diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..8313f2495cfe6845e5ab23b5a686ca575e5be66d 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2469 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek\n", + "import os\n", + "import urllib.request" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données de l'incidence de la varicelle 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": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.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 jeu 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": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"varicelle.csv\"\n", + "\n", + "\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, + { + "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": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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1684 rows × 10 columns

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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1120225176226382286309513FRFrance
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1320224975095321269788511FRFrance
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182022447427122316311639FRFrance
1920224375863330284249513FRFrance
202022427377019505590639FRFrance
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26202236710691781960213FRFrance
27202235715814002762204FRFrance
28202234722667883744315FRFrance
292022337734001739911026FRFrance
.................................
16541991267176081130423912312042FRFrance
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1659199121714903897520831261636FRFrance
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1684 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202310 7 5318 2695 7941 8 4 \n", + "1 202309 7 7026 4546 9506 11 7 \n", + "2 202308 7 8175 5316 11034 12 8 \n", + "3 202307 7 6595 3782 9408 10 6 \n", + "4 202306 7 9595 6017 13173 14 9 \n", + "5 202305 7 6237 3907 8567 9 5 \n", + "6 202304 7 6299 3973 8625 9 6 \n", + "7 202303 7 6063 3798 8328 9 6 \n", + "8 202302 7 6576 3060 10092 10 5 \n", + "9 202301 7 8153 5470 10836 12 8 \n", + "10 202252 7 5171 2717 7625 8 4 \n", + "11 202251 7 6226 3822 8630 9 5 \n", + "12 202250 7 6590 3100 10080 10 5 \n", + "13 202249 7 5095 3212 6978 8 5 \n", + "14 202248 7 4985 3043 6927 8 5 \n", + "15 202247 7 6087 3733 8441 9 5 \n", + "16 202246 7 3033 1392 4674 5 3 \n", + "17 202245 7 3827 1720 5934 6 3 \n", + "18 202244 7 4271 2231 6311 6 3 \n", + "19 202243 7 5863 3302 8424 9 5 \n", + "20 202242 7 3770 1950 5590 6 3 \n", + "21 202241 7 4177 2219 6135 6 3 \n", + "22 202240 7 4883 1472 8294 7 2 \n", + "23 202239 7 2041 331 3751 3 0 \n", + "24 202238 7 1771 419 3123 3 1 \n", + "25 202237 7 1725 499 2951 3 1 \n", + "26 202236 7 1069 178 1960 2 1 \n", + "27 202235 7 1581 400 2762 2 0 \n", + "28 202234 7 2266 788 3744 3 1 \n", + "29 202233 7 7340 0 17399 11 0 \n", + "... ... ... ... ... ... ... ... \n", + "1654 199126 7 17608 11304 23912 31 20 \n", + "1655 199125 7 16169 10700 21638 28 18 \n", + "1656 199124 7 16171 10071 22271 28 17 \n", + "1657 199123 7 11947 7671 16223 21 13 \n", + "1658 199122 7 15452 9953 20951 27 17 \n", + "1659 199121 7 14903 8975 20831 26 16 \n", + "1660 199120 7 19053 12742 25364 34 23 \n", + "1661 199119 7 16739 11246 22232 29 19 \n", + "1662 199118 7 21385 13882 28888 38 25 \n", + "1663 199117 7 13462 8877 18047 24 16 \n", + "1664 199116 7 14857 10068 19646 26 18 \n", + "1665 199115 7 13975 9781 18169 25 18 \n", + "1666 199114 7 12265 7684 16846 22 14 \n", + "1667 199113 7 9567 6041 13093 17 11 \n", + "1668 199112 7 10864 7331 14397 19 13 \n", + "1669 199111 7 15574 11184 19964 27 19 \n", + "1670 199110 7 16643 11372 21914 29 20 \n", + "1671 199109 7 13741 8780 18702 24 15 \n", + "1672 199108 7 13289 8813 17765 23 15 \n", + "1673 199107 7 12337 8077 16597 22 15 \n", + "1674 199106 7 10877 7013 14741 19 12 \n", + "1675 199105 7 10442 6544 14340 18 11 \n", + "1676 