{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autour du SARS-COV-2 Reproduction des courbes du SCMP" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import urllib.request\n", "%matplotlib inline\n", "plt.rcParams[\"figure.figsize\"]=10,10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Données\n", "\n", "Les données seront celles rendues disponibles par [JHU CSSE](https://systems.jhu.edu/) sur github dans [ce dépot](https://github.com/CSSEGISandData/COVID-19).\n", "\n", "Nous nous intéressons en particulier aux données à l'echelle mondiale des cas confirmés.\n", "L'objectif final est de reproduire les courbes du South China Morning Post de cette page : [The Coronavirus Pandemic](https://www.scmp.com/coronavirus?src=homepage_covid_widget)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Téléchargement des données\n", "\n", "Les données sont téléchargées dans le dossier courant si aucune donnée n'est présente. Sinon elles sont recupérées du dossier courant.\n", "La structure choisie ensuite est une structure classique du module pandas." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_link = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n", "data_file = \"time_series_covid19_confirmed_global.csv\"\n", "\n", "try :\n", " open(data_file, 'r')\n", " \n", "except :\n", " print(\"Data file not found, dowloading from {}\".format(data_link))\n", " urllib.request.urlretrieve(data_link, data_file)\n", "\n", "raw_data = pd.read_csv(data_file)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/24/205/25/205/26/205/27/205/28/205/29/205/30/205/31/206/1/206/2/20
0NaNAfghanistan33.00000065.000000000000...10582111731183112456130361365914525152051575016509
1NaNAlbania41.15330020.168300000000...998100410291050107610991122113711431164
2NaNAlgeria28.0339001.659600000000...8306850386978857899791349267939495139626
3NaNAndorra42.5063001.521800000000...762763763763763764764764765844
4NaNAngola-11.20270017.873900000000...69707071748184868686
5NaNAntigua and Barbuda17.060800-61.796400000000...25252525252525262626
6NaNArgentina-38.416100-63.616700000000...12076126281322813933147021541916214168511741518319
7NaNArmenia40.06910045.038200000000...66617113740277748216867689279282949210009
8Australian Capital TerritoryAustralia-35.473500149.012400000000...107107107107107107107107107107
9New South WalesAustralia-33.868800151.209300000034...3090309230893090309230923095309831043104
10Northern TerritoryAustralia-12.463400130.845600000000...29292929292929292929
11QueenslandAustralia-28.016700153.400000000000...1056105710581058105810581058105810591059
12South AustraliaAustralia-34.928500138.600700000000...439439440440440440440440440440
13TasmaniaAustralia-41.454500145.970700000000...228228228228228228228228228228
14VictoriaAustralia-37.813600144.963100000011...1605161016181628163416451649165316631670
15Western AustraliaAustralia-31.950500115.860500000000...560564570570577585586589591592
16NaNAustria47.51620014.550100000000...16503165391655716591166281665516685167311673316759
17NaNAzerbaijan40.14310047.576900000000...4122427144034568475949895246549456625935
18NaNBahamas25.034300-77.396300000000...100100100100101102102102102102
19NaNBahrain26.02750050.550000000000...9138917193669692100521044910793113981187112311
20NaNBangladesh23.68500090.356300000000...33610355853675138292403214284444608471534953452445
21NaNBarbados13.193900-59.543200000000...92929292929292929292
22NaNBelarus53.70980027.953400000000...36198371443805938956398584076441658425564340344255
23NaNBelgium50.8333004.000000000000...57092573425745557592578495806158186583815851758615
24NaNBenin9.3077002.315800000000...191191208210210224224232243244
25NaNBhutan27.51420090.433600000000...24272728313133434347
26NaNBolivia-16.290200-63.588700000000...626366607136776883878731959299821053110991
27NaNBosnia and Herzegovina43.91590017.679100000000...2401240624162435246224852494251025242535
28NaNBrazil-14.235000-51.925300000000...363211374898391222411821438238465166498440514849526447555383
29NaNBrunei4.535300114.727700000000...141141141141141141141141141141
..................................................................
236NaNTimor-Leste-8.874217125.727539000000...24242424242424242424
237NaNBelize13.193900-59.543200000000...18181818181818181818
238NaNLaos19.856270102.495496000000...19191919191919191919
239NaNLibya26.33510017.228331000000...75757799105118130156168182
240NaNWest Bank and Gaza31.95220035.233200000000...423423429434446446447448449451
241NaNGuinea-Bissau11.803700-15.180400000000...1114117811781195119512561256125613391339
242NaNMali17.570692-3.996166000000...1030105910771116119412261250126513151351
243NaNSaint Kitts and Nevis17.357822-62.782998000000...15151515151515151515
244Northwest TerritoriesCanada64.825500-124.845700000000...5555555555
245YukonCanada64.282300-135.000000000000...11111111111111111111
246NaNKosovo42.60263620.902977000000...1032103810381047104810481064106410641064
247NaNBurma21.91620095.956000000000...201203206206206207224224228232
248AnguillaUnited Kingdom18.220600-63.068600000000...3333333333
249British Virgin IslandsUnited Kingdom18.420700-64.640000000000...8888888888
250Turks and Caicos IslandsUnited Kingdom21.694000-71.797900000000...12121212121212121212
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"248 3 3 \n", "249 8 8 \n", "250 12 12 \n", "251 9 9 \n", "252 38 40 \n", "253 63 63 \n", "254 865 896 \n", "255 7 7 \n", "256 336 358 \n", "257 13 13 \n", "258 1 1 \n", "259 994 994 \n", "260 9 9 \n", "261 484 484 \n", "262 354 399 \n", "263 106 132 \n", "264 4013 4100 \n", "265 2 2 \n", "\n", "[266 rows x 137 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En premier lieu, nous voulons faire l'étude pour la liste de pays suivante :\n", "- Belgique\n", "- France\n", "- Chine\n", "- Allemagne\n", "- Iran\n", "- Italie\n", "- Japon\n", "- Corée du Sud\n", "- Pays Bas\n", "- Portugal\n", "- Espagne\n", "- Royaume uni\n", "- Etats Unis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La selection des pays se fait avec la fonction `isin` après la définition de la liste des pays d'interets." ] }, { "cell_type": "code", "execution_count": 4, "metadata": 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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/20...5/24/205/25/205/26/205/27/205/28/205/29/205/30/205/31/206/1/206/2/20
23NaNBelgium50.83334.0000000000...57092573425745557592578495806158186583815851758615
49AnhuiChina31.8257117.22641915396070...991991991991991991991991991991
50BeijingChina40.1824116.4142142236416880...593593593593593593593593593593
51ChongqingChina30.0572107.874069275775110...579579579579579579579579579579
52FujianChina26.0789117.98741510183559...356357357358358358358358358358
53GansuChina37.8099101.05830224714...139139139139139139139139139139
54GuangdongChina23.3417113.424426325378111151...1592159215921592159215931593159515961597
55GuangxiChina23.8298108.78812523233646...254254254254254254254254254254
56GuizhouChina26.8154106.8748133457...147147147147147147147147147147
57HainanChina19.1959109.7453458192233...169169169169169169169169169169
58HebeiChina39.5490116.130611281318...328328328328328328328328328328
59HeilongjiangChina47.8620127.761502491521...945945945945945945945945945945
60HenanChina33.8820113.61405593283128...1276127612761276127612761276127612761276
