Python API- Vacationpy

{ "cells": [ { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "import requests\n", "import time\n", "from scipy.stats import linregress\n", "# Import API key\n", "# from api_keys import weather_api_key\n", "# Incorporated citipy to determine city based on latitude and longitude\n", "from citipy import citipy\n", "# Output File (CSV)\n", "output_data_file = \"output_data/cities.csv\"\n", "# Range of latitudes and longitudes\n", "lat_range = (-90, 90)\n", "lng_range = (-180, 180)\n", "api_key = '8ec952d4e4092c596aad13ddfae6d358'" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "614" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lat_lngs = []\n", "cities = []\n", "# Create a set of random lat and lng combinations\n", "lats = np.random.uniform(lat_range[0], lat_range[1], size=1500)\n", "lngs = np.random.uniform(lng_range[0], lng_range[1], size=1500)\n", "lat_lngs = zip(lats, lngs)\n", "# Identify nearest city for each lat, lng combination\n", "for lat_lng in lat_lngs:\n", " city = citipy.nearest_city(lat_lng[0], lat_lng[1]).city_name\n", " # If the city is unique, then add it to a our 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": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beginning Data Retrieval \n", "-----------------------------\n", "Processing Record 1 of Set 1 | taoudenni\n", "Processing Record 2 of Set 1 | rikitea\n", "Processing Record 3 of Set 1 | caravelas\n", "Processing Record 4 of Set 1 | pisco\n", "Processing Record 5 of Set 1 | hithadhoo\n", "Processing Record 6 of Set 1 | port keats\n", "Processing Record 7 of Set 1 | mataura\n", "Processing Record 8 of Set 1 | byron bay\n", "Processing Record 9 of Set 1 | aykhal\n", "Processing Record 10 of Set 1 | northam\n", "Processing Record 11 of Set 1 | busselton\n", "Processing Record 12 of Set 1 | torbay\n", "Processing Record 13 of Set 1 | klaksvik\n", "Processing Record 14 of Set 1 | bolungarvik\n", "City not found. Skipping...\n", "Processing Record 15 of Set 1 | albany\n", "Processing Record 16 of Set 1 | ushuaia\n", "Processing Record 17 of Set 1 | nioro\n", "Processing Record 18 of Set 1 | wuwei\n", "Processing Record 19 of Set 1 | hobart\n", "Processing Record 20 of Set 1 | punta arenas\n", "Processing Record 21 of Set 1 | codrington\n", "Processing Record 22 of Set 1 | mys shmidta\n", "City not found. Skipping...\n", "Processing Record 23 of Set 1 | hollins\n", "Processing Record 24 of Set 1 | atuona\n", "Processing Record 25 of Set 1 | tuktoyaktuk\n", "Processing Record 26 of Set 1 | georgetown\n", "Processing Record 27 of Set 1 | cape town\n", "Processing Record 28 of Set 1 | batagay\n", "Processing Record 29 of Set 1 | tasiilaq\n", "Processing Record 30 of Set 1 | barrow\n", "Processing Record 31 of Set 1 | husavik\n", "Processing Record 32 of Set 1 | kukmor\n", "Processing Record 33 of Set 1 | bouna\n", "Processing Record 34 of Set 1 | parfenyevo\n", "Processing Record 35 of Set 1 | barentsburg\n", "City not found. Skipping...\n", "Processing Record 36 of Set 1 | castro\n", "Processing Record 37 of Set 1 | senanga\n", "Processing Record 38 of Set 1 | san patricio\n", "Processing Record 39 of Set 1 | nizhneyansk\n", "City not found. Skipping...\n", "Processing Record 40 of Set 1 | mar del plata\n", "Processing Record 41 of Set 1 | sokoni\n", "Processing Record 42 of Set 1 | taolanaro\n", "City not found. Skipping...\n", "Processing Record 43 of Set 1 | new norfolk\n", "Processing Record 44 of Set 1 | cherskiy\n", "Processing Record 45 of Set 1 | bredasdorp\n", "Processing Record 46 of Set 1 | jimma\n", "Processing Record 47 of Set 1 | daru\n", "Processing Record 48 of Set 1 | nedryhayliv\n", "Processing Record 49 of Set 1 | nanortalik\n", "Processing Record 50 of Set 1 | puerto ayora\n", "Processing Record 0 of Set 2 | illoqqortoormiut\n", "City not found. Skipping...\n", "Processing Record 1 of Set 2 | hermanus\n", "Processing Record 2 of Set 2 | isangel\n", "Processing Record 3 of Set 2 | katha\n", "City not found. Skipping...\n", "Processing Record 4 of Set 2 | attawapiskat\n", "City not found. Skipping...\n", "Processing Record 5 of Set 2 | havoysund\n", "Processing Record 6 of Set 2 | grand gaube\n", "Processing Record 7 of Set 2 | sentyabrskiy\n", "City not found. Skipping...\n", "Processing Record 8 of Set 2 | avarua\n", "Processing Record 9 of Set 2 | nome\n", "Processing Record 10 of Set 2 | vestmannaeyjar\n", "Processing Record 11 of Set 2 | bathsheba\n", "Processing Record 12 of Set 2 | chifeng\n", "Processing Record 13 of Set 2 | saint george\n", "Processing Record 14 of Set 2 | anadyr\n", "Processing Record 15 of Set 2 | mugur-aksy\n", "Processing Record 16 of Set 2 | sarkand\n", "Processing Record 17 of Set 2 | zlobin\n", "Processing Record 18 of Set 2 | chimoio\n", "Processing Record 19 of Set 2 | arraial do cabo\n", "Processing Record 20 of Set 2 | norman wells\n", "Processing Record 21 of Set 2 | el tigre\n", "Processing Record 22 of Set 2 | sorvag\n", "City not found. Skipping...\n", "Processing Record 23 of Set 2 | bosaso\n", "Processing Record 24 of Set 2 | sola\n", "Processing Record 25 of Set 2 | upernavik\n", "Processing Record 26 of Set 2 | dikson\n", "Processing Record 27 of Set 2 | severo-kurilsk\n", "Processing Record 28 of Set 2 | jamestown\n", "Processing Record 29 of Set 2 | lazaro cardenas\n", "Processing Record 30 of Set 2 | zaraza\n", "Processing Record 31 of Set 2 | amderma\n", "City not found. Skipping...\n", "Processing Record 32 of Set 2 | falealupo\n", "City not found. Skipping...\n", "Processing Record 33 of Set 2 | maneadero\n", "Processing Record 34 of Set 2 | luangwa\n", "Processing Record 35 of Set 2 | souillac\n", "Processing Record 36 of Set 2 | pacific grove\n", "Processing Record 37 of Set 2 | progreso\n", "Processing Record 38 of Set 2 | garrel\n", "Processing Record 39 of Set 2 | saint-pierre\n", "Processing Record 40 of Set 2 | thompson\n", "Processing Record 41 of Set 2 | kodiak\n", "Processing Record 42 of Set 2 | sobolevo\n", "Processing Record 43 of Set 2 | riyadh\n", "Processing Record 44 of Set 2 | airai\n", "Processing Record 45 of Set 2 | zheleznodorozhnyy\n", "Processing Record 46 of Set 2 | nikolskoye\n", "Processing Record 47 of Set 2 | middelburg\n", "Processing Record 48 of Set 2 | hilo\n", "Processing Record 49 of Set 2 | dobsina\n", "Processing Record 0 of Set 3 | pevek\n", "Processing Record 1 of Set 3 | alice springs\n", "Processing Record 2 of Set 3 | kapaa\n", "Processing Record 3 of Set 3 | port alfred\n", "Processing Record 4 of Set 3 | vaini\n", "Processing Record 5 of Set 3 | maloh\n", "Processing Record 6 of Set 3 | qaanaaq\n", "Processing Record 7 of Set 3 | oistins\n", "Processing Record 8 of Set 3 | andapa\n", "Processing Record 9 of Set 3 | perpignan\n", "Processing Record 10 of Set 3 | mahuva\n", "Processing Record 11 of Set 3 | mahebourg\n", "Processing Record 12 of Set 3 | belushya guba\n", "City not found. Skipping...\n", "Processing Record 13 of Set 3 | sumter\n", "Processing Record 14 of Set 3 | khatanga\n", "Processing Record 15 of Set 3 | chapais\n", "Processing Record 16 of Set 3 | zarubino\n", "Processing Record 17 of Set 3 | shieli\n", "Processing Record 18 of Set 3 | saint-philippe\n", "Processing Record 19 of Set 3 | cattolica\n", "Processing Record 20 of Set 3 | gemena\n", "Processing Record 21 of Set 3 | dunedin\n", "Processing Record 22 of Set 3 | erdenet\n", "Processing Record 23 of Set 3 | gao\n", "Processing Record 24 of Set 3 | nemuro\n", "Processing Record 25 of Set 3 | ribeira grande\n", "Processing Record 26 of Set 3 | port elizabeth\n", "Processing Record 27 of Set 3 | fairbanks\n", "Processing Record 28 of Set 3 | kamenskoye\n", "City not found. Skipping...\n", "Processing Record 29 of Set 3 | butaritari\n", "Processing Record 30 of Set 3 | esperance\n", "Processing Record 31 of Set 3 | salinopolis\n", "Processing Record 32 of Set 3 | egvekinot\n", "Processing Record 33 of Set 3 | navahrudak\n", "Processing Record 34 of Set 3 | gushikawa\n", "Processing Record 35 of Set 3 | burnie\n", "Processing Record 36 of Set 3 | hasaki\n", "Processing Record 37 of Set 3 | yatou\n", "Processing Record 38 of Set 3 | yellowknife\n", "Processing Record 39 of Set 3 | sampit\n", "Processing Record 40 of Set 3 | tiksi\n", "Processing Record 41 of Set 3 | carnarvon\n", "Processing Record 42 of Set 3 | east london\n", "Processing Record 43 of Set 3 | tsihombe\n", "City not found. Skipping...\n", "Processing Record 44 of Set 3 | shizunai\n", "Processing Record 45 of Set 3 | tabou\n", "Processing Record 46 of Set 3 | abiy adi\n", "City not found. Skipping...\n", "Processing Record 47 of Set 3 | hit\n", "Processing Record 48 of Set 3 | berlevag\n", "Processing Record 49 of Set 3 | lima\n", "Processing Record 0 of Set 4 | panjwin\n", "City not found. Skipping...\n", "Processing Record 1 of Set 4 | chuy\n", "Processing Record 2 of Set 4 | van\n", "Processing Record 3 of Set 4 | ixtapa\n", "Processing Record 4 of Set 4 | portland\n", "Processing Record 5 of Set 4 | port moresby\n", "Processing Record 6 of Set 4 | alabaster\n", "Processing Record 7 of Set 4 | gweta\n", "Processing Record 8 of Set 4 | bluff\n", "Processing Record 9 of Set 4 | sao joao da barra\n", "Processing Record 10 of Set 4 | bethel\n", "Processing Record 11 of Set 4 | slave lake\n", "Processing Record 12 of Set 4 | kendari\n", "Processing Record 13 of Set 4 | kuusamo\n", "Processing Record 14 of Set 4 | thinadhoo\n", "Processing Record 15 of Set 4 | nuevo progreso\n", "Processing Record 16 of Set 4 | garmsar\n", "Processing Record 17 of Set 4 | victoria\n", "Processing Record 18 of Set 4 | faanui\n", "Processing Record 19 of Set 4 | fortuna\n", "Processing Record 20 of Set 4 | itarema\n", "Processing Record 21 of Set 4 | bukachacha\n", "Processing Record 22 of Set 4 | tual\n", "Processing Record 23 of Set 4 | yavatmal\n", "Processing Record 24 of Set 4 | sisimiut\n", "Processing Record 25 of Set 4 | kachikau\n", "City not found. Skipping...\n", "Processing Record 26 of Set 4 | asau\n", "Processing Record 27 of Set 4 | naze\n", "Processing Record 28 of Set 4 | kattivakkam\n", "Processing Record 29 of Set 4 | sibolga\n", "Processing Record 30 of Set 4 | vanimo\n", "Processing Record 31 of Set 4 | phek\n", "Processing Record 32 of Set 4 | sambava\n", "Processing Record 33 of Set 4 | eurajoki\n", "Processing Record 34 of Set 4 | tautira\n", "Processing Record 35 of Set 4 | glendive\n", "Processing Record 36 of Set 4 | cabo san lucas\n", "Processing Record 37 of Set 4 | abu zabad\n", "Processing Record 38 of Set 4 | ayan\n", "Processing Record 39 of Set 4 | chokurdakh\n", "Processing Record 40 of Set 4 | kutum\n", "Processing Record 41 of Set 4 | cardoso\n", "Processing Record 42 of Set 4 | ponta do sol\n", "Processing Record 43 of Set 4 | mizdah\n", "Processing Record 44 of Set 4 | nouadhibou\n", "Processing Record 45 of Set 4 | touros\n", "Processing Record 46 of Set 4 | akdepe\n", "Processing Record 47 of Set 4 | durham\n", "Processing Record 48 of Set 4 | buchanan\n", "Processing Record 49 of Set 4 | roebourne\n", "Processing Record 0 of Set 5 | petropavlovsk-kamchatskiy\n", "Processing Record 1 of Set 5 | tessalit\n", "Processing Record 2 of Set 5 | saleaula\n", "City not found. Skipping...\n", "Processing Record 3 of Set 5 | bambous virieux\n", "Processing Record 4 of Set 5 | kpandu\n", "Processing Record 5 of Set 5 | tabiauea\n", "City not found. Skipping...\n", "Processing Record 6 of Set 5 | sioux lookout\n", "Processing Record 7 of Set 5 | mikhaylovka\n", "Processing Record 8 of Set 5 | terrace\n", "Processing Record 9 of Set 5 | constitucion\n", "Processing Record 10 of Set 5 | vuktyl\n", "Processing Record 11 of Set 5 | iqaluit\n", "Processing Record 12 of Set 5 | mananjary\n", "Processing Record 13 of Set 5 | ancud\n", "Processing Record 14 of Set 5 | sangmelima\n", "Processing Record 15 of Set 5 | slupsk\n", "Processing Record 16 of Set 5 | naters\n", "Processing Record 17 of Set 5 | barcelos\n", "Processing Record 18 of Set 5 | tarudant\n", "City not found. Skipping...\n", "Processing Record 19 of Set 5 | governador dix-sept rosado\n", "Processing Record 20 of Set 5 | nsanje\n", "Processing Record 21 of Set 5 | gambela\n", "Processing Record 22 of Set 5 | matara\n", "Processing Record 23 of Set 5 | mount isa\n", "Processing Record 24 of Set 5 | gamba\n", "Processing Record 25 of Set 5 | along\n", "Processing Record 26 of Set 5 | tombouctou\n", "Processing Record 27 of Set 5 | porto walter\n", "Processing Record 28 of Set 5 | lagoa\n", "Processing Record 29 of Set 5 | yangjiang\n", "Processing Record 30 of Set 5 | banda aceh\n", "Processing Record 31 of Set 5 | bilma\n", "Processing Record 32 of Set 5 | ijaki\n", "City not found. Skipping...\n", "Processing Record 33 of Set 5 | sitka\n", "Processing Record 34 of Set 5 | eirunepe\n", "Processing Record 35 of Set 5 | luderitz\n", "Processing Record 36 of Set 5 | lorengau\n", "Processing Record 37 of Set 5 | narsaq\n", "Processing Record 38 of Set 5 | corn island\n", "Processing Record 39 of Set 5 | kenai\n", "Processing Record 40 of Set 5 | pangnirtung\n", "Processing Record 41 of Set 5 | soe\n", "Processing Record 42 of Set 5 | poum\n", "Processing Record 43 of Set 5 | tungkang\n", "City not found. Skipping...\n", "Processing Record 44 of Set 5 | aksarayskiy\n", "Processing Record 45 of Set 5 | biak\n", "Processing Record 46 of Set 5 | kailua\n", "Processing Record 47 of Set 5 | boundiali\n", "Processing Record 48 of Set 5 | umzimvubu\n", "City not found. Skipping...\n", "Processing Record 49 of Set 5 | juneau\n", "Processing Record 0 of Set 6 | mabilbila\n", "City not found. Skipping...\n", "Processing Record 1 of Set 6 | sur\n", "Processing Record 2 of Set 6 | ahipara\n", "Processing Record 3 of Set 6 | barrancas\n", "Processing Record 4 of Set 6 | puerto narino\n", "Processing Record 5 of Set 6 | port hedland\n", "Processing Record 6 of Set 6 | susehri\n", "Processing Record 7 of Set 6 | batemans bay\n", "Processing Record 8 of Set 6 | seoul\n", "Processing Record 9 of Set 6 | bengkulu\n", "Processing Record 10 of Set 6 | kruisfontein\n", "Processing Record 11 of Set 6 | tuggurt\n", "City not found. Skipping...\n", "Processing Record 12 of Set 6 | henties bay\n", "Processing Record 13 of Set 6 | witu\n", "Processing Record 14 of Set 6 | namatanai\n", "Processing Record 15 of Set 6 | saskylakh\n", "Processing Record 16 of Set 6 | madison heights\n", "Processing Record 17 of Set 6 | iquitos\n", "Processing Record 18 of Set 6 | terney\n", "Processing Record 19 of Set 6 | lappeenranta\n", "Processing Record 20 of Set 6 | pemangkat\n", "Processing Record 21 of Set 6 | temizhbekskaya\n", "Processing Record 22 of Set 6 | manta\n", "Processing Record 23 of Set 6 | talnakh\n", "Processing Record 24 of Set 6 | nurota\n", "Processing Record 25 of Set 6 | diamantino\n", "Processing Record 26 of Set 6 | rorvik\n", "Processing Record 27 of Set 6 | wiarton\n", "Processing Record 28 of Set 6 | ballina\n", "Processing Record 29 of Set 6 | monte aprazivel\n", "Processing Record 30 of Set 6 | lebu\n", "Processing Record 31 of Set 6 | kamenka\n", "Processing Record 32 of Set 6 | kimry\n", "Processing Record 33 of Set 6 | itaqui\n", "Processing Record 34 of Set 6 | kavieng\n", "Processing Record 35 of Set 6 | nantucket\n", "Processing Record 36 of Set 6 | phan rang\n", "City not found. Skipping...\n", "Processing Record 37 of Set 6 | taltal\n", "Processing Record 38 of Set 6 | saint anthony\n", "Processing Record 39 of Set 6 | fredensborg\n", "Processing Record 40 of Set 6 | kontagora\n", "Processing Record 41 of Set 6 | labuan\n", "Processing Record 42 of Set 6 | provideniya\n", "Processing Record 43 of Set 6 | pombas\n", "Processing Record 44 of Set 6 | hernani\n", "Processing Record 45 of Set 6 | marv dasht\n", "City not found. Skipping...\n", "Processing Record 46 of Set 6 | irbeyskoye\n", "Processing Record 47 of Set 6 | sungai padi\n", "Processing Record 48 of Set 6 | abha\n", "Processing Record 49 of Set 6 | kodinsk\n", "Processing Record 0 of Set 7 | tumannyy\n", "City not found. Skipping...\n", "Processing Record 1 of Set 7 | chagda\n", "City not found. Skipping...\n", "Processing Record 2 of Set 7 | wonthaggi\n", "Processing Record 3 of Set 7 | wad madani\n", "Processing Record 4 of Set 7 | labuhan\n", "Processing Record 5 of Set 7 | sao filipe\n", "Processing Record 6 of Set 7 | yulara\n", "Processing Record 7 of Set 7 | milingimbi\n", "City not found. Skipping...\n", "Processing Record 8 of Set 7 | samalaeulu\n", "City not found. Skipping...\n", "Processing Record 9 of Set 7 | tateyama\n", "Processing Record 10 of Set 7 | san rafael del sur\n", "Processing Record 11 of Set 7 | bandarbeyla\n", "Processing Record 12 of Set 7 | vila franca do campo\n", "Processing Record 13 of Set 7 | geraldton\n", "Processing Record 14 of Set 7 | gizo\n", "Processing Record 15 of Set 7 | panama city\n", "Processing Record 16 of Set 7 | saldanha\n", "Processing Record 17 of Set 7 | fort nelson\n", "Processing Record 18 of Set 7 | san rafael\n", "Processing Record 19 of Set 7 | gorno-chuyskiy\n", "City not found. Skipping...\n", "Processing Record 20 of Set 7 | port macquarie\n", "Processing Record 21 of Set 7 | ostrovnoy\n", "Processing Record 22 of Set 7 | dokka\n", "Processing Record 23 of Set 7 | businga\n", "Processing Record 24 of Set 7 | cabo rojo\n", "Processing Record 25 of Set 7 | clyde river\n", "Processing Record 26 of Set 7 | balkanabat\n", "Processing Record 27 of Set 7 | kichera\n", "Processing Record 28 of Set 7 | mount gambier\n", "Processing Record 29 of Set 7 | amahai\n", "Processing Record 30 of Set 7 | whitehorse\n", "Processing Record 31 of Set 7 | ust-kuyga\n", "Processing Record 32 of Set 7 | butte\n", "Processing Record 33 of Set 7 | ilulissat\n", "Processing Record 34 of Set 7 | bintulu\n", "Processing Record 35 of Set 7 | skibbereen\n", "Processing Record 36 of Set 7 | curuguaty\n", "Processing Record 37 of Set 7 | tura\n", "Processing Record 38 of Set 7 | padilla\n", "Processing Record 39 of Set 7 | sorong\n", "Processing Record 40 of Set 7 | faya\n", "Processing Record 41 of Set 7 | bubaque\n", "Processing Record 42 of Set 7 | amapa\n", "Processing Record 43 of Set 7 | grand river south east\n", "City not found. Skipping...\n", "Processing Record 44 of Set 7 | pitkyaranta\n", "Processing Record 45 of Set 7 | vaitupu\n", "City not found. Skipping...\n", "Processing Record 46 of Set 7 | palabuhanratu\n", "City not found. Skipping...\n", "Processing Record 47 of Set 7 | burgos\n", "Processing Record 48 of Set 7 | olafsvik\n", "Processing Record 49 of Set 7 | turukhansk\n", "Processing Record 0 of Set 8 | namibe\n", "Processing Record 1 of Set 8 | aksarka\n", "Processing Record 2 of Set 8 | san cristobal\n", "Processing Record 3 of Set 8 | herat\n", "Processing Record 4 of Set 8 | benguela\n", "Processing Record 5 of Set 8 | vila velha\n", "Processing Record 6 of Set 8 | porto novo\n", "Processing Record 7 of Set 8 | kirakira\n", "Processing Record 8 of Set 8 | bisira\n", "Processing Record 9 of Set 8 | santa fe\n", "Processing Record 10 of Set 8 | buenos aires\n", "Processing Record 11 of Set 8 | adrar\n", "Processing Record 12 of Set 8 | surt\n", "Processing Record 13 of Set 8 | chernihiv\n", "Processing Record 14 of Set 8 | karratha\n", "Processing Record 15 of Set 8 | teknaf\n", "Processing Record 16 of Set 8 | magalia\n", "Processing Record 17 of Set 8 | ajdabiya\n", "Processing Record 18 of Set 8 | vagur\n", "Processing Record 19 of Set 8 | tokmak\n", "Processing Record 20 of Set 8 | suba\n", "Processing Record 21 of Set 8 | holme\n", "Processing Record 22 of Set 8 | kangaatsiaq\n", "Processing Record 23 of Set 8 | mrirt\n", "City not found. Skipping...\n", "Processing Record 24 of Set 8 | damghan\n", "Processing Record 25 of Set 8 | guerrero negro\n", "Processing Record 26 of Set 8 | yenagoa\n", "Processing Record 27 of Set 8 | lubao\n", "Processing Record 28 of Set 8 | rimouski\n", "Processing Record 29 of Set 8 | songea\n", "Processing Record 30 of Set 8 | banjar\n", "Processing Record 31 of Set 8 | leesburg\n", "Processing Record 32 of Set 8 | natal\n", "Processing Record 33 of Set 8 | arlit\n", "Processing Record 34 of Set 8 | peleduy\n", "Processing Record 35 of Set 8 | oksfjord\n", "Processing Record 36 of Set 8 | fukue\n", "Processing Record 37 of Set 8 | mingshui\n", "Processing Record 38 of Set 8 | marzuq\n", "Processing Record 39 of Set 8 | sorland\n", "Processing Record 40 of Set 8 | intipuca\n", "Processing Record 41 of Set 8 | mangrol\n", "Processing Record 42 of Set 8 | volda\n", "Processing Record 43 of Set 8 | kyzyl-mazhalyk\n", "Processing Record 44 of Set 8 | merrill\n", "Processing Record 45 of Set 8 | san policarpo\n", "Processing Record 46 of Set 8 | cayenne\n", "Processing Record 47 of Set 8 | chubbuck\n", "Processing Record 48 of Set 8 | cidreira\n", "Processing Record 49 of Set 8 | krutinka\n", "Processing Record 0 of Set 9 | isla vista\n", "Processing Record 1 of Set 9 | praia da vitoria\n", "Processing Record 2 of Set 9 | kuhestan\n", "City not found. Skipping...\n", "Processing Record 3 of Set 9 | aden\n", "Processing Record 4 of Set 9 | alyangula\n", "Processing Record 5 of Set 9 | morehead\n", "Processing Record 6 of Set 9 | alpena\n", "Processing Record 7 of Set 9 | yerofey pavlovich\n", "Processing Record 8 of Set 9 | padang\n", "Processing Record 9 of Set 9 | ambilobe\n", "Processing Record 10 of Set 9 | shymkent\n", "Processing Record 11 of Set 9 | san quintin\n", "Processing Record 12 of Set 9 | gillette\n", "Processing Record 13 of Set 9 | rio gallegos\n", "Processing Record 14 of Set 9 | te anau\n", "Processing Record 15 of Set 9 | huarmey\n", "Processing Record 16 of Set 9 | mahenge\n", "Processing Record 17 of Set 9 | bagdarin\n", "Processing Record 18 of Set 9 | barawe\n", "City not found. Skipping...\n", "Processing Record 19 of Set 9 | pochutla\n", "Processing Record 20 of Set 9 | andros town\n", "Processing Record 21 of Set 9 | yumen\n", "Processing Record 22 of Set 9 | guarapari\n", "Processing Record 23 of Set 9 | cockburn town\n", "Processing Record 24 of Set 9 | margate\n", "Processing Record 25 of Set 9 | flin flon\n", "Processing Record 26 of Set 9 | palmer\n", "Processing Record 27 of Set 9 | haines junction\n", "Processing Record 28 of Set 9 | camana\n", "Processing Record 29 of Set 9 | okhotsk\n", "Processing Record 30 of Set 9 | uspenka\n", "Processing Record 31 of Set 9 | dwarka\n", "Processing Record 32 of Set 9 | port lincoln\n", "Processing Record 33 of Set 9 | broken hill\n", "Processing Record 34 of Set 9 | wajima\n", "Processing Record 35 of Set 9 | polovinnoye\n", "Processing Record 36 of Set 9 | rio grande\n", "Processing Record 37 of Set 9 | katsuura\n", "Processing Record 38 of Set 9 | mae sot\n", "Processing Record 39 of Set 9 | shingu\n", "Processing Record 40 of Set 9 | saquena\n", "Processing Record 41 of Set 9 | dalby\n", "Processing Record 42 of Set 9 | vrangel\n", "Processing Record 43 of Set 9 | alofi\n", "Processing Record 44 of Set 9 | kiama\n", "Processing Record 45 of Set 9 | ranau\n", "Processing Record 46 of Set 9 | bulgan\n", "Processing Record 47 of Set 9 | longyearbyen\n", "Processing Record 48 of Set 9 | kiruna\n", "Processing Record 49 of Set 9 | macas\n", "Processing Record 0 of Set 10 | cap malheureux\n", "Processing Record 1 of Set 10 | ucluelet\n", "Processing Record 2 of Set 10 | talara\n", "Processing Record 3 of Set 10 | lins\n", "Processing Record 4 of Set 10 | ferkessedougou\n", "Processing Record 5 of Set 10 | severnoye\n", "Processing Record 6 of Set 10 | birnin kebbi\n", "Processing Record 7 of Set 10 | bauchi\n", "Processing Record 8 of Set 10 | awjilah\n", "Processing Record 9 of Set 10 | great yarmouth\n", "Processing Record 10 of Set 10 | sembe\n", "Processing Record 11 of Set 10 | college\n", "Processing Record 12 of Set 10 | chiguayante\n", "Processing Record 13 of Set 10 | dombas\n", "Processing Record 14 of Set 10 | pandan\n", "Processing Record 15 of Set 10 | kidal\n", "Processing Record 16 of Set 10 | phan thiet\n", "Processing Record 17 of Set 10 | kibala\n", "Processing Record 18 of Set 10 | dongzhen\n", "Processing Record 19 of Set 10 | muros\n", "Processing Record 20 of Set 10 | waipawa\n", "Processing Record 21 of Set 10 | winslow\n", "Processing Record 22 of Set 10 | coos bay\n", "Processing Record 23 of Set 10 | moussoro\n", "Processing Record 24 of Set 10 | upington\n", "Processing Record 25 of Set 10 | khalkoutsion\n", "Processing Record 26 of Set 10 | evora\n", "Processing Record 27 of Set 10 | daoukro\n", "Processing Record 28 of Set 10 | lompoc\n", "Processing Record 29 of Set 10 | aswan\n", "Processing Record 30 of Set 10 | dolbeau\n", "City not found. Skipping...\n", "Processing Record 31 of Set 10 | mersing\n", "Processing Record 32 of Set 10 | svetlyy\n", "Processing Record 33 of Set 10 | tharad\n", "Processing Record 34 of Set 10 | zhezkazgan\n", "Processing Record 35 of Set 10 | road town\n", "Processing Record 36 of Set 10 | kozhva\n", "Processing Record 37 of Set 10 | bitkine\n", "Processing Record 38 of Set 10 | manokwari\n", "Processing Record 39 of Set 10 | makakilo city\n", "Processing Record 40 of Set 10 | christchurch\n", "Processing Record 41 of Set 10 | clarence town\n", "Processing Record 42 of Set 10 | mareeba\n", "Processing Record 43 of Set 10 | nikolayevskaya\n", "Processing Record 44 of Set 10 | jerome\n", "Processing Record 45 of Set 10 | ellisras\n", "Processing Record 46 of Set 10 | kandava\n", "Processing Record 47 of Set 10 | jalu\n", "Processing Record 48 of Set 10 | miri\n", "Processing Record 49 of Set 10 | lucapa\n", "Processing Record 0 of Set 11 | santa rosa\n", "Processing Record 1 of Set 11 | auki\n", "Processing Record 2 of Set 11 | havre-saint-pierre\n", "Processing Record 3 of Set 11 | digby\n", "Processing Record 4 of Set 11 | solsvik\n", "City not found. Skipping...\n", "Processing Record 5 of Set 11 | pozo colorado\n", "Processing Record 6 of Set 11 | oriximina\n", "Processing Record 7 of Set 11 | belaya gora\n", "Processing Record 8 of Set 11 | kajaani\n", "Processing Record 9 of Set 11 | homer\n", "Processing Record 10 of Set 11 | cururupu\n", "Processing Record 11 of Set 11 | birin\n", "Processing Record 12 of Set 11 | kostino\n", "Processing Record 13 of Set 11 | araouane\n", "Processing Record 14 of Set 11 | jalingo\n", "Processing Record 15 of Set 11 | sedelnikovo\n", "City not found. Skipping...\n", "Processing Record 16 of Set 11 | brokopondo\n", "Processing Record 17 of Set 11 | gasa\n", "Processing Record 18 of Set 11 | nakonde\n", "Processing Record 19 of Set 11 | harper\n", "Processing Record 20 of Set 11 | ugento\n", "Processing Record 21 of Set 11 | verkhoyansk\n", "Processing Record 22 of Set 11 | saryg-sep\n", "Processing Record 23 of Set 11 | champua\n", "Processing Record 24 of Set 11 | dighwara\n", "Processing Record 25 of Set 11 | teshie\n", "Processing Record 26 of Set 11 | yerbogachen\n", "Processing Record 27 of Set 11 | gerede\n", "Processing Record 28 of Set 11 | almaznyy\n", "Processing Record 29 of Set 11 | kamaishi\n", "Processing Record 30 of Set 11 | aklavik\n", "Processing Record 31 of Set 11 | kholm\n", "Processing Record 32 of Set 11 | hami\n", "Processing Record 33 of Set 11 | sazonovo\n", "Processing Record 34 of Set 11 | eureka\n", "Processing Record 35 of Set 11 | varzelandia\n", "Processing Record 36 of Set 11 | novyy urengoy\n", "Processing Record 37 of Set 11 | buraydah\n", "Processing Record 38 of Set 11 | tupancireta\n", "Processing Record 39 of Set 11 | kahului\n", "Processing Record 40 of Set 11 | konya\n", "Processing Record 41 of Set 11 | tamasane\n", "Processing Record 42 of Set 11 | lavrentiya\n", "Processing Record 43 of Set 11 | panguna\n", "Processing Record 44 of Set 11 | kununurra\n", "Processing Record 45 of Set 11 | lyubeshiv\n", "City not found. Skipping...\n", "Processing Record 46 of Set 11 | shache\n", "Processing Record 47 of Set 11 | kandy\n", "Processing Record 48 of Set 11 | melfi\n", "Processing Record 49 of Set 11 | beloha\n", "Processing Record 0 of Set 12 | esfarayen\n", "Processing Record 1 of Set 12 | tabuk\n", "Processing Record 2 of Set 12 | abu samrah\n", "Processing Record 3 of Set 12 | bima\n", "Processing Record 4 of Set 12 | sevlievo\n", "Processing Record 5 of Set 12 | plettenberg bay\n", "Processing Record 6 of Set 12 | coquimbo\n", "Processing Record 7 of Set 12 | marsa matruh\n", "Processing Record 8 of Set 12 | atar\n", "Processing Record 9 of Set 12 | keflavik\n", "Processing Record 10 of Set 12 | nizwa\n", "Processing Record 11 of Set 12 | bela\n", "Processing Record 12 of Set 12 | mezen\n", "Processing Record 13 of Set 12 | vilhena\n", "Processing Record 14 of Set 12 | bardiyah\n", "Processing Record 15 of Set 12 | batagay-alyta\n", "Processing Record 16 of Set 12 | tukrah\n", "Processing Record 17 of Set 12 | santa maria\n", "Processing Record 18 of Set 12 | kavaratti\n", "Processing Record 19 of Set 12 | kargasok\n", "Processing Record 20 of Set 12 | aripuana\n", "Processing Record 21 of Set 12 | katsiveli\n", "City not found. Skipping...\n", "Processing Record 22 of Set 12 | kadykchan\n", "City not found. Skipping...\n", "Processing Record 23 of Set 12 | dunmore east\n", "Processing Record 24 of Set 12 | forres\n", "Processing Record 25 of Set 12 | george\n", "Processing Record 26 of Set 12 | vredendal\n", "Processing Record 27 of Set 12 | lethem\n", "Processing Record 28 of Set 12 | puerto suarez\n", "Processing Record 29 of Set 12 | sungaipenuh\n", "Processing Record 30 of Set 12 | santa cruz del sur\n", "Processing Record 31 of Set 12 | burica\n", "City not found. Skipping...\n", "Processing Record 32 of Set 12 | litayen\n", "City not found. Skipping...\n", "Processing Record 33 of Set 12 | menomonie\n", "Processing Record 34 of Set 12 | opuwo\n", "Processing Record 35 of Set 12 | qandala\n", "Processing Record 36 of Set 12 | hamilton\n", "Processing Record 37 of Set 12 | tahoua\n", "Processing Record 38 of Set 12 | yomitan\n", "City not found. Skipping...\n", "Processing Record 39 of Set 12 | parana\n", "Processing Record 40 of Set 12 | the pas\n", "Processing Record 41 of Set 12 | bargal\n", "City not found. Skipping...\n", "Processing Record 42 of Set 12 | la ronge\n", "Processing Record 43 of Set 12 | carauari\n", "Processing Record 44 of Set 12 | boyolangu\n", "Processing Record 45 of Set 12 | khonuu\n", "City not found. Skipping...\n", "Processing Record 46 of Set 12 | maraa\n", "Processing Record 47 of Set 12 | odweyne\n", "Processing Record 48 of Set 12 | yar-sale\n", "Processing Record 49 of Set 12 | zhanakorgan\n", "-----------------------------\n", "Data Retrieval Complete \n", "-----------------------------\n" ] } ], "source": [ "url = \"http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=\" + api_key \n", "# List of city data\n", "city_data = []\n", "# Print to logger\n", "print(\"Beginning Data Retrieval \")\n", "print(\"-----------------------------\")\n", "# Create counters\n", "record_count = 1\n", "set_count = 1\n", "# Loop through all the cities in our list\n", "for i, city in enumerate(cities[:600]):\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 = 0\n", " # Create endpoint URL with each city\n", " city_url = url + \"&q=\" + city\n", " # Log the url, record, and set numbers\n", " print(\"Processing Record %s of Set %s | %s\" % (record_count, set_count, city))\n", " # Add 1 to the record count\n", " record_count += 1\n", " # Run an API request for each of the cities\n", " try:\n", " # Parse the JSON and retrieve data\n", " city_weather = requests.get(city_url).json()\n", " 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", " city_date = city_weather[\"dt\"]\n", " # Append the City information into city_data list\n", " city_data.append({\"City\": city, \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", " # If an error is experienced, skip the city\n", " except:\n", " print(\"City not found. Skipping...\")\n", " pass\n", "# Indicate that Data Loading is complete \n", "print(\"-----------------------------\")\n", "print(\"Data Retrieval Complete \")\n", "print(\"-----------------------------\")\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>City</th>\n", " <th>Lat</th>\n", " <th>Lng</th>\n", " <th>Max Temp</th>\n", " <th>Humidity</th>\n", " <th>Cloudiness</th>\n", " <th>Wind Speed</th>\n", " <th>Country</th>\n", " <th>Date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>taoudenni</td>\n", " <td>22.68</td>\n", " <td>-3.98</td>\n", " <td>112.68</td>\n", " <td>9</td>\n", " <td>49</td>\n", " <td>5.95</td>\n", " <td>ML</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>rikitea</td>\n", " <td>-23.12</td>\n", " <td>-134.97</td>\n", " <td>72.81</td>\n", " <td>80</td>\n", " <td>51</td>\n", " <td>19.17</td>\n", " <td>PF</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>caravelas</td>\n", " <td>-17.71</td>\n", " <td>-39.25</td>\n", " <td>75.54</td>\n", " <td>56</td>\n", " <td>94</td>\n", " <td>6.53</td>\n", " <td>BR</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>pisco</td>\n", " <td>-13.70</td>\n", " <td>-76.22</td>\n", " <td>64.40</td>\n", " <td>68</td>\n", " <td>40</td>\n", " <td>6.93</td>\n", " <td>PE</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>hithadhoo</td>\n", " <td>-0.60</td>\n", " <td>73.08</td>\n", " <td>81.75</td>\n", " <td>80</td>\n", " <td>100</td>\n", " <td>35.12</td>\n", " <td>MV</td>\n", " <td>1595087159</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>545</th>\n", " <td>boyolangu</td>\n", " <td>-8.12</td>\n", " <td>111.89</td>\n", " <td>72.61</td>\n", " <td>87</td>\n", " <td>94</td>\n", " <td>3.00</td>\n", " <td>ID</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>546</th>\n", " <td>maraa</td>\n", " <td>-1.83</td>\n", " <td>-65.37</td>\n", " <td>84.31</td>\n", " <td>77</td>\n", " <td>1</td>\n", " <td>2.37</td>\n", " <td>BR</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>547</th>\n", " <td>odweyne</td>\n", " <td>9.41</td>\n", " <td>45.06</td>\n", " <td>79.23</td>\n", " <td>41</td>\n", " <td>97</td>\n", " <td>16.87</td>\n", " <td>SO</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>548</th>\n", " <td>yar-sale</td>\n", " <td>66.83</td>\n", " <td>70.83</td>\n", " <td>51.78</td>\n", " <td>93</td>\n", " <td>100</td>\n", " <td>12.28</td>\n", " <td>RU</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>549</th>\n", " <td>zhanakorgan</td>\n", " <td>43.91</td>\n", " <td>67.25</td>\n", " <td>89.28</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>6.26</td>\n", " <td>KZ</td>\n", " <td>1595087256</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>550 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " City Lat Lng Max Temp Humidity Cloudiness Wind Speed \\\n", "0 taoudenni 22.68 -3.98 112.68 9 49 5.95 \n", "1 rikitea -23.12 -134.97 72.81 80 51 19.17 \n", "2 caravelas -17.71 -39.25 75.54 56 94 6.53 \n", "3 pisco -13.70 -76.22 64.40 68 40 6.93 \n", "4 hithadhoo -0.60 73.08 81.75 80 100 35.12 \n", ".. ... ... ... ... ... ... ... \n", "545 boyolangu -8.12 111.89 72.61 87 94 3.00 \n", "546 maraa -1.83 -65.37 84.31 77 1 2.37 \n", "547 odweyne 9.41 45.06 79.23 41 97 16.87 \n", "548 yar-sale 66.83 70.83 51.78 93 100 12.28 \n", "549 zhanakorgan 43.91 67.25 89.28 12 0 6.26 \n", "\n", " Country Date \n", "0 ML 1595087158 \n", "1 PF 1595087158 \n", "2 BR 1595087158 \n", "3 PE 1595087158 \n", "4 MV 1595087159 \n", ".. ... ... \n", "545 ID 1595087256 \n", "546 BR 1595087256 \n", "547 SO 1595087256 \n", "548 RU 1595087256 \n", "549 KZ 1595087256 \n", "\n", "[550 rows x 9 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "citydf= pd.DataFrame(city_data)\n", "citydf" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>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>Date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>550.000000</td>\n", " <td>550.000000</td>\n", " <td>550.000000</td>\n", " <td>550.000000</td>\n", " <td>550.000000</td>\n", " <td>550.000000</td>\n", " <td>5.500000e+02</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>19.518545</td>\n", " <td>17.299982</td>\n", " <td>71.831618</td>\n", " <td>67.001818</td>\n", " <td>48.645455</td>\n", " <td>7.904636</td>\n", " <td>1.595087e+09</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>33.020319</td>\n", " <td>88.503661</td>\n", " <td>14.644635</td>\n", " <td>22.651433</td>\n", " <td>38.599666</td>\n", " <td>5.340705</td>\n", " <td>5.920336e+01</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-54.800000</td>\n", " <td>-179.170000</td>\n", " <td>30.200000</td>\n", " <td>8.000000</td>\n", " <td>0.000000</td>\n", " <td>0.540000</td>\n", " <td>1.595087e+09</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>-7.795000</td>\n", " <td>-58.695000</td>\n", " <td>62.135000</td>\n", " <td>54.000000</td>\n", " <td>6.250000</td>\n", " <td>4.045000</td>\n", " <td>1.595087e+09</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>20.875000</td>\n", " <td>21.640000</td>\n", " <td>74.080000</td>\n", " <td>72.000000</td>\n", " <td>40.000000</td>\n", " <td>6.820000</td>\n", " <td>1.595087e+09</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>47.520000</td>\n", " <td>94.237500</td>\n", " <td>82.000000</td>\n", " <td>85.000000</td>\n", " <td>90.000000</td>\n", " <td>10.387500</td>\n", " <td>1.595087e+09</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>78.220000</td>\n", " <td>177.480000</td>\n", " <td>112.680000</td>\n", " <td>100.000000</td>\n", " <td>100.000000</td>\n", " <td>35.120000</td>\n", " <td>1.595087e+09</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Lat Lng Max Temp Humidity Cloudiness Wind Speed \\\n", "count 550.000000 550.000000 550.000000 550.000000 550.000000 550.000000 \n", "mean 19.518545 17.299982 71.831618 67.001818 48.645455 7.904636 \n", "std 33.020319 88.503661 14.644635 22.651433 38.599666 5.340705 \n", "min -54.800000 -179.170000 30.200000 8.000000 0.000000 0.540000 \n", "25% -7.795000 -58.695000 62.135000 54.000000 6.250000 4.045000 \n", "50% 20.875000 21.640000 74.080000 72.000000 40.000000 6.820000 \n", "75% 47.520000 94.237500 82.000000 85.000000 90.000000 10.387500 \n", "max 78.220000 177.480000 112.680000 100.000000 100.000000 35.120000 \n", "\n", " Date \n", "count 5.500000e+02 \n", "mean 1.595087e+09 \n", "std 5.920336e+01 \n", "min 1.595087e+09 \n", "25% 1.595087e+09 \n", "50% 1.595087e+09 \n", "75% 1.595087e+09 \n", "max 1.595087e+09 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ " citydf.describe()\n", " " ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([], dtype='int64')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cityhumidity=citydf[citydf[\"Humidity\"]>100].index\n", "cityhumidity" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>City</th>\n", " <th>Lat</th>\n", " <th>Lng</th>\n", " <th>Max Temp</th>\n", " <th>Humidity</th>\n", " <th>Cloudiness</th>\n", " <th>Wind Speed</th>\n", " <th>Country</th>\n", " <th>Date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>taoudenni</td>\n", " <td>22.68</td>\n", " <td>-3.98</td>\n", " <td>112.68</td>\n", " <td>9</td>\n", " <td>49</td>\n", " <td>5.95</td>\n", " <td>ML</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>rikitea</td>\n", " <td>-23.12</td>\n", " <td>-134.97</td>\n", " <td>72.81</td>\n", " <td>80</td>\n", " <td>51</td>\n", " <td>19.17</td>\n", " <td>PF</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>caravelas</td>\n", " <td>-17.71</td>\n", " <td>-39.25</td>\n", " <td>75.54</td>\n", " <td>56</td>\n", " <td>94</td>\n", " <td>6.53</td>\n", " <td>BR</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>pisco</td>\n", " <td>-13.70</td>\n", " <td>-76.22</td>\n", " <td>64.40</td>\n", " <td>68</td>\n", " <td>40</td>\n", " <td>6.93</td>\n", " <td>PE</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>hithadhoo</td>\n", " <td>-0.60</td>\n", " <td>73.08</td>\n", " <td>81.75</td>\n", " <td>80</td>\n", " <td>100</td>\n", " <td>35.12</td>\n", " <td>MV</td>\n", " <td>1595087159</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>545</th>\n", " <td>boyolangu</td>\n", " <td>-8.12</td>\n", " <td>111.89</td>\n", " <td>72.61</td>\n", " <td>87</td>\n", " <td>94</td>\n", " <td>3.00</td>\n", " <td>ID</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>546</th>\n", " <td>maraa</td>\n", " <td>-1.83</td>\n", " <td>-65.37</td>\n", " <td>84.31</td>\n", " <td>77</td>\n", " <td>1</td>\n", " <td>2.37</td>\n", " <td>BR</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>547</th>\n", " <td>odweyne</td>\n", " <td>9.41</td>\n", " <td>45.06</td>\n", " <td>79.23</td>\n", " <td>41</td>\n", " <td>97</td>\n", " <td>16.87</td>\n", " <td>SO</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>548</th>\n", " <td>yar-sale</td>\n", " <td>66.83</td>\n", " <td>70.83</td>\n", " <td>51.78</td>\n", " <td>93</td>\n", " <td>100</td>\n", " <td>12.28</td>\n", " <td>RU</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>549</th>\n", " <td>zhanakorgan</td>\n", " <td>43.91</td>\n", " <td>67.25</td>\n", " <td>89.28</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>6.26</td>\n", " <td>KZ</td>\n", " <td>1595087256</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>550 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " City Lat Lng Max Temp Humidity Cloudiness Wind Speed \\\n", "0 taoudenni 22.68 -3.98 112.68 9 49 5.95 \n", "1 rikitea -23.12 -134.97 72.81 80 51 19.17 \n", "2 caravelas -17.71 -39.25 75.54 56 94 6.53 \n", "3 pisco -13.70 -76.22 64.40 68 40 6.93 \n", "4 hithadhoo -0.60 73.08 81.75 80 100 35.12 \n", ".. ... ... ... ... ... ... ... \n", "545 boyolangu -8.12 111.89 72.61 87 94 3.00 \n", "546 maraa -1.83 -65.37 84.31 77 1 2.37 \n", "547 odweyne 9.41 45.06 79.23 41 97 16.87 \n", "548 yar-sale 66.83 70.83 51.78 93 100 12.28 \n", "549 zhanakorgan 43.91 67.25 89.28 12 0 6.26 \n", "\n", " Country Date \n", "0 ML 1595087158 \n", "1 PF 1595087158 \n", "2 BR 1595087158 \n", "3 PE 1595087158 \n", "4 MV 1595087159 \n", ".. ... ... \n", "545 ID 1595087256 \n", "546 BR 1595087256 \n", "547 SO 1595087256 \n", "548 RU 1595087256 \n", "549 KZ 1595087256 \n", "\n", "[550 rows x 9 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cleandata=citydf.drop(cityhumidity,inplace=False)\n", "cleandata" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "cleandata.to_csv(output_data_file,index_label=\"city_id\")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(cleandata[\"Lat\"],cleandata[\"Max Temp\"])\n", "plt.xlabel(\"Latitude\")\n", "plt.ylabel(\"Max Temperature\")\n", "plt.title(\"City Latitude VS. Max Temperature 4/1/20\")\n", "plt.savefig(\"images/Max_temerature.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 13, "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": [ "#Latitude vs. Humidity Plot\n", "plt.scatter(cleandata[\"Lat\"],cleandata[\"Humidity\"])\n", "plt.xlabel(\"Latitude\")\n", "plt.ylabel(\"Humidity\")\n", "plt.title(\"City Latitude VS. Humidity 4/1/20\")\n", "plt.savefig(\"images/humidity.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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yMbhuq++E1P7TptQnipbO7+fKM+fQ39uN4NgLly0aoK9n4ghj+tQKvd3V+jXeySa/Z7QyyQxwxdI5LFs0MEGzSmuSuVHmuMTREHu7q/RU/Zt8X0+VwbPncsXSOZPKb/CcuXz+nLkT6sGb6sjoGKvXb+e4I/roDRgNdjXc013t4urz5jF49tx6vmt58dZZ1LK56tx5k2Tv7a4yrdrFDXdvY//9pkzIe293dcIkM0xuO73dVfp6nHY2fWqlLn9FhMVHzZjwPJHwSebenuBRchpttrGNeunrqbL4qBmB7TesbP1kWbtxiMWr7uCSNZuYPnUK06cGd6TTp1a46tx5nHdc806rIhKY/yx+10Fk6X20Gmd/2INw9kW9DFgL3AgMANuAc1T1Kdcl9f/g7A+7G3i3qjadQS7TRPMRK27ztYcJ8PCqU/MWx2j8PC2qXQLChI615lnj53GT1Q+mRtYeWX7E8UDJwgstKWs3DrH8ps2TvG+qFWHw7Lm5y9NIq+Ua1DbjehXm+S4ozPsoa0zuFBo9LXY9v9fXf70Vr6B2JmitQJAPftb+241pPLZzhKCfjkBucgSlkYUXWlL5gjzEerurbLrsTZnJEoeg9RmNZRzH46ivp8rGT74p8j15vgtsp5AzvtpERUCZoC0VpblZ4pHG+pG8KWJkGqRxB5WbiaPkZqOGoHIN4urz5nHJmk1N78m7rYR1CqZ4H7UVfp4Wo2PK/tOm5GIT9FKzfx6x4jYWr7qDtRuHMk0vD/LOk199RqHmXVMEeXqr1AjyMAqa98naeycJYV5SEF/mMO+qsDmEIin1JjumEuRpMbx7lI2fzG+4HOYbbkoDjEsReUpj/UjeLF8y21fjzcJbpUZQXsdUJ40YspYlKc3WA/iVa7UreLXyY8MjfOG8ecbM70TBdgoZcGhvt68NMW/NKEzrSbMxZhUXKcg+nUeevATVZ0+1i+f3aqhLY9513mgbn1bt8rWNZ0FQOfV76i7ruD1BNMalCop11RWw+ltx5hOWL5nNlWfOmRTH6Ia7t/maiGreVeA/H2Yidk4hA0zx/MjDrpyVF45J9uko9WlCnRctQ9Hph8kV5P3kF+sqjCj1HnStSdg5hZxJ26c4qQ09D7vy6vXbYx2Pikn26aj1Oa1hHUDeL4Rm9vCsydOXPs5vYnDdVl/zzuiYsnr9dt8XelA7ayzPoPmmioixHUIzrPkoI2oLj1qlFRt6HnblrOIimWafDqtPP23x+b3+4buzJM/4OEGk1e7DiPubCMt/UDsdV62HRw97XtCzx1VL2SGAHSkYTyvaXx6aW1ZxkYI0/1oeGvME+GqOeXgqFa2h1yjC46gI4pZ3WP7DRp5RyrMdy9yOFAynVe0va83t/IWzfOcUWo2LFDbKacxTkOZ4z++emmAvzspTyQQNHYrxOCqCuOW9fMnsWHMK3jJrVp7tWOZ2pGA4pmsijTFnRByvnBvu3taSZh5nlBOkOfrZi7PQ4E2pozxt+kUSt7yXzu9n8Jy5E+JShcW68os7ZUJMoryw3keGY6pHhx9FyRp3lWnankplqqN2wJZ365i0yU7HEic+T+O1Zx3bz53376jvXzwyOsbFazbxsW//kmnVSqgfetp734atQ4izhiBMrrgy9/ZUfffIDdpxLG0NPis/9Lz25C1SjijPblxj0FPtosszF9C433TQvski8LojZ/DIkyMMDY9MmEiurVuA8Hr07iVea1891S5G9o6jSv33seAlMwL3fI6yH3OR2JFCDqQR3TJon10vWfjOx1mHEHVdRJhc4G/HDZI5rg96WTRKU7ThLOWIuv7Dr369NEYkjbJvsh+VLqGL4PhkcWJgVbokcE/vMPnzwq5TKJg43hJh9vFmDT2KD3Vcm3qcdQhRbb1hcsWVOcgHffrUKaH24rxI6v1kikdT2nJ4y+MjN25u+uyg+g26J2jfkiiMjeuktBqfHXWRW9QOoTENE7DmoxyI4y0R5p8fN60s9771Ox7VEyOJXHHv2emaGvLwmw+ilTUmpng0pSlHY3kEta0k+0ansc95Ec9uTMME7EghB+J4S4RFVIybVpZ73/odj+qJESZXXJnTyGNWaxla0bJN8WhKU46omnaSfaPT2Oe8iGc3pmECtlPIgTj7qwZdG7TPbtgzs9z7Nuj40vn93LXiJB5edSp3rTjJVyMOkyuuzK3msaa9Dg2PoOzT5tPoGFrRsvPckzcvOaLk268NN9vfuFqZuM95lH2T/ah0yaS0mu1/ngamrWuw5qMciOOdEnbtgpfMmOBV0V3tCvU+SsMrpjaZnGYU1ChyRfEAafTO8u6adcmaTQyu29o0v1lGXW0lWm6UMsrDOylNz6qg8qiIMK4a2oa93keNTJ86cZ9zwNf7KIwo3kfesgjzPppW7WLXHv8RUb/1PsqWsngfWdIjTc8lyDaSbNGeO6bRqsyt1FWeO9GVoW7sOgVL29DMTh9X6w/T5ptp4s3OZxlHv4h9JVql1fJoZeQV995W6r5s+yc0YjsFS6lI03MJgj2mTjx6ZqjnUFTPoqy8n0zxTopLK+XRSpyhOPc2q9sodV+k11ur2IlmS6lI03MJgj2m7rx/R6AmvnbjUCQf+ywxxTspT1qJM5RGLK2w9QqmrTVoBTtSsJSKZhpfEk3ST6u7ZM0m32trWmEUH/ssacfonFFoRQOPem+zUVhZR2lRsZ2CpVS06rnkh599OMxTJszXPi9NPawcTImZlDVZ5bPZ/IMpe7BnhfU+snQ0YbGm/OImhXUIJniYlMHzJQ2K9OxqhzK2sY8slgCC7MN33r/D1wbdH7Li3ISXQrvbu2tkmc9m8w/tuIeCF2s+snQ0YfbhIBu0yVpiu9u7a2Sdz2bzD2X2LmqG7RQsHU1c+7DpPujtZO8OmzPIMp+dMicThO0ULB1NEi8ek7XEdvFKarYWIKt8thLZtl2wcwqWSGQVSbRo2s0+3C75aTZnkFU+O2VOJgw7UrA0pd21J5M1/yS0Q36izBlkkc9OmZMJo5BOQUQuAd6Ls0XqFuDdwCHAt4AZwL3AO1V1TxHyWSZSxjg7edLpNugsSDtWURCN9x3YXfWNxlrGOZmk5G4+EpF+4EPAAlV9JVAB3gZ8BviCqr4MeBp4T96yWfyx2lMwWe7H0MnE2cchaR343bdrz97QPRU6gaLmFKYA3SIyBegBHgdOAm52z18HLC1INksDQVpSb081Z0nMw9qgsyGNWEWXf+++0Hkwv/tGx5T9p00p/ZxMK+RuPlLVIRH5HLANGAFuBzYAw6q6173sUcC3FkTkIuAigIGBgewFtjg7ot28edKG6M8+t5e1G4c66gfTiB1FZUersYqe3j1a32jHbx4s6L7h3aNs/OSbkojcFhRhPuoDzgCOAA4FpgNv8bnUN/6Gql6rqgtUdcHMmTOzE9RSZ+n8fqZPnaw/jI5rx2vEnRit1DSilnXjCM7WnT9FmI/eCDysqjtUdRT4NvA6oNc1JwEcBjxWgGyWAHYGbIXY6RqxKXspdzJx9k72tldbd/4U4X20DVgkIj045qOTgXuAO4GzcTyQLgC+W4BslgCieoOUwRMnTMa48pu+wrkT8KuDXc/vbepFZOvOn0KipIrI5cB5wF5gI457aj/7XFI3AstU9fmw59goqfkRJTJkGaJHpr3Hs8VMytAWiyQsSqoNnW0IZdewARavusN3NNHf281dK07KU9RAwmQEjJffEp0y/KaKIqxTsCuaDaAsK4abeYOUwRMnTMYg9civo7CYTzus7C6CjusUGrWHE4+eyZ337yhUmwjys155632TZMlb+1m7cYjLv3df3bVPBFShr6eKqjMB3Wy3smbeHH55gvBdxYaGR6iIMKZKb3cVEceV8EDP/37129tTreelUcbf73wucJvNxavuSDT3kFZ9rd04xMpb75tgJ+/trrLy9GMm7bbWWAattBHvc3t96jxqnmFyffoda/a8nqkVdu8ZQ3H2sFh0ZB/3PfZMvVz6eqpcdtoxE54TZQ7J256me9IQqKfpbU9BikK/Tz780vC7LkmdZPEO6CjzkZ+dsZEi7I5HrLgtUEu9+rx5hdns124c8l2f4EfYbmVh8vnlqdolIExIN+j5cQl69pVnzuHigH2ZG6+DaHMPadXX2o1DLL9pM6Pjk+uh2iWcd9ys0HJJ2kaa/V6i5rlaEVAmyB9WD82e14xqRRg8e27TXdJgcj2mQbNd2vyui0pabcruvObip5E3UsRq1DBN2itL3qtnB9dtjdQh1OQI2q0srLH6riod10npjoyOsXr99pZ/wKPjyvSp/itWg3ZV88owuG5r5HpIq74G12317RBq+WlWLknbSLPfS9Q8j47pJPmD6jjK85oxOrZv/UxQHXzkxs1cvGZT6h1C7flh6ftdF5U83gEdZT6KatvO2wa+fMnsQC3VK0veNvu4zw3brSyNNIJMO3HZOTLKpssmr1j1i9HfSJi8jefSqq9m10cplyRtJMo9UfOcNM2kz6vdF3R/Wm0pafqN18V9bqvPCaOjRgpRVyrmvaJx6fx++gLiCHllyXsFZtznJpEjzj0VkeYXtZCmN95O2L1B93eJTIizk1Z9Nbs+SrlkVTeN17TaFtN6Xu2+olYnR01fIdb+JHm8AzqqU4iy8rGoFY2XnXZM09WVea/AXL5ktmMPjkBSOfzyVO2SSel2Vyucv3BW5JWrSeVcOr+fu1acxNXnzQss66B2NKY6IUrniUfPTKW+li+ZPSlyZ41qlzQtlzTrptlzfeuzIpPkD6rjKM9rRrUi9eckub9VvPmIkn6cyLp5vAMqK1euTO1heXPttdeuvOiiiyJff/QhL+Cwvm62DO3k2ef20t/bzRnzDuXJZ/fUv3/ytFcU4sbmJ1ujLFGuSVumgRk9rH/4SZ4bHQcc7yNwvDymTanw/N7xluTwy9PK04/hTa948aR8/tWJL61f+8xze6mIoDheON1TKzw/Oj7h/1bqN6ysG8/V5PCyd1x58tk9fPK0V7RcX7V6uPuhJ3lu73j9eG93lU+fOWdCuTz73N5JZZBW3USp88D6PObFvnXszdMB06ZwwuyDOfqQFwQ+b/rUCnvduYiKCK87agbPPLe3/oy+niqf+ot981je+595bi9h1OrRm4Z4vnvbU9CzGsulMX2/tgJOe9kytJP3vP6IWHWStH4vv/zyx1euXHmt37mO8j6yWLIgyHtMgIdXnZq3OKUhb2+6qF5+WWNCe7GL1yylwKQVqHHWswStz7D7TYST945+gfXUXc21nSVdz5MXHTWnYDEXk3Yw85Pl+ru3BcoWNPdS22/C4k/e3nRB9viVpx+TSXpx5TAlOqvtFCxGEKQ1XrxmUyzvjKxkacTrG273m0hGnt50tZHfyOhY3VurqF3V4uwqVwTWfGQxgjDtMO9YUEnWs9j9JuLjty4kC425ce5iTLWeTlEvYpPjMtmRgsUImmmHea40T7Kexe7iFZ+8NGa7j3Y8bKdgMYIo/tx5ad1J1rOYbic2ldq6kIdXncpdK07KRHsuQ/Rek7CdgsUIoq4mzluWmga7bNFAqEZrup24k7GjuHjYdQoW47C7ZlnSpIj2ZJJ7tR92nYKlVNi9cy1pknd7KsumWUHYkYLFYrGkSBm2pbX7KVgsFktOlH1i23YKFovFkiJln9i2nYLFYrGkSNndk+1Es8WSENM9TCytkbR+y+4oYTsFiyUBZfcwsYTTav2aHMaiGbZTsFgSkFbY5zghui35kXdYb5OwnYLFkoA0PEz8tNHr795WP29HH8VRdg+iVrCdgiURnW5PT2OjlKghuj9y42YuWbOpI8u5KEzfCCdLrPeRJTYmbYhTFGl4mETVOsdUO7aci6LsHkStYDuFkrB24xCLV93BEStuy33TmUZsKGL/AHhnHdvP4LqtkesoidbZaeVcFJ0c4DCS+UhE/hr4KvAM8GVgPrBCVW/PUDaLi2meLp1sb/Xi9TBJUkd+m8xEodPKuSjK7EHUClFHCv9dVf8EvAmYCbwbWJU0URHpFZGbReR+Efm1iLxWRGaIyA9F5AH3b1/S57cbpmnmZV+xmQVJ6qhZiO7atpGNdHI5m4hJo/g0iDrRXGudpwBfVdXNIgEtNhrXAP+mqmeLyFSgB/g48N2Ze+sAACAASURBVP9UdZWIrABWAB9tIY22wTTNPK9tFMtE0joK00aDQj53cjmbhmmj+DSIOlLYICK343QK60TkAGA8SYIi8gLgeOBfAFR1j6oOA2cA17mXXQcsTfL8dsQ0zbyT7a1BZFFHtpzNx7RRfBpEHSm8B5gHPKSqu0VkBo4JKQlHAjuAr4rIXGAD8NfAi1T1cQBVfVxEDva7WUQuAi4CGBgYSChCuTBRM+9Ue2sQWdWRLWezMW0UnwZRRwqvBbaq6rCILAMuBXYmTHMK8Grg/6rqfGAXjqkoEqp6raouUNUFM2fOTChCubAao/lkVUftZq9uN0wbxadBpE12ROSXwFzgVcA3cEw/Z6rqn8dOUOTFwN2qerj7/Q04ncJLgRPcUcIhwI9VNVTNspvsWNoZuy2p+ZS1jtLYZGevOr3HGcA1qnoNcEASYVT198B2Eam98E8GfgXcClzgHrsA+G6S51ssRZCFRt+O9up2ox1H8VHnFJ4RkY8B7wTeICIVoNpCuh8EbnA9jx7CmZ/oAm4UkfcA24BzWni+xZIbWXmgtKO9uh1pt3mfqJ3CecDbcdYr/F5EBoDBpImq6ibAb+hyctJnWixFkVVEzU6Ov2MKnRjjK5L5yDX53ALs5x76I/CdrISyWMpEVhp9J8ffMYFOjfEVNczF+3DcQGcARwH9wJewmr3FAIrW5rLS6OPs4FV0GbQjnbqnQlTz0f8AjgPWA6jqA0HrCCyWPDFhRWmW60ii2KtNKIN2pFPndKJ2Cs+r6p5aZAsRmQI092W1WDKmKG2uUTM/69j+CTumnXj0TAbXbc1lHwQTNNp2HKl06pxOVJfUn4jIx4FuEflvwE3A97ITy2KJRhHanJ+t+ZYNQyxfMpuHV53K8iWzuWXDUG626KI12na1vXfqnE7UTmEFTmiKLcBfAj/AWdVssRRKEStKgzTzj9y4ua4x57m+oOhVte26nqId1yBEIZL5SFXHgX92PxaLMRQRFypIAx9TDd0fISvNvejYWEWPVLKk3dYgRCGq99FiYCXwEvceAVRVj8xONIulOXE8dNIiyNYMjoZcEWHMJ3xMUs29mb2+iDLwUoTt3eQ5DJNli0LU2Ef3A5fgRDStqyOq+mR2ojXHxj6yFIFfvJtGuquVVOLhlCG2Tt4ymlwmJsvmJSz2UVTvo52q+q8pymQxmEZN58SjZ07wrCmb5pM2S+f3c8/vnuL6u7f5nu93yygNbdEEz6JG/DThK8+ck5t2bGKZ1DBZtqhE7RTuFJFB4NvA87WDqnpvJlJZCsPP59378rM+8E4Z3bLB37OmZstPyxZtmr0+aE3ElWfO4a4VJ+Uig2llEkUGE2SLStROYaH71zvcUCCfVmDJDT9Np5GyaT5pE1RGFZHUzQRJ7fVZ2bVN0IRNXj9gsmxRiRr76ESfj+0Q2pCoGk2ZNJ+0Ccr7uGrqL8YkvvJZrhswQRM2ef2AybJFJXSkICLLVPV6Efmw33lVvSobsSxxSUszDPOsabyuU8lTGwzyLAJYvOoO3/rOUptPI++tttWiva3CMFm2qDQzH013/ybaUMeSD2nGvvHzeW+kbJpP2uS9LqBxfqJZfWepzbea97TaqsnrB0yWLQqhnYKq/pP79/J8xLEkIU3N0E/Tsd5HEylaG2xW31mOZFrNe15zEmVfK1AkzcxHfx92XlU/lK44liSkrRmWXdPJgyLLqFl9Zz2SaSXvecxJ2KixrdFsonmD+5kGvBp4wP3Mw7OIzVIsRce+MYks9ko2jWb1bXLMnjzaarvGYsqLZuaj6wBE5ELgRFUddb9/Cbg9c+kskSg69o0pdIqGGKW+TR3t5dFWTfCQKjNR1ykcijPZ/JT7fX/3mMUAirZxm4IJPvRhpGXnTlrfJtjZ82ir7bBWoEiidgqrgI0icqf7/c9xAuRZDMFUzTBPTNYQ0x7FxK1vk0ZRWbdVO3JujaiL176Ks6r5O+7ntTXTksViCibPrRRt5y46/TwxeU6lDEQNnX28++/T7t+Xi8jLVfXfsxHLYomPyRpi0aOYotPPGztyTk5U89Fyz//TgONwvJJsqAuLMeQ5txLXPl+0nbvo9KNiwrxHpxN157XTvN9FZBbw2UwkslhaIA8NMYl9vuhRTNHpR8GkeY9OJuoezY08CrwyTUEs0ekEX3yTSWKfL9rOXXT6UeikeQ+TiTqn8A84obLB6UjmAZuzEsoSjNWmiiepfb5oO3fR6Tej0+Y9TCXqnIJ3z8u9wGpVvSsDeSxNMN0XvxMoi32+bMQpVzv3kB1RXVKvA1bjTC5vBn6epVCWYKw2VTztEDPfRKKWa5b7RVgidgoicgJOzKN/BL4I/MbjpmrJEZN98TuFMtjny0jUcrVzD9kS1Xz0eeBNqroVQERejjNyODYrwSz+lMGLpBMw3T5fVqKUqx0tZ0vUTqFa6xAAVPU3IlJtJWERqeDMVQyp6ltF5AjgW8AM4F7gnaq6p5U0klCkrTJK2nn44gfJ8Y5//k/u+u1TE67tj5j+pWu3sHr9dsZUqYhw/sJZXLF0TqR0WyFKun4yDA2PUBFhTJXe7ioiMLx7NLZcjekfObOHh3bsZky1fk1fTxVVGB4ZRdjn0dHXU+Wy045h6fz+3NplnHTWbhxi5a33MTwyOuncflO6+MxZr4okY1Cajc+vlUeaczpx89sJ8xiinsYZeJHIV3Da6jfcQ+8ApqjquxMn7GzxuQB4gdsp3Ah8W1W/5UZh3ayq/zfsGQsWLNB77rkn7JJYNHr2gKOF52EaKDLtKHIc1jeNB57Y5XtPMzkvXbuF6+/eNun4skUD9Rd0FvmPkq4XPxn8iCpXUPpxqFaE814zi1s2DGXeNuLUwdqNQyy/aTOj48Hvjy6Bq86dFypjUJpnHdvPmp9vn/T8NMsjbn5N+H2mhYhsUNUFfueirlP4AHAf8CHgr4FfAe9vQaDDgFOBL7vfBWd19M3uJdcBS5M+PylF2ipNsZMGyRHUIdTOh8m5ev32psezyH+UdL34yeBHVLmC0onD6Jiyev32XNpGnDoYXLc1tEMAGFeayhiU5ur1kzsEcMrjzvt3xJ7T8VvbEze/Jvw+8yDqiubngavcTxpcDfwt+/Z+fiEwrKp73e+PAr41LCIXARcBDAwMpCSOQ5G2SlPspEnTC7tvLGA06j2eRf6jpJs0rSjXBqUTlzTkjUKcOoiadrPrgs6Hld1jwyOx5nSC1vYEKQBx8tuO8xihIwUR2SIivwz6JElQRN4KPKGqG7yHfS71bRWqeq2qLlDVBTNnzkwiQiBFevaY4lWUNL1De7sDV1pXxK96Jx43If9x0opybVC+4xL0nLTLJk4dRE272XVB58NKLm6+g7T8OOVqQvvMi2bmozOBvwJOa/j8T/dcEhYDp4vIIzgTyyfhjBx6RaQ2cjkMeCzh8xNTpP+5Kb7vQXK87ODpgfd0VyucePTMQN/x8xfO8r3Pe9yE/PvJ4EdUuYLyHYdqxZkcz6Ns4tTB8iWzqXaFd3pdQlMZ/dKsdgldAc+uViR2vsNGI3HyW3T7zItmncIXgD+p6u+8H2C3ey42qvoxVT1MVQ8H3gbcoarvAO4EznYvuwD4bpLnt0KR/uem+L4HyfHDD5/A4qNmTLq+dv7O+3cE2lyvWDqHZYsG6ppZRWTSZG8W+e8P0OK8x72jm8F1Wznr2P76+Zq8vd1V+nqqseXyy/fLDp4+SUPt66nS2+0480nD8cGz57LgJTOYVt33U+3trmbSNuLUwdL5/QyeM7cudyP7TelqOskclOb+06Yw5jOf0CUwePbc2PkO0uZr+YuaXxN+n3kQ6n0kIv+lqr6B70Rki6oG+/ZFSdxZFPc3rvfRkexzSd0ILHPnMgJJ2/vIkpwjVtzma+8T4OFVp+YtDtDcY6QMHiVlkDFt0m5LnViGzQjzPmo20Twt5FzLxjRV/THwY/f/h3D2abDExAT/6azjASXJY7M1HWWII1UGGdMmqC0dGDAqaYbdwzwezTqFX4jI+1T1n70HReQ9OHGQLAVjStTULFdat5LHMC+VMniU+L0cw463A8uXzPZdA7Frz17WbhzKZU/rTqbZnMLFwLtF5Mci8nn38xPgvTjrFSwFY4r/dJY216zyWAaPkiieW+3G0vn97D9tsr46OqZtuS7ANEJHCqr6B+B1InIi+zbVuU1V78hcMkskTNJ2s9LGsspjGeJIxV1r0S4M754cOgPMGsW1K1EXr92J4x1kMYxOiO2fVR7LYGvuD8h7kGdVu9AJ7dpUkm7HaTGETvCfzjKPS+f3c9eKk3h41ancteIkozoE6Iz69aNT820CUaOkWgzFVG03TY8oU/OYB52a907NtwlEipJqKnadgplYv3CLxWxaWadgscTW+k3yrTdhDYfF7H1K/PbQiLpPSDtiOwVLKEnWCJjiEWXKGo5Op8h6aJZ24/maV1cntxU70Vxy/CKTBkUrTUKSNQKm+P+bsoaj0zF5n5KwPTQ6ta3YkUKJ8dOClt+8GZT6atBWNZ4kWr8p/v+mjFg6HZP3KUm630M7Y0cKGZOm1t6In5YzOqaTwgO0ovEk0fpNiShpyoil04lSD1n9TpqlnXS/h3bGdgoZUtPk/fYYSIO0dwrzI6m/uAn+/9bX3Qya1UOWv5NmaYftodGpbcV2ChmStS017Z3C/GhV689ypNQMU0YsnU6zesjyd9Isbe95mLiHxrRqF5es2ZR7uy0au04hQ7LeY8BvPUC1IhPmFKC4NQJ2vYIlCqbtxdEJ7dauUyiIrOO3BK369DtWRGM2ab1CWejEdRVxfydBZZRW2UVtt+1aV7ZTyJA8vHCCIpOa0Dit9088OnVdRZzfSVAZ3fO7p7hlw1AqZRel3bZzXdk5hQzpdJu29f6JR6euq4jzOwkqo9Xrt0cquyhzXFHabTvXlR0pZEwn7/hkynqFstDJI6uov5OgsgjaXyKJdh+l3bZzXdlOoQ0w1bbZaqRLU/OVFXYPgeYElVEtZpHf9TWizhVEabftXFe2Uyg5pts2k46UWslXWTsTPw1VgBOPnlmcUDHIo9yDtPizju2fMKdQO55Uu2/Wbtt5FGznFEpOu9o2k+Yr6wWDWbJ0fj9nHduPd/dlBW7ZMGS8/HmVe9D8wxVL5zSdl0hzjqud5wvtSKHktKttM2m+THSD9WrQvT1VVGHnyKivNn3n/Tsm+ewXLX8U8iz3MI+7PLX7dp0vtJ1CyWlX22bSfJnWSTaawZ72bEjvZxIzTf6olEFuu5tbNGynkDFZ21nb1baZNF+mdZJhoZlhsjadp/xptk3Tyj2IdtXu08TOKWRIHnbWdrVtJs2XaUHwomjK3mvykj/ttmlauVuSY0cKGZKXnbVdtZ8k+TLNRBCkQTdeUyMv+dNum6aVuyU5tlPIkDLYWdsRkzpJPzOYFz9tOg/5s2ibJpW7JTm2U8iQsthZLdnRqEE38z7Ki3Zom0WsRynrGpg42E4hQ9p1EtgSDxM16LK3zSIWbZq+UDQtct9PQURmAV8HXgyMA9eq6jUiMgNYAxwOPAKcq6pPhz3L9P0UoDM0iygkKYfGe048eiZ33r8j9bK8dO0WVq/fzpgqFRHOXzgLgBvWb8P78+iudjGtWmF492ioPF65D+yuIkL9nmYy+5UTZBMePaxOooSnPrC7yujYOLv27OtYugTevnCAK5bO4dK1WyaVIThOA43y1p47NDxSD1nhd12Nxavu8B3p9Pd2c9eKkyLnM0rZ1EZ3wyOjvtdWRBhX9X12Xm04LmH7KRTRKRwCHKKq94rIAcAGYClwIfCUqq4SkRVAn6p+NOxZZegULMk2LfG7p5E0Nj65dO0Wrr97W+L7G+XxC7fQeE2QzFE3Tap2CYizH3eU58YlqL6a5c3Lyw6ezgNP7Ao875U3rK6D8nX4itsCn/2IZ2OerNpeEFHzFVWWrAjrFHJ3SVXVx1X1Xvf/Z4BfA/3AGcB17mXX4XQUHUGRW1bmQZKQFc38+/2ekaQcV6/f3vSaqASFcG68JijffnkeHdMJHQI4HYS3Q2j23LjECU8dRFiHUHteTd6wug7KV23bzGbHs2p7QYyMjrHy1vtYvOoOLl6zKXYbNoFC5xRE5HBgPrAeeJGqPg5OxyEiBwfccxFwEcDAwEA+gmZIJ9gpk3i6RPWCqV2XtByDQi4nJcrzsvJKS8urLW546lbTaSa33/kgWRqPZ9n2ghgeGQ00NWWRXtoUtnhNRPYHbgEuVtU/Rb1PVa9V1QWqumDmzHJEjwyjXQPaeUkSiCyqF0ztOlPKMUiD9ZLV5kNpeQ4FPSdK3pKk00xuv/P9Afc0Hs+y7aVVHqZ5fBXSKYhIFadDuEFVv+0e/oM731Cbd3iiCNnyphPWMiRZ7ep3TyPeZ5hQjt3VCucvnBUqd1i+/fJcrYgzh+A91iXOXEPE58YlqL6a5c3Lyw6eHnreK29YXQflK2qbyqLtdVcrXH3ePD5/7tzI5RH2LNM8vnLvFEREgH8Bfq2qV3lO3Qpc4P5/AfDdvGUrgk7YsjJJyAq/e5YtGgh8RtJyDNI4p0+t0KgIdle76OuphsrTGMK5t7s64Z6wfPvlefDsuQyeM3fisXPmMnj23MxCm0QNT93bXWX61IkvxS6BZYsG+OGHT2DZooFJZYiPvN70YJ8GHpavIBmBCfNKQMttr6+nSm/35Dr0k6Gvpxr43GZt2BSK8D56PfBTYAuOSyrAx3HmFW4EBoBtwDmq+lTYs9rB+yiJd4RlMknL0ZZ/+2BCXZogQxTCvI9yn2hW1Z8BQca4k/OUxQRszJiJJF3XkbQc8yp/u14le0zYS6Mdfs+5jxTSpB1GCpZ9lEXLiku75ss0jlhx26QNisDRQB/2rF2wGDZSsCQjK03TJA3WBE0vC9o1X3HJuq3FjedkUts3CbufQgnIal8G0/YzNsGDKAvaNV9xyKOtxfE0Mq3tm4TtFEpAVj74pvj212hXT6x2zVcc8mhrcbzc8mz7ZYtYYM1HJSDvFbBFabBlj9wZRLvmKw55tbWoEWnzkqeMEQvsSKEE5L0CtigN1m4tGkzZtM1GTGtreclj2mg8CnakUAKy0jRPPHqmb4TQE48uLnyIiXsPpEEr+SqjttmIaaOlvOQxbTQeBdsplICsfJ/vvH9HrOOtkra3R6d4j7SD95Jp/vt5yVPGHe5sp1ASstCg89Ri0tZ220F7jkoZtU0/TBsF5iGPaSOkKNg5hQ4mTztv2rbVMtpqk2KaPb7TiTO/U8Z5MjtS6GDy1GLS1nbbRXuOQhm1zXYlyQjVtBFSM+xIoYPJU4tJW9vtJO25jNpmuxI0Qr38e/cVJFH62JFCh5OXFpO2tttp2nPZtM12JWgk+vTuUdZuHGqLOrKdgiUX0vb2yNJ7JKpXk0neTybJkgWm5C/ImwgolTdYGDZKqsXiIWpEU5Min5okSxaYlL+1G4e4eM0m33NlisYaFiXVzilYLB6iejUV4f0U5PVikidWHM+cqNealL+l8/vp7fbfXa1d5rOs+chi8RDVqylv76cwrxdTPLHieObEudaU/NVYefoxbT2fZUcKlo7Hq7F2+W0qzGQtMG/vpzBtOQ1Z0oitFEejj3ptmBxdIoXEgmp3bzA7UrB0NI0a65jPHJufFpi391OYtvyF8+a1JEtaq8PjaPRRrl27cYjlN2323U0N9tVVEavZ29kbzHYKlkwwxVukmVy7nt87SWMFqIgwrhooe96xfMJi6LQqS1qxlYJkVGDxqjsmyBQlJtDguq2MjkdzhClbLCiTsZ2CJXVMjUvkJ1cQ46pNPUny1BabjUxakSUtm72fjDUa20CUkVbc9NtxNXsR2E7BAkTT7KNq/3lF9Yw7GvGTK4iiPUn88nblmXMYXLeVoeERKiITbPCtlGtakTy9Ixa/53nbQJTRTdiagKB8eMljtGrqiLgV7ESzJdJ+tXH2tM3DWyTJHrtR0y/akyQob7BvH+JGe3orE61x9jZuxtL5/dy14iT8p+sn1kHt2odXncpdK06a9DJdvmQ21a7JT6p0yaTjjfLmsQdzu+7zbDsFSyRPkDieJUm9YeJ4wCTxXQ9Kv6+napQnSVjesvDZz8KbJg2PqKXz+xk8Z+6EdQF9PVU+f85cBs+ZGypvHmsbTFo/kSbWfGSJpNnH0f6TeObEnYcIMiuEmRuC5LrstGOMGvInGWm1OgpLe34kLe+sMLnC5M1jtGra+om0sJ1CCpTdrhjFphzlGm85HNhdZVq1i+Hdo4nt/WHzEBURX/fRSsA6A8jXY6iVNtGsrMuwk1fRO63lseNZUBoHdldZvOqO0r4PbKfQIqZ62sQhilbX7JrGchgeGaW7WuEL582LVA5xtS6/DiHseI08PIZabRPNyrosq2mL9OXPYx2JXxrVLmHXnr0Mj4wC5Xwf2E6hRYI03JW33leaRhBFq2t2TVA5XLxmE5d/7z5UYedI8KghrmbXH3B9fwEeKI206n0VpT685048eiaD67ZyyZpNifNY9tGuH/tN6arXQ19PNXUzoV897d6zl6d3j064LqzuTSx32ym0SJAmOzxSrvjqUbS6sGvC7KjeH0mQ5hRXs4tyfVGjuDRszc1s6bVzaeSxHUa7Xvyiqj43Op5JWo31dMSK23yv86t7U8vddgot0gnx1b1cunYLq9dvZ0yVigjnL5zFFUvnxPIp99Oc4tqgw66vaV/NfOWzIm17tjc/tbmUfje/YTuBRS3LKCMbP432nt89NaEtLDqyj189/kxdCRCc1cxB8z8APdUu9owpez0rl1928HR27xlPrD1nvU4mTLuPU/d5reeJi+0UWmT5ktmB8dXL7oXQyKVrt3D93dvq38dU69/DVrP64ffDiWuD9rveT0tsJOt6SdOeHRSbqaZVBuXz6d2j9ZdzMw202cjGT6P9yE2bGfO8yMdUueu3T024Xz3ngtjto8E/8MSu+v9JtOcsvYKaafdx6t5U7yWjNtkRkTcD1wAV4Muquirs+qI32Vm7cYjLv3ffJBtijf7ebu5acVJq6TW+lL14tbLzF85iwUtm8InvbGHXnokvjelTK6iq74+xVfp6qqhSn2SLgne0UaOxXHu7q7x17iHcef+Optrj/L+7PbA+ajSrlzTsvEme4R2FRSVMC292bU27b3yZ1xCBA6dVY9VnVgTVmV85B40Sw57R2N5Wnu4//7B41R2BI+La6A3wHdmdePTMCW141/N7fcs27feGH2Gb7BjTKYhIBfgN8N+AR4FfAOer6q+C7imyU1i7cYjlN29mdMy//NLeGSqsQzCJakUYG1PidjnLFg1wxdI5Tcu1RtBuaEGjtrD7vBS1y1cr9dtdrUQeoZWZRxpiUQXV1VnH9nPLhqFIdRjU3qpdwuA5cyddf8SK2wKjtnrTgcleYo1UKwLKhKB/ee0oV5ad144DHlTVh1R1D/At4IyCZQpkcN3WwBdXRST1il29fntqz8qS0QQdAuzLX1i5egnaDS2MKCt1i1qlmrR+a3nyru4N2hmszPitPwmqqzvv3xF5hXZQexsd11ir9b3pB608n5TGmLL/tClGraYHs+YU+gHvL+NRYGHjRSJyEXARwMDAQD6S+RBm9xtXTb1i45gUykgtf3HsqVF3QwO4OqP1EmmRtH5rZqnGEVOc+Z0y4Fc+YXUVdX4q7irxKHNncdrK8O5RNn7yTZGvzwOTRgp+S1EntQRVvVZVF6jqgpkzZ+Yglj9hGkMWq0vDVuq2A7X8xSm7qLuh9XZXY60mjnM8LZLUb1C+GmMZtUPbaVx/AunUVdzfsbdsw+6LKoNpK9HBrE7hUWCW5/thwGMFydKU5UtmOzbBBqpdksnq0vMXzmp+kQFUK5MjWEahlr+gcm0kaDc0v2ifK08/JrIcaUYMjUPc+m2WL28E0s+fO3dSnspEUPmnUVdJfse1sr36vHmB6fvJ1oipK9FN6hR+AbxMRI4QkanA24BbC5YpkKXz+xk8ey59Pfvst73dVd/JqTS4Yukcli0KNpfVmnVFhGWLBrj6vHlMnzq5UU6fWqGnmm611zTR/t5uBs/eF8HSe857zeKjZkw4XptkhuByXbZooKntNY1on1lEDI1CrX4btfra976eKr3d1UQyBWm3FREWHzUjsD3U0gyiv7e7Xi+NsjY+MqybF5z1CrW8Ranrxny1Ut9Jf8dh6fudi5qvojHG+whARE4BrsZxSf2Kqn4q7PqiXVItFouljIR5H5k00Yyq/gD4QdFyWCwWS6dikvnIYrFYLAVjOwWLxWKx1LGdgsVisVjq2E7BYrFYLHWM8j6Ki4jsAH7nfj0I+GOB4iTFyp0vZZS7jDKDlTtv4sj9ElX1Xf1b6k7Bi4jcE+RiZTJW7nwpo9xllBms3HmTltzWfGSxWCyWOrZTsFgsFkudduoUri1agIRYufOljHKXUWawcudNKnK3zZyCxWKxWFqnnUYKFovFYmkR2ylYLBaLpU5bdAoi8kER2Soi94nIZz3HPyYiD7rnlhQpYxAi8jcioiJykPtdROTvXbl/KSKvLlpGLyIyKCL3u7J9R0R6PeeMLW8RebMr14MisqJoeYIQkVkicqeI/Nptz3/tHp8hIj8UkQfcv31Fy9qIiFREZKOIfN/9foSIrHdlXuOGxDcKEekVkZvdNv1rEXltScr6Erd9/JeIrBaRaamVt6qW+gOcCPwI2M/9frD79xXAZmA/4Ajgt0ClaHkbZJ8FrMNZgHeQe+wU4F9xwswvAtYXLWeDzG8Cprj/fwb4jOnljROK/bfAkcBUV85XFC1XgKyHAK92/z8A+I1btp8FVrjHV9TK3aQP8GHgm8D33e83Am9z//8S8IGiZfSR+Trgve7/U4Fe08saZ+vih4FuTzlfmFZ5t8NI4QPAKlV9HkBVn3CPnwF8S1WfV9WHgQeB4wqSMYgvAH/LxG1HzwC+rg53A70ickgh0vmgqrer6l736904O+SBptXstwAABU1JREFU2eV9HPCgqj6kqnuAb+HIaxyq+riq3uv+/wzwa5yXwBk4LzDcv0uLkdAfETkMOBX4svtdgJOAm91LTJT5BcDxwL8AqOoeVR3G8LJ2mQJ0i8gUoAd4nJTKux06hZcDb3CHTT8Rkde4x/uB7Z7rHnWPGYGInA4MqermhlNGy93Af8cZ1YDZcpssWyAicjgwH1gPvEhVHwen4wAOLk4yX67GUXDG3e8vBIY9CoSJZX4ksAP4qmv2+rKITMfwslbVIeBzwDaczmAnsIGUytuoTXaCEJEfAS/2OfUJnDz04ZhaXgPcKCJH4r8DYK7+t03k/jiOKWbSbT7HjJFbVb/rXvMJYC9wQ+02n+tN8Xc2WTZfRGR/4BbgYlX9k0jYhpbFIiJvBZ5Q1Q0ickLtsM+lppX5FODVwAdVdb2IXINjLjIad47jDBwz7TBwE/AWn0sTlXcpOgVVfWPQORH5APBtdQxpPxeRcZzAUI/i2OxrHAY8lqmgDQTJLSJzcCp0s/tjPwy4V0SOw2C5a4jIBcBbgZPdcgcD5A7BZNkmISJVnA7hBlX9tnv4DyJyiKo+7poTnwh+Qu4sBk53t9OdBrwAZ+TQKyJTXO3VxDJ/FHhUVde732/G6RRMLmuANwIPq+oOABH5NvA6UirvdjAfrcWxpSEiL8eZLPojcCvwNhHZT0SOAF4G/LwwKT2o6hZVPVhVD1fVw3Ea56tV9fc4cr/L9UJaBOysDWVNQETeDHwUOF1Vd3tOGVvewC+Al7neGVOBt+HIaxyuLf5fgF+r6lWeU7cCF7j/XwB8N2/ZglDVj6nqYW5bfhtwh6q+A7gTONu9zCiZAdzf23YRme0eOhn4FQaXtcs2YJGI9LjtpSZ3OuVd9Ex6CjPxU4Hrgf8C7gVO8pz7BI7XyVbgLUXLGpKHR9jnfSTAP7pybwEWFC1fg6wP4tjnN7mfL5WhvHG8un7jyveJouUJkfP1OMP+X3rK+BQcG/3/Ax5w/84oWtYA+U9gn/fRkTiKwYM4Jo79ipbPR955wD1uea/FMUUbX9bA5cD97nvvGzhef6mUtw1zYbFYLJY67WA+slgsFktK2E7BYrFYLHVsp2CxWCyWOrZTsFgsFksd2ylYLBaLpY7tFCwWFxF5Nsa1J4jI6zzf3y8i73L/v1BEDk2Q/iPiRsu1WIqiFCuaLRYDOQF4FvgPAFX9kufchTj+46at4LVYmmI7BYslBBE5DbgUZ5Hkk8A7gG7g/cCYiCwDPoizqvRZnIWIC4AbRGQEeC1OpNMFqvpHEVkAfE5VTxCRFwKrgZk4i47Ek+4y4ENuuuuBv1LVsexzbOl0rPnIYgnnZ8AiVZ2PE3L7b1X1EZx49V9Q1Xmq+tPaxap6M84K2Xe450ZCnn0Z8DP32bcCAwAi8mfAecBiVZ0HjOF0RhZL5tiRgsUSzmHAGjcw2lSczU3S4njgTABVvU1EnnaPnwwcC/zCDZjYjXlB2Sxtiu0ULJZw/gG4SlVvdcNCr0zwjL3sG5VPazjnF2dGgOtU9WMJ0rJYWsKajyyWcA4Ehtz/L/AcfwZnu0w/Gs89gqP5A5zlOf7vuGYhEXkLTjA2cIKwnS0iB7vnZojISxLKb7HEwnYKFss+ekTkUc/nwzgjg5tE5Kc4IdlrfA/4CxHZJCJvaHjO14Avuee6cSJaXuM+wztZfDlwvIjci7Ph0jYAVf0VzuT27SLyS+CHOHs3WyyZY6OkWiwWi6WOHSlYLBaLpY7tFCwWi8VSx3YKFovFYqljOwWLxWKx1LGdgsVisVjq2E7BYrFYLHVsp2CxWCyWOv8fb0VC2AnxW9EAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Latitude vs. Cloudiness Plot\n", "plt.scatter(cleandata[\"Lat\"],cleandata[\"Cloudiness\"])\n", "plt.xlabel(\"Latitude\")\n", "plt.ylabel(\"Cloudiness\")\n", "plt.title(\"City Latitude VS. Cloudiness 4/1/20\")\n", "plt.savefig(\"images/cloudiness.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 15, "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": [ "#Latitude vs. Wind Speed Plot\n", "plt.scatter(cleandata[\"Lat\"],cleandata[\"Wind Speed\"])\n", "plt.xlabel(\"Latitude\")\n", "plt.ylabel(\"Wind Speed\")\n", "plt.title(\"City Latitude VS. Wind Speed 4/1/2020\")\n", "plt.savefig(\"images/wind_speed.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>City</th>\n", " <th>Lat</th>\n", " <th>Lng</th>\n", " <th>Max Temp</th>\n", " <th>Humidity</th>\n", " <th>Cloudiness</th>\n", " <th>Wind Speed</th>\n", " <th>Country</th>\n", " <th>Date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>taoudenni</td>\n", " <td>22.68</td>\n", " <td>-3.98</td>\n", " <td>112.68</td>\n", " <td>9</td>\n", " <td>49</td>\n", " <td>5.95</td>\n", " <td>ML</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>aykhal</td>\n", " <td>66.00</td>\n", " <td>111.50</td>\n", " <td>55.40</td>\n", " <td>82</td>\n", " <td>90</td>\n", " <td>4.47</td>\n", " <td>RU</td>\n", " <td>1595087159</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>northam</td>\n", " <td>51.03</td>\n", " <td>-4.22</td>\n", " <td>69.01</td>\n", " <td>81</td>\n", " <td>93</td>\n", " <td>8.01</td>\n", " <td>GB</td>\n", " <td>1595087159</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>torbay</td>\n", " <td>47.67</td>\n", " <td>-52.73</td>\n", " <td>63.00</td>\n", " <td>51</td>\n", " <td>40</td>\n", " <td>11.41</td>\n", " <td>CA</td>\n", " <td>1595087105</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>klaksvik</td>\n", " <td>62.23</td>\n", " <td>-6.59</td>\n", " <td>51.80</td>\n", " <td>93</td>\n", " <td>94</td>\n", " <td>11.41</td>\n", " <td>FO</td>\n", " <td>1595087160</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>542</th>\n", " <td>the pas</td>\n", " <td>53.83</td>\n", " <td>-101.25</td>\n", " <td>62.60</td>\n", " <td>88</td>\n", " <td>90</td>\n", " <td>5.82</td>\n", " <td>CA</td>\n", " <td>1595087255</td>\n", " </tr>\n", " <tr>\n", " <th>543</th>\n", " <td>la ronge</td>\n", " <td>55.10</td>\n", " <td>-105.28</td>\n", " <td>59.00</td>\n", " <td>100</td>\n", " <td>90</td>\n", " <td>2.24</td>\n", " <td>CA</td>\n", " <td>1595087255</td>\n", " </tr>\n", " <tr>\n", " <th>547</th>\n", " <td>odweyne</td>\n", " <td>9.41</td>\n", " <td>45.06</td>\n", " <td>79.23</td>\n", " <td>41</td>\n", " <td>97</td>\n", " <td>16.87</td>\n", " <td>SO</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>548</th>\n", " <td>yar-sale</td>\n", " <td>66.83</td>\n", " <td>70.83</td>\n", " <td>51.78</td>\n", " <td>93</td>\n", " <td>100</td>\n", " <td>12.28</td>\n", " <td>RU</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>549</th>\n", " <td>zhanakorgan</td>\n", " <td>43.91</td>\n", " <td>67.25</td>\n", " <td>89.28</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>6.26</td>\n", " <td>KZ</td>\n", " <td>1595087256</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>375 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " City Lat Lng Max Temp Humidity Cloudiness Wind Speed \\\n", "0 taoudenni 22.68 -3.98 112.68 9 49 5.95 \n", "8 aykhal 66.00 111.50 55.40 82 90 4.47 \n", "9 northam 51.03 -4.22 69.01 81 93 8.01 \n", "11 torbay 47.67 -52.73 63.00 51 40 11.41 \n", "12 klaksvik 62.23 -6.59 51.80 93 94 11.41 \n", ".. ... ... ... ... ... ... ... \n", "542 the pas 53.83 -101.25 62.60 88 90 5.82 \n", "543 la ronge 55.10 -105.28 59.00 100 90 2.24 \n", "547 odweyne 9.41 45.06 79.23 41 97 16.87 \n", "548 yar-sale 66.83 70.83 51.78 93 100 12.28 \n", "549 zhanakorgan 43.91 67.25 89.28 12 0 6.26 \n", "\n", " Country Date \n", "0 ML 1595087158 \n", "8 RU 1595087159 \n", "9 GB 1595087159 \n", "11 CA 1595087105 \n", "12 FO 1595087160 \n", ".. ... ... \n", "542 CA 1595087255 \n", "543 CA 1595087255 \n", "547 SO 1595087256 \n", "548 RU 1595087256 \n", "549 KZ 1595087256 \n", "\n", "[375 rows x 9 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NHemisphere= cleandata.loc[cleandata['Lat'] > 0]\n", "NHemisphere " ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>City</th>\n", " <th>Lat</th>\n", " <th>Lng</th>\n", " <th>Max Temp</th>\n", " <th>Humidity</th>\n", " <th>Cloudiness</th>\n", " <th>Wind Speed</th>\n", " <th>Country</th>\n", " <th>Date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>rikitea</td>\n", " <td>-23.12</td>\n", " <td>-134.97</td>\n", " <td>72.81</td>\n", " <td>80</td>\n", " <td>51</td>\n", " <td>19.17</td>\n", " <td>PF</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>caravelas</td>\n", " <td>-17.71</td>\n", " <td>-39.25</td>\n", " <td>75.54</td>\n", " <td>56</td>\n", " <td>94</td>\n", " <td>6.53</td>\n", " <td>BR</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>pisco</td>\n", " <td>-13.70</td>\n", " <td>-76.22</td>\n", " <td>64.40</td>\n", " <td>68</td>\n", " <td>40</td>\n", " <td>6.93</td>\n", " <td>PE</td>\n", " <td>1595087158</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>hithadhoo</td>\n", " <td>-0.60</td>\n", " <td>73.08</td>\n", " <td>81.75</td>\n", " <td>80</td>\n", " <td>100</td>\n", " <td>35.12</td>\n", " <td>MV</td>\n", " <td>1595087159</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>port keats</td>\n", " <td>-14.25</td>\n", " <td>129.55</td>\n", " <td>66.29</td>\n", " <td>72</td>\n", " <td>2</td>\n", " <td>1.14</td>\n", " <td>AU</td>\n", " <td>1595086879</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>537</th>\n", " <td>opuwo</td>\n", " <td>-18.06</td>\n", " <td>13.84</td>\n", " <td>72.50</td>\n", " <td>20</td>\n", " <td>0</td>\n", " <td>16.26</td>\n", " <td>NA</td>\n", " <td>1595087254</td>\n", " </tr>\n", " <tr>\n", " <th>541</th>\n", " <td>parana</td>\n", " <td>-31.73</td>\n", " <td>-60.52</td>\n", " <td>81.00</td>\n", " <td>48</td>\n", " <td>36</td>\n", " <td>5.01</td>\n", " <td>AR</td>\n", " <td>1595087255</td>\n", " </tr>\n", " <tr>\n", " <th>544</th>\n", " <td>carauari</td>\n", " <td>-4.88</td>\n", " <td>-66.90</td>\n", " <td>92.10</td>\n", " <td>45</td>\n", " <td>12</td>\n", " <td>3.56</td>\n", " <td>BR</td>\n", " <td>1595087255</td>\n", " </tr>\n", " <tr>\n", " <th>545</th>\n", " <td>boyolangu</td>\n", " <td>-8.12</td>\n", " <td>111.89</td>\n", " <td>72.61</td>\n", " <td>87</td>\n", " <td>94</td>\n", " <td>3.00</td>\n", " <td>ID</td>\n", " <td>1595087256</td>\n", " </tr>\n", " <tr>\n", " <th>546</th>\n", " <td>maraa</td>\n", " <td>-1.83</td>\n", " <td>-65.37</td>\n", " <td>84.31</td>\n", " <td>77</td>\n", " <td>1</td>\n", " <td>2.37</td>\n", " <td>BR</td>\n", " <td>1595087256</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>175 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " City Lat Lng Max Temp Humidity Cloudiness Wind Speed \\\n", "1 rikitea -23.12 -134.97 72.81 80 51 19.17 \n", "2 caravelas -17.71 -39.25 75.54 56 94 6.53 \n", "3 pisco -13.70 -76.22 64.40 68 40 6.93 \n", "4 hithadhoo -0.60 73.08 81.75 80 100 35.12 \n", "5 port keats -14.25 129.55 66.29 72 2 1.14 \n", ".. ... ... ... ... ... ... ... \n", "537 opuwo -18.06 13.84 72.50 20 0 16.26 \n", "541 parana -31.73 -60.52 81.00 48 36 5.01 \n", "544 carauari -4.88 -66.90 92.10 45 12 3.56 \n", "545 boyolangu -8.12 111.89 72.61 87 94 3.00 \n", "546 maraa -1.83 -65.37 84.31 77 1 2.37 \n", "\n", " Country Date \n", "1 PF 1595087158 \n", "2 BR 1595087158 \n", "3 PE 1595087158 \n", "4 MV 1595087159 \n", "5 AU 1595086879 \n", ".. ... ... \n", "537 NA 1595087254 \n", "541 AR 1595087255 \n", "544 BR 1595087255 \n", "545 ID 1595087256 \n", "546 BR 1595087256 \n", "\n", "[175 rows x 9 columns]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SHemisphere=cleandata.loc[cleandata['Lat'] < 0]\n", "SHemisphere" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = -0.5x + 92.69\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_values = NHemisphere['Lat']\n", "y_values = NHemisphere['Max Temp']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "plt.scatter(x_values,y_values)\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Max Temperature')\n", "plt.title('Northern Hemisphere - Max Temp vs. Latitude Linear Regression')\n", "plt.show()\n", "plt.savefig(\"images/linear_regression.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = 0.68x + 78.59\n" ] }, { "data": { "image/png": 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zqJwE7OruL5vZKILJkgrOajlu3DifPz/fkAwiIpKLmT3o7uNqXQ6RuHujrBf+Pxo4iGBkulvpHezoKIJBZkRERKRBxdaMErrJzNYlGEnvm+7+ppnNBK43s68QzNxZqeGvRUREpA7FGmy4+2dyLPsfwRDNIiIiMgBoBFERERGJlYINERERiVXcORsiIlLErAXtnD97EUs7OtmgtYXJE7dg0ti2WhdLpGIUbIiI1EAqwGjv6MSA1CAE7R2dTL35UQAFHNIw1IwiIlJlsxa0M/XmR2nv6AR6A42Uzq5uzp+9qPoFE4mJajZERKhuU8b5sxfR2dVdcJulYSAi0ggUbIjIgJeqaUgFAHE3ZUQJJDZoban4cUVqRc0oIjLg5appiLMpo1gg0dLcxOSJBWdxEEkUBRsiMuDlq2mIqylj8sQtaGluylhm4f9trS0cvH0b589exKZTbmf8zDnMWtAeSzlEqkXNKCIyYKXyNPJNRzmipTmW46aaZnLliFS7SUekGhRsiMiAlH1Tz+XdlauYtaA9lpv8pLFtOfdbqElHwYYklYINEWlIxXqXROkR0tXtPXkb1eqpUu0mHZFqULAhIomWK6gAijZFRL15p14bd7NGsSYd9U6RJFOwISKJlS+/YcjgQUWbIlqHNfPmiq6ix2gyK7ivSozPUaxJp7nJ1DtFEk3BhogkVr78hnw37fTaDM9XhZCmpbmp4L4qlcxZrEln+BqDla8hiaauryKSWKXmMaQ3RbzVmb9Wwwi6oM44aCva8jRfbNDakjfYOee2x4GgxmL8zDlFu7AWO49CZRVJAtVsiEhibdDa0jO/SLq1hzXzXtfqjEAge6CsfK9ta23hvikTMpZlN3Gk9nXKdQtzluvNFV2cNetRbnqwvWitx6wF7ZgVrmlRvoYknWo2RCR2UZ/wS5VrcKyW5iamfX7LjFqJVN7F+bMX9Rw732uzcyMmjW3r2Vd6jceksW0Fg4Br5i0pOirprAXtTL7xYVYXCDQ0mqg0AtVsiEis4hykqtDgWCnFjh0luTPfmBiTJ27ByXlqN7rzVFWkN5mcP3sRXd35I422mLvZilSLeZQsqRobN26cz58/v9bFEJEyjJ85J3JzRb0dO0pPk23PuYOOHDkVTWZ5A45UEHHKdQvzdnU14PmZ+xYtYyFm9qC7j+vXTkQqQM0oIhKrWg5S1Z9jp2pk2js6cXprRbKbgM7ef8uczTGHf3KjPstTUvtqHZZ/OHTlaUgjUbAhIrHKd9Osxs20P8eOOhNsvpyO6ZO2KtibpbOrG/dgDI1szYM0roY0FgUbIhKrqImY9XbsUmpFJo1t474pE3h+5r7cN2VCT1NLannfcCLwVmcX539hG9ZOq+FobWnm/EO2UZ6GNBQliIo0uEqMcNkfpSRiVlLqvDu7unvyJ0pJuMzXNbZQrUi+a11oX/mST0UaSaw1G2Z2ipk9bmaPmdk1ZjbUzDY1s3lm9rSZXWdma8RZBpGBLGreQdxlqEWgkTpvCHqGpGo0oh671FqRQte62L7i6hosUi9iCzbMrA34NjDO3T8BNAGHAecBF7r75sCbwFfiKoPIQBc17yAutQp2KnHehcbXKPWYhfZVDwGhSNzibkYZDLSYWRcwDHgZmAB8KVx/BXA28JuYyyEyINV6uvJiN+C4VOq8S2niyNVMkr48375qdY1Eqim2YMPd283sJ8BioBO4A3gQ6HD3VeFmLwH6axKJyYiW5pxjQFSrW2XcwU45ORJxyTeuRpPlSw8N1DogFKmGOJtR1gYOADYFNgCGA3vn2DTnmDZmdpyZzTez+cuWLYurmCINa9aCdt5duarP8mp2q4yz22t/ciTikG8Ar3zLU2rZNVikWuJsRtkDeN7dlwGY2c3Ap4FWMxsc1m5sCCzN9WJ3vwS4BIIRRGMsp0ii5Xq6Bzj1+odz3uhW5bn5pfbT3tFZVu+NXCZP3CLvJGb9Vaj5ITU6aDUTU9sKTOxWSJzXSKRexBlsLAZ2MrNhBM0ouwPzgbuBLwDXAkcBt8RYBpGGlmvekck3PAyW/4nanT7zg2TvJ/Xa/s5jEme313JzJOJSbtBQq67BItUUZ87GPDO7EXgIWAUsIKipuB241symh8sui6sMIo0u19N9V6EpREPZCYi59pNv2ziU0z223ByJuPQnaMgZGD30ELz5Juy+exzFFamqWHujuPs0YFrW4ueAHeM8rshA0Z8kwvTXFttPuceJMuNrubPClpsjEad+16asWgWnnw4//WnvspUroTn/HCoiSaDhykUSrD9JhOmvLbafco8TZbyLcsfEyJcLUSxHoi4tXAhrrhkEFemBxuzZCjSkISjYEEmwXL0umgdZzsm90mXnEuTaT75tSxGlW2e5XT9rOedKRaxaBaedBmYwdiwsXx4snzQJ3n47SK7Zc8/allGkQjQ3ikiC5csTyF6225iR3P3ksry5BOn7ydcbpZy8imLjXcxa0M6gPLkXxWpTEptY+eijsMsu0NGRufyWW2D//WtTJpGYmdewfTOqcePG+fz582tdDJEBKzuvAoJahELDdxd7HdBnXbF913pSubJ1d8NZZ8HMmZnL99sPrrwSRoyI5bBm9qC7j4tl5yIlUM2GiBRV7pDahWofxs+ckzPQaDLLG2iUk0haU48/DrvuCq+/nrn8T38KmktEBggFGyJSVH+G1M7XQyPfa1e7J3sOkdWrYdo0mD49c/nee8PVV0Nra23KJVJDCjZEpKg45hopdZ91P4fIE0/AbrvBq69mLr/xRjj44NqUSaROKNgQSaCzZj3KNfOW0O1OkxmHf3Ijxm28Tmz5DHEMqV3qPmsxuVpRq1fDD34A55yTuXzPPeHaa2HttXsWJTbfRKQCFGyI1ImoN6OzZj3KlXMX9/ze7c6Vcxdz9dzFrA6XVTqfIY6eH6Xus5TgJPYb+6JFMGECLM2a2unaa+HQQ3OWJ3H5JiIVpN4oInWglN4em039S+RRMttaW3omJWsEUYKIcnvOFLV6NfzoR/C972UunzABrr8e1l0370vHz5yTd5K2ON8f9UaReqGaDZE6UEryYynDcddNPkOFRBkOvOKJpM88E8xPsnhx5vKrr4bDD4+0i3zvQ3tHJ+NnzmFpRyetw5pxh7c6u9TMIg1HI4iK1IFSkh9LmWispvkMNTBrQXve2WBLCrzcYcaMYHTPzTfvDTR22QWWLQvWRww0IP/7YAQBhwNvruiio7MLp7eZZdaC9uhlFqljCjZE6kC+m1Gu5Yd/cqNI+0zU0N0VkGo+ySdS4PXcc7DZZjBoEJxxRu/yP/whCDD+8Q/44AcLlmH8zDlsOuV2xs+c0xMs5Bpa3YBCdVRR5ocRSQoFGyJ1oJR5PqZP2oojdhrNoCIVHAdv388ZSKsg3825HLmaT1KaBxkrVq7qc5xZC9oZP+MuZux2bFCLsdlmQcABMH580I3VnVmfmFC0nKlgJ1VTkV47MWlsGzMO2oq21haMIFcjSmNYozWDycClBFGRCqhE74f0fURpv8+XdJhSkaTIGFU6kXPTKbfnvYE3Nxld3b1rW5qb+OqGzsFnfI1N3sgMHB46+6dsN+2UkstZahJosfcPoLWlmYXTyp+MTQmiUi9UsyHST4WeaEsxaWwb902ZwIWHbst7XauLtt8Xe+rt7Orm1OsfrkitQRzKnVo+n3zNJE2WFmi485UHZvHE9L059fh9egKNhzbYgnEn/pFNTv8z3xqyTVnlLHXQsUIz7aa8u3JV3b1vIuVQsCHST5W+aUbdX5QchG73uk04rPSIoPmaorrdaXvrNf7+u+N54cef53tzLu1Zf/pe32KT0//MQf93Aa8PXzvn8aOWs5S8G6BP00quZrGublfehjQEBRsi/VTpm2bU/U2euAXR+6XUX8JhqTfnYlI377WHNQcL3Dl6/q28cN5+3HfxsXzkjZcAePhDm7PDN//AZlNu57ptJhY9ftRylpJ3k17m+6ZM4PmZ+5KvRVt5G9IINM6GSD9VehjtqPubNLaN+S++wVVzF0dKNoT6unHFMQQ6QNvS51nw2+P7LJ868USu2XavnuMcvn0bNz3YXvT4UcsZZUTUQrk9dTkcu0iFKNgQ6adK3zQnT9yCyTc8TNfq3hCieZDl7ZmSmhOlWLIh1NeNq6JDoLvDsccy6fLLSZ+4/fH1PsyxX/g+7683iuFDBmNZx0m/dk1mGbU/qXKUUs5Cg44VG7I8ruBLpB4o2BDppzjmDenTPlKgvSR1g4vSO6XeblxRRgQt6Mkn4WMf67P4+q324LS9Twq6swLW2ZWzV0fq2MXmLel3OSk+smksnyOROqFgQ6QCKnEzSjl/9qKMbprQmyhY6BiFmkjWHtbMtM9v2Tg3ruOOg9/9rs/iI06+jHuHrN9neaEanYoPb55HlFycSn6OROqJEkRF6ky5CaeFbqjvda3Ouy4xnnoqqKkwyww0jjgimCTNnS8cObHkJM1Sr3e5A5FVOiFWJEkUbIjUmXw3n9ZUL4s8Co3bUG89UUpy4olBgLFFVsDwyCNBrsYf/9jTXJJrpM5ig4SVEgT0Z0yVcnqriDSK2IINM9vCzBam/XvbzE42s3XM7E4zezr8f+24yiCSRJMnbkFzU98kjeXvFR7gKXWjzadQPkclhw2viGef7a3F+PWve5cfemhPLQZb5T7X9O6k902ZULRZIl+QtiLHgFr9GVOlnEBIpFHElrPh7ouAbQHMrAloB/4ETAHucveZZjYl/P30uMohkjSTxrZx9q2P09HZlbG8a3XxvI1iUvN0ZC8rliCZvm2sCYwnnww//3nf5QsWwLbbVu44aVLlz77mb67o6nMd+juminIyZKCqVjPK7sCz7v4icABwRbj8CsjoqSYiBHOi5NLe0cn4mXM4a9ajfWoiZi1o5zvXLSy431xP4FGf1is1LHsfL7zQW4uRHmgcdFBvLUZMgUbKpLFtDB/S99kr+zoo70KkPJGDDTMb0o/jHAZcE/68vru/DBD+v16e4x1nZvPNbP6yZcv6cWiR5Cl082rv6OTKuYv73PTPvvVxiqWB5noCj/q0Xulh2Zk8OQgwNt00c/n8+UGAcdNNPbkY1RDlOijvQqQ8RYMNM9vRzB4Fng5/38bMfhn1AGa2BrA/cEMpBXP3S9x9nLuPGzlyZCkvFUm8KJN0pevs6u7T7JJLriAm6tN6RYZlX7y4txbjJz/pXb7fftDdHQQZ228ffX8VFOU6xJV3UXc5MyIVFiVn4xfAfsAsAHd/2Mx2K+EYewMPufur4e+vmtkod3/ZzEYBr5VUYpEBIH2Apygjg0aV6wk86siV/RpO+4wzYMaMvsvnzYMddyz++iooZVjySuZdlJIzI5JUUZpRBoW5Fum6c26Z2+H0NqEA3AocFf58FHBLCfsSGTBSvSraIuYDrD2sueAf9PjN1sk7xHaUp/WSmxBeeqm3FiM90Jg4sbcWo04CDahdb5GKN0+J1CHzfFMNpjYwuwk4D7gY2AH4FjDe3Q8punOzYcAS4MPu/la4bF3gemA0sBg4xN3fKLSfcePG+fz584ufjUgDSfX8aO/oxKDgZGstzU093V6n3vwInVmDeB2x02imT8rfLTb7mOVMJNZj2jT4wQ/67vz+++FTnypahoFm0ym353xvDXh+5r792reZPeju4/q1E5EKiNKM8g2CppTRwKvA38NlRbn7CmDdrGX/I+idIiJ5ZFetO/QEHG2tLew2ZiR3P7ks502/3CfxKNX5+ZoQ/nbHg+w1Mcc9bbfd4M47oSl6/slA0zqsmTdX9M23KTaIm0iSFAw2wvExDnP3w6pUHhEhd9V6KtC4b8qEqh2z6Bwh06fD977HXlmLjzjyfL5w8uFVzTmIfQyQmOSrXC5S6SySKAWDDXfvNrODCWo2RKom7htHvd+YKtLzI65jvvIKjBrVZ7v/bPhxDjt8BqsHBbUYz1d4IrNCkpxkmW9MlXzLRZIoSjPKv8zs58C1wLuphe7+SGylkgEt7htHEm5M/er5EdcxzzsPpkzps/6ww3/E3NFb91keZ2CUrVozt5YqSlBbi/dapNqi9Eb5LLAd8GPg1+G/X8VZKBnY4s7OT0L2fy0Gj8p1zLaV73Df1N2DHiXpgcYnPwldXeDOkq0/mThCjTwAACAASURBVHN/1bxZ1qImqJioI65qoDAZCIrWbLj7Z6pREJGUuG8c9XhjypY+zka1mnrSj7nPHVdx5t2/77vR3/8Ou+8ePLH/5J8s7eikdVgzzYOMrtW9SQbVvlnWY+1A1NqWWrzXItVWNNgwszNyLXf3H1W+OCLx3zjq8caUS38Hj5q1oD1jcrG1hzUz7fNb5t/n668zabsN+05WtN12MHcuNDf37De9GerNFV00NxmtLc281dmV92aZrzzQ/xtt1AG5qqmUoFYTtEmji5KzkR6aDwX2BR6Ppzgi8d846vHGVGmzFrQz+YaHM2ob3lzRxeQbHwayclN+8Qs46aS+O/nb34IBuLLkemLv6naGDxnMwml7llSeU294mEHQs7zc/Jl6rB1ISlArUg1RmlHOS//dzM4jHLpcJA5x3zjq8cZUaefPXpRxY0/p6g6nqd+4BdZdt+8Lt94aHngA1lgj777LaYbKV57u1d5nOOJyEzvrrXZgIAS1IlFFqdnINgTYrNIFEUlXqRtHvt4AUfZf791jC8l34z/ioduZfudvYGrWittvh332ibTvcp7YS82Hqaf8mXINhKBWJKooORsL6B0puQkYBeSYUUmkfyp9c+9PF9dqd4+t9LmnBwRrvbechT8/nEHZg2KPGQMLF8KQISXtu5wn9nwBSqHtG0G91baI1EqUuVHSazFWAa+4+/uxliqL5kZpfNk3d+id76PcL+vxM+fkvMFFGYUz32sBWluaMYOOFfmTIUvRn3M/a9ajXDV3cU8YMXyNJs49MJgD5YEzz+Pcv/6yz2vmXvh7djr5mLLLmypzKcFRrpwNgKZBlpGzAf1/36WX5kaRehGlGeV77n50+gIzuzx7mUh/xDEoU3+6uBbapiNtZMdK1HiUe+5nzXqUK+cuzlhm77zD3jtsypDuroxeJS+2fogvnvg7ph40tiI38VKf2FPbxtUbRUTqW5RgI2NowHC+lB3iKY4MVHGMfdGf3gClVPvXKii6Zt6Snp8PeeQOzv9rjlkFbroJDjqIjYF5ZZWucgoFKAouRBpb3mDDzE4HpgBrmllqCvjUxJOXVaFsMoDE0U2wP70Bcr22kFoERUPfe5d5Fx3FB1ZmvrZ9zZFM+NrFfHBkK5M33aLvuBkSWZKThEXqSaGajR8DFxAkg/aMU+zu0b59RdIU+9KOo5tgf3oDpL82Sg1HVYOiP/4Rjjyyz2A3Jxwwhb+M2bnn93qc8wWScwNPwhw6IklRNEEUwMxGEHR3HZpa5u73x1iuDEoQTbaoCZD1ehPKVf50lUhoLHruy5fDRhtBR0fG614bvja7fP13vNc8lHzinJa+VHEkAselPwnG9UIJolIvonR9PRY4FWgDHiXI15gL7BpryaRhlDJHRCk3nGoFJ9k1JCMq3BsldYyc+7jmGvjSl/ouv/pqOPxwfjHrUd7PShLNVk9jVtTr7Ky5JGEOHZGkiJIgegowDvi3u3/GzLYEzoq3WNJIKvmlnQow2js6exKIIP4q7qqOl7BiBXz4w/Dqq5nL110XXnwRhg/vWTR90lZMnxR0dc33JF5PY1Yk6Qau4cZFKifKFPPvuXsngJmt4e6PA2PiLZY0knxfzqV+aadP2Q1kD1FFZ1c3J1+3kPEz5/SZxjsRbrghmMp9+PDMQOMPfwB3eP31jEAjWxKmKq/UZ6EaknA9RZIiSs3Gy2bWCtwGzA57prxa5DUiPSqV/JmrCj6Xcmo58jXJxN5U09kJH/0ovPRS5vI114T29uD/iJIwPHaS5gtJwvUUSYpICaI9G5vtDowAbq/mKKJKEE2+Sty0N51ye5/ajEKiJvLlS1o8ePs2bnqwPZ5kxptvhoMP7rv897+HY/o3ume9q9dE4EakBFGpFwWDjXAAr4fcfZvqFakvBRuNLf3m0zqsGfdglM4mM7rdaQtvSFG7oaYY8PzMfYseN98+zYLWi2xl90Z47z34+Mfh+eczlw8dGjSbrLVW6fsUKUDBhtSLgs0o7t5tZv81szZ3T2AjuNS77FqFN1f0DgXeHd7pU80iuWoa0pNEsxXKAyjWnRVyBxqQO5mx4NP6rbfCAQf0ec3MA7/DmO99Z0A81UetzVCth0hjipKz8UHgCTP7N/BuaqG7H1TshWGux6XAJwjuCccCi4DrgE2AF4AvuvubpRZcGkPUPIzOrm7ufnIZMw7aKuNmtNuYkVz3nyV9JvhqbrKCeQBRj5tLdhCTa/Cn71//ILsfuAtrvvhcxrZdg5rY/ltX8fbQDwDQMgAGiYo6OJYG0RJpXFGCjZn92P/Pgb+5+xfMbA1gGHAGcJe7zzSzKQSjk57ej2NIgpXS5XFpR2efLqjjZ87pE2gADF9jcMEbVLldLXMlM6YHLrs++wCX33hOn9ed9blvcOV2fZt06nWMiUqKOrZGksbgEJHSFA023P0uM9sQ2Nzd7zazoUBTsdeZ2VrALsDR4X5WAivN7AB6BwS7ArgHBRsDVikTnuVqFskXNLyVNjNrf47b2tLM8CGDC1brL/vf28y+/CS2eD3H4Fr/+x+b/vjfBRNb63GMiUqKOrZGksbgEJHSRB1B9ESCXiibAaOBi4A9irz0w8Ay4P+Z2TbAg8BJwPru/jKAu79sZuuVX3xJuqgTnhmw25iRfZaXO/BSlOO2NDdx9v5b5n+qnj0b9tqLp7IWn737cVw+bv8gkXSddYoGNvU4xkQlRX2PNIiWSOOK0ozybWBHwhmq3f0pM1s/4r63A77l7vPM7OekTehWjJkdBxwHMHr06Kgvk4TJHssgvTdKOgduejDIUb77yWUZORu5uqcWG7ch1xgKu40ZmbHvnMmJXV3wyU/CggV99jn2W1fx5rARfcpQKLCp1zEmKinq2BpJGoNDREpTdJwNM5vr7juZ2QJ3Hxt2h13o7lsVed2HgLnuvkn4+2cIgo2PALuGtRqjgHvcveC3ibq+Djz5ht7O7n2SGg+jaJDQX3fdBXvkqMy74AL4zneK9qJI72ab3aW30fIRcl0LiDY4Vn9eK32p66vUiyjBxgUEI4YeA5wAfBN42t2nFt252b+Ar7r7IjM7G0iNtfy/tATRddz9tEL7UbAx8JQygFdss3CuWgU77wzz5vVd9+qrsJ5aALNVelbXJM0SW48UbEi9iDI3ymnAO8CTBDkXdwFnRtz/t4CrzOwRYFvgRwS9Wz5nZk8Dn6N/vV2kQZXSTl/xBMJ77glG9Gpuzgw0Zs4MBt9wV6CRR6EeJfWwPxGpjSi9UbrN7FLgHwQ12E+7++ooO3f3hQQzxmbbvaRSyoCTq/0+3wBeFUkg7O6GXXeFe+/tu+6VV2D9KGlKUukeJeqhItIYitZsmNlewLPAJQQDdD1rZnvGXTAZ2CaNbWPGQVvR1tqCETSVfHmn0ZWfhfPee4NajMGDMwON6dN7azEqFGjMWtDO+Jlz2HTK7bHPTFvNY6Wr9KyuSZolVkTyi9Ib5WfAHu7+FICZfRS4BfhYnAUTSR/AK5U42NnVXTTBctaCds657fGeoc9bW5ozu7B2d8PnPgd33933oEuXwqhRFT+XKKNjVmqo7lqOxFnpHiXqoSLSGKIEG6+lAg3o6fq6LMYyiWTIvnl2u/fccHIFGpNvfJiu7t4Gl47OLibf8DBrP/wgnz2m7xwlTJsGZ58d5ynkzT049fqHe36vVIBQy5E4Kz0tu6Z5F2kMUXqjXARsCFxP0GR+CPAM8E8Ad7815jKqN8oAl68bbK5eKNnbmq/miuunscsLfcfFYMkS2HDDipc3l0K9a1qamxjaPChjErqUcnra5DtWsVlwpfGoN4rUiyg1G2sCbwETw9/fAdYnCDociD3YkIEtXzJge0cnsxa0ZzzlprbdZukibvnjqX1fdMYZcO65sZSzkEKjiHZ2decdybScRMhGHYlTM8KKJFeU3ij/V42CiORT6EY9+cagGWLS2DZYvZorZ/2Q8Yv6jovx6W/8Hhu9cTzjcUQQdVj2bOUECI2Y56AZYUWSLcrcKKMJ5kbZJH37KFPMi1TC5IlbcMp1C3M2DXR1O7f+/jYm/eobAIxPW3fxjgcxc7djgaDb1U9reLNN3RBPvf5hunM0Xba2NPP+qtUVCRAaMc+hWB6Kaj1E6luUZpRbgT8AdwKRxteQ5EjCl/SksW2cfN3CzIXu/PZP5zLx6bl9tt/5+Mt4aURmd9URw5prfl6p4+eqdTh7/y2ByiZW1vp8K6nQeBuq9RCpf1GCjZXu/tPYSyJVl6Qv6bawKeXjrz7HXy7/dt8NTj4ZLrwwb3JkR47ky1ooVutQb9e9XhTKQ6ll7xsRiSZKsPFLMzsLmA28n1ro7o/EViqpisR8Sbtz8bJ/stVPz+mzasI3LuXbX9urp7yVTo6Mo+an0WodqqFQHsop2bVeIY0yKlI/ogQbHwW+CuxNbzOKA7vEVSipjrofCnrxYthrL3jiCdKnGL5iu32Z9rlvsPawZqZ9fsuMG3clkyOTVPPT6ArVCKVm082W9N43Io0kSrDxRWATd3+/6JaSKHXZRdIdfvlLOOmkzOU77AC33AKjRnEUcFSel1cyOTIxNT91JM4coHw1Qo3Y+0ak0UQJNh4hGGtDwUaDqasv6SVLYJ994LHHMpf/9rdw3HHBTeyKJ1ja8VDRm1ilminqvuanztSyJmjI4EE9x81V4yUitRUl2FgXeNLM5pGZs6GurwkXRxfJkp5s3eGii+DEEzOXb7cd3HYbbLBBzz5rcRNrHdacc1RPVc/nVouaoOzPBsB7XcnqNJeEHmEi/RUl2Kj+cItSNZWqBcie/AyCoODk6xZy9q2PZ06E1t4O++0HC7MS+y66CI4/PpiFNU2tbmLL31vVZ3lzk6l6Po9a1AQlvalLeUEyUEQZQfQuM9sQ2Nzd7zazoUBTsdfJwJHr6TJdR2cXU29+lE1uvJJtfzQlc+XWW8PttxecoyTum1iuJ8vzZy+ia3XfTrTD1xism0AetcgBSnpTV9KDJZGooowgeizBCKIjgM2A0cBFwB7xFk2SItcXZsrI5W9w2U0/YOtXnslc8ctfwje/2acWIyU9ABgUTimfrRI3sXxPlvnO563O+hivox7VIgeoLpOcS5D0YEkkqijNKN8GdgTmQc8U8+sXfokMJLm+GL/48B38+G+/yFj21Lqj+ehD/4LRowvuL9eU8tkqdRPL92TZFGOAU48qkTdQi2HScwU4Buw2ZmRsx6ykpAdLIlFFCTbec/eVFj6BmpmaUKTHrAXtPTUPI5e/ye9u/iHbvvxUxjbn7P41/t/2+9O29jDuKxJoQP6akiYzVrtX9CaW7wmy252W5qb66KkTs0rmDVR7wLJJY9uY/+IbXDV3cc/IsQ7c9GA74zZep+6bIuqqR5hIjKIEG/eZ2WnAUDPbDfgm8Od4iyVJkLpJHfjInfzkLz/LWPfMOhty9BfP6ZmjpJQv0HwBwGp3np+5b8bx+/sUna/HSVta7kaj9xJIet7A3U8u6zNEfVLK34iT5onkEiXYOA04DngSOIlg2PLfxlkoSYDXXuPDB07kiRcfz1g8fbdjuXSHA8GMVDZGW4lfoFGqlivxNF6sx8lAGVY86XkDSS//QPmcycCWN9gws8vd/Wh37wZ+E/6Tge7KK+H//g+ArcNFz689iiO/+EOWtH4oY1MnCDTumzKhpENEqVquxNO4epwEkp43kPTyiwwEhWo2ti6wThpEdlPEbmNGcveTyzKrdDcaAgcfDP/8Z8ZrL9rrq/x46wPy9iiB8p4uo1QtV+JpNt+2A63HSdLzBpJefpGBoFCwMczMxgI57yTu/lA8RZJqydUUceXcxT3rt7//r0yaunvmizbZBO68Ez7yETZY0E5LgW6iUP7TZbGq5Uo8zeqJOJD0vIGkl19kICgUbLQBF5A72HCgaN24mb0AvAN0A6vcfZyZrQNcB2wCvAB80d3fLKnUUhG5miJaO9/molkz+fTiRzI3PvdcmDo1oxYj/Uu+vaMTg4xEvTifLivxNKsn4l5JzxtIevlFGl2hYOMZdy+tsT233dz99bTfpwB3uftMM5sS/n56BY4jJZi1oD3jqX7fJ/7Fr289L2Ob9jVHcsRh03lhnTaeP2Pf7F0AfQOO1PgUpSaFRi1z+tPrwdu39W3yKeF4eiKWQjRniUjlROmNUmkHALuGP18B3IOCjapKNZ+s9d5yfj1rJp95MXOOkgt2/jK/+vShuA0CgiTPYvtKH4ArVTtQ6UAju8nnpgfbmXHQVv06jp6IJRfNWSJSWYWCjUoEAA7cYWYO/NbdLwHWd/eXAdz9ZTNbrwLHkRI8cMHveOKqczKWvfKBdfjyYefy7LobZSwv1qxQrTEakj4WhCSLPm8ilZU32HD3Oyqw//HuvjQMKO40syejvtDMjiMY34PREUadbCSxVN92dMDhh8Pf/pYxje/PP304P9v58J5ajCN2Gl1S00S1xjhI+lgKkiz6vIlUVqzNKO6+NPz/NTP7E8EcK6+a2aiwVmMU8Fqe114CXAIwbty4voMhNKiKV9/OmgUHHpix6I0PtPLFQ3/EMx/MDOLaWluYPmmrknZfrR4d6jki1aTPm0hlDSq2QTilfPayD0Z43XAzWzP1M7An8BhwK3BUuNlRwC2lFLjRFaq+jeztt+Hznw96jqQHGmeeCatW8c9/Pkb7qE0zXlJuL4zJE7egpTlzupw4enRU6zgioM+bSKVFqdl4wMy+5u5zAczsYGAG8NEir1sf+FM4gdtg4Gp3/5uZPQBcb2ZfARYDh5Rd+gbUr+rb226D/ffPWLRyRCvHHv0T7hv6ITb4QAuTH3mlor0wqtmjY8jgQT2B2NrDmpn2+S3Vfl6EelSURz2VRCorSrDxJeD3ZnYPsAGwLhHG2HD354Btciz/H7B731cIlFF9+/bbcOSRcEtWBdGUKcw6+BtMveW/eZtkKvXFGXePjuymJYD3ulbHdrxGoR4V/aOeSiKVU7QZxd0fBc4Fjgd2A05095fiLthAFbn69i9/CZpJRozoDTTWWgsWLgR3mDGD8//+TP+bZOpARZqWBqAkXrdZC9oZP3MOm065nfEz5zBrQXutiyQiFRAlZ+My4GSCuVKOAW4zs2/GXbCBatLYNmYctBVtrS0YQdJmz1gSy5cHc5SYwb5pg2x997vQ1QVvvQXb9FYmNUpGfaOcR7Ul7bqlamLaOzpxemtiFHCIJF+UZpTHgK+6uwPPm9lOwE/jLdbA1qf6dvZssA0zNxo+PJgYbbvt8u6nUTLqG+U8qi1p101jW4g0rijNKBeGgUbq97fc/SvxFkt491049NCgFmOvvXqXn3xyUIuxfHnBQAMaJ6O+Uc6j2pJ23ZJWEyMi0RWt2TCzzQl6n3wc6OkG6+4fjrFcA9ff/w6f+1zmsqFD4d57YfvtS9pV0jPq03tSjGhpZmjzIDpWdCXuPGolae9/0mpiRCS6KM0o/w+YBlxIkCB6DHmmnZcyrVgBX/saXH11xuIbPnUgZ4w/ivXWXZPJgz7EpDJ2ndSM+uyeFB2dXbQ0N3HhodvW9fnUW1fTJL3/moVXpHFFCTZa3P0uMzN3fxE428z+RRCASH/MmQO7Z/UCbm7mnstu5htPDR7QXRaT2H6vrqb9k7SaGBGJLkqw8Z6ZDQKeNrMTgXZAk6eVq7MTvv51+OMfM5efcAJceCGssQZnzpxDZ1dmdXK932grLYnt90kMkOpNkmpiRCS6KMHGycAw4NvADwkG9Dqq4Cukr3/8A3bdNXOZGdx3H3zqUxmLK32jrbeq/SiS2H6fxABJRKQaovRGecDdl7v7S+5+jLsflBq6XIp47z045pggqEgPNI47Lli3enWfQAPy31DLudEmdeyCpPWkgMq+b0mlQblEJJe8wYaZ3VroXzULmTj33hsEGC0tcPnlmcvd4be/hSFD8r588sQtaG7qm4O7YuWqkr+8kziKJBQZ3KxOJTFAqqSkBrYiEr9CzSifApYA1wDzUA+Uwt5/H048ES69NHP5scfCr38ddF8thfdd9OaKrpITDpNctZ+09vuBnuConBURyadQsPEh4HPA4QSTsd0OXOPuj1ejYIlx//0wfnzf5f/4B+yyS1m7PH/2IrpW54g2KP3LO4m5D0mWtACpkpIc2IpIvPI2o7h7t7v/zd2PAnYCngHuMbNvVa109WrlSjj++KCpJD3QOProoLeJe9mBBhT/ci7ly3ugV+1L9ShnRUTyKdgbxcyGAPsS1G5sAvwCuDn+YtWpefPg058OEjvTzZkDu+1WkUPMWtDOIDO6PXfNBpT25T3Qq/al/6L2ZtKgXCKST95gw8yuAD4B/BU4x90fq1qp6klXF5xySpB3ke6II4JEz2HDKnaoVIJdoUCjnC/vgVy1L/1TykBlCmxFJB/zPDc2M1sNvBv+mr6RAe7ua8Vcth7jxo3z+fPnV+twgfnzgyaSlSszl995J+yxRyyHHD9zTs78ipQ2fXlLleX7TLa1tnDflAk1KJGUwswedPdxtS6HSN6aDXcvOgZHw+nqgu9+F37xi8zlhx0W9DIZPjzWw+fLxTDg+Zn7xnpskVyU9CkilRBlBNHG99BDsPPOQXJnutmzYc89q1YM9RyReqPPpIhUwsCrvUhZtQpOPTXoUbL99r2BxiGHwDvvBD1KqhhogHqOSP3RZ1JEKmHg1WwsXAif+QwsX565/C9/gb33rk2ZQkqwk3qjz6SIVELeBNF60u8E0e5uOOMM+PGPM5cfeCBccQWsuWb/CiixS+JkcgOF3pv6pQRRqReNXbOxZAlsvTV0dGQuv+022G+/2pRJSlZK90upLr03IhJFY+dsjB7dG2jsvz+89VaQi6FAI1GSOpncQKD3RkSiiL1mw8yagPlAu7vvZ2abAtcC6wAPAf/n7isL7aNsS5fCs88GPU0ksdT9sn7pvRGRKKpRs3ES8ETa7+cBF7r75sCbwFdiO/KoUQo0GoDm3Khfem9EJIpYgw0z25BgbpVLw98NmADcGG5yBTApzjJI8qn7Zf2q5Hsza0E742fOYdMptzN+5hxmLWivVDFFpMbibkb5GXAakOrusS7Q4e6rwt9fApRFJgWp+2X9qtR7o0RTkcYWW7BhZvsBr7n7g2a2a2pxjk1z9r01s+OA4wBGjx4dSxklOTSZXP2qxHtTKNFU77tI8sVZszEe2N/M9gGGAmsR1HS0mtngsHZjQ2Bprhe7+yXAJRCMsxFjOUWkgsoZd0OJpiKNLbacDXef6u4buvsmwGHAHHf/MnA38IVws6OAW+Iqg4gEqpUPkWoOae/oxOltDil2PCWaijS2WoyzcTrwHTN7hiCH47IalEFkwCg3AChHueNuKAlYpLFVZQRRd78HuCf8+Tlgx2ocV0Sqmw9RbnOIkoBFGltjD1cuIlXNh+jPlPRKAhZpXI09XLlIhSR5DIhq5kOoOUREclGwIVJENXMe4lDNAGDS2DZmHLQVba0tGNDW2sKMg7ZSjYXIAKdmFJEikj4GRLXzIdQcIiLZFGyIFNEIY0AoABCRWlIzikgRGgNCRKR/FGwUkeTEQKkMJT2KiPSPmlEK0ORQAhoDQkSkvxRsFJD0xECpHOU8iIiUT80oBTRCYqCIiEitKdgoQImBIiIi/adgowAlBoqIiPTfgM7ZmLWgvWDSnxIDpV4U+6yKiNSzARtsRO1posRAqTX1ihKRpBuwzSiFepqI1BN9VkUk6QZssKGeJpIU+qyKSNIN2GBDPU0kKfRZFZGkG7DBhnqaSFLosyoiSTdgE0ST1NNEPREGtiR9VkVEcjF3r3UZiho3bpzPnz+/1sWoieyeCBA81c44aCvdbESkIDN70N3H1bocIgO2GSUp1BNBRESSTsFGnVNPBBERSToFG3VOPRFERCTpFGzUOfVEEBGRpIst2DCzoWb2HzN72MweN7NzwuWbmtk8M3vazK4zszXiKkMjmDS2jRkHbUVbawsGtLW2KDlUREQSJc6ur+8DE9x9uZk1A/ea2V+B7wAXuvu1ZnYx8BXgNzGWI/E0P4uIiCRZbDUbHlge/toc/nNgAnBjuPwKYFJcZRAREZHaizVnw8yazGwh8BpwJ/As0OHuq8JNXgJyPrKb2XFmNt/M5i9btizOYoqIiEiMYg023L3b3bcFNgR2BD6Wa7M8r73E3ce5+7iRI0fGWUwRERGJUVV6o7h7B3APsBPQamapXJENgaXVKIOIiIjURpy9UUaaWWv4cwuwB/AEcDfwhXCzo4Bb4iqDiIiI1F6cvVFGAVeYWRNBUHO9u//ZzP4LXGtm04EFwGUxlkFERERqLLZgw90fAcbmWP4cQf6GiIiIDAAaQVRERERipWBDREREYhVnzkZNzVrQzvmzF7G0o5MNWluYPHELjcIpIiJSAw0ZbMxa0M7Umx+ls6sbgPaOTqbe/CiAAg4REZEqa8hmlPNnL+oJNFI6u7o5f/aiGpVIRERk4GrIYGNpR2dJy0VERCQ+DRlsbNDaUtJyERERiU9DBhuTJ25BS3NTxrKW5iYmT9yiRiUSEREZuBoyQTSVBKreKCIiIrXXkMEGBAGHggsREZHaa8hmFBEREakfCjZEREQkVgo2REREJFYKNkRERCRWCjZEREQkVubutS5DUWa2DHix1uWI4IPA67UuREwa+dygsc+vkc8NdH6FbOzuIytZGJFyJCLYSAozm+/u42pdjjg08rlBY59fI58b6PxEkkDNKCIiIhIrBRsiIiISKwUblXVJrQsQo0Y+N2js82vkcwOdn0jdU86GiIiIxEo1GyIiIhIrBRsiIiISKwUb/WRmZ5tZu5ktDP/tk7Zuqpk9Y2aLzGxiLcvZX2b2XTNzM/tg+LuZ2S/C83vEzLardRnLYWY/DMu/0MzuMLMNwuWJPz8zO9/MngzL/ycza01bl/jPppkdYmaPm9lqMxuXta4Rzm+vsPzPmNmUWpdHpD8UbFTGhe6+bfjvLwBm9nHgMGBLYC/gxileQwAABTFJREFUIjNrqmUhy2VmGwGfAxanLd4b2Dz8dxzwmxoUrRLOd/et3X1b4M/A98PljXB+dwKfcPetgaeAqdBQn83HgIOAf6YvbITzC8v7a4LP4ceBw8PzEkkkBRvxOQC41t3fd/fngWeAHWtcpnJdCJwGpGcTHwD8wQNzgVYzG1WT0vWDu7+d9utwes8x8efn7ne4+6rw17nAhuHPDfHZdPcn3H1RjlWNcH47As+4+3PuvhK4luC8RBJJwUZlnBhWVf/ezNYOl7UBS9K2eSlclihmtj/Q7u4PZ61qiPMDMLNzzWwJ8GV6azYa5vxCxwJ/DX9utHPL1gjn1wjnINJjcK0LkARm9nfgQzlWnUlQvf5DgifiHwIXEHyxW47t67KfcZHzOwPYM9fLcixL3Pm5+y3ufiZwpplNBU4EppGQ8yt2buE2ZwKrgKtSL8uxfd2dG0Q7v1wvy7GsLs+vgEY4B5EeCjYicPc9omxnZr8jaPeH4Elko7TVGwJLK1y0ish3fma2FbAp8LCZQXAOD5nZjjTA+eVwNXA7QbCRiPMrdm5mdhSwH7C79w6qk4hzg5Leu3SJOb8CGuEcRHqoGaWfstrxDyRIWgO4FTjMzIaY2aYEiYb/qXb5+sPdH3X39dx9E3ffhOALcDt3f4Xg/I4Me23sBLzl7i/XsrzlMLPN037dH3gy/Dnx52dmewGnA/u7+4q0VYn/bBbRCOf3ALC5mW1qZmsQJLzeWuMyiZRNNRv992Mz25agivMF4OsA7v64mV0P/JegCvub7t5ds1JW3l+AfQiS71YAx9S2OGWbaWZbAKuBF4Hjw+WNcH6/AoYAd4Y1U3Pd/fhG+Wya2YHAL4GRwO1mttDdJzbC+bn7KjM7EZgNNAG/d/fHa1wskbJpuHIRERGJlZpRREREJFYKNkRERCRWCjZEREQkVgo2REREJFYKNkRERCRWCjZkwDGz5SVsu6uZfTrt9+PN7Mjw56NTs8SWePwXUrPniogMBBpnQ6SwXYHlwP0A7n5x2rqjCQZx08iOIiIFKNgQAczs88BZwBrA/wgmZWshGOSr28yOAL4F7E4QfLwAjAOuMrNO4FPAE8A4d3/dzMYBP3H3Xc1sXeAagsGn/kPavBfhfr8dHncecELSBqASESlGzSgigXuBndx9LMF03qe5+wvAxcCF7r6tu/8rtbG73wjMB74crusssO9pwL3hvm8FRgOY2ceAQ4Hx7r4t0E0Q5IiINBTVbIgENgSuC+e6WQN4voL73gU4CMDdbzezN8PluwPbAw+Ew4m3AK9V8LgiInVBwYZI4JfAT939VjPbFTi7jH2sore2cGjWulzzAhhwhbtPLeNYIiKJoWYUkcAIoD38+ai05e8Aa+Z5Tfa6FwhqKgAOTlv+T8LmETPbG1g7XH4X8AUzWy9ct46ZbVxm+UVE6paCDRmIhpnZS2n/vkNQk3GDmf0LeD1t29uAA81soZl9Jms/lwMXh+tagHOAn4f7SE/yPAfYxcweAvYEFgO4+38JklLvMLNHgDuBUZU+WRGRWtOsryIiIhIr1WyIiIhIrBRsiIiISKwUbIiIiEisFGyIiIhIrBRsiIiISKwUbIiIiEisFGyIiIhIrBRsiIiISKwUbIiIiEisFGyIiIhIrBRsiIiISKwUbIiIiEisFGyIiIhIrBRsiIiISKwUbIiIiEisFGyIiIhIrBRsiIiISKwUbIiIiEisFGyIiIhIrBRsiIiISKwUbIiIiEisFGyIiIhIrBRsiIiISKwUbIiIiEis/j/zTWCjfXIPPgAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_values = SHemisphere['Lat']\n", "y_values = SHemisphere['Max Temp']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "plt.scatter(x_values,y_values)\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Max Temperature')\n", "plt.title('Sorthern Hemisphere - Max Temp vs. Latitude Linear Regression')\n", "plt.show()\n", "plt.savefig(\"images/linear_SHMAX.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = 0.06x + 63.86\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_values = NHemisphere['Lat']\n", "y_values = NHemisphere['Humidity']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "plt.scatter(x_values,y_values)\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Humidity')\n", "plt.title('Northern Hemisphere - Humidity vs. Latitude Linear Regression')\n", "plt.show()\n", "plt.savefig(\"images/linear_NHHumidity.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = 0.16x + 73.7\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_values = SHemisphere['Lat']\n", "y_values = SHemisphere['Humidity']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "plt.scatter(x_values,y_values)\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Humidity')\n", "plt.title('Southern Hemisphere - Humidity vs. Latitude Linear Regression')\n", "plt.show()\n", "plt.savefig(\"images/linear_SHHumidity.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = 0.11x + 46.85\n" ] }, { "data": { "image/png": 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WOHL744aB+eiX359v/rIol8akiZtnSVNaBofuO5IHn3ghMK+Cro9TZkFpAxJNb+kz2VFoY3ihnU2be+kc2s5LW/sXm95paDtffu/Boc9QqUxRZVNruaVV7o2ibisriMjbcKu4/tTXEP0nbjn1+eI2XRqpqp8XkaNxS8EfjVup+lJVLbti9dSpU7US9+1Fy3q44KYVbOmNv2p+R6GdS04cWBmDwim9btGyHmbfsJzevuj8DQt/9i+W07u9fNm4DeUh6NJCm7Dg5EkDZApLf5AcpVSTf4V2YcFJkwIf5nJ5CO6FdvWfHx8U7umHjo/dGMWRu9Au9PUp2wOOn/Kmcdx4b0/o/WFpTJq4eZY0YWVQSjEflj62oeIyC0pboV1AGfAc1JLeuM+kn/Y24etlnqGOQjvve2P3oDpSlBWoqdzqUe4icq+qTq3q5jpQN9Ocqv4vbksCPycAV3m/rwJm+o7/VB1/BrpEJO6unbFZcPuqil6iAFt6+1hw+6qy4ZRet+D2VbEqfFj4cRohcPsPh13au10HyRSW/iA5Sqkm/3r7NDDcOHkIsHBJ8JZKYceDiCN3b0AjVDy+cMmayPvD0hiHRct6mD7/TvaZcxvT59/JomXh+yfGzbOkiZvXxXyopsyC0tbbp4Oeg1rSG/eZ9NMX4xna0tvH1X9+PLRsai23tMq9kTR6QuurvH1WUNV1IrKHd7ybgVvdrvWOrSsNQETOxu1xz/jxlW2w+USA+aia+8LC8R+vJK644VdDJTLVer6S++LkIUBfiMYedjxu/JUQJ65q4ijt6fZs2sIFN60ACOzpxs2zpKk0r8OujgqnluelkfdVGkbU9XHDSqvcG0lWnBWCtiUOrLWqermqTlXVqaNHV7ZCxdiujmpkG3RfWDj+45XEFTf8aqhEplrPV3JfnDwEaJfgHavDjseNvxLixFVNHJX2dOPmWdJUmtfVlFktz0sj76s0jK7OQs3llla5N5JGN0RPFU1u3vfT3vG1DNyHfS9q3AM9iNlHTaSj0F7RPR2F9h2DplHhlF43+6iJzsZdZfiFtngPfxsQdmmhTQbJFJb+IDlKqSb/Cu0SGG6cPASYNW0cQYQdDyKO3IV2CXwYCu1uoD3q/rA0lqPSnm7cPEuauHldzIdqyixuGmpJb9xn0k97Bc9QEC++vI0jDhhdU7mlVe6NpNEN0c1AcVfBM+jfU+Nm4F+8jZ8OBZ4rmvCSZOaUbi458WC6uzoQnGfZyM4C0N9bKx4ToLurI3BAsDScoOtmTulmwUmTdoRfDPv0Q8fvuK+z0MbL2/o497r7efUFv+bCRf1mmQUnT6Kro//eoMenq6PAN06ZzDfeP3nAtcVzfkeFUrn9aQ5LZ7n86+7qYKeh4Q/lyM5C6CB+WB4CA8ZMpu49itMPHb9D1naRihwVwuLyl0N3VwcLTprEN04ZmI9F+S+eefCAfPOXRVQay1FpTzdOvasHF888eFAZTH/1qMC8mjmlO/D6cmU2c0r3oDpcpF2k4vQGjb0FPZN+hg0Z+DrcaWj7AEeFopzve2N3bC2xd7uy+OH1NZVbWuXeSOrpNbcQOBzYHXgKuAhYhNvvfTzwOHCyqm7w3Le/C7wb5779IVUt6w5XqddclkjCGywLJOnRk5ZXWFo0Sx1IiqTKP65XazVxVeM5KsDq+cfEvr4RZM1rrm7OCqo6K+TUkQHXKvCJesmSBuX8/qM8iy6eeXBu5g0UZUpC1qgxkyymPYhKym3xw+srOt7shNUlcFpy3PoVpx6FXTPvlgcrDrtIcV5eKc00llMvcr0NRFaJ4w0V5Q1WqTdV2syc0p2IXHn3DsqLF1yWKa1L1TwLtXi1btzcu8OMV0nYAnz9/ZMCtaxmGsupF1nxmmsq4nhDRXkWtcK8gSDy7h2UFy+4PFHNs1CrV2u1YbfCWE69sIaoDsTpkUV5FrVqTznv3kF58YLLE9U8C3G9WiuNM07YM6d0c/ecGayefwx3z5lhjVBMzDRXB8Z2dQSuvebvTRUHo4PW4wpbu62ZespRYyl5GBsLIk65+8l7eqMoXSux2nUHw/K0q7MQOm5ULl+LcYUR9Zw1c5mlSd285hpBVr3mavX+aXbvsWZNX7Omq1LC1kqsZt1BGLxOWy1r0JXzemuV8sqa15yZ5upAErbi4YX+ounqKDTVw9GsY2B5HiOoZM27coStlVjNuoPBc9eGVL0GXZTXW57Kq9kw01ydqNaTLKjH9sq2oOU480szj4El5UHYSJL20qx0fbVy9aE0T/eZc1vF8Za7RoC758woe79RH0wjyhjNqi34MW+xbJF0nYsqx1rWHaz2eFL3GvXDGqKM0czaQhHzFssWSde5sLUSa113sNrrk7rXqB9mmssYlXpe5RHzPMoWSde5YjnG9ZqrtD7UUn+s7mUT85rLGOZ5ZTQaq3OtR9a85kwjyhhZ67HlZc27WslyOmuVLWxOD/TXs67OAsOGtPHclt6ycfjl6eos8HJvH1t6BzrUdHUUmHt8vwZUeo8qbNrSu2N9tu6AOKtJd+k9RxwwmsUPrx8QxtLHNgTO34uK159XYfOSslh38oJpREYordJTznI6k5iTFjSnp03cXjv+rbOTXn260CYsOHkSMHguUBD++KtJdxzZ2gQCPMt3rHgeFEbUvKWgtGWl7kSRNY3InBVyTJJzP4JoBQ8+yE46g8qzVtnC5vRsVwY0QnHDjZqHU0rvdjdvKO49/virSXeceIIaIehfDT8ojN4+DZ23lJW6k3fMNJdTGrFCdyt48EE20hlWnmEv1riyVZqGctcnHV7Y9dWUSS3lVVwNv5Iw6iVLK2IaUQT11jhqoRE9sVaZc5GFdIaVZ9gq7XFlqzQN5a6vNLw2ESox/hfDr6ZMaimvYj5XEsbYro5M1J1mwBqiEIo91J5NW1D6e6hZaYwa0YtvlTkXWUhnWLn1qdYkW9icniDC5vmUhlcqTxRh+24F4U9XNWUSR7awrCiuhh83X4uyZKHuNAPWEIWQddtvI3pi1a6dlmVNMogsrBEXVm5FWaqVbeaUbhacPImujsKOY2Ev452GDikbbmlejews0FEY/BqJavpGdhZ2yFPURErTVU2ZBN1z+qHjB/z/xvsnc/qh43fE2y4yYGv2mVO6B+SVn3aRQbJkoe40A+Y1F8I+c24LNClkZf/5rHp6ZVWurNPIfGtE3c768xNFK9ThrHnNmbNCCJXONk9izkMl8w+yNt+oSLmVlI1gGlmejVi9I+k4qn1Wqrkvq89WM2MNUQizj5oYe//5ajzYkvB6y+JKz1nwQMsrjSrPSup2FuKo9lmp5RnL4rPVzNgYUQiV2H7jjif5x07Ou355psegqsW8iLJPI8Y1koyj2vHarI/zGv2YRhRB3F5RHC2gtHcW5k2Ud82hEb1to3Ya0eNPKo5qtWzTzvODNUQRxLUvx7GHx51dnnfNoVL7uq3TlR2yWhbVjje1wkr2zYI1RCFUYl+OowXE6YU1i+YQtyfciNUhjHhkuSyq1bJNO88PNkYUQiX25Tj28LBeWNDchFbBbPjZIctlUe14k83xyQ+mEYVQqX25nBYQ1jtr5QfDbPjZIetlUe14k3m/5QNriEKo166VWbTBp4XZ8OtDNWM9WS6LrI5dGclhDVEI9bAvW+9sIGbDT55qx3qyWhZZHrsyksPGiEIw+3L9sTxOnmrHerJaFlkeuzKSwzSiCEyDqT+Wx8lSy1hPFssi62NXRjKk0hCJyGeAjwAKrAA+BIwBrgVGAfcBH1TVrWnIlxZmCzdqJStrvCUVR5bHrozkaLhpTkS6gU8BU1X1dUA7cCrwVeCbqro/sBE4q9GypUnW9z8y8kGS++M0ok6Wi8P2+2kN0hojGgJ0iMgQoBNYB8wAbvDOXwXMTEm2VDBbuJEEaazxVsv+U+XiyOrYlZEsDTfNqWqPiHwNeBzYAtwB3AtsUtVt3mVrgcCaJiJnA2cDjB8/vv4CNwizhRtJ0cg13mr1aosTRxbHroxkScM0NxI4AdgHGAvsBLwn4NLAVUFV9XJVnaqqU0ePHl0/QRuMrVptZI04dbJWTd7qvQHpmObeAaxW1fWq2gvcBBwGdHmmOoC9gCdSkC01zBZuZI04dbJWTd7qvQHpeM09DhwqIp0409yRwFJgMXASznPuDOBXjRKoEs+genkR2coLRtaIUydr9Wqzem8AiIbsi1PXSEXmAacA24BlOFfubvrdt5cBp6vqK1HhTJ06VZcuXVqTLJXsT98Ke9kbRiXYM5FPROReVZ2athxFUmmIkiKJhmj6/DsDe3TdXR3cPWfGgGOT593Bpi29sa41jFbB5r/lj6w1RC2/skJcG/eiZT2BjVBUGIbRCphXm1ErLd8QxbVxR3kB7dpRYPr8O2P3CJPoQabdC71w0QoWLllDnyoi0DGkjS292yNlKZX5iANGs/jh9an3pIPyEmoft4hTRuWuqbWcw9I29+YHd3SsRnYWuOi4g5g5pXvA9bt2FBCBTZt7K4q7ljHX0jpxxAGjue2v69i42cna1VFg7vEHAeHl4w+zc2g7m7f2DXDBHdlZQBWe2xI/XVmqu2k/+/Wg5U1zcW3c+8y5LdifHCi0C719/WejbORJ2NTTtstfuGgFV//58dDzQbIEyRznvnoTJFehXUChd3u8Mo0bbmkY5a6ptZzD0tbXp2wvubbQLpzypnHceG9PaBnFibvWMdc4tAHtIc8cUHGY5dKVpbqb1LOfNdNcy6++HXfmdpgXUJsw4IGA6HkUSaygkPYqDAuXrIk8HyRLkMxx7qs3QXL19umARgjqU0blrqm1nMPSVtoIFY8vXLImsozixF1OZv8qDOddv7ziRghgO+HPXJx6Vkq5dGWp7qb97NeLljfNQTwbd9h+LWEVtNL5FZWMM6W9CkNfDC26VJa4sjV6vC2JfK/kWv/xctfUWs6V5mU15Rr3/BObtgzqzceJrxJqqTtR92ap7qb97NeLlteI4hKmOXVXODO83PE463alPRu9XaTsNaWyxJWt0TPqK4kviWv9x8tdU2s5V5qX1ZRr3PO7dhSq1oDiMraro+r6E3VfJfldy7p7ceOo5HhesIaoAmZO6ebuOTNYPf8Y7p4zg5lTuiueGR51fdzVjtOejT5r2rjI80GyBMkc5756EyRXoV0otA18KVcqW5wyKndNreUclragh77QLsyaNi6yjOLEHRhnm/DS1m2JaUBteON4AbLFqWellEtX3Lp7xAGj675aedrPfr1onzt3btoyVM3ll18+9+yzz05VhgPG7MJeIztY0fMcL768je6uDr543IGhpr6o68+6aikbNg/cgmnbdmVFz3Oc9ZZ9qo4zaWYc8CqeefEVHux5HgVEoLPQxrbtGipLkMwnTB7Lsy9uTSUNUXLNPf4g3nXQnjXlb5wyKndN8fyfH3mWl7e5kZ0Rw4dw+MQ9OGDMLlWn7ajX7TkgzJGdBb783oP5+BH7Dbi+q6NAx9B2XundHjsPguIUgZe2hmtCRU0sqE6cMHksazZs5uVeJ2tXR4FL3vd63nVgcPmUxr/T0Ha2lYwnjewsMHxIO69si5euuHX3miWPx3p+ayGpZ3/evHnr5s6de3kiQiVAy3vNZYkwzzwBVs8/ptHiGBkgbQ/JJIjyOM1bWqLI0/ObNa85c1aokTCf/mp8/cPmNHV1FuolvpFxoryk8vLyDqvX7SJN0wiB7SZbCzZGVANhYzoXLlpRla149lETB9m+AV58eZvt1NqiNIOXVNi4xtffP6lpGiFo3vGbRmANUQ2E9VaD5mPE8fWfOaWbnYYOVlJ7t2vu5wkY1ZFHL6lSzzGgJXZZtd1kq8dMczUQ1isN8w6K04t9ztazM3yEzV/Lai87bMfWS048uCUWBrZ196rDNKIaCOuVhs3HiNOLzWMP2KgfeetlN+vMf6O+mEZUA2G91fe9sXvQml1xe7F56wEb9SdPvexmGNMyGo81RDUQtbvk1L1HVbVCru1YaeQZ8xwzqsHmERmGkRjNMO+pSDNut1DE5hEZhtG0NItGH+Z0AeQuLXnAGiLDMBIlT2NaYTTDROI8YV5zhmEYJZjTRWOxhsgwDKMEm0bRWKwhMgzDKMGW62ksNkaUIs3slWMYSdPI56VZnP4mwkoAACAASURBVC7ygjVEKWFeOYYRnzSel2ZwusgL1hClRBpeOeV6lKahGVnFvNiaG2uIUqLRXjnlepSmoRlZxrzYmhtzVkiJRnvllFuMstz50qX9bX8ko5GYF1tzYw1RSjTaK6dcjzLqfNgGgNYYGY3CvNiaG2uIUiJqef96aB/lepRR521pfyNt8rYdhlEZscaIROTTwBXAC8CPgCnAHFW9o46yNT1BXjn1Gqspt71E1PnPXHd/YJhmnzcaiXmxNS9xNaIPq+rzwLuA0cCHgPnVRioiXSJyg4g8LCIrReTNIjJKRH4rIn/3vkdWG36eqZf2Ua5HGXXe7PNGs2FjntkirtdcccvRo4ErVHW5SMg2pPG4FPiNqp4kIkOBTuALwO9Udb6IzAHmAJ+vIY5cUk/voHI9yrDztlmf0UyYh2j2iKsR3Ssid+AaottFZASwvZoIRWQX4G3AjwFUdauqbgJOAK7yLrsKmFlN+Hkni9qH2eeNZsLGPLNHXI3oLGAy8IiqbhaRUTjzXDXsC6wHrhCRScC9wKeBV6nqOgBVXSciewTdLCJnA2cDjB8/vkoRsktWtQ+zzxvNgs1Jyh5xNaI3A6tUdZOInA5cCDxXZZxDgDcA31fVKcBLODNcLFT1clWdqqpTR48eXaUI2SWL2ofZ041mIotWh1Ynrkb0fWCSp8GcjzOr/RR4exVxrgXWquoS7/8NuIboKREZ42lDY4Cnqwi7KciS9mH2dKPZyKrVoZWJqxFtU1XFjeNcqqqXAiOqiVBVnwTWiEix1I8EHgJuBs7wjp0B/Kqa8PNMFjUPs6cbzUYWrQ6tTlyN6AURuQD4IPBWEWkHCjXE+0ngGs9j7hHceFMbcL2InAU8DpxcQ/i5I6uah9nTjWYkS1YHI35DdArwAdx8oidFZDywoNpIVfV+YGrAqSOrDTPvZHV14bFdHfQENDpmTzfSwlaJbz5imeY8c9qNwDDv0DPAL+slVCuSVc3D1vgysoSte9icxGqIROSjOKeCH3iHuoFF9RKqFcmqJ0+S9vQsjoEZ+cLGLJuTuKa5TwCHAEsAVPXvYfN8jOrIsidPEvb0rI6BGfkiq5YDozbiNkSvqOrW4qo+IjIE0LpJ1eRE2bibdQfVrI6BJUGeyyVvNM2Y5ZYt8MQT0NMDa9e67+Jv/3+NeM0+9BC89rWNk7mOxG2Ifi8iXwA6ROSdwMeBW+onVvNSTjMIe4HlXaNo1p5s3sslb6RqOejrg6efDm4w/P83b66/LACjRjUmngYQtyGag1vmZwXwMeDXuO0gjAoJ0wzOve5+Fty+KrQ3nXeNoml6siXkvVzyRhzLwQBU4bnnwjWO4u9nnmlgKoBCAbq73WevvQb+Lv4fMwaGDm2sXCkRqyFS1e3AD72PUQNRGkBUbzrvGkWWx8BqIe/lklleeWWg6crXeMxcu5aZxf/bt8MFDZRrjz0GNxil/0dUNde/pYm7Md50YC6wt3ePAKqq+9ZPtOYkTDMoEtabzoJGUctYSMU92ZyQhXKplcTGuLZv7zddRY19vPhi8omIYsSI8trH6NHQ5pyIbcyv8cQ1zf0Y+Axupey+MtcaEQRpBqUE9abT1iiSGAtpxtnsaZdLrSxa1sPFC//MrhvWc9iLzzLmhWd47DdXsXq0ss8rm/obkPXrGytYe3twg+FvTMaOhWHDyodVATbmlw5xG6LnVPW/6ypJizBzSjdLH9vAwiVr6AvxiAnqTaetUbT6WEhYLzm1ctm6td90FTX2sW1bZDAzSXjjr9Gjy2sfu+wCNe2rWT9avZ6nRdyGaLGILABuAl4pHlTV++oiVROzaFkPN97bE9oIRfWm09QoWnksJI6nY6xy2b4dHnsMVq6Ehx927rcPPeR+b9xYzyRE8lJhOE+O2I2ndt6NdSN248kRu/PUiN340seP6m889tjDaSlNTivX8zSJ2xBN877968MpMCNZcZqfoB5Xke4M26PrORaSdZv8Zb+8h4NW/41XP7uW/Z5dw/7PrGG/Z9ew18Up7lQiMlDrCNI+xo6F4cMjg5k+/87Acu3u6uBLM1vv8W6GMb88Etdr7oh6C9IqhPWsBLh7TnYf/HqNhdTNJv/KK/C3vwVrH729FQX1m+qliGbMGDchsfSz554NM13lfYwraSw/0iGyIRKR01X1ahH5bNB5Vf1GfcRqXvLa46rXyg8DNERVxr6wnv2eWcNj/3ELdG9zjcfKlfDss3VLWyVsLgzjH7uN4x+7jePpvfblnHOOdY3Hvvu6uSE1smhZDwuuWtww7TDtscesYfmRDqIRS0iIyMdU9QciclHQeVWdVzfJYjB16lRdunRpmiJUTKkGAK7HleuNuTZv5rd33MvVN/6JSY+uYNK6v7H/M48z/rmn0pasn/3379c4DjzQfU+c6AbOy9CoMmvKumFkEhG5V1WDtuJJhUiNSFV/4H2n2uA0E5npcW3bBk8+Ge11tXatM3HF4J3eJ1Fe9SrXYBxwQH/j8drXurGPBnpdNarM8uixlfXxPSMflDPNfTvqvKp+KllxWoOqvd9UnXdV1CKJa9c23gOro4PVw7t4aufdGP3SJrYUhvGP3fbyTFjjuewrp8OrXx24XEletIBGeCzmzWPL5twYSVHOWeFe73s6cCBwnff/ZN85oxxbtvQ3FFFzPhrN2LHRy5WMHQudnbGCOj3C+ypqheDMaIgZIG/jh3nU4IxsUs40dxWAiJwJHKGqvd7/y4A76i5dmvT1wVNPldc+tjS4tzpyZPkZ56NGNXzCYC3eRs244kI15M1jK28anJFd4s4jGguMADZ4/3f2juWTp55yg9cvvNCY+IYNC24w/I3JnnvCkLjFEcyiZT0s+OHyVDSLRmg2zT4ekTftMG8anJFd4r755gPLRGSx9//tuEVQ88nq1fEaoTFjorWPsWNh553rL28MsmCvr6dmk4X0NYI8aYd50+CM7BLpvj3gQpE96V9hYYmqPlk3qWKSR/ftehE1Qz7LE2Xj0uzpyyvNrqU2K7ly3y4iIm/zfhbdsV4jIq9R1f+tj1hGpTS7vb7Z05dX8qTBGdklrmlutu/3cOAQnNecdUWrJOmeZLPb65s9fdViGonRDLTFuUhVj/N93gm8DsjQtPl8URzv6Nm0BaV/vGPRsupduGcfNZGOwsDVkZvJXt/s6auGetQjw0iDWA1RAGtxjZFRBVHzL6pl5pRuLjnxYLq7OhDc2EnWJoXWQrOnrxrqUY8MIw3ijhF9B7ftA7jGazKwvF5CNTv1Gu9odnt9s6evUmzczGgW4o4R+V3TtgELVfXuOsjTEth4h5EESdUjG2cy0ibuGNFVwEKcg8Jy4C/1FKrZsfEOIwmSqEc2zmRkgVgNkYgcDvwd+B7wX8DffC7dRoXYeIeRBEnUIxtnMrJAXNPc14F3qeoqABF5DU5DemO9BGt2bLzDSIJa65GNMxlZIG5DVCg2QgCq+jcRqWk7ShFpx4099ajqsSKyD3AtMAq4D/igqm6tJY564Lend3UWUIXntvTusK1D/dcKW7Ssh3m3PMjGzW7L666OAnOPP2hHPBcuWsHCJWvoU0UEOoa0saV3e6SMSx/bsOOedhFmTRvHxTMPLpsHeRlTSFLmRct6mHvzg2za4vJ/2JA2Xtm2fcd5wXn2dKe03l6pfCM7C1x03EGBcmRhvDKpssljvTQcsZb4EZGf4J6tn3mHTgOGqOqHqo7YbT8+FdjFa4iuB25S1Wu91b2Xq+r3o8Jo9BI/QXvn+Cm0CQj09vXnadJ76yxa1sPsG5YPiKMY94KTJ7H0sQ1c/efHQ+8vtAso9G7vv78N2B5w7emHjh/UGOVl/yA/Scq8aFkPs3+xfED+RZFk3sRJR5h8hXZhwUmTAhutNMszqfjTTkfeyNoSP3HnEf0r8CDwKeDTwEPAOdVGKiJ7AccAP/L+C26Vhhu8S64CZlYbfr0Isqf76d2ugxqIpO3tC25fNSiOYtwLbl/FwiVrIu/v7dNBL6mgRggIDCuPYwpJyrzg9lWxG6Fa4gmLu1w6wuTr7dNAOeo1XrloWQ/T59/JPnNuY/r8O0OdH5IqmzzWS6OfWKY5VX0F+Ib3SYJvAefjtpYA2A3YpKrbvP9rgcAnQUTOBs4GGD9+fELixKNau3mS9vaosJ7wPJ+Soi9AW87jmEKSMjfqnkrC8R8vVz+CSHq8spKV0pMqmzzWS6OfSI1IRFaIyF/DPtVEKCLHAk+rqn+H16Bd3ALfqap6uapOVdWpo0ePrkaEqqnWbp6kvT0qrLFdHbQnuCFeUFhh8Wd5DlSSMjfqnkrCKR5ftKyHtojyb1QZVaKdJFU2eayXRj/lTHMnAh8Hjiv5/Jt3rhqmA8eLyKM454QZOA2pS0SKGtpewBNVhl83guZt+Cm0iRuD8ZH0/KDZR00cFEcx7tlHTWTWtHGR9xfaxY1l+QirBEFh5XEOVJIyzz5q4qD8iyLJvIlKR1ELCdJiwZV7o8qoEu0kqbLJY700+ilnmvsm8AVVfcx/UERGe+eOqzRCVb0AuMAL53Dgc6p6moj8AjgJ1zidAfyq0rCTJMoDJ02vuWJYYV5zxfP18Jor5smW3j7aRehTrYtnWNIkufNp8Z40vOai0jF9/p2h45dRXnP1oBJPvKTKJm+72xoDifSaE5EHVDVwcVMRWaGqwf69cSPvb4iOFZF96XffXgac7o1NhVIvrznzwBmM5Um22WfObYG2bAFWzz+mobJYXck+efOaGx5xrmbjq6reparHer8fUdVDVHU/VT25XCNUT8wDZzCWJ9kmbCykTaThy/XYyiFGpZQzzd0jIh9V1R/6D4rIWbh155oS88AZTJCpJeq40VhmHzUxcI5bn2qox1o9sZVDjEoo1xCdC/xSRE6jv+GZCgwF3ltPwdIkC7PNs0ZxTCjouJE+xZf+edcvH1RORc3VGgYjq0Sa5lT1KVU9DJgHPOp95qnqm1X1yfqLlw7mgTOYMG+ssONG45k5pZvtIeXRytq8kX3iTmhdDCyusyyZwTxwBtMdoiV2t7CWmEVMmzfySNxFT1sOs3EPJGgMotW1xCxi5WTkEWuIjFiYlpgPrJyMPBJr9e2s0ujVtw3DMJqBrM0jMo3IMFKiGffPqTZNpfcdccBoFj+8vqnyxgjHGiLDSIFKVqjOC9WmKeg+/55azZA3RjRx9yMyDCNBmnGlimrTVG6fr7jhGPnFGiLDSIFmXL2j2jTFTXOe88aIxkxzhpEC1c73yfK4UrVpCruv0nCM/GIakWGkQDWrdxTHUnq8nXiLYyeNXtQ0jGpXJCm3z1fccIz8YhqRYaRANfN9osZgsqAVVTuHKei+IK85gOnz78ykNmjUhs0jMoyckKU9hxqN7XGULDaPyMgkWR57MBxJryNXSZmnXT9q0QbTlt0oj40RGZkfezAcSa4KX0mZZ6F+VOuRlwXZjfJYQ2Q05ZyWZiTJnU8rKfNa68eiZT1Mn38n+8y5jenz76yqEQjT+sppg1a384GZ5oymnNPSrCS1KnwlZV5L/UhqBYlqVxW3up0PTCMyqu5tGvmlkjKvpX4kpZFUqw1a3c4H1hAZofM4Nm/dZrb0CkjCBNUoKhlvqmVsKkmNZOaUbu6eM4PV84/h7jkzYmlUtttyPjDTnLHjgZ5784Ns2tK74/jGzb222GRM8raIaSVzfmrZ4yjtHWNtf6Z8YPOIck6QaypU9+BNn39n6Hbgd8+ZkbjszYTlXTA2/yeb2DwiIzGCeuGzb1gOCr3bdcexuD1zG9itHsu7YEwjMeJgDVGOCRoI7u0brOHGnfiXthmlFtKetJjnvKs3SXn6Gc2LOSvkmEp623GuzevAbhYmLeY17wwjC1hDlGMq6W3HuTbJCZONJAuTFvOad4aRBcw0l2OCJvkV2mXAGBFU1jPPoxklK+Mzecw7w8gC1hDlmLCB4KBjzfyCtPGZZEh7nC0JmiENrYi5bxu5x1yEa6cZ8rAZ0tAoWt59W0TGAT8F9gS2A5er6qUiMgq4DpgAPAq8X1U3Nlo+PxcuWsHCJWvoU6VdhFnTxnHxzIN3nF+0rGfQJFBg0LW1zvVJcq5QGHF6kkldkzSVuAjXsvVBcbO2nk1baBehT5WOQhuvbNvOdh1c7qXh9GzagggU+34dhTaGF9rZtLm3orzyy7VrRwERKg6jlCQ33atHHYjzDGzeui0yDdXKFXWfaWDJ0HCNSETGAGNU9T4RGQHcC8wEzgQ2qOp8EZkDjFTVz0eFVU+N6MJFK7j6z48POn76oeO5eObBLFrWw+xfLB8wFhN07dS9Rw0ex2kTkIGu1mE9t6BeXiX3xyFOTzKpa9KkEvmCro1LsY5UGk6cvCoXXrX5PWHObaHnHq1g07161IHAZyBgLDQMAb55yuSq5IpKD5Dp+h5F1jSihnvNqeo6Vb3P+/0CsBLoBk4ArvIuuwrXOKXGwiVrIo8vuH1V2Ydg4ZI1wXN9tuug+T6VLMFfyf1xiON1ltQ1aVLr1gdx8dedSsKJk1flwqs2v9tFKjoeRj3qQNh8uTiNELixwrhyla4XOPfmB0Pvy3p9zxOpum+LyARgCrAEeJWqrgPXWAF7hNxztogsFZGl69evr5tsfSGaYvF4HI+sPtWa5/okPVeokvv8x5O6Jk2S2PogDv66U2k45a6PE141sper73GpRx2o5d6ix2gcuYLmo5Wa3f33Zb2+54nUGiIR2Rm4EThXVZ+Pe5+qXq6qU1V16ujRo+snYBnieGS1i9Q81yfpuUKV3Oc/ntQ1aZLE1gdx8GsRlYZT7vo44VUje3fIPWHHK427lvys5N6ujkLgXK44clWivY7t6sh8fc8TqTREIlLANULXqOpN3uGnvPGj4jjS02nIFpfZR010YzURzJo2LnDGfaFNnI3bRyVL8FdyfxzirAqQ1DVpUuvWB3GZNW1cVeHEyaty4VWb30mVXT3qQOAz0C6Dnr+OQjtzjz8ocKuIOHLF1WSK92W9vueJNLzmBPgxsFJVv+E7dTNwBjDf+/5Vo2Xz0x0yN6XYQwzbOgGCvaeq9XprxFyhOF5nSV2TJrVufVCN15w/nCS85krlSsprLm7elPMSq0cdSOIZiCNX2Hy0kZ0FOocOCb0vq/U9T6ThNfcW4A/ACpz7NsAXcONE1wPjgceBk1V1Q1RY9fSay7oHmGE0mmZ/Jpo9fX6y5jXXcI1IVf+I86gM4shGyhJF1nv3pdh8BqPeJDnXKIvk7ZlvJmyJnwjysnZY3nYHNfJJK3iJ5eWZbzasIWoCmr2n2uzkRZtNek2/vKTbqD+2DUQT0Ao91WYlC3spxSVJL7E8pduoP9YQNQGlPdL9nnmca679Aiu/8T4YOxa++EXoKzM/YutWmD0b3vpW6OiAsBn1v/0tzJoFEya4a+bOTSQNdWH7dnjjG52ct946+Pyzz8LHPgZ77unSfMAB8NOflg/32mvhDW+AnXeG7m74l3+BJ54YeE0xf/yfPfccFFSeZucnuedSntJdpHTVBWs0k8NMc02Af1+iXV5+kWuuu5BHdh/PvZdewXR5Ds47z72UL744PJDNm+FHP4JDDoHDDoM77wy+7je/gb/+FY480r2Qs8yPfgQ9IS+L55+Ht73NNSbf+Q7svjs89JBrkKO4+WbXEH/iE7BgAaxbBxdeCMceC0uXQpuvb/eBD8AnP9n/f+jQQcHlTZtNagwlb+m2cdj6Yg1RE+D39jnhv6+nY9tWnv3pQo596wHuguefd5rL+efDLrsEB9LVBRs2uJ77d78b3hAtWMCi0z/LgttXcYvewC//uJrdlvXU52G88kon96OPVn7vxo3w7/8O8+fDRz4y+PxXvgKvvOIajw5PozziiPLh/vznThv67nf7j+2yC5xwAqxaBa99bf/xMWPg0EMjg2vVvZTylm4bh60vZppLmttuc73i1asHHl+92h2/+ea6RDtzSjd3z5nB+bqaXU44pr8RAjj1VNiyBX7/++hAYixwuWj5uh22fYAXXu4dbNs/9lhn5trie9F8/eswfDg8+GAlyaqe//gPmD7daW5BXHEFnHVWfyMUl95e2HXXgce6utx3FXPyWnV2ft7SnTcNLm9YQ5Q07363G5e56qqBx6+8EkaPhqOPDr+3rw+2bYv+bN8efj/Aww+7RsDP+PHQ2enO1Ugs2/4Pfwjr18MFF7j/K1c689W8eXDQQTXLUJa//tU1NF/7WvD51avh6addA3L00c5kNno0fPaz5U1zH/4w/OEPbizp+efhb39zaTviCDjwwIHX/uQnLuxdd4WTToLHHhsUXJLjLnkib+m2deXqi5nmkqa9Hc480zVEF13EjjVdrroKPvhBGBKR5UceWV5rOeMM16iFsXFjfw/dz8iR7lyNxOoZjhnjTFennQbHHecapClT4HOfiw58+/aBDW3x97Zt/cdEXB5H8clPujGc/fYLNus9+aT7Pv98py3+5jewfDl84QuufP7zP8PDPuYYl/9nneXKAtyYWqmme8IJziy3116uIZ43zzmCrFgxSKNq1bkreUq3fxy2SJY1uLxhDVE9+PCH3RjEXXe5nvLixa43/KEPRd/3gx/ACy9EX7P77uXjDzKxqcYyvZUjtm1/1iy46Sb34m5rcy/6cg3Il77kXtilFAr9v/feO3rM6Npr3VjNLbeEX1Ns4A46yGlvADNmuLz/ylfcuFRnZ/C9ixfDOefApz8N73kPPPWUu/6974X/+Z/+NF56af89b32ra6wmT3aa2rnnhstmJELSc5Rs1YX6Yg1RPdh3Xzj8cPfSOeII933IIeXNUvvtV36coa2MNXXkSNi0afDx554L1pQqpKKe4axZcMMNcPzxsP/+5QM/+2w3vlTk1lvh8ssHahvDhoXf39vrXNA//3nX2Gza5MxnAC+95BqaESNg1Ch3rNQ5YcYMp8X+859w8MDtvndw3nkuPV/9av+xyZOdOfRXv4ITTwy+73Wvg4kT4b77wuU3EqFeHm550uDyho0R1YuPfARuvNG5D990U3ltCJxprlCI/nz4w9FhHHDA4LGgNWvci7h07KgK/LZ9gBHDC8G2/eefh898xpnkbr4Zbr+9fOBjx8LUqf2fCRPcGIv/WFgDAS6Na9e6sZ6RI91n0iR37tRTnSwAr351oCv1jk5AVGP/8MOu4fEzcaJzevjnP8unMQGt1IgmbBzzvOuX29yfjGIaUb048UQ3TnHqqa53fuqp5e9JwjT3nve4+S3F3j/Adde5F+Xb3x5P9jLs6Bl+rcBZb9kHgnqJ557rnC/uvNPlw0c+Ag88MNjjLEl23tmZzvw8+aTTzL7yFafxgGuE3vnOwS7qv/udM8ntt194HHvvPVirWbnSeQhOmBB+3wMPOJPhxz4WOzlGdYSNY/ap2tyfjGINUZWUtUEPH+4G67/3PfciDDGLJb7e1jnnwLe/7RrCz38eHnnEjWF89rMD5xDtt59rmH784/5j//3fTqu4/373/4Yb3Peb3uRewODGuu65x/3eutVNAr3hBthpJ9cIgjOpXXGFC6+rC77zHbZMPJDFM97PJ975qfrZ14cMcSZRP8XxpIMPhmnT+o9/8Yvwlrc4TXXWLOdpN3++c/sumv8ee8xpTz/5iVs9AVz+fuYzTnsrjhF96UuuESp6RN52G1x9tTMzjh3rtKiLL3bei2eemWyaY9JK67qFjWOCzf3JKtYQVUFsG/TMma4hCjGn1cWWPXKk69n/2785j7WuLvfiLF2KZ9u2wcv+/Ou/DnQxPvlk933FFf0v0MWLB5oZf/EL9yk6EWzY4MZ6PvpR58oOLHpsC7858uNcdt1FzNhnGr/bb1r6PdNDDnEODRdc4Cap7rGHmwBbdDkHZ6rr6xvoyfepTzmN6vvfh8suc/n7lrfAJZe4xhhg3DjnHn7uuW6carfdXF585SvhE4rrSKutChA0junH5v5kj4ZvjJck9dwYL4rp8+8M3b317jkz+g+cf74zixUns1YbTgappIed53RWQ1LaR1LhtFr+g8u7865fTl/A+62Z0x2Xlt8YrxkoO5dm1Spnsvr+950XVsjgd15na1faw85rOqshKe0jSS2mlfK/SDGPbO5PPjCvuRCiVtotO8v6Yx9zC14efbQz5YSQ19nala6c3Oh0prlKclKrSicVTlTa/fnfjCtL5231hlbGNKIAyvVGy86lueuuWPHkdbZ2pT3sRqYz7fGQpLSPJMJZtKyH2b9YTpDxvdAmO/I/7TyrJzb3Jx+0rEYU1QMs1xtNqqeV1x5bpRpOI9OZ9j43YXmwa0ehIo0jCS1ywe2r6N0ePAa88/AhA1YLyNveQEZz0ZIaUbkeYJzeaFI9rTz22KrRcBqVzrTHQ4LyptAmvLR1G5u29ALxNI4ktMioNG/a3Fv2umYeQzKyRUtqROV6gHkdu0mSKI2xVMPp6igwvNDGZ667P/XxhbTLLkj723n4EHr7Bmom5TSOJLTIqDT7z9WSZ3kbW8qbvK1CS2pE5XqAeR27SYo4YwZFDSdr4wtZKLtS7W+fObcFXldO46hVi5x91ERm/2L5IPNcoV0G5Ee1eZa1si9H3uRtJVpSIyrXA8zr2E1SVDJmkLXxhSyWXVpa2swp3Sw4eRJdHf2rl4/sLLDgpEkD8qPaPMta2Zcjb/K2Ei2pEcXpAeZx7CYpKhkzyOL4QtbKLk0tLW5eVJNnWSz7KPImbyvRkg1RHvcWaeRaYbH3HKrw2lYlj/UtDnkr+3LyttJ6fFnDlvjJAaW2bXA96nqZnCqJr9GyGdkhb2UfJS8Er8KQ1bTUii3xY1RMlG27Hg9JJT34ONem0dO03m1jGDakbUfdHNlZ4KLjDspsPkfV1enz70z8GbM6GB9riHJAGrbtSsYMoq5Nw1PJvKPqT5B28XLv9og7skFYXU36GbM6WBnWEOWALNnii728nk1baBehT5XuiN5eHG0u6Z5jtRpkHDkuXLSChUvW0KdKuwizq8/NnwAACs9JREFUpo3j4pkHD7q/q7OAKmza0jsgn444YDSLH14/6Jog/Pm6aFkPn7/xr7yyrf9lP/3Vo7jmo2+uSP6kaLSWXg2V5EfSz1ge8idLWEOUA7IwNwYG9/KKS+xH9fbK9TTr0XOspncbR44LF63g6j8/vuOePtUd/6fuPWrA/Rt9Kxf488l/v/+aIIoyLH1sAz9f8jilq/Xc/c8NnPbD/+Oaj7654T3wrHugVZofST9jWc+frJEpZwUReTdwKdAO/EhV50ddn5azQrONefh7+QJ0Dm1n89a+HfEAO7SgKIL2eSm3F06586XpnrBbB39+ZGOoRjLvlgdDX/AigLperl8z2bWjwPMv9w560QMIbp2457b0Bi4emgUenX8Mk+fdEahZVbr3Ttx6lvQeR5Vq2uUIk69dhO2qsccyIXz8Myqvsr4HlDkrhCAi7cD3gHcCa4F7RORmVX0oXckGkpbtt15zY0p7+Qq8tLU/bbNvWA5K6OKZfoJ6e+V6mlE9x6C89j/cpRrJ7BuWD1pKx0+xz1WqmYSZxsDlR9T5LLBoWU+ojJWu1h23biepQVSjaZcjLN1RYZc+Y1H5AUTmVVasGHkhSysrHAL8Q1UfUdWtwLXACSnLNIhmm529cMmayPO9fRqrEYJge3q5WftRqw4E5XUQC5escStNRzRCzUxU3at0te64dTvJFSyiyrnaZytOusuFHZUfjVqhv1XIjEYEdAP+t+JaYFrpRSJyNnA2wPjx4xsjmY9ms/0GbaVcDVG9vShtLqrn+Jnr7o8Vd59qbvM/CaLSnsRq3WHHk9LSy5VdNWUbVK8qDbuaZ70eK/S3AlnSiCTg2KC3pKperqpTVXXq6NGjGyDWQNJe3Tlp2iUo2ysPo9reXlTPMW6etovkNv9rpburIzTtIzsLiazWXe+8LRd+NfGX1quweh53hfLS4832HkibLDVEa4Fxvv97AU+kJEsos4+aSEehfcCxPNt+Z00bF3m+0C4U2sIbq45CO19//6Saen4zp3Rz95wZrJ5/DHfPmbEjrKC8DmLWtHHMPmoihfbaG9VydBTa+dYpk/nWKZNDZSuXZ9VSGmSx3oXVyYuOO6ii8NOq21HlXEv8/nr19fdPqjhtUfnRbO+BtMmSae4eYH8R2QfoAU4FPpCuSINptnXDih5ncbzm/HNfntvSW/e0B+V1lNccEOo111loY1ihnU2bewO95kTcZnFdnQVe7u1jizc5c6eh7RTa20LTG+bp5T/XJgzwyPOHuWtHgd6+7TscRIIopnPq3qMi612tdTKtuu2PNymvuag44qYtzj3N8h5Im6y5bx8NfAvnvv0TVf1y1PWtstacYRhGkpj7dgSq+mvg12nLYRiGYTSOLI0RGYZhGC2INUSGYRhGqlhDZBiGYaSKNUSGYRhGqmTKa65SRGQ98FiFt+0OPFMHcZIgy7JBtuUz2arDZKuOLMsG5eXbW1UbvyJACLluiKpBRJZmyW3RT5Zlg2zLZ7JVh8lWHVmWDbIvXylmmjMMwzBSxRoiwzAMI1VasSG6PG0BIsiybJBt+Uy26jDZqiPLskH25RtAy40RGYZhGNmiFTUiwzAMI0NYQ2QYhmGkSks1RCLybhFZJSL/EJE5KcvyExF5WkQe8B0bJSK/FZG/e98jU5JtnIgsFpGVIvKgiHw6K/KJyHAR+YuILPdkm+cd30dElniyXSciQxstm0/GdhFZJiK3ZlC2R0VkhYjcLyJLvWOpl6snR5eI3CAiD3t1781ZkE1EJnr5Vfw8LyLnZkE2T77PeM/CAyKy0HtGMlPn4tAyDZGItAPfA94DHAjMEpEDUxTpSuDdJcfmAL9T1f2B33n/02AbcJ6qvhY4FPiEl1dZkO8VYIaqTgImA+8WkUOBrwLf9GTbCJyVgmxFPg2s9P3PkmwAR6jqZN88kyyUK8ClwG9U9QBgEi4PU5dNVVd5+TUZeCOwGfhlFmQTkW7gU8BUVX0dbgudU8lenYtGVVviA7wZuN33/wLggpRlmgA84Pu/Chjj/R4DrEo73zxZfgW8M2vyAZ3AfcA03CzyIUFl3WCZ9sK9lGYAtwKSFdm8+B8Fdi85lnq5ArsAq/EcqLIkW4k87wLuzopsQDewBhiF29bnVuCoLNW5OJ+W0YjoL7Aia71jWeJVqroOwPveI2V5EJEJwBRgCRmRzzN93Q88DfwW+CewSVW3eZekWbbfAs4Htnv/dyM7sgEocIeI3CsiZ3vHslCu+wLrgSs8s+aPRGSnjMjm51Rgofc7ddlUtQf4GvA4sA54DriXbNW5srRSQyQBx8x3PQIR2Rm4EThXVZ9PW54iqtqnzkyyF3AI8NqgyxorFYjIscDTqnqv/3DApWnWu+mq+gacifoTIvK2FGXxMwR4A/B9VZ0CvER6JsJAvHGW44FfpC1LEW9c6gRgH2AssBOubEvJ9LuulRqitcA43/+9gCdSkiWMp0RkDID3/XRagohIAdcIXaOqN2VNPgBV3QTchRvH6hKR4o7DaZXtdOB4EXkUuBZnnvtWRmQDQFWf8L6fxo1zHEI2ynUtsFZVl3j/b8A1TFmQrch7gPtU9SnvfxZkewewWlXXq2ovcBNwGBmqc3FopYboHmB/z5tkKE7FvjllmUq5GTjD+30Gbmym4YiIAD8GVqrqN3ynUpdPREaLSJf3uwP3IK4EFgMnpSmbql6gqnup6gRc/bpTVU/LgmwAIrKTiIwo/saNdzxABspVVZ8E1ojIRO/QkcBDWZDNxyz6zXKQDdkeBw4VkU7vuS3mWybqXGzSHqRq5Ac4Gvgbbkzh31OWZSHOptuL6w2ehRtP+B3wd+97VEqyvQWnyv8VuN/7HJ0F+YDXA8s82R4Avugd3xf4C/APnOlkWMrlezhwa5Zk8+RY7n0eLD4DWShXT47JwFKvbBcBIzMkWyfwLLCr71hWZJsHPOw9Dz8DhmWlzsX92BI/hmEYRqq0kmnOMAzDyCDWEBmGYRipYg2RYRiGkSrWEBmGYRipYg2RYRiGkSrWEBmGh4i8WMG1h4vIYb7/54jIv3i/zxSRsVXE/6iI7F7pfYaRd4aUv8QwjAAOB14E/gSgqpf5zp2Jm9OR6dnshpEVrCEyjAhE5DjgQmAobkLjaUAHcA7QJyKnA5/EzWh/Ebe69VTgGhHZglv5eCVumf5nRGQq8DVVPVxEdsNNbB6Nm3wovnhPxy3vPxS34OzHVbWv/ik2jMZjpjnDiOaPwKHqFuK8FjhfVR8FLsPt9zJZVf9QvFhVb8CtDnCad25LRNgXAX/0wr4ZGA8gIq8FTsEtUDoZ6MM1gIbRlJhGZBjR7AVc5y1qORS3Z05SvA04EUBVbxORjd7xI3EbsN3jlg+jg5QXmDWMemINkWFE8x3gG6p6s4gcDsytIoxt9FsfhpecC1pjS4CrVPWCKuIyjNxhpjnDiGZXoMf7fYbv+AvAiJB7Ss89itNwAN7nO/6/eCY3EXkPbpFPcAtoniQie3jnRonI3lXKbxiZxxoiw+inU0TW+j6fxWlAvxCRP+C2Xy5yC/BeEblfRN5aEs6VwGXeuQ7c6siXemH4HQ7mAW8TkftwWzI8DqCqD+EcJO4Qkb/idqEdk3RiDSMr2OrbhmEYRqqYRmQYhmGkijVEhmEYRqpYQ2QYhmGkijVEhmEYRqpYQ2QYhmGkijVEhmEYRqpYQ2QYhmGkyv8HIMafgsFdViYAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_values = NHemisphere['Lat']\n", "y_values = NHemisphere['Cloudiness']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "plt.scatter(x_values,y_values)\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Cloudiness')\n", "plt.title('Northern Hemisphere - Cloudiness vs. Latitude Linear Regression')\n", "plt.show()\n", "plt.savefig(\"images/linear_NHcloudiness.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = -0.06x + 38.04\n" ] }, { "data": { "image/png": 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5ocBEREREckOBiYiIiORGZr1yzOzbwJuAh919n2jZLOACYA/gXuBv3f3xaKbVs4DDCVOln+DuDSfsy9LSlaOccdkdrB4bZ87MIRYt2JOF+w/3Mkkd1yiP7ZbB0pWjfPonN7P22c3zjE0bnMT4xMZN+wPaLud+OFcVZctrp/LT7XJJe7zKdqNj4wyYscGd4ZlDvPbFs7ny9jWbXl/9f+W9ceoltzA2PgHAJIONDsMlOO8i9WQ28quZvYYwg+r3YoHJ/wUec/clZrYY2MHdPxlNbf8PhMDkQOAsdz+w0TFGRkY8i+7CS1eOctLFNzM+sfkLdWhwgNOP3rc0HwaN8thuGSxdOcrHL7qRDRtrX1+DAwYOE7Ftmi3nfjhXFWXLa6fy0+1ySXu8pO3SGhwwNmxwNtZY327+zOw6dx9p6cUiGcvsUY67/xZ4rGrxUcA50d/nAAtjy7/nwdXATDPbhR4547I7tvowGZ/YwBmX3dGjFHVeozy2WwZnXHZH3aAEYGKDbxGUNHuMTqSzSPKa16UrR5m/ZDnzFi9j/pLlLF05mup1ncpPt8sl7fGStktrok5QUut4ImXR7TYmO7v7gwDR752i5cPAqth2D0TLtmJmJ5rZCjNbsWbNmkwSuXpsvKnlRdQoj+2WQTtl1cxr++FcVeQxr5VagdGxcRwYHRvnpItvThWcdCo/3S6XtMfL+ryU8RoXgfw0frWEZYm32+5+truPuPvI7NnZjKg8Z+ZQU8uLqFEe2y2Ddsqqmdf2w7mqyGNe26mt6FR+ul0uaY+X9Xkp4zUuAt0PTB6qPKKJfj8cLX8A2C223a7A6i6nbZNFC/ZkaHBgi2VDgwObGqSVQaM8tlsGixbsycCkpHhzs8EBY7Bqm2bLuR/OVUUe89pObUWn8tPtckl7vKTt0hocsLofzoMDVsprXAS6P1fOJcDxwJLo909jyz9kZucTGr8+UXnk0wuVBmVl6v1QrVEe2y2DynZZ98rph3NVkce8zpk5xGhCEJLmbr5T+el2uaQ9Xny76l45j699hnUTya1IhmPvjY9ecENi1fH0KZNLeY2LQLa9cs4DDgZ2BB4CTgGWAhcCc4H7gbe5+2NRd+GvAIcRugu/x90bdrfJqleOiKRTtp5C3ZK23OYtXpYYmBhwz5IjWj6+euVInmVWY+Lu76ix6tCEbR34YFZpEZFs5LEWpwjSlls7NVIiRZVZjUk3qMZERMosqxop1ZhInnW7jYmIiKSkGinpRwpMRERybOH+wwpEpK8oMBERyUjZ5jYS6QYFJiIiGahuH1IZFRdQcCJSR15GfhURKZW8zm0kkncKTEREMpDHuY1EikCPciQVPSsXaY7GIBFpjWpMpKF2ZpAV6Vd5nNtIpAhUYyIN1XtWrlqT4lItWLY0BolIaxSYSEN6Vl4+6jHSHRqDRKR5CkxalJe7zSzTUdl3rUkLmnlWnpfySqtReruZnyyOpVqwoGjXpUg/UGDSgrzcbWaZjqQ5OuKaeVael/JKq1F6u5mfrI6lWrDiXZci/UKNX1uQl/EJskxH0r4rhmcONTWJWF7KK61G6e1mfrI6Vq3arn7qMVK061KkXygwaUFe7jazTEetfRhw1eJDmrqjzEt5pdUovd3MT1bHUo+R4l2XIv1CgUkL8nK3mWU6OrnvvJRXWo3S2838ZHWshfsPc/rR+zI8cwij+VqwMijadSnSLxSYtCAvd5tZpqOT+85LeaXVKL3dzE+Wx1q4/zBXLT6Ee5Yc0XQtWBkU7boU6Rdq/NqCvIxPkGU6OrnvvJRXWo3S2838FK3sikRlK5JP5l6rM2j+jYyM+IoVK3qdDBGRQjGz69x9pNfpEEmiGhMRkRzTWCvSbxSYiIjklMZakX6kxq8iIjmlsVakHykwERHJKY21Iv1Ij3JERHqo0oZkdGycATM2uDMctSWZM3OI0YQgRGOtSJmpxkREpEcqbUgqwceGqJdkpS3Ja188W2OtSN9RjYmIdEWvepfkuVdLvTmpxic2cOXtazj96H1zm36RLCgwEZHM9ap3Sd57tTRqK7J6bJyF+w/nIq0i3aJHOSKSuV71Lsl7r5ZGbUWS1i9dOcr8JcuZt3gZ85csZ+nK0aySJ9ITCkxEJHO96l2S914tSfP1VCS1JYm3SXE21wApOJEyUWAiIpnr1Uy+eZ9BOD7LM8CAGVB7tue81wCJdILamIhI5hYt2HOLth7Qnd4lvTpuM5ppQ5L3GiCRTuhJjYmZfdTMbjGzP5rZeWY21czmmdk1ZvYnM7vAzKb0Im0i0nnxmgGjdo1AWY6blbzXAIl0QtdnFzazYeD3wF7uPm5mFwKXAocDF7v7+Wb2deBGd//3evvS7MIi0k+qexlBqAFqNtjS7MKSZ716lDMZGDKzCWAa8CBwCPDOaP05wKlA3cBERKQZeR7TJI1KWoucB5FGuh6YuPuomX0euB8YBy4HrgPG3H19tNkDQOI7zcxOBE4EmDt3bvYJFpFSyPuYJmlpXBMpu663MTGzHYCjgHnAHGA68MaETROfMbn72e4+4u4js2fPzi6hIlIq6tEiUgy9aPz6OuAed1/j7hPAxcCrgJlmVqnB2RVY3YO0iUhJqUeLSDH0IjC5H3iFmU0zMwMOBW4FrgSOibY5HvhpD9ImIiWlHi0ixdD1wMTdrwF+BFwP3Byl4Wzgk8DHzOwu4DnAt7qdNhEpr6RRVvM2pomI9KhXjrufApxStfhu4OU9SI6I9AH1aBEpBo38KiJ9Qz1aRPJPgUkfSxrTAXRH2Yx4GW4/NIgZjK2b6HnZnbz0Zs69+v5N/xtw3CvmMrL7rFymtxuaGcMk7bZFHxdFJI+6PvJrJ2nk19YljSA5OMnAYGLD5muilVEl+0VSGcb1quyqg5K4SQYba7zly3yumxkxNe22nRqFtRc08qvkmWYX7lNJYzpMbPQtghLQOA/1JJVhXCfKbunKUeYvWc68xcuYv2R5quntz7tmVc11tYISKPe5bmYMk7TbalwUkWzoUU6fambsBo3zkCxNubRTdq2OVLqhjVrQsp7rZsYwSbutxkURyYZqTPpUM2M3aJyHZGnKpZ2ya/WOfMCs5WOW6VzHa5sm1SiTpPymHe9E46KIZEOBSZ9KGtNhcJIxOLDlB7jGeagtqQzj2i27Vu/I33HgbjXXTaoTs5TpXFdqm0bHxnGSa5Fq5TfteCcaF0UkG3qU06dqjemQtKxXDfny3uOhugw73ctlzswhRhOCkEZ35J9buC9AX/fKqdX+Z8CMje5185t2vBONiyKSDfXKkVwqco+HTlEZtG7e4mWJs4AacM+SI7qdnNxRrxzJMz3KkVxSj4dwR3760fsyPHMIA4ZnDikoSUntP0SKS49yJJfU4yHQSKWtWbRgz8TaJrX/EMk/1ZhILumOV9qh2iaR4lKNieSS7nilXaptEikmBSaSS832eMh7Dx4REUlHgYnkVto73lZHSBURkfxRYCKFUK9GpF4PHgUmIiLFosBEcq9RjUizPXj02EdEJL/UK0dyr9GYJs304KkeqrwS5KSZtVdERLKnwERyr1GNSDNzlmjgNhGRfFNgIrnXqEakmTErNHCbiEi+qY2J5F6aMU3S9uBpdWI8ERHpDtWYSO51chRPTVUvIpJvml1YCqUTPWrUK0f6nWYXljzToxwpjE4NpKahykVE8kuBieRepYYjqW2IBlITESkXBSaSa9W1JEnUo0ZEpDzU+FVyLWnckWrqUSMiUh4KTCTXGtWGqEeNiEi56FGO5FqtcUcgdBuO96hRb5v0VFYiklepakzM7MNmNsOCb5nZ9Wb2hqwTJ1Jr3JEvHrsfVy0+ZIugRHPgpKOyEpE8S/so573u/iTwBmA28B5gSasHNbOZZvYjM7vdzG4zs1ea2Swzu8LM/hT93qHV/Ut+LV05yvwly5m3eBnzlyxv+GWYdnA1zYGTnspKRPIs7aMci34fDnzH3W80M6v3ggbOAv6fux9jZlOAacCngF+5+xIzWwwsBj7ZxjEkZ1odhyTNuCOaAyc9lZWI5FnaGpPrzOxyQmBymZltB2xs5YBmNgN4DfAtAHd/1t3HgKOAc6LNzgEWtrJ/ya8s79QbTfQnm6msRCTP0gYm7yPUYLzM3dcBg4THOa14HrAG+I6ZrTSz/zCz6cDO7v4gQPR7p6QXm9mJZrbCzFasWbOmxSRIL2R5p645cNJTWYlInqUNTF4J3OHuY2b2LuBk4IkWjzkZOAD4d3ffH1hLCHpScfez3X3E3Udmz57dYhKkF7K8U+/kRH9lp7ISkTxLNYmfmd0E/DXwEuD7hMcwR7v73zR9QLPnAle7+x7R/68mBCYvAA529wfNbBfg1+5e9xZOk/gVS9IorkODA/pSFOkyTeIneZa2xmS9hwjmKOAsdz8L2K6VA7r7n4FVZlYJOg4FbgUuAY6Plh0P/LSV/Ut+6U5dREQaSdsr5ykzOwl4N/BqMxsgtDNp1T8AP4h65NxNaK8yCbjQzN4H3A+8rY39S05pZl8REaknbWByLPBOwngmfzazucAZrR7U3W8AkqoRD211nyJZ0AipAroORLopVWASBSM/Bl4YLXoE+ElmqRLJgVbHXZFy0XUg0l1ph6R/P/Aj4BvRomFgaVaJEskDjZAqoOtApNvSNn79IDAfeBLA3f9EjXFGRMpCI6QK6DoQ6ba0bUyecfdnK6PQm9lkoHE/Y5ECqzWzca9HSFV7h+7K63UgUlZpa0x+Y2afAobM7PXARcDPskuWSO/lcYRUzQzcfXm8DkTKLG1gspgwjPzNwN8DlxJGfxUprTyOu6L2Dt2Xx+tApMxSjfyaVxr5VfrNvMXLEp+hGnDPkiO6nRwpKI38KnmWtlfOfDO7wszuNLO7zeweM7s768SJyJY0M7CIlF3aRznfAr4AHAS8jDA42suySpSIJFN7BxEpu7S9cp5w919kmhJpinpm9KfKOda5F5GyShuYXGlmZwAXA89UFrr79ZmkSurSSJT9TfMNiUiZpQ1MDox+xxtLOXBIZ5MjadTrmaEvLJFyUe2o9Ju0c+W8NuuESHoaiVKkP6h2VPpR3cDEzN7l7uea2ceS1rv7F7JJltSjkShF+oNqR6UfNeqVMz36vV2NH+kB9cwQ6Q+qHZV+VLfGxN2/Ef3+THeSI2moZ4ZIf1DtqPSjRo9yvlRvvbv/Y2eTI2mpZ4ZI+S1asOcWbUxAtaNSfo0e5VwX/UwFDgD+FP3sB2yo8zoREWmT5umRfpRqrhwzuxJ4g7tPRP8PApf3ureO5soREWme5sqRPEs7jskcQmPXx6L/t42WiYhIAxqLRCS9tIHJEmBlVHMC8DfAqZmkSESkRDQWiUhzUk3i5+7fIYz++pPo55Xufk6WCRMRKYN6Y5GIyNZS1ZiY2WuiPx+Pfr/IzF7k7r/NJlkiIuWgsUhEmpP2Uc6i2N9TgZcTeutorhwpPbUPkHZoLBKR5qR9lHNk7Of1wD7AQ9kmTaT3Ku0DRsfGcTa3D1i6crTXSZOC0EjNIs1JFZgkeIAQnIiUmtoHSLs0FolIc9K2MfkyUBnwZBJhgLUbs0qUSF6ofYB0gkZqFkkvbRuT+Chm64Hz3P2qDNIjkitqH1Bs7bQPUtsikd5I28bkHOA8QoPXG4Frs0yUSF6ofUBxtdM+SG2LRHonVWBiZgcT5sj5KvA14M5YF2KR0lL7gOJqp31QHtsWLV05yvwly5m3eBnzlyxXkCSllfZRzr8R5sq5A8DMXkSoQXlpVgnLlDv85Cfw6KMwbVr4mT49/FT+jy+bMgXMep1q6RG1DyimdtoH5a1tkUaPlX6SNjAZrAQlAO5+ZzSRX8vMbIDQdmXU3d9kZvOA84FZwPXAu9392XaOkWTpylF+8+VzOfM7izu96/ZMmZIcFLWyLGn50BBMarUTVjEtXTnKqZfcwtj4BAA7TBvklCP3LvQHeTvtHk5eejM/uPp+4tN2Tp8ywGlv6WwNULNpTLN9K/nefmhw07mvXj5/yXJGx8YZMGODO8NV+8xb26J6NThFvp5FkqRu/Gpm3wK+H/1/HKG9STs+DNwGzIj+/1fgTHc/38y+DrwP+Pc2j7GFyl3HX2bvxazXvo8XPLqKbTc8y8jsbdhl8gZYt27zz9q14TGM/pAAABXySURBVGfdOni24/HR1p59NvyMjWV/rKxMmrQ5GGo1qGq0bGCgcToI53rRRTcysXHz1/Dj6yZY9KPQmayIH+bt3DWfvPRmzr36/q2Wr312Ax+/qHNl0mwa02zfSr6Xrhxl7bPrt1o+CVj77PpNAcuGaHb16n0uWrDnFseE3rYtylsNjkiWzN0bb2S2DfBB4CDAgN8CX3P3Z1o6qNmuwDnAacDHgCOBNcBz3X29mb0SONXdF9Tbz8jIiK9YsaLeJluo3CVVG545xFWLeziIrXsISpKCok4tG9cHWMdstx1sv/3mn5kzN/+kWTZ1akuPBtu5fp9/0qWbvoSTdOo90Gwa02zfSr5rvWaSwcY6H3nxfeapV06nP7vM7Dp3H+lE2kQ6LVWNSRSAfCH66YQvAp8Atov+fw4w5u6VW5wHgMRPADM7ETgRYO7cuU0dNLd3HWawzTbhZ4cdepuWdkxMbBkQZRFkbdzY61zCU0+Fnwce6Oph6/bPP6n+a/+76v+/TJ7Ck9tM56ltpke/p8H1L0gfZG23XWJw1ex7LM3yVt63tdbVC0qqX5entkV5q8ERyVLdwMTMbgZqvpXd/SXNHtDM3gQ87O7XRb19INTCbLX7Gsc8GzgbQo1JM8fO23Pj0hkc3PzF1WO17jCrbXXHuX59CDrGxjb/PPFE+In/n7S8smz91o8Q8mbq+meZuv5Zdlr7+OaF965se7/31Fv5r61tX2ub9ZMmwX/skBhILbnzSVazDU9OjQdf03l66rasGZrBE1O35S+DU3Dbst1VXj8LKgFSXmpwRLLUqMbkaGBnYFXV8t2B1S0ecz7wZjM7nDAh4AxCDcpMM5sc1Zrs2sb+a9JdR/9YtGDPrdqYJNnqznry5FBrlcOaq+q2FhCu3626L2/cGGqaYkHU939xAytuupcZz6xlu2fWMeMvTzPjmbXMeGYdM55Zyz7TnVkT6za/5i9/6UEOmzN548bQs+7RR7dad2w7O25Q+9SUSZM61ph94fTpLDxidvh/1qxcXqMindAoMDkT+JS73xdfaGazo3VHNntAdz+J6K0f1Zj8k7sfZ2YXAccQeuYcD/y02X03oruO/lE5p/FeOUnyeoecJPX1O2lSeNSy3Xaw224AvPugg7gjagCb1CtnVh575YyPbwqUfnPtn1j661uZePQx5k56lsPnTmOfbalfs/X00zWPOT44laGJLgRfGzeGdNRJS8uuvx7237/z+xXpsbqNX83sj+6eOFmfmd3s7vu2dfDNgcmbzOx5bO4uvBJ4V6PGtc02fpX+lbq2QaST1q/f3F6qk+2v9tsPvve9ULvSAjV+lTxrVGMytc66tm813f3XwK+jv+8GXt7uPkWSqLZMemLyZJgxI/yISCqNApM/mNn73f2b8YVm9j7aH8dEpKvy1MtCRESSNQpMPgL8xMziA6qNAFOAt2SZMBGRPMnTuCYiZVY3MHH3h4BXmdlrgUpbk2XuvjzzlImI5ITmqhHpnrQDrF0JXJlxWkREOqLTtRuaq0ake9LOlSMiUghZ1G7kdtRokRJSYCKFl/buWG0E+kMWtRsaNVqkeyY13kQkvyp3x6Nj4zib746XrhxtaTspvixqNxYt2JOhwS1nttao0SLZUGAihVbv7riV7aT4atVitFO7sXD/YU4/el+GZw5hhDmWNDifSDb0KEcKLe3dsdoI9I+s5sTSODgi3aHARAot7bN/tRHIpyza/XRilF+1RxLpHQUmUmhp7441s3T+ZDk2SDu1GxqzRKS3FJhIoaW9O+73uXLyWAPQqd4z3R6zJI9lKVImCkyk8NLeHfdrG4G81gB0ot1Pt8csyWtZipSJeuWIlFxeeyR1ovdMFnmrl668lqVImSgwESm5vPZI6sTYIN0esySvZSlSJgpMREoui3E9OqHe2CBLV44yf8ly5i1exvwly2sOhNftMUvyWpYiZaI2JiIll+ceSUntfpppx9HtMUvyXJYiZaEaE5GSK9qopc2042gmb2lrYeqpPt7MoUGmDk7ioxfc0PI+RWRL5u69TkPLRkZGfMWKFb1Ohoh00LzFy0j6VDLgniVHtLTP6loYCDUd7QRoWeyzW8zsOncf6XU6RJKoxqRgOnHXJ9ILvWw3kkVvGvXQEcmGApMC0Qy5Uq0ogWoz124WM/lm0ZtGPXREsqHApEB0hyZxRQpUs2o3klYWtTDqoSOSDfXKKRDdoUlckYZOb/ba7fQovVn0pulWD508nUeRblBgUiCaIVfiijR0eq+v3SzmSurG/Et5O48i3aBeOQVS5F4A0nnzlyxP/LIfjr7sa627avEhmaetmq7d1tQ7x+2cR/XKkTxTG5MCKdp4FJKtIg2drmu3NXk7jyLdoEc5BdOvM+SWWattCOo9Sjjjsjty99hP127zev0ITKQXFJiI9FC7bQg0dHq56TxKP9KjHJEeyqoLuB6dlIPOo/Qj1ZiI9FCWbQj06KQcdB6l33Q9MDGz3YDvAc8FNgJnu/tZZjYLuADYA7gX+Ft3f7zb6RPppl60IYi3aZk5bRB3eGJ8om77lsprRsfGGTBjgzvDfTSmRrtjiWgsEpH0evEoZz3wcXf/K+AVwAfNbC9gMfArd38h8Kvof5FSy2L49XqqR4t9fN0EY+MTdUeOjb8GYEM0xECeR5rtpHZH2C3SCL0iedD1wMTdH3T366O/nwJuA4aBo4Bzos3OARZ2O20iaXVqjpputyFIatMSl9S+pd5r+mFKhHbbAWkqCZHm9LSNiZntAewPXAPs7O4PQghezGynGq85ETgRYO7cud1JqEhMp0fj7GYbgjRtV6q3afSaso+p0W47II1FItKcnvXKMbNtgR8DH3H3J9O+zt3PdvcRdx+ZPXt2dgmUQunmLLtFvgNO03aleptGryn7mBrtTtanyf5EmtOTwMTMBglByQ/c/eJo8UNmtku0fhfg4V6kTYqn28/wi3wHvGjBngwOWM31Se1bktrB1Nu+bNptB9TtdkQiRdf1wMTMDPgWcJu7fyG26hLg+Ojv44GfdjttUkzdrsEo8h3wwv2HmT4l+QnugFli+5Z4O5jKdtA/Y2q02w6ondd3syZQJC+6PomfmR0E/A64mdBdGOBThHYmFwJzgfuBt7n7Y/X21W+T+EmyeYuXkXQVG3DPkiM6fryiT0jX7fKS1mR5nWkSP8mzrjd+dfffEz4DkxzazbRIOXR7LJBuTHefJc2/Ugz1agKLcq2JtEIjv0rh9WI+kSKPxqn5V4qhyG2ZRNqhwEQKr+g1GN2m8ioG1WxJv+p6G5NOUhsTESkrtTGRfqUaExGRHFLNlvQrBSYiIjlV5LZMIq1SYCJ9SbO99jedf5H8UmAifafTc91Isej8i+Rbz+bKEemVIs91I+nUGzFV518k31RjIn1H40OUW6MaEZ1/kXxTjYn0nSLPdSONNaoR0fkXyTcFJtJ3NNtruTWqEdH5F8k3PcqRvqPxIcqt0YipOv8i+aaRX0WkVIo++3M3aORXyTPVmIhIR+RlbBDViIgUmwITEWlb3sYG0YipIsWlxq8i0jaNDSIinaLARETaprFBRKRTFJiISNs0NoiIdIoCExFpm8YGEZFOUeNXEWlbL3rC5KUXkIh0lgITEemIbvaEyVsvIBHpHAUmIiVTpJqEVtNarxdQXvMqIukoMBEpkSLVJLSTVvUCEikvNX4VKZEijSfSTlrVC0ikvBSYiJRIkWoS2kmregGJlJce5UjhZN2GokhtNKo1mlk3T5pJa9I5Of3ofQt7ntIq8rUo0ioFJlIoWbehKFIbjSSLFuyZOLNuHmsS0qa11jk5/eh9uWrxIV1NczcV/VoUaZUe5UihZN2GokhtNJIs3H+Y04/el+GZQxgwPHOI04/eN5dfZGnTWvRz0qp+zbeIakykULJuQ1GkNhq1FGlm3TRpLcM5aUXSY656y0XKQoGJFErWbSiK1EYj7zrVPmLmtEEeXzex1fKynpNKudUyYNbF1Ih0nx7lSKFk3RtDvT06o9I+YnRsHGdz+4ilK0eb3s/Tf1m/1fLBASvlOYmXWy0b3LuYIpHuy1WNiZkdBpwFDAD/4e5Lepyk3KjcRY2OjWNA5aNph2mDnHLk3oWpuu+MLT+Yxyc28JELbuAjF9wAwLTBSYyv34h7uLt8x4G78bmF+6be+9TBSZue7Ztt+Vx/4f7DLF05yqmX3MLYeLiLn2Sw0UMbiepagaUrR/nMz27Z4o6/+pzFz21FPN3VNQ+vffFsrrx9DavHxpk5bRB3eGJ8YtO6ZTc9mFjD0EhS+k9eejPnXbNqiy/D+Ha1akVqtY+onKd4GVSXJ8DQ4CQMWDexMTGtkydZqmu+3VqbWq9PWg7tzxWUVG7VhktaUyRSYZ6T6NvMBoA7gdcDDwB/AN7h7rfWes3IyIivWLGiSynsnerW+dUGB4wzjvnr0gcnS1eOsuiiG5nY2Pw1+65XzG0YnDQq56HBAd760mEuuHZVzTQMDQ5sasC5dOUoi350IxMbtt62cs6Ausec//xZXH//Ew2/rDolnv6Tl97MuVffX3O7t750mB9fN7pVr5rTj96Xj15wA43O0uCAcezLdqtbnvV88dj96l7zSecznr9Gar0+Kd+DAwbOFvlo5lgV8xYvq1turewziZld5+4jbe1EJCN5epTzcuAud7/b3Z8FzgeO6nGacqHRXdTEBu+LlvpnXHZHS19gAOddsyrV/uuV8/jEBs67pv6XaLx25YzL7kgMSmDzOWt0zKv++7GuBSWwZfrrlVmlLGr1GknT/mNigzcsz3oaXfPt9mqp9fqkfE9s8K3y0UoPmnrlNmCW2x5WIp2Up8BkGIh/Ej4QLduCmZ1oZivMbMWaNWu6lrheStP7oOw9FKC9PKZ5Lp9m/83sp9H+Vo+N5/K8VdLUKK+11q8eG0/d/qOd9hJpyreV1zXarpk0N3t+65XbRncFJdIX8hSYJDU13+oTwN3PdvcRdx+ZPXt2F5LVe2nuPsvaQyGunTym6cmQZv/N7KfR/ubMHMrleaukqVFea62fM3OIhfsPM3NosOGx2ulhkqZ8W3ldo+2aSXOz53fh/sPsMC253PJ4rYhkIU+ByQPAbrH/dwVW9ygtuZLUUySurD0Uqi1asCeDk1r7InvHgbs13KZROQ8NDvCOA3erm4Z4D55FC/YMbQ8SVM5Zo2POf/6suus7LZ7+emVWKYt6PZhOffPeDa/bRuWZJp21tNvDqtbrk/I9OGBb5aPV3lynHLl1ualnmPSTPPXK+QPwQjObB4wCbwfe2dsk5UOl+rbfe+VU8ljdg6Naq71y4uW8emyc7YcGMYOxdRNb9LIY2X1Wql45ld+NeuVUjpm3XjmVMqvXK2dk91k1e6KkvW6ryxO27pUzfcoAgwOTNuUzTY+X6vPZbE+Zeq9Pync7x+pkukWKLje9cgDM7HDgi4Tuwt9299Pqbd8vvXJERDpJvXIkz/JUY4K7Xwpc2ut0iIiISG/kqY2JiIiI9DkFJiIiIpIbCkxEREQkNxSYiIiISG7kqldOs8xsDXBfr9ORwo7AI71ORIbKnL8y5w3Knb8y5w3ay9/u7t4fI1RK4RQ6MCkKM1tR5q55Zc5fmfMG5c5fmfMG5c+f9C89yhEREZHcUGAiIiIiuaHApDvO7nUCMlbm/JU5b1Du/JU5b1D+/EmfUhsTERERyQ3VmIiIiEhuKDARERGR3FBgkiEzO9XMRs3shujn8Ni6k8zsLjO7w8wW9DKd7TCzfzIzN7Mdo//NzL4U5e0mMzug12lshZl9Nkr/DWZ2uZnNiZYXPn9mdoaZ3R6l/ydmNjO2rvDXpZm9zcxuMbONZjZSta4M+TssSv9dZra41+kR6TQFJtk70933i34uBTCzvYC3A3sDhwFfM7OBXiayFWa2G/B64P7Y4jcCL4x+TgT+vQdJ64Qz3P0l7r4f8HPgn6PlZcjfFcA+7v4S4E7gJCjPdQn8ETga+G18YRnyF6X3q4TrcC/gHVG+REpDgUlvHAWc7+7PuPs9wF3Ay3ucplacCXwCiLegPgr4ngdXAzPNbJeepK4N7v5k7N/pbM5j4fPn7pe7+/ro36uBXaO/S3Fduvtt7n5Hwqoy5O/lwF3ufre7PwucT8iXSGkoMMneh6Iq82+b2Q7RsmFgVWybB6JlhWFmbwZG3f3GqlWFz1uFmZ1mZquA49hcY1Ka/EXeC/wi+rtseatWhvyVIQ8idU3udQKKzsx+CTw3YdWnCdX8nyXcbX8W+DfCF4ElbJ+7ftsN8vYp4A1JL0tYlru8Qf38uftP3f3TwKfN7CTgQ8ApFCR/jfIWbfNpYD3wg8rLErbPXd4gXf6SXpawLJf5q6MMeRCpS4FJm9z9dWm2M7NvEtoqQLjL2S22eldgdYeT1rZaeTOzfYF5wI1mBiH915vZyylI3iD9uQN+CCwjBCaFyF+jvJnZ8cCbgEN982BGhcgbNHXu4gqTvzrKkAeRuvQoJ0NVbQ/eQmiUB3AJ8HYz28bM5hEaUl7b7fS1yt1vdved3H0Pd9+D8GF5gLv/mZC3v4t6r7wCeMLdH+xlelthZi+M/ftm4Pbo78Lnz8wOAz4JvNnd18VWFfq6TKEM+fsD8EIzm2dmUwiNeS/pcZpEOko1Jtn6v2a2H6Gq9V7g7wHc/RYzuxC4lVCV/kF339CzVHbWpcDhhIaF64D39DY5LVtiZnsCG4H7gA9Ey8uQv68A2wBXRDVeV7v7B8pyXZrZW4AvA7OBZWZ2g7svKEP+3H29mX0IuAwYAL7t7rf0OFkiHaUh6UVERCQ39ChHREREckOBiYiIiOSGAhMRERHJDQUmIiIikhsKTERERCQ3FJiIRMzs6Sa2PdjMXhX7/wNm9nfR3ydUZiNu8vj3VmZpFhHpVxrHRKQ1BwNPA/8J4O5fj607gTCYnkbkFBFpkgITkTrM7EjgZGAK8ChhQr8hwoBrG8zsXcA/AIcSApV7gRHgB2Y2DrwSuA0YcfdHzGwE+Ly7H2xmzwHOIwwEdi2xeVCi/f5jdNxrgP9VtMHARERaoUc5IvX9HniFu+9PmGL+E+5+L/B14Ex338/df1fZ2N1/BKwAjovWjdfZ9ynA76N9XwLMBTCzvwKOBea7+37ABkJAJCJSeqoxEalvV+CCaN6jKcA9Hdz3a4CjAdx9mZk9Hi0/FHgp8IdoyPgh4OEOHldEJLcUmIjU92XgC+5+iZkdDJzawj7Ws7l2cmrVuqQ5IQw4x91PauFYIiKFpkc5IvVtD4xGfx8fW/4UsF2N11Svu5dQAwLw1tjy3xI9ojGzNwI7RMt/BRxjZjtF62aZ2e4tpl9EpFAUmIhsNs3MHoj9fIxQQ3KRmf0OeCS27c+At5jZDWb26qr9fBf4erRuCPgMcFa0j3gD1s8ArzGz64E3APcDuPuthAa3l5vZTcAVwC6dzqyISB5pdmERERHJDdWYiIiISG4oMBEREZHcUGAiIiIiuaHARERERHJDgYmIiIjkhgITERERyQ0FJiIiIpIb/x+F5BjUGfcS/wAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_values = SHemisphere['Lat']\n", "y_values = SHemisphere['Cloudiness']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "plt.scatter(x_values,y_values)\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Cloudiness')\n", "plt.title('Southern Hemisphere - Cloudiness vs. Latitude Linear Regression')\n", "plt.show()\n", "plt.savefig(\"images/linear_SHcloudiness.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = 0.03x + 6.62\n" ] }, { "data": { "image/png": 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+OzAxLRkqhcaaUhRFCSbVMR8RqReRBcBa4HHgdaDFGLPdOeQtoOZsURprSlEUJZhUlY8xpt0YMwbYAzgE2N/rMK9zReRSEZkrInPXrVuXppiJo7GmFEVRgimLt5sxpgV4ChgPNIpI3tFhD2Clzzm3GmPGGWPGDRkypBxiJobGmlIURQkmNeUjIkNEpNH53QCcACwBngTOcQ67CPhjWjJUioljm5h21miaGhsQoKmxgWlnjVZvN0VRFIc0Y7sNA24XkXqskrvXGPOQiLwC3C0iNwDzgV+lKEPF0FhTiqIo/qSmfIwxLwFjPbb/Czv+oyiKonRTNMKBoiiKUnZU+SiKoihlR5WPoiiKUnZ0MTkfNDaboihKeqjy8SAfmy0fIicfmw1QBaQoipIAanbzQGOzKYqipIsqHw80NpuiKEq6qNnNg+GNDTR7KBqNzaYolUfHY2sD7fl4oLHZFCWb6FpZtYMqHw80NpuiZBMdj60d1Ozmg8ZmU5TsoeOxtYP2fBRFqRp0razaQZWPoihVg47H1g5qdlMUpWrIm8LV2636UeWjKEpVoeOxtYGa3RRFUZSyo8pHURRFKTtqdlMUJTNo9ILugyofRVEygUaT716o2U1RlEyg0Qu6F6p8FEXJBBq9oHuhykdRlEyg0Qu6F6p8FEXJBBq9oHuRmvIRkT1F5EkRWSIii0XkK87260SkWUQWOJ9T0pJBUZTqQaPJdy/S9HbbDlxljHlRRHYB5onI486+m4wxP0jx2oqiVCEavaD7kJryMcasAlY5v98VkSWAlipFURSlPGM+IjISGAs872z6koi8JCK/FpEBPudcKiJzRWTuunXryiGmoiiKUiZSVz4i0g94ALjCGLMJ+CmwDzAG2zP6odd5xphbjTHjjDHjhgwZkraYiqIoShlJNcKBiOSwiudOY8zvAYwxa1z7fwE8lKYMipIlNHyMolhSUz4iIsCvgCXGmBtd24c540EAnwReTksGRckSGj5GUXaSptltAnAhcFyBW/X3RWSRiLwEHAtcmaIMipIZNHyMouwkTW+3ZwHx2PVIWtdUlCyj4WMUZSca4UBRyoSGj1GUnajyUZQyoeFjFGUnup6PopSJvFOBerspiiofRSkrGj5GUSxqdlMURVHKjiofRVEUpeyo8lEURVHKjiofRVEUpeyo8lEURVHKjiofRVEUpeyoq7WiJIBGq1aUeKjyUTJP1it2jVatKPFRs5uSafIVe3NLK4adFfvM+c2VFq0DjVatKPFR5aNkmmqo2DVataLER5WPkmmqoWLXaNWKEh9VPkqmqYaKXaNVK0p8VPkomaYaKvaJY5uYdtZomhobEKCpsYFpZ41WZwNFCUC93ZRMUy3LEGi0akWJhyofD7Lu2tvd0IpdUWoPVT4F6JwNRVGU9NExnwKqwbVXURSl2lHlU0A1uPYqiqJUO75mNxFZBBi//caYjwQlLCJ7Ar8Bdgd2ALcaY24RkYHAPcBIYBlwrjFmQ2zJU2J4YwPNHoomS669iqIo1U5Qz+c04HTgT87nAufzCHB/hLS3A1cZY/YHxgOXi8gBwBTgCWPMvsATzv/MUA2uvYqiKNWOb8/HGLMcQEQmGGMmuHZNEZHngG8HJWyMWQWscn6/KyJLgCbgTOAY57DbgaeAbxQpf+JUi2uvoihKNRPF262viBxhjHkWQEQOB/rGuYiIjATGAs8DQx3FhDFmlYjs5nPOpcClACNGjIhzuZJJw7VX3beVKKRRTrpj2SvlnrtjflWCKMrnEuDXItIfOwa0Ebg46gVEpB/wAHCFMWaTiEQ6zxhzK3ArwLhx43zHnqoBdd9WopBGOemOZa+Ue+6O+VUpQr3djDHzjDEHAR8BxhhjxhhjXoySuIjksIrnTmPM753Na0RkmLN/GLC2ONGrB3XfVqKQRjnpjmWvlHvujvlVKUKVj4gMFZFfAfcYYzaKyAEickmE8wT4FbDEGHOja9cs4CLn90XAH4uQu6pQ920lCmmUk+5Y9kq55+6YX5Uiyjyf24DZwHDn/z+AKyKcNwG4EDhORBY4n1OA6cDHReQ14OPO/5qmGiIzK5UnjXLSHcteKffcHfOrUkRRPoONMfdi5+pgjNkOtAefAsaYZ40xYoz5iGOqG2OMecQY87Yx5nhjzL7O9zsl3kPmUfdtJQpplJPuWPZKuefumF+VIorDwRYRGYQz4VRExmOdDpSIqPu2EoU0ykl3LHul3HN3zK9KIcYEO5KJyEeBHwMfBl4GhgDnGGNeSl88y7hx48zcuXPLdblui7qYKpVAy116iMg8Y8y4SsvhRWjPxxjzoogcDYwCBFhqjGlLXTKlrKiLqVIJtNx1X6J4u/XBhsC5whjzMjBSRE5LXbIMMnN+MxOmz2HvKQ8zYfocZs5vrrRIiaEupkol0HLXfYky5vO/wDzgMOf/W8B9wENpCZVFar2Fpi6mSiWoRLlTM182iOLtto8x5vtAG4AxphVrfutWJNlCy2IPSl1MlUpQ7nKXb0Q2t7Ri2NmIzMI72N2IonzeF5EGdnq77QO8l6pUGSSpFlpWC7+6mCqVoNzlTs182SGK8pmKXVJhTxG5E7sMwtdTlSqDJNVCy2rhnzi2iWlnjaapsQEBmhobmHbWaDVHKKlS7nKn5uXsEMXb7XEReRG7Jo8AXzHGrE9dsowx+aRRncZ8oLgWWpYLf2E077x5UG3jSpr4RZFPY2ymlMUidawoWaI4HAAcDRyBNb3lgD+kJlFGSWryWbWslJqkg4W+tEpc0nLwKbYRWesOR5UgyiTT/wE+CNzlbJoEvG6MuTxl2TqopUmmhYUYbOHPmolrwvQ5nkqyqbGB56YcFzmdarlfJVskVf68KKYxlKY8aVLVk0yxvZ4PG0dLicjtwKJUpaphqiV8R1LmQb8xrutmLc7cPSvZIU3zdDGLRWbZXF6tRFE+S4ERwHLn/55A2ULr1CJprJSaJDPnN1MnQrtHrziuedDv5WxpbWPm/OZM54NSObJmns6aPLVAFG+3QcASEXlKRJ4CXgGGiMgsEZmVqnRK2cmbybwUTzEOFkEvZ6U9/JTskjXX/6zJUwtE6flcm7oUSmbwMpMB1IsUNU4z+aRRXHHPAs99arJQ/MiaeTpr8tQCUVyt/wzgLKtwFPCmMWZe2oIplcFPIexwekJxXa8njm3i+gcXs2Fr11i0SZks1JuuNsmaeTpr8lQ7vmY3EXlIRD7s/B6GXU7hYuC3IhJlJVPFhyyG18njpxAa++SKjsww9fQDUzNZZDVihKIowQSN+eztRLEG+DzwuDHmdOBQrBJSiiDrlaWfbdsYio7MkOYs9qxGjFAUJZggs5vbTnI88AsAY8y7IrIjValqmKDKstJd+rz5qrWtnXrH263JMWNdWeK4TVomC3WBVcKIY5ZVE275COr5rBCR/xCRTwIfxcZ3wwkymiuHcLVIVitLd48MoN2YDtPYxLFNmY16nVW5lGwQx9KQdatErRGkfC4BDgQ+B0wyxrQ428dj1/hRiiCrlWWY+SqrrqZZlUvJBnHMsmrCLS++ZjdjzFrgMo/tTwJPpilULZNUgNKkCeuRZdXVNKtyKclRiiksjqUhq1aJWiVqYNHYiMivgdOAtcaYvNfcdcC/Aeucw75pjHkkLRm8qLRNN0plmaSMUdMKmsFdmMZNk8ZkqnKvhAtsOctRpctsJSk1oGecyAQaxaC8hAYWLTphkaOAzcBvCpTPZmPMD+KklVRg0WoIcpmkjHHS8jv27IObeGBec6bzrNyUsxxVQ5lNk1IDeibxDkw7azRQnb3rLAcWjRJepyiMMU8D76SVfjFUg003SRnjpOXnDv3kq+syn2flppzlqBrKbJqUagqL4+bvdyygjggp4Gt2E5Ef4yyd7YUx5stFXvNLIvJZYC5wlTFmg8/1LwUuBRgxYkSRl+pMNdh0k5Qxblpe5qtSXaxrjZnzmz1b4uCdJ6WazKqhzKZJEqawOGZZr2MnTJ+T2ekR1UxQz2cuMA/ojXW1fs35jAG6Bv+Kxk+BfZw0VgE/9DvQGHOrMWacMWbckCFDirxcZ7LqaeYmSRmTSKsa8qxc5M0yfhTmSRKuu909/7PgzdjdGwBp4at8jDG3G2NuB/YFjjXG/NgY82PshNMxxVzMGLPGGNNujNmBnbR6SDHpFEsWCnIYScqYRFrVkGflwi/oKnjnSRIms+6e/2lGx4hKd28ApEUUb7fhwC7sHL/p52yLjYgMM8ascv5+EhsvrmxUg1tukjImkVY15Fm5CGrpelWISbSYNf8rH9Azq9Mjqp0oymc6MF9E8nN7jgauCztJRO4CjgEGi8hbwFTgGBEZgx1LWgb8e3yRS6PSBTkKUWSMOpZQzP1eM3MRdz2/gnZjqBfh/EP3THWp4Cy4Es+c38x1sxbT0mqjSg3ok2Pq6QcycWxTh3x+A6BNjQ2x3dfjUKiA8j2nqHmUhfxNk7TvTxsA6RDJ1VpEdscGFAV43hizOlWpCkjK1bpWSNP99pqZi7jjb2922f6Z8SO4YeLoktIupLDCz1NuV+KZ85uZfN9C2nZ0fhdy9cKkj+3ZxdXcTZCsST2nUtIJO7faFVN3d0UPoxZcreuxE0M3AB9y5vAoFSJN99u7nl8Ra3ux5CuNQsUD5XclnjF7aRfFA9DWbrjr+RW+iids/CGp8YpSnnfQubUQy6wSruhZXhKlmgg1u4nI94BJwGIgH83aAE+nKJcSQJreN17LZwdtL5agwXsorydR0LX87lsgkikyCTNvKc876NwsR1iPSrk90UqNuKDsJErPZyIwyhhzqjHmdOdzRtqCKf6k6X1TLxJre7GEVQ7l9CQKupbffWdBvigyBJ1bCy7E5fZE6+6TfpMkivL5F7qEQqZI0/32/EP3jLW9WIIqh3J7Ek0+aRS5uq5KJlcvjP/AAM9zjt0vmblnUSjleQedWwsuxGF5k7SJrBYUdlaI4u22FVggIk8A7+U3lhDhQCmRNL1v8k4Fhd5uSTsbeLmvQmcvs3KRv5aXt5tfi/bJV9d5bk9TvmKed9i5lXYhLtXhIej+0jCRafDR5Aj1dhORi7y2OxNQy4J6u9UmlYjeHRW3u7kXArwx/dTUrh9ElvMt7rXT9FQLC0rqde8QHnG+HN51ST2XLHu7pRbVOklqVflUu5trVki6QvBzN3fjjqqsUa6Lw085NDbk6NurR8nvxd5THvacmyXATZPGdMnHXL2AoZPno1fepv3eJvmMs6x8ggKL3muMOVdEFuERYNQY85FUJatx1GsmOZL22gpzKy80TZXTa6ySHmpJV7p+4yQtrW0d5s9S3osgE5lXPra1d1VVXnmb9kT1WvBCjEKQw8FXnO/TgNM9PkoJlNtrppbnJiQ9CBzkVu41V6ecg9CVGvBOY05Q1HGSYt+LIGeEOPlVbmeC7uLUEKR8JonIx4BmY8zywk+5BKxVylnAamEyYRBJe20FuZs/N+W4Lq3PcnqNVcpDLY3Gkpdy8KOY9yJokm8Skd3Toha8EKMQpHz2AG4B1orIUyLyXRE5VUQGlkm2miaogCXdS6n1uQlJu577uZWP/8AAz+dSzsjTlYpynUZjyUs5DOjjPauj2Ip34tgmnptyHG9MP7VTw8ErH3P10sXlvhIBRLtLJHPfMR9jzNcARKQnMA44HLgY+IWItBhjDiiPiLWJX6TcY/cbkvhYkN/iZ37bq42kXc+93M3Hf2AAL765MfC5lMN5pFJBLtNyMS4cP/EbbE+64vXLR69t5R5n6S6BTKO4WvcHDgMmON+NwCJjzOfTF8/SnbzdZsxeWtKa9V7sc/UjnuMY9SK8Pu2UotLsboS57dY65fboq/WKt1xUq7fbrcCBwLvA88BfgBv9lr1W4lOuZavLFa+tlukug8B+lLt3p8qm9gka8xkB9AJWA83AW0BLOYTqzgQONr7yChx/PPTpA8OHw7XXQnv4iuYf6t3OjIdvZuHNk3jppnO5+cEZNLZuosl9ralTYfRo2HVX2GUXGDcO7rkHyJin3O9/Dx/7GDQ0wKBBcPLJsGVL4Ckz5zdz4RW/5IkPHsLm3v1o69sPDjkE5s3bedB998EZZ9C62+5s7dXAot0/yNTzrum413KO0WWViWObOsLy5AOTRr3XYv+OMBAAACAASURBVPIoC/maBRlqlaAxn5NFRLC9n8OBq4APi8g7wF+NMVPLJGO3wm8s6JuHDYUTToADDoA//hFefx2uugp27IAbbghM87eP3Ujbilf5xie+jBHhG0/dxi//8F+89cfZOw/atAk+9zmbfn093H8/nHcef3+zhas37ZWN+Ui//CV86Uvw9a/DjBmwYQPMmQPbt/ueMnN+M7f/z0x++5vJPP7BQ7n8jK/Ts0cdX23cyP6trl7LjTeyonEoNx55Mat79uPY1+dy/T3/xQ0tb8O0a8o6Rpc2xZq1ip2bFnQelC80TqFMYXmgc/HSJepicntgx3wOx877GWSMaUxZtg5qdczHD88X40+/ge9/H5Yvt70TsP+vuw5Wr965rZC//hUOP5xnfnE/U9YPYGVLK8e/u5xf/s/l8PjjVqH5MWECz24wfOaMb3XZVdJYx223WbmXLYt+zvr1sPfecOON8G//Fvm0CdPn8JMffZE3G3fnK2dM7tjeRf7165nwy5c6jevcMmsGH135KudN+Z1vOJY0xujSpJSxm2LHvYIiGby3fYenLGnma9Q8qIVxviyP+fia3UTkyyJyt4iswK7dcxqwFDgL6H7u1g8/DHV18MYbnbe/8YbdPmtWYpfydA999FE46aTOSua886C1Ff78Z//EHn0Uhg7lyC+c3ZHmL//7i7Yif/TRYEEGDaJ9W0csWX51//U88YvL6NX23s6xjh/+EHr3hsWLS7jjCNx7r/2+yDPUoC99/rmUsauWcvvBp3Xa3mWsZvDgLtsWD/0Ag7a2dGz3ei7VNhZUitt9sfcaFMnAT5Y08zVqHlTbs602gsZ8RgL3A4cYYz5gjLnQGPM/xpiFxpgdAefVJiefbMdZbi+Ip3rbbTBkCJwS4DXW3m5NQ0GfHSFZ+uqrsN9+nbeNGGHHf159Nd55APvv733e9u3Q0gJ33gmPPcajE87s2DXl5P9g4NaNfOPPt9sxkCVL4Jpr4Prr4cADg+Uvleefh1Gj4Fe/gj32gFwODj0U/vKXwNOOabGNhV23bebRX3+Jf37/DP788y9w6WtPdjm2cFzn4OYlvDZoRKA7cbVNCCylQi32XuPmRb5nmURafulH2V5tz7ba8FU+xpivGmPuN8asKqdAmaW+3o6J3H475E2Vxtj/F14IPQJWpzj+eFtZBn0uvjj4+hs2QKOHpXPAALuvyPPcA6qXfvEnVpYBA+y93nIL4796SceEt3X9BjL145fxuXkP8r3GdbYXMnYsfO1rwbLv2OGtaJ3/f3xhOUd+9/HgQd3Vq2HpUju+9b3vwYMPQt++tlGwZo3vpScOs7Lf+PBNzDzwGC6cdAPP7TOOq3//Q3jkkU7Huif3Hb5sAR9/7Xnu/tjpgXNMKj0hMO6AeCkVarH36nde0ITSNPM1ah5U+tnWOlHW81HyXHwxfPe78NRTcOyx8OSTdgzm8yFTnn7+c3j33eBjBg8OvfxLzRv5f9PndB4LMgbCVhn12m8Mb29t62T7fqb3MM65+BY+uivs/uwcLvzi5Tx52utsP/DojtOePvgEVrW+whFXXGTNjQsXWsUcxLe/bXtHheRs5XMmcPCuu3HE//u1/6Dujh2webP1Sjv5ZLvt8MNhr73gJz+B73zH89IH7t4PgIcPOYVbDz2H4Y0N9Pn6Z+GqC2HatE491vz1fnv3n/nRgz/gmQMO59Drv9oxAO41QB3mghw0sF3qfJZiBsT9HCeiVKjFulsHTej0kyXuteLkZVAeFKZz9sFNPPnqOp1zlAK6pEJcjjvOmn1+8xvb4/nHP6xJKIj29p29JT/q6uzHh/cGDubW0Z/gh4ed17GtIVfPoh+eTY/rr4PJk71PPPdcWLfOKko3p57KX15/m09P/E/fa37/kZuZsGwhE774v52ueVv/FRw6+VI44wzreRfGypX2k+ehh+DWW2HWLC657QXWvvse7/fIsXTIyI5DugzqTppkx31aW+0YU54TToD+/eGBB7yv/dOfwhe/CH/6kx0zy3PDDXDTTfD2252Pf+cdmDAB+vWzjYy+fYsepA86D7wr3jiTNqMOiBdWqMfuNyQzFWoSE0qLeT5+a/nUynIVebLscJBaz0dEfo11UlhrjPmws20gcA92PGkZcG7VTVr9whest9W0aXbOyQ9/GH7O8ccHOwWANWHddpvv7iX9h7PXus5rzDS+vZoerVu9x3Ty7LcfPPNM1+2vvsorg4JXxXh56D6cu+j/6NG+ne31tqjUb36XvX5yjTW3zZoFs2d3rtS9GD7cfjoSfhl69oRx45hz/xpMv66ndLHL77+/7cEVKnFjApU2++/vvd3rvK1b4bTT4P33rYNJ375A8SHuwwa2Sw2b7zd24VZIXr2jB+Y1Z6ZCTWJCaTHPx+u6E6bP6RZLGWSFIIeDUrkNOLlg2xTgCWPMvsATzv/q4qyzbMV53nnWFHTeeeHn/Pzn8MILPHXHw5xz8S2c/tmbOj7nXHwLT93xsHU9DuCxEWM56o0X6fve1o5tpy15htYeveDoo/1P/MQn7HjJs8/u3DZ3LvzrXyz6yOGB1xzXvISVuwzuUDwA1z5xK2Z7u51f8+lPW2W8cWNgOkFEHoM47TSrMNw9uI0b7UTRgw7yv8Dhh9sxrCee6Lz9iSc6n7d9O3zqU/Daa9YLcLfdOnYl7eW1sqU1EU8qv7wT6Bj7qfWgspCcV1oS6eik1OikpnyMMU8D7xRsPhPIu4vdDkxM6/ql4luIeveGCy6wlfknP+k9mF/IqFEwbhzfWtGbuUP2YdGwfTs+c4fsw7dW9IaRIwOTmHPMWbxfn+Nnf/guE5Yt4PwFf+KK537HPUee09n9+oMfhEsu2fn/sMNsz+Szn7U9tZkzrfxHHMGxl3+ahlw9TRvX8ru7vsmkhbM5bPlCTnjteWY8fDNnLHma/z7s3I6kjvvn3zl30f8x45yv2fv+8Y+tSfErX6FYIg/qjhsHZ55p7+32223P5Iwz7LjR5ZfvPO6SS2we5OnZ00aCuPlmO173+ONw2WXw9NM2qkOeL37ROiD8539a09vf/tbx2auf95hWsV5ewxsbEvGkmnzSKLxG+wx0KJfu4C6clFea3/EGIimSWl+6JGnS7Pl4MTTvPed87+Z3oIhcKiJzRWTuunXryiYgRChEEx2dGeahVkApFcFlE8dx8WemUW928KsHvs2Vz97Jr8edyeuXF3iabd/eNeTO3Xfb3tHFF1sldPDB8Ic/dIS07zt0EGv7DeSKv93L7fdfz7TZP6Zp01o+d85U7hxrB+T7t77LtNk/4d6xJ3PUly+06Q4cCL/4hVUGDz4YKy/yBK250oU77rB5/9WvwjnnWMUzZ47t2eTJu7W7ueIKmD7dynrqqfDcczaCw5FH7jzmscfs91e+YhW26/PNsY2JenlNPmlUIp5UE8c2eS4TDTvLVHdwF07KKy1ofaEoiqQ79DKTJFWHAxEZCTzkGvNpcUdGEJENxpgBPqd3UG6Hg9CB3K9/3cY9y08wLTHdqGvWXzNzEXf+7c1OFU4aA6LXzFzUsZxAnUCvHnVsa9tR8cHpSlJKSJq0vN0gvKyWMxp1VPL33dzSSr0I7cbQVGLZSioStls2L4KiG+w95WHPxoAAb0w/NbYsSdAtHQ58WCMiw4wxq0RkGLC2zNePhF9PpPfrr8EfNloPqqlTYyke8HbxzNUJW97fHmnN+idfXdelcEcZEPV7Mf223zBxdMeaNt0Fd170b8ghAi1b22jsk8MY2NjaxvDGBm6aNMYzBphfxRc0oJ7EYPux+w3xbJDkW/3Fuix7KYao6YQp3Mn3LaRth5U4H1k9Tty0MLf3Usin46dI8nWDlwxprXlUq5Rb+cwCLgKmO98R/HTLj18hmvHET+GOpXas4ctfjp2uV0Ww9f3tbNja1uk4P4VSjNnOby7I3OXv8MC8Zg2aSNc8yjcEgE7PxiuPKhl8cub8Zh6Y19ypkhTg7IM7V8RRK+bCe3Erhsn3LQSBtvZgZRGWH9fNWtyheAqJ2pBKOr/jKhI/Gc4+uKnTOwU6KTWINF2t7wKOAQaLyFvAVKzSuVdELgHeBD6V1vVLwW8S2psPPMxHS6xQCiuCkVMe9jzOr+DHbVn52aHzZrXC7Um5laa5IFjSaXvlkR+FeRTXzTdJ2a9/cHGXaxtsD7mYawXlg5fC8LrPsPxwK3YvChtShfew9f3tibpDF6NI/O7xyVfXdQRFLWXicHdZSC815WOMOd9n1/FpXTMpyrlwVt604bW9kGJmp/v1ivwWkkvCCyrN3kAaaZfikhunN5qk7DPnN3fpMbuvXcy1inn2heeU6l3nbkh53UNUOaJSjCIJWvCxFPNfd1vCQcPrFFDY8vCy8SdJnFVGi1GKfr0lP6WXhH262EmZlUrbL4+Cjg871ysfk5Q9yINqeGNDUdeKmw/5c6KkkT9uQJ+cr9IsbEjF6ZEWW26DlKWfIkl0bKetzUYgWbOGp380m1PXrmHwlhYGb9nA4K0tLN5tH37Q0EOVT61TiZZHk09BbvIpyHFbVn69pTTt02nOLUkjba888qMwj+L0RpOUPeicYnsIQfmQq5NOYz7gfZ9h+TH19AOZfP/CTumA9fi87owDO5XtqPlSSrmNrEi2bbMBbNes4Ye932TWS/MZsNkqiCFbWhiytYUD6lrh+28HB/oN4EaPbRNf+TOP7H9EUellHVU+LtJssftRSqDHKAT1lsbtNTAV02KaXj9Jp53v6ba2tXf0BhsDvN0K8yhObzRJ2YvppYRdy30vUb3dwLp7u7cFjXskkV+BUxO2bLERPRxFEfh5912eC8qsq703j3c+iSACQ4fC0KE8vzXHWz13YX3fAazv08j6vo0sHvoBGLFXUlfLFBpY1EWl/PSzMMiYpAxJzS3xC/7odtUF2yqf8amDmLv8nQ5HinoRzj90z04u4zPnN3PdrMUdg94CXZ63W86Z85u5/sHFHWaiwtZ51KWY3S7cW97f3qX34L5eHJfoqL01r2t54Z7f5ZV/UWSI9JyNsaGR1qzhmWdeZvaTL1G3dg27bd3IwC0b2PP9dzmwvpXeb6+jbt1aem9/P/I9pkYu16Ek/rK5h6MkGlnfZwDr+zayrm8jdbvvzp3XnGknPcechgHJvTdudJ5PlVApP/2k5ihEIUo031LNjUk4bAR5IXWJKSNw39w3ee71ndGc2o3hjr/ZQKw3TBzdZX4JdFU80HlGeqF5qKW1zbocO4TlmZcLd65OGNAnR8vWzj2puCbfwjyO0oR031thmtfMXNSRX+Dk31+Xk3uvlanHjrQ9is2bd34vXMjQW+/l3o1v2zGKrS3kdjiV5g0RhHE40vkkRkNDh5Lo8tl9987/d901fDkSDy4IaKQyaFDRopfT0SkLaM/HRdItjyz0aArl8bq/3rk6z0HgSq5V7zdz389Rwo96EV6fdopvel4IwWat/Hhc2HIGUZc8iHtsF9rbOf3aP7B91SpnsNp+dtTV8dauu9GnbRt927bR8P42+ra1skv7+xy3Rx/2bqBDocxbvIKGtm30cY5paHuPPu9voy6SWiuCXXZhRc9dWd27v9NzsD0I25uw3+/0G8CahkZae/budGolyyWU+KzKjPZ8qoQkWx5JOC8kvQ6L35iWn+mmmDGFpIjrIu5H/vi40aKDjo+6r/C4XHsbg7ZsZODqFnhkW6fxh6/Pns/grRsYstn2Iga2btp5os/Yg5s4kfVae/Ri26IGGNJo1y3q25fWHj15p2FXtvTszdac69OzgSnnHGyPc46lXz94/30m/+ZvLKUP6/s28nafRt7r0ROIXgkf5dODCCNsUnWQqTQuXg3ItMdpuwuqfApIygRWqvOCl/Jym0WKUWZxvarqpOtgcho9tzgzzIvp+UD0AfqGXD3fOHoEt8/8O+2rVne4vQ5xXF8Hb2mh6b1NDNy8gcZ3N7Dre1u6JvI9+/VG0IVu7/z3zGi3489uu7Fx14Es2dHAKqc38a+Be7Bw2Ic6KZTWXC921NV3Gce86OpHfOebTfnSKV22A0wYuB8PlVAJhz2TuNMBZs5vDjSVFjOXyqsBOe2s0SVPJlVU+aRGqW61UeY4xPXEi+shtcPs7P2k5XYed4Z5x/b3t9P3/VaGbNlA03ub+FjD+6z/11sM2dLC4K0bOkxPe7dvhh9v4LktHkrCjxvgjMTucCfbpY63+w2gV9MwGkfu0Wn8YW5rjv95ZTPNvXdlfZ9GNjTsQq9ePWOZfPtjvbBmzm/mNqdirItYgZ9/6J6dGjfu7cUuIR5GkGt3MdMBZsxe2sWFG2x0hmLnUvk1IJ+bcpwqmxJR5ZMSpTovRFVScXozfkEo/cZ8CinK7dwYO+/Bx9118HOLucsZtB6ypYVe7Tvl8Bu3jjGeXRy9esHQoWzYZQAvtfXu6Els6T+I444ezcfG7w+7787Da9r57l9Ws3LjtkjebkGV8zjgDOfYd2JW5F7XyJu9/Mb5CivwvFdbobfbuL0GBpqPS7EUTBzbxNzl73Qpk7AzysDZBzdFNjUXayqNe04trYVUSVT5pESpduGovZQoyqzQxThPPgjluL0G8s0HFtKwcadpafAW23sYsnXnALadULeB9m+2UG92RLqPMEqZPrcl15uWXQbQe4/hDPrAnjB0KK/uaOChNTv4Z10/tg0czDv9GvlXXT/67zaQySfvF3u8rXC+y6iTRvExJ41T94NTjz4wMJ04lXMxFXnY2GKc3olXNPO0l5b2itSep7mllXteWEHfntGqqaB3Jug98XvWjT7RGDRKdTKo8kmJtEwSPdq3M3DrRoZsbWH4e5u4rE8/+P5c74l1ziJ8EwlfMjbVJWX79/d1e/3GM2s6Bq3X9+nPtpz1bPIbtA7ySATH/XlgVzPO5o3bYoXs94vunHbEiySCgRYqh1J6J2m3/sPSaWs3kZYbAfvOeEVPyNWJb6Mv6Fnn6oRcvYRGdVCKQ5VPWrz3HhMHtTPx+F1gzVZY/S945K/wvx7mJ49wHFEURuIMGtShHFb02pU57wirG/qzvo+dRJefef123/601ecAqyS8loXI7wvzejpsXDOzYvQQw1aLDBoni9piDxpv80sjrtJIar5V3MCmQTJWYo2auOOQQc8wvy2Ot1tYJO8oCz1mbUpFtaDKZ8uWzoogKDTHu+9WWlqL14Q5r8/gwex9zexAd1Y/BbEn0H9+M7cGrOoIpUcajttDLLUlHuW4sGO8wv7HURp+x/fO1SUWDLRQOYTJWI41avzclgsn/4YR9Hzi9vLCnvXG1jYWTD3Rd393i0SdJLWtfF56CQ46qDLXdoXjCP0MHBgYjqOUllVQyzKsEsm/yHEmaBZeOwpRKox8HvhVUflrhckZRaaw1nhhGmGmr8Lnt+U97zVp/FrgcYOBej3XMBnTXKMGgt2W+/XuEcnhJU+SYy5xn3UhlYgHWSvUtvJZvTr8mIJwHMvq+/FUSx1v1PelfbehnHTsRzjyqNElheMohVJbVn5jRwP65Jh6erTJd3GiPudJ0jYeFsfMfa2oxwUR5gJcmEZQb6xw7kmawUCDlENYj7GYpQXiEFRJtwQonlyddOoVCTYPJ0yfk4h5K+6zLkQ94oqntpXPiSdaV98I+HmEPbC8nmlb+jKxf/80JAzt1ZTaskoiakNhtGMvotjGiyXILt/kk2f5QJ756NRxZPKL7lwv4hkfza/13L8hx1fvXUBUi1JjQ473tu+IbeKKohzCzHNpj+0EVdJ+184/2/xzcAeCTcq8FRbJu9R8VfzR2G6Et6zdL0GSlavfdfMvmd9aP/lj0oq0HaQQ48S/i2ouDDsuSrTxKKGIoDglHHbPXvu91r8JIlcnHeanuBWgl7xhzgxR7iEsrqHfdbzyOCgemp/p0H3tKPHUKjHwn0Yk6iTR2G4ZJyyaQL6VVazpy++l8Luuu3XnFfYfwltWxb6ISc0biWoujHJc/4Zclx5pfrtfGoWhiCbft7CTMojzDP16n1fd2zlsiztP/DwA3TQ5MeTySy3kj283pqPHE6cCs/nwEq1tO+dgRQ0JE7eH7JXnk+9fCIYOM5k7j/0mOLuvEXTtMPNWpQb+k4wH2d3Qng/+Les8fjGmorgSB7WMrrxnQVGBFXP1woxzDopcMbivGfZSJBWxN2o6UY4b++3HPCvyAX1yzL/2xKIdIrzk8SKofPjla1iZArjZWaI9al6F9UiDvMaKjbjsd804ee5lThTggvEjAtcLchOWR9UUabqcZLnnE3/FoxqksU/Od19Drt43kGWUQcWgMZui7cIhtVrYXJggkhpAjZpOlOP8BqTz20uJvh3lvoKek1++Rnm2+fOitOrHXP8YV9yzgOYWu3ZPvmU/c35zR1pB7srFDIDnGzFe14yTXktrW5fyaLDRDaIy+aRRNOTqO21zj4f5yZN3Tsjnk5IdVPng75MgAtPOGt2xfkshUSqYoIrF64WKQj5QYjHXDMPvnhr75JgwfQ57T3k40svsl07h9ijHhR1TX4IHYpRnGPacvPJ18kmjuqx553eenwwG2+ubfN9CT7OjW/GFPdtiGjpBjZi8ybMU4iiwiWObOt5FwfZo3D3OoPsrVNRKNqiI8hGRZSKySEQWiEj6q8SFsNHjxQbA2EIf1uoKIqjizL9QAwJ6Xn4EvbhRK34vvO41Vy9s3rbdt9UdNR2vPItyXNgxUZZYyIdKCZPHi/xz8lNyXvk6cWwTF4wfEaiA8ucFKbcNW9si9WiCnq0414hLUCMmjr6v8zk2rkKcOLaJ56YcxxvTT+0SVTqsgRC156+Uj0r2fI41xozJgj0yrLIOa3UFEVZxThzbxPxrT+TmSWM6elj5Sq6psYFGnxZm0ItbirL0ute+PXt0qQDDXuaoeRbluLBj/Hqm9SIdx8/41EHMOOegop5hXoYfnntQrHy9YeJobpo0xrNxUVgGgnrYQbgVWM6nlr9g/IiiBsCD3ouguTmF7DB0yTf3fJ0keiRR8lDn3mSLijgciMgyYJwxZn2U4yvhap2ku2QpLqB+7thhMauSWgV15vxmrrhngee+qO7eabvAxn1+YQP3XvvckY/rhI65O0HPwZ1Wfs5RkCt1UF57Uegq7Y5pBt4TieM8i6B8DZrzVciAPjm2tbV38sLzu48kUOeDnWTZ4aBSyucNYAPWrP1zY8ytHsdcClwKMGLEiIOXL1+eqkxZDg7oVbFA9Je2WOUaZf5TKd5+SSugqHOKQqNiF3hlHb7PQF58c2Ng5ITC+/HLO7fiKjw3jgeZW7F4reDp5RGZ1Fwev2t6kasX2tsNYQtwJKkYsj73ppyo8im8qMhwY8xKEdkNeBz4D2PM037Hp93zSYKklZdXPDCvQecoL22xLcGgyrDcrttJESQPFO85F9WFPOjcIPfsxoYcG1u9IzWEuaKHyVSKG3ZhBOnTDhrWqYftV24LCepFF/NuleucrJNl5VORSabGmJXO91oR+QNwCOCrfNIiqcKW9AQ3r/T8KCVKs9uV1ysfgtKO2orMWuyrtOSJ6kIedK5fqJZCJZIn/9z8JrMWbo9671Hfi8KwPl7nXRnRjOg3vlTsuxU3Hp1Gpy4/ZXc4EJG+IrJL/jdwIvByueUImsMQl1Lm1URNz4+oUZr9tnvlwxX3LGDM9Y/5zn9qcjz1SpGvHLGvZs5v7uIeHiRPKTJFdSEPOtfPUWTq6V1XS3U/t2JlzFMn0pFH18xcVNR74fc+Bc2hy1PK+k1JUa7rKDuphLfbUOBZEVkI/B142Bjzp3ILkURhy1dufhVAsa3pqOfFidLs56Xlp+haWtvYvG170e7JUa6dJn6V4bH7DfGVJ2huTpBnsZ8LeRRvZD+vtzCPvCgNlEJPST935HZjOvLozr+9WdR74fc+GYOvFx4E3+PM+c2x3y2vBkcUstZD7w6U3exmjPkXUKFFdnZSamELG4yH4lv3QeaXPj3jR44Oij8VZBaJupJjsddOk2LXp5m7/B3PGGRnH9zUMZbR2CeHMfiOweTv2yutXL3Qt2cP33OjmovCymmuTrjujM49psJnUecRNspvzCnuAnt5Nra2cdOkMZ0ixkdZziP/fvnh9W6VYjrT6NTlp9sGFi21sIW1PKO07v1s635RfqOuv+OFX6UWtphW2EqOpVw7TYpdn+aGiaMZt9fARJRlkmkVEvTcgqJhu+997ykPx7peMfLkJ1PHveeg98vv3Spl+ZGoi/IpydFtlU+phS2oJRinZRfUSitlblDUc8MWiqvWll/UxoVfXhUOpOfNq3GXO0hL8UZZhiAMvzwqjKQe5b1IuvIuxtmlFGuGRqcuPzWrfMIq4GILW9hyzgB9evYITSeslRal0oqybkuY6SG/zWsekWBD4VeCUj0RvSrDwvvxawDMXf5Oh4ktv9xBfj5L3kyVlDdU2H26J7cWKr5Sl7f2UxhuE2PUdJOuvIMWmPNLs1RrRiV66N2ZmlxSIa1JZlHGeSDazP8oC6TFlaUhV0/vXJ2v621Ya/2amYs8xzvKPTkvqecXdj9+ziJ+ayh5Ucp8pWIWqfM6rhSyOrel2AmxYedk9X7TIsvzfGoyqnVabpNRXaBLcX+uE4nkoeN3j0ELmIW5zT756roulW4l3E2DFm+L48UUdj9+5pg4zbFSvKHCymlQeSv1uVwzcxH7XP0IV9yzgNUbt3HB+BFdgnVC8d5jUfFLP47nX56wc5KcXqGUTk2a3co1kdCLOO7PXq3admMimXOKvZegAdisuJv6XS+uySvsfsKcLaJQyphYmHzFepiFcc3MRZ1Wem03puO/e3G3tCdeRlk1txjTs19PNOqKtEp5qMmeT1oTG8POd7e0wlqM+VaaV5j+KK1a33V3GnKhawT5VVqVnBAa93ql5FHQpM44KwOV6g0VJl8UD7Mg/MrgXc+v8Dw+vz1/3hX3LEh14mWpFoq4PZmgRo37vLR7e4qlJpVPsRMbwwpd0Joh7vXoQyz6jQAADxtJREFUo74UE8c2saPIVVL97vG6Mw4MDS3vV2lVakJoFDm8KDaPgiZ1XjB+RNf1jOqkY1kEd1uhd6601+fY/YZ0UXZu+cImvXo9l3wZHjnlYa70WfnUb/2jdmMiRU5Iqidcak87rvKKsiKtmubKR02a3eJ63nhFjfYyMeS/r7p3YZcX2G3OijPfoFgPnbB79BuwDlImYWmWa7A2ymRIKD2P8scU3oPf3JzC/Nywta1oM9TM+c08MK+50/iSAGcf3NTpGfotsWA8rlkon994V71PftaLRBrXTKonXKp3WlzlFTatYGVLa0lzhZR41KTyCcJrnZsH5jV7FsjWtnauuGcB181a3LFmS1BUgDBbvd9yy1EVRBz7NhTn/upnZy934EW3HHGVqF86xV57xuylXHnPAqRgOQSwZeSr9y7oUBJ+6/t4RSkvLHMG6yThPj6IkVMe7uTBGEVxrGxp5YLxIzqN+eQ5/9A9udNju5uGXD3H7jeECdPn+K55FGVZi7z7eDFzivLEVV5Bjcf8eVkZ9+wO1KTyCZq/4VY0+VhWYd5NLa1tTL5v56BkWKGP81JEVRBJRvctpgeTRoswTvRk6DwXqVeP9C3GXXoSPgXFrZBaWtu44p4FzF3+Tsfgfdwo5VFd+vNp5ctBlApyeGNDh1x3Pb+CdmOoF+H8Q/fkhomjefLVdYGREwoba0Hvllf59Oqd5RVQ1Im7eYqZ2JpP2+88v0XyqnWydZapyXk+vvM3xL8CiUJ+TodX5eCO2dWQq2NrwaqNpc7LSGotlmLn0JQ6L6lUOSqxQFicNXkKEeCmSWMC5xN5Uey6QlHOK3YBwShzo/xMeVHXOSplTaFiTMFBi+TV0kJ0WZ7nU5M9H9/5GyXq2Xy6hb2Vxj45Nm/buWhWoeIptOWXcu2o2/0otgeTdODFMDmimKnStsWXYmox0CFb3CjlUdfAcbOypZWbJo3xjOoQp1cR1ssMc4P3kivof9j2MIqNSuB3nobZKR81qXySmL/hl24ed+GdMH1O4OROty2/lGsnUfkX+/JHNXFEbYkGyZH0YnrFUmo5irJInFeUcj/TT5isSVac21wNqLwp8foHF9O/Iee5Mqlfz8drnaOsm7U0zE55qEnlM/mkUb5eQoVEXWM+Vye+tuQoFWCplWRSgRvT8q6DeONSQXIkvZhesYR5R+XqhfYdposTQp58tIq4Ucq9js/VCQgdMebcFLqPezlLNOTqaN2+A2PoNMbjhV/+b9jaRq5eyNUJba6bzseDK3Tc8VvnKMlGjFK91OQ8n4ljmzrmZRTS2JDrNK+jR52EKh6AGZ86KDCgYRilVpLFhBvxopS5PBPHNvHclON4Y/qpnqFY4sy7CJIj6cX0iqUwzwf0ydHYkOvI/xnnHMSN546hj898H3e0ijjPzutZz/jUQcw456COsZ385GS/tArnq2xt29Fhds5HNLhmpvd6OUH539Zu6Ne7R5d7uWHi6Ej3GKUc61yb7kFNOhyA98Bprk7o17sHLVvbOtysvVxOCwlbxC3MO8nP7l6p1l1a143rlOAnh9+gdLGL6cXhmpmLPL3Awpg5v5kr713gOa5Y6oJ8XtMDwqJOR3FyqBfh9WmndNke5dxlRTiZRKUUpwTtMXVGHQ4qQKGZKB8aPz82k3ezDiNXL2ze1vk8v8mnbgcEY6yt3D2PwX0uxFv6IEnSsmkXM+8iqtkpbDG9JCqdqDHP/PBrx7W0tnWMk8R9zl6mTLeMzS2tTL5vIdc/uLijURW19+jnJBBmbhRHrnI7eoTdU7nnoimlUZNmtzxuM1HfXj262MvD+nz1IvQosG+DtynJfa35157Igqkn0tTY4DvLPK3I25UkqfA8btMM2OfgDn9SSFJmmrCYZ0HEeW5Rn/PM+c1cde/C0PGvth2GDVvbOt17/wZvs7Mbr7iCsDP/G33SyHvypUWxMQZr8Z2qZWpa+bgpZsC/3Rha27xHhEpxMljZ0lqTM6mTGpfKp5VXZoXRrAuVSlKVTlDMszDiPreorfgo1y6kta0dEULj451/6J6++yaObQpcPj3pcuqOq7j1/e3WwcJFlEZMLb5TtUzNmt0KibpkcJz08viZfMLMUFl3OS2GJE16Ueckxa10/J5XUMyzMPyedZ1HSJ788UHE8fjzomVrGzdNGtNxn3G83dw0lcE12itmXq5eaGzIsbG1LbIZtRrcuJWddBvlE2XJ4KhKyN0KC7Izh7mVqstpMFGVSpxKJ+h5nX/onr4xz8IIKl9RXJALKbW1np/3U2pZScrFPwgvRdvWbujbq0dg76sSsirJURGzm4icLCJLReSfIjKlHNf0MwndMHF0x1iN3zIEA/rkfE1JYa1zPzOUupyGE9X2H2esKeh5jdtrIAXWHurERrkOI6h8FWOK9Lv3ehE+M35ER3qNDTly9fFNVFFJ0pTqR1LmsnLIqiRH2V2tRaQe+AfwceAt4AXgfGPMK37nFONqXQzFxHVKOuaZm6TjYFUbcZ5H1B5i0PPy60FVIr/TuPes0t3LeZqoq3VnDgH+aYz5F4CI3A2cCfgqn3JRTHiSNO3M3X0ANc7ziGpiCnpeWcrvNO49q6i5rHtSCeXTBLh9V98CDq2AHJ7EfZHTfHF0ADX5ijXoeWUtnH61K5WoaDDP7kkllI+X61AXS4iIXApcCjBixIi0ZSqaNF8cbREmT9jz0vyuDN1F0So7qcSYz2HAdcaYk5z/VwMYY6b5nVOuMZ8sUu32/GpD81upJbI85lMJ5dMD63BwPNCMdTj4tDFmsd853Vn5KIqiFEuWlU/ZzW7GmO0i8iVgNlAP/DpI8SiKoii1R0UmmRpjHgEeqcS1FUVRlMrTbWK7KYqiKNlBlY+iKIpSdlT5KIqiKGWnKlYyFZF1wPKQwwYD68sgTrGofMWTZdlA5SuFLMsG1S/fXsaYIeUSJg5VoXyiICJzs+pSCCpfKWRZNlD5SiHLsoHKlyZqdlMURVHKjiofRVEUpezUkvK5tdIChKDyFU+WZQOVrxSyLBuofKlRM2M+iqIoSvVQSz0fRVEUpUpQ5aMoiqKUnZpQPiJysogsFZF/isiUDMjzaxFZKyIvu7YNFJHHReQ153tAhWTbU0SeFJElIrJYRL6SMfl6i8jfRWShI9/1zva9ReR5R757RKRnJeRzZKkXkfki8lAGZVsmIotEZIGIzHW2ZeLZOrI0isj9IvKqUwYPy4J8IjLKybP8Z5OIXJEF2VwyXum8Ey+LyF3Ou5KZsheXqlc+IlIP/DfwCeAA4HwROaCyUnEbcHLBtinAE8aYfYEnnP+VYDtwlTFmf2A8cLmTX1mR7z3gOGPMQcAY4GQRGQ98D7jJkW8DcEmF5AP4CrDE9T9LsgEca4wZ45r/kZVnC3AL8CdjzH7AQdh8rLh8xpilTp6NAQ4GtgJ/yIJsACLSBHwZGGeM+TB2RYDzyF7Zi44xpqo/wGHAbNf/q4GrMyDXSOBl1/+lwDDn9zBgaaVldGT5I/DxLMoH9AFexC6zvh7o4fXMyyzTHthK6DjgIezKvJmQzbn+MmBwwbZMPFtgV+ANHEenrMnnkudE4LksyQY0ASuAgdjVCB4CTspS2Yv7qfqeDzsfSp63nG1ZY6gxZhWA871bheVBREYCY4HnyZB8jllrAbAWeBx4HWgxxmx3DqnkM74Z+Dqww/k/iOzIBnZJ+sdEZJ6zFD1k59l+AFgH/K9jtvyliPTNkHx5zgPucn5nQjZjTDPwA+BNYBWwEZhHtspeLGpB+YjHNvUfD0FE+gEPAFcYYzZVWh43xph2Y80fewCHAPt7HVZeqUBETgPWGmPmuTd7HFrJ8jfBGPNRrBn6chE5qoKyFNID+CjwU2PMWGALlTUBdsEZMzkDuK/SsrhxxprOBPYGhgN9sc+4kKqp+2pB+bwF7On6vwewskKyBLFGRIYBON9rKyWIiOSwiudOY8zvsyZfHmNMC/AUdmyq0VmCHSr3jCcAZ4jIMuBurOnt5ozIBoAxZqXzvRY7ZnEI2Xm2bwFvGWOed/7fj1VGWZEPbIX+ojFmjfM/K7KdALxhjFlnjGkDfg8cTobKXlxqQfm8AOzreH30xHaZZ1VYJi9mARc5vy/CjrWUHRER4FfAEmPMja5dWZFviIg0Or8bsC/dEuBJ4JxKymeMudoYs4cxZiS2nM0xxlyQBdkARKSviOyS/40du3iZjDxbY8xqYIWIjHI2HQ+8QkbkczifnSY3yI5sbwLjRaSP8w7n8y4TZa8oKj3olNBg3CnAP7BjA9/KgDx3Ye2ybdjW3iXYsYEngNec74EVku0IbNf8JWCB8zklQ/J9BJjvyPcycK2z/QPA34F/Yk0ivSr8jI8BHsqSbI4cC53P4vy7kJVn68gyBpjrPN+ZwICsyId1cHkb6O/algnZHFmuB1513ovfAr2yUvaK+Wh4HUVRFKXs1ILZTVEURakyVPkoiqIoZUeVj6IoilJ2VPkoiqIoZUeVj6IoilJ2VPko3Q4R2Rzj2GNE5HDX/8tE5LPO78+JyPAirr9MRAbHPU9Raoke4YcoSrfmGGAz8BcAY8zPXPs+h51zUTWzyhUlK6jyURRARE4HrgF6YicaXgA0AJcB7SLyGeA/sDPLN2OjR48D7hSRVmxE4SXYkPfrRWQc8ANjzDEiMgg78XgIdkKguK77GWyo/J7YAK9fNMa0p3/HilJZ1OymKJZngfHGBry8G/i6MWYZ8DPseiljjDHP5A82xtyPnal/gbOvNSDtqcCzTtqzgBEAIrI/MAkbDHQM0I5VeopS82jPR1EsewD3OMEje2LXnUmKo4CzAIwxD4vIBmf78diFy16w4bpoIAMBXRWlHKjyURTLj4EbjTGzROQY4Loi0tjOTmtC74J9XnGsBLjdGHN1EddSlKpGzW6KYukPNDu/L3JtfxfYxeecwn3LsD0ZgLNd25/GMaeJyCewwTTBBqo8R0R2c/YNFJG9ipRfUaoKVT5Kd6SPiLzl+nwV29O5T0SewS5NnOdB4JMiskBEjixI5zbgZ86+BmzU4VucNNxOA9cDR4nIi9hlDt4EMMa8gnVyeExEXsKu2jos6ZtVlCyiUa0VRVGUsqM9H0VRFKXsqPJRFEVRyo4qH0VRFKXsqPJRFEVRyo4qH0VRFKXsqPJRFEVRyo4qH0VRFKXs/H+axULBqIZY3wAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_values = NHemisphere['Lat']\n", "y_values = NHemisphere['Wind Speed']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "plt.scatter(x_values,y_values)\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Wind Speed')\n", "plt.title('Northern Hemisphere - Wind Speed vs. Latitude Linear Regression')\n", "plt.show()\n", "plt.savefig(\"images/linear_NHwind.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = -0.02x + 7.99\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_values = SHemisphere['Lat']\n", "y_values = SHemisphere['Wind Speed']\n", "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n", "regress_values = x_values * slope + intercept\n", "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n", "print(line_eq)\n", "plt.scatter(x_values,y_values)\n", "plt.plot(x_values,regress_values,\"r-\")\n", "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n", "plt.xlabel('Latitude')\n", "plt.ylabel('Wind Speed')\n", "plt.title('Southern Hemisphere - Wind Speed vs. Latitude Linear Regression')\n", "plt.show()\n", "plt.savefig(\"images/linear_SHwind.png\", bbox_inches = \"tight\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }