WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x21bfeb0ac88>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a set of random latitude and longitude combinations.\n",
    "lats = np.random.uniform(low=-90.000, high=90.000, size=1500)\n",
    "lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "lat_lngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a practice set of random latitude and longitude combinations. \n",
    "x = [25.12903645, 25.92017388, 26.62509167, -59.98969384, 37.30571269]\n",
    "y = [-67.59741259, 11.09532135, 74.84233102, -76.89176677, -61.13376282]\n",
    "coordinates = zip(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.12903645 -67.59741259\n",
      "25.92017388 11.09532135\n",
      "26.62509167 74.84233102\n",
      "-59.98969384 -76.89176677\n",
      "37.30571269 -61.13376282\n"
     ]
    }
   ],
   "source": [
    "# Use the tuple() function to display the latitude and longitude combinations.\n",
    "for coordinate in coordinates:\n",
    "    print(coordinate[0], coordinate[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the latitudes and longitudes to a list\n",
    "coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the citypy module to determine city based on latitude and longitude\n",
    "from citipy import citipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "610"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the requests library\n",
    "import requests\n",
    "\n",
    "# Import the API key\n",
    "from config import weather_api_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=6f1a134139e2ac05bacd17e9ded5dc9b\n"
     ]
    }
   ],
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the datetime module from the datetime library.\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval      \n",
      "------------------------------\n",
      "Processing Record 1 of Set 1 | ust-ilimsk\n",
      "Processing Record 2 of Set 1 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 1 | cockburn town\n",
      "Processing Record 4 of Set 1 | albany\n",
      "Processing Record 5 of Set 1 | ushuaia\n",
      "Processing Record 6 of Set 1 | gasa\n",
      "Processing Record 7 of Set 1 | barrow\n",
      "Processing Record 8 of Set 1 | hilo\n",
      "Processing Record 9 of Set 1 | cayenne\n",
      "Processing Record 10 of Set 1 | yima\n",
      "Processing Record 11 of Set 1 | broome\n",
      "Processing Record 12 of Set 1 | half moon bay\n",
      "Processing Record 13 of Set 1 | tasiilaq\n",
      "Processing Record 14 of Set 1 | bethel\n",
      "Processing Record 15 of Set 1 | saskylakh\n",
      "Processing Record 16 of Set 1 | puerto ayora\n",
      "Processing Record 17 of Set 1 | dikson\n",
      "Processing Record 18 of Set 1 | kingman\n",
      "Processing Record 19 of Set 1 | kapaa\n",
      "Processing Record 20 of Set 1 | ihlow\n",
      "Processing Record 21 of Set 1 | hof\n",
      "Processing Record 22 of Set 1 | ishinomaki\n",
      "Processing Record 23 of Set 1 | hobart\n",
      "Processing Record 24 of Set 1 | zapolyarnyy\n",
      "Processing Record 25 of Set 1 | ormara\n",
      "Processing Record 26 of Set 1 | vaovai\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 1 | grand gaube\n",
      "Processing Record 28 of Set 1 | geraldton\n",
      "Processing Record 29 of Set 1 | jamestown\n",
      "Processing Record 30 of Set 1 | east london\n",
      "Processing Record 31 of Set 1 | zhuhai\n",
      "Processing Record 32 of Set 1 | avarua\n",
      "Processing Record 33 of Set 1 | salalah\n",
      "Processing Record 34 of Set 1 | mys shmidta\n",
      "City not found. Skipping...\n",
      "Processing Record 35 of Set 1 | hermanus\n",
      "Processing Record 36 of Set 1 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record 37 of Set 1 | severo-kurilsk\n",
      "Processing Record 38 of Set 1 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 1 | trujillo\n",
      "Processing Record 40 of Set 1 | rikitea\n",
      "Processing Record 41 of Set 1 | pyaozerskiy\n",
      "Processing Record 42 of Set 1 | busselton\n",
      "Processing Record 43 of Set 1 | ribeira grande\n",
      "Processing Record 44 of Set 1 | goure\n",
      "Processing Record 45 of Set 1 | filingue\n",
      "Processing Record 46 of Set 1 | angra\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 1 | eldorado\n",
      "Processing Record 48 of Set 1 | chulman\n",
      "Processing Record 49 of Set 1 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record 50 of Set 1 | qasigiannguit\n",
      "Processing Record 1 of Set 2 | te anau\n",
      "Processing Record 2 of Set 2 | kodiak\n",
      "Processing Record 3 of Set 2 | klaksvik\n",
      "Processing Record 4 of Set 2 | chuy\n",
      "Processing Record 5 of Set 2 | vaitupu\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 2 | srednekolymsk\n",
      "Processing Record 7 of Set 2 | yellowknife\n",
      "Processing Record 8 of Set 2 | sabang\n",
      "Processing Record 9 of Set 2 | husavik\n",
      "Processing Record 10 of Set 2 | adrar\n",
      "Processing Record 11 of Set 2 | george\n",
      "Processing Record 12 of Set 2 | bluff\n",
      "Processing Record 13 of Set 2 | causapscal\n",
      "Processing Record 14 of Set 2 | vostok\n",
      "Processing Record 15 of Set 2 | nueva guinea\n",
      "Processing Record 16 of Set 2 | new norfolk\n",
      "Processing Record 17 of Set 2 | buenaventura\n",
      "Processing Record 18 of Set 2 | wulanhaote\n",
      "City not found. Skipping...\n",
      "Processing Record 19 of Set 2 | mount gambier\n",
      "Processing Record 20 of Set 2 | upernavik\n",
      "Processing Record 21 of Set 2 | fortuna\n",
      "Processing Record 22 of Set 2 | bara\n",
      "Processing Record 23 of Set 2 | ati\n",
      "Processing Record 24 of Set 2 | saint anthony\n",
      "Processing Record 25 of Set 2 | norman wells\n",
      "Processing Record 26 of Set 2 | mataura\n",
      "Processing Record 27 of Set 2 | qaanaaq\n",
      "Processing Record 28 of Set 2 | vestmannaeyjar\n",
      "Processing Record 29 of Set 2 | georgetown\n",
      "Processing Record 30 of Set 2 | sitka\n",
      "Processing Record 31 of Set 2 | bonthe\n",
      "Processing Record 32 of Set 2 | rawson\n",
      "Processing Record 33 of Set 2 | zhigansk\n",
      "Processing Record 34 of Set 2 | cape town\n",
      "Processing Record 35 of Set 2 | nuuk\n",
      "Processing Record 36 of Set 2 | berlevag\n",
      "Processing Record 37 of Set 2 | san lorenzo\n",
      "Processing Record 38 of Set 2 | constitucion\n",
      "Processing Record 39 of Set 2 | butaritari\n",
      "Processing Record 40 of Set 2 | luoyang\n",
      "Processing Record 41 of Set 2 | chapais\n",
      "Processing Record 42 of Set 2 | oktyabrskoye\n",
      "Processing Record 43 of Set 2 | pevek\n",
      "Processing Record 44 of Set 2 | emba\n",
      "Processing Record 45 of Set 2 | tiksi\n",
      "Processing Record 46 of Set 2 | magadan\n",
      "Processing Record 47 of Set 2 | port blair\n",
      "Processing Record 48 of Set 2 | punta arenas\n",
      "Processing Record 49 of Set 2 | port alfred\n",
      "Processing Record 50 of Set 2 | dzemul\n",
      "Processing Record 1 of Set 3 | birecik\n",
      "Processing Record 2 of Set 3 | vaini\n",
      "Processing Record 3 of Set 3 | ranau\n",
      "Processing Record 4 of Set 3 | bolitoc\n",
      "Processing Record 5 of Set 3 | high level\n",
      "Processing Record 6 of Set 3 | coquimbo\n",
      "Processing Record 7 of Set 3 | fare\n",
      "Processing Record 8 of Set 3 | port hedland\n",
      "Processing Record 9 of Set 3 | castro\n",
      "Processing Record 10 of Set 3 | uthal\n",
      "Processing Record 11 of Set 3 | pyshchug\n",
      "Processing Record 12 of Set 3 | kutum\n",
      "Processing Record 13 of Set 3 | hithadhoo\n",
      "Processing Record 14 of Set 3 | najran\n",
      "Processing Record 15 of Set 3 | kijang\n",
      "Processing Record 16 of Set 3 | jacksonville\n",
      "Processing Record 17 of Set 3 | estelle\n",
      "Processing Record 18 of Set 3 | northam\n",
      "Processing Record 19 of Set 3 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 3 | bogorodskoye\n",
      "Processing Record 21 of Set 3 | samusu\n",
      "City not found. Skipping...\n",
      "Processing Record 22 of Set 3 | urumqi\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 3 | singaraja\n",
      "Processing Record 24 of Set 3 | kaitangata\n",
      "Processing Record 25 of Set 3 | saldanha\n",
      "Processing Record 26 of Set 3 | spring hill\n",
      "Processing Record 27 of Set 3 | san patricio\n",
      "Processing Record 28 of Set 3 | neiafu\n",
      "Processing Record 29 of Set 3 | bambous virieux\n",
      "Processing Record 30 of Set 3 | bengkulu\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 3 | mapastepec\n",
      "Processing Record 32 of Set 3 | katsuura\n",
      "Processing Record 33 of Set 3 | svetlogorsk\n",
      "Processing Record 34 of Set 3 | koster\n",
      "Processing Record 35 of Set 3 | nikolskoye\n",
      "Processing Record 36 of Set 3 | santa marta\n",
      "Processing Record 37 of Set 3 | astara\n",
      "Processing Record 38 of Set 3 | umzimvubu\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 3 | jimma\n",
      "Processing Record 40 of Set 3 | haines junction\n",
      "Processing Record 41 of Set 3 | tshikapa\n",
      "Processing Record 42 of Set 3 | lorengau\n",
      "Processing Record 43 of Set 3 | saint-joseph\n",
      "Processing Record 44 of Set 3 | chlorakas\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 3 | kultuk\n",
      "Processing Record 46 of Set 3 | chokurdakh\n",
      "Processing Record 47 of Set 3 | kudahuvadhoo\n",
      "Processing Record 48 of Set 3 | komsomolskiy\n",
      "Processing Record 49 of Set 3 | manggar\n",
      "Processing Record 50 of Set 3 | khatanga\n",
      "Processing Record 1 of Set 4 | jacareacanga\n",
      "Processing Record 2 of Set 4 | victoria\n",
      "Processing Record 3 of Set 4 | san jose\n",
      "Processing Record 4 of Set 4 | epitalion\n",
      "Processing Record 5 of Set 4 | lebu\n",
      "Processing Record 6 of Set 4 | boljoon\n",
      "Processing Record 7 of Set 4 | lata\n",
      "Processing Record 8 of Set 4 | muyezerskiy\n",
      "Processing Record 9 of Set 4 | codrington\n",
      "Processing Record 10 of Set 4 | ningbo\n",
      "Processing Record 11 of Set 4 | broken hill\n",
      "Processing Record 12 of Set 4 | nizhneyansk\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 4 | filadelfia\n",
      "Processing Record 14 of Set 4 | torbay\n",
      "Processing Record 15 of Set 4 | bensonville\n",
      "Processing Record 16 of Set 4 | nanortalik\n",
      "Processing Record 17 of Set 4 | lavrentiya\n",
      "Processing Record 18 of Set 4 | pizhma\n",
      "Processing Record 19 of Set 4 | saint-augustin\n",
      "Processing Record 20 of Set 4 | yarmouth\n",
      "Processing Record 21 of Set 4 | novorzhev\n",
      "Processing Record 22 of Set 4 | majene\n",
      "Processing Record 23 of Set 4 | chernyshevskiy\n",
      "Processing Record 24 of Set 4 | atuona\n",
      "Processing Record 25 of Set 4 | saint george\n",
      "Processing Record 26 of Set 4 | cabo san lucas\n",
      "Processing Record 27 of Set 4 | porto novo\n",
      "Processing Record 28 of Set 4 | tancuayalab\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 4 | khash\n",
      "Processing Record 30 of Set 4 | kruisfontein\n",
      "Processing Record 31 of Set 4 | portland\n",
      "Processing Record 32 of Set 4 | swan hill\n",
      "Processing Record 33 of Set 4 | bandarbeyla\n",
      "Processing Record 34 of Set 4 | petropavlovsk-kamchatskiy\n",
      "Processing Record 35 of Set 4 | nome\n",
      "Processing Record 36 of Set 4 | port augusta\n",
      "Processing Record 37 of Set 4 | zyryanka\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 38 of Set 4 | huarmey\n",
      "Processing Record 39 of Set 4 | batagay\n",
      "Processing Record 40 of Set 4 | toamasina\n",
      "Processing Record 41 of Set 4 | isangel\n",
      "Processing Record 42 of Set 4 | shingu\n",
      "Processing Record 43 of Set 4 | illescas\n",
      "Processing Record 44 of Set 4 | mizan teferi\n",
      "Processing Record 45 of Set 4 | carnarvon\n",
      "Processing Record 46 of Set 4 | kapit\n",
      "Processing Record 47 of Set 4 | olafsvik\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 4 | hovd\n",
      "Processing Record 49 of Set 4 | saint-francois\n",
      "Processing Record 50 of Set 4 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record 1 of Set 5 | kavieng\n",
      "Processing Record 2 of Set 5 | salamiyah\n",
      "Processing Record 3 of Set 5 | trairi\n",
      "Processing Record 4 of Set 5 | devli\n",
      "Processing Record 5 of Set 5 | tuktoyaktuk\n",
      "Processing Record 6 of Set 5 | caraballeda\n",
      "Processing Record 7 of Set 5 | beringovskiy\n",
      "Processing Record 8 of Set 5 | pacific grove\n",
      "Processing Record 9 of Set 5 | taoudenni\n",
      "Processing Record 10 of Set 5 | imeni telmana\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 5 | nyurba\n",
      "Processing Record 12 of Set 5 | sentyabrskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 5 | fort nelson\n",
      "Processing Record 14 of Set 5 | fairbanks\n",
      "Processing Record 15 of Set 5 | garden city\n",
      "Processing Record 16 of Set 5 | bredasdorp\n",
      "Processing Record 17 of Set 5 | caceres\n",
      "Processing Record 18 of Set 5 | laguna\n",
      "Processing Record 19 of Set 5 | riyadh\n",
      "Processing Record 20 of Set 5 | saleaula\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 5 | falun\n",
      "Processing Record 22 of Set 5 | luanda\n",
      "Processing Record 23 of Set 5 | energetik\n",
      "Processing Record 24 of Set 5 | malinovoye ozero\n",
      "Processing Record 25 of Set 5 | zeya\n",
      "Processing Record 26 of Set 5 | thompson\n",
      "Processing Record 27 of Set 5 | mehran\n",
      "Processing Record 28 of Set 5 | morondava\n",
      "Processing Record 29 of Set 5 | edd\n",
      "Processing Record 30 of Set 5 | egvekinot\n",
      "Processing Record 31 of Set 5 | kahului\n",
      "Processing Record 32 of Set 5 | saint-philippe\n",
      "Processing Record 33 of Set 5 | pangnirtung\n",
      "Processing Record 34 of Set 5 | cidreira\n",
      "Processing Record 35 of Set 5 | bolshaya chernigovka\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 5 | sinnamary\n",
      "Processing Record 37 of Set 5 | kununurra\n",
      "Processing Record 38 of Set 5 | grimshaw\n",
      "Processing Record 39 of Set 5 | samalaeulu\n",
      "City not found. Skipping...\n",
      "Processing Record 40 of Set 5 | nieuw amsterdam\n",
      "Processing Record 41 of Set 5 | tecpan\n",
      "Processing Record 42 of Set 5 | corinto\n",
      "Processing Record 43 of Set 5 | dalvik\n",
      "Processing Record 44 of Set 5 | pyu\n",
      "Processing Record 45 of Set 5 | rawannawi\n",
      "City not found. Skipping...\n",
      "Processing Record 46 of Set 5 | mar del plata\n",
      "Processing Record 47 of Set 5 | jawhar\n",
      "Processing Record 48 of Set 5 | spornoye\n",
      "Processing Record 49 of Set 5 | maracaju\n",
      "Processing Record 50 of Set 5 | batavia\n",
      "Processing Record 1 of Set 6 | lircay\n",
      "Processing Record 2 of Set 6 | lasa\n",
      "Processing Record 3 of Set 6 | lompoc\n",
      "Processing Record 4 of Set 6 | manyana\n",
      "Processing Record 5 of Set 6 | port-gentil\n",
      "Processing Record 6 of Set 6 | saint-georges\n",
      "Processing Record 7 of Set 6 | valle de allende\n",
      "Processing Record 8 of Set 6 | khasan\n",
      "Processing Record 9 of Set 6 | praia da vitoria\n",
      "Processing Record 10 of Set 6 | durban\n",
      "Processing Record 11 of Set 6 | puerto leguizamo\n",
      "Processing Record 12 of Set 6 | ancud\n",
      "Processing Record 13 of Set 6 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 6 | ciudad bolivar\n",
      "Processing Record 15 of Set 6 | talnakh\n",
      "Processing Record 16 of Set 6 | dewas\n",
      "Processing Record 17 of Set 6 | mullaitivu\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 6 | oistins\n",
      "Processing Record 19 of Set 6 | ixtapa\n",
      "Processing Record 20 of Set 6 | lagoa\n",
      "Processing Record 21 of Set 6 | souillac\n",
      "Processing Record 22 of Set 6 | sarakhs\n",
      "Processing Record 23 of Set 6 | ilulissat\n",
      "Processing Record 24 of Set 6 | praia\n",
      "Processing Record 25 of Set 6 | tianpeng\n",
      "Processing Record 26 of Set 6 | malakal\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 6 | barahona\n",
      "Processing Record 28 of Set 6 | inhambane\n",
      "Processing Record 29 of Set 6 | saraipali\n",
      "Processing Record 30 of Set 6 | hauknes\n",
      "Processing Record 31 of Set 6 | tari\n",
      "Processing Record 32 of Set 6 | port elizabeth\n",
      "Processing Record 33 of Set 6 | mitu\n",
      "Processing Record 34 of Set 6 | airai\n",
      "Processing Record 35 of Set 6 | luderitz\n",
      "Processing Record 36 of Set 6 | caravelas\n",
      "Processing Record 37 of Set 6 | jalu\n",
      "Processing Record 38 of Set 6 | mehamn\n",
      "Processing Record 39 of Set 6 | camacha\n",
      "Processing Record 40 of Set 6 | cherskiy\n",
      "Processing Record 41 of Set 6 | gondanglegi\n",
      "Processing Record 42 of Set 6 | tinjah\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 6 | alofi\n",
      "Processing Record 44 of Set 6 | nhulunbuy\n",
      "Processing Record 45 of Set 6 | lat yao\n",
      "Processing Record 46 of Set 6 | gusev\n",
      "Processing Record 47 of Set 6 | clyde river\n",
      "Processing Record 48 of Set 6 | lakselv\n",
      "Processing Record 49 of Set 6 | rocha\n",
      "Processing Record 50 of Set 6 | canon city\n",
      "Processing Record 1 of Set 7 | christchurch\n",
      "Processing Record 2 of Set 7 | eyl\n",
      "Processing Record 3 of Set 7 | narsaq\n",
      "Processing Record 4 of Set 7 | beira\n",
      "Processing Record 5 of Set 7 | esmeraldas\n",
      "Processing Record 6 of Set 7 | leningradskiy\n",
      "Processing Record 7 of Set 7 | kimberley\n",
      "Processing Record 8 of Set 7 | tukrah\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 7 | moron\n",
      "Processing Record 10 of Set 7 | khani\n",
      "Processing Record 11 of Set 7 | sisimiut\n",
      "Processing Record 12 of Set 7 | tabou\n",
      "Processing Record 13 of Set 7 | boffa\n",
      "Processing Record 14 of Set 7 | samagaltay\n",
      "Processing Record 15 of Set 7 | revelstoke\n",
      "Processing Record 16 of Set 7 | bolungarvik\n",
      "City not found. Skipping...\n",
      "Processing Record 17 of Set 7 | bambanglipuro\n",
      "Processing Record 18 of Set 7 | gawler\n",
      "Processing Record 19 of Set 7 | kahama\n",
      "Processing Record 20 of Set 7 | sivas\n",
      "Processing Record 21 of Set 7 | santa cruz del sur\n",
      "Processing Record 22 of Set 7 | fougamou\n",
      "Processing Record 23 of Set 7 | yulara\n",
      "Processing Record 24 of Set 7 | san isidro\n",
      "Processing Record 25 of Set 7 | powassan\n",
      "Processing Record 26 of Set 7 | lar\n",
      "Processing Record 27 of Set 7 | siirt\n",
      "Processing Record 28 of Set 7 | nishihara\n",
      "Processing Record 29 of Set 7 | smithers\n",
      "Processing Record 30 of Set 7 | hlobyne\n",
      "Processing Record 31 of Set 7 | bilibino\n",
      "Processing Record 32 of Set 7 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 7 | rio gallegos\n",
      "Processing Record 34 of Set 7 | saint-ambroise\n",
      "Processing Record 35 of Set 7 | khakhea\n",
      "Processing Record 36 of Set 7 | kenai\n",
      "Processing Record 37 of Set 7 | ziarat\n",
      "Processing Record 38 of Set 7 | mahebourg\n",
      "Processing Record 39 of Set 7 | kyren\n",
      "Processing Record 40 of Set 7 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 7 | aklavik\n",
      "Processing Record 42 of Set 7 | koygorodok\n",
      "Processing Record 43 of Set 7 | chagda\n",
      "City not found. Skipping...\n",
      "Processing Record 44 of Set 7 | saurimo\n",
      "Processing Record 45 of Set 7 | acapulco\n",
      "Processing Record 46 of Set 7 | bathsheba\n",
      "Processing Record 47 of Set 7 | fallon\n",
      "Processing Record 48 of Set 7 | evensk\n",
      "Processing Record 49 of Set 7 | puerto del rosario\n",
      "Processing Record 50 of Set 7 | arraial do cabo\n",
      "Processing Record 1 of Set 8 | paamiut\n",
      "Processing Record 2 of Set 8 | san juan\n",
      "Processing Record 3 of Set 8 | hyeres\n",
      "Processing Record 4 of Set 8 | samarai\n",
      "Processing Record 5 of Set 8 | sorland\n",
      "Processing Record 6 of Set 8 | sur\n",
      "Processing Record 7 of Set 8 | richards bay\n",
      "Processing Record 8 of Set 8 | cedar city\n",
      "Processing Record 9 of Set 8 | dauriya\n",
      "Processing Record 10 of Set 8 | aflu\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 8 | kizukuri\n",
      "Processing Record 12 of Set 8 | talaya\n",
      "Processing Record 13 of Set 8 | dhidhdhoo\n",
      "Processing Record 14 of Set 8 | westpunt\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 8 | imeni poliny osipenko\n",
      "Processing Record 16 of Set 8 | mankera\n",
      "Processing Record 17 of Set 8 | conde\n",
      "Processing Record 18 of Set 8 | chunskiy\n",
      "Processing Record 19 of Set 8 | formoso do araguaia\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 8 | ballarpur\n",
      "Processing Record 21 of Set 8 | ozgon\n",
      "City not found. Skipping...\n",
      "Processing Record 22 of Set 8 | aksu\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 23 of Set 8 | la paz\n",
      "Processing Record 24 of Set 8 | yar-sale\n",
      "Processing Record 25 of Set 8 | kuche\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 8 | alyangula\n",
      "Processing Record 27 of Set 8 | vuktyl\n",
      "Processing Record 28 of Set 8 | bani walid\n",
      "Processing Record 29 of Set 8 | chara\n",
      "Processing Record 30 of Set 8 | korla\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 8 | brae\n",
      "Processing Record 32 of Set 8 | benito juarez\n",
      "Processing Record 33 of Set 8 | hamilton\n",
      "Processing Record 34 of Set 8 | ayan\n",
      "Processing Record 35 of Set 8 | antalaha\n",
      "Processing Record 36 of Set 8 | muros\n",
      "Processing Record 37 of Set 8 | poum\n",
      "Processing Record 38 of Set 8 | vila velha\n",
      "Processing Record 39 of Set 8 | bozoum\n",
      "Processing Record 40 of Set 8 | namibe\n",
      "Processing Record 41 of Set 8 | vardo\n",
      "Processing Record 42 of Set 8 | tayoltita\n",
      "Processing Record 43 of Set 8 | dinhata\n",
      "Processing Record 44 of Set 8 | horsham\n",
      "Processing Record 45 of Set 8 | longyearbyen\n",
      "Processing Record 46 of Set 8 | tongren\n",
      "Processing Record 47 of Set 8 | kesennuma\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 8 | mayo\n",
      "Processing Record 49 of Set 8 | gbarnga\n",
      "Processing Record 50 of Set 8 | makakilo city\n",
      "Processing Record 1 of Set 9 | houma\n",
      "Processing Record 2 of Set 9 | teya\n",
      "Processing Record 3 of Set 9 | selizharovo\n",
      "Processing Record 4 of Set 9 | warqla\n",
      "City not found. Skipping...\n",
      "Processing Record 5 of Set 9 | upata\n",
      "Processing Record 6 of Set 9 | verkhoyansk\n",
      "Processing Record 7 of Set 9 | gazli\n",
      "Processing Record 8 of Set 9 | stanghelle\n",
      "Processing Record 9 of Set 9 | kuching\n",
      "Processing Record 10 of Set 9 | san quintin\n",
      "Processing Record 11 of Set 9 | kamenka\n",
      "Processing Record 12 of Set 9 | delijan\n",
      "Processing Record 13 of Set 9 | lisakovsk\n",
      "Processing Record 14 of Set 9 | sanguem\n",
      "Processing Record 15 of Set 9 | inuvik\n",
      "Processing Record 16 of Set 9 | isaka\n",
      "Processing Record 17 of Set 9 | viedma\n",
      "Processing Record 18 of Set 9 | anloga\n",
      "Processing Record 19 of Set 9 | gboko\n",
      "Processing Record 20 of Set 9 | siguiri\n",
      "Processing Record 21 of Set 9 | solnechnyy\n",
      "Processing Record 22 of Set 9 | ponta do sol\n",
      "Processing Record 23 of Set 9 | alihe\n",
      "Processing Record 24 of Set 9 | bilma\n",
      "Processing Record 25 of Set 9 | qaqortoq\n",
      "Processing Record 26 of Set 9 | nalut\n",
      "Processing Record 27 of Set 9 | hay river\n",
      "Processing Record 28 of Set 9 | marzuq\n",
      "Processing Record 29 of Set 9 | fayaoue\n",
      "Processing Record 30 of Set 9 | bosaso\n",
      "Processing Record 31 of Set 9 | sambava\n",
      "Processing Record 32 of Set 9 | krasnyy chikoy\n",
      "Processing Record 33 of Set 9 | sao filipe\n",
      "Processing Record 34 of Set 9 | lahij\n",
      "Processing Record 35 of Set 9 | alsfeld\n",
      "Processing Record 36 of Set 9 | rocky mount\n",
      "Processing Record 37 of Set 9 | provideniya\n",
      "Processing Record 38 of Set 9 | warrnambool\n",
