WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x2b8e3d8cfc8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a set of random latitude and longitude combinations.\n",
    "lats = np.random.uniform(low=-90.000, high=90.000, size=1500)\n",
    "lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "lat_lngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the latitudes and longitudes to a list.\n",
    "coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new norfolk au\n",
      "arraial do cabo br\n",
      "thompson ca\n",
      "tuktoyaktuk ca\n",
      "mlonggo id\n",
      "utiroa ki\n",
      "marcona pe\n",
      "hilo us\n",
      "dunedin nz\n",
      "bethel us\n",
      "albany au\n",
      "santa rosalia mx\n",
      "moscow us\n",
      "vaini to\n",
      "naze jp\n",
      "butaritari ki\n",
      "palmer us\n",
      "marilandia br\n",
      "port elizabeth za\n",
      "parkes au\n",
      "bam ir\n",
      "vao nc\n",
      "ostrovnoy ru\n",
      "arraial do cabo br\n",
      "george town ky\n",
      "havre-saint-pierre ca\n",
      "atikokan ca\n",
      "butaritari ki\n",
      "belushya guba ru\n",
      "yellowknife ca\n",
      "grindavik is\n",
      "bredasdorp za\n",
      "rikitea pf\n",
      "port shepstone za\n",
      "punta arenas cl\n",
      "mitsamiouli km\n",
      "mataura pf\n",
      "presidencia roque saenz pena ar\n",
      "jinchang cn\n",
      "clyde river ca\n",
      "los llanos de aridane es\n",
      "kenai us\n",
      "jamestown sh\n",
      "marcona pe\n",
      "mar del plata ar\n",
      "esperance au\n",
      "castro cl\n",
      "albany au\n",
      "nikolskoye ru\n",
      "hilo us\n",
      "illoqqortoormiut gl\n",
      "atuona pf\n",
      "vaini to\n",
      "rikitea pf\n",
      "pauini br\n",
      "mys shmidta ru\n",
      "norrtalje se\n",
      "longyearbyen sj\n",
      "ushuaia ar\n",
      "kodiak us\n",
      "yaan cn\n",
      "villazon bo\n",
      "albany au\n",
      "channagiri in\n",
      "busselton au\n",
      "ribeira grande pt\n",
      "samusu ws\n",
      "dumka in\n",
      "sinnamary gf\n",
      "nikolskoye ru\n",
      "new norfolk au\n",
      "carutapera br\n",
      "ushuaia ar\n",
      "butaritari ki\n",
      "saint-philippe re\n",
      "taolanaro mg\n",
      "albany au\n",
      "lachhmangarh in\n",
      "ushuaia ar\n",
      "coahuayana mx\n",
      "tasiilaq gl\n",
      "khatanga ru\n",
      "faanui pf\n",
      "la ronge ca\n",
      "port alfred za\n",
      "nikolskoye ru\n",
      "thompson ca\n",
      "mataura pf\n",
      "lagoa pt\n",
      "san cristobal ec\n",
      "bridgetown ca\n",
      "chimbarongo cl\n",
      "vaini to\n",
      "tommot ru\n",
      "geraldton au\n",
      "sitka us\n",
      "carnarvon au\n",
      "rosetta eg\n",
      "beyneu kz\n",
      "punta arenas cl\n",
      "georgetown sh\n",
      "georgetown sh\n",
      "lorengau pg\n",
      "east london za\n",
      "souillac mu\n",
      "guerrero negro mx\n",
      "tuy hoa vn\n",
      "punta arenas cl\n",
      "lavrentiya ru\n",
      "punta arenas cl\n",
      "new norfolk au\n",
      "punta arenas cl\n",
      "ribeira grande pt\n",
      "saint george bm\n",
      "nishihara jp\n",
      "barrow us\n",
      "hermanus za\n",
      "touros br\n",
      "lolua tv\n",
      "ushuaia ar\n",
      "bredasdorp za\n",
      "puerto ayora ec\n",
      "upernavik gl\n",
      "loreto mx\n",
      "tongliao cn\n",
      "rio grande br\n",
      "kodiak us\n",
      "port alfred za\n",
      "grand river south east mu\n",
      "plettenberg bay za\n",
      "batemans bay au\n",
      "lebu cl\n",
      "hami cn\n",
      "jamestown sh\n",
      "brainerd us\n",
      "rikitea pf\n",
      "athabasca ca\n",
      "punta arenas cl\n",
      "puerto ayora ec\n",
      "new norfolk au\n",
      "ilulissat gl\n",
      "punta arenas cl\n",
      "ushuaia ar\n",
      "manicore br\n",
      "ushtobe kz\n",
      "castro cl\n",
      "taksimo ru\n",
      "hobart au\n",
      "guerrero negro mx\n",
      "batagay ru\n",
      "ushuaia ar\n",
      "anadyr ru\n",
      "taolanaro mg\n",
      "khatanga ru\n",
      "illoqqortoormiut gl\n",
      "hobart au\n",
      "port alfred za\n",
      "balaipungut id\n",
      "illoqqortoormiut gl\n",
      "hithadhoo mv\n",
      "georgetown sh\n",
      "rio grande br\n",
      "kulynychi ua\n",
      "saint-augustin ca\n",
      "jamestown sh\n",
      "orlik ru\n",
      "hilo us\n",
      "mogadishu so\n",
      "baghdad iq\n",
      "castro cl\n",
      "reynoldsburg us\n",
      "inirida co\n",
      "ponta do sol cv\n",
      "hobyo so\n",
      "bredasdorp za\n",
      "albany au\n",
      "klaksvik fo\n",
      "chuy uy\n",
      "carnarvon au\n",
      "butaritari ki\n",
      "ushuaia ar\n",
      "ahipara nz\n",
      "kaitangata nz\n",
      "albany au\n",
      "kapaa us\n",
      "ilulissat gl\n",
      "homer us\n",
      "bundoran ie\n",
      "busselton au\n",
      "anuradhapura lk\n",
      "rikitea pf\n",
      "vagur fo\n",
      "georgetown sh\n",
      "rikitea pf\n",
      "ponta do sol cv\n",
      "castro cl\n",
      "east london za\n",
      "grand centre ca\n",
      "punta arenas cl\n",
      "najran sa\n",
      "aykhal ru\n",
      "san patricio mx\n",
      "attawapiskat ca\n",
      "ilulissat gl\n",
      "nanortalik gl\n",
      "hermanus za\n",
      "edson ca\n",
      "mahebourg mu\n",
      "imbituba br\n",
      "verkhneyarkeyevo ru\n",
      "komsomolskiy ru\n",
      "havelock us\n",
      "maykain kz\n",
      "rikitea pf\n",
      "kyle of lochalsh gb\n",
      "coquimbo cl\n",
      "nanortalik gl\n",
      "bethel us\n",
      "san quintin mx\n",
      "taolanaro mg\n",
      "hermanus za\n",
      "yellowknife ca\n",
      "kaitong cn\n",
      "diego de almagro cl\n",
      "tashtyp ru\n",
      "semarang id\n",
      "leningradskiy ru\n",
      "sentyabrskiy ru\n",
      "hilo us\n",
      "saint-augustin ca\n",
      "grafton au\n",
      "ushuaia ar\n",
      "san patricio mx\n",
      "jamestown sh\n",
      "dikson ru\n",
      "san andres co\n",
      "carnarvon au\n",
      "east london za\n",
      "port elizabeth za\n",
      "kapaa us\n",
      "berlevag no\n",
      "laguna br\n",
      "hilo us\n",
      "zhigansk ru\n",
      "bluff nz\n",
      "benemerito de las americas mx\n",
      "havelock us\n",
      "leh in\n",
      "dikson ru\n",
      "bredasdorp za\n",
      "port alfred za\n",
      "punta arenas cl\n",
      "arraial do cabo br\n",
      "broken hill au\n",
      "saint george bm\n",
      "belushya guba ru\n",
      "imbituba br\n",
      "talnakh ru\n",
      "zhezkazgan kz\n",
      "ushuaia ar\n",
      "para de minas br\n",
      "bengkulu id\n",
      "abbeville us\n",
      "meulaboh id\n",
      "krasnoselkup ru\n",
      "karatau kz\n",
      "luderitz na\n",
      "hilo us\n",
      "provideniya ru\n",
      "amderma ru\n",
      "norman wells ca\n",
      "upernavik gl\n",
      "mount isa au\n",
      "port alfred za\n",
      "georgetown sh\n",
      "taolanaro mg\n",
      "camacha pt\n",
      "jamestown sh\n",
      "itarema br\n",
      "mys shmidta ru\n",
      "tiksi ru\n",
      "donskoy ru\n",
      "hit iq\n",
      "arlit ne\n",
      "ugoofaaru mv\n",
      "longyearbyen sj\n",
      "longyearbyen sj\n",
      "klaksvik fo\n",
      "rikitea pf\n",
      "flinders au\n",
      "albany au\n",
      "thompson ca\n",
      "severo-kurilsk ru\n",
      "busselton au\n",
      "alofi nu\n",
      "ushuaia ar\n",
      "hilo us\n",
      "chokurdakh ru\n",
      "ushuaia ar\n",
      "colac au\n",
      "richards bay za\n",
      "sentyabrskiy ru\n",
      "punta arenas cl\n",
      "bengkulu id\n",
      "sonoita mx\n",
      "castro cl\n",
      "souillac mu\n",
      "khatanga ru\n",
      "biltine td\n",
      "tuktoyaktuk ca\n",
      "diu in\n",
      "rikitea pf\n",
      "puerto ayora ec\n",
      "sorland no\n",
      "egvekinot ru\n",
      "chokurdakh ru\n",
      "ushuaia ar\n",
      "hobart au\n",
      "mataura pf\n",
      "coihaique cl\n",
      "yellowknife ca\n",
      "longyearbyen sj\n",
      "punta arenas cl\n",
      "ribeira grande pt\n",
      "albany au\n",
      "port alfred za\n",
      "lima pe\n",
      "valente br\n",
      "xuddur so\n",
      "nanakuli us\n",
      "umm lajj sa\n",
      "belushya guba ru\n",
      "busselton au\n",
      "kapaa us\n",
      "ushuaia ar\n",
      "bowen au\n",
      "avarua ck\n",
      "oktyabrskiy ru\n",
      "ortigueira br\n",
      "hithadhoo mv\n",
      "thompson ca\n",
      "solovetskiy ru\n",
      "yellowknife ca\n",
      "kapaa us\n",
      "avarua ck\n",
      "bozhou cn\n",
      "hermanus za\n",
      "ixtapa mx\n",
      "bethel us\n",
      "labuhan id\n",
      "dingle ie\n",
      "el estor gt\n",
      "tecoanapa mx\n",
      "tsihombe mg\n",
      "port elizabeth za\n",
      "taolanaro mg\n",
      "onega ru\n",
      "kamra pk\n",
      "kasempa zm\n",
      "rikitea pf\n",
      "faya td\n",
      "vaini to\n",
      "bluff nz\n",
      "vaini to\n",
      "aykhal ru\n",
      "vaini to\n",
      "vaini to\n",
      "saint george bm\n",
      "severo-kurilsk ru\n",
      "busselton au\n",
      "elk point ca\n",
      "mananjary mg\n",
      "emporia us\n",
      "manaus br\n",
      "bushehr ir\n",
      "bluff nz\n",
      "chokurdakh ru\n",
      "avarua ck\n",
      "sabha ly\n",
      "hilo us\n",
      "albany au\n",
      "kapaa us\n",
      "nikolskoye ru\n",
      "clyde river ca\n",
      "atuona pf\n",
      "tahe cn\n",
      "vardo no\n",
      "santona es\n",
      "atuona pf\n",
      "hami cn\n",
      "mar del plata ar\n",
      "sitka us\n",
      "breves br\n",
      "capitao poco br\n",
      "ilulissat gl\n",
      "turukhansk ru\n",
      "port elizabeth za\n",
      "shingu jp\n",
      "bambous virieux mu\n",
      "nikolskoye ru\n",
      "mar del plata ar\n",
      "tshikapa cd\n",
      "changli cn\n",
      "ushuaia ar\n",
      "zelenogradsk ru\n",
      "taolanaro mg\n",
      "busselton au\n",
      "georgetown sh\n",
      "puerto ayora ec\n",
      "port elizabeth za\n",
      "iquitos pe\n",
      "ushuaia ar\n",
      "vilcun cl\n",
      "samalaeulu ws\n",
      "neuquen ar\n",
      "saint-joseph re\n",
      "shar kz\n",
      "siavonga zm\n",
      "punta arenas cl\n",
      "ribeira grande pt\n",
      "mataura pf\n",
      "punta arenas cl\n",
      "kapaa us\n",
      "vaini to\n",
      "upernavik gl\n",
      "whitehorse ca\n",
      "chokurdakh ru\n",
      "punta arenas cl\n",
      "bowen au\n",
      "souillac mu\n",
      "calvinia za\n",
      "jamestown sh\n",
      "yamada jp\n",
      "souillac mu\n",
      "puerto ayora ec\n",
      "rikitea pf\n",
      "punta arenas cl\n",
      "esperance au\n",
      "kruisfontein za\n",
      "quesnel ca\n",
      "iquitos pe\n",
      "kusye-aleksandrovskiy ru\n",
      "poronaysk ru\n",
      "bredasdorp za\n",
      "qaqortoq gl\n",
      "hermanus za\n",
      "attawapiskat ca\n",
      "svetlaya ru\n",
      "castro cl\n",
      "namatanai pg\n",
      "shache cn\n",
      "panzhihua cn\n",
      "kropotkin ru\n",
      "belushya guba ru\n",
      "roald no\n",
      "anadyr ru\n",
      "port alfred za\n",
      "belushya guba ru\n",
      "salalah om\n",
      "upernavik gl\n",
      "puerto ayora ec\n",
      "ushuaia ar\n",
      "taolanaro mg\n",
      "pevek ru\n",
      "thinadhoo mv\n",
      "san patricio mx\n",
      "vaini to\n",
      "ilulissat gl\n",
      "saint-philippe re\n",
      "cidreira br\n",
      "sur om\n",
      "igarka ru\n",
      "mufumbwe zm\n",
      "ondjiva ao\n",
      "nizhneyansk ru\n",
      "la ronge ca\n",
      "avarua ck\n",
      "kloulklubed pw\n",
      "bluff nz\n",
      "ushuaia ar\n",
      "formoso do araguaia br\n",
      "springfield us\n",
      "tommot ru\n",
      "funadhoo mv\n",
      "punta arenas cl\n",
      "kruisfontein za\n",
      "mehamn no\n",
      "upernavik gl\n",
      "atherton au\n",
      "francistown bw\n",
      "juba sd\n",
      "hualmay pe\n",
      "bella union uy\n",
      "bambous virieux mu\n",
      "rishikesh in\n",
      "margate za\n",
      "lavumisa sz\n",
      "rikitea pf\n",
      "saint george bm\n",
      "upernavik gl\n",
      "barrow us\n",
      "new norfolk au\n",
      "ushuaia ar\n",
      "souillac mu\n",
      "busselton au\n",
      "rikitea pf\n",
      "puerto ayora ec\n",
      "krasnyy chikoy ru\n",
      "trinidad bo\n",
      "hualmay pe\n",
      "bardiyah ly\n",
      "provideniya ru\n",
      "tsihombe mg\n",
      "teguise es\n",
      "nisia floresta br\n",
      "hermanus za\n",
      "mys shmidta ru\n",
      "brae gb\n",
      "coffs harbour au\n",
      "bridlington gb\n",
      "te anau nz\n",
      "vanimo pg\n",
      "vostok ru\n",
      "mys shmidta ru\n",
      "barentsburg sj\n",
      "hilo us\n",
      "ribeira grande pt\n",
      "port alfred za\n",
      "new norfolk au\n",
      "unity ca\n",
      "bethel us\n",
      "cabo san lucas mx\n",
      "saint-joseph re\n",
      "kenai us\n",
      "yellowknife ca\n",
      "sinnamary gf\n",
      "toguchin ru\n",
      "sebastian us\n",
      "mys shmidta ru\n",
      "kapaa us\n",
      "henties bay na\n",
      "hofn is\n",
      "saint george bm\n",
      "rikitea pf\n",
      "illoqqortoormiut gl\n",
      "curuca br\n",
      "puerto ayora ec\n",
      "tiksi ru\n",
      "huarmey pe\n",
      "heinola fi\n",
      "la romana do\n",
      "ushuaia ar\n",
      "pangnirtung ca\n",
      "felidhoo mv\n",
      "bredasdorp za\n",
      "katangli ru\n",
      "benguela ao\n",
      "jamestown sh\n",
      "punta arenas cl\n",
      "ushuaia ar\n",
      "mataura pf\n",
      "arraial do cabo br\n",
      "kavieng pg\n",
      "chinchani in\n",
      "illoqqortoormiut gl\n",
      "busselton au\n",
      "bizhbulyak ru\n",
      "utiroa ki\n",
      "tsihombe mg\n",
      "porbandar in\n",
      "saskylakh ru\n",
      "tuktoyaktuk ca\n",
      "salinopolis br\n",
      "torbay ca\n",
      "balikpapan id\n",
      "longyearbyen sj\n",
      "ajdovscina si\n",
      "bure et\n",
      "nikolskoye ru\n",
      "victoria sc\n",
      "dalinghe cn\n",
      "oktyabrskoye ru\n",
      "albany au\n",
      "bengkulu id\n",
      "coahuayana mx\n",
      "busselton au\n",
      "new norfolk au\n",
      "arraial do cabo br\n",
      "cherskiy ru\n",
      "belushya guba ru\n",
      "barentsburg sj\n",
      "cape town za\n",
      "kolokani ml\n",
      "saint-philippe re\n",
      "kiama au\n",
      "souillac mu\n",
      "severo-kurilsk ru\n",
      "rochelle us\n",
      "mataura pf\n",
      "san quintin mx\n",
      "lipin bor ru\n",
      "lorengau pg\n",
      "hithadhoo mv\n",
      "rungata ki\n",
      "kaseda jp\n",
      "samarai pg\n",
      "bredasdorp za\n",
      "tres de maio br\n",
      "vaini to\n",
      "nuevo casas grandes mx\n",
      "wanxian cn\n",
      "haines junction ca\n",
      "iquique cl\n",
      "norman wells ca\n",
      "rikitea pf\n",
      "flinders au\n",
      "grindavik is\n",
      "geraldton au\n",
      "new norfolk au\n",
      "busselton au\n",
      "taolanaro mg\n",
      "verkhnevilyuysk ru\n",
      "hobart au\n",
      "hilo us\n",
      "fort william gb\n",
      "nizhneyansk ru\n",
      "guarapari br\n",
      "saint-philippe re\n",
      "jamestown sh\n",
      "albany au\n",
      "sambava mg\n",
      "luchegorsk ru\n",
      "georgetown sh\n",
      "coihaique cl\n",
      "padang id\n",
      "namikupa tz\n",
      "port-cartier ca\n",
      "vestmanna fo\n",
      "cherskiy ru\n",
      "nizhniy kuranakh ru\n",
      "ribeira grande pt\n",
      "atuona pf\n",
      "ushuaia ar\n",
      "amapa br\n",
      "puerto baquerizo moreno ec\n",
      "khatanga ru\n",
      "cidreira br\n",
      "east london za\n",
      "berlevag no\n",
      "ushuaia ar\n",
      "ternate id\n",
      "airai pw\n",
      "arraial do cabo br\n",
      "anadyr ru\n",
      "hilo us\n",
      "wanning cn\n",
      "ushuaia ar\n",
      "tommot ru\n",
      "haines junction ca\n",
      "tumannyy ru\n",
      "avarua ck\n",
      "klyavlino ru\n",
      "yellowknife ca\n",
      "abancay pe\n",
      "vila franca do campo pt\n",
      "bredasdorp za\n",
      "valdivia cl\n",
      "khatanga ru\n",
      "port alfred za\n",
      "illoqqortoormiut gl\n",
      "wakkanai jp\n",
      "port hardy ca\n",
      "punta arenas cl\n",
      "saskylakh ru\n",
      "bandarbeyla so\n",
      "ostrovnoy ru\n",
      "xining cn\n",
      "qaanaaq gl\n",
      "geraldton au\n",
      "nanortalik gl\n",
      "lazaro cardenas mx\n",
      "ushuaia ar\n",
      "new norfolk au\n",
      "chuy uy\n",
      "rikitea pf\n",
      "kahului us\n",
      "las cruces us\n",
      "severo-kurilsk ru\n",
      "bengkulu id\n",
      "busselton au\n",
      "kaitangata nz\n",
      "mataura pf\n",
      "mumbwa zm\n",
      "asau tv\n",
      "albany au\n",
      "butaritari ki\n",
      "faanui pf\n",
      "thinadhoo mv\n",
      "richards bay za\n",
      "sorland no\n",
      "taolanaro mg\n",
      "taolanaro mg\n",
      "saint-philippe re\n",
      "punta arenas cl\n",
      "samalaeulu ws\n",
      "tuktoyaktuk ca\n",
      "rikitea pf\n",
      "taolanaro mg\n",
      "shubarshi kz\n",
      "champagnole fr\n",
      "asau tv\n",
      "ponta do sol cv\n",
      "sitka us\n",
      "lebu cl\n",
      "cape town za\n",
      "hermanus za\n",
      "faanui pf\n",
      "gizo sb\n",
      "ushuaia ar\n",
      "provideniya ru\n",
      "fevralsk ru\n",
      "mataura pf\n",
      "provideniya ru\n",
      "rungata ki\n",
      "bossembele cf\n",
      "portland au\n",
      "ushuaia ar\n",
      "pitimbu br\n",
      "saint-philippe re\n",
      "mount gambier au\n",
      "nizhnekamsk ru\n",
      "la tijera mx\n",
      "mamontovo ru\n",
      "mahebourg mu\n",
      "east london za\n",
      "tefe br\n",
      "raudeberg no\n",
      "aitape pg\n",
      "kapaa us\n",
      "hermanus za\n",
      "bluff nz\n",
      "dinar tr\n",
      "rikitea pf\n",
      "te anau nz\n",
      "rurrenabaque bo\n",
      "rylsk ru\n",
      "amderma ru\n",
      "sentyabrskiy ru\n",
      "punta arenas cl\n",
      "lapugiu de jos ro\n",
      "bredasdorp za\n",
      "hammerfest no\n",
      "atuona pf\n",
      "punta arenas cl\n",
      "touros br\n",
      "rikitea pf\n",
      "tsaratanana mg\n",
      "mataura pf\n",
      "mahebourg mu\n",
      "puerto ayora ec\n",
      "akhmim eg\n",
      "yenagoa ng\n",
      "mount gambier au\n",
      "yichang cn\n",
      "tasiilaq gl\n",
      "rikitea pf\n",
      "kenora ca\n",
      "albany au\n",
      "kalmunai lk\n",
      "georgetown sh\n",
      "kropotkin ru\n",
      "muzhi ru\n",
      "port elizabeth za\n",
      "lompoc us\n",
      "bethel us\n",
      "ushuaia ar\n",
      "ust-kuyga ru\n",
      "oulu fi\n",
      "khatanga ru\n",
      "suhbaatar mn\n",
      "lucea jm\n",
      "puerto quijarro bo\n",
      "nicoya cr\n",
      "punta arenas cl\n",
      "ushuaia ar\n",
      "victoria sc\n",
      "rikitea pf\n",
      "lusambo cd\n",
      "zhigansk ru\n",
      "bengkulu id\n",
      "tuktoyaktuk ca\n",
      "saskylakh ru\n",
      "port elizabeth za\n",
      "yulara au\n",
      "cidreira br\n",
      "nieuwpoort an\n",
      "puerto ayora ec\n",
      "ryotsu jp\n",
      "dikson ru\n",
      "mataura pf\n",
      "yantal ru\n",
      "jamestown sh\n",
      "toyoshina jp\n",
      "oceanside us\n",
      "kapaa us\n",
      "busselton au\n",
      "upernavik gl\n",
      "kampong thum kh\n",
      "prieska za\n",
      "bluff nz\n",
      "barrow us\n",
      "narsaq gl\n",
      "newport us\n",
      "vaini to\n",
      "new norfolk au\n",
      "kapaa us\n",
      "constitucion cl\n",
      "alofi nu\n",
      "sremcica rs\n",
      "chokurdakh ru\n",
      "gloversville us\n",
      "mataura pf\n",
      "tsiroanomandidy mg\n",
      "bilibino ru\n",
      "khasan ru\n",
      "new norfolk au\n",
      "geresk af\n",
      "ribeira grande pt\n",
      "sakaiminato jp\n",
      "vao nc\n",
      "namatanai pg\n",
      "rikitea pf\n",
      "katsuura jp\n",
      "sokoto ng\n",
      "port alfred za\n",
      "mataura pf\n",
      "ushuaia ar\n",
      "barentsburg sj\n",
      "rikitea pf\n",
      "hermanus za\n",
      "rikitea pf\n",
      "vila velha br\n",
      "ayan ru\n",
      "mataura pf\n",
      "vasteras se\n",
      "sudak ua\n",
      "hilo us\n",
      "nikolskoye ru\n",
      "mahebourg mu\n",
      "punta arenas cl\n",
      "tire tr\n",
      "mataura pf\n",
      "jizan sa\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plainview us\n",
      "kemijarvi fi\n",
      "mataura pf\n",
      "ushuaia ar\n",
      "la ronge ca\n",
      "yellowknife ca\n",
      "westport ie\n",
      "barrow us\n",
      "port alfred za\n",
      "sitka us\n",
      "komsomolskiy ru\n",
      "georgetown sh\n",
      "touros br\n",
      "norman wells ca\n",
      "ushuaia ar\n",
      "marzuq ly\n",
      "ponta do sol cv\n",
      "hobart au\n",
      "rikitea pf\n",
      "bluff nz\n",
      "new norfolk au\n",
      "puerto ayora ec\n",
      "warangal in\n",
      "punta arenas cl\n",
      "saskylakh ru\n",
      "nome us\n",
      "tasiilaq gl\n",
      "urucara br\n",
      "mataura pf\n",
      "new norfolk au\n",
      "ushuaia ar\n",
      "jamestown sh\n",
      "khatanga ru\n",
      "mogadishu so\n",
      "vestmannaeyjar is\n",
      "dikson ru\n",
      "thompson ca\n",
      "kapaa us\n",
      "mataura pf\n",
      "ahipara nz\n",
      "natchez us\n",
      "hermanus za\n",
      "punta arenas cl\n",
      "bluff nz\n",
      "rikitea pf\n",
      "pangnirtung ca\n",
      "yellowknife ca\n",
      "kapaa us\n",
      "besskorbnaya ru\n",
      "cape town za\n",
      "san fernando mx\n",
      "korhogo ci\n",
      "ushuaia ar\n",
      "ambilobe mg\n",
      "rikitea pf\n",
      "bolshiye berezniki ru\n",
      "doha qa\n",
      "broome au\n",
      "carroll us\n",
      "rikitea pf\n",
      "saint-philippe re\n",
      "deputatskiy ru\n",
      "chuy uy\n",
      "avarua ck\n",
      "punta arenas cl\n",
      "tobol kz\n",
      "albany au\n",
      "avarua ck\n",
      "mar del plata ar\n",
      "mataura pf\n",
      "port alfred za\n",
      "attawapiskat ca\n",
      "chokurdakh ru\n",
      "progreso mx\n",
      "illoqqortoormiut gl\n",
      "taolanaro mg\n",
      "saskylakh ru\n",
      "carnarvon au\n",
      "empangeni za\n",
      "poshtove ua\n",
      "east london za\n",
      "hobart au\n",
      "machakos ke\n",
      "puerto ayora ec\n",
      "manokwari id\n",
      "honningsvag no\n",
      "punta arenas cl\n",
      "barrow us\n",
      "la ronge ca\n",
      "arraial do cabo br\n",
      "kodiak us\n",
      "kapaa us\n",
      "bredasdorp za\n",
      "bredasdorp za\n",
      "puerto ayora ec\n",
      "saint-philippe re\n",
      "albany au\n",
      "vaini to\n",
      "beringovskiy ru\n",
      "airai pw\n",
      "mahanoro mg\n",
      "georgiyevka kz\n",
      "cape town za\n",
      "port elizabeth za\n",
      "san jose bo\n",
      "hobart au\n",
      "vila velha br\n",
      "hilo us\n",
      "saint george bm\n",
      "tabiauea ki\n",
      "amahai id\n",
      "yulara au\n",
      "mataura pf\n",
      "ushuaia ar\n",
      "atuona pf\n",
      "chicama pe\n",
      "acapulco mx\n",
      "saleaula ws\n",
      "kapaa us\n",
      "la ronge ca\n",
      "saskylakh ru\n",
      "tuktoyaktuk ca\n",
      "torbay ca\n",
      "mar del plata ar\n",
      "soyo ao\n",
      "leningradskiy ru\n",
      "hermanus za\n",
      "nikolskoye ru\n",
      "hilo us\n",
      "port macquarie au\n",
      "kapaa us\n",
      "terrace ca\n",
      "arahal es\n",
      "hilo us\n",
      "lavrentiya ru\n",
      "khandyga ru\n",
      "busselton au\n",
      "alofi nu\n",
      "barrow us\n",
      "cabedelo br\n",
      "marzuq ly\n",
      "port alfred za\n",
      "bluff nz\n",
      "nushki pk\n",
      "jamestown sh\n",
      "vaini to\n",
      "luganville vu\n",
      "hilo us\n",
      "carnarvon au\n",
      "port alfred za\n",
      "iqaluit ca\n",
      "bluff nz\n",
      "carnarvon au\n",
      "busselton au\n",
      "rikitea pf\n",
      "brokopondo sr\n",
      "naron es\n",
      "sorvag fo\n",
      "busselton au\n",
      "tsihombe mg\n",
      "barentsburg sj\n",
      "tasiilaq gl\n",
      "chuy uy\n",
      "xichang cn\n",
      "barrow us\n",
      "torbay ca\n",
      "faanui pf\n",
      "kamenka ru\n",
      "albany au\n",
      "fortuna us\n",
      "saskylakh ru\n",
      "kurchum kz\n",
      "hami cn\n",
      "laguna br\n",
      "luwero ug\n",
      "buala sb\n",
      "bushehr ir\n",
      "khatanga ru\n",
      "provideniya ru\n",
      "provideniya ru\n",
      "namibe ao\n",
      "arraial do cabo br\n",
      "ewa beach us\n",
      "chilca pe\n",
      "alice springs au\n",
      "aginskoye ru\n",
      "swellendam za\n",
      "usinsk ru\n",
      "sulurpeta in\n",
      "tiksi ru\n",
      "vikulovo ru\n",
      "hobart au\n",
      "saint anthony ca\n",
      "ushuaia ar\n",
      "bluff nz\n",
      "vardo no\n",
      "vaini to\n",
      "port alfred za\n",
      "punta arenas cl\n",
      "sioux lookout ca\n",
      "grand river south east mu\n",
      "santa ana mx\n",
      "kruisfontein za\n",
      "bethel us\n",
      "wenchi gh\n",
      "ushuaia ar\n",
      "kampot kh\n",
      "chibuto mz\n",
      "kavieng pg\n",
      "mataura pf\n",
      "tanjong karang my\n",
      "vardo no\n",
      "lagoa pt\n",
      "atuona pf\n",
      "hermanus za\n",
      "butaritari ki\n",
      "avarua ck\n",
      "rikitea pf\n",
      "lavrentiya ru\n",
      "khatanga ru\n",
      "beringovskiy ru\n",
      "taolanaro mg\n",
      "ngaoundere cm\n",
      "xuddur so\n",
      "sorland no\n",
      "atuona pf\n",
      "albany au\n",
      "tumannyy ru\n",
      "albany au\n",
      "port alfred za\n",
      "ushuaia ar\n",
      "vaitupu wf\n",
      "namatanai pg\n",
      "emerald au\n",
      "rikitea pf\n",
      "santiago de cao pe\n",
      "sitka us\n",
      "punta arenas cl\n",
      "luderitz na\n",
      "puerto ayora ec\n",
      "busselton au\n",
      "veraval in\n",
      "juneau us\n",
      "esperance au\n",
      "mar del plata ar\n",
      "oranjemund na\n",
      "bredasdorp za\n",
      "bagdarin ru\n",
      "east london za\n",
      "jamestown sh\n",
      "punta arenas cl\n",
      "pecos us\n",
      "show low us\n",
      "vostok ru\n",
      "isangel vu\n",
      "kruisfontein za\n",
      "bredasdorp za\n",
      "taolanaro mg\n",
      "albany au\n",
      "agadez ne\n",
      "albany au\n",
      "faanui pf\n",
      "busselton au\n",
      "kavieng pg\n",
      "hofn is\n",
      "maceio br\n",
      "belaya gora ru\n",
      "jamestown sh\n",
      "barrow us\n",
      "florianopolis br\n",
      "marsa matruh eg\n",
      "tiksi ru\n",
      "faanui pf\n",
      "hithadhoo mv\n",
      "salt lake city us\n",
      "ushuaia ar\n",
      "saint-louis sn\n",
      "klaksvik fo\n",
      "nouadhibou mr\n",
      "kapaa us\n",
      "kodiak us\n",
      "taolanaro mg\n",
      "saint-philippe re\n",
      "nchelenge zm\n",
      "rikitea pf\n",
      "hithadhoo mv\n",
      "pevek ru\n",
      "port alfred za\n",
      "rikitea pf\n",
      "vila velha br\n",
      "teguise es\n",
      "thompson ca\n",
      "punta arenas cl\n",
      "sao filipe cv\n",
      "cavalcante br\n",
      "ostrovnoy ru\n",
      "kapaa us\n",
      "touros br\n",
      "albany au\n",
      "talnakh ru\n",
      "bijar ir\n",
      "carnarvon au\n",
      "ushuaia ar\n",
      "busselton au\n",
      "ushuaia ar\n",
      "katsuura jp\n",
      "albany au\n",
      "ushuaia ar\n",
      "esperance au\n",
      "ruatoria nz\n",
      "cabedelo br\n",
      "bluff nz\n",
      "leningradskiy ru\n",
      "hobart au\n",
      "bluff nz\n",
      "rikitea pf\n",
      "bairiki ki\n",
      "camacha pt\n",
      "sterling us\n",
      "rikitea pf\n",
      "jamestown sh\n",
      "klaksvik fo\n",
      "punta arenas cl\n",
      "mataura pf\n",
      "vaini to\n",
      "punta arenas cl\n",
      "ahipara nz\n",
      "sentyabrskiy ru\n",
      "taltal cl\n",
      "merauke id\n",
      "hutang cn\n",
      "cape town za\n",
      "new norfolk au\n",
      "salalah om\n",
      "lebu cl\n",
      "thompson ca\n",
      "taolanaro mg\n",
      "kahului us\n",
      "bathsheba bb\n",
      "pangnirtung ca\n",
      "tulsipur np\n",
      "arraial do cabo br\n",
      "cherskiy ru\n",
      "plettenberg bay za\n",
      "mount isa au\n",
      "gat ly\n",
      "kaseda jp\n",
      "vaini to\n",
      "bucerias mx\n",
      "rikitea pf\n",
      "port hardy ca\n",
      "airai pw\n",
      "vila velha br\n",
      "kameoka jp\n",
      "khatanga ru\n",
      "tuktoyaktuk ca\n",
      "puerto ayora ec\n",
      "illoqqortoormiut gl\n",
      "abomey bj\n",
      "tasiilaq gl\n",
      "mys shmidta ru\n",
      "amahai id\n",
      "mullaitivu lk\n",
      "sciacca it\n",
      "loandjili cg\n",
      "bredasdorp za\n",
      "punta arenas cl\n",
      "georgetown us\n",
      "turukhansk ru\n",
      "nome us\n",
      "bredasdorp za\n",
      "palabuhanratu id\n",
      "rikitea pf\n",
      "kieta pg\n",
      "lebu cl\n",
      "busselton au\n",
      "hobart au\n",
      "san francisco ar\n",
      "butaritari ki\n",
      "ahipara nz\n",
      "illoqqortoormiut gl\n",
      "tamandare br\n",
      "teahupoo pf\n",
      "jamestown sh\n",
      "ulladulla au\n",
      "mataura pf\n",
      "rudbar af\n",
      "bluff nz\n",
      "hobart au\n",
      "busselton au\n",
      "varkaus fi\n",
      "guerrero negro mx\n",
      "butaritari ki\n",
      "nanhai cn\n",
      "bargal so\n",
      "fairbanks us\n",
      "ushuaia ar\n",
      "fortuna us\n",
      "verkhnetulomskiy ru\n",
      "iqaluit ca\n",
      "pevek ru\n",
      "tsihombe mg\n",
      "egvekinot ru\n",
      "henties bay na\n",
      "bengkulu id\n",
      "avarua ck\n",
      "lompoc us\n",
      "rikitea pf\n",
      "qaanaaq gl\n",
      "hasaki jp\n",
      "mataura pf\n",
      "tambovka ru\n",
      "krasnyy chikoy ru\n",
      "burica pa\n",
      "hithadhoo mv\n",
      "qaanaaq gl\n",
      "arraial do cabo br\n",
      "bluff nz\n",
      "los llanos de aridane es\n",
      "souillac mu\n",
      "busselton au\n",
      "puerto ayora ec\n",
      "bethel us\n",
      "albany au\n",
      "padang id\n",
      "tuktoyaktuk ca\n",
      "port alfred za\n",
      "ushuaia ar\n",
      "kaitangata nz\n",
      "amapa br\n",
      "barentsburg sj\n",
      "belushya guba ru\n",
      "namatanai pg\n",
      "victoria sc\n",
      "victoria sc\n",
      "avarua ck\n",
      "clyde river ca\n",
      "rikitea pf\n",
      "portland au\n",
      "port alfred za\n",
      "soyo ao\n",
      "san policarpo ph\n",
      "mataura pf\n",
      "chuy uy\n",
      "hamilton bm\n",
      "ribeira grande pt\n",
      "deputatskiy ru\n",
      "pochutla mx\n",
      "cidreira br\n",
      "bubaque gw\n",
      "caravelas br\n",
      "jamestown sh\n",
      "tiksi ru\n",
      "bredasdorp za\n",
      "butaritari ki\n",
      "busselton au\n",
      "punta arenas cl\n",
      "fairbanks us\n",
      "ngukurr au\n",
      "atuona pf\n",
      "castro cl\n",
      "ulladulla au\n",
      "ushuaia ar\n",
      "rikitea pf\n",
      "colwyn bay gb\n",
      "kindu cd\n",
      "coquimbo cl\n",
      "ponta do sol cv\n",
      "mataura pf\n",
      "vaini to\n",
      "punta arenas cl\n",
      "rodrigues alves br\n",
      "busselton au\n",
      "jaisalmer in\n",
      "albany au\n",
      "kismayo so\n",
      "busselton au\n",
      "illoqqortoormiut gl\n",
      "jamestown sh\n",
      "atar mr\n",
      "udachnyy ru\n",
      "hilo us\n",
      "cape town za\n",
      "east london za\n",
      "esperance au\n",
      "labuhan id\n",
      "nome us\n",
      "vaitupu wf\n",
      "airai pw\n",
      "balimo pg\n",
      "saint-philippe re\n",
      "itacoatiara br\n",
      "tsihombe mg\n",
      "sentyabrskiy ru\n",
      "madaoua ne\n",
      "carnarvon za\n",
      "puerto ayora ec\n",
      "narsaq gl\n",
      "luderitz na\n",
      "albany au\n",
      "lavrentiya ru\n",
      "illoqqortoormiut gl\n",
      "camacha pt\n",
      "caninde br\n",
      "mys shmidta ru\n",
      "zhigansk ru\n",
      "luderitz na\n",
      "almaznyy ru\n",
      "vardo no\n",
      "vaini to\n",
      "torbay ca\n",
      "hilo us\n",
      "mataura pf\n",
      "luanda ao\n",
      "katobu id\n",
      "hobyo so\n",
      "ushuaia ar\n",
      "coihaique cl\n",
      "buala sb\n",
      "moerai pf\n",
      "sitka us\n",
      "cape town za\n",
      "santa marta co\n",
      "marawi sd\n",
      "port hardy ca\n",
      "vaini to\n",
      "kuzovatovo ru\n",
      "ushuaia ar\n",
      "sao joao da barra br\n",
      "westport ie\n",
      "terrace ca\n",
      "mahajanga mg\n",
      "samana do\n",
      "punta arenas cl\n",
      "havre-saint-pierre ca\n",
      "rikitea pf\n",
      "busselton au\n",
      "port alfred za\n",
      "praia cv\n",
      "hithadhoo mv\n",
      "bredasdorp za\n",
      "canavieiras br\n",
      "port alfred za\n",
      "cidreira br\n",
      "taolanaro mg\n",
      "rikitea pf\n",
      "warrnambool au\n",
      "rikitea pf\n",
      "zeya ru\n",
      "ancud cl\n",
      "bubaque gw\n",
      "hami cn\n",
      "srednekolymsk ru\n",
      "bluff nz\n",
      "hithadhoo mv\n",
      "punta arenas cl\n",
      "hermanus za\n",
      "taolanaro mg\n",
      "grafton us\n",
      "rikitea pf\n",
      "rikitea pf\n",
      "nhulunbuy au\n",
      "nikolskoye ru\n",
      "puerto ayora ec\n",
      "cherskiy ru\n",
      "zhigalovo ru\n",
      "longyearbyen sj\n",
      "esperance au\n",
      "castro cl\n",
      "camacha pt\n",
      "grand river south east mu\n",
      "praia da vitoria pt\n",
      "broome au\n",
      "grosse pointe farms us\n",
      "ribeira grande pt\n",
      "ushuaia ar\n",
      "potosi bo\n",
      "punta arenas cl\n",
      "hithadhoo mv\n",
      "ushuaia ar\n",
      "bluff nz\n",
      "new norfolk au\n",
      "nyimba zm\n",
      "hermanus za\n",
      "hilo us\n",
      "touros br\n",
      "comodoro rivadavia ar\n",
      "busselton au\n",
      "chokurdakh ru\n",
      "punta arenas cl\n",
      "guerrero negro mx\n",
      "palu id\n",
      "iqaluit ca\n",
      "nantucket us\n",
      "inuvik ca\n",
      "ostrovnoy ru\n",
      "bur gabo so\n",
      "san cristobal ec\n",
      "albany au\n",
      "batemans bay au\n",
      "ushirombo tz\n",
      "san cristobal ec\n",
      "punta arenas cl\n",
      "wahran dz\n",
      "airai pw\n",
      "rikitea pf\n",
      "lompoc us\n",
      "atar mr\n",
      "butaritari ki\n",
      "avarua ck\n",
      "nizhneyansk ru\n",
      "jibuti dj\n",
      "mitsamiouli km\n",
      "kapaa us\n",
      "kapaa us\n",
      "kavieng pg\n",
      "illoqqortoormiut gl\n",
      "rikitea pf\n",
      "puerto ayora ec\n",
      "nikolskoye ru\n",
      "jamestown sh\n",
      "lukovetskiy ru\n",
      "clyde river ca\n",
      "grindavik is\n",
      "bambous virieux mu\n",
      "romny ua\n",
      "hobart au\n",
      "paita pe\n",
      "punta arenas cl\n",
      "cabo san lucas mx\n",
      "saint-philippe re\n",
      "dikson ru\n",
      "mount gambier au\n",
      "urumqi cn\n",
      "andilamena mg\n",
      "ushuaia ar\n",
      "rikitea pf\n",
      "bengkulu id\n",
      "karaidel ru\n",
      "provideniya ru\n",
      "bairiki ki\n",
      "progreso mx\n",
      "san patricio mx\n",
      "rafai cf\n",
      "olafsvik is\n",
      "broome au\n",
      "vardo no\n"
     ]
    }
   ],
   "source": [
    "# Use the tuple() 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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "597"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the requests library.\n",
    "import requests\n",
    "\n",
    "# Import the API key.\n",
    "from config import weather_api_key\n",
    "\n",
    "# Starting URL for Weather Map API Call.\n",
    "url = \"http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=\" + weather_api_key\n",
    "\n",
    "# Import the datetime module from the datetime library.\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval     \n",
      "-----------------------------\n",
      "Processing Record 1 of Set 1 | new norfolk\n",
      "Processing Record 2 of Set 1 | arraial do cabo\n",
      "Processing Record 3 of Set 1 | thompson\n",
      "Processing Record 4 of Set 1 | tuktoyaktuk\n",
      "Processing Record 5 of Set 1 | mlonggo\n",
      "Processing Record 6 of Set 1 | utiroa\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 1 | marcona\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 1 | hilo\n",
      "Processing Record 9 of Set 1 | dunedin\n",
      "Processing Record 10 of Set 1 | bethel\n",
      "Processing Record 11 of Set 1 | albany\n",
      "Processing Record 12 of Set 1 | santa rosalia\n",
      "Processing Record 13 of Set 1 | moscow\n",
      "Processing Record 14 of Set 1 | vaini\n",
      "Processing Record 15 of Set 1 | naze\n",
      "Processing Record 16 of Set 1 | butaritari\n",
      "Processing Record 17 of Set 1 | palmer\n",
      "Processing Record 18 of Set 1 | marilandia\n",
      "Processing Record 19 of Set 1 | port elizabeth\n",
      "Processing Record 20 of Set 1 | parkes\n",
      "Processing Record 21 of Set 1 | bam\n",
      "Processing Record 22 of Set 1 | vao\n",
      "Processing Record 23 of Set 1 | ostrovnoy\n",
      "Processing Record 24 of Set 1 | george town\n",
      "Processing Record 25 of Set 1 | havre-saint-pierre\n",
      "Processing Record 26 of Set 1 | atikokan\n",
      "Processing Record 27 of Set 1 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 1 | yellowknife\n",
      "Processing Record 29 of Set 1 | grindavik\n",
      "Processing Record 30 of Set 1 | bredasdorp\n",
      "Processing Record 31 of Set 1 | rikitea\n",
      "Processing Record 32 of Set 1 | port shepstone\n",
      "Processing Record 33 of Set 1 | punta arenas\n",
      "Processing Record 34 of Set 1 | mitsamiouli\n",
      "Processing Record 35 of Set 1 | mataura\n",
      "Processing Record 36 of Set 1 | presidencia roque saenz pena\n",
      "Processing Record 37 of Set 1 | jinchang\n",
      "Processing Record 38 of Set 1 | clyde river\n",
      "Processing Record 39 of Set 1 | los llanos de aridane\n",
      "Processing Record 40 of Set 1 | kenai\n",
      "Processing Record 41 of Set 1 | jamestown\n",
      "Processing Record 42 of Set 1 | mar del plata\n",
      "Processing Record 43 of Set 1 | esperance\n",
      "Processing Record 44 of Set 1 | castro\n",
      "Processing Record 45 of Set 1 | nikolskoye\n",
      "Processing Record 46 of Set 1 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 1 | atuona\n",
      "Processing Record 48 of Set 1 | pauini\n",
      "Processing Record 49 of Set 1 | mys shmidta\n",
      "City not found. Skipping...\n",
      "Processing Record 50 of Set 1 | norrtalje\n",
      "Processing Record 1 of Set 2 | longyearbyen\n",
      "Processing Record 2 of Set 2 | ushuaia\n",
      "Processing Record 3 of Set 2 | kodiak\n",
      "Processing Record 4 of Set 2 | yaan\n",
      "Processing Record 5 of Set 2 | villazon\n",
      "Processing Record 6 of Set 2 | channagiri\n",
      "Processing Record 7 of Set 2 | busselton\n",
      "Processing Record 8 of Set 2 | ribeira grande\n",
      "Processing Record 9 of Set 2 | samusu\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 2 | dumka\n",
      "Processing Record 11 of Set 2 | sinnamary\n",
      "Processing Record 12 of Set 2 | carutapera\n",
      "Processing Record 13 of Set 2 | saint-philippe\n",
      "Processing Record 14 of Set 2 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 2 | lachhmangarh\n",
      "Processing Record 16 of Set 2 | coahuayana\n",
      "Processing Record 17 of Set 2 | tasiilaq\n",
      "Processing Record 18 of Set 2 | khatanga\n",
      "Processing Record 19 of Set 2 | faanui\n",
      "Processing Record 20 of Set 2 | la ronge\n",
      "Processing Record 21 of Set 2 | port alfred\n",
      "Processing Record 22 of Set 2 | lagoa\n",
      "Processing Record 23 of Set 2 | san cristobal\n",
      "Processing Record 24 of Set 2 | bridgetown\n",
      "Processing Record 25 of Set 2 | chimbarongo\n",
      "Processing Record 26 of Set 2 | tommot\n",
      "Processing Record 27 of Set 2 | geraldton\n",
      "Processing Record 28 of Set 2 | sitka\n",
      "Processing Record 29 of Set 2 | carnarvon\n",
      "Processing Record 30 of Set 2 | rosetta\n",
      "Processing Record 31 of Set 2 | beyneu\n",
      "Processing Record 32 of Set 2 | georgetown\n",
      "Processing Record 33 of Set 2 | lorengau\n",
      "Processing Record 34 of Set 2 | east london\n",
      "Processing Record 35 of Set 2 | souillac\n",
      "Processing Record 36 of Set 2 | guerrero negro\n",
      "Processing Record 37 of Set 2 | tuy hoa\n",
      "Processing Record 38 of Set 2 | lavrentiya\n",
      "Processing Record 39 of Set 2 | saint george\n",
      "Processing Record 40 of Set 2 | nishihara\n",
      "Processing Record 41 of Set 2 | barrow\n",
      "Processing Record 42 of Set 2 | hermanus\n",
      "Processing Record 43 of Set 2 | touros\n",
      "Processing Record 44 of Set 2 | lolua\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 2 | puerto ayora\n",
      "Processing Record 46 of Set 2 | upernavik\n",
      "Processing Record 47 of Set 2 | loreto\n",
      "Processing Record 48 of Set 2 | tongliao\n",
      "Processing Record 49 of Set 2 | rio grande\n",
      "Processing Record 50 of Set 2 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record 1 of Set 3 | plettenberg bay\n",
      "Processing Record 2 of Set 3 | batemans bay\n",
      "Processing Record 3 of Set 3 | lebu\n",
      "Processing Record 4 of Set 3 | hami\n",
      "Processing Record 5 of Set 3 | brainerd\n",
      "Processing Record 6 of Set 3 | athabasca\n",
      "Processing Record 7 of Set 3 | ilulissat\n",
      "Processing Record 8 of Set 3 | manicore\n",
      "Processing Record 9 of Set 3 | ushtobe\n",
      "Processing Record 10 of Set 3 | taksimo\n",
      "Processing Record 11 of Set 3 | hobart\n",
      "Processing Record 12 of Set 3 | batagay\n",
      "Processing Record 13 of Set 3 | anadyr\n",
      "Processing Record 14 of Set 3 | balaipungut\n",
      "Processing Record 15 of Set 3 | hithadhoo\n",
      "Processing Record 16 of Set 3 | kulynychi\n",
      "Processing Record 17 of Set 3 | saint-augustin\n",
      "Processing Record 18 of Set 3 | orlik\n",
      "Processing Record 19 of Set 3 | mogadishu\n",
      "Processing Record 20 of Set 3 | baghdad\n",
      "Processing Record 21 of Set 3 | reynoldsburg\n",
      "Processing Record 22 of Set 3 | inirida\n",
      "Processing Record 23 of Set 3 | ponta do sol\n",
      "Processing Record 24 of Set 3 | hobyo\n",
      "Processing Record 25 of Set 3 | klaksvik\n",
      "Processing Record 26 of Set 3 | chuy\n",
      "Processing Record 27 of Set 3 | ahipara\n",
      "Processing Record 28 of Set 3 | kaitangata\n",
      "Processing Record 29 of Set 3 | kapaa\n",
      "Processing Record 30 of Set 3 | homer\n",
      "Processing Record 31 of Set 3 | bundoran\n",
      "Processing Record 32 of Set 3 | anuradhapura\n",
      "Processing Record 33 of Set 3 | vagur\n",
      "Processing Record 34 of Set 3 | grand centre\n",
      "City not found. Skipping...\n",
      "Processing Record 35 of Set 3 | najran\n",
      "Processing Record 36 of Set 3 | aykhal\n",
      "Processing Record 37 of Set 3 | san patricio\n",
      "Processing Record 38 of Set 3 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 3 | nanortalik\n",
      "Processing Record 40 of Set 3 | edson\n",
      "Processing Record 41 of Set 3 | mahebourg\n",
      "Processing Record 42 of Set 3 | imbituba\n",
      "Processing Record 43 of Set 3 | verkhneyarkeyevo\n",
      "Processing Record 44 of Set 3 | komsomolskiy\n",
      "Processing Record 45 of Set 3 | havelock\n",
      "Processing Record 46 of Set 3 | maykain\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 3 | kyle of lochalsh\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 3 | coquimbo\n",
      "Processing Record 49 of Set 3 | san quintin\n",
      "Processing Record 50 of Set 3 | kaitong\n",
      "Processing Record 1 of Set 4 | diego de almagro\n",
      "Processing Record 2 of Set 4 | tashtyp\n",
      "Processing Record 3 of Set 4 | semarang\n",
      "Processing Record 4 of Set 4 | leningradskiy\n",
      "Processing Record 5 of Set 4 | sentyabrskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 4 | grafton\n",
      "Processing Record 7 of Set 4 | dikson\n",
      "Processing Record 8 of Set 4 | san andres\n",
      "Processing Record 9 of Set 4 | berlevag\n",
      "Processing Record 10 of Set 4 | laguna\n",
      "Processing Record 11 of Set 4 | zhigansk\n",
      "Processing Record 12 of Set 4 | bluff\n",
      "Processing Record 13 of Set 4 | benemerito de las americas\n",
      "Processing Record 14 of Set 4 | leh\n",
      "Processing Record 15 of Set 4 | broken hill\n",
      "Processing Record 16 of Set 4 | talnakh\n",
      "Processing Record 17 of Set 4 | zhezkazgan\n",
      "Processing Record 18 of Set 4 | para de minas\n",
      "Processing Record 19 of Set 4 | bengkulu\n",
      "Processing Record 20 of Set 4 | abbeville\n",
      "Processing Record 21 of Set 4 | meulaboh\n",
      "Processing Record 22 of Set 4 | krasnoselkup\n",
      "Processing Record 23 of Set 4 | karatau\n",
      "Processing Record 24 of Set 4 | luderitz\n",
      "Processing Record 25 of Set 4 | provideniya\n",
      "Processing Record 26 of Set 4 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 4 | norman wells\n",
      "Processing Record 28 of Set 4 | mount isa\n",
      "Processing Record 29 of Set 4 | camacha\n",
      "Processing Record 30 of Set 4 | itarema\n",
      "Processing Record 31 of Set 4 | tiksi\n",
      "Processing Record 32 of Set 4 | donskoy\n",
      "Processing Record 33 of Set 4 | hit\n",
      "Processing Record 34 of Set 4 | arlit\n",
      "Processing Record 35 of Set 4 | ugoofaaru\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 36 of Set 4 | flinders\n",
      "Processing Record 37 of Set 4 | severo-kurilsk\n",
      "Processing Record 38 of Set 4 | alofi\n",
      "Processing Record 39 of Set 4 | chokurdakh\n",
      "Processing Record 40 of Set 4 | colac\n",
      "Processing Record 41 of Set 4 | richards bay\n",
      "Processing Record 42 of Set 4 | sonoita\n",
      "Processing Record 43 of Set 4 | biltine\n",
      "Processing Record 44 of Set 4 | diu\n",
      "Processing Record 45 of Set 4 | sorland\n",
      "Processing Record 46 of Set 4 | egvekinot\n",
      "Processing Record 47 of Set 4 | coihaique\n",
      "Processing Record 48 of Set 4 | lima\n",
      "Processing Record 49 of Set 4 | valente\n",
      "Processing Record 50 of Set 4 | xuddur\n",
      "Processing Record 1 of Set 5 | nanakuli\n",
      "Processing Record 2 of Set 5 | umm lajj\n",
      "Processing Record 3 of Set 5 | bowen\n",
      "Processing Record 4 of Set 5 | avarua\n",
      "Processing Record 5 of Set 5 | oktyabrskiy\n",
      "Processing Record 6 of Set 5 | ortigueira\n",
      "Processing Record 7 of Set 5 | solovetskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 5 | bozhou\n",
      "Processing Record 9 of Set 5 | ixtapa\n",
      "Processing Record 10 of Set 5 | labuhan\n",
      "Processing Record 11 of Set 5 | dingle\n",
      "Processing Record 12 of Set 5 | el estor\n",
      "Processing Record 13 of Set 5 | tecoanapa\n",
      "Processing Record 14 of Set 5 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 5 | onega\n",
      "Processing Record 16 of Set 5 | kamra\n",
      "Processing Record 17 of Set 5 | kasempa\n",
      "Processing Record 18 of Set 5 | faya\n",
      "Processing Record 19 of Set 5 | elk point\n",
      "Processing Record 20 of Set 5 | mananjary\n",
      "Processing Record 21 of Set 5 | emporia\n",
      "Processing Record 22 of Set 5 | manaus\n",
      "Processing Record 23 of Set 5 | bushehr\n",
      "Processing Record 24 of Set 5 | sabha\n",
      "Processing Record 25 of Set 5 | tahe\n",
      "Processing Record 26 of Set 5 | vardo\n",
      "Processing Record 27 of Set 5 | santona\n",
      "Processing Record 28 of Set 5 | breves\n",
      "Processing Record 29 of Set 5 | capitao poco\n",
      "Processing Record 30 of Set 5 | turukhansk\n",
      "Processing Record 31 of Set 5 | shingu\n",
      "Processing Record 32 of Set 5 | bambous virieux\n",
      "Processing Record 33 of Set 5 | tshikapa\n",
      "Processing Record 34 of Set 5 | changli\n",
      "Processing Record 35 of Set 5 | zelenogradsk\n",
      "Processing Record 36 of Set 5 | iquitos\n",
      "Processing Record 37 of Set 5 | vilcun\n",
      "Processing Record 38 of Set 5 | samalaeulu\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 5 | neuquen\n",
      "Processing Record 40 of Set 5 | saint-joseph\n",
      "Processing Record 41 of Set 5 | shar\n",
      "Processing Record 42 of Set 5 | siavonga\n",
      "Processing Record 43 of Set 5 | whitehorse\n",
      "Processing Record 44 of Set 5 | calvinia\n",
      "Processing Record 45 of Set 5 | yamada\n",
      "Processing Record 46 of Set 5 | kruisfontein\n",
      "Processing Record 47 of Set 5 | quesnel\n",
      "Processing Record 48 of Set 5 | kusye-aleksandrovskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 5 | poronaysk\n",
      "Processing Record 50 of Set 5 | qaqortoq\n",
      "Processing Record 1 of Set 6 | svetlaya\n",
      "Processing Record 2 of Set 6 | namatanai\n",
      "Processing Record 3 of Set 6 | shache\n",
      "Processing Record 4 of Set 6 | panzhihua\n",
      "Processing Record 5 of Set 6 | kropotkin\n",
      "Processing Record 6 of Set 6 | roald\n",
      "Processing Record 7 of Set 6 | salalah\n",
      "Processing Record 8 of Set 6 | pevek\n",
      "Processing Record 9 of Set 6 | thinadhoo\n",
      "Processing Record 10 of Set 6 | cidreira\n",
      "Processing Record 11 of Set 6 | sur\n",
      "Processing Record 12 of Set 6 | igarka\n",
      "Processing Record 13 of Set 6 | mufumbwe\n",
      "Processing Record 14 of Set 6 | ondjiva\n",
      "Processing Record 15 of Set 6 | nizhneyansk\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 6 | kloulklubed\n",
      "Processing Record 17 of Set 6 | formoso do araguaia\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 6 | springfield\n",
      "Processing Record 19 of Set 6 | funadhoo\n",
      "Processing Record 20 of Set 6 | mehamn\n",
      "Processing Record 21 of Set 6 | atherton\n",
      "Processing Record 22 of Set 6 | francistown\n",
      "Processing Record 23 of Set 6 | juba\n",
      "Processing Record 24 of Set 6 | hualmay\n",
      "Processing Record 25 of Set 6 | bella union\n",
      "Processing Record 26 of Set 6 | rishikesh\n",
      "Processing Record 27 of Set 6 | margate\n",
      "Processing Record 28 of Set 6 | lavumisa\n",
      "Processing Record 29 of Set 6 | krasnyy chikoy\n",
      "Processing Record 30 of Set 6 | trinidad\n",
      "Processing Record 31 of Set 6 | bardiyah\n",
      "Processing Record 32 of Set 6 | teguise\n",
      "Processing Record 33 of Set 6 | nisia floresta\n",
      "Processing Record 34 of Set 6 | brae\n",
      "Processing Record 35 of Set 6 | coffs harbour\n",
      "Processing Record 36 of Set 6 | bridlington\n",
      "Processing Record 37 of Set 6 | te anau\n",
      "Processing Record 38 of Set 6 | vanimo\n",
      "Processing Record 39 of Set 6 | vostok\n",
      "Processing Record 40 of Set 6 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 6 | unity\n",
      "Processing Record 42 of Set 6 | cabo san lucas\n",
      "Processing Record 43 of Set 6 | toguchin\n",
      "Processing Record 44 of Set 6 | sebastian\n",
      "Processing Record 45 of Set 6 | henties bay\n",
      "Processing Record 46 of Set 6 | hofn\n",
      "Processing Record 47 of Set 6 | curuca\n",
      "Processing Record 48 of Set 6 | huarmey\n",
      "Processing Record 49 of Set 6 | heinola\n",
      "Processing Record 50 of Set 6 | la romana\n",
      "Processing Record 1 of Set 7 | pangnirtung\n",
      "Processing Record 2 of Set 7 | felidhoo\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 7 | katangli\n",
      "Processing Record 4 of Set 7 | benguela\n",
      "Processing Record 5 of Set 7 | kavieng\n",
      "Processing Record 6 of Set 7 | chinchani\n",
      "Processing Record 7 of Set 7 | bizhbulyak\n",
      "Processing Record 8 of Set 7 | porbandar\n",
      "Processing Record 9 of Set 7 | saskylakh\n",
      "Processing Record 10 of Set 7 | salinopolis\n",
      "Processing Record 11 of Set 7 | torbay\n",
      "Processing Record 12 of Set 7 | balikpapan\n",
      "Processing Record 13 of Set 7 | ajdovscina\n",
      "Processing Record 14 of Set 7 | bure\n",
      "Processing Record 15 of Set 7 | victoria\n",
      "Processing Record 16 of Set 7 | dalinghe\n",
      "City not found. Skipping...\n",
      "Processing Record 17 of Set 7 | oktyabrskoye\n",
      "Processing Record 18 of Set 7 | cherskiy\n",
      "Processing Record 19 of Set 7 | cape town\n",
      "Processing Record 20 of Set 7 | kolokani\n",
      "Processing Record 21 of Set 7 | kiama\n",
      "Processing Record 22 of Set 7 | rochelle\n",
      "Processing Record 23 of Set 7 | lipin bor\n",
      "Processing Record 24 of Set 7 | rungata\n",
      "City not found. Skipping...\n",
      "Processing Record 25 of Set 7 | kaseda\n",
      "Processing Record 26 of Set 7 | samarai\n",
      "Processing Record 27 of Set 7 | tres de maio\n",
      "Processing Record 28 of Set 7 | nuevo casas grandes\n",
      "Processing Record 29 of Set 7 | wanxian\n",
      "Processing Record 30 of Set 7 | haines junction\n",
      "Processing Record 31 of Set 7 | iquique\n",
      "Processing Record 32 of Set 7 | verkhnevilyuysk\n",
      "Processing Record 33 of Set 7 | fort william\n",
      "Processing Record 34 of Set 7 | guarapari\n",
      "Processing Record 35 of Set 7 | sambava\n",
      "Processing Record 36 of Set 7 | luchegorsk\n",
      "Processing Record 37 of Set 7 | padang\n",
      "Processing Record 38 of Set 7 | namikupa\n",
      "Processing Record 39 of Set 7 | port-cartier\n",
      "Processing Record 40 of Set 7 | vestmanna\n",
      "Processing Record 41 of Set 7 | nizhniy kuranakh\n",
      "Processing Record 42 of Set 7 | amapa\n",
      "Processing Record 43 of Set 7 | puerto baquerizo moreno\n",
      "Processing Record 44 of Set 7 | ternate\n",
      "Processing Record 45 of Set 7 | airai\n",
      "Processing Record 46 of Set 7 | wanning\n",
      "Processing Record 47 of Set 7 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 7 | klyavlino\n",
      "Processing Record 49 of Set 7 | abancay\n",
      "Processing Record 50 of Set 7 | vila franca do campo\n",
      "Processing Record 1 of Set 8 | valdivia\n",
      "Processing Record 2 of Set 8 | wakkanai\n",
      "Processing Record 3 of Set 8 | port hardy\n",
      "Processing Record 4 of Set 8 | bandarbeyla\n",
      "Processing Record 5 of Set 8 | xining\n",
      "Processing Record 6 of Set 8 | qaanaaq\n",
      "Processing Record 7 of Set 8 | lazaro cardenas\n",
      "Processing Record 8 of Set 8 | kahului\n",
      "Processing Record 9 of Set 8 | las cruces\n",
      "Processing Record 10 of Set 8 | mumbwa\n",
      "Processing Record 11 of Set 8 | asau\n",
      "Processing Record 12 of Set 8 | shubarshi\n",
      "Processing Record 13 of Set 8 | champagnole\n",
      "Processing Record 14 of Set 8 | gizo\n",
      "Processing Record 15 of Set 8 | fevralsk\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 8 | bossembele\n",
      "City not found. Skipping...\n",
      "Processing Record 17 of Set 8 | portland\n",
      "Processing Record 18 of Set 8 | pitimbu\n",
      "Processing Record 19 of Set 8 | mount gambier\n",
      "Processing Record 20 of Set 8 | nizhnekamsk\n",
      "Processing Record 21 of Set 8 | la tijera\n",
      "Processing Record 22 of Set 8 | mamontovo\n",
      "Processing Record 23 of Set 8 | tefe\n",
      "Processing Record 24 of Set 8 | raudeberg\n",
      "Processing Record 25 of Set 8 | aitape\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 26 of Set 8 | dinar\n",
      "Processing Record 27 of Set 8 | rurrenabaque\n",
      "Processing Record 28 of Set 8 | rylsk\n",
      "Processing Record 29 of Set 8 | lapugiu de jos\n",
      "Processing Record 30 of Set 8 | hammerfest\n",
      "Processing Record 31 of Set 8 | tsaratanana\n",
      "Processing Record 32 of Set 8 | akhmim\n",
      "Processing Record 33 of Set 8 | yenagoa\n",
      "Processing Record 34 of Set 8 | yichang\n",
      "Processing Record 35 of Set 8 | kenora\n",
      "Processing Record 36 of Set 8 | kalmunai\n",
      "Processing Record 37 of Set 8 | muzhi\n",
      "Processing Record 38 of Set 8 | lompoc\n",
      "Processing Record 39 of Set 8 | ust-kuyga\n",
      "Processing Record 40 of Set 8 | oulu\n",
      "Processing Record 41 of Set 8 | suhbaatar\n",
      "Processing Record 42 of Set 8 | lucea\n",
      "Processing Record 43 of Set 8 | puerto quijarro\n",
      "Processing Record 44 of Set 8 | nicoya\n",
      "Processing Record 45 of Set 8 | lusambo\n",
      "Processing Record 46 of Set 8 | yulara\n",
      "Processing Record 47 of Set 8 | nieuwpoort\n",
      "Processing Record 48 of Set 8 | ryotsu\n",
      "Processing Record 49 of Set 8 | yantal\n",
      "Processing Record 50 of Set 8 | toyoshina\n",
      "Processing Record 1 of Set 9 | oceanside\n",
      "Processing Record 2 of Set 9 | kampong thum\n",
      "Processing Record 3 of Set 9 | prieska\n",
      "Processing Record 4 of Set 9 | narsaq\n",
      "Processing Record 5 of Set 9 | newport\n",
      "Processing Record 6 of Set 9 | constitucion\n",
      "Processing Record 7 of Set 9 | sremcica\n",
      "Processing Record 8 of Set 9 | gloversville\n",
      "Processing Record 9 of Set 9 | tsiroanomandidy\n",
      "Processing Record 10 of Set 9 | bilibino\n",
      "Processing Record 11 of Set 9 | khasan\n",
      "Processing Record 12 of Set 9 | geresk\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 9 | sakaiminato\n",
      "Processing Record 14 of Set 9 | katsuura\n",
      "Processing Record 15 of Set 9 | sokoto\n",
      "Processing Record 16 of Set 9 | vila velha\n",
      "Processing Record 17 of Set 9 | ayan\n",
      "Processing Record 18 of Set 9 | vasteras\n",
      "Processing Record 19 of Set 9 | sudak\n",
      "Processing Record 20 of Set 9 | tire\n",
      "Processing Record 21 of Set 9 | jizan\n",
      "Processing Record 22 of Set 9 | plainview\n",
      "Processing Record 23 of Set 9 | kemijarvi\n",
      "Processing Record 24 of Set 9 | westport\n",
      "Processing Record 25 of Set 9 | marzuq\n",
      "Processing Record 26 of Set 9 | warangal\n",
      "Processing Record 27 of Set 9 | nome\n",
      "Processing Record 28 of Set 9 | urucara\n",
      "Processing Record 29 of Set 9 | vestmannaeyjar\n",
      "Processing Record 30 of Set 9 | natchez\n",
      "Processing Record 31 of Set 9 | besskorbnaya\n",
      "Processing Record 32 of Set 9 | san fernando\n",
      "Processing Record 33 of Set 9 | korhogo\n",
      "Processing Record 34 of Set 9 | ambilobe\n",
      "Processing Record 35 of Set 9 | bolshiye berezniki\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 9 | doha\n",
      "Processing Record 37 of Set 9 | broome\n",
      "Processing Record 38 of Set 9 | carroll\n",
      "Processing Record 39 of Set 9 | deputatskiy\n",
      "Processing Record 40 of Set 9 | tobol\n",
      "Processing Record 41 of Set 9 | progreso\n",
      "Processing Record 42 of Set 9 | empangeni\n",
      "Processing Record 43 of Set 9 | poshtove\n",
      "Processing Record 44 of Set 9 | machakos\n",
      "Processing Record 45 of Set 9 | manokwari\n",
      "Processing Record 46 of Set 9 | honningsvag\n",
      "Processing Record 47 of Set 9 | beringovskiy\n",
      "Processing Record 48 of Set 9 | mahanoro\n",
      "Processing Record 49 of Set 9 | georgiyevka\n",
      "Processing Record 50 of Set 9 | san jose\n",
      "Processing Record 1 of Set 10 | tabiauea\n",
      "City not found. Skipping...\n",
      "Processing Record 2 of Set 10 | amahai\n",
      "Processing Record 3 of Set 10 | chicama\n",
      "Processing Record 4 of Set 10 | acapulco\n",
      "Processing Record 5 of Set 10 | saleaula\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 10 | soyo\n",
      "Processing Record 7 of Set 10 | port macquarie\n",
      "Processing Record 8 of Set 10 | terrace\n",
      "Processing Record 9 of Set 10 | arahal\n",
      "Processing Record 10 of Set 10 | khandyga\n",
      "Processing Record 11 of Set 10 | cabedelo\n",
      "Processing Record 12 of Set 10 | nushki\n",
      "Processing Record 13 of Set 10 | luganville\n",
      "Processing Record 14 of Set 10 | iqaluit\n",
      "Processing Record 15 of Set 10 | brokopondo\n",
      "Processing Record 16 of Set 10 | naron\n",
      "Processing Record 17 of Set 10 | sorvag\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 10 | xichang\n",
      "Processing Record 19 of Set 10 | kamenka\n",
      "Processing Record 20 of Set 10 | fortuna\n",
      "Processing Record 21 of Set 10 | kurchum\n",
      "Processing Record 22 of Set 10 | luwero\n",
      "Processing Record 23 of Set 10 | buala\n",
      "Processing Record 24 of Set 10 | namibe\n",
      "Processing Record 25 of Set 10 | ewa beach\n",
      "Processing Record 26 of Set 10 | chilca\n",
      "Processing Record 27 of Set 10 | alice springs\n",
      "Processing Record 28 of Set 10 | aginskoye\n",
      "Processing Record 29 of Set 10 | swellendam\n",
      "Processing Record 30 of Set 10 | usinsk\n",
      "Processing Record 31 of Set 10 | sulurpeta\n",
      "City not found. Skipping...\n",
      "Processing Record 32 of Set 10 | vikulovo\n",
      "Processing Record 33 of Set 10 | saint anthony\n",
      "Processing Record 34 of Set 10 | sioux lookout\n",
      "Processing Record 35 of Set 10 | santa ana\n",
      "Processing Record 36 of Set 10 | wenchi\n",
      "Processing Record 37 of Set 10 | kampot\n",
      "Processing Record 38 of Set 10 | chibuto\n",
      "Processing Record 39 of Set 10 | tanjong karang\n",
      "City not found. Skipping...\n",
      "Processing Record 40 of Set 10 | ngaoundere\n",
      "Processing Record 41 of Set 10 | vaitupu\n",
      "City not found. Skipping...\n",
      "Processing Record 42 of Set 10 | emerald\n",
      "Processing Record 43 of Set 10 | santiago de cao\n",
      "Processing Record 44 of Set 10 | veraval\n",
      "Processing Record 45 of Set 10 | juneau\n",
      "Processing Record 46 of Set 10 | oranjemund\n",
      "Processing Record 47 of Set 10 | bagdarin\n",
      "Processing Record 48 of Set 10 | pecos\n",
      "Processing Record 49 of Set 10 | show low\n",
      "Processing Record 50 of Set 10 | isangel\n",
      "Processing Record 1 of Set 11 | agadez\n",
      "Processing Record 2 of Set 11 | maceio\n",
      "Processing Record 3 of Set 11 | belaya gora\n",
      "Processing Record 4 of Set 11 | florianopolis\n",
      "Processing Record 5 of Set 11 | marsa matruh\n",
      "Processing Record 6 of Set 11 | salt lake city\n",
      "Processing Record 7 of Set 11 | saint-louis\n",
      "Processing Record 8 of Set 11 | nouadhibou\n",
      "Processing Record 9 of Set 11 | nchelenge\n",
      "Processing Record 10 of Set 11 | sao filipe\n",
      "Processing Record 11 of Set 11 | cavalcante\n",
      "Processing Record 12 of Set 11 | bijar\n",
      "Processing Record 13 of Set 11 | ruatoria\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 11 | bairiki\n",
      "Processing Record 15 of Set 11 | sterling\n",
      "Processing Record 16 of Set 11 | taltal\n",
      "Processing Record 17 of Set 11 | merauke\n",
      "Processing Record 18 of Set 11 | hutang\n",
      "Processing Record 19 of Set 11 | bathsheba\n",
      "Processing Record 20 of Set 11 | tulsipur\n",
      "Processing Record 21 of Set 11 | gat\n",
      "Processing Record 22 of Set 11 | bucerias\n",
      "Processing Record 23 of Set 11 | kameoka\n",
      "Processing Record 24 of Set 11 | abomey\n",
      "Processing Record 25 of Set 11 | mullaitivu\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 11 | sciacca\n",
      "Processing Record 27 of Set 11 | loandjili\n",
      "Processing Record 28 of Set 11 | palabuhanratu\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 11 | kieta\n",
      "Processing Record 30 of Set 11 | san francisco\n",
      "Processing Record 31 of Set 11 | tamandare\n",
      "Processing Record 32 of Set 11 | teahupoo\n",
      "Processing Record 33 of Set 11 | ulladulla\n",
      "Processing Record 34 of Set 11 | rudbar\n",
      "Processing Record 35 of Set 11 | varkaus\n",
      "Processing Record 36 of Set 11 | nanhai\n",
      "Processing Record 37 of Set 11 | bargal\n",
      "City not found. Skipping...\n",
      "Processing Record 38 of Set 11 | fairbanks\n",
      "Processing Record 39 of Set 11 | verkhnetulomskiy\n",
      "Processing Record 40 of Set 11 | hasaki\n",
      "Processing Record 41 of Set 11 | tambovka\n",
      "Processing Record 42 of Set 11 | burica\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 11 | san policarpo\n",
      "Processing Record 44 of Set 11 | hamilton\n",
      "Processing Record 45 of Set 11 | pochutla\n",
      "Processing Record 46 of Set 11 | bubaque\n",
      "Processing Record 47 of Set 11 | caravelas\n",
      "Processing Record 48 of Set 11 | ngukurr\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 11 | colwyn bay\n",
      "Processing Record 50 of Set 11 | kindu\n",
      "Processing Record 1 of Set 12 | rodrigues alves\n",
      "Processing Record 2 of Set 12 | jaisalmer\n",
      "Processing Record 3 of Set 12 | kismayo\n",
      "Processing Record 4 of Set 12 | atar\n",
      "Processing Record 5 of Set 12 | udachnyy\n",
      "Processing Record 6 of Set 12 | balimo\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 12 | itacoatiara\n",
      "Processing Record 8 of Set 12 | madaoua\n",
      "Processing Record 9 of Set 12 | caninde\n",
      "Processing Record 10 of Set 12 | almaznyy\n",
      "Processing Record 11 of Set 12 | luanda\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 12 of Set 12 | katobu\n",
      "Processing Record 13 of Set 12 | moerai\n",
      "Processing Record 14 of Set 12 | santa marta\n",
      "Processing Record 15 of Set 12 | marawi\n",
      "Processing Record 16 of Set 12 | kuzovatovo\n",
      "Processing Record 17 of Set 12 | sao joao da barra\n",
      "Processing Record 18 of Set 12 | mahajanga\n",
      "Processing Record 19 of Set 12 | samana\n",
      "Processing Record 20 of Set 12 | praia\n",
      "Processing Record 21 of Set 12 | canavieiras\n",
      "Processing Record 22 of Set 12 | warrnambool\n",
      "Processing Record 23 of Set 12 | zeya\n",
      "Processing Record 24 of Set 12 | ancud\n",
      "Processing Record 25 of Set 12 | srednekolymsk\n",
      "Processing Record 26 of Set 12 | nhulunbuy\n",
      "Processing Record 27 of Set 12 | zhigalovo\n",
      "Processing Record 28 of Set 12 | praia da vitoria\n",
      "Processing Record 29 of Set 12 | grosse pointe farms\n",
      "Processing Record 30 of Set 12 | potosi\n",
      "Processing Record 31 of Set 12 | nyimba\n",
      "Processing Record 32 of Set 12 | comodoro rivadavia\n",
      "Processing Record 33 of Set 12 | palu\n",
      "Processing Record 34 of Set 12 | nantucket\n",
      "Processing Record 35 of Set 12 | inuvik\n",
      "Processing Record 36 of Set 12 | bur gabo\n",
      "City not found. Skipping...\n",
      "Processing Record 37 of Set 12 | ushirombo\n",
      "Processing Record 38 of Set 12 | wahran\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 12 | jibuti\n",
      "Processing Record 40 of Set 12 | lukovetskiy\n",
      "Processing Record 41 of Set 12 | romny\n",
      "Processing Record 42 of Set 12 | paita\n",
      "Processing Record 43 of Set 12 | urumqi\n",
      "Processing Record 44 of Set 12 | andilamena\n",
      "Processing Record 45 of Set 12 | karaidel\n",
      "Processing Record 46 of Set 12 | rafai\n",
      "Processing Record 47 of Set 12 | olafsvik\n",
      "-----------------------------\n",
      "Data Retrieval Complete      \n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "# Create an empty list to hold the weather data.\n",
    "city_data = []\n",
    "# Print the beginning of the logging.\n",
    "print(\"Beginning Data Retrieval     \")\n",
    "print(\"-----------------------------\")\n",
    "\n",
    "# Create counters.\n",
    "record_count = 1\n",
    "set_count = 1\n",
    "\n",
    "# Loop through all the cities in 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",
    "    # 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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "597"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the city count to confirm sufficient count.\n",
    "len(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New Norfolk</td>\n",
       "      <td>-42.78</td>\n",
       "      <td>147.06</td>\n",
       "      <td>57.99</td>\n",
       "      <td>63</td>\n",
       "      <td>98</td>\n",
       "      <td>3.94</td>\n",
       "      <td>AU</td>\n",
       "      <td>2020-10-28 01:09:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arraial Do Cabo</td>\n",
       "      <td>-22.97</td>\n",
       "      <td>-42.03</td>\n",
       "      <td>73.40</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>13.87</td>\n",
       "      <td>BR</td>\n",
       "      <td>2020-10-28 01:07:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thompson</td>\n",
       "      <td>55.74</td>\n",
       "      <td>-97.86</td>\n",
       "      <td>24.80</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>9.17</td>\n",
       "      <td>CA</td>\n",
       "      <td>2020-10-28 01:07:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>69.45</td>\n",
       "      <td>-133.04</td>\n",
       "      <td>12.20</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "      <td>12.75</td>\n",
       "      <td>CA</td>\n",
       "      <td>2020-10-28 01:08:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mlonggo</td>\n",
       "      <td>-6.53</td>\n",
       "      <td>110.70</td>\n",
       "      <td>82.81</td>\n",
       "      <td>73</td>\n",
       "      <td>17</td>\n",
       "      <td>7.81</td>\n",
       "      <td>ID</td>\n",
       "      <td>2020-10-28 01:09:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td>80.60</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>9.17</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:07:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dunedin</td>\n",
       "      <td>-45.87</td>\n",
       "      <td>170.50</td>\n",
       "      <td>54.00</td>\n",
       "      <td>70</td>\n",
       "      <td>37</td>\n",
       "      <td>20.00</td>\n",
       "      <td>NZ</td>\n",
       "      <td>2020-10-28 01:09:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bethel</td>\n",
       "      <td>41.37</td>\n",
       "      <td>-73.41</td>\n",
       "      <td>50.00</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>3.65</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:09:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albany</td>\n",
       "      <td>42.60</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>46.00</td>\n",
       "      <td>77</td>\n",
       "      <td>100</td>\n",
       "      <td>1.70</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:07:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Santa Rosalia</td>\n",
       "      <td>27.32</td>\n",
       "      <td>-112.28</td>\n",
       "      <td>63.66</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>18.43</td>\n",
       "      <td>MX</td>\n",
       "      <td>2020-10-28 01:09:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City    Lat     Lng  Max Temp  Humidity  Cloudiness  Wind Speed  \\\n",
       "0      New Norfolk -42.78  147.06     57.99        63          98        3.94   \n",
       "1  Arraial Do Cabo -22.97  -42.03     73.40        94           0       13.87   \n",
       "2         Thompson  55.74  -97.86     24.80        68          75        9.17   \n",
       "3      Tuktoyaktuk  69.45 -133.04     12.20        92          90       12.75   \n",
       "4          Mlonggo  -6.53  110.70     82.81        73          17        7.81   \n",
       "5             Hilo  19.73 -155.09     80.60        74          90        9.17   \n",
       "6          Dunedin -45.87  170.50     54.00        70          37       20.00   \n",
       "7           Bethel  41.37  -73.41     50.00        75         100        3.65   \n",
       "8           Albany  42.60  -73.97     46.00        77         100        1.70   \n",
       "9    Santa Rosalia  27.32 -112.28     63.66        47           0       18.43   \n",
       "\n",
       "  Country                 Date  \n",
       "0      AU  2020-10-28 01:09:37  \n",
       "1      BR  2020-10-28 01:07:17  \n",
       "2      CA  2020-10-28 01:07:32  \n",
       "3      CA  2020-10-28 01:08:20  \n",
       "4      ID  2020-10-28 01:09:37  \n",
       "5      US  2020-10-28 01:07:11  \n",
       "6      NZ  2020-10-28 01:09:38  \n",
       "7      US  2020-10-28 01:09:38  \n",
       "8      US  2020-10-28 01:07:09  \n",
       "9      MX  2020-10-28 01:09:39  "
      ]
     },
     "execution_count": 10,
     "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": 11,
   "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": 12,
   "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\"]\n",
    "\n",
    "# Import the time module.\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bpMyRWMoOULXlHxj3bmDcZZf12/jDEfpO1pSXM9QQgzhVSM2RjzuM81weiPcbOguKdKYffPl5awAXPk3gDiAJ+Aw45v9rCCn/MFCKL/drfk/6iAbEk6SNGwuk3Imzpem/XiMteHKLNP3Xa6TcibOljRsL+obATtCXc71xY4H0gwXXSUPzLpF+sOC6drR3da837fSG7t70ebbQ37zdH2PuLc1JmUOkzEV/lS56pCD4yVz0V0mXltUtbT2lv694JIAfLLhOmv7rNdLVT38V/Ez/9RrpBwuui9jnpZOmS2MnTOkX3uqrgHj9pilIknRzJ7cu76T8n/Alco+iF+hOyjvf0ZWkBfRYCusrie17Kfl1g/NlzGaTiaTMke2uqQeMoM1iwZ53dae09ZT+/hhnWXk5ufM6bt+WbCzv0OeAiamUHtyDpaiA3Mtvwa4znJe8FfVovsAQaf8yP38+n25Yx/HDRXy6Yd15xWDdoSvTw+7MEkPnYtHSe9COu+6MTRjPpSnkucL5MuZ4nQ7rqSPtrlkqi5GrY7qkraf098c4dfHxHNqxhYOHizl67DhGo7Hd9m1on/VNzcQOHYdh1m1U7Nx43vJWdFG4gBCQOux5V5O75CnseVfzy8dWXNAHW10dlHd1L3wubG5okWIwGo0dyvYVPd9XnC9jvm/pYpo/X4ul4iBejxtLxUFaPn+R7EntAxyE09ZT+vt6nAUFm6hvMdGybwM4bXjUcRwv2knFpjXBg+jQPh1OJ3KlBk36UGzNtWfcf3/hgs689u+GgNQh9AM4fqIch9OLlDOThx//0wWlHYSiu4Pyzu6FSmAAsalZeKxG6upV6PX6Du0E65tMzLtqIWXlvq225ffe3W4botVkZl/hJpQpgxBCoNVoiHG1fq8P7vvCWKGgYBMrVz3fbl61Wk2v6Pj97x4B4OnVz1NvMhGv0zFQryZ91JR25cJp6yn9PS0XziMzJo/nq517O3zfsWs3SZcvIVstp37fB1hqK/C4XQiXLSj9h/apVqnwuOy4GivRGtLb9R9p/s7VOx3VFC4glJWXQ1wqldW1eLV6NMlZKLPHUFxy9ILVFroyh+zqXrjUN3jqNZh3v4ep6minJowFBZuorK6NqGkFNI+YQRfTsvt9XG0mRFwybc11HP34OWZMjmjS/b1Adyap3aEzDdZkMvWalt//7hEaK0txGBtorCzl2adWdktbT+nvSblwHqnW5rDihVdpzJ5N7pKnaMyezYoXXqVamwMaHcrsMbTFDST1krmoEtMZcN1/kXbTE8E5mDF5fLDP1CQDbaX7af7iFQZNXhDsf8bk8efVDkC/Oq/1N74Pzmu9oXneVQs5GncJqpyxKFQ+j0tb9VFMW1/jsmEZnTrTBKSQQ4cO4vKCSqlg1MiRnUojPZFa+nKuI/UHPs3o8JEjOF1ulDJIT0tDyOSYzGZaTWayrl5GZt53P9aHN79G9bYPkLxe4nU67lu6OCh9gm/+brj5p6xvSQ9eCzgiAUEHK5E+EuvJQ7ha61DGJ5J98RQybOVnxVmps2e17K6foVSq+k2aPBNJtTNHs9tyBUvuvOOMaQrlgdGjx3TQ7kLnS61UkJqchJDJqamp7sDve/bu5enVL2H2ayL3LV3MhPHjg2NvNZl5/PHH2NQ2GIBdax9BfclVxCSkMGL4MI4eO461tQHHgQ0AQce22g/+QtKMW9s5uZ344l3qdn2MSqnA6XIjuZy4ETisbShUaoYPyeHPj/+elaue7xNHvb5yXotuH11AWH7v3Vx/xzJSYw3IBwzDXltK67Y3GT77Bsq2vhGxTm+DkZ0LS5T8/Pmd0nDpvPsxVR2nYtMaGoxtDL52GQMGDqN86zqOfvwc8J9k5I6luqSIuqItDL38ZnKmL8RUdZw31r/OhPHjg22XlZcjV7Xf0gi1FMmdNwxbcy0Dr/4vDBOvQ5Ik7I2V5Fw0gpI19/fL2EPR1bOqOFXNir8/16MAdKeD8GfQG3RmgeNwFJ82PZF4ICDVR7I6GucvU/bRMzTZPMTnTsTW6mo3h4EgfiNv+i26gb45XPvOGla/9s/gvO7/6hNctjbqi3eRmjcJW3MtiZkjcTT7fGkdTicxmSNp3fIieVcvoWTLmyRMvQmnsQGFIQO32Rdipr54F1WHdqKd+BMunXEF1SVFHP34OXIm5wf5s+HL17ucvwBfnm1EF4WzhM6k4VNVVdx5z/09ks7y8+eTm5lCxbbXabKa0BrSyZ1zA6o4faf7v4G995JPXiNh2i3BEANWm5FUv+VDaJ/trCWKd1G2/WNM1aX88JbbSdTFBSW13u4X9wbh5wUJ2RdxMm8+1qKC4LUhs24AoHL9M5i/jKfVZCYp52IajhVxctcmtIZ0UoaPbTe+wTk5eJz2dn2Fn19oDenYa0vRZozA47KjVqkwVR0nPjaGcZNncPT4cUAEpby+XCi7elZoVMEAdIE5IcLz6y90pUnEx8aw47nf4PLzZOolczE7PPxgahLzrlrY4Yf84cf/FJzH1KQEEhKTMJnN6OLjkbwezG1WWk1m0iZd044P2+orWbT0Hl5d/SwAi5beg3LiT5AcCoSlDaEfQIsDYi6aRcs3X6Cf8mPkqUPwOB3UmZ04B8/CemATQ/QDgtZHJ/PmY963nnqHgspvj6IwZCGLSaBs+1uk5k1Ca0jHeuoIMQkpAKhVKpp3r8Nlt3Hw/VV43E7Mbz/q02CP7kaTOpiqmhrq/vU2yow8Wos+4dOtbyGPM6DMupijn75Bw7EiErNG0NDaxo2LFqPRxlC+dV2Qp+HcOqBGF4WzgK5CD//ud78jd8lTPZb8/vz4731tXbs0KO00fPk6Tz72QMTyASnE1lyLIX0oAHKlBruxAd2QER2kkUD5+uJdlGx5l7jxC0maqMdjacb29UdUa3P45WMr+OvDv+ybyemC5nbQp+Nsa79HnTN9IY4jhRw/XMSAQUMxNdaQMO0WDOlDsdeWUrXtDZptDcHyy++9mxMVJ2k92Rpx7n752ApSho+latsbePwSZqKwUrb5JVySDPnFV5M+4xe4m6oo/fJVltz/AGvoOw2qq2eFPLbD+M+WNNmdL0mTzYNmzJUYBl+Co6GS0sKXEU4rmqv+GNwfD+Cu3zyKO/cK0mf8AvM3/6Lqm8+xjppBxoiLqT1+OGjD39bYStWhz3HbrdQdP0jCtJtJTBuCqWR78N2xuSEpdypej4uKU9UgeXE7rCiTB2EyNSEMg5CEHJk6Fk9bC0rDQNx2CxWVpxgE6PV6vPGp2M2tvjM6nQanpRVkcsy1J/F63KQMH0v552tJvuY/8XrcuE7sxHSokMTZ/4E8YQCeNiOmne8g08Zj3L0O5ZzFSNpB2Osq8Ljd6KfdjCp9GM76Mozb3sTrduJNGkr5/kISxswlftZiDHKHX+ulnRbR2Tvd34guCmcBXUm+cpUmKLX0RPLrrbNawPqhMwm4MwuNsu0fkzDtZjyqWORaPaqEdDQxcTTsfZ/cK35KXX1DxP76ApGsRDDWooptH/U1lH6XF+InXo82YwSAb5wTr8e25Tsb8Pz8+Xz00UdoPv5Hp3MXKRSDSNTRNGhu8CxHOTAXw6zbMG19rU8l9a6eFR5Xl+PvT0TiX0Ls6wfNX4LQD6CuvgGvXIVu3FU4vylAqYklIXtgu7Iibz6x/nm0njyMYe4dKOIMNDS1EDt0HCp9ChU73ydx1n+giE2komAV6df+Kqg16QaOwK5QYi0qIDY1EUfdCbQZI3Bq9XitRhQxejyttcjjDLiaKlAPGIHkciLkSlxNlcjUsXiRUVffgF6vx9lYiTxWHzyjU8cngrcVydlGyRqfBv+Tuxbx1c5CSr78P0wmM4Pzl9Cm0CNiE1ElpCOm3YR5zzoSLr6chs3PIlPFgORGN+l61BkXgfA54ekn3UDTpqexnjyIYc4dmPd9RELuZDKHjwH+k8r1z+A4UnjOHVCj1kdnAZHsoyNJvt3ZLAectZb9+kEAnvnbX7p1VgsNRta05SWaD39FW30lbTXH29lTh5dvq69EnTYEr8uJkMlRKpVB+2rdwGHY7PZeZ+rqKboLwNba3Mw3Wz9l3+sraGyop6BgEyqlAnmMHrfThiRJuJ025DF68Hra0QF06ugXcAKsqSilsbKUqhMlfLphHSazGfTpyJUaPB4PdrsdSZ+B1dhE8ZEjnQ2j1+gqcBx2U48D0J0OunpePfEl0ev1jBg+DKVSScJFU3A7rBHLBuYRwGWsRz1wJNaTB2n8/EUqX32Qpu3/xFxdSlpqiu952i2o04bgdtpwm5tRtp6k7qu3MFWX0lJ2mOoPVtC8+0MkBPbaUtxtJkx7P0SZkoNx29s4Kotxm+pxG+sw79+A29SEreYYJpORb7Z+inHHOwiXFVv1USSvB1v1UTxtLeBxB+mfMH48y++9m8E5OZhNJryxKSB5kRwWXK21CHU8zqYqEsdfjVCoybnj7yCBLEaP5HaCBJLLgSxGj5ApcJubUGXk4mqtJS3Vty2VkTuWBF38eeGAGtUUzgJOR/INx+keAAfuPfToH3Aa6/HuXw8uG+q4BGJwdFp+0dJ7MJVsR2HIQo4XuVyOrfooWkM61SVFeEbKOw09cKaH1V0FYHv48T9RXHKUmNRBjLpmCRp/qID01FQkYcVqM2I3NqBWqdA4mmnySO3orKyuDXqB9xSDc3I4aqzFpUvFI+QImRxXfTmyGD0ma1Ov2+vJuMO1lUH5P2PNUyv6JZxJd8+rN74kapUK66kjQTv88LJHjbV4EtN9Gpc+FdOeD7FVHEA/9cdoBozAUV1CS+E/cFR9S6KQU6tQYCrZjm7gCGItVZRteROhiiH5ugdRpwzCWvY1rbvexbPjPYRCSdqVd2Or+hZj0Scgk9O0eRVCyFAkpKHJvhhzUQGWoo1IDhuxCUnIJQ9Jg0Zi3fs+Tc21KNQxcMnPSMu/l9wpczBVHWfJ/Q8gU2kYNH8J+soW2ppqkWINKDQ6FOoYbCcPIXndtOxdj0qfjMtuRag0uJpOIVOokLwehFyJp60FebwBZHIcp4qJS8ro0qfmXCGqKZwFdCX5epx2vB43p4r38s07Kzl06GBEyfpMXfQra+pQqDTE6hMZfe1dTLt3JYOvXRaxfn7+fF5d/Sz6UztIVjjwWJqwVBykddsbpAwfy4mNq1HFJfQqU1dv3fkjhe7Iz59PUlISE+78E9PuXUn66KnBtiWvB9v+D0lVuxl10QhiXK2UF6zB5XRR8slrNJbsIyH7IhRxhl6HFVh+791IxZswH9+L1+XAUVtK61evIFw2sqf/sE/DFHSmreh0un4LZ9Ld8+qNL0mMq5Xmz9eSMnwsQIeyUvEm2kr347JbiMkehenrDegn/YjYzIvwuhzIYxNJnvMzjn3+Nrb9H/LQ8mXoT+0gVe2mvugzJKUW/fRbUKcPQ6bSEjt0PLrJNyIUClSxOhQxemwnDxE7+nKUhoEkzV9G+u0rUWeNwfLNJ8iUKmTqWBInXsO0e1cy4vpf0nJ8P7lX/JSZv3oeIVcgjzMwaMzE4FxYUSPy5iP0A1AOHk9j4ct4rUYkwFFdgnnP+8SNmUfLno+IHTEJ49FdeGwWTDvexlF3AplWh8fSjGnHO3hsFjQZubQU/oP0kZed9bDgPUFUUzgL6EryPVVVRdHTD2Oy2sme/sPgQVO4ZH26ZmsBKVA58Sck5U7FUXeCki1vApCce1mn9UNprj9yBIfLjVYGGbZymnGgjkuEtsi09KeJXWdt17RZeeZvPpvvoveOYLLaiRk2hZSZP203Zvmc3F6HFcjPn88LwI9uuZ3WQkAIYhJTyb36Tt8cngVz1f5Ed8+rJ+dYofd+eNcivtq5F3vDSTTFn7Qr+wI+be9o4VpAIJfcxCQk4WmtRSmXgUKGO0aHs6mSJ596ifz8+UE/AmNFMUKrIz4z16etuVxIQqDQp6JUqhl99Z2UbF6Ls6kaw7UP4Dh1BNOedTjry5Fr40iYuQjt8Cm4GiswbX2D+swsMnIvoz5Gg6Z4PSUby3E21aHWxgYleABnmwlvXAqV1bXEXDQdaes7mPd9jKu1BpUumbgRU9AMvAjz3g8x79uAGznKpEzUWXmY935I65a1vkjQXg8ypRbrkS9QuW1k2MqD5xbnU48WB+EAACAASURBVBDL6KJwltCZHXhhYSF5I0e2c16JdOh8uqEIAlKg5FDg9bh8B7HTbqZs+/tdmrIGaA60Eeri//U3BzHXlrPrxXcZPPUaAI59/jbOpjrmXbUQXXx8v+V46GoeAnMccKaqjzBmz7RBp0VHfv58pk+dEtHJqK/V/r4IGdEb9IS3IvmSdBYuJIDCwkLuvefnEcf0z0cf/u5ZxcmDB9UOpxOMtYwYNqxdPodA2X3Hq3E1lAcNChy2NlzN1QitDvVAH/1Cqab+rYdRJGYQN/ZKLAc2Ez/uav8Zkxt1+jASpt1E2faPUcXpyRs5MugkNm7yDJy2Ng4erkOtUpGWmoIqVoez6RSa3EkIuQplcjbxl12LPC4BpUz4zFQrilEnZzF45vUc3bAa5Eo8zVXEjb6c2FFzcDVWYNz+FoNu/gOWioMYC56kqamJ2ro6auvqeaihPjjWc43o9tF5gJ4E6jrdUASBttNSU3Cbm3E7bajThtBWX9lt/fDwBQEXf90l81AkpKO+5CoOrV/DoYKX0Yy5kkuXveD7MW4xUbFpTb8civZkHroas9vSfNp0nGk4iJ6gL0NG9BS9HVdvAzN2VX75vXdTsWkNx4t24lHHgdNGy74N1LeYOrS3/N67icFB8xevYKk8gs1ixFZehK1oI5LTxuFNr2OzWkm68h6Sr/0NusuuxbJ/I67Gk8i0OmSaeDyWZiSnjZiM4R3egUCAO7fV2C7AndxhxLTrXex15TgcdjRZo2jZ8iIecxMOuw1LQxWmXe+jzszjxLaP0E//Kem3PYlu0o1Yj+3AfvIgyuRsPG0tuJ02nE2ncMq11GbOJn3xMyRf+wClzU6W3P/AeRGuJqopnAfoqaQGvc+bEN52XX0DpqqjaBV0Wz/cFNGqTMAw904sBzagUuYTk5BCo0yJbuwCho2d7FO5DQYGX7sMyxf/CKrkZ6IeR5Kau8tUFzrm5qP7qPjXahwttcjcdgz6+NOWxs5G7orOzD/70wS4t+PqykQ1Up2uyn+6YR1Jj/6BioObMe/6J1pDOgMuGkfDsSJuXLSYKZMmBbWQ/Pz5rMFnNHH4gz8jIYhLyWT0VYsBKHrnSeKn+c4bJK8Hj1xJzKi5tG5Zi6upCkVOMsJuQuky01Z5oMM7sHLV8+jHzEEA1e//GYRAHatnWEYSAEcL/w+Pw4YyIQ1d7hSMO9/F1VSJQh2DNl6Po+YYCdNuQaaNR0geVOlD0U/5CeY9HyDXxqKITURmM2Iv2Yp+8o3EDh3XrybOp4voonAeYPm9d/vOEGbe2qVD2umEIghvWzLWIE7t4MnVz3bbVvhec6iLv1wuZ8TwYVR5XGhSB7Xbgw3s8X+9+8xiBXVmFfPkYw90GRMmMObmrEuoOrSThMk3BB3Rmo3mM7IWOpNwED1Bf4SM6Al6M67enhl1V97cZmXKfz6FTK4IOk0mTLsVSaHCrle2O18LfIaNGkvuEl+dAJTaOLQp2cgcZmQxCciUGuTaeFr+5cJ2YAMqpYKhYyaApZ6GCO/AoUMHsWlrkV8zlSH3/MMfRuYNauvq+Mfzz3L9HctIz78XjT/ETNvJw6TlL8O0/Q1yr7yd/W+sQJmU6cupbW5CFqMnZuBwmj+ppm3H2+Rd/mM0ajcVTafQJWUGTXMBNOlDaWwzUVbu7NEz6E9Et4/OA+Tnz+fJxx7wSdZr7kdTvD6ipHY6tv89bTsSwhOIeL1ejCcO4FbFYbPZqK6p8ZnVGmvb1eur84NIVjHacdexaOk9DBs1lksnTmXc5Bkd5iM/fz63XH05pZ+9ibWphtad7xJnrSUzb3yPrY8CCeTV+hSSs4byhz8+0WnZrp5Lb9oBn5ZTXVLE0WPHg/NeXVKEWq3u4aydHnpDZ0ATC0VXz7y78qH3A06TypRsNBptp5ZrkdpUxeqQjLXIkLC31OJqqcZeth+VSs2I1DhijnzM18/cxdcv/R6t1NEcO+AAKVNqEDI52owRxE+8Hpf3uxAzpm2vU/XKr7DufZ/EMbNAJsdpNbPvlSeQvB7aSvfgsTSjEF6ULjO20j1o5RJDDSpatr6Bpng9ebkjkFka8Li+C7liry1FFas7L8xSo5rCeYLuJLUzsf0/Hek2sL9qatyAYeYibEKOte4E5t0fEDdqLsgUnCzejdfSjFS8idbsrB6F3egNwiVMo9FIixSDzQ2Dp99CyWfvEDc2n6FjJmC31LcLp/DG+s9InHsHOr/F1altbxIbG9sj66PwBPLWU0dY8cJagHZRVwPz1Nlz2bN3b4/bCWDG5PGseOE5DHPvJMZf59Tna4mb+Vhvp6/H6M14oeeabU/Lh963NtWiCwksB5G1kEhtytoaad7+Dkk/WEKcfxxt+9bx2+XLmDDeF5760gX3BsuHvz8BB0hJ8iJJEh6XHXmMHrXS9zMZHmKm9MBOygrWokgbhtxhQ509hrZDn6OY9CO8iiQSvRaUp3bwj5dWdzikv+s3j9Lm8RIz+BLcTVU0f/kqOrnrvDBLjS4K5xG6CjoWkJrrj+xm9z8exeOwgUzG9Tfdyntvvd6O6f7wxye6DA/ck+B7K1c9z+Brl+G0GCnb/gHNpb7DMu3QiTirinG1XEzbN5tROK288NdVrFz1PPvf+S7cc0CyCzizPfToHzh2ohyPx41KrSFGrewQBjkckc5DPFYjsalZHPv0NTxCRfO2tzF/8wl5l/+4nUQZsLhyWc14VLGoLprNgXefxjHhiW6lsadXv4Rh7t3EDRoD4Ps7906eXv18h3lsbKgnZdbPIu6Xf/3NwU7b6WxR+GrnXnIm59NwYAOtW15Ea0gnZ3I+ljZrxPJ9ga7GG0pnKH/q4uOp+mglXze1AhIjhg3rUOaBXy7DZrO3c6D8+kR5u/Lg4xFfSOv/xm5sxXxsL5l5l3Xp2BVaJ8DnCuFFLhPUffw/IEnEJg8kZ3I+X+3cy1c793aboCo9NZX6xpN43YmYa6rQaNQkCivJI0cG+4Tvzl5aTWbih0/EVleBftpNqAcMRxGfjHH3OjzGOuo9Dh5avqwDfwdMnENNc30BFp845+cJEF0Uzht0pwmUlZcjpB2c/PpLEq64B/XAkTirj9KyZS2LlvycV9f8L/n58yNKfX/++9PoDe8x4rp7eqxlBKR0mVxBat4kCh5eSNqPHwOvF61WizrZSdZNf6D82cXBNgL0h0pie/buZfVr/8TkUZIw/xdIGh2ethba9n0YDK7XGR0dpcGj2A5/Ttqwiynf+zlJV/4MVdoQHNUllGx5l+GzfkiNXwvInTcMa30DtQ1NKOKTiRkxGeP2t3BaTN0mzImUQD4mcyS1zU0dnlHJ6ysYZWpuVzYg2XbWTn0XlkRl5eXkLrm/XcRMr8fdr2cKPaEznD/Lt66j7lQBI278DRm5Yzt4/ubOG4ZX09hOe7MJNZcu/kMHSR18mt3Im36L3dRMyWfvUKNQEqNW+/b/I2ghBQWbgnV0A4dRXVJE8QerSLhkDgPHXxMMKx+TlEHZ1kIABkz0JahSxBvQ6DS44w0Uf/J8cLvPpxlvQlxzMVpDGtayA7hKPuHPfp8iaK91Dxs1FtfImVhKn0I9YBiS045qwDCSB1+KkMtpevNB3lj/Wbvw7ZHaOd8QPVM4T9CdV+ngnBxO7tpM4uzFaLPGIFOo0GSNInHOnZhtzmA5n9R3J3GDxiCTK3x/dSmQd2WvPIzD92wV6ljsp75FCBG8Zj11hHidrkv6n179ElbUGGbdhohPRqlPRZs9hrhJN9JwrKhLOsLPQ1y732bg6Mm0VB4lYdrNqJKzEEKgGTCchGk3U1r4LoNzcoK0WyxtaPQpyOQKHNVHUSWkoklI4audeyP2F0CkBPLWU0eQq9QdxzjjFkoL321XNiDZdtZOYM56Mu+B9vrzTKEndIY/34ZjRRjm3olVmdDB8zdQRq7SBJ9vV/wdei999FRGzrsZ+8HNfP3MXZ2egYW3Z1UmkDj3DtoqDgbPA8J54mTxPhTxBhQqLUII3M3VxKQOCtIw+NpljJx3M962VureegT7wc0kaeVdarIYa1HoknHWncBjMyKPTUSmVONuqCA2NavXnvznA6KLQh/hTALAQfe+CsvvvRuPw4oyfTjIBEgSIKHJvAiPyx4sZzaZiAmT+rx2C7KEAZ22HQnhtuupueNo+XwNrpoSvB43XqeN5s/Xct/SxV3SbzaZcLaZ0KQPRXK7EEoNCBnKlJxgcL3iI0c6nbtAaIdn/vYXsgakUbZ9A8bqMmKy8nCbm/A4rCgUCmT6dMw1Jzh06CCNDfVUbFqDqeooCpUaqfkktr3vk3f5j5Ep1ezYtStiX4Fn6LS2UbNuBaX/eyen3n2Cxh3v0vz5WuJitB3GmJ13Gdb6iog2/pES0Td/vpb8OdM7HW9nPgOBwGn9gc7ovG/p4uCcfLl1GyWbX6W+eBcAtuZaYjJH+pzN/HC2mUCf3q7t0GB4dlMzu9Y+QuFf72TX2kewm5rbBdULIDVvElP+86+kp6V1Gs4jvI7D6SQuKw9Xa20wKKLCkIGltowTpcf56quvqNnyCi3f7sB46hjNxdtoKvw/hs6+oR0NqXmTiE3OYPZv1jJ09g0cO1HeKa80NTVRu/k5nM01tBS+hL3mOJIk4ag9jmXXPxk89Zpu37PzEdHtoz5AX2Qr685XIT9/PrFxcbhqjiLLHoMQAiFk2E8VI1dqguUCUl9gfxhAponD21rTrr/uLITC909zc3KYdcU0CraspsFkwvvEozxw16LgnnNn9MfrdHhjdNhrSxGqWCSXHaFQ+bxS/cH1TFZ7p8H12s3vrJ8xytTMN+8+jaXsa2LThyJ3mXFYW7DXlaFNTGPcL5/1HWR+9AzWra9i2v42salZ5M65AfXAi3DYjKiSsshd8tcOWxiBrGeylFpSL1sI6jicDRWY97zPjVdMp7bJ2DGwoaWevNwREX0yAvSHJqL/0dzp7Ck52SmvdOYz0J8ezYFnGErnA3ctDh7Opsy8lUGDr8FmaqZki08rCk8+A4RYon3HewE+a2yop+SzdzDMui2Y76Lks1cYaoghOSW11x7w4fymVqmw1hwlLinD5wtgbMDdeBKZXE6DW0PKjx7B1lyL5euNuFtrUeqSkQmwNlUH+wltr754FyWfvUPS5UuCgfHCeSVl5u1MzE+l7NBu6j59kdbPVmPaHk98ajYjL/8xqXmT+sXjvb8RXRT6AL115omEnlh0/OqepfzluZfRzfoP1Jl5OKqLaf78ReK1qqDVwn1LF/ssR0KsV7ymBijeTOvwYb2yEOpq3zM8H2xn9N+3dDGrX/snzV+8gm7yjbhcTjxtLVj3fUjWxVM4sXE12dN/2GmGrfz8+R3mVyaTc+TTN4nRaLloyhwO7dhC24EClHI5X/7P3b6sa2PmoDlSSIPFiXzYVFrkCVgP7MCTMJDhc3/SIYcFEMx6Fjf+h4j4ZLwuJ9r0IWT9cBm1JwuDY7SMu442eTzmmlKse9fxqztv6fTgeML48Vy6c2/wYPrw0ROkzLw9OBahH4AtZyY3LvoZw4fkBHNQD87J4Zm//SU4/4WFhd0z0Wkg9GD40ovbH/zPu2phcN6F0UilF+In/oiybR+RMuLSdslnTFXHicHRzhLN47QH+ezhx/9E3Nh8lMlZPk0xOYu4sfmIU4W9tmYCH78tuf8BrKhxtpkQKi321jri82YieT0kCisn9ryPMkbv27o0ZBOXNoyYYROwlRdhP/IFCROu5eQnq/jbSy8ABNube8/tFL3zJImzbmsXGC+UV7TjrqO6DezNlRCfTuKs2zB98RLqmFiyZ16P1+tk26rlWOsryMsd0WeRdM8GootCH6AvAsD1xKs08MPzP88+T6PFgkwuJzM9hWefejpYLpLU99Av7gpazfSXJ25X9E8YP95nfbTp70Hro1i1MhhcL2f6whCnpe8ybAUks/D5Tc2bhNfr4ch7T1Fy6EPqq2uQxSWScPnP0fil0MqvXsdeXcHw+bfTcHQbVdvfxtlmRLXgGVLzvpNkw3M0W2orSJyoRxGTgEKpxmNvo8VupP7IkaDFy/979km8QkFsSiaZE67o9DAxkgZZvPZhJuSnAj4T28rqWpTZY3Ajp7TZGdHEtr9+THpi3BCY94AlUG0t1FcUc0lWYrvkM4NzcljzlK9ugAdkv1wW5IFlv36QoWMmUN/UHAxtPnTMBGqOfHzanuIylYb4vPl441NxNJzEs+9jvFWHqSrdg0xyo8KLQxHr4wmHEyFkyFUalAnpmJqr0eoMeGM0QQu5QHsKfSrI5KiSs9v1F+AVq82GIuMKvMKLPDYBj92KOudSpJ3vkjRmJt++9d+giSd15q2MyrsMzsKz7EtEF4U+wOkGqwtHeAC6ULPOAH7/u0c6lUq7K9PfDNmZZtGVxjHvqoXtMr2FZthKGTLEdwAYYX41OgNTJk3k0w3rSM4ainbOHe2yrsWMX4hj87MMmXUDbrsVU005Xq8Xh7GBo//awogrbwPaP6fqkiLcbheeNiNKw0AkSUKuVONuNuJw+ZKufLVzL2MX/S4sKN7oiFphJA0yJnUQJ4v3kTB9ni8AXGMFxs0bcTtt6C/JR5OZR31TMyOG917b7C2603DD512v1yMZaxg0fVqXHuWh2k1Amxyck4PdUu8blx+hWyu9scYpKNgUzM+sSxuM2+1GOXgsQqGiafOzKLTxePDicDmQqZUYK4pRpQ8FyQtuJx5jHfr0LNK0Ehq/uenKVc8zaP4Sn4OkthV9xmAkuzmYoQ2+45WvvzmIp6kK7eBxuP05ElwNJ1Ho01AOmYz22H7ix+Zz8fR5PoINhrOaT/tMET1o7gP0VaC03gYa+z4gPNNbIMNWWmpK8JCuu/mNlHVNpolHrlBydPMrlO8vJPGKn5N594vIdamU7yvk24KX2rUzY/J4jn78HJLHjWn3+9hrjuFxOXDVl2Le/R5K/5vSk+CFAUQqO3T2DTR89QatJ7+l5dg+WveuJ/aSK5HHJqLJHoPTZsFms3XZbl+hJ8YNfRUAsK/fEZsbdLlT8Wr12J0uHG1GJK8HeZyBxCt+TtLCh9HNuA1JyGj+12qcdWW4rSbaTuzD+vV6UoaPbdd/+FwMnnoN5t3vYao62oFepQxat7+Ns7ESr9OBq+Ekxh1vEXvx5Ticzi4P3C8ERDWFPkBfBUrri7OJrhzgelpnxuTxfBWyD96TNk4XgXYDmd5kbgf247v52tSAMkbHIH1MxPm95erLWbnqeZb9+kGcLjc6RzOSTYW1uRYJcDWdxON2Ub59PYb8X6DJHg1eLzKVIHHOYio2rCRHNAWf08pVz5MzOZ/SLz5AlZqDaftbeCzNqHRJZF88hQxbOdA7rTA+NoYdz/0Gl9WE1pDO4KnXYG2qRumxs3ftQ7jcHhAKrIe3IFOocbVUozRk4jU3dNluX6Enxg3QNwEA+/Id0Y67DlnLm5hOHkEzYDiy2ES8NhPmA5vRT7sJTfZoXOZmVCk5xE/8Ea1bXqT+/ScQcjkyIUhK0JFhK2d5SP/hc5GaNwlLQyV1u96m5NuN7ehNT0uj8cgx6tf9N5LHjVKf6ut34EjUHmuXB+6n836ebUQXhT5CXzijnOnZxOlYQYXXqS4pYsULz5EzOZ/cJfefliVVb5Gf78v0tuT+BzB5lBhmLkKRNBBr2QGaSj4JHtJ1sEYKcaQq//QlsuYtRqY24LEacRz5griRMzEe2IwiORu8Xl9aRCHQ54yhBdptgQScxuJSsijZ8i6ps36K2n+24ancwfIIIRm6OhQtKNhEk82DZsyVGPyhDA5++CwSEoMW3INdbcBubMC050PUWaOxHt9N8yfPkTj3ThSamKBk2hfhQjpDT8bSl05WfdHW4SNHUGUtIH7slRh3vYuY8hPkSZk46k7gaqpEkzkKj82M5LQhj08mNikL0853EAoV2WNnIFXs4/jhog7ths4Faem0nvwWT+WBoLFDAIHnmjRvCXJDFh5zIy1fvoqzvhx1QhoxcnuHA/fAvN5y9eVnbKV4NhDdPjqP0JNAY135Q5xOGsxITkCGuXdSe2Qfx0+UU2n2YvOHA+gJeuKvUVCwiXGTZxCXPIC45AwunTgVgNREHYmXXQUqLXKHhWFjJzNo/pIO9IfTPGTWDeRMzqd83ZM0fLgCx4ENjLz8x4yafyvKxAzsFQfB60GlVCKEiOhAFpj71LxJ5M65Aeve9zn10i9w7X67nUTb0wCDgT3qYWMnI3dYQKVFqLQkTfsJUnw6Sn0K2sxR6KfehL3yMAnTb0WmUGLd+RYt657oVeDC08XpBEvs6vmG3+ssB0RBwSYunTiVuOQM4pIHMG7yjGA73fGP0+Wm7fhuzAe34Gw6Rf26v1D13J2+yL3aeFxNp/BYzchjExFKNc76MpSGLAxz7uDkrs2dal6hc+HLGNf1cx0xbhoalRJ5nAHdhIW0FW3A868nST5ZyJqnVvDCXx/vMK+BUBtnkqb2bCCqKZxH6E5y6421SADdaRqRwmOrB4ygvqkag1bfIRzAmQbtCwQDc+deQfqMX+BuqqL0y1dZcv8DeF12Lr1pTrtwyN642A70RxpnzvSFHP/0dWY/8GK7+kNmXs/xT19Bo0tElTky6HT3wF2LO5375NzLUMXpg3N/OiEKQsOEBA4qt6x3IE8ciMPpRKPTILxevCmD8Jga0GWPxOSyMWBACk8+86ezJjn29oC3s+cLdLhXWV3bgWcKCjYFNcLkax9AkTSQ2rID3PWbR7lz717eWP9Zl/zjddhoK/6ShOmLiJ+WgtdqxLjzHZRpQ7Cf2I9p2+toL74S7dAJuOpPYNr+DvHjrkKVkYvHYe3yDCMwF6EZ48IR6bl6PaMoKf2kgwYSPq/Lfv1gv6Wp7UtENYXzCN1Jbj0JhdGbkMaR6qhVKiyVxSgT0iOGA+gKPdFUVq56HpE3n9ih41Bq4tD6E4xYUeN0uXtEf2fjjNfpOlw3DMgkMzEG25bnKX92MV5zQzunuwBOR2ruCp2FdsZYi1ql8kXglMuRmitRxidiKtneo8RH5xI9DVURuBcpTPnKVc8Hw55oB+ai1MQRO3QcIm8+T69+qVv+kam1JM+6HXl8EvLYBNQZI9BPuwV3QzmJ027CUV+OccuL1L36ayx71hE/dj7a7DE4qo4QGxd3xnN7Ou9YX9Q9m4guCucZAmEdjh8uCu55B9TpHbt2Y48QfO1MrEXC68S4Wmn5/EViB41B8nqwVR+lddubwXAAXaEnljll5eV441Kwm5ox15ZjaaxCrk/D2WZCKaNH9Hc2zvw50/nmnZXsLdxISUkJp4r30vDl66x68m80VpbiMDZwyZjRwQUhfKsCaDf3kfwOwrc2wq/94Y9PMO+qhRw+coSiV//IiS/eDdIY2GuOcbXiMjZgqTiIacdbZF88Bf2pHR32r/sSPQ3D0lW5wPM1Go3BfA9VZg9bt+/oEAYDQK7ScOjQwXbtHTp0MBj2JFhOqQF9OmaTqVv+USkVKPU+L2q5Jg4hU6BKGYSnrZWEi6YgVyj53X/dj1KtJnHCtSSMmICn/jjmL/+PX92ztNOxjps8g0snTiVjSC4HDh5iwKChzLtqIYtuu71dnon0JH2v37FAP4cOHeSbd1ZyqnjvaVlgnWkonZ4iun10HiNcXXft2ELJZ+8gk8lJzZsEnLm1SKQ6A/UqWsv3U1VciNaQTu6cG1DF6buVaHpimaOLj+dUUxXaIZchU2mRXHbMZfuRyeWMHn0Ry++9u1v6O7NGemP9Z2ROuCLorCaT3PzXPUsijr+3h/KRyodHBS3fuo4VL7zKiGv+k0vn3U91SREnNq6mec/HjB49pp1zV/2RIzhcbrQyOljC9DV6OtbuygUSALWKeH9gOQ+m0v2gS2PggntwtZmCYTBS8ybhsLRgRd0uhIn10FEEHuy1pUG/Eo/LDsbaoKbXFf+MGjmSRmGlHiUeuwWZUoOr8SSqhPTgWVFnYTvCw4AHxjpgYiqlB/dg+noDsSPnI4tPwaZN4WCjiy/3bSN5zs9IGnwJ1lNHeP/ztfxo7mRqe5hqNrSfcfPup3zrOo5/8CT1MRryRo7ssWbYF6F0eopzoikIIX4phDgshDgkhHhTCKERQhiEEJ8KIY75/yaeC9rOJwRUcqfFyJ6XHqNx65u4XE6KP17dqaQRrmns2bu324xa4XWefWolqQmxjL3p10xY/Fhwf93jsKBJTEMRZ2B/0QEW3XZ7u3Z6oqlIXg/Wg5txNpQjeVw4Gysxf70Rl6kxaJ4XCIAHcMc9vyA5a2hQcgvNrhZKc+AQb8isG5i05E/Mfehlxi76XacRUbvb6gqXyh569A/Isy6h5JPX+PJ/7qbkk9cwttnbRQUNjxwaP3A4CZNvxNxm6zDXVSdKaKwspaaitNOgb32FnhogdFdu+b13c2LjatyWZuQKFeayA1gObCJx0kKEkKOIMwTDYLSe/BanpZUhC5a2a2/IgqV4bGaav3gFW1UJLruFttL9SMWbuG/p4m75Z/m9d2Pb/yHxXjMeSxOO6hJMO95GMzC3XRC/r3buJUEXz8zp03h19bMdtgtDx1rf1Ezs0HHopt5CW+leZCotCdNuofn4ARLnLkGWkB6MOGyYeycFW7Z2qVF2NadJY+dhmPUfGE3m036GZksb9Q4FxswpLFp6T59rDGddUxBCDATuA/IkSbIJId4BbgLygM8kSfqLEOJB4EGg/+zxLgCUlZeTmNnMsS8+IGHazRjSh9JWdZT6gmfY/+QdjB49pktJo7cZtQKIJIlnxMvZdqicxPzlaDIvQqG38c4n2+C223n1lZc7rRdOn7nNysgf3MKJrR/S0lTjczYaMxtxaGMHk1PtuOtQZS1AbjVi3v1el/kX+jJvcMTwFP94FFWjjaQ5i4MB3YwbnsYWsp1na64lMXMkjubqdiEs0OiCjoiRaO9v9HRuuiuXnz+fGO7BUbSe1i0v4rC0knrlz4kbNgF76iwsLAAAIABJREFUYyXZmQODYTA0xeuRD5pHRu6Ydu1l5I6lXh9PqkHFsY9WEEi486e/Ph4MidIV/4Ty2NdFRVisNjxOB1p7Y4cgfl1J1KFjDRz8K1NycJsaAV/OZK/ThibzItwt3wWT7C4fRldzfyY8EWgn0IYi3oAudyqntr8dbKOvgiaeq+0jBaAVQriAGKAa+C0w23//ZaCQf/NFYXBODvsK3yVh+k+DqrYmLYf02YsYYTnQZagB6Dyj1v88+0y3zmnhVimaxDQS85ejHTQaAKHSkjh3Cf/8eCWvdlIv4Kiz7NcPBvsZnJODXWdg6t3/HazTevJbNJ6y4PdgljmHAqVWjzYlG7lSTcPe98m94qcdnPkKCjbRajJTuOIOYlOzGDz1GgCOff42zqY65l21sMMYu9rqWrnq+aBWYGuuRWtIR6hjiBm7oF0ojYSpP8Gyfz0s+AlGoxG0eoxl36CMS6SqpgZFfBKuxkpiktK7dEQMz2gmeT2Y26x95tzUU4e7npQbPXoM9ryrSci+iF1rH0ERm4DHZUetUnUIg7Fm7YuYSjq2lzdyZKe82501VLsAfmPHdpif0CB+0LkDaOhYAwf/roZyFLpkwJczWabSYtzzEY7KgzRajSj1qWgG5naZDyMcof3U1TegiDf0iCc6a6feoQjmhLBVH22Xs+Hh/7q/x3R1BSFJUp801KtOhfgF8CfABnwiSdKtQohWSZISQsq0SJLUYQtJCLEUWAqQlpZ22VtvvRW8Z7FYiIuL63f6+xJd0WwymTh+ogxVcjZCJvfljvW4USmVuJqrGD0qr8u29xcdQJ2SAyGJcSRJwlFfRlxKJnKVBo/Tjtviy4er64LZ931dhCp1cPB7mhbqbOCsL+OyS8dGpL2yuhZFnKFdPwZ9PM1Gc4frof0fOlyMJiUbm92OTKEKUI6z6RTxaYOwN5wMjj3QjywmATcy8HpwW5rBH/JArY0FjyvYh0wmIy4urlP6sjLSKa84iVemQB5nQKZU43U5cLbUoEwYgEypQgiZ71m4XbhaqtEaBuBGhtfehsduRhGfDEKGADxtzWjiE1FoYv8/e2ceH2V17//3yUwmk2Qyk4UsBBLCIpEAdUNAEAW9KKlrb61V3K5WvG601vZXrd5b60JrvS1apbcoWutuq6UuaLhQNYgLKCAqBMMiISEhJGSZyT6Zyfn9MQuTZNZkZjJJzvv1yiuZZ555nu9zzpmc5fs9ny9AL9v7lhOaeLo62rC3m9Eb04mL0/SzeyD4e1bPOg/mPM9zenrsdFoaHXmM+5Sz0WjEbDZz+MjRgPcN53O42k5fgi33OL2BsWkGamqPAo6sd9qUMQhdItLaga3lGGlGQ9ARQ573sdodOVDsrf7bhL/r2OOT0egN9Ni6el2ns76Sggn5QbeRRYsWbZdSek1BGPVOwekr+AfwQ6AZeA14HVgVTKfgyaxZs+S2bcfXjPvKOQ8HAtl86twFNEw4B5E2jgSdjuysTKT5CPqydQFnCg6huJt75VZoOvAFTf/3J8779fHOtLnyG7/XKylZz8U/vBrTohvRj52CJtHEz2fpWPHONzSXrKSz6Wi/z5wyex6HzLZeEg86gwl92Tq3M9lzpgLHhQCbLS3kXbic9vhUehJNCI0Oy7c7adj4Z0ScBqwdZKSmMGPGTI7V12Fw5kg2m80craun8VAZ7V/+H6fd+Bt3LLnrGe/9f3e4y9uX5IC3cjv00j0YZi4mbYIjsUyCTkdSdzPii39QWV1Dhw2Ss/JIy5tKU9VemirK0KWNZUbxte6ggObKb2jd9BfGZGa579nQ0EDyAoeU9t59++lJNNF9rIr2bWuZc+NDXu0eCMHKKwRznuc5KclJveS+Pc8vLS2lo6PT7/VKStY7chXv38/xXMW/8mrb4gsudc9SXPRtu8Gc4+05XDO0umMN/OKny3n494/S3QOmBddiTc7Cbrej0WhItpnJq9sS8Lvn7T6fbt2K0BvR6pOxdbWTmJ6DIe9ELF9uJNWYEnBm6BICdLW1ifMucudsCLWNCCF8dgpDsXz0b8BBKWU9gBBiLTAPOCqEGCulPCKEGAvUDYFtMceK++51rJHmX4VxXEHQuRDAe26FpvefIX/O+b3O87f27lpfz557MQ1fb0SbnObIvNZloun9NVx+kfd/LOWH68lacjv6sVMcCVU+eMWdQ7nvEoE32Yq9b/+ZvMXX09XZSVfTUZo/egnDd84ncfIsuusraf16PTWJBRw8vNudI9lkMmEymfiqs4MOW5e7Q/D1jL6WKjwF9jTxeuzdnaRMW4Dls39QOPVujJOmOuph65s8+ut7Wf7zu5mz7LFem+Zqd33C7rfXoDOY3PkGDr71BHE6PQaPaBxPKW3X2rYmZzINjbUB6yYUgt2kFsx5oWx483euv42MaxiY3yiU3Ay+bCstLeXIoQNMmX4yhbPP7r2Z0m6jfM2r/T7jD9d9XD4+w7wrSRo/jbbK3VT/aw3ZU75D4WU/CRhR5JKD8cyFHgk5lKHoFCqBuUKIJBzLR+cC24A24DrgYefvN4fAtpijuNih4f/4U7+lxWJBn5hIhsngWKdftdrvyMJbaN44UwI508/odZ6/DTSu9f0T8k9kz0clVH3wF+ztTdiK7iEjKY6lV17p/TMLlhKfme/Ol8v8KzlQ+iKnTel/n75CgK6k9Uf/tYa21ha67RLDjHNInjqXuCQjOlMWcVot9fs2u3Mk58yYd/yC5lrHRrEgn7EvrrDHdmcGrwSdjtz8ieTWZ3nNsDZx1Wr3urFnoqD4Hiutm/7CEad/ICvN6J4VQH8pbU1cHG31hx06Pp0d1JVtDSoUOBw88OBDPP7Us7Q420nxIkemuVCF2zxH33f9dDkdHZ39fEyesyRRtAT92BPpajUjNQkknlSMZdtar+vsxpQUdn36ASJtHBpNHEhoP/ot3ZaWfvpY4RDxC5ckvovNW7Yx9aJbaI9PpauxBpFoZMyi6+ks29gvkY8ve/09X7gSMUW9U5BSbhVCvA7sAGzAF8BTgAH4uxDiRzg6jh9E27ZYpKRkPS+ve49pV/wSDI546mM7SyhceDmdxvSA0Qt9cyu4RuXBZrnyHJ1lpKdzzJROavGt6NJzmfz9X/iM7Mi//AYOH62HlHQ08Xq06bm01x3izsce8HsPFwVnXkrXnlJSjSnUmjvJWXStI1GK08egTc3B0lhLYdFpfL5hNc2V37ifR5atJ4muXsdCGU25RptZZ13Ve1bgY1nDdX5j3klU79pCyuzvk5RkIk2007HjTZ74veNzU6afzFgvUtq7317D4fQ0uu16ulsaad31HqZTi9mz8RWMmm73/oZI0TdKrfXgl/x9w7OMm/VvTAtBFLHvjK9Hf8yvDEbZ0/cypuhy7B2taA3piHg9GkM6jf96kl27Ovtdu67JguXYO5jOvAq7MZuuql10fFlC/pyL+qU0DUeE10AywvnD0c5Pds88vt5dRtLEk6j56HioRjAzw3A9ny+GZJ+ClPI+KeWJUsoZUsprpJRdUsoGKeW5UsoTnL8bA18p9hnsLkRv8dTpZ1/LoS3vDkhQK1Q5B8+t+a5EOPGZ+cTFxfm8/8SCAmitczhIO8x0Hquiu/Jrigqnel2fbra0sGPzBvbu2++I4uH4iGxiQQG6ZGeOZyFA9iC7O7E1O6KCeuVIdj7Pk4/cz5rHfjdgyQp/ZeStPl3nH/5oLfGFZ9ETn4QmLo6UcScElCHRG9MpHJ9J1bonaHj7f+j+qoS06QtInHgqaaddQFaaMSz/APy1Q0eU2o0YJswkTqMlLjWHtHOWUb/3i6CE21wCh5dccQ0NOadT0wYtrW1odHq/MhhJWRPoaqhyiNfp9CBwyIdnjKe7p/c9Vq5azcSLlzNt8ZU0f/Iq9a/9mravNqDBzqSzL4uIsFyw35Vgv+PeJGXaD+9xtGMnsSB7oXY0R5Bw7EL0Fk892DXnUEYanqOl9oZajOm52Foa0WrH+Ly/6zOZZ13FlEnOEdYXH7Li1/f2Os/tr5hzEdW73kebnEal1Upa9T46drzpHpEtu+MuGjc9j+nMq8BzhHjq2c6R270+12AHircyCiQI19XVRebU2WgTkrF3d1JVU8v47KxeMiReR573/8qrX6LHPpXyXYNfRQ3UDlssFjLGT3OfL23d6MefSNN7x9zHfLUzT7+AOFRL0qRZdLU1c6jqMPbJOf3SnXoyeeFlfPnPP6NNySRh7AlY6yowf/wyKdMWoN3T+x+rpxDdUZmCfkweyB6qn/+ZX/sGSzAhssF+x/vWf1J3M4fff5qCucVuv1Ok5dKDQXUKESQcSXO8xlMfq3KPLiI9svBcw6TTQnfl10yYORuNRvq8f7Drup7lY8jMc6/Ft2rppQO0Bhw5ntf9D3a7HZ1Oh0Gvi7g8RF/81SdAUtYEbI01xOdORatLhJR0Ksu2MzUIGRJPv4SLcNVtoHaYYjTSfniPO9pKaOPpPPwNOtOYgLa4BQ4LTqZldyndTTXoMvKwtzZis9l6fc5bSlW97HLIhbea0RjSSZ2+gKzcPMYwrdd9hvp74ItQvuPe6v97/3kNm7dso3zNHYPyf4QT1SlEkFB22foKBfQcXWRlOHwK5m1vEi8k7//mOre+z0AJJgTRNVpyjYpkfh6Q4zfyIZjZiGf5ZBXNIatojjO64w6fu1ijmbGqb9ns3rOHUxb33iDkWZ+TFy5l36ZXYP6V6HMm011fSf3ml1n9zBP9rvXE7x/uZX+41689CdQO+0ap9TTX0vSBw6cQaAR7sKIC5p+PJl5P6snn0/jxK5jmXQnaeOzWTndymX++/S7lL/2OzAVLyXcms6//8CV+/uNbHXLZ37sFDFlUlm2n/M0/wfhM9zLMylWr2b1nDy3bvyD/zO+RNfMcDnz9Oa07Syg893J3O1x64bksvuDSsLcRf9+RUHfSR9ofEA5UpxBBgo1eCGYK6mqUSYC53UzigqvIGjuZZHsLL697k9NnzQq5sYW6vOVpS2fBeejLNoRFXjoc5RNuvN2zZfsX1JTvZHzR8fBuT3s7jekULrqMg5+spaGxlvgkI4XjHYqegewPZ9RMXwKVs7cotcvPO5PahtqAI9iJBQXsNddiT8vBMMVRLo0fvYj1WCXy5F+5hQozz76B6ZZGDpS+zucbVlNUONW97Hf6rFnce/8Kysr3kpQ1gekXLUNvTO8lONhXYHDs2Fxkuo6mj17GUxAx3G0kGJHASM3whgolnR1BgpWyDiREVlx8XPxtYkEBp1733+RnZdC8+QX2r1tNXXMb99zXP6onEAPJ1OayZcb0Ir9iYME437yVz8G3nqChoaHX5wZi52Dpe09rqxm0esrWPs5XH22kubGxV30umDuLnS88yK43/hekJH/OErJSk/nN/b/qda1j5dsp3/AiVZWV/cTMPOvZczNWelYuO3Z+ic44xqeooT/8tUNXPT3/6uuc8p2ZvPHqCxyrOsALzz8XlOjbnbffjCxbT9uBHXR3tqJJTEEjIDtnLBMn5PfKNpYzYx7zb1/J6TeuICMjo1eHmJGRwek3rmD+7SvJmTGP1PwTaSehl+Dg+KJZfOfyOxk7NpeMjIxeUiDhzGp25MgRt4jk95ZehzWz0K9I4ECltCMtgT1Q1EwhggQ7+gtlCupNJK/zyH7K168KmBnN27UikQkq2JF93/JJSU4iTqcnecF1jB13/HOW+hq/yzaRwLNs6sq2Uv7B62ScdyvtR/bTsrOk12gX4OV17zHlez+lTZNCy5EDHP78DX5241KKi5e4M265rpM6/0rSnPmffY1mXWXYHp9Km9aA1pTN2BtXY687wO+e/AvgX9TQE1/tEALPYIK59pPg2JVc+jTHdyU/RGKiflBifNY2C5hyeh3DkEVZ+V5Ov3FFL5vD1UYeePAhUkxpJC5yhOc27v6I2q//RVJ6LhNPP6ffdUOd4Q3FrDdUVKcQYYJZQwxlCupNJC8+Mx/DCbO55qbbSDWmBC2qFsryTd81VX+KjKE631zHFl9wKQYPiQLX5469+lss1fsRprEcrauny2pFNlWTkZLip1QHh6tsrK1mvnz9cWScBtsnf8dUOJc5t690SwsUFy/pJ8JGYSHNkyaxecu6XtdyhfQm5k7FZu3AOG4qmZMmeS0XVxl+/tcHST/vVoQukbj4OOLGFpK64Fp+s/Jxnn/1db/1G8hfFKx4XCD6LnGOyczqV44DEeNzZaqD43IjlWXbScqa4D6vbs9nVOzfh62rjX89eDUFZ3yXqedf6/M+vnCV1aaPPub3jz1B4vgC4jQa9LknoElO49CWtUw8/RzqyrZ6FVoMtrzCEXwSadTyUQwQyhT0zttvpr3uENr0XKSU2KwdWHZ/SOeR/cTP/iFjL3+A2vELOdBoJe3MpW553mCXb/re1zWy6Sy6kMJlj7mv5yspOwSXgS2Uz8XHwaH1a9i/cwv2BANYO2ja/g51TZaITb3vvP1mDr71BHs2vkLK/KXkXLuSlNO/R8uej6gr29rreQI9r6uc2+qqSMiehM3aga2lkeysTJ/l4rqmrauNhHHOSBwRh72rHQwZ9Gj1veqjbzn4qjdvmdR82R0s/tpIsG3b23muTHW9Prv5ZSYvdOx43/t/z1Oxo5T0JcsZt+wp0s67lYrtpXxT8mxIWc087e+REhGvx9rdjd1uJyHZhCbZRFdTLbW7PmHPxlfQzzyfU5Y/6fe75YtwlXkkUTOFGCDYKahLisDW3c2hZ35M8pTZZM+9FNvBbZjmXkZSaqZ7g5vOlMmhLQ5RtVBC5Pre19fI5mhdvc/n6TvqM5vNHPr6MxqO1nmVse77OWurmYOfvE2H01k7ITsbgENf/x8tW18jMT2HaYuvRGcwRWyEVVy8hKz7V6CZcA42QxbYrKTkTSNBdxUHP1nbS37C83k9ZS4StfRa0rvmptuwlH+CcdxU8nJzMJlMNFd+43NGaKnejzYhma7qPXDSiSB76Olqw95mJiEtx680QjAj0lBmqP5mHX2TQbnqrbqmhltuvtl9TqjZ9Dwz1bmOFY7PpL2hhq1P/xeNB3cx5oI7SRg3DSF7ME48CcT1HHpnJQWiIWhHvWdZaROSkd2diLhkuru70Sca6KguJ87WyZ5/PEbGucuYMHM2XdXfuOv5mptuCzqV6nBwTKtOIUYINAX1lCJIHTuV1srdNJX+lZ4xGXQ21pCSZCI7K5PKw9UhbXALdF9fa8JdXWU+P+MZXumS5mjdWcK079/hV5rjzttvZtkdd2Gxx5N+1jUYM8bRfvBLGso30G05xhk/faa/OFkE/QqWlhZmnLGIltY2qmpqkbp4ErInUV9X1StE05/MheezusXMJk0ixZDsN6TXLbVReCpHP3gG+W8P0NOtpav2AG1f/4tJZ17sPncgCXM87xEoDDbQOrgvP1en1dZPk8gfvs7zPHb8e3AjfLsL3bhp9PTYiddq0Gg0mApm0gQhqZh6ltWEOedjazlG5+FDxGdPovXQPpo3P889dy7n+Vdfp/CMRY5ggSB9Q32JZOhxuFCdwjDh8aeeJW3hTWizT8Bm70E/fhppC/+DQ+8+itFoJE20YzKZSKirD+vGHl8jm4TCBJ+f8Rz1fbp1K7qMPKYtvtItIe05Yu07AtVrBZqTLwBdIh17P6Vr/2eYG49h72yl4qM33GJ5ns/laxQbqi8k0LMfravHUr2XRC29RqGeM4HE2T8kKTWT7KxMx0zAYHA/ayhOSc9zW22t2MxHOfL0g8RpNOTO/57b6elZDv5s93beQDYZgpfNb8lJfPX645gWXINdl4ytq4P4zHy0ho6wz+Q8ReWETo+1Zi/6vCJ67HaIx52nORQ8y2rq+deSkHiAxvUPY+9qJzUtzZ3fefOWbSH7hjwpKVnPPfc9wKF9Bziw5y7itfEUTpnkU1NrqFA+hWGCxWxGkzUZ4jQIrQ4RpyVh7FSQkhee+hMdO96kufIbsjLSaTuwg8ZNzzNh7ndDWlv1hq814eysTL+fc4VX5mRnc8YtjxzvEDi+huptLfpwzRHyi04jW7TQVv4JKXN/wPjr/0jqwhuo2FLCt5te72XHgrmzvK5nP/DgQyH7Qvw9e4ohmawEG6bDn3pdKiguXkKqMYVTF5zH1BOmuGW7+64X9w079ffPwHVuY10Np558ElbLMf758nPo6ssHtEbv7bxg7PG3Dl5Ssp6GDjsyTkPiCXPQJKXSaWnE2lyHLjE57GvlBysqyC08maknTGHSGd+lqfQZug6XYbd303roa3ee5lDoW1ZafTITp5zA26+9zLGqA+4or4H4hlyUlKxn2R13caDRSvb37mHCfz5J6jnLaOiwD6Y4IoKaKQwCV4KQPeV7sff0ECd7mHZioc8kIYMhTqOhq/aAOx0mIo6u2gPEaTT9oj9yUlJ6bewZzCYoX6PJYEfcnnLHnkmCXGkv+45AXVLSHd9sImHCyTR88hrdzUeJT0kj++RFHN36Nl17St123HPfA9SZbVS/+nt3Mp/Ms67i8ad+y7QrftlvdFtdUxP0rtdQww2jsV4crE3h3Azn77lWrlrNhCXLaHv3Obpr96PLnowmORWt1QL27l7PHmyiH1+UlKynvqGRqhXXoTVlkzp9ATlFc6ld/0S/UX0o9C2ruJ8u91lWibKL7nYLjbs/xDh+Knm5Y/36hlysXLWadhJIP/t41GDy5FOxauJiKvIIVKcwYFxCYNap/8aY2bdga67F/NGLlB8+5jNJyGBIitfQVPoMYtGPSBg3ja7qPTSVPkNyvAaI7PZ5b9cORrvdU+44/axrsKeks3/nFrTlG3jykfvd8fueuKSkezrb0LR3YDrjh8RnTkA2VtH46askxsH+3Tvd1/eVzKfFYuk3uu20NNJpTXLMHoKMEQ+lXKO1XjzYNfpQ8fdcrjqcctallH/wOrr5V6LPnoSlcie21uNZ9QYbn+/6viXOuxpDeh49HRaatv6DxJwpZOeO48lH7h/Us3qWlbdsiG77z76Bk05ppPy9v9MRn4DMygoq0c3BigqsbZ3ocya7j2ni9WDK4WAYRA/DieoUBohLCCwhdzpxukS0ySbEWddi3vwi7YS/9z/t9NmUmwV1G/4XW1cb2oRksgtPpdAU/RzbweKSO3ZEE/3zeDSRSeuQCPAiAueSkt6zz0LKrEtIyJ5EfHw8mvEnImd/n44PVve6vq9kPilGY79rHyh9He2cO0jNd+TwDXeMeCSlKoYSf8/lqkPX8uDBT9ZS74y8yrvu0X4RSgONz3d931IKTkba7XTF6zDMOBfz5hfIn5wf8TLua39cnIZ97/+NL0qf5ow5cwLW88SCAhr319BZe8A9U7B3d4K5NqYij0D5FAbMwYoKx25LjRaEoxh12ZOwd1iwtlnCvpZ65+03k9TdzOn/8d+c/8DrnP4f/01Sd/OAfQWDxWKxcMrseRjG5KI3ZmDKySd3UmGvbfuuteisojnMufEhFv7iac645RFa2trdz+Rt3fs39/+K9LRU9KYxaOMkcXFxjtSYSSYS4o+PYw5WVJBfdBq2lkZs1g6klO5kPj++6fp+126vO4QuMbnXc4Q7Rty1Rv/E7x8GYPnP7w5KyiAY6YMHHnwIU04+WkM68SkZJKdm9ivzwd4j0HP19T141uGYwtMoPO9q8vLzeeGpP2H0cPgGis8PZJvr+6aJ1xOfaMAwZhzp088kPtHgbk+RpK/9WUVzOOOWR8jJzg7oGwLn95cuGjc9T0d1Od2drbQd2IEsWz9k32FfqJnCAHEJgZGcAZp4EHFYj36LJtGIThvHxILcsN4vlkahJSXrqag6zIFGK8mnXUzLgR0knn4p2pR0jmk63csCgdbY/T2Tt5SYaaKdMdOOSypPLCigs7WOvFzHTudOcz2yqZqiwqn86r//i9Nnzep17aLCqWDv7vUskYgRD3WpJJjzjxw5wsN/fh7jopsxZhbQWbWLlh3v0F24gGP5UwIuxURKXsFfHXouMfprC8HY5im8p9UlAtBZewBdsjHs3zVvDNZfVFy85LgE/Fu/AyRTp0xhxSCXvSKBkDJ2lx8CMWvWLLlt2zb3a29rgZHC06cQP26626dAZyvpKYmseex3QVV2NG32hcsBuGvX13T3OBLXT582zacj8JTZ87jmqiv45W8eQ9ptpJ93K4l50+mxdqC3t5OVYENftq5Xsp2+a9GBysbzH4Wvz3qe45Jdrt/8MoXjM706+0tK1rP/YAW/33TEMcsz1yLL1g96Pboviy+4lE4PuQ7ALYvhLX4+mPP/+MQqfvtpC4l5M+g2HyUuOY3u+kOYP3mVvO/e6i5zX/H5rnv02xho0vLFZ5/4fJbBOIc927arrjR5J1G/9wva6g+7Zd83b9kW8Pk9k/kkTTwJW0M1jR++4E5XGigUNJRn8OtTGEBbjhah/C8RQmyXUs7y9p6aKQyQ4uLjQmB73n864tFHkcLzy9qRWEvK7O+jSTJxrM/GK8/zyw/XozFkMPa6x6j56x3EJafSY+0AjZauDivGSVMpf7diULObYD7r+tub7LKvUbCUPbTsLMHaZkGXbCSJrkGUnndCFRoM5ny73Y4uZwqIOKTNRpw2AV3OFGwtDXRZj5e5P5tCFVIM5+yiuHgJn2/bxh+efpmkWZcy7qzjsu/BiNl5ft/6Cu8F6hDC8QyxNFOPNH47BSHEeOAKYAGQC3QAu4B3gBIpZY+fj494IhnxEy1WrlpN4qmXsP+9VzDMvgxt9gnESTvtVh1ZvuQTFiwlLj4B7N3Ep4+jp92MLU6LNslIgk7Xb4loMOGwgT5bXLyElatWc/qNK3qNNL05Me+9fwXX33QLxkU39gqPDXdQQKhLDcGcr9FosNbuJzFvBkKrpcfWRXf9IbQpGf3K3Nc9vAkpZi5Y6vP5e+UH95DvCEXWwZPNW7bxncvv7D0jMBio+euv+GjVz+jqk33NW0a/UO8ZDgG6QAmSRho+Hc1CiGeBvwBgyqAbAAAgAElEQVRW4HfAlcCtwL+AJcBHQoizomGkInLs3rOHJplEd0sTCbmFEKfBThwdnZ1enbAu5y6AraUBw/SFWLb+g67afci2RpK6mwe1WW4gBCMyVlKynrLyvQhdIvoxefQkmqiqqQVDVkSCAkLR2A/m/KwxGVhKn6Wj8muELomOb7fTtOk5kk+YHVSZexNStLU0kl90ms/nd5WrS/I7ada/M/76PxI/+4chC8F5Xs+TTksj1jg9uhnnkXXFCkzzr6Txq/fZ89ofWDDX6+rGoO8ZSnBBMMKCIw1/M4U/SCl3eTm+C1grhNAB+ZExSxEtrN02NO1m4lOzsdZ9S8LYE5DddqSUPuUTOlvr0MVnkqCNQ6bmEJ85AcumZ0lIT2PMtGlhzZsczHqwa6QtTGOpPnKEzs4urMcOYT3W4P7yXnPTbQhjFj3dViz7vqblq410Nx+lLiGRqVnJ3m49YEJdagjm/OTkZMYadVS+s5Kenh4EkKDVoCvfyJi4wwHLvLh4CUWFU2mo/BqbcyNhXm6OeyMh9C/rHpuVT//8C1rrKkk7ZxnatFx67N1ByzpYLJZeGwVTkpO8hgmbzric5ILv0N3VSpwuEeOpF9KxbS2bt2zzee1gGayDeCAzDc9yDFbGPpbw2Sl46xCEEJOBJCnl11JKK7A/ksYpIk98HLR89g+SJpyC+eNXMM7+d+KSTNjrD1H/1dZ+G3LcG5lOuo0Tpzodbj3HePSV58Pe2INdD77z9pt7Of17ui20ff0v0CRy7S0/ISFBT4cN0s/6AbaWYzR99k/SzrqW+PRxtH+7g4ZvPwg5QVEgQl3q8Hd+Scl6qmpqyb3oDk4chJNzxX33Osoz/yqM4wp6XadvWdeU76T2izISJ01BWJrQ58+k09KIRkgm5I0nxZDs14fhstlzo2DD+jXUv/UEEy9e7nbWttcdInNBHjpDKgkpaQDIrDzatr4WlhncYDcUhuof8izHsbOzPHJJL/UrBhlLBL1PQQhxD/AQcLcQ4oXImRRdYj01Xl+CsTeUZ5oxYybjZsxF1u6hp7mGpg3/S8MbK7Btf82nfMKjv76LuE4z5WvuQF+2LuwON5f9P7jmBjoKzkKYxvpNsVhcvISMRA0t29dR/9qvaf38n2TM+Xeyim/H3NJK7ZEaulqaqNv4JMgeDKdcgIjX09NuxjhuChlnXsk1N90Ws21g5arVaA3pvVJCJp56Scg2u+pOX7auX931TT/aHp9Kxr8tg6YqdCnpWOsPOeQrtBpMJlPA0bY3mycsWUZWmrHX/YsKpxLXUufYyIXDoW6p3INdE0+zpWVAdeHZ/leuWs3SC8/1+sy+Prtv/wF3ubpmN574e/ZefhinjH362ddyaMu7UUkjGw58zhSEEMuB/5VSuhSbTpJS/tD53lfRMC7SDIfUeJ4EY2+oz+QaSRWed3XQoXauGHSX3ESknpEvvyE+f6Zj7R8wmUw+R2ktbe2MOe9mErMKEEIA0LT7Q9DoyFh8K/rxRXRW70H29JCQM4U4XSIaayupqUYaGnvosMGcZY/FZBs4WFGBRqd3vzabzTTJpAHZ7GtG0ndE3GW1kjR+Gs0fPEPRhcso/+B1EuZdgU2rC1rWwdNmcIywj7S0sGPLZvcxV6hpm70Hff5MOuoO0fzxy8T1dJM956KQ68Jb+395XWhh0D9dfhuFznL1Nrvx9+ye5dhltYYkYx8r+JspNAHrhRAXOV9vEEJsEkJsBv4v8qZFnqFICD8YgrE31GfyN3p08cCDD7kTmZuy85h84gx27S5zj1CDnZkEc56n/UkZOdgaa9CmpLuT+vgapRlTUuisO0R7q4XOzk7sdjvm7eswzb8S/fgi4uJ1JObPRGjj6TxchmxrJC83h9bWNuztZpKz8mK2DUwsKKCrtYmtT/8XpY/cyBfPP0Db/s8GZHPfOnjgwYdYfMGl1B49yqd//gV1ZVsxm82O8jv4FSSaSBh3IoWLLsPy8Us0rH0gqNnhxIIC7NbOXse81V1x8RKefOR+cg6XUvX0LRx76xHsjTVIaxf1+3aiyTsppLoYzHfa9VmNTu93dhOMKCJAgk6HvbvTIW0RBhn7aOHPp/CiEOJ14P8JIW4EfgW8AsRLKc3RMjCSRCpxfaQIxt6BPJO/9WzP5D6mMfm0HPySmi/eRSSl0Vl0IcvuuIs4nZ4JS5b5nZkEO4PxtH/ivIso/+AVUuddgfQzQnUJ77XWvouM06DP/w6dR/bR3Vh9PJVlTw9CxKFJTsWy5TXyvv8TUgzJ7PviYzp2v0/hoh8EXV7RZsHcWVhbLSScdAFp46dhPvgVTZv+SsGpC93nBGNz3zqo+OgNfvfkC0y96BZOmX0DB77+nN0bXiL5xAVojVk0bXoOY9FZVB6uJk1oyEpN5tHHng1q1H7n7Tfz7aFKmiubA46wXe1v7ITJdCRmkjp/KfqcyXTWHqD645dp7PCd5a8vg/lOH/9sa6/P9p3d+MPTh5GV4elTuDyoGVYsEGjz2mTgb8Aa4EFA4ugcRkSnMBxS43kSjL3hfqbHn3qW9HNuxjBhJq3HqkmadBralDFY28yk5s+gnQRSipYEjM4INorD036XyNq+91/C2lDFVB/CYy7hvXGtZvZufInm959GAkgJzTUk5M+gu7sbKW0IEUd8dxtjKksp//CvdFtaGDfnol75HmKtDWzeso1//+FSklLb6WqsId6Qxpizr6OpbKP7nGBs7lsH9ft2kn7OjbTHpzI+PZ0pJ8+l3NZNY+mzpE2YRsGpC2mq2kv97g9o1RLS3oTi4iW89dZb6N/+S9Cbvbp7IGX29937KBJzp2LvI4IYiMG0f/coPzcn5M+68IwkC7eMfbTw51P4q/P9ROCAlHKZEOIUYI0Q4jMp5YNRsjFiREPqeLAa8p7X2L1nDy3bvyD/zO9RcOalbnuXXniuO/QvJTmJhvVrYMmysDxTi8VCxnjHaFvauhHxenQ5U5D2SogDa5vFIRnhwUDTQ0L/OtEZTAFHqK5rx2m0ZBXNwWw2U1tby4Gnf0L9e2vIPHcZyfnTaT+8B3t+Enfd+WO35r5r9NxcOSNm0yMerKggXp/E1BMcGj9ms5nKwxqa66rosduCtrlvHXQ01pI2fhpdjTWAw2ejz5pAgiHNkdvbSY/dRvmaO0Jut0ajMaS0mLp4LUKfQnurxSE0abch9Cm9RBBd+PpeDeY77fqs/aTbQirXvgz3Ta3+ZgqnSClPAhBCfAEgpfwCuEgIcUk0jIs0kd66Hg5Htuc1Tll8BzXlO/n23ado/PxtZsyYydILz+Xlde/1ukf9W0/Qtvk5jrS0DPqZUoxG2g/vwTDBsR4vuzux1h9C5CeBBF2yEcy1wEz3ZwaaHtKzXEKpE89rm81mqmpqsbU2kjH1NPTGdGrX/QF6ejCaTOQUP8RPf3zboO4Xbfquz5tMJtKq99GqhfI1dwRtc986SEzPof3wHpJSPbLomWsddepBtGZOOVlZ1ByrJF5o6OmxExenoftYJblZWb3OC+Z7NRhplcPV1ZSveSgm20I08NcprBdCbAJ0wMueb0gpYysrxCCIZK8eji32fa8xvmgWBoPBLRa2+IJL+91j4sXL0ZetC3od1B8/vul6fvfk03DOjejG5NPy7XZavngX3aK7aC77hiS6kGXrac7P8zsyC2UEFyhmv+8I0fPaB8p307xrE90NhzFkjmfi/IuZcMYF7vLylhwo1kd23tbnO3a8GbLURN86yDzhZCref5oxF93iHhnLsvUk0UVz5Tde6ymUmW/fzWuBZsmyx077VxtIP/takp0+BfNXG5Dpul7nBfpeDVZaJVKRdcMFf47mu4QQRqBHStnq6zyFb8LhyA50jUg7y13LLI8/tZo6iwW9Xk9umgnZ3oS+bANrHnOM0KKRHtLXCPHRX9/lTs15rOIIaQuuxjBhBnbzUXcmtiNhlrKIJgNZn/d1HehdBz/8z2vYvMXhX5lYUMCTj9zf7xzXvUKZ+XrbvBZoltzS1k7huUs5tGUtDY21JKbnUHju5TR91GtMOuwCRIYb/nwKVwMv+xK9c+5uHiul/ChSxg13wuH0DXSNaDjLf/Xf/9Uv721paSm333ar+3Ww0/PBjMj9jRA3vvMGK1etZuzExegKTnZo7ieb3JnYTptSMOD7xgLBrs8HGskHWweBBPKg/wjd897NlhYeXPFbUvNzvZ7rzd5mSwsJDTW9/Bnech8PtwCR4Ya/fQoZwBdCiL8IIW4TQlwuhLhWCPGAc1npEeBodMwcnoQqjDaQa4TjHsOFQOJm/jKxjcTy6Eukxdv8lX/fe3fYwEYcZrO537m+7M27cDkVW0r4dtPrftvyaGrzQ4G/5aM/CiFWAecA84Hv4JDO3gNcI6WsHOhNhRCpwNPADBxhrjcA5TjCXwuACuByKWXTQO8RC4RjySTQNYaDozRcBDNr8pWJbSSUR6BZQDh8WP7wV/733r+CjoJzqGrpIeHbCvTpudBj52hdPSaTqde5vuwdXzQLuIWqdU/Qtac0okuRCt/43afglLjY6PwJJ38E1kspL3OqrSYB9wDvSSkfFkLcDdwNxE5c4AAJhxMz0DVi3VEaLgI5qz0zvU2Z5Hz/iw9Z8et7h9jywWOxWPjFikf9rudHeq3dV/kvvfBcfrPyCcYv+AnahGTs3Z1oJ87C1tqEpbqSnkkFXgMLvNmbW3gyLR+mBHT0jpY2PxQELYgXLpzO67OAZwCklFYpZTNwCfCc87TngEujbdtQM9zE+SJN3/IA/EpyBCPZMVw5WlfvU77BVU61R+vY9ekHvZZswrnW7q18l154Lo8/9Sz2HsmRtQ/TdmA7Wl0ixulnERefQPdnf/NZF56SEJGwVzEwop6jWQhxMvAUUAacBGwHfgJUSylTPc5rklKmefn8TcBNANnZ2ae9+uqr7vdaW1sxGAyRfYAw47LZYrFQVVOL1pCORqfHbu3E1urQ5zEajYEvFGUiXdaRKo/h2EYA6uvrMcvEfsfb6w6h0cajNaTT02N3yFsnmUhITAZ7d0TbkKuO7PHJxCUkYe/qoKfDjCYpFaHRkJ0o0OsTfN47Vtv8cG0jodi9aNEinzmaA3YKQgiNh1LqoBFCzAK2APOllFuFEH8ELMDyYDoFT2bNmiW3bTueiCOUxNWxgsvmUJO9D4aSkvXcc98D7Pu2ApBMnTKFFffdG9KIerBlHWh9PFLlEe42Eo4d68Gw5ulneL5c9iuPPa/+lmlX/NJ9vK5sK/ve/xvWhirOmDMnokldXHVU16WlJ9GEtNtpOfglTe+vwZCVz4qf3sAtN/t3/kar/EJhOP4fgdDsFkL47BQCaR8B7HcK4z0rpSwL3kSfHAYOSym3Ol+/jsN/cFQIMVZKeUQIMRaoC8O9hg3Rir0uKVnPsjvuwmKPZ8zFd6HNGEftwS/5z1/cx5NERy46mHj34RCLHk3p9eysTOrXPNpvPT8+jl4RQVlFcxhTeBrla+4I+2CiL646Eq1tjhF/SjqphXNo/eRlslKTGZebG/AayjcQewTjU/gOsBd4WgixRQhxk9MvMCCklLVAlRCi0HnoXBxLSW8B1zmPXQeMmF3TwRCt9dWVq1bTTgLpZ19L4rhC4vUGkiefiihaEjW56GDkjYfDenM0pdeNRqNXf8mMGTOHrJxcdWQymcjLzSGuw4yl/BMStQ7fTywueyoCE7BTkFK2SCnXSCnnAb8A7gOOCCGeE0JMCfBxXywHXnIm6zkZ+A3wMLBYCLEPWOx8PWqIVuz1wYoKrG0W9DmT3cc08Xow5bjjzSPt7A4mmfpwiEUfbFL4QHjWxb79BwDY+M4b7N+9k43vvOEWgPNWTgvmzgprhj5veN47xZBMVoIN0+FPQ5bfUMQWAZePhBAa4ALgehx7CP4AvAQsAN4FpoZ6UynlTsDbeta5oV5rpBCt2OuJBQU07q9xJP5wShTbuzvBXEtKclJUlkOC2ZE6HGLRI7mztu/SVI/+mNe68FZO3kQSB5uhzxvDoY4UoRPM8tE+HOGi/yOlPEVKuVJKeVRK+TowumMmCW8YaXHxkn4jwXBz5+03k0QXjZuep6O6nO7OVtoO7ECWrUfEaaKyHBLsLCAc5dG3fiwWS7geI6Kzmb5LUxqd3mdd9C2nzVu2BQxfDZT/Oth2HY02GwlU+LdvgnE0X9tX30gIMV9K+bGU8scRsmtYMNxyPIPDrjXgiD5663e4o48euZ/lP7+bsV6WQ8Lt3I3WCNNb/VTV1FJSsj4s94rkc4Qng1jvz+74+9dB5b8eju06FEb68w2WYGYKj3s59kS4DRmODGWO58GMdIqLl/DFZ5/QeqyG1mNH2LFlM8XFS6Lq3I3GCNNb/WgN6WGtn0g9x2Dqwtdnu3sIKv/1cMtdHiqDeb7RMMPw2SkIIc4QQvwMyBRC3Onx82tAEzULY5hIOxp9ESnhs+Hg3A0Fb/Wj0ekjXj/hoG9d2K2dQdeFr3rUxWvd5TFx3kU0f/wK3fWVdHZ29KrroWrX0WKgzxdpwcFYwd9MQQcYcCwxpXj8WIDLIm9a7DNUYZORGsmFQyYilkZS3urHbu2MqbBWX/Sti7hOc0gZxLzJUVi7bZT+7kdsfdohg1646DIsH79Ew9oHetV1ONt1LLUHFwN9vpE+g3LhTyV1E7BJCPFXKeWhKNo0bIhGjmdvRHJj12A2E8XaWq23+rHF9wybmY9nXYS6y9bzs656ybtwOU0yCXu7mfIPXmPcjLle81+Hq10HI+I3FAz0+YbDhspw4C/JzmNSyjuAVUKIfloYUsqLI2rZMGCoQvJCCYWMpoxApKWbQ8Vb/eQtuCHmnImRqiPXdT/d+hkZ5y4ja9wJpABH63R0n7iQA+89S3paqnukG0459pKS9Rw8VIl5/BnILi2itW3I24OLgT7faEnu4y/66AXn799Hw5DhylBs0w92pBPtkXssjqT61o+3HM1DSaTqyPO6npFGebk5ZGdl0mW1otEbOOXHT3q9ZzhmjP951fcxFs6jx97tjnIa6vbgYiDPN1QrA9HG3/LRdufvTdEzRxEMwY50oj1yHy0jqXASqTryvK4r0ih+TJ470sjebiY5K8+9Nh7OduG6d5y2h66j3zo2STqjnKT5yLBtD6Nls56/5aOvcWRF84qU8jsRsUgRFMGMdKI9ch8tI6lwEqk68rzuxHkXUf7BK6TOuwKp1WFtqKJj9/sULvqB33sOdFnLde8Ew1GaP34W5l9JQvYkLNV7EYc/HdbtYTQI+PlbPrrQ+fs252/XctJVQHvELFKEjWiP3EfLSCqcRKqOPK+bVTQHgH3vv4S1oQp9YhLj5lzkPu7tnoNZ1nLdW5ubQ+Giyzj4yVrq66ocQnlKFynm8RmSKqU85Iw6mi+l/IWU8mvnz93A+dEzUTFQhmLfgb/NXLEYnjjURKqO+l5XZzCRlZrMay88ywtP/Ql71Zd+7znQ8MuSkvU0NDTw+dP30lJ7iPKNL9JWV0WctPHjm64HHHkYxk6YzJi8yeROKlRtIcYIRuYiWQhxpkvqQggxD0iOrFmKcBBLI/dYC1eNFSJVR8Fc1997A1nWOl7H1zF5ymEQgvjp55M+diIm0cHTf3+Fp158DdPMRXQk1pIy+/tokkwcE+2qLcQQwXQKPwL+IoQwOV83AzdEziRFOImVNdBIOFRjMWvXQIhUHfm7bqB7DmRZy7OOyze8iHbxraSeOJa4DjPjT5hOY2MT7TtLsO7bSer8pSTmTsVm7aC9w0xWDISqKhwEk09hu5TyJBzJdk6SUp4spdwRedMUI4lwSyeMFsmBoWIgy1qeddzRWEtcfAKaeD1dVqvjBFMO1jYLHY217nwervdHkozGcMdf9NHVUsoXhRB39jkOgJRyZYRtG1aMlFFrpAi3QzXWNsqNNAayrOVZx4npOfR0d2Hv7iFBp3OcYK5Fl2xEq0+ivWYfmjEF2Lu7kHY7NeU7h22o6kjD30zB5TdI8fGjcKJGrYEJt0N1pIu2xQKhKsB61vGEud/F1tJA24EdZGWk01z5DbJsPUl0Ycg7kWObXqSjuhx7WxM9rY3sffvPLJjrNY+8Isr427z2pPP3/dEzZ3iiRq2BCbdDVW2Uizyhzn496/hIRQX6U24m53ApR/a8zcSCAp585H4+37aN36x8gu7ODhre+QOaeB3GcVMomFvM5i3bwmKHYnAEk45zIo6cygWe5yvto+PEorxDLBJOh6raKBdZBhot1lfEb8eWzb2u+fK690g750cYC+fRdfRbmj9+hYnzLmJM4WmUr7kjbHYoBk4wSXbeACpwJNb5g8ePwslQSWh7Mtr2AIRD5ns4YbFYBlS/A20Xwe5TCOX699z3AHXNbTR9/Heq1/4We7uF1PlXcvCTt31+X0aLXHUsEUxIaqeU0lv2NYWToR61jtbRVKyE20aakpL1VNXUOnxWIdTvYNpFMLPfUK5fUrKe8sP1ZC25nYysibRW76Xx07+TetqFtNVV+fy+qFl49AlmpvBHIcR9zkxsp7p+Im7ZMGKoR61qNDWyWblqNVpDesj1O5h2EczsN5Trr1y1mswFS4nPzEcbryMlbxqmMy6ncctrDvkLH9+XWJiFjzaC6RRmAsuAhzm+dKTktD0YakdYLEXijLZlrGhwsKICjU7f61gw9TuYdhFMtFgo1z9YUUF+0WnYWhqxWTuIi4sjKacAaanjBT96SCMtRexwIJjlo+8Bk6SU1kgbMxyJhaWbWInEiYWyGIlMLCjAbu3sdSyY+h1MuwgmWiyU608sKKCztY683LEcraun01yPbKqmqHBq0BFNQy3VMloIZqbwJZAaaUOGK7GwdBPN0ZRrJrBrd1m/mUAslMVIZMHcWXQ0HWVb6buUl5dzuGxbUPU72Hbh2qfwxO8fBmD5z+/uVeehXN91rjQfYcqkAvJS4kis+JAV990btB3B7pdQDI5gZgrZwDdCiM+BLtdBFZLqIBYcYdEaTXnOBPSZOe5Nei4bYqEsRhquMM7l/3kD9n1/p/qTvxEnbfy/25YFrN9wpdUMNPsL5vpqxD98CKZTuC/iVgxjYmXpJhqROL036TX326QXqCyG2vcyHHGVeYIhjTnLVgDQXPkNm7esC+rzgdpFoDoJtDEzlHY3WqLFhjvBCOJt8vYTDeOGA6PJERbIseivLJQUyMCIZBBBMHUSS0EMiugQzI7mFo6n5dQB8UCblNIYScOGC6NpWhxoJuCvLBZfcKmSAhkA7pDM3Bz3sXDNRIORZ4mVmbAiegQzU0iRUhqdP3rg+8CqyJs2fBgtjjDPmQDgdVbkqyzUiHNguMrcbu0M+0w0mDoZTTNhhYNgfAq9kFK+IYS4OxLGKGIbz5lAZ8F56Ms2BD0rUiPOgeEq28PV1ZSveYiJBQUsvfBcVq5azfKf3z0o30wwdTKaZsIKB8EsH/27x8s4YBbHl5MUowyXs7C0tJTbb7s16M8NtRTIcMZV3vt37wzrXpBg60Q5iEcXwcwULvL424ZDHO+SiFijGLGoEWd4CKdMu6oThTcCdgpSyuujYYhi5KNGnIMn3HtBVJ0o+uIvHecT+FkmklL+eDA3FkJogG1AtZTyQiFEOvA3HHkbKoDLpZRNg7mHQjHSUL4ZRaTxF320Ddju/LnY42/Xz2D5CbDH4/XdwHtSyhOA95yvFQqFByoaSBFp/KXjfM71txDiDs/Xg0UIMR64AFgB3Ok8fAmw0Pn3c0ApoLyQCoUHyg+giDTBhqSGO9roMeAXQIrHsWwp5REAKeURIURWmO/pRskthIa38kpM1Af+oCIiKD+AIpIIKQP/vxdC7JBShiWxjhDiQuC7UspbhRALgZ87fQrNUspUj/OapJRpXj5/E3ATQHZ29mmvvvqq+73W1lYMBoPf+1ssFqpqatEa0tHo9NitndhaG8nLzcFojP4m7WBsHkp8ldf4sdmYTKahNi9kYr28fTEc7R6ONsPosHvRokXbpZSzvL3nz9HsKW+RJISwuN4C5CBkLuYDFwshvgvoAaMQ4kXgqBBirHOWMBao8/ZhKeVTwFMAs2bNkgsXLnS/V1paiudrbyy+4FI6iy4kNf+4bEBzZTP6t//CxnfeGOAjDZxgbB5KvJXXt5s+4qazrTyx+r5hN9OK9fL2xXC0ezjaDMpun47mPvIWWo+/UwajeySl/KWUcryUsgC4AnhfSnk18BZwnfO064A3B3oPfyi5hdDoW151ZVup3rUFkZSmhO0UihFIMEl2osXDwGIhxD5gsfN12FE5X4OnpGQ9zZYWdmzewN59+zGbzRz85G1SZn8fjU6vEukoRj0jMf3skHYKUspSKeWFzr8bpJTnSilPcP5ujMQ9VUhfcLjkFLLnXETH7vdpb66n8nA1LbWVaJJMaLXHVx7VTEsxGhmpcvCxNFOICsXFS3j013ehL1tH+Zo70JetUyF9XnDJKUw6+zIKF/2Ari/fof7N3yGtbaSJdjQajfvcQDOtkTiaGgmoehkcIzX9bMgqqSMBFdIXGE85hayiOWQVzaHHbmPHoz+iY8eb2OffRo/dFlDYLpwCborwoepl8IzU9LOjbqagCA5fvpcZM2by6K/vIq7THNRMa6SOpoY7ql4Gz0j1T6pOQeEVf76X4uIlnDBlclBJhVS0V2yi6mXwjFT/5KhcPlIEJlxyCkrALTZR9TJ4RqrkiOoUFD4Jh+9FJdeJTbzVy6H1a8hI1DBl+snDblPiUDES/ZOqU1BElJE6mhru9K0XY0oKPdZODEuWkztOOZ5HM8qnEAOM5NBAJT4YuxQXL2HjO2+wf/dOMjIymHjxcuV4VqiZwlBjsVj4xYpHR2RooAp7HD6M1PBKReiomcIQc7SufsSGBqqwx9gh0Gx0pIZXKkJHdQpDTFdX14gNDVRhj7FBMHIMIzW8UhE6qlMYYhISEkbsCE2NPnsTad+Rr+v7mrHdc98D7vNXrlrN0gvPVfIvCuVTGGqys2EujksAABFHSURBVDKpX/PoiAzZVOGox4m0f8Xf9b35CzotjZQfrsdw9g3u819e95LqCBRqpjDUGI3GESvQp8QHjxNp/4q/63ubsR0ofZ3MBUuVv0fRDzVTiAFG4gYYFyP52UIh0tE9/q7/xO8f7jdja687xPSi0yJmj2L4omYKCkUUiLR/xd/1vc3YigqnQmud1/MHi8u3sWt32YjbdzMaUJ2CQhEFIh3dE+j6nhvVNr7zBivuuzci9nhGOukz80dM4pnRhFo+UiiiQKTlPkK9fqTs8fRtQLPjt9NXoZYRhweqU1AookSk/SvBXr+v9MgTv384bHapndHDH9UpKBSjiEiHxipJ7uGP8ikoFDFEpDa4ua77g2uup665DWurOSKhqJ6+DUDtjB6GqJmCQhEj+BvFJybqw3LdjHHnI2xWyj94FXDk3w7n8o6nr6Kz4Dz0ZRtG7d6U4YqaKSjCxkiWAI8GfTegWVvN1DW38YNrrmff/gMDKs+SkvVcc9NtVFVWUr7hRWxH9xOfmU/q/Cs5+MnbQPiXd1yRTjOmFwVM16qIPdRMQREWlEz24PF00taVbaX8g9dJnX8VUqujR28KuTxddRI/+4dkFM6j6+i3NG5+CZu9h5Rp82lvqHUv74xG6RGFd9RMQREWlEz24CgpWU+zpYUdmzewd99+9n/4BqnzryQ+Mx+9PhGNTh9yea5ctZrEUy9Bm55Ha10Vdl0yxtn/ju3gNrorv4ZOy6iWHlF4R80UFGFBhSIOHNeIPnvORVTveh9tchqtx2owmXKwtTSSl5sDyJDLc/eePejyvovOlIGdOGSPHZutm7a6CjIrPuS1F/6iOgNFP9RMQREWlEz2wHHNsiadfRmFi35A15fv0NPVStu3O8jLzcFkMgGhl6e124a93YwuMRmdTkecRou9tRFsVjU7UPhEdQqKsKCStAwcz2REWUVzmHPjQ5z8w5/R8tk/kOYj9Nht2K2dIZdnfBy0fPYPOmr2EidANlbS+cVbpBkNqkNQ+EQtHynCQqRlHEYy3jZ86Y3pFI7PdIjYvVtB3E+Xh1yeM2bMpCaxgPpta2lorCUxPYdxM+aS21ERgadQjBTUTEERNvqKrqkOITh8zbJ+c/+v3OV5wpTJIZfnnbffjL3qSwrPu5qzfraawvOuxl71ZdCzDRViPDpRMwWFYoiJ1CxrMNdVIcajF9UpjFD6ip7defvN6sscw/gTsyspWc/h6mpuvO2OkOtyoCJ8996/go6Cc6hq6SHh2wqys8a6Q2JVOxrZqOWjEYinpn3hsseUpv0wxlWXPXpT1OqypGQ9ZeV7ic+fiX5MHj2JJqpqasGQxcGKiojdVxEbqE5hBKI2ko0cXHWp0emjVpcrV60mKWsCtsYahBBodYloU9KpLNuuQoxHAapTGIF4hji6MI6bokZ5w5ChqMuDFRVMXngZzR+/QkfNXmSPne76Suo3v6xCjEcBUe8UhBB5QogPhBB7hBC7hRA/cR5PF0JsFELsc/5Oi7ZtIwW1kSy2GEwUz1DU5cSCAkdI7KLLaN+2lurnf4bl45coHJ+p/AmjgKGYKdiAn0kppwFzgduEEEXA3cB7UsoTgPecrxUDQG0kix0G699x1aXd2hm1unTdU2cwcfr1v+bkK35OVmoyv7n/VxG7pyJ2iHr0kZTyCHDE+XeLEGIPMA64BFjoPO05oBRQ0o0DQG0kix08/Ttms5m6Li2W8WdwzU238cJTfwpYJ673D1dXU77moajUpeva99z3AF98WwFIpk6Z4vczipGDkFIO3c2FKAA+BGYAlVLKVI/3mqSU/ZaQhBA3ATcBZGdnn/bqq6+632ttbcVgMETY6vAyHG0GZXew7Npdhj4zH7vdjrW7G6HRIhBYG6rQ6XTk5eZgNBoDXifadlssFqpqatEa0tHo9NitndhaG4O2F1QbiTah2L1o0aLtUspZ3t4bsk5BCGEANgErpJRrhRDNwXQKnsyaNUtu27bN/bq0tJSFCxdGyuSIMBxtBmV3sJwyex6HzDbazY1oU7NJO6UYTZKR9m1rKTzvavRl69j4zhsBrxNtuxdfcCmdRRf2kt5orvyml72ee2GMKSnIHjstbe3uvRSJiXrVRqJIKHYLIXx2CkMSfSSEiAf+AbwkpVzrPHxUCDHW+f5YoG4obFMowkVJyXoaOuzoZ55P5uUPYJxzGQ1b11K/4c9MnHdRTEeEBYp68vSVjL38AWrHL+RAo5W0M5e6/SYWi2UILFcMlqGIPhLAM8AeKeVKj7feAq5z/n0d8Ga0bVMowsnKVauZsGQZU06eCx1mRLyelFMuQJeQSFbRnJiOCAsU9eTpK6lraCR58qmkn30th7a8695LcbSufggsVwyWoZgpzAeuAc4RQux0/nwXeBhYLITYByx2vlYohi2u0bbJZGLihHx0Oh0pk06iu7M95iPCAkWwec4kuqxWNPF69DmT6WisBRyziq6uriGzXzFwhiL66CNA+Hj73GjaolBEEk9JbFeinENffzYs0mAGimDzfLYEnQ57dyfdx6pITM8BHLOKhMKEIbNfMXCUIJ5CESHuvP1mh7LoWVfRaWnkQOnrtNcdoqhwql9RO29ihomJ+ihb719Mz/PZsjKyOPD157TuLKHw3Mvds4rsBT+NssWKcKA6BYUiQnjG+5cfridzwVKmF50GrXU+Zah9SVY/cm9s/YP1nEkcrKggJyUFma6j6aOX3bOKoejIFINHdQoKRQQpLl7CylWrMZx9w/HwzvR08CFD7enABRy/Y9RpG0iWu7S0NHrGKMKGEsRTKCJMKKJ2vs5VTltFtFCdgkIRYUIRtfN1bkKCctoqooPqFBSKCBOKQKGvc7OzMofAcsVoRPkURjkqbWfk8RXeCQ45CW9l3/dc5bRVRAvVKYxiVHL26NHXKRuo7PuWv3LaKqKFWj4axXhL26nJO4lrbrqtX0KYvolilK7N4FApUxWxipopjGIOVlRQuPh4pEtd2Vaqd20hcfYPKVxwnnv0+vm2bby87r1eo9qqmlpKStarGcUA6Vv24IgyKn+3YmgMUiicqJnCKKZvpMvBT94mZfb3MY6b2mv0+vhTz/Yb1WoN6WpUOwhUylRFrKI6hVFM30iXtroqNEmmXpEuxnFTaLFY+sXOa3T6mJV9Hg6olKmKWEUtH41i+ka6JGohTbS7xdvAMXpNMRrd4mcu7NZONaodBCplqiJWUZ3CKMcz0sUVEdNsMGAc5/Ad1H/4Ej++6XpeXvcSnHWV+7gtvkeNagdJIJkIhWIoUJ2Cwo2/0evps2b1Op634Ab1D02hGIGoTkHRC1+j177HVdy8Qm18HJmoTkGhUISM2vg4clHRRwqFImTU5ruRi+oUFApFyIQiB64YXqhOQaFQhIzafDdyUZ2CQqEIGbX5buSiHM0KhSJk1Oa7kYvqFBQKxYBQm+9GJmr5SKFQKBRuVKegUCgUCjeqU1AoFAqFG9UpKBQKhcKN6hQUCoVC4UZIKYfahgEjhKgHDnkcGgMcGyJzBspwtBmU3dFmONo9HG2G0WH3BCllprc3hnWn0BchxDYp5ayhtiMUhqPNoOyONsPR7uFoMyi71fKRQqFQKNyoTkGhUCgUbkZap/DUUBswAIajzaDsjjbD0e7haDOMcrtHlE9BoVAoFINjpM0UFAqFQjEIVKegUCgUCjcjolMQQiwXQpQLIXYLIR7xOP5LIcR+53vnD6WNvhBC/FwIIYUQYzyOxazdQoj/EUJ8I4T4SgjxTyFEqsd7sWz3Eqdd+4UQdw+1Pb4QQuQJIT4QQuxxtuefOI+nCyE2CiH2OX+nDbWt3hBCaIQQXwgh1jlfx7zdQohUIcTrzna9RwhxRqzbLYT4qbN97BJCvCKE0IfL5mHfKQghFgGXAN+RUk4Hfu88XgRcAUwHlgD/K4TQDJmhXhBC5AGLgUqPY7Fu90ZghpTyO8Be4JcQ23Y77fgTUAwUAVc67Y1FbMDPpJTTgLnAbU5b7wbek1KeALznfB2L/ATY4/F6ONj9R2C9lPJE4CQc9ses3UKIccCPgVlSyhmABsd3Lyw2D/tOAbgFeFhK2QUgpaxzHr8EeFVK2SWlPAjsB2YPkY2+eBT4BeDp7Y9pu6WUG6SUNufLLcB459+xbPdsYL+U8lsppRV4FYe9MYeU8oiUcofz7xYc/6DG4bD3OedpzwGXDo2FvhFCjAcuAJ72OBzTdgshjMBZwDMAUkqrlLKZGLcbRy6cRCGEFkgCagiTzSOhU5gKLBBCbBVCbBJCnO48Pg6o8jjvsPNYTCCEuBiollJ+2eetmLa7DzcAJc6/Y9nuWLbNJ0KIAuAUYCuQLaU8Ao6OA8gaOst88hiOQU6Px7FYt3sSUA8861z2eloIkUwM2y2lrMaxIlIJHAHMUsoNhMnmYZF5TQjxLyDHy1v34niGNBxT7dOBvwshJgHCy/lRjb8NYPc9wHnePublWMzYLaV803nOvTiWOl5yfczL+bES7xzLtnlFCGEA/gHcIaW0COHtEWIHIcSFQJ2UcrsQYuFQ2xMCWuBUYLmUcqsQ4o/E0FKRN5y+gkuAiUAz8JoQ4upwXX9YdApSyn/z9Z4Q4hZgrXRsuPhMCNGDQxjqMJDncep4HFOsqOHLbiHETBwV+qXzyz4e2CGEmE0M2+1CCHEdcCFwrjy+0WXI7fZDLNvWDyFEPI4O4SUp5Vrn4aNCiLFSyiNCiLFAne8rDAnzgYuFEN+F/9/e/YRYVcZhHP8+m9ERKtASCildWLSbahZmJYPTRqGgP5CgNC4laNOikIJyH0a0mU1QZEQkYbfatGiREpQhYmFFiINOIFS0aMiNw9PifT0ehpkLTtfumen5wGHOPe+d9/zO5XJ/97znvL/LWuBmSUfoftyzwKztb+rjo5Sk0OW4HwXO2/4NQNLHwHYGFPNqGD46BuwEkHQ3MEKpFNgD9khaI2kLsBX4dmhRttj+3vZG25ttb6a8Me+3fYkOxw3lLh7gJeBx23+3mroc90lgq6QtkkYoF+V6Q45pUSrfEt4GfrR9uNXUA6bq+hTwyX8dWz+2D9reVN/Pe4Avbe+j+3FfAi5KuqdumgTO0u24LwDbJK2r75dJyrWnwcRse0UvlCRwBPgBOAXsbLW9DJwDfgZ2DTvWPscwA9y6EuKmXEC+CJyuy/QKiXs35W6pc5RhsKHHtEScD1OGts60XuPdwAbKHSW/1L/rhx1rn2OYAD6r652PGxgDvquv+THKcHSn4wYOAT/Vz733gDWDijllLiIiorEaho8iImJAkhQiIqKRpBAREY0khYiIaCQpREREI0khopI0dx3PnZC0vfX4gKRn6/p+SXcsY/8zalXLjRiGFTGjOaKDJoA54GsA29Ottv2U+8c7O2s6YilJChF9SHoMeIUySfIPYC8wChwA5mvNmecps0rnKBMRx4H3JV0GHqTMNh23/bukceB12xOSNgAfALdRZn+rtd99lPLII5SCeM/Znr/xRxz/dxk+iujvBLDN9n2Uktsv2p4BpoE3bI/ZPn71ybaPUmbH7q1tl/v0/SpwovbdA+4EkHQv8AzwkO0xYJ6SjCJuuJwpRPS3CfiwFhgbAc4PsO8dwJMAtj+X9GfdPgk8AJysBRNH6VZBtljFkhQi+nsLOGy7V0tCv7aMPq5w7ax87YK2xerMCHjX9sFl7CviX8nwUUR/twC/1vWp1va/gJuW+J+FbTOUb/4AT7W2f0UdFpK0i1KIDUoxs6clbaxt6yXdtcz4I65LkkLENeskzbaWFyhnBh9JOk4pyX7Vp8ATkk5LemRBP+8A07VtlFLR8s3aR/ti8SFgh6RTlB9cugBg+yzl4vYXks5Qfhf79kEfbMRiUiU1IiIaOVOIiIhGkkJERDSSFCIiopGkEBERjSSFiIhoJClEREQjSSEiIhr/AM7geNhZCbq9AAAAAElFTkSuQmCC\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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linear regression from the SciPy stats module.\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to create perform linear regression on the weather data\n",
    "# and plot a regression line and the equation with the data.\n",
    "def plot_linear_regression(x_values, y_values, title, y_label, text_coordinates):\n",
    "    \n",
    "    # Run regression on hemisphere weather data.\n",
    "    (slope, intercept, r_value, p_value, std_err) = linregress(x_values, y_values)\n",
    "\n",
    "    # Calculate the regression line \"y values\" from the slope and intercept.\n",
    "    regress_values = x_values * slope + intercept\n",
    "    # Get the equation of the line.\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    # Create a scatter plot and plot the regression line.\n",
    "    plt.scatter(x_values,y_values)\n",
    "    plt.plot(x_values,regress_values,\"r\")\n",
    "    # Annotate the text for the line equation.\n",
    "    plt.annotate(line_eq, text_coordinates, fontsize=15, color=\"red\")\n",
    "    plt.xlabel('Latitude')\n",
    "    plt.ylabel(y_label)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                   Butaritari\n",
       "Lat                          3.07\n",
       "Lng                        172.79\n",
       "Max Temp                       81\n",
       "Humidity                       65\n",
       "Cloudiness                     44\n",
       "Wind Speed                   3.02\n",
       "Country                        KI\n",
       "Date          2020-10-28 01:07:09\n",
       "Name: 13, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index13 = city_data_df.loc[13]\n",
    "index13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thompson</td>\n",
       "      <td>55.74</td>\n",
       "      <td>-97.86</td>\n",
       "      <td>24.80</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>9.17</td>\n",
       "      <td>CA</td>\n",
       "      <td>2020-10-28 01:07:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>69.45</td>\n",
       "      <td>-133.04</td>\n",
       "      <td>12.20</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "      <td>12.75</td>\n",
       "      <td>CA</td>\n",
       "      <td>2020-10-28 01:08:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td>80.60</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>9.17</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:07:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bethel</td>\n",
       "      <td>41.37</td>\n",
       "      <td>-73.41</td>\n",
       "      <td>50.00</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>3.65</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:09:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albany</td>\n",
       "      <td>42.60</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>46.00</td>\n",
       "      <td>77</td>\n",
       "      <td>100</td>\n",
       "      <td>1.70</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:07:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Romny</td>\n",
       "      <td>50.75</td>\n",
       "      <td>33.47</td>\n",
       "      <td>45.16</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>8.90</td>\n",
       "      <td>UA</td>\n",
       "      <td>2020-10-28 01:11:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>Urumqi</td>\n",
       "      <td>43.80</td>\n",
       "      <td>87.60</td>\n",
       "      <td>37.40</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>4.47</td>\n",
       "      <td>CN</td>\n",
       "      <td>2020-10-28 01:11:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Karaidel</td>\n",
       "      <td>55.83</td>\n",
       "      <td>56.91</td>\n",
       "      <td>28.90</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "      <td>6.82</td>\n",
       "      <td>RU</td>\n",
       "      <td>2020-10-28 01:11:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>Rafai</td>\n",
       "      <td>4.95</td>\n",
       "      <td>23.92</td>\n",
       "      <td>70.36</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>1.01</td>\n",
       "      <td>CF</td>\n",
       "      <td>2020-10-28 01:11:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Olafsvik</td>\n",
       "      <td>64.89</td>\n",
       "      <td>-23.71</td>\n",
       "      <td>39.04</td>\n",
       "      <td>72</td>\n",
       "      <td>100</td>\n",
       "      <td>18.19</td>\n",
       "      <td>IS</td>\n",
       "      <td>2020-10-28 01:10:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            City    Lat     Lng  Max Temp  Humidity  Cloudiness  Wind Speed  \\\n",
       "2       Thompson  55.74  -97.86     24.80        68          75        9.17   \n",
       "3    Tuktoyaktuk  69.45 -133.04     12.20        92          90       12.75   \n",
       "5           Hilo  19.73 -155.09     80.60        74          90        9.17   \n",
       "7         Bethel  41.37  -73.41     50.00        75         100        3.65   \n",
       "8         Albany  42.60  -73.97     46.00        77         100        1.70   \n",
       "..           ...    ...     ...       ...       ...         ...         ...   \n",
       "545        Romny  50.75   33.47     45.16        63           0        8.90   \n",
       "547       Urumqi  43.80   87.60     37.40        51           1        4.47   \n",
       "549     Karaidel  55.83   56.91     28.90        91          90        6.82   \n",
       "550        Rafai   4.95   23.92     70.36        97         100        1.01   \n",
       "551     Olafsvik  64.89  -23.71     39.04        72         100       18.19   \n",
       "\n",
       "    Country                 Date  \n",
       "2        CA  2020-10-28 01:07:32  \n",
       "3        CA  2020-10-28 01:08:20  \n",
       "5        US  2020-10-28 01:07:11  \n",
       "7        US  2020-10-28 01:09:38  \n",
       "8        US  2020-10-28 01:07:09  \n",
       "..      ...                  ...  \n",
       "545      UA  2020-10-28 01:11:39  \n",
       "547      CN  2020-10-28 01:11:08  \n",
       "549      RU  2020-10-28 01:11:39  \n",
       "550      CF  2020-10-28 01:11:07  \n",
       "551      IS  2020-10-28 01:10:36  \n",
       "\n",
       "[365 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.loc[(city_data_df[\"Lat\"] >= 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thompson</td>\n",
       "      <td>55.74</td>\n",
       "      <td>-97.86</td>\n",
       "      <td>24.80</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>9.17</td>\n",
       "      <td>CA</td>\n",
       "      <td>2020-10-28 01:07:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>69.45</td>\n",
       "      <td>-133.04</td>\n",
       "      <td>12.20</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "      <td>12.75</td>\n",
       "      <td>CA</td>\n",
       "      <td>2020-10-28 01:08:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td>80.60</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>9.17</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:07:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bethel</td>\n",
       "      <td>41.37</td>\n",
       "      <td>-73.41</td>\n",
       "      <td>50.00</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>3.65</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:09:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albany</td>\n",
       "      <td>42.60</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>46.00</td>\n",
       "      <td>77</td>\n",
       "      <td>100</td>\n",
       "      <td>1.70</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-28 01:07:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Romny</td>\n",
       "      <td>50.75</td>\n",
       "      <td>33.47</td>\n",
       "      <td>45.16</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>8.90</td>\n",
       "      <td>UA</td>\n",
       "      <td>2020-10-28 01:11:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>Urumqi</td>\n",
       "      <td>43.80</td>\n",
       "      <td>87.60</td>\n",
       "      <td>37.40</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>4.47</td>\n",
       "      <td>CN</td>\n",
       "      <td>2020-10-28 01:11:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Karaidel</td>\n",
       "      <td>55.83</td>\n",
       "      <td>56.91</td>\n",
       "      <td>28.90</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "      <td>6.82</td>\n",
       "      <td>RU</td>\n",
       "      <td>2020-10-28 01:11:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>Rafai</td>\n",
       "      <td>4.95</td>\n",
       "      <td>23.92</td>\n",
       "      <td>70.36</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>1.01</td>\n",
       "      <td>CF</td>\n",
       "      <td>2020-10-28 01:11:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Olafsvik</td>\n",
       "      <td>64.89</td>\n",
       "      <td>-23.71</td>\n",
       "      <td>39.04</td>\n",
       "      <td>72</td>\n",
       "      <td>100</td>\n",
       "      <td>18.19</td>\n",
       "      <td>IS</td>\n",
       "      <td>2020-10-28 01:10:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            City    Lat     Lng  Max Temp  Humidity  Cloudiness  Wind Speed  \\\n",
       "2       Thompson  55.74  -97.86     24.80        68          75        9.17   \n",
       "3    Tuktoyaktuk  69.45 -133.04     12.20        92          90       12.75   \n",
       "5           Hilo  19.73 -155.09     80.60        74          90        9.17   \n",
       "7         Bethel  41.37  -73.41     50.00        75         100        3.65   \n",
       "8         Albany  42.60  -73.97     46.00        77         100        1.70   \n",
       "..           ...    ...     ...       ...       ...         ...         ...   \n",
       "545        Romny  50.75   33.47     45.16        63           0        8.90   \n",
       "547       Urumqi  43.80   87.60     37.40        51           1        4.47   \n",
       "549     Karaidel  55.83   56.91     28.90        91          90        6.82   \n",
       "550        Rafai   4.95   23.92     70.36        97         100        1.01   \n",
       "551     Olafsvik  64.89  -23.71     39.04        72         100       18.19   \n",
       "\n",
       "    Country                 Date  \n",
       "2        CA  2020-10-28 01:07:32  \n",
       "3        CA  2020-10-28 01:08:20  \n",
       "5        US  2020-10-28 01:07:11  \n",
       "7        US  2020-10-28 01:09:38  \n",
       "8        US  2020-10-28 01:07:09  \n",
       "..      ...                  ...  \n",
       "545      UA  2020-10-28 01:11:39  \n",
       "547      CN  2020-10-28 01:11:08  \n",
       "549      RU  2020-10-28 01:11:39  \n",
       "550      CF  2020-10-28 01:11:07  \n",
       "551      IS  2020-10-28 01:10:36  \n",
       "\n",
       "[365 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)]\n",
    "northern_hemi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plt.title(\"Linear Regression on the Northern Hemisphere \\n for Maximum Temperature\")\n",
    "plot_linear_regression (x_values, y_values,\n",
    "                    'Linear Regression on the Northern Hemisphere for Maximum Temperature','Max Temp',(10,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
    "plt.title(\"Linear Regression on the Southern Hemisphere \\n for Maximum Temperature\")\n",
    "plot_linear_regression (x_values, y_values,\n",
    "                    'Linear Regression on the Southern Hemisphere for Maximum Temperature','Max Temp',(-50,80))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v+x9V9X1VXQr8C9dAGHVkxp1Ler201nSuY8adS+okUTLKlTvuvCzrIyitUspJu1GvWRRBZepcrz0aBGj8cuaFWtsUtvDWhS2uD7u5t72NnksKvkjP5RS7EJEzRORhEXl45cqVVRW2v/NSx5pU2/NCuXLHnZdlfSQ9J23ajXrNokgjeyOXMy/kxdAsAdsC9VqqeqWq7qOq+wwfHjhL28iIrVtbUm3PC+XKHXdelvWR9Jy0aTfqNYsijeyNXM68UOtG4RUR2QrA+37V2/4iPdeZ3Qa3ipZRRyYfNoaWQnOPbS2F5i4jX14pV+6487Ksj6C0SkmS9uyF7Yy79C5GTbmdUVNu541336fQ3LOP1QjXLIqguio0SdnlnL2wnQnT5zB6yu1MmD6H2QvbY8/pT9Q69tEtuDVkp3vfN/u2/0FE/hNnaN4ReLDGshklFA12jebhUa7ccedlWR9BaaX1Ppq9sJ3JNzzaQ7e+pnM9TcDQIQU6Vnc2zDWLIqzeg7bFlbPUaN3esYYLbnq8Rz79nWp6H10HHARshlvI42JgNjALGAEsB05Q1Te8478D/DuwFjhbVf8Sl0c9vY/6ottfX6I/XJ8J0+fQHqJDb2ttYf6Ug4HK66Le52dJWJ356ysKf1lahxRQhTfXNF7jG+V9VLWRgqqeErLrkJDjvwd8r1ryZIn1NvJNf7k+UUbV4r5K66Le52dNJYb40rKsWt3Zta/e5cqSvBiaG4q+6PbXl+gv1yfKqFrcV2ld1Pv8LJm9sJ0mCfJpSWagjnMj7iv3mDUKZdAX3f76Ev3l+kw+bEwvYys4I2xR515pXdT7/Kwo9vLXBajLkxqok8jcF+6xhl5kpxLS6jn9xzeJBN5c5g6XD7ZubQnUG1fr+tRLZ17M45Jbn+xSZbS2FJh6zK5d+yqti3qfX0nYkSTPrAgMGtDEOTMXMePOJZHXLqwsScqVJ7tKHP2yUUir5yw9PujmApg41uZN5IHJh40JDPVQDbfMeuvMJ41ri8yn0rqo5/lxdRu1H0j0zKpCx5rOwPSTlMVPWLnqfY+kpWreR7WgXO+jtB4IUV4eSc5vFPy9mU1aCogQ6taY955PreSr1JulWlTiJRMXqC/uf2n6F85+nOseeIF1qjSLcMp+23LZpN1D8yueH1e3UfuBRM9sEMX0o4LwtXesoUlgvff6LB2h+cnjPVIX76M8k1bPWa2QBHmitDdT7D1Buh5aXhqGuB50VuRFZ+4nyEumpdDMj0/asyw//hsXtIcGBWzvWMO19y/vOj/oXrlxQXtXT32dKjcuaGefkcNi76Vahh0pPT9MrmnH7h44Ynh/7frI9KohZ7Xol41CWj1nEl1i0PlxPaQ8kdSzYtK4tkiPkrw0CuVQzuhik5ZCjwa0yNatLWXbrdo71tDs6cDbQiZqRfXOw67P2SF68zjd+5rOdVxy65NdssWxpnMd58xaxDkzF4Wml+ReintOo/a/+/7awOtSZOgQd92CFCUKnDfr0UC5z565CBF6nVcaONF/XWpt46qUful9lDZcQTkhCS6c/TjX3r+8Rw/p2vuXc+Hsx8OSqCtpPCsareeThGLPsL1jDUp3zzAqBMLshe28+8HaXtsLTcLEscNTpefPH7p14O0da5h8/aNMvuHRHmlde//y0LSjrkPpsaXlDtO9r1rdmUodo0pkekUZw9Js71hTdtiRiWOHB16XIoVm4Z331gY2CEXC5IbeDYJf5qBrPnHs8IYKF9MvG4VJ49qYduzutLW2IDjdXlQc9qDjTx0/IvL86x54ITCtsO31JkmvpRqB4fJCOf70M+5c0it8M8CGgwcwd/HKVOlFjdSCwkSX4k877jrEhQOvBUUZm0PmDTSLxD6nYfvnLl4ZWV+d65TO9dnbUptFAq/53MUrU71v6k2/VB9Bep1z2uPDehrrVJm9sD13N0Qaz4paeveUS1rVTTmjn7B9Has76VgdrLqo5iirmEbctQTXi93zkrsiVSzVotDcPY8i6jlJQmlcpKQqrmoQNSqqlY0rC/rlSKEWhPWAgFi1RD0o7XW1thQYOqSQqoeWl5u+HFVQOaOfqHPSppfFKKuYhv/6RBHVIDSF376V43t3hsnY5tlk4q5j0DHVFD2MoUMKoWVptBF0v3RJrQVFm0IY9XZZ7MuU4wJYzjKWUecAqdKLWp6z0CSx6o6wtIPSFUIWK/GlNWhAU1VHEX63z7B6Cuv1+69jUnfxUpLUaVLKveb1pC7LcfZ3Lpu0O6eOHxG6v5GNsnmnHBVNOaOfqHMqsVtB90izrbWFGSfsEVneqLSD5Ih7FU47dnfejGgQsuiJF69FVD0luY7lPkczTtiDoUMKZZ1bSrnXPK/0W5tCLbhskjN6NZI7Wt4npSWhXBfAKL1vWL1EnZNEj5y0vsN6zUOHFGJHnKVyRPWum0VCXUkhfpSRFP+1CKqnYvC6uHAySd3FS8kqcF1ba0uPsBr+kCPvvh/uAZVnbKRQZRpp9bJydPF5JOs6r1a9pEk3LPjdO++tTS3H5MPGUAgxGqxTDXUlLTRJJg1C3LVIE7wuibt4EO0da3qEvi6XoizFBY/8aXas6WTy9Y823PNjjUKVaaQhZZ7CHFdC1nVerXpJk+6kcW1sMLD3wL5zvTLjziWplpicNK6NGSfsQWtLt/okzC+iWaSrDjccXL5iwZ9O3LUIc5MV6HVu8VpHOXZUi6FDCj28n4LcYIvXp5Ew9VENaBR3tL40KS3LOq9mOIU028P0/MURRpqwI6X1M3rK7YHHrVdl6fSjIo/xU2gSEHq8INMaW8PKHzZKKaYb54ZbLoVmAaWHYbql0MzFn9y16385rst5xRqFCskqlEVaXX41Qmg02nT8KLKsn2rVS1bhVsImTaUJO5JElrBjxDM0BK2dXAzGlyQ0dVw+xXTDDOoQHJ4C6AobkpZmEWYcv0dX3mHPZ5TMjfb8mPqoArIKZZFWZ12tEBphob8bLSR41vVTLbtQFuFWWgrNsaEkspIl7D74zH4jWDr9KOZPObhrBDJ/ysH8+KQ9ea9zvYsxRHJbTFS9xnmQ/ejEPQLLccp+24baUcJoKTTzoxP36FEmfzlLZY5b8KhRsEahApKGsojT96bVWVcrhMbcxStTbc8rWdSP/5rNuHMJx+3d1sNdtHh9Sq9lWt3+cXu3denDm0U4bu9ob6ag47OYNBUlS7FMYfNuwu6PsPv6vFnhxtfiiDmMJB5kQfakyybtnsomEnctgvKdcXxPN9fWlgIzTtijIVTHfkx9VAFJpugnCTOdVrdcaWiAMPqKTaHS+gkLH33c3m3cuKC9rEVfwiasRYWVTnp8qVyQfiQTljbQK+1Syrl/g+olagIfJC9TmD0pLPRImIxR1yJNvo2GjRQqICqYV5Eko4C0oQ+S5FsOfSXQXaX1E3bNrnvghchrmXbEl9XxWQRcS1PmUsq5b4LKGRWcLwuvvbT3cSN63mVBvx4pZBHvPohT9tu263eSXlTaAHOn7Ldt4FDen2855WuEQHdJSFo/RUrrKcxgGKe7z2rxprD8o9KptJeadlRaxH9/zF7YztRbnuwKjzEwQMcelWeYDAJdITEmTJ9T1rrqxTUoZj70QmzE2SQy1RRVeOQRmDULrr8eli512//4RzjppMyz67eNQjXWaQ7yckni1VEa6THuhi+mH7fEYTluimnkyCtJ6qdIUD2FzdoN6wgkWfQliFBvHk+u0nqvpndYlGdTWMPQ5rs/Zi9sZ/L1j/Zw2/wg5uVbKndU+Sp9XosqwJM+vC23P7aia5JZccGcuGtbE1Rh4UL34p81C557Lvr4YcOqIka/DYiX9TrNYeeF6Umj1nRNQtwoIEzeZhHWq/Y4py+EtoDyQnSE1VNpw9BSaA7V3YctVVm6P0jec2YuCmyAgu6ncoL2JSXopV5oEvYdPZR/PPtGr7oozTNtYLqgNMKC931m/IjQcDHlPK+lq9qFXbviPdCW9TOhCosWuRd/kpc/wM47wwknwIknwq67xh8fg63RHEDW6zSH3YDFG8kfEwXcFPhy1zWuxHjtX9Hrgpse5+Hn34g0njYK5a4bHTVRqq21pVcDs8/IYaENT9qR1qRxbZw9c1Fiuao+kivR9qwHHly6qkeDIBDolZNGzRL2kp00ro2Hn3+D39+/vCtPJdrQXc7zWvoMFPOG7hhT/k5B2c9E8eV//fXu869/xZ8zdqx78Z9wgnv512Gmdr9tFLJepznKiDlpnFuLtjTWSrnrGidZIzlJoLCiITFqDd1Godx1o8PqKawHGqe7T6vbb0t5H2bp4RK3NvO69Urpq1iBa+9fztzFK3v0ssOC15USFzJ+7uKVvUZOazrXpVbvJA2U579Hip+gUUbkvaQKjz7arfZJ8vIfM6a757/bbnV5+YfRb72P0k4cipvAFfdAZOnumdR4nSRQWBYTn/JAufVb74CF9co/6drMYRR7zxfOfjw0eF0pScoVNcJNO9EvKUkN3i+tWu1e/t/5Duy0k3uRi0BTE4wbB9//fu8GYaed4MIL3Xnr13uLVyssXgzf/S7svnuuGgToxyOFtEPxuAlccatcZWUkTBJSuNgDXNO5rsuQlpZGc0OtJFw21M+4Xq/8s1ibeU3nusiFpDYY2EyhuYk313QmLlfUyG3yYWNS1VPSMN+9DN6bDGbjZ57iyMXzOXLJfWz/hm+i3Q+C01i+aRu37jSBBz/8cf7ttCOYtNc2CXLOJ/22UYB0Q/GoHmeSHlAW7p5JQgqX6taTNAgtheaGd0OtpH7rPemoHvlXeyQowJOXHp76vKjrmLSeis9A7K2vyh6rXuAHry+BMf8P/vlPAOZHnbPDDt06/z32YPail3rJ++CfngCRhlK/+unXjUIaolz2knh/ZNEjDOvd+WWYMH1Oqh5gOT2wPJJ1j7scT6YsvLhKff2HDilw8SfDvdTKzbPcxWmSUu5IsyrPiSoTnn+UQ5+5n48uW8T2b7wYm8Y724zkT9vvzx9Hj6djp12ZfPjYQBnKtWXlmX7rkpqWaroDJmX0lNsDez8CPcIbJ72ieV0/tt5kvV5z0voNcgsFF7p5xvG9Y+hUkmfcGuKV8pOT9qz9faUK8+bx4Oe+zr4vPpnsnO226+75jxuXWr+f5JnMI7ZGcwaEBdqq5Y2fJJxA0h5anhf7qTflLKqTxUI8M+5cEriYfOe64IVaKskzbZBDwc2tSbKusX/xmaoxbx4ceGC3sbdo8D344NAG4d6d94eHH+5p8H32WZg2DfbaqyyDb18JDePH1EceSYbhaXW/SdKMOiZomn5c4LMgnayfRhsdZKWSSZNGVPiJCdPnMHHscOYuXtkjvaTeT1GypF2opRKPtrQ2BX+vNypwXeniM1EE1QX0XIthr6WP8aV7rmH8C08klrVj7G5c8KHj+Muofbpe9MX7ngru+yB5axUappYTTK1RoPyJT5WmGXUM0GtfMSJm6QvJL2OpTra4yEkaD5C8kMV1KSeNKH17e8eaHmqXYnqbtBS67AClaSWVJe1CLZV4tKWxKZTOwSmd6BU0QziO0rpoe/whtvzl2Yxf/jiTEkkFT26+HT854NPcvcN+IMKp40dw2aTdaQUOW9jOYxm+RMOu3bRjd2fasbtX9YVdjfdTFHWxKYjIOcAXcR5jjwOfB4YAM4FRwDLgRFVdFZVOVjaFtCEv/IS14GFptrYUWHTxJ2LzheBZ0kOHFBgycEBDG4WTUsl1qSSNuBDOQQwdUuC9zvWR+v04WWppUwg6t7lJWBegviq+bEvPL+tFeO+9cPHFMHdu/LEeT20+mp9M+DR377gfWw/dgJfffC809tiz045MnG4awq5dLZ7HqJAd5YbgyFWYCxFpA84CdlHVNSIyCzgZ2AW4R1Wni8gUYApwfi1kKncYHtWCh53bsaazK9hZOfmuWt3ZNTO6UUNSJCWLCX/lpFHaE05Cx+pOfnzSnpEvyjhZiscm9T6qxFMn7NyHn38jNpBgop7rfffB1Klwzz2xshR5evgofjLh09y103hUgs2dL3mT7YKodD2RKMKuXS2ex6h7tRp51kt9NABoEZFO3AjhJeAC4CBv/zXAPGrUKJQ7DJ96y5Ohhr4k68yGHZM0ZIA/vyxsHXkLjJfFhL9KJuFt2bsAACAASURBVLVFjfiC0ouzOSWNmJumzsud4xB2rSeNa4tdy9pv4N7u9Rc5avG9HLlkPjtftixZ5rvtxnf2OI4/tO0d+vIPY2svHlVYFNtqUU7YjFrlnXWeNfc+UtV24IfAcmAF8Kaq3gVsoaorvGNWAJsHnS8iZ4jIwyLy8MqV2SwTWU6ogdkL2wN1yOBa9iTrzIaFokjb40nTcw5bD7oYriDpOtG1IIsQEJWmkSRcSNL06h1So0jaNcEBN7Hre9+DPfZg/gWHsOzyo1l2+dHM+Z8zOe++37PzymW9z9l1VxcPaN26bm8fVXj8cT583pcYPLCnJ1OhWSLXUW4pNDNx7HCaQo4JWy8jC5KGjYHsJwYmyTvLPOuhPhoKfAoYDXQA14vIqUnPV9UrgSvB2RSykKmcYXjcOrKTxrX1iozq3x+Ub9QIoa21hXffXxtrzIRofW/UClt5C4yXxUSmStMIOj/I+6gSlU2aSXFZOA5ETrjacHX3Yi6PPhqb1ooNN+X2sQfw57EH8OrOe3Lftz+eSIawuvBvCyrrjDuXBNo9NhjYHDvCqYQged94933WdK7vdWypc0Glo+8k6swsXWBrbmgWkROAw1X1C97/zwHjgUOAg1R1hYhsBcxT1cguVC0nr5USNUmsOHEnrSEwbiJMkvTijkkzuc2ft1EfkqxbnNbFePSU2xn5RjtHLpnPUYvvY9dXE8Tz33rrrkleswdtywWznyzLwF0peZksNnthO5NveLTXKm6FJmHGCXuU9fwnzTeLNHNlaMapjcaLyBBgDa4xeBh4FzgNmO5931wH2RITpufzT9xJ2zMMS1NxHgiTDxsT6/4WN+0+bXiDRpyEc+Hsx0ONpbWwm5S7zGvSkZ2f2NHcv/7VHdJ5kVu7YWmE7K9ttClvHj2J7b/6efjIR9yEsBImATQ11cX+VM3V59Iw484lgct6bjh4QOyovJLRd9bhXIKoeaOgqg+IyA3AI8BaYCFOHbQhMEtEvoBrOE6otWxpCJu0UjpxJ40hMGrimd8vOsodM87DJW5ym59GDIxXGr5hnWrX/31GDqu6v3cWy0b6j088Ee3ZZ7sXc3nkkdhzXtlwGH8eM4Hbxx7Agradexh8WwrNTBsyikkBDUKRcg3clZKXdcRDvQt96uIsw+X7qXbd18X7SFUvBi4u2fw+btTQEKRtsZPOmC6mGdQbqmTRmLR2jKSB/qpN2l73dQ+8ELp97uKVmffcZi9s72E7CgpVHpVH2pHdth0vc9Ti+zhq8b3s/sqz3SddHiLgllvCCSfw/z7Ynjs22S6Rt0+9bUlRZNVTrnTEmGTEkpdRTVpsRnMFJG2x0/Qei//TLNPoJ0lPyi/36Cm3B6azXrXuL4VyZnKGGerXqWbecwvSK4eZ6NLm3bxsKVx+Obf99g8MffqxeGG22KJ7Ja/994fmnt4qd6S0JeV5kaVKe8pZzBBO8pzlZVSTFmsUakBa3WKcZ1MUWdkx8tCbKUcnG7ZsY7MIW24yONOyhumVgwjLY+/1Hez90F85cvF89nj5mV77h5b8XzmktcvbZ+E2uzDj5L0SvciiQr+nWeayL5CFrj/Jc1YL/X81sEYhA+KGoml7iVG9tKBeRlD+ScNA5Lk3U079nLLftoEhoU/Zb9teNgWorKxJe9MthWYu3n0IzJjhDL4+j7kbQs55f+imDPr0yc7bZ4PRXHDzUz3kLjQLGwwcwDkzFzHjziWxwRZbhxQoNEmPEBothWaO27stNshi3iY1+ilHtqxGjElGLPWyvVSCNQoVkmQomrY3HnZ8a0vvkMSVDoXz3JspZxRT9DKKCtWQVVmD5NvmzVc4YvF8jlpyH3uu+GdsGq+3bMxfdv4o8z50IHOHj2HLYRv27nECNDf3eMG/8173nJUkwRZXre6k0Cy0thR6zXHYZ+SwyEi9tQzGloZyZcvz6DgP2CI7FRIV+G6DQS5Q1iYtBd79YG0PVUOYb3Gp4TLu+LD8m0VYr5r5CmSjNm3h/udWRcbGyYq0Ptk17dE+/zxP/PQ3rJs5kz1ein/5s9lmcMIJfL1ze24fuiPrm3rq/LMO8pfkmCT1lUVQwkqIWomuXNmqNX8gjx2rMPI2T6FPERX4rngjd6zppNAkDB1SoGN1+EzUsIlKrS0Fph4THBQtLP+inriSnl1QT8z/EPrdPavRMKQZxVStR7t8eber5wMP9Ni1W8Dhq1o2puOoTzH6K5+Hj34UBvR8xG4LMfhmHeQv7pik9VUtt8okBEWNXbW6k8k3PFqRbFmPjvM8mioHaxQqJOlksM71ypCBA1h40SdCjwmbqLTBoAGhN1eS/Mt1MYybOFXkugdeiFRBJCUqSFs5sqYq9/LlcMMN7uV///3xxw8b1uXtc/NG2/ODe57tIffoKhr2s3CHjKovf6TUKBmqgf8eCHOXLq5EFxVQcvSU2yPvwyx1/X1tnWZbjrNCsgyUVa7hOUn+5fTskp6zTrXiYHplBWlLIGuv7S+8AD/+sZut61/KceRIOO+83g3C0KFwxhnw179CZ2d3ULfXX4crrmD20DFMueXpxHLXKshf3DFh9VVcRCiqQaiWI0LpPRAlQzHoZFhAyVoGdaznaKoa2EihQoo9gfNmPRob3TSud1VOzyfpZLRyenZpQmJU2lOqtLdVKuuWb73GkUvmM+nZf8DlR8cL0NoKJ57I/HETueD1TXnh7Q8SB0YMknvqLU+G9lCL51UzyF/cMWnDnYCLL1RNfXnSkSl0B50snhd27xevRTX1/X3NcG2NQgZMGtfGOSGTzYok6V2FhaCIsw/4h8JhRrRyenZpQmIEUe3FcABob4cbbuCWP17Lpo8mcDrYZJOuwG4cdBAUusM3d9fdBy7pBLrhJIsplZKF6qJSd8iJY4cHuu5GUe2Ac0nvl0KzdN3PSSZi+u171dD359mtuxxMfZQRUb2CttaWRJ4Nk8a1Me3Y3WlrbUEIXjSk2HtOmkbSvJOmNWH7YV1yNXvr4raFlD2tnjxye3s7/PSncMABPdU+22wDZ5/dq0F4Z9AGLPu3U+DOO+GDD7rVPh0dcOWVcOihPRoEiB6thLFJSyF0X9R59Wbu4nRrkVRzAZsiYfeAP+ehQwqBS5NGnV9K3DVNyuyF7UyYPoezZy7ivbXd901rSyEXIWLKxUYKGRHWW0h7cyTp+STxrsjqhkySVhajk2L9bfTGqxzxz39w1OJ72ffFp9zOCyJO3Hhj1+s/4QQ4+OCuF/2G3icNaUcrsxe28+4Ha1OnlwfSylbNBWyKVPoMpRnZVnptSu95v9bq/bW911hoJPpto5C1X3E1JoE1iq6yrLK/9BLceKPz9rn3XibhTdIKY6ONumP7+F7+WZK2vuPCXCS5TvXwb5+9sD0wcB+40eDEscNj12muBlk8Q4MGNHW9qIcOcfdI1EJX5RJl/2hkzyPop41CtfyKs57S3ki6ytCyr1jR/fL/+9/jE9pww26d/8EHw8CB2QsbQtr6juptJrlO9fBvLwbxC1i8rEtXP2lc/DrN1aLcZyhotPpe5/pEYTzKoVxPwkagX9oUytEd14Ms7QNV5+WX4Re/gAMP7Knz33pr+PrXezcIG2wAp58Of/4zvP9+t87/7bfhN7+Bww+vaYMA6es7rLeZNOx4Pe7DqNHNBgPD58PknbC6nLt4ZVWeoSSehI1KvxwpNJJfcbHnVFQzRAVAS0PZaouXX4Ybb2TpL69m9JPx3j5rB7cw4OSTXM//4x8v+0VfKzVLmp5qpTrwcu/DSuoiKu03A9b/bhSi6rIaQemi7Bd5Hc0npV82Co2iqy+StZohUXqvvAI33eSies6b1yuN0SX/VxcG8ecxB3D72AOYP3JPPhjg9LlZrN2b1zAClerAy7kPK62LqPkJeb3/k1DrZ9p/7ds71nSFIG9rgLhHcfTLgHhBi6MUmiXU1a3eZB2UzJ/eZu+u4vB//h9HLr6P/ZcnWMxl8GBu2mECt5a8/KOoNHhaVNC/H51Y2TWrZyCzsFhX/qBvpVR6LyRZcL5S6mU8zzrIXV/GAuIFUdoW5rhtzETd9eqrrud//fXMnzMn/vjBg7sNvoceCoMGde06N8RVNoxK1XJRQf+qPmKqIsU8/FFAwXnLhMlR6b1QTM8fiTcq4GJa6lWn1fD+66/0y0bhkluf7BF5EVzAury6kaUaGq9c2a32SfDyf7+5wO1jndrnuT32Z+5/HB57TthqXWFUOoSPUnlU4v4XZpw8b9ajnDNzUU1eLJPGtTHjziU9GoWiHP5yFXvfYbWepo6roWMvUsvgcJUsLmWE0+8ahdkL2wP9liGfhmYINmpt/cHb/Nc7T8Ch01ywtjgGDuzq+d+y+a6cf/szvYfaRydzQwxb3SyILIxucZOSyr1u1Qw7noUcYWGuS/GHfag3tXLiqPcory/T7xqFKHe/JpHQeDV147XXmPTwn9n/7t+x+QP3xh8/cGD3DN/DDnNqoBKOAdYPGlzWUHv2wvZeIRKaxE0aeq9zPZu0FBAhct2ItMQFHQzrJcfptjdpKfTqoZeSVS83SpZywlz7yZMraa0MvtUakYRdp0ZbRKcSYhsFEfka8HtVXVUDeapOVI+lUh11Rbz2GvzpT26S191399q9eemGAQNcz//EE0Nf/lGUo0KopzGvmH7SyWVxPcm4EBV+sg6JUCpL3KS5uPzz5EpaqwmX1RiRhF2nh59/o8cEuL4+KkkyUtgSeEhEHgH+F7hTG9hlKS5ksH/yUFV6Bq+9BrNnu5f/XXfFHz9gQHfP//DDoaV+boNxvbNq96bSGBPjZI0LUeGnGiER/LLElSvunm0dUmDC9Dk16cXGXeOsDb5h+VVjRBJ2nYIWHGr0UBZRxDYKqnqhiPwH8Ang88B/icgs4Deq+my1BcyaJEGzij2BinoGr7/uXv6zZiV7+Tc3d8f2qfPLP4yo3lmtdLxJRzhxPcmkPcpqhkTwb48qV9Q9W2gW3nlvbZedrJq92KTXOCtDdlR+1RiRxNmYkh7f6CSyKaiqisjLwMvAWmAocIOI3K2q36qmgFlTOukkiGaR5PrKN97o7vnfcUe8AE1N3a6ehx8OQ4aUVY56ENU7q4XXSVQvtXRfmL1Acb7+rUMKgQ4HrS0FNhg0INNed6W92qiJUu++vzbWcykrar3sZFR+RS+jWgSgDPO2a+TJflEksSmcBZwGvAb8DzBZVTtFpAl4BmioRgF6ho4I6m0E9cg2fu8d9v/73fC3Gcle/iLdPf8jjmiol38YYQuzTBw7nN+HeCNl1ZuK6jUCvfYVmoVCk/RyPe7a3yQUmqWHCqml0JyZv76fLHq1Yb3vUSFzRtKuqpaEWoeHicuvVgEoqxVUL68kGSlsBhyrqs/7N6rqehFJsM5hfgnSf377I1uw6OfXsP/COUx8bkGyhIo9/yOOcIHe+ihhC7PMXbyy6l4nccHjSvd1rlOGDikwZOCAQLk612tVRgVBVHNiVVgvthqL4tQ6lEQ9Q1eUXqd9Rg4z7yMfo0sbBBH5nap+VlWfrpJc1aejA26+mUmzZjHpz3/usSts0cH2Q4+m7UufgyOP7NMv/zCiem4/PmnPqnqdlNNL7VjdycKLPsHoKbcHTvp6c00niy7+RCbyxVGtCWNh+u40kwuTOgjUOpR7PULHh12nak74yxtJGoVd/X9EpBnYuzri1IjXX4fNNgvff/zxPPjhj3PBe9vw3OrqLlbeSET13KodZiCu1xi1r9ECICZl9sJ2hOAILWFLpAalkdRBoNahJCx0RX0IbRRE5ALg20CLiLxV3Ax8AFxZA9mqxyabwLnnwvPPO7XPUUe5xV187AvcUx/pcktcz62avam4vKP2ldvjzNLFthruumFhLwQS96bTGo9r3WPuTz30vBDaKKjqNGCaiExT1ahVchuPAQPgRz+qtxQNRz17bknyDttXjtxZuthWy103THWmKdJtpLVFjNoQGjpbRMaq6mIR2Stov6o+UlXJElBu6Gyj/uQ9bEA5IarDypRF6POgtMPcqtOkm3VYdqMxKDd09nnAl4CgLrUCdscYZdEIwczS9qCjylRpbzws7SxcJRtpHXCjNkSpj77kfU/MOlMRacXNedgN18D8O7AEmAmMApYBJ/aVeEt9lXJ7+7WeBFUOaYzTsxe2BwbrK5apUkN33PrDlYy4zJhrlBJlaD426kRVvamCfH8K3KGqx4vIQGAIzqh9j6pOF5EpwBTg/AryMKpIJb39RtBjJ+1BF+shKhRCpe661V5/2Iy5hp8o9dEnve/Ngf2B4ootE4F5QFmNgohsDHwMOB1AVT8APhCRTwEHeYdd4+VhjUJOSdvb948qmhogbEBcD7pYnriZw1m46/ZVl1ojn0Spjz4PICK3Abuo6grv/1bALyrIcztgJXCViOwBLAC+AWxRzENVV4hIr2jRXv5nAGcAjBgxogIxjEpI09svHVUENQh51GOH9aDjFr0pkpW7run9jVrSlOCYUcWXtccrwE4V5DkA2Av4paqOA97FqYoSoapXquo+qrrP8OHDKxCj/sxe2M6E6XMYPeV2Jkyfw+yF7fUWKTFhvdSg7WGLxDSLIDhPl0ZaYD1u0RtwZcuqTJPGtTHt2N1pa21pyPoyGoskM5rnicidwHU4o/DJwNwK8nwReFFVH/D+34BrFF4Rka28UcJWwKsV5JF7GsEDJ4o0vdewUcV6VZZODwsqkl/ibB/VWHjI9P5GrYgdKajq14BfAXsAewJXqurXy81QVV8GXhCR4tvjEOAp4BZcNFa875vLzaMRiAvwlnfS9F7TjCoagSi5rRdvNDpJ11O4iTINyyF8Hfi953n0HG7xniZgloh8AVgOnJBhfrmjETxwINrtNGnvNW5UUelEtlpPhAsrjzUGRl8gyiX1PlU9QETepmfMLcGtu7NxuZmq6iIgaDbdIeWm2Wg0gkdJViquKO+bSvOohxrOfPuNvkxomItGoJHDXIQt8JOn3mYtQiCkzaN0VLD6g7WBK6iVE47CqA1W//Wn3DAX/gSGAtv6j89D7KNGphF6m7VQcVXi2ho1R6CccBR5qvu+itV//kmyHOd3cRPNngPWe5st9lEG5N2jpBYqrjR5JHEFjTo/LI28hdjoy9R7LW8jniTzFE4EtlfVA1V1ovexBqEfMPmwMbQUmntsy3rSVJo8ko5QomRsFAN/X6Xa9V8cibR3rEHpHok00hygepOkUXgCaK22IEb+qMWkqSR5FCf5hVm/WlsKiWWst3tsI09YzIJq13+ju3rngSQ2hWnAQhF5Ani/uFFVj6maVEZuqIWKKyqPuJASLYVmph6za2IZ6xkywvTp1a9/GwlWTpJG4RrgcuBxum0KRgOTV51r2EIyYQ1Cs0iPXmCSMtTDwB8VPK+/2TPqvZa3EU+sS6qI/E1VD6yRPKloZJfUepFXV9gwuZIalvNQhiCSBM8TaMhwH3kkr/d33ohySU1iU1ggItNE5CMislfxk7GMRo3Iq841TK5mkUTn56EMQSTxmLJebHZY8MDKSaI+Gud9j/dtM5fUBiWvOtew/NepJh4xpC1DLdRoSYLnWQjsbMm7q3feiW0UqrEcp1E/8qpzDZOrzWdbKL68331/LR1res9iTlOGWhl9w8oF3WWzF5iRJ5JMXrsoaLuqXpq9OEa1yeuCLVFylfb8wvTGacpQzUlU/hFI65AChSahc3237U6Az4wfwWWTdg89b5OWAiLQsbozV84ARt8nifroXd/vwcDRwNPVEceoNnkNr5FGrizKUC01WmmDtWp1J00lZhEFblzQzj4jh/VY3tN/nn8k1B9dV436kTognogMAm5R1cOqI1JyzPvIKJdqBfsLSzcIf15JzssyEKHRv6nU+6iUIbh1lg2jYalWCI80Iw3/sUnOq7czgNE/SGJTeJzu9RSageGA2ROMhqZaarQow3LQsWnOq7czgNE/SGJTONr3ey3wiqqurZI8hlEzquG6GGQwLzQJCHSu61bVlo5Kgs7zU2iWujsDGP2DqJXXhnk/3y7ZtbGIoKpvVE8sw6g+1ZinEDYCCdpWmtegAU2hjcIGAweULVtew5oY+SRqpLAApzYSYCvgJe833nazKxgNSzXnKYSNQMoN+gfwZsC8jCRYED4jLaGNgqqOLv4WkYWqOi7sWKP/UIteZy3yqHSeQpYyTr3lyaqFwrBFhYy0JFqOE0JD2Rv9iFr0OmvVs61knkKWMs5e2B44O9tPJV5ReQ1rYuSXclxSjX5KLYLp1SpgXyWLvWQpY9w5lQZ0q/eiQkbjEWVoPtf3d/OS/6jqf1ZNKiOX1KLXWauebSXhPrKUMeqcn5y0Z8Wjo7yGNTHyS9RIYSPf59cl/zeqvmhG3qhFr7NWPdtKQixnKWPYOUOHFDJRl1koaSMtqcNc5AkLc1FbZi9sZ/INj/bwty80CzOO36NqNgWozSIpaQzHWcpoi8IY9SAqzEVSQ7NhOEr7EBn3Keq1XGYaw3GWMuY1QKHRf7GRgpGYagWRy4JKXETzXC7DqAY2UjAyIa/ujZW6iOa1XIZRDxK7pIrIeBGZIyLzRWRSNYUy8kle3RsrdRHNa7kMox6ENgoismXJpnOBY4DDge9WUygjn1Qr3HSlVNrTz2u5DKMeRKmPrhCRBcAMVX0P6AA+DawH3qqFcEa+iDKK1jPoWqXrTpux1zC6iTQ0i8gngW8A1wA34hqFIcB1qrqyJhJGYIbmfFBvt8p6528YjUbZK6+p6q3AYUArcBOwRFV/locGwcgPtQpNEYZN0DKM7IgKc3EM8C1gHTAV+B1wkYh8BbhQVZ+tiYRG7smD9041FswxjP5I1EjhMtwo4TjgclXtUNVzgYuA71WasYg0i8hCEbnN+z9MRO4WkWe876GV5mHUBvPeMYy+Q1Sj8CZwsvd5tbhRVZ9R1ZMzyPsbwNO+/1OAe1R1R+Ae77/RAJj3jmH0HaIahX/DGZXX4gzMmSEi2wBHAf/j2/wpnEEb79vmQjQIptM3jL5D1MprrwE/r1K+P8HZK/zRVrdQ1RVe3itEZPMq5W1UAdPpG0bfoOaL7IjI0cCrqrqgzPPPEJGHReThlSvNCcowDCNL6rHy2gTgGBFZBvwROFhErgVeEZGtALzvV4NOVtUrVXUfVd1n+PDhtZLZMAyjX1DzRkFVL1DVbVR1FM6IPUdVTwVuAU7zDjsNuLnWshmGYfR38hQldTowS0S+ACwHTqizPIZh9DHqGY6lUahro6Cq84B53u/XgUPqKY9hGH2XSkOs9xfyNFIwjLKw3p+RhKhwLHa/dGONgtHQWO/PSEoewrE0AvXwPjKMzKh3MD6jcbBwLMmwRsFoaKz3ZyTFwrEkwxoFo6Gx3p+RFAvHkozIRXbyji2yY0QtsAO2mpphBBG1yI4Zmo2GJmwpTcAM0IZRBtYoGA1PUDC+CdPnmPuhYZSB2RSMPokZoA2jPGykYOSWSialbd3aQntAA2AGaMOIxkYKRi4pGpDbO9agdNsEZi9sT3S+uR8aRnnYSKEP0QjhHpLKmDQkQVh6YQbovNWHYeQNaxT6CI0Q7iGNjElsAnHp2WpwhpEeUx/1ERoh3EMaGZNMSmuEMhtGo2GNQh+hEbxt0siYxCbQCGU2jEbD1Ed9hEbwtkkjYxKbQJ7L3Aj2HcMIwhqFPsLkw8YEhnvIk7dNWhnjbAJ5LXMj2HcMIwxTH/URGiHYV9Yy5rXMZuswGhkbKfQhGsHbJgsZ866aMVuH0cjYSMFoKCqd1FYLLJy30cjYSMHoIo898FKZ3n1/be4D3eXV1mEYSbBGwQDyaRwNkimMPKlmbDa10cjYIjsG4EJNB710W1sKbDBoQM1ebv6RQZMI6xLen22tLcyfcnDV5DKMvoQtsmPEEtbT7ljTSceaTqD6o4fSkUHSBsFUM4aRHdYo9GPK6ZVXU38f5MoZxNAhBYYMrN3oxTD6E9Yo9FPK7ZVD9fT3UTaDIi2FZi7+5K7WCBhGlTCX1H5KWK+8WaRrItjQIYXAc6vlWtksErovT5PTDKMvYyOFfkpYb3+9KkunHwX0Hk1AdfX3UaOVokyGYVQXGyn0U5JMsKp1GIm2EJnCthuGkT02UuinJJ1gVcvQGbWc9JXHiXqGkQesUein5HGCVa1kyuNEPcPICzZ5zUhMX+ldj7v0Llat7uy13SbAGf0Fm7xmVExf6V3PXtge2CBAvkJlGEa9MEOzkYi+skZAlLwWxdQwrFEwEtJX1giIktdCZRhGHRoFEdlWROaKyNMi8qSIfMPbPkxE7haRZ7zvobWWrRGZvbCdCdPnMHrK7UyYPqdq6wr0lTUCwuRtbSk0lBrMMKpFPUYKa4HzVHVnYDzwVRHZBZgC3KOqOwL3eP+NCGq54Mzkw8bQUmjusa0RA9GFlWPqMbvWSSLDyBc1bxRUdYWqPuL9fht4GmgDPgVc4x12DTCp1rI1GmF6/rNnLsp81JDX9ZDT4i8HuNAaRdtInlZvM4x6UVfvIxEZBYwDHgC2UNUV4BoOEdk85JwzgDMARowYURtBc0qUfrwa3kGNsAZ0Eopl6AveVIaRNXVrFERkQ+BG4GxVfUsigqH5UdUrgSvBzVOonoT5ZvbC9thw13lbpjJPRHlTxdVX6XyNiWOHM3fxyoafv2EYUKdGQUQKuAbh96p6k7f5FRHZyhslbAW8Wg/ZGoGiLSFJuOtG8w6qFeV6UwXN17j2/uVd+23EYTQ69fA+EuA3wNOq+p++XbcAp3m/TwNurrVsjULSxWig8byDakW53lRJ6r4R528YRpF6eB9NAD4LHCwii7zPkcB04FAReQY41PtvBJC099+I3kG1olxvqqR1byM0o1GpufpIVe/DrZkSxCG1lKVR2bq1JXCVMlumMjnlBt8Lq/ug4wyjEbHYRw1IUIjpQpNrZyttEPpK0LsklONNFVT3pdgIzWhkrFFoQEp7uZu0FHj3g7Vdgd7KNXb2laB31SRoP8W8LAAADaxJREFUhGHeR0ZfwkJn9wEmTJ8TqNJIGwo6q3QMw8g3UaGzLSBeHyCrYHV9JeidYRjlY41CHyCrYHV9JeidYRjlY41CHyCrYHV9JeidEcBTT8Ehh8CQIbD11nDRRbAuZq7Lk0/C4Ye74wcNghEj4ItfhBUraiNzUl5/Hb78ZdhyS2hpgbFj4be/7d5fbjlWrIDJk2GPPWDDDWHbbeG00+Cll3oed/XVINL7c8UVmRe1FpihuQ+QxL0yiVdRHtdtNjJg1Sr4+Mdhl13g5pvh2WfhvPNg/Xq47LLw8958E0aPhs99zr1Qly6FSy6BBQvgoYdgQA5eH2+9BR/7mHtp//znsNlmrgH84IPuY8otx4IF8Kc/uQZkv/3glVdg6lTYf3944gmXp585c1yjVGS77TIvbk1Q1Yb97L333mrE86dHXtSxF/5FR55/W9dn7IV/0T898mK9RTNqwfe/r9raqvrmm93bLr9ctaWl57Yk3HWXKqguWJCtjKqqI0eqXnVVunPOP191++1VV69Od16ScqxapdrZ2XPbkiXuvKuv7t521VVu29tvp5OhjgAPa8h71dRH/YC+spRmw3L77dDU5HqofpYuddtvuaW6+f/lL3DYYbDxxt3bTj4Z1qyBv/0tXVqbbuq+iz3x++93Pe3//d/uY95806laTj21MrmTcNVV8IUv9OyhJ6G0HEG0tvYeRey0k1PBvdp3Q7NZo9APMK+iOlPUZ19zTc/tV18Nw4fDkUeGn7tuHaxdG/1Zvz46/8WLnZ7dz4gR7uW2eHG8/OvXu5fnkiUwZQp8+MOw775u3/jxTu9+zjmw3AsMeNZZ7pyf/zw+7UpYutS9nFtbXR0OHOjq89xzg1/2UeVIymOPwerVThVXyvbbu0ZkzBj41a/KK1MOsEahH2BeRXWmuRlOP901CsV5Qaru/2c/G62bP+QQKBSiP//+79H5r1rlXpylDB3q9sVx5JHOQDt2LLzxBtx2mxvhFLnkEhg50slx883OyPvrX7v0oyht3MC9uP3bouZRvfyy+/7Wt6CtDe64A779bfjlL+HCC9OXI4716+Eb34Add4RPfKJ7+1ZbwXe/C7/7Hdx6q7M/nHkm/PjHydPOETZ5rR9QOlMZnFdRI66c1rA89xzssAPccw9MnOiMkocc4gyWu0YsBbpkCbz9dnTam20Go0aF7y8U4Ic/dC80P21trrH63vei03/mGfcSfeYZZ5jeYAOYPx8GD+4+ZtEi1+tuanIN3a9/HZ0mOA+dOK66yskYxPz5cMAB7iV8//3d2y+9FL7/fSfzkCHpyhHF+efDT3/qVG777Rd97EknwV//CitXpmt4akTU5LUcuA8Y1ca8inLAdtvBQQe5l9zEie57332jGwRwDUlcxy3upTN0KHR09N7+5pvBI4hSdtzRfe+3H3z0o86T5w9/6DlC+dCHnErl0UfhK1+JTxOc54+fY46BM86Ao4/u3jZ6dPj5w4a574kTe24/+GC4+GLnZbX77unKEcZ//zfMmAHXXRffIAAcfzzMmgXLljWcF5I1Cv2EvrKUZkPzxS/Cl74E06bBTTfBj34Uf84hh8Qbg087zdknwhg7trft4IUX4N13e9sa4hg50r2Mn3uu5/af/rTbdvH1r8Pf/x7fWO1T0lEdONCNeEq3h7H99u6cUoqNaFT+YeUI4sYbXZl+8AM3AkhDwhUl84Q1CoZRK449Fr76Vef5s369+47jV79Kpj6K4ogjXC/37bdho43ctpkzncfOgQcmk73IkiVuspi/B79kCXznO04lc/jhsPfeTp9+3nnp0k7LwIFw6KFOFefnnnuc2miHHcLPDSpHEPPmwWc+A1/7Gnzzm8llu/FGd11Gjkx+Tk6wRsEwUlBRaPHBg90L5he/gFNOSaa6GZPBbPIzz4Sf/cw1Suef73rHU6c6Lx2/m+oOO7hG4je/cf+/+U1nBN9vPyfr00+73vL223c3aOvWuZHKuHEuvaYmZ3i+8EI46qj0I5G0XHSRsyt8/vOuTh97DKZPh//4D2dUTloOcK6tf/sb/Otf7v/TT8OkSa4MJ53U024xfLg7H+C445wq8EMfcvUxc6b7/OxnubQnxBI2gaERPjZ5zaglmUwCvPtuN9Hp7rurJ2gQTz6pOnGi6uDBqltuqXrhhapr1/Y8ZuRI1dNO6/5/3XWq+++vOnSom+g2ZozqueeqrlzZfcz3v686ZIjqP//ZvW3tWtXx41X33bd3HlGUM3lNVfWOO1THjVMdOFB1m21UL71Udd26dOVQdWUfObL7f3FSWtDHX08XXKC6004u7cGDVffaS/W3v01fjhpCxOQ18z4yjIRkElr8W99yvcjixDXDqAPmfWQYGVDRJMAlS1xMnl/+0nnGWINg5BRrFAwjIWHrMyeaBPjlL8MDDzi3y7POqoJ0hpEN1l0xjIRUFFp83jwXa2jmzGA3SsPICTZSMIyE2CRAoz9gjYJhpMAmARp9HVMfGYZhGF1Yo2AYhmF0YY2CYRiG0YU1CoZhGEYX1igYhmEYXTR0mAsRWQk8n/K0zYDXqiBOVuRZPpOtPEy28smzfI0s20hVHR60o6EbhXIQkYfDYn7kgTzLZ7KVh8lWPnmWr6/KZuojwzAMowtrFAzDMIwu+mOjcGW9BYghz/KZbOVhspVPnuXrk7L1O5uCYRiGEU5/HCkYhmEYIVijYBiGYXTRrxoFETlcRJaIyL9EZEqdZflfEXlVRJ7wbRsmIneLyDPe99A6ybatiMwVkadF5EkR+UZe5BORwSLyoIg86sl2SV5k88nYLCILReS2HMq2TEQeF5FFIvJwnuQTkVYRuUFEFnv33kfyIJuIjPHqq/h5S0TOzoNsnnzneM/CEyJynfeMlC1bv2kURKQZ+AVwBLALcIqI7FJHka4GDi/ZNgW4R1V3BO7x/teDtcB5qrozMB74qldXeZDvfeBgVd0D2BM4XETG50S2It8Anvb9z5NsABNVdU+fH3te5PspcIeqjgX2wNVh3WVT1SVefe0J7A2sBv6UB9lEpA04C9hHVXcDmoGTK5JNVfvFB/gIcKfv/wXABXWWaRTwhO//EmAr7/dWwJJ615sny83AoXmTDxgCPALslxfZgG28h/Bg4La8XVdgGbBZyba6ywdsDCzFc37Jk2wl8nwCmJ8X2YA24AVgGG59nNs8GcuWrd+MFOiuvCIvetvyxBaqugLA+968zvIgIqOAccAD5EQ+Tz2zCHgVuFtVcyMb8BPgW8B637a8yAagwF0iskBEzvC25UG+7YCVwFWe6u1/RGSDnMjm52TgOu933WVT1Xbgh8ByYAXwpqreVYls/alRkIBt5o8bgYhsCNwInK2qb9VbniKquk7dUH4bYF8R2a3eMgGIyNHAq6q6oN6yRDBBVffCqVG/KiIfq7dAHgOAvYBfquo44F3qr2brgYgMBI4Brq+3LEU8W8GngNHA1sAGInJqJWn2p0bhRWBb3/9tgJfqJEsYr4jIVgDe96v1EkRECrgG4feqelPe5ANQ1Q5gHs42kwfZJgDHiMgy4I/AwSJybU5kA0BVX/K+X8XpxffNiXwvAi96oz6AG3CNRB5kK3IE8IiqvuL9z4NsHweWqupKVe0EbgL2r0S2/tQoPATsKCKjvRb/ZOCWOstUyi3Aad7v03C6/JojIgL8BnhaVf/Tt6vu8onIcBFp9X634B6KxXmQTVUvUNVtVHUU7v6ao6qn5kE2ABHZQEQ2Kv7G6Z6fyIN8qvoy8IKIjPE2HQI8lQfZfJxCt+oI8iHbcmC8iAzxnttDcAb68mWrp9GmDkaZI4F/As8C36mzLNfhdICduF7SF4BNcUbKZ7zvYXWS7QCcau0xYJH3OTIP8gEfAhZ6sj0BXORtr7tsJXIeRLehORey4fT2j3qfJ4vPQI7k2xN42Lu2s4GhOZJtCPA6sIlvW15kuwTXMXoC+B0wqBLZLMyFYRiG0UV/Uh8ZhmEYMVijYBiGYXRhjYJhGIbRhTUKhmEYRhfWKBiGYRhdWKNgGB4i8k6KYw8Skf19/88Ukc95v08Xka3LyH+ZiGyW9jzDyJIB9RbAMBqUg4B3gH8AqOoVvn2n43zG8zZj3jBisUbBMCIQkU8CFwIDcZOXPgO0AGcC67w4M1/HzSR9BxeFdB/g9yKyBhed92lcaOPXRGQf4IeqepCIbIqbxDgceBBffC4v3bO8fB8AvqKq66pfYqO/Y+ojw4jmPmC8uiBtfwS+parLgCuAH6uLs39v8WBVvQE3K/cz3r41EWlfDNznpX0LMAJARHYGTsIFr9sTWIdrjAyj6thIwTCi2QaY6QUVG4iL+Z8VHwOOBVDV20Vklbf9ENxiLg+5cDa0UOfgg0b/wRoFw4jm58B/quotInIQMLWMNNbSPSofXLIvKM6MANeo6gVl5GUYFWHqI8OIZhOg3ft9mm/728BGIeeU7luG6/kDHOfb/nc8tZCIHIELAAcugNnxIrK5t2+YiIwsU37DSIU1CobRzRARedH3ORc3MrheRO4FXvMdeyvwb95C7h8tSedq4ApvXwsuiuVPvTT8xuJLgI+JyCO4MNbLAVT1KZxx+y4ReQy4G7ekomFUHYuSahiGYXRhIwXDMAyjC2sUDMMwjC6sUTAMwzC6sEbBMAzD6MIaBcMwDKMLaxQMwzCMLqxRMAzDMLr4/87hIx4v1ZNtAAAAAElFTkSuQmCC\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",
    "plt.title(\"Linear Regression on the Northern Hemisphere \\n for % Humidity\")\n",
    "plot_linear_regression (x_values, y_values,\n",
    "                    'Linear Regression on the Northern Hemisphere for % Humidity','% Humidity',(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
    "plt.title(\"Linear Regression on the Southern Hemisphere \\n for % Humidity\")\n",
    "plot_linear_regression (x_values, y_values,\n",
    "                    'Linear Regression on the Southern Hemisphere for % Humidity','% Humidity',(-50,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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",
    "plt.title(\"Linear Regression on the Northern Hemisphere \\n for % Cloudiness\")\n",
    "plot_linear_regression (x_values, y_values,\n",
    "                    'Linear Regression on the Northern Hemisphere for % Cloudiness','% Cloudiness',(15,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
    "plt.title(\"Linear Regression on the Southern Hemisphere \\n for % Cloudiness\")\n",
    "plot_linear_regression (x_values, y_values,\n",
    "                    'Linear Regression on the Southern Hemisphere for % Cloudiness','% Cloudiness',(-50,60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3Pw9glb/FYikJ6+opKFfdu7hH6bt0dnVz1b2LQ46wWCwWM6ziLygrOoKWjg3fbrFYLKZYxV9QRjU3JdpusVgspljFX1CmHLU7TY0NfbY1NTYw5ajdKySRxWKpFezgbkFxB3BtVI/FYskaq/gLzOQJrVbRWyyWzLGuHovFYqkzrOK3WCyWOsMqfovFYqkzrOK3WCyWOsMqfovFYqkzrOKvV158EQ4/HIYOhVGj4JJLoLs7/rh16+Css2D4cBg2DE47Dd56q+8+Z54JIv0/ixblcimJWbo0WL5TTok+buVKmDIF9tsPttwSdtwRzjgDVqzou98hhwSXLwJPPJHXVVksxthwznpk7Vr45Cdhzz3hr3+Fl1+GCy+EzZvhiiuijz35ZFi8GP7wBxgwAL77XZg8GR59tO9+48bBtdf23TZmTKaXUTI/+QlMmtT7fdtto/efMwfuuAO+/GU48EB44w2YNg0+8hF44QX9MgD41a/g7bf7HnvJJTB3LnzoQ5legsWSBqv465Hf/AY6O+H222HrreGII7SimjYNvvMdvS2IJ56Ae++Fhx+Gj39cb2tt1UrwH//QLxOXLbaAgw7K/VK47jot99KlyY/dffdkMn70o7rXMtDz2Oy/vy7nttu09Q/6herl/fdh9mz90hxoHzlL5bGunkpw993aWl6ypO/2JUv09pkz8z3/3/4GRx3VV8Gfcop+GTz8cPRx22/fq/QBPvxhGDtW/5aEz3xG9wo6PUnnrr4ahgyBBQuSlVUumpv7K+4PflC7y958M/y4v/9d97JOPTVf+SwWQ6zirwSf+pT2q19/fd/t110HLS1w9NHhx3Z3w6ZN0Z/Nm6PPv2iRVrpeRo/WCizKDx90HMAee/Q/7sUX9Ytl8GBtKftfKL//PaxaBRddpL8vXAgXXwyXXQZ77RUtf1acdRY0NMDIkfCtb/V9CZny3HOwYUN/K9/LTTfpntHHPpZeVoslQ6zirwQNDXoA9PrrQSm9TSn9/QtfiHYHHH44NDZGf84+O/r8a9dq69XP8OH6t1KPmzBBW+933gk33KBfVkccAU8/3bvPyJHwi1/Af/833H+/dpNMmADf/na07Js3B7/kvNviBqkHD4avfx3++Ed97q9+FX796/jB3SBZzjsPdtsNjjwyeJ8NG3Q9nHyyHty1WAqAdThWirPPhh/8AB56CA49FB58EJYt01ZoFL/9LaxfH71P3CAlBCshpeKVk8lx553X9/djjtEW8Q9+AG1tvdtPPVWPMxxzjHZxzZ+vX4pRXH657hX4aWzs/X+nnaJ9/u5Lx+WQQ7QL6z//E+bNg/Hjo2VwuegiPe7x8MN9z+/lzjvhnXesm6cGqKWlUK3irxQ776wVzrXXasV/7bXaXx7n5th1195eQhgDYjpyw4dDR0f/7evWBVv03uNWreq/vaMj+rimJu2+uvPO/r+deirceiscd5y2nOM45xw9PuBy113wu9/1HRcZPDi+HD+f+5xW/M8+a6b4f/UruOoquPFGPbgdxk036Xs2cWJymSyFodaWQs3N1SMiO4rIgyKyUEQWiMh5zvZpItIuIvOcT4RDu8b58pd1NEh7u7Z846x9yMbVM25cf5/88uXw7rvBPvyo4yDc9+/H31t4+2244ALt4pk5U0cMxTFqlFai7mfMGBg0qO+2ffaJLydMNhN3zG23wTe/CT/+sXbhhLFunR70ttZ+1VNrS6HmafFvAi5USj0rIlsBc0RklvPbNUqpn+R47urghBO0r/mUU7S/2MTHnIWr59Of1tbq+vWw1VZ624wZ2jL/xCeij/v+9+Gxx/SALegwxVde0b+F0dmpFeABB/Tdfv752h//wAO6Hr78ZR0PP2xYtPx5cOut+q9fRj8PPaQnrX3jG/HjEXfcARs3WsWfMZVwudTaUqi5KX6l1EpgpfP/ehFZCFRfnyhPhgzRSuSXv9TKIcpd4rJ7Bitwfe1relD1hBP0BKxXXtGx8N/6Vt8Qz1131S+CP/5Rfz/4YB0G+sUv6slP7gSuj360N4Z/3Trtijn9dH386tVwzTW6V3Pzzb1l33WXdm/97W/6un/+c9h7bz0+cN11pV9jFNOm6ZfepEn6eh95RL8ITzgB9t23d78vfUn77196SX9fuFBPVhs3Tlv6Tz7Zu29LC+yyS9/z3HSTnuW7xx75Xk8dUSmXy6jmJtoDlHy1LoValqgeERkDTACecjZ9Q0SeE5E/icjwkGPOEZHZIjJ7VZBfuVaYPFn/jXPPZMnw4Tqapbsbjj0WLr1Uu1z8g6ZBETI33aRfBmefrV8ABxygLVuXwYO1ErziCu3XP+ccrdgffrjXz71mjd7+la/o0FaAESN0iOf11wePBWTJuHFanrPO0jL+5S86FcNf/tJ3Pzd01uWpp/SLbf58/dI4+ODez/e/3/fY1at1HSeNFLJEUimXS60thSoqbqCw1BOIbAk8DFyplLpdRLYHVgMK+D4wUikVqfUmTpyoZs+enaucFeM739FuFnfylsViCWXs1LsJ0lgCLJl+TK7nrsaoHhGZo5TqF1mQa1SPiDQCtwE3KKVuB1BKveH5/ffAXXnKUFgWL9aTnH79a21xW6VvscRSSZdLLS2FmmdUjwB/BBYqpX7q2T7Ss9tngRfykqHQfPWr8B//oV0N555baWkslqogyOUiwKHjWiojUJWSp8U/CfgC8LyIzHO2fQ84VUTGo109S4Gv5ihDcXnooUpLYLFUHZMntDJ72RpuePLVHpePAm6b087EnUbUjEWeN3lG9TyGfhn7uSevc1osltrnwUWr+vn53QFeq/jNsI5li8VSVdRaTH0lsIrfYrFUFWEDudUaU18JrOK3WCxVRa3F1FcCm6TNYrFUFa4fv9pi6ouEVfwWi6XqqKWY+kpgXT0Wi8VSZ1jFb7FYLHWGVfwWi8VSZ1jFb7FYLHWGVfwWi8VSZ9ioHouliqnGVMGWymMVv8VSpdTaAuDlpp5fmlbxWyxVStRqVNWswMqhkOv9pWkVf4Fpm9vOtJkL6OjsAmD40EYuPXavumiYlniKnKwsrfLOQiGbnLtWX5qmWMVfUNrmtjPllvl0be5NQLt2QxdTbp0PlG6V1HM3t1Yo6gLgpSjvpArZ344PHdfCbXPaY89d5JdmObBRPQXlqnsX91H6Ll3dquSFpd0Hs72jE0Xvw9E2t72kci3lpajJykpZED2JQg5qxzc8+arRues9w6dV/AUlyvIo1Sop5cG0FIfJE1r54Qn70NrchACtzU388IR9Kt5zK8WaTqKQg9px0ELsQecu6kuzXFhXT0EJ68a7v5VCvXdza4kiJisrxQU15ajd+7iJIFwhJ2mv/nPXe4ZPq/gLypSjdu/n4wdobJCSrZKi+oYttUES5e0niUIOa8dCX8s/7NxFfGmWC6v4C4rbIPOI6inlwbRY4ijVmjZVyGHt+MQDWnlw0aq6tORNEaXCvGLFYeLEiWr27NmVFqOmsFE9llrAtuNoRGSOUmpiv+1W8VssFkttEqb4bVSPxWKx1BnWx2+xWADrNqknrOK3WCx1n7um3rCuHovFYif11Rm5KX4R2VFEHhSRhSKyQETOc7aPEJFZIvJv5+/wvGSwWCxm2El99UWeFv8m4EKl1B7AQcDXRWRPYCpwv1JqN+B+57vFYqkg9Z67pt7ITfErpVYqpZ51/l8PLARageOB653drgcm5yWDxWIxo95z19QbZRncFZExwATgKWB7pdRK0C8HEdku5JhzgHMARo8eXQ4xLZbcKHrETL3nrqk3cp/AJSJbAg8DVyqlbheRDqVUs+f3tUqpSD+/ncBV+xRdMZaCP2IGtDVdhEyaltqmIhO4RKQRuA24QSl1u7P5DREZ6fw+EngzTxksxafW1wewETOWopFnVI8AfwQWKqV+6vlpJnCG8/8ZwF/zksFSHdS6YrQRM+WnbW47k6Y/wNipdzNp+gM1Y0RkRZ4+/knAF4DnRWSes+17wHTgZhH5EvAqcFKOMliqgFpXjDYNdnmxk9HiyTOq5zGllCil9lVKjXc+9yil3lJKHa6U2s35uyYvGSzVQa2HEtqImfJS6z3ILLAzdy0Vp9YVY1GXSKxVar0HmQU2V4+l4tRDKGE9r/ZUbkpxrdVydJkXq/gthcAqRktWpF1hrp7GBqzit1SMerGuLOUlbQ8yamyg1tqlVfyWilBP1pUlf4KMiMenHpaojHoaG7CDu5aKYCMvLFmR1QTAWo8u82IVv6Ui1JN1ZcmXrIyIWo8u82IVv6Ui1JN1ZcmXrIyIegq7tT5+S0VIG3lhsfjJcmZ0vUSXWYvfUhHqybqy5Es9uWiyItTiF5GfA6E5m5VS5+YikaVuqBfrypIv9TABMGuiXD1uAvxJwJ7ADOf7ScCcPIWyVB4bY2+pJqwRkYxQxa+Uuh5ARM4EDlVKdTnffwPcVxbpLBXBxtjXH/ZFX1+YDO6OArYC3CyaWzrbLDVKPc1gtNgXfZZUywvURPFPB+aKyIPO908A03KTyFJxbIx9fZH0RV8tyq3cVNMLNDaqRyl1LXAgcIfzOdh1A1lqExtjX18kedHX+jKZpVBNs9FjFb+zhOIngf2UUn8FBonIh3OXzFIxbHhcfZHkRV9Nyq3cVFNP2SSO/1fAwcCpzvf1wC9zk8hScWyMfX2R5EVfTcqt3FRTT9nEx3+gUmp/EZkLoJRaKyKDcpbLUmFseFz9kCQO3q4fHE41zUY3UfxdItKAM5lLRFqAzblKZbFYyorpiz4P5VYrg8XVNJHMRPH/N3pQd3sRuRL4HHBxrlJZLJZCEqXc0ijwaoqEMaFaesqiVGhWht6dRMYBhwMC3K+UWpi3YF4mTpyoZs+eHb+jxWKpCH4FDronEDc2NGn6A4Guo9bmpsQLqVj6IyJzlFIT/dtNk7RtC2xQSv0CWC0iYzOVzmKxVDVpo33sYHFlMAnnvBT4LnCRs6kR+HOeQlksluoirQKvpkiYWsLE4v8scBzwLoBSagU6hYPFYrEA6RW4nTNSGUwU//tKDwS4UT1b5CuSxWKpNtIqcDtnpDKYRPXcLCK/BZpF5CvA2cDv8xXLYrFUE6WEMuYVCVMrYaJ5YBrVcwRwpPP1PqXULINj/gR8BnhTKbW3s20a8BVglbPb95RS98SVZaN6gqmlhl1L15IHaUMl09Zptd+PtFFGtUZYVI/pmrvPA01od8/zhsdcB/wC+B/f9muUUj8xLMMSQi3FP9fSteRBmvoppU6r/X60zW3nwpvn0+0zam1q8V5Monq+DDwNnICevPWkiJwdd5xS6hF6c/hbMqaWkmXV0rXkQZr6KaVOq/l+uC8tv9J3sWGiGhOLfwowQSn1FoCIbAP8E/hTynN+Q0S+iF7a8UKl1NqgnUTkHOAcgNGjR6c8Ve1SS/HPtXQteZCmfkqp02q+H0EvLS82TFRjEtXzGjojp8t6YHnK8/0a2AUYD6wErg7bUSn1O6XURKXUxJaWlpSnq11qKf65lq4lC9rmtjNp+gOMnXo3k6Y/wLCmxsD9ouqnlDqt5vsR9XKyYaK9mCj+duApEZnmTOZ6EnhJRL4lIt9KcjKl1BtKqW6l1GZ0ZJDN65+SWop/rqVrKZWghU7efX8TjQOkz35x9VNKnVbz/Qh7OTWI1N3AbhQmiv9loA0njh/4K9pa34qEE7lEZKTn62eBF5Icb+mlluKfa+laSiXIVdHVrdhyyMBE9VNKnVbz/Qh7aV39+f2qQv5yYRTO2bOzyHCgQxkcJCI3Aoeg8/y8AVzqfB+PfoksBb6qlFoZV5YN57TUC2On3k3QwyXAkunHlFucqqTaQ1GzJHE4p4hcAtyslFokIoOBv6GV9iYR+Q+l1D+iTqiUOjVg8x8Tym2pIewDGU+9LHSSZ1uoltTIlSQqqudk4PvO/2eg3UItwAeB64FIxW+xeKn22PA8CFJ+1bSKU1psW6g8UT7+9z0unaOAG52B2YWYT/yyWIDqjg3Pg6BBXFf5/fCEfWj2RPIMaTTNnl4d2LZQeaIU+EYR2Rvtnz8U+Lbnt6G5SlVn1IMLpJpjw/MgSvlNOWp3Nm7qXd107YaumrKITdtCPTwXlSJK8Z8H3Ip271yjlFoCICJHA3PLIFtdUC/d3jx919WoIMKUXzYChIkAACAASURBVHtHJ+fPmNdvey2lGzBpC/XyXFSK0D6kUuoppdQ4pdQ2Sqnve7bfEzJwa0lBvXR784oND3OZtM1tL6ncvEnzwquV3pFJW6iX56JSWF99halWF0hSK7uUtL1RRCmIIluGQYO4ccS9LPLo+eRRprcttHd00iDSR6lPntBatc9FtWAVf4WpxvC9tN3wUsLswhRQtSoI/4swbmJMXO8oD9dInu4W9/iw8qvxuagmrOKvEK4ia+/oRKDPg1/08L1yW9lRCqiaFYT3RThp+gOB1wF65mycpZ3HPcn7PscNcFcirDXIwHBldbcdOq6FBxetymVMqVzjVVETuE6IOlApdXvm0tQJfkWmoEf5mzzklabcVnYRFUTWhF2HaaqEPO5J3vc5qvy8XINRBBkYU26ZD6LTZrjb/vzkqz3HZNkLKueAdpTFf6zzdzvgI8ADzvdDgYcAq/hTEqTIXKX/+NTDKiNUAsptZRdNQeRBqdeRxz3J+z7Hle93DbpZS/O6z4F5kjbHp7TJqhdUzp50qOJXSp0FICJ3AXu6OXWcRGu/zFSKOqNa/dIu5baykyqIaqWU6wi6J4K2GidNfyCVksz7PicpvxzWcB69oyzKyEMvmEwJHONLpPYGOm2DJSXVnO8cyp+9sZrTBJcL7z0B+owbuXMDxl92X6Iw17zvc5LyyxHeWWrvKK/z56EXYrNzisgvgN2AG9Ft6RTgJaXUNzOXJoRay85pshB0NU5KyhNbH+ZEDRRX64LjYVlLIbtxsba57VwwY15shJWfrOo0jwXiUy+2rpT6hjPQ+zFn0++UUnekksICxPtz7azF/tSKO6ccRLkG0vqMK/3iDXP3QXbPx+QJrYGzpl3EkePQcS3cNX8lHZ1dQHa5lMo5XmUUzulE8NjB3AyJUmTVOinJUgyilCQk9xkXwRCJm/CW1fPRGlJ33sCLtrnt3Dan12WWZS6lchk4sa8qETlBRP4tIutE5G0RWS8ib+cuWYn41y0t+hR+L9U++GupLEFjIl6S+oyLkD7BP4YRRBbPR72kkzDpo/wYOE4pNUwptbVSaiul1NZ5C1YK1Zq/xaXaB38tlcVVksOH9l+kPc2geFEMkckTWnl86mGhyj+L58NkwLko9VEKJq6eN5wc/FVDtbtKamVSUlK8fuRhTY2IQMeGrkhfZ5jvOen2cl9f2LlNZo6ayOy6DNrmtnPZnQtYu0H7owcPTO6PLtrs6Lyfjzh3S9HqIw0min+2iMxAL7i+0d1Y5Jm71f5GrpVJSUnw+5HdgTMI9ymH+Z5nL1vDbXPajbf7yy3H9QWdu21uO1Nund9nluiFt8xnAL0TiZLKPHvZGjo29NZlR2dyf3TRDJFKPx9Fq480mIRzXhuwWSmlzs5HpP4kDecMC2erlpmx9UhUCKKL//6FHdMgQndAuw7bXo52YdImJ1x+X49lHoeJzFHhiUmvudJRPUWjWuqjlHDOs/IRKT9q4Y1cb5j0xvz7hB0TpNyjtpejJ2jSCzVV+lHlebnq3sWhMelJr9mG0/al2usjKknbd5RSPxaRn0P/9qOUOjdXyUqg0l1BS3LiQhDdfUyOSWrxl8M3m7VfeFRzU6zVGaXcB4gwdurd9tmoU6JGetwB3dnAnIBPoXEjAJZMP4bHpx5mG3bBiQtBDOqxhYXenXrgjom2l6MnaBIm6F1gPYqmxgYOHdcSG7kW9VLpVqoqI94s2RCl+HcRkQ8BNyilrvd/yiWgpT7wh9E1NzUyfGhjZA6XsNC7Kybvk2h7OYwCkzDBacftReMA6XNc4wDh9ING9zvuwUWrYmPJ416mYcdZap/QwV0R+Qk6HfM44Dngn8DjwBNKqTVlk5DK5eqplgGcesX0/qS5j95jmoc2ohSs64wOLS3nNYXlrhFgyfRjQssLc6f5j8tDZkv5CRvcNYnqGQRMRL8EDnY+HUqpPfMQNIhKKP48EiZZeolTFia/m9yfNPcx6BgvRWgH4y+7r0/Iq0tzUyPzLj0y9LioSKjNSiVW3PX6nFTLyy5M8ZvM5mgCtgaGOZ8VwFMGJ/yTiLwpIi94to0QkVlOCohZIjLc/BLKS7mmZRcttUQ55ImbWW0y89r0/qS5j0HHJDm+HIgk2+4S5v4J8/nHtYdaSF+QlGrPDAARil9EficijwMz0Fb+P4GTlFITDUM8rwM+5ds2FbhfKbUbcL/zvVC4DT2sS5xl6F/RGlC55IlTFibKxHSSXprJfGlCS8tNR0joZ9h2F/9YQ0PAm8Kta5P2UO2TJdNQCy+7KIt/NDAYeB1oB14DOkwLVko9AvjHAo4H3IHh64HJxpKWAW9DD2OASGaKsGgNKEyeC2+en6nyj1MWJsrENJ9RmrxHJiGWlZ6eX0o+J2/E2+aIuQ0m7bMe80rVwssuVPErpT4FfAj4ibPpQuAZEblPRC5Leb7t3dW8nL/bpSwnF+K6+KC7xFlZwUVrQFEToi6YMY8xGbl/4pSFiTIxXZUrzepdaUJLy01Wq5JF1bVJ+6yX1dG8Lq8BIf60anrZRfr4leYF4B7gb+ionl2A8/IWTETOEZHZIjJ71apVeZ8OMFe4WVnlRbOWos5b6jJ+XuKUhYkyMV22L83ygf5jhg9tpLkpOrS03GS1LGJUXZu0z3Ivw1lu2ua2M+Hy+zh/xrwel1fQJMBqe9lFhXOei47kmQR04YRyOn+fV0ptji1cZAxwl1Jqb+f7YuAQpdRKZ9H2h5RSsbVVrqgek3wxLqWEv7kULSIiLprFTymylhrVY8mOtrntTJu5oCdKaPjQRi49di+AQrXPchP3PKSNhConaXL1jAFuBS7wLbZeCjOBM4Dpzt+/ZlRuJgTl+PEuWu0lq9zfkDy1RFZK0S2nvaOzJ51Bc1MjGzd1s9lg4dE0qa79sl9z8vhIhe//vV7I+h7HlbNxU68dt3ZDF+fPmEdzUyMnHtDKg4tW9Rx/6LgWrrp3MefPmNfTZrJa8zaI037/BI+/3DtUOGmXEdzwlYMzP08Qca7fzUqVbPxVilDFr5T6VikFi8iNwCHAtiLyGnApWuHfLCJfAl4FTirlHFkTpIgPHdfSJ5UvlDf3t5+slsHzl+N2Xzs6u2gcIIhAt4H2TzIeESd7EZb4KwJ53eOwcsIUXEdnF7fNae+x8MPaTF73ya/0AR5/eQ2n/f6Jsij/uLZdTT59P0Zr7qZBKXVqyE+H53XOLAhSxBN3GlEYt0Opi8x4rfwwujYrYsLBe0jS+ONkNwnzLMI9KAUTCzyrhYTCyjl/xjyuundxz7njFmefNnMBABfePD80w2lS+Uzqwa/047anJUyWuMSBh45ryVSOPrz7LrzwAjQ0wMR+npqSyU3x1xJFSsFaSiRQEh++gacncc8nbRina1EmtYCLNk5gaoFnFe0VtX97RycXzJjXx2UTRkdnF1NuCVf63jJNyLtnl+S+R8kSt8D7g4sSBp28/TY8/zw891zfzzvvRB+3cCGMG5fsXDFYxV9llJLe1yRc1SUqtXHaAa042aPSLCe1gIvoNjK15LNK4Rxnsbp3N06hQ+8KYFEETQYLIs+lUU3cid6Xwob3N4XK4i5Uc/6MeYHnWtHRCWvW9FXm8+frz/vvl3QdDBoE++4LJ58Mu2cfLWQVf5VRyiIzphZjU2MDJx7QGji2UUpER5zsYb+HvayirqeI6y6bWvJZLSQUZ7H6GSAYDeqHYfICAfN6mLTLiEC3zqRdRoSWHecu9L8UghixYR1jl86Dq+cy+bnn2HPWP9nl9VdoCApk/FGoKP0ZOlQrc+9nn32guTlBIdlgFX+VUcoiM1EWYFCERtZjG3Gyh/0eNiYRZQEXbXIcmFvyWS0k5C3HxA2jlA7lTLISmJdWwx6JaT3c8JWDE0f1BN5fpehevpxH//spzljyL8atWsK4N5cybvWyaEFn6D8fjNpn6637K/O994attoouu8LEZucsApVKy1xrFG3egClp5C7iusuVrH+T8R33pe/fr7FBQEW7e5JcRyb1oJR2rcyaBffdp/9mxNohW/HSB8byoeMOgf32g/32486Nw5j+yKuFGS8yJfWau5baIYklWaSB0TQWsIm7pNzX6LfA3bEL1w1RjnNfdueCQIverZuwuvZvO3RcS5/4fvdYkzoNvZ/7fgCeflor8Vmz4OGHM7v+97ZpYf6InZg3fDSLWsawaLsxvDxiR94fGLzqmX+C5rHAsR/ZNTN5Ko21+C39qNaegZ8oJWRyjXm9GCpVv2FWf3NTI9OO2yvzgVWArQYofrlrFx9fNk9b5k8+WdI5gnjuA7vyxM4HsNcZJ/DRM46n7cXVgfctSVRbJXuGWZJ6IZYiYBV/eSmimyRr4q4xT+VcqfrN6rx/fXopt/7lH+z6wjOcsvABdl/xUpZiaj7yETjiCP358IehsdcyL+U6Lm57nhuefDUyXLkajZwwrKvHYkwRB0azJu4a84wKqlT9Rp73vfd0vLgbkuiGJwYkSDze+STmkEPgiCP40orhPDi0lc0D+iaHM30BlVJ/d81fGaj0qyHvTpZYxV+DBLkowNxHnlUceRFIOivTvcY8lXPZ6nfDBj3701Hit931CLuseIlhG9/tv2+SsETgpRE7sGLrFjbLAB4dM55/7XsQr2w/lvZ17/Xb16/QHwhZL9i0btPWX9vc9sDlKqG68+6kwSr+GiNoAsuUW+aDQFd3b26VKbfOZ9rMBYELiGcVRx4nZ94Dq0lnZXqvMU/lXFL9rl/ff+anyexPYP+4HfbZp39o4siRfdZzjFrk/ZpPjTO6rlLrNm39RaVSr0ajphSs4k9IkaJdgpg2c0E/F0VQGF5Xt+qxfvyzG7OKIw+jXLNqo9w1rgUado15vvz89bv74E383x07+dhjt8OvPMr8vf7WcyIaGnoV+H77wb77cg/bcuUzb6W+r1FK27TdlFq3adtnVI8i7txFf+6TYgd3E1D0aJe2ue2h08tNKNfgbbkGN6OsU5NufUkP+6pVfafxu/93m82iDWXIkH5W+d1qG759/2tlaZdZPQOVUKRh7W740EbmXnJk6HFFf+6jsIO7DqU0uKg1aS+YMS+zBpxWxlJXBct6Ifmwayg10Zzp+EWUdWoab96zTSl4/XX4+9954W+P0v7IU+zU/jLjVi1NUCvBvDOoiVdG7sxWH9qfsYcd3Dv7c9gwo+N/MP0Bo6ymw5oaEdELsmcxG9g0CVrQvkkSH5ou2uNdVyJojYCwnoa76EzYuYOyklY6/Uep1JXFX8qb29SaLtUSKEXGMAsX9OxL18cfRlYWd9w1pLX4g8ptHCB9xi+855q9bA1/fvLVfuVM2mUEz766js73NzFq/Sr2eHMJ+7y1jM81rmWH5f+GRYvSXnoP64ZsyaKWMby43VgWtYxhychd+MKXj+bYj+wWei1p207UfY/KdZRFW41TyKVeY1wZUbH57n6Q7uUXF/efxSp8eWMtftKH6LkNwIRSLYFSwgjDLFx3Kb2ofOpZDt7GXUMpg3Nx4xeiNtPy5kqe+unT7LLyFX69/N+MW7WEsWszWESupYVntt6R+cN3ZNF2Y1m43Vhe2mZHNg4c1OelFfZia39kOcd+ZLfMrcgkWU1LOZ9X0Q9ramT9xk09i/W4AQPQt1dQakhsXBnfu/05OruCV4F11xLYuGlzTxkdnV00NTYYrewWl822mgeE60rxp3UxJElnbFJemmNXdHTGWlhRXdnJE1q5IKLHcuIBeiGULFxWcfWceHCuuxteeonxT87i5DeXsMeqpYxbtZQd172RSj4vr285gkUtY1m03RgWtYzhZz84Q6fBHTQocP/PG4Qixt3Di25/PvQFnKbtJM1qmuZ8fus3KCyyq1tx2Z0LMnHpxe27oqOTi9ueZ0OI0ncJktP05RMlZ7Utru6nrhR/2jCypA9jKZZAmIzDmhpjI2HiFGpUj8CbgrnUKBuTep6893ZMHtThDHw+Ct+7Sg9+rlgRWu4vDc//2tYtLB21Cy9/YGee2XoHFraMZemIUXQ7E4bC1hpobW7SIY0lXlvUPnlYkUmzmqY5n6nx480DVOoYS1wZNz613Ej2IEye6aieVDUM7EZRV4o/rYshrAHohck39ylPKG1JtjAZRTDqNkcNmoWVrZRZ2bFs3Agvvsh/v/8czz30KLu+/grjVi2lZUNH7z4XmRfXh513ZsVOu3HHpm14fpudWNQyhmXDRzKwoSHUxz8MuD/gesPWGjCx4EzaUNQ+Ub2uUqzIsPse5aNOcr4seyKHjmsxDueNqkuTMbewNNMmL7ywc1e70oc6U/xp43/DGsC04/Zi9rI1fXJ/KOC2Oe1M3GlEqsYRJmOYwkjyQKYqe8MGWLCg/4ShNeHrnh7gfGL54Ad74st7Ys1Hj+4zYcjLKKB1bjt/STgrOei3tGsNmLShqH3CrPA8rEi/HKVE9cSt5uXS3NSbUyeqJ2JqaETVZdSYlSvLpcfulXrOQN7zWSpJXUX1lEJY17TUmHTTLm+mse/r1/eZyj//nkcYu+IVtn5/Q7Jygth77/6zP0eNClXm9UZeMeF5x8WbZLYcAAwb2hj7Yil1foXLxW3PB0ZtQf/In1pU3ibUdVRPFjc+rCuddADLHxnx7vub+qRSCFsfdFhTY7+QzD6WS0dH79qf7rqfhrM/94v6ccCAHgX+wjY78Yd1W/L40FEMah1VyAco7F776zLO8g2LDQ/LQx93fpc8rMhyzYQe0jig5xzNTY18Zr+RPXXhtmXXrRIlQ1jvQQFjpt7dswRkUCy+lysm6/GYG59a3sfyD1rnoGjttNLUvMWf96y7JJa4aT5wt8H/6M+PMbb9JR3F8uZS9li9hN3fXErj5tJmf3Y3DqJhv75W+T1qW658OlqhVdLfafLyDpMxyKfvJSgPv2ne9riY8qzqKOr6854JbXJdeTwHQecpVU6TMmqpd1C3+fiL8FD0yPLD+3m/fQV7vLmEcU5I4h5OeGLJ+Bdy3m8/jpq1hsUbG/rtajJRyjRCpBxpHkzrOOxeh0XxeDGJw487Nq+2Fnf9WblO/Od028CAiCgo97qSyuDtUcVhWn9ZuF1rbTC3bl09eec+nzx+FE2vr2DWjFlst2QxEzqWc+A77Wx9xb/67ft4wrLfHjSUhc5EIb1c3Fj+ve1oFvz0c0bH/+uuuwO3R117mNsgzDorR47+sMHAaTMX9Hkgw2SJU/r+Y5Nek7t/Xm0tbjA060yi/jZgMucgqQyu+yVq1nHQedLsZ3p8KRM8q62XUPOKP9VDsXkzLFvWM/jZ/shTdM+dx+i32gN3P8r5mPBW09bOZCGtzBduN5Z/b7MjGxsHA70WRpSFbUqaaw9r/GFWczlmL4Y9uB2dXbTNbY+dp2Bi8ZvE4ccdm6UC9iqTMMndejENUzZVUKYx+97ryjpUOuw8acoyydMf1ftIYyhBvmsol8qASguQN1OO2p2mxgYGbO5m7Jp2jl70GN99/AZu/fuPYOxYHW3i/zQ0wM47w+TJcMkltP7j7lClD8AHPgBHHgnf/jb8z//AvHl6UFWpPp+2Z1/joxfO4LRTfsD3D/8Kt+x7BItH7cbQYVsiaKXuditdub0kjfNOU0aU1VyqPGmJe1G5hF3vqQfu2G+7fx9/HH7U/l6E3pS+Wdwz6FUm7RFKH3rrZfKEVn54wj60Njf1a0dhZboKqm1u/3ZtYiH7r8tEhiCmHLW7zrdkeJ64spLWv7dewmgeGrwgO0T3EopMRSx+EVkKrAe6gU1BPqhMuP9+Jn/yk0xOc+wOO8C++/K/7w5zZn+OYcmIVjY16CpL47dNEtGRRfRHUBmHjmuJTM0QZjW5A86V6NJGTdbxKqmoOvPG7ZtE9Qwe2BvB4kaZCPRRxAKcdtBo45nTpphY3EGKN+o8SdwYUT2nqOUJ00TPuPtPm7mgJ72CaVRPWFlJ6t+krqM6i3m7kvOiIoO7juKfqJRabbJ/6sHdRx6BT3yi9/uYMb0ThdyJQzvvrC38EPIYOKsUJoNXlRrginNDTLj8vsAZmFkPLkddP5RnMk+U31sg1bmjyvyZL2FZLQ5yhmEyxhD1rJdrbYm01Ofg7sc/Ttuzr5X0sGY9cJYlUcrS+1vz0EaUMktYlWWcualP2cRPGjQDE2DD+5v6+PmTyuW3/t/duCnUMnYtbAW8vu49zp8xr2d7XCz/7GVreuLNG0Q49cAde+LQ/UT1utIqkyhfetKcT+UmyeBp0oFWkzGGASKhbSzt2EalqZTFvwRYi36GfquU+l3APucA5wCMHj36gGXLliU+TznygVeKOMvUNE4a8um9JApzNbSa2ua293EJxJVrKpcpYRkv42L5BwBBOSRPP2h0H+XvHWT0u5XyWOfBS1EsVD9J2lGaZ9W0PUSVU+SonqJZ/JOUUitEZDtglogsUko94t3BeRn8DrSrJ81JssgHPnlCK7fMfpXHX+7NTbP/6GG5TsgxIW5QKYliy6P3kqTuTf2kbq4bv+L3XndcnSZNse0Sldvee11B5YclDr7xqeU9Yw9+Ze9t8El93V78Pb9KhuWmIUk7SvO8+3s3zU7KCb/CiSqnGmcGV0TxK6VWOH/fFJE7gA8Dj0QflZwsBl4ubnu+j9IHePzlNVzc9nyPtZZUiWcRApYm9CyIvLqlSeo+iTstrFz/fIOwOk2j4Exy28fF8gfRrVQfmcPGkkpR+t7y127o6teTcDENeSy3VZukHaV93v2Ke+zU5PNfqo2yh3OKyBYispX7P3Ak8EIe5wprzEks3LCc3+72i9ue54IZ84zC5FxKDQFrm9tOWADcqOYm4+sLC7lrm9vOpOkPMHbq3Uya/kDktYSRpO6DwvAaG4R3N27qJ0NYuUEWeVCdmtTN8KGN/cIS4+ZPeGP5kxAbUUL6tZSD2pmCfm0nScijaRvPCtN21Da3nQEhiQAVJGrHWeiNolMJi3974A7RN2kg8Bel1N/zOFGagRe/ZRM28adbKdrmtvdJyezS2aUXYIdgCz6tZRI30cQbUx6Xhz3KX5m2N5IoqZyHoO72O+9t6nHpeGVIutqUv06DjvfiXXzbleeqexdz6LiW0Hw/3usKKj/Mx2+Ku3JXUos7rD0p9AstLu9RVMqGVOs1pMDkGY5b1QySteNqHbBNQtkVv1LqFWISQmZF0uiEIKUXRoMIV927ODQUzO3Ge+VwSRMpZDIIpXzn8kf1rOuMz8NeyrR1/9J8jQOE4QZpeqFvd3vS9Af6hW66MrgDkKa5hPx16m8TQTH9QL92cNucdk48oJUHF63ql7HTe11hbS4oqsctK47mofGrrwWRNjooTcqGvDB5hk3HbUxfVkWLasqDmk/SloQkyblOP2h0oLXvxzQ7YVz0gYlsaZKv+c+Xdt5ClvHMaWTIMvoq69jsqFTRcS/zpsYGBg8cEBiKm1SBu+XF1YnpcxAUdRXVvvIaJzCJxXdJE8FW5KidOIoW1VNIoiwY18LzxmCbWGxBZaaxKOKsK1M/bZzVmHbeQpYzGNPIkKWVFjWAPGn6A4niyf3ROkH17p9Z7c/3n3b1tbR1YnLPGgdIoLslrH3lmdMmSW6lqPQLQVRrLp44rOL3kLRrHOcvdssMImkIWFTjNgn3M3XhZJ1sK82AWFoZsgqrC7sWodf9F6UA/MoiKjTQRGZTN1YQfuXvXZwkDBNFuuWQgbHulrgw16zGCUyeQ5ekDo485a4kVvF7SKpwvA9V2KSbKUftnklXMUy2sKgc//mSxMq717Sio5OhgxrY8H4358+Yx4U3zw+dcZrlgFgSSzVt3UYdF6ZIghT4+TPmcf6MeT33XsRMuSTpCZVSt2ksVhNF2uEbg4lrX1n0osIIai9hL651AS6zKKo1F08cVvH78CbnGj5UL9Yc1RC9FluQMoH+A4VpuoqmyjDsQW8e2hiY6yZoOrp7Tf41TbuV6vnuV/5ZD4iZWMIXtz3fZ5zFtG7jlGHcCz2InolXhhZlEpdDKXVb6qSmMAXq723E9fhK7UXF4W8vYeMUw5qSuXqKnLKlFOp+cDerafJ5LcaelLDzNTc1snHT5tiUA152ueiewIiOBhFe/uHR2QmdAJOVm+LqNmrw0u82G3/ZfYEDq6XS3NTIvEuPTHRMmt6NycBnlIFjOkAct1/Q72Ev1Cyejba57Uy5ZT5dm/ueobFBuOpz+xm/WIqassUUO7gbgIkv9sKb54emMA4rx2u5JO0qluoWCit3XWcX15w8ngtvnh8Ykz1t5oJ+542aw1AJTPOqxHXDo3733jsITmyXBUldDmkHGYc1NcZew9oNXUy5NXjeiWlvI26/JO6YLNwokye0ctmdC/r1cru6VeKULVB7oZ01r/ijFKlJ/K+r5KIetKjudJKuounDHXVNUeebPKE1NEKko7Or34SpMH91Q8gMybwxjdeO64bHDV4mmUWdlqSugrSDjKa3qqtbcdmd/V/+pgPQEO+eM3XHZOVGCXJtQvT8nCCyChooEjW9AlfcVPOklkWYQoiy6oNWGPKHwrmYpHKIu6a4VYhMH6rOrm6aBgY3j1MP3NGojKwxuV/e2cthmKywtaKjMxPLc4DQ7/6nGfRO0nP0ptwIU35BrN3Q1addTbllPhMuv6+k1B1hck2a/gCHjmvJdVW3MAOlUoZLkahpxR+nSNNYFmFJxoLo2e5vZyHtzuThjrumyROil8AzUXq95W7m9ING9zwoDSL9UgmXk7j75V8RKwxvHUWdK+p8zU2NofmSvPv89PPjueqk/RIvSRgkj8l202UbTejarFi7oSuT/DxBBos7G9qkbtLkj4pLt1LP1PTgbtwM0KgBp7AFupPOxA0biAwqx2QgOIsVwfyuog3vbyrL6lalEnW/0qYuTrquQdyAZRZ584MmdJkGHySZfZ6GuOUXwyglyCHtfY+qi2oaoC2FuhzcjfOvRw3chD3UJknGvOUkmXVpEq+ddMzAxGeb5FrzwHRAO2yOAegVsWYvWxMZ7k/yTwAADZNJREFUWhtVpncgcLDj4jIZ2BvS2Bv+K/TvfQXJEjQ7N2x2qzec1s2sGaXw0rinBggMGTiADV3xqeRMxryCiOvN+kNztxjUwJWf3Sd08pdJCG/UfIQkazhUijxTRdS0xV+qRZZFxUdZOm5yMX/cf1y+E/81NTYIWwwa2CcJG0Rbq/7r9Cq+5qZGph0XPX8hK9LeI/8cA5fTDxrNxJ1GJCozj5WbkqyGFtc79BNlJUe1txUR7p/GAdIv9NEE015hlFyHjmsJvJcNA4SrT9qPC2bMS5UTC/R9Oj/E+IL+mV2L0hPIqjcZZvHXtOKHyidYCruBJx7Q2i/Nr+mN9a+q9M57m/o8tE2NDQxpHGDkvkm6tF2pdekv492Nm1IlIAubY+AuRh6kZBpEuPrz/WO407ghTJPmgVkUSZxi9uNfIN0ljdsxzK1pgqmLMUquoBBjF9M6TLMgehhFcHFmNf8nTPHX9OBupZU+hA+2PrholfFiLP6BLYDHpx7GkunHMHTQwH6WWmdXd2g0h7/LbbooTNDg3Pkz5jH+svuMB8qCygiLMY9zWYQpCrfcsGOCBijTTMs3cakkiQxqd9qoKWEDrVGD+2ERX6XMyzCVOUquqPO7kXFxAQlRciQJaHDPWWnyThVRsz7+SmTVM/WpA8a+/7jrSNoQ/A9IVA6VsVPv7pPvPshd0dHZZVyvpnH4QXL6ibJSo34Lin0P6yEEpbOIO8a/D5hZ/A0iiZKNRcXwh8Wdh41ZmLqY/CTNRRUmV9T9cuefuHJH5cQKwyQFhf+clSbvVBE1a/GbWrJZ0Da3nQmX38f5CZZgNA3PSxuS2tzUaBQjHdWQvNeRxYQn05dU2DwHL1FzCbqVirTwglblCto/rIcQdYyLW9em1ma3Uv2s4jjSWH+TJ7T29BYfn3pYaE8giOam/ktSAiUvyxh2Lxs87cCVe+n0Y7jm5PGJw2Pd4+Pq1WQeSDmIm49TKjWr+MuVVc+1yINcK1EK0fTGxl1HWDnTjtsrMp4/So6g64ib9GJSr8bWioHWu2LyPmwxKFhu91rDZA5alSts/7B76FfSw4c29sT2e+s6qmy/zG65j089jGtOHh97TNKEY2H4r6XZWTbTi9um/C+NLAysKybvw+kHje5z27cY1MDVJwXn1Al6eZkS1wb9q9hVirj5OKVSs66ecmXVi3NfhClE0xwgpYSken8Pw3981FKSUWvbmtRrkCsjKFGXaT6VKz+7T2gYqntskpDcpAueJEllEFZ2kEwma8iCeToGE4JCfE3cN1kZWFdM3qcsEwPj3GlRk/rKTZ6pImpW8YeFiB06rsW4DJPGH9fA41aNiruxJrH9pTYQ7/Fx4adBia9Mu6BBL6lSEnWZvPT8a92eeEB4XYXJM6ypseSc8VHX6pfJdCzEnxM/KVHt27RNVVvaYveaps1c0C+woLFBeHfjpj5jW0Ww/vOgZl09Dy5alWi7Hzetqz93id93GdXAs/DJmXb50kxpDyLKBTV5QitzLzmSn6XwsXqvx9tND7OwkkSLhHX72+a2c9uc9h7L2V1PICwSKejaGwcI776/qSQfdljZLrfNae9Tnqm1XIpyjcv5ZErevug8mDyhlXmX9m3Hw4c2gtLBClmkqCg6NRvHX2pqg7A87P486mETeaphElRUeeUKgzXJ455WljRT9vNMZ9E2tz00Zt1bnknceakTjbJcJ6IIYdOlUu51M8pF3aVsKLULGhZf7t8e5G5wp+RfMGMeV927OPcHIet1QfP0LQadC8zSZmSVJgDC68d/7WOn3p247DBMxxGC3HtBs7NLuUdZBj+Us73kRa0usRhGzSr+LNeAjcPb8Csxf6DaG22Y4ij1hRYXa28aiZSlD9ukPNOB/1KoNt983tRbfdSsj7/UcKjhIWuihm13Kef8ARfTOQHVRqkvtLhQVdNIpCx92KbllRKymKUc9UK91UfNWvxQWhf00mP3Ysqt8+nq7vXHNjYIlx67V+RxlbC+y9m7KSelWmHuvc8qEqm9o5MGkcAMnKaUw5qvJjmKQr3VR80O7mZBmkGrSg0S1cIAm58sB61LrZ9qX3S7FGqxbdULhcrOKSKfAv4LaAD+oJSaHrV/pRR/GupZQeRBUZROrUZ9xGHbc3VTmKgeEWkAfgkcAbwGPCMiM5VSL5Zbljyoty5j3hQlYqTaB9DTknXEmKUYVMLH/2HgJaXUKwAichNwPFATih+Ko6ws2VFvUR8u9frCq3UqEdXTCiz3fH/N2dYHETlHRGaLyOxVq8xm21oseVFvUR8utRoxVu9UQvEHpZbqN9CglPqdUmqiUmpiS4t5fh2LJQ/yzpZYVOr1hVfrVMLV8xrgTcC9A7CiAnJYLImoRxeeHbOqTSqh+J8BdhORsUA7cArwHxWQw2KxGFCPL7xap+yKXym1SUS+AdyLDuf8k1JqQbnlsFgslnqlIjN3lVL3APdU4twWi8VS79Rsrh6LxWKxBGMVv8VisdQZVvFbLBZLnVEVSdpEZBWwLOFh2wKrcxAnC6xs6SmyfFa2dFjZ0hMn305KqX4ToapC8adBRGYHJScqAla29BRZPitbOqxs6Ukrn3X1WCwWS51hFb/FYrHUGbWs+H9XaQEisLKlp8jyWdnSYWVLTyr5atbHb7FYLJZgatnit1gsFksAVvFbLBZLnVGTil9EPiUii0XkJRGZWmFZ/iQib4rIC55tI0Rkloj82/k7vEKy7SgiD4rIQhFZICLnFUU+ERkiIk+LyHxHtsuKIptHxgYRmSsidxVJNhFZKiLPi8g8EZldJNkcWZpF5FYRWeS0vYOLIJ+I7O7Umft5W0TOL4JsjnwXOM/CCyJyo/OMpJKt5hS/Z03fTwN7AqeKyJ4VFOk64FO+bVOB+5VSuwH3O98rwSbgQqXUHsBBwNeduiqCfBuBw5RS+wHjgU+JyEEFkc3lPGCh53uRZDtUKTXeE+NdJNn+C/i7UmocsB+6Disun1JqsVNn44EDgA3AHUWQTURagXOBiUqpvdGZjU9JLZtSqqY+wMHAvZ7vFwEXVVimMcALnu+LgZHO/yOBxZWuN0eWvwJHFE0+YCjwLHBgUWRDLyB0P3AYcFeR7iuwFNjWt60osm0NLMEJLCmafB55jgQeL4ps9C5ZOwKdVfkuR8ZUstWcxY/hmr4VZnul1EoA5+92FZYHERkDTACeoiDyOa6UecCbwCylVGFkA34GfAfY7NlWFNkUcJ+IzBGRcwom287AKuBax032BxHZokDyuZwC3Oj8X3HZlFLtwE+AV4GVwDql1H1pZatFxW+0pq+lFxHZErgNOF8p9Xal5XFRSnUr3e3eAfiwiOxdaZkAROQzwJtKqTmVliWESUqp/dHuzq+LyMcrLZCHgcD+wK+VUhOAd6ms26kfIjIIOA64pdKyuDi+++OBscAoYAsROT1tebWo+KthTd83RGQkgPP3zUoJIiKNaKV/g1Lq9qLJB6CU6gAeQo+VFEG2ScBxIrIUuAk4TET+XBDZUEqtcP6+ifZRf7gosqGfz9ec3hvAregXQVHkA/3CfFYp9YbzvQiyfRJYopRapZTqAm4HPpJWtlpU/D1r+jpv7lOAmRWWyc9M4Azn/zPQvvWyIyIC/BFYqJT6qeenissnIi0i0uz834Ru+IuKIJtS6iKl1A5KqTHo9vWAUur0IsgmIluIyFbu/2g/8AtFkA1AKfU6sFxEdnc2HQ68SEHkcziVXjcPFEO2V4GDRGSo89wejh4UTydbJQdQchwIORr4F/Ay8H8rLMuNaJ9cF9ra+RKwDXpg8N/O3xEVku2jaDfYc8A853N0EeQD9gXmOrK9AFzibK+4bD45D6F3cLfisqF96POdzwK3/RdBNo+M44HZzr1tA4YXRT50IMFbwDDPtqLIdhna+HkB+F9gcFrZbMoGi8ViqTNq0dVjsVgslgis4rdYLJY6wyp+i8ViqTOs4rdYLJY6wyp+i8ViqTOs4rfUHSLyToJ9DxGRj3i+f01Evuj8f6aIjEpx/qUism3S4yyWrBhYaQEsloJzCPAO8E8ApdRvPL+diY6pLtrMcIslEqv4LRZARI4FLgYGoSfwnAY0AV8Dup28KN9Ez5h8B50BcyJwg4h0orPCLkSnzV0tIhOBnyilDhGRbdAT+VqAp/Hkk3LKPdc571PAfyqluvO/Yks9Y109FovmMeAgpROH3QR8Rym1FPgNcI3SedofdXdWSt2Knn16mvNbZ0TZlwKPOWXPBEYDiMgewMnopGrjgW70C8diyRVr8Vssmh2AGU6iq0HonPFZ8XHgBACl1N0istbZfjh6wY9ndPoVmqhwQjxLfWAVv8Wi+TnwU6XUTBE5BJiWooxN9Paih/h+C8qNIsD1SqmLUpzLYkmNdfVYLJphQLvz/xme7euBrUKO8f+2FG3BA5zo2f4IjgtHRD6NTkoGOqnW50RkO+e3ESKyU0r5LRZjrOK31CNDReQ1z+dbaAv/FhF5FFjt2fdO4LPO4tsf85VzHfAb57cmdPbE/3LK8A7QXgZ8XESeRadJfhVAKfUiekD5PhF5DpiFXj7PYskVm53TYrFY6gxr8VssFkudYRW/xWKx1BlW8VssFkudYRW/xWKx1BlW8VssFkudYRW/xWKx1BlW8VssFkud8f8BNRC5VqG5tCkAAAAASUVORK5CYII=\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",
    "plt.title(\"Linear Regression on the Northern Hemisphere \\n for Wind Speed\")\n",
    "plot_linear_regression (x_values, y_values,\n",
    "                    'Linear Regression on the Northern Hemisphere for Wind Speed','Wind Speed',(10,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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",
    "plt.title(\"Linear Regression on the Southern Hemisphere \\n for Wind Speed\")\n",
    "plot_linear_regression (x_values, y_values,\n",
    "                    'Linear Regression on the Southern Hemisphere for Wind Speed','Wind Speed',(-50,23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}