WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a0f4fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from citipy import citipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f6206630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x1e954f77f40>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "0e33ff90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the latitudes and longitudes to a list.\n",
    "coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c4a40ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "635"
      ]
     },
     "execution_count": 11,
     "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": 14,
   "id": "6d90cd57",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (config.py, line 5)",
     "output_type": "error",
     "traceback": [
      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
      "  File \u001b[0;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3398\u001b[0m in \u001b[0;35mrun_code\u001b[0m\n    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "\u001b[1;36m  Input \u001b[1;32mIn [14]\u001b[1;36m in \u001b[1;35m<cell line: 5>\u001b[1;36m\u001b[0m\n\u001b[1;33m    from config import weather_api_key\u001b[0m\n",
      "\u001b[1;36m  File \u001b[1;32m~\\OneDrive\\one drive\\Data Analytics UCB\\Class_folder\\Module_6\\World_Weather_Analysis\\config.py:5\u001b[1;36m\u001b[0m\n\u001b[1;33m    g_key = AIzaSyBPFTaXYkIA7gAzXNIC_mb96f8Oo-5jhq4\u001b[0m\n\u001b[1;37m                                                ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "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": 13,
   "id": "f3e5e835",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'weather_api_key' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [13]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Starting URL for Weather Map API Call.\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttp://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[43mweather_api_key\u001b[49m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'weather_api_key' is not defined"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aaa8e68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a27744d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d78401f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the time library and the datetime module from the datetime library \n",
    "import time\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5ace227d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval     \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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "065742fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a84cc4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 50 of Set 12 | hot springs\n",
      "Processing Record 51 of Set 12 | rikitea\n",
      "Processing Record 52 of Set 12 | mahebourg\n",
      "Processing Record 53 of Set 12 | saskylakh\n",
      "Processing Record 54 of Set 12 | mae sot\n",
      "Processing Record 55 of Set 12 | barrow\n",
      "Processing Record 56 of Set 12 | tuktoyaktuk\n",
      "Processing Record 57 of Set 12 | new norfolk\n",
      "Processing Record 58 of Set 12 | torbay\n",
      "Processing Record 59 of Set 12 | east london\n",
      "Processing Record 60 of Set 12 | fort nelson\n",
      "Processing Record 61 of Set 12 | iqaluit\n",
      "Processing Record 62 of Set 12 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record 63 of Set 12 | kapaa\n",
      "Processing Record 64 of Set 12 | albany\n",
      "Processing Record 65 of Set 12 | sao filipe\n",
      "Processing Record 66 of Set 12 | charleston\n",
      "Processing Record 67 of Set 12 | grindavik\n",
      "Processing Record 68 of Set 12 | thompson\n",
      "Processing Record 69 of Set 12 | ahipara\n",
      "Processing Record 70 of Set 12 | souillac\n",
      "Processing Record 71 of Set 12 | fortuna\n",
      "Processing Record 72 of Set 12 | bato\n",
      "Processing Record 73 of Set 12 | port alfred\n",
      "Processing Record 74 of Set 12 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record 75 of Set 12 | tautira\n",
      "Processing Record 76 of Set 12 | itarema\n",
      "Processing Record 77 of Set 12 | hobart\n",
      "Processing Record 78 of Set 12 | greece\n",
      "Processing Record 79 of Set 12 | batouri\n",
      "Processing Record 80 of Set 12 | mataura\n",
      "Processing Record 81 of Set 12 | isangel\n",
      "Processing Record 82 of Set 12 | portland\n",
      "Processing Record 83 of Set 12 | hokitika\n",
      "Processing Record 84 of Set 12 | mantua\n",
      "Processing Record 85 of Set 12 | broome\n",
      "Processing Record 86 of Set 12 | marcona\n",
      "City not found. Skipping...\n",
      "Processing Record 87 of Set 12 | cape town\n",
      "Processing Record 88 of Set 12 | castro\n",
      "Processing Record 89 of Set 12 | inverness\n",
      "Processing Record 90 of Set 12 | puerto leguizamo\n",
      "Processing Record 91 of Set 12 | busselton\n",
      "Processing Record 92 of Set 12 | luganville\n",
      "Processing Record 93 of Set 12 | codrington\n",
      "Processing Record 94 of Set 12 | west odessa\n",
      "Processing Record 95 of Set 12 | jamestown\n",
      "Processing Record 96 of Set 12 | puerto ayora\n",
      "Processing Record 97 of Set 12 | palabuhanratu\n",
      "City not found. Skipping...\n",
      "Processing Record 98 of Set 12 | boshnyakovo\n",
      "Processing Record 99 of Set 12 | sakakah\n",
      "Processing Record 1 of Set 13 | coquimbo\n",
      "Processing Record 2 of Set 13 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 13 | falealupo\n",
      "City not found. Skipping...\n",
      "Processing Record 4 of Set 13 | port augusta\n",
      "Processing Record 5 of Set 13 | kathmandu\n",
      "Processing Record 6 of Set 13 | coos bay\n",
      "Processing Record 7 of Set 13 | namibe\n",
      "Processing Record 8 of Set 13 | champerico\n",
      "Processing Record 9 of Set 13 | provideniya\n",
      "Processing Record 10 of Set 13 | kabare\n",
      "Processing Record 11 of Set 13 | berlevag\n",
      "Processing Record 12 of Set 13 | teguise\n",
      "Processing Record 13 of Set 13 | avarua\n",
      "Processing Record 14 of Set 13 | shimoda\n",
      "Processing Record 15 of Set 13 | saint george\n",
      "Processing Record 16 of Set 13 | bredasdorp\n",
      "Processing Record 17 of Set 13 | vaini\n",
      "Processing Record 18 of Set 13 | samarai\n",
      "Processing Record 19 of Set 13 | olga\n",
      "Processing Record 20 of Set 13 | severo-kurilsk\n",
      "Processing Record 21 of Set 13 | ushuaia\n",
      "Processing Record 22 of Set 13 | ilulissat\n",
      "Processing Record 23 of Set 13 | palmer\n",
      "Processing Record 24 of Set 13 | port elizabeth\n",
      "Processing Record 25 of Set 13 | la asuncion\n",
      "Processing Record 26 of Set 13 | gorno-chuyskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 13 | yulara\n",
      "Processing Record 28 of Set 13 | bluff\n",
      "Processing Record 29 of Set 13 | yakshur-bodya\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 13 | lompoc\n",
      "Processing Record 31 of Set 13 | byron bay\n",
      "Processing Record 32 of Set 13 | mar del plata\n",
      "Processing Record 33 of Set 13 | victor harbor\n",
      "Processing Record 34 of Set 13 | lushoto\n",
      "Processing Record 35 of Set 13 | pangai\n",
      "Processing Record 36 of Set 13 | lebu\n",
      "Processing Record 37 of Set 13 | punta arenas\n",
      "Processing Record 38 of Set 13 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 13 | erzincan\n",
      "Processing Record 40 of Set 13 | novokizhinginsk\n",
      "Processing Record 41 of Set 13 | kalmunai\n",
      "Processing Record 42 of Set 13 | hermanus\n",
      "Processing Record 43 of Set 13 | westport\n",
      "Processing Record 44 of Set 13 | pasni\n",
      "Processing Record 45 of Set 13 | floro\n",
      "Processing Record 46 of Set 13 | ribeira grande\n",
      "Processing Record 47 of Set 13 | copiapo\n",
      "Processing Record 48 of Set 13 | kodiak\n",
      "Processing Record 49 of Set 13 | aflu\n",
      "City not found. Skipping...\n",
      "Processing Record 50 of Set 13 | mehamn\n",
      "Processing Record 1 of Set 14 | terrace\n",
      "Processing Record 2 of Set 14 | rocha\n",
      "Processing Record 3 of Set 14 | longyearbyen\n",
      "Processing Record 4 of Set 14 | santana do livramento\n",
      "Processing Record 5 of Set 14 | nikolskoye\n",
      "Processing Record 6 of Set 14 | nanortalik\n",
      "Processing Record 7 of Set 14 | hilo\n",
      "Processing Record 8 of Set 14 | saint anthony\n",
      "Processing Record 9 of Set 14 | mogadishu\n",
      "Processing Record 10 of Set 14 | fengxian\n",
      "Processing Record 11 of Set 14 | clyde river\n",
      "Processing Record 12 of Set 14 | nizwa\n",
      "Processing Record 13 of Set 14 | cidreira\n",
      "Processing Record 14 of Set 14 | hutchinson\n",
      "Processing Record 15 of Set 14 | lunglei\n",
      "Processing Record 16 of Set 14 | geraldton\n",
      "Processing Record 17 of Set 14 | saint-augustin\n",
      "Processing Record 18 of Set 14 | mulege\n",
      "Processing Record 19 of Set 14 | chokurdakh\n",
      "Processing Record 20 of Set 14 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 14 | dujuma\n",
      "City not found. Skipping...\n",
      "Processing Record 22 of Set 14 | sakaiminato\n",
      "Processing Record 23 of Set 14 | carnarvon\n",
      "Processing Record 24 of Set 14 | natal\n",
      "Processing Record 25 of Set 14 | rungata\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 14 | mys shmidta\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 14 | noumea\n",
      "Processing Record 28 of Set 14 | laguna\n",
      "Processing Record 29 of Set 14 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 14 | iskateley\n",
      "Processing Record 31 of Set 14 | saint-philippe\n",
      "Processing Record 32 of Set 14 | youhao\n",
      "Processing Record 33 of Set 14 | kununurra\n",
      "Processing Record 34 of Set 14 | padang\n",
      "Processing Record 35 of Set 14 | nizhneyansk\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 14 | arlit\n",
      "Processing Record 37 of Set 14 | atuona\n",
      "Processing Record 38 of Set 14 | alofi\n",
      "Processing Record 39 of Set 14 | katsuura\n",
      "Processing Record 40 of Set 14 | faanui\n",
      "Processing Record 41 of Set 14 | qaanaaq\n",
      "Processing Record 42 of Set 14 | dingle\n",
      "Processing Record 43 of Set 14 | bandarbeyla\n",
      "Processing Record 44 of Set 14 | mareeba\n",
      "Processing Record 45 of Set 14 | tame\n",
      "Processing Record 46 of Set 14 | plettenberg bay\n",
      "Processing Record 47 of Set 14 | touros\n",
      "Processing Record 48 of Set 14 | ixtapa\n",
      "Processing Record 49 of Set 14 | stephenville\n",
      "Processing Record 50 of Set 14 | tomatlan\n",
      "Processing Record 1 of Set 15 | algeciras\n",
      "Processing Record 2 of Set 15 | banikoara\n",
      "Processing Record 3 of Set 15 | constitucion\n",
      "Processing Record 4 of Set 15 | rorvik\n",
      "Processing Record 5 of Set 15 | meulaboh\n",
      "Processing Record 6 of Set 15 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 15 | dikson\n",
      "Processing Record 8 of Set 15 | uglich\n",
      "Processing Record 9 of Set 15 | slave lake\n",
      "Processing Record 10 of Set 15 | mayo\n",
      "Processing Record 11 of Set 15 | viedma\n",
      "Processing Record 12 of Set 15 | itaituba\n",
      "Processing Record 13 of Set 15 | watsa\n",
      "Processing Record 14 of Set 15 | ponta do sol\n",
      "Processing Record 15 of Set 15 | wahran\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 15 | suleja\n",
      "Processing Record 17 of Set 15 | tiksi\n",
      "Processing Record 18 of Set 15 | cortez\n",
      "Processing Record 19 of Set 15 | beloha\n",
      "Processing Record 20 of Set 15 | bambous virieux\n",
      "Processing Record 21 of Set 15 | dao\n",
      "Processing Record 22 of Set 15 | elur\n",
      "Processing Record 23 of Set 15 | talnakh\n",
      "Processing Record 24 of Set 15 | skalistyy\n",
      "City not found. Skipping...\n",
      "Processing Record 25 of Set 15 | yellowknife\n",
      "Processing Record 26 of Set 15 | srednekolymsk\n",
      "Processing Record 27 of Set 15 | camana\n",
      "Processing Record 28 of Set 15 | arraial do cabo\n",
      "Processing Record 29 of Set 15 | maragogi\n",
      "Processing Record 30 of Set 15 | iracoubo\n",
      "Processing Record 31 of Set 15 | usakos\n",
      "Processing Record 32 of Set 15 | hithadhoo\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 33 of Set 15 | oksfjord\n",
