diff --git a/module3/exo1/analyse-syndrome-grippal-Copy2.ipynb b/module3/exo1/analyse-syndrome-grippal-Copy2.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f9ae3f1ced17fb84317a37078e616fd11ad20ce1 --- /dev/null +++ b/module3/exo1/analyse-syndrome-grippal-Copy2.ipynb @@ -0,0 +1,2543 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal CORRECTION" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"syndrome-grippal.csv\"\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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2020253031916614283.024049.02922.036.0FRFrance
2120252931867313815.023531.02821.035.0FRFrance
2220252832328518131.028439.03527.043.0FRFrance
2320252732145317129.025777.03226.038.0FRFrance
2420252632194517422.026468.03326.040.0FRFrance
2520252532332318546.028100.03528.042.0FRFrance
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.................................
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2146 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202550 3 190419 176434.0 204404.0 284 263.0 \n", + "1 202549 3 133235 122213.0 144257.0 199 183.0 \n", + "2 202548 3 82609 73827.0 91391.0 123 110.0 \n", + "3 202547 3 58860 51442.0 66278.0 88 77.0 \n", + "4 202546 3 40657 34407.0 46907.0 61 52.0 \n", + "5 202545 3 47645 40423.0 54867.0 71 60.0 \n", + "6 202544 3 44837 38385.0 51289.0 67 57.0 \n", + "7 202543 3 55400 47640.0 63160.0 83 71.0 \n", + "8 202542 3 75239 66515.0 83963.0 112 99.0 \n", + "9 202541 3 86860 77648.0 96072.0 130 116.0 \n", + "10 202540 3 79169 71180.0 87158.0 118 106.0 \n", + "11 202539 3 72930 64872.0 80988.0 109 97.0 \n", + "12 202538 3 61435 54131.0 68739.0 92 81.0 \n", + "13 202537 3 46373 39689.0 53057.0 69 59.0 \n", + "14 202536 3 25581 20702.0 30460.0 38 31.0 \n", + "15 202535 3 22717 17480.0 27954.0 34 26.0 \n", + "16 202534 3 21429 16177.0 26681.0 32 24.0 \n", + "17 202533 3 16766 12022.0 21510.0 25 18.0 \n", + "18 202532 3 19900 14303.0 25497.0 30 22.0 \n", + "19 202531 3 18470 12625.0 24315.0 28 19.0 \n", + "20 202530 3 19166 14283.0 24049.0 29 22.0 \n", + "21 202529 3 18673 13815.0 23531.0 28 21.0 \n", + "22 202528 3 23285 18131.0 28439.0 35 27.0 \n", + "23 202527 3 21453 17129.0 25777.0 32 26.0 \n", + "24 202526 3 21945 17422.0 26468.0 33 26.0 \n", + "25 202525 3 23323 18546.0 28100.0 35 28.0 \n", + "26 202524 3 23154 18577.0 27731.0 35 28.0 \n", + "27 202523 3 24391 19307.0 29475.0 36 28.0 \n", + "28 202522 3 18755 14333.0 23177.0 28 21.0 \n", + "29 202521 3 23760 18671.0 28849.0 35 27.0 \n", + "... ... ... ... ... ... ... ... \n", + "2116 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2117 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2118 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2119 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2120 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2121 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2122 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2123 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2124 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2125 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2126 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2127 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2128 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2129 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2130 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2131 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2132 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2133 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2134 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2135 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2136 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2137 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2138 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2139 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2140 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2141 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2142 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2143 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2144 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2145 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 305.0 FR France \n", + "1 215.0 FR France \n", + "2 136.0 FR France \n", + "3 99.0 FR France \n", + "4 70.0 FR France \n", + "5 82.0 FR France \n", + "6 77.0 FR France \n", + "7 95.0 FR France \n", + "8 125.0 FR France \n", + "9 144.0 FR France \n", + "10 130.0 FR France \n", + "11 121.0 FR France \n", + "12 103.0 FR France \n", + "13 79.0 FR France \n", + "14 45.0 FR France \n", + "15 42.0 FR France \n", + "16 40.0 FR France \n", + "17 32.0 FR France \n", + "18 38.0 FR France \n", + "19 37.0 FR France \n", + "20 36.0 FR France \n", + "21 35.0 FR France \n", + "22 43.0 FR France \n", + "23 38.0 FR France \n", + "24 40.0 FR France \n", + "25 42.0 FR France \n", + "26 42.0 FR France \n", + "27 44.0 FR France \n", + "28 35.0 FR France \n", + "29 43.0 FR France \n", + "... ... ... ... \n", + "2116 59.0 FR France \n", + "2117 64.0 FR France \n", + "2118 97.0 FR France \n", + "2119 93.0 FR France \n", + "2120 80.0 FR France \n", + "2121 116.0 FR France \n", + "2122 149.0 FR France \n", + "2123 281.0 FR France \n", + "2124 395.0 FR France \n", + "2125 485.0 FR France \n", + "2126 544.0 FR France \n", + "2127 689.0 FR France \n", + "2128 722.0 FR France \n", + "2129 762.0 FR France \n", + "2130 926.0 FR France \n", + "2131 1113.0 FR France \n", + "2132 1236.0 FR France \n", + "2133 832.0 FR France \n", + "2134 459.0 FR France \n", + "2135 207.0 FR France \n", + "2136 190.0 FR France \n", + "2137 198.0 FR France \n", + "2138 224.0 FR France \n", + "2139 266.0 FR France \n", + "2140 219.0 FR France \n", + "2141 176.0 FR France \n", + "2142 163.0 FR France \n", + "2143 195.0 FR France \n", + "2144 308.0 FR France \n", + "2145 213.0 FR France \n", + "\n", + "[2146 rows x 10 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_file, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
19091989193-NaNNaN-NaNNaNFRFrance
\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1909 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1909 FR France " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]\n", + "#raw_data.isnull().any(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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21321985053637302592795.0681809.011551074.01236.0FRFrance
21331985043424937390794.0459080.0770708.0832.0FRFrance
21341985033213901174689.0253113.0388317.0459.0FRFrance
213519850239758680949.0114223.0177147.0207.0FRFrance
213619850138548965918.0105060.0155120.0190.0FRFrance
213719845238483060602.0109058.0154110.0198.0FRFrance
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2145 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202550 3 190419 176434.0 204404.0 284 263.0 \n", + "1 202549 3 133235 122213.0 144257.0 199 183.0 \n", + "2 202548 3 82609 73827.0 91391.0 123 110.0 \n", + "3 202547 3 58860 51442.0 66278.0 88 77.0 \n", + "4 202546 3 40657 34407.0 46907.0 61 52.0 \n", + "5 202545 3 47645 40423.0 54867.0 71 60.0 \n", + "6 202544 3 44837 38385.0 51289.0 67 57.0 \n", + "7 202543 3 55400 47640.0 63160.0 83 71.0 \n", + "8 202542 3 75239 66515.0 83963.0 112 99.0 \n", + "9 202541 3 86860 77648.0 96072.0 130 116.0 \n", + "10 202540 3 79169 71180.0 87158.0 118 106.0 \n", + "11 202539 3 72930 64872.0 80988.0 109 97.0 \n", + "12 202538 3 61435 54131.0 68739.0 92 81.0 \n", + "13 202537 3 46373 39689.0 53057.0 69 59.0 \n", + "14 202536 3 25581 20702.0 30460.0 38 31.0 \n", + "15 202535 3 22717 17480.0 27954.0 34 26.0 \n", + "16 202534 3 21429 16177.0 26681.0 32 24.0 \n", + "17 202533 3 16766 12022.0 21510.0 25 18.0 \n", + "18 202532 3 19900 14303.0 25497.0 30 22.0 \n", + "19 202531 3 18470 12625.0 24315.0 28 19.0 \n", + "20 202530 3 19166 14283.0 24049.0 29 22.0 \n", + "21 202529 3 18673 13815.0 23531.0 28 21.0 \n", + "22 202528 3 23285 18131.0 28439.0 35 27.0 \n", + "23 202527 3 21453 17129.0 25777.0 32 26.0 \n", + "24 202526 3 21945 17422.0 26468.0 33 26.0 \n", + "25 202525 3 23323 18546.0 28100.0 35 28.0 \n", + "26 202524 3 23154 18577.0 27731.0 35 28.0 \n", + "27 202523 3 24391 19307.0 29475.0 36 28.0 \n", + "28 202522 3 18755 14333.0 23177.0 28 21.0 \n", + "29 202521 3 23760 18671.0 28849.0 35 27.0 \n", + "... ... ... ... ... ... ... ... \n", + "2116 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2117 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2118 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2119 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2120 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2121 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2122 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2123 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2124 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2125 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2126 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2127 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2128 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2129 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2130 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2131 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2132 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2133 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2134 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2135 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2136 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2137 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2138 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2139 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2140 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2141 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2142 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2143 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2144 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2145 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 305.0 FR France \n", + "1 215.0 FR France \n", + "2 136.0 FR France \n", + "3 99.0 FR France \n", + "4 70.0 FR France \n", + "5 82.0 FR France \n", + "6 77.0 FR France \n", + "7 95.0 FR France \n", + "8 125.0 FR France \n", + "9 144.0 FR France \n", + "10 130.0 FR France \n", + "11 121.0 FR France \n", + "12 103.0 FR France \n", + "13 79.0 FR France \n", + "14 45.0 FR France \n", + "15 42.0 FR France \n", + "16 40.0 FR France \n", + "17 32.0 FR France \n", + "18 38.0 FR France \n", + "19 37.0 FR France \n", + "20 36.0 FR France \n", + "21 35.0 FR France \n", + "22 43.0 FR France \n", + "23 38.0 FR France \n", + "24 40.0 FR France \n", + "25 42.0 FR France \n", + "26 42.0 FR France \n", + "27 44.0 FR France \n", + "28 35.0 FR France \n", + "29 43.0 FR France \n", + "... ... ... ... \n", + "2116 59.0 FR France \n", + "2117 64.0 FR France \n", + "2118 97.0 FR France \n", + "2119 93.0 FR France \n", + "2120 80.0 FR France \n", + "2121 116.0 FR France \n", + "2122 149.0 FR France \n", + "2123 281.0 FR France \n", + "2124 395.0 FR France \n", + "2125 485.0 FR France \n", + "2126 544.0 FR France \n", + "2127 689.0 FR France \n", + "2128 722.0 FR France \n", + "2129 762.0 FR France \n", + "2130 926.0 FR France \n", + "2131 1113.0 FR France \n", + "2132 1236.0 FR France \n", + "2133 832.0 FR France \n", + "2134 459.0 FR France \n", + "2135 207.0 FR France \n", + "2136 190.0 FR France \n", + "2137 198.0 FR France \n", + "2138 224.0 FR France \n", + "2139 266.0 FR France \n", + "2140 219.0 FR France \n", + "2141 176.0 FR France \n", + "2142 163.0 FR France \n", + "2143 195.0 FR France \n", + "2144 308.0 FR France \n", + "2145 213.0 FR France \n", + "\n", + "[2145 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " #print(w)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]\n", + "#data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation\n", + "comme nouvel index de notre jeux de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans\n", + "le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", + "entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations\n", + "que nous avions supprimées !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], + "source": [ + "periods = sorted_data.index\n", + "#print(periods)\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !\n", + "\n", + "Toute la colonne 'inc' est représentée par des chaînes de caractères à cause du trait dans la ligne de la semaine 19 de l'année 1989" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)\n", + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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M+ENGTEOLRlr51Y4Wrvy/z/PL7SeyvSs5S7dlaZRPJho6eyqTaNHQxGAFwi1LQ8c0Us/WIz38wy93A3CsZzjLe5O7dA36KHLaKHXZEy6nA+GZJZnOfZoCYiQQorzIoS2NNPLotuOUuh04bDY6zIJ8mvF0DfqpLZ24hIhFsdPOsHZPZQwtGpoYvIEQc8vdo6Kh52mkHI83wLzyIkrddto9WjQmwpjYl9g1BTp7KtNo95QmhtFAuOES0JZG6hnyhSh125lXUUTHgC/bu5OzdA36qZsk3RYM95Q/GCYUVhnYK40WDU0MI4HYQLjO7kk9Hl+QsiInc8vdkX4RmvF0DfomzZyC0aKFuv5UZphUNERkoYg8IyL7RGSPiHzeHK8RkU0ictD8Wx21zldFpFlEDojIlVHja0TkDfOzO8V0VoqIW0R+bo5vEZGmqHU2mt9xUEQ2pvLgNePxBsIUOW16RngaGfQGKHc7mFtRxIA3qIO4cQiHFT1DfmqTsDSKtGhklGQsjSDwJaXUGcAFwC0isgL4CrBZKbUc2Gz+G/OzDcBK4CrgLhGx0h/uBm4Clpuvq8zxG4FepdQpwB3At8xt1QC3AucD64Bbo8VJk3pGAiGKXHbcTp09lS6i3VMAHTquMY6+kQChsErK0igyr1WvvlYzwqSioZRqU0rtMN97gH1AA3AN8IC52APAteb7a4BHlFI+pdQRoBlYJyL1QIVS6hWllAIeHLOOta3HgMtMK+RKYJNSqkcp1QtsYlRoNCkmHFb4g7G1p7RopJ5BX5AytzPS97pdxzViuHPzQS7/7nPA5BP7IKp7n7bYMsKUYhqm2+gcYAswTynVBoawAHPNxRqA41GrtZhjDeb7seMx6yilgkA/UJtgW5o04DXjF0W6YGHaCIcVQ/4gZUWOiKWh4xqxvNTchdMufPqSJbzz1LpJl9fuqcySdMqtiJQBvwS+oJQaSJA7He8DlWB8uutE79tNGG4vFi1aNNF+aSbBelIrdtqx2QSX3aZTblPMcCCEUlDmtjOvXItGPLyBEGfUV/BPV69IavmiSHFNLRqZIClLQ0ScGILxU6XUr8zhdtPlhPm3wxxvARZGrd4ItJrjjXHGY9YREQdQCfQk2FYMSql7lFJrlVJr6+omfzLRxMfyCVs+YpfDppswpZhBr1HuosztpKLYgdtho8Oj3VPRDJulbJKlOOKe0tdqJkgme0qAe4F9SqnvRn30BGBlM20EHo8a32BmRC3BCHhvNV1YHhG5wNzmDWPWsbb1YeBPZtzjaWC9iFSbAfD15pgmDViWhvXk5nLY8If001sqGfSZolHkQESYV1GkLY0xjARCFE9SOiSaSCBcu6cyQjLuqYuBTwJviMhOc+wfgW8Cj4rIjcAx4DoApdQeEXkU2IuReXWLUsr637wZuB8oBp4yX2CI0kMi0oxhYWwwt9UjIrcBr5nLfUMp1TPNY9VMgvWjs57cXHabjmmkmIhouI1zPK9Cz9UYy8gULY1ITEO7pzLCpKKhlHqR+LEFgMsmWOd24PY449uAVXHGvZiiE+ez+4D7JttPzcyxRMP6EbqdWjRSTbR7CmBueRH72gayuUs5hzXBNFmKdfYUAM/s7yAYVlyxYl5av0fPCNdEsHoSWK4Bl92mU25TjGVplJqWRl25W8c0olBKMRIIUTIF95Rbz9MA4N4Xj3D3s81p/x4tGlPkse0t7G7py/ZupAWvGfQuckTFNAr8h5hqLNEoNy2NimIng74gYV03CTDmBSkFRVOKaZjZUwUe0xj2Bylxpb8GrRaNKXLr429y/0tHs70baWHU0jAuC7dDp9ymmkFvADAC4TAa2xjSTYSAURdTyVRiGg49TwOMrLOpWGjTRYvGFBjyBRnyh+gcnJ3uBOtH546yNHTKbWoZMm+Klnuq1G2Ix5CvsG94FsNjXKTJ4LQLdpsUfMvXIX8wcj2lEy0aU6DT9D3P1nLW3rExDYcdn7Y0UorHG8Rlt0WEucz8kVtuq0InMsF0Cm4WEaHIYYu4VwuVYZ+2NHIOK2A5Wy2N6BnhYLqndEwjpQz5ghHXFECpyxEZ14y/BpOlyGkvePeUtjRyEMvS6Bnyz8qbaSQQHj25T+e+p5RBXzDimoJo95QWDYiKq01DNArZPRUKK7yBsLY0co3oEtbdQ7PP2hgJhHDZbdhtxrQct065TTkebzAyRwO0e2osw2ZCwFRiGmDMCi/k+Jt13kp19lRuEZ1PPxvjGt5AKJLzDnpyXzoY8gUpj3IhlOrsqRjGViVIlkJ3T0Wyztza0sgpOqNEo3MWTsjyjpmJq6vcpp6x7qlRS6Nwb3jRDFs3vylaGsVOe0GXEYlk5WlLI7fo8PioKzeawszGYPjYQnE65Tb1DJr9wS10TCOWsVUJkqXIaS/oMiLW9TPV8zYdtGhMgY4BL2fUV5jvZ59oeAOhyEQpsKrcatFIJUbXvtFzXOKyI6JFw2I05XbqMY1CTrkd1pZGbtI16KOhqoiaUhedg7OvMulIIBxTvsHtsBMKK0K6xEXKGPQGIy4pMOYYlLocOhBuMqOU24J2TxnXj45p5BDBUJjuIT915UXUlblnsaUxekm4HLrlayoJhY1ifNHZU2AEw7WlYTASCOGwCU771G5NRU473gJ2Tw37tKWRc3QP+VEK5pa7mVvhnpUxDe/YmIb5w9VtNFPD2Aq3FqVuhy4jYjLsn1oDJosip62gq9xaKbd6nkYOYVkWdeXuWWtpjPhjYxpW+q22NFLDaAOm2KfBcrd2T1mMzeBLluICT7mNxDT0jPDcwZrYN7fcTV25YWkYHWlnD2NLXIxaGlo0UkH/sFHhtqpkrHvKod1TJtOt1GrN05htv8lkGdKWRu5hzcuYW1FEXbkbfzDMwMjs+qEPeINURKWDWjENLRqpoXfYD0B1iStmvFRbGhFGAqFIGZupUOS0E1YUbLbfsC+E3Sa4Hem/pWvRSJIe8wdfU+KiptQVMzYbCIbCDPqCVBSPWhpWJVbtnkoNPUPmNVQaKxplboeeEW4yMk1Lw7pZFmra7ZA/SInTjshEnblThxaNJBn0BnHYhCKnbVZOyPKYvaujLQ3rh1ioT2+pJmJplI61NOw6EG4ydoJpsljrFGr3vmFfKCPptpCEaIjIfSLSISJvRo19TUROiMhO8/W+qM++KiLNInJARK6MGl8jIm+Yn90ppiSKiFtEfm6ObxGRpqh1NorIQfO1MVUHPR08XsPfLyKRQOZsEo0Bs6NcZfF40SjkAGMqsSyNquLxMQ3tnjIY9k8vEG4lcBRqpdshfzAj6baQnKVxP3BVnPE7lFKrzdfvAURkBbABWGmuc5eIWFfA3cBNwHLzZW3zRqBXKXUKcAfwLXNbNcCtwPnAOuBWEame8hGmiEFfkHIzSByxNGaRS8GKz1RE3dAqzYBtnxnA1cyM3iE/lcVOHGPmIJS5HPiDYe0GxEr7nvrNz4qDFKp7asSfQ5aGUup5oCfJ7V0DPKKU8imljgDNwDoRqQcqlFKvKCO94UHg2qh1HjDfPwZcZlohVwKblFI9SqleYBPxxSsjeLwBys1JWVYZiNlUZK5/xBCGiqjsKcv33juLYjfZpGc4MC6eAbr+VDTD/iDFzql7za2+9oVqFQ/5g5TkkKUxEZ8Vkd2m+8qyABqA41HLtJhjDeb7seMx6yilgkA/UJtgW1nBck/B7PyRW+6paEvDyvKx3Cqa5OjweHmpuWtc+mfvkJ/qMem2oHtqRGMEwqdhaTgsS6MwRWPYH6I0A+m2ANOVpruB2wBl/v0O8FdAvNC9SjDONNeJQURuwnB9sWjRokT7PW083iALqoqAWSoaI+NjGkVOO6UuuxaNJOj0+Pj6b/ew/6SH5o5BAB776wtZ21QTWaZnyB+5hqKZje7O6eINhKeVcut2FnhMwxdkYU1JRr5rWpaGUqpdKRVSSoWBH2HEHMCwBhZGLdoItJrjjXHGY9YREQdQieEOm2hb8fbnHqXUWqXU2rq6uukc0qQY1UlNS8M1+54M41kaYGT69GrRmJTn3+rkyd1tNFYXc8OFiwE4ORBb1LJ32D9ujgZENWKaRdfTdAiGwvhD02tZWlzgMY1MWhrTEg0zRmHxAcDKrHoC2GBmRC3BCHhvVUq1AR4RucCMV9wAPB61jpUZ9WHgT2bc42lgvYhUm+6v9eZYVvB4A5Sb6ah2M/V2Nv3I+0cC2G0y7sKrKXXNqvko6aKtfwSAH3xiDX/7nuVArFtPKUXPkD9uTEM3YjKYbn9wMGpPQeHWSRvyZS6mMem3iMjPgHcBc0SkBSOj6V0ishrDXXQU+AyAUmqPiDwK7AWCwC1KKet/8WaMTKxi4CnzBXAv8JCINGNYGBvMbfWIyG3Aa+Zy31BKJRuQTylKqZiYBhg/9Nn0Ix8YCVJhphRHU12iLY1kaO33UlPqoshpj/RYjxaNkUAIXzA8bo4GzE5353SYbi8NGM2eKtRGTNMtvzIdJhUNpdT1cYbvTbD87cDtcca3AavijHuB6ybY1n3AfZPtY7rxBcMEwyqScguzr17QgDcwzjUFhqVxqHMwC3uUX7T1jVBfacQrnHYblcXOGLGNzAaP457SgXCDmVgao+6pwhMNv3l/ykSxQtAzwpPC8veXR7fpdDki5YhnA/0jgZjZ4Bba0kiOtn4v9ZXFkX/XlLrojjpvvUPGNRTP0piNk0Wnw3AKLI1CLI+eybLooEUjKQbNEhvl7rHuqdnzIx8YCcTUnbKoKXUy5A8V5BPcVGjtG4nJjKouccbMb4nULisdL8zWE6J1nRUq0+0PDqPVCwrRPTWUwVavoEUjKay6TLHuqdlVL2jAG4xJt7WoKXUDelZ4IoZ8QQa8wTGWhpueodFz1meKRlUc95TLYaPUZae3wM+xJZrTufnZzAqvhdjyddiXuVavoEUjKeI1z5l1MY0J3FPWk3H30OxrOpUqrMypaEujptRJT9Q5SxTTAKgpcxX8zPuuwdFGZ9Oh1O2ItD0tJLSlkYN44sQ0Zpt7qn8kfiDcmlfQO1TYT8GJaO0z5mNEWxrG/JZAZFZ475Afm4yfB2NRU+Iq+EmUVs+a6YtGYfZatyyN6bj1poMWjSSI756aPZaG10wHje+emn29Q1KNZWlY2VMAtaUu/KFw5CmwZ9hPVYkrko47lupSbWl0enwUm1UIpkOpy4Fnlvwmp4InjicknWjRSIK4ouGyM+QPEQ7nf3vJ0V4a4y86K9tHZ1BNTGufFxGYXxkdCDfFdtA4byd6R5hTFt81BYal0T1Y2Oe4c9BHXbl72o2EymbRg9xUGNKikXtYN9XSMTENgOFZkFU0UQkRGO39UOiuk0S09Y8wt9yNM6rkebSF5g2EeOVwNxcurZ1wGzXa0qDT45u2awpml/U/FSKiEeehLx1o0UiCQV+AYqc95qYwm2bxjpZFHy8aDruNqhKnFo0EjJ2jAVFl5Yf8vNTchTcQ5vIV8ybcRnWpi+ECT23u9PioK5u+aMy2OGOyaPdUDuLxBmNcUzC7ZvFaFW4TBmkL/Ck4ES29IzRUxReN7iE/m/a2U+Z2cP6SxJYGFLZF1zFjS2N2pcEny5AviN1MOc4EWjSSwOMLjjP9Iu6pWXCRDpjut8o4k/vAeAruKXB/+0R4vAGOdg9x+vzymHErFtQ96OOP+zq49LQ6XAl+1IXeu8QXDNE/EpiRaJS5nbPC8p8qg16jAvd0Y0FTRYtGEhiWxti+zlb3vvy/SLvN/Ph4ZbvBiGtYLixNLHtaB1AKVjVWxoyXux047cLv32ija9DHFWdM7JoCqC0r7C6JXeZDycxEw86gPziu+dVsZ9AXyphrCrRoJMWgNxBTQgRmV72gkwNeXHZb3LLdYGSNeXxaNOLx5ol+AM5siBUNEaG6xMWuln7mVbi5atX8hNspdEvDmqMxd4aBcKVGa1gVCkNRvX4ygRaNJIgX05hN3dba+73MrZg41bGi2MnASP4fZzp440Q/9ZVFzIkTwLVE+G/edcqk3egKPaYx04l9MLuSU6bCoC8Y8XxkAi0aSeDxjlfy2RQIPzngZX7F+DakFhVFTjzeQMGZ/cnwxol+Vo2xMizmVRQxr8LNR89bGPfzaCqLndikcOfDpEI0ZtNvcioYopE5SyNz35RhkTn4AAAgAElEQVTHDPrixTRmz1NN+4CPFQsqJvy8vMhBWBk1bjJpBuc6Hm+AI11DfGB1Q9zP/+3aVQRCyfW8ttuEqgLOUrNEo7Y0FZZGYbmnBn3BuL3n04W2NCbBHwwz6AtSVRIrGiVOKxCe3xeoUoqT/ZNYGmYq7oAOhscwURDcYmFNCUvrypLeXnWJs2BrfHUOeqkucSbMMJuM2ZScMhWGfMGMFSsELRqTYpW0Hts8x2YTSlz5XyDN4wsyEggxr2LiJzwrnuMp8H4PY7E6Go5Nt50uRuOmwqwmPNPZ4ADlbuPhptBEI9PuKS0ak2B1X6udoLdzvotGe79RoXXeJDENGC03ojFoH/AhwoxmMUdTY1bGLURmOrEPRi2NfP9NTgWlFEO+8Yk66USLxiRYgcl46ahlbkekimm+cnLAEI1k3FMeLRoxdHq81Ja6cNhT8zOqKS3cmMaJOLPqp0ohBsJHAiHCityyNETkPhHpEJE3o8ZqRGSTiBw0/1ZHffZVEWkWkQMicmXU+BoRecP87E4x8ztFxC0iPzfHt4hIU9Q6G83vOCgiG1N10FOhO4FozIb6/SdNSyO6QutYrKcYnXYbS8eAj7ry1AUgrX7shZal5g2E6PD4aKwumdF2ZlNySrJY3Q5zbZ7G/cBVY8a+AmxWSi0HNpv/RkRWABuAleY6d4mIlTpyN3ATsNx8Wdu8EehVSp0C3AF8y9xWDXArcD6wDrg1WpwyRe9wAtFw5X+BtPYB7Z6aLh0e34wmo42lptRFMKwiZV0KhdY+ox9JY/XMLI0Slx2RAhONDBcrhCREQyn1PNAzZvga4AHz/QPAtVHjjyilfEqpI0AzsE5E6oEKpdQryniMenDMOta2HgMuM62QK4FNSqkepVQvsInx4pV2rB4HVXGK+c2G+v0nB7xUlTgTpoXqQHh8OjzehAkEU6WmQHuXtPRaojEzS0NEzAe5/HYZTwUrvTin3FMTME8p1QZg/p1rjjcAx6OWazHHGsz3Y8dj1lFKBYF+oDbBtjJK77CfqhJnXL/1rAiED/gSxjMAipx2XA6bTrmNIhRWdA36mZtK91SBdkkcFY2ZWRpguIwHC6jkjVXeJ6csjSkSrw6FSjA+3XViv1TkJhHZJiLbOjs7k9rRZOke8lMzQSG/Unf+P9W0D3iZO4logOGiiuc2aesf4V8ffzOSfloo9Az5CYUVc1NpaYzp9lcotPQO47BJQhdpshjWf37/JqeCdaz5IBrtpssJ82+HOd4CRNdMaARazfHGOOMx64iIA6jEcIdNtK1xKKXuUUqtVUqtraurm+Yhxad3yD9hIb+yWRIIn5/Eja+iyDEuprHtaA/v/d4LPPjK29z97KF07WJO0uExYkGpjmlAYVoaC6qKJ+yfPhUKrRGTdf/Jh9pTTwBWNtNG4PGo8Q1mRtQSjID3VtOF5RGRC8x4xQ1j1rG29WHgT2bc42lgvYhUmwHw9eZYRukZ8o+b2GdR4nIwEggRytM+4eGwonsoORdLebFzXEzjJy8fxS7Cu06r4w9vniyornMdkVpJqXNPFW5MY5iFNTN3TcHscBlPhUjXvlyapyEiPwNeAU4TkRYRuRH4JnCFiBwErjD/jVJqD/AosBf4A3CLUsq6k9wM/BgjOH4IeMocvxeoFZFm4IuYmVhKqR7gNuA18/UNcyyj9Az5407sg6jy6Hla6bZ32HCxzCmLf3zRVBQ5xsU0jvcMs2JBBZ96x1IGfUGe2d8xwdqzj86BmZfyHkuJy4gd5Vul2yNdQ9zz/CEe3nJsWhl2Lb0jNFbNLAhuUVqglkYm3VOTfpNS6voJPrpsguVvB26PM74NWBVn3AtcN8G27gPum2wf04VSit7hiS2N6LzweP21c53OweSfliuKnJwwUyMtjvcMs+rMei5cVsucMjeP72zlvWfWp2Vfcw3LPTXTWczRiIjRWjfPROPuZ5t5dJuR5/J29xBffd8ZSa87OkcjNZZGobmnBr1BbALFSRTFTBV6RngCBrxBAiE1oaUxWrYgP90yUylHXVHsiHFPDfqC9A4HWFhdgt0mXH3mfJ450EEgFE7b/uYS7QM+KosTpypPh+pSV9517+se9HP6/HKuWDGPx7a34A8mfw1YDyKNKXNP5X+ccSpYdacy1eoVtGgkxPItT9QGNd+793WZlkYy7qnyImeMe+p4zzBAxBd9zqJqfMFwwWRRdXi8KXVNWdSW5p+l0TPsZ06Zm4+dv4juIT+b9rYnva51HTWk0D2Vrw9xFs0dHn7w3KGkKgNkumsfaNFISKSEyAQ31XwvWzAlS6PIgS8Yxhc0fpAR0TAnZFn9OPacGEjHruYcHR5fStNtLarzUDT6hgNUlTh55/I6GqqKeXjr20mvu/N4HyJwWooqBZe7HfhD4SlZO7nGHX88yDef2j/OHRyPQS0auUWkWOEklka++lA7PT6KnLakLrrRooXGsR43J2QtrDFEY+mcUtwOG3taC0Q0BnwpndhnUVPizDvR6B32U13iwm4Trl+3kJeau9mb5HXw2tEeTp9fQWWcigvToTzPS94M+YJs3mdYatvf7p10+UyXRQctGgnpSVCsEPK/T3jXoJ+68ol7g0czWrTQ+DEe7xmm1GWn2mxO5bDbOL2+gr1t/enb4Ryhd8jPib4Rls9LvsFSslSXusxYWn48KYfCiv6RQOQ6+OQFTZS67Nz93OTzdgKhMDve7uP8JTUp2x+rrItVUy3f+OO+drwB4/8+GdHweDNbFh20aCSkJ0GxQojuFJafPtROj485SfaCsLLDLEvDyK0viRGclQsq2Ns6MOurtL5+3Pgxn7so9fUzraSLvuH8eFIeGAmg1GgJlMoSJ5+4cDG/293Kka6hhOvuaR1gJBDivKbUicb8SiPGZlVvzjd+u6uV+RVFXLi0NinRaB/wpsXiTYQWjQT0DvlxOWyUuOJnyOR7ILzT40u6gdBYs/94z8i4AnMrF1Qw4A1GagnNVna83YfdJpw1QZvXmRCpP5UnLirrwSo6WeRT71iKTYRfbDs+0WoAvHbEmHZ13pLUiW+9WeK/NQ9Fo2vQx3NvdfJnZ9Vz3pIa9rUNJLy3BENh2ge8Ge0PDlo0EtI3bJjdE7lvip35XYq5czD5bmmLaw2BePOEYUkcjzOLd+UC4ya6p9VwUf369Ra2HO5O4R7nBjuO9XJGfTklaejLHKk/lSeiYbVDrioZjUnUlbtZ2VA56ZPyliM9NNWWpPRJeU6ZG7tNONmffw8uP9tyjEBIsWHdItYsriasjESBiegc9BFWiXvhpAMtGgnoHfZTVTxxOupoKeb8E41AKEyvmSqZDPMqiljVUMEf97XTNehn2B+KZE5ZnD6/HLtN2NNqPCF98dFdfPSeV/naE3sYyfMOhxahsGLX8T7WpME1BaOWRr7M1bDa045NS1+zqJpdLX0JYzN7Wvs5J8Xn0W4T5pW7acszSyMQCvM/W97mkuVzOGVuGecsqkIkcVyjtc84xgWVqZnjkixaNBJgpRImIl8nE/UM+VFqajOaLz9jHjuO9fIDM8i5tin2B1/ktLOsrpQ9rQPsP+lBKTh/SQ33v3yUq+98gQMnPSk9hlSjlJo0HnPgpIchf4hzF6dHNKyYRneeWBq9cdxTAGsWV+MNhCfMovIGQpwc8EYs2FQyv7Io72Ia/7unnfYBHxsvbAKMGGJDVTGHE8x7ajOtKW1p5BB9I/4kRCM/+4RPZY6GxeVnzEMpuPfFI1x6ah1nNVaNW2ZFfQV7WvvZ12bcLL7zkbN5+FPn0zcS4Pbf70vNzqeJ720+yPo7no+cm3hY7oJzFqZHNKpK8qtooRWwry6N/Z2cu9i4NiZ6Uj7RN4JSsKgm9aJRX1Wcf6Kx9yR15W7effrcyNiimhKOmfOh4mEdo7Y0coje4cCEs8Et8rV7X2dkNnjyorFyQUUk0Pj5y5dPsEwl7QM+XjzYRUWRg4aqYi46ZQ4fPW8hLzV35ezNUCnFo68d52DHIJ9+cNuEFXvf7hnCZbelrFbSWFwOG3Xl7kkzj3KFnmE/DpuMm+tTX1lMQ1Ux24/FFw3rZpgOS6O+ooi2fm9eZfFtO9rLeU3VMeXhDdGYODbT2uelxGWnolin3OYESin6hwORJ7+JKHXlqWh4pl6lVUT460uX8RcXNU2YbrrSnBm+eX87Z9RXRJIIrj6znlBY8fSekzPc8/Swt22A1n4vV59Zz87jffx0y7G4y53oHWFBVRG2FPR+mIjzmqrZeiTjBZ2nRd+wn6oSV9xkkXMXV7P9aHzRGC1Dkx731EggxMBIfvwuT/Z7OdE3wprFsanHC2tK6Br0TXh/OTkwwvzKoozWnQItGhMy7A/hD4WTck/l4zyNjoHpVWndeFETX3v/ygk/t8qJBEIq8h4MMVlcW8Lv3mibxt6mnz/u7UAEvvb+lSydU8rLzV1xlzvRN0JDmqwMi/OaajjRN5JUGYls0zs0OrFvLGc2VHBywEt/nDknx7qHKXLakk75ngr1prumbSD3zx/AtreNB4S1Y+JklhV2vDe+i6q1z5tx1xRo0ZiQ0QBfYtHI1+59J/pGmFPmSnmV1qoSFw1VxoV8Rv2oaIgIV59Zz8uHuiNpmrnE5v3trF5YRV25m/OX1rL1SE/c5lonekfS/kNdZ86Qfi0PrI1ErQOs7LqWvvE3vWM9wywaMzk0VViB4XzJoNp2tJdipz3mIQtG4z3HuuOLRlv/SMaD4KBFY0KsAF9lgpRbyN9OYSf6vCyoSs/Nz3JRraiP/RGcv7SWUFjlXBZVp8fH7pZ+Lj9jHgAXLqvF4wtG5ptY+IJG74d0Wxqnz6+g3O1gSx6IhjWXKR7W5M94kz0t0UgHVtwtX4Lh297u4eyFlTjtsbfjiGjECYYHQmE6PD4WaNHIHSJZIUm5p/JPNFr70vfEvG5JDZXFznG1mZbVlQJwqDO3grxvnjDEwSpncYH5pP/qmImJ1k2oIU1ia2G3CWubqnntaO6LRo9ZrDAeVrLAWNFQSnGsZzgt8QwwXK42gaPduXWdxWPYH2Rfm4e1i8eXUqksdlJe5IjEf6Lp8PhQysgUyzRaNCYg4p6awPS2KHUZJcODeVJgDowfbWvfSNosjb+8eAnP/59343bEur4WVBZT7LTT3JFbPTf2munBp9cb5bnnVhSxrK6UVw7FisYJ8+aXbtEAWLekluaOwYTpv9lGKRUJhMejqsRJqctOyxiffPeQMTk0XZaG025jcW0pP3zuMNd8/yU8OVzx9sBJD6GwYlXD+JI0IsKimhLejiMaJ7M0RwO0aExIn1nNtWqSks352L2vbzjAsD+UNjeL3SZUxrHQbDZhaV1pzjVq2ts6wKKakpiWve84ZQ7PH+ziG7/dy7BZxbjFDEyn2z0FcPEptQC8fCh+QD4XGPKHCITUhNa4iNBYXTLO0rDcLekSDYCff+YC/v6q09h1vI8ndrWm7XtmiuWqPX2CfiITzdVoyeADzFi0aExAnzmfIN7NL5pIT408Ko9uZeU0ZLjQGcCyurKctDRWjglCfvGK0/jI2kbue+kI9zx/GDBceiKj2TnpZOWCSqpLnDz/Vu6KxmSdLcFwUY0VjcOmezKdojG3vIibL13G6fPLI/3Lc5H9Jz0UO+0TnotFtSW09IwQHpOUcaRrCJH0nsOJmJFoiMhREXlDRHaKyDZzrEZENonIQfNvddTyXxWRZhE5ICJXRo2vMbfTLCJ3iplSISJuEfm5Ob5FRJpmsr9ToW8kQInLPs7FMharp8agN39Eo9UUjXS5pxJxytwyTvSN5EwtqkFfkKPdQ+OC9pUlTv7jg2exckFFZM7Eid4R5pa7cTnS/6xltwkXnzKHFw52ZmSS2pce3cUvt0/t5mplJyVykSysKRnnnnrxYCc1pS6W1qW+H0k0IsJ1axey63hfziVfWBw46eHUeWUTzvtZNqcMfyjM4TGTPY92DbGgsjjl2Y/JkIqr/91KqdVKqbXmv78CbFZKLQc2m/9GRFYAG4CVwFXAXSJiHfHdwE3AcvN1lTl+I9CrlDoFuAP4Vgr2Nyl6EwT4opkfKcWcHznhEG1pZF40lpk3isNduWFtHDg5gFKMS3e0OHdRNbuO9xEKK06kMQ4Uj0uWz6HD4+Ngmi2zN0/088sdLfxm54kprWfVPkp0Thqri/F4g/Sb7t5QWPHcW51cempdzOzndHHt6gU47ZOXac8GSikOtHs4fX78aw/gvAmSMo50D7NkTmla928i0vHIdA3wgPn+AeDaqPFHlFI+pdQRoBlYJyL1QIVS6hVlPFI9OGYda1uPAZdJOhK745BMsUKAplrjP+7tPCn7AIal4XbYJmwulU6WzTXOV664qKyCehOKxuIqhvwhDpz0GBP7Miga71heB8Dzb3Wm9XseMy2M/VN8Gj8RsVgntjRGM6gMa2Pn8V56hwMxNZbSSW2Zm/OaanIyfblz0EfPkD9hf/Sm2hLmVxTFiIZSiiOdgzTNybxrCmYuGgr4XxHZLiI3mWPzlFJtAOZf6+poAKLlvsUcazDfjx2PWUcpFQT6gdqxOyEiN4nINhHZ1tmZmh+YkRUyuWjMKXNR5nZwdIIJONlCKcVDrxzl5eauce6N1j4vDVXFGS8/AIbI2iR30m73tA5QXeJkfkX8G59VLuXxXSdo6R1haQaf7hqqillWV8oLB9MX1/AFQ/xm5wlcdhudHh/dg8lna7X2jVBV4kzYV2TsXI0/7e/AbhMuNQUxE5zVWMX+kwP4grnhErWYLAgOhovtgqU1vHq4J/I77h0OMOANRh5YM81MReNipdS5wHuBW0TknQmWjXeHUgnGE60TO6DUPUqptUqptXV1qbkY+5KoOwXGf+ri2pKcKzD3yqFu/uXxPXzsx1u49vsvxdwMMu1miabIaWdxbSn72+KXzM4029/u5azGqgkFdFFNCXPKXNzz/GGcduHjFyzO6P5dsryOLUe6JyygOB12HOvln379BiP+EI/vbKVvOMDGi4zjmorvP5kyFmPnajyzv5M1i6onTTBJJWc3VhIIKfa35VZcwzrXiSwNgAuW1tI16OP23+3j3d9+lq1HDKtjaV0eioZSqtX82wH8GlgHtJsuJ8y/HebiLcDCqNUbgVZzvDHOeMw6IuIAKoGM2JlGA6bkLuymOaU5N5Ho4a3HqCx28u8fOJP9Jz184t6tkfIdmXazjGXNYmPi2tiMkEzTPWjEC85fOnGPahHhnEXVKGXMP5k3gUWSLi5ZPgdvIMyOJPpFJ8ttT+7lp1uOsfEnW/nXx9/k3EVVfPqSpQDsm5JoTP7wUVnspKLIQXOHh/7hAHvbBnjH8jkz2v+pctZCo0z77paJu+Blg4Ptg8wpc1E7Sf2tC5cZzpUfv3iEI11DfHfTWwD5Z2mISKmIlFvvgfXAm8ATwEZzsY3A4+b7J4ANZkbUEoyA91bTheURkQvMeMUNY9axtvVh4E8qA6kk4bCif2TysugWS2pLaekdSdilLJN0D/p4es9JPnhuAx87fxE/3riWt9o9/OiFw3R4vHR6fFl7SgHjyal3OMBbHdl98rNmXJ+/ZJzHM4YrzphHQ1Uxf/3OZZnYrRguWFqL0y48nyIX1c7jfbx+rI8Lltaw9UgPtaVufvjJtcytKGJOmXtKFmBr38ikadsiwnlNhntl+zGzJ3jTxCKdDhZUFlFb6mJXS//kC2eQZEupLKop4Yz6Ct55ah3nLqrirfZB7DZJ24z6yZhJIfZ5wK9Ns94BPKyU+oOIvAY8KiI3AseA6wCUUntE5FFgLxAEblFKWTb3zcD9QDHwlPkCuBd4SESaMSyMDTPY36Q50TdCWEF9kvMYFteWEAorWnpHspbRYKGU4p4XDhMIKa5ftwgwXBznLqrixYNdkUyNC5YmvlGmk/OtjJBD3QkzR9LNq4d7KHLaODPObNxoPnLeQq5b25iVGFCp28G5i6p54WAnX3nv6TPe3k9eOkKZ28GPbljLK4e6OaO+IlLp+Iz68qSD4R6v4VdPpozFhctq2by/gyd3t+GwCasXjm/elU5EhLMaK3kjx0SjpW84qWZeIsLjt1yMy2Hj0W3H2XGsj8bq4nG1qjLFtL9VKXVYKXW2+VqplLrdHO9WSl2mlFpu/u2JWud2pdQypdRpSqmnosa3KaVWmZ991rImlFJepdR1SqlTlFLrlFKHZ3KwyXLQfAI+dV5iX6OFJRRHsxzXCIcVX3x0Fz987jB/fvaCmP2/aNkcdp/o5+k9JylzO8ZNZsskC2tKaKwu5tXD2c1o2XKkhzWLq5Oad5ENwbC4ZPkc9rQORFJcp8vxnmF+t7uN69Y2Ul7kZP3K+TFPq6fPL+etdk9SJXGsORrJxMasB5THd7ayqqGSYlfm5xac1VjFwQ5PztSJC4bCtPZ5WViTnJvYukavPrOeEpc9qw+nekZ4HN5qN9JBT52bnGg0mf+B2Q6G7zjWy69fP8FnLl3K9z66Ouazdyyfg1LwuzfaOK+pGkeWnlIsLlhay5Yj3VmLa/QPB9h/coB1TdmzuJLlmtUN2G3CfS8emdF2vv9MMzYRbnrn0rifn72wCl8wzEOvvj3ptqZSVeCM+goqihyEworzmtLTJncyLlpWS1jBJ368Zdxkw2xwcsBLKKwi5eOTpdTt4K6Pn8uX15+Wpj2bHC0acXir3cO8CnfSGR61pS7K3Q7enmYw3BsIpaQe01bTR/+Zdy4bN8P07MYqSlx2lMqua8rCimscaM9OXOOpN9tQCi45NbNB2emwsKaE95+9gJ9uOTbtdrnHuof5xfYWPnb+ognLoLxvVT3rV8zjtif3Tjo3pK0veUvDbhPWmXGjsd3pMsX5S2v5/sfO5VDHIO/73gtZ7yB53Gzj2jhF0QB412lz4xY4zBRaNOJwsH0wadcUGK6LpXWlkWqpU+U//3CA933vhRn35XjtSA+nzC2LO2nP5bBFmvvkgmhYBfleOJjeiWsT8fDWY5w2r5xzMuxfny43v2sZw/4QP3x+6h7at7uHuOmhbThsws3vmjiYb7MJd3x0NQtrSvj+M80Jt9naN4LdJswtTy7u9+7T6yhy2rJmaQBcfVY9T37uHSyuLeUzD21nWxZLz1vd+JJ1T+USWjSiCIcV4bCiuWOQ5Um6pizeeWod29/undLkKIARf4hfbD+OLxiO9HWYDqGwYtvbvQkzUz50biOrF1ZlNZ5hUV9ZzKnzyrJSkG93Sx+7W/r5+AWLshqrmAqnzivnw2sa+eHzh/jT/vak1xvyBfngXS9zcsDLj25YO2nKcKnbwZUr5/P6sb6Ec0OOdg8xv6Io6VIg15+3iBf/4T2Tppemm8W1pfz8MxdQ7nbwyGvZKy3S0juCLUPFL1ONFg2Ttv4RPnDXS/xi+3FGAiFOnTe1YmpXrpxPWMHmfR2TLxzFk7tb8ZjFDnfPILvjwEkPHm8w4ZPcn5+9gN/ccnHW4xkW71xex9ajPRkvXvg/r75NsdPOtec0TL5wDnHbNatYUV/B53+2k1+/3pJUIcPXj/XRPeTnux85m3eemtzE1wuX1uIPhdk+wdyQ/uEAm/d1RKzFZLDZhDlZFgyLEpeD951Zz1NvtEXK3mealp5h5lcUZaT4ZarJvz1OExVFTjy+IP/46zcBWD4F9xQYLU4bqoqn7Ct9eOsxltWV0lBVzM4ZTD6y5hxkOgd+Jrzz1Dr8wTCvHumefOEU8Xb3EL/acYLr1jbG9M/IB4pddn50w1qW1pXydz/fxQfvfpkXJ5m/seNYLyKwdgrXxXlLarDbZFwTKotHtxkPVhsvaprK7ucUH1rTyJA/xB/ezE5so6V3hMYszbOYKVo0TErdDv77+nMi5vbYVqWTISKsXzmPF5q7kk7r29c2wOvH+rh+3SJWL6xi1/Hpi8bLh7qoryyKlG3IB9YtqcHtsPHwlmNxW1qmgzs2vYXDLnz23adk5PtSzYKqYn79NxfzrQ+dycl+L5+4dwtfeOT1SBXZsWx/u5dT55ZPSSDL3A7OaqyM2wCqw+PlwVePsq6phpULsheMnSlrF1ezsKY4Uqwx0xzvHc6r32o0WjSiWLmgkv/80Fl8dO3CaT2Frl8xH38wzMvNyfnpH95yDJfDxofXNHL2wkpaekemHBMB6B8J8MyBTq5aNT9vfPRg1KG6ft0iNu1t593ffpadMxDNZGju8PD4rlb+8uIlzM1wOZBUYrMJHz1vEc/+n3fxd5efym93t/HJe7cQGpO+HA4rXj/Wy7mLpx7sv2hZLbtb+hmIapX677/fx7rbN3O8Z2TCtN18wWYTNpy3iJcPdWe8k6QvGOLkgHfK6ba5ghaNMVx7TgPf+vBZ01r33MVVuB02Xjk8ubtl2B/kN6+f4Ooz66kqcXF2o1UfZ+pxjd+/0YY/GOYDeeajB/ja+1ea/cRt/GzLsbR+1y93nMAmwo3vWJLW78kUboedz1++nO9cdza7W/rHNVE61DnIgDcYqdQ7Fa5YMZ+QUnz+Z68TCIU52e/lvhePcOXKefz+c5dw+Yp5qTqMrPGRtQtx2oWfvhr/uusa9KGUQinF/S8dYdPe9pTMK3p4yzGUgmVz09uEKl1o0Ughboed85pqJvQFR/Pr10/g8QX52PlGqY9VDZXYBH65o2XKF+avXz/BsrrSScth5CqLakt475n1/C6NgclwWPHEzlYuWT4nZwKyqeKa1Qs4d1EV//n0ATxRlsGOY0Yg+9zFUxeN1QuruP3aM3nmQCdfeGQnP3npCGGl+OerV0zYeyTfqCt3c9Wqen6x/fi46+7l5i7O//fNfG/zQV493MPXfruXTz+4jXP/bRNn3vo0n3rgNXYd78MXDHHHpre48o7nIx0xJ8IbCPHD5w7x9d/uZf2Kebxv1fx0Hl7a0KKRYi5cVsv+k56EbqZj3cN886n9nLOoirXmD7rU7eBv37OcJ3e38Q+/3J10i8/jPcNsPdLDB85pyCvX1Fg+vKaRQV8wbZOudlnDMY4AAAyfSURBVBzr5UTfCNesXpCW7WcTEeFf/mwFXYO+iI/+jZZ+7nr2EHPKXNPuAfKx8xfxz1efwe/eaOOHzx/m8jPmZa1IXrq44cLFeLxB7n1hdLZ9x4CXzz2yk1BYcc/zh/mvp/czp8zNd647m6tWzufPVy9g65Eervn+S6z416f53uaDHOzw8M2n9k/4PS29w7z728/yH0/t592n1XHn9efkTBbjVJlJwUJNHKyJc1uO9PC+M+vHfd4/EuCWh3cgwJ0bzom50X/h8uUEQmHuevYQ157TwMWnTD5b+XGzRec1q/PPNRXNuqYaFtYU86Pnj/DeVfXjeh+HwmpG7UF/s/MERU4bV6zIz6e7yThnUTWrGip4bHsLF58yhw/d/TI1pS7u+viaGT1MfOqSpThswn8+fYDPXJrfcYx4nNdUw5+dVc9//6mZK1fNZ/ncMr782G4GfQF+8Ik13PLwDnYc6+PvrzqND61p5ENrjC4O/3Dl6Wza186BkwOsbarhzRP9/PefmllWV4YvGOLMhkq2vd3L4ztb+fZ1Z/HY9hZ6h/389FPnc9Gy2rx+wJNMNK3PJGvXrlXbtm3L2vcHQmFWf/1/WdVQySXL57DxoibKzaB6c8cgn35wGy29w9z18TVcEccv7A2EeMe3nmHFggoe/Kt1Cb9LKcXl332O2lI3j/71hWk5nkzy9J6T/PX/bGf9inl85yOrjY6IXUN86Re76B3y88TfvoMy99Sfc473DHP5d5/j6rPq+e5HVk++Qp5y/0tH+Npv93LqvDJO9nvZ/KV3RSrYzpRgKJy3T8aT0T3o4/LvPkdNqYv3nWkIyDeuWckNFzbxz795g9/tbuO5v393wuSYYX+Qy77zHG39XkRAKbAJzClz4/EGGQmE+Pxly/m7K07N4JFNDRHZrpRaO+lyWjRSz2cf3sGTu9sA+Nx7TuGL60/jeM8wH7r7ZcJK8YNPrEmYN//9Z5r5r6cP8NTnL+GM+on9x7tb+nj//3uJ//jgmZEy6PnOvS8e4bYn9+K0C7Wlbjo8XkrdDgZ9QT5+/iL+7dozk97W4c5BPN4gdz3bzPNvdbH5S5dmrWNhJugd8rPu3/9IIKT456vP4FOXzD7LIF28fKiLz/3sdboG/Zy/pIafffoCbDYhGAoz5AslVYeue9DHSCBEXbmbN0/0U1Pqxu2wce33X8Imwp++fGnC1rjZRotGFgmHFcOBEF96dCevHOrmV39zMTc9tI0uj4/Hbr5o0rpW/cMBLvrmZuZWFPGtD50VqRk1lq89sYeHtxzjtX+6PKPtM9PNa0d72Lyvg65BHw1VxWxYt5Afv3CEe188wgN/tY5LJ5nZrJTi/pePcvvv9hE0kwq+vP5UPvue5ZnY/azydz/fyZsn+vnd5y7Jy9nG2aRr0MeDLx9lw7pFKX246PT4CIbDOV8yRItGDmBZAi67DadduP+v1iU9Y/vVw918+Re7aOkd4dxFVXzlvWfEiEf7gJdL/+sZrlo5n/+74Zx0HULOMOIP8YG7XqKld4RHP3PhhBk8g74gX/3VG/x2VyuXnzGP969ewIneEf7qHU24HZnv45BpgqEwIaUK4lg1qUWLRo5w4/2vsfN4H/f/5TrObJxaSuyQL8jDW45x/8tHCYTCPPPld1Fq+vS/8svd/HJHC5u/+C4W1c6ujJaJaOsf4YN3vcygN8j15y/ihgsXx5SW3n9ygL/5nx0c7R7iS+tP4+ZLx5eI12g08dGikSN4AyHCSs3Il7njWC8fvOtlbnn3Mr68/jQe39nKFx/dyV9ctIR//fMVKdzb3OdI1xDf/t8D/OHNkyilOK+phhKXnWF/iF0tfVQUObnz+nNyovy7RpNPaNGYZXzhkdd5cncb8yqKONFnuKx+8hfrZlUsYyq09o3w0Ktv88qhbkJhRbHLzuKaEv7+qtNTljGk0RQSyYpG7obyNTH885+toKrERfeQnxvfsYSNFzXNaN5CvrOgqph/uOr0bO+GRlNw5IVoiMhVwPcAO/BjpdQ3s7xLGWdOmZuvvX9ltndDo9EUODmfkyciduD7wHuBFcD1IlJYjnyNRqPJEXJeNIB1QLNS6rBSyg88AlyT5X3SaDSagiQfRKMBiG7m22KORRCRm0Rkm4hs6+zszOjOaTQaTSGRD6IRL9obk/KllLpHKbVWKbW2ri65PsgajUajmTr5IBotwMKofzcCrVnaF41Goylo8kE0XgOWi8gSEXEBG4AnsrxPGo1GU5DkfMqtUiooIp8FnsZIub1PKbUny7ul0Wg0BUnOiwaAUur3wO+zvR8ajUZT6My6MiIi4gEOjBmeA3Sl8Gsqgf4C2Z4+d6khlecx14851dvT1+DMSOb8zQFKlVKTZxIppWbVC9iWzNgMv+OeQtmePne5dx5z/ZjTsD19Dab5/E3lHOdDIDwX+W2BbS+V5Pqx5vK5s8j1Y871c5jrx5vT5282uqe2qTGVGuONaZJDn7vUoM/j9NHnbmYkc/6mco5no6VxT5JjmuTQ5y416PM4ffS5mxnJnL+kz/GsszQ0Go1Gkz5mo6Wh0Wg0mjSRl6IhIgtF5BkR2Scie0Tk8+Z4jYhsEpGD5t9qc/wKEdkuIm+Yf99jjpeIyO9EZL+5nVnfpyNV58787A8issvczg/MMvYFQSrPY9Q2nxCRNzN9LJkmxdfgsyJyQER2mq+52TquTJHi8+cSkXtE5C3zPvihSXcglaldmXoB9cC55vty4C2MXhv/CXzFHP8K8C3z/TnAAvP9KuCE+b4EeLf53gW8ALw328eXD+fO/HeF+VeAXwIbsn18+XgezbEPAg8Db2b72PLp3AHPAmuzfUx5fP6+Dvyb+d4GzJn0+7N9AlJ0Eh8HrsCY1FcfdWIPxFlWgG7AHeez7wGfzvbx5Nu5A5wYaYIfzfbx5ON5BMqAF80f/qwXjRSfu4ITjRSfv+MYk/qS/r68dE9FIyJNGEq6BZinlGoDMP/GM1U/BLyulPKN2U4V8OfA5nTuby6RinMnIk8DHYAHeCzNu5yTpOA83gZ8BxhO+87mGCn6/f7EdE39i4jEa6Uwa5nJ+TPveQC3icgOEfmFiMyb9EuzrZIzVNgyYDvwQfPffWM+7x3z75XAIWDZmHEH8BTwhWwfU76dO/OzIgz31BXZPq58O4/AauC35vsmCsjSSMU1CDSYf8uB/wVuyPZx5cv5wygdooAPmf/+IvDQpN+b7QOfwQlzYlS+/WLU2ITmGUYfjreAi+Ns6z7gzmwfUz6eu6hlNgL/L9vHlm/nEbgZoz/MUYzeMX7g2WwfWz6cuzjb/ItCuQb/f3v3EyplFQZg/Hlz3JShWBoG6t2JKFG0Cm8kuFJauhFRU1wIQriTQnBTO7ukLrwFJVobaZUUBBKkoLgUw24ounHhJvBfun1bnHNpGNT7zTDXb4b7/GCYmTOHM+ccDvNyzsy835DWXgBPgFfq89XAjbneeyyPp+oW9DtgJjOnul46T/nwot7/XOsvA34FPsvMyz1tfUFJEHZovvs9CoY1dxGxJCJW1ccdYBvw9/yPYDQMax4z81Rmvp2ZE8AkcDMzN8//CNozxDXYiYg36+PFwMfAQvj12bDWXlK+i9xci7YAf83ZgbYj5oBRdpKyrboOXKu3bcAblO8kbtX75bX+EUpEvdZ1W0mJvgnMdJXvb3t8YzJ3b1EukHUduAGcBDptj2/c5rGnzQkWwPHUENfga5Tjmdk1eBxY1Pb4xmX+6mtrgUu1rd+BNXO9v/8IlyQ1NpbHU5Kkdhg0JEmNGTQkSY0ZNCRJjRk0JEmNGTSklywiDkTE7j7qTyyE7LcaD522OyAtJBHRyczptvshDcqgIfWpJon7jZIk7j1KeobdwHpgipIT6B/gk8y8FxF/AFeATcD5iHgd+Dczj0XEu8A0JU3/bWBfZt6PiPcp6W2eUjLgSiPB4ylpMOuAbzPzHeARcJDyr/jtmTn7gf9lV/1lmflRZn7V085Z4HBt50/gaC0/DXyamR/M5yCkfrnTkAZzN//P4/Mj8DnlAjcXanbuRcC9rvrnehuIiKWUYHKxFp0BfnpG+Q/A1uEPQeqfQUMaTG/+nceUDKHP2xk86aPteEb70kjweEoazJqImA0QO4CrwIrZsohYHBEbXtRAZj4E7kfEh7VoF3AxMx8ADyNispbvHH73pcG405AGMwPsiYhvKFlFT1Kub3CiHi91gK8p2VdfZA8wHRGvAneAvbV8L/B9RDyt7UojwSy3Up/qr6d+ycyNLXdFeuk8npIkNeZOQ5LUmDsNSVJjBg1JUmMGDUlSYwYNSVJjBg1JUmMGDUlSY/8Bz8ds8rPP+ckAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", + "1er août de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]\n", + "#print(first_august_week)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2021 743449\n", + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2010315\n", + "2022 2060304\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2023 2873501\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2024 3670417\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal CORRECTION" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Incidence du syndrome grippal CORRECTION%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + } + ], + "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 +} diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..90d2c84c09da7e79c45fe844254e7314b874e60a 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2440 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", + "\n", + "| Nom de colonne | Libellé de colonne |\n", + "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", + "| week | Semaine calendaire (ISO 8601) |\n", + "| indicator | Code de l'indicateur de surveillance |\n", + "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", + "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", + "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", + "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", + "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", + "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", + "\n", + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"varicelle.csv\"\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202550764853608936210614FRFrance
120254976350363490669513FRFrance
220254875147326070348511FRFrance
320254775150325870428511FRFrance
42025467313614514821528FRFrance
520254574733207073967311FRFrance
62025447346117635159528FRFrance
72025437378419635605639FRFrance
82025427398121275835639FRFrance
92025417377919445614639FRFrance
10202540725139644062426FRFrance
112025397306313674759528FRFrance
122025387119502448204FRFrance
1320253771120112229204FRFrance
14202536715753202830204FRFrance
15202535713271622492204FRFrance
1620253471438482828204FRFrance
17202533735796926466519FRFrance
182025327238404809408FRFrance
19202531757030130829020FRFrance
202025307710235901061411616FRFrance
21202529763853384938610614FRFrance
2220252875584312380458412FRFrance
2320252775667285084848412FRFrance
2420252675872328584599513FRFrance
2520252575953369882089612FRFrance
2620252474580255866027410FRFrance
2720252374911266371597410FRFrance
28202522768373940973410614FRFrance
2920252174693265367337410FRFrance
.................................
17981991267176081130423912312042FRFrance
17991991257161691070021638281838FRFrance
18001991247161711007122271281739FRFrance
1801199123711947767116223211329FRFrance
1802199122715452995320951271737FRFrance
1803199121714903897520831261636FRFrance
18041991207190531274225364342345FRFrance
18051991197167391124622232291939FRFrance
18061991187213851388228888382551FRFrance
1807199117713462887718047241632FRFrance
18081991167148571006819646261834FRFrance
1809199115713975978118169251832FRFrance
1810199114712265768416846221430FRFrance
181119911379567604113093171123FRFrance
1812199112710864733114397191325FRFrance
18131991117155741118419964271935FRFrance
18141991107166431137221914292038FRFrance
1815199109713741878018702241533FRFrance
1816199108713289881317765231531FRFrance
1817199107712337807716597221529FRFrance
1818199106710877701314741191226FRFrance
1819199105710442654414340181125FRFrance
18201991047791345631126314820FRFrance
18211991037153871048420290271836FRFrance
18221991027162771104621508292038FRFrance
18231991017155651027120859271836FRFrance
18241990527193751329525455342345FRFrance
18251990517190801380724353342543FRFrance
1826199050711079666015498201228FRFrance
18271990497114302610205FRFrance
\n", + "

1828 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202550 7 6485 3608 9362 10 6 \n", + "1 202549 7 6350 3634 9066 9 5 \n", + "2 202548 7 5147 3260 7034 8 5 \n", + "3 202547 7 5150 3258 7042 8 5 \n", + "4 202546 7 3136 1451 4821 5 2 \n", + "5 202545 7 4733 2070 7396 7 3 \n", + "6 202544 7 3461 1763 5159 5 2 \n", + "7 202543 7 3784 1963 5605 6 3 \n", + "8 202542 7 3981 2127 5835 6 3 \n", + "9 202541 7 3779 1944 5614 6 3 \n", + "10 202540 7 2513 964 4062 4 2 \n", + "11 202539 7 3063 1367 4759 5 2 \n", + "12 202538 7 1195 0 2448 2 0 \n", + "13 202537 7 1120 11 2229 2 0 \n", + "14 202536 7 1575 320 2830 2 0 \n", + "15 202535 7 1327 162 2492 2 0 \n", + "16 202534 7 1438 48 2828 2 0 \n", + "17 202533 7 3579 692 6466 5 1 \n", + "18 202532 7 2384 0 4809 4 0 \n", + "19 202531 7 5703 0 13082 9 0 \n", + "20 202530 7 7102 3590 10614 11 6 \n", + "21 202529 7 6385 3384 9386 10 6 \n", + "22 202528 7 5584 3123 8045 8 4 \n", + "23 202527 7 5667 2850 8484 8 4 \n", + "24 202526 7 5872 3285 8459 9 5 \n", + "25 202525 7 5953 3698 8208 9 6 \n", + "26 202524 7 4580 2558 6602 7 4 \n", + "27 202523 7 4911 2663 7159 7 4 \n", + "28 202522 7 6837 3940 9734 10 6 \n", + "29 202521 7 4693 2653 6733 7 4 \n", + "... ... ... ... ... ... ... ... \n", + "1798 199126 7 17608 11304 23912 31 20 \n", + "1799 199125 7 16169 10700 21638 28 18 \n", + "1800 199124 7 16171 10071 22271 28 17 \n", + "1801 199123 7 11947 7671 16223 21 13 \n", + "1802 199122 7 15452 9953 20951 27 17 \n", + "1803 199121 7 14903 8975 20831 26 16 \n", + "1804 199120 7 19053 12742 25364 34 23 \n", + "1805 199119 7 16739 11246 22232 29 19 \n", + "1806 199118 7 21385 13882 28888 38 25 \n", + "1807 199117 7 13462 8877 18047 24 16 \n", + "1808 199116 7 14857 10068 19646 26 18 \n", + "1809 199115 7 13975 9781 18169 25 18 \n", + "1810 199114 7 12265 7684 16846 22 14 \n", + "1811 199113 7 9567 6041 13093 17 11 \n", + "1812 199112 7 10864 7331 14397 19 13 \n", + "1813 199111 7 15574 11184 19964 27 19 \n", + "1814 199110 7 16643 11372 21914 29 20 \n", + "1815 199109 7 13741 8780 18702 24 15 \n", + "1816 199108 7 13289 8813 17765 23 15 \n", + "1817 199107 7 12337 8077 16597 22 15 \n", + "1818 199106 7 10877 7013 14741 19 12 \n", + "1819 199105 7 10442 6544 14340 18 11 \n", + "1820 199104 7 7913 4563 11263 14 8 \n", + "1821 199103 7 15387 10484 20290 27 18 \n", + "1822 199102 7 16277 11046 21508 29 20 \n", + "1823 199101 7 15565 10271 20859 27 18 \n", + "1824 199052 7 19375 13295 25455 34 23 \n", + "1825 199051 7 19080 13807 24353 34 25 \n", + "1826 199050 7 11079 6660 15498 20 12 \n", + "1827 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 14 FR France \n", + "1 13 FR France \n", + "2 11 FR France \n", + "3 11 FR France \n", + "4 8 FR France \n", + "5 11 FR France \n", + "6 8 FR France \n", + "7 9 FR France \n", + "8 9 FR France \n", + "9 9 FR France \n", + "10 6 FR France \n", + "11 8 FR France \n", + "12 4 FR France \n", + "13 4 FR France \n", + "14 4 FR France \n", + "15 4 FR France \n", + "16 4 FR France \n", + "17 9 FR France \n", + "18 8 FR France \n", + "19 20 FR France \n", + "20 16 FR France \n", + "21 14 FR France \n", + "22 12 FR France \n", + "23 12 FR France \n", + "24 13 FR France \n", + "25 12 FR France \n", + "26 10 FR France \n", + "27 10 FR France \n", + "28 14 FR France \n", + "29 10 FR France \n", + "... ... ... ... \n", + "1798 42 FR France \n", + "1799 38 FR France \n", + "1800 39 FR France \n", + "1801 29 FR France \n", + "1802 37 FR France \n", + "1803 36 FR France \n", + "1804 45 FR France \n", + "1805 39 FR France \n", + "1806 51 FR France \n", + "1807 32 FR France \n", + "1808 34 FR France \n", + "1809 32 FR France \n", + "1810 30 FR France \n", + "1811 23 FR France \n", + "1812 25 FR France \n", + "1813 35 FR France \n", + "1814 38 FR France \n", + "1815 33 FR France \n", + "1816 31 FR France \n", + "1817 29 FR France \n", + "1818 26 FR France \n", + "1819 25 FR France \n", + "1820 20 FR France \n", + "1821 36 FR France \n", + "1822 38 FR France \n", + "1823 36 FR France \n", + "1824 45 FR France \n", + "1825 43 FR France \n", + "1826 28 FR France \n", + "1827 5 FR France \n", + "\n", + "[1828 rows x 10 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_file, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Y a-t-il des points manquants dans ce jeux de données ? Non" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]\n", + "data = raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il restent deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation\n", + "comme nouvel index de notre jeux de données. Ceci en fait\n", + "une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans\n", + "le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
1991-01-07/1991-01-131991027162771104621508292038FRFrance
1991-01-14/1991-01-201991037153871048420290271836FRFrance
1991-01-21/1991-01-271991047791345631126314820FRFrance
1991-01-28/1991-02-03199105710442654414340181125FRFrance
1991-02-04/1991-02-10199106710877701314741191226FRFrance
1991-02-11/1991-02-17199107712337807716597221529FRFrance
1991-02-18/1991-02-24199108713289881317765231531FRFrance
1991-02-25/1991-03-03199109713741878018702241533FRFrance
1991-03-04/1991-03-101991107166431137221914292038FRFrance
1991-03-11/1991-03-171991117155741118419964271935FRFrance
1991-03-18/1991-03-24199112710864733114397191325FRFrance
1991-03-25/1991-03-3119911379567604113093171123FRFrance
1991-04-01/1991-04-07199114712265768416846221430FRFrance
1991-04-08/1991-04-14199115713975978118169251832FRFrance
1991-04-15/1991-04-211991167148571006819646261834FRFrance
1991-04-22/1991-04-28199117713462887718047241632FRFrance
1991-04-29/1991-05-051991187213851388228888382551FRFrance
1991-05-06/1991-05-121991197167391124622232291939FRFrance
1991-05-13/1991-05-191991207190531274225364342345FRFrance
1991-05-20/1991-05-26199121714903897520831261636FRFrance
1991-05-27/1991-06-02199122715452995320951271737FRFrance
1991-06-03/1991-06-09199123711947767116223211329FRFrance
1991-06-10/1991-06-161991247161711007122271281739FRFrance
1991-06-17/1991-06-231991257161691070021638281838FRFrance
1991-06-24/1991-06-301991267176081130423912312042FRFrance
.................................
2025-05-19/2025-05-2520252174693265367337410FRFrance
2025-05-26/2025-06-01202522768373940973410614FRFrance
2025-06-02/2025-06-0820252374911266371597410FRFrance
2025-06-09/2025-06-1520252474580255866027410FRFrance
2025-06-16/2025-06-2220252575953369882089612FRFrance
2025-06-23/2025-06-2920252675872328584599513FRFrance
2025-06-30/2025-07-0620252775667285084848412FRFrance
2025-07-07/2025-07-1320252875584312380458412FRFrance
2025-07-14/2025-07-20202529763853384938610614FRFrance
2025-07-21/2025-07-272025307710235901061411616FRFrance
2025-07-28/2025-08-03202531757030130829020FRFrance
2025-08-04/2025-08-102025327238404809408FRFrance
2025-08-11/2025-08-17202533735796926466519FRFrance
2025-08-18/2025-08-2420253471438482828204FRFrance
2025-08-25/2025-08-31202535713271622492204FRFrance
2025-09-01/2025-09-07202536715753202830204FRFrance
2025-09-08/2025-09-1420253771120112229204FRFrance
2025-09-15/2025-09-212025387119502448204FRFrance
2025-09-22/2025-09-282025397306313674759528FRFrance
2025-09-29/2025-10-05202540725139644062426FRFrance
2025-10-06/2025-10-122025417377919445614639FRFrance
2025-10-13/2025-10-192025427398121275835639FRFrance
2025-10-20/2025-10-262025437378419635605639FRFrance
2025-10-27/2025-11-022025447346117635159528FRFrance
2025-11-03/2025-11-0920254574733207073967311FRFrance
2025-11-10/2025-11-162025467313614514821528FRFrance
2025-11-17/2025-11-2320254775150325870428511FRFrance
2025-11-24/2025-11-3020254875147326070348511FRFrance
2025-12-01/2025-12-0720254976350363490669513FRFrance
2025-12-08/2025-12-14202550764853608936210614FRFrance
\n", + "