61Hong KongChina22.3000114.2000022588...1065106510651066106610791082108410871093
62HubeiChina30.9756112.270744444454976110581423...68135681356813568135681356813568135681356813568135
63HunanChina27.6104111.708849244369100...1019101910191019101910191019101910191019
64Inner MongoliaChina44.0935113.94480017711...227232232232232232232235235235
65JiangsuChina32.9711119.4550159183347...653653653653653653653653653653
66JiangxiChina27.6140115.72212718183672...937937937937937937937937937937
67JilinChina43.6661126.1923013446...155155155155155155155155155155
68LiaoningChina41.2956122.6085234172127...149149149149149149149149149149
69MacauChina22.1667113.5500122256...45454545454545454545
70NingxiaChina37.2692106.1655112347...75757575757575757575
71QinghaiChina35.745295.9956000116...18181818181818181818
72ShaanxiChina35.1917108.8701035152235...308308308308308308308308309309
73ShandongChina36.3427118.14982615274675...788788788788788790792792792792
74ShanghaiChina31.2020121.449191620334053...668669670671671672672672673673
75ShanxiChina37.5777112.29221116913...198198198198198198198198198198
76SichuanChina30.6171102.71035815284469...564564564564564564564575577577
77TianjinChina39.3054117.3230448101423...192192192192192192192192192192
..................................................................
112ReunionFrance-21.135155.2471000000...452456459460465470471471473477
113Saint BarthelemyFrance17.9000-62.8333000000...6666666666
114St MartinFrance18.0708-63.0501000000...40404040404041414141
115MartiniqueFrance14.6415-61.0242000000...197197197197197200200200200200
116NaNFrance46.22762.2137002333...179859180166179887180044183309183816185616185851185952184980
120NaNGermany51.00009.0000000001...180328180600181200181524182196182922183189183410183594183879
133NaNIran32.000053.0000000000...135701137724139511141591143849146668148950151466154445157562
137NaNItaly43.000012.0000000000...229858230158230555231139231732232248232664232997233197233515
139NaNJapan36.0000138.0000222244...16550165811662316651165981667316716167511678716837
143NaNKorea, South36.0000128.0000112234...11206112251126511344114021144111468115031154111590
166ArubaNetherlands12.5186-70.0358000000...101101101101101101101101101101
167CuracaoNetherlands12.1696-68.9900000000...17181818181819191920
168Sint MaartenNetherlands18.0425-63.0548000000...77777777777777777777
169NaNNetherlands52.13265.2913000000...45236454454557845768459504612646257464424654546647
184NaNPortugal39.3999-8.2245000000...30623307883100731292315963194632203325003270032895
201NaNSpain40.0000-4.0000000000...235772235400236259236259237906238564239228239479239638239932
217BermudaUnited Kingdom32.3078-64.7505000000...133133139139140140140140141141
218Cayman IslandsUnited Kingdom19.3133-81.2546000000...129134137140140141141141150151
219Channel IslandsUnited Kingdom49.3723-2.3644000000...558559559560560560560560560560
220GibraltarUnited Kingdom36.1408-5.3536000000...154154154157158161169170170172
221Isle of ManUnited Kingdom54.2361-4.5481000000...336336336336336336336336336336
222MontserratUnited Kingdom16.7425-62.1874000000...11111111111111111111
223NaNUnited Kingdom55.3781-3.4360000000...259559261184265227267240269127271222272826274762276332277985
225NaNUS37.0902-95.7129112255...1643246166230216809131699176172175317460191770165179017218110201831821
248AnguillaUnited Kingdom18.2206-63.0686000000...3333333333
249British Virgin IslandsUnited Kingdom18.4207-64.6400000000...8888888888
250Turks and Caicos IslandsUnited Kingdom21.6940-71.7979000000...12121212121212121212
255Bonaire, Sint Eustatius and SabaNetherlands12.1784-68.2385000000...6666666677
257Falkland Islands (Malvinas)United Kingdom-51.7963-59.5236000000...13131313131313131313
258Saint Pierre and MiquelonFrance46.8852-56.3159000000...1111111111
\n", "

69 rows × 137 columns

\n", "
" ], "text/plain": [ " Province/State Country/Region Lat Long \\\n", "23 NaN Belgium 50.8333 4.0000 \n", "49 Anhui China 31.8257 117.2264 \n", "50 Beijing China 40.1824 116.4142 \n", "51 Chongqing China 30.0572 107.8740 \n", "52 Fujian China 26.0789 117.9874 \n", "53 Gansu China 37.8099 101.0583 \n", "54 Guangdong China 23.3417 113.4244 \n", "55 Guangxi China 23.8298 108.7881 \n", "56 Guizhou China 26.8154 106.8748 \n", "57 Hainan China 19.1959 109.7453 \n", "58 Hebei China 39.5490 116.1306 \n", "59 Heilongjiang China 47.8620 127.7615 \n", "60 Henan China 33.8820 113.6140 \n", "61 Hong Kong China 22.3000 114.2000 \n", "62 Hubei China 30.9756 112.2707 \n", "63 Hunan China 27.6104 111.7088 \n", "64 Inner Mongolia China 44.0935 113.9448 \n", "65 Jiangsu China 32.9711 119.4550 \n", "66 Jiangxi China 27.6140 115.7221 \n", "67 Jilin China 43.6661 126.1923 \n", "68 Liaoning China 41.2956 122.6085 \n", "69 Macau China 22.1667 113.5500 \n", "70 Ningxia China 37.2692 106.1655 \n", "71 Qinghai China 35.7452 95.9956 \n", "72 Shaanxi China 35.1917 108.8701 \n", "73 Shandong China 36.3427 118.1498 \n", "74 Shanghai China 31.2020 121.4491 \n", "75 Shanxi China 37.5777 112.2922 \n", "76 Sichuan China 30.6171 102.7103 \n", "77 Tianjin China 39.3054 117.3230 \n", ".. ... ... ... ... \n", "112 Reunion France -21.1351 55.2471 \n", "113 Saint Barthelemy France 17.9000 -62.8333 \n", "114 St Martin France 18.0708 -63.0501 \n", "115 Martinique France 14.6415 -61.0242 \n", "116 NaN France 46.2276 2.2137 \n", "120 NaN Germany 51.0000 9.0000 \n", "133 NaN Iran 32.0000 53.0000 \n", "137 NaN Italy 43.0000 12.0000 \n", "139 NaN Japan 36.0000 138.0000 \n", "143 NaN Korea, South 36.0000 128.0000 \n", "166 Aruba Netherlands 12.5186 -70.0358 \n", "167 Curacao Netherlands 12.1696 -68.9900 \n", "168 Sint Maarten Netherlands 18.0425 -63.0548 \n", "169 NaN Netherlands 52.1326 5.2913 \n", "184 NaN Portugal 39.3999 -8.2245 \n", "201 NaN Spain 40.0000 -4.0000 \n", "217 Bermuda United Kingdom 32.3078 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184980 \n", "120 183879 \n", "133 157562 \n", "137 233515 \n", "139 16837 \n", "143 11590 \n", "166 101 \n", "167 20 \n", "168 77 \n", "169 46647 \n", "184 32895 \n", "201 239932 \n", "217 141 \n", "218 151 \n", "219 560 \n", "220 172 \n", "221 336 \n", "222 11 \n", "223 277985 \n", "225 1831821 \n", "248 3 \n", "249 8 \n", "250 12 \n", "255 7 \n", "257 13 \n", "258 1 \n", "\n", "[69 rows x 137 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listePays = [\n", " \"Belgium\",\n", " \"France\",\n", " \"China\",\n", " \"Germany\",\n", " \"Iran\",\n", " \"Italy\",\n", " \"Japan\",\n", " \"Korea, South\",\n", " \"Netherlands\",\n", " \"Portugal\",\n", " \"Spain\",\n", " \"United Kingdom\",\n", " \"US\"]\n", "donnees_pays_interets = raw_data[raw_data[\"Country/Region\"].isin(listePays)]\n", "donnees_pays_interets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous filtrons les données de lattitude et longitude qui ne sont pas utiles pour cette étude gtace à la fonction `drop`. Nous vérifions que les clés existent bien." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"60 1276 \n", "61 1093 \n", "62 68135 \n", "63 1019 \n", "64 235 \n", "65 653 \n", "66 937 \n", "67 155 \n", "68 149 \n", "69 45 \n", "70 75 \n", "71 18 \n", "72 309 \n", "73 792 \n", "74 673 \n", "75 198 \n", "76 577 \n", "77 192 \n", ".. ... \n", "112 477 \n", "113 6 \n", "114 41 \n", "115 200 \n", "116 184980 \n", "120 183879 \n", "133 157562 \n", "137 233515 \n", "139 16837 \n", "143 11590 \n", "166 101 \n", "167 20 \n", "168 77 \n", "169 46647 \n", "184 32895 \n", "201 239932 \n", "217 141 \n", "218 151 \n", "219 560 \n", "220 172 \n", "221 336 \n", "222 11 \n", "223 277985 \n", "225 1831821 \n", "248 3 \n", "249 8 \n", "250 12 \n", "255 7 \n", "257 13 \n", "258 1 \n", "\n", "[69 rows x 135 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "try :\n", " donnees_pays_interets = donnees_pays_interets.drop(columns = [\"Lat\", \"Long\"])\n", "except KeyError:\n", " print(\"Les clés ont déja été supprimées\")\n", "donnees_pays_interets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour les pays d'interets, nous ne nous interessons pas, dans cette étude, aux dépendences ultra-marines ou aux \"résidus\" de colonie.\n", "\n", "Les données sont filtrés en ne gardant que les pays qui n'ont pas de nom de province. Le cas de la Chine est particulier et sera traité après." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le filtrage se fait en gardant uniquement les pays qui n'ont pas de nom de province grace à `isna`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Province/State Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 \\\n", "23 NaN Belgium 0 0 0 0 \n", "116 NaN France 0 0 2 3 \n", "120 NaN Germany 0 0 0 0 \n", "133 NaN Iran 0 0 0 0 \n", "137 NaN Italy 0 0 0 0 \n", "139 NaN Japan 2 2 2 2 \n", "143 NaN Korea, South 1 1 2 2 \n", "169 NaN Netherlands 0 0 0 0 \n", "184 NaN Portugal 0 0 0 0 \n", "201 NaN Spain 0 0 0 0 \n", "223 NaN United Kingdom 0 0 0 0 \n", "225 NaN US 1 1 2 2 \n", "\n", " 1/26/20 1/27/20 1/28/20 1/29/20 ... 5/24/20 5/25/20 5/26/20 \\\n", "23 0 0 0 0 ... 57092 57342 57455 \n", "116 3 3 4 5 ... 179859 180166 179887 \n", "120 0 1 4 4 ... 180328 180600 181200 \n", "133 0 0 0 0 ... 135701 137724 139511 \n", "137 0 0 0 0 ... 229858 230158 230555 \n", "139 4 4 7 7 ... 16550 16581 16623 \n", "143 3 4 4 4 ... 11206 11225 11265 \n", "169 0 0 0 0 ... 45236 45445 45578 \n", "184 0 0 0 0 ... 30623 30788 31007 \n", "201 0 0 0 0 ... 235772 235400 236259 \n", "223 0 0 0 0 ... 259559 261184 265227 \n", "225 5 5 5 5 ... 1643246 1662302 1680913 \n", "\n", " 5/27/20 5/28/20 5/29/20 5/30/20 5/31/20 6/1/20 6/2/20 \n", "23 57592 57849 58061 58186 58381 58517 58615 \n", "116 180044 183309 183816 185616 185851 185952 184980 \n", "120 181524 182196 182922 183189 183410 183594 183879 \n", "133 141591 143849 146668 148950 151466 154445 157562 \n", "137 231139 231732 232248 232664 232997 233197 233515 \n", "139 16651 16598 16673 16716 16751 16787 16837 \n", "143 11344 11402 11441 11468 11503 11541 11590 \n", "169 45768 45950 46126 46257 46442 46545 46647 \n", "184 31292 31596 31946 32203 32500 32700 32895 \n", "201 236259 237906 238564 239228 239479 239638 239932 \n", "223 267240 269127 271222 272826 274762 276332 277985 \n", "225 1699176 1721753 1746019 1770165 1790172 1811020 1831821 \n", "\n", "[12 rows x 135 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "donnees_sans_colonnie = donnees_pays_interets[donnees_pays_interets[\"Province/State\"].isna()]\n", "donnees_sans_colonnie" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions l'écart entre le nombre de pays qui nous interessent et la taille des données après filtrage." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Il y a bien un seul pays d'écart\n" ] } ], "source": [ "ecart_pays = len(listePays) - len(donnees_sans_colonnie)\n", "if ecart_pays == 1:\n", " print(\"Il y a bien un seul pays d'écart\")\n", "else:\n", " print(\"Attention il faut vérifier le traitement, il n'y a pas l'ecart attendu !\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il y a 1 pays d'écart, la Chine qui n'est pas intégrée pour le moment." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous traitons ici le cas de la Chine. Après avoir selectionné les données qui correspondent à l'état de Chine, nous récupérons les données de la province Hong Kong d'un coté, et les données de toutes les autres provinces de l'autre coté." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "donneesChine = donnees_pays_interets[donnees_pays_interets[\"Country/Region\"] == \"China\"]\n", "donnees_HongKong = donneesChine[donneesChine[\"Province/State\"] == \"Hong Kong\"]\n", "donnees_chine_sansHK = donneesChine[donneesChine[\"Province/State\"] != \"Hong Kong\"]\n", "donnees_chine_agregees = donnees_chine_sansHK.sum()\n", "\n", "donnees_chine_agregees[\"Province/State\"] = \"China \\ HK\"\n", "donnees_chine_agregees[\"Country/Region\"] = \"China \\ HK\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous renommons les données propres à Hong Kong." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Province/State Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", "61 Hong Kong Hong Kong 0 2 2 5 8 \n", "\n", " 1/27/20 1/28/20 1/29/20 ... 5/24/20 5/25/20 5/26/20 5/27/20 \\\n", "61 8 8 10 ... 1065 1065 1065 1066 \n", "\n", " 5/28/20 5/29/20 5/30/20 5/31/20 6/1/20 6/2/20 \n", "61 1066 1079 1082 1084 1087 1093 \n", "\n", "[1 rows x 135 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "donnees_HongKong.iat[0, 1] = \"Hong Kong\"\n", "donnees_HongKong" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Province/State Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 \\\n", "0 China \\ HK China \\ HK 548 641 918 1401 2067 \n", "\n", " 1/27/20 1/28/20 1/29/20 ... 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 \\\n", "0 2869 5501 6077 ... 83030 83037 83038 83040 83040 \n", "\n", " 5/29/20 5/30/20 5/31/20 6/1/20 6/2/20 \n", "0 83044 83046 83062 83067 83068 \n", "\n", "[1 rows x 135 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "donnees_chine_agregees = pd.DataFrame(donnees_chine_agregees).transpose()\n", "donnees_chine_agregees" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Après ce traitement du cas de la chine afin de séparer Hong Kong, l'ensemble des données est concaténée avant de tracer les graphes." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "donnees = pd.concat([donnees_sans_colonnie, donnees_HongKong, donnees_chine_agregees])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tracer des graphes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Echelle linéaire\n", "\n", "L'ensemble des données est tracé sur une echelle linéaire.\n", "L'axe des abscisses est la date (format américain), l'axe des ordonnées est le nombre de cas confirmés." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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btjUYN25c67i4uCObN29uuHPnzobBwcHBADk5OYaYmBjH4olCRbZ/Ls+999573mAw0KVLlxwPD4+CsLCwXIDAwMDcxMREB0kUhBBCVJ2L6ablj2fjYOhSCPq7tSOqU+65556LGRkZtmlpabZaa6ZOnZr27LPPni2tfkW2fwZQxbbrzs3NLXE7axsbG+zt7S93aDAYrLIddHWR8SwhhLC2i2dh0UBIj4fhyyVJqIT9+/c7Go1GvLy8Cvv3739h8eLFnpmZmQaAP//80y41NfUvP4wrsv0zgIeHR8G+ffsci4qKWLNmTeOaOJfaRkYUhBDCmrLPmJKEc0dh+Apo28vaEdUZljkKYBohmDt3bpKtrS2DBg26cOTIEcdbbrklCMDZ2dm4dOnSP318fC5PdhwzZkzGtm3bXAMDA0Nat259qaTtnwFee+211Pvvv9/f29u7ICgoKPfixYs33A9sSRSEEMJask+b7pGQcQxGfANt7rJ2RHVKUVHR3tKOvfzyy6dffvnl01eWW1Yw2NjYVGj759K2di6+quHK7afr04oHkERBCCGswqYwBxY/COePwyPfQuse1g7phlPXt3+uKZIoCCFETSsqIOTILDgfLUmCFdX17Z9riiQKQghRk7SG9f/GPeMADPwY/HtbOyIhynTDTcoQQgir2vUu7F9MUsuHocsoa0cjRLkkURBCiJpy6Bv4+Q24aShJrUZYOxohKkQSBSGEqAmJ22HN49Cqh+mSg6o39+MR9ZzMURBCiOp2dAcsHwaegTB0MdjaWzuiesHGxiY0ICDg8m2S16xZk9CuXbt8a8ZUH0miIIQQ1SnpF1g2FNzbwOg14HRD3tyvWhTf66EkBQUF2NnZ1WRI9VK5iYJSaiFwH3Baa93BXPYqMBE4Y642XWu90XzsBWA8UAQ8qbXeYi4PBb4CnICNwL+01lop5QAsAkKBdGCo1jrJ3GYM8JL5Pd7QWn9tLm8NrADcgX3AKK21ZJFCiNrl2G+w9GFo3BJGr4UGntaOqHqsftyP01FVus00TYNzeOCTa95sas6cOR6bNm1yy8vLM+Tk5Bi2bNmS0K9fP//MzEybwsJCNWPGjBMjR448Hxsba9+/f/+AsLCw7IiICBcvL6/8LVu2JLi4uOjIyEiHSZMmtUxPT7e1sbHR33777dGQkJC88raurq8qMkfhK6BfCeXva61vNj8sSUIwMAwIMbf5VCllY64/F5gEBJgflj7HAxlaa3/gfWCWuS934BWgGxAGvKKUsqTis8zvHwBkmPsQQoja4/geWDoEGjY3JQkuTawdUb1juYVzUFBQcJ8+fdpayvft2+eyfPnyP3fv3h3n7Oxs3LBhQ0JUVFT0jh074qZPn+5rNBoBOH78uOOTTz55OiEh4Yibm1vRokWLGgOMGDGi9eTJk0/HxsZGRURExLRo0aKg+NbV0dHRUQcOHHDetGmTi5VOvUaVO6Kgtd6plGpVwf7uB1ZorfOAP5VSCUCYUioJaKi1/h1AKbUIeADYZG7zqrn9d8DHyrRd19+ArVrrc+Y2W4F+SqkVwN2AZcrw1+b2cysYoxBCVK/k/5m2i3bxgjHrwNXL2hFVr0r88q8KpV166NGjxwUvL68iAKPRqKZOneq7e/duF4PBwOnTp+1TUlJsAXx8fPIsW0F37tw5JykpySEjI8Nw6tQp+9GjR58HcHZ21oCuyNbV9dX1zFF4Qik1GogAntZaZwA+wO5idVLMZQXm51eWY/6bDKC1LlRKZQIexcuvaOMBnNdaF5bQ11WUUpMwjWTg5eVFeHj4NZ8oQHZ2dqXbWpPEXbPqatxQd2OvbXG7Xoin08EZFNg1ZH+7F8nfFwtcfQPA2hZ3feLs7Gy0PJ8/f757enq67eHDh6MdHBy0j49Px9zcXAPwl62hbWxsdG5urqG07acrsnV1fVXZRGEu8DqgzX9nA48CJa330WWUU4k2ZfV19QGtFwALALp27ap79uxZWtUyhYeHU9m21iRx16y6GjfU3dhrVdzH98Cy18G1CbbjNtLdzbfUqrUq7nosMzPTxtPTs8DBwUGvW7fO9cSJE2UuOXF3dzc2a9Ysf/HixY1GjRp1Pjc3VxUWFqr+/ftfePXVV5tPmjTpnJubm/HPP/+0s7e318V3pKyvKnUfBa31Ka11kdbaCHyGaQ4BmH7d+xWr6gucMJf7llD+lzZKKVvADThXRl9ngUbmulf2JYQQ1hG93rRdtLOH6XJDGUmCqDkTJkw4d/DgwQYdOnRov2TJEvfWrVtfKq/NkiVL/vzkk0+aBgYGBnft2jUoOTnZdtCgQReGDBly7pZbbgkKDAwMfvDBB9ueP3/epry+6oNKjSgopby11mnmlw8Ckebna4FlSqn3gOaYJi3+obUuUkplKaVuBfYAo4GPirUZA/wOPAT8bF4NsQV4q9gExr7AC+Zj2811V5jbrqnMeQghRJU4sMx0M6XmXWDEyvq7uqGWsWwZXdyTTz6ZjmkFHQDe3t6FBw4ciCmpffGtoWfOnHnK8rxjx455u3fvjruyfmlbV9d3FVkeuRzoCXgqpVIwrUToqZS6GdOQfxLwGIDW+ohS6hsgCigEHtdaF5m7msL/L4/cZH4AfAEsNk98PIdp1QRa63NKqdeB/5nrzbRMbASmASuUUm8A+819CCFEzTsaDmv/Ca3vhGHLwb5qVwkKYW0VWfUwvITiUr+YtdZvAm+WUB4BdCih/BIwpJS+FgILSyg/yv9f7hBCCOs4EwsrR4NHADy8SJIEUS/JXg9CCFEZF8+a7pNgaw+PfAOObtaOSIhqIbdwFkKIa1VwCVaMgOxTMHYDNGph7YiEqDaSKAghxLXQGtb8A5L3wJCvwbertSMSolrJpQchhLgWv34Ikaug9wwIecDa0QhR7SRREEKIijq6A356DUIehDuesnY0N7zk5GTbAQMGtPb19e0YEhLS/uabbw5atGhRI2vHVd9IoiCEEBVxPhm+e9S0wmHgx6BKukmsqClGo5EBAwb49+jRIzslJeXwkSNHor/55pujycnJZd550aKwsN7fULHKyBwFIYQoT2YKfD0AivJh6BJwuCE2Daywl3992S8hI6FK14b6N/bPef3210vdbGrdunWudnZ2+rnnnjtjKQsMDMx/8cUXTxcWFvL444/7/vrrr675+flq4sSJp5999tmz69evd3399de9mzZtWhAVFeW8cePG+H79+gWEhYVl79u3z6V9+/Y5jz766NmZM2f6pKen23711VdHe/XqlbN9+3bnp556qsWlS5cMjo6Oxq+++urPTp065c2ZM8dj/fr1jXJzcw3Hjx936N+///l58+alvP/++56RkZFOX3zxRTLA7NmzPaOjox0///zzlNLOpzaTREEIIcpy/jh8dR/kZsDI76FJoLUjEsDhw4edbrrpppySjn3wwQeebm5uRZGRkdG5ubnqlltuCRowYMAFgEOHDjXYv3//kaCgoPzY2Fj75ORkx5UrVx4NDQ09dtNNN7VfunSpR0RERMyyZcsavfnmm969evVK7NSp06U//vgjxs7OjtWrV7s+99xzvlu2bEkEiIqKcj548GCUk5OT0d/fv8Mzzzxzavz48edCQkKC8/LyUhwcHPSSJUs858+ff6wmP5+qJImCEEKU5mwCLLof8rNg9GrwCbV2RLVSWb/8a8qoUaNa/PHHHy52dnba19c3LyYmxnnt2rWNAbKysmyioqIc7e3t9U033XQxKCgo39LOx8cnLywsLBcgMDAw9+67775gMBjo0qVLzhtvvNEc4Ny5czZDhw5tnZSU5KiU0gUFBZevO91xxx0XPDw8igD8/f0vJSYmOvj7+2fffvvtWStXrnTr2LHjpYKCAmV5j7pI5igIIURJ0g7Cwr9B4SXTJk+SJNQqHTt2zD106NDlyx2LFy8+Hh4eHpeRkWGrtVazZ88+HhMTExUTExOVmpp6eNCgQRfgr1tQw1+3mjYYDDg6OmoAGxsbioqKFMC0adN87rrrrqz4+Pgj69atS8jPzzeU1N7GxuZyEjFp0qSzX3/9tceCBQs8Ro4cWae3ppZEQQghrnTsd9PlBltHeHQzeHeydkTiCgMGDMjKy8tTs2bNamIpy87ONgD06dMnc+7cuU3y8vIUwKFDhxwuXLhQ6e+7Cxcu2Pj6+uYDzJ8/v0I7ft19990X09LS7H/44QeP8ePHnyu/Re0llx6EEKK4+K2wchS4+cCo1dDIr/w2osYZDAbWrVuX+Pjjj/vNmTOnmbu7e6Gzs3PRq6++mvLoo49mJCUlOXTs2LG91lq5u7sXbNy4MbGy7zVt2rSTEyZMaD1nzpxmPXr0uFDRdg888EDGoUOHnJs0aVJUfu3aSxIFIYSwiFwF30+CpsGmiYsuTcpvI6ymZcuWBevXrz9a0rGPP/44FUgtXnbfffdl3XfffVmW1+3atcsvvtX0qlWrkko6ds8991xMSkqKtBz78MMPT8DVW1pv3749ofj7/f777y5Tp049RR0nlx6EEAJg71fw3XjwvQXGrpckQVTa2bNnbVq1atXB0dHReP/992eV36J2kxEFIYTYPQ82TwP/PrJdtLhunp6eRcVHIOo6SRSEEDe2mI2w+XkIug8e+tK0bbQQ4jK59CCEuHGdjITvJ0LzzjD4c0kShCiBJApCiBvTxXRYPhwcGsKwZWDnZO2IhKiVyk0UlFILlVKnlVKRxcreUUrFKKUOKaV+UEo1Mpe3UkrlKqUOmB/zirUJVUodVkolKKXmKGXaUUUp5aCUWmku36OUalWszRilVLz5MaZYeWtz3XhzW/kZIISoOGMRrBoP2adg2FJo6G3tiISotSoyovAV0O+Ksq1AB631TUAc8EKxY4la65vNj8nFyucCk4AA88PS53ggQ2vtD7wPzAJQSrkDrwDdgDDgFaVUY3ObWcD7WusAIMPchxBCVMz2t+Dodrj3XfDpYu1oRCU5Ozt3tnYMN4JyEwWt9U7g3BVlP2qtLXt07gZ8y+pDKeUNNNRa/6611sAi4AHz4fuBr83PvwN6m0cb/gZs1Vqf01pnYEpO+pmP3W2ui7mtpS8hhChbzAbY9S50GW16iHpFto+uelWx6uFRYGWx162VUvuBC8BLWutdgA9QfHvNFHMZ5r/JAFrrQqVUJuBRvPyKNh7A+WKJSvG+rqKUmoRpJAMvLy/Cw8MrcYqQnZ1d6bbWJHHXrLoaN9Td2K8lbqecE4TufZocV38ONBiA0YrnW1c/75KcmP6iX158fJWuKXUICMhp/tabFdps6srtoxMTE4/cc889bdPS0uzz8vIMkydPPvXMM8+cBdMoxPjx40//+OOPbo6Ojsb169cn+Pn5SXZRhutKFJRSLwKFwFJzURrQQmudrpQKBVYrpUIAVUJzy0YapR271vISaa0XAAsAunbtqnv27Fla1TKFh4dT2bbWJHHXrLoaN9Td2Cscd/5F+PwesHek4fgfuLNRi2qPrSx19fOurYpvHw2wdOnSJC8vr6Ls7GzVuXPn4JEjR2Y0a9asKDc313Dbbbdlf/TRR6mTJ0/2/eijj5r897//TbN2/LVZpRMF8+TC+4De5ssJaK3zgDzz871KqUQgENOv/uKXJ3yBE+bnKYAfkKKUsgXcMF3qSAF6XtEmHDgLNFJK2ZpHFYr3JYQQVzMaYfUUOB0No74HKycJ9U1Ff/lXpyu3j541a5bXhg0bGgGcPHnS7siRI47NmjW7aGdnp4cNG5YJEBoaenHbtm0NrRVzXVGp5ZFKqX7ANGCg1jqnWHkTpZSN+XkbTJMWj2qt04AspdSt5jkGo4E15mZrAcuKhoeAn82Jxxagr1KqsXkSY19gi/nYdnNdzG0tfQkhxNW2vgxRa6DvG9D2bmtHI6pB8e2j169f77pjxw7XiIiImNjY2Kj27dvn5ubmGgBsbW21wWD66rO1taWwsLCkUWpRTLkjCkqp5Zh+2XsqpVIwrUR4AXAAtppXOe42r3C4E5iplCoEioDJWmvLRMgpmFZQOAGbzA+AL4DFSqkETCMJwwC01ueUUq8D/zPXm1msr2nACqXUG8B+cx9CCHG1PQvg948hbBLc9ri1oxE14Pz58zZubm5Frq6uxv379zsePHiwgbVjqsvKTRS01sMZ1RsBAAAgAElEQVRLKC7xi1lrvQpYVcqxCKBDCeWXgCGltFkILCyh/CimJZNCCFG6mA2mPRza/R36vQ1KfjzeCAYPHpy5YMGCJoGBgcFt27a91KlTp4vWjqkuk70ehBD1U8pe026Q3jfD4C/AYGPtiEQVy8nJ2Q9Xbx/t5OSkd+7cGV9WG4Bx48ZljBs3LqP6I63b5BbOQoj6Jz0Rlg8Fl6YwYqXsBinEdZBEQQhRv5w/Dl8PBG2ER74zJQtCiEqTSw9CiPoj6yQsuh/ys2DMOmgSaO2IhKjzJFEQQtQP+Rdh2cOQdQpGrwHvTtaOSIh6QRIFIUTdZzTCD49B2iHTnAS/W6wdkRD1hiQKQoi6L/wtiF4Hfd+EwL9ZOxoh6hWZzCiEqNMan9sHO9+Bm0fKDZVuMJZtpmNjY+3nzZvnXl792NhY+4CAgJDqj6x+kURBCFF3ZZ2kffQH0DQY7n1Xbqh0g4qPj3dYuXJluYmCqBy59CCEqJvM8xJsinLhoYVg52TtiG5YPy2K9juXml2lN6tw93HJ6T26fYU2m3rxxRd9jh496hgUFBQ8fPjws8OGDTs/YsSI1pb9HT788MPjffr0+cvdGUNDQ9t99NFHx7t3754L0KVLl6C5c+ce69atW25Vnkd9IImCEKJu2vslHA0nIfAftGva3trRCCt68803U2fPnu21ffv2BICsrCzDrl274pydnfXhw4cdhg8f3iYyMjK6eJuxY8ee/fzzzz27d++efOjQIYf8/HwlSULJJFEQQtQ9WSdh22vQ+k7SvPvSztrx3OAq+su/puTn56vx48e3jIqKcjIYDBw7dszhyjpjx47NeOedd7zz8vJS5s2b5zlixIiz1oi1LpBEQQhR92x+HgovwX0fwOFa9R0laoE333zTq2nTpgWrVq3602g04uTkFHplHVdXV2OPHj0uLFu2rNHatWvd9+7dG2WNWOsCSRSEEHVL7GY48gP0egk82gKSKNzo3NzcirKzsy/v+pWZmWnj6+ubb2Njw8cff+xRVFRUYrvJkyefHTx4sP8tt9yS7eXlVXIlIasehBB1SNZJWPM4eHWA25+0djSilggLC8u1tbXV7dq1C37ttdeaTp069fTy5cs9OnXqFBQXF+fo5ORkLKldjx49cho0aFA0btw4uexQBhlREELUDUYj/DDZdKvmwV+A7VWXncUNxrJltIODg/7999/jih+Li4u7fCnhk08+SQVo165dfnx8/BFLeVJSkp3WWj344IMXairmukhGFIQQdcNvc+Doduj3H2gaZO1oRB338ccfe9x6663tZ8yYkWpjY1N+gxuYjCgIIWq/pF/gp5kQfD+EjrV2NKIeeOKJJ9KfeOKJdGvHUReUO6KglFqolDqtlIosVuaulNqqlIo3/21c7NgLSqkEpVSsUupvxcpDlVKHzcfmKGW6hZpSykEptdJcvkcp1apYmzHm94hXSo0pVt7aXDfe3Nb++j8KIUStdOEEfDsW3NvAwI/l7otC1LCKXHr4Cuh3RdnzwE9a6wDgJ/NrlFLBwDAgxNzmU6WUZUxnLjAJCDA/LH2OBzK01v7A+8Asc1/uwCtANyAMeKVYQjILeN/8/hnmPoQQ9U1hHnwzBvJzYOgScGxo7YiEuOGUmyhorXcC564ovh/42vz8a+CBYuUrtNZ5Wus/gQQgTCnlDTTUWv+utdbAoivaWPr6DuhtHm34G7BVa31Oa50BbAX6mY/dba575fsLIeoLoxFWT4GUP+CBT2ReghBWUtk5Cl5a6zQArXWaUqqpudwH2F2sXoq5rMD8/MpyS5tkc1+FSqlMwKN4+RVtPIDzWuvCEvq6ilJqEqaRDLy8vAgPD7+mE7XIzs6udFtrkrhrVl2NG2pf7G0Sv6ZF8vckthlD8pnGUEpstS3uiqqrcYsbT1VPZizp4qEuo7wybcrq6+oDWi8AFgB07dpV9+zZs7SqZQoPD6eyba1J4q5ZdTVuqGWx754Lyd9D1/G0vXc2bcuYl1Cr4r4GdTXu2sTZ2bmzZYmkqD6VXR55ynw5AfPf0+byFMCvWD1f4IS53LeE8r+0UUrZAm6YLnWU1tdZoJG57pV9CSHqur1fm27RHHQf9P+vTF4UwsoqO6KwFhgDvG3+u6ZY+TKl1HtAc0yTFv/QWhcppbKUUrcCe4DRwEdX9PU78BDws9ZaK6W2AG8Vm8DYF3jBfGy7ue6KK95fCFGXRX4P6/4F/veYto62kRXcdcGWuR/4nU0+VqXbTHv6tcz525Sp5d6fOzMz09CvXz//zMxMm8LCQjVjxowTI0eOPB8bG2vfr1+/gM6dO1+MjIx0btOmzaVvv/02ydXV1fjMM894b968uVFeXp6ha9eu2UuXLj1mMBgICwtrFxoamv3LL780zMrKspk3b15Sv379sqvyvOqiiiyPXI7pS7ydUipFKTUeU4LQRykVD/Qxv0ZrfQT4BogCNgOPa60t98+eAnyOaYJjIrDJXP4F4KGUSgCewryCQmt9Dngd+J/5MdNcBjANeMrcxsPchxCiLjsZCav/AX7d4OHFcudFUSHOzs7GDRs2JERFRUXv2LEjbvr06b5Go+mOzUlJSY6TJ08+ExcXF+Xq6mp85513mgA8++yzpyMjI6Pj4+OP5ObmGlasWOFm6a+wsFAdPnw4etasWckzZ85sbqXTqlXKTde11sNLOdS7lPpvAm+WUB4BdCih/BIwpJS+FgILSyg/imnJpBCiPriUCd+MAkc3eHgR2Ffpj1NRzSryy7+6GI1GNXXqVN/du3e7GAwGTp8+bZ+SkmIL0KxZs/y+ffteBBg1alT6nDlzmgKnNm3a5Pree+81u3TpkuH8+fO2wcHBuUAmwJAhQzIAunfvfvHZZ5+Ve/Qgd2YUQlib1qaRhPPHYcx6cPWydkSiDpk/f757enq67eHDh6MdHBy0j49Px9zcXAOAumJ+i1KKnJwc9fTTT7fcs2dPlL+/f8FTTz3V/NKlS5dH1x0dHTWAra0tRUVFMkEG2etBCGFth76BmPXQ+xVoeZu1oxF1TGZmpo2np2eBg4ODXrduneuJEycujwKkpaXZb9u2rQHAsmXL3Lt3756dk5NjAGjWrFlhZmamYd26dY1L61uYSKIghLCe7NOweRr4hsFtj1s7GlGHFBQUYG9vrydMmHDu4MGDDTp06NB+yZIl7q1bt75kqdOmTZtLCxcu9AgMDAzOyMiwfeaZZ854enoWPfLII2eCg4ND+vfv79+pU6eL1jyPukAuPQghrGfjs6Zto+//GAyyg5+ouIiICCc/P788b2/vwgMHDsRceTw2NtbeYDCwbNmy41cemzNnzok5c+Zctaz+jz/+iLU89/b2LkxNTT1c9ZHXPTKiIISwjtjNELUa7poGTdpZOxpRh/z3v/9tMnLkyDavvfZaqrVjuRHIiIIQouYVXDJdcvBsB7f/y9rRiDrmueeeO/Pcc8+dKatOu3bt8uPj44/UVEz1mSQKQoia9/tHkJEEo1aDjZ21oxFClEEuPQghalZmCux6D9oPhLa9rB2NEKIckigIIWqO0QjrpoI2wt+uui+bEKIWkksPQoia89scSNgKf38XGrWwdjRCiAqQEQUhRM04vgd+mgnB98MtE6wdjagHnJ2dO1uer1y50q1ly5Yd4uPjrXLb5Q8++MAjMDAwODAwMDggICBkyZIljSrTz2+//ea0cuXKy3tPPPXUU81nzJhh1duVyoiCEKL65ZyD7x6FRn4w8CPZOlpUqTVr1rg+88wzfps3b44PCAjIr0ibgoIC7OyqZiJtYmKi3ezZs70PHDgQ7eHhUZSZmWlIS0ur1PdrRESEc0RERIOhQ4dmVklwVUASBSFE9bLs5ZB9Csb/aNr4SdQr576L8ys4ebFKd/Kya9Ygx/2hwHI3m9q8ebPL448/3mrdunXxISEheQBxcXH2Y8aMaZWenm7r4eFRuGjRoqSAgID8wYMHt2rcuHHh4cOHnW+66aac2bNnnxg/fnyL6Ohop6KiIvXiiy9e3qJ6xIgRrS17Rnz44YfH+/TpU+odHNPS0uwaNGhgdHNzKwJwc3Mzurm55YNphGDKlCktc3NzDS1btsxbtmxZUpMmTYrCwsLavfvuu8l33nlnTlpamm3Xrl3bJyYmRv7nP/9pfunSJUNQUJDL008/nQYQHR3tFBYW1u7EiRP2kydPPvXSSy+drorPuKLk0oMQonrt/hTiNkHfN8Cni7WjEfVIfn6+Gjp0qP+qVasSOnfufPnWzZMnT24xYsSI9Li4uKihQ4emT5kyxc9yLDEx0fHXX3+N++yzz1KmT5/u3atXrwuRkZHRu3btin3ppZd8L1y4YGjevHnhrl274qKioqJXrlx59N///neZE2puvfXWHE9PzwI/P7+ODz30UKtly5ZdzobHjh3b+q233kqJi4uLCgkJyZ02bVqpW1c7OjrqF1544cSAAQMyYmJioiZOnJgBkJCQ4Lhjx464//3vf9Hvvvtu87y8vBodkpMRBSFE9Tm+B7a+AkH3QbfHrB2NqCYV+eVfHezs7HSXLl2y582b59mtW7fLMezfv7/Bpk2bEgGmTJly7rXXXvO1HBs0aFCGra3pqy88PLzhli1bGs2ZM6cZQF5enkpISLBv2bJlwfjx41tGRUU5GQwGjh075lBWHLa2tuzcuTN+x44dzj/++GPD559/3i8iIqLBiy++eCorK8vm3nvvzQaYOHFi+pAhQ9pc63n27dv3vJOTk3Zycip0d3cvSElJsW3btm3BtfZTWTKiIISoHpmpsHKkaV7C/R/LvARR5ZRSrF279uiBAwcaPP/8880q0sbFxcVoea615rvvvkuIiYmJiomJiUpLSzvcpUuXS2+++aZX06ZNC6Kjo6MOHz4cVVBQUO53pcFgoFevXjn/+c9/Ti5ZsuTo+vXry5zMaGtrq4uKigDIyckp8x+Hg4ODtjy3sbGhsLCwRv8xSaIghKh6BZdMSUJBDgxbDk6yk6+oHq6ursbNmzfHf/fddx7vv/++J0Dnzp0vfv75540B5s+f7961a9fsktr26tXrwuzZs72MRlPu8OuvvzqBaetqb2/vAhsbGz799FMPyxc6QOvWrUOu7CcpKcnul19+uTxHIyIiwtnHxyffw8OjqGHDhkWbN292Afjiiy88brvttmwAPz+/vD/++KMBwNKlSy//A2nYsGFRdnZ2rfpulksPQoiqpTWsnwon9sGwZdA0yNoRiXrOy8uraPPmzXF33XVXUJMmTQrnzp17fMyYMa0+/PDDZpbJjCW1e/vtt09MmjSpRVBQULDWWvn6+uZt3749YerUqacHDx7cdvXq1Y3vuOOOLCcnJyNAWlqardb6ql/z+fn56plnnvE9deqUnYODg3Z3dy/47LPPjgN8+eWXf06ZMqXlk08+aWjRokXe8uXLkwCef/75U0OHDm2zYsUKjx49elyw9NW/f/+sd9991zsoKCjYMpnR2iqdKCil2gErixW1AWYAjYCJgGXDjula643mNi8A44Ei4Emt9RZzeSjwFeAEbAT+pbXWSikHYBEQCqQDQ7XWSeY2Y4CXzO/xhtb668qeixCiCu2eCweXQ88XIOhea0cj6rGcnJz9luf+/v4FxbeF3r17d9yV9VetWpVU/LWLi4tetmzZsSvrdezYMS8uLi7K8vqTTz5JBQgPD28wYcKEq1YcBAYG5pf0fgDdu3fPPXjw4FXbYHfu3PlS8fewbHvt5eVVFBkZGV2sakbxdtbY6KrSiYLWOha4GUApZQOkAj8A44D3tdbvFq+vlAoGhgEhQHNgm1IqUGtdBMwFJgG7MSUK/YBNmJKKDK21v1JqGDALGKqUcgdeAboCGtirlFqrtf7LByqEqGFHw+HHl0yTF+98ztrRCFGlhg8fXmvubVCTquo6SG8gUWt9VWZWzP3ACq11ntb6TyABCFNKeQMNtda/a601phGEB4q1sYwUfAf0Vkop4G/AVq31OXNysBVTciGEsJZzf8K3Y8EzEB6cB4ZadZlVCFFJVTVHYRiwvNjrJ5RSo4EI4Gnzl7kPphEDixRzWYH5+ZXlmP8mA2itC5VSmYBH8fIS2vyFUmoSptEKvLy8CA8Pr8TpQXZ2dqXbWpPEXbPqatxwfbHbFObSef80HAry2df6X+T+vrdqgytDXf3M62rc4sZz3YmCUsoeGAi8YC6aC7yO6ZLA68Bs4FGgpOUcuoxyKtnmr4VaLwAWAHTt2lX37NmzpGrlCg8Pp7JtrUnirll1NW64jti1hm/HQE4yPPId3fx7V3lsZamrn3ldjVvceKpibLA/sE9rfQpAa31Ka12ktTYCnwFh5nopgF+xdr7ACXO5bwnlf2mjlLIF3IBzZfQlhKhp+5dA1Bq451Wo4SRBCFH9qiJRGE6xyw7mOQcWDwKR5udrgWFKKQelVGsgAPhDa50GZCmlbjXPPxgNrCnWZoz5+UPAz+Z5DFuAvkqpxkqpxkBfc5kQoiZdOAFbXoSWd8Bt/7R2NEKIanBdiYJSyhnoA3xfrPi/SqnDSqlDQC/g3wBa6yPAN0AUsBl43LziAWAK8DmmCY6JmFY8AHwBeCilEoCngOfNfZ3DdFnjf+bHTHOZEKKmaA3r/w1F+TBwjkxeFDVOKRU6ceLEyyPSM2bM8HrqqadK3UsBYP369a5bt25tYHk9ePDgVl9++eV13RHMx8enY2V3i7xS8a2za4vrOjGtdQ6myYXFy0aVUf9N4M0SyiOADiWUXwKGlNLXQmDhNYYshKgqh7+FuM3wt7fAo621oxE3IHt7e71x48bGaWlpJ729vQsr0ubnn392dXFxKSprN8iKMhqNmAa56ze5M6MQ4tpdSIONz4BfN+g22drRCCtbvXq13+nTp6t0m+mmTZvmPPDAA2VuNmVjY6NHjx595q233vL66KOPUosfO3HihO24ceNapqam2gO89957x1u2bFmwaNGiJgaDQX/zzTceH3zwwXGAHTt2uMyZM8frzJkzdq+//nrKuHHjMgBefvllrx9++ME9Pz9f3Xvvvefff//9E7Gxsfb9+/cP6N69e9bevXtd1qxZk1D8fe+55562aWlp9nl5eYbJkyefeuaZZ86CaaRg/Pjxp3/88Uc3R0dH4/r16xP8/PwKY2Ji7IcNG9amsLBQ9e7d+/J9Go4dO2Y3ePDgNtnZ2TZFRUXqo48+OtavX78Sb0Vd3WSsUAhxbbSGdf+Cwny4/1Mw2Fg7InEDe/bZZ09///337unp6X/5D/Gxxx7ze+qpp05FRkZG//DDD4mTJ09u1a5du/zRo0efmTx58qmYmJgoyxfvqVOn7CIiImLWrFkT/8orr/gAfP/99w0TEhIcDx06FB0dHR114MAB502bNrkAJCUlOY4bNy49Ojo6KjAwML/4+y5dujTpyJEj0QcOHIiaP3++18mTJ20AcnNzDbfddlt2bGxs1G233Zb90UcfNQH4xz/+0WLChAlnIiMjo5s1a3Z5R8iFCxe69+7dOzMmJiYqOjr6SLdu3XKq95MsnYwoCCGuzYGlEL8F+r0Nnv7WjkbUAuX98q9O7u7uxiFDhqS//fbbTS17MgD8+uuvDePj450sr7Ozs20yMjJK/HE8cODA8zY2NoSGhl5KT0+3A9i8eXPDnTt3NgwODg4GyMnJMcTExDi2adMm39vbO793794lXrqYNWuW14YNGxoBnDx50u7IkSOOzZo1u2hnZ6eHDRuWCRAaGnpx27ZtDQH27dvnYtkS+7HHHkt//fXXfQFuvfXWi4899lirgoICw0MPPZTRvXv33Kr4vCpDEgUhRMVlpsDmF6Dl7RD2mLWjEQKAF1544VSXLl2Chw0bdtZSprUmIiIi2sXFpdxJBI6OjpfrWOYcaK2ZOnVq2rPPPnu2eN3Y2Fh7Z2dnIyVYv369644dO1wjIiJiXF1djWFhYe1yc3MNYNpW2mCe8Gtra/uXraINBsNVMfbv3z97586dsatWrXIbO3Zs6yeffPLUE088kV7euVQHufQghKgYrWHNE2Asgvs/kVUOotbw8vIqGjBgQMayZcs8LWV33HHHhVmzZjW1vP7tt9+cAFxdXYuysrLKvV7Wv3//C4sXL/bMzMw0APz55592qampZf64Pn/+vI2bm1uRq6urcf/+/Y4HDx5sUFZ9gC5dumR/9tln7gCfffbZ5cUBcXFx9j4+PgVPP/302ZEjR57dt29flc4BuRbyL10IUTF7v4Sj26HvTHBvbe1ohPiLF1988eT58+cvf5EvWLAged++fQ0CAwOD27ZtG/Lxxx83ARg8ePD5DRs2NAoKCgrevHmzS2n9DRo06MKQIUPO3XLLLUGBgYHBDz74YNvz58+XmWAMHjw4s7CwUAUGBgZPnz69eadOncpdWfHpp58eX7BgQdMOHTq0z8zMvNz/li1bXIODg0Pat28fvGbNmsbPPffcqYp9ElVP3QhLOyy6du2qIyIiKtW2rt5uVeKuWXU1bign9vREmNcDfLvCqNW1ajShrn7mdSlupdRerXXX4mUHDx5M6tSp09nS2oi65eDBg56dOnVqVdKx2vOvXQhROxXmmXaFtLWHBz6tVUmCEKL6yWRGIUTZts6Ak4dg2HJw8y2/vhCiXpGfBkKI0kWvgz3zoNsUCPq7taMRQliBJApCiJKdiYMfJoNPKPR5zdrRCCGsRBIFIcTVLl2AFSPA1hEeXgy2DtaOSAhhJTJHQQjxV8Yi+OExOHcURq8BNx9rRySEsCIZURBC/NW2VyF2I/T7D7TuYe1ohCiVjY1NaFBQUHBAQEBI//7922RlZV3Td9rzzz/frDriio2NtQ8ICAipjr6tQRIFIcT/27cIfpsDt0yEbnKLZlG7OTg4GGNiYqLi4+OP2NnZ6dmzZzepSDuj0UhRURFz5szxru4Y6wO59CCEAMAlKwF2Pg9t7zZt+CREBUVFT/O7mB1XpbcYbuASmBPcflaFN5u64447sg8dOuQE8Oqrr3otXbrUE2DUqFFnZsyYcfrK7aFDQkJy8vLyDEFBQcGBgYG577zzTup9990XEB8ffwRgxowZXtnZ2TbvvffeiR07djhPnDixlbOzs7Fbt27ZP//8s1t8fPyR2NhY+xEjRrS27Ofw4YcfHu/Tp0+5d2OsayRREEJAXhbBUe+CS1MY/AXYyP81iLqjoKCALVu2NOzbt++FXbt2OS9btsxj79690VprQkND2/fu3TvL09OzKCkpyfGzzz5LWrJkyXEAZ2fnxjExMVFgulxQWv8TJkxo/emnnyb16dPn4j/+8Y/Lk3aaN29euGvXrjhnZ2d9+PBhh+HDh7eJjIyMrv4zrlny/wZCCNjwDE65p2DoenB2t3Y0oo65ll/+VckyIgDQrVu3rH/9619n33nnnSZ///vfzzds2NAIcO+992Zs377ddciQIefL2h66NGfPnrW5ePGiwTJSMGbMmHNbt25tBJCfn6/Gjx/fMioqyslgMHDs2LF6uTzouhIFpVQSkAUUAYVa665KKXdgJdAKSAIe1lpnmOu/AIw3139Sa73FXB4KfAU4ARuBf2mttVLKAVgEhALpwFCtdZK5zRjgJXMob2itv76ecxHihnVwBRxaQVKr4bRudbu1oxGiwixzFIqXlbV/UWnbQ4NpG2ij8f8PX7p0yVBef2+++aZX06ZNC1atWvWn0WjEyckp9BrCrzOqYjJjL631zcU2DHke+ElrHQD8ZH6NUioYGAaEAP2AT5VSlp2y5gKTgADzo5+5fDyQobX2B94HZpn7cgdeAboBYcArSqnGVXAuQtxY0hNh/VPQ8naOtRxi7WiEuG5333139saNGxtlZWUZLly4YNi4cWPjXr16ZZVU19bWVufl5SkAX1/fwnPnztmePHnSJjc3V23ZssUNoEmTJkUNGjQw/vTTTw0AFi9efHnILTMz08bb27vAxsaGTz/91KOoqKgmTrHGVceqh/sBy6/7r4EHipWv0Frnaa3/BBKAMKWUN9BQa/27NqVui65oY+nrO6C3UkoBfwO2aq3PmUcrtvL/yYUQoiIK8+C7cabNngZ9BqrMHXSFqBPuuOOOnBEjRqR36dKlfWhoaPtRo0aduf3223NLqvvII4+cad++ffDAgQNbOzg46KeffjotLCysfe/evf39/f0vWerNnz8/acqUKS1vvvnmIK01rq6uRQBTp049vXz5co9OnToFxcXFOTo5OZU6YlGXXdc200qpP4EMQAPztdYLlFLntdaNitXJ0Fo3Vkp9DOzWWi8xl38BbMJ0eeJtrfU95vIewDSt9X1KqUign9Y6xXwsEdMowljAUWv9hrn8ZSBXa/1uCTFOwjRagZeXV+iKFSsqda7Z2dm4uJS6dXmtJXHXrLoUd5vEr2mR/D2HO0wn3bNbnYq9OIm7+vXq1euG3mY6MzPT4ObmZgSYPn16s7S0NLsvv/zSKvMyqktZ20xf72TG27XWJ5RSTYGtSqmYMuqqEsp0GeWVbfPXQq0XAAsAunbtqiu7/3td2ju+OIm7ZtWZuE8ehh1roPNIOt4/DahDsV9B4hbV7ZtvvnGbPXu2d1FRkfLx8clbtmxZkrVjqknXlShorU+Y/55WSv2Aab7AKaWUt9Y6zXxZ4bS5egrgV6y5L3DCXO5bQnnxNilKKVvADThnLu95RZvw6zkXIW4YxiJYNxWcGkOf160djRC13sSJEzMmTpyYYe04rKXScxSUUg2UUq6W50BfIBJYC4wxVxsDrDE/XwsMU0o5KKVaY5q0+IfWOg3IUkrdap5/MPqKNpa+HgJ+Ns9j2AL0VUo1Nk9i7GsuE0KUJ2IhpEaYbtEsSyGFEOW4nhEFL+AH03c7tsAyrfVmpdT/gG+UUuOB48AQAK31EaXUN0AUUAg8rrW2TBGdwv8vj9xkfgB8ASxWSiVgGkkYZu7rnFLqdeB/5noztdbnruNchLgxXEiDba9Bm17QUVY5CCHKV+lEQVJL6iIAACAASURBVGt9FOhUQnk60LuUNm8Cb5ZQHgF0KKH8EuZEo4RjC4GF1xa1EDe4zdPAWAD3vQeqpKk+QgjxV7IplBA3itjNELUG7nwW3NtYOxohRB0hiYIQN4K8bNj4DDRpD92ftHY0QlSJadOmNfP39w8JDAwMDgoKCv75558bXGsfS5cudZs+fXq1bDddX8heD0LcCLa+DJkp8OgW0w2WhKjjtm3b1mDLli2NDh8+HOXk5KTT0tJsLXdZvBaPPPJIJpBZDSHWG5IoCFHfJfxkWunQ/Z/Qopu1oxH10NTo434xFy9V6TbTQQ0ccz5o36LUmxqlpqbaubu7Fzo5OWkAb2/vQgAfH5+OAwcOPPfLL780BFi+fPnRDh065C1btszt7bff9i4oKDA0bty4cOXKlUf9/PwK58yZ4xEREdFg0aJFxwcPHtzK1dW16ODBgw3OnDlj9/rrr6eMGzfuhl0WaSGXHoSoz3LPw9p/gmc76PVS+fWFqCMeeOCBCydOnLBv1apVh5EjR7bYsGHD5dtcNmzYsOjw4cPRjz322Ol//vOffgB9+vTJPnDgQEx0dHTUQw89dG7mzJklXm44deqUXURERMyaNWviX3nlFZ+S6txoZERBiPps03OQdRImbAU7R2tHI+qpsn75Vxc3NzdjZGRk1ObNm11/+ukn1zFjxrSdMeP/2jvzMLuKMnG/dc65a9/bt/dOOulOd/bQSTqxQwKEJDDKIgZFg7KoJBnUUUScYQZxdEZG+I3jBoOjPiCDDsQZNsEBJoJKxAAGISSQxSydPZ1O7/f2dvd7zqnfH+f2kqWzdJZOh3qfp3Lq1KnlOze3b31V9VV932oAxxU0wOc///nIP/3TP5UD7N27133dddeNbWtrc6XTaa28vDx1tHo/+tGPduq6Tm1tbTIcDrvO3hudu6gZBYXifGXzs7DpaVh0N4w5L73fKt7nGIbB4sWLe/793/+98Qc/+EH9888/nw+gaf1dmxBCAtx+++0Vt912W+uOHTu2/uQnP9mfSqWO2v95vd4+dwCn4gvpfEIpCgrF+UhnveM+unweLPj74ZZGoTjtbNy40bN582ZP7/17773nGzt2bBpgxYoVBQA///nP82fPnh0D6Onp0SsqKjIAjz32WOFwyDxSUUsPCsX5hm3B/34RpA0f/xno6s9ccf7R3d2t33HHHRXd3d26ruuysrIy9fjjj++fM2dOKJVKiZkzZ061bVs89dRTewC++c1vNt50000TSktL03PmzInV19d7jteGwkH9gigU5xtrHoT9a+C6h6GgarilUSjOCAsWLIi/9957R/VY/A//8A9t999/f9PAtM985jOdn/nMZzoPz3vHHXeEgTDAc889t2/gs3g8/t7pk3jkopYeFIrziYPvwh+/A9Ufh5obh1sahUJxHqBmFBSK84VUD/z68xAohcX/rnw5KN6XHDx4cPNwy3C+oRQFheJ8QEp48Q6I7IFbXgRf/nBLpFAozhPU0oNCcT6w9hHY8mv4q3+CqgXDLY1CoTiPUIqCQjHSObgefvdNmHw1zP+74ZZGoVCcZyhFQaEYyZhpeOF2yCmGjz8MmvqTVigUpxf1q6JQjGTWPAitW2HxA8ouQfG+oq6uzj1p0qTqgWl33nln2be+9a3SY5V7/fXX/cuWLSsHWLlyZfCVV145adfUY8aMmdHU1HSEjd/A9DfeeMM/ZsyYGWvWrPGdTlfWK1euDF5++eUTT0ddJ4oyZlQoRiptdfD6D6D6EzDlw8MtjeIcwbYlnYkM7dEU4WiazniaSDzNx2aNIeBRP/kLFy6ML1y4MA7w6quvBgOBgHXFFVfETmcbb7/9tu/GG2+c8N///d+758+fn5g/f36CEezKesjfGiFEObACGAXYwCNSyh8JIf4F+DzQls36DSnlS9ky/wjcCljAHVLK32XTa4HHAB/wEvBVKaUUQniybdTiHIhxg5RyX7bMUqDXHd7/k1I+PtR3UShGHJYJL3wZ3Dnw4e8PtzSKM0wibdEeTfV1/uFYivZomnA07aTFUtl4mkgshX0UFwUXVhYwuTR4RuS769mN5Tuae06rm+nJo4LxH1xfM2RnU3Pnzp1SW1sb/dOf/pTb09OjP/zww/uuvvrq6MqVK4P3339/6cMPP1y/YsWKYk3T5DPPPFP44IMP1s+cOTO5fPnycQcPHnQDPPDAA/VXXnllrLm5WV+yZMn4SCTimj17duxYPiA2btzo/dznPlf1i1/8Yu/ll18eBzgRV9aWZbF06dKKt956K1heXp6ybZtly5aFly9f3vHss8/m3nXXXeUFBQXmjBkz4r1ttbS06J/+9Kcr6+vrPT6fz37kkUf2z5s3L3HnnXeW7du3z93S0uLat2+f9zvf+c6BP//5z4FXX301t7S0NLNq1apdHo/nhB1ZnIp6aQJ/L6V8VwgRBNYLIV7JPvt3KeUPB2YWQlwA3AhUA2XAKiHEZCmlBTwEfAF4C0dRuBp4GUep6JBSThRC3Ah8D7hBCFEA3APMAWS27RellO97v+GK9wlrHoSGd+ATj0KgeLilUZwGoimTgx0JGjsTNHQmONiRYFdrD1sbu2nsSh61TI5bpyjooTDHTXmBn9kVeRTmeCgKuCkMeCjIcfeFosD778Ri0zTF5s2btz399NOhe++9t+zqq6/e0ftsypQp6VtuuaUtEAhY9957bwvAtddeW3XnnXe2XHXVVdGdO3e6r7rqqkl79uzZ8vWvf73s4osvjv7whz9seuqpp0JPPvlk0WBt3nDDDRMfeeSRvVdddVV0sDy9rqw3bNjg/fjHPz5x+fLlHStWrMg/cOCAu66ubsvBgweN6dOnT1+2bFk4Ho+L22+/vfKVV16pq66uTi1evHh8bz1f+9rXympqauKrVq3a/eKLLwaXLl1atX379q0A+/fv97z55ps73n33Xe9f/dVfTX388cd3P/zwww1XXHHFhGeeeSb02c9+9ohTKgdjyIqClLIJaMrGe4QQ24Bj+e7+GPCUlDIF7BVC7ALmCiH2AblSyj8DCCFWANfhKAofA/4lW/5Z4CdCCAFcBbwipYxky7yCo1w8OdT3UShGDE2bYPV3ndMXZ1w/3NIojkHKtDgQidMRz+DSNaSUNHclOdiZ4O1tKf6nfh0HOxIc7EzQlcgcUtalC8YV5nBhlTMTUBzMKgA5HgqzV59bH6Y3O5RTGfkPFTHIgWID0z/5yU92AFxyySWxu+66y328OtesWZO7c+dOX+99NBrVOzo6tLfeeiv461//ehfAjTfe2PU3f/M31mB1zJ8/v/vnP/950ZIlS7oM4+hd7NFcWb/xxhuBT3ziEx26rlNRUWFedNFFPQAbNmzwjh07NjVjxowUwKc//enwo48+Wgywdu3a4HPPPbcrW2fPF77wBSMcDusAH/rQh7o8Ho+cO3duwrIscf3113cDVFdXJ/bu3Xvcz2Igp2XBSghRCcwG3gbmA7cLIW4B1uHMOnTgKBFvDSjWkE3LZOOHp5O9HgCQUppCiC6gcGD6UcocLtsXcGYrKC0tZfXq1UN6x2g0OuSyw4mS++xypuUWdoba9X+PywjwTt4nMF977bTVrT7zoWFLSUdS0hKXNMVsmmM2LTFJc9ymLS4ZbH7Xo0uKfK0U+jRqiwSFPheFPo0ir6DQJwh5BJqQOEvbXRAFotCBE3ZKZ1rXBDKIQ6698QxgZq8XYOI9jw7rLC0tNbu6ug7RlCKRiF5VVZXqve91GW0YBpZlHfftpZSsW7duWyAQOOK/TTvBHUX/+Z//Wb98+fJxt9xyy7gnnnhi/9HyHM2V9bGWMwZTio5Wptetdu/Sgq7rGIYhe+XXNA3TNE/qm3DKioIQIgA8B/ytlLJbCPEQcB/OksB9wP3AXwNHE0weI50hljk0UcpHgEcA5syZIy+77LJB3+VYrF69mqGWHU6U3GeXMy73K9+C2H64+VdcOvnK01q1+swHx7QlrfEUu1qj7G6Lsbc9xv5wjPr2OAcjCdKm3ZfX49IozvcxeqyX6jwveXlePF6dlGmTtiVevwu336ChpZGiUWWkLJu0JWm2bfZbNmkpSdmSjC1JS6dMWjohIyGDzIaT4/cXjGdmae7p/WCGkVAoZJeUlGReeOGF4Mc+9rGelpYWffXq1aG77rqr9UTrCAaDVnd3d5+ycemll3Z/73vfK7nvvvtaAN58803fJZdckrjooot6fvGLXxR+//vfb3rmmWdyB5Y5HE3TeOGFF/YsWrRo8t/+7d+WPfjgg40nIsuCBQuiv/zlLwtvv/32cGNjo/H2228Hb7rppsisWbOSDQ0N7i1btniqq6tTTz31VEFvmYsuuqjnv/7rvwp/8IMfNK1cuTKYn59vFhQU2MdqZyickqIghHDhKAn/I6X8NYCUsmXA8/8EVmZvG4DyAcXHAo3Z9LFHSR9YpkEIYQAhIJJNv+ywMqtP5V0UinOe/X+GNf8BtcvgNCsJ5ytp2yZm2cStgVerLx63bOK2Tcx00uO2TWs0zY7mbg6G46S70shoBhE1EZn+318pQPp0ZI6BHOND5riQfgM7xyDp0egSgl19uZOQ6pdJ60mgd4FueTD2h9FtiW6DbkkMG+feAiOb7rMkQRsnjy0xrP48ut1fxhhwbwAuBC7pXA2gZOr5txv+8ccf33vbbbdV3H333eUAd999d2N1dXXqeOV6WbJkSef1118/4eWXX8578MEH6x955JEDn/vc5yomT558gWVZYt68eT2XXHJJ/Xe/+93GJUuWjL/gggumXXzxxdHRo0enj1Wvz+eTL7/88q758+dP+bd/+7dMTk7OcTvvpUuXdqxatSo4efLk6qqqqmRNTU0sLy/P8vv98sc//vH+xYsXTywoKDDnzZsX3bZtmw/ge9/7XuPNN99cOXny5At8Pp/92GOP7T3Rdz8ZxLGmO45Z0JkLeRyISCn/dkD66Kz9AkKIvwPmSSlvFEJUA08Ac3GMGf8ATJJSWkKId4Cv4CxdvAT8WEr5khDiy8AMKeUXs8aMn5BSfiprzLge+EC22XeB2l6bhcGYM2eOXLdu3ZDeV422zi5K7sNI9cDDlzrxL64BT+C0N3E2PvPOjMmmngQAl+YH0I7huMqSkkjGJJwxCadNwhmLqGWRtiVJy6bDtOjImNQ1NmHkFdBjWsRMy+n0bZuEbWMeSxgpEUkL0ZVB606jdWfQohlI9f+m60Lgc+n4DI0cXSdH0wgIjQAClxS4AI+m4dYEbk3Do2t4dYFH13DrGh5N4DE0vJqGx3CC26WjuzTq6/czacoENF1DNwS6oaEZAl3X0Ixs2oB4bz5N19D0bH5NoOkCoQsnrgmEdvrXF4QQ66WUcwambdy4cV9NTU37aW/sfUxXV5cWCoXs5uZm/cILL5y2Zs2a7RUVFcf8Gp8uNm7cWFRTU1N5tGenMqMwH/gssFkIsSGb9g3gJiHELJylgH3A3wBIKbcIIZ4BtuIsoX05u+MB4Ev0b498ORsAfg78Mmv4GMHZNYGUMiKEuA94J5vv3uMpCQrFiMW24X+/CJ31sOylM6IknAl6lYKNPXE29STY1BNnf7J/IDbZ72XZmEJStmR/Mk1rKkM4Y/YpBx0Za9D1fQBNgt+UeFM6/kgX7pSFOyMJmOC2JK6MxG1K3JbEbYKRtkmaNp3SpM2yCFsmyexASQPGeN1U5ORQOcpHVb6PiQU5lOX78PhcuLw6bq+By6Pj8ugYbg3DpaHpQx+lJ1bXM+uyiiGXV5x/XHHFFZO6u7v1TCYj7rrrrqazpSQcj1PZ9fAnjm4r8NIxyvwr8K9HSV8HTD9KehL45CB1/QL4xYnKq1CMWF69D7avhKu/B+MuHm5pDsGWkqQt6ciY/CHczUttXeyMJ+k0nen9Xiq8bmYGfXymrJCZQT/hjMmP97XwjZ0HAQggyDMFOWmbQFJSFLdwR018MQt/SuJP2fhTEk9GYtgSv0cn3+fGH3ARTXZRUVWGL+jGG3DhzXHh9hkIl6AhkWZnR4x1jV38eV+ESFZRmToqyILyPKaPCTFzbIgpo4J4jHNjB4Hi/cvatWvrhluGo6GO6VIozmU2PQN/esCxS5j3N8MtDVJK3uuO80q4mz9GetjUE2fg4muVz83FeQEKXAbFLoMJUmds1Ib2NF31cbrDLTSFk/REkixJmISDGjkpiS8t0Q2NQIGHQL4Hf64Pf6Eb/3g3vqAbf25/8AZd6ANG8qtXr2bBwinsaOlhw4FONjW08peD3dQ195DOKitFATeLJhezYFIRl04soiTXe5Y/udOHtEzMzn1k4mEsM4mZ6saMt2MlOshYaUxpYdkmprScHRCGB9NwUz3js/iCp+UUYcX7DKUoKBTnKg3rHIdP4y6FD/8AjrGef6aIWRY7YynqYkm2RBO83N7FgWQaDfhArp/byovxmiBiJpOikN+Qorulk87WBN3tCfZakl7rKsOjEyryEizwUjYxRKDQiQezV3/QfcLr6xnLZt2+Dl7f2carGxPc9urviKedlcxcr8GMsSGWz6+kekyI6WW5VBbmoGnCOdHSzkCyC6xMNqT7r3Z/mm2mSGcSpMw4GStJKpMkbSZIm0nSVpKUmSJtp0hbKdJWmrSdIW2bpG2TlLTISIuUlSFtp510mSFlW6SlSVpaxNIp/nufQVrapJCkkVjI7PbG/nhGOEfZ9m57tIf4NXihbA7jlaKgGAJKUVAozkW6GuDJmyB3NHxqBRgndT7KSWNLSaPU+N+WDrZGE9TFktTFktQn0312Ai5glnDxsaiHKY1pzJYOutoasbK7AQ4AjS6NULGPgtE5VNUUkVfqJ6/ER6jEjz/XPeh+8COwTOjc7/iziOwhQpANyVFsiLjY0JzmvRaLngy4NMk4Tw/XjLEYH+yiIqeVkKuDtJUgnU6T2pli3fYUbyTCxGOtJMwEcU2QEIK4phHPXp17QVxoJDVBSgjM06SYuaTEg8AtwY1wgtDQNYlfgFvoBNBwCQ0DDUOI7FXDQGAg0MGJCw3DG8LwF2N4gui6C8Pw4vKE0D1BDMODobnQNRcuzYUhBLplYlgmo0Z94LiyKhRHQykKCsW5RqwdnrgBMglY+iLkFJ72JsJpk7e6oqztirGpJ87mngRRcmHrfnSgzNIYHZdMi5iEmlMUhDMURG00CZohyBQ5nX/FBQWESvqVgUCex5kVkNJ5j6566GmBzjywRoFtQbQFoq1Y0RY6uxsIx1qIJNrpseJE7TSdiQR7evzsz4yjIVNJS7qSmFWEM55O43E34/LXE8ypQwZ206Kn+R04R7cNdiitAAIuPMKLX7jway58mgu/7sane8jTPfh1L37Di1f34tHdeHQvLt2Dx/DgNry4dR9uw4vH5cNt+HC7/LhdPjyuAC7Di8fw4tbcuHUneHQPLs01qHJ0pneZSNMk09xMeu8+0vv34x13ZpVNxfmLUhQUinOJroPwy+ucHQ43PgEl005Ltb2KwZsdUd7sjLIt5vgOcAMVKcGsiEn+gQSjIxZF3RYuIQgWeckr8RMan0veRX5CJT7ySvwE8j1oyYjT4SdaHKXAzsDWjaTr3yIc2Ukk1kIYk7CuZ4NG5LB4p6ZhC4GdycOKV2IlqrES5djJ0fT+NOmubvzBVspz91AYCDMq2EnI6yHgCpDjmkCOexbtjWGqp9SQ48klx5OH352Lx+XDo3v6Omyf4cNn+NC188dg0YpGMVtbMZubSR88SKaxEbOxkczBRjKNjWRaWsDqP2nYX/sBvNNOz/fpXKGurs69ePHiSTt37tzSm3bnnXeWBQIBa9GiRdG/+7u/K0+n01o6nRbXXXddxwMPPHBChx8pDkUpCgrFuUJ4N6y4DhId8JlfQ+X8oVeVVQzWRHr4U3sPO9KOtb/bhsoOi8sPpqhsNRkdMfH7DEoqc0n40lx40wwKynIIFnoPMRjspavuN2z+1b+wN3aQvS6DAy4XYV0jrOtENJ0eXYMgECw4pJxPc1No+CkwgoTsSnzJKnKiZTRGcumJOe14XYLpZTnMKs9jzrhCaiuKGBU6vtHh6tWruWz6ZUP+rM417FQKs63NUQJaWzFbWsi0tmK2tmG2tPSl2/H4oQU1DaO0FFdZGb7aWnLLynCNHYOnshLXuHEYxe8v52G33npr1ZNPPrn74osvTpimycaNG0euBeswoxQFheJcoGUL/PLjjiHdsv+DstknVTycNnm9uZM/NnayNpZgn+aMJF2mpLzN5PK2DJVtJhe43BSPDlA4sYDCRQEKxwTILfIihODVP76Kb6LJ7tg2Gvc10h5vo6f7AB0du9nX08DedIQwFvgBfyGG0BnrLaTIHWKqO5fCUAUFgTEU+gop9BaS780nlQzS0K6xrTHBpoYu3mvs6jM6LAp4uGR8PnMrC7iwqoCpo3LRz8BhQecK0rIw28NOR9/Wiu+NN2jduDGrDLT2KQBW55HrJ8LtxigpwSgtxTNtKoFFCzFKSrNpJbjKxuAqLUG4XMPwZsDzXy6ndetpdTNNyQVxrvvpkJ1NRSIRo6KiIgOOr4fa2tqju+BUHBelKCgUw03DOvjvJeDywfKXoWTqcYtYGZu/7Ovg1wfCvJpKsDPrY8ZlSsrbTa7qkczS3HygIMCoCUEKFuSQP8qP4dLpSHawLbyN1R3b2V23m6Z3m2iMNtIUbcKuP/SkWU1KQrbNuIzJQs1LVWktVTNupqpwCmMCYzC0/p8Qy5Zsa+rmrT1hfrsnwvr9rXTEnXMS3IZGdVkun5pTzsyxIWaV51FVlHPixo3nKHY6jdXRgRWJYEYiWB2dTrwjghXpwGxv758FaG93Ds/KkguENQ2jqAijpATX2LH4aj+Aq6SkXwkoKcFVWoIWCo34z+ps84UvfKFl2rRp0+fNm9dz5ZVXdn35y18O+/3+oR1F/D5HKQoKxXCy93V44kYIFMMtL0B+5RFZpC3pbI3Tur+Hffu7eKU7ypqgzd5iA4RgdNriuqjBpaEAl1bkM3p+EI+/f2TZnmhnXcsa1q1fx/qW9ezq7PdCUOIrYUxwDDXFNcxJFVDT9BpjbEFZbjnFxdPxV8xHVMyDgglH7LywbMmWxi7e2hPhrT1h1u6N9LlKriz0c8UFpdSU51EzNo8po4K4TuEUw7OBlBI7FsfqiBza8Xdk45EOrI6OPiXAikSwY7GjVyYEel6eowSUluKZPBmjpBhXaa8CUMo7u3exYPFixCCuiEcUpzDyPxWO5Wr6hz/8YdPy5csjK1euzH3mmWcKf/WrXxWeqwcaneucB99QhWKEsv0l+NUyKBgPtzwPwVFIW9LVlqC1vpvW/T207e+h5UAPO/I0No1zUzfWTaZYZ5RlcKsvwE1VJUwfFQTAtE32dO3h901r2Nm5k50dTmiJO37a/Iaf2SWz+cj4jzCzaCZTCqYQ8oQcWcK7MV9bgFE8Hv76d+DOOULceNpkQ30n6/Z38M6+CBvqO+lJOSfMVhb6+fD0UVw0vpB54wsYHfKdlY9wMGQmgx2LYcfj2LEYVlfXgM4+gpnt9J3Rv3O1OjqQ6aP7+hEuF3pBAXpBAUZ+Pu6x5U68IB89vwA9P9+JZ/PoubkI/diGk3a4/fxQEoaR47marq6uTlVXV7fdeeedbYWFhbOam5v1UaNGWUevTTEY6luqUJxtpIS3fwa/+0fk6Fm0L3qMA2+mOLhjI817ukgnnM63rcBgx/QA79WE6NAhV9O4qTSPT44uZE6un7ZEG++2rOHldzbzl/a/sC2yjYTpOFwyNIMJoQlcOOpCpuRPoba0lmmF0w5ZKsAyoXWbYx/x+g+QQoMb/qdPSWjtTvYpBev2dbC1qRvLlggBU0qDfHRWGRdWFpwWxUBmMljRqNO5HxLi/fH4UeLxOPlNTey5/4FDnsvMsZ0wa4EAen4+ekE+rtJSvNOmoefnYRQUOB1/QT5Gfrbjzy9Ay/Grqf9zkGO5mn7qqadCn/rUp7o0TWPz5s1eXddlUVGRUhKGgFIUFOc00rbBNJGWhbRssEwnDZwOt9f76WHXPq+ofSuSR883sA55rLzHrL//md7URGrXrr77I+SwMlirH0Cve56I90LWrLuJ+GurAcgt9FBSGWB3sc56zeRAJo0r3c7Hevx8qCDIhbk5NLSu593N7/Jky3vs69oHgEdzUZE7jktDl1JVUEVFsIJROaMwhO7ImwC5vZVU5C1Skb0Q2Qsde6Gj3tnWKEHqXtaO+gyJ59+jrul1drR009qTAgluQzCzKIfrS4NMLMlhYnGAHLdE2u3Q0Y58K0NnrL+Tt2JxZDKJTCawE0nsZDJ7n8ROJbGTKWQikb1PYSeTcJyOvQ9dR7jdCK8HzeNBeLyITAZRWIARCqF53AiPB+EZ8NzjRvP70XJy0AIBtJyc44/2YzHSsRg0NPQnDnV1exAPvd5tW+nqiQ5W6LS100vg8svRg8GTr/ccZzBX03ffffeYr3/96+Ver9c2DEM++uijew01gzMk1Kem6MNZo41htbc7RljtYayuLmQm4wQz43TamQwyY2Ink9jdXVg9UeeZZSNtC2wJluV06JbldPK2BaYT70uzTCdtgDLQ+6wkk2GbfVwX7uccRcCeE8pZAhxgOt8/4slk4MNHKdGI4+VwTjb0YwF12eDcHTwhGUKH3I3mBeAFxg/Sfi+RbBgWLMtRMhKJPh8TLiDZOPK2x4dw/k/PFuNf+s15qSjU1tYm33777R2Hp69cufLE/hQVx0UpCu9DMk1NRFevJrm9DrO9vV8xCIeRyRPcQeRyoXk86Lm5aMGgsy1L1xCaDpqG0DRn5KdpYOgI3UAYOmi6M5rLpqFrzjNdd0aLuo4wdOobGhg3fnw2bUA+Qweh9fs9EPTF+6aG+6aID7vvSz5a/sPzHlpGCHHks4FlstGt27YxacIUOlvidDQ7obs9SUC0MDNnJTl6B20ln0Sr/it2eyRvdkb5SzSJLQQVXjfTcwSFsolwbA97u/dxp1+ALgAAFoNJREFUMHoQCxuP7uGCwguYUTSD6uLpBI0c6DoA4R3QtgM690KiE5LdgI1AIoWB9I/BdJdhu4owRYjmdIBdcZ21MTdbCSCBokyUyu4WKiMHqOpuojjReaRbWF1HDwadEfmA0JumB4Noudl4INA/gs/Jcb4HJzNtf5JT/GvXvsPceXNPqsxQ2skWGkKZoxdbu3Ytc+cOLveQljqOUcY1evTJ16dQoBSF9xWpXbto+tY9JN59F8Cxyi4uxiguwlfxAcdCu6gQo6gIvbAIo7gIPS8P4XI5wTAc4yvDOOPrtVtXr6b4DB5vezoxMxaNOzup3xJhe5uPd3cB5KAZgklV7Vw+5QkKu15F+grYfs3j/Nw1jRfbOug2bUaVaVS7WtB7VrO+7Q1WpToAyAnlMH3CdGYXXUNtoJw5iRSe5s3QUwf1q6F5M1Y0Siamk7HyMV0VZNKFmAnIxASZriRNPRZ1vhJ25pWzI7+cnXljibr94IMirZtru+q4hlbKCn3ok0s50OVlYu2N6Pl56KE89LwQesgJwus9Z9forYYGPBMmDKmsbUsyto1pSUyrP56xbDKWjWk7ccuWWLbElhLLdnxj2LbEkk66lM4uEGtAui2d+nvTZbZsb54diQLqGo5e5+B1HNrOwLK98vXG++rM3n//+hJGq1OcFUNAKQrnKc7hLu0YBw6Q2LyZxHvv0Xr/A2h+P8V/fyfBD34Qd1XVOfvjfy4jpaSzJc6BbRHqt0Y4WNeBmbbRDQ1vIcxaNJ6KklaKdv8UbdvzSDvIO7O+wjcKr2VzuwevFmaqqx275/c01v+WTUgqcytZVL6ImuIaZhZOZ4JpoW1bibXqcdL760lEdbrjXtLJHNJRg0xXAVa8fxq529XFzsJx7BozhZ1l49g+oYSw8ABgCJgcMvjIqBxqKouYNWkUU0aHjjjcaPvq1eSfBeXMtiWxtEk0ZRJNmvRkr8mMRcq0SZk2adMmZVrZ62HxjE26tyO3JE2tSX6xZy1m9j5t2ZiHdPgS07LJ2NlrNt3MdsAnh3QmsbLXw+8Pv4LE+ZgHyVe3HoF08h1Wn64JNCHRBehCoAvQsuHwuBASHdA00HDK9T3H+aGPxSbBMO9GUYxMlKIwQrHTaTLZ890zjY2YTU3OGe9NTf3nvGcyFAL7smVyFiyg7Dv/+r47yvVUsW1Jd1uClr1dHNjeQcO2CLEuZxtdqNjHtPlljKsupGxyHu+uepLaxBOw8llMl59VF3yerxd+jEYtSLnspCr+G3rCL3FQpphZNJMbqj/P5TEfpXv2kl73FzL1L5Fu6WR/tyAT1bFNDcfyAdA0XKNHY1VVsWfMFHaExrJN5LIloXEg2m/MPaE4h0Vj85g5NsTM8jwuGJ2L13XqPg4sWzqde28Hn8z0dfKHd/rRVIZoyqQnaRJNpkkkUyRTaVKpNOl0GgMLl7AxsNGwnY6N/qAjnc5S2H33Lt1RegwNdCGduJCMNU3cKb2/fLYukQ1ab6ctJMIAYTgdc39HL/uNUg8xWs0ao0qJHPhsuJEMydYx5PrQaRdF8f5AKQrnOHYsRmrPXlK7d5HevYfU7t2kd+8mfeDAIae8oWnOKW5lZfhmzXLOeS8bzfamJqbPmoUeDOKrrVUzCMdA2pKeSJJIY4xIU4xwY5RIY4yO5nifK2VvwMXYqfmMnZLP2KkFhIp9xONdtGx+kfZf/ooLD7xBUnPzaPlNPFR+A126m0DXe9Tue4kJjfuZmQwxtctHaViiRd4l072elC2o7xVCA3dRAa6qUfgnTccafwENBWPYqoXYGtPY1NjDztYe7AzQDmPyvMysDHHz2DxqxoaYPjZErnfwY3xNy6YnadKVyNCVyNCdzPTFN+xO8efoX+iJJ+iJJ0mlUtimSTqdpjuWIJpIomMP6OQtXNgYInvNpruzIV/YFEkLjQHfUwF4Tup/BcMQGIaGpoFhCDRdYOgCTQddg2QyQTAYQGgCTQg0TUPXNTRNZNN0NM0ZdWuaRAiJEHb2mjUx0bKKRfYZwlFeEP3pCDtrjmJD3zWraAh56D1O/eCU4/A0bLq6OgiFcp10+vOAjey1M8neD3yGtLPplpNXDigne9MOzSulhc/3WUANEhQnz4hWFIQQVwM/wplde1RK+d1hFmnI2KkUqbo6Ujt3kdq1i9Ru52o2NvVncrlwj6vAM2UKuR+5BldFBa6ysmOe855avZrgCFnrPxtIKUlGM3S1JehqS9Dd7lw7muN0NMXIpPpH5jl5bgqK3YyeHSQvKMnNSRNxp9kY30vd7t9QvGEzk5O7mBHfSZU0afCU8tMxN/BGehpTNuzi+0/cR1VjE9ohI9EIutfGCLlwjSlEm1tJeNx0WsfMoCU0libbxcHOJA0dcQ52JmjfngYSQIKCHDczx4T44LQSqor8jAoYYGXojCbojnWzcVsra95NEk+msiP4FOlUmkwmjZXJYFsm2CYGNi5hYWDjEWk8RgqPnqJAM9HDNvmaRaGw0TQbISyEbqOFbLR8C9GXbqM75ipOR+4CV/Ze16UTNImmZfNrNpqwsp2whRAmYIK0kGSQMuN0ctJCYiGlhZQmQ9+TeGaQAyYfjkRDoIE42lV3ngutL5/bSCMtv2Oci3bIcy1bTkMDdBDCqas3T2/dh7d5eBwx4LnyiaQYGiNWURBC6MBPgSuABuAdIcSLUsqtwyvZoUjbRqbT2PF4/w6DcNjZehhux2xrJ7V3L8lt2/r2kgu3G/eECfg/UIvnUxNwT5iAZ8IE3OXlw+f0ZZiRvb/QtkTa0tmCmY2baYtUV4Jkew/JjjjJnhSpnhTpeIZUzCKTtEinbJJJSKQElu2sBgvA0sA2Uth6Epc7hnTFiepJug2bTp+LhGaQNgV2XFKQDjO/az2LO9ZRnHGMDne7KnhDzKe7uwRtr5eZ8Rg1rk3YhkFqwkzqps8n6s8l7g/R5c2lycil2XTRkrRoS1pELQltOIEGdCS5uklQS1OiJZnoS5Bn9FDqjpCrd2NE02g7M7TtMQnrFppmoWkmumbh1ywCmoVmmGi5vc+coGsWmm47V81E0010LYPQTtMWVKkhpAFSR9g6SANh6iB1sA0nzdaQtgFSQ9pusLzQm25pYGsgBdgCYQmQAmFrILMdotSyZQXYGkIKkBq2LdGE1rtagJTO/61E9A3GhdSQ2fz99fbe62D3xgfI0ZtXHnp/RLyvQxZ936t+jj6DJ3FcfPcvgBwfcXhdR1R97JYTX0mTM+UEGhpB+P3+2fF4/L3e+//4j/8oXLduXc6KFSvqj1XuVFiyZEnl4sWLu5YvX97R0tKiX3bZZVO++MUvtnz1q18Nn6k2h5sRqygAc4FdUso9AEKIp4CPAaddUfjxHdfg6+lmz2MSzQYhJZotyc5MolsSw7QxTIluSVwZm0DUJNhj4s4M/hNgaRD3G3TnGjRP99Na6iVc6KY7aCC1lPMq6a2wDScc8nMislOcDrI/+RCElDy7+XvHecPB6z0e2eOHhrpp7BAOr+PXm//tmM/lceSUAvCC8Er07Fq4wEbPlnPWry10aaNLGw0LlzTxyTSjSVMl0wgpIYoTgDAeHvCNpt43i3pZQjd+TKlh+XSsUg04/OjjFH2agATDNPEbCXx5ScqMBH5XnBxXnBxXjBx3DK+RQjvKe5k4Zxcc843t3s5ay3bYTmctbB0hXQjpdNwi25kL2wW2jmYbTjk7O/KV/Z1yf1x3Ok36O9b+POLQb404Usqj/V/1poi+fxiYcsyyhzwXEiGEo08ci75qD6/POjzDoVKewJdbnuAfgET2bWG0LQttkIOfjvfOfZzEH17lSS35KI5HOBzWP/jBD05etmxZ2/msJMDIVhTGAAMdkTQA8w7PJIT4AvAFgNLSUlavXn3SDVXs2M/kfYeOvCxnUIMtIGNARoe0y7mmXLCnUNA2HqJejYzhpHXmQFeOyF4h7iH7h56dhmUQBzOKs4yRDYN5ze3VHHYD9E3ynuhcTzobugYmZrLhhGs5Fr3r2sc47VDgLNiduo2j4gxxxAzCKTJh5wcYtW//aa2zl39e88/luzp2nVY30xPzJ8bvm3/fkJ1N7dixw7106dLKcDhsFBYWmitWrNg3adKk9JIlSyqDwaC1cePGnLa2Ntd9993XsHz58g7Lsli6dGnFW2+9FSwvL0/Zts2yZcvCy5cv7zi87p6eHu1DH/rQpE9+8pPhu+++uw3Atm2+9KUvjX311VdDQgh51113NX3+85/vWLlyZfDee+8tKygoyNTV1flmzJgRf/755/dqmsbTTz8d+vrXvz62oKDAnDFjRnz//v2eP/7xj7uOfJvhZSQrCoOo/4clSPkI8AjAnDlz5GVDWK9/ruXL/K5hL7n5+Uitz/oJIfr/lKXoPSDFSZMCCoSgaMAvsdSy5wP15eo/sKe/nmxcc1Yn0bTsiEscerhQdhUya1UFCDStN95/IFFjYyNjx4zpby8rv0A48veOwjTRl8epRj90IlV35ND6rL+y9enZnNlDkIQQWVE1hK4NEM9ZWxWitx0nrmmOkZrznL66N2/cTE3NLLI1OXVrA95fZFdy+9rV0TXn3qNrA4w2hdOuJujfDdj/Gbq0rIzZD0LTNOcd6T/w5mgGoIMZhb7xxhssWLDg0Lyn+Qf/RA1ST7Td3vpef/11Fi5ceHrqPNF3PuFsg2d87bXXWLRo0Um1ezJGvWeiToDVq1czlN8jRT+pVEqbOnXqBb33XV1d+hVXXNEF8MUvfrHi5ptvDn/lK18JP/jgg4Vf+tKXyletWrUboKWlxbVu3brtGzZs8H784x+fuHz58o4VK1bkHzhwwF1XV7fl4MGDxvTp06cvW7bsqDMF//zP/1x+0003td9zzz2tvWkrVqzI27x5s2/btm1bmpqajLlz50678sorowDbtm3zbdiwYU9lZWWmtrZ26iuvvBJYsGBB7Ktf/eq41atXb586dWr62muvrTqzn9bQGcmKQgNQPuB+LGfoRNQlN9w2Yv+oR6rcsfouasfPHG4xThqv5iXHdaTnxZGAS7jw6CNvfloX+qHOrhRnnVMZ+Z8KHo/H3r59e99yc6+NAsB7772X8/LLL+8G+NKXvhT59re/PbY330c/+tFOXdepra1NhsNhF8Abb7wR+MQnPtGh6zoVFRXmRRdd1DNYuxdffHH3b3/727yDBw82jxkzxsyWD37qU5+KGIZBeXm5OW/evOif/vQnfygUsmfMmBGbMGFCBqC6ujq+e/dudzAYtMrLy1NTp05NA9x4442RRx999JzclnJuO4g/Nu8Ak4QQVUIIN3Aj8OIwy6RQKBSKcxyv19vvAu5wR28nwA033NDx13/9161XXXXVpI6ODu145T0eT99DXdcxTVOcTHvDzYhVFKSzd+p24Hc4pn7PSCm3DK9UCoVCoTgXmD17duzRRx/NB/jZz35WMGfOnMFcdQKwYMGC6PPPP59vWRYHDhww3n777WN60PrWt77VumDBgu6PfOQjE5PJpFi0aFHPs88+W2CaJo2NjcbatWsDCxYsGNTwrKamJnngwAFPXV2dG+Dpp58uGMp7ng1G9HydlPIl4KXhlkOhUCgU5xYPPfRQ/dKlSyt/9KMfjeo1ZjxW/qVLl3asWrUqOHny5OqqqqpkTU1NLC8vzzpWmYceeujg9ddfX7lkyZKqF154Yc+bb74ZmDZtWrUQQn77299uqKioMDdt2nTUsoFAQD7wwAP7r7766kkFBQXm7Nmzz1lr9hGtKCgUCoXi/cvAMxQA7rjjjjAQBpgyZUr6rbfeOsL99HPPPbfvaHXous5DDz3UEAqF7ObmZv3CCy+cVltbGz9e+Weffbbv/mc/+1kDjv1cH4sXL+5ZvHhxn73DwDMePvKRj/TcfPPNW2zb5pZbbqmora09J5UFpSgoFAqFQgFcccUVk7q7u/VMJiPuuuuupoqKCvNMtvfggw8WPfnkk0WZTEZUV1fH77zzzvYz2d5QUYqCQqFQKBTA2rVr685me/fcc0/rwC2W5yoj1phRoVAoFArFmed9NaOwfv36diHEUI8mKwLOyWmh46DkPruMVLlh5Mqu5D7zjBtuARTDx/tKUZBSDvkwCyHEOinlnNMpz9lAyX12Galyw8iVXcmtUJxZ1NKDQqFQKBSKQVGKgkKhUChGJPX19cbixYvHl5eXT58wYUL1okWLJm7atMmzcuXK4OWXXz7xaGVuuOGGcevXr/eeSru7d+92LVy4cNJgz+fOnTvl9ddf73OSVVdX5540aVI1wOGy3XHHHWWXXnrppEQicXqdwpxG3ldLD6fII8MtwBBRcp9dRqrcMHJlV3K/D7Ftm49+9KMTb7755vDKlSv3ALz55pu+xsbGY7pgffrpp0/ZheaECRMykUjEiEajIhAIDPks5rvvvnvU22+/HfjDH/6w0+fznbNnOitF4QTJeqEccSi5zy4jVW4YubIruYefxm98szy1c+dpdTPtmTQpXvadfx3U2dTKlSuDhmHIr33ta229aZdcckmi91ksFtOvvvrq8Ye7dp47d+6UH/7whwcWLlwY9/v9s2+99dbW3//+9yGv12uvXLlyV3l5ufnEE0+Evvvd747OZDJafn6++fTTT+8pLy8/5EyF+fPn9/z2t78NXn/99d1Deb977rmn9JVXXsn74x//uONUlI2zgVp6UCgUCsWIY9OmTb6ampojTk7sZdu2bb6f/vSnB3bt2rWlvr7e88orrwQOz5NIJLSLL744WldXt/Xiiy+O/vjHPy4GuOKKK6IbNmzYvm3btq3XX3995N577x11eNlrrrmm6ze/+U1osPZvueWW8VOnTr1g6tSpF1xzzTWHLFOsX78+8NhjjxWvWrVqRygUsk/uzc8+akZBoVAoFKfEsUb+w8XRXDsfnsflcskbb7yxC6C2tja2atWqXIC9e/e6r7vuurFtbW2udDqtlZeXpw4ve9VVV0W/8Y1vjD08vZcVK1bsWbhwYRwcG4XFixf3KQsVFRWp7u5u/YUXXggtX76849Tf9syiZhQUCoVCMeKYMWNGYuPGjYMudxzNtfPheQzDkJqm9cb78tx+++0Vt912W+uOHTu2/uQnP9mfSqWO6Cu9Xq8cNWpUeuvWrUcoIMejuLg4s3Llyp3/+I//WP5///d/x/RSeS6gFAWFQqFQjDiuvfbannQ6Le6///6i3rTXXnvN/5vf/OaIJYaTpaenR6+oqMgAPPbYY4WD5bvyyiu7X3zxxUGXH47FzJkzU08//fSuW2+9terNN9/0DVXWs4FSFBQKhUIx4tA0jRdffHH3H/7wh9zy8vLpEydOrL7nnnvKejv4U+Gb3/xm40033TShtrZ2SmFh4aCOoa677rqu3//+90NSFAAWLVoUf/jhh/ctWbJk4pYtWzxDredMI6Q8p40tFQqFQnEOsnHjxn01NTUj5QjqM0Z1dfW0devWbT+XtzeeCBs3biyqqampPNozZcyoUCgUCsUQ2bJly7bhluFMo5YeFAqFQqFQDIpSFBQKhUIxFGzbts/ZY4cVJ072/3HQ8xyUoqBQKBSKofCXtra2kFIWRja2bYu2trYQ8JfB8igbBYVCoVCcNKZpfq65ufnR5ubm6ahB50jGBv5imubnBsugdj0oFAqFQqEYFKUFKhQKhUKhGBSlKCgUCoVCoRgUpSgoFAqFQqEYFKUoKBQKhUKhGBSlKCgUCoVCoRiU/w+gQAvYhwWrAwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axes = donnees.set_index(\"Country/Region\").drop(columns = [\"Province/State\"]).transpose().plot()\n", "leg = axes.get_legend()\n", "leg.set_bbox_to_anchor((1, 1))\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous retrouvons la même allure de courbe que sur le site du SCMP." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Echelle logarithmique\n", "\n", "Les mêmes données sont tracés en echelle logarithmique" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "axes = donnees.set_index(\"Country/Region\").drop(columns = [\"Province/State\"]).transpose().plot()\n", "leg = axes.get_legend()\n", "leg.set_bbox_to_anchor((1, 1))\n", "axes.set_yscale('log')\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ces données sont bien cohérentes avec les données affichées. \n", "Pour tous les pays considéré au dessus, l'augmentation des nombres de cas confirmés ralentie. Il faut faire attention avec le second graph en echelle logarithmique.\n", "\n", "Cependant il m'est dificille de dire beaucoup plus de chose à ce sujet. Certaines limites apparaissent : \n", "- peut on comparer US et France par exemple ? La différence de taille des pays et la démographie est très différente.\n", "- Les politiques de dépistage sont différentes d'un pays à l'autre rendant la comparaison encore plus difficile, selon moi." ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }