WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b409d94f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e549284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x258391ec300>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a set of random latitude and longitude combinations.\n",
    "lats = np.random.uniform(low=-90.000, high=90.000, size=1500)\n",
    "lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "lat_lngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "529134a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the latitudes and longitudes to a list.\n",
    "coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6febf111",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "5.333942126574485 130.3140526536625\n",
      "11.088600716588928 -115.40397215964758\n",
      "34.469613444798156 175.27122303062077\n",
      "82.64358273405148 -166.6567425371\n",
      "-8.543652960963868 -49.37681913967165\n",
      "-51.84461487939739 78.91704257075247\n",
      "-22.442814152339466 141.77127846119066\n",
      "35.267622181361276 -169.07145896848175\n",
      "-6.901491016238097 -46.51339153729444\n",
      "61.222000598470146 -39.12767233850519\n",
      "85.71947212993607 -95.30021294654298\n",
      "37.07404954726117 -68.30510832986\n",
      "6.751915124035932 63.72966097081141\n",
      "-60.55551342557266 -56.13128768112806\n",
      "-38.15701417182679 19.6879376950165\n",
      "-52.93887255622748 134.85162237565447\n",
      "-18.774180214518694 32.9550553205448\n",
      "53.28802865722972 152.17924058639812\n",
      "48.93796637705407 -105.34850581123972\n",
      "1.4509268467567296 -179.6179346517983\n",
      "47.57417535209038 122.62163542403613\n",
      "45.0301864061347 -96.34657975770654\n",
      "-81.25960781542612 -150.38997020774707\n",
      "-80.52142094633942 -145.85167949163397\n",
      "53.320595711921726 -66.95200068231674\n",
      "-33.602768981462795 144.6143546095817\n",
      "72.71024831814105 151.1022067669996\n",
      "-19.792118005881534 -116.06554571338015\n",
      "65.13699841894046 -106.89466468381103\n",
      "-63.07581062968434 133.9664257545242\n",
      "-83.65327945706356 -55.074954927566\n",
      "-88.28528805808521 142.36951084934066\n",
      "22.183794100155055 -158.37213454700805\n",
      "-10.346111906564047 20.57026788242095\n",
      "-2.540935636276245 110.66734540217925\n",
      "23.86886976198842 -108.24245529664483\n",
      "-1.3202105488877152 162.31671425150432\n",
      "6.105761598706607 -74.07245984525996\n",
      "17.335237043382563 -148.01678471747277\n",
      "-57.62241852636258 79.66751830961749\n",
      "71.2417931732875 -12.203027017543889\n",
      "-38.25657451773991 54.25741106938733\n",
      "83.72144274128462 -72.48456423028121\n",
      "-6.8399465040501894 -143.71608969218227\n",
      "-48.80038846794968 35.674592540492114\n",
      "-88.23819247462293 -60.23624686852375\n",
      "-71.99879480797215 -87.16849961016435\n",
      "85.81128157220192 -158.98952313455027\n",
      "-69.08996905564165 -114.35795334680536\n",
      "-20.793502687855252 136.52126102695973\n",
      "31.560410159920707 151.4644968635488\n",
      "-5.708526671279216 169.57370486925925\n",
      "1.1294608017497438 -146.27656210834317\n",
      "-55.452961337320154 -45.78135639751238\n",
      "18.200068170164684 -160.28025302537839\n",
      "44.881069479444164 91.88376092269749\n",
      "-82.21431979373578 -56.757847763309556\n",
      "-88.05270362930057 136.11100336743664\n",
      "53.62999247239148 -143.03034682378285\n",
      "5.21238224231233 -9.448272868315371\n",
      "30.36158885291968 79.83790786229736\n",
      "26.74022889542188 -84.23846765465842\n",
      "65.49171944389079 61.48768167603899\n",
      "66.51022123723018 -104.75013262511972\n",
      "-39.38543550590774 -35.03702623606455\n",
      "-51.4915461776342 78.52394318018185\n",
      "-6.105686292562083 147.6659889572311\n",
      "-80.99947377479502 82.04728495596862\n",
      "44.58728370684756 76.74342898449487\n",
      "81.64431699307258 132.4025055750975\n",
      "-10.73201944093681 -92.16797901587941\n",
      "-34.44218625564644 49.62055724471\n",
      "-83.9417877041902 20.165525378629326\n",
      "-87.0466478077187 55.798474062722875\n",
      "-88.07233583265254 -139.13883640610777\n",
      "-41.434523570094036 99.50052615595496\n",
      "87.50242877650561 -144.13794096797028\n",
      "-16.550397162981795 88.78228303922941\n",
      "-88.61837621687383 65.02000576011136\n",
      "-0.19156972431289887 -156.8782404238024\n",
      "-34.30377993271949 -85.43961260299471\n",
      "-64.0363603353208 -118.35053182508196\n",
      "8.311205495876052 -148.68333539509712\n",
      "6.0587710262747265 -68.6811645761196\n",
      "-35.924033365712226 141.04480371990883\n",
      "-61.42968079228254 -141.0246403649733\n",
      "-1.1842063363240527 -172.89612934051573\n",
      "11.57134968722157 -116.42636926874988\n",
      "-20.68536907628574 -35.875834465249795\n",
      "41.336622942846645 101.43639774578986\n",
      "-83.0792465469494 -49.815389981025476\n",
      "-68.2497553682333 -172.27205752840885\n",
      "-34.53773948115705 81.75239100040113\n",
      "67.46770777610118 16.192313617502037\n",
      "-26.434012949465192 34.51919164880792\n",
      "-87.96926753363076 178.6949933291998\n",
      "10.440020099846933 -101.29653113268877\n",
      "-42.29762569226498 177.3468539570373\n",
      "54.1796410276589 133.5539070025436\n",
      "-46.5737311018464 45.58944188213445\n",
      "76.44307582925356 -31.96708883059506\n",
      "17.625669311081822 -4.315970540196389\n",
      "84.19695655107489 -159.17857912763066\n",
      "-34.91274123668515 58.120343228041605\n",
      "37.224772994706086 19.529837005835446\n",
      "-84.91869208224722 29.94527896157689\n",
      "-24.099025070870738 -39.828874752813476\n",
      "-71.12779179763386 0.09313423366046436\n",
      "84.26945121161754 -176.65831290360586\n",
      "-68.15787666115108 -145.6134474965731\n",
      "-16.00188055243774 -13.22140607258379\n",
      "-85.63889538137963 -42.755134329609064\n",
      "38.768541024313976 -88.75670126390047\n",
      "21.680692673906734 70.0057213939449\n",
      "-37.81178524965726 -91.66832710795846\n",
      "-1.6583006734105652 -156.2625450233455\n",
      "67.76359169566578 -137.02294257183013\n",
      "64.45783641984292 142.53607940972427\n",
      "24.20062187887102 5.629462251696509\n",
      "-41.890262651090985 33.617910515743176\n",
      "7.960257998697017 -57.50863172275639\n",
      "6.99845949393233 94.04299599629445\n",
      "-79.01626170203491 -112.04322707829952\n",
      "67.25304158894238 -164.45374200246908\n",
      "63.89313953992951 58.20741773925954\n",
      "43.144454149170855 118.41750593602285\n",
      "30.75528649594142 -32.85569822454758\n",
      "-43.80319932719886 94.53984838261977\n",
      "85.81818180255354 -42.849645004293905\n",
      "71.4495477191582 -49.65414143271934\n",
      "84.1616150002429 -105.90983495616224\n",
      "14.517012537892228 43.646765922960014\n",
      "62.656713404114925 139.66664528503804\n",
      "20.497488570694287 33.883928295373835\n",
      "-77.58965100128421 46.41488401232493\n",
      "-38.33752205110282 -100.30590555567402\n",
      "51.11872340256784 -71.98327621962764\n",
      "83.14399102466692 151.07871396811106\n",
      "73.91900234674068 78.54024744151548\n",
      "-57.71432527746802 -36.867641354921005\n",
      "25.236886018204146 178.12296825871363\n",
      "73.51462648747412 94.12750610472216\n",
      "-83.48472977294725 105.39840146275793\n",
      "27.05271340868117 70.41692237941726\n",
      "-42.57241542713875 -118.25139822958793\n",
      "59.184038181049004 11.054899959461864\n"
     ]
    }
   ],
   "source": [
    "# Use the print() function to display the latitude and longitude combinations.\n",
    "for coordinate in coordinates:\n",
    "    print(coordinate[0], coordinate[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca7f671b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the citipy module to determine city based on latitude and longitude.\n",
    "from citipy import citipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c92b0e54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vaini to\n",
      "along in\n",
      "rio muerto mx\n",
      "leh in\n",
      "illoqqortoormiut gl\n",
      "fortuna us\n",
      "albany au\n",
      "hobart au\n",
      "hermanus za\n",
      "rikitea pf\n",
      "rikitea pf\n",
      "bengkulu id\n",
      "upernavik gl\n",
      "bonavista ca\n",
      "hobart au\n",
      "ghatal in\n",
      "attawapiskat ca\n",
      "champerico gt\n",
      "arraial do cabo br\n",
      "hermanus za\n",
      "atuona pf\n",
      "geraldton au\n",
      "kaitangata nz\n",
      "bayburt tr\n",
      "hermanus za\n",
      "codrington ag\n",
      "kefalos gr\n",
      "hermanus za\n",
      "illoqqortoormiut gl\n",
      "berlevag no\n",
      "punta arenas cl\n",
      "tautira pf\n",
      "ushuaia ar\n",
      "yellowknife ca\n",
      "rikitea pf\n",
      "salinopolis br\n",
      "boguchany ru\n",
      "vaini to\n",
      "ushuaia ar\n",
      "qaanaaq gl\n",
      "albany au\n",
      "hilo us\n",
      "linqiong cn\n",
      "baykit ru\n",
      "jamestown sh\n",
      "iqaluit ca\n",
      "lompoc us\n",
      "atuona pf\n",
      "ibra om\n",
      "plouzane fr\n",
      "yellowknife ca\n",
      "babanusah sd\n",
      "ambodifototra mg\n",
      "puerto ayora ec\n",
      "qaanaaq gl\n",
      "padang id\n",
      "hithadhoo mv\n",
      "nikolskoye ru\n",
      "linqiong cn\n",
      "bubaque gw\n",
      "victoria ar\n",
      "ushuaia ar\n",
      "barentsburg sj\n",
      "puerto ayora ec\n",
      "izmit tr\n",
      "winslow us\n",
      "clyde river ca\n",
      "college us\n",
      "punta arenas cl\n",
      "stokmarknes no\n",
      "bambous virieux mu\n",
      "olafsvik is\n",
      "polewali id\n",
      "avarua ck\n",
      "ushuaia ar\n",
      "rikitea pf\n",
      "constitucion mx\n",
      "cayenne gf\n",
      "seoul kr\n",
      "saldanha za\n",
      "morehead pg\n",
      "kristinehamn se\n",
      "punta arenas cl\n",
      "lompoc us\n",
      "ponta delgada pt\n",
      "kapaa us\n",
      "vaini to\n",
      "castro cl\n",
      "ushuaia ar\n",
      "calderitas mx\n",
      "dali cn\n",
      "rikitea pf\n",
      "maykain kz\n",
      "vaini to\n",
      "aykhal ru\n",
      "nikolskoye ru\n",
      "new norfolk au\n",
      "bolshiye uki ru\n",
      "steinbach ca\n",
      "hasaki jp\n",
      "hinton ca\n",
      "poum nc\n",
      "mataura pf\n",
      "kapaa us\n",
      "upernavik gl\n",
      "hailar cn\n",
      "san pedro ar\n",
      "punta arenas cl\n",
      "hilo us\n",
      "atuona pf\n",
      "petatlan mx\n",
      "albany au\n",
      "rikitea pf\n",
      "deputatskiy ru\n",
      "hermanus za\n",
      "busselton au\n",
      "rikitea pf\n",
      "carnarvon au\n",
      "coquimbo cl\n",
      "hermanus za\n",
      "ntcheu mw\n",
      "cape town za\n",
      "fairbanks us\n",
      "bembereke bj\n",
      "carnarvon au\n",
      "tumannyy ru\n",
      "caravelas br\n",
      "senanga zm\n",
      "carnarvon au\n",
      "airai pw\n",
      "broken hill au\n",
      "rikitea pf\n",
      "albany au\n",
      "busselton au\n",
      "atuona pf\n",
      "kahului us\n",
      "komsomolskiy ru\n",
      "constitucion mx\n",
      "carnarvon au\n",
      "carnarvon au\n",
      "bredasdorp za\n",
      "taolanaro mg\n",
      "castro cl\n",
      "belgaum in\n",
      "tabou ci\n",
      "tumannyy ru\n",
      "barrow us\n",
      "vaini to\n",
      "san rafael ar\n",
      "kavieng pg\n",
      "dafeng cn\n",
      "khatanga ru\n",
      "ayabaca pe\n",
      "hasaki jp\n",
      "butaritari ki\n",
      "punta arenas cl\n",
      "amderma ru\n",
      "avarua ck\n",
      "sinnamary gf\n",
      "namibe ao\n",
      "albany au\n",
      "ushuaia ar\n",
      "puerto del rosario es\n",
      "codrington ag\n",
      "pijijiapan mx\n",
      "cabo san lucas mx\n",
      "taolanaro mg\n",
      "vaini to\n",
      "puerto ayora ec\n",
      "alta floresta br\n",
      "kapaa us\n",
      "tsihombe mg\n",
      "higuey do\n",
      "tasiilaq gl\n",
      "mocambique mz\n",
      "galdar es\n",
      "chernyshkovskiy ru\n",
      "kodiak us\n",
      "ugoofaaru mv\n",
      "boende cd\n",
      "bluff nz\n",
      "vilhena br\n",
      "takoradi gh\n",
      "dunmore east ie\n",
      "margate za\n",
      "mayo ca\n",
      "kuanshan tw\n",
      "puerto ayora ec\n",
      "punta arenas cl\n",
      "hermanus za\n",
      "avarua ck\n",
      "upernavik gl\n",
      "windsor ca\n",
      "faanui pf\n",
      "castro cl\n",
      "pokrovsk ru\n",
      "ormara pk\n",
      "ushuaia ar\n",
      "tautira pf\n",
      "naze jp\n",
      "jeremie ht\n",
      "qaanaaq gl\n",
      "san cristobal do\n",
      "bairiki ki\n",
      "qaanaaq gl\n",
      "pevek ru\n",
      "cap malheureux mu\n",
      "vaini to\n",
      "half moon bay us\n",
      "waipawa nz\n",
      "san joaquin bo\n",
      "emirdag tr\n",
      "ferndale us\n",
      "shache cn\n",
      "albany au\n",
      "ajdabiya ly\n",
      "bolungarvik is\n",
      "saskylakh ru\n",
      "aklavik ca\n",
      "teguise es\n",
      "zapolyarnyy ru\n",
      "umzimvubu za\n",
      "busselton au\n",
      "rikitea pf\n",
      "shache cn\n",
      "paita pe\n",
      "svetlyy ru\n",
      "pevek ru\n",
      "pevek ru\n",
      "taolanaro mg\n",
      "busselton au\n",
      "katobu id\n",
      "butaritari ki\n",
      "sofiyivka ua\n",
      "beringovskiy ru\n",
      "castro cl\n",
      "grand centre ca\n",
      "ongandjera na\n",
      "the valley ai\n",
      "saskylakh ru\n",
      "taolanaro mg\n",
      "tsihombe mg\n",
      "yellowknife ca\n",
      "east london za\n",
      "albany au\n",
      "singosari id\n",
      "olafsvik is\n",
      "puerto ayora ec\n",
      "kodiak us\n",
      "ushuaia ar\n",
      "hithadhoo mv\n",
      "port augusta au\n",
      "barentsburg sj\n",
      "pevek ru\n",
      "natal br\n",
      "pacific grove us\n",
      "ayolas py\n",
      "rikitea pf\n",
      "yarada in\n",
      "mataura pf\n",
      "punta arenas cl\n",
      "erdenet mn\n",
      "nikolskoye ru\n",
      "bluff nz\n",
      "jamestown sh\n",
      "san andres co\n",
      "kutum sd\n",
      "new norfolk au\n",
      "hilo us\n",
      "cape town za\n",
      "dikson ru\n",
      "rossland ca\n",
      "amravati in\n",
      "rikitea pf\n",
      "albany au\n",
      "ribeira grande pt\n",
      "raudeberg no\n",
      "nanortalik gl\n",
      "constantine dz\n",
      "port alfred za\n",
      "laguna br\n",
      "sao filipe cv\n",
      "diego de almagro cl\n",
      "anadyr ru\n",
      "cidreira br\n",
      "esperance au\n",
      "puerto ayora ec\n",
      "mataura pf\n",
      "vaini to\n",
      "los llanos de aridane es\n",
      "kruisfontein za\n",
      "cape town za\n",
      "tuatapere nz\n",
      "ancud cl\n",
      "gorakhpur in\n",
      "castro cl\n",
      "port keats au\n",
      "vozhega ru\n",
      "ilulissat gl\n",
      "ankang cn\n",
      "nanortalik gl\n",
      "ushuaia ar\n",
      "atuona pf\n",
      "ushuaia ar\n",
      "cape town za\n",
      "kavaratti in\n",
      "port elizabeth za\n",
      "yellowknife ca\n",
      "busselton au\n",
      "belushya guba ru\n",
      "sabang id\n",
      "mataura pf\n",
      "ushuaia ar\n",
      "stony mountain ca\n",
      "bredasdorp za\n",
      "hermanus za\n",
      "davila ph\n",
      "mataura pf\n",
      "souillac mu\n",
      "punta arenas cl\n",
      "bluff nz\n",
      "atuona pf\n",
      "urucara br\n",
      "ushuaia ar\n",
      "petrolina br\n",
      "new norfolk au\n",
      "dubbo au\n",
      "yellowknife ca\n",
      "ribeira grande pt\n",
      "bolungarvik is\n",
      "hermanus za\n",
      "bethel us\n",
      "holme no\n",
      "saint george bm\n",
      "rikitea pf\n",
      "hermanus za\n",
      "avarua ck\n",
      "mataura pf\n",
      "bambous virieux mu\n",
      "mataura pf\n",
      "punta arenas cl\n",
      "tarazona es\n",
      "bintulu my\n",
      "ushuaia ar\n",
      "wanning cn\n",
      "port elizabeth za\n",
      "ramhormoz ir\n",
      "hermanus za\n",
      "olafsvik is\n",
      "rikitea pf\n",
      "atuona pf\n",
      "georgetown sh\n",
      "talnakh ru\n",
      "berlevag no\n",
      "bluff nz\n",
      "jamestown sh\n",
      "illapel cl\n",
      "jamestown sh\n",
      "taolanaro mg\n",
      "bulgan mn\n",
      "rikitea pf\n",
      "roma au\n",
      "solnechnyy ru\n",
      "albany au\n",
      "hithadhoo mv\n",
      "bocana de paiwas ni\n",
      "agua branca br\n",
      "erenhot cn\n",
      "maicao co\n",
      "thompson ca\n",
      "bredasdorp za\n",
      "marcona pe\n",
      "barentsburg sj\n",
      "tasiilaq gl\n",
      "kavieng pg\n",
      "saldanha za\n",
      "baculin ph\n",
      "tumbes pe\n",
      "illoqqortoormiut gl\n",
      "broome au\n",
      "rikitea pf\n",
      "esperanza mx\n",
      "castro cl\n",
      "bluff nz\n",
      "hasaki jp\n",
      "lebu cl\n",
      "bilibino ru\n",
      "lebu cl\n",
      "hermanus za\n",
      "grand centre ca\n",
      "busselton au\n",
      "xiongyue cn\n",
      "butaritari ki\n",
      "bluff nz\n",
      "kruisfontein za\n",
      "barcelos br\n",
      "new norfolk au\n",
      "ushuaia ar\n",
      "jamestown sh\n",
      "iqaluit ca\n",
      "kahului us\n",
      "medea dz\n",
      "atuona pf\n",
      "arraial do cabo br\n",
      "adrar dz\n",
      "port elizabeth za\n",
      "mumbwa zm\n",
      "tsihombe mg\n",
      "ugoofaaru mv\n",
      "ancud cl\n",
      "hobart au\n",
      "petropavlovsk-kamchatskiy ru\n",
      "east london za\n",
      "bogorodskoye ru\n",
      "mataura pf\n",
      "punta arenas cl\n",
      "ushuaia ar\n",
      "barrow us\n",
      "griffith au\n",
      "kemijarvi fi\n",
      "albany au\n",
      "aswan eg\n",
      "bethel us\n",
      "saint-joseph re\n",
      "mkuranga tz\n",
      "tura ru\n",
      "taolanaro mg\n",
      "dikson ru\n",
      "lazaro cardenas mx\n",
      "bandarbeyla so\n",
      "busselton au\n",
      "khatanga ru\n",
      "ushuaia ar\n",
      "port alfred za\n",
      "samusu ws\n",
      "bambous virieux mu\n",
      "khatanga ru\n",
      "gryazovets ru\n",
      "ponta do sol cv\n",
      "ostersund se\n",
      "illoqqortoormiut gl\n",
      "albany au\n",
      "aljezur pt\n",
      "dikson ru\n",
      "rikitea pf\n",
      "touros br\n",
      "chokurdakh ru\n",
      "jamestown sh\n",
      "yellowknife ca\n",
      "cape town za\n",
      "ushuaia ar\n",
      "olafsvik is\n",
      "mataura pf\n",
      "port hardy ca\n",
      "kangaatsiaq gl\n",
      "lively ca\n",
      "qaanaaq gl\n",
      "ayagoz kz\n",
      "busselton au\n",
      "agadez ne\n",
      "kapaa us\n",
      "lebu cl\n",
      "maragheh ir\n",
      "rikitea pf\n",
      "tidore id\n",
      "puerto ayora ec\n",
      "kloulklubed pw\n",
      "mys shmidta ru\n",
      "santa fe us\n",
      "mar del plata ar\n",
      "dzhebariki-khaya ru\n",
      "terrace bay ca\n",
      "tuktoyaktuk ca\n",
      "souillac mu\n",
      "najran sa\n",
      "saint-philippe re\n",
      "puerto ayora ec\n",
      "albany au\n",
      "jabiru au\n",
      "vaini to\n",
      "mataura pf\n",
      "lasa cn\n",
      "visby se\n",
      "luanda ao\n",
      "quelimane mz\n",
      "merauke id\n",
      "dikson ru\n",
      "along in\n",
      "hilo us\n",
      "hilo us\n",
      "bjornevatn no\n",
      "hilo us\n",
      "nouadhibou mr\n",
      "hermanus za\n",
      "ponta do sol cv\n",
      "hongjiang cn\n",
      "vaini to\n",
      "merritt ca\n",
      "ondjiva ao\n",
      "tsienyane bw\n",
      "tifton us\n",
      "busselton au\n",
      "rikitea pf\n",
      "monywa mm\n",
      "lao cai vn\n",
      "westport ie\n",
      "cooma au\n",
      "kolpashevo ru\n",
      "cape town za\n",
      "acapetahua mx\n",
      "barentsburg sj\n",
      "vilaka lv\n",
      "mahebourg mu\n",
      "balkanabat tm\n",
      "burica pa\n",
      "saskylakh ru\n",
      "avola it\n",
      "barrow us\n",
      "tabiauea ki\n",
      "marsta se\n",
      "mount gambier au\n",
      "rikitea pf\n",
      "rikitea pf\n",
      "butaritari ki\n",
      "yellowknife ca\n",
      "thompson ca\n",
      "dzhebariki-khaya ru\n",
      "carndonagh ie\n",
      "san andres co\n",
      "norman wells ca\n",
      "saint-philippe re\n",
      "makat kz\n",
      "biak id\n",
      "plavinas lv\n",
      "busselton au\n",
      "mataura pf\n",
      "punta arenas cl\n",
      "arraial do cabo br\n",
      "pangnirtung ca\n",
      "tuktoyaktuk ca\n",
      "barrow us\n",
      "vestmannaeyjar is\n",
      "tsihombe mg\n",
      "lebu cl\n",
      "lagdo cm\n",
      "hermanus za\n",
      "taoudenni ml\n",
      "barrow us\n",
      "alofi nu\n",
      "namibe ao\n",
      "puerto ayora ec\n",
      "tuktoyaktuk ca\n",
      "meulaboh id\n",
      "bluff nz\n",
      "jiangyou cn\n",
      "maloy no\n",
      "butaritari ki\n",
      "cape town za\n",
      "sao joao da barra br\n",
      "mundi in\n",
      "mataura pf\n",
      "bluff nz\n",
      "kirkwall gb\n",
      "ahipara nz\n",
      "bluff nz\n",
      "chokurdakh ru\n",
      "butaritari ki\n",
      "taolanaro mg\n",
      "tiksi ru\n",
      "nouadhibou mr\n",
      "rikitea pf\n",
      "tsihombe mg\n",
      "luderitz na\n",
      "laguna br\n",
      "atambua id\n",
      "haines junction ca\n",
      "saint anthony ca\n",
      "mataura pf\n",
      "bubaque gw\n",
      "punta arenas cl\n",
      "kahului us\n",
      "rikitea pf\n",
      "butaritari ki\n",
      "nouadhibou mr\n",
      "cosio mx\n",
      "portel br\n",
      "shangzhi cn\n",
      "new norfolk au\n",
      "mattawa ca\n",
      "tasiilaq gl\n",
      "gulshat kz\n",
      "kapaa us\n",
      "fuxin cn\n",
      "svetlyy ru\n",
      "rikitea pf\n",
      "coahuayana mx\n",
      "mayumba ga\n",
      "tromso no\n",
      "malwan in\n",
      "thompson ca\n",
      "busselton au\n",
      "punta arenas cl\n",
      "morros br\n",
      "mataura pf\n",
      "lakes entrance au\n",
      "mar del plata ar\n",
      "mareeba au\n",
      "port alfred za\n",
      "saint anthony ca\n",
      "hermanus za\n",
      "severo-kurilsk ru\n",
      "yamada jp\n",
      "raahe fi\n",
      "mahajanga mg\n",
      "ragusa it\n",
      "rikitea pf\n",
      "rabo de peixe pt\n",
      "satitoa ws\n",
      "longyearbyen sj\n",
      "nemuro jp\n",
      "kavieng pg\n",
      "cape town za\n",
      "katsuura jp\n",
      "mataura pf\n",
      "rikitea pf\n",
      "arraial do cabo br\n",
      "east london za\n",
      "tuatapere nz\n",
      "tabuk sa\n",
      "attawapiskat ca\n",
      "lavrentiya ru\n",
      "albany au\n",
      "dikson ru\n",
      "naberera tz\n",
      "butaritari ki\n",
      "awbari ly\n",
      "ushuaia ar\n",
      "kavaratti in\n",
      "castro cl\n",
      "minot us\n",
      "hobart au\n",
      "chuy uy\n",
      "airai pw\n",
      "ribeira grande pt\n",
      "sinnamary gf\n",
      "kapaa us\n",
      "rikitea pf\n",
      "upernavik gl\n",
      "mataura pf\n",
      "atuona pf\n",
      "pisco pe\n",
      "kruisfontein za\n",
      "cape town za\n",
      "sentyabrskiy ru\n",
      "puerto escondido mx\n",
      "kapaa us\n",
      "busselton au\n",
      "luwuk id\n",
      "busselton au\n",
      "aketi cd\n",
      "new norfolk au\n",
      "khatanga ru\n",
      "wagar sd\n",
      "cape town za\n",
      "avarua ck\n",
      "souillac mu\n",
      "punta arenas cl\n",
      "ushuaia ar\n",
      "sadu ro\n",
      "hilo us\n",
      "mataura pf\n",
      "rikitea pf\n",
      "carnarvon au\n",
      "ponta do sol cv\n",
      "broome au\n",
      "punta arenas cl\n",
      "mnogovershinnyy ru\n",
      "wewak pg\n",
      "kaitangata nz\n",
      "butaritari ki\n",
      "bredasdorp za\n",
      "rikitea pf\n",
      "niamtougou tg\n",
      "sitka us\n",
      "port alfred za\n",
      "busselton au\n",
      "bredasdorp za\n",
      "busselton au\n",
      "jamestown sh\n",
      "barentsburg sj\n",
      "mataura pf\n",
      "olafsvik is\n",
      "serebryanyy bor ru\n",
      "namatanai pg\n",
      "hervey bay au\n",
      "busselton au\n",
      "flinders au\n",
      "manono cd\n",
      "ushuaia ar\n",
      "rikitea pf\n",
      "carnarvon au\n",
      "carnarvon au\n",
      "buckeye us\n",
      "taolanaro mg\n",
      "iqaluit ca\n",
      "cape town za\n",
      "lagoa pt\n",
      "isla mujeres mx\n",
      "guerrero negro mx\n",
      "kapaa us\n",
      "moussoro td\n",
      "yellowknife ca\n",
      "anloga gh\n",
      "bluff nz\n",
      "kapaa us\n",
      "rikitea pf\n",
      "cidreira br\n",
      "hobart au\n",
      "margate za\n",
      "castro cl\n",
      "illoqqortoormiut gl\n",
      "punta arenas cl\n",
      "pangai to\n",
      "upernavik gl\n",
      "codrington ag\n",
      "khatanga ru\n",
      "nikolskoye ru\n",
      "gat ly\n",
      "jamestown sh\n",
      "ribeira grande pt\n",
      "havre-saint-pierre ca\n",
      "torbay ca\n",
      "powell river ca\n",
      "yellowknife ca\n",
      "pevek ru\n",
      "jamestown sh\n",
      "toliary mg\n",
      "umm bab qa\n",
      "amderma ru\n",
      "busselton au\n",
      "torbay ca\n",
      "ratnagiri in\n",
      "rikitea pf\n",
      "punta arenas cl\n",
      "puerto ayora ec\n",
      "victoria us\n",
      "praia da vitoria pt\n",
      "georgetown sh\n",
      "ribeira grande pt\n",
      "mataura pf\n",
      "sinkat sd\n",
      "norman wells ca\n",
      "albany au\n",
      "taolanaro mg\n",
      "mehran ir\n",
      "cidreira br\n",
      "avera pf\n",
      "belushya guba ru\n",
      "tecoanapa mx\n",
      "corrales pe\n",
      "bambous virieux mu\n",
      "stephenville ca\n",
      "barrow us\n",
      "rikitea pf\n",
      "mataura pf\n",
      "teacapan mx\n",
      "namatanai pg\n",
      "ushuaia ar\n",
      "saint-philippe re\n",
      "bar harbor us\n",
      "east london za\n",
      "yulara au\n",
      "nikolskoye ru\n",
      "mataura pf\n",
      "taolanaro mg\n",
      "hit iq\n",
      "nortelandia br\n",
      "souillac mu\n",
      "priargunsk ru\n",
      "nome us\n",
      "wuwei cn\n",
      "arraial do cabo br\n",
      "hithadhoo mv\n",
      "sentyabrskiy ru\n",
      "bambous virieux mu\n",
      "arraial do cabo br\n",
      "san policarpo ph\n",
      "punta arenas cl\n",
      "santa teresa br\n",
      "eirunepe br\n",
      "faya td\n",
      "barrow us\n",
      "longlac ca\n",
      "robertsport lr\n",
      "bredasdorp za\n",
      "ushuaia ar\n",
      "olinda br\n",
      "kapaa us\n",
      "east london za\n",
      "tiksi ru\n",
      "airai pw\n",
      "moranbah au\n",
      "lompoc us\n",
      "nikolskoye ru\n",
      "kapaa us\n",
      "cape town za\n",
      "cherskiy ru\n",
      "catamarca ar\n",
      "albany au\n",
      "souillac mu\n",
      "vostok ru\n",
      "geraldton au\n",
      "norman wells ca\n",
      "khatanga ru\n",
      "hermanus za\n",
      "sao joao da barra br\n",
      "rikitea pf\n",
      "new norfolk au\n",
      "fortuna us\n",
      "magadan ru\n",
      "mezen ru\n",
      "barrow us\n",
      "viedma ar\n",
      "hermanus za\n",
      "andijon uz\n",
      "new norfolk au\n",
      "hobart au\n",
      "teluknaga id\n",
      "albany au\n",
      "kupang id\n",
      "puerto ayora ec\n",
      "punta arenas cl\n",
      "ngukurr au\n",
      "seoul kr\n",
      "atuona pf\n",
      "samusu ws\n",
      "taolanaro mg\n",
      "konongo gh\n",
      "bluff nz\n",
      "aklavik ca\n",
      "rikitea pf\n",
      "shimoda jp\n",
      "roald no\n",
      "yar-sale ru\n",
      "college us\n",
      "shache cn\n",
      "busselton au\n",
      "fayaoue nc\n",
      "rikitea pf\n",
      "atuona pf\n",
      "port macquarie au\n",
      "carnarvon au\n",
      "severo-kurilsk ru\n",
      "yellowknife ca\n",
      "atar mr\n",
      "ponta do sol cv\n",
      "rikitea pf\n",
      "ambilobe mg\n",
      "bluff nz\n",
      "busselton au\n",
      "hermanus za\n",
      "cape town za\n",
      "codrington ag\n",
      "kiruna se\n",
      "patos de minas br\n",
      "chokurdakh ru\n",
      "ngunguru nz\n",
      "attawapiskat ca\n",
      "rikitea pf\n",
      "klaksvik fo\n",
      "ushuaia ar\n",
      "isangel vu\n",
      "viljoenskroon za\n",
      "kahului us\n",
      "saint george bm\n",
      "bengkulu id\n",
      "kalabo zm\n",
      "haines junction ca\n",
      "port alfred za\n",
      "bilma ne\n",
      "asosa et\n",
      "tasiilaq gl\n",
      "tumannyy ru\n",
      "egvekinot ru\n",
      "malumfashi ng\n",
      "constitucion mx\n",
      "ushuaia ar\n",
      "wundanyi ke\n",
      "geraldton au\n",
      "mafeteng ls\n",
      "grand river south east mu\n",
      "sueyoshi jp\n",
      "ushuaia ar\n",
      "severo-kurilsk ru\n",
      "albany au\n",
      "manggar id\n",
      "atuona pf\n",
      "kapit my\n",
      "albany au\n",
      "pevek ru\n",
      "puerto ayora ec\n",
      "sovetskiy ru\n",
      "touros br\n",
      "dhidhdhoo mv\n",
      "barentsburg sj\n",
      "bredasdorp za\n",
      "sfantu gheorghe ro\n",
      "new norfolk au\n",
      "ponta do sol cv\n",
      "bonavista ca\n",
      "bredasdorp za\n",
      "la ronge ca\n",
      "rikitea pf\n",
      "ribeira grande pt\n",
      "jamestown sh\n",
      "barentsburg sj\n",
      "kemin kg\n",
      "bagdarin ru\n",
      "hithadhoo mv\n",
      "hobart au\n",
      "liverpool ca\n",
      "illoqqortoormiut gl\n",
      "ushuaia ar\n",
      "zhezkazgan kz\n",
      "busselton au\n",
      "qaanaaq gl\n",
      "pacasmayo pe\n",
      "kapaa us\n",
      "dubovskoye ru\n",
      "palabuhanratu id\n",
      "chuy uy\n",
      "acapulco mx\n",
      "teguldet ru\n",
      "vaini to\n",
      "vaitupu wf\n",
      "vaini to\n",
      "georgetown sh\n",
      "tiksi ru\n",
      "east london za\n",
      "acari pe\n",
      "puerto ayora ec\n",
      "aksha ru\n",
      "barrow us\n",
      "mataura pf\n",
      "albany au\n",
      "tulagi sb\n",
      "richmond za\n",
      "bredasdorp za\n",
      "barrow us\n",
      "kodiak us\n",
      "uspenka ru\n",
      "ushuaia ar\n",
      "hay river ca\n",
      "mataura pf\n",
      "yellowknife ca\n",
      "thompson ca\n",
      "esperance au\n",
      "aginskoye ru\n",
      "dharchula in\n",
      "nikolskoye ru\n",
      "dikson ru\n",
      "taltal cl\n",
      "vila franca do campo pt\n",
      "qaanaaq gl\n",
      "albany au\n",
      "hofn is\n",
      "yellowknife ca\n",
      "faanui pf\n",
      "darnah ly\n",
      "kokopo pg\n",
      "thompson ca\n",
      "illoqqortoormiut gl\n",
      "belushya guba ru\n",
      "palmer us\n",
      "bredasdorp za\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tasiilaq gl\n",
      "tiksi ru\n",
      "punta arenas cl\n",
      "santa fe pa\n",
      "tasiilaq gl\n",
      "lebu cl\n",
      "kapaa us\n",
      "kapaa us\n",
      "maningrida au\n",
      "mahebourg mu\n",
      "atraulia in\n",
      "khatanga ru\n",
      "dikson ru\n",
      "mataura pf\n",
      "carnarvon au\n",
      "avarua ck\n",
      "sao filipe cv\n",
      "cherskiy ru\n",
      "necocli co\n",
      "tuatapere nz\n",
      "illoqqortoormiut gl\n",
      "sentyabrskiy ru\n",
      "hambantota lk\n",
      "sentyabrskiy ru\n",
      "bam ir\n",
      "hamilton us\n",
      "hilo us\n",
      "punta arenas cl\n",
      "faanui pf\n",
      "ayan ru\n",
      "whithorn gb\n",
      "albany au\n",
      "new norfolk au\n",
      "punta arenas cl\n",
      "georgetown sh\n",
      "hermanus za\n",
      "hermanus za\n",
      "victoria sc\n",
      "ndola zm\n",
      "kapaa us\n",
      "kaeo nz\n",
      "rimavska sobota sk\n",
      "avarua ck\n",
      "port hedland au\n",
      "san jose gt\n",
      "surab pk\n",
      "vaini to\n",
      "punta arenas cl\n",
      "lebu cl\n",
      "narsaq gl\n",
      "yichun cn\n",
      "thompson ca\n",
      "cherskiy ru\n",
      "pisco pe\n",
      "chuy uy\n",
      "saskylakh ru\n",
      "nizhneyansk ru\n",
      "vaitupu wf\n",
      "esperance au\n",
      "jega ng\n",
      "east london za\n",
      "komsomolskiy ru\n",
      "jibuti dj\n",
      "yaan cn\n",
      "saldanha za\n",
      "zhirnovsk ru\n",
      "atuona pf\n",
      "oranjemund na\n",
      "la ronge ca\n",
      "albany au\n",
      "mys shmidta ru\n",
      "klaksvik fo\n",
      "yorkton ca\n",
      "fairview ca\n",
      "bell ville ar\n",
      "cidreira br\n",
      "mataura pf\n",
      "chapais ca\n",
      "punta arenas cl\n",
      "grindavik is\n",
      "warmbad na\n",
      "yulara au\n",
      "saint george bm\n",
      "barrow us\n",
      "provideniya ru\n",
      "illoqqortoormiut gl\n",
      "gonaives ht\n",
      "grindavik is\n",
      "grand gaube mu\n",
      "karaton kz\n",
      "cape town za\n",
      "nanortalik gl\n",
      "ushuaia ar\n",
      "inuvik ca\n",
      "manta ec\n",
      "asosa et\n",
      "tasiilaq gl\n",
      "coahuayana mx\n",
      "amderma ru\n",
      "bredasdorp za\n",
      "kaitangata nz\n",
      "omboue ga\n",
      "alappuzha in\n",
      "isla vista us\n",
      "kodiak us\n",
      "hobart au\n",
      "honningsvag no\n",
      "ponta delgada pt\n",
      "majene id\n",
      "atuona pf\n",
      "punta arenas cl\n",
      "punta arenas cl\n",
      "hobart au\n",
      "atuona pf\n",
      "kodiak us\n",
      "bilibino ru\n",
      "rawson ar\n",
      "marsabit ke\n",
      "solnechnogorsk ru\n",
      "la primavera co\n",
      "flin flon ca\n",
      "provideniya ru\n",
      "tuatapere nz\n",
      "kapaa us\n",
      "male mv\n",
      "butaritari ki\n",
      "new norfolk au\n",
      "walvis bay na\n",
      "pangkalanbuun id\n",
      "vaini to\n",
      "kattivakkam in\n",
      "vaini to\n",
      "omsukchan ru\n",
      "beyneu kz\n",
      "kribi cm\n",
      "rokytne ua\n",
      "longyearbyen sj\n",
      "saldanha za\n",
      "bredasdorp za\n",
      "bluff nz\n",
      "namibe ao\n",
      "amderma ru\n",
      "narsaq gl\n",
      "hobart au\n",
      "taolanaro mg\n",
      "pulaski us\n",
      "salalah om\n",
      "san patricio mx\n",
      "batagay-alyta ru\n",
      "nikolskoye ru\n",
      "bluff nz\n",
      "east london za\n",
      "rikitea pf\n",
      "arraial do cabo br\n",
      "amderma ru\n",
      "potosi bo\n",
      "saint george bm\n",
      "juba sd\n",
      "victoria sc\n",
      "tiksi ru\n",
      "mar del plata ar\n",
      "namibe ao\n",
      "acatlan mx\n",
      "rikitea pf\n",
      "harper lr\n",
      "busselton au\n",
      "alihe cn\n",
      "ushuaia ar\n",
      "cape town za\n",
      "samusu ws\n",
      "les escoumins ca\n",
      "ushuaia ar\n",
      "port lincoln au\n",
      "salta ar\n",
      "sturgeon falls ca\n",
      "ciudad bolivar ve\n",
      "lavrentiya ru\n",
      "smithers ca\n",
      "avera pf\n",
      "warqla dz\n",
      "san quintin mx\n",
      "kodiak us\n",
      "makakilo city us\n",
      "lebu cl\n",
      "alim ph\n",
      "alofi nu\n",
      "ushuaia ar\n",
      "nizhneyansk ru\n",
      "haibowan cn\n",
      "puerto ayora ec\n",
      "douglas us\n",
      "khatanga ru\n",
      "bluff nz\n",
      "dukat ru\n",
      "hobart au\n",
      "kaithal in\n",
      "alamogordo us\n",
      "ushuaia ar\n",
      "bredasdorp za\n",
      "naze jp\n",
      "sitka us\n",
      "hamilton us\n",
      "tiksi ru\n",
      "mayo ca\n",
      "cape town za\n",
      "camacha pt\n",
      "atuona pf\n",
      "zaterechnyy ru\n",
      "hermanus za\n",
      "puerto ayora ec\n",
      "east london za\n",
      "ushuaia ar\n",
      "pokhara np\n",
      "sao joao da barra br\n",
      "kapaa us\n",
      "port alfred za\n",
      "isangel vu\n",
      "illoqqortoormiut gl\n",
      "bataipora br\n",
      "fare pf\n",
      "kapaa us\n",
      "zhigansk ru\n",
      "sentyabrskiy ru\n",
      "salalah om\n",
      "khatanga ru\n",
      "punta arenas cl\n",
      "yellowknife ca\n",
      "illoqqortoormiut gl\n",
      "tuktoyaktuk ca\n",
      "illoqqortoormiut gl\n",
      "nacala mz\n",
      "grindavik is\n",
      "ushuaia ar\n",
      "tuktoyaktuk ca\n",
      "alekseyevskoye ru\n",
      "kavieng pg\n",
      "ambilobe mg\n",
      "port hedland au\n",
      "luderitz na\n",
      "punta arenas cl\n",
      "saint-pierre pm\n",
      "vaitupu wf\n",
      "lorengau pg\n",
      "douglas us\n",
      "noumea nc\n",
      "khatanga ru\n",
      "beloha mg\n",
      "lorengau pg\n",
      "lakes entrance au\n",
      "bredasdorp za\n",
      "rawannawi ki\n",
      "busselton au\n",
      "teguldet ru\n",
      "sungairaya id\n",
      "chirongui yt\n",
      "bluff nz\n",
      "lompoc us\n",
      "thompson ca\n",
      "hilo us\n",
      "butaritari ki\n",
      "bethel us\n",
      "port alfred za\n",
      "karauzyak uz\n",
      "saint-philippe re\n",
      "punta arenas cl\n",
      "doctor pedro p. pena py\n",
      "tshikapa cd\n",
      "belushya guba ru\n",
      "butaritari ki\n",
      "malakal sd\n",
      "makakilo city us\n",
      "solnechnyy ru\n",
      "tsienyane bw\n",
      "mataura pf\n",
      "monetnyy ru\n",
      "progreso mx\n",
      "castro cl\n",
      "mar del plata ar\n",
      "illoqqortoormiut gl\n",
      "atuona pf\n",
      "ribeira grande pt\n",
      "esperance au\n",
      "powell us\n",
      "ushuaia ar\n",
      "punta arenas cl\n",
      "barrow us\n",
      "hermanus za\n",
      "punta arenas cl\n",
      "atuona pf\n",
      "vaini to\n",
      "necochea ar\n",
      "ushuaia ar\n",
      "ozark us\n",
      "santa cruz del sur cu\n",
      "sao felix do xingu br\n",
      "belushya guba ru\n",
      "punta arenas cl\n",
      "hithadhoo mv\n",
      "korem et\n",
      "xiaoshi cn\n",
      "castro cl\n",
      "mar del plata ar\n",
      "birao cf\n",
      "thompson ca\n",
      "bluff nz\n",
      "port hedland au\n",
      "asau tv\n",
      "ribeira grande pt\n",
      "saint-pierre pm\n",
      "ushuaia ar\n",
      "bambous virieux mu\n",
      "mar del plata ar\n",
      "ushuaia ar\n",
      "port blair in\n",
      "arraial do cabo br\n",
      "punta arenas cl\n",
      "la ronge ca\n",
      "rawson ar\n",
      "bluff nz\n",
      "bluff nz\n",
      "thompson ca\n",
      "utica us\n",
      "busselton au\n",
      "puerto ayora ec\n",
      "georgetown sh\n",
      "georgetown sh\n",
      "pisco pe\n",
      "ribeira grande pt\n",
      "roald no\n",
      "bluff nz\n",
      "jabiru au\n",
      "ushuaia ar\n",
      "tiznit ma\n",
      "kabompo zm\n",
      "ushuaia ar\n",
      "hilo us\n",
      "hilton head island us\n",
      "pacific grove us\n",
      "magadan ru\n",
      "pacific grove us\n",
      "ixtapa mx\n",
      "caravelas br\n",
      "morro agudo br\n",
      "punta arenas cl\n",
      "nguiu au\n",
      "busselton au\n",
      "cherskiy ru\n",
      "mahon es\n",
      "sao filipe cv\n",
      "malindi ke\n",
      "nouadhibou mr\n",
      "coahuayana mx\n",
      "naze jp\n",
      "hasaki jp\n",
      "joshimath in\n",
      "mar del plata ar\n",
      "marsa matruh eg\n",
      "rikitea pf\n",
      "ziarat pk\n",
      "filadelfia py\n",
      "dikson ru\n",
      "le mars us\n",
      "benguela ao\n",
      "mount isa au\n",
      "vilhena br\n",
      "ciechanow pl\n",
      "ha giang vn\n",
      "lebu cl\n",
      "vostok ru\n",
      "ponta do sol cv\n",
      "kodiak us\n",
      "victoria sc\n",
      "puerto madero mx\n",
      "broken hill au\n",
      "vaitupu wf\n",
      "kununurra au\n",
      "belushya guba ru\n",
      "katsuura jp\n",
      "port elizabeth za\n",
      "san isidro ph\n",
      "cabo san lucas mx\n",
      "nikolskoye ru\n",
      "barrow us\n",
      "conceicao do araguaia br\n",
      "mahebourg mu\n",
      "mount isa au\n",
      "kapaa us\n",
      "bom jardim br\n",
      "tasiilaq gl\n",
      "qaanaaq gl\n",
      "nantucket us\n",
      "kulhudhuffushi mv\n",
      "ushuaia ar\n",
      "bredasdorp za\n",
      "new norfolk au\n",
      "manica mz\n",
      "sobolevo ru\n",
      "assiniboia ca\n",
      "vaitupu wf\n",
      "nianzishan cn\n",
      "watertown us\n",
      "mataura pf\n",
      "mataura pf\n",
      "sept-iles ca\n",
      "griffith au\n",
      "chokurdakh ru\n",
      "rikitea pf\n",
      "yellowknife ca\n",
      "new norfolk au\n",
      "ushuaia ar\n",
      "hobart au\n",
      "makaha us\n",
      "saurimo ao\n",
      "pangkalanbuun id\n",
      "eldorado mx\n",
      "buala sb\n",
      "cimitarra co\n",
      "hilo us\n",
      "busselton au\n",
      "husavik is\n",
      "taolanaro mg\n",
      "qaanaaq gl\n",
      "atuona pf\n",
      "port alfred za\n",
      "ushuaia ar\n",
      "punta arenas cl\n",
      "barrow us\n",
      "punta arenas cl\n",
      "mount isa au\n",
      "hasaki jp\n",
      "lata sb\n",
      "atuona pf\n",
      "mar del plata ar\n",
      "nanakuli us\n",
      "hami cn\n",
      "ushuaia ar\n",
      "hobart au\n",
      "sitka us\n",
      "buchanan lr\n",
      "joshimath in\n",
      "bayshore gardens us\n",
      "verkhnyaya inta ru\n",
      "yellowknife ca\n",
      "laguna br\n",
      "mahebourg mu\n",
      "finschhafen pg\n",
      "busselton au\n",
      "boralday kz\n",
      "tiksi ru\n",
      "puerto ayora ec\n",
      "taolanaro mg\n",
      "bredasdorp za\n",
      "port alfred za\n",
      "rikitea pf\n",
      "busselton au\n",
      "barrow us\n",
      "bengkulu id\n",
      "taolanaro mg\n",
      "faanui pf\n",
      "lebu cl\n",
      "rikitea pf\n",
      "hilo us\n",
      "puerto ayacucho ve\n",
      "horsham au\n",
      "rikitea pf\n",
      "saleaula ws\n",
      "cabo san lucas mx\n",
      "linhares br\n",
      "zhangye cn\n",
      "ushuaia ar\n",
      "vaini to\n",
      "bambous virieux mu\n",
      "straumen no\n",
      "xai-xai mz\n",
      "kaitangata nz\n",
      "acapulco mx\n",
      "waipawa nz\n",
      "zlatoustovsk ru\n",
      "tsihombe mg\n",
      "illoqqortoormiut gl\n",
      "goundam ml\n",
      "barrow us\n",
      "saint-philippe re\n",
      "lixourion gr\n",
      "kruisfontein za\n",
      "armacao dos buzios br\n",
      "hermanus za\n",
      "mys shmidta ru\n",
      "mataura pf\n",
      "jamestown sh\n",
      "ushuaia ar\n",
      "effingham us\n",
      "kutiyana in\n",
      "lebu cl\n",
      "faanui pf\n",
      "aklavik ca\n",
      "ust-nera ru\n",
      "gat ly\n",
      "east london za\n",
      "georgetown gy\n",
      "sabang id\n",
      "punta arenas cl\n",
      "nome us\n",
      "vuktyl ru\n",
      "chifeng cn\n",
      "ribeira grande pt\n",
      "busselton au\n",
      "ilulissat gl\n",
      "ilulissat gl\n",
      "yellowknife ca\n",
      "zabid ye\n",
      "solnechnyy ru\n",
      "barbar sd\n",
      "port alfred za\n",
      "lebu cl\n",
      "dolbeau ca\n",
      "chokurdakh ru\n",
      "dikson ru\n",
      "mar del plata ar\n",
      "butaritari ki\n",
      "talnakh ru\n",
      "albany au\n",
      "jaisalmer in\n",
      "rikitea pf\n",
      "skjaerhollen no\n"
     ]
    }
   ],
   "source": [
    "# Use the print() function to display the latitude and longitude combinations.\n",
    "for coordinate in coordinates:\n",
    "    print(citipy.nearest_city(coordinate[0], coordinate[1]).city_name,\n",
    "          citipy.nearest_city(coordinate[0], coordinate[1]).country_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6298010b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "633"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list for holding the cities.\n",
    "cities = []\n",
    "# Identify the nearest city for each latitude and longitude combination.\n",
    "for coordinate in coordinates:\n",
    "    city = citipy.nearest_city(coordinate[0], coordinate[1]).city_name\n",
    "\n",
    "    # If the city is unique, then we will add it to the cities list.\n",
    "    if city not in cities:\n",
    "        cities.append(city)\n",
    "# Print the city count to confirm sufficient count.\n",
    "len(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5863dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the requests library.\n",
    "import requests\n",
    "\n",
    "# Import the API key.\n",
    "from config import weather_api_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f0b9af34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=ed54220d527d00d778ba67c1079904ee\n"
     ]
    }
   ],
   "source": [
    "# Starting URL for Weather Map API Call.\n",
    "url = \"http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=\" + weather_api_key\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b1b238e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=ed54220d527d00d778ba67c1079904ee&q=Boston\n"
     ]
    }
   ],
   "source": [
    "# Create an endpoint URL for a city.\n",
    "city_url = url + \"&q=\" + \"Boston\"\n",
    "print(city_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "191f8264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a 'Get' request for the city weather.\n",
    "city_weather = requests.get(city_url)\n",
    "city_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8d6b498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [404]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an endpoint URL for a city (Boston spelled wrong - 404)\n",
    "city_url = url + \"&q=\" + \"Bston\"\n",
    "city_weather = requests.get(city_url)\n",
    "city_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5aa93a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an endpoint URL for a city (Boston spelled correctly - 200, must format)\n",
    "city_url = url + \"&q=\" + \"Boston\"\n",
    "city_weather = requests.get(city_url)\n",
    "city_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d0139e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"coord\":{\"lon\":-71.0598,\"lat\":42.3584},\"weather\":[{\"id\":803,\"main\":\"Clouds\",\"description\":\"broken clouds\",\"icon\":\"04d\"}],\"base\":\"stations\",\"main\":{\"temp\":70.47,\"feels_like\":70.97,\"temp_min\":65.21,\"temp_max\":74.68,\"pressure\":1012,\"humidity\":80},\"visibility\":10000,\"wind\":{\"speed\":9.22,\"deg\":180},\"clouds\":{\"all\":75},\"dt\":1652625014,\"sys\":{\"type\":2,\"id\":2013408,\"country\":\"US\",\"sunrise\":1652606573,\"sunset\":1652659108},\"timezone\":-14400,\"id\":4930956,\"name\":\"Boston\",\"cod\":200}'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the 'text' of the 'Get' request.\n",
    "city_weather.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "59b014ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'coord': {'lon': -71.0598, 'lat': 42.3584},\n",
       " 'weather': [{'id': 803,\n",
       "   'main': 'Clouds',\n",
       "   'description': 'broken clouds',\n",
       "   'icon': '04d'}],\n",
       " 'base': 'stations',\n",
       " 'main': {'temp': 70.47,\n",
       "  'feels_like': 70.97,\n",
       "  'temp_min': 65.21,\n",
       "  'temp_max': 74.68,\n",
       "  'pressure': 1012,\n",
       "  'humidity': 80},\n",
       " 'visibility': 10000,\n",
       " 'wind': {'speed': 9.22, 'deg': 180},\n",
       " 'clouds': {'all': 75},\n",
       " 'dt': 1652625014,\n",
       " 'sys': {'type': 2,\n",
       "  'id': 2013408,\n",
       "  'country': 'US',\n",
       "  'sunrise': 1652606573,\n",
       "  'sunset': 1652659108},\n",
       " 'timezone': -14400,\n",
       " 'id': 4930956,\n",
       " 'name': 'Boston',\n",
       " 'cod': 200}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the JSON text of the 'Get' request >> easier to see data structure!\n",
    "city_weather.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "551e59c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City Weather found.\n"
     ]
    }
   ],
   "source": [
    "# Create an endpoint URL for a city - Handle the 200 error code!\n",
    "city_url = url + \"&q=\" + \"Boston\"\n",
    "city_weather = requests.get(city_url)\n",
    "if city_weather.status_code == 200:\n",
    "    print(f\"City Weather found.\")\n",
    "else:\n",
    "    print(f\"City weather not found.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32ff7568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'coord': {'lon': -71.0598, 'lat': 42.3584},\n",
       " 'weather': [{'id': 803,\n",
       "   'main': 'Clouds',\n",
       "   'description': 'broken clouds',\n",
       "   'icon': '04d'}],\n",
       " 'base': 'stations',\n",
       " 'main': {'temp': 70.47,\n",
       "  'feels_like': 70.97,\n",
       "  'temp_min': 65.21,\n",
       "  'temp_max': 74.68,\n",
       "  'pressure': 1012,\n",
       "  'humidity': 80},\n",
       " 'visibility': 10000,\n",
       " 'wind': {'speed': 9.22, 'deg': 180},\n",
       " 'clouds': {'all': 75},\n",
       " 'dt': 1652625014,\n",
       " 'sys': {'type': 2,\n",
       "  'id': 2013408,\n",
       "  'country': 'US',\n",
       "  'sunrise': 1652606573,\n",
       "  'sunset': 1652659108},\n",
       " 'timezone': -14400,\n",
       " 'id': 4930956,\n",
       " 'name': 'Boston',\n",
       " 'cod': 200}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the JSON data. \n",
    "city_url = url + \"&q=\" + \"Boston\"\n",
    "city_weather = requests.get(city_url)\n",
    "city_weather.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "976514a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# put it in a variable\n",
    "boston_data = city_weather.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e4a410af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'US'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select specifics...\n",
    "boston_data['sys']['country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8c034885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1652625014"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_data['dt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ba0dbad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.3584"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_data[\"coord\"][\"lat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dba38869",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.3584 -71.0598 74.68 80 75 9.22\n"
     ]
    }
   ],
   "source": [
    "lat = boston_data[\"coord\"][\"lat\"]\n",
    "lng = boston_data[\"coord\"][\"lon\"]\n",
    "max_temp = boston_data[\"main\"][\"temp_max\"]\n",
    "humidity = boston_data[\"main\"][\"humidity\"]\n",
    "clouds = boston_data[\"clouds\"][\"all\"]\n",
    "wind = boston_data[\"wind\"][\"speed\"]\n",
    "print(lat, lng, max_temp, humidity, clouds, wind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2af63675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2022, 5, 15, 14, 30, 14)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the datetime module from the datetime library.\n",
    "from datetime import datetime\n",
    "# Get the date from the JSON file.\n",
    "date = boston_data[\"dt\"]\n",
    "# Convert the UTC date to a date format with year, month, day, hours, minutes, and seconds.\n",
    "datetime.utcfromtimestamp(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "39f23381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2022-05-15 14:30:14'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.utcfromtimestamp(date).strftime('%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be1834b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval     \n",
      "-----------------------------\n",
      "Processing Record 1 of Set 1 | vaini\n",
      "Processing Record 2 of Set 1 | along\n",
      "Processing Record 3 of Set 1 | rio muerto\n",
      "Processing Record 4 of Set 1 | leh\n",
      "Processing Record 5 of Set 1 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 1 | fortuna\n",
      "Processing Record 7 of Set 1 | albany\n",
      "Processing Record 8 of Set 1 | hobart\n",
      "Processing Record 9 of Set 1 | hermanus\n",
      "Processing Record 10 of Set 1 | rikitea\n",
      "Processing Record 11 of Set 1 | bengkulu\n",
      "Processing Record 12 of Set 1 | upernavik\n",
      "Processing Record 13 of Set 1 | bonavista\n",
      "Processing Record 14 of Set 1 | ghatal\n",
      "Processing Record 15 of Set 1 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 1 | champerico\n",
      "Processing Record 17 of Set 1 | arraial do cabo\n",
      "Processing Record 18 of Set 1 | atuona\n",
      "Processing Record 19 of Set 1 | geraldton\n",
      "Processing Record 20 of Set 1 | kaitangata\n",
      "Processing Record 21 of Set 1 | bayburt\n",
      "Processing Record 22 of Set 1 | codrington\n",
      "Processing Record 23 of Set 1 | kefalos\n",
      "Processing Record 24 of Set 1 | berlevag\n",
      "Processing Record 25 of Set 1 | punta arenas\n",
      "Processing Record 26 of Set 1 | tautira\n",
      "Processing Record 27 of Set 1 | ushuaia\n",
      "Processing Record 28 of Set 1 | yellowknife\n",
      "Processing Record 29 of Set 1 | salinopolis\n",
      "Processing Record 30 of Set 1 | boguchany\n",
      "Processing Record 31 of Set 1 | qaanaaq\n",
      "Processing Record 32 of Set 1 | hilo\n",
      "Processing Record 33 of Set 1 | linqiong\n",
      "Processing Record 34 of Set 1 | baykit\n",
      "Processing Record 35 of Set 1 | jamestown\n",
      "Processing Record 36 of Set 1 | iqaluit\n",
      "Processing Record 37 of Set 1 | lompoc\n",
      "Processing Record 38 of Set 1 | ibra\n",
      "Processing Record 39 of Set 1 | plouzane\n",
      "Processing Record 40 of Set 1 | babanusah\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 1 | ambodifototra\n",
      "City not found. Skipping...\n",
      "Processing Record 42 of Set 1 | puerto ayora\n",
      "Processing Record 43 of Set 1 | padang\n",
      "Processing Record 44 of Set 1 | hithadhoo\n",
      "Processing Record 45 of Set 1 | nikolskoye\n",
      "Processing Record 46 of Set 1 | bubaque\n",
      "Processing Record 47 of Set 1 | victoria\n",
      "Processing Record 48 of Set 1 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 1 | izmit\n",
      "Processing Record 50 of Set 1 | winslow\n",
      "Processing Record 1 of Set 2 | clyde river\n",
      "Processing Record 2 of Set 2 | college\n",
      "Processing Record 3 of Set 2 | stokmarknes\n",
      "Processing Record 4 of Set 2 | bambous virieux\n",
      "Processing Record 5 of Set 2 | olafsvik\n",
      "Processing Record 6 of Set 2 | polewali\n",
      "Processing Record 7 of Set 2 | avarua\n",
      "Processing Record 8 of Set 2 | constitucion\n",
      "Processing Record 9 of Set 2 | cayenne\n",
      "Processing Record 10 of Set 2 | seoul\n",
      "Processing Record 11 of Set 2 | saldanha\n",
      "Processing Record 12 of Set 2 | morehead\n",
      "Processing Record 13 of Set 2 | kristinehamn\n",
      "Processing Record 14 of Set 2 | ponta delgada\n",
      "Processing Record 15 of Set 2 | kapaa\n",
      "Processing Record 16 of Set 2 | castro\n",
      "Processing Record 17 of Set 2 | calderitas\n",
      "Processing Record 18 of Set 2 | dali\n",
      "Processing Record 19 of Set 2 | maykain\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 2 | aykhal\n",
      "Processing Record 21 of Set 2 | new norfolk\n",
      "Processing Record 22 of Set 2 | bolshiye uki\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 2 | steinbach\n",
      "Processing Record 24 of Set 2 | hasaki\n",
      "Processing Record 25 of Set 2 | hinton\n",
      "Processing Record 26 of Set 2 | poum\n",
      "Processing Record 27 of Set 2 | mataura\n",
      "Processing Record 28 of Set 2 | hailar\n",
      "Processing Record 29 of Set 2 | san pedro\n",
      "Processing Record 30 of Set 2 | petatlan\n",
      "Processing Record 31 of Set 2 | deputatskiy\n",
      "Processing Record 32 of Set 2 | busselton\n",
      "Processing Record 33 of Set 2 | carnarvon\n",
      "Processing Record 34 of Set 2 | coquimbo\n",
      "Processing Record 35 of Set 2 | ntcheu\n",
      "Processing Record 36 of Set 2 | cape town\n",
      "Processing Record 37 of Set 2 | fairbanks\n",
      "Processing Record 38 of Set 2 | bembereke\n",
      "Processing Record 39 of Set 2 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record 40 of Set 2 | caravelas\n",
      "Processing Record 41 of Set 2 | senanga\n",
      "Processing Record 42 of Set 2 | airai\n",
      "Processing Record 43 of Set 2 | broken hill\n",
      "Processing Record 44 of Set 2 | kahului\n",
      "Processing Record 45 of Set 2 | komsomolskiy\n",
      "Processing Record 46 of Set 2 | bredasdorp\n",
      "Processing Record 47 of Set 2 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 2 | belgaum\n",
      "Processing Record 49 of Set 2 | tabou\n",
      "Processing Record 50 of Set 2 | barrow\n",
      "Processing Record 1 of Set 3 | san rafael\n",
      "Processing Record 2 of Set 3 | kavieng\n",
      "Processing Record 3 of Set 3 | dafeng\n",
      "Processing Record 4 of Set 3 | khatanga\n",
      "Processing Record 5 of Set 3 | ayabaca\n",
      "Processing Record 6 of Set 3 | butaritari\n",
      "Processing Record 7 of Set 3 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 3 | sinnamary\n",
      "Processing Record 9 of Set 3 | namibe\n",
      "Processing Record 10 of Set 3 | puerto del rosario\n",
      "Processing Record 11 of Set 3 | pijijiapan\n",
      "Processing Record 12 of Set 3 | cabo san lucas\n",
      "Processing Record 13 of Set 3 | alta floresta\n",
      "Processing Record 14 of Set 3 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 3 | higuey\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 3 | tasiilaq\n",
      "Processing Record 17 of Set 3 | mocambique\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 3 | galdar\n",
      "Processing Record 19 of Set 3 | chernyshkovskiy\n",
      "Processing Record 20 of Set 3 | kodiak\n",
      "Processing Record 21 of Set 3 | ugoofaaru\n",
      "Processing Record 22 of Set 3 | boende\n",
      "Processing Record 23 of Set 3 | bluff\n",
      "Processing Record 24 of Set 3 | vilhena\n",
      "Processing Record 25 of Set 3 | takoradi\n",
      "Processing Record 26 of Set 3 | dunmore east\n",
      "Processing Record 27 of Set 3 | margate\n",
      "Processing Record 28 of Set 3 | mayo\n",
      "Processing Record 29 of Set 3 | kuanshan\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 3 | windsor\n",
      "Processing Record 31 of Set 3 | faanui\n",
      "Processing Record 32 of Set 3 | pokrovsk\n",
      "Processing Record 33 of Set 3 | ormara\n",
      "Processing Record 34 of Set 3 | naze\n",
      "Processing Record 35 of Set 3 | jeremie\n",
      "Processing Record 36 of Set 3 | san cristobal\n",
      "Processing Record 37 of Set 3 | bairiki\n",
      "Processing Record 38 of Set 3 | pevek\n",
      "Processing Record 39 of Set 3 | cap malheureux\n",
      "Processing Record 40 of Set 3 | half moon bay\n",
      "Processing Record 41 of Set 3 | waipawa\n",
      "Processing Record 42 of Set 3 | san joaquin\n",
      "Processing Record 43 of Set 3 | emirdag\n",
      "Processing Record 44 of Set 3 | ferndale\n",
      "Processing Record 45 of Set 3 | shache\n",
      "Processing Record 46 of Set 3 | ajdabiya\n",
      "Processing Record 47 of Set 3 | bolungarvik\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 3 | saskylakh\n",
      "Processing Record 49 of Set 3 | aklavik\n",
      "Processing Record 50 of Set 3 | teguise\n",
      "Processing Record 1 of Set 4 | zapolyarnyy\n",
      "Processing Record 2 of Set 4 | umzimvubu\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 4 | paita\n",
      "Processing Record 4 of Set 4 | svetlyy\n",
      "Processing Record 5 of Set 4 | katobu\n",
      "Processing Record 6 of Set 4 | sofiyivka\n",
      "Processing Record 7 of Set 4 | beringovskiy\n",
      "Processing Record 8 of Set 4 | grand centre\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 4 | ongandjera\n",
      "Processing Record 10 of Set 4 | the valley\n",
      "Processing Record 11 of Set 4 | east london\n",
      "Processing Record 12 of Set 4 | singosari\n",
      "Processing Record 13 of Set 4 | port augusta\n",
      "Processing Record 14 of Set 4 | natal\n",
      "Processing Record 15 of Set 4 | pacific grove\n",
      "Processing Record 16 of Set 4 | ayolas\n",
      "Processing Record 17 of Set 4 | yarada\n",
      "Processing Record 18 of Set 4 | erdenet\n",
      "Processing Record 19 of Set 4 | san andres\n",
      "Processing Record 20 of Set 4 | kutum\n",
      "Processing Record 21 of Set 4 | dikson\n",
      "Processing Record 22 of Set 4 | rossland\n",
      "Processing Record 23 of Set 4 | amravati\n",
      "Processing Record 24 of Set 4 | ribeira grande\n",
      "Processing Record 25 of Set 4 | raudeberg\n",
      "Processing Record 26 of Set 4 | nanortalik\n",
      "Processing Record 27 of Set 4 | constantine\n",
      "Processing Record 28 of Set 4 | port alfred\n",
      "Processing Record 29 of Set 4 | laguna\n",
      "Processing Record 30 of Set 4 | sao filipe\n",
      "Processing Record 31 of Set 4 | diego de almagro\n",
      "Processing Record 32 of Set 4 | anadyr\n",
      "Processing Record 33 of Set 4 | cidreira\n",
      "Processing Record 34 of Set 4 | esperance\n",
      "Processing Record 35 of Set 4 | los llanos de aridane\n",
      "Processing Record 36 of Set 4 | kruisfontein\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 37 of Set 4 | tuatapere\n",
      "Processing Record 38 of Set 4 | ancud\n",
      "Processing Record 39 of Set 4 | gorakhpur\n",
      "Processing Record 40 of Set 4 | port keats\n",
      "Processing Record 41 of Set 4 | vozhega\n",
      "Processing Record 42 of Set 4 | ilulissat\n",
      "Processing Record 43 of Set 4 | ankang\n",
      "Processing Record 44 of Set 4 | kavaratti\n",
      "Processing Record 45 of Set 4 | port elizabeth\n",
      "Processing Record 46 of Set 4 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 4 | sabang\n",
      "Processing Record 48 of Set 4 | stony mountain\n",
      "Processing Record 49 of Set 4 | davila\n",
      "Processing Record 50 of Set 4 | souillac\n",
      "Processing Record 1 of Set 5 | urucara\n",
      "Processing Record 2 of Set 5 | petrolina\n",
      "Processing Record 3 of Set 5 | dubbo\n",
      "Processing Record 4 of Set 5 | bethel\n",
      "Processing Record 5 of Set 5 | holme\n",
      "Processing Record 6 of Set 5 | saint george\n",
      "Processing Record 7 of Set 5 | tarazona\n",
      "Processing Record 8 of Set 5 | bintulu\n",
      "Processing Record 9 of Set 5 | wanning\n",
      "Processing Record 10 of Set 5 | ramhormoz\n",
      "Processing Record 11 of Set 5 | georgetown\n",
      "Processing Record 12 of Set 5 | talnakh\n",
      "Processing Record 13 of Set 5 | illapel\n",
      "Processing Record 14 of Set 5 | bulgan\n",
      "Processing Record 15 of Set 5 | roma\n",
      "Processing Record 16 of Set 5 | solnechnyy\n",
      "Processing Record 17 of Set 5 | bocana de paiwas\n",
      "Processing Record 18 of Set 5 | agua branca\n",
      "Processing Record 19 of Set 5 | erenhot\n",
      "Processing Record 20 of Set 5 | maicao\n",
      "Processing Record 21 of Set 5 | thompson\n",
      "Processing Record 22 of Set 5 | marcona\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 5 | baculin\n",
      "Processing Record 24 of Set 5 | tumbes\n",
      "Processing Record 25 of Set 5 | broome\n",
      "Processing Record 26 of Set 5 | esperanza\n",
      "Processing Record 27 of Set 5 | lebu\n",
      "Processing Record 28 of Set 5 | bilibino\n",
      "Processing Record 29 of Set 5 | xiongyue\n",
      "Processing Record 30 of Set 5 | barcelos\n",
      "Processing Record 31 of Set 5 | medea\n",
      "Processing Record 32 of Set 5 | adrar\n",
      "Processing Record 33 of Set 5 | mumbwa\n",
      "Processing Record 34 of Set 5 | petropavlovsk-kamchatskiy\n",
      "Processing Record 35 of Set 5 | bogorodskoye\n",
      "Processing Record 36 of Set 5 | griffith\n",
      "Processing Record 37 of Set 5 | kemijarvi\n",
      "Processing Record 38 of Set 5 | aswan\n",
      "Processing Record 39 of Set 5 | saint-joseph\n",
      "Processing Record 40 of Set 5 | mkuranga\n",
      "Processing Record 41 of Set 5 | tura\n",
      "Processing Record 42 of Set 5 | lazaro cardenas\n",
      "Processing Record 43 of Set 5 | bandarbeyla\n",
      "Processing Record 44 of Set 5 | samusu\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 5 | gryazovets\n",
      "Processing Record 46 of Set 5 | ponta do sol\n",
      "Processing Record 47 of Set 5 | ostersund\n",
      "Processing Record 48 of Set 5 | aljezur\n",
      "Processing Record 49 of Set 5 | touros\n",
      "Processing Record 50 of Set 5 | chokurdakh\n",
      "Processing Record 1 of Set 6 | port hardy\n",
      "Processing Record 2 of Set 6 | kangaatsiaq\n",
      "Processing Record 3 of Set 6 | lively\n",
      "Processing Record 4 of Set 6 | ayagoz\n",
      "Processing Record 5 of Set 6 | agadez\n",
      "Processing Record 6 of Set 6 | maragheh\n",
      "Processing Record 7 of Set 6 | tidore\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 6 | kloulklubed\n",
      "Processing Record 9 of Set 6 | mys shmidta\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 6 | santa fe\n",
      "Processing Record 11 of Set 6 | mar del plata\n",
      "Processing Record 12 of Set 6 | dzhebariki-khaya\n",
      "Processing Record 13 of Set 6 | terrace bay\n",
      "Processing Record 14 of Set 6 | tuktoyaktuk\n",
      "Processing Record 15 of Set 6 | najran\n",
      "Processing Record 16 of Set 6 | saint-philippe\n",
      "Processing Record 17 of Set 6 | jabiru\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 6 | lasa\n",
      "Processing Record 19 of Set 6 | visby\n",
      "Processing Record 20 of Set 6 | luanda\n",
      "Processing Record 21 of Set 6 | quelimane\n",
      "Processing Record 22 of Set 6 | merauke\n",
      "Processing Record 23 of Set 6 | bjornevatn\n",
      "Processing Record 24 of Set 6 | nouadhibou\n",
      "Processing Record 25 of Set 6 | hongjiang\n",
      "Processing Record 26 of Set 6 | merritt\n",
      "Processing Record 27 of Set 6 | ondjiva\n",
      "Processing Record 28 of Set 6 | tsienyane\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 6 | tifton\n",
      "Processing Record 30 of Set 6 | monywa\n",
      "Processing Record 31 of Set 6 | lao cai\n",
      "Processing Record 32 of Set 6 | westport\n",
      "Processing Record 33 of Set 6 | cooma\n",
      "Processing Record 34 of Set 6 | kolpashevo\n",
      "Processing Record 35 of Set 6 | acapetahua\n",
      "Processing Record 36 of Set 6 | vilaka\n",
      "Processing Record 37 of Set 6 | mahebourg\n",
      "Processing Record 38 of Set 6 | balkanabat\n",
      "Processing Record 39 of Set 6 | burica\n",
      "City not found. Skipping...\n",
      "Processing Record 40 of Set 6 | avola\n",
      "Processing Record 41 of Set 6 | tabiauea\n",
      "City not found. Skipping...\n",
      "Processing Record 42 of Set 6 | marsta\n",
      "Processing Record 43 of Set 6 | mount gambier\n",
      "Processing Record 44 of Set 6 | carndonagh\n",
      "Processing Record 45 of Set 6 | norman wells\n",
      "Processing Record 46 of Set 6 | makat\n",
      "Processing Record 47 of Set 6 | biak\n",
      "Processing Record 48 of Set 6 | plavinas\n",
      "Processing Record 49 of Set 6 | pangnirtung\n",
      "Processing Record 50 of Set 6 | vestmannaeyjar\n",
      "Processing Record 1 of Set 7 | lagdo\n",
      "Processing Record 2 of Set 7 | taoudenni\n",
      "Processing Record 3 of Set 7 | alofi\n",
      "Processing Record 4 of Set 7 | meulaboh\n",
      "Processing Record 5 of Set 7 | jiangyou\n",
      "Processing Record 6 of Set 7 | maloy\n",
      "Processing Record 7 of Set 7 | sao joao da barra\n",
      "Processing Record 8 of Set 7 | mundi\n",
      "Processing Record 9 of Set 7 | kirkwall\n",
      "Processing Record 10 of Set 7 | ahipara\n",
      "Processing Record 11 of Set 7 | tiksi\n",
      "Processing Record 12 of Set 7 | luderitz\n",
      "Processing Record 13 of Set 7 | atambua\n",
      "Processing Record 14 of Set 7 | haines junction\n",
      "Processing Record 15 of Set 7 | saint anthony\n",
      "Processing Record 16 of Set 7 | cosio\n",
      "Processing Record 17 of Set 7 | portel\n",
      "Processing Record 18 of Set 7 | shangzhi\n",
      "Processing Record 19 of Set 7 | mattawa\n",
      "Processing Record 20 of Set 7 | gulshat\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 7 | fuxin\n",
      "Processing Record 22 of Set 7 | coahuayana\n",
      "Processing Record 23 of Set 7 | mayumba\n",
      "Processing Record 24 of Set 7 | tromso\n",
      "Processing Record 25 of Set 7 | malwan\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 7 | morros\n",
      "Processing Record 27 of Set 7 | lakes entrance\n",
      "Processing Record 28 of Set 7 | mareeba\n",
      "Processing Record 29 of Set 7 | severo-kurilsk\n",
      "Processing Record 30 of Set 7 | yamada\n",
      "Processing Record 31 of Set 7 | raahe\n",
      "Processing Record 32 of Set 7 | mahajanga\n",
      "Processing Record 33 of Set 7 | ragusa\n",
      "Processing Record 34 of Set 7 | rabo de peixe\n",
      "Processing Record 35 of Set 7 | satitoa\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 7 | longyearbyen\n",
      "Processing Record 37 of Set 7 | nemuro\n",
      "Processing Record 38 of Set 7 | katsuura\n",
      "Processing Record 39 of Set 7 | tabuk\n",
      "Processing Record 40 of Set 7 | lavrentiya\n",
      "Processing Record 41 of Set 7 | naberera\n",
      "Processing Record 42 of Set 7 | awbari\n",
      "Processing Record 43 of Set 7 | minot\n",
      "Processing Record 44 of Set 7 | chuy\n",
      "Processing Record 45 of Set 7 | pisco\n",
      "Processing Record 46 of Set 7 | sentyabrskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 7 | puerto escondido\n",
      "Processing Record 48 of Set 7 | luwuk\n",
      "Processing Record 49 of Set 7 | aketi\n",
      "Processing Record 50 of Set 7 | wagar\n",
      "Processing Record 1 of Set 8 | sadu\n",
      "Processing Record 2 of Set 8 | mnogovershinnyy\n",
      "Processing Record 3 of Set 8 | wewak\n",
      "Processing Record 4 of Set 8 | niamtougou\n",
      "Processing Record 5 of Set 8 | sitka\n",
      "Processing Record 6 of Set 8 | serebryanyy bor\n",
      "Processing Record 7 of Set 8 | namatanai\n",
      "Processing Record 8 of Set 8 | hervey bay\n",
      "Processing Record 9 of Set 8 | flinders\n",
      "Processing Record 10 of Set 8 | manono\n",
      "Processing Record 11 of Set 8 | buckeye\n",
      "Processing Record 12 of Set 8 | lagoa\n",
      "Processing Record 13 of Set 8 | isla mujeres\n",
      "Processing Record 14 of Set 8 | guerrero negro\n",
      "Processing Record 15 of Set 8 | moussoro\n",
      "Processing Record 16 of Set 8 | anloga\n",
      "Processing Record 17 of Set 8 | pangai\n",
      "Processing Record 18 of Set 8 | gat\n",
      "Processing Record 19 of Set 8 | havre-saint-pierre\n",
      "Processing Record 20 of Set 8 | torbay\n",
      "Processing Record 21 of Set 8 | powell river\n",
      "Processing Record 22 of Set 8 | toliary\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 8 | umm bab\n",
      "Processing Record 24 of Set 8 | ratnagiri\n",
      "Processing Record 25 of Set 8 | praia da vitoria\n",
      "Processing Record 26 of Set 8 | sinkat\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 8 | mehran\n",
      "Processing Record 28 of Set 8 | avera\n",
      "Processing Record 29 of Set 8 | tecoanapa\n",
      "Processing Record 30 of Set 8 | corrales\n",
      "Processing Record 31 of Set 8 | stephenville\n",
      "Processing Record 32 of Set 8 | teacapan\n",
      "Processing Record 33 of Set 8 | bar harbor\n",
      "Processing Record 34 of Set 8 | yulara\n",
      "Processing Record 35 of Set 8 | hit\n",
      "Processing Record 36 of Set 8 | nortelandia\n",
      "Processing Record 37 of Set 8 | priargunsk\n",
      "Processing Record 38 of Set 8 | nome\n",
      "Processing Record 39 of Set 8 | wuwei\n",
      "Processing Record 40 of Set 8 | san policarpo\n",
      "Processing Record 41 of Set 8 | santa teresa\n",
      "Processing Record 42 of Set 8 | eirunepe\n",
      "Processing Record 43 of Set 8 | faya\n",
      "Processing Record 44 of Set 8 | longlac\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 8 | robertsport\n",
      "Processing Record 46 of Set 8 | olinda\n",
      "Processing Record 47 of Set 8 | moranbah\n",
      "Processing Record 48 of Set 8 | cherskiy\n",
      "Processing Record 49 of Set 8 | catamarca\n",
      "Processing Record 50 of Set 8 | vostok\n",
      "Processing Record 1 of Set 9 | magadan\n",
      "Processing Record 2 of Set 9 | mezen\n",
      "Processing Record 3 of Set 9 | viedma\n",
      "Processing Record 4 of Set 9 | andijon\n",
      "Processing Record 5 of Set 9 | teluknaga\n",
      "Processing Record 6 of Set 9 | kupang\n",
      "Processing Record 7 of Set 9 | ngukurr\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 9 | konongo\n",
      "Processing Record 9 of Set 9 | shimoda\n",
      "Processing Record 10 of Set 9 | roald\n",
      "Processing Record 11 of Set 9 | yar-sale\n",
      "Processing Record 12 of Set 9 | fayaoue\n",
      "Processing Record 13 of Set 9 | port macquarie\n",
      "Processing Record 14 of Set 9 | atar\n",
      "Processing Record 15 of Set 9 | ambilobe\n",
      "Processing Record 16 of Set 9 | kiruna\n",
      "Processing Record 17 of Set 9 | patos de minas\n",
      "Processing Record 18 of Set 9 | ngunguru\n",
      "Processing Record 19 of Set 9 | klaksvik\n",
      "Processing Record 20 of Set 9 | isangel\n",
      "Processing Record 21 of Set 9 | viljoenskroon\n",
      "Processing Record 22 of Set 9 | kalabo\n",
      "Processing Record 23 of Set 9 | bilma\n",
      "Processing Record 24 of Set 9 | asosa\n",
      "Processing Record 25 of Set 9 | egvekinot\n",
      "Processing Record 26 of Set 9 | malumfashi\n",
      "Processing Record 27 of Set 9 | wundanyi\n",
      "Processing Record 28 of Set 9 | mafeteng\n",
      "Processing Record 29 of Set 9 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 9 | sueyoshi\n",
      "Processing Record 31 of Set 9 | manggar\n",
      "Processing Record 32 of Set 9 | kapit\n",
      "Processing Record 33 of Set 9 | sovetskiy\n",
      "Processing Record 34 of Set 9 | dhidhdhoo\n",
      "Processing Record 35 of Set 9 | sfantu gheorghe\n",
      "Processing Record 36 of Set 9 | la ronge\n",
      "Processing Record 37 of Set 9 | kemin\n",
      "Processing Record 38 of Set 9 | bagdarin\n",
      "Processing Record 39 of Set 9 | liverpool\n",
      "Processing Record 40 of Set 9 | zhezkazgan\n",
      "Processing Record 41 of Set 9 | pacasmayo\n",
      "Processing Record 42 of Set 9 | dubovskoye\n",
      "Processing Record 43 of Set 9 | palabuhanratu\n",
      "City not found. Skipping...\n",
      "Processing Record 44 of Set 9 | acapulco\n",
      "Processing Record 45 of Set 9 | teguldet\n",
      "Processing Record 46 of Set 9 | vaitupu\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 9 | acari\n",
      "Processing Record 48 of Set 9 | aksha\n",
      "Processing Record 49 of Set 9 | tulagi\n",
      "Processing Record 50 of Set 9 | richmond\n",
      "Processing Record 1 of Set 10 | uspenka\n",
      "Processing Record 2 of Set 10 | hay river\n",
      "Processing Record 3 of Set 10 | aginskoye\n",
      "Processing Record 4 of Set 10 | dharchula\n",
      "Processing Record 5 of Set 10 | taltal\n",
      "Processing Record 6 of Set 10 | vila franca do campo\n",
      "Processing Record 7 of Set 10 | hofn\n",
      "Processing Record 8 of Set 10 | darnah\n",
      "Processing Record 9 of Set 10 | kokopo\n",
      "Processing Record 10 of Set 10 | palmer\n",
      "Processing Record 11 of Set 10 | maningrida\n",
      "Processing Record 12 of Set 10 | atraulia\n",
      "Processing Record 13 of Set 10 | necocli\n",
      "Processing Record 14 of Set 10 | hambantota\n",
      "Processing Record 15 of Set 10 | bam\n",
      "Processing Record 16 of Set 10 | hamilton\n",
      "Processing Record 17 of Set 10 | ayan\n",
      "Processing Record 18 of Set 10 | whithorn\n",
      "Processing Record 19 of Set 10 | ndola\n",
      "Processing Record 20 of Set 10 | kaeo\n",
      "Processing Record 21 of Set 10 | rimavska sobota\n",
      "Processing Record 22 of Set 10 | port hedland\n",
      "Processing Record 23 of Set 10 | san jose\n",
      "Processing Record 24 of Set 10 | surab\n",
      "Processing Record 25 of Set 10 | narsaq\n",
      "Processing Record 26 of Set 10 | yichun\n",
      "Processing Record 27 of Set 10 | nizhneyansk\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 10 | jega\n",
      "Processing Record 29 of Set 10 | jibuti\n",
      "Processing Record 30 of Set 10 | yaan\n",
      "Processing Record 31 of Set 10 | zhirnovsk\n",
      "Processing Record 32 of Set 10 | oranjemund\n",
      "Processing Record 33 of Set 10 | yorkton\n",
      "Processing Record 34 of Set 10 | fairview\n",
      "Processing Record 35 of Set 10 | bell ville\n",
      "Processing Record 36 of Set 10 | chapais\n",
      "Processing Record 37 of Set 10 | grindavik\n",
      "Processing Record 38 of Set 10 | warmbad\n",
      "Processing Record 39 of Set 10 | provideniya\n",
      "Processing Record 40 of Set 10 | gonaives\n",
      "Processing Record 41 of Set 10 | grand gaube\n",
      "Processing Record 42 of Set 10 | karaton\n",
      "Processing Record 43 of Set 10 | inuvik\n",
      "Processing Record 44 of Set 10 | manta\n",
      "Processing Record 45 of Set 10 | omboue\n",
      "Processing Record 46 of Set 10 | alappuzha\n",
      "Processing Record 47 of Set 10 | isla vista\n",
      "Processing Record 48 of Set 10 | honningsvag\n",
      "Processing Record 49 of Set 10 | majene\n",
      "Processing Record 50 of Set 10 | rawson\n",
      "Processing Record 1 of Set 11 | marsabit\n",
      "Processing Record 2 of Set 11 | solnechnogorsk\n",
      "Processing Record 3 of Set 11 | la primavera\n",
      "Processing Record 4 of Set 11 | flin flon\n",
      "Processing Record 5 of Set 11 | male\n",
      "Processing Record 6 of Set 11 | walvis bay\n",
      "Processing Record 7 of Set 11 | pangkalanbuun\n",
      "Processing Record 8 of Set 11 | kattivakkam\n",
      "Processing Record 9 of Set 11 | omsukchan\n",
      "Processing Record 10 of Set 11 | beyneu\n",
      "Processing Record 11 of Set 11 | kribi\n",
      "Processing Record 12 of Set 11 | rokytne\n",
      "Processing Record 13 of Set 11 | pulaski\n",
      "Processing Record 14 of Set 11 | salalah\n",
      "Processing Record 15 of Set 11 | san patricio\n",
      "Processing Record 16 of Set 11 | batagay-alyta\n",
      "Processing Record 17 of Set 11 | potosi\n",
      "Processing Record 18 of Set 11 | juba\n",
      "Processing Record 19 of Set 11 | acatlan\n",
      "Processing Record 20 of Set 11 | harper\n",
      "Processing Record 21 of Set 11 | alihe\n",
      "Processing Record 22 of Set 11 | les escoumins\n",
      "Processing Record 23 of Set 11 | port lincoln\n",
      "Processing Record 24 of Set 11 | salta\n",
      "Processing Record 25 of Set 11 | sturgeon falls\n",
      "Processing Record 26 of Set 11 | ciudad bolivar\n",
      "Processing Record 27 of Set 11 | smithers\n",
      "Processing Record 28 of Set 11 | warqla\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 11 | san quintin\n",
      "Processing Record 30 of Set 11 | makakilo city\n",
      "Processing Record 31 of Set 11 | alim\n",
      "Processing Record 32 of Set 11 | haibowan\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 11 | douglas\n",
      "Processing Record 34 of Set 11 | dukat\n",
      "Processing Record 35 of Set 11 | kaithal\n",
      "Processing Record 36 of Set 11 | alamogordo\n",
      "Processing Record 37 of Set 11 | camacha\n",
      "Processing Record 38 of Set 11 | zaterechnyy\n",
      "Processing Record 39 of Set 11 | pokhara\n",
      "Processing Record 40 of Set 11 | bataipora\n",
      "Processing Record 41 of Set 11 | fare\n",
      "Processing Record 42 of Set 11 | zhigansk\n",
      "Processing Record 43 of Set 11 | nacala\n",
      "Processing Record 44 of Set 11 | alekseyevskoye\n",
      "Processing Record 45 of Set 11 | saint-pierre\n",
      "Processing Record 46 of Set 11 | lorengau\n",
      "Processing Record 47 of Set 11 | noumea\n",
      "Processing Record 48 of Set 11 | beloha\n",
      "Processing Record 49 of Set 11 | rawannawi\n",
      "City not found. Skipping...\n",
      "Processing Record 50 of Set 11 | sungairaya\n",
      "Processing Record 1 of Set 12 | chirongui\n",
      "Processing Record 2 of Set 12 | karauzyak\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 12 | doctor pedro p. pena\n",
      "City not found. Skipping...\n",
      "Processing Record 4 of Set 12 | tshikapa\n",
      "Processing Record 5 of Set 12 | malakal\n",
      "Processing Record 6 of Set 12 | monetnyy\n",
      "Processing Record 7 of Set 12 | progreso\n",
      "Processing Record 8 of Set 12 | powell\n",
      "Processing Record 9 of Set 12 | necochea\n",
      "Processing Record 10 of Set 12 | ozark\n",
      "Processing Record 11 of Set 12 | santa cruz del sur\n",
      "Processing Record 12 of Set 12 | sao felix do xingu\n",
      "Processing Record 13 of Set 12 | korem\n",
      "Processing Record 14 of Set 12 | xiaoshi\n",
      "Processing Record 15 of Set 12 | birao\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 16 of Set 12 | asau\n",
      "Processing Record 17 of Set 12 | port blair\n",
      "Processing Record 18 of Set 12 | utica\n",
      "Processing Record 19 of Set 12 | tiznit\n",
      "Processing Record 20 of Set 12 | kabompo\n",
      "Processing Record 21 of Set 12 | hilton head island\n",
      "Processing Record 22 of Set 12 | ixtapa\n",
      "Processing Record 23 of Set 12 | morro agudo\n",
      "Processing Record 24 of Set 12 | nguiu\n",
      "City not found. Skipping...\n",
      "Processing Record 25 of Set 12 | mahon\n",
      "Processing Record 26 of Set 12 | malindi\n",
      "Processing Record 27 of Set 12 | joshimath\n",
      "Processing Record 28 of Set 12 | marsa matruh\n",
      "Processing Record 29 of Set 12 | ziarat\n",
      "Processing Record 30 of Set 12 | filadelfia\n",
      "Processing Record 31 of Set 12 | le mars\n",
      "Processing Record 32 of Set 12 | benguela\n",
      "Processing Record 33 of Set 12 | mount isa\n",
      "Processing Record 34 of Set 12 | ciechanow\n",
      "Processing Record 35 of Set 12 | ha giang\n",
      "Processing Record 36 of Set 12 | puerto madero\n",
      "Processing Record 37 of Set 12 | kununurra\n",
      "Processing Record 38 of Set 12 | san isidro\n",
      "Processing Record 39 of Set 12 | conceicao do araguaia\n",
      "Processing Record 40 of Set 12 | bom jardim\n",
      "Processing Record 41 of Set 12 | nantucket\n",
      "Processing Record 42 of Set 12 | kulhudhuffushi\n",
      "Processing Record 43 of Set 12 | manica\n",
      "Processing Record 44 of Set 12 | sobolevo\n",
      "Processing Record 45 of Set 12 | assiniboia\n",
      "Processing Record 46 of Set 12 | nianzishan\n",
      "Processing Record 47 of Set 12 | watertown\n",
      "Processing Record 48 of Set 12 | sept-iles\n",
      "Processing Record 49 of Set 12 | makaha\n",
      "Processing Record 50 of Set 12 | saurimo\n",
      "Processing Record 1 of Set 13 | eldorado\n",
      "Processing Record 2 of Set 13 | buala\n",
      "Processing Record 3 of Set 13 | cimitarra\n",
      "Processing Record 4 of Set 13 | husavik\n",
      "Processing Record 5 of Set 13 | lata\n",
      "Processing Record 6 of Set 13 | nanakuli\n",
      "Processing Record 7 of Set 13 | hami\n",
      "Processing Record 8 of Set 13 | buchanan\n",
      "Processing Record 9 of Set 13 | bayshore gardens\n",
      "Processing Record 10 of Set 13 | verkhnyaya inta\n",
      "Processing Record 11 of Set 13 | finschhafen\n",
      "Processing Record 12 of Set 13 | boralday\n",
      "Processing Record 13 of Set 13 | puerto ayacucho\n",
      "Processing Record 14 of Set 13 | horsham\n",
      "Processing Record 15 of Set 13 | saleaula\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 13 | linhares\n",
      "Processing Record 17 of Set 13 | zhangye\n",
      "Processing Record 18 of Set 13 | straumen\n",
      "Processing Record 19 of Set 13 | xai-xai\n",
      "Processing Record 20 of Set 13 | zlatoustovsk\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 13 | goundam\n",
      "Processing Record 22 of Set 13 | lixourion\n",
      "Processing Record 23 of Set 13 | armacao dos buzios\n",
      "City not found. Skipping...\n",
      "Processing Record 24 of Set 13 | effingham\n",
      "Processing Record 25 of Set 13 | kutiyana\n",
      "Processing Record 26 of Set 13 | ust-nera\n",
      "Processing Record 27 of Set 13 | vuktyl\n",
      "Processing Record 28 of Set 13 | chifeng\n",
      "Processing Record 29 of Set 13 | zabid\n",
      "Processing Record 30 of Set 13 | barbar\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 13 | dolbeau\n",
      "City not found. Skipping...\n",
      "Processing Record 32 of Set 13 | jaisalmer\n",
      "Processing Record 33 of Set 13 | skjaerhollen\n",
      "City not found. Skipping...\n",
      "-----------------------------\n",
      "Data Retrieval Complete      \n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "# Import the time library and the datetime module from the datetime library \n",
    "import time\n",
    "from datetime import datetime\n",
    "\n",
    "# Create an empty list to hold the weather data.\n",
    "city_data = []\n",
    "# Print the beginning of the logging.\n",
    "print(\"Beginning Data Retrieval     \")\n",
    "print(\"-----------------------------\")\n",
    "\n",
    "# Create counters.\n",
    "record_count = 1\n",
    "set_count = 1\n",
    "\n",
    "# Loop through all the cities in the list.\n",
    "for i, city in enumerate(cities):\n",
    "\n",
    "    # Group cities in sets of 50 for logging purposes.\n",