1828 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 \\\n", + "period \n", + "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", + "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", + "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", + "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", + "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", + "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n", + "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n", + "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n", + "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n", + "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n", + "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n", + "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n", + "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n", + "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n", + "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n", + "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n", + "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n", + "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n", + "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n", + "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n", + "1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n", + "1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n", + "1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n", + "1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n", + "1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n", + "1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n", + "1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n", + "1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n", + "1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n", + "1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n", + "... ... ... ... ... ... ... \n", + "2025-05-19/2025-05-25 202521 7 4693 2653 6733 7 \n", + "2025-05-26/2025-06-01 202522 7 6837 3940 9734 10 \n", + "2025-06-02/2025-06-08 202523 7 4911 2663 7159 7 \n", + "2025-06-09/2025-06-15 202524 7 4580 2558 6602 7 \n", + "2025-06-16/2025-06-22 202525 7 5953 3698 8208 9 \n", + "2025-06-23/2025-06-29 202526 7 5872 3285 8459 9 \n", + "2025-06-30/2025-07-06 202527 7 5667 2850 8484 8 \n", + "2025-07-07/2025-07-13 202528 7 5584 3123 8045 8 \n", + "2025-07-14/2025-07-20 202529 7 6385 3384 9386 10 \n", + "2025-07-21/2025-07-27 202530 7 7102 3590 10614 11 \n", + "2025-07-28/2025-08-03 202531 7 5703 0 13082 9 \n", + "2025-08-04/2025-08-10 202532 7 2384 0 4809 4 \n", + "2025-08-11/2025-08-17 202533 7 3579 692 6466 5 \n", + "2025-08-18/2025-08-24 202534 7 1438 48 2828 2 \n", + "2025-08-25/2025-08-31 202535 7 1327 162 2492 2 \n", + "2025-09-01/2025-09-07 202536 7 1575 320 2830 2 \n", + "2025-09-08/2025-09-14 202537 7 1120 11 2229 2 \n", + "2025-09-15/2025-09-21 202538 7 1195 0 2448 2 \n", + "2025-09-22/2025-09-28 202539 7 3063 1367 4759 5 \n", + "2025-09-29/2025-10-05 202540 7 2513 964 4062 4 \n", + "2025-10-06/2025-10-12 202541 7 3779 1944 5614 6 \n", + "2025-10-13/2025-10-19 202542 7 3981 2127 5835 6 \n", + "2025-10-20/2025-10-26 202543 7 3784 1963 5605 6 \n", + "2025-10-27/2025-11-02 202544 7 3461 1763 5159 5 \n", + "2025-11-03/2025-11-09 202545 7 4733 2070 7396 7 \n", + "2025-11-10/2025-11-16 202546 7 3136 1451 4821 5 \n", + "2025-11-17/2025-11-23 202547 7 5150 3258 7042 8 \n", + "2025-11-24/2025-11-30 202548 7 5147 3260 7034 8 \n", + "2025-12-01/2025-12-07 202549 7 6350 3634 9066 9 \n", + "2025-12-08/2025-12-14 202550 7 6485 3608 9362 10 \n", + "\n", + " inc100_low inc100_up geo_insee geo_name \n", + "period \n", + "1990-12-03/1990-12-09 0 5 FR France \n", + "1990-12-10/1990-12-16 12 28 FR France \n", + "1990-12-17/1990-12-23 25 43 FR France \n", + "1990-12-24/1990-12-30 23 45 FR France \n", + "1990-12-31/1991-01-06 18 36 FR France \n", + "1991-01-07/1991-01-13 20 38 FR France \n", + "1991-01-14/1991-01-20 18 36 FR France \n", + "1991-01-21/1991-01-27 8 20 FR France \n", + "1991-01-28/1991-02-03 11 25 FR France \n", + "1991-02-04/1991-02-10 12 26 FR France \n", + "1991-02-11/1991-02-17 15 29 FR France \n", + "1991-02-18/1991-02-24 15 31 FR France \n", + "1991-02-25/1991-03-03 15 33 FR France \n", + "1991-03-04/1991-03-10 20 38 FR France \n", + "1991-03-11/1991-03-17 19 35 FR France \n", + "1991-03-18/1991-03-24 13 25 FR France \n", + "1991-03-25/1991-03-31 11 23 FR France \n", + "1991-04-01/1991-04-07 14 30 FR France \n", + "1991-04-08/1991-04-14 18 32 FR France \n", + "1991-04-15/1991-04-21 18 34 FR France \n", + "1991-04-22/1991-04-28 16 32 FR France \n", + "1991-04-29/1991-05-05 25 51 FR France \n", + "1991-05-06/1991-05-12 19 39 FR France \n", + "1991-05-13/1991-05-19 23 45 FR France \n", + "1991-05-20/1991-05-26 16 36 FR France \n", + "1991-05-27/1991-06-02 17 37 FR France \n", + "1991-06-03/1991-06-09 13 29 FR France \n", + "1991-06-10/1991-06-16 17 39 FR France \n", + "1991-06-17/1991-06-23 18 38 FR France \n", + "1991-06-24/1991-06-30 20 42 FR France \n", + "... ... ... ... ... \n", + "2025-05-19/2025-05-25 4 10 FR France \n", + "2025-05-26/2025-06-01 6 14 FR France \n", + "2025-06-02/2025-06-08 4 10 FR France \n", + "2025-06-09/2025-06-15 4 10 FR France \n", + "2025-06-16/2025-06-22 6 12 FR France \n", + "2025-06-23/2025-06-29 5 13 FR France \n", + "2025-06-30/2025-07-06 4 12 FR France \n", + "2025-07-07/2025-07-13 4 12 FR France \n", + "2025-07-14/2025-07-20 6 14 FR France \n", + "2025-07-21/2025-07-27 6 16 FR France \n", + "2025-07-28/2025-08-03 0 20 FR France \n", + "2025-08-04/2025-08-10 0 8 FR France \n", + "2025-08-11/2025-08-17 1 9 FR France \n", + "2025-08-18/2025-08-24 0 4 FR France \n", + "2025-08-25/2025-08-31 0 4 FR France \n", + "2025-09-01/2025-09-07 0 4 FR France \n", + "2025-09-08/2025-09-14 0 4 FR France \n", + "2025-09-15/2025-09-21 0 4 FR France \n", + "2025-09-22/2025-09-28 2 8 FR France \n", + "2025-09-29/2025-10-05 2 6 FR France \n", + "2025-10-06/2025-10-12 3 9 FR France \n", + "2025-10-13/2025-10-19 3 9 FR France \n", + "2025-10-20/2025-10-26 3 9 FR France \n", + "2025-10-27/2025-11-02 2 8 FR France \n", + "2025-11-03/2025-11-09 3 11 FR France \n", + "2025-11-10/2025-11-16 2 8 FR France \n", + "2025-11-17/2025-11-23 5 11 FR France \n", + "2025-11-24/2025-11-30 5 11 FR France \n", + "2025-12-01/2025-12-07 5 13 FR France \n", + "2025-12-08/2025-12-14 6 14 FR France \n", + "\n", + "[1828 rows x 10 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data = data.set_index('period').sort_index()\n", + "sorted_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste. Il ne manque pas de données." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !\n", + "\n", + "Toute la colonne 'inc' est représentée par des chaînes de caractères à cause du trait dans la ligne de la semaine 19 de l'année 1989" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EsF55bIOfNtK/1tMjzwghWgHsBTAoTdu6ArqizkGubeXWObywcgdW+8FxknQBpSJt253ESo7ktSSdg+TaDLcfe2MzfvD3JcWXXwQ60waokth7qAXrdh7s6GZY4UwciKgPgAcBfFUIsQ+eiOgoABMAbAbwC5mVeVxY0m3P6G2YRkRziWju9u3bXZteEtr3hHTHzZwDTa34n+dWOx9Wc22p7L+075a0eH7qzjB85sGItVKqaiqCmFiJGUO//OdK7DnYnFhWroRDcMUS5vJCaL+q4ANVAT7w6xdwzs+f6ehmWOFEHIioFh5huEcI8RAACCG2CiHahBAFAL8DMMnPvgHAEcrjowBs8tNHMemRZ4ioBkB/ALv0dgghbhdCTBRCTBwyZIjbG5aIdj0hLdTr9p1E0x9bihueWIaZhtO/pSLt26R5/ScWbUlZejqUGlvC5Crkv/yzDNayUtVsaoe9/X9fsMl6vxTo3zHjHDys21XdXAPgZq1EAO4EsFQIcZOSPlzJ9kEAi/zrRwBM9S2QxsJTPL8ihNgMYD8RTfbL/AyAh5VnrvCvPwLgadENhZPq7rrSb//tvy7EX15ZF/ze3eDtYp05B1eZuWNwm1Lw7PKQi6zEzjR902PUgUVrW7LSvRTvGWHf2/P9+19eL74SC/7yyjpcfc+8SFr3m9WdFy6H4M4E8GkAC4lIOsP/FoDLiWgCvG3JWgBfBAAhxGIiuh/AEniWTtcIIaRQ+GoAdwHoCeAJ/w/wiM8fiWgVPI5hammvlQ6tbQXMXLIVF514eEyZ2K7WSirnUOG67pnjEYbLJ70NANDsL1Ty1HES0i7Ceu6kfu3Ma0j8EBz/si4LZSk6B/loRy3I1z+0kEntzF+2eyGROAghXgC/93nc8sx0ANOZ9LkATmTSGwF8NKktlcIfXlyL6Y8vxS2Xn4pLThnRUc3oUJ1DiyQONY7EwVnpkDJ/2vLL9Jy1zJT59QXdtLy7MGklEQf/P0fIXdyTSCzauBcnjOhXltgSjrGbMlQBshPSAHb5isF1Oxti97qizoErOyAO+VAM9PD8jWhsKZMlkOF1zK9ZPTvMtN/CRSHtXFYZnuWa3+Ig0gKAZ5Zvw/t//QLufXV9cmYHZArpzoOMOADoU+8xUPub2ucwlUmuH9E5VLB+frHwEqVYafaanfjKvfPx08eXllSXbfdqQzVxDmkRV0jzcOmTkoiD1Dkw91w5h7d2eBumpZvt3nddkSmkOw8y4oDwMFWrv0Bu2duIMdc9hgde21ARnUOLgbcuRDiH8tcblM2kSeVoja8B3XfII5Sb9zbyZaQ8xNWZF4W0TY+57O4gnYNNpOf6TtKUtlwiz2og3qXiYHMrxlz3GB59o3JWXtWAjDgg3MFJy5DV2w8AAB6at6Eig9nkyTTKOVRuFnET3RRcho1FUBC4/fk1qepM7agvVe7qgv7tTOu7S59USufg7tupOPcnJnQFsdKmPd6G6aZ/rOjgllQWGXFAuDBWymNHS1shsstoaeU5B9FOnMP7bzHHYHCp97kV23HzP9NNjPTnHKpnESlVmW465+BSbNKQfGLhZuw40GR4VrIOxdUNhBumsnF+1fNZi0bYJ13gZSzIiAPCyazPw4bmNuxq4CdeGvz66VX48p9DW3KTMrC9FsTlW3lfOxJb9jaiqdWsiE6jpHa1tddRXdOutNaYziqUKlbae7AFV98zD5/9w6up2+Tq8ygXcA7l+SJdYUENuKkObkelkQX7gRqkJToRF6zfgwVlKH/znkOR380G4tBeOockTP7ZLOv9YhisdlNIV8GUdT3T4dJS2yE46bl2oza+gnpt9cSIA9/Otb4Fn2lR/8U/lmPfoRb88NKYhTqLjv86pSPjHLoR5K5IfvRyCpdWbz+A/3ttQySto3UONpS7VjKsULY+3ra/Ec+vbB/fWS5IL1ZyPWVeYZ1DoJAO62lsacOOA02x8cUtdC+t3oH/fc7TLZksX3/99CrMmO3uErwzGyZIyG/S1c9sZMQBilipAiqH3z69KpZmEiu1p/sMHXKxKEZ84GIWmabUSdNnVZUpq0uRew+1pMrvmq8UPRincvj8jLmY+JN/xsVKyvVR33oc1/x5XuDyGyifWKmadEnFQvbrxj2H0GzQH3YFZMQB4cQIdmllJBKNjOzeNKDa032GCcWwyrYJL7uyq7PgTy7aHFy7KqRd/FjJQ4kqmlrbsKsh9Ohq6n9O3/PCqh1emt4WJVNbQeCxNzajd30Ne5/De25+zno/bKtTtk6DV9fG/IOmQjUTy4w4QNE5VKDsxpY4ITCNh470yioXkmLYftsj5TaFLKUtRZdZoba7cFy6aTEAfOmPr+HtP56Z+OzWfZ7JJVeLPr64d+xdpxIHe10rth5IbI/XlupdDF2h9pXKMZZaVrUhIw5QPlAF5EqcCMk0QdrrhDQH2SZXr6wqXLgCU47OsFikb6NbfhfiUMMQh2eWu+ljfvbEMq81zPdJa0abHYILob5DqStGNXPUGXGAKlZKzrtsSzo3AmlCRHakzkGiTd9Rsrmi72RraxhwphOzDimxu6HFyR233tcc8rnSp6hLl3CLlKrvKNcZIK6eWUu3RsRy1Q71HUrtlioYrkZkxAHhwmWSDat47a3dqcrmxAKmhTJCNDpo1BRjgeGy7lfxBikRLm3ffTAUL3z+7rn47sOLgt+mBaRYzkHin35gpsSFm6kmfs4hnocM1ya4cJ1cPVfNmIsv/Wle/EZKvLWzwdmhYCko524/4xyqGOf997O468W1ANx2AWlNC9NwDiIiVuqYQaMPVpe3Laat5ZLg6eV0VL/d4ItwJB6Zn+x3x0nnwCikJb75oBcvQY6bhqZWPDx/Yywf6z7DwZQ17Tdy4YQqZcq6q6EZ7/r5s/ieQpQrhfQzxFJW9dKGjDis2dEQeGN1ESulHQp87JxkzqGjBo2+SOxsSI5zbJ3wFQ44U5JjOkcU03YXMYwLcUjzdt/52yJ85d75WLB+TySdbb/FlLVYuHl6Nef56eNLi9J5AcD+Ro9ze3HVzqKeTwN1E6dGUyyurFJbUzl0e+KgwmVCp12LeLESnzfKOVQG/1hsj7esEwcXMZqLPqFSO/q89kHSBLFxxayEuNrPLt8WS2vHAIIBNvknpQ82R82nXbyyigRpjMu4dzrvYsly+/NrsGjT3uSKbOW3A+eovsNzK0o7rJmJlToZXHQPrkinkA6vTQvuup0HMfEnMzHmuseMSs8texsjdvAqpv3xNWt7ixHZOjAOFRMn6PraShCHX8y0Oxm8sgjfRq4ox9ts2x93ux4/BMeInlJa5bSW+TBkGpRzziahnEOseklDRhwicBFRpB2EXJmmhd+Fc7j8dy9jxwFv4TdNxsk/m+VkBw+EcRxW+nbqLjuZmJzfQlDCcw6VmQZ6/5pckxSLOWvsYgoXJ4Sl+FZK022mrPPW7Yml6d+ZG0pqkgtXfcAhWFald8rtsREvJ3fSqTkHIjqCiJ4hoqVEtJiIvuKnDySimUS00v8/QHnmeiJaRUTLiehCJf00Ilro37uF/BFHRPVEdJ+fPoeIxpT/VZMRHJC2zYOUGxSuLDfOgc9T6qEbHZ+4Yw4AoMk/tV3MIu4yWSo1BXRrnlaDudUWQ9AiG1rbCvj47S9b8xgnd9k2suXruX2N4djR282ehUg5Fr73t2RlcBWvhc4op0+lJHFeR8KFc2gF8HUhxHEAJgO4hoiOB3AdgFlCiPEAZvm/4d+bCuAEABcBuJWI8n5ZtwGYBmC8/3eRn34VgN1CiHEAbgZwYxneLTUqwZhyZbodgjOcMChzI195M3r830WspDfBxmbb4hiXA3U10SHcauAcPn3nnNRlu1jfmMRYTp+pnU2AT/7BP4JrfYHjqlm8KTzT4/I+s5bFdS86knbK7SkeKhZfu39+2cqq5kOgicRBCLFZCDHPv94PYCmAkQAuBTDDzzYDwGX+9aUA7hVCNAkh3gSwCsAkIhoOoJ8QYrbwtiR3a8/Ish4AMIVc+NgyoxI1cq9hmh+RiWPIoyq4K7Hgltu3kpLL8Gzq6iKo1czBTKI2U0AcwLzAu7RNivh0dMDwVepOzhMXK8Vf9udPLS9XkwIk9Wmx3dae3b1siz0eShpUs5faVDoHX9xzKoA5AIYJITYDHgEBMNTPNhLAeuWxDX7aSP9aT488I4RoBbAXwCCm/mlENJeI5m7f3jEundOOQZZzMFkrGa5VqNY5lZBXFlOmywCvFOfQoMm5TUp61YmcjlLcg3/zwTeKftZJHFehfot956R6mIGcJuiTsV4Npb5vZxNbdQnHe0TUB8CDAL4qhLD5kOAlKeZ02zPRBCFuF0JMFEJMHDJkSFKTU6P9PDw4KKQNbcmpnENZW+WhKM7B0hJrwJkyYF+jRhwMlMpmbFDKBN1tsAqLVlf8tjaV2CFFVr2bitnBmoJWlQKTzqirotNzDkRUC48w3COEeMhP3uqLiuD/lwLHDQCOUB4fBWCTnz6KSY88Q0Q1APoDKM0XbhEo5Tv9a+V2jLnuMazalsxyGhXSDvNC1b9WhHNw0TloC63Vt5IMjBLLVBk5gIk42MQOxhjPDt3LuWQvF4b371HcATyHPDGvrAmjn3PjUUzbkp6phClyNaNTcw6+7P9OAEuFEDcptx4BcIV/fQWAh5X0qb4F0lh4iudXfNHTfiKa7Jf5Ge0ZWdZHADwtKthr5/33s7hnzltGt8XFLFuPLvAch726Vjs0xhRm9q2UrJBWxUqpzBwdM7soYeNlm++RQ55y4sSR/dl0q5my0dQ0udGcS3ZLkdHyk+TvDmUA4aYmDZehW70lrcmcA8BipmjShsblrAQHWey2/Y1FWaZ1FKqXNLhxDmcC+DSA84hovv/3PgA3ALiAiFYCuMD/DSHEYgD3A1gC4EkA1wgh5PbqagB3wFNSrwbwhJ9+J4BBRLQKwNfgWz5VAo0tbVizowHf/usip4NArpDP6hOa25W6KKTdnNlFM7W2FXjfOkJg7PWP40d/X5JY5n2vrk/Mo8PJZbchyz+W2E8fJ+HoYX0ivwf3qWPz2WjDWzsaMOa6x2InoV2+QZNB7l4uhbTLiNRdTrjU/Y0HorqSpIW+2jkHOQZb2kRiDPRqQjWfczBr6XwIIV6AeRMzxfDMdADTmfS5AGKRyIUQjQA+mtSWckBarfStr4m7EHD4TtzEO9TchuX+ITJ9h8rNUxMRUndNpqbYzkLcMmslbmHCkkrZ8B9eetNQaoi0XmcBx8NcFdojnX/csGigGUM1tuVSxkB+ZMEmTDluWFJRETSWECYyUQdM5LZJKKLuDbsPRn4n1cO6gSmi3iR2qGjOoainOh5VTBu63wlpKVMnSscWX3LKCABAr7p8kCbdA3/l3tdDZ2fa4E9jraTumkxtU8U++q5jzY4G9pmGJm9326s2z94vFTZnaS7nHMZc9xhWbI3rakb074FPTX6bte4a3rMh0w7zqvSm32/6GQmX8WEK+erCNzj5rUph0WRrrv4u+knypFflOIeidr0Jj3B9vmnPIUz8yT+x1jC+ubYUs8npCFQz59DtiIOcbALxcWpbDD5/9lgA4Q5q5pKtGP/tJ7Bk0z7MXh26WHDye+9EHAzPWrgLE0suzT171iUyih2Ga++LHyw65GAqqS9aag8UCgJn/9fT+OvrG5y+ix4LoD2m7aKNZkdzRG6NiLlZZ/Vc9jKSiFAaB5KlgFssH1mwCTsONFk9oOqP/asE8+T2RBXThm5IHELtnTHgCbfLlJNDEpAnF3neTRdv2pvaB42RK3BgqW2cg4kll64x6mvK87njJ6Tj9f59wSbMXbsryF2MrHl/Y2viiVl90VL7tqVQwPpdh/DNBxY6+c0qp6WMWp2t6t0Hk12iJyHgHGx5EspIenXudlGiQt0vl34Yj2HE5Cfe4Hud5REtR+XwqxkZcahCeJyDiKWZIBchOYmafBNG3X1DTCGdwreSC+ewR4k4pjfYvLiVNgKTxCucB9h//8vr+Mj/zA7eP9FtAtNRrQWRePK1Nm/mHKLl28sBgJaCwOW3v4ynl3mK6Y6euESOCmkngwB7nmLul6N/dGd9bNAhf1Y99oY5lKg+9HtWSIRabmRipSqC/BQFIZxCJUrInacIiIPcjecBnWH/AAAgAElEQVSt3lT5nW+Ya++hFpw+/Z947a3dmkI63hh9EXYVK4UcEXs7EUm7yh8/mmwFlah8dW9OBLqJpekbunB0DU2tmL1mJ66WIStLmrfEXKUtgRxjZURxzT3z8PX7F0TSkr5hIufA3NfTRvTvYS+Eq1fjFLh2uIxbvS0d6b4kDTLiUEWQk40d7FIfwdzMaTvggDjU5qJy7pTy33nrdmP7/ib8atbKRFNW3UQvLlbilaOlDj+9P+54YU3kt81LdnjOIYlzsD9vgh5FU9bS3FrADxXTXZcof7rrDRexyZFDerPp6vsU2/8mzuHcYzTvAIFC2rvYtr8JD87boGVJaoX9Pu/SO5poc1FirlUTK7FzL/njuXiZrUZUcyu7H3EI/ps5B/6DRU/6yoWkNpezBkXhxvWLq3ew+VVrGa4NumWM3n5TLINS54n++MtroofX6yxxjk1l6HBZADYxMudaTawnv8/Pn1qGP8/xFZjkKFZKacEDAKMH9gquJ44eYMnJw6ZTMd2pyfHvbENSlmSdQzxD3AVH+oGmP8ITh2LKSd2UDkE1E7HuRxxE+N+kc+C+lxygutlgjqLlcBJTHX96mbe6iHIOyYNGn0j7G/lgK6WeMUia9LpnVLYNMbbf/jtMD2+8sSFu2aOHCZXWXL/715uR9EUbQ3dgpr7VOS+XXlPz1OR5UZJ6/ZtPnBorY+9Bc4wOXpzjritzRaI1k0s7imiI/gi3qOccqIOLl9lyQAhPD3bxScPLVF5ZiqkIuh1xkMNRCObDSJETM90CnQO0PBQtp9idgBDC6RCc3tw/vPgmlvsuhPcYLF9K5hwSnh/Ymz+VDHgiDr8UaxkqEdCVzGnAWWzppZneJx572dxmqd9Rs+g7eg7vP3lELO3NnXH7/TuvmOgdgtPS56/fE+E8gXAhtPVwMudQ3l2/EII1f42XkSwOUseG6UyN7iG2UpxDW8GTONSUMEZVVDOH0+2IQyg6ipMAEbsIIYmDFEsHSl5EJ3Daj60OfBdrJRVCAD/8+xK875Z/4Q8vvonNBp8yld6dnHvM0MQ8SW1Qp5rLIhs8p81RpyD3hnQT58Xhew8vipWlWq6l0YdyC+KU44aBmHuX/fbFmD8nl++btPgvT4hRwD1vE+U0tRbYb6Ev7noO7hm1KznfX2u2H8BH/me21rbKDHq5+UgzRm3IFNJVBJvoyKZzyOclcShE8vzngwsiuoBiP/a+Qy2anx6XRU74bRIR5aspX7FI9sHvIgKz31cX04h4JmGR1WX2HOfQpOlqTO+zvzEq3rE1+ZH5nkNh9d171CrEIYWNklHf4mjK6oKkcr7KHEKMPM/NF4syWe9zLg9XLitWMmygAI9j+NvrcX9ilVp0pSuaupoy+c6qXtrQ/YiDhEB8UfvNM55fIu6D1fly9YAQ+HnW74oqSeOD2+3rL9iwN+IXyW036FR0onsFznUF97zxfpDPnDHRWkm5Vk89q4usy268zcHnuKkpen/amsy1pYfBtj6p3cWsD8cP75cqfyk76YG969gNhtUU3FCdbjPhZq0UXu/TCPgZNzzN+hOrlLimxZ//A3qZRalpkHEOVQT5LdoKAicpMXUjeZiRLUUGckfkErBHrU9FdPEztNOQriJpYOk7YZPI5T03P28tJ6kt0jeRbUImleFircRCe8wUQzraFj6PvuDq+d51dGhCmgtOzIf364sQK9l8fJHXCBaD+tThYxNH8TcZFLsEnTCiny/eit+zKYGNcdJjFFj7mWDKOmn6rIjJMXcAE6hcXAjJmY4c0BP5HOEdYwdWpJ5qQPcjDkVOk3qdOJh2n7pMlcl35rjBifU5cQ4JE0ASP1lWsVG2kojQbc+u9uuxcQ72OuT8X7/rIHYr1jvJYqUonHQOhixNeuAeS1F5zUDBez59/3pWczw8hTR/N58jDOpTHyvLVk8xyOeIVYwDnKVRXGd25RljMPPac9DXPwMREytpZXzzwYXxirSPfOcLbyY3vEKQkoPafA4njewf85CQFhnnUEVwVfTqiImVDHCx/VZTTIvfP5cmxzlwDcwjFxjTOYjE5x0fs3MOCWIlvyPmrSvNm6aLy2fT+8QUvZYyZHvVso4aEsaWKIdE2rRjB9IrRF3FSnq+HJGRu7HpC55d4QWGHD2oF8YP64unfAKhE2+XZulcpYlbUFGpA9JyfNXmyen8RRIya6UqghNxYNJyOUJtngKFlKmYpJ2RDpPiUvdAqfvfB9xZZ1WUVhRcdRu2jEmcQ8r04L6yCgzsXedorcTnkV5ZXdyMq+deTh8zAI98+Ux89swxbLsS22Oox1ZETS4+cmxv7krg9Xw5MhMpmxnqtfd57jsW+mdTRhzWE2eNH8zMj+SGVZMjDDlGavM5EFGRJsCCva42dD/i4GIFZPhg+RwFohxTHquSLqF8FboH1bNufCaxLhNkNt0ltStcJ4BVrKH91if8nDf5kOER76bcfeX6tNEDSuIc0kx09dwLgXDyqMPQi3GJfqi5zeowzm+R+Y6Jc8hTOnNZ96wxEBnGsfab94sUNjJHlErpL1FM6NpKIUIcUJy47pjvPBlcZ5xDFcHFAsWUJafsFNw5B/vX/80zK9n0uppkr5LunIOXrxiZOOC2sGzYfbAiMu80yBM5WSuZiEBM5GF588iJeWWRPrxf1Pncjx5dEtGhcFiznQ9i452h4dvgcQ5puBNXEWQUbcJrh9s5h3ge9UBjTtlcmerj8J96SFOHZyoFKZr1xErFcQ7NyiatU3MORPR7ItpGRIuUtB8Q0UYtprS8dz0RrSKi5UR0oZJ+GhEt9O/dQv6Wgojqieg+P30OEY0p7yu6Y7ic2CY2H0V4t2Q5h/Ba91Mk0dvBH727zqE0uAzg19ftsU4U10mk7jT71tckimeIgKe+eg4evPqdyOWKFxsCiC3i9o2E5Byi3Iu+mG/dZw9239jSFovnHNZh4xxycc7BZhBgbYVaRDRnoeCddNbH9fIt+4Nv+t8fPQVXvHM0ayChnpLOU3zMJo2tQ83JAZ/aE60K56B7RygG1Usa3DiHuwBcxKTfLISY4P89DgBEdDyAqQBO8J+5lYjkKncbgGkAxvt/ssyrAOwWQowDcDOAG4t8l5IhP9Rn73qVvZ9TYvqaIni9vGZn5PfyhDMEJlw6Ie5mQUeStZKEaQD3ra/BkYN5r6KRehyqSYo9UMwkeOG685zqPebwvjht9EDjDjfWlpTiOAnVBDnQBwteNyDTkkx0G3ULqYQ2SKSNV+AsGtR+txYEavIUsXR7Zvk2XPjL5/HAa57n1z71eeQ1B5QSkT5jdtrcM+ppbS7u+b9W7oilxd6jQquuaq1UmydWVNvY0oY3NuxxKs91DncEEomDEOJ5APz2No5LAdwrhGgSQrwJYBWASUQ0HEA/IcRs4W0V7gZwmfLMDP/6AQBTKGm7WAJKEX0QhZPMZPnz3IpoeMLV2w/E63FYKl2cjbkrmA2iiTw5cR9O7SV77IFi2Of+PWtTKSNdg+MUa7nzsw+dFFzrOofwmbAtAB97WUWxa0PPunwqhbSzUYEuKioI1OQoostZvc0b00s2ec4MiTzLHY4AqfE2PLFSchv+7Z7XgmvOKeHSzftiae0FGbq2V10evetqYv64AOC7f1uES37zIjbvtUWu81BN+hQdpegcvkxEb/hiJ+mreCSA9UqeDX7aSP9aT488I4RoBbAXwKAS2mWFbaHT772k7VxzOW8BTGP109om8KG3j4ykOZ1hcMizJUFkkVRfPkdu7+LCOSDdITjnHlQV0syeQV2YVc7OBiGAF4rYfQ7t1wPvO+nwWB4udoNsVz7BQZuNUJHlfXrU5p3sNW16tI9PPCLeHi1nmxCoyeUiB89km2Regr/wc5xDXhUruY23nopIVY8U54pKyfIlMehZm0fv+ho0NMfbJ70H7zuU3PZKHdYrB4olDrcBOArABACbAfzCT+dGqy6WVdNtz8RARNOIaC4Rzd2+vbgA4mk4B125KK0tdA+QNhSEwIj+PZ3bEOZJzvTTx5c6tcFUElFcQchBz3LB8cOYsiwV2RpRIvRYzU5iJRR/nkIu+qphQoQ4aNUncw4W4hDUEEeP2pwTVyXzcNVw3CnLOeSji7pOQMg/C8GG+FSq8AhINM9GJkZHb8Xqq6Fo4lDUY4mQnEPPujx61eVxsCm+Fsh+dVn4q02noqIo4iCE2CqEaBNCFAD8DsAk/9YGAOp2ZBSATX76KCY98gwR1QDoD4MYSwhxuxBiohBi4pAhQ7gsyW1PcU+fPJJ1TkMcBNyClehwWbTTnnPQkWMUhOzzWs/wj9jl/W5lMMGSUjqwk+WOPKynMZ8QIn4aWoGtW+U7ynYJIbQ2Rh/W400AwIQjDguubdbFVoV0Lm7KyuUlRfylwyEMh885UESMKuuRxhQ5cuPaONHT1NtfjuVTTx0fYBZfF1RqQy6dY9bX5FHDEDsgHjXShml/fA2vGMy4OxpFEQdfhyDxQQDSkukRAFN9C6Sx8BTPrwghNgPYT0STfX3CZwA8rDxzhX/9EQBPiwradyW5eND9EQHA4D7SyZbHORxyJA5CCF/sEJ3FLjJ8l8HtYtMv28HBY/Ndno/+Nk0IK+Et8oum0T65cg4FYVcUN1tcpHCHHG0KaS6mwZ8+/47weatYKfpbPS+RIzdT1nChit/jCJfenNY2gQUb9uK5FduDk8l6UUT8wq9ndhVjqn3WXOTZnFI9EZsg21/juxWxeZF1NQKQDj+rDYlBX4noLwDOBTCYiDYA+D6Ac4loArxPvxbAFwFACLGYiO4HsARAK4BrhBByJb0anuVTTwBP+H8AcCeAPxLRKngcw9RyvJgJ9s8Vd8b3p6vegXFDPbcI3pgVTs7dgHCi6QuRm86hjJyDIT2XIxQczj7obWF97hNhx4GmWHpYhmur0kElvIRw92rdBECw4Vz1R+QC84Wzx+LKM8f65Yb51d8SkmDLhZcLCtNHibWc1Avq/Wv+PC+45ogOtyCSL+9LCqJjKkP99ks27cNZ4wfH3pngZvPPHYKT+M7Fx2H640shNMJdrIuKcnMO+xpbIERYbi5nfuc0YiUAeH5FcSLySiOROAghLmeS77Tknw5gOpM+F8CJTHojgI8mtaOjcNb40ElejjxrC9dxF4ggdPbf4VkXAlKqWMl1J+fCORCAH9liShTJOpDhmkvLUViPtTbTdl/P5hdy3PB+gZhKdlcgy0d0kW1qkf7+c36b7PVwcbElPGJn4PoYsRIHPbytCi7inp5v3NA+QRApyTHHiBDBuItW4Ymn+M3I4D716Fmbx8HmtghB4LgbJ5RZ+HCyv2ns5SvLcyTHWzyvbHGxvsyqBdkJacd7QMg623ZI44eGztdkrqJ0Dg6D21msZFgqc44KaR2cnTmR3T2HXkslBIeeWCm5fE+slNwemRRVOEuCH666alFSl1Hvn0NIWtt++U/+hLx81vQaRHGhUj1zqt4meqpJUDocPawPfvOJtwe/X3vLU+LrfVWfzylESBjHVJ8enuknz3mq1+p5kuKIQ6V0DtJaKZ8jVsEOhO/SWqRIrFrQ7YiDbU+ZNJ7k7si2C1bZ/YK+kLhWBLfB7WwGZ8jmqpBWJ4DJuiJHZPSPBAAvrQoJSmtbAS2GtutdleiyW7WGibg3SRIruXAO/vdTzzHEyoq2Qb6W9I2VRqGug2Dh+ijOOQzoVRvLZ+cc4tNf3XCcNW4I+vcMy9xmMJ2uq8kpcnY9rkMIKU7jzFPV9qmvVSznoH//vQdb8PD8eMQ4FU2tbc6bJemt1uZWpM03e69mFxk2dDviYOccEmSmOX9nZMmmTi6jzsFJId0OOgdH3zBqlquVA0oRJMzhGbPfCq7f/+sX8P/+8npiXV6x9oKjkeL4uANcHS4bUo5zkMRfXXTVok4e1T9yvxxunTnkc/FNh6rLkDh7vGfVx/o9Yhqncn/6ujykrxc/Qp8n9TX5iIWOacPR22/fQeZsgDqW1fnC6VZcoE+Nr90/H1+5dz5WbYsfSpU45jtP4tr77eFS1TaadCgyqa0gcNS3HseX/mSYM1WO7kccirwHhA7IrCILZbTICamO72MP7xs8b9ulFLvbGDUgbsJpKqp3fU3kxOrw/j3YfOrj+glwiTRTeFlCMPto3e79QKrOwSpWEkX7+59+mac2O8sP2CQgIov0Hz/3DvTrURPqJopQrh4xsKd8OLLQqOB0GboI5vQxA3DhicP8dsbBiWxcRIN7tFPL9bW54D1t8yPQ0zD3C4p4Tg1V0dPBx5iK0YN6sXVs8vUmJhPmvYe8d3p4/ib2vo58jiI6rgj8NDn/n1ocxmb5n+dWO5VfDeh+xMFR53DMsL6x+9Jcc/dBc7CRFmW1DReI8L4aZ9gm0ilWZsrtHk0L7IjDekR85pjWLhdCVS6PJ3oxqmUYV4XqokBVEFo3AcKtvdxrD+3XA317hA4BC4Vov/XvVYsTRvQPGpB2F3zlGWPw1FfPASDFSl5BepApTiGti2D61NeEbj5Y8+N4e9R6YtZbfsIdWiS2ekWsJISZo7Wd1i4IoXBqYbuk0tx1eD1yzVl+HdFaOBGhijRnl4DwbIdtnnJ09oYnlqWqpyPRDYmD2279I6fF4/PKwfD5GXONZagyeRFwDtHdo2k3qKLY8IHchDcVVV+Tjwxu04Lp5HjPpXFFIMniQ1WOR3QOCW12W2x4nZHqR0ow91VZtEoPXOJk9+tRE8SFULPrxGHvoZbYQqe/stourjs4fbTKObhybZ7OwbvWDTai1mZmQqWOMbWfhPBOg7/+3Quc2tK/Vy161PJOAG1IivCowzsVblJIS1PWTCHdqeAqVjIdbCoIYfX3suNAc3DkXxUtTDvnSACeElBODpu1UbGcA2//zsN9QY/rUVzrkPjkO97mXJuKpAk2SQnwTlD7zbYJcFMUB2catHTVikgwIqqcIg7i9BU2tBkW1qa26M52xdb9iSekCcoJaaY7uD6wxfywbTIiCmmliMgjFuV4VOcQrbN/z1oc1qsu/pABnBNI/XyKDnnYLo2KQ3Kq8dCq3v9ObqzU/YiDK7hddJLnUYmfP7UcgMo5ANdddCyW/+Qi1NXkggmjmrrNvPYcPPeNc/Hy9VPMskwHcIPbWJajZZBLU5I4nT49Eo/UsEiypnrviaEjPFJ39Ak6B5dFQC6UMQsqrXyOeIScQ3hXJWQm6BsGWc/G3fp5CI5DZHaxtntMH9g4NdOd+powtoTN1NvW5cJAFG2WZZxiWz4fizgXcIF8/ZJzSKMAzxkIr1w7Zry01visS7yWjka3Iw7WtSbC2sZvy0NwSZC7oOA0JXk20boduroQjB/WF6MH9cbh/XsgR47eUjmwp14NWR15ByfHrQmLeLHWfEl162EoXXQOK7cdwJ/mvGXJ4eHGJz358Ovror75Pasogb0HW7Bsy/7Yt1IPhKntu/WTb0cSVCMFWQ8AfPDWlyL5ZGxnFfo7z1q2zSrn18Vcm/YcCtxxA5zOgW9zROdQKM4NtU2sZFqvpTsPiT/7bklsfp5MY74l4Bzi902GIyYfSrKEV9aaTbvPPWao8V61oPsRB8dzDtwg0e2adeXv2f5pamnOaDwh7RdhIgBJiq7vXHyc8d7nlCD3gDew56/jA4/ok87IObiY3iYQTVf78caWaEGRHSVLsBG5H+ocRHCaVccX7p6L9bvMJ5Pff7LnOmzdroMAEHMLIjmHh173vNDPWrYt3iZG58DFmNahbhh0DiXSBor3B+/zyixW0nHGDU/j6/+3wHjfGLI0b9Y5RNtsbkubRhQlCsKsC6vTlCbDpLUdxfsiUaxk4RxM4t/QQktP5+tQsY/x4VZt6HbEwbbOJS1Euh399z9wPHuyM9i9MrtHtQzToJPnKYqBtGuXaG4r4Fez+FO4rhYg5RArub7Nf2iLUxLRicZz0PRGjnXGyvQ7Rrrb1r+T1Dls2csfClPFGmmNuKKLpLnvLz5pRFwhzeoV/HsJX6AfI/Yb2Dsq57d9YmkWq5uycuIiri0qUYnoHCztjhkCaP9VCMu9A02t+PKfvXM3/XvWoq0g8LX75wdBhUxj2+Rgz8USziWaXUej2xEHR6mSQeegE5Ao+yp1CHJAr/RDhEYtNhDMsjaDfFc/nKZzGPsbPVnrUUPiIT71VtsUjPrics2549h8rjESbLCVMai3WdmYVLf6maInpN2sg2x11vgG9/Hv5H13k3w6p4iD0rbBVZz4zqMGOfnsCsRKHOFQnh/sH3CT+Mw7R+NL7zqKrTs4hxEpK9xFJ5qyMrcLQuD0MZ5Opp9yKhvCnaOVv2RQLle88ubOIHDWoD51WLuzAQ/N24h/u8dzdGjcxBnep1IHH9sb3Y84MN953ncvwOfPGhu5Z9Q5GHZFQLgQy+RP3DEHQFRxplq6tBq2xXo9/1oZPXgmbb+5QatPJN1E74QR/Yx5J44ZAA4hJxSv7+aPn2K8x5WxYffB2L3+vWpjIgIJbl4+vnBzPBHwxQlhfeccXVzMj+CMgoVzAISROBCFrtDTnv/4rO/9FfCIN7dzHmE4rKgTUq99/O5Wh37705NHR+IqAKq76vi3ivhWMoqV/Dxs24FfTT0VQDQ+toCFOBh0IqxC2vL+uhJeV+LbNnFe2/Xn+QZ3Njca3Y84MENzYO86b7eh3GNPoDKyTBVBHAAtXTVpo0i6eRJF/NNo2aQyi3MdTkQRXYh+IvSBL50RqUdFnpn0Eq1tBbywKs4KHz9c6lfCtElj4lY58n3OuvGZeJth7lcuXe7ogDjnIDtfCBG4e0gLOQ6kWEk3p5W6ANPCX5MrzqHhfdMmB+7hZUVctwQiEgfWQWbZecB8cBOIL1yc3yWpaObe2mTKGmmL5ZxDW0FgYO865JV5KITAX1/faNQPxfUKIbdm4iq4r6LO9UUb9wWbD5nXFL9FtdDi0uPt5dOrFd2POBg+kK78476v7pZYL2qQHxRIr0Pfvcv7ckd6+aToGQBpbVEoCLyxYQ/+pjkMk4OPc3VABLz4zfNw3XuPZeuOuiOIvqXJyVlBCFzymxfx6TtfYesDgAXrQ6X3zoZ4XAfbponIC2D/EkN8ogeqOCOBqM5BFSulwWcVRb6sUroiGT0oKr6TugBTf+XzFHCFpYgYCN576FY5pr40uVIHgDu1U83xZ6O/uTgUkuDxC6z3v03jHCJzysI5CIXwyGfmGQwpuLLVcomYhdj/bYvcJvHf/1gRKV8aJuhIq5Cu5njRHLodcTBC+6Ama6XIrke5/P4Hjsf3P3CCnxwdBFFnZuGuRu78zzs2atYmF7k/vLQWl/zmxZi/F9k2VqwET0zztoGejxlbJC19UuSZBQHwJskSXzlnKuN/n18TpO1gdqk2jkve+9Sdc9i6bYhYK0HR1TCyatNC/d4TD8exh4fuUgIux7c+++K7jozkl+IebgEFPM5BLgScG20T9FeV7f/mg29o+fgdPNdXcqzoBCZed/RhTnRkW9xUriDJlJW3VvLLUcSuH77tpXhGBSaLpJpcLuYuW+bkuJokYmtyva2K0qLp5k2WxIdOHclXWkXodsTBNmzVe7zZZFQBrU6oD582Cr3rvYVAH2wR4qDcl7tLPQi9VKyuMDios3MOFJQBJExonTgYBrWtzzjRCufAz7ZpqvUXIi4Pt9Co/WWyVhKIcxrcggf4h7hUt9zBLpN/TnIOpkUgn6OAcL+0ukSrFBEPXWuyhOL8Cck8+ji4+KThkd/6osl1lXXJV5SzJqs/m/5Fjc3tKprX88kyetXn0aDFng7dnTAirYQKTWM3Z+AcTFCrUfVh+vyvFnQ/4mBSloEio99F5yAEcOa4QQA8m2tdpiqjh6nR5NRiAwWftgOV4itToBNZDxs0RWmrbKMJ+uKpv7JUfNt2/Vw/3Xnl6fjDZ0+PpNmUcaqSXE9jHcapxEFbfNTFKa5TsUxC5VZAYAzlSHGPqTjJOTy3Yntgsvjg1e80121skseh2BSvKrhFyiTKCeOi81AJ4gNfeqdfvts4OGiI+REiXk5BeSlXn04mzqFvfY3RxQ1rKWVY3WVeE/EwHoIzEME2A9EsVjdWaSQSByL6PRFtI6JFStpAIppJRCv9/wOUe9cT0SoiWk5EFyrppxHRQv/eLeT3IBHVE9F9fvocIhpT3leMwjTsSBuUpnMO+kC44zOn4+9fPgs9avOxiXjyqP4YN7RP7OyBrnPQFy1pMssZ8Pzj2nOCQckrpGUZklCF9350qSf2+uXHJ+ArU8azi97L10/Bgu+9B+ccPQTfft9xsTJi9TFpQ/rU493aCVBbGccOj3vAvfb8owHwYoCoKEm5NuxeJY4aGjf99Z6LkkndBYdOACURktY8utgpn8uhtSCwVTkHceLI/mzdNkgOJf4qkmhF28X1sU0JLEWPAGPppJQ9ccxAz3zVYRzsb2zFJb950ZqHXaBVZbehHnUTIQRDNP0He9XVxFxrBGIlThluGJyyz5IO9en3TZsGNR8XFKza4MI53AXgIi3tOgCzhBDjAczyf4OIjgcwFcAJ/jO3EpEUut4GYBqA8f6fLPMqALuFEOMA3AzgxmJfxgmG76ArpM2cQ7SonnV5nOSfiNYHf1NrIWKWJ2uSRZhMA6WbDk4McvSwvqFYiVk55WLAWVLIBfuyU0fi2guOZnwCEQ7v3wP9e9Xi7s9NwpFDpOVMOs6Bmxy2CWCzjDn68DjhiIi/lMuAIEKKlaL49/PGs/WbZPfcoSwgvpH40jnR8wCqziGsI1l0oHeRlL/HxUXh/Wg6Sx0AxPt/aL8emHLcMPzfl96Jj542Km6rr32SiD6Hgcy/55BZeZ5kyirz2DZwan4T51Bbk0NzG3+P13cYiEPwbPS+POdh9K1kaL/Koajzplr11InEQQjxPADdScilAGb41zMAXKak3yuEaBJCvAlgFYBJRDQcQD8hxGzh9fTd2jOyrAcATCF9S1RGmFhWfVCadQ5RsVK0jOgubUEdRikAACAASURBVPv+piBcZKQeqeiycA6eczhTN/CDUm13oHNQMul265yrae63bfCa+kmHVefAEAd5OvcrU5QF3S+2QRFbRCLBBXUJ39QUuOSUEcF9k2z3/OOH4T3HH46TRvZHn/oaZZfpl8v0kxBR31kqDjS1YldDM15VfOvoXfKrqRPYtnAwWuVo+WJO+5S2yVuH9+uBWy4/FV/0vQSfPmZgxHxUgtWzWNpIljHpkkd+G0/nkLxaeieq+Xu1OYopkSUBWMlEgjMSBylW0vZgf/yc9OEUtsUFUf9RSnqVUodidQ7DhBCbAcD/L2UIIwGsV/Jt8NNG+td6euQZIUQrgL0ABhXZrkSYvqM+KM2+lcLfPWq1CSTrAPDoG5uwcONeLN+6n80DhPbz+qIVxKo2TMe+Lh5OJXehmLLqizAnVor+Tp7wnF5ElnvlGWOCNNOEn/OtKeyiLU/LJnnJjMRLyIXtlRHabrn81Fi7VCz8wXvwvpOGo3+vWvz938/CkUN6K2IlwT6392ALtuxtDO9rs+gVP5b2/70WDnm9at08loMck3rPyXr79ghPEm/ccyiI0vdr/51JqVcuQDmfYNYoY4Ez/dQ5B5szO1kGYF8oQ87By7PnYHNgLnz1uUcFeVzWWm8DEM14/HBP7FSTp5jIdeMe76yE7p4FsHEOdrGSSSFtar4pFGraWBLthXIrpLmZzHH4Mt32TLxwomlENJeI5m7fzoerLBbFcA6XToiao6ky73+t8BSR0tUFBzmAY5yD71vJFLJxWD/+hKzeViB6+rNWV3xrXa8TRJOpnsS333ec8TwIED1xLQzlDOvXI7X/Ia4u79r7v3DjXjS2FIzEToVuakqInrIG4v2yr7EVs9fsNN7f6rtiUH0TmfwA2SDHZEx84v+XXNF5xw7Fs8tD538TjjgsqFNftDmmXHcDA8Qt18gvo6WtwJ+nMCyUFxw/LFIG4PXrvsYWTPjRTGzYfQiXThgRBjiCeXFVq/U2ACFGDegZbA5q8rmIyPWvr6v70jhMi//uBs9KTN/Zy3kZ9K1238QImDafNhc3HYliicNWX1QE/78cmRsAHKHkGwVgk58+ikmPPENENQD6Iy7GAgAIIW4XQkwUQkwcMqQ41whmziEKnnOIHoLTF3W5+Dy5aAvum7seHNSdkclaSZqy2nYUOtcioVuyqASGExXYfofy6ng9HzhlBL5wzpGxflKVnCpsYgBX1+ES/RXfO+qTspzAPj7p/bg0IkWsFO62VUhf/Kb7d312krWdJnCcIqeQ/uElnmFBLkc49vC+yOcown3lAhFN2CdW0SDihDvGsfmEavy3n8BbO+OHwmRuVZxz9+cm4R1HhkIAdfO0Wzl3wYkVk2Dz/lqbo8i4v/Y+s6dZwByUp7mtgB0HmmJ9N3KAWeewcut+owm6KuLN5cL+aG4rVOUBuWKJwyMArvCvrwDwsJI+1bdAGgtP8fyKL3raT0STfX3CZ7RnZFkfAfC0qKATEr3gvoqriahCOv5sUhAeKV5Q/bjr3i5VfzktgUKaIw7x8n/sB7eXeVT8/sqJ+MqU8cEJaHlf3ZXE9B96+3XTVilWspge6v2kLojR/MD89YYTryk5h5MUyx+1G+LrWTTBdoJYLSO0UpFp0VznHD0ERw/rE+oktFLOOMpbECPxuR0IVaxtmg5LQuVY876rDtX1iSw6x3IO8XrUyHV63Woem9KB41TjIsHwd5SjVUVcZvHV8P6hwz+bm47afI615DPBds5hd0Mz7nhhTSRNcjmyy9VxdcHNzwdO/HSoc7q5tYDnv/FunHuMt8nV3dxUA1xMWf8CYDaAY4hoAxFdBeAGABcQ0UoAF/i/IYRYDOB+AEsAPAngGiGEfOurAdwBT0m9GsATfvqdAAYR0SoAX4Nv+VQpqBPti+86EnO+PcX7Ydo2K0iKs8DN97gS2Pu/cut+/HKmd0xf92nkyYAFJo2Nql56KZZP+oJ13PB+uPaCo5X73n91B6XrB2ILVkzO7F9w7ywXRoMoCogumgUhjCdeXWkDK8KKHIKzvx+3mHDiHt1aSYcMxmSKLiZ/q5xavB63txZAxPLmWM16K5/zuEx18x30A4V9JkPXmowYEr3fgs/zz6+dE5QB6N4A+LK2H2jCb54O3cirXLAnVuLb8ouPnRJspF5ft9uYryafS4w9rsKmEN6451Ak2JO6mTQ53nOpZ92ugzhiYC9M8b0jJJ1g7wgkajaFEJcbbk0x5J8OYDqTPhfAiUx6I4CPJrWjXFA/Y78etRFZpwoT52BXuPGiqFgbBDD19pex0x8QHOcgRLxNH1Asb/RiY2IwZrLqiC2mhrZzc8e4E1USIpOXKeMLZ481to0Dy3qrxCglBwJwZqpxy52YLiZHEXEPdw4CSDg0xm3LtOwEAEIEcQUA4DsXHx+rS18HFdoQHEjbfdAcXEYliMb2Gnb044b29e97v1VuyUSsv3Lv69ijtOeBuRsC1zMgc1v696xFv5612NXQjKcWb8HU00OfZGpVtYpvKxfYRDo69zO0X3hgzTY/OKjjQRIvaS6+btdBjBrAi2Q7CsUF9u3EMJ1lSFxsvVypOQfeRj5qoRDXOfjWGEra6WMGRLiQpF2yvG+1hGAWRq5Mbof2Lf+AXBKBkbBFKXO1XP7mgwsx9zvDImnRSHB8W/7w2dOx60Azu8+0cg6KhY9eZ0EI432ZZhNtcFZYcdGO3XwUAPLktVMq1k8c2S8kDhTfHJiUyUmS3GTuwhcrtZrFSvLXHo1Q7VdOMyeNBPk+NbmcsT2ebyX+3uhB8QXY9l4mlytAssFGvJ7wWs5LKVJLQcvaDd3OfYY63UwiEIDf2SXqHJiRHbf68Ceikhw/50AxhRt3FkJ/JlqPBxvnELdW0sv0/quvfPKo/ljw/ffgCF/xbHpGh83CxRV6uE69DL3uv73uebN99zFD8eHTRjmx/+oO2aRzyBH53kfjbVDbZXd6mPzuLjt6KVaS8TB+ctlJwXfldAn8d3AzH7WSBsmpRjgHPQ//zt+86NhIHqEQXh1y0a/NE+56cS2bpzZPxnF/wXHDYmnq+ZAvvzsa8MpmSW3zrXT6mAH49OTRkbQ7Fd2FNNyQ4sBi4m5XGt2OOLhyDhz0CG06klxK+5lidcetiHwORamKO0WtQidCUr+gnxSN1qM3LSYjAhBdUHrX1UStcAwiFR1ct+Uo+r8YkOEaADYZwngmFSjf1yQ6kyfYw2hv8WJyzK5dL6McIF//IRe4OiWeMyEuMuEWMo6IsHkcFNKHlAOKrlylPOMAhNySKcayFBftPNCMh14PXdkfe3joWqMmT8bobVy6Hpo1cs8mGvTzcmtCfU0exw2P+gz708vrguv3nzLcL8OfY13IWqlLQB0I+sBlF7OcXVTAcg4G8YGabPKtNGP2WqVu+yJcHOeg/WZEYLK9YTvsz5gV0vH65XuXsk5GrJUSqIzL5oyguE0I6uC/TyLnYBHpJR3uk3mSTBzz/oZFNYuW7eF8gb3vxMPjbYWDQjqJa/b//+SxpUFakujTVI4QZj3Av/mhbI9UQuSeNnoAfvnx8MR5TS7nGQww7eXKVRdmvYl6/k8p3IBNt0Rk3/TIzZ4cB13JlLXTIrLQWTgH3qrDLirgwMpdRXRBMZmyLt60z5JHa1ts0ZYyYAtxMBCDSFv99qpt09vK1auDm6jl8JIScZ+RWF60DboIAYiabAohDFyBtzO979V18ZtBu+yEmXOPrndRbT4XG28jDosegPTESuEON0cU4RzUzcy4oX3wPan4Vd8nl+wmW4rSVKgxCTgxbGzsuxAH3yDAtFa+9ySPuKnc62mjB6C3YkUkD3tyFkss56C+l9ZINf/TX39XJIyrfs5hleKag4ismxV5S/aRidPpSHQ/4hBZ6Cz5mLRcwm7Q5HIj+pucOIckT4+JYiX/Zxq5t+nEdBrdh6lLud2VbHOSWacqk9Zh4/7ibQivh/Wrx39ceAxbnipW4ghOLkfYtr8JW/fFdSBBHiKrOaXLQllXk4uNt9AZYlhOW0FEXLEE/UnRhW9grzqWY3HlHFRCc/HJw3GTslvnRap6PckvLTmHJJcV6mJ6zLCoea90DcJZLOkhXwE756C+89jBUZcnuljp/JueC+6t3nYgMr/0zZEcV/J7VKNn1u5HHJRlX/X4qS8CpgNTVjGNYZepl+ENlDC9h+a5VT+JHT6plmuvW76PjTgkTVVZphp/OE6Eor8PVwL9DFPM/rih39Bsdi2i4iSLu+uIWCkFJ2JaqFQLobYCL/5x0ZGoTVn2Y92psZtYqS4fJw5cOQUhIq5Y1EhxNnl6mJ6scyCEbkEAN5GRyzvG2xJ3GfK+k0JRWEAclEVbNfEGQi5btZySSOIc9PeSxOTnHznZaM23aU9ct6Vznau3xx3+AeF8ysRKVQA5Du74zESccdRgYz7uYyWZJ3JTIWatRGFZAO8Gg7OK0ndBSYeqgkNwzASReGn1TuM9tcwdSkzoM8ZF+0xfFK5/73HB9cQxA3HftMk4eVR/VkywcMNetgwdtuAveiQ4azlm6YFSRmjW2dpWYJ0CuhAhNQ/nHsKJONTkrJsRINQ5yMU0r4iIpLJawkwcks0xiSji0VQviuWwjHJKa00RncN/ffhk3PrJ04K7nBhG9xkmTb5bCgU0tkRPHnPzWnbx1NOPiPWRrId7v427PWd+X/rTa/G3IIp8Y1P35jLOoXogP8GYwVF7Z/3bm+zybU6yeMUk34bAxt94Ejtavz7IkxTDslzb4jL3rd3B9b+de5TxNHdTS1jG584cw9YjoceveMeRg1CT4+3o+7h4l4W7y/A0RMaWVdbXWhAscXBZ2KMcTfw+R2B0IsiJleL1ENoK4SJWkwu5gBxFnR+aiJoU5djribpfcVn3XayV7vn8O2L1AGEgH11uL3+2RVyTaCJWRcl72o9nRu7x1kpe3JUbPnyyUSHNfvKEARcRKxnyyPElN537GltiYWE7Ct3wEFxggxJJ1xc5bofBKQijZcTBi5XCcWUSRemi0UMt0QTJkZxx1CBcdupIRjTl/Xd1B/z19/DydwBYu7NBSbOLt7hO4KxmgJCQJCmSVcKyUzvroD6Z9K5RzsEkVooGY6phdv2uVjeAt6vld9XJZdQljDfAs5MvFEJrpXyO0K9HDYb2rcd33388jj28H8YP7YOV2w7YuaWEtkglvPpbvx9/Jvqb64d3jB0Y+V2bIzS3qu8TbwdgV+CqiuIGLWSpHucBiIoP9SbKTRnXdzZjD7UdLnmkaOvkH/wDALD2hosTn600uh3nIKF/N32HyC1m+s46qUyAc8ftsfoylYu3qypFJZp0zsGv7G0De+FjE4+ADtkWyTn835fiMYzff3IYZJ5brCTBfHj+pvhNrR22cjwxWTxdJg3vb3dBrj76rb8ujPSNuuA8sWiLczkmPL9iOxb4DgJbCyWIlfznTFwGlz56oKbwzEWJ6ukKF6CWo5qy5nOEmnwOr3z7/EAWL+sycg7MeIvlgaa/YMrg2qaXoUMnvof374HNew8pjh3j8wewi3dt5w9YU1ZFP6ATsJ88upRtB2DnylsLhch6YereQCFtIXaLNu7Fl/88r931Et2XOGi/9YHMDb5k4sAsJFq5m/YcwsY9h7B1v93SRR9MsbCTfrGmxSdwn+EPYM5bqnqCk2+7sYmxdtjKMXEOJ/uKZhnYxwjl0acWb43oStTX1+v4xUdPiRaTQq7bVvCUvEWLlfz/JvcLXD+9TXPtkNcME45g3KF7vpWixMHUXiu35CBW0p+x3QfMY9aGPj1qcbC5DU8v86IAmKzybJyD6fxBXU3OeAjOxDmorj102DjV1jaB3vUhN290EihFYFpbZy7ZCsBz/HfZb1/Eo29sxobdcVfplUS3Iw6mSaArtZI4B1PcAn1w6XP15TWeO2/bLiCXi9ev55eTJok4SD823CLHiUzSwiWIDSHuO+bqc4/CVWeNDX6PPKwnTLAqpJX69TqOHhaPPx0+Z7wFwHOh3FYQyOc5Ymd/Fgj7X/ebFcCBTuVI360zCz95Lrt/+U/Py6mVOBjqcXidRGLAtU3nil1MWWX8bXmYzsSZSvGQevgtzBOKlVTU1+TYeffciu2BY8KY23qKlqni8nd4jv+kZ1UVBSHQpz4qtT/cD9KltlluHvV2feHuuQCAM2942hhOuNLofsQhcLOs7Ui0HR7HMdYpi6l0PKdD/3zFuEngFNL6jieJOMhqX1jlRaTjLGbqEohDg2XXZAIve457yTz28L6JJ5olXJ2SqX125ODeOGlU1ATWxVpJorGlgNaCQC2z83fyiySJg0msZCIaaj2Op6j3HmrBAf9b2YiDqbhixqiLWGmMdi5Az8NZ6uVzUd1GTOegWSvxOjvvvz6H6mt4h3zrdoU7cr28D/oH/bh6Bvepx7B+9Rjcpx4HNbPsgohuToQAamsIHzp1JC5TDg+mMWUVwhM/3fbsaqzcygcUKie6H3GQZn5auj6Jxw3tAx0q52Ca9GU59Uvxcw5GsZKhPr153KJh3NX6aNKU4JdPepshZ7xdkbbk4sQtzelZV2HQUcoBseNH9Ivdj1orJbx7axtaCwV+sXUiDt5/k1hJ31VycDlL4OkLwt9cfcFhQ4vOQYWM66xCxsUOymRO9etIekfuG3icg9ntd6iQLhjrlWl6iN76mnziIqyXJnUBpvHiKfNF5CwQ4BEm3UikubUQE02bOAcurbmtgDU7GnDjk8swb93uWP5yo/sSB+1bqwvllWeMwaSxcTm4OiFMC6ueOqBXHZtP4pyj4+FOc5TsajngHIwLfDSda29SeMbJR0aDDU0750hrfhO4k+WcyxATXHUF37gotLhiTUVT6PMaWwpobRMscXBheHT3CBwmH2nXtbgodPMU9UDKVZfEOejJLv2UFDjqtk++PbEeU1ttVlH6TtvmleBDt0aDS9U4xHnQi2sJ6jHkh0ecY5HctD4UgicONRbi8Lm7Xo38bm0T2LTHO1uhn5SvBLofcfD/6zsBdSKaTuSqA8SsaIz+vuHDJ1nbw425HFFs17PvUNT2WVbvyjlw4hFdzxIrI0eYODq0kEnKD5g5AJ3YmUxvOZg2e73q8sbf3KJ8keJ0zqTHkP6WCkKgua0QC63qtdWhH/wva+uzJHGOq25Dle1zbQv7wjBWipBlm9y1AMCkMQPx3pOGIwZdT2Foa1tErKQRIf9zSNckvFjJS9TNgFvbRCLnoH8TqdswfQupzNcJEaezbGqNj6fgTAaT/7kV2yO/W9oKwTzixmW50f2Ig8ENs7rYJyl5bXn0AT+4Tz2bL2gPW09UDgoA7zpmiJbHLtOO6VSK4BzUeoBkHYWeX03T/Qzp721fnvgJrc+naFjOeP5edTX4j/cczT4rIV2qmHZ6gKv9uvffxjkkleMivnpqid18V22DKw1w4dRsZxhMVn0mZa+KfI4iegE9T5xz4NrGv+jYwb3j4zDhXVsDImQWyS3etBf7GuM6h0g9ELxYSVpWOegcmtsKsSBBlUS3Iw4mqJPYtJOKukTg85gW6zTQB+Kz/3EubvpY3CoDsLU1+pvjHJJ0Dl754XWSKS9g4oTinIMuZrItlKZ5o+/+1W9itun30k1rgnxKCH4yA1El6YQjDrPWY4sklrT2u+hl9h5KPk2b5Bpd7ysXc3p93Knj3kgctPq5b1STwDnoh+BcDxg+941z0bs+rnPQdWF6eS2BzoFHjgjLtsSVw5y1YWtBoC4f5XbD09yGCtS2tomAG3KZi6WipBqIaC0RLSSi+UQ0108bSEQziWil/3+Akv96IlpFRMuJ6EIl/TS/nFVEdAuVQ6trgGncqwuLi6jGtCOUk6QmR3jyq2fH7sdNAO31AN7BIF0MI8eekXNw0Dmk5QRcdismHzs6cTj2cM3M1PLFTQu5vogRUdAfxVrmyNsCnhiA6yP1HX94SdwFNhDKoG0EOLktGnEwmLJK3GgQYYZ9Yt79qrCZDnP1AtGxYRpXeu28ziEXEbHI8KfBM37RUtzD6xziaaMH9fbChxbioiZbG6Vy3LQkmcaZPmYbfeOO+lqdc4jWY0NbQQSbKpe5WyrKUcO7hRAThBAT/d/XAZglhBgPYJb/G0R0PICpAE4AcBGAW4lIfvnbAEwDMN7/i7uxLBcMCumoyIh/VN0tmXaE8uzAtHOOjESnCsp2Ekkk5wlOkCaYsgbtKvKcg0oEnYiDoS0qp/D6dy/A6EFRM8ftvvvrM8cNwhNfiRJV42LFJCfZhMtkkzhB9v2WvY1YuHEvuytUv4/pW+3wrVdsnGQi5xAT3TDtzanjlv8+gR8vI8GM/ua65refiCqY9f5VN1dmzkHnhFw4h+h9/RAcL1Ziq0dNPh48qSXm0DL6jBRDGRXShk7VOYdDvocDfVGXDvpcwoQK0Yk4BwMuBTDDv54B4DIl/V4hRJMQ4k0AqwBMIqLhAPoJIWYLb8berTxTdpjOOairWpJIAjDvCOWkMS5ODmKnNdsbIr+55iRxDkmR4wA3BbPsi7p8zi0/0x6iqAXKgN5xC66zj/a8vY4e1DsWXtFVrKRCV+hLhP7z+edk6+X5kM1MqFF1fict8DYCnNSbSRZBQHSzYapKjlWj3FxrCWdhZ9MxANHNkklZGt+QxfN4OgezU73QZXeyKStbtk4cNPFmnHMwK75t6frw2t3gbRb6Ms4m80ROYqWCAJ5d7impdUlCJVAqcRAA/kFErxHRND9tmBBiMwD4/+XxwZEA1ivPbvDTRvrXenoMRDSNiOYS0dzt27dzWZIbbDjn4KJsjlor8XlqE1wV6I996O3xV12uHXDRWWsgHHw2T5tJ4PQQOmR7Rw7oWfQZjhwlO8ULTHM5MYHhmb494i5BJB59YzObHugcDITF5R1tEQQl5He1KaST6nLhICO6MuOY88eksR3R33/47OnxMrT30AlRrUIQBhmMMPT6TZyDqtw1+layHYIzEclcnHPQiYU+KkJrJbd59j+f8jgsvZ6Nvgkq5wmA84jAoU0IDPQJN+cOp9wolTicKYR4O4D3AriGiM6x5OV6V1jS44lC3C6EmCiEmDhkSPx8gAsCU1bLLsa0u887EBA5iExrwn9eGEY1+8llJ+LSCSwdDLDg++9h06VYxMg5uCxyjidwgdJM51SPnleeMcaYR61PxYUnxOMeA8ADjDPB5LZ4/40KaQf6py4Upn6WBN3WxUndrzfxRMbEWu2vJD2YC0cMACO4BUy3ftN1DkrdgxjOkAP3DXSrOs6sPEehrsBV5wB4YrcY5+Av/v/14ZNj5REhcNFv+lR6/eceMxTD+tXjhg9F9T/Su2s9s+OvyfEnt3UUhMB9c9cn5isXSiIOQohN/v9tAP4KYBKArb6oCP7/bX72DQBU96GjAGzy00cx6RVByDloLLvy26yQDtNN8ne5azFNxM8p/oSOG877/nn720ILmN51PPvIycJVqNV/Tol7mxZ6OMPiygivzzhqEJtHFs/1W11NLtYP17z7qKIOAgV+d0xtdShDXb/MIgzvv/SllVSbGu1MQhWvXHnGGHyCOaHuZEiRYK3k8s7659c3Fup8cBWpcnoffbNjcgdiE/eYvgnHOUidQm2N94x0+XHc8H4YPbBX4JvMVZnfozaPOd86H1O1b9Vo0Dl4ZSPiWdeENI4jy4GiiQMR9SaivvIawHsALALwCIAr/GxXAHjYv34EwFQiqieisfAUz6/4oqf9RDTZt1L6jPJM2RHqHKLpLpZI5JAnn2IxNSm1Lz55hH+fEpXGpg2H2lY9sFEayPcpxUTXRVcTcg58GfrCopsEuiKJc5DtuP35NeYyIqIcezk2yCzfuPCYSLQziUbFfclxw/sa9EZhh5k4wSTOIcI1m97HYFIa1KESKUMh+sLItdekVFdBRGhqkzGz+cWWQz4Xt5r76eNLI+XIGCO96/Lo17MWW/zQqGqI1GhdbvNCciCSCKkY1KceG/ccSjy97aKXKCdKCfYzDMBf/QFbA+DPQogniehVAPcT0VUA1gH4KAAIIRYT0f0AlgBoBXCNEEIe7bwawF0AegJ4wv+rKGzyzyTZLZAsznEZM0altp/MKa90GOXIyp1SLIODk9glEIeoPsduyWIU6TkoZ53akrSLTilWKqVvk55U5dCmfouIlSyKWMD2zuEN6TlUhz4n3vBDvEpEiJShHp3b5rK5WPPliYL4JpyRRBrOQbrGluVIK6DmtkJErr/BDwlaLKSfMk7iMLRvPfYeaknkHNo7nkPRxEEIsQbAKUz6TgBTDM9MBzCdSZ8L4MRi25IGLrJm00KoKopNO3pZjsuOwiSakvWX4lLbZTfoVo7kHJLbkiQy8sqx715dRHqlQJZjNk2M/uYWy3xkI1G+NulQQ64aDSCUMWLabASuJgzkSBVXfMHgP0uv/p9LtxrbYSKY+kLOHoJzsqALXWOkidSXz0etlQRDfKVuramlgH4DQuJgstRzHZcPL9gIgBcr5XNe+GH99LaOZVv2OdVVLnS7E9JB98fESuruln9WmqN5+fk8NqsbHUlmqLUOK4/JysHFFt8FSa7BVdzGiEb0+pNEdsb7Cb9dIYs3LZT6wnbrp+IO5KKE17TgJrdFLlQmm/UPvz1UxblElOOs2gDggdc8Y8A1Ow6w99Vd8WcN+qmk8ay2w2RGrG+GuPd+da1NR+OhobktMPdOE6mvNheN57BLmc/yEJqMSviBU4ajX89w7/zpyWPYMl2nVqOFc5A6FBNnIOuQMTvaC92OOMhZqy8OLhPeZYFMI1Yycg5yQU5xDkEHRd4nuS0mSOKz+2BzQk6gp0F5rrYl6ayEq/iqWHpHCd9HT+7LuJ2OGCYYFnap2xozyKzvaUk40NS/V20Q88C0kXBxWyFhGiu7HL6tTjT/33njjHk37uEjlunt+/2VE2N5licYWujgOA3b/G0riIBjUBfjIX09onBYrzos/dFFuObd4yLEVj/ZnFSXZz4JugAAEohJREFUCdz4l6FeTToHl41mJdDtiIPJlFX9bTpgosrDTb7q04iVjMrZgHNId4I52o7yyMWlZ8gk6yjATTlrkp3LnbZpIujJtjMONgSOzgxb+3gQqHh71NOsSW4M1Gh3OqT5os1MWK5fpu+sbjCSzI17Gsa1HreDg179O47kRYi2etS2/vt54zBuaNxaz7TBMIEjmrq3Xj2vJAqSc/v05NE4TfE+3LMuzxzys4tDXTB2cG/2DIh3CM7MOZRLpJoW3Y84GA7BqYPBtPBHZOeGRUG6TSjFWimwECohWpg6nmw21F9815H4wQeON1eQQgfmcvDPNMmCQ33Gfoumf/z0I9hcZ44zL1pASOy27uNjeOvV92bGwprtoXjGRBzcxErJ7pfl4UHToqmOEVM5N33MUw2a1hhpZvlFS7yONAYBJg5GJRqmBS+tzyBuPTV5QpacuCQKcjE+xeA8ce3O0FOBcT6nWLjPGjeYTc/5HI1pnnYQbeiOxIF3n6EOVtPOwyUG7o4D3qIjfanYYAwhmZNyfgfOwUGBqysPVVz/3uNwpe0cRIqBacrqonMQCTtkzp6cw51XeKd7TQtUU8J30b8xNxbUiHimekLdlrkDmx04B4lBvfkF75JTRiS2RbrDMBEsaWZ51nh+8QLi88U2F0z19EyItwEAx+gOGRPALai969NxDmbnlcq1w8aHw92fmxRcmwh8nqRYie8403pUaXQ/4uD/tyk4Tcf/XbxVBnkdspp0CoFYyYFzMG201CddjuabkIalNXMxYbqZc/Ad5jnUN/2DZsO2unwOw/v3wM8+yHsovfaCo61l683rVRfnHNTDd0nfyHa3NYUTNZNZs3ri3LT4JJ2El0Rh9MDexjz6AmUr0mW0LdnEW95MV76bqY6rzz0quOa4fFN/ys2WJChSCe0am4XNo1xffHI8wNHQfuFaYtrQ5HOEFVsP4HsPL4rd+/K7x8WcHj7/jXcntqsc6HbEQSJ+CC55IEwc7YV15EJ76nBZkE0LYU3AOSS3yeRjRX2fa95tVh4moRwsrYs4rqGp1Vqfq413LkeYff0UfPi0Uez9xNjNWv1J38B0v2A5wSshFymXA33GsLQO4lAK8vJlTzv7SLz67fPxNovyfJhm0ssRnG9c6IVpddmL7G/i41CoJ+FN1lfqvOHabNLVyXnVWihgy95GzPZPr5dywFPOsw+9fWRsEVfvA2ZdjBxD/1q5I5L+4bePwn9ceAyG9Y/2ve07lROlHILrlCjFp85lp47E5CMH4fD+/EEhFS6LGSfPBtSzBeZGHTGwJ9bvOmT0O6S+z+lj7LGKbSiHMkwu/ID5nZ5a7Im+Xli1A58/Oy77Vr1n6p400yBJj6OKSy6bMMKS089v6J8GP3Rnb4bzkJDuok2WMCpcNgqmxWdfo7cQm8QTuRxhSF97xEL92dED4wuU7AoXDvtQMy/eIwfxriRMqhKZuy9x/nGe78+8Ila64KbnsN8fl0YC70DlkgxQ1KJ7GL6zPIin4xe+rqi9D79JdD/i4P930R9wcCEMQGkfdEAvjxuwLcwzr30XWgvC7C2yTEqschTzvLIjSlrkTN5b1XjASYeFbEiq34XLccGhZm/hsVnfhJxDcj0uhxBNY+Hs8UMw+ciB+Nb7jksswwS131ZNfy/bN7mQOiTinYYDkypmKPL6SFv8ejgHgToevPqMwFdZraKQ3q9sWEwm5S5zOPA95qD7MxHvJP2k2r728MYq0e3ESsJgrlSKXF7FiSP7lVzeCSP7Y+zg3vjk5NHGPD1q81YRSbnM38oRlE+dZEksvGnxVomDHjg+DZLMg130Iy74wv9v79xjpKrOAP77dheQNwKyriyPpeJjxQdCeQiCMULBRzGpNj5ZtQ21wWptmiitYo02QVNNVWyUWohtfaWxtVBtrTVqUmsVqE+eiqWKxWpbRPBZktM/7rm7d2fm3Lkz9zF32O+XTObumTNnvvvtvfc75zvnfN+J4+jd1BA6aiu3zyFInPAlg/v24sFF04sSLFVCUBfO6AD2Pcq1/62Tx5etUyoKLXQZ8EERwsv0bmwIBI/05C586Lt0WxiHqRS+UYiSWrjSZbo+bcO7/m+uqMZp0OOMg0/hMy+pgIez7XxEnDR+A/o08dR3T+q2EqVSklr95hvTue3NVbcRTJ5evude+vPgMsDP0nQrdRs5VK/FqeOGsfXG+QwNCV/tj4CiGIckcpPHIdLkrD9wiHAvxQoB37lgozKj2jXn0F1Al26jhNH2V0ZFCZ0fJ0HPYc0DQn8nDXqeW8mxzyGpkcNeGzrANZ8AXsrF4Nb8NEhq5ODfSD9w5EqOQnDTWDlXjeviv/vCSbQvfRyI1qNzt19uF3H0unH58BNvLmBIBFdBnJFDVpQLhx6knLFx7QkI/k6U1XylosUW5mt2GWf/2o+y/8O1tyLK5tr2lkFs3Nm1euuJK2d1hg736cz8mEHuaJ+eZxwcaUKTmvO5YNoYHn313ZLL2nzCPkuKpOYc/F5/nJy1lbiVXMPz4JLSOBPS/gNlapvL3ZOMWykKx44azNrtu0qm5Syk1iOHKBzT6vn24yyAANh64/xQY+h35KI8KEtFUi4cObhGIP4mxbD7dZuN8eT37AsJnocrN8uc9uZO47B92Wkl6/j7p3TkkCKukUNSiTTGNw9k3TWnJNJWHJKYK4CuGynKEN5F99DTZeYcIsgdZ86hqbGBNZfNdOa46BaTKkTWU45sLpmlrBLu6fgi7+7+tKKMfHlmSttQ/nbtnFBX2lcnt7Ju+67Qdsp1RPxFC1GuyeDD1NdhobuonG7DRpB+56n1wNLXU9A4uUYXi2aN47Ynw4Pq7bJJh97bUzqvRBr0OOPgUzTn4Cjv6fi9/jhzKElMSAeJ41YCOLrV/VAP3syrnv27c4XPPR3FQeMqZXDfXpFXnyRl7NMmzDAA3HzWsbE7Yp0T+ZE2iQYn0kuPHFwsP/d47n1uO0eE7dq2zbsD83UdD3csFw5zQRfyaYQ4WEnR4yakXZeF3wOY7Fg7XY9cfvKhPLJ4Rqw2Wg/0lgtG8e+6CN6Lroe/n8rUlU8AYOnpXgyoz/elt+47KF2cJbNZc8qR1S8YyJq4hq4z7EWFHRZ/tdLHn3cPKe4SZ+zw/lx3xlGhIzt/L4arRnAjn8utFIVbzvb2PERZvpsUPW7kMH/CwRzWPKBo96X/AKuXHloUvjP38NhtPLBoGi++9UFiE2Eu/S49o52lYQEA6VraeOiIynNHRyU4cuiY7l5KnBUPLZrGU1veD63j8lMnzalHH8zEUbXvPPlupSj+9+AksJ8f5byfPt+tzmHNlcVzCvKT8yZx3wv/4AuOfObBhSdxni1nThzJwAOaMu0E9DjjMGZY/5LrvcfYLemnZzBZXE80DzqAeRNK78LOmiltQ3lk8QyOienrDyN4/8ZZoZUUU8cNCw2PnSWl8lzXAtfEcpDVl83gnV2fdNv5XWrUeuOZE2LNp40e1o8l892bC5PqbDY2CHMd0RDSIjfGQUTmAbcBjcA9xphlWf7+IUP6svmGebHWX/dUHr18ZresWoVcd0Y716/ZmMhvHecIr5wG+9Mocn/CX+bp2nEM3sopf/WUT6Fvf/MN82LtPYjK/V+fWnbj0R+vnJXY3qSkyIVxEJFG4E5gDrADWCsiq40xyTxRIpLFhbI/ctQh4T35juljuX7Nxtire7LAj3/jhzBR8sd5U0bTt1cjCyLEvgpSeH9ndb+fELJnwyeOaystcmEcgCnAG8aYNwFE5EFgAZCpcVDSoaFBePibJ3Cowy+bJ/zlhgMihGZQakNDgzij7oYRNS6a4pGXO2Ak8Hbg7x3A1BrJoqSAK4Jm3hjctxdXzTuCOe0jai2KkjAD+jSxfdlpvL/ns4pys/RU8mIcSrnbiv57IrIIWAQwevTooi8oSlxEpFsyGWX/o1x4csUjL7OvO4BgUuBW4J+FlYwxK4wxk40xkw86qHzCHUVRFKU68mIc1gLjRaRNRHoD5wCrayyToihKjyUXbiVjzD4RuQx4HG8p60pjzIYai6UoitJjyYVxADDGPAY8Vms5FEVRlPy4lRRFUZQcocZBURRFKUKNg6IoilKEGgdFURSlCEkqA1rWiMgeYIvj49HAW2WaGAzsjlkniTagvLxJ/U4S56O6ra5OVrrN6nxAdZtWnbTvscONMeWDORlj6vIFrAv57P0I318Rt04SbUSRN8HfSeJ8VLc51m1W56O6TfV8Ur3Hwp6dwdf+6lb6IEKdNQnUSaINKC9vUr+TxPmobqurk5VuszofUN2mVSereyyUenYrrTPGlEzkG/ZZHqkneetJVqgveetJVqgveVXWytuv55HDiio/yyP1JG89yQr1JW89yQr1Ja/KWmH7dTtyUBRFUdKjnkcOiqIoSkrUjXEQkZUi8p6IvBYoO1ZEnhORV0VkjYgMsuW9RWSVLX9ZRE4KfGeSLX9DRG6XFBIFJyjr0yKyRUResq/EM9CIyCgReUpENonIBhG5wpYPFZEnROR1+35g4DtLrP62iMiXAuVZ6DZJeVPVb6WyisgwW3+viCwvaCt3ui0jb950O0dE1lsdrheRkwNtparbhGVN/ZnQSZQlTXl4AbOA44HXAmVrgdn2+BLgBnu8GFhlj0cA64EG+/cLwHS8BEO/B+bnWNangckp67UFON4eDwS2Au3AzcDVtvxq4CZ73A68DPQB2oBtQGOGuk1S3lT1W4Ws/YGZwKXA8oK28qjbMHnzptuJwCH2eALwTla6TVjWVPXaTe4sfiRBJY+l+wP3Q7rmTUYBG+3xncAFgXpP4uWpbgE2B8rPBe7Oo6xZXwiB3/8tMAdvg2GLLWsBttjjJcCSQP3H7Y2VmW6TkLcW+i0na6DeRQQetnnVrUvePOvWlgvwH7wOQ+a6rVbWrPVaN24lB68BX7bHZ9OVTe5lYIGINIlIGzDJfjYSL+uczw5blkdZfVbZ4eO1abgSgojIWLxey/NAszFmJ4B994evpfJ9j6QGuo0pr08m+o0oq4u86rYcedXtV4AXjTGfkbFuY8rqk4le6904XAIsFpH1eMO1z235Srx/8jrgx8BfgH1EzFWdEpXKCnC+MeZo4ET7ujAt4URkAPAw8G1jzIdhVUuUmZDyVEhAXshIvxXI6myiRFkedBtGLnUrIkcBNwHf8ItKVEtFtwnIChk+E+raOBhjNhtj5hpjJgEP4PmTMcbsM8ZcaYw5zhizABgCvI73EG4NNFEyV3VOZMUY84593wPcj+caSxwR6YV30d5njPm1Lf6XiLTYz1uA92y5K993ZrpNSN5M9FuhrC7yqlsnedStiLQCvwEWGmO22eJMdJuQrJk9E6DOjYM/Uy8iDcA1wF32734i0t8ezwH2GWM22qHbHhGZZodjC/H8f7mT1bqZhtvyXsDpeK6ppOUS4GfAJmPMrYGPVgMd9riDLj2tBs4RkT7WDTYeeCEr3SYlbxb6rULWkuRYt652cqdbERkCPIo3//SsXzkL3SYla1bPhE6ymNhI4oXX294J/A/P2n8NuAJv5n8rsIyuCd+xeJM9m4A/AWMC7Uy2Ct0GLPe/kzdZ8VaCrAdeATYAt2FX2SQs60y8YfQrwEv2dSowDG9y/HX7PjTwne9b/W0hsLIjI90mIm8W+q1S1u3Af4G99tppz7lui+TNo27xOmQfBeq+BIzIQrdJyZqFXoMv3SGtKIqiFFHXbiVFURQlHdQ4KIqiKEWocVAURVGKUOOgKIqiFKHGQVEURSlCjYOipICIXCoiCyuoP1YCUXwVpdY01VoARdnfEJEmY8xdtZZDUeKgxkFRSmADpP0BL0DaRLzNiwuBI4FbgQHAv4GLjDE7ReRpvLhYM4DVIjIQ2GuM+ZGIHIe3I74f3karS4wxu0RkEl5srY+BP2d3dopSHnUrKYqbw4EVxphj8EKuLwbuAM4yXoyslcAPA/WHGGNmG2NuKWjn58BVtp1Xgets+SrgcmPM9DRPQlGqQUcOiuLmbdMV2+aXwPfwkq88YSMlN+KFSfF5qLABERmMZzSesUX3Ar8qUf4LYH7yp6Ao1aHGQVHcFMaW2QNsCOnpf1RB21KifUXJDepWUhQ3o0XENwTnAn8FDvLLRKSXjbnvxBizG9glIifaoguBZ4wxHwC7RWSmLT8/efEVpXp05KAobjYBHSJyN17kzDvw0ozebt1CTXgJmjaUaacDuEtE+gFvAhfb8ouBlSLysW1XUXKDRmVVlBLY1Uq/M8ZMqLEoilIT1K2kKIqiFKEjB0VRFKUIHTkoiqIoRahxUBRFUYpQ46AoiqIUocZBURRFKUKNg6IoilKEGgdFURSliP8Dgjdvfsu5um4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)\n", + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics au printemps. Le creux des incidences se trouve en automne." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-150:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe au printemps, pas vraiment à cheval\n", + "entre deux années civiles, mais quand même nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n", + "1er septembre de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er septembre de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er septembre.\n", + "\n", + "Comme la varicelle est très faible en automne, cette modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an décembre 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_september_week[:-1],\n", + " first_september_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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+ "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu zéro au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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