      "Processing Record 39 of Set 9 | sorong\n",
      "Processing Record 40 of Set 9 | ko samui\n",
      "Processing Record 41 of Set 9 | parga\n",
      "Processing Record 42 of Set 9 | tilichiki\n",
      "Processing Record 43 of Set 9 | iacu\n",
      "Processing Record 44 of Set 9 | ransang\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 9 | pinega\n",
      "Processing Record 46 of Set 9 | havelock\n",
      "Processing Record 47 of Set 9 | hobyo\n",
      "Processing Record 48 of Set 9 | baoning\n",
      "Processing Record 49 of Set 9 | deh rawud\n",
      "City not found. Skipping...\n",
      "Processing Record 50 of Set 9 | touros\n",
      "Processing Record 1 of Set 10 | masvingo\n",
      "Processing Record 2 of Set 10 | cap malheureux\n",
      "Processing Record 3 of Set 10 | jumla\n",
      "Processing Record 4 of Set 10 | malindi\n",
      "Processing Record 5 of Set 10 | kalmunai\n",
      "Processing Record 6 of Set 10 | port lincoln\n",
      "Processing Record 7 of Set 10 | petauke\n",
      "Processing Record 8 of Set 10 | raga\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 10 | monrovia\n",
      "Processing Record 10 of Set 10 | tocopilla\n",
      "Processing Record 11 of Set 10 | linjiang\n",
      "Processing Record 12 of Set 10 | latnaya\n",
      "Processing Record 13 of Set 10 | tokur\n",
      "Processing Record 14 of Set 10 | chiman\n",
      "Processing Record 15 of Set 10 | tual\n",
      "Processing Record 16 of Set 10 | chicama\n",
      "Processing Record 17 of Set 10 | aracruz\n",
      "Processing Record 18 of Set 10 | mitha tiwana\n",
      "Processing Record 19 of Set 10 | tuatapere\n",
      "Processing Record 20 of Set 10 | clarence town\n",
      "Processing Record 21 of Set 10 | kieta\n",
      "Processing Record 22 of Set 10 | teahupoo\n",
      "Processing Record 23 of Set 10 | linqiong\n",
      "Processing Record 24 of Set 10 | sao joao dos patos\n",
      "Processing Record 25 of Set 10 | nampula\n",
      "Processing Record 26 of Set 10 | banda aceh\n",
      "Processing Record 27 of Set 10 | verkhnevilyuysk\n",
      "Processing Record 28 of Set 10 | oudtshoorn\n",
      "Processing Record 29 of Set 10 | atlantic city\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 10 | yamada\n",
      "Processing Record 31 of Set 10 | yuncheng\n",
      "Processing Record 32 of Set 10 | nadvoitsy\n",
      "Processing Record 33 of Set 10 | eureka\n",
      "Processing Record 34 of Set 10 | salym\n",
      "Processing Record 35 of Set 10 | panaba\n",
      "Processing Record 36 of Set 10 | noumea\n",
      "Processing Record 37 of Set 10 | pisco\n",
      "Processing Record 38 of Set 10 | iralaya\n",
      "Processing Record 39 of Set 10 | valentin gomez farias\n",
      "Processing Record 40 of Set 10 | killarney\n",
      "Processing Record 41 of Set 10 | toora-khem\n",
      "Processing Record 42 of Set 10 | agadez\n",
      "Processing Record 43 of Set 10 | iqaluit\n",
      "Processing Record 44 of Set 10 | ambulu\n",
      "Processing Record 45 of Set 10 | port hardy\n",
      "Processing Record 46 of Set 10 | borzya\n",
      "Processing Record 47 of Set 10 | cartagena\n",
      "Processing Record 48 of Set 10 | mersing\n",
      "Processing Record 49 of Set 10 | xichang\n",
      "Processing Record 50 of Set 10 | darlowo\n",
      "Processing Record 1 of Set 11 | jipijapa\n",
      "Processing Record 2 of Set 11 | fukue\n",
      "Processing Record 3 of Set 11 | ampanihy\n",
      "Processing Record 4 of Set 11 | marsabit\n",
      "Processing Record 5 of Set 11 | oktyabrskiy\n",
      "Processing Record 6 of Set 11 | atar\n",
      "Processing Record 7 of Set 11 | wamba\n",
      "Processing Record 8 of Set 11 | marigot\n",
      "Processing Record 9 of Set 11 | ust-kuyga\n",
      "Processing Record 10 of Set 11 | hambantota\n",
      "Processing Record 11 of Set 11 | bocana de paiwas\n",
      "Processing Record 12 of Set 11 | abbeville\n",
      "Processing Record 13 of Set 11 | ouesso\n",
      "Processing Record 14 of Set 11 | kidal\n",
      "Processing Record 15 of Set 11 | anadyr\n",
      "Processing Record 16 of Set 11 | kondinskoye\n",
      "Processing Record 17 of Set 11 | kanigoro\n",
      "Processing Record 18 of Set 11 | gigmoto\n",
      "Processing Record 19 of Set 11 | sinkat\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 11 | raudeberg\n",
      "Processing Record 21 of Set 11 | opuwo\n",
      "Processing Record 22 of Set 11 | todos santos\n",
      "Processing Record 23 of Set 11 | villarrica\n",
      "Processing Record 24 of Set 11 | pacifica\n",
      "Processing Record 25 of Set 11 | wharton\n",
      "Processing Record 26 of Set 11 | hirado\n",
      "Processing Record 27 of Set 11 | oda\n",
      "Processing Record 28 of Set 11 | kumba\n",
      "Processing Record 29 of Set 11 | barcelos\n",
      "Processing Record 30 of Set 11 | bonavista\n",
      "Processing Record 31 of Set 11 | tempio pausania\n",
      "Processing Record 32 of Set 11 | kandara\n",
      "Processing Record 33 of Set 11 | dwarka\n",
      "Processing Record 34 of Set 11 | puerto carreno\n",
      "Processing Record 35 of Set 11 | taltal\n",
      "Processing Record 36 of Set 11 | lolua\n",
      "City not found. Skipping...\n",
      "Processing Record 37 of Set 11 | oranjemund\n",
      "Processing Record 38 of Set 11 | esperance\n",
      "Processing Record 39 of Set 11 | morant bay\n",
      "Processing Record 40 of Set 11 | walvis bay\n",
      "Processing Record 41 of Set 11 | churhat\n",
      "Processing Record 42 of Set 11 | muswellbrook\n",
      "Processing Record 43 of Set 11 | kawalu\n",
      "Processing Record 44 of Set 11 | faanui\n",
      "Processing Record 45 of Set 11 | roberval\n",
      "Processing Record 46 of Set 11 | sidi bin nur\n",
      "Processing Record 47 of Set 11 | inta\n",
      "Processing Record 48 of Set 11 | vila franca do campo\n",
      "Processing Record 49 of Set 11 | caserta\n",
      "Processing Record 50 of Set 11 | ribeira brava\n",
      "Processing Record 1 of Set 12 | bafq\n",
      "Processing Record 2 of Set 12 | wilmington\n",
      "Processing Record 3 of Set 12 | jam sahib\n",
      "Processing Record 4 of Set 12 | torit\n",
      "City not found. Skipping...\n",
      "Processing Record 5 of Set 12 | mulege\n",
      "Processing Record 6 of Set 12 | urengoy\n",
      "Processing Record 7 of Set 12 | shirpur\n",
      "Processing Record 8 of Set 12 | ouegoa\n",
      "Processing Record 9 of Set 12 | constantine\n",
      "Processing Record 10 of Set 12 | skalistyy\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 12 | severo-yeniseyskiy\n",
      "Processing Record 12 of Set 12 | vanimo\n",
      "Processing Record 13 of Set 12 | tricase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 14 of Set 12 | naumburg\n",
      "Processing Record 15 of Set 12 | tigil\n",
      "Processing Record 16 of Set 12 | dubna\n",
      "Processing Record 17 of Set 12 | kadykchan\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 12 | torva\n",
      "Processing Record 19 of Set 12 | ruatoria\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 12 | nantucket\n",
      "Processing Record 21 of Set 12 | traverse city\n",
      "Processing Record 22 of Set 12 | uri\n",
      "Processing Record 23 of Set 12 | ipixuna\n",
      "Processing Record 24 of Set 12 | ahipara\n",
      "Processing Record 25 of Set 12 | zhezkazgan\n",
      "Processing Record 26 of Set 12 | minas\n",
      "Processing Record 27 of Set 12 | pingzhuang\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 12 | smidovich\n",
      "Processing Record 29 of Set 12 | gornyye klyuchi\n",
      "Processing Record 30 of Set 12 | dingle\n",
      "Processing Record 31 of Set 12 | seoul\n",
      "Processing Record 32 of Set 12 | dubbo\n",
      "Processing Record 33 of Set 12 | lake charles\n",
      "Processing Record 34 of Set 12 | amahai\n",
      "Processing Record 35 of Set 12 | maragogi\n",
      "Processing Record 36 of Set 12 | coahuayana\n",
      "Processing Record 37 of Set 12 | susangerd\n",
      "Processing Record 38 of Set 12 | bundaberg\n",
      "Processing Record 39 of Set 12 | pochutla\n",
      "Processing Record 40 of Set 12 | imeni vorovskogo\n",
      "Processing Record 41 of Set 12 | mount darwin\n",
      "Processing Record 42 of Set 12 | babaeski\n",
      "Processing Record 43 of Set 12 | weligama\n",
      "Processing Record 44 of Set 12 | tarko-sale\n",
      "Processing Record 45 of Set 12 | svetlaya\n",
      "Processing Record 46 of Set 12 | tombouctou\n",
      "Processing Record 47 of Set 12 | tembisa\n",
      "Processing Record 48 of Set 12 | faya\n",
      "Processing Record 49 of Set 12 | oussouye\n",
      "Processing Record 50 of Set 12 | portree\n",
      "Processing Record 1 of Set 13 | champerico\n",
      "Processing Record 2 of Set 13 | biak\n",
      "Processing Record 3 of Set 13 | uttiramerur\n",
      "Processing Record 4 of Set 13 | kavaratti\n",
      "Processing Record 5 of Set 13 | harper\n",
      "Processing Record 6 of Set 13 | puerto escondido\n",
      "Processing Record 7 of Set 13 | uwayl\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 13 | concordia\n",
      "Processing Record 9 of Set 13 | juneau\n",
      "Processing Record 10 of Set 13 | silver city\n",
      "-----------------------------\n",
      "Data Retrieval Complete      \n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "# Create an empty list to hold the weather data.\n",
    "city_data = []\n",
    "# Print the beginning of the logging.\n",
    "print(\"Beginning Data Retrieval      \")\n",
    "print(\"------------------------------\")\n",
    "\n",
    "# Create counters.\n",
    "record_count = 1\n",
    "set_count = 1\n",
    "\n",
    "# Loop through all the cities in our 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\n",
    "    \n",
    "    # Log the URL, record, and set numbers and the city.\n",
    "    print(f\"Processing Record {record_count} of Set {set_count} | {city}\")\n",
    "    # Add 1 to the record count.\n",
    "    record_count += 1\n",
    "    # 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 data to ISO standard\n",
    "        city_date = datetime.utcfromtimestamp(city_weather[\"dt\"]).strftime('%Y-%m-%d %H:%M:%S')\n",
    "        # Append the city information into the 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 occurs, 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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "556"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(city_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ust-Ilimsk</td>\n",
       "      <td>57.96</td>\n",
       "      <td>102.73</td>\n",
       "      <td>17.47</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>8.63</td>\n",
       "      <td>RU</td>\n",
       "      <td>2020-01-04 20:06:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cockburn Town</td>\n",
       "      <td>21.46</td>\n",
       "      <td>-71.14</td>\n",
       "      <td>80.60</td>\n",
       "      <td>69</td>\n",
       "      <td>40</td>\n",
       "      <td>11.41</td>\n",
       "      <td>TC</td>\n",
       "      <td>2020-01-04 20:05:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albany</td>\n",
       "      <td>42.65</td>\n",
       "      <td>-73.75</td>\n",
       "      <td>42.80</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>6.93</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01-04 20:05:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>-54.81</td>\n",
       "      <td>-68.31</td>\n",
       "      <td>57.20</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>5.82</td>\n",
       "      <td>AR</td>\n",
       "      <td>2020-01-04 20:03:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gasa</td>\n",
       "      <td>27.91</td>\n",
       "      <td>89.73</td>\n",
       "      <td>2.66</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>7.29</td>\n",
       "      <td>BT</td>\n",
       "      <td>2020-01-04 20:06:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Barrow</td>\n",
       "      <td>-38.31</td>\n",
       "      <td>-60.23</td>\n",
       "      <td>77.27</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "      <td>18.05</td>\n",
       "      <td>AR</td>\n",
       "      <td>2020-01-04 20:06:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>19.71</td>\n",
       "      <td>-155.08</td>\n",
       "      <td>75.20</td>\n",
       "      <td>73</td>\n",
       "      <td>40</td>\n",
       "      <td>3.36</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01-04 20:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cayenne</td>\n",
       "      <td>4.94</td>\n",
       "      <td>-52.33</td>\n",
       "      <td>84.20</td>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "      <td>9.17</td>\n",
       "      <td>GF</td>\n",
       "      <td>2020-01-04 20:06:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yima</td>\n",
       "      <td>34.75</td>\n",
       "      <td>111.87</td>\n",
       "      <td>37.58</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>14.36</td>\n",
       "      <td>CN</td>\n",
       "      <td>2020-01-04 20:06:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Broome</td>\n",
       "      <td>52.47</td>\n",
       "      <td>1.45</td>\n",
       "      <td>46.00</td>\n",
       "      <td>87</td>\n",
       "      <td>40</td>\n",
       "      <td>6.93</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-01-04 20:06:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City    Lat     Lng  Max Temp  Humidity  Cloudiness  Wind Speed  \\\n",
       "0     Ust-Ilimsk  57.96  102.73     17.47        93          97        8.63   \n",
       "1  Cockburn Town  21.46  -71.14     80.60        69          40       11.41   \n",
       "2         Albany  42.65  -73.75     42.80        86          90        6.93   \n",
       "3        Ushuaia -54.81  -68.31     57.20        41          40        5.82   \n",
       "4           Gasa  27.91   89.73      2.66        98          98        7.29   \n",
       "5         Barrow -38.31  -60.23     77.27        55          75       18.05   \n",
       "6           Hilo  19.71 -155.08     75.20        73          40        3.36   \n",
       "7        Cayenne   4.94  -52.33     84.20        70          75        9.17   \n",
       "8           Yima  34.75  111.87     37.58        95         100       14.36   \n",
       "9         Broome  52.47    1.45     46.00        87          40        6.93   \n",
       "\n",
       "  Country                 Date  \n",
       "0      RU  2020-01-04 20:06:18  \n",
       "1      TC  2020-01-04 20:05:35  \n",
       "2      US  2020-01-04 20:05:03  \n",
       "3      AR  2020-01-04 20:03:55  \n",
       "4      BT  2020-01-04 20:06:19  \n",
       "5      AR  2020-01-04 20:06:19  \n",
       "6      US  2020-01-04 20:05:13  \n",
       "7      GF  2020-01-04 20:06:20  \n",
       "8      CN  2020-01-04 20:06:20  \n",
       "9      GB  2020-01-04 20:06:20  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the array of dictionaries to a Pandas DataFrame.\n",
    "city_data_df = pd.DataFrame(city_data)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Ust-Ilimsk</td>\n",
       "      <td>RU</td>\n",
       "      <td>2020-01-04 20:06:18</td>\n",
       "      <td>57.96</td>\n",
       "      <td>102.73</td>\n",
       "      <td>17.47</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>8.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cockburn Town</td>\n",
       "      <td>TC</td>\n",
       "      <td>2020-01-04 20:05:35</td>\n",
       "      <td>21.46</td>\n",
       "      <td>-71.14</td>\n",
       "      <td>80.60</td>\n",
       "      <td>69</td>\n",
       "      <td>40</td>\n",
       "      <td>11.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01-04 20:05:03</td>\n",
       "      <td>42.65</td>\n",
       "      <td>-73.75</td>\n",
       "      <td>42.80</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>AR</td>\n",
       "      <td>2020-01-04 20:03:55</td>\n",
       "      <td>-54.81</td>\n",
       "      <td>-68.31</td>\n",
       "      <td>57.20</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>5.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gasa</td>\n",
       "      <td>BT</td>\n",
       "      <td>2020-01-04 20:06:19</td>\n",
       "      <td>27.91</td>\n",
       "      <td>89.73</td>\n",
       "      <td>2.66</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>7.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Barrow</td>\n",
       "      <td>AR</td>\n",
       "      <td>2020-01-04 20:06:19</td>\n",
       "      <td>-38.31</td>\n",
       "      <td>-60.23</td>\n",
       "      <td>77.27</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01-04 20:05:13</td>\n",
       "      <td>19.71</td>\n",
       "      <td>-155.08</td>\n",
       "      <td>75.20</td>\n",
       "      <td>73</td>\n",
       "      <td>40</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cayenne</td>\n",
       "      <td>GF</td>\n",
       "      <td>2020-01-04 20:06:20</td>\n",
       "      <td>4.94</td>\n",
       "      <td>-52.33</td>\n",
       "      <td>84.20</td>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yima</td>\n",
       "      <td>CN</td>\n",
       "      <td>2020-01-04 20:06:20</td>\n",
       "      <td>34.75</td>\n",
       "      <td>111.87</td>\n",
       "      <td>37.58</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>14.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Broome</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-01-04 20:06:20</td>\n",
       "      <td>52.47</td>\n",
       "      <td>1.45</td>\n",
       "      <td>46.00</td>\n",
       "      <td>87</td>\n",
       "      <td>40</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City Country                 Date    Lat     Lng  Max Temp  \\\n",
       "0     Ust-Ilimsk      RU  2020-01-04 20:06:18  57.96  102.73     17.47   \n",
       "1  Cockburn Town      TC  2020-01-04 20:05:35  21.46  -71.14     80.60   \n",
       "2         Albany      US  2020-01-04 20:05:03  42.65  -73.75     42.80   \n",
       "3        Ushuaia      AR  2020-01-04 20:03:55 -54.81  -68.31     57.20   \n",
       "4           Gasa      BT  2020-01-04 20:06:19  27.91   89.73      2.66   \n",
       "5         Barrow      AR  2020-01-04 20:06:19 -38.31  -60.23     77.27   \n",
       "6           Hilo      US  2020-01-04 20:05:13  19.71 -155.08     75.20   \n",
       "7        Cayenne      GF  2020-01-04 20:06:20   4.94  -52.33     84.20   \n",
       "8           Yima      CN  2020-01-04 20:06:20  34.75  111.87     37.58   \n",
       "9         Broome      GB  2020-01-04 20:06:20  52.47    1.45     46.00   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        93          97        8.63  \n",
       "1        69          40       11.41  \n",
       "2        86          90        6.93  \n",
       "3        41          40        5.82  \n",
       "4        98          98        7.29  \n",
       "5        55          75       18.05  \n",
       "6        73          40        3.36  \n",
       "7        70          75        9.17  \n",
       "8        95         100       14.36  \n",
       "9        87          40        6.93  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the output file (CSV).\n",
    "output_data_file = \"weather_data/cities.csv\"\n",
    "# Export the City_Data into a CSV.\n",
    "city_data_df.to_csv(output_data_file, index_label=\"City_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 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 grapg properties.\n",
    "plt.title(f\"City Latitudde 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": 21,
   "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 plot  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 grapg properties.\n",
    "plt.title(f\"City Latitudde vs. Humidity \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Humidity (%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig2.png\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 plot  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 grapg properties.\n",
    "plt.title(f\"City Latitudde vs. Cloudiness (%) \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Cloudiness (%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig3.png\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UCvgHSAPWA+OAoYAAscAzwN2e0k+aNEnZWL9+vQpHtNzBJRzlDkeZldJyBxtf5Aa2KCc6NShePUqpGmADMEspddiUqQVYBZweDBk0Go1GYxBIr54MEUkzf8cD3wK+EJGh5jYB5gA7AiWDRqPRaLoTSK+eocAzpp0/AviHUmqNiKwTkQwMc882oH8tbaPRaDQhTiC9ej4HTnWy/bxAlanRaDQaz+iZuxqNRuMniovXMvOiORScNIGZF82huHhtX4vkFL30okaj0fiB4uK13LpkKRnTrqZwZgGW8lJuXbIUIOSWctQtfo1Go/EDy1asJGPa1aSNOJGIyCjSRpxIxrSrWbZiZV+L1g2t+DUajcYP7C0rIyW3oMu2lNwC9paV9Y1AbtCKX6PRaPzAqLw8LOWlXbZZyksZlZfXNwK5QSt+jUaj8QOLFi6gauPz1Oz/gg5rOzX7v6Bq4/MsWhh6Hut6cFej0Wh6SXHxWpatWEltxX6Orv4tdFghIpLoCDpt/KE0wKsVv0aj0fQCe2+eiTNvoWzTK5RtLmbMxTeQUzghJL17tKlHo9FoeoGjN0/VV9tIP+86GqPTQta7Ryt+jUaj6QWO3jxN1RUkDBtLS2tr57ZQ8+7Ril+j0Wh6gaM3T3x6No0HdxMbE9O5LdS8e7Ti12g0ml7g6M2TMXoC1eueJKGtJmS9e/Tgrkaj0fQC24DtshUrKXmzjFF5efxg/lze3byBko1/YVReHsuXLA6ZgV3Qil+j0Wh6zezZs0JKsXtCm3o0Go1mgKEVv0aj0QwwtOLXaDSaAYZW/BqNRjPACORi63Ei8pGIfCYiO0XkXnP7KBH5UES+EpG/i0iMp7w0Go1G4z8C2eJvAc5TSp0CTABmicgZwFJguVJqNHAc+GkAZdBoNBqNAwFT/Mqg3vwbbX4UcB7worn9GWBOoGTQaDQaTXdEKRW4zEUiga1AAfBH4HfAZqVUgbl/OFCslBrnJO08YB5AVlbWpNWrVwNQX19PUlJSwGQOFFru4BKOcoejzKDlDja+yD1jxoytSqnJ3XYopQL+AdKA9cBUoNRu+3Bgu6f0kyZNUjbWr1+vwhEtd3AJR7nDUWaltNzBxhe5gS3KiU4NilePUqoG2ACcAaSJiG3G8DDgUDBk0Gg0Go1BIL16MkQkzfwdD3wL2I3R8r/cPOzHwKuBkkGj0Wg03QlkrJ6hwDOmnT8C+IdSao2I7AJWi8j9wKfAUwGUQaPRaDQOBEzxK6U+B051sv1r4PRAlavRaDQa9+iZuxqNRjPA0Ipfo9FoBhha8Ws0Go0dxcVrmXnRHApOmsDMi+ZQXLy2r0XyO3ohFo1GozEpLl7LrUuWkjHtagpnFmApL+XWJUsBwmqhFU/oFr9Go+lkILR23bFsxUoypl1Na30tH69awrbVD1NZ08Cv7rmvr0XzK14rfhFJNF0zNRpNP8TW2m0uupjC6/9Ac9HF3Lpk6YBS/nvLymi2VFOy/kUSJn+X3B/9npSzr6bkYFW/qgeXil9EIkTkKhF5Q0QqgS+Aw2aI5d+JyOjgianRaAKNrbWbNuJEIiKjSBtxIhnTrmbZipV9LVrQGJWXx54NL5J29pXE54xBIiKJzhhBxtSr+lU9uGvxrwfygf8BspVSw5VSmRjxdjYDD4rID4Mgo0YTFoS7mWRvWRkpuQVdtqXkFrC3rKxvBOoDFi1cQGPlPqLSc1BK0d7aRHtdNSOKJvWrenA3uPstpVSb40alVDXwEvCSiEQHTDKNJozoD4OCo/LysJSXkjbixM5tlvJSRuXl9Z1QQWb27FkUFY7h2P7ttA/KJTYmhuE52ajaw/2qHly2+O2VvohEikiOiIywfRyP0WgGMv3BTLJo4QKqNj5Pzf4v6LC2U7P/C6o2Ps+ihQv6WrSg8sA9dxBftpHhyREUnJCHqj3c7+rBozuniNwE3AMcATrMzQo4OYByaTRhxd6yMgpndjeTlLxZ1jcC9QBbz2TZipWUvFnGqLw8li9ZHNI9luLitSxbsZK9ZYa8ixYu6LW84VgPvuKNH//PgEKl1LFAC6PRhCv9xUwye/assFFwgTSvhVM99ARv3DkPALWBFkSjCWe0mST49AfzWl/hssUvIovMn18DG0TkDYwF1AFQSi0LsGwaTdgwEMwDoUZ/MK/1Fe5MPcnm937zE2N+NBqNE/q7eSDU6C/mtb7ApeJXSt1r/19EUozNqi7gUml8xtkgF+D3gS+NJlRYtHCBYdOfdjUpuYaNv2rj8yxfsrivRQt5vPHqmQyswuwBiEgtcK1SamuAZdN4ibNBrvm33UNHazOjLr0pbP3KNRp39Ma8Vly8loPl5Vx34y0DslHkzeDu08B/K6XylFJ5wI0YLwK3iMhwEVkvIrvNMA8/M7cvEZFyEdlmfi7s1RlonA5ySdEsGonVA1+afs3s2bN4+41XKN25jbffeMVrpX/rkqV0xKUO2JhE3ij+OqXUu7Y/SqlNgDfmnnbg50qpscAZwI0iUmTuW66UmmB+3vRZak0XnE21JzWb1gZLl00Dbfq9RuMMW0MpMiZuwDaKvFH8H4nIYyIyXUTOFZE/YXj5TBSRia4SKaUOK6U+MX/XAbuBXP+IrbHHNsjVhdoKYhJTumzSA18ajY5JBCBKKfcHiKx3s1sppc7zWIhIHrARGAcsAn4CWIAtGL2C407SzAPmAWRlZU1avXo1APX19SQlJXkqMuQIpNwWi4UDhyqISkonMiYOa2szrZajKNVBbGpm57b2+mqG52STkpLiOdMgyB1IwlHucJQZwk/ur0r30BGXyuDEaGrajEjz1tZmIpprGV2Q7zKdxWLhSGUVLS0txMbGkpWZ4fJZ8uVYX/GlvmfMmLFVKTW52w6lVEA/QBKwFfiu+T8LiMTobTwAPO0pj0mTJikb69evV+FIoOV+881i9a0LL1P5Raeob114mXrzzWKn23xF13fwCEeZlQo/ud98s1gVnj5drXzun+rC5evVOb94QhWePt3t82FLc84vnvCYxpdje4Iv9Q1sUU50qjdePWnAj4A87LyAlFI3e5E2GiOS5/NKqZfNdEfs9j8BrPGUj8ZzTBJXPuQDyVNBo/EG2zNxsLyckifu98obyN6BAjC+zXEBx3S+HNtXeBOr502M+Pvb+SZIm0dERICngN3KbpaviAxVSh02/34H2OG9uAOT/hDyV6MJJWbPnsWGDRso3bnNq+N9mSUcDjOKvVH8cUqpRZ4P68bZwFxgu4jYavdXwJUiMgEjwmcZML8HeQ8owqEFodH0Z3yZJRwOM4q98ep5TkSuF5GhIpJu+3hKpJTapJQSpdTJys51Uyk1Vyk13tx+qV3rX+MC7YWg0fQtvgThC4eAfd60+FuB3wF3YLTSMb9PCJRQmq6EQwtCo+nP+DJLOBwC9nmj+BcBBUqpo4EWRuMcHZNEo+l7fAnCF+oB+7wx9ewEGgMtiMY1s2fPYvmSxcTtWkPJE7cQt2tNyLUgNK7xZRH2cF+wXRMeeNPitwLbzIlc9vH4PbpzavxHqLcgNM7xxSPLYrFw2wPLtfeWJuB40+J/BWOi1fsYE7FsH41G4wFfVok6UlmlV5TSBAWPil8p9YyzTzCE02jCHV88slpaWrT3lheEqjksVOVyhkvFLyKvi8gl5uxbx30niMh9InJtYMXTaMIbZwH0XHlkxcbGen3sQMVmOmsuujgoIZW9VebeyhUqLwd3Lf7rganAFyLysYi8KSLrRGQv8BiwVSn1dFCk1GjCFF98urMyM0Le/7uvCeYC6768ZLyRK9gvLXe4VPxKqQql1G1KqXzgCuDXGK6dJymlZiqlXg2WkBpNX9PTlpovHlkpKSnae8sDwZzM6MtLxhu5gvnS8oQ3Xj0opcowwitoNAOO3sZK6k/+331NMCcz+hJzxxu5QimGjzdePRrNgCaUWmoDnWCGQ/BlfMYbuXzJL9Boxa/ReEDHSgodgjmZ0ZeXjDdyhVIMH69MPRrNQEbHSgotgmUO8zXmjie5QimGj0vFLyLb+SYoWzeUUicHRKIg4GlRE43GHl9jJfXn+8vZucXHxwW9zGDVp79fMqEyhuOuxX+x+X2j+f2c+X01YRy7Ry9qovEVX1pq/fn+cnVuD91xa9DLhPCvz77EnTvnPqXUPuBs061zu/m5Hfh28ET0L3qgTtMTZs+exdtvvELpzm28/cYrLpVOf76/XJ3bkcqqoJfZ0/q0d8v9qnRPSM+uDSTeDO4misg5tj8ichaQGDiRAoseqNMEkv58f7k6t5aWFhcpAldmT+rTcQJVR1xqn02g6mu8Ufw/Bf4oImXmrN0/AWEbqiGUXKo0/Y/+fH+5OrfY2Nigl+msPm2t+ZwTChkyPJ+hI/O7TLZz7D1ExsR53XsIlVAL/sKbIG1blVKnACcDtiUUP/GUTkSGi8h6EdktIjtF5Gfm9nQReVtEvjK/B/X+NLwnlFyqND0jlB/C/nx/uTq3rMyMoJfpWJ+21vzREdOJueDnxM9YQFN8Bofi8zpb9T3tPYRSqAV/4dGdU0SygN8AOUqp2SJSBJyplHrKQ9J24OdKqU9EJBnYKiJvAz8B/qOUelBEbgduB4K2lFQouVRpfCfUB/v68/3l6twC6dXjbX3aWvOVLVFEx6cSnzGCyOhYqra8TOEFP2TZipU9dsu17ykAxrfZUwjX6+qNH/9fgFUYa+4CfAn8HXCr+M1F1A+bv+tEZDeQC1wGTDcPewbYQBAVP4SOS5XGd8LhIezP95ezc9uwYUPQy3TEFg7hwBdfEpdivIjisvM5Vl3RGRbh0Ycf7OKWa21t9moJU2ehFpot1Xz64UcUnDQhLF12RSmXrvrGASIfK6VOE5FPlVKnmtu2KaUmeF2ISB6wERgH7FdKpdntO66U6mbuEZF5wDyArKysSatXrwagvr6epKQkb4sOGQItt8Vi4UhlFS0tLcTGxpKVmUFKSkqv8w21+t6xcxdxGSO6bW+u2s+4k4o6/4ea3N5gL3NvrmdP0x4+fJjKo8ewWq1ERkaSOWQwQ4cO9UnuvuKr0j10xKXSrgQVEYlIBB1tzXQ01BCXkk5Ecy2jC/K71E12ViYxMTEe68aWd2SM8UJpb26gyVJNVEIq8UkpWFubaa+vZnhOtl+eOU/4Ut8zZszYqpSa7LjdmxZ/g4gMxpzMJSJnALXeCikiScBLwC1KKYuIeJVOKfU48DjA5MmT1fTp0wGjdWH7HU4EUu7i4rWdS/al5BZhKSul6onlfjExBLq+fZ2c88DvDBurfXe9Zv8XxO16i4U3/nfQ5A4ENpl7cz17mva+X9/P0seeI/2860gYNpbGg7upXreExfPncvddd3old1/S1NTMrUuWEj/xMo6rBKyNtdR99BK5487AeuAzli9Z3E1Gb+W25Z1h9hQ++PN9xI3/NgUTRpCamgpAzf4a4l5/mrffeCUAZ9cVf9S3N149i4DXgHwReQ94FrjJm8zNRVxeAp5XSr1sbj4iIkPN/UOBSp+l1nQhXH3HPQ2aORvE7c+DpzZ6cz17mvaRx1eRft51JI0cT0RkFEkjx5N+3nU88vgqf51WQLHFyhmyfwNtb/2epvUriW+qIqeprNcNIMc4PK3HDpA//rROpQ/h57LrscVvDs6eCxQCApQopdo8pROjaf8UsFsptcxu12vAj4EHzW8d17+XhFK4V19wZ68HnA7iLl+ymOVLFvfLwVMbvbmePU1bZ7EweNjYLtsSho2l0mLxSuZQIFhjK3HxCVRvX0fauZd3bgs3l11vvHoSMFr9I5VS14vIaBEpVEqt8ZD0bGAusF1EtpnbfoWh8P8hIj8F9mMs8qLpBeEaRMydknL3UnA3c7Y/0Jvr2dO0ySkpNB7cTdLI8Z3bGg/uJjkINutQx9GT7FDJNr58/c8A5J0zx2PsplDEG1PPKqAVONP8fxC431MipdQmpZQopU42ff8nKKXeVEodU0qdr5QabX5X90J+DeHrO+5uck5/ngHrah6CxWJh5kVz2Ll7N9ue+zVfv/Oiz9ezp/fCzfOuoXrdk9Tv206HtZ36fdupXvckN8+7xi/nHM44ms+GFU1mzCU3cOTD18N2pTRvBnfzlVI/EJErAZRSTeLtCK0mKISr77i7qJfLVqz0W3nTqOoAACAASURBVC8mlKJlupqH8PGWLQzOyKS56GJOnXkLh0q28fWbj1P98euMGzfe6+vZ03vBNoD7yOMrqbRYSE5JYfH8azwO7A4EnPVMcwonULcxmdKd21ykCm28UfytIhLPN149+UDggnP0cwKlhMLRd9yTkvIlFLIrQm3ClysT1iOP/5YHfruUtBHZAAwrmkxSUhJxu9b47CnS03vh7rvuHPCK3tnzGa6mVHd4o/jvAdYCw0XkeQzb/U8CKVR/JdSUUCjgSkn5qxcTahO+XI1r1FksnX7i9ttDfYC+P+Hq+bzq4vN58h9PsL9oFqRmQ20FzZ++Rk5afNhO4PLGq+dtEfkEOAPDq+dnSqmjAZcszHFsOdw0/1oefexpvyqhQPQenMndV/ijFxNqHk+uWo/JKcZEIHvCvVUZbrhqJLzyxjN0tDbTuK2Y1gYLkXGJtNQcZcjJ36PQHNwNtwact2vungucD8wApgZOnP6BM//0A4cq2LFje8BCzPojcJQruXsbjKovg6r1NFpmoGR2Nfh687xraK+vDrsB+v6EK4eCL0tLGXXpTZy9cBkzFj9J9oU3kXHhzVR9tS2s5s3Y41Hxi8ifgAXAdmAHMF9E/hhowcIZZ5NoopLSaevAbyF7AzFpy5Xcvcmzty+o3irgnni5BDIao6tFue++606G52S7Xaw7WC/QUI5+GkhcNRJAurwQWlpbSRg2lqbqis5t4eZx5o2N/1xgnDKD+ojIMxgvAY0LnJkXImPiiI2Oomrj824HLL013wTChOFK7t7c0L2xsftjTKQnYwWBHhdwZcJKSUlxOZAbrPGhgTwO5crLbPQJXc1zsTExNB7cTXx6dmfacDPLeWPqKQHso2INBz4PjDj9A2ctB2trM0Vjxzpt7dkeKF9amoFY8MOV3L3Jszf++P7q1Xi7bKK3MvdFizhYYTnCNfyHP3DVG/vNvXd36TUmtNVQve5JMkZPCFuznDct/sHAbhH5yPx/GvCBiLwGoJS6NFDChSvOWg7t0R2drXdv1msF9y1Ndz7wgZC7p/TGFa6vBmbdydxXLeJg1UWoDYYHG0/Pp63X+J35c3l38xZKnrglbObN2OON4r874FL0M5yZF4ZPvdbrmOL2uHro/OHu6Mys5BgHxxu53dGbF1Qw/aft6yIlOZnK1x5l1KU3OZ1Y1hfuocGqi/7os+4PwnGejDu8WXrxHaXUOxgDu+lAvW2buV3jBEfzgjdxun013/hqwrDHlVkJ8Flud7jqPnsja7BCUTjWReLUHxMRE0f9O093k7mvQkkEqy7CNfyHxjdctvhFZA1wu1Jqhxk++RNgC0Z45seVUn8IlpD9AVscFneDtoEw37jCU2RMW+t38a030dTU3Ouwtj1JH6xQFE7rYtb1xO1aw6cfGYOtNrt+xZFK2j5Yz8jxp3eG5T1Uso2qo8dIGpIDKMYUFPDAPXc4lbOncy+CURc22SxVhzj87L20NDUSFRPDmIICz4k1YYU7U88opdQO8/c1wNtKqR+Z6+e+B2jFb+LpYS4uXsuBQxVGi9KNXTiYMXdcmZU++cf2LjbsjrijferVEYwuticTm71df+ywsyj5zz9os3aQP/406iu+5otX/4hExpN50Y1EDc6lYu9nzL/tHh6DbvdBb8YHAlkX9rLln34te7Z/jNpWTOH53ycuJX3AePYMFNyZeuxj7p8PvAnG+rlARyCFCie88cRZtmIlUUnpXnlK9MZ84wuuzEptHXTx6oiMiev3Xh2eTGz2PYLscWcxduaVNG//N58+Op8Dax4lKiKCzNkLic8tJDouicT8iUjRrG51FsoeM/ayVR6rJjF/Iunn/oh9m98MKTn7mv4yx8Gd4j8gIjeJyHeAiRjxejADtkUHQ7hwwJuHeW9ZmdM4LH054cOVLTcmOqrfhkN2hSe7tqNdP7NoCmfe8BDZWVmkpSSjEOKy8zv3R0bHQWp2tzoL5VDT9rK1tLYSGR1HXHZ+5ySlUJGzt9gr7q9K9/ikuAM5sS/YuFP8PwVOwgjI9gOlVI25/QyMGP0avHuYR+Xl9TgOS6BaGK4GXU8aO9bv8wNCHU8D0O56BKPy8ohJTKG5Yk/nPmtbM9RWdKuzQMy98AfFxWupsdTxybtv8eVXpURGRmBta6a5Yk/nJKVQkLO32BT3ofg8LJGp1Dc28f1r5nPfrz0uLwKEdo/NV1za+JVSlRihGhy3rwfWB1KocMIb97dFCxfw9b791OyvgaRM9u/aStW7L1A4LIPi4rUuzTmB9hl3ZTO2H2C2tjaH3epCPcGd/dzToPv1tyym+p1nSZ82l6jBuTTu/YyokrdY9NC9nXkUF6/laFUlJc8vJWPqVYwomgT1lX1et7Z7LGvKJZTvWEdU4iAkNpHW0i00fvZvCs//fmcPKNzvgWUrVhI5/BTKd3xI2tlXEjNkOGkXLOT3T67itMmT/epuHep4G6TNZ0TkaRGpFJEddtuWiEi5iGwzPxcGqnxHgh10y979bfbsWQzPyabh3Wf4+A/XU7etmJMuuZ6kc69121W0tTBa62v5eNUStq1+mMqaBn51z31+kd0Zjq3fiOZanwaYQ9kG2lPZ3PUIZs+exRN/WEp+egxHX1tKxaqbyD64gcceurfbjOykc6/lpEuM6//xH66n4d1ngjLxx9152+6xE869nMIZV9Dy2RscW/N7mjY9S356DMc3vRDwFaZ8uS69ub/2lpVR9eWnpJ19JfE5YwAhccQ4EibP4Y57H/CYb6j22HqCNxO4espfgBXAsw7blyulHg5gud0IZMvZW0+clJQUBg8ezGnXPdCld+Bs8o/NS2jjpveI37UHa0Q0g6f/hPTsfJoPl1KydoXbnkJvsW/9btiwgenTp3uVLpTjvLhb+erdzVvculd68try5G3j6C6aPe4savZ/QdyuNUFR+u6uiX0rNrNoCplFU+iwtlPyxC18+tH7AZXNG/l6eqwzRuXl8f4n20nPzqe+dAut2RmUPfV7IhNTOVZRypQbfuc232C6WweagLX4lVIbgZBYTzfQtjlvPXG8GQ+wH0AaOfdBWiWa+FNmEzUoB4mIJDpjBOln/YC5824MuVZ1KNtAnckWOfwUfv/kC24H6/wxoNeXg7qerklft2J9uWd6e38tWriACNXOsQ//RfWW14lMTCPrqt+SfNp3iEjJoLW+1m2+vZmMGGqIGXSz+w6RRzGXW3SGUupmj5mL5AFrlFLjzP9LMAaLLRiTwX6ulDruIu08YB5AVlbWpNWrVwNQX19PUlKSp6K7sGPnLuIyRnTb3ly1n3EnFfmUV0+pr6/ncMUROuJSu3j4WFubiWiuZXSB4RXyVemezmOsVisNVQeIHpQLyopERqGs7aAU7bVHSM42Bo3b66sZnpPd61m2jlgsFlpbW6k4UklsbCxZmRluy/C1ni0WC0cqq2hpafEqf19wvE+cydZwtBwVGY1Y2+mwthERGU1UXDwxHa1Or4cNx2vmCW/zqK+vp6Ojw6914umaWCwWDhyqICop3bjnenA/9eSZ9Fa+nh7risOHD3P4SBVRqZlkp8ZR2Qgd1jbosKKaLCQOyelRvsHEl/qeMWPGVqXUZMft7hT/j82fZwNFwN/N/1cAW5VSt3oq1InizwKOYrxQfg0MVUp5XOJp8uTJasuWLYBvpgcbMy+aQ3PRxV1MLLautq/rmfaUDRs20NTU3NlVdewq2loNBSdNoPD6PxARaVjh3l/5P8ROvAyi40hOSqLd2k5r3XFaPnuDKdfdH7BzsbV0b73pRl47OsSprI74Us/23XZXddEbHO8TZ7K9fc/3iRyUQ8b0HxGXnU9zxR5q3nuB+KYqDu8zvHQcrwfQaQrxdqFtb8/1tdde47YHlvu1Try5JjZT1q7du2lpayc6AsaNG+/1rOKePJO+yGd/7KH4PKq+2kZTdQXx6dlkjJ5ATlOZT/d+zgmFJF14GzednsqKLfW0t7cjiWkcWX0n02970q0MoYAv9S0iThW/S1OPUuoZpdQzwGhghlLqUaXUoxiTuSb0RGCl1BGllFUp1QE8AZzek3x8JVTij3jTVXTsehdMm0P9B6uJbqii4IQ8Giu+pu6jlxh11iWdxwTCbGDrVkfGxHndrfalnnvTbe/JAJ8z2To62hl05hXE54xBIiKJzxlD8unfo81ueqI/TCHemgiOVFb53VTmrfPBooULSM7IYex//Q8Tb30qaD7qvtwzU8+YTNnmYmJPuYicuQ8Te8pFlG0uZuoZ3fQa4Po+OWnsWLLiFfFxcYwZXUBuzlAa935GdELKgIlP5M3gbg6QzDf2+iRzm8+IyFCl1GHz73cwAr8FnN6GQvDn2rbuBgKLi9dy7Ngxdvz5l0hsIpFR0cSlZRDTXM3gfeso2fEqbZY6cqdcQmbRFGprazlSWYWl/EvaLHV+HfD9ZtCvvnObJ9c1X+q5p65x3g7S2q8VbB+D5ujq33a2aFMSE4hKGkRL3XHaWhrpaGuFqDjosHam9deAnjfhFlpaWkjJ7WpasIXRcBfnyd396c01KS5ey9x5N9LUDtVv/ZVRZxn3V/XwU5g770bSUpJ7fd+7wpd75t3NWxhzyQ00RqfRUn2IhLQMhlxyA+9u3tDtWHcDwbZraj3lRjqs7ajaw1g/X0P78SP83z1XkJySws3zrglL2723eKP4HwQ+FRGb7/65wBJPiUTkb8B0YIiIHATuAaaLyAQMU08ZMN93kXtGT+OcBHvlo8jhk4irbiVh8hwi4pKJsFQQUfJ2Z9Av23EHd+VxXCVgbaylaec6cqdc4le5Olu6Ob6tMuRtPfc0/K+zgGr1Ey/jd39czoS5d3VeI/u1gm3X79SZt3Qq7kULF7BsxUoOtFRTb+0gInEQMUmxtB0uocWqOl+i3iomfzQOYmNjsZR1rZOyTa/QSKzLOE/e3J+eGhu3LllK9Ok/YHDhWbQc+ZqS9X+j5kAJR0o/J/70H1A49YKAemh5e88YjYUJ3c1uG//S7Vh3QQht5puD5eWUPHE/KcnJREsHY35yX+fL/YU1z3vl2x+ueBOWeRUwBfiX+TnTNAF5SnelUmqoUipaKTVMKfWUUmquUmq8UupkpdSldq3/kCXYKx9VfbWN9KlXMyj/VBIzcknMKWDkrOs7y7OZDQ6seZSqV5fS8tkbFM64ghPOvdxvphLbZKOtzy+lqd5CTXW137u/PTW/OfOQOVK+j+bmZratfpiPVy2htb62c61gd9dv0cIFHF73LNJSR0JiEqp6P/VbXmHEOd/pUo+evLb8NZU/KzOjW53s3/QvTrhwnsv7r7f3py19Su4YOqxtxOeMIe3sK9n34b9JPv17pOSOCRkPLV/Mbp48qWbPnsXognxKd25j8ODBjLr0ppD0RgsU3rpzRgJVwHFgjIhMC5xIoUWwXPFs5TRVV3TGfYmMjqOltbVbebNnzyItJZnpi59iynX3k1k0xWu5PCkpx8lG1iZLQCYb9dQ1zvHhr9z1Icc/W0f6+fPI/dHvSZj8XUrWv0hHh5W9ZWVur9/s2bNIoIWWbWsof/bnNG55mcIZl5N3zhyfrq+/GgcpKSnd6iQ5IY6cwq5DavbXubf3py19VmYG7XXVtLc2EZt1Au3N9UQmpJKVmdGjfAOBL40Ff74k+iMeTT0ishT4AbCTb6JyKmBjAOUKGYK98lF8erYRIyVnDNa2ZmJjYpyW1xO5XNly7SeROXaRkzJqOO26BwIy2agn5jdHm/tX6/5O8qkXkji8qHOAlrOvpKXueGdduKuncePGO/Uq8eX6+nMqv2OdzLxojlv5e3t/Oqa3jRlFRUUxSBo71xzwNd9A4Mt4wNQzJvP7J5eRMHkOyUPzSbTW0fTJq07HZgbiqmPetPjnAIVKqYuUUpeYnwGzzm6wVz7KGD2BmvdeoH7fdtpqq0hoq3Fanq9y2dtyh13zv50t48pdH/q1BRloHHsKrccOMOzEU1CtjbS3NqGUIio9h462FhYtXOCxnvxxfQM5CSrQ8tunT05KJDO2ndSDH/CrRTfR9Mmrfe4J54g3kyWLi9fywpr/MOy0C7B+9R7lL/2G0n8t56qLz3d6fKh4/QUTbwZ3v8YIw9wSYFlCkmAtjmJfTnVTFU3rVxIbHcWQsWNZ5KQ8X+WyteRVS1SnLZezr2Tv+y8Tk5TqtxZkIHEcQH304QdZtmIlzdZ6knKGcqSyiubaKtTxcuLzc7rUhat68sf1DeRUfk/y9VZ+d+lPmzy5c3tKcjLxyspNv7idUStWMvWMyZ1eVN6u0lZcvJY77n2AL0tLAWH0CXn85t673Q48+zpgbt+rTWxWDBpRCCI0VB7gkcedB2ML1jMeSricwNV5gMhLwCnAf7BT/t7M3PUXvZ3AFQr0tdy2iUh19Q3GTM3kdCIiozm46mcMSkkic1AKlro6Y6Hx45bOhcYvHXKU5Y/+sc8fBFeToK66+HxeWPOfbtsfuuNWLr00eB1Tf3j19PU94grHui/b9Aplm4sZc8kN5BRO8OoeKS5ey/zb7qG98AISRp1C+7Fyqjc+R0pkG0/8YanbuDzeTmazpakddiYphWdRs/UNarb/h4zzfkriiJOwlLxP6sEPOvMI1fr2REAncNnxGsYs2/eBrXYfjR9w52Hjz0iXtpZ8amoqw3OyiWiqxVLyPlEdrUTExJE49cdOFxr3NTpnoHA1gPru5i1OB4l7EuagN/XtbbymcMSx7qu+2kb6edfRGJ3m9Spty1asRIpmkZg/kei4JOJzC0k/90c0Euu3uDyOHkqN+3eQPuOnSPIQOqxtpOSO6ffeOt7ijTvnM84+wRCuv+POw8bfq/24suWeMHI4I2dd3+UBGznreoZkZFK6cxujC/Jd2lGDGX7ZceyhtraWI03Cxk3vdbpm9kbphsLqShaLJSRDWjvWfVN1BQnDxtLS2tq5zdM40N6yMkjNNlYnM4nLzqe1weI0XU/Gmhw9lNpqjhCbeyIdba2011UbcY+c5BGMeznUwpW7VPwi8g/ze7uIfO74CZ6I/Rd3rRp/zx9w5T5Z19Do9AH7dNs2hgzP55NtnzFkeH6XVYr6QknaD6DW1tZy4FAFTZZqUkcW+aV8X+vb3w9ycfFaDhyqCMll/RwHr+PTs2k8uJvYmJjObZ7GgUbl5UFthbE6mUlzxR5iElOcpuvJgLljrzY6eRDNB3YSITA8J5vU1NRueQTjXg6FRoUj7lr8PzO/LwYucfLR9BJ3rRrbvtraWr78qpTtO3dxpEnYtXt3j8tzZo5w9oDt+WwztY3NxM9YQGxGHvEzFrD0sec6lX9fhF+277FUVFTQXl9N3UcvM+rsS/1Svi8tTMcH+VB8Ht+/Zj45JxT2+CWwbMVKopLSu9Rp/MTLQiL8tqPXS8boCVSve5KEtho6rO2dq7S584JZtHABatdaGvZ8QltzPU3lJVS/8ywJtDhN1xNPG8de7YiTz6T2nVWomnL27d/P55veZt/aJ7rk4epe/tU99/ntxR6K4crdefX8QETeAz5VSrUHS6CBhCcPmkMl26iRZKKS04lLiaNh/w4sjc0+xeTxNOjozCOlrPgJ0iZdTNLI8SCtxvd51/HI4yu5+6472bl7N0mjLuHAzl3ExsR0dqEDuQSdvefFvk3vkTqyiMIZl3eZvNab8n3xZrJ/kCt3fWgs5XfBQuJT0mmOVz0KbbC3rKxL2Oba2lqOqwSa2mHK9X/o00VtnHm9/GD+XN7dvIGSjX8h4tabPI4DzZ49i8fA8OrZ8CTfePXc7zRdTzxtHNMkJyaQFBNB4+dv0dpgISYxhQQH50RnczCaLdWUHKwi6dxrfV60xxmhuGSjuxb/MOB/gUoR2SAivxGRi0QkPUiyhQW96fI7tmoO7trC5/9Yxo4d2zl27BhfvrSc9vpqIqNiaD78FdXrn0Zik7hi7jVeleVNF9OZCchad4z00+d0ySth2FjqLBaKi9dS19hMk6WauCHD6YhP5cChCg6VbAu4y6etxzLtnLMp/PbcTqUPvXc59aWFad872Pv+66SdfSWJI8bR2m5FUofSlDeNK+Ze6/IaObtnRuUZayvU1tay64svKP16L001VUjSYOrqG/q8lWir+0cffhCAZ1e/CMCjDz/ochwIvjnXoSPzmTvvRioqKzlzyhRu+9l/IxGRXDH3GpKG5HDq6Wd1q6ueDJjbpxmSkcmw2TeQ8e3/ZsjFi8j49n+Tcd41XerQaY93w4tkTL2qSwudom9z/7I/siOuiIgZN3F0xPQu43EnFBYRk5pBVFI6qdkjuphG+3qxG2e4W2z9FwAiEgNMBs4CrgWeEJEapVTorVAQZHobwM2+hbLtpd1YGpsZcc53yDvHmK3Zse+31L37LDXtbURGxxARE0/6uT9GRcXQnBrtsSx3gars0zjOFh0yPJ/Gg7uNlr5J48HdJKeksGzFSkac8x3KP3qZyOhYI459fTVfv7OK36xc5mMN9oxA+M370sK07x3UV+wjJiqOuooyBMXefftRKUNpbm1la+khrr9lMU+Y6ZatWMnO3bupa2xmUMFEmiNTef+T7Wy+Zj5zLphOq+UoX356gOjck2g9VkrjjnUkjDmTfQcOMhL/tBJ743bq6n5/6I5bux3neK5N8Rkkn/49IhNS2XPwE97787OknHE52VN/RvuxcvZsfK6zrvzVo9m5ezcxwy8kOj6VuJQ4rG3NHG9t5cC2bcy8aA6XXXgBx44do/K1Rzvdly3lpTRW7uOkokmAERKkdOMr1FWV09FhpbWxnqa2DlqIJ33iZdxx7wPsP1hOnTWKwRf/gpicQloO7uLBP/8FgLvvujMkl2z0ZgJXPJACpJqfQ8D2QAoVLnirWN1hU7qOC1KkjTiRzGk/pG5bMVMX/ZkPn7yThMnfJXrIcCKaakkbUeCxrJ52MW+edw1LH3sSzrsOThxD/b7tVK97ksXzr+HZ1S9SeP0tJGUMZ+/7L3OsuoL4QVkk0BI0E0SgJtx4G0LC9iBXDz8Fq7WN9rrjxOWMoaO9hfb641gtlcQOHUPc6d+l5v0XuPGWRcSkZZEx7WqSRl2CHKvgyDvPkDb+fIZfchsNB3bx2jurmDLldOq2rsH6/j/paGsm+dQLSSqahrW+miOVVajaw71qJfa2oXLHvQ/QlHceB+o6iP26jKzMoWRMu5ojlVVOy0gadQmRlmqOvPkIGbMWkjRyPO2tTVSs+5SU6dcQnTyE6Lgkok3XTsumv/r07Hiita2dyMZa4s1Vu6Ji4qk7Vk5NXQPvf7Kdiy+aTcuwSUQ0fED9O09zuKGRUXl5FBWOgfpKKiu+omT9i8Sf9l0y03JoKd9Nw/a3iUpIJXrwcGpVPJWlpbSpSNJnzSduxDgA4keMh+nX8MjjT3L3XXeG5AQxl4pfRB4HTgLqgA8x/PiXuVoqcSDiT9uds7xGFE3i47dWUrP/CxqPVZCSnkN7nbEsnjdl9XQW7t133QnAI4+vpGXizTStX8ni+ddw91138u7mLVjKSzsX5oZvVisKJj0Ns90TnLWSly9ZzNx5N5J44jnUffhPYqb9EJWWS0ejhZpNzxNfMIXaj1+hre44+5ssjD/PiLB5YOcuItKySZ/xU+o/WUP66ZeROGIc7ZPn0NZuZcgFC4jPzKNhz1aqt7xObM6JEBWN5ehexJx81FN601ApLl7LrpIvGTb1Z0TFJmJta+bAoQqGZWXS0nLQaRkHdu4iccQ4iIyGxEGAEXiwtfYog7ILsNYd60wXl53P0QYLe8tau5XdU6IjoO6jlzp7pvX7dnD83edJnngxWWdfTkRiA+U7PiR33JkMaSrj04++WZHs1iVLqaxpIO3sq7EmZ2FtqCEudyxRyUOwfPgSGRfdQl3ph4AYMbVyx35TsEQQk13AcYulc1Mw71dvcGfjHwHEAhVAOXAQqAmGUOGCP213zvKqr/ia2NhYPl11F611x2jeu63TLc2bsnoTg+Tuu+7k6IE9TJxwCkcP7Ol8GQy0uCauxkkA0lKSOfPyBYy74EqaP3mFIy8sxrL1Ndrrqmk9XELKlMvJ/tEyJDaR4yqB2tpaYmNi6GhrJTZ3LG21lQBY25pJHmquvWu6PCYVTCZ98iXUbvorVS/eS9tHf+91K7E3cZiWrVhJQuZI2qsPISJExcQTlZzO/l1biY2NdVpGbEwM1rZmYtJzaT2yt/NcY1KH0FpRSkREZGc6d66dPWXcuPHkjjuDxi0vU/7szzn67z8RN2oiaaddhkREEhEdR9rZV1L15afdot8uX7KYpsoyWpVgrasCFB1NdUQNyqG9toLmij3UfvBPMgenGVF0y+287VQHrRWlJPt5DWx/4m7pxVnAacDD5qafAx+LyFsicm8whAt1/KkEnQ30fvn6nxl2znc5986/MXrm1Rzf/E/qyr/yuqyehj52RyDyDGXcueLZXtaZRVOYct39ZFz8c5InXkRETBypZ11JbFY+qr2V6NRMrI3GamlZmRkIiuaDu4lOzaS9tYn2umoSrXXExcZ2cXmMjE8mUiAreyjPPf7HXtdxbxoqe8vKyJ9+OTXv/Y2mQ1+iOqy0Ve2n6t0XuoRuti/DNpEqLrcQy8cvdwYezBhzKpYNq2ir2uuVa2dPWbRwAdYDn1F4wQ+Z9vOVREZFkTR2KhHqmxXW4rLzaag66LQOomLjibS2EJM+jKjkISBC095PsTbVUfv+aqKjomiPSSZWrFSve4rm/TvoaG+jaf92LBtWcfO8a/x2Lv7G7cxdZbADeBMoBt4D8vnGx39AY68Etz0yn92rf0vl3hLmzrvRZ59uR4W658WHiIyMYv+Ha/l41RKSMoaTd8ZsDqx51CeF684zoqceSY55Ar32ee6pLPf9+n6GDM8nNjWj20Qzf+Culez4spbaw9RsWAWqg6j0XDpaG+lorCFl7DnUbn6R6n272Ld/P9bqAxxf9wTRg3ORhmrSlBEyODdnKI89dC/ZBzdQseom8Et8YAAAIABJREFUjr62lPz0GKexbNzhqi69aai4SpucmMCeDS/SeryCitd+z77HF3DsrT8RbW1m/4GDncc6+tKnqTpav3qfhLY6mtavpO2t35OvKrj9hh+Rc+hdr8/T1/vDfrnN3at/y+YH52Jtrqf92AFaaqtobWoAoOHALiJUe7cXjs2Joe6jl+k4tg9EsDYcp+6jl0iddAnR0dGMu2wBI2ddT0FBAbkpURxb8zDlf76WpvUruf2GH3X2kntKIGf7urPx34zhyXM20Iah9D8AnsaLwV0ReRpj8lelUmqcuS0d+DuQh7H04vfDfczAdqPeumQpacNPoXzHZhJN74Wj0thpFoiPj3OXTWdes2cby+l976c3kXn+POKGFtBcsYeS9X9j9LnfIS0lmdKd23ott7+WlPRHPj3N475f38/Sx54j/bwFDB42lsaDu41BaWDa1HO8Pgd3OBsnKdv0CjWWOm76xe2kJCd3DgxmJifTHGmlToTmvZ8Ql3UCUQLZJ59NWU0llk0v0BSfRExiCslRHYxoL8ey/lGG5OWxaMli4uPjmD59euc525SXLSKmt9EpPdWlq0FGd+sZH2uyEjf+26SbAdaq1j1FW/1xxl7xc+IyRnSawJYvWczyJYu7lPGblcucyu2tYvT1/rA//tSZt3CoZBtfvv5nMgsmYynZSFRCKs3WNqxN6VjeWcUvb7y+Wz57y8rsnBheoeHoITpiU2ivPULk0a8oOP/7ZBZNocPazuG6Or4u2eXVuXiLu3P2Rpd4wmV0ThFZhjGg+15Plkg0V+mqB561U/wPAdVKqQdF5HZgkFLK42hVqEfntHnklLz1VxImf5f4nDG0tzYR0VRLZmw7cbvWcMcvb/Fa7pkXzeHLpFOIyZtAVEw8AE2HvsSy6a9MKsjpbGX7Q2bHBUjidq3pkr+n+vY2H3/I4siQ4fnEz1jQxe20ft92mtav5MXnnurVfWJTujt2bKeRWE64cB45hRO6RaZ0jBpZXLyWX91zHyUHq8iYehUjiiaxf9dWjm99g7Ezr+w2IO6qrnsSnRJg4hlTOTbyPGRQbufkOlV7mIZ3n2Hw4MFu3ThdXYfdq3/L2P/6HyTVCH3d0tpKU8XXNO94m2k/+18uy6rh1SNpPl93b6/BBx9+xODzr2fk+NM7x7fcleV4Hl9+VUpjTRUtn73BqLMuYe/7r9NQeYDf/OpWxhQUOK1PZ3Xx+aa3qdtWzNkLv3Fb9vc5uyvfVpYvusTn6JxKqUVKqRd7ui6uUmojUO2w+TLAFuDtGYxFXsIeX5ZN9Da/EUWTOpfCsy0u0li5z282UF8H+lx1O/2xcEtP86izWEgYNrbLNttEs95gP6A78danGHbaBZT+aznbHpnPkQ9fZ8wlNzCsaLLT6fezZ8/i04/e56WnHmVM/Wcc/sfdHPvPExSaLURP52er5yvmXkNlTQOt9bVeT/O3ed5EjxjfZXJdQ2QSu0q+9BgrxtV1qLNYSMktIDU1lTGjCxh/UhFxmSOxNjd0O9ZfC/bYXwPiUogeMZ4Dhyqora31WNaOHdsp+fdzbHjoOj588k5q9nxKwrCxNFVXdI7HTF/8FJERES5fos7MYmrXWhJoCYpjQ6AXRPLGj9+fZNleJEqpwyKSGeTyA4KvyyZ6k19zfSXDHRYXKSoc47dBVF9cPd11O/2xcEtP80hOSXE50aw3OLo9nnDu5aSPGkfcrjXsLSsjp3CCER3UbP3GREXS4BBDyd59b+ZFc2hO6Trh3dn5WSwWbntgORnTrmZw7reR9lZK1q8GILNoikf3XXvPm+icMUZvMTmd8i8+JCFzZOf5tNbXUlnTwBVzr+HMKVM6W/8pycns+GB9t95CckpKt+tDbQUxiV3r2Z+zUe2vQcLgbOOchgznSGWV02BrNoqL19JILCkTLiZ9eBHNFXuo2vAsHS0NxKdnd5E1tjC2W3p74lULn666G1CMKSjgsYfu7ZQt0P74gV4QyeNCLL3KXCQPWGNn6qlRSqXZ7T+ulBrkIu08YB5AVlbWpNWrjQegvr6epKSkgMncEywWCwcOVUBMPG1NDUQmDoKISKLooKOxxoh/HxHhtdy2/KKS0omMicPa2kx7veG/35M48/b5HqmsoqWlhcjICNo7ICZliNsy6uvrOVxxhI641C6xZKytzUQ015KVmdFrWXt6vocPH6aiqpqolCFERMfR0dZMu+Uo2RnpJCcn9/g+2bFzF3HmpB97Giv3oZRCEgZBVAwRkdFIRAQdrU20WyrJzxvpVF5vz+/o0aMca4bImDhaWlpQEZGo9lY6GmpIHJLTWeejC/Jdyh2ZPJiWuuNEJqUTER1LR1sLbTVHiB+USXRcIu3NDTSb+0GIiRTa66tJT02mqvo4ViVEJQ9GIqPpaG1Cmi0MGZRKdW1dF/lbLUdRqoPY1EwGJ0ZzrKGtx/eo7b5sampCASKC1WolblCWW5lt81ls93RsbCzt7e2QMIh2IpDIKEQisLY00l5bSWzKIGKTBnXW/7ChWV3WFPb1egUSdzL4oktmzJjh1NQTbMVfAkw3W/tDgQ1KqUJP+YS6jR+62oTbOiA2OoqisWM7W1O+yu2PFZ0c83O0Ge997dHOlbccy7CVf9mFF3D7/7P35oFRluf6/+edfZ/sCVkgIUAggIAEQRZREAQFi60b1OVoXau0llpta+uup/TU5VvxFIVKFQUXXBAkqJUdZAmbQGKAhOx7JrPvM+/vj2GGLJOVoJ7+vP9BJzPP+yz3+yz3c93X9cQzjFv8GjFx53atwYCf4hUPcfrEkV7XNdr3gT619+lnnuXvr6/CZrWiNxj41T2hRLOe9Hf7eoTlBKPFlKsKCzj98UsMnHodJV/nEzP9TpQDhiCaKrF+/R5poyaS6iqLGuvtqeTgslf/l3xfLhKpLEI9LdUaqVv7J8YtfLjbGH/28FHUuaX47CYQRSSAJiEVd10JqpRsfE4rPrcL48VXYxg7O5QB7mvm1Jb3sNWWoB+QTeLQsdQX7sXZ0oAYDKJRKXn3Xys4UFDQoZ/D0ow/uXo26zd90ScfDfulNGMMld98jSZvARKVHm9TOfZDnxE/fi7ZE2bgqf6WU1vew9tcGTmlAB18+sDKx5jw0AoEqTS0mLjdBAN+6t9/HGNMbOS9nDYpjwEDBrD0pVc6+Nv53lv117vbWTn9ocD1XYd6PgVuB/5y9t/13/HzL5j1d2Zef5cXLWsz69rFqAo3cmjvzjbfbb1IqBJTUMRnUHLsAEPGToqaPNabunYWNnrpyUd7fUGWn7+ZnXsLiDHoGXfR6PPinSnb9QlLX1vNsPn3MyJ9MsVfvY8vECR79ASwN1C66XUGTr2OwdOvp3T7hzgOb8S8tR65PpbcmTeSkDOe4hUPdfGc25l+87lL2mimVCqxloWO9+F+Lj+2H9zWNvDdaBPCgYICaqwe9BNvQJ05Fk/daVq2rcJSXogybgCKkVcSl30xllMHsR3/CuQqEuNiKd6fT8yUn6MMglICVV8uR6JQk/rTP6JMHoy1eA/3PvIEQa+bETf/ITLBrtn4DhPy8vjys0/Ytm0bDz7wy16NXdjCflm46U20E29AmZpD0OdBkClg/E9o/PpdzCd24m04gxgM4e+379pNaclpDAYDidPvjPh0Q9F+/IEg+155EGXcAJLHXYk0fjCi20Zs1mhyrrqVxh3vMG1SHms2fsVvFj9AThTm0/PJyO8vtFz4+xcqP6Yn0ot9MkEQ1hKCf+YIglAlCMIvCE34swRBOAXMOvv/P9p3YJ1dFhUWFXW4tG2/SAydcRP2I/mUHv2a4uJiCrZt4pv3X2TapA4biW6tv7jJz1fcois5wZRRkxkxayHuY59z+JV7URVuRIOHzKkLaCjch4gEr6kaeUwymqGTSMqd2CMK5+7am5yU2OZCUbTUoi7bwQer34jkYHTW7hdefZ2EK+/BkH0xAUcLEm0ssZffgUQbQ/yV96BMykJ0mFHEp2O45Dr8ZwpoOLqFmCkLkScORCZXIE8cCAotmrHXoE4dFpErFHLn4ER5Qfjkw37paK5FmRLyT4lcCcEg6syLCDgtyBIHIY1JIXbWfaTcuYz4a39PjVvGidNluK0h/MjJz9+i7NA2Eub+ioQFf0Cb91OqD2zGWrizg27D319fhTRjDG6riR0v3EfxF28jzRgTac/5JLq1H++esLV+H3bBJn5RFBeKojhAFEW5KIrpoij+UxTFZlEUZ4qiOPTsv+1RPz9aF3Y+CR3RnLmm+AhWp7vDJHKiqKjNIpGUO5GMSfOo/3w51R8+T+DUbtInzGbNxq967cj9hVY43wWkOznBpNyJXHr/X0lJTubLzz5h1KjRlO36hOKt60i46pckXPsI2nHzsH+7m9Lt63pE4dxdew0GQ7dZ0Z2122G3o0kfgVytQ5DKUBiTUGWMQvS60GbkojAmIJPJyM7KRGWIx22qwdlch+ws/1NSYkIIRea0II1JiWQUJyclgjEFr6MtUqq/ECZZmZmU7fqEgM9NzZtLaFy/FMep/QgyGb7mKgSpHF9jOfq8a1EPHo9MpUeRnE3sZbch1cVTsi1ED12+73PirvgF6syxyLQxyI0JaEdfieXwJrQ5k6kX9Zw8dRp0SVhMzVQf34tEG0PabS+gyfsp1cf3cvx4KD3pfDLyW493OFwnHzgaVIYfhPJW2L7rUM+P1kc73yNke2rYmuIjFH38Cpohl9LgkSGc5Xznsp/T9O5/U7brExpPHWHGXTewb+XfICYN/YDB7TDMo3rNpthfaIXzJchrX4+wnKAmJpGGwn0RrLdaRiQj9cY77iVm9oMh7n27Ga8YRH/RLOr3vd8ppUJv29vd8b6zdkuk0gjCSfT7EOQqPDXFSBTqUIx/wFDclhAiJrb6FHYZuF1WfBXHIncZWo2GBqUGX1M5Eq32HC/UBUTwTJuUF0rCm3k3UkMyQacFy9738Q8ag6v0IHJDIn5bE7KYVCQyJQgCBP0oUoYQ9Dhw2howV3yL3+NAkZqDGAyg0hqQGmKRqI20fLUSzfCpSOUhWuaSYwcQ5Ar0l/wMiVyFIJGGUHiX/AzX1nOQXOgbeqf1eNc3NCLTx+FrqkQTn9In9t4LZRdsx/+fat+XaPL57nDb00uc/vgl8LpIvOyWCN7bYrFgSBtC0OOibG8+yjHXoIhLRznmGizF+4jPHt2mzL7s+vqL3+h8CfI6kxP0le6leOsHKMdcQ+JPHiVj3uLIAqvXqEIqW02VyIMeBmcOYvz0q4gx6HuFBz8f7Hdn7U5PScS0ZSX28mMgkeCqOI559xoGjJuBefdaHBXHUcikVBUWULrpdeQSGDo4E7FwM6KlNhJailWCunQ7SUo/ep32guPXd+4tYNj8+9ElpkHQh0StQzv6SuxHPydoN6HLmYRMF4/fXEvQ74FgEBDw1p1GptaTmzMMVeFGJIKAp7oIhVyOVBoif/PUnkSQSPA1VYIYxNdUif1IPoIYRKoxIopBRFHE73Uh1RhRys/tg/siAgNtx9vtduFrrMC8ey1Zk0Nqtf2JxT8f+3HH30MLIzMKi0+iSRpE9uWLcBvi+kxz0Nmtf/g5RcUnCQSDSMQgI4bnUN/UzLhZbS8Pe0sB3Z77v/iLt/HUl6JOHQb6uAjnu0SpZti8+3HKYwj6vaDUYbjkp1TsXkPcoNxIIlJfdn39xU3eW3GLzqiV//jE0xwuLQNEBiQn03jgMzRTb0UTk0hyUiJGoxGzTseLy5YzcsQI3GqRmIHnNIjMFd9i0OuZdc0CThQV4fX5kUtCzJCtx7Wn7e3MN9qImxw83Eawp3HHO7z68otnkTfLsVrMIFWQPuUnpE24muIt71H36QtIRD9ShYqs6def++2nr+DY+Sa1Z5FdK15e2qG+d904n483bKJg5WP4A35kMjnDhwzmQEFBBNXz3P+83OFyvSforRNFRYyb9RDpZ5FM9Q2NOIWBWPxudCol3lN7iMuZRPPB9SAGUWaMItBSi3nnanS4ee6JUH1LTp+kess/YcYv0GXk4qk9ScvWN0jKuRhnwVndiLgUcmbeSNGHLxMrOAm67VS++zw+cx2IASQ+N7OuWXBeCLrW4928bx+K+AxyZtx0Xu/MhbAfJ/4eWDjM4sqcQfq0X+M31XBq+1pyrrg+suvuD14agHsfeQLvsCtJuOR+/OY6LLvepriqCTx2aoqPkJ577kK1r04UDhdkTZ5P8da1MGVhCMFRfRKh6msUchmpOWOx2R0guFDFpaCMS8Xy9XsUfbmWYDCAyhD3vaoI9WZC7azPF82biUtQMu6OpyOLR9XKx7jookltoKvhBfaVv/2lw2JTvnkFLpuZhozpMH0GUks99iObqFFnttkU9MQ/WidwtefKWbPxqzbcM6WbXsd0YAOjRo1m0byZkck0jG6CkHDKgZc/RpM0iLE3LaFk2zpUo68ifuykyKmxM2RXe/4e6cBLUFqDJJzlobJUHYqgoFpz9YR/G63P737oUSQKFYPm3B35zHbwcMSvjUZjaKGt+Jbh06bx5WefRBaPQx4zli0rCAYCSKRSMgYks+yl/weEIJ1p1y5BeuJrKj5/lSaPE61OR5pRR9rlN3SAZQ4dnEnjllUERt5H7KSfIaoMBBwtOA+u7zBufbHWnFu/eXIpCp2RYMD/g1DeCtuPE38PLBxmqbQFkSm1yFOHwZSFnNnzERPueLJXu+6uxDAAhNw5KFNHIlGokWmNCJfdhmXn20gkAqWbXken0523fFtrOmGAM3s+ovFsPPul118NsRpWnw7F/rNikClUuGpOok8aiG7UDIo+fJlLJ17Sp536+d5V9AUj3Vmf//31EAdN6881SYOoKDxIzNRZkd+HF9hoi43c58CfdyPS9NGhTNm4NESg5kg+o677Za82BfUNjT2qZ3puHjqdDlXhxsjJJxo8Nj4+ngl3PRf53beb3iAua0wk+xW6PzWG+674i7eJmbIowkNVt+UwmuHTOP3VWmy5t1H8xQckDh0baW+0PneiRJ87p81nA6de16Vfd7dozrpmQeQ5MQOHM3zuHRHMfaenwqce57GnnkOmMSIqg8j08WiTBqLS6Ggs+Iic2bf0Wxy+ffbvS08+9r3H9+HHGH+PLHxTHxaWgBCPt8tU1+tdd1cojzNlZWBMAakMhNDQKJIHE3BZERHQ4OkXHvzWcciEnPHkzL6FjIEDIxeU4b9bq08iIOCqOYl591qGXLaAUZdeQUpyUq/inq3tfO4q+grh7I6DpqFwH/tW/oltf72LYMBH3b9XdhrPbh/7rW9qRpM1BkEqRxAkCAoV6kFjcDRV9zqe6/F4uqxn+8/PlJV12Z/t262OS8HfXB1BLkH3p8bOeKjcDZU4y4+hu+R6FPHpHZAx0frc67CG/LuVZU5dcF5+3dX71JV2hNVmQ60zIJfLUWv1SKXSyDvdH3H4sK/qpt/J9D+tYdwdz+AUfzj77B9OTX7AFt4hJycNCFEz6OPwNVYg1xh6vevuDuVx0lIH2viQXJ0gwVtfilRtQCGTMKqfmDm7C5OE/731ngfwTk/FWfAROVdcT1LuRMwV355XjPJ80Dh9lQ7srM/1BgNluz6h+vg+YqYsJC4lG0dlIf4tK9posHY9EQn4m6sR4geBGARBgs9UA4LQ601B6wSu9vXszGda92c4Ru52+6jfuROlRkvt0l+gTcoga/J8sibPp+jL1cSOv4ZgYFiPTo2d8VAJUgn6CT9FNWAoIHRAxkTrc4XWAJY64BxIwFp9mlGjRvfZr7t7nzo7MRj0elx2Kz6fD9FhQ6nW4q0vQR2X0i9x+P7Q476Q9uOOvwcW3gGLllrSkxPxlh2hYfMyBhllvd51d4XymDYpj+bda3GWHsLntOKu+hbzjrcQXdZ+VyfqDrUwd+4cVr/+KgqFgpzZt5CQM75f0BznqwLVlxyAzvr8V/fcQcWuj9Ff8lNksak4mmoIyNRoxl/Ht6dKehRKGjo4E9OO1QSaygj6vXiqizHvfAul1tjrvtJpNXzz/osUbNtEcXExVYUFkXp25jN6rYav//EIW/5yJwfffApbXSnu8qOgi0c9+Rbi5/8O5ZhrKN76AfbGSgxSH/HlW3q8uw73XeLQsZh3r4moaAkCSNQ6JGIAURRx2q0EZGocdlsHQZZwnTV4EAs39ys6qC+oqfz8zTS0WPE7LQgeO35bM9YzRzHtDLWzPxBLF5pd83ztxx1/D6z1DvlMWRnDMjNZ/s9X+rRyd7bbBliz8SsGTbqa6iOfY972T8SzqJ6RuSMi3C595QEJ88SfKi0jEAigUCjQqBQR9Enr9um1GgSJFKvNxm8X39/t7rc3dWodd3VbTZRsW4ezoZzcnGHk52/u0849KzOzQx0W33tnt30+d+4clq9ajVxjwGVuRKaLQ6JXIDMkYtq6ioMWFTfc/SuSdQoEqYwwn88nn30e4d1Jio9B6Q9g37MGj90McjUBWxM52YN4/uy4zrpmQbd9k5+/GZPFRvqE2TSe3E31nvcQvS4GJMbx1rvr2gi+tPaZsECKLi0XX0st5l1v42uqJPnah1GnDSNoa8ZtqcPjcnDyi9WMzs3luSceA+CPTzzNDbfeCYikJCej12qwnS0/XM9wXf/4xNN46s/g+OQvSCXSEAyysRyXKBL0J+N3tCC4baiTMrn3kSeIV0upPllMSdExxGAQmVzODfPnsGjhwjZIqmFDhnToi9Y+1R4pFeZTao/O6uqSv71vNDU2kHXtYtQGPd5j72GvKyfg9yH4XKQOHcCSTn4f5uFSyGWMPMvDFfar9uN7odk1z9d+nPh7aF1dMvV2Mo5WVutLqsHTrwc6EkP19WI0P38zdz/0KNaAnNh5vwNDMp7K49iP5lOjzmyDtohNN1H81fvoxs4le/QEJDovLkHJK3/rSCrWlzq1nkjCYiUjc8eDvaHbtnR2Wbdo3swOdaisqWuzkHQ2fiNHjOBkSw2azLH4gwKiRIq34QzyhAy8jRVoJlxPzaGN5N35NDXHtvCXf7yFYdL1pEz7Nf7mapp2rEbhN5GZosQq05wd/6WdIls6a+OLy5Zz/cJbGDz9egZPv56Gwn0UfbkW/5hrGHXpFZG2th6HWdcsYNCcuxGMAyg5U4ZEqUE/7hpM/34d3aBRIEiwlh/DU7Kf5Jl3IcoUaI1y7n3kCZwtjXhVcSRc+yiCLh5LxQnqjn3OiCujw5RdgpK8u54DXRIlxw5g2vkOlv0fYbj0RgRJGgG3HeveD0mdcBXOgITiHasJaOKJv/wXKFOyCTSU8NGWNyJltUZStX9WuN/UF/8ERcbVSJ0WbPs/pERIYc9rq8mcNJecux/qEc9T67IkWXqO15ZgKv+GIbVVyCbkMfGukFRnmHSwfTmtSeRc6jr0rdT1wu9N/NSFkbJvvm8Jv72roNdw4+/afgz1nKedL2dM2HpyNOzrxeiLy5bjREnc9NuQJWYhU+vRDB6PZtw8Gk8dwYkS4SzaonzvJuKm34Y2+2Iamk1IFaoun9GXOs2dO4eExCTG//xRLpo6i5i4uB7/Ltpl3c69BR3qINPF9ejCeMmD99G4cw2+xgqCAT+e2lNY9ryHIW9+iM44cyyi101Ds4nGU0cwXH4H8sQs5Cod6rQc4qbfRlCbQHx8fIew2WNPPYcr8zIqbUFOl5YhGAdEbWN+/ma+3rcfp6mefSv/FMkcjrvsVoTYtE77NewzRqMRnVaLJi6Z2OGTEARw15UQ8Llxntob4eNRqdTEDByOkDsHm8tL3PTbUKfl4Pd50Awej2HyIsr3burwrNZj3NBsQpt9McapixARaNn6Br6Wamz7PkI7ZjZufRqarDF43fYQhcKgUUjkSuQDcoibcRcfbNjcrb+En+eUxyA3JqIbNJqYKYtoPHmYuBl30XjqSI997cVly1Ff/BPMgh5RG4dxxDRipiykdOdHBALnRNe741pqPHWEmCmL0A0ajdyYiFMegxMlwZxZbco2TL+D/3l1BUC39Bvfp/048Z+n9RfpWE9i310tDl1lFJ8pK8PrsKJKyUYURRAkCHIVspgUXKa6NmiLMHojrCDW+hnRrK+xzL7+LtrdRLSypApVj+Kpc+fOISc9Eevud6hf83ssez9Af/E1SDUxSHVx+Ew1yIxJ2B0OLHWVyJOzCQbPTRiqlGy8DmuHZ4XVsKRpIxH0SbgkakrLK3FIdW2+G944xM+8G3lcKpq8n1K8dR32unJk8SFBlM76p7XPJCcl4reZcFQWoklIxbT9LRwlhwg6WiJ8PMlJiaEfGlMI+NwRlI7o9yFRqJAnZuIy1XV4Vuv+9Xi9SOUqpMYUBEFAmTIUmSGJhPkPox12KcFgAH9zNQSDKNPOqqMJEkRRRJM+Ar/P1+24h58Xflakny1NESWtzn7b3s6UleGQ6pHp45Ap1AiCgC7zInzmOrwuR7f3AmE1L9OZ4zTv+QD76YLIu+F1WAnqk9qUrc3IJSjIIpe4fcn+/S7sx4n/PK2/LnF6cknV2eJg0Ou7PHVkZWai0Bpw15UgCAKIQUSfG785lM14Dm1BBL0RVhALP6Oz2GR3C1ZnC9L5Ui50V4eA193jsp5/6nGSYrSMuO5BYqfdgiBTYtm9FsWAoZh3vIV6xGVIZAoUcan46kKx/fBu0V1XgkJr6PCsF5ctRxmfjquxAiRSpCodUn0CVUWHMej1rSQW78SVeRkJ2aMQgwHkCRnETL6ZgN+H88zRc5N1lP5p7TN6nZYY0YZ1+yrUPivZcQpSqrYheOz4Ko6d490BsNSFIJl1JQAIMjlBrxtfY1lEpar1s1r3bxjS7GupRaLWo7toFgG7CU9dCaLXhbepEtOO1SExmeqzqmRiEEEQcFYVIZPLux338PNaw6fddSUojAk4q4o6KGl1Nc5ZmZnYaksiCwhAwFKPLjENv62py914WM1LOXYeKQufR3fxPEwFG7Ce/BqlQoFCa8BnqmpTtruuBG1i+g/mErdyTL3EAAAgAElEQVQz+3Hi76E9/cyzJGRkozQmkpCRzdPPhGKD/TWBdYU5Dltni4MYDHR56pg2KQ9XQzm1n/0/nKUF+KxNOEsP4jy8kcShY9ugLQZNupqG/GVUvfsEVR8+j72xijOfvtIpyqGrBaurMFh/cthEK8tvN/W4rHDfJ1Rsw7P5fzBteoFAcyXUFRFwWhAIQRE1A0fRsm0V3oYy3A4rrupiTNvfioq4OlNWhjb3Mqxfv4+nvgRRDOBrrsKybx3mluYOerImsxWZVIrEZUGUKRB8LmTFX0R4dKL1T3ufSajYxrvLX6S2vITD+/dwaO9OPlj9BuqyHW3KEQs3o1crMG1/C1d1MTK5EmfpQax71jBo0tUdntW6f5Pi43CUHMJW8AlBtx2JXIlEqcW6+x2q33gQ957VGKQ+knMnYtr6T1zlx0MqYLXFmLas5Ib5c7od9/DzND4zPksj9vJjmHevIXHYOExbVpI4dGyPfWbJg/fhLPgER8VxxGAgkpOSNnY6Oq22y934i8uWM/jqe5Dp4lCo9Uh1cegn/BTLoc1ofGY0eHAdXN+h7MRh47rd+Hzf9h97udufClZPP/NsiEFwxn3Ep4/AWVXE0tdWAr3njOnKustS7Aydsvjh35Ma5dRRvCkUAlqz8SuG3/wH6qvLaSlYj6+lFqVcjkGnIdVVxt/O8rM89tRzFBYWIqoMGPIWII9PR1DpMTvcrFm7Nmp/doWYaX1hDW2xzOFLtP7QL41Wh4xpd/aprJiYGAZmZCAGA9gcTqoazHiP5dNQ8BHquBQyx19B/YnNmD5fhlSpgoAPQWfgFw/8OoL0mDt3Dnqthuqj/yZgN9O08UUEQYLMkIBaraGhuZlBo2dT/MXbeO0t1H70F4zjryYQCO3Ivc2V6Iwx3HXjfD757E0Ot1Luitb23vpMWDv2wd88TOWHzxIMBFAqlaQkxtOya00b1FAYkaTXanDsfBOrzUaKXo/b04LF6aZh/VL8E54gaG1AIZOhUiqwWVpwVW8lEBRp/HQpAqDVann0wXt5/M9/iryb7ce99Ttr0OsRD76Pr6kZj8+PWgLZYh3/de+tfLxhE9uffZ/2qKDO3vlrp6/lvQ0vgkyBIiaZpJyLqd/7KfZpg0NILqmU9JREXn35xTYXzF/v20d82lXI5CJ4HUiCfmRqLb6GUhIqtvH8yyE6jf95dRkmQYY2MZ20URMJVB5lyZOP9qsoS3/bf+TE398d/vfXVxE3476IqLdu0GiYcRd/f305TX/+E/DdCDBD9Bfd8NRzUUWyszIz29xBpOfmwayfRZWRy8/fjFOUoRmQjWbCz0ATi0RjRKpSEXP5nby74SWGzby5DZqidX2itfd8qZPPp1+2bdvW499G85cwgubFZcs7yPDJVBqqfB7SJ8ym+vjeNkiPMLdOVUMzoqhAkKuQaAwEnVbwOkibeBUlX6ym+vheYqYsQj8zGVvZN7Qc2EBg1hCc5kZcJ7aQNnE+K9/fQNDrZtwdz3SKgOlL34TbLDcmMnH+Qx02LJ0hkkJ98hcOFBSw9LXVpMx8AMGQiFQlIyhXkXTZz5HEpeO3t+Db8wHGwWPxVR0nbdQkApVHmZCX12V9oj1v5av/rwO0cs3GrzqgglrzGbXnOTpQXMHQmTfRePIwjsYqqra9h0Qfj9SQRNr9b+CtKaZ6yz+59e5fsnrF/wIh/h9FfAaC34skNgW/zUTmoIGIllpGTJsaeXfmzp0TkaA8U1ZGqqssAgftauPzfU/8F1Rzt7+st5q756uZ2d6UxkQyH1iFRHpunQwG/JS9egceS2OPyrhQWsGtoZpxl92KLD4N55mjyIq/4LW/PsXih39Pzt0vd6h7WC83bOE+O/Lu3zBevQSZLh5BpmDJ6AAvHA5Qu/phjAMGReBvPenPrsahNcdMtInnfK03/d3TeobzDmy1pSRfdR+uwu3oJ90Q4a+RuCwkKf1889ZTiPpkDFMWIokbiLfhDJZdaxBcZgZmZVFbXoJh9uLIRsLnsmM+dYA/XJHK0lUfkzV5Pkm5E/lm15fYjuS300Doux+3tnGXTKbc4sfntKKOSyFr8nwUOmOk7K765PA3x1BfcW4jdE9KOUt3m5GqdWgSUvEHBTz1pdgOfEz85BtCmd+zb+my3q2fF85AtlafxLf/vTZaB53Vq+jdtnxGXX3+xZM3Ezvrfh6bP4oXj4feC3fFcUybX+Gys6py7tx5eO0WireuI2bKQmRxqfgqjqEu29FjHx0ycmyP3r3eWn9o7v5Hxvj7O2tObzDgrCpq85mzqgi9wdDJL747e3HZcrKuXcyIWQtxHvyY+nf/hPvY58SrpW0SSVpbtDuIcJ+p41Lw1p9BkKsifEGe+lIU8WkRNEVD4T6KP1/Njl27u4xbdhXH7y801Pla+EhfYfFx8tRpLBYL0JHvxb79DU5sWIF+7FwU+jhUmWOxN9cgT8wEiCA9DGlDcDgcqMfMBYUW0WVBkZCBcdoi/B4HLz35KFqNGqnGiN/rQhRFBKkURVwaEomUiXc9GyHPu1DKV/n5mymuasQw5ecRBariretwW01RkTztn22zWtGkj4h8Lgb8qNKHI4pBpHIVoiiiSBmCz9LQY/6b8PPCqlVBtRFDzmRcftoAFbrjXerJ5wGfG/mAoW0+U6aNIOBzRzizDGlDSMqdSM4V1+Ms+Ii6tX+i+asV3U76rWP6ZquNsl1tF7ofShLX9zLxC4JQJgjCMUEQjgiCUNDf5fcnYgTgV/fcERG5CAb82MuPYdqykl/dc0c/1LZ76w6qGXbSiXc9y+WPrOTS+/+KzeEEep7SHu6zrMnzsR74CHflccSAD9Hnwfr1e6jShqOOSwlN+lvXoRw7j0G3/qXLvIWuLqx/CCntEdrcs0f61oI07fleWucdaBNS8ZtqkMek4Kg+CRBBQZUc3QuCgCJ9JLLYNCTaWIJuO+q4VBRyGXPnzmHkiBHECk4kLgvupkokLgsSewOCtF3k9QIoX+Xnb+bWex5AO+E6AnINfo8LdeowYqYspGTbuqhIHovFwslTpzm08wvMVhsqtbrNRkiQynBXfYsgSEI8PkJIKEVuTArx+/SA/yb8vLBqlUyhxlNfijYpo82GoLN3O8xn1JPPpXIVvtpTbT7zVBchlavIysxs84zwezVu4cNcOvGSbif91mCGjHmLKdubT+n2db0CMHwXF8Lf547/ClEUx0Y7hpyv9bfq0eN//hOP3nsrrq3LKXv1Dlxbl/Povbfy+Nn4/oW07hLEulvkeoIWgnN9ptAZyRgzlebNf6fmX78m6DSTPCIP57c7SRw6ljO7Pw1x2+jiSElJ6Xan3hmWuS+Lc3+/EOFTx9AZN2He8y6+pkqkWiPlx/Z38JfWC1XW5PmYd69FO2g05t1rI/w1/qZySj97HYlKj6+5EhERQaZAqo3FUX6MYUOGkJ+/mabGBorXv4q7/gwZA1JIUvqRfPslEoJtfLa/lK/C/TZgUDY337cEh1fEOHwKgtqA22rC57Iji0vF2VDeAclTVVhARVV15O4heeJ85DIZzf9eEdkISeQqWrasQOVqwmdpxFdbjHnXGlRpOSE0Tg/4b1ozwkqk8ghCJmvy/DYbgq54lxp3vEPp9nXsW/EYW56/nSOrn2HuFVM7fF+vVmDZ/i9Er4ug3xcK82z5J3q1giUP3tft/NGZH7Y/xabn5jFs/v3U79vQ4ySu/koI7c6+lxi/IAhlQJ4oik09+X5vY/zQv6ie/rC+xvi7u69ofSnW21h5+z5qzYMS5uu589aFrN/0ReRvO3btZtCtfyEl5RwuvC9xy97Wu7ff76y/W7e5rr6ecYtfQ5BKObnvS6yFu/DbTIhuK3/+3UNtFvb249BQuI9TW97DWVuCzhiDz+PG6fUjIhB35b3YDm/COOkGAk4L9iOb8DVVoFQo8IkCsZfdRtBlw1m8C7+5lpHDc3juicfw+by88tobbXw2pKq1CpvVit5gYO4VU6lrtpxDvpxFH0Xz8faqcUGvC3n2RMyHNyORKpDHp6EZPg15XBoyewPx5VtYcM1Vkefh9xCQyBGDQQSJhKDHhVStR6WPQeY243B7CSDw12ce55E//PksnTgICOD3IsiVAEgFGDE8J8I3Fc33wpPqrfc8gMsP2qQMYjOG0XSmCHtDBaLXycicoTz/1ONAdH6cp595lhdWrkGTtwD9gGy0ARuuQ+tZNG9mB36fNWvXMu7ii/ndY08iSCQkGHWsWvFapH5PP/Nsm37/1T13MCEvrw3VyMDc8ZiObQkR/WlUWK1Wxi1+rY2IT2/fjZ7cT/ZHjP/7mvjPAC2ACLwmiuLrUb5zD3APQHJy8vh3330XALvdjk6n+w5r2z/W13ofP1GIKnFgh8/djRWMGhmSALRardQ3NOLxeFAqlSQnJWLo5v7BarVSWVOHTBeHVKEi4HXjt5vISE1p89v29T51uoSgyohU0SohxutG4rYwdEh2r9oWrrfL7Q7FugG1Wh21/uHnisEAHruFYMCHIJGilMKI4cM79EFSYsK5hKVO2mxvrEKi0ocmKEEKEgmi14Xf2oBMJiMzIz1Sj676C6CkrByJNo6g245UG4sYDBCwm0AUkepiEaRyxICPgNOKVGNEkCuQCBKCPjdyr40Rw3M69HX7Z/rcTtzmRhQ6A7Kzu/WA24aAgCCTIyEYqXP4twGFDkGuQvT78FvqQSpDposDiRSCAQIOM4JcgVz0o1EpMFsdyAwJBD0uAh47Ul0CEoUS0efBb2tCkCkR/Z4QgaBUjlQbQ4pRRa3FQ8DZgkShIehxQDCkayuRKwg4WpCrteB1RforWl/GGfWYLVZcbnco7CWCVB/fqq4tSAWRxLhY7A5nB3/vqW+G+yY5KRGzX97B96ONtdfahCgGCSJBqotDkCkIuOwE3XZkulgEqRS/tQmJ2oBSrY3o/vb23ejJ+96bueSKK674QU38qaIo1giCkAR8CSwWRXFHZ9/vy47/u7buThgXasff13r1tNz29T6fE0Zn9bv7oUdxosTrsIYSpfCw4uWlbcobMnIssVMXcWr7x8RMWYgqJRt37WkaNi/j9/ffHoHyhet0c3aQwYMGtikj2q796PrXiLnsdlQZI/E2lmHZvRbd4IvxlR1kfDv9g676cs+hY8Re+yi+pkps33yB8dKbse5bh27c1ch0cSh1MXjsZoJeF9b9H5N4zUMEnGaUOiPVK3/J1MmT+cnVs/nnm29HmFHNVhvJE+dHSPtOnjqN09yI5+hnyDPHYz6xE8PEnyHVxSLzuzFtf4vsOAWH9+9h1jULaBp4OQ1eOfKEgQiChNo1f0A/fh66nMkEnFZEnwt3zSlsO1bx8Zo3ufWeByJonZJ/3EPc7AdQpA5DkEhBkOCpPEHzF8uQ6RMRAz5iLrsVqTaO30+N52/HZHjqTmHd9yGGiT8LqcbJVQy86XFcNSfbIHuADr5Xun0dVQe+4KIbl4AuiZOH92A5nI9+5BUYc6ciV+tw1ZykZfubBO1NjL31zx38r7cIttsnpLC+PqaD70d7N8IIK5/TStptLyBIpFR+8AyGCddhGDQSd1MlyYKNoi/XEju+LbFeb96N72rH/73g+EVRrDn7b4MgCB8DlwCdTvw/dLuQiRphOKEpY0wEhywR/fzugbvPq159xdh3lbDVF/vjE0+HoKjTbwlN5nUlmLa/xR+feLpNmVmZmRzcto6YqbeExOEBeeJAEqct4u+vr+ogoSjT1XXAS7dvc1LuRPjgZSz7P8K84y3kxiTi8uajHTyOqhNbOVN2Tqkq3PbO8hW0iengaEGeMBD9RbOx7v8QT823GC69EQkiCl0MHqcdWVw6fmt9ZBfuri3BL0pw585DqtdTYvJGmFEd3+yl+vgWdIkZJOVOxOP1okkfgXnrP3E4dmKcsgjlgKH4mitRpw7DMOkGTmz4G0NGjqWuvp6kjLlIZBLwe0GhQvS6kBmTCXhcSJUaZGo1UnEwitgY5s6dg81qJf4sWifocaJIzQFR5GzwBkXacESPi6DMhigGkCdmEbA1gyCELrSTBhOwNqJIziboshF024AQz07zWWRP2L/a+17jycNo8hZExlCdMhhh2s9xHNxAwvhQn0uNyTgaKyEYoPiLtyOw1zA2vqdUyOf8wB75rHXdor0bYYSVJv6cII3f2oQ8MTNysZ80dCLBYICiD1+m+Pj6Pr0b3xWr53c+8QuCoAUkoijazv73bODp77oe/WkXUm1n7tw5HCgoiMQu0y4LxS7XbFzPhLy8Lsvvql7nwxfeXbZob+xUaRkJ1z4amczVqcOQGJM5VrQXpTExEl9d8uB9XLfodpSWBpr3fIDPXI9UF0PmxKupiwLZi0bS1pkqlHL0TNRZ45HKlcjl8lb8O6k9akNWZiY16kyqCz5Bl7cAeUIGmpypeOtOYRRciBojAZ8bqVqHq/woEpWeoMeBz1SN5ev3iB0zk5iBw/HaC4mbfhvyhAwamk0Y0oYh08ZyZs8GknInolQoIlw11voqFEmZiD43gkyOz2VHVBkQFBpy7n4Z0z8ewVZ7Bt2gUfjsplCGqsaA31yHIJGBTIpCpSJWcJIwIjTZ6w0GTPs/wV1dDICrZD+qjFEIUh0iAt7qbxGUaiRqPQT8+BrPINXGhhYHUcTbUIrUkIi3vgSJWo/kLIdNNGRP+3FwNFaRdtm5cIhSoSAYn4HZXA+Ax2HFduYo8thUYmfdh8zvpnjrewAk5IyneFMZr/ztLz2aNCPAgtTonD/R/CSMsMqaPJ/irWthykJk+ng8daeQxCRFQlgqQxyXTrykz3kW/b2x6sy+D1RPMrBLEISjwH7gM1EUfxgEFn20Cw1N3Lm3gItuXELe5VeTk5NDem5ejzDvXdWrv5FP7a3nCBwRWXxa5P+adq7FVXOK+GuWkPnAKtRX3MfS11ZzoKCAtORELAUb0Y2/lpRF/0381EVU7t2IUtGR+CsaSVv7NpduX4fXYcVxJB93xTcEfB5sFYU0bftXrxTPljx4H4HKo6SNmoj3m020fLoU97613HztHCj8PMI5E2yuwH7oU4JOKzVvPIir4COC1kZyZtwEQDDga8OMmpyUiFRjxNFQSTDgR+MzR7hqNHEpuKsK8duaUWqNeGwtBBwt6JIGIpHKGDrjJhxHN+OsKkKpNeAqO4K/pRbr3g+QWGsZnTucJKUf16H15/SEr5iK9fg2tOPmETvzLmyHPsNdcZyA04Kn8jgtW/+JKm0kottGwNGMbfda/OZ6xKAfd8U3WHavRZk+EvP2Nwk4zGiy887x7Awdy5lPX6G5uZkTRUUcWf1MG5ijRPSjDdgifZqclIi36jiCQhWZ9O2H89EMnwaiiKhPQjfxes7s2RCZtHuLYAt43VH5pZoaGzj4zlK+2fUlZpOpDcJKoTMydPp1WHe9jbe6CPuOfxEj2tDrtP32Dn0XrJ7f+Y5fFMVSYMx3/dwLaRdCbacD+uSSO9v8vSdhma7qdSF3Fr0JfQ0bMoS6M0fRZl+MVK7CcnwLcbMfQJWWg0Qqa0OPMTAtFVf65WjThiCVqwgkDUI3di6aog007ninTThsxh8eYtqktqHN9m02W22MuOG3KAhw8st3MG9ZiQio5VJWvPVGj/uidbmegIUxF4+OxP/D49hQVITH50fj8yLIFQSDMiRBNz65ghNrnmXojJuQjNbjritBnpCBUqHAaDQSW30KuwyKVzyEXqthgFFN+bb3CASCOBv+QcLlt+MjgLv+DJ6i7YyYeSMQCmNl2Wyc/HQZdq0RbWI6w2beiOXYVpKqtlO8YmOHMa9rtpA19y4cMgMSlRbd8KnYD23A9O9/IAgSgl43Uo+VEUMGc938q/l4wyaKPv87vkl/pHnjiwT9Xnz1pYhBP0mJCUiKP8d1PMSzo6jYj0ShQjvtdsalDaGm+Ailm17HdGADo0aN5ncP3M2ajesx63QY0oYgWmpRnPw3qeogFSvuJyhCzNRFaHKmgCAQdFoQ9InY6ira7Op7choN/72qupriFc+24Sb6zZNLSZx+JyPPZmkf+GI5uTnDIvxGLy5bTm1ZGeOHZLLk5afP+dOOf11wupb+tP9Irp7v2vo7Ltd+4jT94xFKjh1gyNhJEaRKTzDvTY0NFL+zNAI9w94Q9SUJT06LH/49WcuWt4F1PvqbxezYuYudewuiSs91xmrY09DXc088xr2PPIFXKgFjSii2nDIEhfwcD70mfQQNVitWg57s0RNoaDbhtjSiVCjIHj2B2qINLJo3s004TBWj54VXV/LmO+9S32Qimrzg8ePHSM0Zi0Qqi2TLhi8De/vydjbhdMZL81+//gPkXkVMzAB8pmqOrH+dhRf9joav3sAw/hqGXhQStncdWs/q118F4N5HnkAY9RPip6SApQ734U9JrNiCrciJaLWRMXH+uaxfIG5AOqNzRxAfHx/hkflbu0vz1naiqAhVxlyCPj+CIGAcMo4Rk6+i9v3HOX3iSJvNyM69BRF45qeffsro3BxOReQUQ/KOrX3r6337iZ8ZUguTSGXo04YSM+kGmr9aEXm+WvRweNXjBAJ+FEoVGqWc+ORkqKkL0Tqf3IPMkIgqcywArtKDiF5Hn+RA586dw9q1azFbbWzbto2de75GDAbRpmRhtFtIGTWZlFGTKd2+jop9G0LvRiew8P8LE317+3Hi7wfr791z+4lz6IybKPpyLeVSSQe0QDSLLBxRdi4vPflYB6x360WmpvgIS1/7R0Tezq9oYOlrfyU+MxeXOjEqIVl7jHRvLo7nzp3Da2fbfOb4emQyGcGGEqRZ5w6FYXqMrMxM3PYGhg1ti3jIysyMhMPCfSZQjcnmAoWGpGsfRRIzAEvpYWqPfEbuVbdQ01xDs3UfXz13K4bU7MhF4XeRUr/44d8TzJ2LZvB4BLkKeWwqCBICbgdSvwvrtlWUHl5H7ogRET+6eNI0/Dmz0WaODZ12YlPwBYIIVdsik/JvnlyKuWJUu83HYz3yw/z8zZgtNnT15WgGjweJDK/XxbeH9uBtaubpZ56NSoIWtmhyiq2J0zj6LfKBo6msqcPhdGIyW5EPHA0qA00DL4/43IhxCyj+6n20Y+cSq5Jy6utPMV5+B6pBY/FUFWI98AmiGESi1mPdu454g65HhG/tT5xPP/MsemMsQs4VKMqPoZ/wUwSVFonbRvHWdZGyqo/vRX3JTeRMm/2DYtc8X/uPJGn7IVpv6p06OAfd1Y/g9QcibJuuyhMUffgyKclJ3Sak9QYC2v67rSGDE+96ljkx9fzly1Ia85eRcu1v2xCSaXxmTn/8EgOnXtcGcZQcH8uAqx/sEwT1HAX2XWjOUmCbtqzk0XtvZUJeXqdQ0vZQviulhfzxhdcwXHozMVmjcbtD8Vx3+VG8Rz9DlCnR5S1AVGiQ+N3Y9n9I2qhJ2Iq/Jl4t7TQpqj9MbohnwJ3/i1StQwwGAYGAx8FDg+rYJhsfta90CQNIueMV5Kpz+G2f207dqsXYm2qBrkXBu2vDrGsWsPdoEQGFFuPUW5AnDMTbVIFl52pkPmeIunjC7Ai0FM6N6Y3XzeetYrFLgrR9K/+EJu+nyBMycJvqUMWl4GuqxFnwEbHT/yvic0Dke7Uf/Tf6STciUemQaGJAEHBXHKNly0pkhiREexN6rZoYg77XUOWEjGyeeOZ5/rh0GfqJP0M5YCgBl52A04w84MFZ8BEAyjHXoIlJZNjQIVHL+T7s/yyc80fr3PLzN2NzupFaTWgHjiLgc1NZU0eMRNFjtEBvdtztv9saMggQDEvmeRwRqT6pXIXb0ogo0+MLBKk+vo+YKQuJS8nGUVlI7eZleDevgDl39yr0lZ+/mZ17C1CLHho3/I2A14MxLp5H772jTRZt+5MVEDqyL/0F2qQMsibPJ5irwu+0okweDIAoikgUIbnJlqZqUn/2GKoBQ3E2lKPW6fGPnEHVrrfRG2PRzVmMzGri4LZ1XLfodnJzhkVCF/1hogj+lhqk6hCSCUHAb6qBQSGsRfSxEvA3VyNPy4l84m+uBoTI/4fr135x7Mku9UxZWSjr+JIFmPasxW8LIYE0OdNwHd6AbuJCGk/ubjPxh+vp8XgwpOW2Ka89QVoYDRMz+Wb8QfA1VmDe8y45V1xPfTufi0vJBkGC19qEMmUIot+Lz9aMVBuLatAYJDIlkqCfoERKxrzFpOaM7TVU2Wa1IpGr8FkaUJz1EYlCjd/aiKCLw1xeRDDgR+lwYpw8DxjSxdiE7IfGFtCV/Tjx/8DsxWXLGTj1Oqr3f4RUrgxh2+0mSrev4vnlL3ZfAL27bG7/3daQQQBJWDJPqY3gl8O4ZWv1SUAgZsrCCBxTO3AU/mk/J75yWwhd0cPQV+vj+cRZD3Wa/NI+Zh7+Xca8xbSIGgJOC8VbP4DcW5Eo1ARNlaAZjiAIBL0huUkEAVVKNgGfG7VKxbChQwgOzmT79lVkXbsYr90SShSbegtxcak0Vxzr1yO+RqWkZcdbxE6/HXliJt7605h3voVw8X8B0cdq6OBMSnasJm76bahSsrGXH6dpyz/B62HWNQsik0xfocVZmZmYTteATEnM5XciMyQhkStwVxZiD/gRfC4cjVVtfhOup1KpxFrW0d/CBGkxA4dH7h5ObXkHW20J+gHZ5My4iaTciZhPnW7jc+ELbpkuDm9DGYqEDCQSCaLLirvuNEGPHZngI/uGR0IaE/Qeqqw3GEJZ08YkvPWlKAcMRfR7EQB7+XEk+kQSrrwHvE6q9q9Hq9V2GQr8IYuuRLP/SFrm/8t2pqyMzKkLInSw1W/9Fs+RjWjw9NiBOoNqTpuU1wFi2f67rSGDwYAfiRjAtGUlSTkXY969JkJIpvGZcRZ8AgF/5CRgP11A1bpnadn9HqdKQ5DRnkLSWk9YNruDBo8MS/ql3HrPAxEoaDSIaPh36bl5DExPQxOTiHrkDAgG0AkezDvfxlVdjBDw4iw9iP3gejQxiVjdy1QAABhpSURBVDgqC9sIkIfgoAKGtCGc3vFJSIwmNoMAMkTjgH6ljH74V79EtDVh3vEWdW8/gnnHm/gtjSh0MZ1CAp9/6nEMUh/WXW9TsfJBGvL/jnZwHhN+u6oNkVdfocVLHrwPDR6atvyToKMFJFI8taewHfiImPHzcBTuQCL6o8J/k5MSOfPpK+xetoStS+9i97IlnPn0lQhxWvg3Cp2RpBgtf/7db0iK0aLQGTv43KBJV0eE4g3DJ9Oy402cZw6h1MUgC7jxFm5h9IgckpKSSM0ZG7WdPYEq/+qeO/Bbm1Cl5WDZvRZX+fGQAHtzBfZvPidh6k2ojIkImhh0eQs4s/vTLuGaPxSa8Z7ajzH+78h6Wu/W8cmGwn2c2bMBR0MlUr+LIdnZWG22Hh0joxGwtac1aB1+6Yys7dHfLKa2trYNqkcpl5E7YgQp8Ube+zQf4/Q7IeDBUbQL4+SbUCcOxFv5Dbb9H6HBw6hRo9vUN1rd/vvFV4hd8FiIHgBQ6uOQa/RUrfo1GQMHsmjezKj1tzbWkDbrF5Tv3YTLFBKPHzTpam6/SIfJZOKFZa/hcDiQSKXExxhISU6mrr4eJ0oGX30PqTljI9BCT0s9CbPvp3HnWgbc9gKCVE7Q6yJgbSArIz2Cbumqn3t6vG9NAqZSq4k36ljyqwdZv+mLTstojZCJueJOlImZBIJBlAoFGp8Z4fCHVFTXIL/kJgxpw0hOSsRoNFJVWEDlxlc6xMKjjcPz//MSksQs/PYWFDHJxFw8F1X6CKpWPsAflyzucJEfRsfc/bsnUI9fgCw2DX9LNfb9HxK0NoZI3s4meGk1ah7+1S/byC92RRBY39BAY0MDyFUEAz6kchV6tYLVK/43qjJae/LC7sZl7dq1LH7kT1hMzUgVSsRggKAgJX7GL5AnZyP6fYCA6PfQ9NEzXDZ1ShtCuNYkbqLPy6Tfr+530ZVo9n+WpK239v+niT98ZJRmjKHi0HY0Y6/GbzfhPLkH4/h5DL1oYhtYZn9ygISf3/qFWXzvnVx77bWd1tObmEN9UQHBgB/jlEWokjLx2U0hQjJzHZ5vNjH6ugfaLDKtY9Bluz7hzJ5NiDIlMZffgTxh4FlysyCCy4L3WD45s2/pVGHp6L8eB2NqJAQSpnx4/jd3sOytdZ1yCoXbWVhUhNXpJnbIxVirT+N0OJCotMRe/l8oEzPx200o1DoCtd8yzH60Q191x1vUm4Whpz6SOjgHxezfIjcmhhA+Pjeelnrq3vszQ2f9vI0cpMpjovLLVWROmkvm1AWROna2kGoEP9pptyMYB4RI77xexJZq4su3cGjvzqh+8O2pU7yw34ksZRhiMIDf0YLPVI3py+Xo865FN3ImrrIjOA+uJ04r555bboi6gEQr+95HnkDInQPGEIRVLNwcwdSfL2dU+/4eMnIsTR4BxUVXo8m6OESr4XPjOH2AjPrdkfZHAyA0fLGctLxZjLzqlkh5F+oi+EcFrv9AC2cfVu38ENXoq1ANGoOn4hixl92OIuMiahoa+nSM7EkIIBoXeGVNXdSs2/DRduRVtzBq9kIC5loEuRKvpQExGECQq1Gk5uBorMZrt0Tq2/5I3HjqCIbL70A3aibWr9/D11SBRBODt6mCpq1vRPjYO1NY8vkD6MbORZ6QAYIEeUIGurFz8QeCXR69w9mRuSNGMHDqdVib6oiZcTf6cXMJet00b34VZ+lBlFoDQWsDjTvXdDjid3e8v1Dc6l6fn4DTgkyhRhAEZAo1XnsLgkzJ4OnXk3PFDXiOfkbj+qWUffISmZPmMnj69W3q+PfXV0WtuxgM0LjjHURLLUMGZ5Khl6Au28FzTzwWtS6htgrIkocgSGVIlGqk2lhkxhQkChXeqsKQJGP2eLTj52N1+3lh5Zoe9cljTz2HkDsHITYNmUyOLCED37CZ3HTbnTz21HNYG2soeve/OfTSL3rEdd+dZWVmEnA7sR/Jx9tciSgG8DaW4zi6GTEYiHwvpMF9F7pBoyNJhglX3EnV7vUXLBO+v+3Hy90foM2dO4dAwI9xWB5ylY56ayOKAUMAAbctJGHQW9Hynlx4RbsYjEZ2Bm2RE0m5E9EmpOE316EO75T8Xlxlh5Dp4zmzZwMT7ngyKkGXy1RHbMoQAtpYVMYETPs+xG9tJOiyIpPLQ5d/Fd+2uShsHQLzB/ykDBmJxdqM0+MBQJWYTjDgjrpQtO+zM2VlWKUtkQvqgEKLftQMrIfzse56B1fBR8g1BnLSE7vsg2jPuFAcTnIJ2PZ/eO7yv64E6951CIIQGY+k3IkEA37+/cQNZE5d0KGOnS2ktQ5nRGS+JxfzZ8rKkMjkeOpLUaWHkD2CTIG3sSwU9rE2ACCRq5DFpmKxNBE/8xfd9kl+/maOHz+OoiWAz9qIVBuLdvSVyOQKPFI1demXkz13QuT02x8ImjAfVOykG7Dt+zDCB5U1dQG2w+d27WEyO5/LjsdhQfT7EAyJiH5PrwAN36f9OPH/YO0cfC+MPFDEZ0T+2ttEo55kF0ebyKKRnUHHhUQM+rEd3oTMkIgieTC+pgrsh/NBkOIy1XVK0KWOSwnJ9OkT0A3JQ501DltlEZbda9Dp9G0UltZsDNEyhEMZGo2Rln+/Rs3Jb1AmZaFJSEUqV+GoOA6CgpriIxHUR2d9lpWZyZ5Dx0IQQkCpNeK2NqMbPRPvyV2MvflhGne8w/NRoKjdLaYniorQZc2n8kRhJB+jtwt2NBs1ajQ16kwaCz6i+ey9hi5jOP46aZvvtUfWdPZ564VUfXZG6Gl4IiszE5lKg/XrFQhTFqEYMARPVVHofmf4NLy1ocUo6HPjb6lBBPQD2nLTR+uTBx5aAto4tOOuQTFgGL6mCqxfv4+3uQq5IR7T7vewffMFuTNvjJyy+oMQMTdnGM1SKfHTb42MmWipRdWSGfme3mDAfuYogiEJmS4OQa7CVXEcQab8QUM4W9uPoZ4fqA0dnIlpx2pcNScxjpmFecdbodCDXNanY2RPCKzCE1lYY/XYiUJcdisGvb5Dee2REz6XA+3wKdj2raP+nUf4/9o79+go6iyPf27SIQ+SEJAQRMAEwSjogICsCiL4WoIPhll2dVVEd33ADu7MoEdlQB3mrO7oenyMzDkozDiMgzIDMg4wCc4LBpRDUHzLy6CAQBAR84aEJHf/qEpokk7n1XR10/dzTp9U/aq66ts31bd/9av7u7escDnpI26gvqaKhJT0xqiib775hncWzeHt+bM4+MlGMgcNo2zdyxz/+guOH6ug5mAR5RtfRSq+Ib50X6PORx+Zy7M/eYivCleRPORKUjIy6d/3LM675hZKNi2nuuww8b4uHCv+jPLNK0hITuXz/JdavfWeNXM6cVpL5ZdbGwufx4tSf+BTOFYWdAghWPRIw3yMo2VHSOrZr7Ge74EdH3R6ZnBDUrjca29j7P0LnDz3R4oClmlsGlkTqFThjrXLSBx6HZmTHqLf9fe1azhq1szpUHOUrPNHUrpxKcUv/4Ajb76AL6M3VdvX06XvYOqOVlC1awuVW1bhE05KxgbNf5ALCtbw5VdH6DHuDnzdejkpobMGkDp8ItTXknHFNHpPfZrUUVOaFYnvLI8/Nofk3evplxbHwAHZaGlxwIigw2t/RW35EYhPoPrADsoKl9F39KSIjeJpivX4I5Qn5j3K3T98yMkCWFlGPErF+l+T0C2NJL+p/P60JT9JsN7IrJnTuffBx6jNvZaUnKFQsZ/aqjoOfVtGQcGaZseCE5Opkn3Qs09/qs8ZgS+tB8QlUL7nY+qrqzi7m4/JjQ8Tp3FxXi/2bt3Cp6sWkts3k4dn3M4bf3qTnetfBoRBA7J5Yv6LzbTm5U0gIz2N3MuvPRE90c3JqV++eQWVGxaT3KM3ueOnkNw9ixSqqdywmPeLihqPG8hWWWd0p3jNfGovv5W0M8+hR9xRju59m4WvBE/UFixVxzXXfbfT8zHac17/JGJNtVw8cmSL7VPv+T7Jo24iJSOzMQqoJDU14NBLoGurIVfPOXqQ6vpSqhPjSIhLpGtcGQeqjlBe+Drlm5YTHx/P4NxBTL7hlpOSsQW683xm/gLiEhJJ6n+hUzmssgStP058SgZxCUkkZmYjIiSdOYjE0f/OrnW/ZcTAbEJBW9KvPPrIXJ5+bj5lhcs48rcj+FLPIHPoeHx9zmP9Kw+fNK8iUrGonjDREd3tiQgJVWWsi0Zdxp7SWo5XlZHcozc/vX8GSz4ubzU6oeH8ycMnURmfRnnxLqrefYP777qFRx+Z2640EsE+d6DjvD1/FmnD8vjOmGsa267vfpAX5z9Plfqa2SRQRMueNQtDmqph4JBh5N79HId3bOGLjaucUNPuWcSX7qN4z66A72ntGjkVM0MbdAYLQwwWXZOXN6Hd13Zrn2PgkGEc0VS6fCeP+G5ZaN1xJM5H9cGdHN3+Ft3H3UlcSjcSk7uitcfYt+j7/OHVxe22RUd9SUHBGv7tznvJuHYmXftfQE1FCdWVpU4U2kf5nDV6Mp/nvxQwlDkUWMqG05z2FDwJ1YPE8soqLp1xwhH4kkpIPyurXZW5ynfv5oLsbGYteKaxva1pJFqbARnoWUUK1ejWNZT079fYVptQj9bXkXnFtGY2+flL/9usYhcT7iZp62qemDf9pEylHf3SNgybNTxohRM/dB2hrTND2/vj0JaH/nPmPd4sQVxlXT1z5j3eIdu0dl3nZGdTUyp89Y/FdB//n3Tpk0vNge2UbXqdrgNHkuiLg+PlHD9Win67n8G554a1d910dn2tL4m6ylLKC5eRM/pGSiSN9CvupPqD1Y1RSxBZM3htjP80IVTFYBqrE/nRnspcLRWQaOtxWwuRDPSsYuFzT/LiU/NOauvXpzfllVUBbdJSRMvWbdtCFn4Z6kI3bZkZ2pHw0bbo3FlURErO0JPCR1NyhrKzqKjF43aGWTOn823Re6QPGUvl+6s5tORByt9ZQY+c86nf/wl9usJ5gwa2Gmp6qmg6u/7gaz+m8v1VpJ43muMZ/fGl9aBrv8Ec/fariJ3Baz3+04RQFYNp2qOuqzkWkpqfba1Z0JY7g2C57xtYt25dizZpKdKl+ngtA0IUfhnqVN1tsUtH7vraprP1BHGhJC9vAmkpSaSedyk1A0c1RtekpXZtjNn3MmSy6d1c4aK5jVk8q2tqSEpP4ljxZ425h0IRyRVqPHH8IjIBeB6IBxap6s+80HE6EapiME0dQdyP7gvJl6utjjCU1cxasklDaGjT9oQ42hT731baM1TXGm2xS3uysrZHZ9MEcccO7uLI+ldOelgeaoacfz7HkpWM/ieyfpbs3c4FF1zoaUpkaH5dZQ4axu6/L6LnDTNQXxqVez+hfPMKcsc7mUzDUeOhvXhRbD0e+AVwDbAPeEdEVqrq1nBrOZ0IZQ/T3xGE8mF6WxxhKKuZBbNJoEiXZ+YvCHkJzVDRFrucihKgcHKE2eHKMrp0TSc9/jhPzPufTh03GKGuahdKAl1XN907lQ2b1nHITQHSf8xkeuaOaBw6iwTdJ6GqYX0BlwJv+q3PBmYHe8+IESO0gbVr12o0YrrbTn5+gV49cZKeM3ioXj1xkubnF7T7GB3RnZ9foLmjxumYBxbqxGfX6pgHFmruqHEdOn9HaE1za3Y5lfqDnftUXSOhuA6CEQu6gXc1gE8NeziniEwBJqjqXe76VOCfVHVmk/3uAe4ByMrKGrF06VIAKioqSE1NJdow3eGlo7rLysqc5GTV1SQmJjqzbdPTT4HC5oTC1l7oj7VrxGvao3v8+PEBwzm96PH/K864fsP6VOCFYO+xHr93mO7wEY2aVU13uAlFj9+LcM59QD+/9b7AAQ90GIZhxCReOP53gEEikiMiXYCbgZUe6DAMw4hJwh7Vo6q1IjITeBMnnPNXqvppuHUYhmHEKp7E8atqPpDvxbkNwzBiHUvZYBiGEWNERXZOEfka2OOu9gQOeyino5ju8BKNuqNRM5jucNMe3WerambTxqhw/P6IyLsaKC41wjHd4SUadUejZjDd4SYUum2oxzAMI8Ywx28YhhFjRKPjf8lrAR3EdIeXaNQdjZrBdIebTuuOujF+wzAMo3NEY4/fMAzD6ATm+A3DMGKMqHL8InKfiOwQkU9F5Cm/9tkiUuRu+2cvNbaEiDwgIioiPd11EZGfu7o/EpHhXmtsQET+T0S2u7r+ICIZftsi2tYiMsHVViQiD3utpyVEpJ+IrBWRbe71/AO3vYeI/EVEPnP/dvdaa1NEJF5E3heR1e56jogUupp/5+bgiihEJENElrvX9TYRuTRKbP0j9/r4REReE5GkkNg7UMrOSHwB44G/Aonuei/372DgQyARyAF2AfFe622ivR9ObqI9QE+3bSJQgFO49BKg0GudfnqvBXzu8pPAk9Fga5zcT7uAAUAXV+tgr3W1oPVMYLi7nAbsdO37FPCw2/5wg+0j6QXMAl4FVrvrvwdudpcXADO81hhA82LgLne5C5AR6bYGzgK+AJL97HxHKOwdTT3+GcDPVLUaQFUPue2TgKWqWq2qXwBFwCiPNLbEs8CDgP+T9EnAb9RhE5AhImd6oq4JqvpnVa11VzfhpM6GyLf1KKBIVT9X1RpgKY7miENVi1X1PXe5HNiG80WfhOOkcP9+1xuFgRGRvsB1wCJ3XYArgeXuLpGoOR0YC/wSQFVrVLWECLe1iw9IFhEfkAIUEwJ7R5PjPxe43L3F+YeIXOy2nwV86bffPrctIhCRG4H9qvphk00RrduP/8C5M4HI1xzp+gIiItnARUAhkKWqxeD8OAC9vFMWkOdwOjH17voZQIlfRyESbT4A+Bp42R2iWiQiXYlwW6vqfuBpYC+Owy8FthACe3uSnbMlROSvQO8Am+bgaO2OMyxyMfB7ERmAM1TSlLDGqLai+8c4QyfN3hagLWy6g2lW1T+6+8wBaoElDW8LsH8kxQNHur5miEgq8DrwQ1UtczrQkYmIXA8cUtUtIjKuoTnArpFmcx8wHLhPVQtF5HmcoZ2Ixn3mMAlnWLUEWAbkBdi13faOKMevqle3tE1EZgAr1BnY2iwi9TjJijyv6NWSbhG5EOef9qH7he4LvCcio/BYdzBbA4jINOB64CrX5hABtm6FSNd3EiKSgOP0l6jqCrf5KxE5U1WL3aG/Qy0fIeyMBm4UkYlAEpCOcweQISI+txcaiTbfB+xT1UJ3fTmO449kWwNcDXyhql8DiMgK4DJCYO9oGup5A2dsCxE5F+cBzWGc6l03i0iiiOQAg4DNnqn0Q1U/VtVeqpqtqtk4F+BwVT2Io/t2N7rnEqC04bbTa0RkAvAQcKOqVvltilhbu0RNdTd3bPyXwDZVfcZv00pgmrs8DfhjuLW1hKrOVtW+7rV8M/B3Vb0VWAtMcXeLKM0A7vftSxHJdZuuArYSwbZ22QtcIiIp7vXSoLvz9vb6yXU7nnB3AX4LfAK8B1zpt20OTjTHDiDPa61BPsNuTkT1CPALV/fHwEiv9fnpLMIZK//AfS2IFlvjREvtdDXO8VpPEJ1jcG7RP/Kz80ScMfO/AZ+5f3t4rbUF/eM4EdUzAKcDUIQzHJHotb4AeocB77r2fgNn2DjibQ3MA7a7fu8VnIi6TtvbUjYYhmHEGNE01GMYhmGEAHP8hmEYMYY5fsMwjBjDHL9hGEaMYY7fMAwjxjDHb8QcIlLRjn3HichlfuvTReR2d/kOEenTgfPvFjdLq2F4QUTN3DWMCGQcUAFsBFDVBX7b7sCJr460maqGERRz/IYBiMgNwFyciYLfALcCycB0oE5EbgPuw5k9WYEzGW8ksEREjgKX4mTYHKmqh0VkJPC0qo4TkTOA14BMnIk34nfe24D/ds9bCPyXqtad+k9sxDI21GMYDm8Bl6jqRTjpnB9U1d04+c6fVdVhqrqhYWdVXY4zE/RWd9vRIMd+DHjLPfZKoD+AiJwP3ASMVtVhQB3OD45hnFKsx28YDn2B37nJurrgFMAIFWOB7wGo6p9E5Fu3/SpgBPCOm8QvmchLFGachpjjNwyHF4BnVHWlm3L4Jx04Ri0n7qKTmmwLlBtFgMWqOrsD5zKMDmNDPYbh0A3Y7y5P82svxymNGIim23bj9OAB/sWvfT3uEI6I5OEkCAMnMdgUEenlbushImd3UL9htBlz/EYskiIi+/xes3B6+MtEZANOuu8GVgGTReQDEbm8yXF+DSxwtyXjZFJ83j2G/wPaecBYEXkPpyjPXgBV3YrzQPnPIvIR8BecWryGcUqx7JyGYRgxhvX4DcMwYgxz/IZhGDGGOX7DMIwYwxy/YRhGjGGO3zAMI8Ywx28YhhFjmOM3DMOIMf4fIzhc+eQZe4cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plot  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 grapg properties.\n",
    "plt.title(f\"City Latitudde vs. Wind Speed (mph) \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Wind Speed (mph)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig4.png\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linear regression from the SciPy stats module.\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a function to create perform linear regression on the weather data\n",
    "# and plot a regression line and the equation with the data.  \n",
    "def plot_linear_regression(x_values, y_values, title, y_label, text_coordinates):\n",
    "    \n",
    "    # Run regression on hemisphere weather data.\n",
    "    (slope, intercept, r_value, p_value, std_err) = linregress(x_values, y_values)\n",
    "    \n",
    "    # Calculate the regression line \"y values\" from the slope and intercept.\n",
    "    regress_values = x_values * slope + intercept\n",
    "    # Get the equation of the line.\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    # Create a scatter plot and plot the regression line.\n",
    "    plt.scatter(x_values,y_values)\n",
    "    plt.plot(x_values,regress_values,\"r\")\n",
    "    # Annotate the text for the line equation.\n",
    "    plt.annotate(line_eq, text_coordinates, fontsize=15, color=\"red\")\n",
    "    plt.xlabel('Latitude')\n",
    "    plt.ylabel(y_label)\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                    Saskylakh\n",
       "Country                        RU\n",
       "Date          2020-01-04 20:06:21\n",
       "Lat                         71.97\n",
       "Lng                        114.09\n",
       "Max Temp                   -21.41\n",
       "Humidity                       95\n",
       "Cloudiness                     62\n",
       "Wind Speed                   6.04\n",
       "Name: 13, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index13 = city_data_df.loc[13]\n",
    "index13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3      False\n",
       "4       True\n",
       "       ...  \n",
       "551     True\n",
       "552     True\n",
       "553    False\n",
       "554     True\n",
       "555     True\n",
       "Name: Lat, Length: 556, dtype: bool"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " city_data_df[\"Lat\"] >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Ust-Ilimsk</td>\n",
       "      <td>RU</td>\n",
       "      <td>2020-01-04 20:06:18</td>\n",
       "      <td>57.96</td>\n",
       "      <td>102.73</td>\n",
       "      <td>17.47</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>8.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cockburn Town</td>\n",
       "      <td>TC</td>\n",
       "      <td>2020-01-04 20:05:35</td>\n",
       "      <td>21.46</td>\n",
       "      <td>-71.14</td>\n",
       "      <td>80.60</td>\n",
       "      <td>69</td>\n",
       "      <td>40</td>\n",
       "      <td>11.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01-04 20:05:03</td>\n",
       "      <td>42.65</td>\n",
       "      <td>-73.75</td>\n",
       "      <td>42.80</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gasa</td>\n",
       "      <td>BT</td>\n",
       "      <td>2020-01-04 20:06:19</td>\n",
       "      <td>27.91</td>\n",
       "      <td>89.73</td>\n",
       "      <td>2.66</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>7.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01-04 20:05:13</td>\n",
       "      <td>19.71</td>\n",
       "      <td>-155.08</td>\n",
       "      <td>75.20</td>\n",
       "      <td>73</td>\n",
       "      <td>40</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City Country                 Date    Lat     Lng  Max Temp  \\\n",
       "0     Ust-Ilimsk      RU  2020-01-04 20:06:18  57.96  102.73     17.47   \n",
       "1  Cockburn Town      TC  2020-01-04 20:05:35  21.46  -71.14     80.60   \n",
       "2         Albany      US  2020-01-04 20:05:03  42.65  -73.75     42.80   \n",
       "4           Gasa      BT  2020-01-04 20:06:19  27.91   89.73      2.66   \n",
       "6           Hilo      US  2020-01-04 20:05:13  19.71 -155.08     75.20   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        93          97        8.63  \n",
       "1        69          40       11.41  \n",
       "2        86          90        6.93  \n",
       "4        98          98        7.29  \n",
       "6        73          40        3.36  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df[(city_data_df[\"Lat\"] >= 0)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Northern and Southern Hemisphere DataFrame.\n",
    "northern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] >= 0)]\n",
    "southern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] < 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Ust-Ilimsk</td>\n",
       "      <td>RU</td>\n",
       "      <td>2020-01-04 20:06:18</td>\n",
       "      <td>57.96</td>\n",
       "      <td>102.73</td>\n",
       "      <td>17.47</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>8.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cockburn Town</td>\n",
       "      <td>TC</td>\n",
       "      <td>2020-01-04 20:05:35</td>\n",
       "      <td>21.46</td>\n",
       "      <td>-71.14</td>\n",
       "      <td>80.60</td>\n",
       "      <td>69</td>\n",
       "      <td>40</td>\n",
       "      <td>11.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01-04 20:05:03</td>\n",
       "      <td>42.65</td>\n",
       "      <td>-73.75</td>\n",
       "      <td>42.80</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gasa</td>\n",
       "      <td>BT</td>\n",
       "      <td>2020-01-04 20:06:19</td>\n",
       "      <td>27.91</td>\n",
       "      <td>89.73</td>\n",
       "      <td>2.66</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>7.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-01-04 20:05:13</td>\n",
       "      <td>19.71</td>\n",
       "      <td>-155.08</td>\n",
       "      <td>75.20</td>\n",
       "      <td>73</td>\n",
       "      <td>40</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City Country                 Date    Lat     Lng  Max Temp  \\\n",
       "0     Ust-Ilimsk      RU  2020-01-04 20:06:18  57.96  102.73     17.47   \n",
       "1  Cockburn Town      TC  2020-01-04 20:05:35  21.46  -71.14     80.60   \n",
       "2         Albany      US  2020-01-04 20:05:03  42.65  -73.75     42.80   \n",
       "4           Gasa      BT  2020-01-04 20:06:19  27.91   89.73      2.66   \n",
       "6           Hilo      US  2020-01-04 20:05:13  19.71 -155.08     75.20   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        93          97        8.63  \n",
       "1        69          40       11.41  \n",
       "2        86          90        6.93  \n",
       "4        98          98        7.29  \n",
       "6        73          40        3.36  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "northern_hemi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\n for Maximum Temperature', \n",
    "                       'Max Temp', (10,-40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values, \n",
    "                       'Linear Regression on the Southern Hemisphere \\n for Maximum Temperature', \n",
    "                       'Max Temp',(-30,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values, \n",
    "                       'Linear Regression on the Northern Hemisphere \\n for % Humidity', \n",
    "                       '% Humidity',(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\n for % Humidity', \n",
    "                       '% Humidity',(-50,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values, \n",
    "                       'Linear Regression on the Northern Hemisphere \\n for % Cloudiness', \n",
    "                       '% Cloudiness',(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\n for % Cloudiness', \n",
    "                       '% Cloudiness',(-50,60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"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', \n",
    "                       'Wind Speed',(10,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"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', \n",
    "                       'Wind Speed',(-30,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}