WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import datetime\n",
    "\n",
    "# Import API key\n",
    "from api_keys import API_KEY\n",
    "\n",
    "# Incorporated citipy to determine city based on latitude and longitude\n",
    "from citipy import citipy\n",
    "\n",
    "# Output File (CSV)\n",
    "output_data_file = \"./output_data/cities.csv\"\n",
    "\n",
    "# Range of latitudes and longitudes\n",
    "lat_range = (-90, 90)\n",
    "lng_range = (-180, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The above script has generated 648 different cities using random numbers.\n"
     ]
    }
   ],
   "source": [
    "# List for holding lat_lngs and cities\n",
    "lat_lngs = []\n",
    "cities = []\n",
    "country_codes = []\n",
    "\n",
    "# Create a set of random lat and lng combinations\n",
    "lats = np.random.uniform(low=-90.000, high=90.000, size=1500)\n",
    "lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "\n",
    "# Identify nearest city for each lat, lng combination\n",
    "for lat_lng in lat_lngs:\n",
    "    city = citipy.nearest_city(lat_lng[0], lat_lng[1])\n",
    "    city_name = city.city_name\n",
    "    country_code = city.country_code\n",
    "    \n",
    "    # If the city is unique, then add it to a our cities list & also add its corresponding country code\n",
    "    if city_name not in cities:\n",
    "        cities.append(city_name)\n",
    "        country_codes.append(country_code)\n",
    "\n",
    "# Print the city count to confirm sufficient count\n",
    "print('The above script has generated {} different cities using random numbers.'.format(len(set(cities))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 1 of Set 0 | ferme-neuve\n",
      "Processing Record 2 of Set 0 | punta arenas\n",
      "Processing Record 3 of Set 0 | cabatuan\n",
      "Processing Record 4 of Set 0 | albany\n",
      "Processing Record 5 of Set 0 | ushuaia\n",
      "Processing Record 6 of Set 0 | rikitea\n",
      "Missing weather information for city named: mataura ...hence skipping\n",
      "Processing Record 8 of Set 0 | nara\n",
      "Processing Record 9 of Set 0 | kaitangata\n",
      "Processing Record 10 of Set 0 | anloga\n",
      "Processing Record 11 of Set 0 | eyl\n",
      "Processing Record 12 of Set 0 | konskie\n",
      "Processing Record 13 of Set 0 | santa maria da vitoria\n",
      "Processing Record 14 of Set 0 | hermanus\n",
      "Processing Record 15 of Set 0 | tasiilaq\n",
      "Processing Record 16 of Set 0 | new norfolk\n",
      "Processing Record 17 of Set 0 | constitucion\n",
      "Processing Record 18 of Set 0 | castro\n",
      "Processing Record 19 of Set 0 | igarka\n",
      "Processing Record 20 of Set 0 | chicama\n",
      "Missing weather information for city named: marcona ...hence skipping\n",
      "Processing Record 22 of Set 0 | meulaboh\n",
      "Processing Record 23 of Set 0 | baruun-urt\n",
      "Processing Record 24 of Set 0 | njeru\n",
      "Processing Record 25 of Set 0 | winslow\n",
      "Processing Record 26 of Set 0 | hirado\n",
      "Processing Record 27 of Set 0 | cape town\n",
      "Processing Record 28 of Set 0 | qaanaaq\n",
      "Processing Record 29 of Set 0 | iqaluit\n",
      "Processing Record 30 of Set 0 | busselton\n",
      "Processing Record 31 of Set 0 | avarua\n",
      "Processing Record 32 of Set 0 | khatanga\n",
      "Processing Record 33 of Set 0 | brae\n",
      "Processing Record 34 of Set 0 | tucdao\n",
      "Processing Record 35 of Set 0 | mar del plata\n",
      "Missing weather information for city named: avera ...hence skipping\n",
      "Missing weather information for city named: olafsvik ...hence skipping\n",
      "Processing Record 38 of Set 0 | rassvet\n",
      "Processing Record 39 of Set 0 | port alfred\n",
      "Processing Record 40 of Set 0 | petropavlovsk-kamchatskiy\n",
      "Processing Record 41 of Set 0 | halifax\n",
      "Processing Record 42 of Set 0 | aswan\n",
      "Processing Record 43 of Set 0 | port elizabeth\n",
      "Processing Record 44 of Set 0 | puerto madryn\n",
      "Missing weather information for city named: kuche ...hence skipping\n",
      "Processing Record 46 of Set 0 | arraial do cabo\n",
      "Processing Record 47 of Set 0 | yarada\n",
      "Processing Record 48 of Set 0 | magadan\n",
      "Processing Record 49 of Set 0 | bluff\n",
      "Processing Record 50 of Set 0 | jaypur\n",
      "Processing Record 1 of Set 1 | nebbi\n",
      "Processing Record 2 of Set 1 | la ronge\n",
      "Processing Record 3 of Set 1 | korfovskiy\n",
      "Processing Record 4 of Set 1 | erdaojiang\n",
      "Missing weather information for city named: falealupo ...hence skipping\n",
      "Processing Record 6 of Set 1 | tuktoyaktuk\n",
      "Processing Record 7 of Set 1 | hasaki\n",
      "Processing Record 8 of Set 1 | riverton\n",
      "Processing Record 9 of Set 1 | alugan\n",
      "Processing Record 10 of Set 1 | jamestown\n",
      "Processing Record 11 of Set 1 | lebu\n",
      "Processing Record 12 of Set 1 | hobart\n",
      "Processing Record 13 of Set 1 | clyde river\n",
      "Processing Record 14 of Set 1 | coquimbo\n",
      "Processing Record 15 of Set 1 | dikson\n",
      "Processing Record 16 of Set 1 | bambous virieux\n",
      "Processing Record 17 of Set 1 | isangel\n",
      "Processing Record 18 of Set 1 | rodrigues alves\n",
      "Processing Record 19 of Set 1 | matay\n",
      "Processing Record 20 of Set 1 | winneba\n",
      "Missing weather information for city named: nizhneyansk ...hence skipping\n",
      "Processing Record 22 of Set 1 | wilmington\n",
      "Processing Record 23 of Set 1 | sur\n",
      "Processing Record 24 of Set 1 | verkhnevilyuysk\n",
      "Processing Record 25 of Set 1 | benito juarez\n",
      "Processing Record 26 of Set 1 | faanui\n",
      "Missing weather information for city named: bengkulu ...hence skipping\n",
      "Processing Record 28 of Set 1 | kahului\n",
      "Processing Record 29 of Set 1 | labuhan\n",
      "Processing Record 30 of Set 1 | eidsvag\n",
      "Processing Record 31 of Set 1 | hammerfest\n",
      "Processing Record 32 of Set 1 | rehoboth\n",
      "Processing Record 33 of Set 1 | lure\n",
      "Processing Record 34 of Set 1 | hamilton\n",
      "Processing Record 35 of Set 1 | thompson\n",
      "Processing Record 36 of Set 1 | nanortalik\n",
      "Processing Record 37 of Set 1 | pevek\n",
      "Processing Record 38 of Set 1 | bangassou\n",
      "Processing Record 39 of Set 1 | oak ridge\n",
      "Missing weather information for city named: laguna ...hence skipping\n",
      "Processing Record 41 of Set 1 | kudahuvadhoo\n",
      "Processing Record 42 of Set 1 | leningradskiy\n",
      "Processing Record 43 of Set 1 | inhambane\n",
      "Processing Record 44 of Set 1 | vaini\n",
      "Processing Record 45 of Set 1 | katsuura\n",
      "Processing Record 46 of Set 1 | pontalina\n",
      "Processing Record 47 of Set 1 | vardo\n",
      "Processing Record 48 of Set 1 | werota\n",
      "Processing Record 49 of Set 1 | palma\n",
      "Processing Record 50 of Set 1 | hilo\n",
      "Processing Record 1 of Set 2 | tutoia\n",
      "Processing Record 2 of Set 2 | butaritari\n",
      "Processing Record 3 of Set 2 | bredasdorp\n",
      "Missing weather information for city named: illoqqortoormiut ...hence skipping\n",
      "Missing weather information for city named: bacuit ...hence skipping\n",
      "Processing Record 6 of Set 2 | saskylakh\n",
      "Processing Record 7 of Set 2 | beira\n",
      "Processing Record 8 of Set 2 | mahebourg\n",
      "Processing Record 9 of Set 2 | vincennes\n",
      "Processing Record 10 of Set 2 | bethel\n",
      "Processing Record 11 of Set 2 | barrow\n",
      "Processing Record 12 of Set 2 | puerto escondido\n",
      "Missing weather information for city named: lushunkou ...hence skipping\n",
      "Missing weather information for city named: kleck ...hence skipping\n",
      "Processing Record 15 of Set 2 | jacmel\n",
      "Processing Record 16 of Set 2 | hithadhoo\n",
      "Processing Record 17 of Set 2 | cherskiy\n",
      "Processing Record 18 of Set 2 | sorland\n",
      "Processing Record 19 of Set 2 | wanning\n",
      "Missing weather information for city named: samalaeulu ...hence skipping\n",
      "Processing Record 21 of Set 2 | quatre cocos\n",
      "Processing Record 22 of Set 2 | srednyaya yelyuzan\n",
      "Processing Record 23 of Set 2 | mayumba\n",
      "Processing Record 24 of Set 2 | ahipara\n",
      "Processing Record 25 of Set 2 | coahuayana\n",
      "Processing Record 26 of Set 2 | xining\n",
      "Missing weather information for city named: karkaralinsk ...hence skipping\n",
      "Processing Record 28 of Set 2 | georgetown\n",
      "Missing weather information for city named: burica ...hence skipping\n",
      "Processing Record 30 of Set 2 | provideniya\n",
      "Processing Record 31 of Set 2 | chokurdakh\n",
      "Processing Record 32 of Set 2 | nouakchott\n",
      "Processing Record 33 of Set 2 | san patricio\n",
      "Processing Record 34 of Set 2 | yulara\n",
      "Processing Record 35 of Set 2 | fairbanks\n",
      "Processing Record 36 of Set 2 | jalu\n",
      "Processing Record 37 of Set 2 | veraval\n",
      "Processing Record 38 of Set 2 | luderitz\n",
      "Missing weather information for city named: halalo ...hence skipping\n",
      "Processing Record 40 of Set 2 | maku\n",
      "Processing Record 41 of Set 2 | geraldton\n",
      "Processing Record 42 of Set 2 | nanchong\n",
      "Processing Record 43 of Set 2 | okhotsk\n",
      "Processing Record 44 of Set 2 | saldanha\n",
      "Processing Record 45 of Set 2 | te anau\n",
      "Missing weather information for city named: tsihombe ...hence skipping\n",
      "Processing Record 47 of Set 2 | takoradi\n",
      "Processing Record 48 of Set 2 | roma\n",
      "Processing Record 49 of Set 2 | ixtapa\n",
      "Processing Record 50 of Set 2 | presidencia roque saenz pena\n",
      "Processing Record 1 of Set 3 | baykit\n",
      "Processing Record 2 of Set 3 | atuona\n",
      "Processing Record 3 of Set 3 | mokhsogollokh\n",
      "Processing Record 4 of Set 3 | ilulissat\n",
      "Processing Record 5 of Set 3 | ponta do sol\n",
      "Processing Record 6 of Set 3 | whitehorse\n",
      "Processing Record 7 of Set 3 | blagoyevo\n",
      "Processing Record 8 of Set 3 | portage\n",
      "Processing Record 9 of Set 3 | kodiak\n",
      "Processing Record 10 of Set 3 | puerto ayora\n",
      "Processing Record 11 of Set 3 | lusambo\n",
      "Processing Record 12 of Set 3 | egvekinot\n",
      "Processing Record 13 of Set 3 | komsomolskiy\n",
      "Processing Record 14 of Set 3 | agudos\n",
      "Processing Record 15 of Set 3 | victoria\n",
      "Processing Record 16 of Set 3 | haines junction\n",
      "Processing Record 17 of Set 3 | buala\n",
      "Processing Record 18 of Set 3 | poum\n",
      "Missing weather information for city named: taolanaro ...hence skipping\n",
      "Processing Record 20 of Set 3 | gamba\n",
      "Processing Record 21 of Set 3 | muros\n",
      "Processing Record 22 of Set 3 | chuy\n",
      "Processing Record 23 of Set 3 | andenes\n",
      "Processing Record 24 of Set 3 | yellowknife\n",
      "Processing Record 25 of Set 3 | benguela\n",
      "Processing Record 26 of Set 3 | riyadh\n",
      "Processing Record 27 of Set 3 | tha chang\n",
      "Processing Record 28 of Set 3 | solwezi\n",
      "Processing Record 29 of Set 3 | isla mujeres\n",
      "Processing Record 30 of Set 3 | mpika\n",
      "Processing Record 31 of Set 3 | sao gabriel da cachoeira\n",
      "Processing Record 32 of Set 3 | eyemouth\n",
      "Processing Record 33 of Set 3 | chimbote\n",
      "Processing Record 34 of Set 3 | charlestown\n",
      "Processing Record 35 of Set 3 | kamenka\n",
      "Missing weather information for city named: amderma ...hence skipping\n",
      "Processing Record 37 of Set 3 | denia\n",
      "Processing Record 38 of Set 3 | carutapera\n",
      "Processing Record 39 of Set 3 | lorengau\n",
      "Processing Record 40 of Set 3 | kavieng\n",
      "Processing Record 41 of Set 3 | kargasok\n",
      "Processing Record 42 of Set 3 | rawson\n",
      "Processing Record 43 of Set 3 | port hardy\n",
      "Processing Record 44 of Set 3 | grodzisk mazowiecki\n",
      "Processing Record 45 of Set 3 | toumodi\n",
      "Processing Record 46 of Set 3 | kiama\n",
      "Processing Record 47 of Set 3 | vestmanna\n",
      "Processing Record 48 of Set 3 | severo-kurilsk\n",
      "Missing weather information for city named: gat ...hence skipping\n",
      "Processing Record 50 of Set 3 | palmer\n",
      "Missing weather information for city named: lasa ...hence skipping\n",
      "Processing Record 2 of Set 4 | vostok\n",
      "Processing Record 3 of Set 4 | kapaa\n",
      "Processing Record 4 of Set 4 | urucara\n",
      "Processing Record 5 of Set 4 | east london\n",
      "Processing Record 6 of Set 4 | kalmunai\n",
      "Processing Record 7 of Set 4 | plettenberg bay\n",
      "Processing Record 8 of Set 4 | saint george\n",
      "Missing weather information for city named: barentsburg ...hence skipping\n",
      "Missing weather information for city named: gazion ...hence skipping\n",
      "Processing Record 11 of Set 4 | port antonio\n",
      "Processing Record 12 of Set 4 | fortuna\n",
      "Processing Record 13 of Set 4 | chapais\n",
      "Processing Record 14 of Set 4 | ylivieska\n",
      "Processing Record 15 of Set 4 | iida\n",
      "Processing Record 16 of Set 4 | kirovsk\n",
      "Missing weather information for city named: mys shmidta ...hence skipping\n",
      "Missing weather information for city named: abu jubayhah ...hence skipping\n",
      "Processing Record 19 of Set 4 | beringovskiy\n",
      "Processing Record 20 of Set 4 | pangnirtung\n",
      "Processing Record 21 of Set 4 | longyearbyen\n",
      "Processing Record 22 of Set 4 | carnarvon\n",
      "Processing Record 23 of Set 4 | norman wells\n",
      "Processing Record 24 of Set 4 | santa maria\n",
      "Missing weather information for city named: crab hill ...hence skipping\n",
      "Processing Record 26 of Set 4 | ulaangom\n",
      "Processing Record 27 of Set 4 | cockburn town\n",
      "Processing Record 28 of Set 4 | kirakira\n",
      "Processing Record 29 of Set 4 | shelburne\n",
      "Processing Record 30 of Set 4 | port hedland\n",
      "Processing Record 31 of Set 4 | seydi\n",
      "Processing Record 32 of Set 4 | kogon\n",
      "Missing weather information for city named: codrington ...hence skipping\n",
      "Processing Record 34 of Set 4 | eucaliptus\n",
      "Processing Record 35 of Set 4 | kachug\n",
      "Processing Record 36 of Set 4 | oktyabrskoye\n",
      "Missing weather information for city named: balimo ...hence skipping\n",
      "Processing Record 38 of Set 4 | qingquan\n",
      "Processing Record 39 of Set 4 | segou\n",
      "Processing Record 40 of Set 4 | vung tau\n",
      "Processing Record 41 of Set 4 | tual\n",
      "Processing Record 42 of Set 4 | ridgecrest\n",
      "Processing Record 43 of Set 4 | port-gentil\n",
      "Processing Record 44 of Set 4 | saint-philippe\n",
      "Processing Record 45 of Set 4 | moroni\n",
      "Processing Record 46 of Set 4 | mecca\n",
      "Processing Record 47 of Set 4 | namie\n",
      "Processing Record 48 of Set 4 | nikolskoye\n",
      "Processing Record 49 of Set 4 | ovalle\n",
      "Missing weather information for city named: artyk ...hence skipping\n",
      "Processing Record 1 of Set 5 | brandon\n",
      "Processing Record 2 of Set 5 | madingou\n",
      "Processing Record 3 of Set 5 | dimovo\n",
      "Processing Record 4 of Set 5 | nishihara\n",
      "Processing Record 5 of Set 5 | neuquen\n",
      "Missing weather information for city named: belushya guba ...hence skipping\n",
      "Missing weather information for city named: faya ...hence skipping\n",
      "Processing Record 8 of Set 5 | aksu\n",
      "Processing Record 9 of Set 5 | darovskoy\n",
      "Processing Record 10 of Set 5 | kefamenanu\n",
      "Processing Record 11 of Set 5 | san matias\n",
      "Processing Record 12 of Set 5 | malaya purga\n",
      "Processing Record 13 of Set 5 | uray\n",
      "Processing Record 14 of Set 5 | cody\n",
      "Processing Record 15 of Set 5 | hambantota\n",
      "Processing Record 16 of Set 5 | pacific grove\n",
      "Processing Record 17 of Set 5 | sambava\n",
      "Processing Record 18 of Set 5 | kaputa\n",
      "Processing Record 19 of Set 5 | kharp\n",
      "Processing Record 20 of Set 5 | taoudenni\n",
      "Processing Record 21 of Set 5 | talnakh\n",
      "Missing weather information for city named: airai ...hence skipping\n",
      "Missing weather information for city named: tawkar ...hence skipping\n",
      "Processing Record 24 of Set 5 | caravelas\n",
      "Processing Record 25 of Set 5 | mahanoro\n",
      "Missing weather information for city named: samana ...hence skipping\n",
      "Processing Record 27 of Set 5 | lulea\n",
      "Processing Record 28 of Set 5 | emmett\n",
      "Processing Record 29 of Set 5 | mount isa\n",
      "Processing Record 30 of Set 5 | parnu-jaagupi\n",
      "Processing Record 31 of Set 5 | mao\n",
      "Missing weather information for city named: sataua ...hence skipping\n",
      "Processing Record 33 of Set 5 | tautira\n",
      "Processing Record 34 of Set 5 | madang\n",
      "Missing weather information for city named: tabialan ...hence skipping\n",
      "Processing Record 36 of Set 5 | korla\n",
      "Processing Record 37 of Set 5 | ermelo\n",
      "Processing Record 38 of Set 5 | richards bay\n",
      "Missing weather information for city named: paredon ...hence skipping\n",
      "Processing Record 40 of Set 5 | fasa\n",
      "Processing Record 41 of Set 5 | qasigiannguit\n",
      "Processing Record 42 of Set 5 | altay\n",
      "Processing Record 43 of Set 5 | nantucket\n",
      "Processing Record 44 of Set 5 | yeppoon\n",
      "Missing weather information for city named: louisbourg ...hence skipping\n",
      "Processing Record 46 of Set 5 | souillac\n",
      "Processing Record 47 of Set 5 | ribeira grande\n",
      "Processing Record 48 of Set 5 | sao felix do xingu\n",
      "Processing Record 49 of Set 5 | laela\n",
      "Missing weather information for city named: vieques ...hence skipping\n",
      "Processing Record 1 of Set 6 | talcahuano\n",
      "Missing weather information for city named: cozumel ...hence skipping\n",
      "Processing Record 3 of Set 6 | muzhi\n",
      "Missing weather information for city named: kadykchan ...hence skipping\n",
      "Processing Record 5 of Set 6 | miri\n",
      "Processing Record 6 of Set 6 | lake charles\n",
      "Processing Record 7 of Set 6 | kieta\n",
      "Processing Record 8 of Set 6 | matamoros\n",
      "Missing weather information for city named: rincon ...hence skipping\n",
      "Processing Record 10 of Set 6 | broome\n",
      "Processing Record 11 of Set 6 | moindou\n",
      "Processing Record 12 of Set 6 | ostrovnoy\n",
      "Processing Record 13 of Set 6 | esperance\n",
      "Processing Record 14 of Set 6 | turukhansk\n",
      "Processing Record 15 of Set 6 | cayenne\n",
      "Processing Record 16 of Set 6 | mareeba\n",
      "Processing Record 17 of Set 6 | robertson\n",
      "Processing Record 18 of Set 6 | pusad\n",
      "Processing Record 19 of Set 6 | igrim\n",
      "Missing weather information for city named: nguiu ...hence skipping\n",
      "Missing weather information for city named: kismayo ...hence skipping\n",
      "Processing Record 22 of Set 6 | myaundzha\n",
      "Processing Record 23 of Set 6 | alofi\n",
      "Missing weather information for city named: ler ...hence skipping\n",
      "Processing Record 25 of Set 6 | tiksi\n",
      "Processing Record 26 of Set 6 | vanavara\n",
      "Missing weather information for city named: sentyabrskiy ...hence skipping\n",
      "Processing Record 28 of Set 6 | kankaanpaa\n",
      "Processing Record 29 of Set 6 | kloulklubed\n",
      "Missing weather information for city named: lolua ...hence skipping\n",
      "Processing Record 31 of Set 6 | sawtell\n",
      "Processing Record 32 of Set 6 | solnechnyy\n",
      "Missing weather information for city named: attawapiskat ...hence skipping\n",
      "Processing Record 34 of Set 6 | zeya\n",
      "Processing Record 35 of Set 6 | maragogi\n",
      "Processing Record 36 of Set 6 | sola\n",
      "Processing Record 37 of Set 6 | kita\n",
      "Processing Record 38 of Set 6 | taltal\n",
      "Processing Record 39 of Set 6 | saint-augustin\n",
      "Processing Record 40 of Set 6 | grindavik\n",
      "Missing weather information for city named: ngukurr ...hence skipping\n",
      "Processing Record 42 of Set 6 | whyalla\n",
      "Processing Record 43 of Set 6 | havoysund\n",
      "Processing Record 44 of Set 6 | kirillov\n",
      "Processing Record 45 of Set 6 | balabac\n",
      "Processing Record 46 of Set 6 | muisne\n",
      "Processing Record 47 of Set 6 | singarayakonda\n",
      "Processing Record 48 of Set 6 | sochi\n",
      "Processing Record 49 of Set 6 | coihaique\n",
      "Processing Record 50 of Set 6 | cidreira\n",
      "Missing weather information for city named: grand river south east ...hence skipping\n",
      "Processing Record 2 of Set 7 | lompoc\n",
      "Processing Record 3 of Set 7 | bandarbeyla\n",
      "Processing Record 4 of Set 7 | kenora\n",
      "Missing weather information for city named: aflu ...hence skipping\n",
      "Missing weather information for city named: samusu ...hence skipping\n",
      "Processing Record 7 of Set 7 | barcelos\n",
      "Processing Record 8 of Set 7 | nyurba\n",
      "Processing Record 9 of Set 7 | port macquarie\n",
      "Processing Record 10 of Set 7 | cedar city\n",
      "Processing Record 11 of Set 7 | acajutla\n",
      "Processing Record 12 of Set 7 | san policarpo\n",
      "Processing Record 13 of Set 7 | karratha\n",
      "Processing Record 14 of Set 7 | tibiao\n",
      "Missing weather information for city named: liku ...hence skipping\n",
      "Processing Record 16 of Set 7 | barentu\n",
      "Processing Record 17 of Set 7 | starkville\n",
      "Processing Record 18 of Set 7 | sibu\n",
      "Processing Record 19 of Set 7 | sladkovo\n",
      "Processing Record 20 of Set 7 | upernavik\n",
      "Processing Record 21 of Set 7 | cabo san lucas\n",
      "Processing Record 22 of Set 7 | nuevo progreso\n",
      "Processing Record 23 of Set 7 | aklavik\n",
      "Missing weather information for city named: vaitupu ...hence skipping\n",
      "Processing Record 25 of Set 7 | estevan\n",
      "Processing Record 26 of Set 7 | luangwa\n",
      "Processing Record 27 of Set 7 | yumen\n",
      "Processing Record 28 of Set 7 | englewood\n",
      "Missing weather information for city named: mullaitivu ...hence skipping\n",
      "Processing Record 30 of Set 7 | hwange\n",
      "Processing Record 31 of Set 7 | morehead\n",
      "Missing weather information for city named: aasmae ...hence skipping\n",
      "Processing Record 33 of Set 7 | barra patuca\n",
      "Processing Record 34 of Set 7 | touros\n",
      "Missing weather information for city named: bargal ...hence skipping\n",
      "Processing Record 36 of Set 7 | monrovia\n",
      "Processing Record 37 of Set 7 | port-cartier\n",
      "Missing weather information for city named: kamenskoye ...hence skipping\n",
      "Processing Record 39 of Set 7 | reconquista\n",
      "Processing Record 40 of Set 7 | tessalit\n",
      "Processing Record 41 of Set 7 | teya\n",
      "Processing Record 42 of Set 7 | izhmorskiy\n",
      "Processing Record 43 of Set 7 | saint-francois\n",
      "Processing Record 44 of Set 7 | marivan\n",
      "Processing Record 45 of Set 7 | drogheda\n",
      "Processing Record 46 of Set 7 | wasilla\n",
      "Processing Record 47 of Set 7 | matara\n",
      "Processing Record 48 of Set 7 | thunder bay\n",
      "Processing Record 49 of Set 7 | praia\n",
      "Processing Record 50 of Set 7 | nome\n",
      "Processing Record 1 of Set 8 | sasolburg\n",
      "Processing Record 2 of Set 8 | kaoma\n",
      "Processing Record 3 of Set 8 | bang len\n",
      "Processing Record 4 of Set 8 | aljezur\n",
      "Processing Record 5 of Set 8 | buchanan\n",
      "Processing Record 6 of Set 8 | hof\n",
      "Processing Record 7 of Set 8 | southbridge\n",
      "Processing Record 8 of Set 8 | kraslava\n",
      "Processing Record 9 of Set 8 | cusco\n",
      "Missing weather information for city named: yanan ...hence skipping\n",
      "Processing Record 11 of Set 8 | lagos\n",
      "Processing Record 12 of Set 8 | saint-pierre\n",
      "Processing Record 13 of Set 8 | santa cruz cabralia\n",
      "Processing Record 14 of Set 8 | shetpe\n",
      "Processing Record 15 of Set 8 | tupik\n",
      "Processing Record 16 of Set 8 | broken hill\n",
      "Missing weather information for city named: paradwip ...hence skipping\n",
      "Missing weather information for city named: alappuzha ...hence skipping\n",
      "Processing Record 19 of Set 8 | canjulao\n",
      "Processing Record 20 of Set 8 | marawi\n",
      "Processing Record 21 of Set 8 | jining\n",
      "Processing Record 22 of Set 8 | mookane\n",
      "Processing Record 23 of Set 8 | ipatovo\n",
      "Missing weather information for city named: saleaula ...hence skipping\n",
      "Processing Record 25 of Set 8 | hobyo\n",
      "Processing Record 26 of Set 8 | san lorenzo\n",
      "Processing Record 27 of Set 8 | tecoanapa\n",
      "Processing Record 28 of Set 8 | birjand\n",
      "Processing Record 29 of Set 8 | walvis bay\n",
      "Processing Record 30 of Set 8 | cajaruro\n",
      "Processing Record 31 of Set 8 | aqtobe\n",
      "Processing Record 32 of Set 8 | deputatskiy\n",
      "Processing Record 33 of Set 8 | barra da estiva\n",
      "Processing Record 34 of Set 8 | college\n",
      "Processing Record 35 of Set 8 | karanganyar\n",
      "Processing Record 36 of Set 8 | qaqortoq\n",
      "Processing Record 37 of Set 8 | hondo\n",
      "Processing Record 38 of Set 8 | moron\n",
      "Processing Record 39 of Set 8 | erzin\n",
      "Processing Record 40 of Set 8 | paoua\n",
      "Processing Record 41 of Set 8 | dudinka\n",
      "Processing Record 42 of Set 8 | mehamn\n",
      "Processing Record 43 of Set 8 | kenai\n",
      "Processing Record 44 of Set 8 | dulce nombre de culmi\n",
      "Processing Record 45 of Set 8 | tiarei\n",
      "Missing weather information for city named: chkalovskoye ...hence skipping\n",
      "Processing Record 47 of Set 8 | conakry\n",
      "Processing Record 48 of Set 8 | flin flon\n",
      "Processing Record 49 of Set 8 | itarema\n",
      "Processing Record 50 of Set 8 | la rioja\n",
      "Processing Record 1 of Set 9 | hanzhong\n",
      "Processing Record 2 of Set 9 | axim\n",
      "Processing Record 3 of Set 9 | sahuaripa\n",
      "Processing Record 4 of Set 9 | gorele\n",
      "Processing Record 5 of Set 9 | lowestoft\n",
      "Missing weather information for city named: karaul ...hence skipping\n",
      "Missing weather information for city named: naftah ...hence skipping\n",
      "Processing Record 8 of Set 9 | portland\n",
      "Processing Record 9 of Set 9 | mikuni\n",
      "Processing Record 10 of Set 9 | marseille\n",
      "Processing Record 11 of Set 9 | kirya\n",
      "Processing Record 12 of Set 9 | sao joao da barra\n",
      "Processing Record 13 of Set 9 | kangaatsiaq\n",
      "Processing Record 14 of Set 9 | ranghulu\n",
      "Processing Record 15 of Set 9 | kaka\n",
      "Processing Record 16 of Set 9 | kang\n",
      "Processing Record 17 of Set 9 | sao pedro do sul\n",
      "Processing Record 18 of Set 9 | nisia floresta\n",
      "Processing Record 19 of Set 9 | ruidoso\n",
      "Processing Record 20 of Set 9 | youkounkoun\n",
      "Missing weather information for city named: one hundred mile house ...hence skipping\n",
      "Processing Record 22 of Set 9 | merauke\n",
      "Processing Record 23 of Set 9 | suleya\n",
      "Processing Record 24 of Set 9 | bilibino\n",
      "Processing Record 25 of Set 9 | borzoy\n",
      "Processing Record 26 of Set 9 | atasu\n",
      "Processing Record 27 of Set 9 | chitrakonda\n",
      "Processing Record 28 of Set 9 | port augusta\n",
      "Processing Record 29 of Set 9 | elizabeth city\n",
      "Processing Record 30 of Set 9 | byron bay\n",
      "Missing weather information for city named: haibowan ...hence skipping\n",
      "Processing Record 32 of Set 9 | hengshui\n",
      "Processing Record 33 of Set 9 | port lincoln\n",
      "Processing Record 34 of Set 9 | puerto carreno\n",
      "Missing weather information for city named: vila ...hence skipping\n",
      "Processing Record 36 of Set 9 | dinar\n",
      "Processing Record 37 of Set 9 | fiumicino\n",
      "Processing Record 38 of Set 9 | miraflores\n",
      "Processing Record 39 of Set 9 | tabuk\n",
      "Processing Record 40 of Set 9 | hirara\n",
      "Processing Record 41 of Set 9 | chokwe\n",
      "Processing Record 42 of Set 9 | camden\n",
      "Processing Record 43 of Set 9 | vila velha\n",
      "Processing Record 44 of Set 9 | muravlenko\n",
      "Processing Record 45 of Set 9 | sitka\n",
      "Processing Record 46 of Set 9 | kamien pomorski\n",
      "Processing Record 47 of Set 9 | sisimiut\n",
      "Processing Record 48 of Set 9 | ankang\n",
      "Processing Record 49 of Set 9 | kropotkin\n",
      "Processing Record 50 of Set 9 | sandnessjoen\n",
      "Processing Record 1 of Set 10 | nykoping\n",
      "Processing Record 2 of Set 10 | george town\n",
      "Missing weather information for city named: cumaribo ...hence skipping\n",
      "Processing Record 4 of Set 10 | chute-aux-outardes\n",
      "Missing weather information for city named: satitoa ...hence skipping\n",
      "Processing Record 6 of Set 10 | ambilobe\n",
      "Processing Record 7 of Set 10 | changji\n",
      "Processing Record 8 of Set 10 | jiangyou\n",
      "Processing Record 9 of Set 10 | mineiros\n",
      "Missing weather information for city named: umzimvubu ...hence skipping\n",
      "Processing Record 11 of Set 10 | neiafu\n",
      "Processing Record 12 of Set 10 | kiunga\n",
      "Processing Record 13 of Set 10 | san cristobal\n",
      "Missing weather information for city named: utiroa ...hence skipping\n",
      "Processing Record 15 of Set 10 | kazanlak\n",
      "Processing Record 16 of Set 10 | yar-sale\n",
      "Processing Record 17 of Set 10 | shakawe\n",
      "Processing Record 18 of Set 10 | torbay\n",
      "Processing Record 19 of Set 10 | port hawkesbury\n",
      "Processing Record 20 of Set 10 | dukat\n",
      "Processing Record 21 of Set 10 | qom\n",
      "Processing Record 22 of Set 10 | saquarema\n",
      "Processing Record 23 of Set 10 | ovsyanka\n",
      "Processing Record 24 of Set 10 | praya\n",
      "Processing Record 25 of Set 10 | oriximina\n",
      "Processing Record 26 of Set 10 | kendari\n",
      "Processing Record 27 of Set 10 | gunjur\n",
      "Processing Record 28 of Set 10 | susehri\n",
      "Processing Record 29 of Set 10 | zhigansk\n",
      "Processing Record 30 of Set 10 | garmsar\n",
      "Processing Record 31 of Set 10 | williamsport\n",
      "Processing Record 32 of Set 10 | aden\n",
      "Processing Record 33 of Set 10 | maltahohe\n",
      "Processing Record 34 of Set 10 | artyom\n",
      "Processing Record 35 of Set 10 | kashi\n",
      "Processing Record 36 of Set 10 | ternate\n",
      "Processing Record 37 of Set 10 | hofn\n",
      "Processing Record 38 of Set 10 | latung\n",
      "Processing Record 39 of Set 10 | ibimirim\n",
      "Processing Record 40 of Set 10 | san carlos de bariloche\n",
      "Processing Record 41 of Set 10 | rio hato\n",
      "Processing Record 42 of Set 10 | kilgore\n",
      "Processing Record 43 of Set 10 | topeka\n",
      "Processing Record 44 of Set 10 | svobodnyy\n",
      "Processing Record 45 of Set 10 | phalaborwa\n",
      "Processing Record 46 of Set 10 | sao filipe\n",
      "Processing Record 47 of Set 10 | cascais\n",
      "Missing weather information for city named: barbar ...hence skipping\n",
      "Processing Record 49 of Set 10 | balugaon\n",
      "Processing Record 50 of Set 10 | yamada\n",
      "Processing Record 1 of Set 11 | paris\n",
      "Processing Record 2 of Set 11 | florianopolis\n",
      "Processing Record 3 of Set 11 | santiago del estero\n",
      "Missing weather information for city named: talah ...hence skipping\n",
      "Processing Record 5 of Set 11 | irati\n",
      "Processing Record 6 of Set 11 | iquique\n",
      "Processing Record 7 of Set 11 | gizo\n",
      "Processing Record 8 of Set 11 | bloomfield\n",
      "Processing Record 9 of Set 11 | hervey bay\n",
      "Processing Record 10 of Set 11 | kavaratti\n",
      "Processing Record 11 of Set 11 | noumea\n",
      "Processing Record 12 of Set 11 | mersin\n",
      "Processing Record 13 of Set 11 | santa cruz\n",
      "Processing Record 14 of Set 11 | sorong\n",
      "Processing Record 15 of Set 11 | obigarm\n",
      "Missing weather information for city named: brajrajnagar ...hence skipping\n",
      "Processing Record 17 of Set 11 | sivas\n",
      "Processing Record 18 of Set 11 | birmitrapur\n",
      "Processing Record 19 of Set 11 | mamallapuram\n",
      "Processing Record 20 of Set 11 | aksay\n",
      "Processing Record 21 of Set 11 | tabou\n",
      "Processing Record 22 of Set 11 | hami\n",
      "Processing Record 23 of Set 11 | aplahoue\n",
      "Processing Record 24 of Set 11 | namibe\n",
      "Processing Record 25 of Set 11 | zheleznodorozhnyy\n",
      "Missing weather information for city named: urumqi ...hence skipping\n",
      "Missing weather information for city named: tumannyy ...hence skipping\n",
      "Processing Record 28 of Set 11 | kilifi\n",
      "Processing Record 29 of Set 11 | grand forks\n",
      "Processing Record 30 of Set 11 | palomares\n",
      "Processing Record 31 of Set 11 | bahia blanca\n",
      "Processing Record 32 of Set 11 | hovd\n",
      "Processing Record 33 of Set 11 | narrabri\n",
      "Processing Record 34 of Set 11 | tarakan\n",
      "Processing Record 35 of Set 11 | loves park\n",
      "Missing weather information for city named: san quintin ...hence skipping\n",
      "Processing Record 37 of Set 11 | douentza\n",
      "Processing Record 38 of Set 11 | nelson bay\n",
      "Processing Record 39 of Set 11 | manzhouli\n",
      "Processing Record 40 of Set 11 | oistins\n",
      "Processing Record 41 of Set 11 | vredendal\n",
      "Processing Record 42 of Set 11 | luau\n",
      "Processing Record 43 of Set 11 | oksfjord\n",
      "Processing Record 44 of Set 11 | manacapuru\n",
      "Processing Record 45 of Set 11 | pisco\n",
      "Processing Record 46 of Set 11 | montanha\n",
      "Processing Record 47 of Set 11 | katima mulilo\n",
      "Processing Record 48 of Set 11 | lukiv\n",
      "Processing Record 49 of Set 11 | beloha\n",
      "Processing Record 50 of Set 11 | launceston\n",
      "Processing Record 1 of Set 12 | gullane\n",
      "Processing Record 2 of Set 12 | bocanda\n",
      "Processing Record 3 of Set 12 | cristalina\n",
      "Processing Record 4 of Set 12 | naze\n",
      "Processing Record 5 of Set 12 | opuwo\n",
      "Missing weather information for city named: bac lieu ...hence skipping\n",
      "Processing Record 7 of Set 12 | cotonou\n",
      "Processing Record 8 of Set 12 | roebourne\n",
      "Processing Record 9 of Set 12 | mount gambier\n",
      "Processing Record 10 of Set 12 | poronaysk\n",
      "Processing Record 11 of Set 12 | tokur\n",
      "Missing weather information for city named: kabare ...hence skipping\n",
      "Processing Record 13 of Set 12 | kutum\n",
      "Processing Record 14 of Set 12 | chisec\n",
      "Processing Record 15 of Set 12 | dingle\n",
      "Processing Record 16 of Set 12 | ginir\n",
      "Processing Record 17 of Set 12 | inuvik\n",
      "Missing weather information for city named: raga ...hence skipping\n",
      "Processing Record 19 of Set 12 | puerto baquerizo moreno\n",
      "Processing Record 20 of Set 12 | flinders\n",
      "Processing Record 21 of Set 12 | korem\n",
      "Missing weather information for city named: doha ...hence skipping\n",
      "Processing Record 23 of Set 12 | champerico\n",
      "Processing Record 24 of Set 12 | yelan\n",
      "Processing Record 25 of Set 12 | balasheyka\n",
      "Processing Record 26 of Set 12 | cairns\n",
      "Processing Record 27 of Set 12 | tarashcha\n",
      "Processing Record 28 of Set 12 | lavrentiya\n",
      "Missing weather information for city named: lata ...hence skipping\n",
      "Processing Record 30 of Set 12 | havre-saint-pierre\n",
      "Missing weather information for city named: maunabo ...hence skipping\n",
      "Processing Record 32 of Set 12 | ingham\n",
      "Processing Record 33 of Set 12 | malartic\n",
      "Processing Record 34 of Set 12 | atherton\n",
      "Processing Record 35 of Set 12 | garowe\n",
      "Processing Record 36 of Set 12 | padang\n",
      "Processing Record 37 of Set 12 | chipinge\n",
      "Processing Record 38 of Set 12 | berlevag\n",
      "Processing Record 39 of Set 12 | kuantan\n",
      "Processing Record 40 of Set 12 | jalgaon\n",
      "Processing Record 41 of Set 12 | lere\n",
      "Processing Record 42 of Set 12 | kurud\n",
      "Processing Record 43 of Set 12 | sovetskaya gavan\n",
      "Processing Record 44 of Set 12 | kaeo\n",
      "Processing Record 45 of Set 12 | bemidji\n",
      "Processing Record 46 of Set 12 | nemuro\n",
      "Processing Record 47 of Set 12 | starogard gdanski\n",
      "Processing Record 48 of Set 12 | morwa\n",
      "Processing successfully completed. Retrieved weather information for 563 cities\n"
     ]
    }
   ],
   "source": [
    "#create a dataframe to store all the cities and countries generated with citipy\n",
    "df_countries = pd.DataFrame({'City': cities, 'Country': country_codes})\n",
    "\n",
    "#add additional blank columns to store information from openweathermap api\n",
    "df_countries['Latitude'] = ''\n",
    "df_countries['Longitude'] = ''\n",
    "df_countries['Max Temperature (F)'] = ''\n",
    "df_countries['Humidity (%)'] = ''\n",
    "df_countries['Cloudiness (%)'] = ''\n",
    "df_countries['Wind Speed (mph)'] = ''\n",
    "df_countries['Date'] = ''\n",
    "\n",
    "# keeps count of those cities whose weather information is successfully obtained\n",
    "city_count = 0\n",
    "\n",
    "# initializing # of variables\n",
    "api_call_count = 1 # count for API calls made to OpenWeatherAPI, max 60 counts allowed for free API calls\n",
    "sets = 0 # counter to count the number of call sets\n",
    "t0 = time.time() # get current time\n",
    "\n",
    "#loop through all rows and fill in values for blank columns in dataframe\n",
    "for index,row in df_countries.iterrows():\n",
    "    city_name = row['City']\n",
    "    country_id = row['Country']\n",
    "    \n",
    "    # create a dictionary to contain the query parameters\n",
    "    search_parameters = {\n",
    "        \"q\": str(city_name)+\",\"+str(country_id),\n",
    "        \"units\": \"IMPERIAL\",\n",
    "        \"mode\": \"json\",\n",
    "        \"APPID\": API_KEY\n",
    "    }\n",
    "    \n",
    "    #api call for all Cities \n",
    "    target_url = \"http://api.openweathermap.org/data/2.5/weather\"\n",
    "    country_info = requests.get(target_url, params = search_parameters).json()\n",
    "    \n",
    "    try:\n",
    "        df_countries.loc[index,'Latitude'] = country_info['coord']['lat']\n",
    "        df_countries.loc[index,'Longitude'] = country_info['coord']['lon']\n",
    "        df_countries.loc[index,'Max Temperature (F)'] = country_info['main']['temp_max']\n",
    "        df_countries.loc[index,'Humidity (%)'] = country_info['main']['humidity']\n",
    "        df_countries.loc[index,'Cloudiness (%)'] = country_info['clouds']['all']\n",
    "        df_countries.loc[index,'Wind Speed (mph)'] = country_info['wind']['speed']\n",
    "        df_countries.loc[index, 'Date'] = country_info['dt']\n",
    "        \n",
    "        #print log for each city\n",
    "        print(f'Processing Record {api_call_count} of Set {sets} | {city_name}' )\n",
    "        \n",
    "        city_count = city_count + 1 # only increment if you get all the weather information\n",
    "    \n",
    "    except KeyError:\n",
    "        df_countries.loc[index,'Latitude'] = np.nan\n",
    "        df_countries.loc[index,'Longitude'] = np.nan\n",
    "        df_countries.loc[index,'Max Temperature (F)'] = np.nan\n",
    "        df_countries.loc[index,'Humidity (%)'] = np.nan\n",
    "        df_countries.loc[index,'Cloudiness (%)'] = np.nan\n",
    "        df_countries.loc[index,'Wind Speed (mph)'] = np.nan\n",
    "        df_countries.loc[index,'Date'] = np.nan\n",
    "        print(f\"Missing weather information for city named: {city_name} ...hence skipping\")\n",
    "    api_call_count = api_call_count + 1        \n",
    "    if api_call_count == 51: # loop to limit 50 API calls per minute. The OpenWeatherMap API limit is 60 calls/min\n",
    "        t1 = time.time()\n",
    "        api_call_count = 1 # reset it for next 50 calls\n",
    "        sets = sets+1\n",
    "        time.sleep(60-(t1-t0))\n",
    "        t0 = time.time()\n",
    "print(f\"Processing successfully completed. Retrieved weather information for {city_count} cities\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City                   563\n",
      "Cloudiness (%)         563\n",
      "Country                563\n",
      "Date                   563\n",
      "Humidity (%)           563\n",
      "Latitude               563\n",
      "Longitude              563\n",
      "Max Temperature (F)    563\n",
      "Wind Speed (mph)       563\n",
      "dtype: int64\n",
      "                      City  Cloudiness (%) Country        Date  Humidity (%)  \\\n",
      "0              ferme-neuve            68.0      ca  1546893493          54.0   \n",
      "1             punta arenas             0.0      cl  1546891200          58.0   \n",
      "2                 cabatuan            88.0      ph  1546893493          92.0   \n",
      "3                   albany            90.0      au  1546891200          72.0   \n",
      "4                  ushuaia            75.0      ar  1546891200          61.0   \n",
      "5                  rikitea            80.0      pf  1546893494          96.0   \n",
      "7                     nara            92.0      ml  1546893494          28.0   \n",
      "8               kaitangata            12.0      nz  1546893494          63.0   \n",
      "9                   anloga            20.0      gh  1546891200          61.0   \n",
      "10                     eyl            44.0      so  1546893495         100.0   \n",
      "11                 konskie             8.0      pl  1546893496          56.0   \n",
      "12  santa maria da vitoria            44.0      br  1546893496          62.0   \n",
      "13                hermanus            20.0      za  1546893496          92.0   \n",
      "14                tasiilaq            92.0      gl  1546890600          53.0   \n",
      "15             new norfolk            20.0      au  1546891200          77.0   \n",
      "16            constitucion             0.0      cl  1546893497          90.0   \n",
      "17                  castro            75.0      cl  1546891200          76.0   \n",
      "18                  igarka            48.0      ru  1546893497          78.0   \n",
      "19                 chicama            20.0      pe  1546891200          78.0   \n",
      "21                meulaboh            56.0      id  1546893401         100.0   \n",
      "\n",
      "    Latitude  Longitude  Max Temperature (F)  Wind Speed (mph)  \n",
      "0      46.70     -75.45                12.66              6.17  \n",
      "1     -53.16     -70.91                57.20             16.11  \n",
      "2      16.95     121.67                67.69              2.15  \n",
      "3     -35.02     117.88                60.80              5.82  \n",
      "4     -54.81     -68.31                51.80             24.16  \n",
      "5     -23.12    -134.97                80.07             15.90  \n",
      "7      15.17      -7.29                70.98              7.40  \n",
      "8     -46.28     169.85                61.44             18.25  \n",
      "9       5.79       0.90                82.40              2.24  \n",
      "10      7.98      49.82                73.41             18.25  \n",
      "11     51.19      20.41                 2.58              8.63  \n",
      "12    -13.39     -44.19                81.37              6.62  \n",
      "13    -34.42      19.24                58.87              3.83  \n",
      "14     65.61     -37.64                23.00             11.41  \n",
      "15    -42.78     147.06                59.00              4.70  \n",
      "16    -35.33     -72.42                58.92              7.52  \n",
      "17    -42.48     -73.76                53.60              8.05  \n",
      "18     67.47      86.57                 1.54              6.62  \n",
      "19     -7.84     -79.15                78.80              9.17  \n",
      "21      4.14      96.13                78.67              4.05  \n"
     ]
    }
   ],
   "source": [
    "#change all data recieved from openweathermap api to numerical data\n",
    "df_countries['Latitude'] = pd.to_numeric(df_countries['Latitude'])\n",
    "df_countries['Longitude'] = pd.to_numeric(df_countries['Longitude'])\n",
    "df_countries['Max Temperature (F)'] = pd.to_numeric(df_countries['Max Temperature (F)'])\n",
    "df_countries['Humidity (%)'] = pd.to_numeric(df_countries['Humidity (%)'])\n",
    "df_countries['Cloudiness (%)'] = pd.to_numeric(df_countries['Cloudiness (%)'])\n",
    "df_countries['Wind Speed (mph)'] = pd.to_numeric(df_countries['Wind Speed (mph)'])\n",
    "\n",
    "#display dataframe df_countries after openweathermap api calls removing those with NaNs elements\n",
    "df_countries = df_countries.dropna()\n",
    "\n",
    "# Reorder columns as we're interested in for the final DataFrame\n",
    "df_countries = df_countries[[\"City\", \"Cloudiness (%)\", \"Country\", \"Date\", \"Humidity (%)\",\"Latitude\",\"Longitude\",\n",
    "                       \"Max Temperature (F)\", \"Wind Speed (mph)\"]]\n",
    "\n",
    "print(df_countries.count()) # displays the count of rows\n",
    "print(df_countries.head(20)) # displays first 20 records\n",
    "df_countries.dtypes # displays the Dataype of dataframe\n",
    "df_countries.to_csv(output_data_file, encoding=\"utf-8\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Data - Latitude vs. Temperature Plot\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(df_countries[\"Latitude\"], df_countries[\"Max Temperature (F)\"], color='b', alpha=0.5)\n",
    "plt.grid()\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Max Temperature (F)')\n",
    "plt.title(f'City Latitude vs Max Temperature ({datetime.datetime.now().strftime(\"%D\")})')\n",
    "plt.savefig(\"./output_data/LatVsMaxTemp.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Data - Latitude vs. Humidity Plot\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(df_countries[\"Latitude\"], df_countries[\"Humidity (%)\"], color='blue', alpha=0.5)\n",
    "plt.ylim(10,120)\n",
    "plt.grid()\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Humidity (%)')\n",
    "plt.title(f'City Latitude vs Humidity ({datetime.datetime.now().strftime(\"%D\")})')\n",
    "plt.savefig(\"./output_data/LatVsHumidity.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Data - Latitude vs. Cloudiness Plot\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(df_countries[\"Latitude\"], df_countries[\"Cloudiness (%)\"], color='b', alpha=0.5)\n",
    "plt.grid()\n",
    "plt.ylim(-20,(df_countries[\"Cloudiness (%)\"].max()+10))\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Cloudiness (%)')\n",
    "plt.title(f'City Latitude vs Cloudiness ({datetime.datetime.now().strftime(\"%D\")})')\n",
    "plt.savefig(\"./output_data/LatVsCloudiness.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g/QD+n/NdALwbwHJnlwcAXB6lDIQQQgipXHp6kruQedSWsDsB3AjgkPP9KAB9xpgx5/sWAK0Ry0AIIYRUFN3dwPbtwNVXA8uWJcP1FhdtbcldyFyMMdEcWOQDAN5njLlORBYCWArgYwCeNsac5OxzLIBHjTFv8/n/tQCuBYCWlpZ3PPjgg+N+37dvHyZPnhyJ7OUKyyw7WF7ZwfLKDpZX9rDMwjE4COzcCTQ07MPQ0GQcPAgcPAi0tOh6kpWGLY+qKn0FlUch29eiRYueM8YsyLRflLMj3wngUhF5H4B6AA1Qy1iTiFQ71rBjAGzz+7Mx5j4A9wHAggULzMKFC8f93tXVhdRtJD0ss+xgeWUHyys7WF7ZwzILx7JlGnx+zjld2LhxIQD93tysv1UiYWZHxtG+IlPCjDH/AOAfAMBawowxHxaR/wGwGMCDAK4C8HBUMhBCCCGVRk+PzgL0kpQYqLhI6kLmcSRr/RyAT4vIH6ExYv8VgwyEEEJIWZLkGCgynqIoYcaYLmPMB5zPm40xZxljTjLGfNAYM1wMGQghhJBKoKND3Y9jY8ChQ/q5t1e3k2TBZYsIIYSQMqK9HVi6FKiuBrZs0ViwpUuT6Y6rdLhsESGEEFJmtLcDe/YA998ftyQkHbSEEUIIIYTEAJUwQgghhJAYoBJGCCGEEBIDVMIIIYQQQmKAShghhBBCSAxQCSOEEEIIiQEqYYQQQgghMUAljBBCCCEkBqiEEUIIIYTEAJUwQgghhJAYoBJGCCGEEBIDVMIIIYQQQmKAShghhBBCSAxQCSOEEEIIiQEqYYQQQgghMUAljBBCCCEkBqiEEUIIIYTEAJUwQgghhJAYoBJGCCGEEBIDVMIIIYQQQmKAShghhBBCSAxQCSOEEEIIiQEqYYQQQgghMUAljBBCCCEkBqiEEUIIIYTEAJUwQgghhJAYoBJGCCGEEBIDVMIIIYQQQmKAShghhBBCSAxQCSOEEEIIiQEqYYQQQgghMUAljBBCCCEkBqrjFoAQQgghhaW7G9i+Hbj6aqCtDejoANrb45aKpEJLGCGEEFJGdHcDd9wBjI0BxxwD9Pbq9+7uuCUjqVAJI4QQQsqIzk6guRmorgYmTNDPzc26nSQLuiMJIYSQMqKnRy1gXhobdbsf3d2qoPX00HVZbGgJI4QQQsqItjagv3/8tv5+3Z6KdV329tJ1GQdUwgghhJAyoqNDlamxMeDQIf3c26vbU7Guy+Zmui7jgEoYIYQQUka0twNLl2pM2JYtqlQtXervYuzpUVell3SuS1JYGBNGCCGElBnt7cCePcD996ffr61NrWTNze62INclKTy0hBFCCCEVinVd9vZmdl2SwkMljBBCCKlQrOuyuTmz65IUHrojCSGEkAqmvZ1KV1zQEkYIIYQQEgNUwgghhBBCYoBKGCGEEEJIDDAmrMzg8hOEEEJIaUBLWBnB5ScIIYSQ0oFKWBnB5ScIIYSQ0oFKWBnB5ScIIYSQ0oFKWBnR1qbLTXjh8hOEEEJIMqESVkZw+QlCCCGkdIhMCRORehH5nYi8ICLrReTLzvbjReQZEdkkIj8QkdqoZKg0uPwEIYQQUjpEmaJiGMC7jTH7RKQGwG9E5DEAnwbwr8aYB0XkXgDXALgnQjkqCi4/QQgpF1JT7px9dtwSkWxh2qT0RGYJM8o+52uN8zIA3g1gubP9AQCXRyUDIYSQ0sQv5c7OnUy5Uyy6u4Fly4Crr9b3XMqdaZMyE2lMmIhUichaALsAPAHgFQB9xpgxZ5ctAFqjlIEQQkjp4Zdyp6qKKXeKQaGUJ6ZNyowYY6I/iUgTgB8B+CKAbxpjTnK2HwvgUWPM23z+cy2AawGgpaXlHQ8++OC43/ft24fJkydHLXpZwTLLDpZXdrC8soPllZ7XXwdqagARd1td3T7s3TsZxx0Xn1ylRK5tbPt2YGwMqPYELNnvM2aEP45fHRoDjI4ikXVYyD65aNGi54wxCzLtV5Rli4wxfSLSBeAcAE0iUu1Yw44BsC3gP/cBuA8AFixYYBYuXDju966uLqRuI+lhmWUHyys7WF7ZwfJKz7JlaoFpbna3nXhiF159dSGuuio2sUqKXNvY1VerBWyCx1d26JBO+Lr//vDH8atD+z2JdRhHn4xyduRUxwIGEZkI4L0ANgD4FYDFzm5XAXg4KhkIIYSUJn4pdw4eZMqdYlConJNMm5SZKGPCZgD4lYh0A/g9gCeMMT8F8DkAnxaRPwI4CsB/RSgDIYSQEsQv5U5LC2fWFYNCKU9Mm5SZyNyRxphuAKf7bN8M4KyozksIIaQ8SE2509UVmyhlRaa0EVZ58u5zzTW5KU9Mm5SeosSEEUIIISR+7MzH5ubxMx9TLVRUnooDly0ihBBCKgSmjUgWVMIIIYSQCqGnB2hsHL+tsVG3k+JDJYwQQgipEAo185EUhtBKmIhMEpGqKIUhhBBCSHQwbUSyCFTCRGSCiPyNiDwiIrsAvARgu4isF5Gvicjs4olJCCGEkHxh2ohkkW525K8A/ALAPwBYZ4w5BAAiMgXAIgBfFZEfGWO+E72YhBBCCCkEnPmYHNIpYe81xoymbjTG7AHwQwA/FJGayCQjhBBCCCljApUwrwLmxIK1ePc3xvT4KWmEEEIIISQzGZO1isinAHwJwE4Ah5zNBgCNmYQQQgghORImY/7fAzjZGPNW1MIQQgghhFQKYVJUvAGgP+NehBBCCCEkNIGWMBH5tPNxM4AuEXkEwLD93RjzLxHLRgghhBBStqRzRx7pvPc4r1rnRQghhBBC8iTd7Mgve7+LSINuNnsjl4oQQgipULq7dUHtnh5dTqijg3m9ypWMMWEiskBEXgTQDeBFEXlBRN4RvWiEEEJIZdHdDdxxhy4ldMwx+n7HHbqdlB9hAvPvB3CdMWaWMWYWgE8C+GakUhFCCCEVSGenLiXU3AxMmOB+7uyMWzISBWGUsL3GmF/bL8aY3wCgS5IQQggpMD09QGPj+G2NjbqdlB9h8oT9TkT+L4DvQ5O0fgg6W/IMADDGPB+hfIQQQkjF0NamLsjmZndbf79uJ+VHGCVsvvP+pZTt50GVsncXVCJCCCGkQuno0BgwQC1g/f2qlF1zTbxykWjIqIQZYxYVQxBSPDjzhhBCkkl7O7B06fgx+pprOEaXK2HWjmwC8BEAszB+Ae/roxOLRIWdedPcPH7mzdKluXdyKnWEkEqhGONde3vpjaG8D+RGmMD8R6EK2IsAnvO8SAlS6Jk3nE5NCKkUON75w3LJnTAxYfXGmE9n3o2UAj092km85DPzxqvUAe57Zyefgggh5QXHO39YLrkTxhL2bRH53yIyQ0Sm2FfkkpFIaGvTQE8v+cy84XRqQkilwPHOH5ZL7oRRwkYAfA3A03Bdkc9GKRSJjo4ONRX39gKHDrmfOzpyO16hlTpCCEkqHO/8YbnkThgl7NMATnIy5h/vvE6IWjASDXbmTXMzsGWLvucTlF9opY4QQpIKxzt/WC65EyYmbD2AA1ELQopHIWfecDo1IaRS4HjnD8sld8IoYQcBrBWRXwEYthvLJUUFp9XmTylOpyaEkFzgeOcPyyU3wihhP3ZeZUcUObNykYFKICGEEFJ5hMmY/0AxBImDuKfVJkEJJIQQQkg8BAbmi8hPROQSEanx+e0EEflHEbk6WvGiJe5ptYVOnEoIIYSQ0iHd7Mj/DeDPALwkIr8XkUdF5Jci8iqA/wvgOWPM/UWRMiLinlYbtxJICCGEkPgIdEcaY3YAuBHAjSIyC8AMAIMANhpjymK2ZNyr1be16fmsGxRgbhVCCCGkUggTmA9jzGsAXotUkhiIe1pt3EogIYQQEgZOIouGUEpYORPntNq4lcBKh4MKIdnR3Q3cfTewejUgApxzDvCJT7DflDucRBYdFa+ExQ1zq8QDBxVCsqO7G7jpJuCVV4Ajj9RtXV268sZXvsJ+U87EnUmgnKESRioSDiqEZEdnJ7B7N9DQAEycqNtEgF272G/K3are06MPq144iawwpEtR8aKIdAe9iikkIYWGM1MJyY6eHmB4GKivd7fV1+u2Su431qre2zveqt5dRnfJuDMJlDPpUlR8AMAlAH7mvD7svB4FsDx60QiJDg4qhGRHWxtQVwcMDbnbhoZ0WyX3m0rI98gFuqMjUAkzxrxujHkdwDuNMTcaY150Xp8HcFHxRCSk8HBQISQ7OjqAqVOBgQHgwAF9DQwA06ZVdr+pBKu6nUTW3KwxgM3NjJ8tFGFiwiaJyLuMMb8BABE5D8CkaMUiJFo4M5WQ7GhvB267bfzsyIULOTuyUvI95jOJrNxj5vIhjBJ2DYD7RaQRgAHQD6CklysiBODM1HzwG1RJ+dPeDtx7b3b/ScoNOIwcucjKfI/p4Uz09KSLCQMAGGOeM8a8HUA7gPnGmPnGmOejF40QkkSCApEHB+OWjCSNpASth5EjV1npqktPJcTM5UNGS5iItAC4DcBMY8zFInIagHONMf8VuXSkJEjKky4pDkHpPfr64pOJJJOkpIIJI0c+stKqHgzTW6QnoyUMwLcAPA5gpvN9I4AbohKIlBZJedIlxSMoEHlkJB55sqG7G1i2DLj6an1nO42WpASth5EjKbKWG5yJnp4wStjRxpiHABwCAGPMGICDkUpVQZT6TYGm5sojaFCtrY1HnkzYPnb55cCVVwKbNvGBoVgk5QYcRo6kyFpucCZ6esIoYftF5ChoUD5E5BxocD7Jk3KwIvHpsfIIGlSbmuKW7HC8fay3V2f0rVunmd/5wBA9SbkBh5EjKbKWG4yZS0+Y2ZGfBrACwIki8lsAUwEsjlSqCiEp8RL5UCnTs4lLUHqPPXviluxwvH1sYEAfEIaGgA0bgJYWPjBETVJSwYSRIymyliOMmQsmoxJmjHleRC4AcDIAAfCyMWY0cskqgHIIWPROzx4aAtauBd56C7jwQrVCsOOVJ36DaldXLKKkxdvHGht1Bmd9vet24gND9GRzA840yWdwUF3L9vd589SyGWZSUBg54lQWKm2CU3f3+Jxz55xTmTnnMrojReQIAJ8HcIMxZh2AWSLygcglqwDKIQbBPj0ODwNPPqnb3vMejQ8qNdcqKT+8feyUU/RBob9fF6GmuylZZArP6O4Gdu50f9+0CbjxRmDjxtIN57CUQ2hKNnR3AzfdBKxcqfeKmhp9iLv55vK95iDCxIR9E8AIgHOd71sA/FNkElUQ5RKD0N4OTJ8OvP/9wMUXAzNmMN6GJANvH5s2DZg7FzDGbZ+MTUkOmSb5dHYCVVXu71u3qjK9bVvpTwqqtAlOnZ0al9nQABxxhL4aGoBdu8r3moMIo4SdaIy5HcAoABhjBqFuSZIn5RSwyAB9kkRS+9icOcC3vw38+Mfq1irFvlauZBpDenpUCbNYi6bXm1CqY06ljZ89Peo9qa93t9XX67ZyveYgwgTmj4jIRLizI08EMBypVBVEuQQsMkCfJJVy6WPlTqYxpK0NOOhJjtTYqAmCvbNyS3XMqbTxs60NWL9ewwMmTtRtQ0NAXV35XnMQYSxhXwLwMwDHish3ATwJ4MZMfxKRY0XkVyKyQUTWi8jfO9uniMgTIrLJeW/OdCySfMrFtZovpZ73jZC4yDSGdHSoEmZ/b23VGa8zZ5bGmJNubKi08bOjA5g6VevvwAF9DQxoyEC5XnMQYdaOfAJAB4CPAvg+gAXGmK4Qxx4D8BljzKkAzgHwSWfJo88DeNIYMxuq0H0+N9FJkign12quVFpwLSGFJNMY0t6uaUXs77NnA7ffri7mpI85mcaGShs/29uB224DLrhAV9oYHQUWLgS+8pXyveYgwrgjAeACAO+CuiRrAPwo0x+MMdsBbHc+7xWRDQBaAVwGYKGz2wMAugB8LhuhSTKpdLdPOeR9IyROMo0hEyeqFcnL4hLIWhlmbKi08bO9Hbj33riliB8xxqTfQeRuACdBrWAA8CEArxhjPhn6JCKzAKwCMA9AjzGmyfNbrzHmMJekiFwL4FoAaGlpeceDDz447vd9+/Zh8uTJYUUgYJllS7bl9frrOtVaPNNWjNGnvOOOi0DAhMH2lR0sr+wp1TKLa2wo1fKKi0KW16JFi54zxizItF8YS9gFAOYZR1vHJ6epAAAgAElEQVQTkQcAvBhWEBGZDOCH0DxjAyLhJlYaY+4DcB8ALFiwwCxcuHDc711dXUjdRtITRZmVc4LBbMtr2bLDg2vt96uuKrh4iaOU+qRfuwWK25ZLqbyKSboxpRTLzMaC7dqlMU+nnKIpfYoxNiSxvLq7gXvu0SStxmiS1uuuS8Z9I47yChOY/zIA73yFYwGEinIRkRqoAvZdY4zN/rFTRGY4v88AsCu8uCRJMAZqPJUWXFuq+LXbm27SRJFsy/FSbmOKvZ7WVqC6WmdzPvWUJpitxLGhu1v7WVeXWgZrazVh6003lW4d50sYJewoABtEpEtEugD8AcBUEVkhIiuC/iRq8vovABuMMf/i+WkFAKv7XwXg4ZwkJ7FTaQkGM1FpwbVJJdMMVb92u3u3WirYluOl3MYUez2zZwPvfKem0xgb0wSzlTg2dHZqP0tN0rp7d+nWcb6EcUd+McdjvxPAlQBeFJG1zrabAHwVwEMicg2AHgAfzPH4JGbKYe3LQlNpwbVJw1oempvHW1K8Nzy/djvsk/mw0ttyHJTbmOK9npYWfR06pA9plThO2CSt3sS0di3XUq3jfAmzgPdKABCRowCcDw2sfy7E/36D4Mz678lGSJJMUhMM7tgBrFmjU46XLSuv+DBSGoSZheaXGLOu7vBjlXOyzKRSbklLy+168oVJWg8n0B0pIj8VkXnO5xkA1gG4GsC3ReSGIslHEow3Bmr7dvXtDwwAZ50VPpaDyU1JIQmz/Itf7N7UqRo0zXi+eCm3uMpyu5586ejQfpaapHXq1Motk3QxYccbY9Y5nz8G4AljzCUAzoYqY6TC8cZAPfOM+vYXLgy/gHe5BeGS+GlrG7+WIHC45cEvdu+22zRRJOP54qXc4irL7Xrypb1d+9nChZqeY2REE7bedlvllkk6d+So5/N7APwn8KfEq4cilarCKOU0DzYGysY+TPCo9ZliOZjclORLat+ZNw9Y4UwXamxUBay3F7jmmvH/C4rdY7srPn7jX2pC1kIfP2w9F2JsZpzoeNrbNUUFUdJZwt4QkU+JyF8AOAO6fiScxbxriiFcJVAu1qAwFohUwriOCAnCr++sWAFceiktD6VC1ONfPscvl7GZJJt0lrBrAPwjgPcC+JAxps/Zfg6Ab0YtWKVQLtagjg4doID0FggvSQ9aHRzUJ/JStFBWAkF9Z926wlpSSHREPf7lc/xyGZtJsgm0hBljdhljPm6MucwY83PP9l8ZY+4ojnjlT7lYg3KJfUhy0Gp3N7BzJ5+Ck0y59J1KJuo6zOf49r87d2py0YcfBtau1RchhSLsAt4kIpJuDcqGbGMfrOLmjbm45ppkPGV2dgInnsin4CRTTn2nUom6DvM5flsbsGmTWlbr63XiUX+/Zr3v7uY4QApDmIz5JEKSbA0qBu3t6jq6/359T8rA1tMDVFWN30YrS7Ko9L5TDkRdh/kcv6NDFTARVcKGhnStw7lzKze7Oyk8VMJihlOYk0lbG3Dw4PhttLIkC/ad0ifqOszn+O3twPHH68PXwIAmFz33XOCkk/gwRgpHoDtSRP4dgAn63RhzfSQSVSDZuPFKOZ1FnCxfDtx1F7B1qy6mu2QJsHhx8P4dHcDzz+tTc9iJBqT4lMr0f79+Ww6EHY/S7ZeuDlP/d/bZ2ctoj2+Pdeed4cfO+fMPd2f29vJhLF+6u4G779ZYu74+XVNz4ULguutKoz8XknSWsGcBPAegHpqiYpPzmg/gYJr/kYjglOncWL4cuPFG7ewzZuj7jTfq9iDa23WdN1pZSL4E9dvBwbgly4+w41Gu45bf/3buzG28y1UGurwLT3c3cNNNwOOPA2++qS7eN9/U7zfdVHn3s0BLmDHmAQAQkY8CWGSMGXW+3wvg50H/I9HBKdO5cdddGlTb1KTf7ftdd6W3hk2cyFQHJH+C+m1fX/B/SoGw41Gu45bf/6qqchvvcpUhyZOHSpXOTmD3bs2WX1cH1NS42fN37668+1mY2ZEzARwJYI/zfbKzjRQZm5XeC4PFM7N1q1rAvDQ06PZSg+7o0iOo346MxCNPoQg7HuU6bvn9r6oqt/Eun7GzVFzecZDLeNTTAwwPA2NjOuEBAKqrdeLD8HDl3c/CBOZ/FcAaEfmWiHwLwPMAbotUKuJLLlnpicaADQyM3zYwoNtLCbqjS5OgfltbG488hSLseJTruOX3v4MHcxvvOHYWnlzHo7Y2tYBVV6siBuh7dbVur7Q6yaiEGWO+CV20+0fO61zrqiTFhfEJubFkiSpdfX1abn19+n3Jkrglyw6vS2XChHCLpJP4Ceq31i1eqoQdj3Idt/z+d/BgbuMdx87Ck+t41NEBTJ2qDyHDwxobOTys36dOrbw6CZuiogrAbgC9AOaIyPnRiUSC4JT83Fi8GLj9dr3pbd+u77ffnj4eLIkwQ3xpEtRvJ06MW7L8CDse5Tpu+f2vpSW38Y5jZ+HJdTxqbwduuw246CLg6KM1D9vRR+v3226rvDrJGBMmIv8M4EMA1gM45Gw2AFZFKFciSGL8TbnEJxS7bBcvzqx0LV8OfPazwBtv6Pevfx147jngM5+JTq5sqIQM8Unsc2HJNg1DV1fRRSw4YcejXNNEpB4/tcy8ZV5bqzf04WH/Y6ceq7s7/dqw9thr17ppFObPL16bTHpfyGc8am8H7r03OtlKiTCWsMsBnGyMeb8x5hLndWnUgsUN42+io9hlawfbq6/Wd7/zLF8OXHst8Prr7raxMeDmm1UZSwLl7lLxtouaGuCxx/TaPvGJ5Pc7jheZKXQZpbaXlStVSaupyXzsTLLY3zduBDZvViVs82ZdxqgY9VoK7ancx6NiEUYJ2wygJmpBkgbjb6KjmGUbdjC76y5g716Vp7paXyKqiP37vxderlwod5eKbRfDw8Dq1bptyhRNmht0AwqjYKdj+XJNEjl7tr6nyx0XRnaOF8EUuoy8x3v5ZZ3x3NCgnzMdO5Ms9vdt29Rt3NSk71u3FqdeS6E9lft4VCzCpKg4AGCtiDwJYNhuLPeM+UwHER3FLNuw+YG2btWnudT1Io0B9uxBYigXd7Qftl2sWqVT1ydO1PIfGHBvQKnuojvu0N+8CnbYG4FN4tvQMD6JL5B9vCDHi8wUuoy8x+vv13q0nzMdO5Ms9nfvcevr9Xsx6rVU2lOU41HS3bGFIowlbAWAWwE8Bc2gb19lDac0R0cxyzZs8Ghrqz5xHjo0fruIWmMqmXytTWGx7aK/380fNDSk9eVXZ/laC7xJfCdM0PeGBt2eq+xeOF6Mp9Bl5D1eY6O2FdteMh07kyz2d3tcwD12Meq10ttTKbhjC0WYFBUP+L2KIVyc0N8dHcUs27CD2ZIlwJFHqjxjY/oyRt2Sn/pU4eUqFYo5GNp2UVur09YHB/XGd+qp/nWW72zRrVtdK4cl1yS+HC8yU+gy8h7v5JPVYjowoJ8zHTuTLPb3mTO1Hfb16Xtra3HqtaNDY9Aeewz48Y/1ffPmymlPpeCOLRSBSpiIPOS8vygi3amv4okYD8XydxfLypAkihlLEHbgX7wYuO8+4Ljj3G3V1cBXvpKc2ZFxUMzB0LaL0093XcDnnKNKmV+d5WstKGQSX8bHZKbQZeQ93ugocMEFGtc3Opr52Jlksb/PmQOccIJaSU84QWMHi1WvxqT/Xs5UUjqedDFhf++8f6AYgiSRqONv8o1pyffcQf72YvjiC1m23d3APfdoMLcxeuO+7jr3HGHXfktNY9HVpYO6PUcpxyfkKn+xY1Ps1HWvvDNm+NdZR4f2l9271Xq1e7fOjLvllnDnWrLEjQFraHAtKV/4Qu6yl1KbiINcy8i2h+OPBz7+8fSpKAolS5z12dkJnHgisGCBu623N5p1FZM4tnnTX+zcCWzYAOzaBUybpvLGLV8hSaeEfUhEfgtgjTFmrFgCVRL5LMidT8dJp/wB8SmGudDdrWkk/vhHdScCOlV9yxY38V++g6lfed18s1pMRkaSM3AFkY+yH1dusjB11t4OXHopcOutav2YOlWvb8UKtWBk+r9VuO+6S5W41lZVwEotiW+5422/J5yg/RsAzj8/+eNTrhTr4SdOQ0A67APWm28CL77ozlqfOTMZ8hWSdDFhxwD4NwC7RKRLRG4TkfeLSIWHKReOXE2u+cbppHMx5eJ+itOl2tmpT0gNDcARR+iroUGtIoVymaWWyciIKn1r1pRG0KhX/t27gRdeAJ59Frj++swyJz3Wad06tVZecQWwaJG6i7Jxly5erBbPTZv0nQpY8vC236Gh7FJRlCrFCsxPauyV9WBs3arxuU1NwHnn6cNVEuQrJIFKmDFmqTHmPADTAdwEYA+AqwGsE5E/FEm+sibXjpZvx0mn/GWrGMY9i6WnR90SdjYdoJ+Hhwv31JhaJhs2qNVtZCRZA1cQVv6dO4GnntIA46OPVuU1U10lPdapkmJHKhVvHR88qP3bposAyrO+i/Xwk+T+096uls/Fi/VBa/p03Z4U+QpFmBQVEwE0AGh0XtsAPBOlUJVCrh0t346TTvnLVjGM+0mqrQ2oq3OnkQP6ua6ucE+NqWXiHfwtSR4YrPwbNrj5t4aHNb4iTF21t6uF8/779T0pChjAqfyVgLeOq6qyS0VRqhTr4Se1/+zYATz+uFr5kzBRrBL6d7rZkfc5MWE/AHAuNE/YB40xC4wxHyuWgOVMrh0t34aZTvnLVjGM+0mqo0OViYEB4MABfQ0MaHxQoZ4aU8uktlaz659yirtPkgcGK/+uXaqc2tQPp5ySbOUxDEl3l5L88dZxfX12qShKlWIFy3vLdvt2jbcbGADOOisZYRaV0L/TWcLaANQB2AFgK4AtAPqKIVQlkYuVId+GmU75y1YxjPtJpb1d00jYqekjIzpV3QblF+oc3jI54wyduVRXV5yBId+YOyv/tGka6DpxInDuuWreT7LyGIaku0tJ/njr2JjsUlGUIvmGeHjHi+3bw4cbPPOMxtotXKgzkpMQZlEJ/TtwdqQx5s9FRADMBXAegM8AmCciewA8bYz5UpFkJClkk3Yh3THS5dAJeyw7iwVws0n39qo8mSjU0157u6aoiJLUMkmVPdvyD0uhZi+1twPf+IZ7rMZGV3m0dZVNffjtGxdJTw2RxBQApYat464u4Kqr4pYmf9K1iXxnzXvHi7GxzOOFLVs7I3OCxzSTBEt50vt3vqRdO9IYY6CB+H0A+p3XBwCcBYBKWIwUumHmeqPIVSFM6tRobzlceOHhOWlSy+mGG6KVN5sBOVMdpqurbOojaN+//uvoyqFUyZQOhlQemfpaPukpUseL6mr/dVf9CJOOhg8UhSdQCROR66EWsHcCGAXwWwBPA7gfwItFkY4UhXwVolwUwnye9qIi01NkHIpj2AE5rGxBdZVNfQTt28dghcNIV642EbCFN7jKIFNfyyc3Xz4KXCavRlIfnEuddDFhswAsB3CWMeYEY8yVxpi7jTEvGGMOpfkfKTHimOEYd0C/H6nl4H2K9Pu9GOUUNuYuyrQlYfcdGQl3rkoibLnGneqFFI9MbcIv5nfzZp25mCkuNJ8Y3UzxV3HPhC9X0uUJ+7QxZrkxZnsxBSLFJw6FKNfBIsrEsJnKIY5yCjsJI8q0JWH3ra0Nd65KolhKNCkdMrWJVGVoZEQnJNTVZVbQU8eLsbHsJ20FTRRL4oNzORAmTxgpc+KY4ZjLDM+orQWZyiGOcgo7OyjKtCVh921qyu0ay5liKdGkdAjTJrzKUEuLzsYOo6CnjhfV1YVzF8Y9E75coRJGYsnFksvU46itBZmeIuPKWRMmjUmUaUvC7jtxYj5XWZ4US4kmpUO2Y1+2Crp3vJgxo3DxWpWQsysO0s6OJJVBPikv8gkmDhPQ7z3+888DZ589/vdCWgtSy+HUU8cPjoVIDZJ6TYUKwI46bUmYfbu6wp+rVMg3bYctJzuxo7MTuPNO/d3blvNJ9VLueMt14ULg4x9XF12u9ZHtOaM4TzZ9LZ9A/UJSqPGPjIdKGAGQ2wzHqGfLpB5//Xpg1SpN1mjXESv0YOQth66uw68j39QgUZZZuefTKTaFSNuRbmbtzp1uChTe4PzxlltNja5UsXIlcP75+dVH2HPmW++FIEkKOseYwkN3JMmZqN2Dqcc/4wzdvmZN6ZrDGYBdOtxzD/Dyy6r4r1ql1peguspUr36/V1WNP1YYt3Ol4S23l1/WMmto0M/p+k4+/Syb/xajP+cSulGqRDnxKqnQEkZyJp+cNLkcv6VFn4B/9zsdjErRWhB1mZHC0N0NPPEEMGWK3vQHB4GnngLOOSc4bUe6evX7vaqK9Z4Jb7n19wMiun6kjZ9Ll0Yl136WzX+L1Z/TWaDKJb9cpeYhoyWM5EzUwcR+x6+vBy67rHStBQzALg06O4GjjtKbvohOOqivB9auzS5tR7qZtQcPst4z4S23xkZN1TA05AaqZ5tGJUx5FyJdS7HqtZzyy1Wql4BKGMmZqGfLlONsnHK8pnKkpweYP19v+IODevM3BnjrrezSdsybpw8La9dqjOGmTe7vBw+y3jPhLdeTT9YyGxjQz7mkUQlT3oVI11Ksei0nxaVS07RQCSM5E3WsQjnGQpTjNZUjbW1q+Tr3XLWCDQyoRey97w2ftuPSS4EVK/Sm3N4OzJ0LrFunVormZnWv26D9SouDCYu3XEdHgSOP1Ik5o6O5pVHJZj3cfNK1FKs/l5PiErdVMS4YE0byIurZMuU4G6ccr6mU8YupsTPSmps1DtHOSLvuuuDjpNbrsmXj1wicMweYOlW/L1umlrFKjYPJhtQZyx/5SPb/y+ecUZ4nX6JKXxFHnFmSZoEWEyphJC/KJSi0kghbZ5VQt+mUoEwpIzKVT5ig7c5OdbG98ILedBobgdbW+BayD1PnqfvMm6cWvtT/RJ1ri0SjuAT1iUsv9a/nQlGpaVrojiQ5U05BoZVC2DqrlLpNF1OTLmVEmPIJ415ZuxZ48UWNO7OzMF98UbcXmzDXlLrPpk3AjTcCGzeO/8/y5eHbT6W0tShIdYeOjABHHKEJgZct0/aULX594uBB4NZbo6+jSkzTQiWM5EwSgkIZT5MdYeuss1OXbXrhBeAnP9H3sbHSDPhNR64xNWHKMUzQdl+f/n/iRHcW5oQJur3YhLmm1H22blXlcdu28f+5665k5doqZ6zicsMNwP794xf6tgmBs8GvT2zZ4sbhsY4KC5UwkjNxB4XyCTp7wtbZ2rXqevBaaNati8dCky/pFPVcg4HDlGOYoO2mJlXQ7AzMwUH9Hsdi6GGuKXWf/n5tH94ybGxU5Szs2BD3OFIuhEkIHAa/PrF7t8YzeimVOkr6gzpjwkjOxL2mmXfQAdz3dPE05Rp7Eva6wtaZ10ID6PvwcH4WmjjKPlPge64xNWHLMVPQ9vz56j7ats2NCTvxRA3iLzZhril1n8ZGbRNepbG/X+Pa+vvDjQ1xjyPlQqESAvv1iZoarVMvpVBHpTDxhZYwkjNx58jJ9gm6XC1n2VxX2DrLZKEJerpMt71QZZ/Nk20mV1euKQYK1fY7OoDqauDtbwcuuUTfq6vjyR8W5ppS92lt1fQdM2eO/8+SJfHm2kq69SMKCpUQ2K9P3HKLtsswdZSksi8FVzeVMJIzcefIydaVVAodMheyua6wdTZ/PvC2t7k5siZO1O/z5wcrVN73VEWrUGWfrTIX1m2YbTBwodp+3H0oW1lS95k9G7j9drXcef+zeHF8ubbK9WErE37KbK4JgVP7RNj6TFrZl4Krm+5Ikhdx5sjJ1pWUhHUbo3DJZXtdYerMlu3b3z6+bDs6gt3Au3cHu4cLVfZ+5969G7j+euCEEw4v0yhdXYVq+/kcp9DtKYwsfvssXpzbsXLZNxO5hCmUA34pHmxC4EIdP9Oxklb2peDqphJGIiPdDcLvNyC7G0rYvDL2XGvWAOvXA6efDkyfrr+98ooGEV99dfi8SLne6KKKT4hioElXtnfe6a9QjYwEP3UWSsZUZW7HDp0wMDamSVULFfNVCHLJuZVN21q+XNMGjI5q0PTwcP7tqVhxe5nOk8/vxXrYKpX40mLKmYQHXS+lkAA2MnekiNwvIrtEZJ1n2xQReUJENjnvzemOQUqXdGZpv98+9Sl9mn7oIVWMNm4Mzl/ljTcA0ruSvOc66yx1ra1cCWzfrjmOnn5a41ky5UXauFHleugh4Mor9QaYLVG5Q6OKzQty0wW5gWtrg93D2coYFFeSeu6XXtKynDatsDFf6WQIE/MSxi0zOJi766a7WxUwEVXAhoZUGT14MPf2VCxXUqbz5Pt7MZa/SZrbLUimrVuBm24qnpxJW3ooSe7+IKKMCfsWgD9P2fZ5AE8aY2YDeNL5TsqQdApH6m8jI2qhGhhwbyjr1x+elyqXgc97rhkzgIULdUr9M8/oAHXOORrPki4v0tiYyjM0pPKJ6A2wEPl3CvGUWOyBJkihmjo1WNHKRsZ09Zx67l279P3UU93/FyLmK0iGsElIwyjcfX25K+WdnWoBa2x084vV12vZ5tqeihUz6df/X35ZlyNatgy45570cmSSsxgThpIYX+on09iYGyZQDDnjnqzlR9ITwEbmjjTGrBKRWSmbLwOw0Pn8AIAuAJ+LSgYSH5nM0t7fNmxwP9sbCqBKUn29+1su8QapcrS0ABddpDerVDlSZbT/t3JYuRobdWBLPW8ms38pxCeFPZefq3LPnvTu4bAypqvnZcvGn2PaNJ2h19Li/r8QZZoqg1UUliwBjjtOXdr2pmb3z3bJonTuW0tQm+rpcR9YbLusr9d2uWhRbtdcLFeS9zw7dwJPPaUJRgHtH088AbznPcFyZJKzGMvfJM3tBvjLdOiQuqm9RClnpS49lA9ijInu4KqE/dQYM8/53meMafL83muM8XVJisi1AK4FgJaWlnc8+OCD437ft28fJk+eHJHk5Ukxy2z7dn0Kq/ao+d7v3s99ffpUD7hKlzG6z5QpasECgNdf13w1Iu4xjdH/Hndc/nJ4v8+YoeW1d+9k7Nmj2+x5Dx3Sz/X17nkHB/WGUlWlr4MH9dXS4t4kw+yTC4ODWoYjI+oSbGrK73i5Uqj2lU09R1WmXhlGR4F9+1TpGhrSej90CJg8Wffxky1du7Ptec+efdi/f3LgPvbajNG6HRtTeWbMUDmGhnSfCRN0+8GDeozjj8/t2sPIXAi859m7V8sS0Os48kjXneVVUO3+Rx6pfTIKObPpR1GWVa792U+m6up9eOutyb5lWcg6LRcKeY9ctGjRc8aYBZn2S2xgvjHmPgD3AcCCBQvMwoULx/3e1dWF1G0kPcUsM28QujcgculS/d372+OPq1uppka31dfr/sYA3/62+xS1bNnhliT7/aqr8pfD+1t7u5ZXW9tCXHml3uQaG92b37x5Oj3fnjedbDZ2zcpTyGDtdNdX7KfPQrWvbOs5isBjrwxdXe4afDt3ula3iRPVve0nW5h6eeyxLvz4xwsD91m2TGMR16/XPuHtF7fcAjz2mN5Qt25VC1hNjW6/+OLcrrlYbcl7npUrVdEYHgbOPVcnzGzfDjz5JPD+9x8ux5492icLLWe21x5VWfkdd/NmtfaOjKRv337/fdvbuvDTny7EiSfGPz6UAnHoFcXOE7ZTRGYAgPO+q8jnJ0UiXQxQ6m9nnAHMnQuceabrUrE3Gu9AkUu8QTZyBOVFuuUWlWf3bpVv3jy1unjPu3atvh5+WG/aO3f6m/0zxSdkG/eWxNiUfMm2nqOI+fDK0Nen9T80pG10aEi/9/UFyxambU2cmH6fVFe4fRAYHdUg/KVLNZ7xxBOBK67QBxa/VBFhKVZsofc8IvqyChig1/ve9wbLEYWc2fajqMrKL17uj3/Umd2ZxgM/mVpbgdtuS3ZgeqVTbEvYCgBXAfiq8/5wkc9fsiRpOnRYWdLFAKX+Zo+5f78Oyk1NeqOZM+fwwTc13gDQm2+QPPaz/Z935lyYOKXFi1WOdOk2Xn1VrSUjIzrYbdgAvOMdOiMzG7KNe0uNr9mwQZUDkeROmc9EEuJKrAx3361pMIaHVREaGlKX2Z496qJ8/HF1S37xi5rINtsyT9f+2tqA1avHr9lnJ4f09BQmDtCvL3stt1FhZbcpBOrqVOEOa6kpdAxk2BivQo7Dy5frQudbt6qytGTJ4XJs2KDtbWQkfQyiJbVcurqCyyroWpJ0r6kEIlPCROT70CD8o0VkC4AvQZWvh0TkGgA9AD4Y1fnLiSStfxWVLPa/mzdrbE1jo/+x/ZS3IHkAHUzWrlUlad48tRrkInO6Qb+zU9NcrF6trpWJE1Uh+/Wv9QadLgdZKtkG/Npg/5ERDXCur1cZRJK3Rlo2FHOiQToOHFBl+re/VQtUX58qQm+9pYls9+5Vl+DmzcCkSePbXr79pKMD+NGP3DUlrSv8pJPyn3jQ3a0K5i9+ARx1lCqQcYwrUSrc2SgTYSbNFHLsW74cuPFGnak9Y4a2qxtv1LbW369K/0svAS++qH3aG79VCOUw6FouvRRYsSIZ95pKIcrZkX8d8NN7AraTAJKUhThKWXI5dtB/7r5bLVK7d2uQNaCKUUODG9OTj8zeQe/559U9ecwxelMeGtLvxui5L7oo/GCW7QxKa0l4+WV3hpmNr6mrK58s4XE8ndu21dMDHHus1u3+/VofM2eqBcNOABgc1O9vf7traU3Xlru7NfYpnYJuXeG33qrteOpUVcBSXeHZYm/AL7+sE18AfYA47zzXBVdsq2Ohz5etwhQmqWchx7677tKxyK7Fat+tRf2VV9QCVlenDwIDA2qRnT49O+UwiKBruesubcNJuNdUCsWOCSM5kKT1r6KUJZdjB/3nl7/UgQxQZaiqSl1Iv/99fjJ3dwOf+IQO2o89psHQdXUat1FbC8yaBZxyip7PZpHPJlYrl3iopUtV8RoZUYXAxtfEPWW+UDFYtI0AACAASURBVMSVGNO2rf5+VVZmzQJOO03rdvp0VcrsbF4bNG/LPF1bttczNpb5ehYv1livK65QK+7s2YWLf7LtxeYY27ChfNpMFDFeYcansItXb92qSpiXhgZtQ8ceq59HR1XZnzoVOOIIrR+/8cB7rbt3Ay+8ADz7rC7lZSeUpBJ0LVu3xnuvSdLi38UisbMjiUuS1r+KUpZcju39z44dasLftQvYtk3dmvYmMzqqypLND5aLzEEWhDlz3OWPTj7ZTR0wc+b4AS3MYJaLe6a9Hbj88uS0kUJTTEuw1+K2ebMqt42NWp/emLCBAX23ebqGhlyFzZZ5UH3Y66muzi3OJ1+sy7uxUd2q1noLaGzS7NmFO1dceN36dlzo69PvucSwAsFjzbRprrJw002qCA0P66zWZ5/VwPjU47a2qjxNTe62gQHdPjys1vMJjolk507gD3/QMW3RosPHA3utNudafT1w9NEq286dKlvq+YPG2tZWfY9jHElS2E0xoSWsBEhSFuIoZcnl2PY/GzfqANTXpze3mhodgPbt0wFpbExfQO4yB1kQbILMkRF3BmVrq+Zt8mZyDzuY5TLbL0ltpNAUyxKcanGbOVOXtZo0yc3dNDio1rCBAa1b7/bWVrfM09VH3JZtu7TM1Kn6UGJzjRmj1ztvXnHkiBJ7jTt26DUNDqqluq4udytq0FjT2qrHvPVW1/pu6/eVVzQ0IpUlS7QN9fVp++jr0+9Llhy+9E9Li8bsffjD/uOB3X/DBncm7fCwKodVVf7Wv6D2uWRJ/uNIrtascpzpHQYqYSVAsaaOxy1LpmP7dW77n23bVMlqagLe+U513Rw8qHE3kybpgHTwoP6eq8z25mmDpAHXDTVzJvChD7luo3e+U+N3amszD2aFMMEXs40U22VQV6ezEL3pP6J4Ok+9CcyZo8ta7d8PnHCCtp0TTtAZr7ffDpx99vjtXldhuvqIe309ewPu6dF2W12t7fnYY/V6163LfIxCEGU7ste4Zs34WMkzzsj9xh401syercf89a/VOmrTiUycqN9Xrz78WIsXaxtqatIxqqlJvy9enP0Dld1/1y691sFBrU8bFuGn3Ae1z8WL8xtH8gkdiPvhJC7ojiwR/EzlcU0ljnLmWrrp1OlM1SecAJx/vmvCP/NMvbH19en7EUdorNRVV+lN5otfdN0BYVMLWBP+qafqkzCg1oPaWv9p9an14+dWLKQJvhgzCnMJAs73fG+84br/DhxQReykk4CvfKWw5/KbmXrSSapo33//4ftnyskVVB82CHxsbHxaBm8QeNRMmqRxjDU1es1nnqkWl0OHorvp2f6wdq3e4LdsUSVh0iRtT889p3WazQ0/aPyzSsYVV2jZirh1m8+NPXWs2blT26MdZw4c0LHGcuAA8Oab/hMw5szRZL89Pao8/eIXwKOP6n6XXqrjVJiQBHut11/vukdPP13jFg8eDFbug9pnPuNIPqEDSQq7KSa0hJUocQUrx0UmU7WfCf+sszQJ7BlnaBbxq67S6debNmm8T1+fvm/cGK7s7BNnba1aDAAN9j/9dH+lKYxbsdRM8MWWt7NTLYsXXKA3t9FRDVpubS28wlksC5W9aVZXF9+ybceN2lp1O06frmVqV6+L6qZnz7txo/a5V1/VvjM0pOfcv1+Vwnvuye54mca/ujpN73DyyarwPfWUugjzuUbbTmwMlnV1HnmkKk1vvaXl+dZb7hqfqTJ65a+pUUVu5Ur93Nur41RHR/iQhPZ24BvfABYs0NmN06bpcQ4eLG5IQj7WrHIOqUgHLWElSpLSVhSDTPmz/KaYV1XpwGTLY9kyLacXXnBjugYH1b1gUwtkCoD3Bs1ffHH+1sckLgQMpF84Ol95s7Hg2vNNmOBmVD90yJ1gUUjCpCkoFO3tqoT4WdiixDtunHaaKhEiGk9UVxfd9drz/va3qpz09+t5R0bU0rhvnyoOfq67TNcB+I9/nZ36ELZ+vbvm5/CwWpg+9zndx9sW6+rcdTrTtcugtDAXXKCB+Hv2uLOxjzpKXZapEzCszM3NqoDZmZIvv6zWsdRrCYPfpJ6WluLeD/KxZkWZMy7JUAkrUYp98447i3Kmzh2mA9sy6+93B73U1AKZKLTLL4km+HQux3zlzdb9Wqzyse17YEDbgXVTl9tNwDtutLRobrB0M+8Ked6aGrWC1de7i7MPDmpCYxtjaS1y2VyHJbUP9/SoO7mhQWcy2n7e3OzmarNt0VqjAHU1pmuXdqz5yEf0e1OT6/6bMgV45hm1vj//vMYN2oeHVBmt/N7xyLt4eS5jeer4ZK+pWOT7IJOUJM3FhEpYiVLMm3cSpg6H6dzeJ+DU5YkAt8xSUw6kphYoJvkOWlEox+msDOnk3bo1/fJRmY6daxLNfPG27/Z29xyp8heqrO1xjj9ey6uYDzSp40ZLi7rSFi0q3HJFfuXU1qZ59SZO1H3q6rTviagVrKFBU2VccEH6Y519tv91AIf3YbvP9OmuIuT9j7ctZmuNCkoLU1+v25ctcxeB9+KXwsSOR4DryotjPCpE+65Ua1Y+MCasRCmm/zwoDujuu4s3Sy7M7L9McSK2zFpbx6cWmDkzvtiDfGY1RhUXmC6uw5scdsUKYNUqDazeuFFjZDLJkm3MSDFmfXpTj6xapa+XXx4fnxS2rDPN+EuNBSp2LGehx43U612+3L+c5s1TN2RTk05IqKtTBay6Wsu9uVlj/667bvyxU49l816FuY5M+3jbYn+/KlDWMg4ELw9kr3fHDrXsBR0/3fm9v518slpgd+1SRfShh1QpzDdViHdVhkzjcyHHklxS7FQytISVKMV84vAz/Q8NAU8+Cbz//cWzjmUyVWeysnjLbP9+d3bknDnhnvqicsnmaoKPKi4wjJXhwAG1Wljr1K23AjfckFmWQlhwN24sbD1YV9nq1XoTbmhQ5fyJJ9w0KHffrYrZyIhe8ymnuBMS7Lm//nVNMzA8rO3q1FP1Ju3tE946E0lfZ1G0t0KOG34W8ltvVeUhtR2sWwdceKG66MbGtIwaGrQ8J01yUzN45fBr3zbv1bJlma8j07V622IYa1Tq9fb3uzFkW7YcfvxM5/f+9ra36azRwUE3kH/FCh2bcqmb5cs1cewnPwk88ojG26WbfVppMcZJgkpYCVMs/3nqjXP9euBnP9NZVQ8/rNPb587VRKXXX683oGzTPxSCMHEiuZZZavzIY48B3/kO8N736tN7HANVkHL88MPZLeRrbwR24e/t23X22ty5GlOT6gJMtRz192tMkY3rsfhZErJ1L3Z3j89C/vvfA//5n6oAnnRSYZR/6yqziS4BLYejjnJd2r/4hcb7WAXt6ad1hqy9vq9/Hbj5Zr0p19erRWP1at3HeyMLG8sZZXsrxLjR3T0+JcIpp7gzLbdsGZ91317fDTe41+St+6C68ysrb96rMNeRbh9vWzz5ZG3LgLuYeWq79FNUTjxR33Nx5XplW7ZMZ3HaY+/cqQrrRz6i7s10/ThVWZ83z+0z1tq4fbu6fe+5x38Gaph2GXdccLlCdyTJiNd0vm4d8POf6w24qUlvSL/4hc56WrdOF6z2pn/YtKl47pYoUwx4FQ87g2vKFE0Gmen6okpKmXq9O3fqjaS2NpxLIdU1tnKlukFaWnQgX79e90l1Afb0qMJlp+c3NOj/h4fVRWPxK/ts3Yt33z0+C/mOHerW+vnP9VqHh/NPkdHR4aYVMMZNdjl/vhtbeNRRekOzSTjr69Vy0damZXT77a4CduiQyjVhgs469N7IwrbRfNpb1Nh2s2uXrkZhldIdO9SKs3v3+P3t9WVb935llS7vVbbXYCdivPCC9p2FC1W5Hx31ly1bV3o2Lj7vsW3qCztJId3//M5x661aNzZprF3fdng4ePZppnZZaSmRigktYSQjXrP6D36gN5pJk9wODqiFYtYsHTi86R+2bg2X/qEQRBnEbZ8UV61yLSbG6CCe6pbyEjSpITUZYy5PlanX+/zz+vn008OtSZgpMPnoo/2f8v0sR1ZJWbNG171LV/bZWGJWr3azkO/bpxamqiptW34WqVxob1cL05o1Wp+NjVqGNsdUT48qZPYGVl+vdb9nj9ZBZ6fe4OrqVAGrqlJlYWxMH0a8SoO3zozxt7gArov08cfVdT5pkpbxoUPp21sxsO1m2jR3ggugMxDtmoi9varIrl2rCu6FF7qu3bBy27J6801V2nbv1mV1ChEr5TcR4xOfSC9btq70bFx83mPb5YcAd4WPoP/5nWN0VNuenYEKqDXswIHg2aeZxs5M10IrWe7QEkZCYYMtjzxSTfczZ2pHtwtjHzigNwgb3Apkn/6hEDJGFcRtnxRtAO++fZpccvt2vdGsXev/P79JDQcP6tNqvk+Vqdc7PKzT64OmxKeSS2Ay4G85mjBB24aNjylU2XtvJG++qecZGdHXzp1altYiFUQYS+R112m7Pv98fdmcWXZmX329pnOYOFEVNRFVLNrb3ZQWNTXaBw4eVDmHhvQ43mBxb50FWVwAtWauWqUKmF2A/vXXVcGLO4+cbTennuouVl9Xp9aX6mrgllu0LT75pO7/nvfo9WTbxtvb3YeV3bvVyjZxosZK5WOByTXhcLaTGvwsZzZcILUteo/d16f9yi49BATXud85pk51LdO2jw4N6btNMp1KprEznRWQVrL8oCWMZIV90m1q0rXm3nxTb0pHHKHBpVu3Rpf+IczTVuqTtr0B5/uEZp8Ua2tVAbFut+OOc5dHsk/6XvxiLV56SWOoVq0KDvIOS2pcSbop8alkG5jsPaef5aimBrjssvxSHaTW8ezZwIsvqtKzd6+r5NTXq2KyY4e2taAbYdj0KpmCqO0xzj9/vOXEluNpp6lVzspl0y/ceGNwG+3q0lUc/LDKZ22tXq+XuPPI2XZj84xt2ODGhtlyXbdOJ+14rUbA4W08U59et06tsvY49fX5WwKD4p/Wrk0/VmQ7qSHVcmbDBRoa/K3iNkedVWrPPdd9oAqqcz/r3DHH6MPh6Kh+txaw007Tfht0jemslOmsgAzqzw9awkhWLFmig0VfnypeTU36uuUWfUqfOXN8+ofW1sKkf8jlaSufJ7TU6d2ADsBnnKEKVHW1KmDV1TrAzZ3r/ySdGmuxY4da0Gprxwd5Dw3lb93IdeFf7zT5gQH9nOm/fpajfJdI8auvAwf05g7ok72IWtyamvR8VVWHZwX3Wr6uv173C2P1CJpan8lK0NGhrttzz1XZJkxQ+b7yFeAzn8mtLKxVc8YMVzlua9PyiCudisXbbqZO1XCDBQvGr05hLSd2bcWHHz7cYhymf0axqLNf/NMrr+hklExjRTbpF1L7o1+4wNiYaxVvb9eynDNHy9W6t9P1Rb8+X1UF3HabhgVUV+v1fPCD2hdWrMhtPEw3tlTqwtuFgpawhJF037pdtPiuu9Tq1doKfOELut3KfuCAay2bPTv6BKL2PbXMcn1CszeHCy883Hpyzz2qnPX2qsIycaIOqtOmBbvuvLEWa9aoxWjKFDfIG9Cb08UX51dG2T6pp+5/wQUq0/Cw3vyz+W8hlkgJmn02PKwWge3bVVltadHys64wr1Uj1fK1erXGbjU06P+A3G4Q6awE3rKYPr0w/dZaHj7wAVVk/KxNcRGmnbW16aScdevctB+pFuMw/TOKpNR+8U/r1umDVCGtOanl5BcusHWr65a25z3hBDd3WqZ+nK4uFi9WBXjNGt3XLtuWyzWmO08SV/0oJaiEJYjly/WpaHRUn4SGhrKbfl8MBa67WwesE05QN0FYc3amY3Z2qiISlNoinQth82Z/d1MuSzt5p96/6136bgdNO1jZKezNzWrZWrUKeO01tYh1dwOf/7yrrKYOXiMjmp1840ZVIFKDvHMlte5vuCFcXeRSZ0HnyneJlKD62rLFtUZu2qQ3LhtreNJJ49MhpN7YjzgCeOMN4Ec/UrfvqaeqFTKKRbnzcYP7pRlYsWJ8UHpNjVqik/BQ5tduvP14yxatq/p6rVMbkzR3rj7ItLQA3/2uWs5PPdXtY6n9M1VhGhvLnNIkTMhCqkJx/PHaliw7dqjiu22bK0emcg86d7pwARvr5iW1zWcibB8Om4bi7rv14UVEY8jshIWg84SZEJXuuJUO3ZEJobtbFTARVwFbv14HnTDT74sRHBnFOewxN20an9pi48bxxw6aQt3XFxxkm23KitSp94cOuVPvUxcL7+1VGR95RN2Lhw7pGnivvKJKyfLl7nG9LozLLtMbT1CQd7Zlt2yZ5hG68kqVJ+rA2CjbWZCbaPNmN0P5wIC6bC65RN+rqsYrr6lT/QcG1B158KBaaLu69HhRuvOyLSO//Ves0OvzBqXPnZt/UHpU2GvYuFHLd2zMDQp/9VUtf+uufeIJvcaZM7W+bR8DDu+fqa7g6urgh9Jsyj3VrTh/vtv2duxQmfr73dU0Uo8TdqUA7386OrQ9P/YY8OMf6/vY2OGKUVRWpDBpKG66SVPV1Na662nefHP6NpfJXZ/rcSsFKmEJobNTLWCNjeNzEW3dGs51kuuMn2xlLPQ57DG3btVrbmrS923bxh87KCahqSk4HiHbGCnv1Hub56m+3l38N3Wx8G3bNEjfuhcnTVLLy+ioumv9sDLV1rrxVCef7AZ5h8V7w+nt1Tazfr0qkJnqJZ+8ZVG2s9T62rRJb4YzZ+qNqq5ufIZyv5mF3hvNhg26z/TpWjejo+oWa22N9gk82zIK2v8nP1Fr8xVXqPV0zpzC9+lsSNdu7DVs2+b24yOP1L4xe7Y+oEyfrlayo47SfU87TetTROsqqH96FaYZM4LrLp+26W17GzaoTDaY3Xuc7m7tqx0dqkTZpaduvVUVqkzn9s72BfR6rOVo+3Y95iOPuMszFZJM42Fnpyr8DQ06jh1xhH7etStzGaaLlcvnuJUAlbCE0NPjWsAs9fXaeMM8FRUjODKKc9hj2hQJgH9qi6CnLe8TrCXX5JCpU+8PHXKn3qfeHNrb1SVbX683ldpa3V7tOPi3bvU/R6HSaHhvOHaGolUYgeB6ydeSFWU7Sy2brVvVbTFnjntjO/FEdWUFBUb7TfWvqgL+/M/VCnnRRarERUm2ZRS0/9atyQl4ztRu/Prx9OnqcjfGzR321lvaZwF3dmVjo/vQlU+8Wz5t09v2tm3T/5133vg4wrVr9Zqff14fugB1rw0Pq4Kf2udTz93ZqWPGxRer9frii7Usjj22MCk9srnGoDQUw8Nu/QH6eXg4/zE+iuOWC4wJSwhtbdoo163T71YRqakJ5zopRnBkFOewx7QpEtKltgiKSfCLR/izPxsfkxMmRip16v2ECRqT4xcM3d2tbpehIa23xkYdOMfG9PfW1uDz5Bo758Ub32HLzpvjK6hespms4BfjEnU785bN1VdnH9PnjfexGe6DpvpHFUOZbRkF7d/aqu9JCHjO1G78+nF1tSodth6amzVFQl2de9yWFu03ixZljoHyzlj2q69826a37fkdp69PZ0SPjKglx1q1XnrJXSlgxw7Xcl5bq5N2LGvXuhN67MPe1KnuupNhUnrkS6Y0FOvX65hmJwzZXHfpQjgy9aFcjltJ0BKWEDo69Il93jzXAmaMpn4I0wmzdb3lKmOhz2GP2do6PrWFjcXIdGy/p7tLL81tKnbq1PuJEw+feg+4VoGZM9UKNjqqgfX792vckQ2gjhKv281a7vr79eaQrl7CWguCLB/z5kXfzvyu0RLmpmpdIw88oK5ev6n+Uca25ZMqxLv/kiXFK+tMZGo39hpSU9ScfLK+HnhA6+S663K7JltfNoYqKOaqEOWVKfShsdH1WNgHn2OOUdlWrnTHgIEBtY51d+vr1VfdPjo4qEsTvfKKtuckpHno6NBxb2BAr+HAAf08bZp/GYbtQ9ket9KoWpZPZsUicd999y279tprx2177bXXMGvWrHgEioCWFnW17Nqlyth552ng4qJF2f3/tdd0Ntj06YdPa863zMKcI9dj7tqlFiVA4yTmzg1/7JYWjZ257DJ9f+gh3d7cPD4NxGuv6e+ZZLHXN3v2a7j88lmHyWAXwD3mGL3p7N2rg+vIiN5w/umf3NmRUTFlCvDLX7qfa2q0DGfM0JleQWW3Zo3eIG2ZAPp9+vTxZWOvMbUM9+7VY/u1gUL3Se811tW5Lq1rrnHdROlI116Dri9TGwlD2H5iyyto/0WLCt/fciVTu0ntx/v2qaLR26vt8cwzdZ9cxxBbX21tr2HPnlm+9VWo8SnoONbFPWWKKlSAPoBVV6uScdRRqpyNjmo83Jln6gSf117TOLOqKi0fwHXH7dql4/y2beH6pR/d3Vo+//3fWk9Tprj9I5s+adeM3b1bV2c4dEhniH/uc/5lGLYPZXvcOCnkGPblL395+7Jly+7LtB/dkQkiGzfV8uXjc3UtWaI3/igbdRjTs98+QPr/FcI95yWb1BR+8trnkq4uf7ns8a3roa5OB9zmZp31lCvZuMfa29Xi520Dt9zir/x5j1tXpzeWE09Mv75mujIsRH35pWRYt+7wNCU2m7jdLqIpRPzSmASVU7o69Lu+oHrItn6CZvDZY9j1FIHDy6KzE7jzzuLkCvz/2zv/KKuqK89/N1VQP4D6QSh+WFACAbX8UZaIBHGJOpLE/MKEOCvOSqftsWdlqU3sOONKOul0j70cXRnH1Zk4tLqy0iaZblfT3aRiSFrT7Y8w0CKKCpQFiAJCSQEWYBU/6wdVnPnj+866p27d+9697716tx61P2uxqPfefe+eu++55+yz9z57R7muKGkI7DW7udrssW6qnaj9x23X1q3A4sXeZzaFxL59fE7d/mA3DeQiw3ShD7W1jFXctInP3tSpDDY/eZIxh+Mc/9L58964M38+rWDWXVldzd+y58mm7m3UihD+76Tr3/39jFnLJLMoz5BNV3LqFJW0JUtoDR1tyleSqCWsCFm7luVQjOEAcOIE8NvfciK+/PLw7+UiM/uwAzQjd3fTSmEDpcOO+eUvGXBaURH+vXTnDFvhpTsm6qoy0zWFyWvrVu7csxmwJ070Bs0bbohmpQm61kzy9R//s5+xaPqiRZyEtm8ffrz/d21R6cpKtjfMWhDVYuYStX/527RnD6+lr48r5b4+undLSijnG2/k6nrixKGfl5YyMDpKX/ITdn0AXUq2bfY+DA6yjf7345zbf921tfvxxBNz8Mor3vNhZVFRwXubzXniELXfxbEy5cPKaNtlrSd79wI7dwK33LIfu3fPwWuvsf9a15/bH/Jxr4JwZbB3L5/5pUu5w3lggArH+PG0iFnsM1Ndzb/r6nhfL7uMz+zcuUMtidlaCF1ZHztGb8CGDcC0afthzJy0Y3S2/TvTM3TsGGXS0eHFg+3fz+9deeXI9OdcScISpjFhRcjq1VxR1dR4JVKqqsLTIuSDKNu/g47p7ORA6r43OEhrRroUCVHiDXKNW8qlkO+bb3I1fuAAJ86zZz0LxkjJN5vjg46bN4+DfLrSKyMZY+hvU0cH++/OncFpSlavHp7+oKKC38s2ZUPY9dkAcr9cbRtySc3hv+7SUj4bNq3I0aPAq69yItu0KVq6kVyJ0+/SpSFwiRPfFJb2oqWFio0N6G5o4Lhx6hSwZQvvk42v6u5mwP6rr/KYfNyrMKwMmpupPLk7d6+4glbboGcmqL/t3ctUFG5ptKglkSx+WX/0EWutdnZ6cWruuBl2v+PIzN6zbdtohXz//eBnqKOD42JlpfecHztGJezJJ3O4CRcYqoQVIXbScqmqCk+LkA/a2zkY2jpw69cPr3cYNPj29XmxXsDwQSIsmDNbpa+2lgNhlDQQuQTD9vTQUuMyaVL2gbS2LUeOhNfaCzreJajt2V5jvlJpRGm7nUxPnQpOU2JTNWRKYxKHsOuzO11d8pUuIt3z8dFHVLzOnGE/OnMmOFFwvilUbcagDRXpFlrt7ZR5eTkV7smTuXgAGI9VXc3J/cQJxmBVVlJm77xDd9+2bd44NRIyDJLb/Pm0bAU9M25/a22lwrh9O63pNtdYNhtD/LLetYtj4bRpnqLvjptx06Fs2wbccw+VzmuuYc6673+f7W1q8hTP1tbhz9CRI1SQP/6Yx1trWH8/8NJLmqjVojFhRUh9vRc3Yzl5Mn1ahFwpK+OAVlXl7e7ZsGGoeyFoi7i7HR0YPkiEpUiIEteVa9xSUHv37KHF5e67vZgd/++0tDAgH/BM8T092dV/tLETW7dyhd/Xx/YE1drL1PagiS6Xbfv5jtULa1NJCbB7N699zx5uLigp8RQvm6ohShqTOARdX5C89u7lRL92LfttYyNdKXHPne752LWLCsfEibzGiRO9vG8juZW/ULUZg+Kb0qW9aGiga9Et6VNaSplcfDGVggMHaHUZP573Z+JE9ovTp4f2l9deo7JwySXZX5OfMLk1N4en2rB9bd8+9u+ZM/l682amUbHKUpxnzi/rzk7K6bLLvGPccTNOOhTrAt69m0owQBfl5Mkcd8eN84qN19Z6123rhnZ18fXgIENnBgd5fyZMoMs23+k3ihW1hBUhq1ZR6eruphm4u5uvRzItgjFDX589S/fJyy97boQgk/u0aXxI7Xudnfw/bJCwRFlNZ5vCwOJv73vvcUCsrw825Vva2znY2gLStjzL8ePx3HWuJWDxYrpUjh7leW2tvTAXp9v2dJm2C5G6JC7+th8/7hXm7u3l4G/THbipGvzpD+rrRz4Ni83a39jIya27m1YMO8nEObf/twcG+GxMm8bnoqyME1x/P61hYYmC88lI9I+oVtR0VriVK6lcnTjB56Cnx5vAlyzx3F4AP7M7Eq3yZQyPLy/3qkmMhnQ9VvHs7+diwlZGeffd7AvLu7KeNo1jhlsk3B0T46RDaWtjH3Qz3Yuw7TYpNBBc77OtjUaCsjIvd2JJCRXkiRM5fmqiVqJKWBFyp5uNvAAAIABJREFUxx3AY4+xkx8+zP8fe2xk0yL09zMAtaKCE31nJydNG+BtV2P+wfeRR4BHH40+SFiiDHK5TiD+AezQIa5IFywINuVbGho4cF5//dD6j8uXx1vZuZaAmTO9XVa2hNPSpQyMDRqsbNszZdoeSbditrhteuMN9onPfIaZw60MRLjKfvBB9usHH+TrefPY3+fN433K97WEZe3/1Kd4P2pqOKl0dMQ/t/+3S0v5bDzyCGVw7BgtBMuXUw5hiYLzSdT+EbfUVZT4sXSLqKYm7vY1hoHqR47QXd3Tw37+4IO0iFVW8lmpraXsJk6kW9CtzVpdTTfhSPaTqM+VVTyDco1la4F0Zf3EE+xXrqLvjolh7bbPmPv+3LlUnNxM95WVHHPc+xaUVHvuXI7xdXW8JyJsz+CgF3qgiVqJuiOLlDvuGPlcVC7WjH3zzXRL2hWsHQABKhX+Ade/Hfrzn/cSqaZzVbiZz+13/buFohyTiWwytFsXQG0tFVN7DffdF/28wHB36owZHKDOnfPcvF1d4YNVUxO/kynT9ki5FXPBtsnKwL+t/+DBoW6dQl5DWJ+YMYP/bPuyaY/7224KlCeeGJrWwVoBwxSifGb6zyTbbNIgRCGT29KObw8/zGfCJlBet47n9svMfn/WLC8nGTDc/ZYvsumTdhydNo07CM+f58JpypRoKSmitMkdExsbvfvU2kpZbtxIec6dy/E4bJx46CEu8t1M91VVjL2bMIFt37OHVsa5c3m87YszZvC7lZVcXH34oRffNzBAj4MmaiWqhCmRcAfM7m4+hH19XlmOdFnX3cF73Tov91MmxSnKIJfr5Ozms9m2jcpAQwMHr0svDS9zU1npFZLORvkDhsdnNDZ6cXfnz0fLFRQnJ1oUWlu5c2nzZq5elyzxiovnOvGnK4PU1ze03EtDw9CyUyOZK8vtA26OMn+ZJpsXrrOTk2hQrF62RF1QpFOIgGj3KK4SF6fUVRBh5wu7ZsC79/v2DY3nKi/3rNMPPTT8+3/xF9EWeXHlkC/Fd+VKJmfds8cL07AuuhUr8tOf3DHxhRfY7r/8S24COH7cc3Xv3ctybkDwgt7uAt+7lxbJs2f5DJSW0qK1cSOD7q+8khZ72xdXrKCl+ORJuog7OzmWDA5y3Kyp8QL6C2lIGK2oEqZEwh0wM9Xks4QN3m1tmevEFQK3FMq+fZz8jx718g4tXuwN4P7Jzw7uuVgD/JaACRPoSqmvj67c5TOwurWVO5/27vUCcdevZ3Du5MkcaLO1hIQpDytWAD//uXdOm/m/o4N/T57MieTv/54bJW69dagCn6tyZts1OMg+MG4c731lpde+devYL9raPDd1fX1+rEG2De4En67Oadgz9eSTnCQzWauysWrlouhnOp9/EeU/fvNm3o+qKm+scc8dtAi75JLclNlMx773HvCNb9D6EyVhsEtTE/tOZycXcQsWMD62rCz/SklrK0NHbJjG8ePs5+PGcawRoUVs9erg8zY10V3+5JMcBzo72falS6kMr19PBWzBAh7f388g/lWr6Cq+6ip+p6eHz/HMmcAXv8hjDx/m7tVCLLJGOxoTpkTGxh6kq8nnMhrqoaXDTmg2/9RFF3GQ6eujYnbunDcoZ5tTLB1B8RmPPAI8/XT0XEH5DKxuaaGy4QbiVlVx1e7P9ZZrjiw3ncjs2Z4btrKSsT1Tp3obJQC6azZuZJLi99/PX71H2y4bh+fPUWbTnRw6RItFdzcnm44OTmi55p6KW8My7JnavDla/3QDwzds4L/du72kn0HksgEm1/x3dhe1Gwie7txRLVZx2uUe29lJ95uI96zF7YP9/cysb8us2WSu+R4XW1oY02WVo95ejhGnT/Pz0pQJJl1qo6Ymjkd33knF86tfpTJVW8vn9eBBHmdTrBjD8xnDczY2eorn4KB37IYNVATzXbe1GFFLmBKbqK6Tkdj+ns94GLvCt3mqACoA48cDX/oSlUz72/l2+1lydafmIy7O0t4+PE9WeTktLP5cbzt3UjEBot2DbdvYF2yg9GWXcYK18nPLvfz617wf773HFXVFBQf19nZOWB0dXH3HdYuFXbO/D9hA6d5eb7V+5AgntLo6ft7Tw5xUZ86k//1M/TWuqy/smRKJnjdu/HgqbeXlXrqZF18Md69GTTkRRNBz09vL8l5BMvEf39jI3ah2V7UNNA9zL0a1bsV5nt0yZb/7He95ZSWvw71f9v9MY9NIjItBtLdzA8umTey7JSVUhHp6qCjZ3aVRUhv5n9/GRj4LH35Ii9i77/L3p0xhnxJh/9q1a3iYha00cs016dMUjRXUEqZkhX/3EzB891S+t7/HtRpkwq7wq6u5otu2DXj9dSaD3LKFKzX/sS4jMXDmypEjtGpE3cVmaW2lO66jg5Yvu1ru7eWEY/NZ2RXviRNe8Hime9DaSplaRcfmbtqzh/Lzy7a6moM94O3MsjvJbP4099h8JBZ1d6v19nJCcVfrp05xt+LgoFceZtw4r0xL2HVn6q9xrcVhz9SSJdH6Z0MD+7kNkrahBTZvk7/9Dz3E+otuDGScHbb+e3vkCOVaVhYsE//x06fTrTVtmrej1Ma/+cebONatOM9zQwP76muvUQGrqOCipKuLz4NNahp1bCpU2piGBi6gystpvbKLGYDPly35ZFMbhe2ADXp+N23y2t7dzd8dGOA9WrDAS7Fj44fnz6fSdfAgZbds2dAd8lYxjztuXQioEqbkTNhkA+Q3PUK+XYJ2MDx3jiu63l7PGrNx49Ade6Mx3xYwVPbjx3PH1fr18bJw29+or6c77uxZ1ng7dsxL/njoEGOz3niDk7YxXOFGuQctLYwdCcvd5JdtfT0nibo6Dvg2R9SsWd5K3JKPxKJdXTynm4PMKmR2tT55MhWzw4e9vFXnzw9NmOyX6f33M7B5+/bwEkRxlfuwFAP33hutf65cydggN7fdgQM89rnnhk687jNdVkYF5Nvfjl5Sx5WvbdfWrXx/4cLgZzjoOSsp4U7IZ57xEpy6ff6FF/i9NWu8+2YJU2jjPM8rV3ouyIkTqUQYQ8Vw1y4vqfLgIO/1b37D/8Pc1YVKG7NyJRUjY9juhgYv7vLcOcZ4PvAAXe7XXccY38cfZ0LW11/3xo6WFgbS+5/f99+n8l9Tw9c2VtLelw8+oNJ9+DDf7+9nG5YsGZr2IpNifqGj7kglZ9K5VOIM2JnIt0vQDoYrVnBQsEHXdufnRx8N3XadL7dfPrE19rZvH+oS2L3bS3ORyczv3r+qKloBDxzg4DhlCguET57M1f7773Ole9113krWuu3cber2d9vb6X6webbspFVdzfPZdrmyXbAA+MpXWNrkpZdopVmyhNaozZs5IUTdPZoJ976eOePtjty/n8XY7TXOmMG+cewY5dLX51nC/G48q8B0djK2zVr+rr9+qAsWyM7VF+bCjtI/m5qYh2zrVq+sTF2dl5vNuu9y3REZJN/2di/foFu82R9on+k6bNv6+jy36pQptOLaKh7298MU2jjPs8171dXlKeg2S7xNpitC93RFhWctCnJXx9mEkStNTXxmRbh4+cQn2KfLyth22/eOH2e7RDh+nDxJuS5Z4rV1/nxel93BXFLCa7R5Kpct45gzMMBNNhdfTIV51iwuRK6/3ttB2dFBhe6Tn+S9D1LMgbHjnlQlTMkqzsr9ztat3EnoMhKBpnFiKaJeU1MTV23NzRwATp+mVczddODfzQWwhM3993NAqa+nST+J7dbbttGNaPP4GMOJ4dw5vnbjmsLk4Cq306dzB9P581zRL1vmydtaIQBPObFBtlVVlON771EOPT10VzY3U5YbNgA33TQ0/1ltbfpJyaYleOUVXkNNDXDttTxXLqlB/AQpNQ89xDZabFzL1KnsJxUVntXOH3dklYRp07wyS0BwCaJ8KvdR4wvvu49t3r2bz6kI+8nSpVyA2LakW/DEGTPcdvnlCgQn+7TH+1OmPPAA+3xTE/uUdasaw9QLAJX+z342s0LrP09LC12vQdfT3BycqqSykvd4yxYqIFOmsN9PmkQl0XVXj1S+tXTU1fHf0aNsy9atfP3oo14/3bDB2zFpDPt1WRkXTDNneuOuzZO3Ywfw+99zjLEVPrq6uFnr1Ve5WKmp4TOza5dX19jGcc6bR2W8tjaaYn6ho0rYGCebgcH9jk0psGaNZyHJpq5eFKJaDeJek1uL89gxDiIDA1ztBa3K1q7lLr2qKg5S3d18DURXxPK1waC721MKyss5MNpJ1VWQ0uWU2rePE4bdag545WJs0XZrvZo92wvSra4eGmQ7MMAB+uRJr7j55s0cnN95hxOAOzneeGP6nFc2fUh5OSc7m9jyzJmRz/oflj7k8GG6a90akl1djMObPn3ooqSxkbEzwNASREGJiQu54reK31138R7X1NAKMX06ZWz7ZNiCx03rcfAg7/GvfsUcXZn6f5Rn2M3btnOnt2vWFoPeuJETt7uZoreXz+Kll9KVFkdJj5J7bds2utdsTqyyMj433d1055WWUnYff8y21NfzOXTd1fmyLsbFBuD7X1tF++RJL/nquHF85qqqeG12bLL3rLeXCtjgIK1dR49SKZ0+nQpXT8/Qep/2HvnjON1kzFEU8wsZVcLGONkMDH53gP37ww85OF51FSfhXLM/+4lqNYh7TatWeUpUTw//7+9nHMXx48NXZatXc2CxA6z9Pyzfjh83P1lHR/pJLJOyVlPDgb+nh+6GAwf4fllZ+C6kp56iImMnncOHgeef5+vZs/leSQmVMqvEWRfLjh38vb4+5s9qb6erBvDiRQBem7UAdXZypfvGG0Mnx3T3yb7evt2rsdfTQ3ldffXIT1xBfe2RR2gp8Wf47+1l6agvfIGf7djhucWsC9YmeE26ZJSlqYkpEsIUrXTKUksLJ+G2Nt7vujp+/vDDtF76XbP+/pvuGXYVIrsbr6eH/9uQAYDuXRvHZxcdCxdSmfjyl+PlIYySe62piYpgWxufneZmL9+XfTbOnGE7envpOl+6dGjR8JHaYZ2O7m5anq691nuvq8srkt7V5ZURsiksSkt5PWVlw0MxnnuO8p43jyEKEydy/DhyhN+dNct7Vjdt4iL95Mmhyqhfwcpl9+2FgAbmj3GyyeVlv/PuuxwYp07lqkgk+7p6UfHvygw6R9xrcmtxDgxwEFq+3FMi/INGR4e3ArdYk3sUbBzXjh1efIkIJzE3GDXK7rrmZiq91j02Zw4tArbGm38Xkj+nVGcng+7r6vh9m5R0xQoqdWGcPUv34lVXcUJ+7TVaLMrLqcDZHEQ23UN5OSd9976lu0/2M/td97cK5aoI6mtBgfTbtlFWdsOIrSLx9tuU69VXM67uiSdGhwJmSRecni54vL2d77k7LKuref/dQPR0G3bCnmFXIbI7ZM+f52+PH89zjR/Pz8vLuQABGL80YUJ2m2Wi5l675BIq1s3NbHd/P58xOwaOG8dxYPJkPn+lpUPbksQO6/7+9EXSu7oYYzk4SLkC3k7H73zHuzf2WVi4ELj8cu/5njSJFmIrnxtu8NLZlJVxfDt5kgpr2AaIQm1UGK2oJWyMk03OGvsd1x1QWsrcT8uWZa6rl+/ad2Hti3NNthanuxI3xhs03FWZ67602IEmHfa6n32Wk8rkyZ6iV11NBci18ESx6NlV5NVXD11F2pWrNfMfOcIA2QMHGPc2bhxdx1aRrqnhNVjrSFubF6uxe7en/DQ3091z001sz+WXc8VrC/SeOMHVMcDVsDHe5Ohf2Wa6T9blaeOqenu9a0zKVRG0aj9+nJn8LTNmUG5x3WKFJpNlOcxN2tBABcV1O9nFhKscZ2Nld61F1dVcJNi0IAD7U0kJLbaf/CRjCG37Z87MT/kwIFrutYYGbyE1aRLbdPgwn4Mgq2cSFp8JE3ieoGfMvf9nz9LdOjhIuYbFuDY0UMlqa+NruzAaGODYMGMGg/DffZdjZGkpF7iZytQV2iU/mlAlbIyTzcBgvzNhwnB3QKYJshDBqbkMdu7AdO4c2+kfNFz3ZVUVlZeTJ4Ef/CD8d93rvugixkidPcvV4qRJwZNYFPdFpon08cep3L35Jv8fN47n27ePLggRDpxWwXHP4RZtt9idYPbY6dPpdtm5k6+NoXI3aRItRB9/zHJD9947/P5muk+PP05ZtbVx4D9/nqvuJF0VQfJevtzLo2YpL4/vFkuCbCa/lSvpPnfzq/X2cvJ2n/1s3G+uQtTYSCvt8eNUvM6d89ItzJrlKRK5jhth/dDmXgtbJPhrK1pL2Pz5dF0HKRqF3mFdU+MtxIKesbjys7K68kouMGyZt+XLPYu1DeC399EucJVgVAkb42QzMNjvPPnk0BQCYRYPl0IEp+Y62NmBaf16Bi/7sQPK6tXe7sgf/CD9QONed2MjV4rW/VdSwkls/vyhk1jYCr2sbHhx66DJ3srh/vv5O5WV3g7H/fu9OBsbhL9woXeOdHFB/slp+nTe+6lTgb/7O0/un/tc5l1z6e6Tu0q3lscFC/JvOY1rmfVPXFbBBsZGTEtTE+MXH36Y/beujgpYkPstrkXa7XN1dVToe3v5vf5+9rPrruMzk68cfWH9EGAt1aNHuQgoK/N2FtrvPfoo5bBxI5XEuXM5ZkTZKVoIKiryq/i5siorA265xbsPY+kZyCeqhClZDQxNTawp5k5gUdwBhQpOHenBLu7qzr1u667asIHKxZw5VMD8E0uQErRvn+fii1JMuKmJQbTd3V46AoAxfB0dVASN4cq2rm6o+zXd5BQ04NbUxJd7uuMLMWHlwzIbR+l3n5dPfzq8VNBo5447hhfK9ve9bHOg+XPGffe7/KylhQuJkVDEg/ra2rV8vrq6vDqW/p2GABWdr33Nu8Z164ZvUEiSfD9HYb83GvMoFgOqhCk5EfcBL1TdtNGG/7qvuIJm/EOHqCQFTWJBk3t9vZds8ciR4GLCfgXCjV2xMWilpVTErLUqblxQ0IBrg6SLiXwmJY2bCmFgYOTzRI0kma45n+63TNZpl3zEnLa20sJlrcfW5VpVFT9uc6wwluO6ckGVMKWgjNXtyEHXXVqaececf2C7+26uyAEvqL68nK7FsAlg5UrgrbdY/87Wjjt1ii4kO0FlYwn1f2f9+ni/EcZIb9xwKWTaAP+EXVrqley5UCevuH0rV8tkvmJObUyo3blsFy8HDw6N/0si7YRyYaEpKpSCMla3I+frut1t7jZ9Q1BQvf/cjzzCAPtz5xhbc9NNjGcZTXJvbQXuuYdK1wsvxKt/mS2FTBuQTTqYsUau9WHzVV+2vZ0KmFuLsryc8WH+uM1Cp51QLizUEqYUnLFqts73Ti6biTooqD7o3E89ldu5RxJrwdi9m6VfAKZBWLp0ZK1FhbTMjlVX/Nq1QzexpCvxlatlKV+WqYYGKmA7dvC13cAyfnzmuM2xYNlX8odawhSliHAtajaXmT+oPl+7xgqJtWD093sZ8svLmW1+JK1FhbTM+pOj2pp7xXi/omJLfHV3Dy3xtXZt8PG5WpbyZZlauZLu4iuu8CxgxnBXaFDc5liz7Cv5Qy1hilJkuBY1f/xUse5IshYMNzmrtT6MtLWoUJZZf6B6Y+OFP2HHLfGVq2UpX5Yp916Vl3upGILu1Vi17Cv5IRElTERuA/BjACUAfmqM+WES7VCUYudCmQCsq+6yy1gCCUifbb9Yce/X+vUXxr1LR0eHl5vOkq7EVz5y/OV7R6aijCQFV8JEpATA3wD4NICDALaIyDpjzM5Ct0VRlNGBtWDU1jIZbKZs+0pxkE2Jr1yVH1WelGIiiZiwxQD2GGP2GWP6AawBcHsC7VAUZZTgxtacO8f8ZS0t3EygE2rxsmoVla7ubsbBdXfz9apVSbdMUUYHYmzioEKdUOQOALcZY/5L6vU3AHzKGLPKd9w3AXwTAKZPn37tmjVrhvzO6dOnMWnSpMI0+gJBZRYPlVc8VF7xGCvy6upiYLstO1RXN3SHaBzGiszyhcorHvmU1y233PKWMWZRpuOSiAkLKPyAYZqgMeYnAH4CAIsWLTI3u1WEAaxfvx7+95T0qMziofKKh8orHiqv+KjM4qHyikcS8krCHXkQwGzn9SwAhxJoh6IoiqIoSmIkoYRtAbBAROaKyAQAdwJYl0A7FEVRFEVREqPg7khjzICIrALwr2CKimeMMTsK3Q5FURRFUZQkSSRPmDHmeQDPJ3FuRVEURVGU0YCWLVIURVEURUkAVcIURVEURVESQJUwRVEURVGUBFAlTFEURVEUJQFUCVMURVEURUkAVcIURVEURVESQJUwRVEURVGUBFAlTFEURVEUJQFUCVMURVEURUkAVcIURVEURVESQJUwRVEURVGUBFAlTFEURVEUJQFUCVMURVEURUkAVcIURVEURVESQJUwRVEURVGUBFAlTFEURVEUJQFUCVMURVEURUkAVcIURVEURVESQJUwRVEURVGUBFAlTFEURVEUJQFUCVMURVEURUkAVcIURVEURVESQJUwRVEURVGUBBBjTNJtyIiIHAVwwPf2VADHEmhOMaMyi4fKKx4qr3iovOKjMouHyise+ZTXxcaYukwHFYUSFoSIvGmMWZR0O4oJlVk8VF7xUHnFQ+UVH5VZPFRe8UhCXuqOVBRFURRFSQBVwhRFURRFURKgmJWwnyTdgCJEZRYPlVc8VF7xUHnFR2UWD5VXPAour6KNCVMURVEURSlmitkSpiiKoiiKUrQUpRImIt8Skd0iskNEHnPe/56I7El99tkk2zjaEJEHRcSIyNTUaxGRJ1LyahWRhUm3cbQgIv9LRN5NyeVXIlLjfKZ9LAARuS0lkz0i8mdJt2e0ISKzReT3IrIrNW79aer9KSLyooi8n/q/Num2jiZEpEREtorIb1Ov54rI6yl5/aOITEi6jaMFEakRkbWpsWuXiFyv/Ss9IvJA6nlsE5F/EJHyQvexolPCROQWALcDaDLGXAHg8dT7lwO4E8AVAG4D8KSIlCTW0FGEiMwG8GkA7c7bnwOwIPXvmwCeSqBpo5UXAVxpjGkC8B6A7wHax8JIyeBvwD51OYD/lJKV4jEA4L8ZYxoBLAHwJykZ/RmAl40xCwC8nHqtePwpgF3O6/8J4EcpeXUB+ONEWjU6+TGA3xljLgNwNSg37V8hiEg9gPsBLDLGXAmgBBzfC9rHik4JA3AvgB8aY/oAwBjTmXr/dgBrjDF9xpgPAOwBsDihNo42fgTgOwDcAMDbAfxfQzYDqBGRmYm0bpRhjPk3Y8xA6uVmALNSf2sfC2YxgD3GmH3GmH4Aa0BZKSmMMYeNMW+n/j4FTpD1oJx+kTrsFwC+nEwLRx8iMgvAFwD8NPVaAPwHAGtTh6i8UohIFYBlAP4WAIwx/caYbmj/ykQpgAoRKQVQCeAwCtzHilEJuwTAjSlz4f8TketS79cD+NA57mDqvTGNiKwA0GGM2e77SOUVjbsBvJD6W2UWjMolBiIyB8A1AF4HMN0YcxigogZgWnItG3X8b3DxeD71+hMAup0FkvYzj3kAjgL4Wcp9+1MRmQjtX6EYYzpAT1o7qHydAPAWCtzHSkfyx7NFRF4CMCPgoz8H21wLmvSvA/BPIjIPgAQcPya2fmaQ1/cBfCboawHvjQl5AellZoz5deqYPwfdSM/arwUcP2ZklgaVS0REZBKAXwL4tjHmJI07ih8R+SKATmPMWyJys3074FDtZ6QUwEIA3zLGvC4iP4a6HtOSio+7HcBcAN0A/hkMqfAzon1sVCphxpjlYZ+JyL0AWgxza7whIufBek8HAcx2Dp0F4NCINnSUECYvEbkK7GDbU4P9LABvi8hijGF5Aen7GACIyF0AvgjgVuPlcRnTMkuDyiUCIjIeVMCeNca0pN7+SERmGmMOp8IBOsN/YUxxA4AVIvJ5AOUAqkDLWI2IlKYsFdrPPA4COGiMeT31ei2ohGn/Cmc5gA+MMUcBQERaACxFgftYMbojnwN9thCRSwBMAAturgNwp4iUichcMOD8jcRaOQowxrxjjJlmjJljjJkDPqgLjTFHQHn9YWqX5BIAJ6zZeqwjIrcB+C6AFcaYs85H2seC2QJgQWpX0QQwuHVdwm0aVaTimf4WwC5jzF87H60DcFfq77sA/LrQbRuNGGO+Z4yZlRq37gTwijHm6wB+D+CO1GEqrxSpMf1DEbk09datAHZC+1c62gEsEZHK1PNpZVbQPjYqLWEZeAbAMyLSBqAfwF0pS8UOEfknUIgDAP7EGDOYYDtHO88D+DwYXH4WwH9OtjmjitUAygC8mLIgbjbG3GOM0T4WgDFmQERWAfhXcIfRM8aYHQk3a7RxA4BvAHhHRLal3vs+gB+CIRV/DE4K/zGh9hUL3wWwRkT+B4CtSAWiKwCAbwF4NrUQ2geO6eOg/SuQlNt2LYC3wfF8K5gx/19QwD6mGfMVRVEURVESoBjdkYqiKIqiKEWPKmGKoiiKoigJoEqYoiiKoihKAqgSpiiKoiiKkgCqhCmKoiiKoiSAKmGKohQNInI6xrE3i8hS5/U9IvKHqb//SEQuyuL8+0VkatzvKYqiBFGMecIURVGicDOA0wA2AYAx5mnnsz8C0AbNuK4oSoKoEqYoSlEjIl8C8AOwesZxAF8HUAHgHgCDIvIHYCLLW0GlbD+ARWBiyx4A1wPYBWCRMeaYiCwC8Lgx5mYR+QSAfwBQB1ZHEOe8fwDg/tR5XwdwnybvVRQlDuqOVBSl2Pl3AEuMMdcAWAPgO8aY/QCeBvAjY0yzMWajPdgYsxbAmwC+nvqsJ81v/3cA/5767XUAGgBARBoBfA3ADcaYZgCDoPKnKIoSGbWEKYpS7MwC8I+pAsUTAHyQx99eBmAlABhj/kVEulLv3wrgWgBbUqWtKqDFkRVFiYkqYYqiFDv/B8BfG2PWicjNAB7K4jcG4HkGyn2fBdV2EwA8Ps9PAAABAklEQVS/MMZ8L4tzKYqiAFB3pKIoxU81gI7U33c5758CMDnkO/7P9oOWLQD4qvP+BqTcjCLyOQC1qfdfBnCHiExLfTZFRC7Osv2KooxRVAlTFKWYqBSRg86//wpavv5ZRDYCOOYc+xsAXxGRbSJyo+93fg7g6dRnFQD+CsCPU7/hBtf/FYBlIvI2gM8AaAcAY8xOcDPAv4lIK4AXAczM98UqinJhI8YEWdoVRVEURVGUkUQtYYqiKIqiKAmgSpiiKIqiKEoCqBKmKIqiKIqSAKqEKYqiKIqiJIAqYYqiKIqiKAmgSpiiKIqiKEoCqBKmKIqiKIqSAKqEKYqiKIqiJMD/BwROwo45/eiFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Data - Latitude vs. Wind Speed Plot\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(df_countries[\"Latitude\"], df_countries[\"Wind Speed (mph)\"], color='blue', alpha=0.5)\n",
    "plt.grid()\n",
    "plt.ylim((df_countries[\"Wind Speed (mph)\"].min()-5),(df_countries[\"Wind Speed (mph)\"].max()+5))\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Wind Speed (mph)')\n",
    "plt.title(f'City Latitude vs Wind Speed ({datetime.datetime.now().strftime(\"%D\")})')\n",
    "plt.savefig(\"./output_data/LatVsWind.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}