    "    if (i % 50 == 0 and i >= 50):\n",
    "        set_count += 1\n",
    "        record_count = 1\n",
    "        time.sleep(60)\n",
    "\n",
    "    # Create endpoint URL with each city.\n",
    "    city_url = url + \"&q=\" + city.replace(\" \",\"+\")\n",
    "\n",
    "    # Log the URL, record, and set numbers and the city.\n",
    "    print(f\"Processing Record {record_count} of Set {set_count} | {city}\")\n",
    "    # Add 1 to the record count.\n",
    "    record_count += 1\n",
    "    \n",
    "# Run an API request for each of the cities.\n",
    "    try:\n",
    "        # Parse the JSON and retrieve data.\n",
    "        city_weather = requests.get(city_url).json()\n",
    "        # Parse out the needed data.\n",
    "        city_lat = city_weather[\"coord\"][\"lat\"]\n",
    "        city_lng = city_weather[\"coord\"][\"lon\"]\n",
    "        city_max_temp = city_weather[\"main\"][\"temp_max\"]\n",
    "        city_humidity = city_weather[\"main\"][\"humidity\"]\n",
    "        city_clouds = city_weather[\"clouds\"][\"all\"]\n",
    "        city_wind = city_weather[\"wind\"][\"speed\"]\n",
    "        city_country = city_weather[\"sys\"][\"country\"]\n",
    "        # Convert the date to ISO standard.\n",
    "        city_date = datetime.utcfromtimestamp(city_weather[\"dt\"]).strftime('%Y-%m-%d %H:%M:%S')\n",
    "        # Append the city information into city_data list.\n",
    "        city_data.append({\"City\": city.title(),\n",
    "                          \"Lat\": city_lat,\n",
    "                          \"Lng\": city_lng,\n",
    "                          \"Max Temp\": city_max_temp,\n",
    "                          \"Humidity\": city_humidity,\n",
    "                          \"Cloudiness\": city_clouds,\n",
    "                          \"Wind Speed\": city_wind,\n",
    "                          \"Country\": city_country,\n",
    "                          \"Date\": city_date})\n",
    "\n",
    "# If an error is experienced, skip the city.\n",
    "    except:\n",
    "        print(\"City not found. Skipping...\")\n",
    "        pass\n",
    "\n",
    "# Indicate that Data Loading is complete.\n",
    "print(\"-----------------------------\")\n",
    "print(\"Data Retrieval Complete      \")\n",
    "print(\"-----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bec4e925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Vaini</td>\n",
       "      <td>-21.2000</td>\n",
       "      <td>-175.2000</td>\n",
       "      <td>60.96</td>\n",
       "      <td>94</td>\n",
       "      <td>20</td>\n",
       "      <td>5.75</td>\n",
       "      <td>TO</td>\n",
       "      <td>2022-05-15 14:34:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Along</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>94.7667</td>\n",
       "      <td>71.42</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>1.30</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:41:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rio Muerto</td>\n",
       "      <td>27.4167</td>\n",
       "      <td>-110.2333</td>\n",
       "      <td>80.02</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>9.33</td>\n",
       "      <td>MX</td>\n",
       "      <td>2022-05-15 14:41:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leh</td>\n",
       "      <td>34.1667</td>\n",
       "      <td>77.5833</td>\n",
       "      <td>53.47</td>\n",
       "      <td>25</td>\n",
       "      <td>79</td>\n",
       "      <td>8.41</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:41:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fortuna</td>\n",
       "      <td>40.5982</td>\n",
       "      <td>-124.1573</td>\n",
       "      <td>56.93</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-05-15 14:41:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albany</td>\n",
       "      <td>42.6001</td>\n",
       "      <td>-73.9662</td>\n",
       "      <td>71.64</td>\n",
       "      <td>82</td>\n",
       "      <td>95</td>\n",
       "      <td>3.62</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-05-15 14:33:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hobart</td>\n",
       "      <td>-42.8794</td>\n",
       "      <td>147.3294</td>\n",
       "      <td>54.27</td>\n",
       "      <td>79</td>\n",
       "      <td>40</td>\n",
       "      <td>11.50</td>\n",
       "      <td>AU</td>\n",
       "      <td>2022-05-15 14:31:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hermanus</td>\n",
       "      <td>-34.4187</td>\n",
       "      <td>19.2345</td>\n",
       "      <td>68.90</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8.86</td>\n",
       "      <td>ZA</td>\n",
       "      <td>2022-05-15 14:34:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rikitea</td>\n",
       "      <td>-23.1203</td>\n",
       "      <td>-134.9692</td>\n",
       "      <td>76.80</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>19.33</td>\n",
       "      <td>PF</td>\n",
       "      <td>2022-05-15 14:34:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bengkulu</td>\n",
       "      <td>-3.8004</td>\n",
       "      <td>102.2655</td>\n",
       "      <td>78.49</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>3.20</td>\n",
       "      <td>ID</td>\n",
       "      <td>2022-05-15 14:34:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City      Lat       Lng  Max Temp  Humidity  Cloudiness  Wind Speed  \\\n",
       "0       Vaini -21.2000 -175.2000     60.96        94          20        5.75   \n",
       "1       Along  28.1667   94.7667     71.42        98          97        1.30   \n",
       "2  Rio Muerto  27.4167 -110.2333     80.02        43           7        9.33   \n",
       "3         Leh  34.1667   77.5833     53.47        25          79        8.41   \n",
       "4     Fortuna  40.5982 -124.1573     56.93        97         100        0.00   \n",
       "5      Albany  42.6001  -73.9662     71.64        82          95        3.62   \n",
       "6      Hobart -42.8794  147.3294     54.27        79          40       11.50   \n",
       "7    Hermanus -34.4187   19.2345     68.90        51           0        8.86   \n",
       "8     Rikitea -23.1203 -134.9692     76.80        71         100       19.33   \n",
       "9    Bengkulu  -3.8004  102.2655     78.49        90         100        3.20   \n",
       "\n",
       "  Country                 Date  \n",
       "0      TO  2022-05-15 14:34:26  \n",
       "1      IN  2022-05-15 14:41:25  \n",
       "2      MX  2022-05-15 14:41:51  \n",
       "3      IN  2022-05-15 14:41:52  \n",
       "4      US  2022-05-15 14:41:52  \n",
       "5      US  2022-05-15 14:33:33  \n",
       "6      AU  2022-05-15 14:31:07  \n",
       "7      ZA  2022-05-15 14:34:40  \n",
       "8      PF  2022-05-15 14:34:32  \n",
       "9      ID  2022-05-15 14:34:39  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the array of dictionaries to a Pandas DataFrame.\n",
    "city_data_df = pd.DataFrame(city_data)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f87cd3f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vaini</td>\n",
       "      <td>TO</td>\n",
       "      <td>2022-05-15 14:34:26</td>\n",
       "      <td>-21.2000</td>\n",
       "      <td>-175.2000</td>\n",
       "      <td>60.96</td>\n",
       "      <td>94</td>\n",
       "      <td>20</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Along</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:41:25</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>94.7667</td>\n",
       "      <td>71.42</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rio Muerto</td>\n",
       "      <td>MX</td>\n",
       "      <td>2022-05-15 14:41:51</td>\n",
       "      <td>27.4167</td>\n",
       "      <td>-110.2333</td>\n",
       "      <td>80.02</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leh</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:41:52</td>\n",
       "      <td>34.1667</td>\n",
       "      <td>77.5833</td>\n",
       "      <td>53.47</td>\n",
       "      <td>25</td>\n",
       "      <td>79</td>\n",
       "      <td>8.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fortuna</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-05-15 14:41:52</td>\n",
       "      <td>40.5982</td>\n",
       "      <td>-124.1573</td>\n",
       "      <td>56.93</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-05-15 14:33:33</td>\n",
       "      <td>42.6001</td>\n",
       "      <td>-73.9662</td>\n",
       "      <td>71.64</td>\n",
       "      <td>82</td>\n",
       "      <td>95</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hobart</td>\n",
       "      <td>AU</td>\n",
       "      <td>2022-05-15 14:31:07</td>\n",
       "      <td>-42.8794</td>\n",
       "      <td>147.3294</td>\n",
       "      <td>54.27</td>\n",
       "      <td>79</td>\n",
       "      <td>40</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hermanus</td>\n",
       "      <td>ZA</td>\n",
       "      <td>2022-05-15 14:34:40</td>\n",
       "      <td>-34.4187</td>\n",
       "      <td>19.2345</td>\n",
       "      <td>68.90</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rikitea</td>\n",
       "      <td>PF</td>\n",
       "      <td>2022-05-15 14:34:32</td>\n",
       "      <td>-23.1203</td>\n",
       "      <td>-134.9692</td>\n",
       "      <td>76.80</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>19.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bengkulu</td>\n",
       "      <td>ID</td>\n",
       "      <td>2022-05-15 14:34:39</td>\n",
       "      <td>-3.8004</td>\n",
       "      <td>102.2655</td>\n",
       "      <td>78.49</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City Country                 Date      Lat       Lng  Max Temp  \\\n",
       "0       Vaini      TO  2022-05-15 14:34:26 -21.2000 -175.2000     60.96   \n",
       "1       Along      IN  2022-05-15 14:41:25  28.1667   94.7667     71.42   \n",
       "2  Rio Muerto      MX  2022-05-15 14:41:51  27.4167 -110.2333     80.02   \n",
       "3         Leh      IN  2022-05-15 14:41:52  34.1667   77.5833     53.47   \n",
       "4     Fortuna      US  2022-05-15 14:41:52  40.5982 -124.1573     56.93   \n",
       "5      Albany      US  2022-05-15 14:33:33  42.6001  -73.9662     71.64   \n",
       "6      Hobart      AU  2022-05-15 14:31:07 -42.8794  147.3294     54.27   \n",
       "7    Hermanus      ZA  2022-05-15 14:34:40 -34.4187   19.2345     68.90   \n",
       "8     Rikitea      PF  2022-05-15 14:34:32 -23.1203 -134.9692     76.80   \n",
       "9    Bengkulu      ID  2022-05-15 14:34:39  -3.8004  102.2655     78.49   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        94          20        5.75  \n",
       "1        98          97        1.30  \n",
       "2        43           7        9.33  \n",
       "3        25          79        8.41  \n",
       "4        97         100        0.00  \n",
       "5        82          95        3.62  \n",
       "6        79          40       11.50  \n",
       "7        51           0        8.86  \n",
       "8        71         100       19.33  \n",
       "9        90         100        3.20  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_column_order = [\"City\", \"Country\", \"Date\", \"Lat\", \"Lng\", \"Max Temp\", \"Humidity\", \"Cloudiness\", \"Wind Speed\"]\n",
    "city_data_df = city_data_df[new_column_order]\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bbee9ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the output file (CSV).\n",
    "output_data_file = \"weather_data/cities.csv\" \n",
    "# Export the City_Data into a CSV.\n",
    "city_data_df.to_csv(output_data_file, index_label=\"City_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "66d22f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract relevant fields from the DataFrame for plotting.\n",
    "lats = city_data_df[\"Lat\"]\n",
    "max_temps = city_data_df[\"Max Temp\"]\n",
    "humidity = city_data_df[\"Humidity\"]\n",
    "cloudiness = city_data_df[\"Cloudiness\"]\n",
    "wind_speed = city_data_df[\"Wind Speed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "06ee9521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1652630395.063085"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the time module.\n",
    "import time\n",
    "# Get today's date in seconds.\n",
    "today = time.time()\n",
    "today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bea6f7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import time module\n",
    "import time\n",
    "\n",
    "# Build the scatter plot for latitude vs. max temperature.\n",
    "plt.scatter(lats,\n",
    "            max_temps,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Max Temperature \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Max Temperature (F)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig1.png\")\n",
    "\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a89afebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for latitude vs. humidity.\n",
    "plt.scatter(lats,\n",
    "            humidity,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Humidity \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Humidity (%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig2.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b05aa8d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for latitude vs. cloudiness.\n",
    "plt.scatter(lats,\n",
    "            cloudiness,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Cloudiness (%) \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Cloudiness (%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig3.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "696de051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for latitude vs. wind speed.\n",
    "plt.scatter(lats,\n",
    "            wind_speed,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Wind Speed \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Wind Speed (mph)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig4.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d8f87adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linregress\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Create a function to create perform linear regression on the weather data\n",
    "# and plot a regression line and the equation with the data.\n",
    "def plot_linear_regression(x_values, y_values, title, y_label, text_coordinates):\n",
    "\n",
    "    # Run regression on hemisphere weather data.\n",
    "    (slope, intercept, r_value, p_value, std_err) = linregress(x_values, y_values)\n",
    "\n",
    "    # Calculate the regression line \"y values\" from the slope and intercept.\n",
    "    regress_values = x_values * slope + intercept\n",
    "    # Get the equation of the line.\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    # Create a scatter plot and plot the regression line.\n",
    "    plt.scatter(x_values,y_values)\n",
    "    plt.plot(x_values,regress_values,\"r\")\n",
    "    # Annotate the text for the line equation.\n",
    "    plt.annotate(line_eq, text_coordinates, fontsize=15, color=\"red\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Latitude')\n",
    "    plt.ylabel(y_label)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c80bd311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                   Champerico\n",
       "Country                        GT\n",
       "Date          2022-05-15 14:41:54\n",
       "Lat                          14.3\n",
       "Lng                      -91.9167\n",
       "Max Temp                    83.59\n",
       "Humidity                       70\n",
       "Cloudiness                     99\n",
       "Wind Speed                   3.36\n",
       "Name: 13, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index13 = city_data_df.loc[13]\n",
    "index13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8e1f11e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4       True\n",
       "       ...  \n",
       "578     True\n",
       "579     True\n",
       "580     True\n",
       "581     True\n",
       "582     True\n",
       "Name: Lat, Length: 583, dtype: bool"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df[\"Lat\"] >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5c126ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Along</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:41:25</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>94.7667</td>\n",
       "      <td>71.42</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rio Muerto</td>\n",
       "      <td>MX</td>\n",
       "      <td>2022-05-15 14:41:51</td>\n",
       "      <td>27.4167</td>\n",
       "      <td>-110.2333</td>\n",
       "      <td>80.02</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leh</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:41:52</td>\n",
       "      <td>34.1667</td>\n",
       "      <td>77.5833</td>\n",
       "      <td>53.47</td>\n",
       "      <td>25</td>\n",
       "      <td>79</td>\n",
       "      <td>8.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fortuna</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-05-15 14:41:52</td>\n",
       "      <td>40.5982</td>\n",
       "      <td>-124.1573</td>\n",
       "      <td>56.93</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-05-15 14:33:33</td>\n",
       "      <td>42.6001</td>\n",
       "      <td>-73.9662</td>\n",
       "      <td>71.64</td>\n",
       "      <td>82</td>\n",
       "      <td>95</td>\n",
       "      <td>3.62</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>578</th>\n",
       "      <td>Ust-Nera</td>\n",
       "      <td>RU</td>\n",
       "      <td>2022-05-15 14:58:03</td>\n",
       "      <td>64.5667</td>\n",
       "      <td>143.2000</td>\n",
       "      <td>32.05</td>\n",
       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>Vuktyl</td>\n",
       "      <td>RU</td>\n",
       "      <td>2022-05-15 14:58:03</td>\n",
       "      <td>63.8567</td>\n",
       "      <td>57.3094</td>\n",
       "      <td>48.04</td>\n",
       "      <td>82</td>\n",
       "      <td>92</td>\n",
       "      <td>6.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>Chifeng</td>\n",
       "      <td>CN</td>\n",
       "      <td>2022-05-15 14:58:04</td>\n",
       "      <td>42.2683</td>\n",
       "      <td>118.9636</td>\n",
       "      <td>55.08</td>\n",
       "      <td>41</td>\n",
       "      <td>90</td>\n",
       "      <td>6.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>Zabid</td>\n",
       "      <td>YE</td>\n",
       "      <td>2022-05-15 14:58:04</td>\n",
       "      <td>14.1951</td>\n",
       "      <td>43.3152</td>\n",
       "      <td>97.45</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>Jaisalmer</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:58:05</td>\n",
       "      <td>26.9147</td>\n",
       "      <td>70.9181</td>\n",
       "      <td>98.28</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>406 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Country                 Date      Lat       Lng  Max Temp  \\\n",
       "1         Along      IN  2022-05-15 14:41:25  28.1667   94.7667     71.42   \n",
       "2    Rio Muerto      MX  2022-05-15 14:41:51  27.4167 -110.2333     80.02   \n",
       "3           Leh      IN  2022-05-15 14:41:52  34.1667   77.5833     53.47   \n",
       "4       Fortuna      US  2022-05-15 14:41:52  40.5982 -124.1573     56.93   \n",
       "5        Albany      US  2022-05-15 14:33:33  42.6001  -73.9662     71.64   \n",
       "..          ...     ...                  ...      ...       ...       ...   \n",
       "578    Ust-Nera      RU  2022-05-15 14:58:03  64.5667  143.2000     32.05   \n",
       "579      Vuktyl      RU  2022-05-15 14:58:03  63.8567   57.3094     48.04   \n",
       "580     Chifeng      CN  2022-05-15 14:58:04  42.2683  118.9636     55.08   \n",
       "581       Zabid      YE  2022-05-15 14:58:04  14.1951   43.3152     97.45   \n",
       "582   Jaisalmer      IN  2022-05-15 14:58:05  26.9147   70.9181     98.28   \n",
       "\n",
       "     Humidity  Cloudiness  Wind Speed  \n",
       "1          98          97        1.30  \n",
       "2          43           7        9.33  \n",
       "3          25          79        8.41  \n",
       "4          97         100        0.00  \n",
       "5          82          95        3.62  \n",
       "..        ...         ...         ...  \n",
       "578        98         100        8.84  \n",
       "579        82          92        6.31  \n",
       "580        41          90        6.35  \n",
       "581        22           0        6.67  \n",
       "582        13           0       19.71  \n",
       "\n",
       "[406 rows x 9 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.loc[(city_data_df[\"Lat\"] >= 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a9cdabee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Along</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:41:25</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>94.7667</td>\n",
       "      <td>71.42</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rio Muerto</td>\n",
       "      <td>MX</td>\n",
       "      <td>2022-05-15 14:41:51</td>\n",
       "      <td>27.4167</td>\n",
       "      <td>-110.2333</td>\n",
       "      <td>80.02</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leh</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-05-15 14:41:52</td>\n",
       "      <td>34.1667</td>\n",
       "      <td>77.5833</td>\n",
       "      <td>53.47</td>\n",
       "      <td>25</td>\n",
       "      <td>79</td>\n",
       "      <td>8.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fortuna</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-05-15 14:41:52</td>\n",
       "      <td>40.5982</td>\n",
       "      <td>-124.1573</td>\n",
       "      <td>56.93</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-05-15 14:33:33</td>\n",
       "      <td>42.6001</td>\n",
       "      <td>-73.9662</td>\n",
       "      <td>71.64</td>\n",
       "      <td>82</td>\n",
       "      <td>95</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City Country                 Date      Lat       Lng  Max Temp  \\\n",
       "1       Along      IN  2022-05-15 14:41:25  28.1667   94.7667     71.42   \n",
       "2  Rio Muerto      MX  2022-05-15 14:41:51  27.4167 -110.2333     80.02   \n",
       "3         Leh      IN  2022-05-15 14:41:52  34.1667   77.5833     53.47   \n",
       "4     Fortuna      US  2022-05-15 14:41:52  40.5982 -124.1573     56.93   \n",
       "5      Albany      US  2022-05-15 14:33:33  42.6001  -73.9662     71.64   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "1        98          97        1.30  \n",
       "2        43           7        9.33  \n",
       "3        25          79        8.41  \n",
       "4        97         100        0.00  \n",
       "5        82          95        3.62  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.loc[(city_data_df[\"Lat\"] >= 0)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d62d965a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Northern and Southern Hemisphere DataFrames.\n",
    "northern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] >= 0)]\n",
    "southern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] < 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bab62afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VRQmhfEYFlzzzGVXVtpl2RWUVlzzzGYAqK4qiKM2AKipKq6ax1pBxr8yuU1JcqqprGPfKbFVUFEVRmgFVVJRWSyasIQsrq2KNK4qiKJlFg2mVVkuYNSQqXYsKY40riqIomUUVFaXVkglryPAB3SksyE8YKyzIZ/iA7o2STVEURYmGun6UVkvXokIqfJSSONYQ10UUFueiWUGKoihNh1pUlFZLc1hD3DiYisoqDPVxMOUzKjJ2DEVRlLUZVVSUVktZaTFjjuxFcVEhAhQXFTLmyF6xrB2pFJFMxMEoiqIowajrR2nVlJUWN8oNkyo9WbOCFEVRmhZVVJScJij+o7niQlIpIpmIg4mLxsQoirI2oYqKkrME1UGZNm8JT0+vaJZqsakUkeEDuifICPVxME2hUFxW/hmPvj8f47zXSrmKorR2NEZFyVmC3C6Pf/B9s8WF+AXkAqz4fQ3lMyoC42CAjAfZls+oSFBSXDQmRlGU1oxaVJScJcjtUmOSp+rw9Rtj2XDXG/3CFyxdWV03XllVnWDJSN5fv7FvZLz0/rhXZjdQUlw0JkZRlNaKWlSUnCUoziNfJPL6mUgfListpn3bhjp9mCWjKYJsw7bVSrmKorRWVFFRcpagOijH7bpZ5PoomUofDlISKiqr2GLkJPqNfSNB+WmK0vtB2wpopVxFUVotqqgozUr5jAr6jX3Dd3JPJij+45qyXpHro2TKshGmYPhZapqi2JzfPgU4YbduKd1Jca67oihKLqExKk2AiNwPDAR+Msbs4IxtAIwHSoC5wLHGmKXOskuA04Ea4AJjzCtZELvJidvNOCy2JGp9lEylD/tl9yTjjUGJUno/LunuMxNdpBVFUbKFmIDARCV9RGRvYDnwkEdR+QewxBgzVkRGAp2MMSNEZHvgceCPQFfgNWBbY0zwjAj07dvXTJs2rUnPI9P0G/uGr9JQXFTIlJH7JowlT65gLRLpVpZt7H7cfblKQtBTI8CcsYemtc/GKjNB+4pz3RWltSMi040xfbMthxIdtag0AcaYt0WkJGn4cGAf5/8HgTeBEc74E8aY34E5IvINVml5r1mEbUbiuGFSVYSNSiYtG15LSdDkH8dSk0lLR9i+tHquoigtGVVUmo9NjDGLAIwxi0RkY2e8GHjfs94CZ6zVEccNk8nJtbFl9P0IK/QWlUwpY6n2FXTd80TqasEoiqLkKhpMm338cm19PQsicpaITBORaYsXL25isTJPUIBp/x6dGwR6NkXWTBhxg00z0fAwLJMobrBrmGIXVLSuxhjt9KwoSs6jFpXm40cR6eJYU7oAPznjC4DNPOttCiz024Ex5h7gHrAxKk0pbFPg54bp36Ozbzn8o/oUJ4yDv8UiEzEe6bpgGmupCbJ0ALFdQGHWKncfwyZ80qBYXmOL0CmKojQ1alFpPp4HTnb+Pxl4zjM+RETaicgWwDbAh00qSRYDqMtKi5kycl/mjD2UKSP3ZfKsxb4ui8mzFqe0WGSimBtkrtZKXIIsHekcP1U6dFlpMbUxK/oqiqLkAmpRaQJE5HFs4OxGIrIAuBIYC0wQkdOB+cAxAMaYL0RkAvAlsAY4N1XGT6P45z9h2DD7/6OPwvHHN9mhohDmskhlschUjEe2gk1dGS8aP7PRx48SNJyNTs+KoiiNRS0qTYAx5jhjTBdjTIExZlNjzH3GmF+MMfsZY7Zx/i7xrH+tMWYrY0x3Y8xLTSrc4YfX/3/CCSAChx4Kv/7apIcNojGxKE1dzK05JvCy0mKKM3T8ZGtVsrLWFEXoFEVRmhpVVNY2ttrKun7+9z/o2tWOvfgidOxolZbXX29WcRozeWZKwcj2BN5cx89EALCiKEpzowXfWigZK/hWUwOXXgr/+Efi+Jlnwu23Q9u2kXbTmKDWdLdtqmJumagiG5dsH19R1ha04FvLQxWVFkqTVKb98EPYddfEsfbtYepU2GmnwM0yqTDERSd4RVHioIpKy0MVlRZKk5bQr6qCs86CRx5JHB81Ci6/HPISPYatsUS7KkCK0jpRRaXloTEqSkMKC+Hhh20sywsv1I+PGgX5+bDNNjBvXt1wayvRnqm0Z0VRFKXxqKKihDNwoFVYfvkF9tnHjn3zDZSU2ODbf/87MHjVQKQqr37ErRSbSbJVV0VRFEVpiCoqSjQ22AAmT7ZKy7//XT9+1llMuWQ/xj9xKR2rfmuwWTrWiGxbNFqbhUhRFKUlo4qKEp8zzrAKy9y5sPXWAOw671M+ufU45l4/kH2/SSysG9cakQmLRmMsMtmsq6IoiqIkooqKkj6bbw5ff21TnEeNqhu+/+mrmHv9QG5+YRztqn8H/K0RQcpEYy0ajbXIZLuuiqIoilKPZv20UJo066cRnHLBPdx51wW0dxQUl7KTbmTx9r3rsmf8UpoBOrUvwBiorKpusO+oWUSZyEKKk/WjGUKK0nLQrJ+Wh/b6UTJK2amH0qdjN9asWsXo/97N8Z+8DED5w7a/0L3PH035rTcy7rVvGigpAEtXVpOfJxTkCdW19Up0HItGKotMFMUiamfkdDsvK4qiKNFQ14+SUdwy7RtvuD6XHnQeJSMmcvzga+qWn/HeU5TtsjlPX3sMJUv8XTE1tYa2bfLSLvUeFmOS6UBdzRBSFEVpWtT100LJVdePly1GTsL76Vr395Xc+vw/2Pe7RLlH73cm/+kzyKY7e5g79tC0jhtWKXfcK7MzWpwu+RxdBJiTpvy5grq0lNaIun5aHmpRUeqIkykTZd1ky8bydu057ZhR9BvzOtOuva1u/MrX/83cfxzGcw8OZaMVSxt9HmHN9zKdetxaM4SynSKuKIriojEqChAv1iLqusMHdPe1bAwf0J2+pftSfvARjLn3de5/ajQ9f/qOnX74mmm3nwTA3465DEjfIhEUY9K1qNDXohJFsfCzMPTv0ZlH3p/fYN3+PTqnJ3iOEObSUquKoijNiVpUFCBerEXUdcMsG+7yS87Yj7LTb6Pk4he4dp/T6ra94clrrCvoiCNg+fIMnWX6qcdBFoaJnyzyXX/yrMWZEjljxLGYadE7RVFyBbWoKEC8iSlo3YrKKvqNfSMhliFV9oy7bNwrs7l31yN5ccAJjN4mj/3PPgZ+/hnKy2G99ezKb74Jf/pT9JNySLaEHNWnmMmzFseKvQhSzvwylyDehN4csSBxs5MaY3lSFEXJJBpM20LJdDBtnNojQeu6uIGrjZ5s16yB4cPh5psTx889F266CQoKQjcvn1HBqOe/aFCTpSBfaJMnVFXXArZ2y6E7dqlTXjoWFiAClSur6xSHoeNn+gbNBhE1ONcv8FewfZKKM6i0BN2zosICOrRr00BJCgtIVteP0pLRYNqWhyoqLZRMKyphExOQ8Iu/f4/OPPr+/NCJO90sGleWBhaGlXNhzz0TVywqgnffhZ49I51PuhQW5NOuTZ5vEbo8gXxpWPMl6oTeXEpfUHZS2PE060dpjaii0vLQGBUFCI4nARrEZqRSUiD9WIbAbJP2JWAML0z5H6/3ctw/lZWwww42lmXMGNt/yMHPVZMuVdU1iNAgtgWg1gBiLRPp1HxJdZ0yVZMlqsvGe7yy0mKmjNyXOWMPZcrIfVVJURQlK2iMilKHXzxJv7FvNJjwo/wyTzeWIVWg7iUvfUvVIcPhkOEM+N9U7n72OrvSpZfa1/bbwyuvZDzoc+nKajq1L/BVfqprDB3atWHmlQeG7sPPQhEUC+IlE+cSxQqWyeMpiqJkClVUlFDSmbQEImXR+LkVwgJ1h034hBqP1eSVbfegZMREerb5nUmTb4SpU+HLL2GzzZgDDDtkKE/32i+2/EHntHRlQ9ePS6rrVD6jguFPfUJ1jZW/orKK4U99wuBdNuPp6RWh1p88EcpnVMS2aLjXuKKyqi7uxUWAwoI8VjpxOl6aOmBWXUqKosRBXT9KKOlMWgZrGQlKfw0rJhZ0PIEEJcXLF2va0e+wqykZMZHLDzinbvzGF29i7vUDeWj85ay/qnEpzqksEamu0+gXvqhTUlyqawyTPl1U53IDe57J1BgTu9ia9xpDQ/kN0K4gv9m7RGshOUVR4qKKihLK8AHdKcj3mz7DCZuAwtw7fnVOkq0ByYhzPICHdz6UkhET2fusf7Ng/Y0B2HvuDD69ZQhzrx/I3t9Nj30uUVjx+5rQyTbIGrN0ZXVdLMjcsYdy0+De5EvD6x03ViVKjE7lyurQOjdx6q40Ri7tjaQoShjq+lES8DPLd2jbxjfjJRXeCci7z6CYjIrKqoS6KqnWd/FTYuZ36sKe59yPmFr+9s4jnPveBAAeevJKAJ7cYX/+PuBcVrcJT3GOSmVVdUa6JpeVFjN0/EzfZVHccF53Tyq6FhUmxCW52w4dP5Oi9gUsX7WmLpspU12htZCcoihxUUVlLSJVbEBQUbDGZM8k7yNsAnUtCclBvalSeMMwksdjg/7CY4P+wgZffcozj/yNgtoajvn8NY75/DWq8/I54qQb+fwPW4fuJ5VVB8JLzBcVFvgqe0WFDRWldIutxUnJTnbxJG/rZwHKRAl9LSSnKEpc1PWzlhAlNiDILN9You6jxhhfN8PwAd19YzcAXzeJF3dCHj6gO99068E2w5+j+7BneGoHG2RbUFvDxAcvYu71A/m/tx9GTMPg0sKCfE7YrVukc3Cr8ya7SUYN6klBXqKsBXnCqEENa8CkW+Y/akq2Xwp11G0ba/lI99wURVl70YJvLZS4Bd+iVJ6NWhSsOSjIE9Zdpw1LV1aTL+IbSFtYkM9RfYoD027zRbjx2J0auDa8heu+f+xZHnTcQS7fd9yE44Zcy4KiPyRUhy0ZOSmy/H6F2uJku6STGRP1/gk02Gfce9+YqrnZzvrJ9vGV7KIF31oe6vppZkRkKHAG1pPwGXAq0B4YD5QAc4FjjTFLM3ncKLEBRe0LfE3+eeIUNmtGqmtNnSxeJcWvvPycxcuZ8u2ShO39FAW/OjFbvD+fkhETWX/Vcu4oH8te82ay2bIfeffuM+wKd94JpVaRC3Lf+OHnJknV9wgaTqI3De4deRKNEs8DJFjUXLmibusSFLMSRQmIch2airg9jxRFyT7q+mlGRKQYuADoa4zZAcgHhgAjgdeNMdsArzvvM0pQDIB3PMi41q5Nnm9V1nSJn0NUj6ukuJVSy2dU8PH8ZT7HiKZZuS6dX9dZl5OGXEPJiIn87ZCL6lf4619t5dt+/bhury4N3DdhxHWTNDZ118+tEoY32Nlv24J8Cb1Xydk6LSH1WLOOFKXloYpK89MGKBSRNlhLykLgcOBBZ/mDQFmmDxolNmBZgLWgqro2IY01VVxIKhprnPEqAEGxFSurayNNkteU9eLE3brVnVO+COuceZrV2ubPh+22sytOncoh++3I12MOZUjF9Lp03psH966rgZJM3ADRxk6ifm0Qbh7cm7ljDw1UOBZWVtVZQaqqa+quQ3FRIeOO3inlMStS3Isg+Zsi9TkKmnWkKC0Pdf00I8aYChG5AZgPVAGvGmNeFZFNjDGLnHUWicjGmT52ctpvUfsCjIGh42fW1S8JMv+7k5wby+KXXeLGlLgdh1f8vsbXTVJcVMgPy1YFFm+LglcBCJtgqqprGDbhE4aOnxnohiifUcHkWYupNaZh3MVmm9lKt8bYXkJ//zsAYx+5krEAxx4LF/yHafMalqdPJ0A01STqTT1243aSZfZzq5TPqCAvIM6nY2FBwr2sMaZO9rLS4pSpzuLsP6yqcPK4n/tl6PiZTJu3hGvKevnuI25cSdD6mnWkKC0Ptag0IyLSCWs92QLoCnQQkRNjbH+WiEwTkWmLFy+OfXy3sNhNg3uzqrqWyqrqBBN9/x6dfX95u5VmvftJ/uU+7pidmHHFgXUN7EYN6hlowWmMkpKsAKSaYGqMCXRD+LkqLho/k96jX038hS9i+wgZA59+Cuuvb8cnTIAOHbjmiB0pXfBV/erAUX3ix2GEueeSK8261zCVe8XdLigYWaRhVlaySygM72cjinsR/C0vBnj0/fm+5xHXpeS3/tDxMykZOYmVq9c0cN9p1pGi5DaqqDQv+wNzjDGLjTHVwDPAHsCPItIFwPn7k9/Gxph7jDF9jTF9O3funLYQQSb6ybMWB7plXBeBa653rTBBnXWDujGXlRYHukrcdYPwS6sNUq78cCdg9zwuGj/T123kFm/znQh79YJly2D1ajinvlz/M48OZ+71A7n89X+TV2uvZVzC3HNh6cNh7qGg7fJFGHNkLyoDKua6VpCy0mI6tQ8viueuG1RVuH+Pzr7rJ5OsEIedQ1V1DaNf+MLXfRSkCIFTH6YR3a4VRWl+VFFpXuYDu4lIexERYD/gK+B54GRnnZOB55pSiLDGf34FyKDeRRDnV22QqT5sQg76Ve4NoPUe4+npFbFiXly5U2W4pIwNKSiAO+9kixETGXLcdXXDp097jm/HHc5zVx8Js+MFaIYpd6liKOLGXtQaU+cK8cM7fuVhDa1jfuuWlRZzVJ/iBMXRtZSUeJSJMCtYRWUVW13yIpeVf5byHJaurPb9PKa6Vm636yAlW1GU3EIVlWbEGPMB8BTwMTY1OQ+4BxgLHCAiXwMHOO+bjLCJYoWPabwgT/h1VXWsQMkwpSZsQo5TECzMyhAU8JsvErkAXZQAy65FhbzfbUfbxfmiCby6zW4AbLRyGfToYd1GN94YnFKVhOueS55EU7m4orpdksejXG/3fvlZVtx1XSvVIz41bdz3qVyMLjXG8Mj78+uUlajxI+7nMcr6GjyrKC0HLfjWQolb8M1LqlLrndoX0L5tGxZWVtGxsIAVq9c06PzrIsCcsYcm7HvYhE98YyK8xeVSyRclcDKoSJkANw3u3eAcCwvyY1XajSKv37UsLMjnoQ5z2OWSvyau3Ls3vPgidOkS6fje65Dce8dLQb7QoW0bllVV1xWymzxrceB2yTVmorRWcJd3LCxAhLqgaVehidtqoW2+sDrgM+WSL8K3Yw6J1RoA4Gafe59M1M+i0vrQgm8tD836WQtxJ6GLAprfVa6sZsYVBwK2om1YkbOi9gX0G/tGwqQYFCwb9Vds1IJgYRkcfs0N3ViPKIXNogZYBh1nl9KDYOQ58MMPMHAgTJ8OM2dC1652w8ceg+OOC9yvX++dgnypKzrnLcJXXWPq7lFFZRWPvD+/bj/e7VxFJlkRCbveyXJUVlVTWJCfUIiu39g3YrdaSKWkQH3AsN81XuhY65LJF0lYv6KyqkGfJg2eVZSWhSoqaylhqadR038L8oXlq9bUVZD1q2obtN9MMHxAd19rhjsJBU3Aftsc1ae4zgqRyqqQvDxUsfrDH2DaNOv6ueUWGDrUjh9/vH0deqhVWtxMIgc/t5YbWzFqUM9YFgZ3u5lXHhhp/VRyuGnfQKT4mXTxuu+Sr3FQOwOvchPVYqQoSm6jispajN9ED7By9Zq62hhBVot8se6GqCXlm+JXbJA1I2wSSmcbv7ofF42fyUXjZ1JUWMCoQT1TT3wicNFF9vW//8E++8CiRTBpEnTsaNd57TXYzzZLDAuOjdpAMHm7dAjarsaYutLzccvvR+W4XTcLXFYccEy/jLJsluxXFKXxaIxKC6UxMSpeymdUMOr5LxooHG4cA/hbIMYc2Yuh42dGyrhJbg7Y0ghq6OglssJC/S/8H5Ys5+r3H+X4dyYkrtC1K/3Pvo85KxoqI8Uhbo8w0o3JSHXubsG5uDEqYeSLcNyum3FNWa9Aa0hQbJCmGiup0BiVlodm/azllJUW06FdQ8Oat6leUIZOFFdOYUF+i1ZSgEjWgtDaKx68GVE1eflcusef2e6yl3jrAU9G+sKFTL7iYOZeP5A958xI2L5kw8LYLrTGWLNS9Q9yi6m1a5NHp/a2NklQRk+qejcd2uZz8+DefDvmkDolJSh7LOxzqShK60IVFSVlDY6glNmgRnatrZhW1N5GbhGyMIJiPi5dtK6NY1m2DNq2rVv2yITLmXv9QM6f8jgYw9Rvl9C/R2ffwmpgr/mJu3XL2ATuKgRh18BgFbVV1bWcsFs32uQ3XLcgTzjBkSuIFatrEpS9sPiYLUZOYtTzX7By9Zq0zktRlJaDxqgoafc/SSfeoyUSp+T/0pXVdb/4/UhZmG399eH339li5CRO//AZLpt8PwDD3n2UYe8+yrcbFDOccYwZvHezXXd3v6ncO1XVNTz+wfe+12vdddrU9fEJCoR19+Fa8sLiY4AEd6VrbfHKqyhK60AVFSVl9kwYa0OgYlDgZhDuROtHVKWwa1Eh9/7xSO7945F0rPqNu5+9lt2+/5ytllTwzPXHw/VQdt99MPK0eCeTBsndlcMUt6BlS1dWs8XISbGKscUN0vUqOYqitB7U9aPkvL/f22PI29OluUgVp5FMWIZN1Mq73v44ywrXY8jxYykZMZFLBpxXv9Lpp9tson33hSVLIssXB79miMmVi6PixpmkosipgBv3uoNWnFWU1ohaVBQgdy0jfqnBzW3id4/jlx3lR8eAfknefaVy2wQ1NXy890HsOmYkZZ2q4YAD4JtvYPJk2HBDu8KkSXDIIVFOKxK+9Vx8quNmEtcok3ytohw107V6FEXJPpqe3ELJVHpyrhOUHputEujJZe2DityduFu3upiMdAhqDwC2RHydYlNbC1dfDaNGJazzcu/9Wf2vuxi021aNKngWJkcqkivCxtnO25bBpffoV0MVRW/hvorKqjo3VXEGYni0aFzrQdOTWx7q+lFymrhdgZsabwbUlYf1DFzv0ffnN8pFFWYZSMgsysuDK6+k/OMFHHHG7axqYzOGDpr5GoN23xpEePT2pxJSfIeOn5nQnThdOcIoLipkzthDQ7N84h5z1KCegW6n4qJCjupTzNPTKxLcVJC603cqUjXZVBSlaVFFRclp4nYFbk78Oke7mBTLUxEWyOxmFiXLMmPDEnoMe4Zthz3L4zvWl8t/8v6LmHv9QEa8+QB5tTUYoitS6cSJeDsqr/g9XvpwWBB3WWkx447ZKSGW6ubBvZnrpM1PnrU4MCspqNO3S1gcVFCadGPur6Io0VHXTwtlbXH9ZKsCaRRTfyq3SJALIyqlV70a6Frq1L6AGVccWCdnUJBqv7kzeXT8ZQlji9bdkCHHj2HNFltF7mYd1MDSlcUYEpoeQvSOym613ca6VNK9H6k+Y2Fduhtzf5XsoK6flocG0yo5TTZqtUQN4E2VPttYq8+Vh/UMVBCWrqzmsvLPeHp6Ragy8P4WpZSMmMi6v6/k1uf/wb7fTaPL8l94656z7AodboXzzrPZQwGENbDMF6FypVVQvC0EonZU7tS+IGOxRunejzCLSVi/q1yw6inK2oC6fpScJ6gyblMR1dQf5hbJRBPGstJiikIyiB7/4PtQZaCwIJ/jdt0MAZa3a89px4yiZMRELho4rH6lCy6wcS677go//RS4r6BzrTHGN24jagxRWJyPS9T09HTvR6o4qKgp5YqiNA2qqKRARHYWkQtE5HwR2Tnb8ihNT9DEVVFZlTBJeuvPQH2p/UzWoRk1KHgiDyu85spwTVkvTtitW0KfnfKe/dnuspd4+ZVpsOOOdvDDD2GTTaxl5emnG+zPPddO7YMVJ68yl8raINjMqFTXKE4ga/L98BqJ1ikI/qpLFQeV63WGFKW1ozEqIYjIFcAxwDPOUBnwpDHmmqwJ5bC2xKhkg7COwdno0BuUlitSX3PEixu/4iU05sYYuOEGuPjixB0deSQ89BB06FA3lKqbshu34Rf34aYrB6UL+8kY5HJKlZ4eJ7YpbF1o/S0i1jY0RqXloYpKCCLyFVBqjFnlvC8EPjbGbJddyVRRaUr8Ji4vzVHDJbley/JVaxIKrRUW5CMYVlbXNti2qLCAmVce2GA8El99BXvvDT//nDj+9tuw114pA1bda1M+o4LRL3xRFwwcRUnxUxaC7kGqQNa49Xf8lCRoGBDsp+xojZWWhSoqLQ8Npg1nLrAOsMp53w74NmvSKM2CO8kEBbI2dQ2X5El76crquq7U3syaIPmiVM8NZLvtYPFiWLMGhg2DW2+143vvDcC43csY2e8U1uQ3/OrwpiUnT/CuchMUmBwUFxTUW8jPXeNVGIKUqaB751eZ2S8gOLmfUC5UTlaU1o7GqITzO/CFiDwgIv8BPgeWi8itInJrlmVrFWS7j08QZaXFgcXKmjrbw7dsfY2hQ7s2CQHF+QGZOkHjsWjTBm65xbqF3nmnbvjo98r55oYyZt4yhK1/nl833qFtvaXBT34vVdU1jHr+i4SxsE7JUQJZk2NZgohz76IUG9QaK4rS9KhFJZxnnZfLm1mSo1WS679GG9NVujFErcYbFEwbFmQbRKj7Ys89wRhGP/EBfUcN49DZUyhatZzX7vsrAGP/dAovDDixbv0oFqfKKlu0zt0mKAW42BOrkuyW6Tf2jbqxFb+vSZkOHffeRUlLzrXKyYrSGlFFJQRjzIPZlqE1k6p+RbbJRg0XiDZBgp3Egyb3OERRGMtnVPDAzJ/5T9klnAsMmD2Vu8uvA2DkWw8w8q0H4LEd4OWXU9YzcfHe5zClMNkt4ydvKsL6/QQpaVEUVa2xoihNjwbThiAiA4Grgc2xSp0AxhizflYFo3UE02rFT3+iZqxkqmpvWCaPO8F7A2O9bLByGfc+fRU7L0x0dYwY9DfGb7dPymPP9dznqEGpqTKPkgm7JqmuYSqZ/LYvyBc6tG1TF0/Uv0dnJs9arMG2OYIG07Y8VFEJQUS+AY4EPjM5dqFag6KSa52RM0GmMkCi7icTx0uVyVOQJwkZR74Yw0kzJnH1f+9KGH5ri5254PARLGvXocEmAtzk7QSdIXn98GYjea/XytVrfBWwfBFqjYl0TVNlaCWTjRR3pR5VVFoeqqiEICKTgf2MMQ1zQLNMa1BUstXHp6loivNpjtTXuBaKVPyxZikTJvwd5s9PGD/x2Kt5d4vShDEBitoX1JXhj3J+6cjrKkVR+w95iXMPo8qWrIxrinPzoYpKy0MVlRBEZBes6+ctbAYQAMaYf2ZNKIfWoKhA6/qCzrSFqLkUuVR1Y+JS57qrrYXLL4frrktYPr7XAVx+4F9Z3aZhldso55eOvEWFBXRo1yZthSzqPYxj7RFsLEvJhoVM/XZJwnYtWWHPdVRRaXmoohKCiLwKLAc+A+qsKsaY0VkTyqG1KCqtiagxN42NxfBOmkGFyuIqf6m6MMfBb1I//bx/cfed59HGY5z8Pb8NR550I19sslXK7YPk9Wb9hNWPKcgXqmvS/66LGjeVSetUS3aB5jKqqLQ8NOsnnA2MMWmW+PRHRIqAe4EdsHWwTgNmA+OBEmyRuWONMUszeVyl6YmSARInJTtV6qvfvoY/+QkIdZNy1JRvN7PGNzg0TxL2CfYX/1F9iht0bw5KAT7s9EH06rQFtVVVXPfK7Rz1+Ru0q1nDpAcuBOCWPYZw857HYyQvUmpvqkygZKprTGDxONfasrCyirwYBeb88MsUShdNcVYUixZ8C+c1EcmoogLcArxsjOkB7AR8BYwEXjfGbAO87rxXWhhRuuzGKRCWqlmeb2G4WtPAchCnAJlfA75xx+zEuKN3atCU75qyXpGb9bn7XVPQjmGH/h8lIyZyytGj6pZfOPUJ5vxjEG/fdTo7m2WRZA2SO4ig4nGjBvWs685947E7NapTst/1O3G3bmkV4dMUZ0WxqOsnBBH5DegArHZejUpPFpH1gU+ALb1ZRCIyG9jHGLNIRLoAbxpjQr8Z1fWTm6Ry68RJyU4VoxI3HqKxKd9x4omC1vU7p/VXLeeO8rHsNW9m4k7uugv+8pfYcm51yYu+VpF8EW48dqeU59AUcVNxM5XSzYhSUqOun5aHun5CMMasl+FdbgksBv4jIjsB04ELgU2MMYucYy4SkY0zfFylmfDrGeMlToGwVAXnohZWC9p/HFK5rMJSdP3cT+66HQsLkPad+POQa+haVMiwiikcefsV9qBnn21fe+4J5eWw4YaRZA2r2Jvq/rgyNkZB8F6LjoUFtst1jO0FOGG3bqqkKIqDWlRCEBEBTgC2MMZcLSKbAV2MMR+mub++wPtAP2PMByJyC/ArcL4xpsiz3lJjTCef7c8CzgLo1q1bn3nz5qUjhpJFMpnJEyeepLEZJGGBvVHjMlIFh3rPp8uvi3l4/OVsvWRB4krPPQeDBqUta9DxM1n/Jm6MSmFBHusU5MdK0VbSRy0qLQ+NUQnnTmB34Hjn/XLgjkbsbwGwwBjzgfP+KWBn4EfH5YPz9ye/jY0x9xhj+hpj+nbu3LkRYijZwi+GIV0lIk48SWMnvqDAzorKKoZN+CTSxJwqONQbc7No/c7sf+ZdlFz8AuP2Oql+pcMPBxEYMgRWrvTdT5RYIS/JDQ1dC1A6DTJTNWR08UasVFXXsqq6lpsG965rOKkoSj1qUQlBRD42xuwsIjOMMaXO2CfGmJ0asc93gDOMMbNFZBQ2BgbgF2PMWBEZic02ujhsPxqjomSKKNaETKTdujEiQRNxqjiOHZfO5+nHRlCw/LfEBVOnwu67JwzFsZBksv5NOlVzG3M8JT5qUWl5qEUlnGoRycdxMYtIZzz1VNLkfOBREfkU6A1cB4wFDhCRr4EDnPeK0uREtSYMH9Cdgvz4mSteaowJtVSkiqP5tFM39vl7OaxeDeecU79gjz2slWXYMKipie3GCVLA0kkPbkwskKYjK4o/qqj4ICIPOP/eCjwLbCwi1wLvYhWLtDHGzHTcNzsaY8qMMUuNMb8YY/Yzxmzj/F3SuDNQlGhETZcuKy2mQ9vGx96HpUr7uWySWVhZBQUFcOedYAy88Ub9wn/+E9q0Yc+9dqDdt19HcuOUz6ggSP1KR+mIcg5B5In4ylk+o4J+Y99gi5GT6Df2jbRcUorSklFFxZ8dAYwxjwIXA2OARUCZMebJbAqmKJkkLPYkmWUhlV8zccwotVCSlYfyom3pN+Z1drhoAu9stwcAG62o5I17z2bu9QM5/aNyqlav4aLxM30n+XGvzA5MF49aOwXqlYmh42eyTkEe7QvCv1r9lCM/i5Ofxeui8TPpPfrVBueiCo3SWlFFxZ/2IlIqIjsD7YH3gKlAoTOmKC2GsAksyGogznZeMlWArGtRYaBMZaXFTBm5LzcP7p0yINY7iS9v156TBl1KyYiJnDeoPrzr8jfuZe4/DuOFBy5k9YKFDRSBIKXJEF7J10uyMrF0ZTVV1cEe4sKCfE4IKAKXbHEKCs6trKpOOJdMBgQrSq6hdVT8KQZuxP+HjwE04k3JWeLUNBk+oDtDx89sYFUwUDdhevdVkCd1+0qHwoJ8+vfonLKNQKoaMu4yv0l84nZ7M3G7vem8fCn3PzWKXj9+S68fv+WjO2z20BXf/J2yx68BgmvRhFl1kvGTI+wKuVlYj74/33e5V3kKi1txlZqy0uJQF55mESktHbWo+PONMWZfY0x/n5cqKUqTkAnTvd+v+2TFwvurvay0OHBSdRUI774Q2xvHWx4+2fIhQL+tNqib7F3LgZsqPXnW4shxMW5pe7+03VTBp4vX7cRhp9xCycUvcPW+Z9SNX/XEtTb49rDDuGTP4rRL5rv3K042VHFRYULBPj+846msWO41SNUXSlFaMmpRUZQcIE6zwjCi1vFYWFlVZ3kJIl+kYS+hGkOHdm2YeWV9C6y+m28QK8tm6PiZvuMVjkxRzzdyZV4R7tuljPt2KWPLXxYw/olL6bx8CUycyMCJExkInH/GDUzcsEesbtPxC7slKkB+hfKirOPFVWTiVDxWlJaG1lHxQUQONMa8mm05wtA6Kq2LTNXyiFrHo1P7AlZV1wZOgIUF+YHLGts3KMwKEaeKblCV33UK8qz1J4CCPGFIn65sd8sYjn9nQuLCs8+GW2+1mUUh9B79KpUxgovdsvjXlPVqcA5e15oxNmg5uT/S6Be+aHBO3muVyYrHrR2to9LyUNePD7mupCitj0yZ7qP8gi4syMcYAhUR10UTFKfR2F/pYSm8je30PObIXlx5WM/QFOHqWsMjH1Vw6R5/pmTERI448Yb6hXfdBW3bwnrrwWef+W5fPqMiVEnxC5I1wORZi33PYcrIfblpcG9WVddSWVXdIBi2rLSYGVccyM2DewdWHM5kxWNFyTXU9aMoOUCmTPd+roKCfKFD2zYJv9SD3C/uPtwJLpVrIh3cfV8UIEMc5SysgeC4V2YHWm68VqcZxT0oGTGRLdsLb3z1MDzxBCxfDjvuaFe46iq47DIb1wKhilRxUWFaSmeUYNjkAGNvnJH71/3ftdQMHT9T+wcpLR61qIQgIuv4jG2UDVmU1k3c/jRB+Pb/OXonZl55YEJQapgC5J0A/X6lA40O+i0rLc64xcYbjDzuldkMH9A9VvbOnJUGHn/cFpJ77rn6BVdcAXl5sN128P33oQrH8AHdIwXJJhNFuQmqqbL95S+lrL3iWme01orSElGLSjgficiZxpj3AUTkKGzxt22zK5bS2oiSjhtnX6m2Gz6geySLRvK+GhP0m1zavn+Pzjw9vSIjFpsguY7qU9zgGEEkKBKDBlmF5Zdf4Mgj4e23YdYs6NaNOcDwgy/kyR0PSNi+U/uCtC1RUSxqQYHSK6trGf7kJwChqcqjnv+C39fUNjpgW1GaG7WohHM8cJuIjBORR4Ez0RoqShORKh0308fq1N4/YDSVtSVKanEyfr/yn55ewVF9ijMSVxEk1+RZi1NWu3WpqKxqaGXYcEN46y2rtNx9d/3xXrqFudcP5NEnLmX9VcspLMjnysN6AunFi0SxqIVlOFXXmrp7ELReZVV1WvdOUbKNWlRCMMZ85vT4eRj4DdjbGLMgy2IpSka48rCesX/5pxv0G6ZIZKJjcJhcroLgV9gumVArw1ln2decOazYuz8dFsyj37xP+fSWIXb57pPAJ14kClEsavki1IRkabop50J4wblk4qaFK0pzo4pKCCJyH7AVtvfPtsALInK7MeaO7EqmKI0nHXdTukG/TV2QLJVcQT19/EhZ0XWLLejw/VyorYXRo22wLcChTsr2SSfBPffAOg1C3BoQp9NzmJICVjkZNuGTWEqKi7qAlFxGXT/hfA70N8bMMca8AuwGaK8fpdUQ192UbtBvOgGmcUglV1yFKNL6eXlWUTEGPv64XjF5+GEoLIT8fAipdRS3P08U91UqZSaIVC4gDcJVsokqKiEYY24ynop4xphlxpjTsymTomSTdOt1ZCqrKV254ipEsRWo0lKoqoJVq+B05yuithZ22cWmNV9yCdQkur7ixvuE1Z9JhV9tl2SClDNteKhkG61MG4KIbIPN8tkeqLPjGmO2zJpQDlqZVsk1Urkx4rg5mkK2qCXvM1bR9b//hQMPTBzbdFN4803YaqvAKsIC3DS4t++1Sr6GUVoIFBbks3O3jkz5dknoekFVkEuvetW30m/cqsm5glambXlojEo4/wGuBG4C+gOn4t9RWVHWaqKkLccNMM007drkRVJUMlbR9YADrFto2TIYMgRefhkWLICttwbg/MPO59btDqwrJOdS1L4g9Fp6ZYvSFHHMkb1SZvYEWbfKZ1QEtiPQhodKc6Gun3AKjTGvYy1P84wxo9D0ZEVpQLppy1FpTIyEq0RF6c3j7W6cieOXz6ig37+ms8VO59FvzOtMu/qWumX/98JtzP3HYTzz8DA2WLkMCG5vEHQto7iDykqLQ5WKMPdd2P3ThodKc6EWlXBWiUge8LWInAdUABtnWSZFyTmaMqunsZ2lRz3/ReQux0FWhXSO77fdSQXbMubjBZRtDBxyCHz6KTsvnM3Ht50AwAfj7mHIz1199+d3LetSryfMxM+LL2IbVeYFpDZ73Td+rrlUVXgVpTlQi0o4FwHtgQuAPsBJwMnZFEhRcpGmzOppjLUmVQPBZIKsCukcP3S74mL45BMbcHv99XXLdx1+FnOuH8i/nr2OwtWrErYNupZlpcXcdGxvCvJ9miEam7bsp6R43T1BAbMdC/2LAhYVFmgqs9JsqKISgjHmI2PMcmPMAmPMqcaYI91y+spagjFw3XWw2WY25XTvvWHmzNTbrV5t62tsvbXdbuut4cor4fff69cZNcr+5PV7jRnTVGcUD2Pg2muhWzebfrvzzvDKKw1Wu3azVTw64XJm3HIcM28ZwiNP/J1df/o6/Ff36tVw7LGw5Zb2GnXuDAcfDNOnJ6y2sLKK/Noaznn/SSbfcyazbyjjvTtO5rSnb00pflzXk59bJ11rUaTtRODii+11/uIL2GADAA7+31S+uulo5l4/kL4LvkCA/j06Bx6rrLSYcUfvVJf1FJTlky/imxUVpFSJ4JutNWpQz9BzV5RMoq4fH0Tk+bDlxphBzSWLkmXGjoWrr4Zx46BHD/jnP2H//eHzz+EPfwjebuRIuOsuuOYam7r68ce2A29lJdzixCmccQYcdFDiduXl9hf2wQc31RnFY+xYq3BddRX07g2PPAKHHQZTptjUW4Dvv2efc49n8TY9uXbvkSxZsZrzppfzyJNXUHDlEcH7rqmpT93daiv49Ve46SbYd1+YMcMqMFhLwrBHrqHfvE+4pd9xfLvBpnT57Wd2Xr4wpfhxXU9+bp10i9wFbWewClGDrKftt7e9hdasYWrZyewx6TEAnnp0BAAP7XI4zxXfwuG7bO57PG+g7RYjJ/muU2sMc8Ye2mA86DpVrqwOzEBSlOZC05N9EJHFwPfA48AHJGX6GGPeyoZcXjQ9uRlYtQo22QSGDbMddAFWrICSEvjLX6wSEsQf/gAnnAA33lg/9n//B48+Cj/+GLzdoYfCd9/BV19l5BQSKCmxVpxTTom2/urVsNFGcNFF9dVXAfr0gS5dYOJE+/6uu+Dcc+0kW1Rkx5Yutdvefjucc07KQ7nxEZU/LWHGrccz+8JL6HXjaACm3v4Iu1x4CgefehvfbNQNiJ5C3Hv0q7FcPy7JsRt+rQZSHT9VSnTYPtxsnl2+/5wnHxuZuHCDDeCdd6xiE0BQNlBQSnHc9Vsymp7c8lDXjz9/AC4FdgBuAQ4AfjbGvJULSkqrZNIkW+lzzpzE8Tlz7PjzoUaupmHqVPsr/9hj68c6dLAWhZdeCt+2uho6dkwcKyrCN+LRZckSW3vjuOPqx95/H9q0gfvvrx9btsy6ok48MfKppMW338Jvv1kLkpcDDrByrl5t31dXWxnXXbd+nXXXtWMRfgh54yNWFKzDqvwCXp3xfZ0LZo+3n2fJH/ekauvusYrMlc+oYMXqNXHOuI7kDtLpFLnzbudHWJyLe/yPNtuBkhET2X7ok7y07R524ZIl0LOntUZdf73vNY5bYK+pC/IpSmNQRcUHY0yNMeZlY8zJ2LL53wBvisj5WRat9XLQQdC1Kzz4YOL4Aw/Y2IVDDgnetqYG1qwJf9XWxpdp1ixbBn2bbRLHt9vOLgvjjDNst90pU2D5cvsL+F//gvPOC97mqafspD9kSP3YbrvB8OEwdCjMn2/HLrjAns9tt8U/pziscoI527ZNHG/Xziop331n3x91FLRvby1PP/1kX0OHQqdOcMwxKQ8z7uVZrP59NZ2XL+WSN/9DbV4ez3Tfq34S/+ADNunbiykLnmHOnccx5aqBlF19PiwMd/2Me2U21TXpWYyT3TrpdrZ2twsqvhTkckk+/sq2hZxzxKX0G/O6/Zy4jBxpFfkdd0y4HnGVq3SUMS2rrzQXGqMSgIi0Aw4FjgNKgFuBZ7IpU6smP9+6JB580Aadithfig8+aJu8tQn5qO63H7yVwtB18slW6YnD0qXWMpCfVKeiUydYudJO1smTuMvYsbak+p571o/99a/1LiQ/nnjCBqtuu23i+OjR1uJ02mlw/vnw0EP2fadO4fKv8bEm1NYmjufnNyg4VseWW9plH31kFSaXDz+0f5c4lU67doXJk2HgQLjVCXDt0sUG3XYODgB1GfTKw4x4yyqoP7fvyKlHj6Ki48aIO4n/8IO9dzvtZK/Rb7/ZANQjjrAWpyT5XTdSlKqtfkSxJMStshs3zmX4gO7Bna1Li+2z8dNPcPjh9hp89pnNJALba+jEE9Pq4Bx1/camjCtKHNSi4oOIPAhMxTYgHG2M2cUYc7UxRn8yNCWnnQbz5tkS42Anv3nz4NRTw7e7+247mYa9Ro0K3t6YROuLtyeL3yTumtrD+qeMG2cDT2+7zSpRt95q41OCFJVFi+x6XrePS9u2Vjl5+20YPNhaa8IsTC4FBYmvefNsHxrvWLIFy0vHjlaea6+192LJEns+r71ml7sK3KJFcPTRNnblpZfsq08fG2/jWoFCeLffQA77802cftTlfL7J1tz39FVs/fP8+kncGPt67jl73oMH28n4ww/hjTcS9uV1IwVRVFiQYDk4cbdusS0JcXvfxHWtRLJwbLwx5Xc+Rb/rXuOKA86uHz/pJPvZPPhg6yZsApq6wJ+ieNFgWh9EpBZY4bz1XiABjDFm/eaXKpFWG0y77762H8pDD9kv3P/9Dz74IHybmprUsRB5efblx5tvQv/+9e//9Cc7dued1s3y+++JVpVx46zis2IFvvz8s7Uy3HEHnHlm/fjdd1vXT0UFbJxUN/CWW6y7ZN48G3+STG2ttbZ88onNICotDT9faNi5d9AgOOssa/lw2WIL2HDD4H0sXmwVg8mT7fvNNrPKzqhRNn6opMQGCT/zDHz9tVV+wFqbttnG/uJ3rSwBeH+d59fW8Op9f+Xz4h6YBx+0E/Mmm1jrznvvJV6PwkK44QZrZXJIVVI+E3180g08zXSvI79g3e6//cizT19G+x8XJa786qs2tijGvr2y9u/RmcmzFqfsMSTgm1WUS2gwbctDXT8+GGOa1NIkIvnANKDCGDNQRDYAxmNdTHOBY40xS5tShpzljDPs5D5mjJ38vFkzQTTW9dOnj7W6uKy3nv3bo4dVgr75Brp7fvnOmmWXBfHddzbWpHfvxPHSUmuxmTevoaLyxBPWTeSnpIBVZNzjnn++ta4EKV4ufZO+i9u2tYpF8ngYnTtbq8WCBfbXeffucPPNNquppMSuM2uWDe50lRT3WD172oDcFHhreSysrGJ+1y3ZM/9XNnQn8e22S6w/42JMg2uQqlR8JlJr062rEuZaSUeJ8bNqzF5vEw646BGmDP8T/zvzQrb9zx12gdsc8bTTrALerl3gfv3cOo+8X28ZC1MEgwrEKUpjUNdPdrgQ8OafjgReN8ZsA7zuvF87OfJIO8kNGWJ/NXsDS4NorOtnvfXs5O2+XKVkjz1g/fXhySfr1125El54IbzOyeZOnYuPP04cdwuZuRO8y9y5Ns7Az+0DMHs2/P3vNh36ySft+dx0U/Dxm4JNN7WKx5o1NgPptNPql22+ua0r42YBgVUsPv+84bkGUBesOmo/+q9YwIY7eBTDgQPh00+tpcrl7betMrjTTgn7CYr5cK0dmYifCDpGupN0Oq6k8hkVgQrDwsoqyj/9gcOLB1IyYiKD/vxPasT5qr//flu4r7AwsHChnwIUlTBvqKKkiyoqzYyIbIoN0r3XM3w44AYLPAiUNbNYucM669j6I+++a4Ml3bocYXTvnqho+L0iTpgNZBk50lamveMOeP11m8VSW5vgbuChh2yw77x59v0mm0BZGYwYYS0hkydbxWLkSLt9coDpE0/Y7Y8+uqEMNTXWGlRaal0sO+xgg2svuyx15lEmePhhO7m9+aY9z913t26wSy6pX+eMM2zGyRFH2CDfiRPt+S9aZF1NLqefXtc5GIDHH4c//xkee8zu//HHrXti0SJ7ri5nnWXdU4cdZpXExx6zbsH9908MVsZWb02eKzOdZjt8QHcK8hrOyCtWr0kr8yVuvIer2ATRtagwYZ+fdtmWrS5+nu7DnuGlUsf9s2qV/UyJ2OB1T1ZcY/ozVQZ0WlaUxqCKSvNzM3Ax4M2X3cQYswjA+bt2Nz4sK7N/vb/as8XIkdaaMWaM/WX/66+2hsgmm9SvU1vbME7mwQftBH7rrTYA9I47bJG4++5reIwnnrDuK78MmX/8w2Z0PPBAvZtj+HDrVjr55MTA36bA7UVz0EH2uH/8o1UqvDVT+vSBl1+22TgnnWSVj5Ur7XXyWjzcNHKX7t1tkbj/+z8YMMAqP1272tgabwzO+utb91OnTtbCdu659npNmJAgavmMCp6eXtEgqOyoPvGyX1JRVlrMuus09JpX15i0gknjupLCLB6uUuZnbfm9TVvOOfBC+zmd5Klce9VVVvncaiuYO7dR/Zm0o7LSFGgwbTMiIgOBQ4wxfxWRfYC/OTEqlcaYIs96S40xDXJPReQs4CyAbt269Znn/oJvbVx8MYwfX1/sTVEiEDfItTHBrVuMnITfN2c6waRx5Q46NsDNg3tTVlrMVpe86NuIMF+Eb8d4MsaWLrWWvKTsqSsOOZ+Heg0IlLkgT0BIqFOTiUDl5kCDaVseOgs0L/2AQSIyF3gC2FdEHgF+FJEuAM7fn/w2NsbcY4zpa4zp2zlCfYoWx+zZ8OyztjDa+eerkqKEklxwLCxmw2/buHEhXoIsB3kisd0/cVOXw+JwXCXBT0nxHe/Uybo0jUmw9l314m3MvX4g4x8byXYFqxukcI87ZqeEJohRq/UqSjpo1k8zYoy5BLgEwGNROVFExgEnA2Odv89lS8as8pe/2FTkQYNsWrCiBOCXmSLga2nwm9jD4kKiTLZ+BdnAKgJxC58lZz2lsu6EFoNzKA5IIfaW829oURpAmTG29s2AATBrFrt+/zkvXXMkAGccdTn03SdBNlVMlOZAXT9ZIsn1syEwAegGzAeOMcYsCdu+1dZRUZQIBFlQkpWVIHdESUB3YYC5EV035TMqGDbhE1/rRVM38/NW380XocaYhPTrVI0UIzVaNIYvzxvB9neOSzj2pO3/RM299zJo961piajrp+WhFpUsYYx5E3jT+f8XYL9syqMoLYmgQFMDdRN3vkhgIK27jh/lMyoStgmKZSkrLWbo+Jmx5MsEXiVFqHfn+JWxT5YbgpW8BhYlEc7c7CAqRvyJHj/N4alHL2bd1VUc+uVbsIfT/+q99xLbKyhKE6BBAIqitDiC4jS8E3eNMTw9vcI3ZiRISQESMndSxbIEydFU2S/JLQKSz8Kb1pzcSBFI2V4gWcFy38/aeAt2GPok2/ztWR7p7akhtPvuNsV5+PCmz0BT1lpUUVEUpcXhF4DqF6MSVI+kOESR8E7WqWqcxA2EjUtywPCo579IWYwtnbRml2QFK/l9dX4Blw0413Zx9mYK3XCDrQXUpYtte6EoGUQVFUVRWhx+TfuCbCQVlVX0G/tGgmVl+IDuDQrDuXgn51Q1TiI1D0wTP2tOZVXqgmodCwvoN/YNSkZOYqtLXqQkRVaUi5+CFaqI9e9vs4V+/dUW4wPb6bp7dxDhs2GjUvfgUpQIaDBtC0WDaRUlkbgNCS8r/4xH358fGnwbVuNk+IDuoY37GttXKIpykYxffROXoKwoCO+DFKXejKtUHfDJG9z6QmLwbWWPXpx05BV8XlOYkevSWDSYtuWhikoLRRUVRUnEL5MlmeRsnFSTcFB2zFF9inl6ekXosRpbAC2ssJsfRYUFiMDSkDL2UbOi4pKsVHVevoT7nxpNrx8Tm1KeO2gEb+y4T1Zrrqii0vJQRaWFooqKojTEmxHjRzqVY/2UmbBjJJNu1+Ygi0qeQK3P13ZxUSELHTdRKnkyZfVxCVSqjOH0ac9x+Rv3Jgy/s90e7PXBy/WdypsRVVRaHqqotFBUUVGUYOKWpY9LXGtHOpaLINdUkBVHsPE1YQpUU9V3Kb3q1VBLDsCWvyxg/GMj6byyMnHB5Mmwzz4ZlykIVVRaHhpMqyhKq6Ops3Hiph+HdUP2I6zBYlDGkrdWShCZ7CLtJej3rngilr/bcFN2Of8Rthr+HI/v6ekU3r+/XfGcc6A6XNlJzoJKp1u10vJQRUVRlFZHU2bjgL8ilIqoReDcirfJlhMDTJ61mOEDutugWQ8FeVLnxunQ1l+uDm3zmywuZFlANpIxNLhObdu1pfDWm+3CqVPrF9x1F7RtCx07wuefN9hXY/szKS0XVVQURWmVJBc8y+Qk7acIuY37gohihXEn46CCdHXKTnJutef9ytX+rqEVq2uazBIR1igxVGHcfXersKxYAUOG2LFff4VevayV5dpr68w1qWraKK0XjVFpoWiMiqLkJpH66ASQKiXZVYTC4m+i1kzJpIWpMefcgOeeg7KyxLEePdij/wgWrt+wa3zcAGmNUWl5qEVFURQlgzTG7RTmHnJjbFIVoYvilsq0JSKjrrbDD7dWlMWLYc897disWUz916nMvX4gR37+esLqTdWuQMkdtCmhoihKhnGbFsYlKGsnX6Ru4g9KjXYnbG9Dwjh9fRpLuuccyEYbwTvv2P/vussG2wL/nHQT73XbkUXrd85ogLSSu6hFRVEUJSJNnXUSlK1047E71SkBUTKa3PicoDYBYEvttxjOPhuM4dUXpjJq8KX8sN5GGQ+QVnIXtagoiqJEIDkOw806ATI2WXqtIUFF2aKs4xJWV0XCtJgc5cCBu3PgwN0ZlW1BlGZFg2lbKBpMqyjNS1MXkWsKymdUcNH4mb7LgoJQo/T2acloMG3LQy0qiqIoEUgVxJpMLkz4ZaXFjH7hC9+qsX5BqMnVcCsqqxj+1CeMev4LllVVt0rFRcl9NEZFURQlAkHZJX7juVSc7MrDekaq0ls+o6JByX6wnZgrq6qzfh7K2osqKoqiKBGIU5Y/l4qTRU0dHvfK7Ej9i7TImtLcqOtHURQlAnGCWOO6iZoab+qw65IaOn5mwjnEkS1b56GsnaiioiiKEpGotUKCsm2yXZzMLwbFzVxK1XnZS3OeRy7E+ijZRV0/iqIoGaapuzenQ1AMiuvKidposSBfmu08cinWR8keqqgoiqJkmKbu3pwOYTEoCyur6mQuSlEIrkPbNs12HrkU66NkD3X9KIqiNAEZLymfglQukrC4kjyROivF72tqQ4+zrKphqnNTkWuxPkp2UEVFURSlhROlam5YDEqNMVzyzGe0a5PXwIKRTHPGp+RqrI/SvKjrR1EUpYUTxUWSKgalqrqGyhTWkuaOs8nFWB+l+VGLiqIoSgsnioskalflIIqzkHETJyVcab2ooqIoitLCieoiceNmgvoWdWpfwKrq2gTrTGFBflYDgZs71kfJPdT104yIyGYiMllEvhKRL0TkQmd8AxH5r4h87fztlG1ZFUVpOcR1kQStf+VhPXMuW0lR1KLSvKwBhhljPhaR9YDpIvJf4BTgdWPMWBEZCYwERmRRTkVRWhBxXSRR1neXuXEuyZVtw46jRdqUTCLGROnuoDQFIvIccLvz2scYs0hEugBvGmNCo8X69u1rpk2b1hxiKoqyllA+o4JRz3/RIKjWdf8ACdlF3mVeRSbVOtlERKYbY/pmWw4lOur6yRIiUgKUAh8AmxhjFgE4fzfOomiKoqyFuAqGX+aPm0EUJbtIi7QpmUZdP1lARNYFngYuMsb8KiJRtzsLOAugW7duTSegoihrHX4KhpewImveZVqkTck0alFpZkSkAKukPGqMecYZ/tFx+eD8/clvW2PMPcaYvsaYvp07d24egRVFWStIpUh0LSoMLLTmHY+yjqLEQRWVZkSs6eQ+4CtjzD89i54HTnb+Pxl4rrllUxRl7SZMkXAziKJkF2mRNiXTqOuneekHnAR8JiIznbFLgbHABBE5HZgPHJMd8RRFWVsZPqB7gyBYsLVVrjysp29GkF9GjxZpUzKNZv20UDTrR1GUTLM2pBVr1k/LQy0qiqIoChBcBXZtUGCU3EUVFUVRFCWQKJ2ZFaUp0WBaRVEUJRCti6JkG1VUFEVRlEC0LoqSbVRRURRFUQLRuihKtlFFRVEURQlE66Io2UaDaRVFUZRAwuqiaDaQ0hyooqIoiqKE4pe2rNlASnOhrh9FURQlNpoNpDQXqqgoiqIosdFsIKW5UEVFURRFiY1mAynNhSoqiqIoSmw0G0hpLjSYVlEURfElLKtHuyQrzYUqKoqiKEoDomT1BDUxVJRMoq4fRVEUpQGa1aPkCqqoKIqiKA3QrB4lV1BFRVEURWmAZvUouYIqKoqiKEoDNKtHyRU0mFZRFEVpgGb1KLmCKiqKoiiKL5rVo+QC6vpRFEVRFCVnUUVFURRFUZScRV0/iqIoSizCKtYqSqYRY0y2ZVDSoG/fvmbatGnZFkNRlLWM5Iq1AAV5wrrrtKFyZXXOKy4iMt0Y0zfbcijRUYuKoiiKEtlK4lextrrWsHRlNeBfal9RGoPGqCiKoqzluFaSisoqDPXKRvmMigbrRqlMq6X2lUyiioqiKMpaTpy+PlEr02qpfSVTqKKiKIqylhOnr49fxVo/tNS+kilUUckRROQgEZktIt+IyMhsy6MoytpDnL4+ZaXFjDmyF8VFhQhQVFhAQb4krKOl9pVMosG0OYCI5AN3AAcAC4CPROR5Y8yX2ZVMUZS1geEDujfI5AlTNpIr1mq6stKUqKKSG/wR+MYY8x2AiDwBHA6ooqIoSpPT2L4+WmpfaUpUUckNioHvPe8XALtmSRZFUdZCVNlQchWNUckNxGesQSU+ETlLRKaJyLTFixc3g1iKoiiKkl1UUckNFgCbed5vCixMXskYc48xpq8xpm/nzp2bTThFURRFyRaqqOQGHwHbiMgWItIWGAI8n2WZFEVRFCXraIxKDmCMWSMi5wGvAPnA/caYL7IslqIoiqJkHVVUcgRjzIvAi9mWQ1EURVFyCe2e3EIRkcXAvBibbAT83ETiZIJclk9lS49clg1yWz6VLX1Sybe5MUaD/FoQqqisJYjItFxubZ7L8qls6ZHLskFuy6eypU+uy6fER4NpFUVRFEXJWVRRURRFURQlZ1FFZe3hnmwLkIJclk9lS49clg1yWz6VLX1yXT4lJhqjoiiKoihKzqIWFUVRFEVRchZVVBRFURRFyVlUUVkLEJGDRGS2iHwjIiOzLMv9IvKTiHzuGdtARP4rIl87fztlSbbNRGSyiHwlIl+IyIU5Jt86IvKhiHziyDc6l+RzZMkXkRkiMjGXZBORuSLymYjMFJFpOSZbkYg8JSKznM/e7jkkW3fnmrmvX0XkohySb6jzLHwuIo87z0hOyKZkDlVUWjkikg/cARwMbA8cJyLbZ1GkB4CDksZGAq8bY7YBXnfeZ4M1wDBjzHbAbsC5zrXKFfl+B/Y1xuwE9AYOEpHdckg+gAuBrzzvc0m2/saY3p4aG7ki2y3Ay8aYHsBO2OuXE7IZY2Y716w30AdYCTybC/KJSDFwAdDXGLMDtv3IkFyQTckwxhh9teIXsDvwiuf9JcAlWZapBPjc83420MX5vwswO9vXzZHlOeCAXJQPaA98DOyaK/Jhu36/DuwLTMylewvMBTZKGsu6bMD6wBycxIZcks1H1gOBKbkiH1AMfA9sgG0HM9GRMeuy6SuzL7WotH7ch9llgTOWS2xijFkE4PzdOMvyICIlQCnwATkkn+NamQn8BPzXGJNL8t0MXAzUesZyRTYDvCoi00XkrBySbUtgMfAfx2V2r4h0yBHZkhkCPO78n3X5jDEVwA3AfGARsMwY82ouyKZkFlVUWj/iM6Y56SGIyLrA08BFxphfsy2PF2NMjbFm+E2BP4rIDlkWCQARGQj8ZIyZnm1ZAuhnjNkZ6wI9V0T2zrZADm2AnYF/GWNKgRXkoKtCRNoCg4Ansy2LixN7cjiwBdAV6CAiJ2ZXKqUpUEWl9bMA2MzzflNgYZZkCeJHEekC4Pz9KVuCiEgBVkl51BjzTK7J52KMqQTexMb75IJ8/YBBIjIXeALYV0QeyRHZMMYsdP7+hI2x+GOOyLYAWOBYxgCewiouuSCbl4OBj40xPzrvc0G+/YE5xpjFxphq4BlgjxyRTckgqqi0fj4CthGRLZxfRUOA57MsUzLPAyc7/5+MjQ1pdkREgPuAr4wx//QsyhX5OotIkfN/IfaLelYuyGeMucQYs6kxpgT7GXvDGHNiLsgmIh1EZD33f2wcw+e5IJsx5gfgexHp7gztB3yZC7IlcRz1bh/IDfnmA7uJSHvn2d0PG4icC7IpGUQr064FiMgh2PiBfOB+Y8y1WZTlcWAfbCv2H4ErgXJgAtAN++VzjDFmSRZk2xN4B/iM+jiLS7FxKrkg347Ag9j7mAdMMMZcJSIb5oJ8Hjn3Af5mjBmYC7KJyJZYKwpYV8tjxphrc0E2R77ewL1AW+A74FSc+5tt2Rz52mPj3LY0xixzxnLl2o0GBmMz9mYAZwDr5oJsSuZQRUVRFEVRlJxFXT+KoiiKouQsqqgoiqIoipKzqKKiKIqiKErOooqKoiiKoig5iyoqiqIoiqLkLKqoKIqCiCyPse4+IrKH5/3ZIvJn5/9TRKRrGsefKyIbxd1OUZTWT5tsC6AoSotjH2A5MBXAGHOXZ9kp2GJquVb9WFGUFooqKoqi+CIihwGXYQuR/QKcABQCZwM1Tl+V87EVQZdjOxT3BR4VkSps5+6vgL7GmJ9FpC9wgzFmH6dg2ONAZ+BDPD2pnP1e4Bz3A+Cvxpiapj9jRVFyEXX9KIoSxLvAbk6zvCeAi40xc4G7gJuMMb2NMe+4KxtjngKmASc4y6pC9n0l8K6z7+exVUQRke2wlUb7Oc0Xa7AKkqIoaylqUVEUJYhNgfFOY7e2wJwM7ntv4EgAY8wkEVnqjO8H9AE+su1bKESbyinKWo0qKoqiBHEb8E9jzPNO/55RaexjDfWW23WSlvn17xDgQWPMJWkcS1GUVoi6fhRFCaIjUOH8f7Jn/DdgvYBtkpfNxVpIAI7yjL+N49IRkYOBTs7468DRIrKxs2wDEdk8TfkVRWkFqKKiKApAexFZ4Hn9H9aC8qSIvAP87Fn3BeAIEZkpInsl7ecB4C5nWSEwGrjF2Yc3IHY0sLeIfAwciO1yizHmS2wA76si8inwX6BLpk9WUZSWg3ZPVhRFURQlZ1GLiqIoiqIoOYsqKoqiKIqi5CyqqCiKoiiKkrOooqIoiqIoSs6iioqiKIqiKDmLKiqKoiiKouQsqqgoiqIoipKz/D/75+28kp2vkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for Maximum Temperature', 'Max Temp',(10,40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "92d2f2fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for Maximum Temperature', 'Max Temp',(-50,90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c6250f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for % Humidity', '% Humidity',(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "24831433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for % Humidity', '% Humidity',(-50,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d83948e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for % Cloudiness', '% Cloudiness',(30,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d7ff1c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for % Cloudiness', '% Cloudiness',(-50,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "88437232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for Wind Speed', 'Wind Speed',(40,35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5a2baf92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for Wind Speed', 'Wind Speed',(-50,35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a211ffcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#YAHOO!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}