      "Processing Record 34 of Set 15 | cherskiy\n",
      "Processing Record 35 of Set 15 | udachnyy\n",
      "Processing Record 36 of Set 15 | kaitangata\n",
      "Processing Record 37 of Set 15 | georgetown\n",
      "Processing Record 38 of Set 15 | ukiah\n",
      "Processing Record 39 of Set 15 | faya\n",
      "Processing Record 40 of Set 15 | gat\n",
      "Processing Record 41 of Set 15 | togur\n",
      "Processing Record 42 of Set 15 | farmington\n",
      "Processing Record 43 of Set 15 | vila velha\n",
      "Processing Record 44 of Set 15 | kandrian\n",
      "Processing Record 45 of Set 15 | butaritari\n",
      "Processing Record 46 of Set 15 | eskasem\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 15 | gympie\n",
      "Processing Record 48 of Set 15 | hasaki\n",
      "Processing Record 49 of Set 15 | gedo\n",
      "Processing Record 50 of Set 15 | rio gallegos\n",
      "Processing Record 1 of Set 16 | norman wells\n",
      "Processing Record 2 of Set 16 | shibetsu\n",
      "Processing Record 3 of Set 16 | umzimvubu\n",
      "City not found. Skipping...\n",
      "Processing Record 4 of Set 16 | lata\n",
      "Processing Record 5 of Set 16 | esperance\n",
      "Processing Record 6 of Set 16 | samusu\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 16 | bima\n",
      "Processing Record 8 of Set 16 | sovetskaya gavan\n",
      "Processing Record 9 of Set 16 | bakhtiyarpur\n",
      "Processing Record 10 of Set 16 | morant bay\n",
      "Processing Record 11 of Set 16 | caravelas\n",
      "Processing Record 12 of Set 16 | sorvag\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 16 | fairbanks\n",
      "Processing Record 14 of Set 16 | airai\n",
      "Processing Record 15 of Set 16 | bavly\n",
      "Processing Record 16 of Set 16 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record 17 of Set 16 | namatanai\n",
      "Processing Record 18 of Set 16 | moose factory\n",
      "Processing Record 19 of Set 16 | aguas belas\n",
      "Processing Record 20 of Set 16 | korla\n",
      "Processing Record 21 of Set 16 | paamiut\n",
      "Processing Record 22 of Set 16 | grand gaube\n",
      "Processing Record 23 of Set 16 | aketi\n",
      "Processing Record 24 of Set 16 | palamos\n",
      "Processing Record 25 of Set 16 | pullman\n",
      "Processing Record 26 of Set 16 | port lincoln\n",
      "Processing Record 27 of Set 16 | ayan\n",
      "Processing Record 28 of Set 16 | pisco\n",
      "Processing Record 29 of Set 16 | acapulco\n",
      "Processing Record 30 of Set 16 | boyolangu\n",
      "Processing Record 31 of Set 16 | puerto del rosario\n",
      "Processing Record 32 of Set 16 | vestmannaeyjar\n",
      "Processing Record 33 of Set 16 | north bend\n",
      "Processing Record 34 of Set 16 | progreso\n",
      "Processing Record 35 of Set 16 | lavrentiya\n",
      "Processing Record 36 of Set 16 | yumaguzino\n",
      "Processing Record 37 of Set 16 | muros\n",
      "Processing Record 38 of Set 16 | safaqis\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 16 | shitanjing\n",
      "Processing Record 40 of Set 16 | khatanga\n",
      "Processing Record 41 of Set 16 | saint-joseph\n",
      "Processing Record 42 of Set 16 | tanout\n",
      "Processing Record 43 of Set 16 | cabo san lucas\n",
      "Processing Record 44 of Set 16 | northam\n",
      "Processing Record 45 of Set 16 | poronaysk\n",
      "Processing Record 46 of Set 16 | bengkulu\n",
      "Processing Record 47 of Set 16 | tabialan\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 16 | xuanhua\n",
      "Processing Record 49 of Set 16 | znamenskoye\n",
      "Processing Record 50 of Set 16 | novoorsk\n",
      "Processing Record 1 of Set 17 | ostrovnoy\n",
      "Processing Record 2 of Set 17 | boldumsaz\n",
      "Processing Record 3 of Set 17 | namie\n",
      "Processing Record 4 of Set 17 | ranong\n",
      "Processing Record 5 of Set 17 | bolungarvik\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 17 | udankudi\n",
      "Processing Record 7 of Set 17 | katherine\n",
      "Processing Record 8 of Set 17 | takahata\n",
      "Processing Record 9 of Set 17 | saldanha\n",
      "Processing Record 10 of Set 17 | cabo rojo\n",
      "Processing Record 11 of Set 17 | burica\n",
      "City not found. Skipping...\n",
      "Processing Record 12 of Set 17 | kumbo\n",
      "Processing Record 13 of Set 17 | monetnyy\n",
      "Processing Record 14 of Set 17 | bafoulabe\n",
      "Processing Record 15 of Set 17 | bairiki\n",
      "Processing Record 16 of Set 17 | svetlogorsk\n",
      "Processing Record 17 of Set 17 | aklavik\n",
      "Processing Record 18 of Set 17 | kadaya\n",
      "Processing Record 19 of Set 17 | college\n",
      "Processing Record 20 of Set 17 | kruisfontein\n",
      "Processing Record 21 of Set 17 | praia da vitoria\n",
      "Processing Record 22 of Set 17 | pabrade\n",
      "Processing Record 23 of Set 17 | san julian\n",
      "Processing Record 24 of Set 17 | key west\n",
      "Processing Record 25 of Set 17 | shamary\n",
      "Processing Record 26 of Set 17 | anadyr\n",
      "Processing Record 27 of Set 17 | oeiras do para\n",
      "Processing Record 28 of Set 17 | liberty\n",
      "Processing Record 29 of Set 17 | bethel\n",
      "Processing Record 30 of Set 17 | victoria\n",
      "Processing Record 31 of Set 17 | honiara\n",
      "Processing Record 32 of Set 17 | jabiru\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 17 | hamilton\n",
      "Processing Record 34 of Set 17 | la ronge\n",
      "Processing Record 35 of Set 17 | upernavik\n",
      "Processing Record 36 of Set 17 | lampazos de naranjo\n",
      "Processing Record 37 of Set 17 | hami\n",
      "Processing Record 38 of Set 17 | abu kamal\n",
      "Processing Record 39 of Set 17 | kiruna\n",
      "Processing Record 40 of Set 17 | vaitupu\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 17 | galiwinku\n",
      "City not found. Skipping...\n",
      "Processing Record 42 of Set 17 | sao raimundo das mangabeiras\n",
      "Processing Record 43 of Set 17 | jimenez\n",
      "Processing Record 44 of Set 17 | aguimes\n",
      "Processing Record 45 of Set 17 | clovis\n",
      "Processing Record 46 of Set 17 | santa rosa\n",
      "Processing Record 47 of Set 17 | ancud\n",
      "Processing Record 48 of Set 17 | sentyabrskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 17 | temaraia\n",
      "City not found. Skipping...\n",
      "Processing Record 50 of Set 17 | bridlington\n",
      "Processing Record 1 of Set 18 | shieli\n",
      "Processing Record 2 of Set 18 | kopychyntsi\n",
      "Processing Record 3 of Set 18 | nuuk\n",
      "Processing Record 4 of Set 18 | grand centre\n",
      "City not found. Skipping...\n",
      "Processing Record 5 of Set 18 | comodoro rivadavia\n",
      "Processing Record 6 of Set 18 | cururupu\n",
      "Processing Record 7 of Set 18 | tunceli\n",
      "Processing Record 8 of Set 18 | linfen\n",
      "Processing Record 9 of Set 18 | san pedro\n",
      "Processing Record 10 of Set 18 | yar-sale\n",
      "Processing Record 11 of Set 18 | amapa\n",
      "Processing Record 12 of Set 18 | san carlos de bariloche\n",
      "Processing Record 13 of Set 18 | leningradskiy\n",
      "Processing Record 14 of Set 18 | biscarrosse\n",
      "Processing Record 15 of Set 18 | broken hill\n",
      "Processing Record 16 of Set 18 | zhigansk\n",
      "Processing Record 17 of Set 18 | egvekinot\n",
      "Processing Record 18 of Set 18 | ust-kuyga\n",
      "Processing Record 19 of Set 18 | caxito\n",
      "Processing Record 20 of Set 18 | rio grande\n",
      "Processing Record 21 of Set 18 | prainha\n",
      "Processing Record 22 of Set 18 | brae\n",
      "Processing Record 23 of Set 18 | olavarria\n",
      "Processing Record 24 of Set 18 | alice springs\n",
      "Processing Record 25 of Set 18 | nueve de julio\n",
      "Processing Record 26 of Set 18 | ondorhaan\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 18 | tuatapere\n",
      "Processing Record 28 of Set 18 | malakal\n",
      "Processing Record 29 of Set 18 | chara\n",
      "Processing Record 30 of Set 18 | pangnirtung\n",
      "Processing Record 31 of Set 18 | thongwa\n",
      "Processing Record 32 of Set 18 | calvinia\n",
      "Processing Record 33 of Set 18 | dwarka\n",
      "Processing Record 34 of Set 18 | ust-ilimsk\n",
      "Processing Record 35 of Set 18 | tessalit\n",
      "Processing Record 36 of Set 18 | chimbote\n",
      "Processing Record 37 of Set 18 | townsville\n",
      "Processing Record 38 of Set 18 | panjab\n",
      "Processing Record 39 of Set 18 | nsukka\n",
      "Processing Record 40 of Set 18 | port hardy\n",
      "Processing Record 41 of Set 18 | kuopio\n",
      "Processing Record 42 of Set 18 | palu\n",
      "Processing Record 43 of Set 18 | lyantonde\n",
      "Processing Record 44 of Set 18 | sibolga\n",
      "Processing Record 45 of Set 18 | duz\n",
      "City not found. Skipping...\n",
      "Processing Record 46 of Set 18 | kismayo\n",
      "Processing Record 47 of Set 18 | hofn\n",
      "Processing Record 48 of Set 18 | baijiantan\n",
      "Processing Record 49 of Set 18 | maniitsoq\n",
      "Processing Record 50 of Set 18 | jumla\n",
      "Processing Record 1 of Set 19 | rwamagana\n",
      "Processing Record 2 of Set 19 | mount gambier\n",
      "Processing Record 3 of Set 19 | waingapu\n",
      "Processing Record 4 of Set 19 | lasa\n",
      "Processing Record 5 of Set 19 | mombetsu\n",
      "Processing Record 6 of Set 19 | awjilah\n",
      "Processing Record 7 of Set 19 | ust-nera\n",
      "Processing Record 8 of Set 19 | kyra\n",
      "Processing Record 9 of Set 19 | laramie\n",
      "Processing Record 10 of Set 19 | adrar\n",
      "Processing Record 11 of Set 19 | barreirinha\n",
      "Processing Record 12 of Set 19 | blagoyevo\n",
      "Processing Record 13 of Set 19 | zwedru\n",
      "Processing Record 14 of Set 19 | clarence town\n",
      "Processing Record 15 of Set 19 | taoudenni\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 16 of Set 19 | sloboda\n",
      "Processing Record 17 of Set 19 | kuala terengganu\n",
      "Processing Record 18 of Set 19 | antalaha\n",
      "Processing Record 19 of Set 19 | sofiysk\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 19 | tiznit\n",
      "Processing Record 21 of Set 19 | castlebar\n",
      "Processing Record 22 of Set 19 | dianopolis\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 19 | san patricio\n",
      "Processing Record 24 of Set 19 | tasiilaq\n",
      "Processing Record 25 of Set 19 | the valley\n",
      "Processing Record 26 of Set 19 | maykain\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 19 | pimentel\n",
      "Processing Record 28 of Set 19 | thinadhoo\n",
      "Processing Record 29 of Set 19 | san angelo\n",
      "Processing Record 30 of Set 19 | dien bien\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 19 | karaul\n",
      "City not found. Skipping...\n",
      "Processing Record 32 of Set 19 | sandpoint\n",
      "Processing Record 33 of Set 19 | puri\n",
      "Processing Record 34 of Set 19 | lagoa\n",
      "Processing Record 35 of Set 19 | narsaq\n",
      "Processing Record 36 of Set 19 | mao\n",
      "Processing Record 37 of Set 19 | sao jose da coroa grande\n",
      "Processing Record 38 of Set 19 | oranjemund\n",
      "Processing Record 39 of Set 19 | angoram\n",
      "Processing Record 40 of Set 19 | jaroslaw\n",
      "Processing Record 41 of Set 19 | dicabisagan\n",
      "Processing Record 42 of Set 19 | launceston\n",
      "Processing Record 43 of Set 19 | tazovskiy\n",
      "Processing Record 44 of Set 19 | shegaon\n",
      "Processing Record 45 of Set 19 | ikorodu\n",
      "Processing Record 46 of Set 19 | klaksvik\n",
      "Processing Record 47 of Set 19 | datong\n",
      "Processing Record 48 of Set 19 | kroya\n",
      "Processing Record 49 of Set 19 | wadi musa\n",
      "Processing Record 50 of Set 19 | san francisco\n",
      "Processing Record 1 of Set 20 | kutum\n",
      "Processing Record 2 of Set 20 | manzanillo\n",
      "Processing Record 3 of Set 20 | staraya sunzha\n",
      "Processing Record 4 of Set 20 | marsa matruh\n",
      "Processing Record 5 of Set 20 | gladstone\n",
      "Processing Record 6 of Set 20 | bonoua\n",
      "Processing Record 7 of Set 20 | young\n",
      "Processing Record 8 of Set 20 | lugovskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 20 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 20 | atambua\n",
      "Processing Record 11 of Set 20 | plyussa\n",
      "Processing Record 12 of Set 20 | alizai\n",
      "Processing Record 13 of Set 20 | abonnema\n",
      "Processing Record 14 of Set 20 | vila franca do campo\n",
      "Processing Record 15 of Set 20 | mildura\n",
      "Processing Record 16 of Set 20 | aginskoye\n",
      "Processing Record 17 of Set 20 | pevek\n",
      "Processing Record 18 of Set 20 | owando\n",
      "Processing Record 19 of Set 20 | kieta\n",
      "Processing Record 20 of Set 20 | necochea\n",
      "Processing Record 21 of Set 20 | guelengdeng\n",
      "City not found. Skipping...\n",
      "Processing Record 22 of Set 20 | yenangyaung\n",
      "Processing Record 23 of Set 20 | sitka\n",
      "Processing Record 24 of Set 20 | san quintin\n",
      "Processing Record 25 of Set 20 | helmsdale\n",
      "Processing Record 26 of Set 20 | half moon bay\n",
      "Processing Record 27 of Set 20 | sorland\n",
      "Processing Record 28 of Set 20 | fershampenuaz\n",
      "Processing Record 29 of Set 20 | neryungri\n",
      "Processing Record 30 of Set 20 | porto torres\n",
      "Processing Record 31 of Set 20 | phan thiet\n",
      "Processing Record 32 of Set 20 | coelho neto\n",
      "Processing Record 33 of Set 20 | siocon\n",
      "Processing Record 34 of Set 20 | xixiang\n",
      "Processing Record 35 of Set 20 | kathu\n",
      "Processing Record 36 of Set 20 | dutlwe\n",
      "Processing Record 37 of Set 20 | beringovskiy\n",
      "Processing Record 38 of Set 20 | sechura\n",
      "Processing Record 39 of Set 20 | shelburne\n",
      "Processing Record 40 of Set 20 | bukachacha\n",
      "Processing Record 41 of Set 20 | coihaique\n",
      "Processing Record 42 of Set 20 | vostok\n",
      "Processing Record 43 of Set 20 | nelson bay\n",
      "Processing Record 44 of Set 20 | qafsah\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 20 | abeche\n",
      "Processing Record 46 of Set 20 | salou\n",
      "Processing Record 47 of Set 20 | cabedelo\n",
      "Processing Record 48 of Set 20 | tayoltita\n",
      "Processing Record 49 of Set 20 | hualmay\n",
      "Processing Record 50 of Set 20 | ruatoria\n",
      "City not found. Skipping...\n",
      "Processing Record 1 of Set 21 | sao joao da barra\n",
      "Processing Record 2 of Set 21 | karatau\n",
      "Processing Record 3 of Set 21 | saint-denis\n",
      "Processing Record 4 of Set 21 | aykhal\n",
      "Processing Record 5 of Set 21 | buraydah\n",
      "Processing Record 6 of Set 21 | kemijarvi\n",
      "Processing Record 7 of Set 21 | crixas\n",
      "Processing Record 8 of Set 21 | kalabo\n",
      "Processing Record 9 of Set 21 | komatipoort\n",
      "Processing Record 10 of Set 21 | mirina\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 21 | kasane\n",
      "Processing Record 12 of Set 21 | camacha\n",
      "Processing Record 13 of Set 21 | pacific grove\n",
      "Processing Record 14 of Set 21 | redmond\n",
      "Processing Record 15 of Set 21 | saint-ambroise\n",
      "Processing Record 16 of Set 21 | matay\n",
      "Processing Record 17 of Set 21 | baykit\n",
      "Processing Record 18 of Set 21 | qasigiannguit\n",
      "Processing Record 19 of Set 21 | yanan\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 21 | puga\n",
      "Processing Record 21 of Set 21 | okhotsk\n",
      "Processing Record 22 of Set 21 | salamiyah\n",
      "Processing Record 23 of Set 21 | trinidad\n",
      "Processing Record 24 of Set 21 | pfarrkirchen\n",
      "Processing Record 25 of Set 21 | las matas de farfan\n",
      "Processing Record 26 of Set 21 | kisangani\n",
      "Processing Record 27 of Set 21 | kavaratti\n",
      "Processing Record 28 of Set 21 | bonavista\n",
      "Processing Record 29 of Set 21 | pillaro\n",
      "Processing Record 30 of Set 21 | lowestoft\n",
      "Processing Record 31 of Set 21 | borba\n",
      "Processing Record 32 of Set 21 | channel-port aux basques\n",
      "Processing Record 33 of Set 21 | kaeo\n",
      "Processing Record 34 of Set 21 | elko\n",
      "Processing Record 35 of Set 21 | muroto\n",
      "Processing Record 36 of Set 21 | yanam\n",
      "Processing Record 37 of Set 21 | hirara\n",
      "Processing Record 38 of Set 21 | himeji\n",
      "Processing Record 39 of Set 21 | umba\n",
      "Processing Record 40 of Set 21 | middelburg\n",
      "Processing Record 41 of Set 21 | vilyuysk\n",
      "Processing Record 42 of Set 21 | pierre\n",
      "Processing Record 43 of Set 21 | mumford\n",
      "Processing Record 44 of Set 21 | alugan\n",
      "Processing Record 45 of Set 21 | denpasar\n",
      "Processing Record 46 of Set 21 | jishou\n",
      "Processing Record 47 of Set 21 | kovdor\n",
      "Processing Record 48 of Set 21 | rakai\n",
      "Processing Record 49 of Set 21 | kamenskoye\n",
      "City not found. Skipping...\n",
      "Processing Record 50 of Set 21 | bari sadri\n",
      "Processing Record 1 of Set 22 | tabiauea\n",
      "City not found. Skipping...\n",
      "Processing Record 2 of Set 22 | jacarezinho\n",
      "Processing Record 3 of Set 22 | pontes e lacerda\n",
      "Processing Record 4 of Set 22 | sobolevo\n",
      "Processing Record 5 of Set 22 | antofagasta\n",
      "Processing Record 6 of Set 22 | pata\n",
      "Processing Record 7 of Set 22 | tasbuget\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 22 | louisbourg\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 22 | saleaula\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 22 | prokhorovka\n",
      "Processing Record 11 of Set 22 | pompeu\n",
      "Processing Record 12 of Set 22 | limbang\n",
      "Processing Record 13 of Set 22 | chapais\n",
      "Processing Record 14 of Set 22 | guymon\n",
      "Processing Record 15 of Set 22 | henties bay\n",
      "Processing Record 16 of Set 22 | pinawa\n",
      "Processing Record 17 of Set 22 | borovsk\n",
      "Processing Record 18 of Set 22 | stornoway\n",
      "Processing Record 19 of Set 22 | along\n",
      "Processing Record 20 of Set 22 | todos santos\n",
      "Processing Record 21 of Set 22 | bathsheba\n",
      "Processing Record 22 of Set 22 | bubaque\n",
      "Processing Record 23 of Set 22 | paucartambo\n",
      "City not found. Skipping...\n",
      "Processing Record 24 of Set 22 | basoko\n",
      "Processing Record 25 of Set 22 | inhambane\n",
      "Processing Record 26 of Set 22 | kudahuvadhoo\n",
      "Processing Record 27 of Set 22 | qui nhon\n",
      "Processing Record 28 of Set 22 | montecristi\n",
      "Processing Record 29 of Set 22 | warri\n",
      "Processing Record 30 of Set 22 | blyth\n",
      "Processing Record 31 of Set 22 | nuevitas\n",
      "Processing Record 32 of Set 22 | tambura\n",
      "Processing Record 33 of Set 22 | giddalur\n",
      "Processing Record 34 of Set 22 | kodino\n",
      "Processing Record 35 of Set 22 | batemans bay\n",
      "Processing Record 36 of Set 22 | doctor arroyo\n",
      "Processing Record 37 of Set 22 | jocoro\n",
      "Processing Record 38 of Set 22 | evensk\n",
      "Processing Record 39 of Set 22 | puerto madryn\n",
      "Processing Record 40 of Set 22 | playas\n",
      "Processing Record 41 of Set 22 | el balyana\n",
      "City not found. Skipping...\n",
      "Processing Record 42 of Set 22 | tolaga bay\n",
      "Processing Record 43 of Set 22 | juifang\n",
      "City not found. Skipping...\n",
      "Processing Record 44 of Set 22 | chuy\n",
      "Processing Record 45 of Set 22 | hailar\n",
      "Processing Record 46 of Set 22 | odweyne\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 47 of Set 22 | maloshuyka\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 22 | rawson\n",
      "Processing Record 49 of Set 22 | madinat sittah uktubar\n",
      "Processing Record 50 of Set 22 | hareid\n",
      "Processing Record 1 of Set 23 | solnechnyy\n",
      "Processing Record 2 of Set 23 | grahamstown\n",
      "Processing Record 3 of Set 23 | mariental\n",
      "Processing Record 4 of Set 23 | hobyo\n",
      "Processing Record 5 of Set 23 | mentok\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 23 | yingcheng\n",
      "Processing Record 7 of Set 23 | novyy starodub\n",
      "Processing Record 8 of Set 23 | bur gabo\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 23 | visby\n",
      "Processing Record 10 of Set 23 | salinopolis\n",
      "Processing Record 11 of Set 23 | cayenne\n",
      "Processing Record 12 of Set 23 | kanigoro\n",
      "Processing Record 13 of Set 23 | eyl\n",
      "Processing Record 14 of Set 23 | patiya\n",
      "Processing Record 15 of Set 23 | mangan\n",
      "Processing Record 16 of Set 23 | elizabeth city\n",
      "Processing Record 17 of Set 23 | sola\n",
      "Processing Record 18 of Set 23 | alta floresta\n",
      "Processing Record 19 of Set 23 | rensvik\n",
      "Processing Record 20 of Set 23 | olafsvik\n",
      "Processing Record 21 of Set 23 | leh\n",
      "Processing Record 22 of Set 23 | tarudant\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 23 | atar\n",
      "Processing Record 24 of Set 23 | praia\n",
      "Processing Record 25 of Set 23 | machaneng\n",
      "Processing Record 26 of Set 23 | spearfish\n",
      "Processing Record 27 of Set 23 | kangaba\n",
      "Processing Record 28 of Set 23 | gafargaon\n",
      "Processing Record 29 of Set 23 | kota kinabalu\n",
      "Processing Record 30 of Set 23 | dakar\n",
      "Processing Record 31 of Set 23 | sulina\n",
      "Processing Record 32 of Set 23 | chagda\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 23 | istok\n",
      "Processing Record 34 of Set 23 | palmas bellas\n",
      "Processing Record 35 of Set 23 | axim\n",
      "Processing Record 36 of Set 23 | zyryanka\n",
      "Processing Record 37 of Set 23 | porbandar\n",
      "Processing Record 38 of Set 23 | altoona\n",
      "Processing Record 39 of Set 23 | gormi\n",
      "City not found. Skipping...\n",
      "Processing Record 40 of Set 23 | wum\n",
      "Processing Record 41 of Set 23 | kahului\n",
      "Processing Record 42 of Set 23 | husavik\n",
      "Processing Record 43 of Set 23 | marystown\n",
      "Processing Record 44 of Set 23 | aligudarz\n",
      "Processing Record 45 of Set 23 | zhezkazgan\n",
      "Processing Record 46 of Set 23 | isiolo\n",
      "Processing Record 47 of Set 23 | tyup\n",
      "Processing Record 48 of Set 23 | jensen beach\n",
      "Processing Record 49 of Set 23 | jaisinghnagar\n",
      "-----------------------------\n",
      "Data Retrieval Complete      \n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "# Loop through all the cities in the list.\n",
    "for i, city in enumerate(cities):\n",
    "\n",
    "    # Group cities in sets of 50 for logging purposes.\n",
    "    if (i % 50 == 0 and i >= 50):\n",
    "        set_count += 1\n",
    "        record_count = 1\n",
    "        time.sleep(60)\n",
    "\n",
    "    # Create endpoint URL with each city.\n",
    "    city_url = url + \"&q=\" + city.replace(\" \",\"+\")\n",
    "\n",
    "    # Log the URL, record, and set numbers and the city.\n",
    "    print(f\"Processing Record {record_count} of Set {set_count} | {city}\")\n",
    "    # Add 1 to the record count.\n",
    "    record_count += 1\n",
    "    \n",
    "    # Run an API request for each of the cities.\n",
    "\n",
    "    try:\n",
    "            # Parse the JSON and retrieve data.\n",
    "            city_weather = requests.get(city_url).json()\n",
    "            # Parse out the needed data.\n",
    "            city_lat = city_weather[\"coord\"][\"lat\"]\n",
    "            city_lng = city_weather[\"coord\"][\"lon\"]\n",
    "            city_max_temp = city_weather[\"main\"][\"temp_max\"]\n",
    "            city_humidity = city_weather[\"main\"][\"humidity\"]\n",
    "            city_clouds = city_weather[\"clouds\"][\"all\"]\n",
    "            city_wind = city_weather[\"wind\"][\"speed\"]\n",
    "            city_country = city_weather[\"sys\"][\"country\"]\n",
    "            # Convert the date to ISO standard.\n",
    "            city_date = datetime.utcfromtimestamp(city_weather[\"dt\"]).strftime('%Y-%m-%d %H:%M:%S')\n",
    "            # Append the city information into city_data list.\n",
    "            city_data.append({\"City\": city.title(),\n",
    "                              \"Lat\": city_lat,\n",
    "                              \"Lng\": city_lng,\n",
    "                              \"Max Temp\": city_max_temp,\n",
    "                              \"Humidity\": city_humidity,\n",
    "                              \"Cloudiness\": city_clouds,\n",
    "                              \"Wind Speed\": city_wind,\n",
    "                              \"Country\": city_country,\n",
    "                              \"Date\": city_date})\n",
    "\n",
    "    # If an error is experienced, skip the city.\n",
    "    except:\n",
    "            print(\"City not found. Skipping...\")\n",
    "            pass\n",
    "\n",
    " # Indicate that Data Loading is complete.\n",
    "print(\"-----------------------------\")\n",
    "print(\"Data Retrieval Complete      \")\n",
    "print(\"-----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "146639e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e19cfc7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Jaisinghnagar</td>\n",
       "      <td>23.7000</td>\n",
       "      <td>81.3833</td>\n",
       "      <td>76.23</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>7.74</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-07-18 23:17:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>34.5037</td>\n",
       "      <td>-93.0552</td>\n",
       "      <td>82.40</td>\n",
       "      <td>83</td>\n",
       "      <td>27</td>\n",
       "      <td>1.99</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-07-19 04:58:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rikitea</td>\n",
       "      <td>-23.1203</td>\n",
       "      <td>-134.9692</td>\n",
       "      <td>74.48</td>\n",
       "      <td>74</td>\n",
       "      <td>4</td>\n",
       "      <td>12.80</td>\n",
       "      <td>PF</td>\n",
       "      <td>2022-07-19 04:58:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mahebourg</td>\n",
       "      <td>-20.4081</td>\n",
       "      <td>57.7000</td>\n",
       "      <td>71.26</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>19.57</td>\n",
       "      <td>MU</td>\n",
       "      <td>2022-07-19 04:58:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Saskylakh</td>\n",
       "      <td>71.9167</td>\n",
       "      <td>114.0833</td>\n",
       "      <td>44.37</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>15.39</td>\n",
       "      <td>RU</td>\n",
       "      <td>2022-07-19 04:58:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mae Sot</td>\n",
       "      <td>16.7167</td>\n",
       "      <td>98.5667</td>\n",
       "      <td>91.42</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>6.91</td>\n",
       "      <td>TH</td>\n",
       "      <td>2022-07-19 04:58:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Barrow</td>\n",
       "      <td>71.2906</td>\n",
       "      <td>-156.7887</td>\n",
       "      <td>42.82</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>17.27</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-07-19 04:58:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>69.4541</td>\n",
       "      <td>-133.0374</td>\n",
       "      <td>66.20</td>\n",
       "      <td>72</td>\n",
       "      <td>75</td>\n",
       "      <td>4.61</td>\n",
       "      <td>CA</td>\n",
       "      <td>2022-07-19 04:58:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Norfolk</td>\n",
       "      <td>-42.7826</td>\n",
       "      <td>147.0587</td>\n",
       "      <td>49.50</td>\n",
       "      <td>61</td>\n",
       "      <td>45</td>\n",
       "      <td>1.72</td>\n",
       "      <td>AU</td>\n",
       "      <td>2022-07-19 04:58:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Torbay</td>\n",
       "      <td>47.6666</td>\n",
       "      <td>-52.7314</td>\n",
       "      <td>62.89</td>\n",
       "      <td>86</td>\n",
       "      <td>81</td>\n",
       "      <td>18.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>2022-07-19 04:58:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0  Jaisinghnagar  23.7000   81.3833     76.23        84          92   \n",
       "1    Hot Springs  34.5037  -93.0552     82.40        83          27   \n",
       "2        Rikitea -23.1203 -134.9692     74.48        74           4   \n",
       "3      Mahebourg -20.4081   57.7000     71.26        68          75   \n",
       "4      Saskylakh  71.9167  114.0833     44.37        64         100   \n",
       "5        Mae Sot  16.7167   98.5667     91.42        74          40   \n",
       "6         Barrow  71.2906 -156.7887     42.82        75         100   \n",
       "7    Tuktoyaktuk  69.4541 -133.0374     66.20        72          75   \n",
       "8    New Norfolk -42.7826  147.0587     49.50        61          45   \n",
       "9         Torbay  47.6666  -52.7314     62.89        86          81   \n",
       "\n",
       "   Wind Speed Country                 Date  \n",
       "0        7.74      IN  2022-07-18 23:17:44  \n",
       "1        1.99      US  2022-07-19 04:58:10  \n",
       "2       12.80      PF  2022-07-19 04:58:11  \n",
       "3       19.57      MU  2022-07-19 04:58:11  \n",
       "4       15.39      RU  2022-07-19 04:58:12  \n",
       "5        6.91      TH  2022-07-19 04:58:12  \n",
       "6       17.27      US  2022-07-19 04:58:13  \n",
       "7        4.61      CA  2022-07-19 04:58:13  \n",
       "8        1.72      AU  2022-07-19 04:58:13  \n",
       "9       18.10      CA  2022-07-19 04:58:14  "
      ]
     },
     "execution_count": 21,
     "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": 22,
   "id": "c6b95fca",
   "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>Jaisinghnagar</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-07-18 23:17:44</td>\n",
       "      <td>23.7000</td>\n",
       "      <td>81.3833</td>\n",
       "      <td>76.23</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-07-19 04:58:10</td>\n",
       "      <td>34.5037</td>\n",
       "      <td>-93.0552</td>\n",
       "      <td>82.40</td>\n",
       "      <td>83</td>\n",
       "      <td>27</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rikitea</td>\n",
       "      <td>PF</td>\n",
       "      <td>2022-07-19 04:58:11</td>\n",
       "      <td>-23.1203</td>\n",
       "      <td>-134.9692</td>\n",
       "      <td>74.48</td>\n",
       "      <td>74</td>\n",
       "      <td>4</td>\n",
       "      <td>12.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mahebourg</td>\n",
       "      <td>MU</td>\n",
       "      <td>2022-07-19 04:58:11</td>\n",
       "      <td>-20.4081</td>\n",
       "      <td>57.7000</td>\n",
       "      <td>71.26</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>19.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Saskylakh</td>\n",
       "      <td>RU</td>\n",
       "      <td>2022-07-19 04:58:12</td>\n",
       "      <td>71.9167</td>\n",
       "      <td>114.0833</td>\n",
       "      <td>44.37</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>15.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mae Sot</td>\n",
       "      <td>TH</td>\n",
       "      <td>2022-07-19 04:58:12</td>\n",
       "      <td>16.7167</td>\n",
       "      <td>98.5667</td>\n",
       "      <td>91.42</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Barrow</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-07-19 04:58:13</td>\n",
       "      <td>71.2906</td>\n",
       "      <td>-156.7887</td>\n",
       "      <td>42.82</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>17.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>CA</td>\n",
       "      <td>2022-07-19 04:58:13</td>\n",
       "      <td>69.4541</td>\n",
       "      <td>-133.0374</td>\n",
       "      <td>66.20</td>\n",
       "      <td>72</td>\n",
       "      <td>75</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Norfolk</td>\n",
       "      <td>AU</td>\n",
       "      <td>2022-07-19 04:58:13</td>\n",
       "      <td>-42.7826</td>\n",
       "      <td>147.0587</td>\n",
       "      <td>49.50</td>\n",
       "      <td>61</td>\n",
       "      <td>45</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Torbay</td>\n",
       "      <td>CA</td>\n",
       "      <td>2022-07-19 04:58:14</td>\n",
       "      <td>47.6666</td>\n",
       "      <td>-52.7314</td>\n",
       "      <td>62.89</td>\n",
       "      <td>86</td>\n",
       "      <td>81</td>\n",
       "      <td>18.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City Country                 Date      Lat       Lng  Max Temp  \\\n",
       "0  Jaisinghnagar      IN  2022-07-18 23:17:44  23.7000   81.3833     76.23   \n",
       "1    Hot Springs      US  2022-07-19 04:58:10  34.5037  -93.0552     82.40   \n",
       "2        Rikitea      PF  2022-07-19 04:58:11 -23.1203 -134.9692     74.48   \n",
       "3      Mahebourg      MU  2022-07-19 04:58:11 -20.4081   57.7000     71.26   \n",
       "4      Saskylakh      RU  2022-07-19 04:58:12  71.9167  114.0833     44.37   \n",
       "5        Mae Sot      TH  2022-07-19 04:58:12  16.7167   98.5667     91.42   \n",
       "6         Barrow      US  2022-07-19 04:58:13  71.2906 -156.7887     42.82   \n",
       "7    Tuktoyaktuk      CA  2022-07-19 04:58:13  69.4541 -133.0374     66.20   \n",
       "8    New Norfolk      AU  2022-07-19 04:58:13 -42.7826  147.0587     49.50   \n",
       "9         Torbay      CA  2022-07-19 04:58:14  47.6666  -52.7314     62.89   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        84          92        7.74  \n",
       "1        83          27        1.99  \n",
       "2        74           4       12.80  \n",
       "3        68          75       19.57  \n",
       "4        64         100       15.39  \n",
       "5        74          40        6.91  \n",
       "6        75         100       17.27  \n",
       "7        72          75        4.61  \n",
       "8        61          45        1.72  \n",
       "9        86          81       18.10  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_column_order = [\"City\", \"Country\", \"Date\", \"Lat\", \"Lng\",\"Max Temp\", \"Humidity\",\"Cloudiness\", \"Wind Speed\"]\n",
    "df = city_data_df[new_column_order ]\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4435d9c",
   "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": 24,
   "id": "198ccf25",
   "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": 25,
   "id": "45cfe156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1658262602.3186164"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the time module.\n",
    "import time\n",
    "# Get today's date in seconds.\n",
    "today = time.time()\n",
    "today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "99e85dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import time module\n",
    "import time\n",
    "\n",
    "# Build the scatter plot for latitude vs. max temperature.\n",
    "plt.scatter(lats,\n",
    "            max_temps,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Max Temperature \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Max Temperature (F)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig1.png\")\n",
    "\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1bccdbbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import time module\n",
    "import time\n",
    "\n",
    "# Build the scatter plot for latitude vs.humidity.\n",
    "plt.scatter(lats,\n",
    "            humidity,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Humidity \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Humidity(%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig1.png\")\n",
    "\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bf6eb5cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import time module\n",
    "import time\n",
    "\n",
    "# Build the scatter plot for latitude vs. cloudiness.\n",
    "plt.scatter(lats,\n",
    "           cloudiness,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Cloudiness(%) \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Cloudiness(%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig1.png\")\n",
    "\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e32b2ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rKCj0CoMt0d3RhBLOqaCg0OOEE91lnfITiucGF8HdfPcDAEqY7ABAGfErKCj0OEq20IGNovgVFBR6nPKKCizDitpsswwroryion8EUmiDovgVFBR6nMGY6O5oQlH8CgoKPY6SLXRgozh3FRQUepzBmujuaEFR/AoKCr2Ckuhu4KKYehQ6xG63H9Gx2MnEmivx6ApHGsqIXyEhK1euYm9VdbDo+BEYi51MrLkSj65wJKKM+BUS8vATS9GYMo7YWOxkYs2VeHSFIxFF8SskpLyiArXO0GbbkRSLnUysuRKPrnAkoih+hYSMLCzE73G32XYkxWInE2uuxKMrHIkoil8hIUtuuA6fs+GIjcVOJtZciUdXOBJRnLsKCZk/fx5vv/02hneePSJjsZOJNVfi0RWORBTFr9AhFouFD999s7/F6DWSiTVX4tEVjjR6zdQjhMgXQnwihNgmhNgqhPh/oe13CyH2CyE2hX7O6i0ZFBQUFBTa05sjfh/wSynlN0IIM/C1EOLD0GePSCkf6sVzKygoKCgkoNcUv5TyAHAg9LdDCLENGNZb51NQUFBQSA4hpez9kwhRCHwGTACWAD8D7MBGgrOCxjjfuQa4BiAnJ2fK8uXLAXA6nZhMpl6XuadR5O5bBqPcg1FmUOTua7oi95w5c76WUk5t94GUsld/ABPwNXBh6P8cQE3Qv3A/8Gxnx5gyZYoM88knn8jBiCJ33zIY5R6MMkupyN3XdEVuYKOMo1N7NY5fCKEFXgP+JaV8PfSiqZFS+qWUAeAZYFpvyqCgoKCg0JbejOoRwN+AbVLKh6O2D43a7QJgS2/JoNA9orNR7ti5S8lGqaBwhNGbUT0nAYuAzUKITaFtvwUuE0JMAiRQAVzbizIodJHYbJQBw0ElG6WCwhFGb0b1fA6IOB+911vnVDh8orNRAqh1hkg2SkXxKygcGSi5ehTaoGSjVFA48lEUv0IblGyUCgpHPoriV2hDbDZKv8etZKNUUDjCUBS/Qhvmz5/HI3ffiqF0BWXP3ITKbVOyUfYRR3p94yOVwViTuUPnrhDCACwAZgF5gItg+OW7UsqtvS+eQn8QnY1y9erVnHrqqf0r0FHAkV7f+EhlsNZkTjjiF0LcDXwBnAisB54CXiaYfO2PQogPhRDH9oWQCgpHOgOlvvFgHL32J4O1JnNHI/4NUsq7E3z2sBAiGyjoeZEUFI4+EtU3Lnuvos9kGKyj1/6kvKKC4rnto+D68r51h4Qjfinlux19UUpZK6Xc2PMiKSgcfQyE+saDdfTanwzWKLhOnbtCiLFCiGeEEB8IIT4O//SFcAoKRwsDob6xsoaj6wzWmszJrNx9BVhKMKGav3fFUVA4OhkI9Y3Do9fwqm0YHKPX/mSw1mRORvH7pJR/7XVJFBSOcvqjvvHKlat4+ImlwdG+2Uzt248z8twbsQwL2vjrPvsXj9x9a5/KNNgYjDWZEyp+IURG6M93hBD/A7wBtIY/l1I29LJsCgoKvUg8Z65q1TM4P32WA80tg2b0qtB1Ohrxf00wg2Y40dqvoz6TwKjeEkpBQaH3iU3Il1ZwDMxbjKF0Bd9+9Wb/CneEED2jGllYyJIbrhsQL9KEil9KObIvBVFQUOhbBmso4mBhIIfHJhPVYxBCLBFCvC6EeE0IcVNoRa+CgsIgZrCGIg4WBnJ4bDK5ev4BjAceB54ASoAXelMoBQWF3mewhiIOFgZyeGwyUT3FUsrjov7/RAjxXW8JpKCg0DcM1lDEwcJADo9NZsT/rRBiRvgfIcR0gjl8FI5gwjlbtmwtVXK2RDFYctkkK+f8+fP48N032bl1Ex+++6ai9HuQgTyjSkbxTwfWCiEqhBAVwJfAbCHEZiHE970qnUK/EHZKuUsWYMgqwF2ygJvvfmDAKrm+Irpdihc/OmDbZbDIeaQR+7IF2qQ4N5SuGDAzqmRMPf0vpUKf0jbMryn4W6m7Gz/8cQC2y2CR80giUQTPI3ff2ueL8pKh0xG/lHIPYAesQGb4R0q5J/SZwhHGQHZK9SeDpV0Gi5xHEgM5giceyYRz/g74HngM+HPo56EkvpcvhPhECLFNCLFVCPH/QtszQrn8d4R+px/mNSj0MEqYX3wGS7sMFjmPJAbbyzYZG/8lwGgp5alSyjmhn9OS+J4P+KWUchwwA7heCFEC/Ab4SEo5Bvgo9L/CACLaKQUMKKdUf9JTzrredhAPZKfikcpge9kmY+PfAqQBtV05sJTyAHAg9LdDCLENGAacB5wa2u15YDWgZIEaQESH+bkLz8BQ+sGAcUr1Jz0R/tjRak6jsWfWRSphml3ncFMrLLnhuuB9POUngyLBnZBSdryDEFOBtwi+AKKTtJ2b9EmEKAQ+AyYAlVLKtKjPGqWU7cw9QohrgGsAcnJypixfvhwAp9OJyWRK9tQDhsEk94EDB6g9WI/f72d4Xh5+v4+hQ4f2t1hdItzedrudmto6Wltb0ev15GRnYbFYevx8yZ5nx85dBAzWNtW2/B43KreNobk5g6aPRDOY+nY00X1kb1U1GlMGap0Bv8eNz9lAfl5ul/pKX/W1rrT3nDlzvpZSTo3dnozi30qw3u5mIBDeLqX8NJkTCyFMwKfA/VLK14UQTcko/mimTp0qN24MFvsarMW/B4vc9/7uPh546gUyTrualOHjuGFsM7+5/U5uvXYRd95xe3+LlzSrV6/G5XJHRtexo7CeHP1Gj+I7O0/R+EkUL34UlfrQZDvg91H2zE0se/LRQdFHYhksfTuWsNxzzz4fd8mCNgutmip/wFC6YkBG5HSlvYUQcRV/Mjb+g1LKx6SUn0gpPw3/JHlSLfAa8C8p5euhzTVCiKGhz4fSRROSQu/y2NPPkXHa1ZhGTESl1qDSGck47Woee/q5HjtHXy2C6qtIi66cZ7DZgo8GBptjtidIRvF/LYT4gxDiRCHE5PBPZ18SQgjgb8A2KeXDUR+9DVwR+vsKgmYkhQGCw24nZfi4NttSho/DYbf3yPH7cnFRXz3QXTmP4ngdeByNL+NkFP/xBKNyfk8XwjmBk4BFwGlCiE2hn7OAPwJzhRA7gLmh/xUGCGaLhZZ929psa9m3DXOStsrORvN9Ge/cVw90V84zf/68Abua82jlaHwZdxrVI6Wc050DSyk/51ARl1hO784xFXqfX1xzJQ88tQxCNv6Ax0XDx8u49dorO/1uMvnH+zIHfF9FWnT1PIOxVN+RzNEYBZVwxC+E+KkQoqPPRwshTu4dsRT6izvvuJ1br12E65OlVDx5JQFHXdKO3WRG8305re6r0bUyih/8hJPVPf5Q0ABx469+M6CT8B0uHY34Mwlm5vyaYBnGOsAAFAGzgYMMosVXA7UE2kDkzjtujyj6rkQQJDOa781RePQ9vvXmG3G53H02uu6r8wzGfjxYZB7IFbN6mo5KL/5FCPEEcBpBe/2xgAvYBiySUlb2jYiHz9F0Q/uTZPKP99a0OvYeBwwHj7h7PBj7cazMVWWbuPS6JaRwPRMmTBxQL4GjKbldh85dKaVfSvmhlPJuKeW1UsqbpJRPDSalD4MvgdJgJVknWW/kgI+9x2qd4Yi7x4OxH0fL7HA20yTMWGZfid86PBLRde/v7hsQNQ6OprDOZKJ6Bj1H0w3tT/rT1n003OPBeI3RMtfU1qExZ5CaX4KrsYa0gmMwTj6PPz35zICoHRDP/1RVtokmu4O8UcUMyR/N0BGjjwjb/1Gh+I/GON3+or8qOh0N93gwXmO0zK0eD2qtAXf1LowZuQA0q80EhGZAzGJiZ6z7Sjey/Z2/YjluLrozfolxznW4jFlUGQsHfWGbo0LxH41xukcbsffY73Efcfd4MPbjaJl1GjXNlVto+uIlRs48BwDHgV2kZg1v853+msXEzlj3rnicwhnz0Y6agdaahWnERNJOWkjdjk0D3sTWGQmdu0KIJR19MWY17oDmaIzTPdqIvceqm29sc48HS2RJRwzGfhwtc/O2bdhb3BScfAFDiqfQVPkDLRvfZPgJZ7T5Tn/OYqKjs4rGT6Lw5PPZ+sN2DJZgUj1fcxNNlT/w7UsPgdvOypWrBnT7J6KjcE5z6HcxcALBVAsA5xDMtDmoUBbNHPlE3+PoMNTBGA2TiN7sx731coyWOXyOsmduYmRhIb+8eiEvrviIpsoJAy6dcdhMpdfp8HvduCu30vDVm6SfthjzqOPwVm7usB8N5MFGQlOPlPIeKeU9wBBgspTyl1LKXwJTgOGJvqegMNAYjNEwvUWilBrROZTST17I1zuruGDhFUyeMatHbdmxPqA777h9wC5+C5upUrxNeG111K9/HdOk+aTml+BvtjFi4rSE/WigF7xPphBLAeCJ+t8DFPaKNAr9SrwRSk8VB+lP+jJNxECmo5lP+OXocdrY8ekbpJ38UzIy8qjvZFTbEwzU2Xi0map22za8Bw+iTx+KNtBKTl4uVquVgCk1bj8a6GsCknHuvgB8JYS4WwhxF7Ae+EfviqXQ1yQaodh7KCtnfzIYo2F6g45mPuGwy/K175B20mUY88ai0aci0ocdtbMjODRD2b+7jFNnzaIww8DYMUVYrVYgcT8a6KG3nSp+KeX9wJVAI9AEXCml/H0vy6XQxzz8xFKMk8+jtlXD1h+2U9uqwTj5PGpq6/pbtMMmPGXfV7qRsrIyNq5+j+9ffphZM9rVpzii6UgZhV+OroZqDLmjAfB73eh1ugGlsPqTrkRVDfTBRrLhnCmAXUr5F2CfEGJkL8qk0A9s3baNRplCwGjFMCSfgNFKo0zB5Xb3t2iHzfz581i44HR2vvEI+1/7Pf4dXzD8hDN4ccVHA8bm2hd0pIzCSk2bYsF9YCc+jwufo4Gc7KwBpbD6k64sUBzoobed2vhD5p2pBKN7ngO0wD8J5u9ROELweH2oW2wYswoA0OiMuFtsSGnsZ8l6hjXrNjJp0R0x5fUmDBiba1/QUYK8cBv89q57KVv1BFmzFlJQMgVpOzBgomwGAsn6IwZ66G0yzt0LCBZj+QZASlklhDB3/BWFwYZWBY6vXkOt1WPIHY27eheOr15DTlnI3LPPH5AhaV1BcfB2rozCSi3i5H/5rQGnsPqC6CAHc2oKQqXG7nB0uf9Ht3d5RQUPP7GUDRs3smbdxn5/npJR/B4ppRRCSAAhRGovyzTgGMjxuD3FhAkTqTIWUrfxdeobqjFm5GIZMhRU6qDDtwfj3/ujPZPJHHo0kMyIdaBG2fQF0ZFP6cMbKPvoZUyT5jN64gm4nbVd6v/xMpM+8NRfKZwxn+LFN/XrepJkbPwvCyGeAtKEEIuB/wDP9K5YA4eBHo/bUyy54Tr8e7+j+Iyfcsovl1J8xk9p3PkNemtWj8a/91d7DnSbq8LAIDryac+698iYfTmpoydTW9/Q5f4fG0XVok0j47Srqduxqd/XkyQT1fMQ8CrwGkE7/51Sysd7W7CBQrKLfzqrNTvQiee4MqcY0BpS2ux3uBEe/bWYqiczhw72e62QmOjIp3CEk1proNUTXMrUlf4fG0XV6vGQMnwcrobqyLb+iphKxtQDsB2QUsr/CCFShBBmKaWjNwUbKCRjGz5SUgLETvHnnn0+fk/bqJ7DNY/0p629J0wYyd7ro8E82NMMhDaLNgkaM3JxV+9COyQfvU4HdN7/o6+hye5g23+Wox01g1aPB7/fT3Pl1khm0mSO11t0OuIPmXdeBZ4KbRoGvNmLMg0okonHPVJTAiy54Tp8zoYeNY8M9PjmzkjmXg9U8+BAnqkMlDaLNgmOmHEWDZ/+g+Zd35CdmdFp/4+9hiH/tZj9G/9DQ+nn6DPyCDgbqPvPM5jyj+l3c2MyNv7rCYZu2gGklDuA7M6+JIR4VghRK4TYErXtbiHEfiHEptDPWd0VvK9IxjY80FfpdZf58+eRn5fbo3lUBrutvbyiAkzZbN+xk81bS9m+YyeYstvc694cCHRXeXdHsYbPlTeqGGtuAQZLJqYheRw/bWaPK+SBMniKNgk2fv4iozN05O5bzYGX7+y0/8degzTnMmTOlTi++5CqF36Ft/RDcoomYv/uw37PS5SMqadVSukRQgAghNAAMonv/R14gvbpHR4J+Q0GBcnE4x7JESMWi4UP332zx4430OObO8NiNrNr8wZSR0/GYDHg97rZtXkDueZDEc5dNWeFzQPnnXUG9//p0YQmjsMxKXY1d0z4XMbJ56EZNg+jowHvhjdJHT2ZXbs2svimW3kmifNGX19HJpyBFG7bLqw1SdNT7DW0ejyYRh6H3WDk1FuWARDw+yh75iZ2bt3U25fRIcmM+D8VQvwWMAoh5gKvAO909iUp5WdAw2HKNyDorKrUYB/F9jX9VaWrJ5ABP85NK/Ee3AsygPfgXpybViID/sg+XTFnRY/EDVkFHY7ED2dU3NVZafhcLdo0RGoGxoKJpJ28kJbKrWTMvpwW9EmdN9mZRkdt1h8mqs7kjidT7DXodTpa9m0bEDb9WISUHQ/ehRAq4OfAGYAA3geWyc6+GPxuIbBCSjkh9P/dwM8Imo02Ar+UUjYm+O41wDUAOTk5U5YvXw6A0+nEZDJ1fmV9jN1up6a2jtbWVvR6PTnZWVgslsjnA1XuzlDkbsuWraWozZl4nDYCfi8qtRadyYrfUc+E8SVAsC/srapGY8pArTPg97jxORvIz8tt0ycAduzcRcBgRa0zkKb10+RV4/e4UbltjCka3e7chtDK6mjcdZWRcyci+jxhEp0n+lwutxtUGgjN+L31+9APGY7n4D60asGE8SUdtnWy503UZhlWMw02R1Jt2VW6K3dOdlZSsnrdLbib6tCZLOhN6T0me1f69pw5c76WUrZLStWp4gcQQuiAYwiaeMqklJ5OvhL+XiFtFX8OcDB0nN8BQ6WUV3V2nKlTp8qNGzcCbQtsDCaSlXsgRDZEc6S3d1eZe/b5uEsWxKR++AFD6Yo2JrFk72PR+EkUL34UlVrDeTlNvFWTltAckOy54xFtJkqUriHeuWpbNbjVKah0RlprduFY/xqZMy/G/vk/mVKUx4fvvtmu6E30dW/dto3jf/EUKvUhq3Ki64vXZg8/sbTb19xZe+zbv58HHnk87v2Jvi+xco8sLEwoU1jm8DXMmjG1x1fqdqVvCyHiKv5kcvWcDSwFdhEc8Y8UQlwrpVzZNXFBSlkTddxngBVdPcaRTH+FhQ60l81ApqN8N9EkGzraFf9QsueOR1d9K+FzpUw+D5fbjau6AfuGNzGPnkzDp//Aova2M2XG67+Or7+lqmwTw0sO6Z5E1xevzW781W963PYflvPmG6+nePGjcZ+z6Ptis9moqa3Dvn87Xrsj+DKbe1NcmWLTNAAD8nlKxrn7Z2COlHIngBBiNPAu0GXFL4QYKqU8EPr3AmBLR/sfbfRH8YZEL5twTpHOHI5HKolehj3tnI5W5uTkRvxDCxecnjBHUnfP3ZV1DLFFSFwtLgItzTQ37WPMqEJ+f8997Y4Vr/8WnHwBu997GpPJ1K3Sir0ROBGWU60zRHwlsc9Z+L44J59Ho0zB32LDtfVjhk0/h8rP30j4Mhssa3qSUfy1YaUfYjdQ29mXhBAvAacCQ4QQ+4C7gFOFEJMImnoqgGu7KO8RTX9ENsR7WBvyj+PPy17k2EuWYMjKjTi2YGB13s7o7kyms4e3J3PZRCtYd+EZGEo/YOGC03lxxUcdnr8v6Oq5yisqGDotGOratOtbWnasI9DcSKDFhvPTZznQ3NLll9XhzHI6kjP4nDkj22Kfs7B8i665HpcPUrPzKZ5zMdkl0wESvsx6cvDWmzPxZBT/ViHEe8DLBBX2xcAGIcSFAFLK1+N9SUp5WZzNf+uuoEcD/REWGu9lU7f9W1Kmnh+So6lPy8b1VGe32+3ccv8jfRL6eLiEFezq1au54fr/Ye7Z5w/osn2JCIe6ohI4t6/DeuKPUaflIg+U4qr8gscfurPL8vdG+G8k+iav42ib+fPnkWYxMz3G1l948vk0bHgnuL4lRqaeMk11NPjoiXKoyYRzGoAaYDbBEXwdkAGcAyw4bAn6gYG6grE/wkLjhdE11+3DPLRtpEdfLEjrydWbNbV1fRb62NP09/m7SzjU1fb1e1hnXoYuawTS7USfVXhYi7F6Ovw3/Jz5Pe5uV9KaMGFiXJl6amV6by9o63TEL6W8skfONEAYyDa4/ljcFG8qrZI+Uv1tUzH1RfxxT460W1tbsQxrG+KY7Mirvxfk9ff5u0t1TQ0qVQotdfuRQoXPUY/BkoHP2YBl2NgBU/sg3Jf27d9P2TP3JXzOVq5cxcG6Wsr+9UCkMA3O2g5NTT1lmupts28yuXoeFEJYhBBaIcRHQoiDQoif9sjZ+4GBsjQ8EX25uClsVrHXVbFt+R/45pGfYyhdwa+vX4zrm7doqvwBoM8WpCU70k1mxqbX67s98urvBXn9ff7usHLlKlrQY551Ocb88eB1gRAEfF70Ot2Ae3HNnz+PMUWjEz5n4QGiafZVjD9nMY5NK9nw6GKa1zzf4WCsp7LA9nZOq2Rs/GdIKW8RQlwA7CNo4/+EYPnFQcdAWhren0TPfI6fe1NkZBK2qZ8wdWobh2NfpFVIZqSb7IwtJzuLumce6ZPQx56mv8/fHR5+YimjzrqGJmHGPHEOtq9ex3zChUivG6veT936t3qsfGO0H8hiNiMDfhwhx3FPOUBjZ5+5E2ZGYvX7opBNbzi1o0nGxq8N/T4LeElKOajTMAz27JA9RWczn/DMY8L4kj5Lq5DMSDfZGZvFYjmskVd/ppXoqoN7IPisyisqyCueRH5eLubcUZjGzsC54TXqXr2HIZWre+zFFe0HGnrJvVQPP5VdDR7ST17Yoxk9+9vP0pP1I+KRzIj/HSHED4AL+B8hRBbg7pGz9wO9/SbtaXojpGvlylV8uf4r5KZSVKnppIyZQdro48nOzO5zB2Ls9S1ccDpr1rWPlgiTaMb2zcub28S933jtVZx77rm9orCTvSfduXdd9UF1x2fVG30qerZmtVphTBFNJRMPe4VtLNEv/u07dpI6ejI6axZ71r3O9Kvv65ZPKF57DAQ/S2+G7ibj3P2NEOIBwC6l9AshWoDzekWaPmAwTaN7wxEdPmbanKsQQ0vwN1Vj+/LfqFOtOKp2tcky2dvEu74XVyROIwDxzUEVn79JC/o2tYH3VlWzcuWqHr+vXSnE0p1711UHd+z+HqeN2qZmLl50JSdOn95OqfdWcENvDahilfKWLZuZHFo12+rxYLAYUOeOpj5U1Sqe2bajF12i9li44HSe/ufjtKDH02xHl2ohhVaeefSBw7qegUJCxR+O04/ZFv1v3Pj9wUBfLoI5HHojnjx8TG8ztLqd6LJGYJ15GbbP/4lagMzQ9eQlJCVLV64vnoKp/PwNii64uc1xNKbqXol7T1bm7t67rvqgovevLV1P2SevknbST5AaHW6rtp1S7601Cr0xoIqnlFu2bKfi8zcZNfsi9Dodfq8b78G9kQyYXfEJGY2GhO3xxjvPotIZMJfMA2su2KqRpYlNSIMt7UlHI/5zQr+zgZnAx6H/5wCrGcSKf7BQXlFB+vAG1i+7HVdDNcaMXEbMOIsDh2GOCSuKvT9sx2DJoLW5CaHR4q3fS8mPf0nj5y/23AUkKUs0nTna4ykYc4qBvOJJbfZT6wy9YrZKVubuBhGMLCykqmwTLdo0Wj0e9DodKd6mhCaG6BlQ+dp3SDvpMrRD8lG5bKQVFLVT6r0Z3NDTA6p4SnnUWdew841HAKgr3Yizbi8EfIyYPi/iE4qeZXT0orvt1zclbI9vd1dw/JX3Rs0sJ9JUkB/3BTmQQ8QTkdC5K6W8MhTDL4ESKeWPpJQ/Asb3mXRHOebUFMo+epmUqRcy7PI/kzL1Qso+ehlzakrnX05AWFHodTqEWo1pyDC0+EkbMQ6DJaNPbZjddbTHOl7HjxvX7jh+j7tXriVZmbt7bbNmTGX7O3+lpakOfUYeLU11bH/nr8ya0S7BItDWId5SX40mIw+fo4Gc7CygvUPSnJrCl3+9hdUPXs36ZbdTW7p+wAY3xHOw5hVPQkeAfRs+QD/uFIZf9Fuy51zJ/k2f4fz02bg+oY6ctInuE8iknbsDPUQ8HslE9RRGJVaD4Cresb0kj0IUQqXGNGk+2iH5IFRoh+RjmjQfoVJ3+5hhRZHibcJrq8O5ZzNNX7xI1phJfR4rHi+KZ8+qZzhYV9ulCJV4x/E5G3rlWpKNse9qLP7KlavYsXMXD/7l/0gdPZWWr9+m6oVf0frduxTOmM+adRvjfi86+gO3HW/lZvLzcoMOVtq+bFauXEW9y49h4pnkXHofKVMuYNuHL1H+9uM92lY9FWWUSCkLrY5jL1nC1FPPYnzJeI6fPY8pP7mVIVnZ7UbYnb2AE92nsUVFHX4v+hq/XP8VbnvbYMeBvtI6maie1UKI94GXCI7+LyUYx69Ae9teT+bftjscjJ54ArX1Dbhtdeh1OkZPPIED2zotgJaQ2KyLrV4fRhXkuSpY0sdO7lizjcVsJuBxY5p3I3kh+/21t9xF5l33dhinHc/8kz/rql7LqxN7rni27K7YvMOmghuvvQqPz49l9InoxpxEYXYWeUOHRvLAdyTT/PnzIseRBfkETKntHKwPP7GUEfMWI6xDqamtw6czkj7lbDL3fNxjbdWTZo9EDmOdVhN3NB7PXNWZ0znRfQLifm/hgtM5ftpMyvbVBVfzXnIV3tKvKfvoZVQqdSSJ20CdRYVJJqrnhpCjd1Zo09NSyjd6V6zBQWwnryrbxANP/ZXMwhLcaitrv9nMuiuv5dfXL+aUWSd3+fgjCwtxO2sZO+ZQBEtT5Q+H3aEGknM7Wpa5Z59P6qwrInZVYR2Kr/gM9mx+nxP/O37e9HjHgWCxir6QuSf2e/iJpajzj8PrakabmQ9+H+q04dTUHSQ1JQVpO9DOYdmdlNFhe7ZKrYnMCAL+sZRteatL1x97/huvPVRLqSedx4mu5+EnliYdatlRm4T7SEf3Kfp74ayptTYf2fNuQJtVwL6aOoaMnoDXH2DHx/9mSPGUAR8iDsmN+MMZOBVnbgyxnbxFm0barMup+XApQ8/7NRm5o2neW8qflz3HpOOO7fLxB9uag8Ml1tFWU1tHysjjcKx/JWHe9HiEqytdff1NgyLCoryiAru6EfU5M8mYdj4Na1/COvMy1NZs9mz+CmPFZ5F7fjgpo3siNj3e+aNDZ3vaeZzoerryXHR3oBP7vXDW1P3LH8IwtChocjVn4HTaGD3xBL5dvSxSoWughoiHSSZXz4VCiB1CCJsQwi6EcAgh7H0h3EAn1nHU6vEgLFmg1mLMG4tQqUktmEDK1POpqa3r8vF7e/XeQCPWHtvq8eCr39+mWHVnttOwYgoYrN3O8NnXK2FHFhbSXLcPlVaPqWgqGVPPwb7+FepevoP6j56J3POVK1ex6Jrr2VtZSdkH/+Rg2dddciT2RA6geI5MjSkjcv7uOLW72t799VyEn3djRi7u6l0AqLUGWj0ecNZy4vTp/bLSuzsk49x9EDhXSmmVUlqklGYp5eFVOT5CiO3kep2O1urd6DKG4ff7cbvdtDjtqNPygkWru0E4guXxh/4IBEvRDaRU0j1JrGJq2fYpVW89iLN6T9IRKPGqK3UlwqInU0Mny5IbrkMlfQQ8bqSUGArGk3XaVeRNnYvBaOTGX/2G46fN5Npb7kI77ccMv/IvwQivT16ltnR90o7EnlCY8aJkokNnu+PU7k5790dKjfDzPnLmOTR98RKuqu34WpuRjfsHfBK9WJJR/DVSym29LskgJLaTp3ibsK9/Bf3QsXg8HgJ+HwGXHdliwx8IdFt59Icy6gtiR3pARDF988jPadm1kYzZV5B3+Z86jUAJH+uzz7+g7P0X8LmbI591JcKiP0Lz5s+fx6+vX4zfcRDbtjWI5gZE5QZqNn1C/oIbKV78KHtsPnzFZ5CSM4qA34sxbyxpJ11G+dp3umSuOVyFGV5nsH3HTjZvLWX7jp143S2R83f15TKYQiHDz7vOZGXM7Auwf/5P9i27nsw9Hw+6mXgyin+jEOLfQojLQmafC+Ot6j0aie3kQypXc8kZJ+MsXU3zjvX4mxuguYHmb1egM6V1uzP31MMxEJJ5RcsS72UGBJPDTZjI8Zf9mrGTT0Ld6oRQBEp2uiXhAhp3yQJGLPoj+kkLcDsaqS1dD3TNjt1fybnuvON2Ro3IZ4K7lMAnj2P/7kPGnvPfDC+ZikqtwdtiJ2XkcSDA52jA53GhzxlFc+3edqPN3rzP8dYZuJvq2qwziH25AAnl6c327ul2iH7eGz9/kSlFebzx4vN8s27NoFL6kJxz1wK0AGdEbZMozl4gvuPoPyNG4y//Ets3b2LMyKV4zkXoTend7sw94TAbaKsLO4v+6EoESvSxhM3G3gCoU02Uf/ESOpO134t7J8PKlauoqa2LRMps3batzWpkY0Yuvvr9oDNSMHwYNbV12Pdvx6ihzWizt+/zmnUbKZwxn7rv3qXpk79hzMhFd+rPWfP+hwmvqyN5equ9B1p/H2gcdRW4+oIJEybiLlnQpjP7PdWdOrgSrQdosjuoKtvE8JJDo6quPhzJhNnFCxNMtr5nV3OVRBfmbvV48B4ow7VjPc37y5h79vlYzOakFUL0izH8khBqF7Y9pR2aGuLJ3B+RVGEldfON11O8OBi26vj62zb3fOTMc9j24QukTzkb8zFjkbYDiH1f8sjTT7bLEWScfB61rRr2/rA9mPJh8nk9Vuy7vKKC4sU3MWr2RZH99KamhIOazvpddHtjyqay9Gvq1rxI8fCsyAi9OzlwOjpv+PPzzjqD+//0aIfHjM39X9toZ+S5Nw76l0lCU48Q4pbQ78eFEI/F/vSdiIOPrq4kjTV7HCw4lQeeeoEqYyHFix8lZ/o5bH/nr+wr3djtaIzOptSJTC92e+cBXN3xQYQLcweMVny2Gpq2rkF33FkMW3gf7pIF1Dba2bPqmaSchLFOdqvVilbAKSeflNCO3ZGpqa8jRsJx/G57A5/9+TrKPvgn6UWT2f3e05Hr15msWNReMvd83KFcW7dto1GmEDBaMQzJJ2C00ihTKN3WNTfdvb+7j0uvW8IWQwmqOTdysOBUbr77AcypKV1Kj9FZvwubT5rXPM+GR4OVrsafsxjT7Ku49pa7WHzTrd3ybSU6b+m2bZH7bsgqiHvMsIkob1Qxl1x5beQ5rB9xGna/Fo/TNuD9EZ3R0Yg/3FPirxXvBCHEswSLsddKKSeEtmUA/wYKgQrgEillY3eOP5DpaCVpvFFUvPUAGaddTd137zJq9kWR0dXeFY/j+MzcrTjhzqbUiUZIyYShdmfRTrgwt86ahW3TB1im/wh1ajpCqBDWoagnnU/NyidoWf4H9FoNJePGJbzmeKN0nzbQ4YsxLLOwDmXn7gpaPQFk4Sncds/9fWKzje4H+/buRWXeRuDUYrz+AD67HVvNfqS9huY1z3PA4WBkYSHPPPpAp3J5vD7ULTaMWQUAuCu3Ur/+dbwHDzL37POTrgnwpyefIe2MG0gtmIDf66bJAWmTz0N8+xp1n/0r6bZOxpQzf/48Hn5iKSdcfX+b/SpL5tGyaWWXF4OtXLmKJruDb9Z8gGXYWHKys7Bardj376TV62NUpK82tTtmtInINPIc1PYG9n/1OqasfET6MDJOWUT52jciK3QHa/W+jpy7o4UQJwD/klI+H/uTxLH/DsTend8AH0kpxwAfhf4/Iol1cFksloSjzC1bNrcZnbhcLqTRSkP5lkgYY+HJ55NmMXc7GqOzMLtEI6TW1tZOj90dB52juYXi0y+hZePruPaVInQpSL8Pd2sre/buQ1swEY0li3GX/i/mrLwOFVa8SJL8vNxOTU2YstlbVR0ZHWsLJlJatr3Xnd6x/cAvVUhtCmpTJjk/eZC0WYtQGU1ItZ4WqeHxh/6Y9D3XqsDx1Wu4qrbj2PEV9etfxzRpPrkX35P0iPnhJ5YSEBpS80sQQqDRGdGYM2hWm7E7HF1q62TDO+P1Iay5eJrbzjiTXceRM/0cXFs/pqWpjsp9+9lXupHytx+n2enk25ceYv2y2yORX9HHjB7EeHx+UgsmRKKn9DodmsxhuBqqqTpwgE3fb2b9x++xr+oA9/7uvg7bdKDR0Yh/OPAX4BghxPfAWuAL4Mtkyi9KKT8TQhTGbD4PODX09/ME0zsfmctQ45BoZHxw+R8ioyKbzUYAgc/RiHHYuFC89ks46/YetsPLa6tj/dP/S8DvJzU1lV/ecG3kgU00MtMX6zs9bnccdCMLC3FbMhg58xxs+3chPS3ohpcQcDfjd9nxH9hFSmZu0qO8rqZsGFlYyPbSr9EVTkKjMwLga6giJXtEwtS7PZVvPbYfoFJhPekyhFaPUKnR5xZhPWkh9e88FDElJHuuCRMmUmUspG7j6zRV/kD6aYtJzS9BG2iNm6Y53vVV19Sgzx6Nu3oXxrxgPka11oDtwC4mFBZ2ua1ThI9vn7sDEIwZVcgj99yZMJla+Bmoqa2jpaYcr8vJ9vf/QePe7bgaqtGmWBhhTWknc7zZsykrn/K179Bcu5cGn4u03Hyyz7gObcFEfA1VocivfehM1khfjfYXhfP9G0KFXo7JzmLnpnWgNXKgppaAy4lr26eYp5zDA0+9AASjswYDQkrZ8Q5C6ICpBHPynxj6aZJSlnR68KDiXxFl6mmSUqZFfd4opUxP8N1rgGsAcnJypixfvhwAp9OJyWTq9MIGGk6nk4o9lRhCU/BoWmr3oNZo0Zgy8EoI+Hz4mxtRp1hRG0wEPC589lpGF47AYom/ds5ut1NTW0drayt6vZ6c7KzIvna7nT37qpAGCyqdEen34nPUoxaSwvzhWCwW7HY7e6uq0ZgyUOsM+D1ufM4Ghg/NiThME5Hou/l5uR3KW7F3Hz6fDxmQIARCCIRGi9AaCbidpKQNQWNIBcBdV8mE8Z12uTbt3VE/sdvt7KqoRGPNRqXVE/C24nc2oDen43fUtzlXV6+vs3uxq7wChBqVRoveZKWloRptZgE5qYKaltBBpMRbX4klb3SXrj1a1paGGrQZeciAH51Wi1odzOoae7zY63PW7UNogm2iMWcE28fjxu84yKgR+e2uOVFbd6XdwvuqUtLwoYKAH5+zEYQK6fegMQ8JyeFCuO0MSbfSYHO0O7bf6yElp7CdLI4Du0nJzAO1Fo/Xi1BryDZIqqpr0KpVEZl27NxFwGANHtPvx+P1QsBPoKUJgyUTj/0g3lY3qNQIjQ5NigWVPpWAx0XAUcdxEyckdZ8Oh67owDlz5nwtpWyX0zuZcE4jwZBOa+inCtjcBTm7hZTyaeBpgKlTp8pTTz0VCI4uwn8PJlavXs1b733QLtqnqfIHDKUfRkYrn33+BdYRJWRPOh1vmpFWTzU6jZrm955g/+6yuMdeuXIVt9z/CFmn/ATLsBLsFTupe+aRiE187tnns910HLrCnNDo1oirqhr75/9kSlFeJNY6UVRPMu3dnQLhDy19HpupAG/dHsyTF6BKTcfbUI5jwxvI5kbOvO8NsIXb6ANuuP5/kj5fMv3k+Gkz2WPz4W2xY8zIZeTMc9CZNO3ONffs80P3LTeyramyCcM7z7arJxu+F8bJ59GsNuM4sIuWjX/ml1cv5ISpU7nl/kdwFZ4SGXU2ffEcPvtBUqZfzB0XnsCft2jA56Gl/Bu8369kwgX/006ezgi3zZfrvyLz9MWMmDgt8vKO15ax11e+4Rt2f/Y62qFj8B/cg99+EI3w8+vrF3Pujde3O1+itu5Ku4XlXnTN9bh8kJqdz8iZ57DzszfRHncWaqPAlJpKTnYW0tbCtj/czrhL/7fdsbctfyS0ve0z9u1zf2D27S+hUmsiM4obpqRy+1338MoLz0X6jsvljtj4LcOCiRd3v/c0KbQyYcJEltxwHedfuojC659DpT6kPgN+FRVP3kCrreupWbpKT+jAjkovPk2w6IoDWE/Q1PPwYTpja4QQQ6WUB4QQQ4HawzjWoKOjUMHw9PnQwxLzchg3LuFxk4mJ56QzUWsPhWYackdzsNlOeYUnsi3emoTOpvGxCvjxh/6YVGhck91B/oIbafzoJdJmXop+WDHS40ajN6KZ/TMaP/g/An5f3HDKnorR/v09d3LtLXchJv0YrLlUN1QjP3+Zpx68p81+XVlHcds99+MsmI3Do0WlCZA6cjKa1HT+9OQTjCl8n6xTrkBYh7K3qhrtkHzSZl5Kw0dP41j/CnLBeDy19fhtNbg2vUfB5NndCiUNt8Fv62opWxOsqFZQMgWctW2OF74fn33+BSNGnoOw2QBoNg3DOnUB9R8tQ2tIjSj9jswY8V7EW7dtwzTyHLaveoWWHesINDeSmjkUtW1fQrm1KlBlDcfVUE352ndoqa2goHAirQ1VjB0TvAcBUyoOuz2uX0mv1VD32b9oyD+Ouu3fBnMgSR+5OTltisFbrVZ06mpOnD69TZ+JF5jx+6UPt9nHbLHQsm8bphETI9ta9m3DnGB2OxDpyLlbAOiBamA/sA9oOszzvQ1cEfr7CqBr+WAHOcksZ+9OIq1kqgxhq8bvdUc+d1fvQpdq6XRtwY6duxKufOxKGGfsvi4fNMoUfM4GtFkjIBBAaPUEfF505gxUAU/CNurJZf4BjxvHppXUv/cXHJtWEvC42+2TbOKxlStXUVq2HVXeeLRDClCbMnA7GtENKSAgNGwrK6PGJajctx+VkAQc9UiNjkBzA//731dglG7Ep0/i37Ack99OnquiW6Gk4bY2zb6K8ecEQyQ3PLqY5jXPt0n4dvPdD1BlLETojOx5+T6+fv4etq//EI05A701m7SCYzjtt88zadEdCQvBQNBME9sPrr3lLppsDhp/+BLn9nWYZ/yYIZf+Ad2xZ9GCPmEfaUGPftKCSMU5qUuh4as30esO1YK279+J2WKJe09Kxo1j4YLTqfjsDRy2BvwSVKYhNNnahwcnCrGOl9YiehVwmsVM3XuP4dyzmYDfh3PPZho+XsYvrhk8S54SjvillPNEsLr6eIL2/V8CE4QQDQQdvHd1dGAhxEsEHblDhBD7gLuAPwIvCyF+DlQCF/fIVQwiOksR21lO9Xh05lxdcsN1XHvLXTT7A6SMPA5f/X4aPnsBi9rb6dqC6EVFh1O4O3bf1Ox8/C02NKYMZMNeGFKI39uKSkCm2k3xzJlxzQHQc3VjH35iKSPPvbHd7CpW/mQXdT38xFJSskfgrd+HYXgJ6AxoTBk4KzahTbXQ7GrGZW+IhEj6HA1kqFsZO306d95xO6tXr25jzguPom/81W/ambM6MnXFtnXuhJkhE8+KNvuo849j/5b1DDnzeqTRir/Zhu3LfyM9blr3fEfxnIuSatua2rp2/aCyZB7qxuXYN71Pxpk3oM8rJuBxgdHKqLOuSdhHRp11DU3CjN/nwTB0DJmnXE7tqscZPmZCmxngL665khdX/CvuPfntXfeitmSRNftyDLlBJ3XDp//A4nEEB11dLNYTb4bpeOtJbCsfodbjxWyxcOu1Vw4axy50YuOXQc/vFiFEE2AL/SwAphFU5B1997IEH53edTGPLrqaPzyZKkNPETRDbF+9jHB0xe/vua/T5FnRWS4Pp3D3li2b8e9txNVYgzEjl/T8sdR89Rra7JHYv1yOedqPUKdYSde04vrmLZZ0YN7oqWX+ycqf7Mu4vKKC0acupPQ//0actBDd0CI8B/fS9MVL6DRq0o87HcdXr6PW6oMKydnA7k+f4/dLH24nW0fmLKBDU1cy1xWuAZB20mUY88bidTlp1WhJnXAa9Z8+j85oonxtsNJbdNRLPFpbW7EMi3E+W3ORCNRqDWpTOt76vQiNFhWSvOJJlH3293bHCco9CbOzmZraOty2OlIzc7GmGBhSuZqyz/7epu1PmDo17j25eNFVDDn3VnQ5o2n1eCE9H9OJl1L97kPsLnszcr5ki/XEG+CMPe96DKUrEg5OBjod2fh/QXCkfxLgJRTKCTxLHzh3FZInGcXU1ZfJIeXhjGyLVR7JKuDwFN4yaQEZ+SW4q3dR88VLWIYMpWXPBvQqcH2yFL1Ww5Bx4xKWgAyPcrds2UzLlu2MOusa8oontXHAhRcpJZNqoisvkGTaLxKietK5lH+5HL+zCXWKGZ1Gjbe5iUmn/RhP9Q7K175OfUM1xvQcUmiNe9zOUg50NNNK5rpGFhay9pvNZOSOBkBrNBHwefFnj0SlTyHv8j/hq9/Ptg+DM8NnHj300olFr9djrzh0PpvNhrt2D63OxqBfqbkR88jj8HlcqFy2hG0cLXe0M3rEpElxFWzieyJRpQ0NRu+o1AihQpOZj8frjRSMiaazQIGeLi4zEOjIxl8IvApMk1KOklIuklL+n5TyOylloG/EO3o43EyCXUm3G+9csdviLc2PfWCT9UeEp/AaU0ZkCm+ediGNO7/hhaef5MCeXRzcu4v9u8uSSrEw+ea/MfyEM9j5xiOs/9PP2PnGIww/4Qwm3/y3LqWa6InCJPGOl56TR+H0s1DrjbTW7kHlbWFYThY4a9EPO4b02T8j8+wlGMedytCheXGP1ZHfpjOfTux17f70VTa98Du2btsWud/hGgDNe0uRUuLzuPA4G1G1OjFnF3SaETWanOysyPmaGhrYuWkdzZs/IOOkSzEfN5e6j/+GfefXeG11pHibErZxvPuxZ9UzHKyr7dJzMbaoCOfub5F+XzAc1OvGXfk9KRm57fxAnfmpolcBb9+xE1vIAT7Qa+p2Rkc2/iV9KcjRQKKRRV9mEox3rsU33YpKZ2DEvMWRbfWrnqHu7cfxH3dHwuiarphAYqfwRksGgRRD0tcXOwIeNfsiMkZOYNvyPzBp4f+2G/0mk2qiO/6UZI7327vupTxcjDsUTbNn1TP88Mqf0E69JJhe2bmfxq/fxa+OPwrtbNTe0WfR1/XNy5tpQU/RBTeTVzwp0rceuftWfn39Yv687Dl8U8/HPHQ0vvq9uLd9QvGci8kOR9AkUZPXYrFEauF+uX49usx8xp/xE/TDjqGmtg6/20n9qsfITE/rcEYXez8sZjMBjxvTvBvJC5kwr73lLjLvuhdHc0vCsOH777qNC6+6HqHWoEnLxddUjeu7lRwz7wrKP3+xzb6dzazCq4D3b/kYtdHKruZm/PV7cX37Nr+8emGH7TKQSarmrsLhr9y02+2R+G7VSDNbDuzi0uuW8MurN7Jm3cYeK1DdGfGyNzr8GtJK5rU9/7xgFIjKbaPsmfsSKsVkTSDxpvAdhajGtvfWbds4fu5NbfaxDCtqF9ZXW7qe8i/exjHswqRy03TVBJYMew/UYD3xxxhyRiLUaqyh9vz+H/fg3/w+jvWvYMzIZdzcy9CZrHHvc2d+m86czYnCg6P71pIbrmPUO++x49O/40CiVmsYPutHkTw0ALu+W0fl/io0pgyESoVWSKxpaZGY9rDc4fMVjZ9E8eIHIzHuVqsVS+tktv3wGUajsdO2i74fc88+n9RZV0RkF9ah+IrPYM/m9znxv+MHHIT/PiY/h53rX6a12QZSkjpkGC31Ve1G6R2Zcdqk+07JoHztq3ibagl4mjFaMnn6n69wwtSpgy4zJyiKPyl6YkReU1uHcfJ5NAkzmtQMrOPyIvHd5hRDXKXWGzbErdu2ocs/C63RisFiwO9143Y0ETBntzv/AYeDMUWj2bl102Gds6upjuNGUcSkKYa2YX1pBcdQW7qesk9exTztQnSZw9pk3OyLhzMst8sHmcUzCfi97K2qBoLt6Xa7+a//fjBm4Y8v7n1OZjaSzEwlkWL75uXNwTaefRWzFwbbeM+qZ7Bt/oSmkROwDCti13frKF+5jNRJZ2OZei6tVdtp/GQZjS1uqoyFkbaN9qfEzlRqS9dT9tHLZJ6+mOIT53Tp2YmVvaa2jpSRx+FY/0rCgIMwF5xzFg889QI55/ySlOHjaNm3jYqPl/Hjaxe12a+jmVX0+V2mPNJPW4w6NY2af99O5pnX0/DpP/jtXfcOSsWfTAWuo57oUfLWH7ZT26rBGMpxniytra00q81ozBlodEaEEKTmlxAQGjxeX5cLVHcXj9cXDKMMyaDRGVGnWvEc3Ntr509m/UI08eL0C06+oE2a4rA9/hfXXBmxC5d/8TbmaReiMWWg1Wi7VW/3cPwsYblTs/NprdkdSW4WLJqSOPY8UTtH+21mzZjKomuuR2/NYkj+aDZs3JiUTyfRGgRvgHZtPGLeYrLTLZH7VPHmI6SMm0Xa9AsRag2G/PGkzbocX6ubHf95idqmZn57171tjh1rp9/x8b8xTZrPiInTurzmIlb2Vo8HX/1+jBm5kW2JkratWbeRsef8NylpWbQ2VJGSlsXYc/673XqEjvw80ed3t7aiMWfibaxCa83BmDeWjFMWsWN3+3MPBpQRfxLEGyU3ejzUdiHHuV6vx3FgF9Zxh5x57updpGYNR23b1y7Vbd1n/2LhgtOZe/b5PZIYLEw4e2MkpLB6F8Lbgn39KzSNKepW8ZFkzGBdManEG6UWnnw+DRveaROHHRvWZ9tTyohTLic3Nxe1OpiDKtmZU0/M6sJyj5x5DmWfvAQnXYY+ZxT2/dsR+77sMPa8I+793X088NQLZJx2HZmh0esDTy0DOk8Klmi2pdNq4jqIDzgcfLNuDQB6axbm488CoQIZIOBpQW3JQqVLIeu8W9BKH2WrnmjjSI+dqXjqaxg/8YQ2+Z7C96SzfhNef1JZMg+subhr99C8+QPGn/GTyD4dF+eZ1H52FRNG2tnMKtx2MuCntW4P9i//TcbUcwDQZA4jWIxw8KEo/iSIzXGu0Rlxt9ho9fqSPkZOdhYtG/+MJjWd1FBIY9MXLzFswnTyXOmRXD3hzrdwwem8uOKjHnf4RmdvrG+oxpiRS+H0eegqv4qrVBPFOocf2q3btuFocVNw8gUUL76pR+RMNP2eMGFih2F9c88+H7dRhpRMU+R7ycxculNTIJHcYRt5+drXqavdGyyPGKqUdcLUqfz2rnv5dncFIBlb1Fb5xlOGjz39HBmnXRdJEWAaMRFOu5rHnl7aqeJPpNgefmJppyGfZosFT/VOjPnBxGP+FjuBFhuatBxUOgNaSwZZsxa2c6TH2undzlrIyGhzHnNqSlIv2oDHTcumlXia7ajUanzNdjyoEwYcxN6LwwnVjW67xrVfgiWH9Onnkzp6Cj6Pi5by79rdv8GCoviTIN4o2fHVaxi7YCizWCz88uqF/OnJJ2gQGlKzhjNswnT8e7+LRDlEd77jp82k1uZj//KHIgnEupqiN5o2MfAcioEPPzy/j5Mqt6NjJSpWkV0y/bAd090tf9imjF9ObmTanszMpbux2u1K8739OCPPvZEhxVPa1PuNbguX0HP8lfdGri2s8LxeTyTZXrQytNuayBze1hGeMnwctUmErEJixdZZG//imiv541+fg9k/Qz9sHK3VO2ne/CEpxSeh1afgczRQUDKF1tb4uXcg8b1MUanJmtXxizbeyurdn76aVEGiniqhGW67lStXBfM6CYGrtgJs1WjKPuD+mLxOiejJtN49gaL4kyDeKHnYhBnkuSq6dJw777g9YpYor6ggz1URN7Rt5cpVlO2rI3veDRiGFuGu3kXZJy8xZvYFHIgqlZhsR4pW1JPn3kTF52+y841HqE0xdFjZKhHRo+O9W0tJLZiAWqunfO3rZJdMP2zHdHfDLKO/5y48A0PpB0lfW3dWA8czD6lWPYPz02c5EAo3jM6NEx3uaI0q3xdWeJdccE7cWUfF9tIeTwqWTBuHZxN/fnIpB51OEALThNNIHT4ObaCVnLxcpO0A+mGJazYkOs+Nv/oNQ+OYmmJXF8cz+bVuW90u4CDe8xCe2fRUqO5Toeso3/JW8BwP3tPtlA/9Xau303z8A4GpU6fKjRuDTplkU5L25Bs2+sbFy6qZDNFydybboTTKh4qEuKq2R9Ioh0cz0fKUv/042ekW7KEyfdHHTJjxM4kl5/HaOxiy9ygOZzPleyoJSBBqNQ1v/oHTb38h6WP3Jl1NXdude5xsu0ZGiyXz8BgzEH4vzvWvMO70S8gumU5TQwPfPn4t997+G5buNJGbmxuxiQf8Ptb9cRFuTQoZp10diVBp+HgZt167qE/zwyRqowdvuxmtVtfp8xYvO2t0lFZs24XbV1iHBusbeDzIxv1k7vk44odIJNeeVc+QaVRH4v1nzZjKmnUbk0453pP643Cev3h0pW8LIbqdj3/Q0dNv2J5c6JOMbOUVFRRcchX7aurAnIFaa0CTkUdL7R6WPHpvO3u0x2nD7teiHnEaE+KEzPX0kvORhYVUlW2iSZjRWbNxVnyHfeM7eJvtfPrQtVgMmg6X+A8kYk018UbriYjNPzRy5jkMKZ7Srl1vu+d+PGNOR5s2HOH1IHRGUiafy87P3kQ/7Bh2bd6ALjMftc6Ay97A3tC6+HCd2MlTpjBrxlQee3optXZ7vyUFS/QceL2euH16w8aNEWVrTk2h3uWPLBKs+PxNtr/zV+C/25gco00xYeeur/iMDhe9xT4PsfH+VWWbeOCpv1I4Y34bP9SDt90c9zp7Wn8MxJQPR6Ti7wlHXSydRaUkO0LobKXgw08spbqmhoYX7yNv+gK8GjUtDdW01u1BCBFxqEbH/ZevfYeMUxaBzhg3vjmRGcOcmtKtqKElN1zHpdctwTL7SmTDfhxfr8B0/Hz0uWPwHihDteuTZJu1X4n3gNd99i8ef6hzf0e8/EOJSmRuKysjY8JC1KYM1Gotfo8LGfDjqNnDns1f4dy0knFzL0NvSsPx1d8xT7uQ6mqQtgNtlOHxUSPWE6a2G8T1CfGeg2eW/a1dn27IP44/L3uRUWddg2qkmR/eeRTz8Qto0VpJU2sYNTuY+XPvisepfV9Dq9eHVkXkOQifJ/Oue9nTyaK3zuL9W7RpZJx2NXXfvcuo2Rd1urq7p/VHTyUV7EmOyDj+7hT/Phy6kpc+kWxbtmyOHOP4G5/CMPFMKte+jaahAl9jFa4t/2HCxUtwlyzA0eKmqmxT5Puuhmo0mcPa5CzvKHdLU+UPlL/9OPUuf1IyxzJ//jxSaKV10wpq3/8/zJPPRqNWYVv9LE1fvoLTI9vFdw9EDievf6L8Q5Wfv9EuD40/IAm47AidAaFWo9anEGhuJNDaTP1Hz1AcMvloDKkUz7mI1k0r2PPCb3B++iwpwsdV113Ppdct4WDBqV2+V31BMDtn2z5dt/3byIJFmZqB9HowFE6iprYuku+m8OTz0Ws1mLPyGHfp/7bJtRS+NkdzCyf+94Ocessypl99X8SHFP0sdxbv3+rxkDJ8HK6G6sg+lmFFtLa2xr2entAf0WtCDtbVtqsFcDg5oXqCI1LxJ1s4o6foigIZWVhIxedvsn7Z7ax+8GrWL7udis/fbLOgJi0jg6JJMzAMK6bsrcep+/Ap1ARQqdRxFzNpUyy0lH9HTnZW3OuNt4AqO93CiHmLu13MZMKEiRSfuQi9KQ2tVov9uw8xn3ABQ3/6IJaTfkLZvroBo5gScTgPeHlFBXnFk8jPy0XlsuE+uBejJQNLnPxDKhnAtv41Wg/sQAb8eGp2Yd/wJhoBJ06fhsFyKNQxu2Q6xWcuoqR4DC6hJ3XWFfitw7HMvpImYcbhbD6swjO9gV6vb/e8NdftQ5MxLLJgUWvNxtd4AImIjLTt+3fS6vV1+OzEe5YrPn+TJrsjstBu1oypbQY2sjFYb2LkzGC8vV6no2XftjYLv+z7d6LXx3dKW8xmtnz5CZu3lkYSs3VFf8QOBE2zryLgcdO85vmkFjD2BUek4u/prIud0RUFMmvGVCrWrUR/3NnkLXoI/XFnU7FuJQT8YMpm+46dbN5ayq4NH+M+sJO0OT9n1PXPkjrjx3z39tP8597LqNv+LWpXU0SRj7Bq0JR9gLQdSHi90atAP3z3TewOx2GNasJtrE2x0LjhLawn/hhdzih0egParAKyZi0cMIopEYczQAh/12q1MnZMERPHl5BjlJTEyT9UUlKCMbcI+7pXqH7h19jXvYIxt4iSkpI2fRWI3DuhUkcUoquxhtT8ksgqYDh0rw53tXFPEJ2dM9z/VNKHr35fpNxn2qQzaVrzD3yNVbjdrsh1alW064duewNfrv+KovGTqK+vp/ztx9lXupGysjJWL72NHWveRj3lIlRzbuRgwam8uOIjFi44PfI8ZO75GIvai85kJeD3keJtouHjZWSNmdTm+YgeKIVZuXIVtY12Gr9+Fzwu/HoTOzetY8+qZ5LWH/EGgiPPvZHMzMyksuf2BUek4u9qioDDpSsKJNFScq8/wK7NGwgYrRiG5GMrW0fKlPMwZBfibXFAagbpp16FZkgh+kkL8BvTWHLDdezcuolvv1rLUw/e06XrPdxZUbiNR1g1tNbuQZM+FK1Gg/R7IvHdvWVai0esAuzttMxdSSF8/123kWrbReZx/0XOgpvIPO6/SLXt4v67bmvTV911lZF7F/1iNmbk4q7ehVproNUTrJFs378Ti9ncpdKXc88+n7xRxQzJH83QEaN77EURzs4Z3f9+ff1iXN++TXPlFmTAjzrFgvC6cG18nfrX741c54QJE9v0wza5fRY/SuqsK/BKFT8s/wOVL96Be38ZGadfg7FoBn5DGk3CjHHyebz57vuRY2RmZnLNTy+OyDOkcjW3XruIPFdFm+fDEhUOG26fixddhXrS+RTMPJeWr9+gZvntuDe/T6ZR3aUV3H1pau4OR6RzF3on62IiB+6sGVP587KHSQmlt031O3B981bcxSKJlpJva2lGblqJzpqFOnc03sYDqFPT0agFHmcjaks2GmsOjR//DY0po135uq5eb5vFTqZsKku/pm7NixQPz4qbJjge4XNOnjGL+uof8KYH/Qz5ofjuZF4iPRE2F89Ju7equtPrOJxordjvxkshHB0J0lEMeLgdV69ezQ3X/w8AI6NW1oZTQJinXYjRkhF5QRmln6zZV3TqhAy3j3Hyeejyz0LdYsO27lW+c6byo5/fSPHwrC4t4EvUHvG+H71gMXPEMTTu/KaNwo1daBUvt4/h+HPxblqJxpCCrboSQ34JAkEg4EdjzsBm91FVtp0Trr4/cv9fXNF5uHV4VXp0//Gt/5radW+wv6kahECTkoa5+CRqt7zXrq/GCxGdP38e5tQUvvzrLXhb7JFor86qmPU1R6zi72kShXht2LiRF1d8xPATzqBu+xfsX/tvVNLHr69fHLfTJfLwa3R6csYex963/4yvtRkkeA5WIjOGIYRA+r14asvRmtPJz8vFbBodt3xdsoRlu+2e+ykt205K9gjGn7MYgyWjy6Fr9991W7BtCn6CZVhh0qskO2rTeA9UIn57173tVjlrTCPaREr1xorJ2NQE0SmEY5Xw4byYhxRPwVm3l/J3H0EG/FSp1YwZVUh1TQ2TY0aWmLL5cv16isZPilxv2PRQ26pBa7SiyxiGb/rFODa8Qfa8G9jzxb96ZUFR9ILFLVs2Y7PVxa0LEL3QKl5uH6y5eJrteFvs6DKG4anZjT53DFL6UGsNNO3fRQDB+mW3R1Iw5006JekonHD7eJw2pNaAadJZ6LJG4Knbg+Or16ld829UXhfX3nJXJBw1UYjoho0bqXf5MUw8k4xQfetkqpj1NYriT5JEIV6PPf0Hxl0aLAQSDlFrqvyBNetWxD1OoqXk2Zlp1Gz/jtxzf4mvuYm61S/g+PY90mf9FE3mcNyVm3F9v4qSM36C1WqlqfKHwx5BzJ8/j4efWMoJV9/f5kXU1dC17o6c47VpOAzw2EuWxK01G6vEgbirnAMn3hSJlEoUj92T8do9Hasd26bm1BSG5OUzYt7iSL9peflhKj5/M9LvbDZbZE1A8eIHD6V8qKvi+Lk3sfeH7RgsBlpbW9HnFtFoP4hhaBH1LXayzr2G3951b7Au886dHKrL3DMzgY7qAkTbvOPl9sFWjS7VgsaQgsgdh+2Ll4I+pawROHZ9g23dqwi9mcwzr0ebkYdrz3dUfvMeDX57h6P0W2++EZfLHbl3G567G8u0H6HLHonaPARt5nA0qenUr/wLUq1FRNWsSBQiGtYH4UVnvlAVs8w9H/e7XT8aRfEnSaIHO7YQSHh7ogc+kZK87Z77cQ8/Fe2QfOrXvkLmGf+N13EQx/pXkC1NBDQGNP5WhhRP6VIOmmSuK314A+uX3Y4rlI5ixIyzIqkhkqU7prV4bVq3/VtSpp7fTkHcds/9tEhNOyVtlK1kzVqINqsAoVJjzBsLJ11Gq6OxTaRU9LHCL7WejNcOL2qzq0y43cEwQWE7QLbZ3KXjRBM7ozDFKM5RZ13DzjceISOUPz96TUD0eo6Dy/8QjGLR6fB73UgJnro9aK3ZuKt3BX0I9gZ+2FtD+kkLyZ31//DV72fXZy+w+KZbeYb4L8KwUj3vrDO4/0+PdjibSvbFGG9gJEtXkUIr1jEz2b9lHSkjJmJbuxxfUxV+jxvp86HLHY3fZUdvGEPKyMkIoHnNc21e7LGj9IDhIDff/QAWsxn7/p24GqoxTc5Dbc5EaHUgQTesGKHVE2hxg/VQVFA4RLTpk7+1uZ6wPlCpNVGrrzuvYtbXHJHO3d4gkTO0qznWIX59XLvDweiJJ6By2fA0VKHLGkHaMTMZcvrVnPabZ5l61X1It73LzurOoj7MqSmUffQyKVMvZNjlfyZl6oWUvv9PamuDTsodO3f1WqRIvDZtrtuHeejoNtssw4rYvnNn3LC/HbsrKCiZgs/RgM/jQkqJJiOPgLc1YerhsJOtJ51ws2ZMpeyt/6O5oRZNxjACbid1X7xM5b79PdJ+8WTNK56EJcUQcWJGrwkIYxlWhFZFMDGatwmvrQ531Q80ffEiKQUTaPriJUbOPIddq1/FMv1iUkdPRmswYRxWTMbsy2lBHzc6Kzpk0ZBV0OnagmSDCcLObuenz/LpfQv59rk7GJKq45qfXkyeqwKjqw5Z9gkG5wEMqRbME05HN7QIv7OBho+epvGLf4NGh9qag88faNNnIqP0HZtQqTWodQayQimXwxFq3vp9CE1wPYwQAm9tBdq0ocHU1LZD6wAShYh2Rx/0B/0y4hdCVAAOwA/44uWSGGgkMtF0N8d6LObUFLa+eB/eFjvC78W9/wcCuUVIvz+44MVZy4nTp3cpt0cypgyhUmOaNB/tkHwQKlTmLFImnY1nywcUL/5zZFQUpift5fHaVCV9pPodbfYLPkgirpIGScPmj7Fv+5rm+gOoTRmkDhuD8bJTGD9uXMIVk9FFtC3DxpKTnRVJkdCdh/TNd99HVzgJ+9fv4F/9d7RpOaQdezreiq97pIRmIt9QybhxbXPbRK0JCO8TLpP48BNLqd22DVeLC5/TgVcNY069CJ3JSkvtHrJm5UfCLwEMuaM52GynvMLTTp62s6UmhHUorsJTuPAnPyM1NRWtCnJzchAqdTBCKZS5NOu0K2lWm3Ec2EXLxjfb1K2NTfU94tRLKDz5/LjO2rlnn0+VsZCKbz8Pmn1yx+CpKqNx9d/QZQ5Hp1GhUrXtM4lG6QeaW3j8oTv57V33Urr+VdQpVvTDS/DUVmBb+xK67FH4DpQhS1fRVJCPZVgRKd4m9n28jMIZ89ukiO4pfdDb9KepZ46U8mA/nr9LdGTHDjuwupvHZ+XKVRGHUFrBRJo2fUDjp3/HMv0iTAXj2blpHZqyD3gqlAL2cNND/PaueyPfr66pYeyp19PcYsNtq8Pv92MedTx1X7/RZlT027vuxSX0PZphMF6b/vr6xby44i2aTKY2D86YUfEVX86QTCrWrSTjtKsZEpXALM26IOHLeuGC09sU0dakplPp8ZC+f0fCaKzY+xVrNy4t207GBT8mPbsQpEQG/GjVKg5s/SSu4uwqyaQZ7mifWFNcJE33qqfwBoKjW8/BvejSctEZU4FgoSBdqoWRhXnt5AmbbmpL19OsMrH+iQfRWHPwBVQY51xHwyd/w17rxDr1PEZPPAGctXjfepIflv8BlcFEatZwhp9wBi+u+CiSfqIrqb7LKyqwqxvJPOWniMwRgEQ/fBzmEy7E8c0KsixGxhYVtekziUbpIwsLI+1z7+/u44+P/ZVG1KjNWehyRuKr/Ibbbrm53XN+wbWLWLNuI2XP3NSj+qAvUGz8XSCRHTveQxWbAye6LmksDz+xlBHzFiOsQynfU4lx1BQCnmaaPv07dl0KGqOJXIO/yw7JeHZVt72Bsn11mGZfRfHcIhr+egvVO7dSNGkGVquVzVtLCTQdaFfe7tvdFRx/5b09XhA+XpvGe3Agfv54a1o6ljk/okWbhqtuL+hNmE+4kNqD9aSlpcdNzRv9QjRl5VO+9h2aa/fi1MALoYIpiYjX/n9e9jA6yxD8thpabDU4t67GZ6tBZTChQSY9g7Db7QlzJyXjQI/nEE5RqbnxV79h5BNL4x4vfC1uewOl7/+TAALzqOPxNuynfvXz+B11HLRqIiac6Oya3733Dxp3fY+Y9muyLrqH1v2leBuXc/CT55G+ANZp55E6ejK19Q2MHXMoLPOkGx6mtnQ95Wvfwb53OxcsvAIhBNlzr6XRr8fZYkMzZBSGKRew+YPl5Gszyc7MbpemYe03m8nIG0NAgsfrwd/agjZ9KNhreGrp8+36TOwo3e9xt3l5rly5ijXrNmJN0eMNgB4HJRleltz5WLt2i9cvHn5iabCtB0C+/c7ol7TMQohyoJFg3bKnpJRPx9nnGuAagJycnCnLly8HwOl0YjKZ+lDarmG329lbVY3GlIFaZ8DvceNzNjB8aE7bELUotmwtxRCq7uVyuxFqLVJKvA370A8pCC7zP1hJ0aiR1NTWETBYUesOvUj8Hjcqt40xRW1t4zt27mq3r7NuH2qjBaMpGEvtczfjsjegSbFiNFlwOe34WmwYLRloDKmkaf3UN3tpqa/CPHTUoXP6/Xh9Xjz1+zGbTORkZ7WJz+4N7HZ7MD1vayt6vZ6c7Cwq9+7DkFWA3+/H4/Ui1BoEgiyth7qGRvLzctvJFd3e0bjrKpkwvqRDGeK1aYvDRqDFRkAGQKjQmDMRah0BTwv+5kaGZmUydOjQTq+ttdVDQytt+k2G1YyzuaXNNSfTzon6YXR7RF+Lp8WB29GA9PkACVKi0mgxpGWhUqnx2A8iZQC9NTuYRbSxBo/LidaaQ67FQLXTi1BpkD4PPmc9BAJIGUCl1aPSp5BqScflcuGz1WJMG4Lb0YjQpRBobUGdmobPfhBtRh4y4AdAqDQgBN6GfWgzhiE9LrQeB+OOKY5c366KPWgs2ah0RqQMIP0+NATQeByRZyG2z5hSUyLtmZuTjU6nw2KxJNVeh9PWPUlXdOCcOXPipmXuL8WfJ6WsEkJkAx8CN0opP0u0f3fy8fc0yZpXEuXevrxYsPjqn8c95pfrvyLz9MWMmDiNmto63OoUWmt20vDBUlQ6I2qjGVWrgxnHjQuO4hc/2m4B2DeP/JwJEya2C3WMzVO+YdltnHDTM6RlZGCz2aipraNxx9c0rV1OVmYGOdnZ1DbaGXnujViGFXHukIM88viTpAhfJE7dZrOxt6oan7OB1k0ryBp7PJWfv4E5xcD4ceOSGu30VL7zcHvXtmoIGK1odEZcVdu5eYqBV8tVcXOeH05+9HAtguj2LysrY/9rv0eXakU7cR6a9GEAqARk6bwMqVzd7rix119fX8+1N/w/VjQemmntK93IzjceYdKiO7pcByKZawxfy55vPmP3529jOfkydDmjadn+JY6vXsdYMIFjz7kaq9XK959/iCM0WgdYv+x2mvbtZMj5/8tvTsnmoU0gdAak30f1C78i7ZTLcX63irQZF2P74kUmnHEZ1Q12HKGFWClTL6R+7SuYT7gAfc4o6t75E6Yp5wXt6bZqhEaH39mI/eu3sZxwHi3rXmZ0ho5vv1obuZ57f3cff172YtyFk8n0pbAuWblyFYuuuR6XD1Kz8xk585xgnYQEtRWi03jLgJ8du8vRZeYz5rQfRxzrvVmToify8fdLVI+Usir0uxZ4A5jWH3IkS09k34zNBBh9zHE/uonGr99l56Z1pKYYaN69kcZPnsM86Sxyfvw7Uo87E3QpbNmyOWHSqhb07eQD2i2lLykeS8Pmj1m79H9Z/8Qv2P/BM4iAF1PuSMxZedx/12088+gDke+o3DYeuftW7r/rtkiKgn379tFSvZvaD56iuXYvld9+RtoZN2A665akMkfe+7v7uOTKa1n7zWbsaitVxsIOv9NRZFI4dYJ9/3ZUai2uqu00ffESepM1YYTO4aRqiNf+qX4HKunD02wjfdxMUrPy0BsMjBpZSF7xpHYyxOtPpWXbQa1ts1+z2kxAaLqVSC+ZiKXwtexZ9y6Wky/DMLQYAn70OUWkz7mall0bD6UuDi2iCuNqqEafXYi3oQoIKn2BwFNbjsaaE7T324IRThmzfsKOj/8dCctsrt2LPmcU3qYatFkj0Gq1pB0/D9uXy/HW70UG/Pid9TSufhbvwT3YvnyZ4tMvwdHc0uZ67rzjdpYvfZgJ7lICnzzOkMrV3fKv3Xz3A2in/ZjhV/6FlKkXUvbJq9SWrm/XXtH3begl91I9/FS21zrx6Sw0Nx1k0yuP8s0/72f9stv59qWH+HL9VwM2UWGf2/iFEKmASkrpCP19BjCgc/h2Jd47UfSFvljf4TFVKjU7Pv4321cvw+P1kzLhdIwjJxFwO1CrBH4EBxubqK+vj9R1DY8CKz9/g6ILbu50cQwEle4DT72A+ZSfkTtsHJ6qMho++RuFk08la9y0dt+JHl1s2LiRPz95L06nE5XOiHXi6bj2lWI87kz02SPxOBtIKyjp0Pa/cuUq/rzsRdLOuCFSdH5/qOh8vO905tMI7//jy6/i4Ed/QwCpQ4ZF2j1Rce3wPYhnL+9oNhLPger65i1+ff1iHnv6Oexla7EMG0t+Xm7ChXbx+lNK9gg8rmYgEwguxmrcWwapGWzfsTMSdZTsorBkcsCHr6W1sZr07FEEvK34nfWoDCbU5kwCra5IbqDwIqowxoxcZFYR9i+XIxeMJ9Aq8Tbsx752Oabjz8JTV4HGlI7fcRAp1Lhq9/CvF4N290XXXI+9bC1aczqyYS/q4cegyykiZcyJQWXfsB+tNYe0k3+CYXgJAUcdBou2S4XSkyV8L2SrhoDfG1kHUr729XZpFqLv2/YdOzEML8EXCOD4diW5F92Je38ZdR89TfqkM8g94wa8lZv7vcRiIvpjxJ8DfC6E+A74CnhXSjkwX4shuhLvnWg0GZ0JcOXKVXy5fj2VNm8k7Wt2yXRO/O8Hyc3JYUhmOmkT52DMyEE2VWHf9AHWmZeRe8m9pM66ApXOgPPTZyOjeHOKgbziSUnJF04SpzZa8DVVo7EMIeu0n9O4d3uHMewrV67ixRUfkTb7Sob9/EmyFvwSV1UZ3qYajCOOo9XRGKkH0NFxHn5iKSlTzye1YEJkwVXaScGMo/G+k2zK67TcfLL+6xoKFj+J9ZQrcNkbKH/78bij+I4Ue2ezu0QJAO+843ZeePpJrPu+JFvvw2xKTTiTiNefRp96ET5nA02VP9DU0MDOTetwbHgT0+gpBIxW9lZVdyk9cDKzmvC1qHxuWnasw+9sQJ1iRWUw0Vr1A0KjQ4WkqfKHyGg9fLysMZNo+WENKgT+5kYOPHsDtjUvYDr2DFRaI46vXidtylmYh45C7W9FozdEzhlup4JjT8T+5XLsO7/G7WhAlzkcodFhnrwg6Neq24PXVkOqz3ZY2XU7mjGG70VOdlZkLYg+ZxTNtXvbnTP6vrV6PHhbWzCOOA7Z6gRAnzcWy4yLad7zPf5mGyMmThtQ6bOj6fMRv5RyN3BcX5/3cOhKBZ1Eo8lwVE9Ysegy8xE+DwFjLnurggtDohObHRQttLhsNG54B+uJl2DMGYnG4yCtoAjmLcZQuoJvv3oTCNpzk5UvnCSuZXdFxCYuM3LZ/+lzHSqVsALe6wigSzEHR/wn/piD7z2Kp34fKq2enPy8Ds8dPr95zjn4ve5IPWFD7mjq6vZx3OSJcfdPtOIz1kcydPQEnE4nPp0RTYqJ7HRLl2YQEByNaqf9GNmqQYRy3zfkH8eia64nzWKOvCji2W47mknE1put+PzNSESRq6EabYoF7bFXYChdESnIPmLyqdTs/A7v8BI0GXns2fwVxorPkooJTzaNxvz58/jtkhu5/8m/ozntajTWLFr3baNp9XNIKTnw7zsZPfNErr7kHN58932+fe4OwukcLjxtBq++swqQmI79L7y1u2n67AUCPjepI4/HMmY67gM7cHz1OgUnX9Amb1FYtgZXHfWrHkPoUjHnFjDshHm0WoajseZg+/Q51Ns/In/SJJb0QqlTo9HQ7tmuqa3Dvn87Rg3t2it6X71Oh7PFhb+lCq01B51Wi6vZgS6rEOf6VyMzvoAptV9LLCZCWbmbBF21CcdbmRsmrEDHnPZjmtYux3twL+pUK3s2fxU55pIbrsP1zVtk630Itx19ei6tTTW43G5Kf/iB/Q4/n33+RWT00hX5wp03eoTjPrATbYolYVphODTa0et0EPCi02rRZQXjp22f/QNhP4DZlMq+0o18//LDbNmyOe5K4ZGFhaT6HfgcDbhbHLS0tNC4axMel5PczPZRTwlXTKemREbmGCxoCybS0GQnJzuLieNLMJqChedjiTeDUOcfx6U/W8wFC6/A5nCi0hgio+zyDR+zf8s6tNN+zNBL7mW76Th+9PMbOX7azLj223j3PnYWkb/gRnatfo0tK58nZcoF5Fx6H4aJZ+KXKg7W1eL1+tDojaTlF1M85yJaNr5O9Uu3U//RMx3asGNHtkDCfhjNnXfcTmqghcYPnmT/0p9T//7jmAqPpXjBtajVarZu28afnnyG1uFTmH37Sxx/ZXBNx9qvvib7jOtQp1jx1+6C5gZMmTmoPC70fhf7//FLWja+TvGciyg8+fw2M7pwOx3Ys4uM9DTyL76NlBMXos4Zw8gRBUw//WxG5A/n4P6KyOKzrtQcWLlyFZNnzOK8SxdRn3sCVc3ELWIT/eyYTalk631Y930ZN6w3et/szAy8dRU0fvY81kln4Pe4CTQ34rfVoLVkRr4zEFftghLHnxSHk743lvAINhwVUr72dVrqq8Ft55UXnm1zzNvuuZ9WWx2OPVtIHXk8GoMRr9uFu+o7jFnDI2aI2AyHHckXtutmnfIThuccSsc81KKn1aNul1Y4XJD60AtjaDB0zZwBjfswZQ4lhVay937Kpsdewd7ipuDkCyIrLmNtnOHzq0afSosrHW9DFY4Nb5Ay8nhe/3gdY353X5si4okWJaWo1GTNCirwlMxcfA1VaIfkU1Nbh9Vqxe9xdzDjOTSDqC1dz/4t69DNuIyssdNw7P6Oxg1volKpMBSMZ89/niZtxkXozOnsq6lDVziJ7NSMLmW0jLXpDy+Zyu60bAwT5oLOiLrVydCxxyJSvOyx+Rix6I+47A2UffIqxXMuYvrV99FU+QPOT59NGCt+uAnnppwwrU0UUG3perZ9+BKZpy9Gn1XQbkGVc/J57PrXfYw4ZyKaFB25F/wGn6OB4TlZfPv4tRSfuahdRFG8+7Fy5SocLW7U9gZSCybg97rZW1VNmnREVlh39bpWrlwVKdIu9lQjVBoOfPA0+xz1mIbkMerkcyK5qLrybEfvW15RgQVobm7E525BNlThdxyk6YuXsJScQuW+/UkvCOwPFMWfJIfrRAoTPV3MLpneJmws+iEOp0sGFc3fv4/Omo3MzMfbsB/nd6vQ4e/QidvRdcChzju2sJClf3uch59YGjd7YjiqI9ELI5jL/d5OMzDGLoC5YOEVSG0KuszhZM9ehKloKs49m3ns6aVtFH+iB/PGX/2GoSF7azhffdrMS5EaHU2VP+DTBjqc8YRlLF/7DuZpPyKgTUGjT8U88jikDNDw1ZsMHzmJ1sZqhMFMq7sVKVT47A3o0nLxhjJaRl9bIt9BPHOV392MIXsEE0PrBrbv2IlKZ8XbYic3N5e9ATBPu5DyL95GZ7JS/vbjqHQGTCUL4irARAEIt91zf1Jhsx3lxa/ct5/UggmotXrK175Odsl0mtVmECp8DVVAYdBsZ86gsvRrxhYVUfdZx2kLos10KePnYFv3KmqtHkPuaFz2g2z7z9Ok64NOe2Ed2ibldlYHwQPhviJK5pFaOInG9W/g2LSKtFk/RTukAF9dBWUfLWd0RkqbPpbssx1vseaia67HEQoFLZx8Ko17t1O39ZOkFgT2F4ri72M6W3ofHuG4Ck9j+Kz/x95/3ELKMbOwr38VT1M12rShZEw7H9sXLwLJpf6993f38djTz+Gw2zFbLPzimivb2ahv/NVv4trSW1tLgcQvjOhOnWwGxvnz5yEDAQqvfrxNPHzK8HHUxqmcFe/BHPnEUqrKNtGiTaNVmkktnknjZ//A31TF2OnTyZ91VYcKzjn5PJrVZhr3bmfI5AsRUuKorURtMKMfNo7Gj5ax77n/B14XnoOV6AqOQ21IRXpbcZR/g0af0ubaOhqZxvMR6VItoaRfQb9Gq8eD9HsxZuRGFvpVV0PtntJIjeSO8v3Ha3tM2ZTGFChJNFqOfcFG58VXHziAq9kB1jzsNfuw2Ww4DuwiNWsYTV+8ROCUm5EBNd66SurWvMjSvz3e5ljxoqbCbcV3P2CZdgFi2xc41r1CTWM1wmjFa6vDlTuCVpUkY8almIYX462roOyTlxgz+4IOs8eWV1TASWei1hoQag2px89HO2REsLBKZj6mSfMR+1Yn/H5XmD9/HmkWM9PjrK0pe+amAan0QVH8fU5nU8toJ6pGn4ouYxjS3UzA70MgkDKAt/FQSoXObIjh8M2M064jM5TL5oGnlgG0GVkncmCrR6sSphEIEx69VdfU0PDXW9osZEkkn9lioWXfNkwjDjl0W/Ztw5zkSsdZM6Zy35/uR2p0SG8rQqtH+Dzc/uv/x5133B6prhTL/Pnz2LBxI3968hG8Hm9wham7GW1eMdLvxd9iw+84iCYth0yLEY1Hz8HvP0CVmoEqtwhP/V6cm1aiw9fm2joK+Y33sk+htU3SL9m4H5/DHykQbrVakbYDjDj5JD58902Kxk+KzHDCRL94RhYWUvH5m9Tt2BRJr03aMFKyRySdZiP6BRvOi29Tq/H5/Pi9TficjahS09m24VPs614jZ8yxWEYdj8/RQPmy/0VjNDHUou80vUF0W4XNdJbxpxAYMR6jx4ev2UbztyvwelxYTlyAJn0oPn8gEmq5a/U/mVIUbPd4s6yRhYVst1XjT88l4HFhzJ+I31mP9HlQq9WMnngCB7a9k1Q/S4auBH8MFBTnbj/QkfM32onq97ox5Iyi+Yc1mKeeR+5P/4R58gKavv8I67DRSS08euzp58g47WpMIyaiUmswjZhIxmlX89jTz7XZL56DuPztx/EFiDglDxacyqXXLWlTrzXacXn8jU9hmHgm2z58ieotazuU7xfXXEnDx8tw7tlMwO/DuWczDR8v4xfXXJlUG/7f08sQKVYyzryeoVf/lcwzr0ekpvH3f77Y6XfXrNsYXA2bP5aM6Rdg++o1PHUVwdWijoM0fLwMjb+VTKMalUZH/owF2Ne9TNU/lmBb+zLWqefidTW3ubaOQn7jhYA+8+gDbeokZ+75GLWQkQLhsW3XWVrjWTOmUrFuJdqSuVjPWoIsnEHTti8xWNLjytQZ4f6wZ/NXaC2ZBOw1NHz0NN76fbh++Bzz2Gkc3L2VA7t/QJuWw9Czf4Fao6VVqjt1vka31ciZ59D0xUt46ypxtbTgczbg2PA6aZPOxO9yYCychL/Fjt/bGkm53VK7hyU3XJcw9HbWjKnI0lU07/oGjTkT78E9CECr1TJyRAE4a7uklFeuXMXx02ZiGpKHachQJs+YFXcRYXcWBPYXyoh/gBHrRHVV7yT9lEWoUtPxNR3AmJaF9qRLOLD6WQpFfadOZofdTubwcUAov47XixhSSFNjY5uatPFmItnpFnSWIaQV5GKz2WgSZiyzr6R104rIQ5YifGSdckWk4pAmYxjGCf/Flpf/zMkzT0woX3i28djTS6kNmaBuvfbKNrOQaGKXytcdbCDrgtswFEwAKVEXTCT9tKvZt+KhTts4bBZxNVQzbMGv8au0OL56HZ+tFl1aDuqAl1N+9SybHrsWZ4sLnaOF9NMWo9Eb8bW6cB3YgbfFga26MhId0tmor6MEf2HefvttDO89G3cm2JmJcM26jeTPvRKnJg2V0Ypp7AzUKRYOfvAktaXrO52BJZLr4kVXgcGCr9VF+uSzyJh2Ho6dG2jc8A4+byu27z/EU38iLV+/wbi5l6EzWTtN3Bfr5wLY8fG/sO/fiWFoEdbj52EqmkrTpvfxNR5AaHTI5gbcbhuycT8lxWMjPqV4s6w161bw1IP3cNs999NwoIyG/zxF1qyFFEychrQd6FKa5JUrV7H4plux+7UMOfdWWu11bNvwNudfdjnjjynm/rtu69Hgj75CUfwDjCU3XMfim26lBT2tziZaHY2YT/wxRr2OYXlDg7HB/rGUbUsuD0jYpGIcXhJMYqZS460qQ61PaWfvjVVOReMnRZKR1dTWoTFnoE/PpemTv0Uesm+fu4Pj52dHIn1SLDnorEOwffpcp/l37rzj9oSKPppY+/l3n/8H9e591K96HF32SEzHziXgdeP8/kP8Ph9D8kfz+IP3JTxeWPEYM3JxV+/CkD8eff4ECPjxNVbh+e7dYDWtFjfG4lk4t65GY8okELBCcyPN33+A3pzO5JufitjNFy44/bDzsFssloT3tDPlUl5RgWrkORhTMyLrIzQjJtCg0rDj438zpHhKl2WaP38eJ04PRvtsWv4Q6VMX4Ny5MbK2RJM1Atfub1BptJH8NgG/j29e3tyheTD2JaYzWclOS2WE9RiYcglNwozP48I66QzqP3se87FnUDz5JHDWUvftZ9x/922Ra472a9hsNmpcgj2ffwEQUcqRQcPLK9q0WyJzYDQPP7GUFvRkzP4p/hY79u8+JH32z9CkD6W++oe4q8gHA4riH4CodAbMJfMwW3M5+MFSpL2GYaNnRpx+XbEf/uKaK3ngqWWYT/kZ+mHjaN1XGkzRcOJZkRQNiTrsyMJC/B43EHQ+GiwG3Ad2RPwLwem6oLL0a3SFkyIKx9dQRUr2iE5Hft2pK2Cz2ZBpw0g/7Woc36zAMu1CGj5citDqSTt5IaqUNLwVG6mqrsGQlo1Go2ZsUVGbkVkkQmnMJCo/eQ7DcfORfj/NWz7Ee3AvQqVm20t/YPR/XUbzkBJ0ecdg+2YFnob9SJ+HzDk/w7n+1TblDdesW8HCBafz2NN/aONE7wllENtOjz/0x7ipQrYc2IV13KHc+d66CkxZ+Xhqd7XLGZ8s4bbSplhwH9hJ4zfvYZ5xMdqcUUivB31WIWpTOuVrl5NdMr1t3qgEDuVELzEIJhVMm3wezc3g97hQNTdg2fk+B3a+307+6JlDdOJA64iSNvmqDkcpl1dU4Gl2Y8gdzf7X/4j1pMvQ546heec6mje9D831LLrm+gEbvZMIRfEPMMK5+SPx1OrL2fbhS+zRaJhw4pwuj9rCI+r7HnqYgJRo9KkUTj+TsWdeHow86CAiaMkN17F7TyVNlU3oNGqaK7fg+Cq4IAeCL6AxowopW/Mi2akZqEPFzpu+eIkxp15E+eeJ7e1drSswdFo223fsxNncjNqUidqSReMnf0ObORyVIQXTpPmoTRn4D1bgKF2DOG8KaWf/ClPeKKrLv+PaW+7iKdoqgYefWMqug5V41y5HChXWmT9Gn3cM3oZ9NH70DCmZefh0OrQFEzAXnYAM+Clf9gv0lixcWt2hOsXpOfiqd7D3oI1xl/5vZMT/4op/ccLUqYelENpkoJxzDgf9jrjttOSG67j0uiVoUtMjOZCaQjmQ8kYOSWp2GO9FDGCUrbQc2EXzu39B+n3oskchfV58zY0Q8IFQ0VJfze5PX2XHf14kY87PqY1a+Rx2KIfbPJkXvaOiggmFhSx5/qmE7Rc9c6hxiaB/4KvXySmaSNkH/6S5di+X/mwxo0bk42hu6VYWWIvZzIFmiW1PKZ6mGnTZo2jZvRHnt++TNvMSzPnHYCv9PNj2rYsRWh1aFZGqZwP1ZaAo/gFG7PQ1u2Q6gYCfba89StmWt5IatcV7gGeffFK7NL3J1AZ+++23MbzzLM3btkUWZ7Up+H5PsGTdni/+RX2LHWNGLsVzLmqX4CqWeFEwzsnntUuNMH/+PCxmM6UfLqf14B48jdVo04eiyzsGjXkIfnsd/mYbGmsO2lQr9jVrEAYTGnMmutwctIYUUkdPxqNWtVtPMH/+PIrGT8KutpJywo+CUSOATBuCv/kidq1+lQmL7gqm1DBn4K2rRGM0UbvqSdRGEylTLyQjdzTNe0tpeudhRkw+r1uFahIVLV+5chV/evKZYEK70OKmJgekTT6v3XHnz5/HL6/eyJ+efIIGoSE1azjDJkzHv/c7liQxSIj3Il58062odAZGzFvM6QuL+Oyha3G3ttCycz363CK05kwQKgLeVryOevZt+AC13oR13EkE/N5IKhLLsCI2vbYtqRd9V2Pqw31pz+dfYB1RQk7RRGp2bibtpMswW3Ow7/qWXVv/Q/HpC3FbMrq0qG3lylXUNtqRHhe2tS+hMpqDKSg2rcJy4sWY8sfhbbGjySrEPOsKGte9RsbMi3F89Rq7RC6XXreEFK4fkC8BJaqnh+isqHmyxIveMFgyOHH6tE6X3oflSBTp0J3Ig7Ddef/uMl5+7inyXBXtCr7//p47yU5LZdKlv+KEK+9GZ7J2euzYKBibzUajTMHlo11ytKbGepy7NmKeci65P3kA8+RzaNn2Gdq0XLRaDWpDKn5bDUaTFa+tloDLgdDoECJ4bLXWANbcdil25559PtU1NTTu2YYwZ+F2u3G5XLiaHaTkjKSldg+O/TsQ0kfTD19y4N2/YPbboNVJ2kmXYRg6Br/PEyrAYQwuaopuuyQiaOIVLV98061MnjGLixddidvlQjekACEEGp0RjTmDZrU57rX8Y/mrFBUMo3hEHha/Df2+rzHKVm781W867ZPxUlm0oEeUzItsQwjUpgyaN3+I9LQgdAa89fvwt9jQ67Qce8kSzLkFtNbsjsgazH2zk1avL6lke10lHCF3ysknUXzmIhr3biftpMsw5o3F02wnZdRkMmZfzp5173X5nA8/sZSR597IxPOuRYcPb205De8/ia9hP6ZhY5F+Dx5nI3prFpqcIvzNTZhGTMRy4qXUlH2DZfaV+K3Dk0pV3tcoI/4e4HCXy0eTTG3VjkgUT75m3Yqk0zokorPIlK4cO15yLH+LjdTs/Da284efWEptfRNZ8/4fIjTCVxnNpJ+yCMfXb5NvVtGs8uBY/wp6czpaaxY+VzPS24pGE/Q5+L1usFVjMZuZe/b5bNmymRb0jDrrGo6fdhUbn70d557NGEcch0qtwd/chO/gXjLSrex84xECoRF00axz8e/9Du+enRgtGbgP7kWv05Gfl0tzTj6OA7uguDhyjcn4YmKLlnucNux+LeoRp5F57EIOfrQMR8X3WEYdj9ZoQq01YDuwiwmh48bre3Wf/YvLL72IF1d8RNbsK9qk4Ii+X9HEWwDmabaDNTfyvzEjF091JZZpF+JY/xqeg3uR0g/H/5oWlxtM2YyceQ5bVvwVdKn4XXaEVk+zQYVWRdxw155KYBZ+bppr95KeMwqfx0XA58FgSEGdMpr6hkOzj2TPGZ1eJRx9VL1lLd8t/xPNO9djGTYWlUqF1pBCS8UWtGk5AKgy8vE7m0jNL2kTCJFsmdKeKlLUEYri7wG6kq+/Mw43NKyj1bO9GXnQ1WO3f8Ftx7X1Y4rnXBzZ59BDKjEMHY3WECw353U5catUtFbvwlC6gn/89S9s2LiRx55eiruhHqFLwe9swF/rwZs5jJby7/B/v4JaESB11hX49zZimbSAJmHGrFZjnXgatm/eRQZ8GHKLoKWRlu8/AIeTST+7t415rKlyAgeX/4EcowzWHgiRNfZ49m14k6ZRo7r0wo69X+Vr3yHjlEWgM6LX6bBOOZumjSsQQkX6MTNo3ltKy8Y3WbI0WAkrUd977Ok/MO7S/yWt4JhIfdvm2r0JHZHJrC4eOfMcvn35YVSGVKyTzqBxwztYTrwE3ZB89EPy2bV5AxkpWlQ6IymT5qO25uBv2Idq1ydkWVN7dZFT+HrCuf4tw8ZiMOgh4MVduzfugseOahzHa5Pa0vXsWv0qKhmgZeOb5GZfg9SZaa7cgv3Lf2OdchYArTW70abl4K7e1SYQIpkXTk8OIjtCUfw9QLKpCpLlcBT0YFlFGPuC89odDJt+TmRkBYfktpjNVJd/R+royaFl+GoCjfs5dsL4iNNy/vx5kSLtW7ZshoCX2jf/gEolGFtUhEwzYpp9FWkFx+BqrCEjvwS/z0NNbR2G4SWoUzOo++D/aDWlBf0Up1/C98v/FHeUqtdq2uWi8e/9jl9evZA161Z06YUde79cDdVYMoehbnWSk52Fx+fHOvks6j96BufaF1FJH7++fnGbMM54fc9ht2MZVkRt6XrKPnmVtJMuIz1nFPaytQmdw52tLtaZrFiMepyf/R2/Wkf6KVdgzBmJDPgZecqPqFz7Njafl6zTrkKbVYDP0UD+1FnIMUU0r3m+0/w9h0N4lKzTanB88S8yTr6A7ImnsWvzBprW/htdSiof//6KSPutXLmKvVXVHUYfRbeJ295A2UcvY5o0n/ELT6Bh88fsfOMR9GpBq1+SM3U+gYzhOPdsxv7lv0kZOYmmL15qEwiRzDPYk4PIjlAUfw8wkJTt4ZqK+pLoF1x4pNNUOSGu3NfechcetSpoerBVoyn7gPsfvCdyrOiR0uS5N5GSeZCCMeMiyrdo/CTyQko8Er8/dAxuW11wlbTeSFrBMUy/Ohj/31T5A2aLJe59LQnVFe6JBTvR94ucXLQpFlrKv6No0oxI+O6e+kp0ahUnTm7vJAz3PY/T1ia3v16nxb5/J+Vr34nYvH0eF5ZhY8kaNSqucxjazjSfefSBdtteeOb/ALh40ZVojaloPA7UAT21332Mz1FPa7MNTwC8B6sw6INV5yzDijjgcPD4Q3/sMH/Pb++6lx27KwDZLgS3I6Lv/fFzb6KqbBO733uahg3vkGI0YleB8bj5ZIfq8r644i3eeOc9rvvFEtIKgiNyj9NGbVMzFy+6khOnT2/Tzg8/sZRvo+piW61W0mZfRMbICRhKV0T6QukHf6bV68Pg9eDe8mH7QIgknsGeHkQmQlH8PUBvKNvu2vkG4ypC6Fzup0KflYcim5Y8eE+ba4odKcmAv82DbE5NiSjxcDZP87QLMVoySPE2se/jZRTOmE/A74vcv19cc2XCRVk9ZTaLvm534RmMsGqoL/sAOXIEAVMq0nYAY8Vn7VJ2hwkv+LP7tWScsghLlGmr/O3HabY7IzZvn6OB/LxczAmKgySzujjMidOn47Zq8Tjr8TSnkjL1QkzWXKpe/R0+Ww2W0ZMRanWbFMuxL/pwimlzagr7autp1ZgZcu6taDKHRUJwr964kTXrNnb4HMRLe20ymTCUrgAgL7bovMnEt8/dGVmcWL7hY8q/eBvziZdg0Bo4aAi0WwNQNH4SxSfOaZOIrSMTavj6urp+oq8GkYri7wF6Wtkerp1vMK0i7ClHVvRIqbZ0Pe4JqVhO+glSo8Nt1VL11pPYl92GymAiNWs4liG5NH7wBIEUAyPGjeOCaxexZt3Gdg9q2HzUmy/R8P1avXo133619pDSSOKc8+fPI/ue+1GPOA10RmRzE8acEbRMnI/ji3+i9vva1AEG2PLlJ9TX1DL37PO73d7hwU5tUzPqGb9CO0SLq6GGtClnY/9uFUKlJv2YGbidDez+9Dl+H/JJQPv+/eVfb8El9WTN+inGYUHneOroybiE5I+PLSXrtJ/DSWey3VbdZj1GmM5GyfE+A4nf48Zms1H+5QqsJy1ElzUCv7OBJqFqFzLbVYXc3Wewr2bsiuLvIXpS2faVna+/SfYFl8x+0Q9m+dp3UM/4H7RZWlQuG8KahfrYBeg2vUfOvP/BcWAXto1v8uvrF0cU+z+Wvxp3VWx/vEQTjYwTvRjtDgcTTpyDw9kcTJ1hysBSnIt97b/JzB1KYMu7ZI8ahfT72bV5A85NKxn3o5u6HNceKyMETT4gULlsqJCkjZ+NRp9C7fv/R8tXr2BMzyGF1g5nZ94WOwGfH1VGfmQftdZAwJSNDzW6wkmotQb86bk0+wPcds/9HTphIcaJG+ezsUVF+JwN7Nm8H5+zEW3mcHzOBgzmdIRaTXMzOKJCZvtKIffVjF1R/AOQvrLz9TfJvuCS2S/6wWypr0aotRHTRk1tHSkjj8Ox/hWKi4uhuJimUaN4451ngyGPvRxB0V2SfTGGFV9tqwaNOZirx1W1ndTsfEac8VOa1zzfppbvuLmXHXKiH8aAYv78eZw4fTo6tWDsmCK279gZrKWcmhbxl4SLDEUT27+NGbkEnA5aa3ZjHBmMIPJ73Xjq96HLGHYo95DOSMrI49i+elmb43WmlON/dhter4f6j+5G+gO4KzdjHnkcWqMJKWWbkNno9u4LE2pfDDaUBVwDkM5S8B4pdJTKuKv7zZ9/KPUx7uBqy3DB61aPB1/9/khoXfj7O3ZX9Mqiop4i3qKqePKF0wLb929HpdbiqtpO0xcvMXLmOUFl53Dw4btvkpuTw4n//WCbyKlk0zQnYskN1+FzNkTq0Dbv+oaGT//BiBlnJVwkGNu/R848B+lyYPv8X7j2l+F1O2ne9Q2Oda+QWnhsm+/66vcDos226Hsfu7iwo88sFgsnTp/GmNMvpXXrf/A1ViEDfportwRDZmPkDi8WS2Yh5UCnX0b8Qoh5wF8ANbBMSvnH/pBjoJLstLIvFnr0JsnaTZPdL/ygr1y5it17KpG2lqCDtHE/jV+/y7i5l7X5vs/TytZ3/05LQzVqUwZp42cxavLsw1KEPUlXKppBMIZ939p/k5qdT/GciyJlPcPt1FOOw9iKbn/63R0YSt+kvKKCXLMZ+f/bu/fgqMozjuPf3wQTgiEV5CICchM7pTogRkStThCtBC/YaqdMtUU7lkErVJ2ON7QtM9apl8Fqxxlqta0VlXqrUmtbtdYL7RRBKspFFJSrKEGFGIsg4ekf7xtYk92QhGX3bPb5zGSy55zN7i8nJ09O3vOe9+1eysfzHsx4VpxudM7uXcsp004+mHszjb16OncrZ/OqhWzrN4zOhwzhs/dX8dFL9zN0cPO8LZ0lt7StMUvfI4+jdsFj1Naub9ZltiPKeeGXVALcBZwGrAcWSJprZstynSWpWvNvZa5u9NifWvsHrq3tqzU1e8YYWvH0ag7u2pWGks93T3JSt2FlmMO2ojulw8dT2W8YOzatZsu/HmTZhxsZ0LVr2tfNtbYU6pqacdx/9127j4nKvoc360aYjXbqdDO6ban7lJNGV7VqILjGrNC862i6njE/uPxq6ubNZvOndZQeWEllyefcNCPzkNttlZple8NWhqfpMtsR5eOMfxSw0szeAZA0B5gAeOFPsbd2vo5wAbi17abtaV9tOrZ9054yvbpVwlFnQdcesKuBsj5DqTzuXD78253YkAHZ/2bboT1/8CDzfspGO3WY0W3K7ikzKwYcRafKrdx568xWza2QmnVv71tTM47f0Dii5w4GDTx0vxTlQuoFly0ys9y+oXQeMM7MLo7L3wWOM7PLmjxvMjAZoHfv3sfMmTMHgPr6eioqKnKaORuynXvJ0mV07nlYs/Wf1a7lyK8OS/MV7dNR9/eSpcuwip6opBO2qwEzA4mGjzdyQKeSrO7D1kqXua6ujg821bJ9+3bKysro3asnla2cl3h/WPTaYsp6DmT3CHhAr87GurWrGTlieN5ytUdHPbZTjRkz5lUzq2q6Ph+F/1vA6U0K/ygzm5rpa6qqqmzhwoUAvPDCC1RXV+cialZlO/dpZ5zTbJjlxh4Urf2XuzU66v4+7YxzeKti+BcmkNn23lvUzZvNMYcfmtV92FqFsK979B9C+Zg9Z/wAlw7ZyowbrmPzulV5TNZ2hbC/02lLbklpC38+evWsB/qnLPcD3stDjoJWiBM8J8mVl03ZPSH355/Vs23DCj568Q90YbvvwxZMm3wRHz1/D/Vr3mBXw07q17zBzrrNTJt8Ub6juTbIRxv/AmCopEHABmAi8J085ChohTo0Q1LU1Izj18D0GT+P/cLF0MEDuWnGjb4PW9DYjn/n3bPYFHv1HFJzI1dM+2Gek7m2yHnhN7Odki4D/k7ozvlbM1ua6xwdQTFelMom33/t85Mbrv/ChdzWTFrukiUv/fjN7Gng6Xy8t3POFTu/c9c554qMF37nnCsyXvidc67IeOF3zrkik/MbuNpDUi2wJi72ADbnMU57ee7cKsTchZgZPHeutSX3ADPr2XRlQRT+VJIWprsTLek8d24VYu5CzAyeO9eykdubepxzrsh44XfOuSJTiIX/7nwHaCfPnVuFmLsQM4PnzrV9zl1wbfzOOef2TSGe8TvnnNsHXvidc67IFFThlzRV0gpJSyXdkrL+Wkkr47bT85kxE0k/lmSSeqSsS2RuSbdKelPS65L+JOmglG2JzNxI0riYbaWka/KdJxNJ/SX9U9LyeDz/KK7vLulZSW/Hz93ynbUpSSWS/ivpqbhcCJkPkvRoPK6XSzq+QHJfEY+PJZIektQ5K7nNrCA+gDHAc0BZXO4VPw8DFgNlwCBgFVCS77xNsvcnDEO9BuiR9NzA14FO8fHNwM1JzxzzlcRMg4HSmHVYvnNlyNoHGBkfdwXeivv3FuCauP6axn2fpA/gSuBB4Km4XAiZ7wMujo9LgYOSnhvoC7wLlMflh4ELs5G7kM74LwF+YWbbAcxsU1w/AZhjZtvN7F1gJWFC9yS5HbgKSL2SntjcZvaMme2Mi/8hzJIGCc4cjQJWmtk7ZrYDmEPInDhmttHMFsXHnwDLCb/oEwhFivj5nLwEzEBSP+AM4J6U1UnPXAmcDNwLYGY7zGwLCc8ddQLKJXUCuhBmK9zn3IVU+I8ATpI0X9KLko6N6/sC61Ketz6uSwRJZwMbzGxxk02Jzp3i+8Bf4+OkZ056vrQkDQSOBuYDvc1sI4Q/DkCvPEZL55eEk5hdKeuSnnkwUAv8LjZR3SPpQBKe28w2ALcBa4GNwFYze4Ys5M7LRCyZSHoOOCTNpumErN2A0cCxwMOSBgNK8/yc9lHdS+7rCE0nzb4szbqc5W4ps5k9GZ8zHdgJPND4ZWmen6T+wEnP14ykCuAx4HIzq5PSfQvJIOlMYJOZvSqpOs9x2qITMBKYambzJd1BaCJJtNh2P4HQrLoFeETSBdl47UQVfjM7NdM2SZcAj1to2HpF0i7CYEV5n7w9U25JRxF+aIvjL3Q/YJGkUeQ5d0v7GkDSJOBMYGzc55CAfb0XSc/3BZIOIBT9B8zs8bj6A0l9zGyjpD7ApsyvkHMnAmdLGg90BiolzSbZmSEcF+vNbH5cfpRQ+JOe+1TgXTOrBZD0OHACWchdSE09TwCnAEg6gnCBZjMwF5goqSxO4D4UeCVfIVOZ2Rtm1svMBprZQMIBONLM3ifBuSWNA64Gzjaz/6VsSmzmaAEwVNIgSaXARELmxFE4E7gXWG5mM1M2zQUmxceTgCdznS0TM7vWzPrFY3ki8LyZXUCCMwPE37d1kr4cV40FlpHw3IQmntGSusTjZSzhWtC+5873les2XOEuBWYDS4BFwCkp26YTenOsAGrynbWF72E1sVdPknMTLtquA16LH7OSnjkl33hCD5lVhGarvGfKkPNrhGao11P283jgYOAfwNvxc/d8Z82Qv5o9vXoSnxkYASyM+/sJQrNxIeSeAbwZ6979hB51+5zbh2xwzrkiU0hNPc4557LAC79zzhUZL/zOOVdkvPA751yR8cLvnHNFxgu/KzqS6tvw3GpJJ6QsT5H0vfj4QkmHtuP9V6eO0upcriXqzl3nEqgaqAf+DWBms1K2XUjoX53Yu4OdS8cLv3OApLOA6wk3Cn4InA+UA1OAhjhGylTC3ZP1hJvxqoAHJG0DjifcVVllZpslVQG3mVm1pIOBh4CehDudlfK+FwDT4vvOBy41s4b9/x27YuZNPc4F84DRZnY0YTjnq8xsNTALuN3MRpjZy41PNrNHCXeCnh+3bWvhtX8KzIuvPRc4DEDSV4BvAyea2QiggfAHx7n9ys/4nQv6AX+Mg16VEibAyJaTgW8CmNlfJH0c148FjgEWxEH8ykneQGGuA/LC71zwK2Cmmc2NQw7/rB2vsZM9/0V3brIt3dgoAu4zs2vb8V7OtZs39TgXfAnYEB9PSln/CWFqxHSabltNOIMHODdl/UvEJhxJNYQBwiAMsHWepF5xW3dJA9qZ37lW88LvilEXSetTPq4knOE/IullwnDfjf4MfEPSa5JOavI6vwdmxW3lhJEU74ivkXqBdgZwsqRFhEl51gKY2TLCBeVnJL0OPEuYi9e5/cpH53TOuSLjZ/zOOVdkvPA751yR8cLvnHNFxgu/c84VGS/8zjlXZLzwO+dckfHC75xzReb/1yVB2WaKJuoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import time module\n",
    "import time\n",
    "\n",
    "# Build the scatter plot for latitude vs. wind_speed.\n",
    "plt.scatter(lats,\n",
    "           wind_speed,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Windspeed\"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Windspeed (mph)\")\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": 41,
   "id": "39960578",
   "metadata": {},
   "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",
    "\n",
    "    \n",
    "# Import linregress\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Create a function to create perform linear regression on the weather data\n",
    "# and plot a regression line and the equation with the data.\n",
    "def plot_linear_regression(x_values, y_values, title, y_label, text_coordinates):\n",
    "\n",
    "    # Run regression on hemisphere weather data.\n",
    "    (slope, intercept, r_value, p_value, std_err) = linregress(x_values, y_values)\n",
    "\n",
    "    # Calculate the regression line \"y values\" from the slope and intercept.\n",
    "    regress_values = x_values * slope + intercept\n",
    "    # Get the equation of the line.\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    # Create a scatter plot and plot the regression line.\n",
    "    plt.scatter(x_values,y_values)\n",
    "    plt.plot(x_values,regress_values,\"r\")\n",
    "    # Annotate the text for the line equation.\n",
    "    plt.annotate(line_eq, text_coordinates, fontsize=15, color=\"red\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Latitude')\n",
    "    plt.ylabel(y_label)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a8f94bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Northern and Southern Hemisphere DataFrames.\n",
    "northern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] >= 0)]\n",
    "southern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] < 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5eb8923c",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=-0.43641763518023097, intercept=87.94314788547943, rvalue=-0.6626297744414847, pvalue=2.196455164094533e-48, stderr=0.025643755987203077, intercept_stderr=1.111472044621255)\n"
     ]
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere for Maximum Temperature', 'Max Temp',(10,40))\n",
    "\n",
    "print(linregress(x_values, y_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d2e2fb46",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.7564036552838768, intercept=79.42178175857003, rvalue=0.7549656180927858, pvalue=7.254694160508044e-32, stderr=0.05130413155246871, intercept_stderr=1.3236409331328352)\n"
     ]
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for Maximum Temperature', 'Max Temp',(-30,20))\n",
    "\n",
    "\n",
    "print(linregress(x_values, y_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "84f1f0c5",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.0014409060807849053, intercept=71.36973373013312, rvalue=0.0013570470460696634, pvalue=0.9791889815472923, stderr=0.05520005471107382, intercept_stderr=2.3925246248458807)\n"
     ]
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere for Humidity', '% Humidity',(40,5))\n",
    "\n",
    "print(linregress(x_values, y_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c192bcf4",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.08823946444736154, intercept=74.22047270362553, rvalue=0.06736259727454198, pvalue=0.38850694837509203, stderr=0.10205505476959845, intercept_stderr=2.63300915225518)\n"
     ]
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for % Humidity', '% Humidity',(-50,15))\n",
    "\n",
    "\n",
    "print(linregress(x_values, y_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f7e087a6",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.006157924766353212, intercept=60.090229087444285, rvalue=0.0032258376933696166, pvalue=0.9505560815731726, stderr=0.09924045453056082, intercept_stderr=4.301358621618037)\n"
     ]
    }
   ],
   "source": [
    "# Linear regression on the Southern 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 for % Cloudiness', '% Cloudiness',(10,10))\n",
    "\n",
    "\n",
    "print(linregress(x_values, y_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b35681e6",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.5775521727153456, intercept=62.13389300545905, rvalue=0.2155254841939692, pvalue=0.005291674332802029, stderr=0.20433469912441427, intercept_stderr=5.271812691027653)\n"
     ]
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for % Cloudiness', '% Cloudiness',(-50,60))\n",
    "print(linregress(x_values, y_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9ad38a73",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.006550273643695215, intercept=7.735710646233921, rvalue=0.023227349150322832, pvalue=0.6552032875226507, stderr=0.01465688730009914, intercept_stderr=0.6352704534918364)\n"
     ]
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere for Wind Speed', 'Wind Speed',(10,30))\n",
    "print(linregress(x_values, y_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "33b664b5",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=-0.04538390379253706, intercept=6.875030034756749, rvalue=-0.12112434218541561, pvalue=0.12006166828146544, stderr=0.029042841421875815, intercept_stderr=0.7493021040842636)\n"
     ]
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere for Wind Speed', 'Wind Speed',(-30,30))\n",
    "print(linregress(x_values, y_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31c9be35",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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