGQz0WT9OxJNAnubRE5IckyKUrW4tVe/Z9v10UoBoCaHYaJs5bbRgDZnXtz8wIGDr6LvcpeYUr3EwHosHEtnz5wISsn9Ge//30XcUwyTSfRqt96fT+S3fnOLSnRzfi2wxDxuWgEU+x4WTn8BrQEtvl/BDDGmF1SL543dOWgJBOnEhexku/LjXgaH9CziBcXVEY990XzpzPmnUdCxq4441be3PcIwDKnjD7V2XyVKKMqltQlA4pY//RuPbMTeTKPJ1opnhVFLCubppJYl5BZKdWISC4wH6g0xvQXkTZAOVAMrAQGGmPWu51DlYOSbIInh1b5vpACeF4ITGThBMIwwyN/5ixba9uO9OjvFvDUtNEh5xh/9EU8dNgA8vOapcRUMqpiSYjDOUCBL4fqmh22Mqdz8qxYWMnQ8kUxN1/yaoZrSrWjEvU5CDAY2NMY81cR6Qi0N8Z8kiTh/gT0AnbxK4e7gHXGmPEiMgJobYwZ7nYOVQ5KqomlKJ8vR6hxeMz28vS698jXIhTLPr98z9uPXRMy9tIBfZh87gjeuzW5Vl+764Ol8L4dd3JSrxVOxcLKkGq0gRUS1OdV5Dgo3gCJ+hzcPuuiRraKSNTn8CBwBHCe//Um4IEkCdYBOAV4NGj4dKz+Efh/lybjWoqSCF7t+4X5PiaefUidQzUcL3Z5u4nvm7adKB4+gx7XT+HrXTsCcObSObw3qh9ftu/CzHeXepLPC04Tr5cIrkSoWFhJ2bTFEWXKb562mLJpi6msqsZEkaN1gY/zDutEy7xmIWOxPPW75a44ZsY3QrzkORxmjOkhIgsBjDHrRcR7Gyx3JmPlUewcNLa7MeZH/7V+FJHdknQtRYmb0pIiRr70X6prdthunzyoe8Tk4zX+Pty+XehixlpX0IrjL/snedtruHfGPfRf/gH7/e8b9jvmQGp9eeQu/Ry6dEngTp1NYrkeczDiZeKs5bYrrtodBi/en8BTffj7vmnrdsZMX8rQ8kWeTGBuyZAQJTO+EeFl5VDj9wsYABFpR1C+Q7yISH/gZ2PMgjiPv0JE5ovI/LVr1yYqjqI4Eog6clIMQEQkjNf+1wH7duCpuLKqms3btkf9x9zWzMd1pSMoHvYqk460FvW5Ndtg332tYP/Zs+O4U4tzD+sY03iySKTkRUDx2iXV1dSa6DWxgvBSur0plOdw9DmIyJPGmItEZDAwCOiBZeY5CxhljJmW0IVFxgEXANuBFsAuwEvAocAx/lVDe2CuMcZ1Ta8+ByVVxBK5FM0ebRcB41QnqXWBj99rausUUo7AEXu14cNv1zlev/+X73H/9LtCBx95BC6/3PH6dsoqXKZcEc49rCN3lB4U9T1IJMonFr9OQK4dxoRcx2tORq4I9wy0z0UJvg8330MmGy4li7gc0iLymTGmh//vbkBfLH/aO8aYL5Ms4DHAn/0O6YnAr0EO6TbGmGFux6tyUFJFrBOWk+PTKQLGTenYhcK28OXYFueD+smy72+rePTBa0O2fXvupZy615ls2e7spE00SicZx5dNWxxhWsrNEXIgZNzpvLF8Xl5ka+yRS/E6pAtEpEREegAFwEfAf4B8/1iqGA8cLyJfA8f7XytKRojVfBCcbBWcBHfz84ttawg52fFzRWz3N6a+r0Q4tcZggLd37sx+o15n1mufWCXEgb2nPsYXd57Cv58bRf623yNkhcTLXyd6fGlJERPPPoTCfF/dWOsCH/ecfUidk9/NRAexdfPzIptX82BjxG3l8BvwKfbJiMYYkzVrKl05KKki1pUDWP8wkwZ192yOslshuB13/uGdQuo5OVVoDZhOcrdsps3gszly1X/rtv3csjWnXTiJn3ZpWxdaWzxipuM17Rzu4bjVSYq1rEYiVCys5KbyRZ72jVW2xpYcF69ZaaExpiSlkiUJVQ5KqnAydfhyhZZ5zWyjimKpC2SXFOfmi4BIs4abnd2XK2Ask4yYHdz+zr+4eMGrIfvMfWYmt/7QwlVeL6aUaAX80kkqigU2RhNTonkOitJkKS0pYqcWkRHfNbVWWQmnukBezFGBfUtLivhwxLGsGH8KH444ltKSIvp0a+dYPyjcHOKWO1FTa+oUm5Ecxh53JcXDZ3DrCfUJdcecfwofjuzLycs+cDyPFxNMPB3iYulAFwtezEux1qdqal3n3JSDa1ayojQVqhwcwFVbahzt0U4Tdq5IVNt1oJe1W9RNsPKJxc4eYErJyVR8tpqrLhxXN/bgK+NZOaE/13841bYMbDSFF6t93i6MN1kJZnaynH94p5h8B+GKK5nd9xoCjklwxpg30ymIomQrbt3GSkuKHB2j4SYIAQ7fqzUrf61mTVV13RNnaUlRiC07WnmIwLUDBK7vtQ8F1LcdfaP9QRQPn8Fev65m1uPX4ttRy80fTOHmD6Ywo9v/Y2j/P1GT64u4phPh70dggrWz0XvpQBc4h1stKie7v9Nn4wW7BkfJ7L7XEPCSIa0oTZp4uo2VlhQxf9U6psz7vm5CMRCSpxB4Up6/al1IpdZoE7zTtXduYe8DscOY0MS973btQJeyV2hV/RtTn7uF/X9eQf9l79N/2fssa9uZiy+8i7J+3T2dO0C03tRe+j+nsgudG3aKy677ni9X2Lx1O8UjZob03YjW9KkhENXnICItbMbapkYcRck+4g1nnLNsbdSErOqaWqZ8/L3n8uB21x5VsYSbyhfFVDm2qrrGdjW0IX9nTr74Pvb5cwWvHdQHgG6/rOKjewdR2qMDfS97iOIRM+k+9s2o5p9oNnqnJ+7gcbtzhJMKu7+T4jJAy7x6E14g+xpCLXFV1TXcVL6oQddg8rJy+FRELjfGzAMQkQHAOGDflEqmKFlEPCYKr7ZoL5Ygt+Q6u/LaibI9txl3njeKbSc8zddDb6XsvacBeOexqwG48OyxlG3dDjg/sUdbGXhZkXl9D532izf01MmUmO/LYfM2730+bnnpvw129eAlWuk84D4RmSgiU4DLgazJcVCUbCUZtuhoK5VURspUVlUzdNpiHjhiIMXDZ3BV6ci6bU9PG83X407h2zETHI+PtjLwsiJLpAtdIg5vp8irrdtjKyu3xaUeV7YTVTkYY5YAdwJXAX2A64wxq1MtmKI0dOKJIgpn0qDudeGtdqQ6UiZ4VfNG1yMpHj6DU4ZMrhu7efp9VqG/q6+G2tAnai+hrXZhvMG4hfSGXyucREJPnRSXWze8xkZUs5KIPAbsDRyMZUp6VUTuN8YkpaeDojRWgqNy1lRVU1jgY0N1TUwTTDRna7Ty0qlg6R/2oXj4DHb77VemTynjDxt+hocesn6OOgpmzICdd464/1gzir2E9ILl/LU7pxeHtxt2psRYIsLA0psNFS9mpc+BPsaYFcaYWcDhWBVaFUWJQvCTcUFes5ifPANPuk7JYmX9upKT4ATkVKspGutbtWXenM9g82Y48URr8L33YJddYNddYdWqhOTy4oz25QhjTjvAdpsXh3esxFq2fPBhneK+VqbxYlaaZIJqbBhjNhhjLk2tWIrS+HB7Yi3wOf8rBmzldrbz0pIi7h3YHZfDXckBBh3asc584lXRCDCod0frybqgAF5/3TIr3XyztcO6dVBcTGmPDuy2dGFcSW7RnvADXfecViLxZGxH447Sgzj/8E6eGh+df3gnT2XOsxUvoaxdROQFEflCRL4L/KRDOEVpTLg9sRokpBppME4VWoNt581yI30bhfm+qPb6HcCMxT/WrW52aWEvQ6S88OKCytCJPicH7r7bclQ8Wt/59+Vn/szKCf057Yu5MYWdOr1fRYX5rBx/CotGn+BqokpVRdU7Sg/i23Ens3L8KUwe1N1WAU0e1L1BKwZwKbxXt4PIB8BoYBJwKnCx/7jRqRfPG1p4T2kIRGscZDX42eG5Qmugoqhbwbv1m7d6iphZ6a9M6rVZTvA1nArX7TliJr2/X0L51JEh4/cdMYjnTr2cNRt+d/VD2L1fgSS0aI2V0klDrtTqVnjPS55DvjHmHRERY8wqYIyIvI+lMBRF8UhgwnAqJ121pYZJg7pHlIoIzrIOJvBknajjNfycsTi43a6xR2E+H2OV5+i8fg2vP3E9BTVbuf6jcq7/qJxZXQ7nhtOGhTjdwyfaAT2LmLNsbUT5ilRlRsdDImU6shkvlsrfRSQH+FpErhORM4DdUiyXojRKSkuK6kp6hxOo1RQc2umUZS3Uh2+6OV69OF8DdZbwnzMW97Tb+YNt/qta78H+f3qRg298jkXtuwDQ7+t5LL/nTGY+cCl/e+JdikfMZGj5ohDfyosLKinr15WiwvyI96ExV0TNBrwoh5uwOsHdAPTE6vs8JIUyKUqjJhZHqVsZh8DTqtv5ouVa+HKF0afWR/uUlhQx+HDvETabt253dDDb2fw3ttiJ0gsnsXfZKzx/0HEA7LV+DZ/cN5iVE/rTZe3KkHNU19Ryk19h2NFYK6JmA1F9Dg0B9TkoDQ2vdmqvDXTczhe8rbDAhzGwobrG9bqjKpY4mrPC8dqL2amB0RUfv8gtc58IGbvorNHM3fvQqNfORCOhxkS8neCmu53UGHNaEmRLCqoclMZKMrqPeVFEdgoklkJ+bpN0NEd8gL7ffMxjL/41ZOyvx17GY4eW2u7f0LuwZQPxKoe1wA/AVOBjwnpJG2PeTbKccaPKQWnMJBIN40W5eJ28o1FUmG8ro1ujnFyb3hX7/fwdrz9xQ8hY+UHHc8uJ11Gbk1t3rUSighpyhFGAZNxDvMohFzgeOBerdMZMYKoxZmlMV08DqhwUxR4vZimv/ZbdCO9zEKyAikfMdDxu8qDujuGqbTev54VnhlFc9WPdts/26MrFA//K4klnxy1rY+gFnax7iKuHtDGm1hjzhjFmCFbJjG+AuSJyvecrK4qSUbyEuSbq1LXrkBYcSeSUTSzYO60nDerOyvGn0PXgvTnmyn/R9eaXeGufwwDosWY5iycPhJ13hu/iy8UdM31pg+8FnY5+1q55DiLSHDgFa/VQDPwDeClpV1cUJaW4tTiNto8TrQt8FOQ1qzNnOB1bWVXNkeNnOxaqM1BXAsTuaXflr9Z5tzbL4/IBt4ExlL33NNfOmwabNsHee1s7TpkC553nSfaKhZWOvpSGFPmUzNwWJxxXDiLyFPAfrCJ7Y40xhxpj/mqMabitjRSlieElbDaW0uL5vlxGn3pASC6GU94GEFXpuD3pRkx0Ikw8egh7Dp8BTz9dPz54sFX+dOjQqPK7Xa8h9YJORVHBcNzyHC7AKtF9I/AfEdno//lNRDYmemER6Sgic0TkSxFZKiI3+sfbiMhbIvK1/3frRK+lKI0Vp2qtAbzUFwrfp3WBr64uU/DfTrWJEulbES3D2nH8ggusGk5Tp9ZvmDzZUhI9esAO+5IhbtdLpCBfuklFUcFwMpbnICLtgfbGmM9EZGdgAVAKXASsM8aMF5ERQGtjzHC3c6lDWmmKZJNjNTxyxquZKtYQ2HxfLgN6FjFj8Y915qHWBT7u3Rf6nNsv8iQbN1r+CT9OzvfWBT4W3n5C1PurrKqui7DKdH2njEUrpRsReQW43/9zjDHmR78CmWuMcVWHqhyUpojXBLlM4BahFEy0stbhE2Cfbu0o/+QHasIaY/hyhYlnHUJphzzYzaa6z1dfQZcucSlUt1DfhhblFE6ihfdSjogUAyVY+RS7G2N+BPArCNs6TiJyBXAFQKdODbehhqLESzqckl6we4JtXeBj/ZboSXRzlq113R7urD5y/OwIxQBQU2uYOGs5pSOOtcxNNTVQXAxr1lg77Luvdb7XX4czD4rpidut6VAgQqihKgc3Mq4cRGQn4EXgJmPMRvHYV88Y8wjwCFgrh9RJqCjZiZdIpFQT/lQdqJY6oGcR5Z/+QE2t+7+mF0UWrHzczhZyLp8PKv3+l8GD4dlnrb9POolSoPTll6G0NOq1vcjYkKKcYiHO/lHJQUR8WIphijEmECL7k9+cFPBL/Jwp+RQlm0mHUzIaTvH2c5atZeJZh4RUfLUjmiILKJ/KKIrB9VxTpliriX/8o37sjDMs5/WECdY2h2sfOX52/Ndt4GRMOYi1RHgM+NIYc2/QpunUV30dArySbtkUpSGQqk5nseBm2iotKWLh7ScweVB32y53XhSZlz7SYPkcoirF66+3FMHnn9ePjRhhdbC78ELLFOUnWCm5kW5lnE4yGa30R+B9YAlWt0KAW7D8Ds8DnYDvgbONMevczqUOaUXJDLE4xeOJrvHSma51gY/Rpx7gWkzQ9npr18JRR8GyZfVjPXrA229z5MMLo9aDynS0UjJoENFKiaDKQVEyQ6zRPxULKxkzfWldGGqOwA5TX0gPCJnQN2/dbpvRHC0iKya5tm61/BIvvlg/lNuMfpc8wMo2ofsGWrM2FuKqraQoihKNWExbFQsrKZu2OGSyDwQeVVZVU/bCYsqmLQ7pBLd523Z8OaFBKvGaoxxrDzVvDi+8YCXO3X67NVS7nbn/upKVE/pzxKr/1u3aWP0LdujKQVGUtBBv9dfwWk6JmKO8Pvl/Ou4BDr3lupCx20++gR53DGvQZqRwdOWgKErGiTfks2pLTUgtJy+Tc6K1hw4deS0Vn63m8qvvrxv7y2v/oLRHB7jpJsfyHI0JVQ6KoqSFeE0y8RyXjDDf0pIi/vXgtVaE0/ff12de//3vkJsLxx8PmzfHLFtDQZWDoihpoaxf1wj/QTC+XInLv2BH0sN8O3aEn36C336DY46xxt5+G3baCfbYoz7hrhGhPgdFUdJGeLRScKOgwnwf/Q9pz5xla7O/feeOHXDjjXD//aHjn34KvWxN+FmJhrIqipJ1ZFNV2YR48EG49trQsWnT4KyzMiNPDKhDWlGUrCMdrS7TwjXXWH6Jt96qHzv7bKs8x1//6lieI9tR5dBUePBBOOUU2HVX60s7d673Y195BQ46CFq0gP33h/LyyH2WLoUTToCCAmjbFq6+2mrlmC1s2wZ/+Qvssw/k51u/R4+2EqCCiec+5s613lO7n35BPQa++QauvBIOOcRyaAZs102UeKrKRmtulFGOO85SBF9+aZXkACtvIicHzjnH+g42IFQ5NBWefhrWrQudrLzwwQcwYAD06QOvv24pmHPPhTffrN9nwwY49liorrYUx913W9mm55+f3HtIhBEjYPx46ynvtdesSf+uu2DYsPp94r2PHj3go49CfwIK9KST6vdbutS69r771pWQbsrEGm4aXoSvsqqam8oXccDtb2SXkujWDWpr4Zdf4OCDrbHycmjenGXtijnptpezS14H1OfQVNixw3qC+fxzaxUwZ463J9d+/ayCZLNn14+dfLLVYeuDD6zX48ZZP99/D4WF1tj06XD66alx0BUXw5gxcNFF3o/5wx+sEgn33FM/9qc/WRU7f/rJep3M+5g40VJIP/xgRbNA/WcAlj36l19iW8GlkGR0FYvnmk6d3uyc0m5JdHXNfoJkzsQ92fHKJyvIveQS+i+dGzL+9gtzOG7AMWmXJxj1OaSTmTOtCWDFitDxFSus8enTMyNXThwf9datlhIZODB0/JxzrKfjDRus14sWWRNnYEIFyzQjYr0fAPPmQbNm8Pjj9fts2GCFCKZjhVFTA61ahY4VFobag73ch1emToWjj65XDBDfZ5AG7J7IR760JOVPt3bhpgN6FvHigkpbWdzMTYFmP5m+Jzvumr2C6/r/meJhrzLx/11QN37cWX2s71awryKLyM5va0PmxBOtCeGpp0LHn3wS2rWznrqdqK2F7dvdf9KZmfntt9ak2q1b6Ph++1lyfPWV9fr33yEvL3SfZs2syfDLL63Xhx8OZWUwdKj1ZA5www3Wee67L7X3AXDZZfDww/Dhh5YP4f334Z//hOuCSiR4uQ8vfP01LFxomd8aAJl0DJeWFIVkP89ZttZRlmjJcJVV1XWTfzY5u+uUmggP/N8giofP4KrSkfU7BB5AHngg7bK5ocoh2eTmWuaOp56qfyo1xnp9wQXWZONE375WByu3n0suScttALB+vfU7+EkaoHXr0O377AOLF4fUw2fBAkvZrQuqtj52LHTubN3DK69YfpB//av+fE6EK0iwlErwWDTz6Pjxlu/kj3+0Gs4fdRSceWZdobWY7iMaU6dan9WAAd6PySDZ0m7U7Zprqqop69eVaH0io60yMnFPdkrtja5HcuS4dyDYHH7ddZaSuPZa6zuXYVQ5pIJLLoFVq+rtyXPmWK8vvtj9uIcftmzbbj9jxjgfb0zohJmsL1h469bARBwYv/xyqzb+9dfD//5nOV6vucZSlLlBJQzy8iyF8N57MGiQ9TTvtpIKEK4gV62CSy8NHQtfqYUzcSI884y1Snn3Xasr2JQpocrB631E47nnrKfBNm28H5NBEq1DlEzcZCktKWLw4e794qOtMoLH0xX55FrKo2dP6/9p9Woo8vtDHnzQeog8+mgrIztDqHJIBXvtZTl7n3jCev3EE9C7NxxwgPtx++wD3bu7/3Ry+ed4993QCbNv38TuI/BEX1UVOh54HVhRdOsGjzxiPTG3b29FaPTubcm7++6hxx5yiBUOu3WrNfF6IVxBtm9vhaEGj516qvPxv/wCo0ZZLSGvu85aNVx/vfV63Dj4+efY78OJxYstE1QDMSlBdrQb9SrLHaUHOXaWCxBYZbidJ50+CU+lPIqKLAWxebP1YAHWQ9Quu1jm6IApNo242DiUhLjsMutJdNw4eOml0CgZJ/r2tSZ4N4YMsfwXdvTsaU2UAXbe2bO4tuy9t6Vkli2znmICLFtm2eGDwzEvuQTOO8+yt++2m5UjsOuu1vsQzOTJ1vHdulkT9HvvRXfUhkcJ5eVZEUteo4e++84yFXXvHjpeUmKtsFatqi+q5vU+nHjuOSuP4vTTve2fBQQmqWyI7PEiS2lJkWv0UmCV4XYeN59EKu47IHNUCgpg1izLbPrnP8OkSdbDTefO1vaPPrL8d2lAlUOqOPNMy3Z4zjnWB33OOdGPefjh6MvItm2dt+28c3LDRps3t/Ibpk2zkrcClJfDEUdERv+0aGGFyYJl5tmxIzTSaflyuPVWuOMOy3Hfs6f15b/55uTJbEfgH+uzz+DQQ+vHFyywfhcXh+4f7T7cKC+3VjE77ZSQyOnG8+SVBrzKUtavq20obGB14HaebPJJ2JKTA/fea/08+qj1oAnW/x1YJtHzzkupCKocUkWLFlZc/QMPWCaGcKeuHV1TuIyfPx9WrrTi7sFaofzyS+gT+NNPW0/O335bP6HedptlIrvpJigttZK4XnsN3nij/twbN8Kdd1rmmmbNLB/LPfdYzuaA3b221lr1lJRY+QU5OZaDetQoK7EuPCIqmey+uyX78OFWRNLBB1thq2PGWGUO2rXzfh9g+TvefdfKeA5m3jwrZPnee+3l2LLFeu/AquK5caPVgQws30tBQRJvuvGTyIqnVb7Ptv1oKxdzVca47DLrZ+5c62ENrLll8GDr/+cvf4n0CyYDY0yD/+nZs6fJSt56yxiwfmeaIUMsWcJ/hgyp3+eJJ6yxFStCj335ZWMOOMCYvDxjunY1ZurU0O2bNhlz/PHGtG5tTIsWxvTqZR0TzN/+ZkxBgTFffVU/tn27MYcfbkzv3tbfXunc2ZI1FjZsMObmm43Zay9Lxr33NqaszJiNG2O7D2Os96xz58jxG280plUrY37/3V6GFSvsPwO791xJKd3HzjKdh8+I+Ok+dlamRYvOV19Z38/Ad2fkyLhPBcw3DvOqZkinkmHDLDNDIAFOUZSsINE2ollBVZW1crjmGivIIw7cMqTVrJQKli+HL76wkqxGj1bFoCgZwqmExh6F+Y7O7AZDYWFkP4kkosohFVx5JXz8MZx2mpUFrChK2gmv3RQIV4XozmxFlUNqyJJiaorSlHELV/1wxLF1+2Q6fDdbyVrlICInAn8HcoFHjTHjMyySoigNiGjhqtkUvpuNZKUxXERygQeAk4D9gXNFJD6Pi6IoTZJsKgvSEMlK5QD0Br4xxnxnjNkGPAc0nJRTRVEyTjaVBWmIZKtyKAJ+CHq92j9Wh4hcISLzRWT+2rVr0yqcoijZj6eaRooj2epzsEv3CwlLNsY8AjwCVp5DOoRSFCX7cOv4pn6F+MlW5bAa6Bj0ugOwJkOyKIqSpbiFq6pSSIxsNSt9CnQRkT1FJA84B8hQf01FUbKVbOr41tjIypWDMWa7iFwHzMIKZX3cGLM0w2IpipJlZH111QZMVioHAGPMa8BrmZZDUZTspVGUwchSstWspCiKEhUNV00dWbtyUBRFiUY2dbFrbKhyUBSlQdOUwlXdwnaTjSoHRVGUBkC6w3bV56AoitIASHfYrioHRVGUBkC6w3bVrKQoSqMmnXb6VJLusF1dOSiK0mgJ2Okrq6ox1NvpKxZWZlq0mEl32K4qB0VRGi2NqbxGuqvMqllJUZRGS2Mrr5HOsF1dOSiK0mjRbnDxo8pBUZRGi5bXiB81KymK0mhxK6/RWKKYUoUqB0VRGjV2dnptEhQdNSspitLkaExRTKlClYOiKE2OxhbFlApUOSiK0uQoLPDZjmsUUz2qHBRFaVJULKxk0+/bI8Z9uaJRTEGoclAUpUkxcdZyanaYiPGaWsNN5Ys4cvzsBlleI9moclAUpUkRza9QWVXN0PJFjKpYkiaJshNVDoqiNCm8+BUMMGXe9016BaHKQVGUJoVd1rQdBjyFtlYsrOTI8bPZc8TMRmWS0iQ4RVGaFIEkt6Hli4j0PIQSzQTVmJPpdOWgKEqTo7SkyDGcNZhoJqjGnEyXEeUgIhNFZJmI/FdEXhaRwqBtI0XkGxFZLiL9MiGfoigNn2jmnqotNa7HeynQ15iT6TK1cngLONAYczDwFTASQET2B84BDgBOBB4UkejGQUVRlCC8dIBzWxXkitStANx8CI25JHhGlIMx5k1jTCALZR7Qwf/36cBzxpitxpgVwDdA70zIqChKw8WLucfOMe3LFXw5Qq2xvBHR2oo25pLg2eBzuAR43f93EfBD0LbV/rEIROQKEZkvIvPXrl2bYhEVRWlIeDH32LXdbJnXLCJBzs2HkO7WnekkZdFKIvI28AebTbcaY17x73MrsB2YEjjMZn/bgAJjzCPAIwC9evWKFnSgKEoTYo/CfCptFES4uSe8nPeeI2bans/Nh5DO1p3pJGXKwRhznNt2ERkC9Af6GmMCk/tqoGPQbh2ANamRUFGUxkpZv64hIabgzdzTKt9HVXWko7pVfvTIJq80lCZDmYpWOhEYDpxmjNkStGk6cI6INBeRPYEuwCeZkFFRlIZLvOYesbNduIzHihdHebaQqSS4+4HmwFtivevzjDFXGWOWisjzwBdY5qZrjTG1LudRFEWxJR5zj1N4a7SwV6+4OcqzbfWQEeVgjNnHZdudwJ1pFEdRFAXw7quIl4aUF5EN0UqKoihZQapDUxtSXoQqB0VRFD+pDk1tSHkRWnhPURQliFSGpgbO2xCilVQ5KIqipJGGkhehZiVFURQlAlUOiqIoSgSqHBRFUZQIVDkoiqIoEahyUBRFUSKQ+pp3DRcRWQusivPwtsAvSRQnmahs8aGyxYfKFh8NWbbOxph2dhsahXJIBBGZb4zplWk57FDZ4kNliw+VLT4aq2xqVlIURVEiUOWgKIqiRKDKwd9NLktR2eJDZYsPlS0+GqVsTd7noCiKokSiKwdFURQlAlUOiqIoSgRNWjmIyIkislxEvhGRERmW5XER+VlEPg8aayMib4nI1/7frTMkW0cRmSMiX4rIUhG5MVvkE5EWIvKJiCz2yzY2W2Tzy5ErIgtFZEaWybVSRJaIyCIRmZ9lshWKyAsissz/nTsiG2QTka7+9yvws1FEbsoG2fzyDfX/D3wuIlP9/xtxy9ZklYOI5AIPACcB+wPnisj+GRTpSeDEsLERwDvGmC7AO/7XmWA7cLMxZj/gcOBa/3uVDfJtBY41xhwCdAdOFJHDs0Q2gBuBL4NeZ4tcAH2MMd2D4uCzRba/A28YY7oBh2C9fxmXzRiz3P9+dQd6AluAl7NBNhEpAm4AehljDgRygXMSks0Y0yR/gCOAWUGvRwIjMyxTMfB50OvlQHv/3+2B5Zl+3/yyvAIcn23yAQXAZ8Bh2SAb0MH/D3ksMCObPlNgJdA2bCzjsgG7ACvwB8tkk2xh8pwAfJgtsgFFwA9AG6w+PTP8MsYtW5NdOVD/ZgZY7R/LJnY3xvwI4P+9W4blQUSKgRLgY7JEPr/pZhHwM/CWMSZbZJsMDAN2BI1lg1wABnhTRBaIyBVZJNtewFrgCb857lERaZklsgVzDjDV/3fGZTPGVAJ3A98DPwIbjDFvJiJbU1YOYjOmcb0uiMhOwIvATcaYjZmWJ4AxptZYS/0OQG8ROTDDIiEi/YGfjTELMi2LA0caY3pgmVWvFZGjMi2Qn2ZAD+CfxpgSYDOZNb1FICJ5wGnAtEzLEsDvSzgd2BPYA2gpIucncs6mrBxWAx2DXncA1mRIFid+EpH2AP7fP2dKEBHxYSmGKcaYl7JNPgBjTBUwF8t3k2nZjgROE5GVwHPAsSLyTBbIBYAxZo3/989YdvPeWSLbamC1f/UH8AKWssgG2QKcBHxmjPnJ/zobZDsOWGGMWWuMqQFeAv4vEdmasnL4FOgiInv6nwTOAaZnWKZwpgND/H8PwbL1px0REeAx4EtjzL1BmzIun4i0E5FC/9/5WP8kyzItmzFmpDGmgzGmGOu7NdsYc36m5QIQkZYisnPgbyzb9OfZIJsx5n/ADyLS1T/UF/giG2QL4lzqTUqQHbJ9DxwuIgX+/9e+WI78+GXLpFMn0z/AycBXwLfArRmWZSqWrbAG6+npUmBXLIfm1/7fbTIk2x+xTG7/BRb5f07OBvmAg4GFftk+B273j2dctiAZj6HeIZ1xubDs+ov9P0sD3/1skM0vR3dgvv8zrQBaZ5FsBcCvQKugsWyRbSzWg9HnwL+B5onIpuUzFEVRlAiasllJURRFcUCVg6IoihKBKgdFURQlAlUOiqIoSgSqHBRFUZQIVDkoShgisimGfY8Rkf8Len2ViFzo//siEdkjjuuvFJG2sR6nKMmkWaYFUJQGzjHAJuA/AMaYh4K2XYQVc55tmfeKEhVVDoriARE5FRgF5GElQQ0G8oGrgFp/HZvrsTJTN2FVPe0FTBGRaqwqwF9ilVT+RUR6AXcbY44RkV2xkiDbAZ8QVPfLf94b/Nf9GLjGGFOb+jtWmjpqVlIUb3wAHG6sYnDPAcOMMSuBh4BJxqrz/35gZ2PMC1hZvoP926pdzj0a+MB/7ulAJwAR2Q8YhFUkrztQi6WUFCXl6MpBUbzRASj3Fy/Lw+o5kCyOAs4EMMbMFJH1/vG+WE1lPrXK5ZBPhosbKk0HVQ6K4o37gHuNMdNF5BhgTBzn2E79ar1F2Da7OjYCPGWMGRnHtRQlIdSspCjeaAVU+v8eEjT+G7CzwzHh21ZirQQABgSNv4ffXCQiJ2EVmgOrUNpZIrKbf1sbEekcp/yKEhOqHBQlkgIRWR308yeslcI0EXkf+CVo31eBM/wN5/9f2HmeBB7yb8vHqpr5d/85gp3KY4GjROQzrPLZ3wMYY77AcoK/KSL/Bd7CavWoKClHq7IqiqIoEejKQVEURYlAlYOiKIoSgSoHRVEUJQJVDoqiKEoEqhwURVGUCFQ5KIqiKBGoclAURVEi+P/arY9TKxI5sQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "# 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)]\n",
    "    \n",
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Max Temp\"]\n",
    "\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for Maximum Temperature', 'Max Temp',(10,40))\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, (5,5), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel(\"Max Temp\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "genetic-bottom",
   "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 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",
    "# 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)]\n",
    "    \n",
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Humidity\"]\n",
    "\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for % Humidity', '% Humidity',(40,10))\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, (40,20), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel(\"Humidity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "expensive-duncan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xdi8vjBGVVz1yTUlRKmRsY1UO7bkDnniio8N32TL44IP244YM4f3agdw57IiU6WfVXoPZUb0LdbU1fD5ApJVf1FLnUUA+z0Oph5KGhSkFIzLC6mnlE8fuFdGy/eOdDJ86N+/lJoMc7+V89apHm2rJvWiCBAhcc/RA7r7jEYa/9QYHbVrLyM3us+f29+GGxEH9+ztzzwUXtPf8DzkEamt5e1Ejv+7G85EtNUcYEWqFGEXnGnxR6MmlphSMyAirp5VPHHvna+9RU822j3em/BKFmqTn59CMi426cx02b2riD//1F4YMbeXIDxpTvf8z1q/njMQ5H/aqYc0+w3h/wunsedyn2xXA3nu7uQAZ6O7zkTwu0wJD0LFdS6XXH0YKkLCfW0tzYURC2BO9giRrq64SZnzh8IzXyTX1Qr74Xcert+pnOoo0BUlLC7z2Gldddzd1b77ByM1rOWjTWoZt2UiPZJKN3r1dbH/ypZ/87Lef58u/0JRCGomg+KX0qEu7vw2LGrl+9rKME+WSx+by3FqaC6OohN276bzIjhdeCgGim6Tnd51ce6sF6yW2tcGaNV0neq1YAS0t/BxolR6s6TeIFQOG8eAhx7Mykedn/s1fcykgSohSGQUEwe95S97fBWvfY9Y/1neYIJdLOblSWnezhCj1pHBxohCpM5K2Xb+euF/ZUZlusl0nFxt1vu2Yepa3bOf47Rv4t/eXMOLJuTBsGHz0kXP6btvWfsLQoa63f9ppTFstvNBnEKv2GsxHPXt1KLdKpOQUQpK4zO3IZgptbmlNrbCXrZywKM07WmRKPSlc3Chkrzxfh2FU4YVhXiendly1CmbNYuudf6D+9RWZF3VZvx7Gj4evf72j0zdtYZff+6y5nO1FZWQnyLoV2do57Oe2KEpBRK4Avo7LTvAycCHQB5gFDAPWAOer6haPIgpKqSeFixuF7JXnayqIysQQ5nUyteNe25qYtO4fcOzP4JlnupyzR6fvq/YczJxRn+MfR53Mn351YdZr1lT3YLvHnJC6EnOIx5EgplCv9TOgMOHLkSsFEakDLgMOUdVmEbkXmAgcAjyhqtNFZCowFbg6avmg9JPCxY1C98rzNRVEZWII5TrbtvGfrGDrX/7ISa/+Pfvxe+8NEydy7ntDWDRoJCodF3kP4gK+tuFlT4VQ3UPKbtJWsUg+H5kc5NU9hDagNYM/wS+QojsUy3zUE6gRkRbcCOFN4Brg+MT+u4CnKJJSiEuoYFyIk+MvSdF8Si0tbqJXck3fj9rTNnwqw+FtPavpMSmxoPtJJ0Gvjnb/t6fPR7M8y151veeF9Z5izvhi+C+jSifT72R7Wth0Oj3EP5CiO0SuFFS1UUR+AawDmoHHVPUxEdlHVTcmjtkoIntnOl9ELgYuBthvv/0KImOlTGePkrg4/iAin5IqvPBC+8v/7bezn3Pmme7lf+aZ0LcvAD2ynJLtWfarq58tOy73Mm50/p0Mnzo343GqhbsHxTAf9QPOBoYDTcB9IvKVoOer6m3AbeDmKRRCxjj2bI3wCN2ntHIlzJoF99zjwjyzccwx7uX/hS/APvvkfr00sj3LfnWt8kjUV1Wk+QeVSDGsFsUwH50ErFbVTQAi8gBwNPC2iAxMjBIGAu8UQbYUcerZGuGSt09p40a47z7X8/97ALv/oYe2L+g+YkQeknbEywzk9yx7OTcbm5oZd8CePLvqvS77Jn16SLdl7Uy+5rpimPmiTEtRDKtFMZTCOuAzItIHZz4aDywAtgGTgemJvw8WQTbDyN47e/99aGhwL/9HHsla3lt992L2wccyZ9SxvLzvgdT06hl6xEg+Jq+GRY2+kS3/XLeVcQfsyfNvbKFVlSoRJn16CD+pHx2a3PnK3p3zopS1uzIWw2pRlDQXIjIN+BKwE1iEC0/dDbgX2A+nOL6oql27KWlYmov4U4qTBJM/5J07dnDs6n9y1itPc+byp9tTO3jRp4/r+U+cCCecAD17lkQ6Da/r+KVYCHJ+WOTbRlG1bXeuWQwZg1ByaS5U9Trguk6bP8KNGowKoWQmCba1wXPPpZy+9e++m3myVzrnnONe/qef7rmmL5RGOo1cz8n1mO6SbxsVI3Q812vGMbzdZjQbRaMQkwSzjjyWLm2P+Fm1Kmt5z+33CeaMOpZHD/osW/rsQZUIq248LbA8pZJOI5dzgp4flGz3JN82KoYTNtdrxjG8PVtEm2EUjLB7UcmRR2NTM/u+v4nT5t3Ngaef4LJ1Jj+jR8NPf9pVIRxxBEyfDm+8AaoMu/ohhl39EP8y6QbuOeLzbOnj5gbnmtphyoSR1FRXddhWqHQauV4n0zm5nB+E9HuitI8GGxY1dkv27pzXHXK9ZjFk7C42UjCKRii9qC1b4C9/cWafxx/PbvYZOrTd7n/44Z7pncMKxyzldBqZ1pgQcQvRhyVnkNFgqacq6c414xjebuspGEUjp7z3O3bAww87s89992Ut+/3euzJn1OeYM+pYXhx8KKt+flZOsl3b8DJ/fH5dl+1f+cx+oUfflDPDPRLqCbB6+ulRi2MkKDlHs2GARy/qpAOp37ISLp7mFED6Or5enH8+TJzICUtrWL2ta7bJfBK3JV/8ybTFhQrHjJpMyq5PdQ9uOPcTBem9RmFTL8UItjhjIwWjOKjCkiXuxX/PPbCua6+8Cyef7Mw+55wD/fp12R2nFbeKgdfoB1wunV+df0RB1h8u5D2xe54fNlIwisvq1S7Nw8yZThFk41Ofci//L34RhgSfPRtH+22U+CW4a1MKkhq+0PfE0tyHjykFIzw2b4b773cv/7/+NfvxBx7YnubhsMNCEcHSk3iTLXKqULHz6amhZ8xbyRWzFjNj3spuK4eGRY2eIbW51CVO5qcoZDWlYOTOtm3w0EPO7PNggGwke+3VHvFz9NHQwyKhi4FXRFWSQsbOhz1RMVmeF0HrUjITKAMQlaymFAxvdu6E+fPbJ3s1Z+l9VVW1v/xPOaVLbv9CEWVPL069ys5M+vQQX59CIWPnwzbzZCovSS7zAOJkfopKVlMKhnP6vvhi+8t/48bs55x2GkyaBGed1WFN36iJsqcXp15lJpKRU1FGHyUJe6Ki33m5OJnjlIYiKllNKVQar77a/vJfvjz78Z/9bLvTd+DAwsuXI1H29OLUq/TiJ/WjixJWG3Zoqld5dbU1Od2LOKWhqO1TnXEVtrBlNeNuufLWW3DTTW7BlvQ0DyNHwnXXdVUIo0bBtGluERjV9s9zz8Fll5WkQoBoe3px6lWWGmGnewirvLikoWhY1MiHO3Z22V5dFf5a2TZSiDsffOCcvTNnwtzMS/d1YN99ndln4kQX+hnzVbSi7OnFqVdZaoQdmhpWeXEJY54xbyUtbV2DBHbt1dOijyqWjz+Gxx5rN/20Znaypejdu93pO348VFdHI2fERLkyla3d3T3CDhcOq7w4hDF7jUa3Nnc1J3UXUwqlRlsbPP98+0zfzZuzn3P22e7lf8YZsNtuhZexhIiypxeXXmWSaxteLrs0HZVKlKNUS3NRTF55pb3n/9pr2Y8/7jj38j/vPBgwoPDyGbHFEvqVF2Gn87A0F8Vmwwa491738n/xxezHjx7dPtP3gAMKL59RdniltLjnhfWmFGJIlKNUUwph0tTkcvvfcw88/nj244cMabf7jxkTe6evEQ1BJtB5zVzOdZEgIxhRTGqMyvdhSiEfduyARx91Pf9Zs7Ifv9tu7RE/xx3nZv4aRh4EnUAX1iJBRnbiPqmxM6YU/GhthWeeabf7b92a/ZzzznMv/9NOgz59Ci+jUVEEnUDnldJi0qeDZ501/EmODjI5gOM2qTEdUwrQMbf/zJmwdm32c8aPb8/tv9dehZfRMAg+ga5cFwkqFTI5fjsT10mNlakUVOGSS+CWW7Ife+SR7Wkehg4tvGxlRCknjytl2fzIJTSxWCktKgG/hHxJ4jqpsTKVwqZNXRXC8OHtTt/Ro83p201K2c5ayrJlwybQlQbZRgFxvieVqRT23ttFCvXta7n9C0QpJ48rZdmyEbcJdOWK14gNXFK+ON+TylQKAHvsUWwJyppSTh5XyrIFIQ5pGcodrxFbOawNXblKwSgopZw8rpRlK1Xi6oMpFOU8YjOlYBSEUrZ9l7JspUicfTCFpFxHbGZQNwpC/Zg6bjx3NHW1NQjOzloqQ+tSlq0U8fPBGOVHoJGCiFSpapZczYbRkVLuSZWybKVG3H0w5UQUZrygI4XXRWSGiBwS6tUNwyh5vHwt5oOJlqQZr7GpGaXdjNewqDHU6wRVCp8AXgVuF5HnReRiESneau2GYURGXJasLHeiMuMFUgqq+oGq/k5VjwauAq4DNorIXSJyYKgSGYZRUpgPpjSIyowX2KcAnA5cCAwDfgncDXwOeBg4KFSpDMPoNmHan80HU3yiCqUOaj56DTgbmKGqY1T1V6r6tqreDzwaqkSGYXSbqOzPRnREZcYLOk/hX1X1b+kbRGScqj6rqpflelERqQVuBw4DFLgIWAnMwo1E1gDnq+qWXMs2jDCJ66StOKfyMDIT1YS5oErhN8AnO237bYZtQfk18KiqfkFEegF9gH8DnlDV6SIyFZgKXJ1n+YbRbeI8acvCSMuTKMx4vkpBRD4LHA0MEJHvp+3aHchr+bBE1NKxwAUAqvox8LGInA0cnzjsLuApTCkYRSTK3nbYIxJL5WHkSzafQi9gN5zy6Jv2eR/4Qp7X3B/YBNwpIotE5HYR2RXYR1U3AiT+7p3p5EQ47AIRWbBp06Y8RTCM7ETV2y6E/d/CSI188R0pqOpfgb+KyO9VNcByZIGv+Ungu6r6goj8GmcqCoSq3gbcBjB27FhbhbzEiatNHqLrbXuNSK6fvaxb0ULJsuPY9kbxyGY++k9VvRy4SUS6vIBV9aw8rrkB2KCqLyS+349TCm+LyEBV3SgiA4F38ijbKCHibJOH6BLneY08mppbaFjUaGGkRqRkczT/IfH3F2FdUFXfEpH1IjJSVVcC44FXEp/JwPTE3wfDuqZRHOIeARNVb9tvwZa4tJVRPmQzHy1M/P1ryNf9LnB3IvLoDdykuB7AvSLyNWAd8MWQrxkqcTaLREU5RMBE0dueMmEkl89anHFfnNrKKA+ymY9exs0jyIiqfiKfi6rqYmBshl3j8ykvauJuFokKi4AJRv2YOqbNWcaW7S1d9llbGVGTLfroDOBM3KzlR4EvJz4P43wBFYnllw9GpUXANCxqZNz0+QyfOpdx0+fnFD103ZmHVlRbGaVLNvPRWkjNXh6XtmuqiDwL/LiQwpUq5WAWiYJKioDp7uixktrKKG2CzmjeVUSOSaa6EJGjgV0LJ1ZpY2aR4FRKBEwYTvVKaSujtAmaEO9rwH+JyBoRWQP8Ny5fUUVSaWYRIzs2ejTKhUAjhUQU0uGJFBWiqlsLK1ZpY0N9ozM2ejTKhWzRR19R1T92ynuEiACgqr8qoGwljQ31jXROOHgAf3x+XcbthhEnso0Ukn6DvoUWxDDizJMrMufh8tpuGKVKtuijWxN/p0UjjmHEE/MpGOVC0OU4h+NmIQ9LPyfP3EeGUXaYT8EoF4KGpDYA/wPMAdoKJo1R0lhqD2+iSp5nGIUmqFLYoaq/KagkRkljqT38sYg0o1wQ1exLEojIvwAjgMeAj5LbVfWfhRMtO2PHjtUFCxYUU4SKYdz0+RnNI3W1NTw79UTfc22EYRilhYgsVNVM+ecCjxRGA18FTqTdfKSJ70YFkK8j1UYYhhEvgiqFc4D9E+spGxVIvo7UuK+pYBiVRtA0F0uA2gLKYZQ4+ab2sFBNw4gXQUcK+wArRORFOvoULCS1QsjXkWqhmoYRL4IqhesKKoURC/JJ7WGhmoYRL4ImxAt7OU6jQrBQTcOIF0FnNH9A+7KcvYBqYJuq7l4owYzywZIHGkZ8CDpS6JAQT0TqgaMKIZBhGIZRPIJGH3VAVRuwOQqGYRhlR1Dz0blpX3sAY2k3JxmGYRhlQtDoozPT/t8JrAHODl0awzAMo6gE9SlcWGhBDMMwjOKTbTnO3+JjJlLVy0KXyDAMwyga2UYK6SlIp2GT2AzDMMqabMtx3pX8X0QuT/9uFBdLR20YRiEI6mgGizYqGSwdtWEYhSIXpWCUCJaO2kjHRo1GmGRzNKent+gjIu8ndwFqaS6Kg6WjNpLYqNEIG98ZzaraV1V3T3x6pv3f1xRC8fBKO23pqCsPv1GjYeRDXmkujOKS74I3Rvlho0YjbEwpxJD6MXXceO5o6mprEKCutoYbzx1t5oIKxEaNRtiYozmmWDrq0qGYjl5bxMgIG1MKhtENiu3otUWMjLApmlIQkSrcjOlGVT1DRPYEZgHDcAn3zlfVLcWSzzCCUArhwTZqNMKkmD6F7wHL075PBZ5Q1RHAE4nvhlHSmKPXKDeKohREZDBwOnB72uazgWQajbuA+ojFMoycMUevUW4Ua6Twn8BVQFvatn1UdSNA4u/emU4UkYtFZIGILNi0aVPBBTUMPyw82Cg3IlcKInIG8I6qLsznfFW9TVXHqurYAQMGhCydYeSGhQcb5UYxHM3jgLNE5DRgF2B3Efkj8LaIDFTVjSIyEHinCLIZRs6Yo9coJyIfKajqNao6WFWHAROB+ar6FWA2MDlx2GTgwahlMwzDqHRKaUbzdOBkEXkNODnx3TAMw4iQok5eU9WngKcS/78LjC+mPIZhGJVOKY0UDMMwjCJjSsEwDMNIYUrBMAzDSGFKwTAMw0hhWVIrEFvT1zAML0wpVBjFTvVsGEZpY+ajCsPW9DUMww9TChWGpXo2DMMPUwoVhqV6NgzDD1MKFYalejYMww9zNFcYtqavYRh+mFKoQCzVs2EYXpj5yDAMw0hhI4UKxyayGYaRjimFCsYmshmG0RkzH1UwNpHNMIzOmFKoYGwim2EYnTGlUMHYRDbDMDpjSqGCsYlshmF0xhzNFYxNZDMMozOmFCocm8hmGEY6Zj4yDMMwUphSMAzDMFKYUjAMwzBSmFIwDMMwUphSMAzDMFKYUjAMwzBSmFIwDMMwUphSMAzDMFKYUjAMwzBSmFIwDMMwUliaC6MssBXkDCMcTCkYscdWkDOM8DDzkRF7bAU5wwgPUwpG7LEV5AwjPCJXCiIyRESeFJHlIrJMRL6X2L6niDwuIq8l/vaLWjYjntgKcoYRHsUYKewErlTVUcBngEtF5BBgKvCEqo4Ankh8N4ys2ApyhhEekSsFVd2oqv9M/P8BsByoA84G7kocdhdQH7VsRjypH1PHjeeOpq62BgHqamu48dzR5mQ2jDwoqk9BRIYBY4AXgH1UdSM4xQHs7XHOxSKyQEQWbNq0KTJZY88rr8D48dCnDwwaBD/6EbS2Zj8vSVsbHHkkiMBDD3kf19Dgjhk7ttsi50L9mDqenXoiq6efzrNTT+yqEJYtg1NOcfXv3x++/W348MOuBT34IIweDbvsAoccArNmBRNg506YPh1GjIDevWHwYLjiCu/jL7/ctdMPfhC4joYRBUULSRWR3YA/A5er6vsiEug8Vb0NuA1g7NixWjgJy4gtW+Ckk9xL7sEHYdUquPJK96L/yU+ClXH77dDY6H/Mjh3w/e/DPvt0X+Yw2boVTjwRDjrIveTffReuugo2bnRKLMnf/gbnnQeXXAK/+Q08/DBMmgT9+jmF4seFF8ITT8B118HBB8P69U4RZ+KVV+COO2D33UOromGEhqpG/gGqgXnA99O2rQQGJv4fCKzMVs6RRx6pRgBuuEG1tlZ169b2bT/7mWpNTcdtXrz3nmr//qq3364KqnPmZD7uxz9WPeYY1cmTVQt1b+68U3Xo0NzOueEG1b59Vbdsad/24IOuLi++2L7tlFNUTzih47mnnqo6bpx/+Y88otqzp+qyZcHkGT9e9dprXT2uvDLYOYYRIsAC9XivFiP6SID/AZar6q/Sds0GJif+nww8GLVsBWPuXOjRA1av7rh99Wq3ffbswl7/kUdgwoSOPdOJE6G5Gf761+zn/7//B+PGOfOTF+vWwc9/Dr/+deb9Z5zhetDNaWGiv/ylM9MsWxasHvmyeLEzZ9XWtm875RRnvpk7133/6CN48kk4//yO506cCH//uxtteHHHHW4kcsgh2WW5/35YvhymWhyFUZoUw6cwDvgqcKKILE58TgOmAyeLyGvAyYnv5cHnP+/s+Hfd1XH7738PAwbAaad5n9va6uzVfp+2Nv/rr1jhXsjp7Lefs6+vWOF/7ksvwZ13wi9+4X/clVe6F+onP5l5/+9+B5s2wTXXuO/Ll8O118K0aXDoof5ld5cdO6BXr47bevZ0Cnn5cvd91SpoaenaTqNGufZ99VXv8l94wZmmvvMdp3j79IFzz4U33+x4XHOza6fp02HXXbtfL8MoAMWIPvqbqoqqfkJVj0h8HlbVd1V1vKqOSPx9L2rZCkZVFVxwgVMKmnCDqLrvX/2qe0F5MX48VFf7fy66yP/6W7Z07CUn6dfP7fPju9+FSy+FAw/0PubJJ2HePPjpT72PGTgQbrrJ2eqfeAImT4YxY7I7WtvaMivA9G3ZHOYHHghLlriXfpKFC9157yUes2Q7dG6nfv067s/EW285Bb94Mcyc6ZTowoVwzjnt9xvgxhtdO3zlK/7yGkYRsdxHUXHRRXDDDfDUU3DCCe5Funatc1D6ceut8MEH/sf075/9+pkc+aqZtyeZORNWroQ5c7yP2bkTLrvM9fr33ddfhkmT4IEH4PTTXS99yRKnMP348Y/daKIz1dXt/w8dCmvWeJfxjW84s9Z3vwvXX+8czZdc4q7d+fqd2yP5UvdrJ+edcE78vfZy2wYOhOOOg/nznWJfvdqNtubP9y/LMIqMKYWo2H9/OP5414s84QT396ijsptODjywY28zEz2yDPj69YOmpq7bt27NPIIA16ueMgWuvtr1zpua4P333b5t25yi6tvXmYWamlzPP3mNjz92vfCmJmcmSX+BT5rk7OpnneXCN7Nx8cXOH5HkoYfgtts6+mF69/Yv4+CD3TlXXOGUbI8erlyR9kip5Iigczslv3u1U/Lc/fdvVwgAxxzjTFbJUOCpU+HUU50syTLb2pwvo6kJ9tjDlIVRGnh5oOPwiV300d13q/bpo7phg/t7883ZzznuuGQ/1PszebJ/GZ/7nOrEiR23rVvnzp09O/M5W7b4X/OAA9xx3/ue/3F/+EN7mVu3qu63n+qYMW7fo49mr39n8ok+StLcrPrSS6pvvaW6c6fqHnu034MdO1Srq1VvuaXjOf/7v6o9eqg2NXmXe9xxqp/5TMdtra2uvJtuct8PP9y/ndavz69OhpEH+EQf2UghSs4919nnJ050vcSJE7OfE4b56NRTYcaM9t49uHj9mhpn4sjEbrs5E1c6b73levo33OCibcA5V+vrOx43fbozl9x6q3PUJrn8cjeCmD/ftcPXvw5Ll7pechTssoubmAbOn9PW1h5t1Lu3G8Hddx9885vt58yaBZ/9rL+MZ5zh5ids3tx+L55+2o22Dj/cfb/99q6T5SZOdO3/7W+7gAPDKAW8tEUcPrEbKaiqXnqp6xlOmhTdNd97T3XffVVPOkn18cdVb71VddddVX/4w47HHXCA6kUXeZezerX6zlNIkmmewpw57txHHnHf331XdeDA7KOczuQzUti6VfWqq1QfesiNTq6+2s0ruPPOjsc984xqVZUb/Tz5pOqUKaoiqvPmtR+zZo075q67OpY/ZIgbLcye7UaEgwe79vbD5ikYRYJSmqdQ8SR71dkihsKkXz8X8dPaCmee6Xq1V1zR1YEbJJInH957z9nwv/ENF54LsOeezh9x113+juwwqKqCRYtcpFd9vWuL++5zEWHpHHOM83f83/+5eR2zZ8Of/tRxNrOqa6P0MODdd3ejn379XO//0kudH+HeewtbL8MoAOKURjwZO3asLliwoNhi5MZVVzmTRHLimmEYRsSIyEJVzZigzHwKUbFypYtEuflm11M3hWAYRgliSiEqvvlNN/P1rLNcXL9hGEYJYkohKp56qtgSGIZhZMVsGIZhGEYKUwqGYRhGClMKhmEYRgrzKRhGHjQsamTGvJW82dTMoNoapkwYaWtCG2WBKQXDyJGGRY1c88DLNLe4iX6NTc1c88DLAKYYjNhj5iPDyJEZ81amFEKS5pZWZsxbWSSJDCM8TCkYRo682dSc03bDiBOmFAwjRwbV1uS03TDihCkFw8iRKRNGUlPdccW2muoqpkwYWSSJDCM8zNFsGDmSdCZb9JFRjphSMIw8qB9TZ0rAKEvMfGQYhmGkMKVgGIZhpDClYBiGYaQwpWAYhmGkMKVgGIZhpIj1Gs0isglYW2w5AtAf2FxsIQqI1S/elHP9yrlukH/9hqrqgEw7Yq0U4oKILPBaJLscsPrFm3KuXznXDQpTPzMfGYZhGClMKRiGYRgpTClEw23FFqDAWP3iTTnXr5zrBgWon/kUDMMwjBQ2UjAMwzBSmFIwDMMwUphSKCAicr2INIrI4sTntLR914jI6yKyUkQmFFPO7iIiPxARFZH+adtiXT8R+XcReSlx3x4TkUFp+2JdNwARmSEiKxJ1/IuI1KbtK4f6fVFElolIm4iM7bSvHOr3+YT8r4vI1FALV1X7FOgDXA/8IMP2Q4AlQG9gOLAKqCq2vHnWcQgwDzeJsH+51A/YPe3/y4BbyqVuiXqcAvRM/P8z4GdlVr9RwEjgKWBs2vbY1w+oSsi9P9ArUZ9DwirfRgrF4Wxgpqp+pKqrgdeBo4osU778B3AVkB6xEPv6qer7aV93pb1+sa8bgKo+pqo7E1+fBwYn/i+X+i1X1ZUZdpVD/Y4CXlfVN1T1Y2Amrl6hYEqh8HwnMUS/Q0T6JbbVAevTjtmQ2BYrROQsoFFVl3TaVS71+6mIrAe+DPwosbks6taJi4BHEv+XY/3SKYf6FbQOtvJaNxGR/wP2zbDrh8DNwL/jepn/DvwS9wOUDMeXZGxwlvr9G84M0eW0DNtKrn5+dVPVB1X1h8APReQa4DvAdcSkbpC9foljfgjsBO5Onpbh+NjWL9NpGbaVZP18KGgdTCl0E1U9KchxIvI74KHE1w04W3ySwcCbIYsWCl71E5HROJvsEhEBV4d/ishRxKR+Qe8d8CdgLk4pxKJukL1+IjIZOAMYrwljNWVUPw9iUz8fCloHMx8VEBEZmPb1HGBp4v/ZwEQR6S0iw4ERwD+ilq87qOrLqrq3qg5T1WG4B/WTqvoWZVA/ERmR9vUsYEXi/9jXDVz0CnA1cJaqbk/bVRb186Ec6vciMEJEhotIL2Airl6hYCOFwvJzETkCN7RbA3wTQFWXici9wCu4ofulqtpaLCHDpkzqN11ERgJtuMiqb0HZ1A3gJlwEzuOJkd7zqvqtcqmfiJwD/BYYAMwVkcWqOqEc6qeqO0XkO7iovyrgDlVdFlb5lubCMAzDSGHmI8MwDCOFKQXDMAwjhSkFwzAMI4UpBcMwDCOFKQXDMAwjhSkFw0ggIh/mcOzxInJ02vdvici/Jv6/ID2rag5lrknPNGsYxcDmKRhGfhwPfAg8B6Cqt6TtuwA3UTFuM2UNw5SCYfghImcC1+JSFL+LS45Xg5vM1ioiXwG+C4zHKYk1wFjgbhFpBj4LLMelb96cyO3/C1U9XkT2Au7BTbD6B2k5bRLlXpa47gvAJXGbZGXEEzMfGYY/fwM+o6pjcCmKr1LVNcAtwH+o6hGq+kzyYFW9H1gAfDmxr9mn7OuAvyXKng3sByAio4AvAeNU9QigFaeMDKPg2EjBMPwZDMxK5LHqBawOsexjgXMBVHWuiGxJbB8PHAm8mEhBUQO8E+J1DcMTUwqG4c9vgV+p6mwROR63ml6u7KR9VL5Lp32Z8swIcJeqXpPHtQyjW5j5yDD82QNoTPw/OW37B0Bfj3M671uD6/kDnJe2/WkSZiERORVILsL0BPAFEdk7sW9PERmap/yGkROmFAyjnT4isiHt833cyOA+EXkG2Jx27BzgHBFZLCKf61TO74FbEvtqgGnArxNlpDuLpwHHisg/cYsVrQNQ1Vdwzu3HROQl4HEgPQ27YRQMy5JqGIZhpLCRgmEYhpHClIJhGIaRwpSCYRiGkcKUgmEYhpHClIJhGIaRwpSCYRiGkcKUgmEYhpHi/wOsmGqx6yqIlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "# 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)]\n",
    "    \n",
    "# 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 \\\n",
    "                        for % Humidity', '% Humidity',(-50,15))\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, (-50,15), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel(\"Humidity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "liked-excitement",
   "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 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",
    "# 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)]\n",
    "    \n",
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Cloudiness\"]\n",
    "\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for % Cloudiness', '% Cloudiness',(-50,60))\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, (10,20), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel(\"Cloudiness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "northern-extreme",
   "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 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",
    "# 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)]\n",
    "    \n",
    "# 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 \\\n",
    "                        for % Cloudiness', '% Cloudiness',(-50,60))\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, (-30,30), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel(\"Cloudiness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "weekly-selling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LX6lbsmztlgM/H3q5rkNnVzeX3LKGrdv6AGhuzDH/lEOGlGXwu0dJ3j+3482c1MKNK7uLAr7VPqjLM6tHRH5OsXtrAGPMN9ISqhDN6lGSRnPXYd95t7m+4AKsX3Bi6sfv7Opm7g2r6esfKkVumLDwU1MycR86u7qZv3gNPb1WwzS2KcfFJx+SCdnC4JXV4+fqWQGsBEYBhwFP5z9TgX7vzRQl+2h9msrXplm4ZF2R0gfo22kycR9s48BW+gDb+3ZWUKLk8FT8xpirjTFXAwcCM40xPzfG/Bw4Bkv5K0rVEjcrpFrKF4eh0rVp/K51FoKntWwchPHx7wXsCryW/75LfpmiVC1x6tNUW/niICo9Ytcvi2av5saKx2BquXhdGMW/AOgSkWX57/8IzA/aSET2Bv4AvA/YCVxhjPmZiOwGLAImABuATxtjtkaWXFFKYO7sia4+fj9rNwvB0KRJq7Z9GObOnujp4585qaXijWwtF68LTOc0xlwFHAncnP98KO8CCmIHcL4x5iDgKOCrInIwMA+42xhzIHB3/ruilJU4MznVsgVYCTraW1n4ySmMbcoNLGtuzLHwU1NYtnZLxd0slXaFpUmgxS8iAhwL7GeM+a6ItInIEcaYh/22M8ZsBjbn/39TRJ4CWoGPA0fnV7sauAf4TuwzUGqWtLv6Ua3dWrYAK4XXPThv0SrX9f0a2aSfl0q7wtIkjKvnV1iumlnAd4E3gRuBD4Y9iIhMANqBh4A9840CxpjNIrKHxzZzgDkAbW1tYQ+l1AhZ9KfHcQ8p8YjayKb1vFTSFZYmYUbuHmmM+SqwHSDvjx8R9gAisgtWQ3GuMeaNsNsZY64wxkwzxkxraWkJu5lSI2QxoyLNib6VoUR1s2TxeckyYSz+PhFpID+YS0RasHoAgYhIDkvpX2OMuSm/+CURGZe39scBL8eQW6lxsupPr1ULMGtEdbNk9XnJKmEU/39iBXX3FJEfAJ8ELgzaKB8b+C3wlDHmJ46fFgNnYWULnQX8JarQSu2j/nQlSiOrz0s0wmT1XAN8G/ghVrC2wxhzfYh9Twc+D8wSkVX5z8ewFP5xIvI0cFz+u6IMoZYzKpRk6ezqZtu7O4qW6/PiTdgibbsD24wxV4lIi4jsa4xZ77eBMeZ+vIvbHRNFSKX+qOWMCiU53GouwdBib0oxYdI5LwamAROBq4Ac8Ccsi15RUkP96UoQbkFdgNEjh+uz40MYi/8TWKmYjwIYYzaJyK6pSqUoMaj0EH+l/GhQNx5h0jnfNVbtZjurZ3S6IilKdLI+qYiSDpWuMFqthFH814nIr4FmEfkn4C7gynTFUtKmlqpMQvnzuGvt+lUrmgQQj0BXjzHmMhE5DngDeD9wkTHmztQlqzC17DbI4qjYUilnl78Wr1+1okkA8Qib1fM40Ijl7nk8PXGyQa2/2HGqTGa9ISxnHne1V+nM+r2MiiYBRCfQ1SMiXwYeBk7FGrz1oIick7ZglaTWh39HtY6rwX9ezi5/NQcUq+Fepo266cJZ/HOBdmPMqwAi8l7gAeB3aQpWSar5xQ5DVOu4XBZuKZZoObv81TxK1Otenn/das5btKomegB+xOnNV/u8u26EUfwvYlXktHkTeCEdcbJBVl7stLrkUatMlqMhTMK9Vq4ufzVX6fS6Z/3Gmgyl1tyahUQ1Yjq7upl7/Wr6dg5OFrN1Wx9zb1gNVO81CpPV0w08JCLz84O5HgSeEZFvici30hWvMqTlNojSxUyzSx61ymQ5Uuaqyb1WzVU6w9yzrF53N6K6baIaMQuXrBui9G36+rMxIXxcwlj8z+Y/NnZRtZodxOXlNgCYvmBpLAs8qkWbtnslinVcDgu32txr1RpQdLuXbmT1ujuJ00uM2pvP+oTwcQmTznmJ/b+IjAV68gO6aprCF7tUV0RURZ4lRVgO/3lW3Gu1TuG9HCYy4OZxEua6267I7p5eGvL7aS1jjCCOcRTViAmaEL5a8VT8InIRcJ0xZq2IjAT+CkwFdojIZ4wxd5VJxkxQqgUeVZFnTRGmbeFmwW9ea2mOXjjvpVuRszDXvXC7SsQI4hhHUY2YubMnFvn4AXINUhUxHS/8LP7Tge/l/z8LKx7QgjWI62qsEbx1Q6kWeFRFngVFWE4qPRCn1sdueBH3unsVR4PyjWmIaxxFMWLs9eopq+ddh0tnNnCtMaYfeEpEwg78qhlKtcCjKvJKK8JKUEm/ebUPyiqFONc9yOAph0uyXMZRlOtTLb1GPwX+jogcCrwEzAT+1fFbU6pSZZBSH7I4irxaA4jVSJZiKtWAn+/b/j1tsmYcVVOv0U/xfxO4Acu9c7k98Up+Fq2uMsiWKZJ4yEpV5NViTVQjWYupZB2/7KByuiSzZBxVU6/RU/EbYx4CJrksvx24PU2hskolH7JqsiaqkXqLqZSK0xCqVFZP1qimXmPd+eqrlWqyJqqRrLkNqoEsWdtZoJp6jar4q4RyWxP16FZSRRaPenxW3KimXqMq/jKQxItRTmtC3UpKWPRZGaSaeo1+A7hO9dvQGHOT3+8i8jvgJOBlY8yh+WXzgX8CtuRX+7d8zKBmCfNihGkYymlNVLNbqbOrm0tuWcPWbVbOtQgYQ0m+51qwaNM6h2p+VpwUPjfNjTnmnxI9V79aeo1+Fv/J+b97AB8Glua/zwTuAXwVP/B74BfAHwqWX26MuSySlFVM0IsR1mIqpzVRTUEqJ51d3cy9YTV9/YOjLO2RKHEt0VqwaNM8B69nwi/VM2u4PTc9vX3Mvb66K3D64Vmd0xhztjHmbKxZtw42xpxmjDkNOCTMjo0xfwNeS0bM6iVIiUapStnR3sryebNYv+BEls+bldoDWa0TWC9csm7Iy1tInKqT1VQ11Is0z8HrmRComglOvJ6bvp3VXYHTjzBlmScYYzY7vr+EVbYhLl8TkcdE5Hf5om+uiMgcEVkhIiu2bNnitVrmCVKiWbSuq3UC6zDXLOp1zeL9iUqa5zB39kTEZbmBqlGatVqB048wiv8eEVkiIl8UkbOA24BlMY/3X8D+WMXeNgM/9lrRGHOFMWaaMWZaS0tLzMNVniAlmkXrulrrzYe5ZlGvaxbvT1TSPIeO9la8+ljVojT9rkM13ecoBCp+Y8zXgF8DU7AU9hXGmK/HOZgx5iVjTL8xZidwJXBEnP1UE0FKNKvWdbncSkkyd/ZEcg1u9qdFnOua1fsThbTPobXKG0ev5yY3rLorcPoRKp0zn8ETFMwNRETGOdxGnwCeKHWf1YBfpD+rKWDVmMliy+eW1dMgMsSvHbU6Y7VdCydpn0M15a+74fbcxM3qqRYkaE6VfFrnj7CyeyT/McaY9wRsdy1wNLA7Vlzg4vz3qVguwA3AVwriB65MmzbNrFixImg1JSG8arSX290Tp/Ep3GbmpBZuXNld8XNJArfrAdlolJI2FIL2V42GSVSSOEcRWWmMmVa0PITifwY42RjzVKQjJogq/vIyfcFS13S81uZGls+bVRYZ4jQ+btsIuPqgy3kuSeB2brkGAcOQSUKqtVFzEnTvs2KYpElS5+il+MMEd1+qpNJXyk8WMlnipCC6bVPtgUewlMD5160uOre+flM0M1S1pZq6EXTvayHFNoi0zzGMj3+FiCwCOoF37IVBI3frnWruimah2FScxieKMq+WwKNt+bnNi+tFOQdPpTHvbtC9D/q9mt89m7SNrzAW/3uAbcDxWKN5T8YqxaB4YL+s3T29GAZHSlbLgJYsZLLESUH0G0zkpJoCj35THHrRIEPPuLOrm+kLlrLvvNuYvmBpYs+h8zmH4nl34x4n6N77/V7t755N2mnEYdI5z3b5nJPI0WuUau+KZiGPP07j47XNZ49qq7oxCTZxLDxn7yBNRRhm3t04BN17v9+r/d2zSdv48ivS9m1jzH+IyM9xcZUaY76RiAQ1SJLdtEp1W9MsNhXmnOJOVRl1m6zj5XZrEOE9jcMH0g+dOPPq0yyilta8u0H30e/38xatSlSWSpH2s+zn47cDuppOE5GkfOS1UiDML73S75zCND5ujUg1Zev40dnVzda33ylanmsQFn5yCkBg/nyavuIk592NWh3T69nIQnwqKdI0vvxcPfuLyAeBa4wxVxd+UpGmRkiqm1bt3VY3N8M1Dz6f2DnVij/XDbti5La+nUW/9eczecK45NL0Fbs95zZRnnf7XJ29F7s6ZtR7mYX4VDXgZ/GPB34GTBKRx4AHgOXA/xpj6r7qph9JddPiWmtZyWpIO72yVmrBu+FXaXSnYeAcg6zCNEfVJjXvblB1zCj3shbdfWngN9n6vwKIyAhgGlZN/nOAK0WkxxhzcHlErE6S6KbF6bZmyT2UdnplFsYbpEVc/7lbo3/pqZNTU4RJPOdJV8eslslQKkmYdM5GrJTOMfnPJuChNIVSLOJ0W7PkHko7vbIWKmd6EXQObr97ub6AgYJ7duZL0qmdpVCP1TErjafiF5ErRGQ5sAj4EJar51P5Uslnl0vAeiZOWmWWrOC00ytr2Z/rV2k01+BeNTKo0c9qTKQeq2OGZmdxjCcJ/Hz8bcBI4GmgG3gR6ElFCsWTqN1WL/fQMBE6u7rL2gVO299ay/5ct4qRAGObclx8snu2SymzvVXqmtmuqb5+M1BJFZKpjpmVWJcv/f2wciXcdZf1WeYy1ckzz8D++yd6WD8f/wkiIlhTLX4YOB84VERewwrwXpyoJEogbqmRy9ZuGfJguwXzwBrUU25ffzlevMKG0R6lWsox05A7zj7DNvr2vr0C50nO9pbktSmMRxmTXLG1uLGuVJ7Zl1+GpUsHlfvGjeG3nTUL9t67tOO7EFidE0BExgPTsRqAk4D3GmOaE5fGA63O6V6trxD7pQE4/7rVrvVdylWVshIVFJM4Zhpyp3ktgp4L53FKrbqa9HmkWQXWa9/2/t0Ueuzzc1rtd94J99wTTdjJk+HYY63PRz4Cu+wSbXsfvKpz+o3c/QaWop8O9JFP5QR+BzyemGQZIo2CU6XI4bQ6wtRssbvty+fNqvgIxkq4FZI4Zhpyp3kt/J6LwmfXqzfY3dNL+3fv8HQhpXUeXs9id09vyW5Jv+fcy/r3Pb/WHNx996Byf+GF8MKMHj2o2I87Dt7/fmuGoAri5+OfANwAnBdmspRqp7C1Lyw4BeVxkXh1UcMW6rIf+EqPYKxEkDmJY5a6D7dGO81r4bUPgSKr2StuALB1Wx9zb1g9ZL2wx4p7Hn4jf0t954JGFRc1WP397PHkKjo2rOIfNnRx1AsFkwNeEHDAyZMtpX7ccTBjRqJWexr4+fi/VU5BKk2YglPlUPxeVofdAwnCVuwzJ7XwpwefL/p95qTyTFxfiYYniWOWsg+vRntMY46e3uKaOklci6jydrS3snDJOtcaP339hktuWePp4w57rLB+cq8eCJT+zrnt+71v9zB942pmbOhi+obVtL65ZYhCv9lvh7vsMmixH3ssHHhgxa32Ugg15249kFbBKSdeL4RzuZdq7zeGxlxDoI/fTn9btnaL6zpey5OmEvOwJnHMUvbh1WiPyg0rundJXYs48vo9y1u39Q00CoW93TDHihJUtb+fm5Rbsq8PbrkFHniAjhUr6Lj33kibv37gQdz43oO4d+8pPDz+EHpHjKq5mb1sVPHnSbLglBteL8SKja8VzQnrRqvD1++X1WM/oJXO569EqmUSxyxlH17XtmdbH5efPjWVaxFH3qBn3YnT8g5zrKhxALsHErrXsn49XHklXHEFvPpqqHOweWtEI/dPmMr9+0xl5QGH8ZUvn0DHYeMHfh8D7NbVzTNL1rG9p7ci8b1yESqrp9KkndXT2dXN/MVrXLvjkEwGhleWQRgXTq5BGD1iOK/39oVWGlmYNzeLpJlimoVrHmZC9pmTWlj08AtF0zZ6IcD6BSeGWnffebe59lr99uE0iobt7OcDf3+Gjz37EHMeuC7UMQvZ/JHjuKj1I9z/voPoHTEKsAaD7TJqOD3bwr9DtUDkrJ56wSsdbphYxbCSavW9rEE/pS9Ac1OOt7bvGGiUwgabK+FqyTpp1zGq9DV3O7+5N6weMiF7d08vN67s5vQj9ubW1ZsHnquxTTmMoeRYRFAcoLOrmytvfJD9H3uI4zc9zqzux+j4+yY6op7s2WfDnDlw5JFFvvZPujTAfTsNTSOG03XR8VGPVJOkpvhF5HdYOf8vG2MOzS/bDasExARgA/BpY8zWtGQIg1dQd9yY0qy0QsvLK8DnZfE3N+ZYdfHxTF+wtCgQFybwVcujWuOSdopplGueRs/D7fzcql729vWzbO0WVl08VAl65bFHjZFceMMqDnhhHTM2dDFjw6rBDJkLoAMClfyTe+zLfRPaeWHMntwxZRb/9pkPRbo2Ud2cVTHCN2HStPh/D/wC+INj2TzgbmPMAhGZl//+nRRlCCQNX7ib5ZVrEHLDZEj3ujHXwGmHt7p2u99+dwedXd0lyadVCodSjrhH2Mlj0uh5lJq2GslYeOmlwbz2u+4ayGvvIFixvzmikfsntLN8wlTu32cKffvuz/ILjvF0lV1yy5pEypb4FbbLQjXbcpKa4jfG/E1EJhQs/jhwdP7/q4F7qLDiTyPt0MvyGtuUo2nE8KKX6rbHNhdZ9X39Vi3ySufj1xJZuZZp9TyiBG390j072lthxw5YsQJuuwrOuwsiZsgwdergoKUZM2D0aG///+vbAe+Ga+u2vkgDuoJcbk4Lf5hLj7vS9YvKQbl9/Hvag8GMMZtFZA+vFUVkDjAHoK2tLTWB0vDL+mV3uPkYe1xyqu39XH76VPXVJ0SlffA2afU83M4v1yBDfPzgOGcPqz0U73nPoGI/9lg44IDAvPaghtev4YqiiP16Ll4DNQuphTkd/MhscNcYcwVwBVhZPWkdJw1feFTL0m/9IPnK7Z+sZn9oVuIeafU8Cs9v711H8L3xvbQ8fD/b/3oHh61fPbjy90Ps0MVqL4Wghnfu7ImJ5fR7udzClD6B2u9Rl1vxvyQi4/LW/jjg5TIf35WoVRDjjEj0syy91p85qWVIpcnLT59aVIkyCf9k2PNyO955i1axYuNrfL9jcujjVRK/ex1mgF0SjUUUV0Tg8Qqs9o4XXgidIfPWyNHct88Unjjog0w9+zSOO2VGKqNRneczpjHHqNywgbTKmZNaWLhkHectWsVezY005Ya5zjOclCIO04DUQ4+63Ip/MXAWsCD/9y9lPn5s/JQsFFuRUaa7c7NEZ05qGTKwy02pe/mKowTDojQeXnPo2qUhqkX5uxF2gJ3f9QmrsDvaW1mx8TWufegF+o2hQYTTDm91dUX8/bW3+PN/Xsf7mzZz8JqHI/vaeyYewo1jJ3Hv3h/g4b0PYXtulLv7Z8XbXNq2KfHeT+H59PT20Zhr4PLTpwIUJ0EME3INMiQbKUlF7NXbahBhpzFV14uNS2oDuETkWqxA7u7AS8DFQCdwHdYkL89jzegVOHF7Fsoye2UcNDfmeGfHzsRL7oYZDOQVLAP4aUHvoJTj2PgdT6CoR1JNRB1gV6goAFcr3u05KFSGu7+9laNffJyv79zIqHuXsufrEcpqOH3txx1nTdjhsNr9yhMXksZAM7/nC/B8p0aPLE6CSIJKlAuvJGUfwGWMOdPjp2PSOmaaeAZsXXLzk8gKCBMATCIYFiXQ6Hc8E+GYWSTqALvC6q2jcsO8M3Um72llyORL+nb87W+RBiyt2WM/lk+YypzvfcXytTc1hd42icqkpRAnkP16b1/RGIOkyEqcp9JkNribNaKkykHpL1GYAGApwbCwszY5mTt7IuctWuW5zaYeq466s+xvElPolQM/F0BQSY3evn5G97zCKRtX8w/rVzFjQxfj3nLUkQko6fvGiCYrpz3/2dg8rsjX3trcyJzjo1vjSaR4lkLQc1yJ9Fod36KKPzReAblRuWGuJW7DlvH1sjzcjidYL8r0BUsH1vWqMdTclPM9btCsTW4+Vds37VbuGWBMY465N6we4p/t6e1j7vX+dd6zgNf9Pe3wVm5c2c2777zLBzY/PTAa9cgX10Q7wNSpAyV9D7njLd4ePjL0prEDvx7n5ZvimTBBgewspNfWI6r4Q+LVRYR4D29QUNV5vO6eXgQGLG3nuvNPOaRI2QK8tX2H56CXKLM2FWIHcK958Pkhln9jrgER9xIBfTtN5t1AHe2tPLX6aV6+6Tamr+9i+sZVjHvTstrDZD6+ObKJB/Zt529tUwas9sYRw119x28vvS20XK2O52zqJXcMaeTDZHH5PbflcHeErehZz26XSqDVORMgTrpflKCq17pjm3J0XXR8kUJw21eYmv92gDZurRk/NxDAhpAVHlNjxw545BGe+sONbLt9CYc//0TwNk7a23l68pEs6BvP8nGT2J6zKj865zoOc+3CBlztipZBPTS7cciii63UVNisTHxfbhmTwiu4q4q/QkQpXxuUveOlcMMqDpuxTTm298XPUPJTaGXL+vn73wdHot51F3R3h970jZGjuX+fKQP+9o3N42gd21TUECehzOZevzpUWeTW5kbefmeHZ8lwm8IUSLBKES/81JSKKaFSM2iqYeL7rGcJqeLPGElY/Pb64B4ks/cVxsJszDUwcviwwJ6DH51d3a5upzD7Ca1Md+yAhx8eVOz33Rcol5N1ex3AsvEfYPmEqTwy/uABq92LKLXoo9D+3TtcY0Nx8AtAV3L+Bb8U2TA586XMb+B8nprzJadf7+1zrc0Tdp9Jy1gOtB5/xogyutcve6e7p5efBtTz8cvwERjiqnEjbIaS/QJHzTQqtJrefXETD1zyPxy2cwNtjz4QyWpnzJihee377TckQ+YEn96TG2llmHjVZ4pK0HSchde8nG6JoBTZoBhFUCqo30hr5/PkbGCTrs1T6Znu4qKKv0JEzSd2BncLlwO+I4W9UuoKrZJIU+D5nFfgfgqs9o777os2EUd7+6Binz49Ul67X3pj4TVOM8MkanqwG2Obclx88iGe19s+jk25SxCHOUe/MS9+qaB+5xK2Hk/hPuOQlYqvUVHFX0Gi1G73slLtgVPL583y3FfY3kVS1Svnzp7IT66+l8OefZQZG1YxfcOqwbz2gJx2GPS1379vOz/85XlFVnspzJ090dW/nmsQTv/g3p5zGCdN1PRgr5LeA/tzcbHlhsmQe5f2RDSFzJzU4pn668TLOvZ7Hv3OJaq1XUoDn5WKr1FRxZ8CSXanw1gvzgfd79hBMkXqhfT1wSOPuPraOwiejMNptc/63z6e2+bud2X//YP2FAn7XJzjH2zLOW5wL869jpoe7CefvdwtqwcYKPTnN/AuDW5dvTnUen5zA4D78+jnlgzT00iqNk+1jgTW4G7CJB3l98vosbFdNolnGGzeDHffzfPXLWbUvcvY441Xwm9r+9rzg5b8FLib3HEmmPfad5yX0mvScmeDkRsGhYUkhwHk52tuEOHMI/eOXLwuisyFI6Vtd1VrvtDfokde8Ay22wQFIuNewwnzgscrOOWNco/9gqpuVriTLGXdpI0GdxMgzAuQdHc6yHoRrC51rGP7WO1OPKfBaW8fVOwzZkBjPL9modUUdYL5sEG+sD5tr0nL+/sNTj3vUj3Y+j2vZ/uNiVW51MsFWHiebordOcgvjJsl1yC+bomoVWmjPONegxLDlML2c7G4PU92Vk+1WORpo4o/JGGVSNJR/iDrxQA3ruxm2j67uR6j5a3XOGLNavjiH+DOO2HTpvAHb25m6fgPcFfrZGvy6+b3DfyUdLqaU9lFmWA+apAvTCMcdtLysFz70Asll6x2O8/C0dOxCNiB1zWcv3jNkKq0bu/D2Kacb7yi0KCx9wvF5ZoL9x3kYtF6PP6o4g+J3wvgfMDCRvmj1G63j+82R+jw/h0c9OJTvPLtP9P5zEqmbIg4GvWww4ZmyBRY7ed4dNdLzUjxI0rjGSfIF9QIJ+3z7jdmyIQ6cSxOr7kQSiWonEYpVWkvPrm4nEiuQbj45EM8ffQ9vX3MX7wmVIOtyj0+qvhD0NnV7anoenqHTgQdJsof1QXR8b5hdIzfBE/dyUs33cqebwVOYTDA66N24a0Z/0jrp06xFPx++4XeFvzr0Uchip84Soqcn3KPm2qXRKplIfb+4qZQppkX7rfvUqrS+lnlfimoXqOUs54bX02o4g/AVtJ+LFyybuCv7VMcOXyYp0+x0HobsNrn/hm2PwPLl3sea0+XZY+97wBWT5zG5y/8EotHT+BH9z6fWIZBUD36MERt6MJORTl39kRf5R431c6romWhjz8ucWI+cRqj3DCr0Q6qDOHXEJZaldbLKvcblBhHTiUaNa/4S02tDJNOaSsy50hBe3q5gWNt2jQQRL3p5tsiWe2MHTswGvWOcYfyzYffcM3cob2VU4BTPnRA+H0H0Ooz+CssUX3tbpai11SUdtnkMEG+sPffL9XSmdXTlBvGyFzDwPyxc2dPLJpSMamRonNnT3R1m5z+wb257bHNrlk9hVU4m0Y08Pa7xc+ynRwQ9VqUkr/e0d46JBvJiVfNqKznxlcTNa34C2vH2NkZEL6bHeYFbRChb/s7HL75//EPG7qYvmE1H+x+0rOeb5DV7uZrH2jAntvKmMYcw4SBl3jk8GGhziUOSQxQ8XPHeDXMhZbi9AVLXRuPZWu3+I5atvdjpz2eu2gV5y5aRVNuGCOGN3j2ypzb/fvNjw+xTkePaOAHnxiaDuiWceNUyIXEsl4L2xAD0/bZzTdw3Nk1WO5iu1saErBs7Rbfw/r50ksxqi4++RDPMQul7jspslx5sxRqWvFfcsuaomyMvn4TaTLyZkdmwh5vvsqMjdZI1BkbV6dmtRdyYefjQzI4Cn2gPb19qQ29T2KAipebomlEw5DKon4uIC83x6ae3sAgn1vxuG19O9mWV4Rex+3s6ub861fTX+Arefvdfs53TC7j5sryS6UUiGy9Llyyrmi0cVBgtlCupOvUlBpcDZOZU0nKXeKinNS04veytjyrIvb1wUMPDea1L19OV4TjPfa+A1i+j1XSd0XrQeze0uya8ng8cOn4cJZEZ1d3qLS9NIfel/qCe/nM3dwOzvOwrS0/33YYy3nhknWB6Zhu12/hknVFSt+m36F0o9aGMURXHHEylMLKVUnfeZYzc8pd4gLK18OoacXvhm2184VFlnLfHG5YOUDPqF24f5+pA/Xad07Y19PfCfD2O96zYIV94P3mxS0krayHUh9GN8vOr8a87QIKmkMgrOUc9roUrhc27TPqdY8SH7GJk6EURi71nXtT7sqb5exhVETxi8gG4E2gH9jhNqQ4Cd7/bg+3//QLDDcRcjEOP3ywrO/06Uz9j/u8J8HI35hLT53MpadOLgpWJeGCifKQRbHcwirzpB7GwoZuX5/h/Hs1N4ayVsNazmEzYgqvX9B29vpRMm7iKto4sRa/CeSTqFOTFp1d3YnVUiqFclfeLGcPI72oYDAzjTFT01L6AD/aZVOR0u8ZtQu3T5rBBbO/xj985TdM+M6tHHThX+l89EUwBlasgAULLMXf2BhYFDLoxti/R6Gzq5vpC5ay77zbGBYyXz6KQrGVeXe+cJetzJ2BQBu/h7EUvF4e24oP0+CFtZz9slZsvKqVNgxzv/4NjsqXc2dPpDHXEHiM5sZcrBoxdiPd29c/MH6itbkxcF9ucjXmGvjxp6ewfsGJvhVdK4U9O5nT2Nq6rY+5N6x2fT7TxOv6pdVDKmcPo6ZdPe3fnUvnKaez8K5nfF0Mfso7zIQZ3T29XNj5uGfsIGjiCCdhAnICfHj/3djwam8s90sUy8LLku3u6S1pNKpXKYpROcsWCbKiw76AnV3d3LiyWGGMaBCaAorA2d///ebHh8QjCrN6nK6s7p7egTRO+2/UAmSF8hc+D4Xpql4kEZgvN25BbLCSMs6/LlpGXqmU+/qVs4dRkeqcIrIe2IrVW/+1MeYKl3XmAHMA2traDt+4cWOkY3gp2Shz3UL4ibH98KoY6FYlMOp0dVFcNkGTrTtltfex/wW3hxqsFafioZ0qWRjkbcw1uObnx6nkGHVqvAs7Hx+Shx+luqZXXMLLVRHGpZHm1H5xnp20lV9QNdparqyZxvy9WavOOd0Ys0lE9gDuFJG1xpi/OVfINwZXgFWWOcrO/fzSUVvVoCJpYQiaOMJ5U726dTuNKWqYgvzvzqwYrxm8CincR9gRunF9kds8MntuXb2ZUblhA+dm15dPMxvmws7Hh6RhRq2u6RWX2LqtONbjNuG67dJwrpdW9z9s7CZrs3alnVVTScrZw6iIj98Ysyn/92XgZuCIJPfvp2Sj+O3cfKtRaW7M0dHeGvoF9mqA3Jb7nafTjw/RCno5ffhRMlCiKqP5i9d4ytXT2zfEdfbOjnjFEqJcz2sfesF1Xa/lhfidf2FcxM+l4VwvivxRCBu7SSvG40WYeIzTdWrHwqYvWFr2GEAadLS3snzerNRjMGVX/CIyWkR2tf/HSmuPWFLSHz8l29HeyqWnTqa1uRHBO0hWqDj7jSGq6m/MNQzMghT2BY7SMPmdZ9Tccq99u8njdR2iKKMLOx/3zpZyIa6yiXI9S61LFHT+ToXlZ9U672taAUa/AXF+34OWl4JXPKYQ55y7zgSF8xat4sJO/7paikUlXD17AjeLZUEPB/6vMeZ/kjxAkDsnTA59KWVwBYq6aV61Vgpf4CjdPb/zjJIVE3StCuWZOamFRQ+/MMRiLZzf1Q97UFpU4iibKNez1EqkQW5Bp8Lyw9mApNH991OOzU25IlnKFXAMY6z4zblrgGsefJ5p++xWk66gJCm74jfGPAdMSfMYadaXCcIZdLO7onbFzv7C0aMeLUnYwV1+5xk04tV5PYKuVaE8nV3dLHqkwP0RoTsUNChttEcxsbjKJuz1PGq/sSx/trgMx1H7jQ19HBhayM3GT2E58TIGklRkfq6rnm197DvvtoEGJol3KSx+71yhMeVVz99AzcYAkqSSefypEdad40ccJeN8IQq7olu39RWV9LVrrcTF7zz9csut1HTDeYtWsXDJOk47vDXStXIrgVDom/bD7wUf25TjB5+YXNb8aZsNr7rL5bXcid3In7doFaNHDudzR7W5XtOgc1/4ySmemTWl+LOd2/u5rkz+4wzilvouhcXrnWttbizyeZc6Yrneqdk8/lKtpJmTWkLNWWpTmGIY1sdeaqqo13kW5pY7s3p2Guh1FCi75sHn+exRbZ6ZK4XpfGH9w1547UNgSDqjWyngoLEDpUyu7jdmwav0hr2tW5G25sbc0NLcPuful57ptv/zFq1ixcbXQmUbhSl/4UZvXz/nLloVaxyC331wq2S6bO0Wz+yz7p5eJsy7bUhm19zZE4cU+HOidfuDqVnFXwqurgwfBIpe2rBKMG62UBjsRiFoLIKfb9RN6XilhoZ94dzcBwJ89qi2IYOioqYVljq5uh9++/Fq5N1KdkSZZMbPiIjizy410B81hTNogna/SqYGPJ+vnt4+5jqqoq7Y+FpRAUOtPRSOmnT12MTtHoep5ujETeGFVYJRZrKKS5hGyPaNwtDrdv51q12VTmFzFXUSjkL3weWnT/W1XsOkFcZNPQyjGP32EyWNE4bOnzC2KTcwWM2rhIbX/p33zI8kXB9u5+H1fvndh7A1mLwMIqd79Psdk7n89KllcUPVGjVr8Zcy8CTKi+Kl8Py6ok7c8uSTHikZtoiYW1VMr4bJHkFbSsXOKOuHmcwligsq7EjmMDIEXV9nGmehtb+9bye3PbbZd3Bfqe41r+2bG3O8s2Nn6N6Acx9+71cSKaB+BlHhvL6q6KNTsxZ/KQNPwlrrDSKcdrhVj73Q6ulobw1UKG6NRpQCamEJW0QsbFVMGPRJl6vYl9c9aW7KDRlvEWbbwmtcqgxB19fezuuZDKrxNHf2xJLGTniNBZh/yiFDel7NjTnGFqRzOnFa4X7vl9+YlST87+rDL52aVfylWB1hRg/mGqw6Ln5ddL9Rr17d0jRGStqulSBmTmrJbA13L+VlDL4NlZuscXzefudsX183pencLqrLxTmW4rNHtcV2r/llfzlHiq66+Hi6Ljrecz9OK9zv/fIbdBbWCAHrHSta5hgvUosjd8tFzSr+Uoa6B81Baqfd+XXRwVtZ/fT0qZ5WclojJTvaWwPLLyxbuyXw+ti9nHJ3r72U1+s+o3+9GtegfPHW5kY+d1QbzY2DityuGmpTqHQAui46np/6+Jw9ey2NOd/01c6ubpat3TLE9x3Vnx2lFIDXc+Jc7vd+BTU0zt+8fPmtzY0s/OSUIY1pc2OOhZ+aMlCHKqhnrA2DNzXr4y9l4ImXYrAreNoTd3t10e2SxXNnT/SdCByK/fnOOX6dxO3eOvc/pjFHrkE8A9fdPb187qi2oqqYTvqN4caV3RUZHenmzw0aqOaGX0qlPciqMJV367bBjBIozkxxxo+8UkzdMqKcZT3cnpNSyjLHJcy745WZ5Xz2w1Q/PWq/sTz6/Ouux/Lz3wf1jAsH0XX39BYVwKtnKlKWOSrTpk0zK1asiLxd3CCpXyncKNU67RRFv/z4MPuKW5rVbf+5YcIOY/C67XZJ5GVrt7Cpp5dhHmUMkigLnARB19Dt2nltI/mPXzm45sYco0cOD52L73asKOWlw5RlTqNscti5I7waNLdzLKx+OnCOMeaW8Cvf3Jhr8HwexjblfN1ZtUbWyjKXhbgR/6BSCGH9w0G51n77ilN7Psz++3YaxjbleGv7DtfqkL19/Sxbu2VAqXhNkZiV0ZGFA9UKcSvja///bzc9xra+QTVvj1r1o6e3z9O95HZNvHLwwzacQa6/tMomh3l3/MaJ2NfRKY9XqYgHn9vKs5d+LJJ8ftNK+r2fXr30eqNmffyl4OejjKrwvPLjwwyqspVD3BfYS9aebX3sMsq7zXdul1ZZ4CSx/ddemS9u16GjvZV3dsTr7Ua5JqXGbIKOVe6yyW4EnYstT6nVT514xc/KMS6mFlDF74FXMCyOwnPmxzuDUUFjdku1qv2Uht+Uks7tyj3vaClEbaTiKomZk1pCX5NSG86g61/OsslehDmXTT29noHcOKPXvYyzoAQGZ8C+nlHFHxGvdLTGnPel9MqPdxsBW7hdKfgpjaDJzm2SKHhXLqI2UnHLZSxbuyX0NSm14Qy6/mM8FJnX8jQIk6K5V3MjZx65t+tvXsuDcDPOgmQ5acq4WMeqNWrax58GXgXE7OCVV+0QvzKyzY05zzK+XoQJvvnJCsXlmAvr5Tj3k0VFX0jQ+RZy5pF7RyrEZ2MXSdsrX2rC79rYvzmzwJwlG8Lgd/292q4US0AVYct2/nWrXXtRtjFhrxd3TuMosnjFfCqVkZY1ajqrpxJ4KeSg7IwomRlJTcpczkm0s4ozvTAOYa57UOZRU24YPzz1A0D0CVe8slvs1OOwJPEseGUw+WW2pUmaE9VXC15ZPar4y0RSyhr0gU4LvxRBP4Kue1Ag34swz0cSz0KSz2aWjImkGsVqpi7TOStN4UvgzI8v5aXIQkCvFvFLEdxpjGejEHTd48654JaKWkgSM2T5ZQZFfT6z5BYs57SR1YYGd1Piws7HOW/RqiFZPDeu7Gbu7IklFzarhhTLasQrEPvjT09h/YITPTNG/K57Z1d3lFkpiwhqVJIIvteqIVFNGWnlRi3+FLAnEy+0EONaUYWUcx7UeiIoOBznugfNLxxEmMa8VCu7Vi3jqMH+ekIVf4IE1YWHZKwofaDTw0+JxrnupdzvcjXmtWxIZMn1lCUqovhF5ATgZ0AD8BtjzIJKyJEkYevuJGVF6QNdGaJe97CT4BTinF82bdSQqD/KrvhFpAH4JXAc8CLwiIgsNsY8WW5ZkiRMDZ/CwVFK7eNlTTv98FnIhFFDor6ohMV/BPCMMeY5ABH5M/BxoKoVf1CX3mtwlFLbhLGmVekq5aYSir8VcJbpexE4snAlEZkDzAFoa2srj2Ql4NelL6XCplL9qGJXskYl0jndstuKEh+MMVcYY6YZY6a1tARPhVhp4sy2pSiKUgkqYfG/CDirMo0HNlVAjkTRAJmiKNVCJRT/I8CBIrIv0A2cAXymAnIkjnbpFUWpBsqu+I0xO0Tka8ASrHTO3xlj1pRbDkVRlHqlInn8xpjbgdsrcWxFUZR6R2v1KIqi1Bmq+BVFUeoMVfyKoih1RlVMxCIiW4CNMTffHXglQXGSRGWLh8oWD5UtHlmVLYxc+xhjigZCVYXiLwURWeE2A00WUNniobLFQ2WLR1ZlK0UudfUoiqLUGar4FUVR6ox6UPxXVFoAH1S2eKhs8VDZ4pFV2WLLVfM+fkVRFGUo9WDxK4qiKA5U8SuKotQZNav4ReQEEVknIs+IyLwMyPM7EXlZRJ5wLNtNRO4Ukafzf8dWQK69RWSZiDwlImtE5JsZkm2UiDwsIqvzsl2SFdkcMjaISJeI3Jol2URkg4g8LiKrRGRFxmRrFpEbRGRt/rn7UBZkE5GJ+etlf94QkXOzIFtevvPy78ETInJt/v2IJVtNKn7HvL4fBQ4GzhSRgysrFb8HTihYNg+42xhzIHB3/nu52QGcb4w5CDgK+Gr+WmVBtneAWcaYKcBU4AQROSojstl8E3jK8T1Lss00xkx15HpnRbafAf9jjJkETMG6fhWXzRizLn+9pgKHA9uAm7Mgm4i0At8AphljDsWqbHxGbNmMMTX3AT4ELHF8vwC4IANyTQCecHxfB4zL/z8OWJcBGf8CHJc12YAm4FGsaTozIRvWJEJ3A7OAW7N0T4ENwO4FyyouG/AeYD35xJIsyVYgz/HA8qzIxuCUtbthVVW+NS9jLNlq0uLHfV7fLM6QsqcxZjNA/u8elRRGRCYA7cBDZES2vCtlFfAycKcxJjOyAT8Fvg3sdCzLimwGuENEVubnr86KbPsBW4Cr8i6y34jI6IzI5uQM4Nr8/xWXzRjTDVwGPA9sBl43xtwRV7ZaVfyh5vVVBhGRXYAbgXONMW9UWh4bY0y/sbre44EjROTQCosEgIicBLxsjFlZaVk8mG6MOQzL3flVEflIpQXKMxw4DPgvY0w78DaVdYcVISIjgFOA6ysti03ed/9xYF9gL2C0iHwu7v5qVfFXy7y+L4nIOID835crIYSI5LCU/jXGmJuyJJuNMaYHuAcrTpIF2aYDp4jIBuDPwCwR+VNGZMMYsyn/92UsP/URGZHtReDFfM8N4AashiALstl8FHjUGPNS/nsWZDsWWG+M2WKM6QNuAj4cV7ZaVfwD8/rmW+8zgMUVlsmNxcBZ+f/PwvKvlxUREeC3wFPGmJ9kTLYWEWnO/9+I9fCvzYJsxpgLjDHjjTETsJ6vpcaYz2VBNhEZLSK72v9j+YKfyIJsxpi/Ay+IyMT8omOAJ7Mgm4MzGXTzQDZkex44SkSa8u/sMVhB8XiyVTKAknIw5GPA/wOeBf49A/Jci+Wb68Oyer4EvBcrOPh0/u9uFZBrBpYb7DFgVf7zsYzI9gGgKy/bE8BF+eUVl61AzqMZDO5WXDYsP/rq/GeN/fxnQba8HFOBFfn72gmMzZBsTcCrwBjHsqzIdgmW4fME8EdgZFzZtGSDoihKnVGrrh5FURTFA1X8iqIodYYqfkVRlDpDFb+iKEqdoYpfURSlzlDFr9QdIvJWhHWPFpEPO77/s4h8If//F0VkrxjH3yAiu0fdTlGSYnilBVCUjHM08BbwAIAx5r8dv30RK6c6i6PCFcUTVfyKAojIycCFwAisATyfBRqBfwb683VRvo41YvItrOqX04BrRKQXqyLsU1hlc18RkWnAZcaYo0XkvVgD+FqAh3HUksrv9xv54z4E/Isxpj/9M1bqGXX1KIrF/cBRxioc9mfg28aYDcB/A5cbq077ffbKxpgbsEaffjb/W6/Pvi8G7s/vezHQBiAiBwGnYxVUmwr0YzU4ipIqavErisV4YFG+0NUIrJrxSfER4FQAY8xtIrI1v/wYrAk/HrHKr9BIhYvhKfWBKn5Fsfg58BNjzGIRORqYH2MfOxjsRY8q+M2tNooAVxtjLohxLEWJjbp6FMViDNCd//8sx/I3gV09tin8bQOWBQ9wmmP538i7cETko1hFycAqqvVJEdkj/9tuIrJPTPkVJTSq+JV6pElEXnR8voVl4V8vIvcBrzjWvQX4RH7y7X8o2M/vgf/O/9aIVT3xZ/l9OAO0lwAfEZFHsUokPw9gjHkSK6B8h4g8BtyJNX2eoqSKVudUFEWpM9TiVxRFqTNU8SuKotQZqvgVRVHqDFX8iqIodYYqfkVRlDpDFb+iKEqdoYpfURSlzvj/ASjQuyl+Np6yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "# 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)]\n",
    "    \n",
    "# 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 \\\n",
    "                        for Wind Speed', 'Wind Speed',(40,35))\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, (45,25), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel(\"Wind Speed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "rubber-batch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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",
    "# 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)]\n",
    "    \n",
    "# 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 \\\n",
    "                        for % Wind Speed', '% Wind Speed',(-50,35))\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, (-20,20), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel(\"Wind Speed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "laughing-berry",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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