199104 7 7913 4563 11263 14 8 \n", + "1677 199103 7 15387 10484 20290 27 18 \n", + "1678 199102 7 16277 11046 21508 29 20 \n", + "1679 199101 7 15565 10271 20859 27 18 \n", + "1680 199052 7 19375 13295 25455 34 23 \n", + "1681 199051 7 19080 13807 24353 34 25 \n", + "1682 199050 7 11079 6660 15498 20 12 \n", + "1683 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 12 FR France \n", + "1 15 FR France \n", + "2 16 FR France \n", + "3 14 FR France \n", + "4 19 FR France \n", + "5 13 FR France \n", + "6 12 FR France \n", + "7 12 FR France \n", + "8 15 FR France \n", + "9 16 FR France \n", + "10 12 FR France \n", + "11 13 FR France \n", + "12 15 FR France \n", + "13 11 FR France \n", + "14 11 FR France \n", + "15 13 FR France \n", + "16 7 FR France \n", + "17 9 FR France \n", + "18 9 FR France \n", + "19 13 FR France \n", + "20 9 FR France \n", + "21 9 FR France \n", + "22 12 FR France \n", + "23 6 FR France \n", + "24 5 FR France \n", + "25 5 FR France \n", + "26 3 FR France \n", + "27 4 FR France \n", + "28 5 FR France \n", + "29 26 FR France \n", + "... ... ... ... \n", + "1654 42 FR France \n", + "1655 38 FR France \n", + "1656 39 FR France \n", + "1657 29 FR France \n", + "1658 37 FR France \n", + "1659 36 FR France \n", + "1660 45 FR France \n", + "1661 39 FR France \n", + "1662 51 FR France \n", + "1663 32 FR France \n", + "1664 34 FR France \n", + "1665 32 FR France \n", + "1666 30 FR France \n", + "1667 23 FR France \n", + "1668 25 FR France \n", + "1669 35 FR France \n", + "1670 38 FR France \n", + "1671 33 FR France \n", + "1672 31 FR France \n", + "1673 29 FR France \n", + "1674 26 FR France \n", + "1675 25 FR France \n", + "1676 20 FR France \n", + "1677 36 FR France \n", + "1678 38 FR France \n", + "1679 36 FR France \n", + "1680 45 FR France \n", + "1681 43 FR France \n", + "1682 28 FR France \n", + "1683 5 FR France \n", + "\n", + "[1684 rows x 10 columns]" + ] + }, + "execution_count": 10, + "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": 11, + "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": 12, + "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": 13, + "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 !" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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hOaSwosu7NAf3zcj7TESlaJi2uUg0K6mEYEarVRl2w+4hNOc0aEr51mrMqolMXmVtxHpvMXw2jbIlEwlZnmT8uO357bjg+vICzH3vTdZggZrCgVK6i1L6PPt7CMCrAOYCOAPA9Wyz6wGcyf4+A8CNlNICpXQzgI0AVhFCZgNoo5Q+Sa24uxtc+/D3ugXAKWQS2QVO+snDdkRCRShrwvcFpdSOVtJNCtOkifkcCrqJZiYcuOkqrxvIaAQLupoAAK/sHMT8ziaH5tCgqYnYxN2agxg+m0ap9ImEFA7jx9dvfhEvbfcPn04j1ykJQvkcmLnnKABPA5hJKd0FWAIEwAy22VwA24TdtrOxuexv97hjH0qpDmAfgGlhzi1pgkqqkitDOukb4/a1O6CbFEtnWCXDS6bpGbkSR85DUTfRlGXCwbQa++wdLWFacw4LpjXZ2y2Y1uTwOeQySkJmJeszcZuu6AQfTaGm1URCmpUmLpP12gQWDoSQFgC3AvgqpXSw2qYeY7TKeLV93OdwISFkDSFkTU9PT61TjgWv5KKFl9yNf3/gDc/t045WemJjH6a3ZHHOinkArBvRy8Y5OBat8QilFEXDRHNWtY/TP2o5uqe1ZNHNGg4BwIKuJruMOA9zTdLnwM1LYm6F1BwkaRAk+XAq+xxACMnAEgy/p5TexoZ3M1MR2O89bHw7gPnC7vMA7GTj8zzGHfsQQjQA7QD63edBKb2GUrqSUrqyu7s7yKnHDi+kd8UDr3u/Tp01jYyEVw2GSdGYVZFjlViLumknqInsjSgcSgYFpUCT4HPgUVDTmnMghGBep9XTYkFXEzJMc1AVgoyq2BnVccKFAteKRM0hjSZLEwkpHMYHHh1XjSnrc2C2/2sBvEop/Znw0p0Azmd/nw/gDmF8NYtAWgTL8fwMMz0NEUKOZe95nmsf/l5nA3iITtB6AF4Tr5u+4SK4VSXpG6NkUmiKgoxWjiLyWqnsDXATV4P7DFpyluagGxR9w5Zw6Gq2OtHdetHx+PTxC3HCkulQmUNaJQQZlj0dN9ysxB3yzaJZqbi/aQ4T8nGZ8uwZytfcZrL6HLTam+AEAJ8CsI4QspaN/ROAywHcTAi5AMBWAOcAAKX0FULIzQDWw4p0uphSypdxFwG4DkAjgHvZD2AJn98SQjbC0hhWR/xcscFFFHePB3kIe4YKaG/MYDCvJ65SGqYJja3OAWs17eVz2DsaLdeBr9K5z6FkmHYmKG9TOrOtAd/5yGEArMQ3ANAUgmxCSXA8vyPv8j0A+1+0ku7KrTEpte8JSXIEqYg7WQV3TeFAKX0M/v7YU3z2uQzAZR7jawAc7jGeBxMuExWe5Rgk2ah32BIOI0Ujcc1BN8rlKwBga/8ovnfX+ortopqVuHDgphvdpOhjPaq55iDCo5UUblZKQDhwbSRfLEdrAcDi7maMFvYP4UCItYARNbOzf/EEXti6F1suP30cz2z/YNh1n3U1ZyuSTqe0z2F/xn1Zg6wCeocLaGvMQFNI4tmRhkmhqcTu/vbN21/23C5qjfmy5sAd0qZtb+1o8hAOzKyU1RQmHOL/HsqaA8vvMCgOmtmKRdOaMbKfmJX4qk38fl/YGk9ei6Q2bs1hGlsoqUK03pT1OUgs+CTvXgGLqwK+eudmJZWQxAui6SaFqii2CcEvx6EQsWUo37/ZdkhTjBV1NGZUx4PA4aGsGaY5JFI+w/Y5lDUHVbGytQsJHM/N1r7RcXcE83QgL432+a0DeH33UNqntF/hzoaezqL2xEXhZPU5SOEQEC4D3JO9mD/Ak4IHRktoa8xAVUniN4ZhUuZzsCYJv+iofMSWoXyy5aGsOusX3ZhVPbe3Q1lVgqyWjM/BNiuVDFBKLf+LWq4QmyT9I0W8618exnf/75VEj1MLLpa9HP5nXfUEPnDFI+me0H6GW3P44BGzADh7jExWn4MUDjXgF9nWHFyTvSgcxJIRbQ2WWSlxn4NpOnwOXsfLaUpkzYFPPnYoq0kxVjTRmPEWDmWHtJKYz4GblUxqPYC60HgoaeEwxCpv/nlDOvk2foQJlJDEj1s4rDigE7/81Ap8+68Otccmq88hSLSSBOX8BvcklxcmoYxQMqK9MQNVISlEK1lRKTyU1et4OdalLQpFW3Pg5TNM5HXDLrjnRhXyHLIJ+Bz2jhaxQTCZ5HXD1qKyCYXOekGptUAo6CbaXT0s0sAKlKCyKus44e7d0NmUxWFz2vH0pj57TPocpii8V0AQs9J0IUu4vTEDTVGSz3MwKDRV9Dl4CIeMGl1z4MIhV3ZI54uGr+bAzUqakkyew33rd2Mor+NjK628ynzREg5qgj4OEbHI4YeufBRHfve+RI/ni605eH9ev+sjiYeRgu5YFM5sawBQvv8B6XOYsnCzEm8s457kxoR4en5jAMCcjobUNAdNMCuJx/vIkXNw+VlHoCETn+bAQ1l/eM9rGBgt+puV3BnShhlrn2Pey2Fxt1VTKl8yHZFbSQsH7oMxKMWmnpFEj1UNr2glET/NThIPIwXDsSjkGrNYW2yymvzknVMDPqHx327NQVyRNwgP4tIZLdBSEA48Qmd2uyWYxOiJK889CqtXLUBOUyNH7xRcSXAA8PzWvTUd0jwJjtJ4ba/8vbiDPK8bduRWTrU0lSST7Pn3Md79gUkNzaGUQtTW/sxwQfc0J4pViSerz0EKhxpwjYFfYLdtd2t/uVexeBMsnt6SkuZgZUh3Nmcxv6vRc5ucpkSuylo0rP0bXCvRBj+zkktzsN4jvomKy2gurPIlp88h7uO58dJMzHGYBHhypth8SWSyrlonCyMF3Q7vbhYWSmJwymT1OUiHdA34/MKvr3vC+dpNL5a3Fe6BxqwV/590c3GuOQDAO+Z2YFv/WMU2DZnomgMvR+HWFPx9Ds5oJQBW8b3KfLm64BMxfzDHigZKBovcEooQ5rRkbO5ewiGvGw7NKk24ENg+4Lz+JdPSoCZRe5RJRd9wEfO7mvC/XzwO8zvLZesdmsMkDRaQwqEGfBLyMyuJGKaJh75xMgZZNnKaPgcAdlVUN3GEsm7qGUFDRsHM1gbHuK9w4IX3mEMaiFtz4MLBOv7fXPMUAGDR9Gbb/5Kk38HrvUeL6QsHfn9xzeGknzzseJ1SywTmp+FJ6odSih17x3Dckmk4ZmGX4zXR5zBZNQdpVqpBhVlJ0AS4wOB9BAyTYnF3C5bP7wCAdPIcDGpXQBWrkopYZqVoE+Xru4ewdEYLFIXgT199lz3uNjNx+MNhJaU5TR9xwK+HezK2NIdyz4mk8NLExlIu9tc3XLAFbrXvdn8rQpgWg3kdwwUdczsqF2VitJIUDlMUnvxWNiuVLzS/6FwYuC1I6eU5WJOvv3CIHsq6cc8wls1oBQAcNKvVFogNPg5pbsUQfQ5xCoeyWcl5fM1lVkoK7oMRSWMSvvaxzbj5WavR4oofPGCP+zV5ss5r/6gzlTY7mAlvrofGLmoOk9UhLc1KNeCTkGmblcoTDu+RwFV2w+UM1BQlhdpKpu1zaPURDg0Raw1RSrFrXx7zhYegrSGDobzua1biD4SWkHCwzUoVmoMiOKSTm6y9BE8axf6+zyrufuyY+Y7xfMmws7bdxNEiVlLJjr1MOHhoDiJJzwFJITWHGvDranr4HI65zFq5Hb2gEysO6MQ/f/hQx76qQioERtzogs+hmuYQZYLgCx9RVebmpFrCwRGtFGM3OC603Q5yMecjyeJ7XsIhTrPSYL6E7/3f+sDvuXe05NvQSZqVkqGXlayf0ZareE187JMOSkkKKRxq4NYcvJyqrQ0abr3oeBzJzEscTU3BrOTwOXhP1FGrlPKbW6y+Oo0l/vi9r25rDgqyWgI+B2pFabkdrapKHC1Tk8Lrc//84Y247fntsbz/7556C795fDN+/eimQNsPjBbx9OY+z9ekcEgG3pfdK89heksWxy62nNTS5zBF4ZO7YVompf98aGPFNtyM4UZNpfAetcPmxB7K7z90pv13Q0aNlCHNFz6icPinDx0CADhibrvnPl6aQ7wOaasFaYPru1dJ2efw9r7aLRzrxUs4PPFmH75+sxXafMfaHbhj7Y6635/3BdjUGyz7enPvCP7x1nUAgHNXOU1OaTvK9xcG8yWoCvHUnjVVwY0XHofGjDppfQ5SONTAFDKk7163C28PVk44WdVHOJB0SnarLrMSIcCvzltpb5PTFOR1o+6MYW4aU4VY+eXzO/D6Dz6I9xw8w3OfWSxj+9A5beXQ0jgd0pRCUayHUHT+iXkOF/3+eezaV5n3EQe1tJKv3LgWX7lxbdVtqtHaYK1GxSTLauwRMrWnNTvNHOPdc2KqMpTX0dagVc0h0ZTke7okhRQONTBdoaxeVNUcUnBI88mRaw5uGZDTlIpWkkF4e18e+ZKBqx62tCV3Ux+/zw0Axy6ehtv+9nhceNJiO88hztBSw6S2sBJ7JSuEOIT175/aGtsxRZKu+son9Lf6ggkHEXfb1slq1pjoDI6VbCHuh6omnwibFDJaqQaG7XMo5zO48dMckvY5mCaFScsJZ34OaT6J6waFzyYVDOVLOPZHD+LgWa147W2rNLZXx7dqHL2g0zq+nSEdt+ZgnU9WUzDGHO4mpQ6h9VbAlXdY/Mx0zT6hvWHh903vcMFRoiEI01qcwkFqDskwmNfR1lj9uqSR65QUUnOogRitpPioj/6ag5KocODmHs3Oc/CemLjDOowWwyNfuGCw3qe+EgxJ5Tnw88kJ379umo7rkVThOXeY7A2fXQUAmNPRiEO+9cfI7y9eq4c37Kl4nVIKhQALuprwHaGxzL+vXl5hA5+sNu+JzlC+hLYamoOmKL7dGSc6UjjUgEcrbe0fxRX3v+65TcZPc0g4lJVPIOVJsnpXNncXu2p4RbjULxys/eIun8HNSqIwMEzq0OSSUOlHizp+5zJXvevAbpyxfA6KrH1qVMTV5s69lX4TnWmN56yYh7lCTZ8ZrQ2Ouj7A5I2zn+gMjum+1gROGkEpSSHNSjXgPofn3hrw3aaazyHJksl84hMdst88/RCsWuSu8+LfJc4Pr4Qqtc7ibTzcNM6oGcOEw6zE0Q0K8TSLCUyMd6zd6TmeVaP3zeCIQs3L+c2jpXIZxWHKasgoKBrO+3GyTk4TnUCawyT2OdTUHAghvyGE7CGEvCyMfYcQsoMQspb9fEh47VJCyEZCyAZCyKnC+ApCyDr22pWEufgJITlCyE1s/GlCyMJ4P2I0/CZUccWQ8xEOLTkNIxEnxJ6hAtZs6a96buKK/nMnLcY75rnyLZTweQZD+cps33o1B/5deb1nvZiCQ1rUmAyTYmZbgx1jHqdw/uE9r+Kn921Ak49fIaspsWVJi6t9LwHHmx3lNNXu683/z7iu02SdnCY6g3m9tkN6ikcrXQfgNI/xKyily9nPPQBACDkUwGoAh7F9riKE8CfpagAXAljGfvh7XgBggFK6FMAVAH5c52dJBNPHLHT1J1bYf/uZlVpyGoYLeqSmM2f+/HGc/YsnPV8rJ5pVn7S5mSHMTTropTnUKRyasxoI8dZG6oUnwQEuzYH11L7xwuNw/JJp0E0Tr+zch6/dtDaS7Z1Simse2YT/eGijvWr/23cvcWyT09QKc9wP73m1ruPV0hxGCtZxslql5uC+TpN1cprIjBUNDBf0Cue/m66m7Lg3hKqXmsKBUvoIAO+layVnALiRUlqglG4GsBHAKkLIbABtlNInqTVT3gDgTGGf69nftwA4hUyg4vNeE8rfnbLM4fz1Myu1NGgwTBqpIuoOD3uz+9xUpfpl1Oz+0sHPY9Bjla/UKRwUhaAlp3m+Z72YppXnADg1N/F6aaqCokGx+pqn8IcXdkR6SMU+CbwUyWdPXOTYJqtVBiBc88imigZRQeBhvxmVeGp8XHjnNMWpOWTUSp+D1BxiZ/uAFQXnVyafs7i7GZt6h9M4pdiJ4pD+EiHkJWZ26mRjcwFsE7bZzsbmsr/d4459KKU6gH0ApkU4r1jxWnR98tgFDrOSr3BgD+1QIfqK2Uv7sDUHtYbmwCb1MLZnXhrA633qgRfqiwvRIe2OVuJkVcvnw48bZcnx/Nayz4kLB3fpDr/7oB6hyIVMY0b11Bx4O9icpqJJOI8GTanHcf5IAAAgAElEQVRYLEifQ/xss4VDU9XtlnS3oHe4iH0ez9NEp17hcDWAJQCWA9gF4Kds3Ovxo1XGq+1TASHkQkLIGkLImp6ennBnXCderR8ziuLIQs34TM5cgAzHMCl6Pd88RK6mWUkJb1bymsj9QnmD0NqgxWtWMst5Dp8/abFjnJNRFYewiGJWWr9zEIA1WY8Vrfds0BT8/nPvxB+/ehIAf9/T3tFi6ONxbaMx6y0c+PWxNAfRrKTa1ztThzlREgyuSfq15uUs7m4BAGzqmXzaQ13CgVK6m1JqUEpNAL8CsIq9tB2AWNhlHoCdbHyex7hjH0KIBqAdPmYsSuk1lNKVlNKV3d3d9Zx6aLx8DhlNQUdT2RGVU70dlFxz4Ku8KPBJTtQgvArieVFPnoGXzyGK5mAJh+jfw56hPE694hFs7R+1hdW7DuzGf336GADOVbKmKg4fQCThsMsSDjxUNasq0FQFJyydjoNntQGoIhzqWDXqrE9HVlPs6yYuVLigzWqKI3Q3p5V9Dvz7kZpD/GwfGENOU9DdUlmRVWQ680nUcw+MN3UJB+ZD4HwUAI9kuhPAahaBtAiW4/kZSukuAEOEkGOZP+E8AHcI+5zP/j4bwEM0igc3ZrwmFE0hjnoqfuYEHsmwc28+soAwTIorH3wDiy69x15VipVPq8EnizCTo5e2U69DGrC+izjMa5t6RrBh9xDW7xx0hNZ6fcaMShzqvF9wQRA2sGRAw6TYN1ZCzqMDnt99sM+nlHY1eG/wjKqgwK63mDMjag7ivaipiq0xEMJr+0ifQ9zsGy2hqzlbszc3X5hNxkS4mnkOhJD/AfBuANMJIdsBfBvAuwkhy2GZf7YA+AIAUEpfIYTcDGA9AB3AxZRSvnS7CFbkUyOAe9kPAFwL4LeEkI2wNIbVcXywuPDUHFzRSbV8Dl/83XNoyCh47fsfrPs8SgbFDU++BQDYPVTA3I7GiiQ4PzS7TWfwG9TLlFGvQxqwNIc3e6JrDvy8dMGsBACHs+qwnz5+oT2WVRWHUI6iOQzmS2jIWO1W+4YLnpU4/TWHesxKFBnF0gp4OK54/rbPweM8uM+BgEBTZYZ0EuR1w/d6i6h1+PsmCjWFA6X0XI/ha6tsfxmAyzzG1wA43GM8D+CcWucxXng9WO7J2G9yFp3WUXs4GybF7PYG9A4XsGNgDHM7Gh3d1qqRqSNaySubOapZycvJHRbRNCbK6K7mLLZcfrpjW00ljiKEUTQHw6ToaMoiXyqgf6RY4YwG/BcJfk14qqGbpt2bgl8L8V4Uo5Xc8OtECKASJdFe2lOV0aLV5dBPMyiUTN+KBCJaHVr7REGWz6hBlGva4iqWFqWPs26amNlmlcHesXfUHgOsyo/VqCdaycs/EcUh3dGYxWBej1xfSdRoamVsuzW8KIfWTYo2Juz7Roo+moP3ZDFQh3AoGRSaoiCjKg5tifPLv2xix6x8hPlihWByZ+iOF1v7RnHoP/8Jf3jB6sdRYOXu+4YL2Mqq5BZ0w9O06KasOUy+ayCFQw2qrTYf+sbJ+M+PH+X7eour7so963bVfR6GSTGr3XJ+8cbmPESyrUZ9F62OwnteZqVaIbPVWDqjBYZJsTlg8xo/RI2mlpmrUjjUJ+lNk4JSoI11/OobLthtUkX8qvPuqyNayTDNqg5pjleGbllzIJO6Kuh48QyrSPCnV97GvtESDvrmH3H1X97Ev963AZ+/YQ0Aq3xJELNSPc/eREEKhxpUK7+wuLsFH37HHN/XM6ozW/VrN71Y93nobCUJADv2Wg2HeFJXd0tD1X3LGdLBVy9xaw4Hz24FALzKon7qJZzm4Hy9XrMSdwTziXgwr4cyKw0XwmuMukHtxkVemgPHq2S0Q3NQFOmQDsnru4fY72Ec+b37AAC3Pb8D+8ZK6GF9oy3hUNusxLV6aVaagkQNQQuyugiCblL7BuNJWHuGLCHR3Vo9nM6Odw9lVqrtawnD4uktyKjEUQK8HsTzSktz4PuJGlqjR30lv2s9UkekGi8DklHL/b/zHtVe+QT14DdOxl1fPhGAM3ot7qqgt7+wA8u/d9+4CJx9YyXc8tz2SOVogsAXMKKWSylFUad2FF8hoEM6M5Ud0vsz+ZIRuTl7TquMta9nkjVM077B+EqyZ6iA1pzmOVGJ2P0cQtg9vTSHKA7prKagvTETOVO0KPhtQvsc6tUcuHAQGsk3eKwa/TSHeorx6aZpaw4lw0RRN3HSTx4GAJy0bDoefaPXsf0SlmzlgFgLgzhNGt+6/WUMFXSMFA20N6a7tjzz549jc+8IVhzQiUXTmxM7jldGO6XWM1E0TBR0A/mS6ak9uimHWE8+7U1qDlWoJ8rEDV/Z8Yk1jFN6455yVqWlOVg3GLe79wwVamoN4rFDhbLGbFYCLBt41FVf0RGtFNKsVOfqjQvlhdOa8E5WDn1LX6XvxM/MUE+Oi+WQtlqeFg3T0dOhs6l6sbeGrPVYf2zlfKhKvN0IxZ7qaWIK/qp6NLEwjHq8P0V5cfX5G57Dxj3D4XwOk1BzkMKhCv0j4R2JbjKaNUHxVWeYkNb3/ewv9t+6QSs0hz1DBUwPIBzsUNaIDukoZiXAWulHnajCmJXcyYFRzUpZVcE1n1oJoJxXIeKnwdUzmRkmhaYSludAsVVod+ruEe0mp6lY/71T8f8+dAgyqhJrBz6ufaUdHitm7MfRTKkao0UDy+c7y95TSlHSrc/8yOtW6Z5A0UqTuISJFA5VGGBRJrWigapBWOko/h713tiiz4E/7L0BNYd6VFuvCSWycFBIpHBSoNzkBgBqBU9ltHiFg6oqaG/K4PlvvR8/OLMiZcczvBWor7ZWyTChKYrlkDZMRy/saTWEAwA0ZTUoCom9jzl/q7RDM8WFWlRTby1GizqOmNuOWy863h6jqNSmw+Q5SM1hisFvSHe+Qhi4JaasOQS7sd1qu5/PoVZtF0BoExpi9RK3Qxqwvouo5ohSCLNS1iU9ovocuI+jqznraW/2Cm8F6jMrGay2Es9z2CZqDjV6CIioioJSjBMTv358FZ0WA0I4cJwdBb0YLRpoyqoOsyT3OYgEMytJn8OUhFfTfHswX/d78NuLtxMMKhzciVO6Qe36LCXDxFjRwFBBx4y2AD6HOjKkvUJ4ozikAaY5RPU5COdVywcSl1nJqx2rF34OypGiEUoo7to3hife7CuHshqmndsCWAmFQckoJNaJydZeU57s+oYF4VBKzudgmBQF3URTVnPcPxS0wjS035fP2J/pH7Em6CjXlaff83j0oD6H3S6BZJhln0NBN4Uch+AO6TA3aNGwknxEM06U2kpAXD6HEA5p18Nbb54DnwdrHc9vsuATTpDoFgA479pnrONS2HkO4v2Q1RScs2IePnjErJrvpSokVv+AbVZK2YYuag5JmpVGWWRZIM0hwPUkhMQeFJAWUnNg3Ltul0N1B6wbsq1Bw7mrnM19wuDWHAoBNYc9rq5lP7v/dTzw6m4A1k3aMxwsxwGor59DyTBxzsp5uOoTR9tjtUJHa6EoJFJ9I8ClOdQSDq7X6/V32JpDDSdHtQqdYUxLPNFqcKxkm8bEaCVVAf7lnCPx3oNn1nyvjJpMElycTu4g9I2kY1bigqcppzoWA5R6R/AFIW4BnRZSODAu+v3z+PB/POYYGxgtorM5ix+ddQTWfefU+t7Y5XMI6pDuG3YKhzVvlTuRlQyKPYNMcwjhkA46SRgmhUmBGa0N+NAR5ersUX0OCimvwuvFEcoaurZSNJ9DlFDeMBFLHexe2TtasnMndu4raw612sKKJLVqTVs49AwV7EVOKsIhq1bcP+7P7BXR54UWs2kvLaRwQHnSdCdo9Y8Ua8aU14JPKDxaKahZqdqNV9RNe3UZRDgQQqxkqICTBD+2++GILhzi9TmkZVbi5xzF5zISooRGOxcOY0XPek1hNDirB3X8wiFpG3rJMPHrRzdh/c5BjBZ13LF2J45fOh0ZlWA0wALrF395s65SLVyIN2U1l+ZQ6XMIqknEnaWeFlI4AMj7TMQDo0VHTPl1nzkGt/3t8Z7b+sFvr/aQ0Ur8ZvqHUw+qeK3EHJRZTcH05trCAQh3g/Kb3p3xG0coa72JaBxx9VZrJe9OgqvbIR2wb0Y1wiQ/ijkxbgEX9jwmq+bw7JZ+/ODuV/GhKx/Fxj3D6B8pYvUx81mb1urfpWlSXH7va/grlyUgCFyzb8qqDjOi6WFWCqo5ZFRF+hwmK35+gIGRkqMd6LsPmoGjF3SGeu+KUNaAkwTXZo5Z2FXxWlE3sW1gFPM6GgM7iTNK8GQovp07FDQOzSFOn4NPEVSb5fM78LX3HYibv3AcgAiagxldOIRZvXcI2qqn5hDiPDRVSSSyKGmHtFidgAdFtDZoaMpqNYVDoUqhwlqUzUoaMq5oJffzE/R5kprDJKaq5hDRrFROgmM+h4D2Un4zeUXAFA0T2/rHML+rKfB5aCFq7PCbvsKsFINDOuqcIk6ytSbJpqyGr7xvGWYw01s9qzdKy1Fi9QgHnvsQdJUJOB3pXvWaQgmHSao5iI2h+ISdVRU0ZlWMlgwYJsUTb/Z67hulb8pooRytpFZEK5W/R00h+NJ7lwZ6T00hk7JNqBQO8NYceNG9zgDZqNUoaw6Wz6EQcJLgD7RX+GPRsDSH+V2Ngc9DVZTAqxee4BS3z0El9dc34oTJc7CPG6Eb1+lXPoZP/8YKLa3Vq9sLnkVbNIJPWNx88e2/OjS65qAoiazyk46+Ef1/vSxyL5dRbbPSrx7dhI//6mn8hZWyEAn6jHnx+6e3oimrYk57o0NzMCl13D+//NQKzGitXiqfoyok9byQOJDCAd5OYh5XXauOTVBymgpCwvscvLJuKbXU7rkdwTUHqzpnQGc41xwS8DlEXcWGKbzH4aa3oGYlSikeem03BvMlrN81iCG2mqxDNthmyTCag25QHDSzFZ85YZGn5hDGMW41+0nArJTwZCcKBx7WndMUNGVVjBR0vMmKUt65dqdd64gT9Bnz4qlNffj4qgVob8o47i/39QuaswIkp70ljRQOKKuh4s0wwBLgOpsqO22FQYx9dyeVVYOv9qrVb2lvDH5uWohoJT+fQ0TZABKTz4FPmIE1B8I1h2DH+NMru/HZ69bgi799zjEeVnM4cl47/vWcIwEAxZA5JtwZ6tbegHAhtR3NGQyMlGKfnJL2OYjCoUcQDgfOasWL2/fa9+itz2/Heb95xmHmqldz4ImmvKmTKIQr6yoFvxc0NbjWPpGQwgFlzUG0qXPNIWooK39HSi2ncNCHyjBNEOLfIwAAmnNhVi/Bb1C/UNZqSV5BUOMQDoZpC8XgmoP1O2gYLS+b8sSbfY7xsJrTNz5wEGaxvt9hNIeSSe2SJ56aQ4h2rUu6W5iParT2xiGoNyEsKA7hMFw2K5122CyMFg08tanfsb34XBVCVD4W4dfIXnzEqTlIn8PkhEcQiQtDXnQvqlnpOx85DMtmtGDpjBaoavBkmJJp1fOvNiGFKQioKcHNSiWfUNaoxGFWGs7rtoM56GTNhX5Qf0eTz/ca5Hgff+cC+29RuIczK5m21iZeA171NYzmwJsAib1B6kWsD5WG5tDESqDvYaVDcpqCZTOtz8O7INrnIzxXQSMC3fBr5KUV8FuHvxZGc5DRSpMYvtLIl0xc/ec3AQiaQ0ThsGpRF+7/+slozKrQFBK4QibvGFfNvhxKOKhKVSdi/0gR37r9ZRR0o+xzqBUrGhJCotWpAqzEsNntliM+KYe037sGsfX/8KNH4ISl0+z/+XdYDDFh8XLdgDOUlU+WYRSYpTOYcOiJLhzE+ydpn8NgXsf8TsunxjWHrKagmd3z7kspXtt6NQduXq62KOKLxbCaQ9olzuOg5tNPCPkNIWQPIeRlYayLEHI/IeQN9rtTeO1SQshGQsgGQsipwvgKQsg69tqVhNkoCCE5QshNbPxpQsjCeD9ibcTQtx//8TUAZZ9DRwi7fi00RQmsXuoGRUZRqpoQmkNqDtW0lsvvfRW/feot3P3SLnsSiFs4qBFrK+VLVnvGOR0N7P2C7aeEFA5+JU6CaipEEC+25hDCDFMyqB0MIE5UTcyMGEbAtjdm0JhRK8qx1IP4GZKOVhoaK2Fep7UI6Bks+xyas973vEM41Kk5FKpoDhxuZg7S6IczlQvvXQfgNNfYJQAepJQuA/Ag+x+EkEMBrAZwGNvnKkIIF7FXA7gQwDL2w9/zAgADlNKlAK4A8ON6P0y9eK00eNE9LcYJMox6aZgmVJVUdYKG0xyqH5ur1JSWy3V7hVFGIWpVVm6HntXeYL9f0OMCwX0OftEuYctnUFr+DsNMprpp2rkO4jXgfavDfoctDVpdPSXciDk6Sec5FHQTHU1ZEAI7WiyrKlAV4hnBJ34nYbotuo8JVNcc3n/oTJy7agGmBaxMACQXTpw0NZ9+SukjAPpdw2cAuJ79fT2AM4XxGymlBUrpZgAbAawihMwG0EYpfZJahssbXPvw97oFwCkkquczJF42yv6RYmxhrBxrgg7nc6g2H4XRHGplSPNnS1GEJDgt3sugRFxBceEwp70RCglWMhkIb1YShYO4iqynZDkv4REmgqak03K0knB8WyiGlNmtOQ1DdXSjczMktOq8/N7XsH0gXie3SIFFpYk2fj4teC2K9Fg0B4Mdy/++OmR2K3501hEhs9SnrubgxUxK6S4AYL9nsPG5ALYJ221nY3PZ3+5xxz6UUh3APgDTkCJuzaF/pIje4YId0hYXWhjNwaDQFKUiQkhcvYYpI15LteWvEJDEfA4KsVbT9cJLKkxvyeHa84/Bx1bOD3jccA7psWL5fhC/43oK7xFi9YEOF61k2t+9qDlcufoo/PCjR2DpjNZQ59Cc0+rqY+3GXZjytud3RH5PP4q6gZym2LZ9UUg3eZiWnGalaNFK1UxGwyEKKHLUqepzCInX00OrjFfbp/LNCbmQELKGELKmp6cyM7Je3GaEo79/P554sy9UREIQQvkcmEPazcy2clZmuFjr6tU5eSQKIWUTCJ+YTlw6PfBxqhG2E9w//WEdvvTfz9v/8xDTjqYM3nPwjEAVaflxgRBmJV3UHMqryMA+B9dmvGFPUHSDegqHzuasIxoqKC25+sxKwwUdNzy5xb433MLBry1qHBQNExmV2Ka0rHAdvDRm3WFWKl+/MB34bJ9DlUWR+zsIwv6WBLebmYrAfu9h49sBiMu5eQB2svF5HuOOfQghGoB2VJqxAACU0msopSsppSu7u7vrPPVK/FYaSYRy+mkO/7tmG354z6v2/7ppejqjD5hWzooOY33LqErV1Qt/hkoGrYj3vv6zq/D6Dz4Y+Fh+KCRcVdZ12/c5QjD3sgczTPKfdVzrd3DNwdusFDbPgR8tqwUvegjwaKXKUNZ6sXwO4Ve8D6zfjX++4xW8ySKdBl2mqWrml6jwZEcugMTr0OKR3+OnOdzw5Fv4xK+fCnxMoLrmcMjscFobwPp4T0Wfgw93Ajif/X0+gDuE8dUsAmkRLMfzM8z0NEQIOZb5E85z7cPf62wAD9GoXehD4ueAjFs4VPM5/MMtL+GaRzbZ//tpDvWWsLDyHKqZlazX8iWjovAe72UclbDRSnuG8o6WkAMjZc0hDHarxnoc0sLXXe93H9qs5BOtVC+W5hB+xcsdwdzBW8+quR50w7RapKpq2ayUqTQrZVUFJx9oLRL9Qlm/fecreHyjM5nRD9shrXoLvQe+fjKOXxJei45Dc/i3B17HwkvujvQeYQkSyvo/AJ4EcBAhZDsh5AIAlwN4PyHkDQDvZ/+DUvoKgJsBrAfwRwAXU0r5k3YRgF/DclK/CeBeNn4tgGmEkI0Avg4W+ZQmvppDzDb3MDeJwUJZ3ZiU4tTDZmIZi18PfOwa0Up83nQKh5gd0iGa/ZgmRe9w0SEcduwdQ2uDVpcvyIqUCratKBxEbSNo+YxPHnsAgPIqM6MRPLulP0RdrXK0UtR6VgATDnU4pHmFUv58DLqEQ1K9nMV+Ijnb51CesLlD+qNHzcW5qywzW60kOD+t0TQpFl5yN/7zoTeqag4fWzkPi6c31/NxQgWi+PFvD7wBIJyZLCo1PZqU0nN9XjrFZ/vLAFzmMb4GwOEe43kA59Q6jyTxu3Cxaw4hQtp007QnhsaMasfemybwy0+trPPY1aKVrPMq6KZtM4/fIU1824Re9/hmPLaxF78+/xgAViixYVJ7Qn3k9R7c8ORbOHR2W33HVoIX3hPzHESBGjR09tTDZmHL5afb/+/am4duUlx296v4/pkVj0AFJd2M9btvadBCdaLjjLDJv6ibeKtvBP/ypw2O10eL0Z3cXohmzQaPjGR+T8ztbLTNb7WS4IqGiQalUiPg1/df73sdP/uYVQfLa1H4k7OPrOuzAPH6HHSTxr5o8yN4uMsUxm/CTsbnUH0FwW2tulkOZ2xp0MrCoc6VQy2HNJcbhZJh9yiOPc9B8Q8n/c7/rXf8z7NiR4s6Xty2F+exstmz24OVSa44dogcCzFOXtxHrfOh5BPQpt5gWcpibaU4aMlpKBomCroRyk9Q1hwMz8ikegROEEThwDUH8Vl8bqvVT/3dB3Wjj5kaayXBFUqmZ1az+DwWAvgc6iFMufxaFGJeOFRDls+Af8eo2KOVAlRG5c5Qg+U5AM647nq1ylqrF/5A5XXTdojWE9dfjTCd4PawrFiTAmf8/HF7fLerpk7gY4dYvflpDlF6SANAV8DEKZ1F6sQFv3/CmpZEzcHLnJGY5mCUo4a8NIfvfuQwLO5uxuFz2j01By//jl+9JfH6XnrbOnaseB3tWY2E8jlVw69rZRJI4QB/4TAePocR9sCJ9XVE4RAmFNRx7BrRSlwocZ9DEqsTJYRDmpdpdvOZ4xfVdeygzvC+4QKe2dxvm9ZEU1yYgndedFVxpL++ewiUNZQxabwmPZ6rETYRjk/+RcPE9oExAMB/feYYvPCt92NGay45n4NoVmKrfbFsxhnL5+Khb7wbilCYUnyGvZ5nv3pLXlaDuC0GDQH6XgfFr2tlEkizEuBriw+agRsUNYDPgT+QluZg3aRiae56zUoZpbpZia+WCyUTCiGJ2DXDmHZ6PGoBrf/eqZ4JUHEe+w8vWOaTeZ2N2NY/Fqvm4GcqWr9zEB+68lH8/QcOxIOv7WHbxvf98+iugdEiFiK4U5WbjQolSzisWtSF9xxk5btOb8klrjmIoazTW72rFZR7dQiag8fz7Jc17bVgitti0JzVMFoyQCmNXPZeag4pk5bmkPGJWhAjKfhqzOFzEDSHek2XqqJ4To66YeIHd63H1j6rFEJetzSHuFdP1jmQmufPzRduzeFfzzmybsEABNdauLPzJ39tOSDF7yyqmc0vWomHiD7yei9e2LoXAByRapd+8GD8/ONH133cDlYsjmeYB4VnVfO2tPM6ym1pm3Nq8j4HVbE1KD+THH9GxGe4pJsVE7xfvSWvxVrURYCbxqwKw6Sx9MCI0gI1LFI4wF9zSCsJTrRx8wdON8o+h7OOLucPrjygE/WQUYlnItaTm/rw68c2CzHtyZmVSIAe0ly72eMSDl3N0UqZBNUcCroJhQCLu60VdhyOxIe+cTIA/wmKr477WQY4AAwKdYy+cPISnP6O2XUfv4sJhwHh/YPANYOeoQJ27ctj2cxyAlgTWw0ngWhW4uaY6S0+mgMToqbLrOTOhfHTHLzuCXF1f8fFJ+D3n3tniLOvhJdaH41BmKYpHKRZCVU0h9hDWb0nqBFBPecPpJUEZx3/Q0fMxsbLPogtfSM4YFqUWOvKY7sX0wXdtJKwEhAOaoA8B6619Lgcz1E78lm1pWpvV9BN5DTVLtEQRwji4u4WLJ7eXMW0YR1jrzB5b+4diXxcTqctHEJqDmxifmm7pc2I2cHNORU7947FdIZOROHAy374FcHUPHwOJdYtcPdgeYHhN6nWylw/cn5H8BP3gftLRksGwiztHt/Yi+ffGsCXT1lmj53588fx3Dffh2ktwavC1ovUHGCt0g+f24YvvGuxYzx2h7Tq7XMQVxSjdrSSM2JFUxUsndFa96TNzUruqBO3kzVfspr9JGVWqjXZ8u+nZ6jgCFuNWiFXURCoC1+hZCCXUeyua0cviD45AJb/yk9z4CXSefdBADhpWTz1rADLIa0Qp/AJAg9lXbvNEg5ijklbQya0JhKUguBz4Asnv8RHxfY5iL0mTLS5tvcz6fH7UVUInv1/78MDX39XtJP3oJFpDmMhfTSf+PXT+On9r1eM379+dyznVQspHGDdIK25DP7mGGeVzyQ0By+fw7fusPsolTUHw7t8Rr1kPFZYAFA0nA9NvmTGnoTFIYR4huKKqzdul+0ZKmBBV7mOVEdUzYEQBMk/LDB7taoQ3PmlE3Dtp4/Bdz9yGJZHXEE2ZBRfzYF/Zn5pvnjyksAVZ4OgKAQdTVmH8AkC1xx6h4vIaYqj0OGcjkb0DhcDZ32HQfQ5cDOrVz0lwMfnYNCKnAZ/zcHa7+cfPwrdrbnQFW+DwM1KNz27rcaW3rgXVEn30uBI4QCrRLKmkoobKq0eyo++0Wv/bfschDyHOOCRMm7NxR2OOFrULdNOEtFKincorngOumkiXzIwmNcdRQbbQpQn90JRghX942YlAHjHvA60NWRw/vELcfvFJ0Q6foOm+k6k7iiy6S3ZyFEtbjqbMqEd0mI0UktOc5zTXOac3rWvvryTaoi9nHkpEt4L241Xr46SUVm0spbPIWhplHrggRS/enQzXmAJfGFw56cUUyriJ4UDygln7giHqHHtbjI1+jgDZee0EXOWLDdRlVyaizgxtzZo2DdWYuWSE/I5ePlchHLSJZ3akUpHzG3HqoVduPb8lZEny+AOaSP2UEbA0hx8zUqulaDbJBIH7Y2ZmoXzKKX45u3r8OyWfhSZ74nT5Fq5z2HCIQm/g+hzOHvFPGy5/HRfzeOlRkwAACAASURBVFFMghvMl/Dgq7s9fWa+3z17HurNfg8C1xwA4I3d4Xt5i8EJQPhkxnqRwgHWyk1VlFBNw+vBT3PIaQoufNdiKKScjCaWbY7r2AAq+kmIyTnzO5uwb6yEkp6MQ5qHgrr9HuIKtWSado7DvK4m3PzF43DKITMjHztoi9ZCyYy9fAJgJUL5aw7OiStME6eg5DS1ZpaublL87qmtOOcXT1bkMLh7N/P+zjsGEhAOIZpNiUlwX/mfF3DB9WuwY2C0wl/olx/An0evIpdxIQqHl3bsDb2/Wzj0j0TvBx4EKRxQdv66hUPcFRC9fA6mSVHQTTRmVMcEYviU7K772LyXcRXNYX5XI/IlE89s6Y81CYujeCQsAc7uWiXDtDWH7hgjMhZOa8babQM1J0jRrBQnDRnVt4SD+5zi7kAIsIZDNWzVoslxxGVuFCc4AJjRZl2bPXWWM6mGu59INUSz0ovb9wGw+k6479+r//Kmp1mRC+Y4nzU3TUKe0lt94VurDo45BXVfSN9RvUjhgLLzV1WcmcFxW/a8esnyCaMpqzqqr1rVF2M0K/EVVoXmUL7x5neWbfx+5Sui4NeRTTR36Aa1cxxmBOz0FoSzV8xD73ARz2z27CNlk5RZKadVMytZ3wePzkqig7pl0qwuHMSFw6irc5y7+xpfmcdVM0ikHuGgm9Sh7WRUBU9c8l48ccl7AQC7Bwt4YVulvd/WHJI0KwmLzqBVmYFyk6qhCs1BCofUECfiJLtb8Y5QokbCzTqNWUtzGEtIc/By3AFuzaEsHDbFGGfPKfdydo6LwiFfMvC7J98CIdHDV0UWMOe2n939lue2Y82WfjtaKW6CmJV+dNYRWDajBYfPbY/9+FnNOwlSRJy43G1F3ZoDIVYDqCSSsvj31BDgWdSEJDhR+GZUBXM6Gm3fiEXl88Q/c5KaQ6Pw3YXp68CF417XPRs2X6VeZBIcrAxpfnM0ZBTYZX1iVh24D8GkAF+o8Mm5IaOiMavaBcLi9jlw4eeeIMQs17nCg5TEitBu1+nSHMQmMq+9PYQNu4ewfH5HrA55PuH7Ra38/f++CAA4aGZrIguEXEbxLf7Gr8mKAzpx/9dPjv3YQLBgCLFSgDuKze1zAKyqqXELh/6RIjb1jiCrKqE1BxEvTcBLOOu25pDcOjmnKTjr6Ln4wws7QrULzaqWttnrqjNWT1e/epCaA5wNNMSJYdWirliPU47JFrpWsRvW8jkoDrNSvD4H673uW78bR3znT7bGwn+vWtSFTmGlfuph0Z3AboKYlXii1leErNA44Ne11mRW0I1kHNKaiqJhetq9wzhg6yUToFWpOMF+83Yr94YnA7qjlYBgfoywvPOHD+APL+zwPJ4X/J5yJ5iJ3+U3Tz+EbeMhHFLwORBC8LOPLce7D+wOFDG3uXcE379rvX1OvUNOM5KMVkoRcSLmdW4e/MbJWFhnW0A/uCbw9ZtexFt9ltlmTBAOjay0r26YMEwa6wqWH/vye1/DUF7H1n7LMTZa1HHQzFbc/IXj7CiZOe0NuPoTK2I7NqdsVvLXHHgT+8ZsvKt3fl1rVbVMyqzEBY7XZFrSk1+9ZtRwDmlevoM7nr00h6wWrjd2EPjK2ut4XvD7eshlBhMd0u8+yOozPVZVc0i+u5oWwO8DAD++9zVc+9hm23y0mTWJmtGawzvmtWMor6fSLlQKBzh7J/AJOYnQNl4r6e51u3AFS4vnq5kmwefAV7cNMa5g/ZJ8RouGPRFPY5rDJ487IPZGP0B5deZePImawz72QLht3FHh19WrHr74wCYVrcQduF6aS4mZNZNcvWZ9Ci9SSnH5va9h457hikg2oFwywyvMOwnhwAl6/fl3NuISDmIoKz93b+HANYfkp8JMgGZfQFkgcx7e0AMA+PtTD8Kph82CziIck0YKBzi7rvEJOYlQTnF1Mqvdsu/zG7aBRSvlBeEQ5wrW7/MMF3S7JPi0lhzWfecDuOjkJbEdV4TPfW7VWozj5n/HLRy4/drL7i/aogul5KKVAG+fR8mMt/ObFxlVsWs4iewZKuAXf3kTn/6vZzwjaQ5klVi96jJlA5iq6iWo5sj7ObjLh4taGDeNeZuVeIZ0CppDrT7uJsUda3f4dntc0t1iVwoI27ipHqRwACuPzW4mvspIJENYuAF5CWKnz8ESDna0RoxJeW7Nga+Y+oaLmCaUQ25tyMReuoGj2JpDpc+hk5VY5lpEY4TeDV7wMGWvydlRMr2YjM+BayNek2lSSYciGc3bIc3PZ6Sge2oWS2dYZSu8mi8l4XMIi6IQKKQyukpcDNmF76qYlZJYDLrRajTcuvX57fjKjWvx26fesscWMdP2XV8+ESsO6EQLEw7uz5sEMloJ1kRZ1hy4cIj/ZhFXJ/yhqvA5iJpDjJOUO/KDTwp9wwVMT6H8L+DdtQuwBMK0lhwGRktlzSGBbPUGTfVUx90rykTMSpp/XoBVyyph4cB8Du5uZPz+G8zrnpPn+w+diTOWz/EMEEjSrBSmVLqqkIqMbodZSaumOVjnn2RtJY5XnhMAfO76NXh99xAuOLGyBe7fHDMfH1s53w7rbslZiyh37kMS7PfCwWQ9ezU7WombleK/WURVkD9Uo0KeQ2PWKuscJs47KO2Nzqzbom5irGhgpGg4NIcksR3SLs1h92ABh8xuw8Y9w4LmkEw4qVc4o3tSTMKsZAsHL4d0QrWsRPhnEiPzALFEPMU5v3jSHv+7U5Yhx3o4//vqozzfM26zkvheYYUDz7Kf2ZbD7sGC4/tUWN20aqGsqZiVfPq4P/CqVYK7JecRLqwpjnwfvk0aEUv7vVnJfXNwzSGJm0XsSc1XsGNCnoPbIR2n5uDujFU0TPSxGi3TfVowxo1tVhKej4JuoGeogANYAt7gmA6FJDNB5wJqDm6HYBxUyyguGiYyWtI+B1Z40Z3n4mOe+MChM3Hxe5ZWfc9cRrV7L8SBaCoJIxw0RbEd0rParCxzt5moKat6m5WM9MxKtfq4e52D+37lEYXu6KwkiPQEEkK2EELWEULWEkLWsLEuQsj9hJA32O9OYftLCSEbCSEbCCGnCuMr2PtsJIRcSZIyenvAJXnZ52D9TmIld+4x83HXl09Ec7ZcBI2vZqxoJWslxlXkODWHCuGgm+gbtpyMaWkO/CsV8xx277ME1AFCBnNTVkvE75Hzyeh1Txpz2hsrtolKdbNSCj4HngSpVy/ZDgDnrpofKEs7bs1BNJUE6ffNURViC7mZTDi4v08eJu6mvDhMw6xU3SHtdW+6zUdcOEwWzeE9lNLllNKV7P9LADxIKV0G4EH2PwghhwJYDeAwAKcBuIoQwme/qwFcCGAZ+zkthvOqCqUUn79hDR58dQ+AsqaQ01QQkkxSjKYqOHxuu8NWO1YyoCkEGbXcfYzH/cepOeQ01REBVNTLmkMaLQcBb7PSDlbyWWzsk4RJCeDd2ConCPeYs+RCTMe2o5W8HNLp+BwAoOBq7uTVBzpoo6GcpqDok3FeD6LZNUx3VsusxDQHVp/K/X02+GoOfHGYkkO6ygfbJ5TF4POPOyqpJacho5JUAgGS8DmcAeDd7O/rAfwZwD+y8RsppQUAmwkhGwGsIoRsAdBGKX0SAAghNwA4E8C9CZybzVBBx/3rd9st97hwmN6SRUdj/FUxRRzCoWjaQoFPirwpS9yO0Y7GjL1SLBqmnQg3R2jHmSReSXC79lnCQUw4bE5KOPhpDkXn2KwEvo9aDumkNQc+WbrNGl5mpaDnEne0Ur1mJVUhGBzjPgdvs1Kjz8IgXZ9D9Z4iYkTYJ9+5APe+/Lbd7IjT1ZzFG5d9KLFzFIkqHCiA+wghFMAvKaXXAJhJKd0FAJTSXYSQGWzbuQCeEvbdzsZK7G/3eAWEkAthaRhYsGBBpBN3J81ws9JnT1yEvzpyTqT3roX4UI2VDDSwyZD7O3hmZJxJcIDVanMn69xV0E3ct343DpvThhlt6QgHr/IZvMLkzNbyOcQdxsrJaUpFhvR/PPgGrn18s2MsiYk6W0VzsJorJexzYD4Nd66Dl1kp6Co6brMS15int2Rxxd8cGXg/TejVwSvbepqVfJLgVIUkFr7tPM9yH3dCCF7YOoD7hH7QvUIl5IvfsxTfPePwivdI0eIeWTicQCndyQTA/YSQ16ps6/WpaJXxykFL+FwDACtXroyUP+622fGVQ1NWwwHTkg3iEh+qsaJuaw5cOOwdsybMuDWHzuayRlTUTby4bS8+6xE+lxRe/RzsPI+siuasipGiEXsCHCeXUR2lOgA4Grj/x7lHhbJ1hzr2OEcr+RVe9Jowg9rf4w5lHWCJdrdffALmCeXjayHmAx04sxUKKTumOU05Dfs8EvnirmFWjXJQAEVWIzjvN884zEZcc9jwg9MSrQ4dlEh3JKV0J/u9B8AfAKwCsJsQMhsA2O89bPPtAERj5jwAO9n4PI/xRHF7+9O6QQAgK0TNjJWMslmJ/ea2x7iTsToay47nsaIB3aSe4XNJIT4c9nmUDCjEeo37PpI6pwafcEbOew6egTOWeyqtkcmq/klwRd2MvV+5Gy4c3MJptKhDVQiu+dQKYduAmkPswsG678OWav/8SYvtvw+e1Yrnv/V+HDm/w7HN3I4G278lohvU7nWSNHYfdxYE0+q6z3uHi8hqyoQQDEAE4UAIaSaEtPK/AXwAwMsA7gRwPtvsfAB3sL/vBLCaEJIjhCyC5Xh+hpmghgghx7IopfOEfRLDrTl4raCSwrJ9s6qoJdP2NXDhkJTPoU3wpfAoiCRCRv3ocGVBA1Zv38aMCkKInTXuXvXFRS5TvVVmQ4LfRdmsVHmfDRf0wIXm6j6+j89hpGCgKaM6KvIGzfGJy+fw/NYB/OSPr+H13UPIauXAjKCczArrAda5e/WbXtDVjN7hon3fmybF1r5RXPvY5oqud0nBrRP8GrjNub3DhVSfx1pEuSNnAvgDs4FpAP6bUvpHQsizAG4mhFwAYCuAcwCAUvoKIeRmAOsB6AAuppTyq3IRgOsANMJyRCfqjAYq0895WGcaiCuufFHQHLLWjfHHV94GEL/PoU3oTcyrnya9YhVpZ5qLWKcnXzJsswB/qJNwCAP+Dmn+WhKJj+L7A96aw3BeT6RvtIiXWamom7j1+e3QVOLQ1oKupLOsR4Rp0kiFGr9x84t2FdhZbQ2h7eozA3QMXMhCpd/qG8Xhc9vxr/dtwFV/fjP8yUaACwduVnV3OuwZSq9aQRDqviMppZsAVHiNKKV9AE7x2ecyAJd5jK8BUOl9SRCuOVz07iW4+s9vVjiokySnKbZwGisZ6GY3iVtTiDu8UZyA+PHTVGF5/aQBoc3hmCAcDJdTMW4aMypGit7X2d0GM26qRSsNFXS7Zk7Sxxcd0k+82WvbvEXhEEZzACxTVYNS3320d7RoCwagMh8nCEHOl3cC3NI3gsPntuOW57bX2CN+bLNSlf4dU0VzmJRQSnHqvz2CTT3WDfmZExZirGjUzAaNE4dDWvQ5eLRijBPRrMQn6DRvRl7CQ2xzWCiZtobEbbFJZCgDli1772gJz28dwNELOh2vJf09+AmHkYKOobxeYX+OG+5HEM1AewYtB+g/nnawQzgGjVYSnez1Fon80n+/4Pg/ztawIku6W9CcVfHYG7348DvmJNKnuxa2z40tgrz8XxNJOEycM0mJ57fuxeu7h+3Qt2nNOXznI4c5bK5J48xzKK+cw9pawyJqDg++ZsUJJFGB1A9NVdDWoDl8DqLm0Jixzq8pIfv7dKahnXXVE3h5xz7HRK0kPFtwk8JP738dT2/qA2AtVA779p8AWNVwk8R2SAufuZclQX76+IVoFjqvBe1lwieyak7+alBK8dSmPrxzUZfdebDTw18QhI+tnIfD57b5vt6QUfH+Q2fij6+8DUqp43rfetFxdR0zLDwKjGsOoomTVwhIoo9Kvex3wmHNln7774yabIMVP9x5Dk3ZSuGQRK5Fa65yAko6M9dNZ3PWDlkErImFf+4fnHk4vnjyEhyzMN72rJxuoUzI+p2DDlNi0tUTRC3wM9c9C6Ds9wGQmllJ1Bx6h4poZgUfRfNiUM2BC3GvshRBKOgmdJPiXQd226XBg5Tt8OInZx+Ju758UtVtls1sxd7REoqG6RAOS7tb6zpmWMptgsuaQ1dzFou7m7H6GCtvy6tvxnix35mVvnDyEjTlNHzr9pdDNfuOk5xLc2h0JcEBwOVnHRH7cds8Mr9zCWsrbjoaMw6zUr5k2JPMrPYGXPLBgxM7tujsW79rEMcvnWb/n7TmIMJNOOJEkLRDekZrDoTANqcCVnTMdA9nbtBsYa5tuBvtBIUL55achk8eewBOXNqNYxcnszAAyouvl3cMOsJakyrX4qasOTDhoBtYPr8Dv/n0MXjsjV4AVjjrRGG/0xwA4MNHzMb7DpmBv3tven4GkSyLmqGUOswqor0xiUQwrwkobRtnZ3MWewbz9v9jgs8haUTh8Nrbg47s4DSFA3e8i0Iy6XyTjqYsDp/Tjkff6LHH+ka8o2OC+rq4kPNz8teCC5WmrIr2xgyOWzIt0Qxg/kz99dVPOMbTitjTXJVxC6Vyv/I5HelUKQjDfikcOpuz+PX5x+DrHzhoXI6fVa14e25z5Csa0d6YxEMyEYTDMQu78NrbQ9jJVm4FQTgmjVh9dt+Y7jArpemg5Db6gRQ1BwBYtagLL27fB0opDJPijd3Ddt/weuAaX72RflyopJWImZaG4EfGbVbSy/d+EsUeo7JfCofxhjed4WGEjSmtnL0ewjTzHADg1MNmAQBufW47Pvnrp7GlbyQ14dCS0+zvYHCslLrmcNeXT8RfHz3PXhSIZiWeQZ0kM9tyKOomhgs6bnx2K/YMFbDigM7aO/oQl1kp6TBiTlr3mR8VDmlBcxjvc/NCCodx4Mh57dBNigd5B6iEI1U4bQ0ZnH7EbNx44bH2WNqp+vM6rRXSfz2xBY9t7IVJk4/S4hBC8PJ3T8Wnj1+IwXzJUdcmjbiEw+e2Y+G0JhgmRckw0T9SNivNTCh8V6SLNXUaGClha/8oCAEufNfiGnv5w7O66zUrDacsHNK6z/zgvhzRIS0KhSv+5kjcJDyb481+55CeCJx84Aw0ZBTcsdYqIZWGSQGwzFY//8TRjrG0zUoNGRVZTXFMxmn5HDhtDRqGC7pdLhxIz+fAJ4OCbmLvaBEKAV7+7qmJhe+KdLHCi30jBYwWDHQ2ZSOZL/mk7tdNTmSkoGP9rkFHJBrXOPYXs1I5Cc4SDgXddDx/Hz1qnud+44XUHMaBxqyKGa0N2NJnRY64E6DS6q8ApJvnwGlryDiiMtLWXtoaM6AUeG3XkD2WVilk/n3nSwZ27s2jvTGTimAAyppD/0gRo0LZFo67WF0tuIM3SG2if7jlRZzziyfRI5SlLpuV0rn+46452ElwVjBKQTdTjxYMg9QcxomWnIb1uwYBOBOg1n/v1FQjZ3Ip2LrdtDVq6BUam1DvCu3JHZ993zet2YaZbTn0DBXwtfctS+XYvPXrjoEx/N9LO/GRhHuHiHSxBDNLOOgVk/L/fuG4ipLe1chpCjSFBHJIv7htHwCr4CMvFzNcSNchPd52/YwQymr3iZ9AGdFupHAYJ8SkJ9GslNYqkjNemgNgfe4fnXUE3rloWo09Yj5+o1BkTlWw6Uenp3Zs/n0/+kYPirpZ0ekrSbpaROFgVDRVympKqAAFQgiasmqoaKX3/vQvuOfvTsJfXu/Bb1iTpdR8DuNuViqHshZKlnAYb4FVDSkcxgnRlJSWz8GLtDOkgXIyXkdTBh9+R3orZ/v4gqY2N+UQQm5Ce2HrXqgKwcGz0snOBaz2q1lNKWsOMUyWzTktkFmJCk2U/vDCdtz78tvoGSogqymJNzriNHlMxI/943tSOTYAW2PaPZi3S7dPZM1h4p7ZFMepOaQTreTFeNRy4aXD2xPu1e2HWMXzPz9+dJUt44drDi9s24sl3c2prhwJIehqyqJ/pGj1cYhJOIyGjFYihNh5LmneA27N4fgl00J1nIvKtOYsmrMq3uobxcaeYQCYUCW63UjNYZzgdtZcSFV+KsA1h/ESDofOacPBs1rxw7OOsFdzacF9Dv0jRbxzUXKlIvzoaraEw5hQtiQKra5CikHYPZgH7xTbneLkKK7S//PjR+H4JdNTOzZgCcUDpjXjjT1DeGXnPjRlVZx8YHftHccJKRzGCa45jJdJ6b0Hz8BDr+2pvWECcPV+vIRDS07DH7/6rnE5tujjSao8dTWmtWTRN1LESEGPRXOY1pzF9oHK9ptuxJADMUpM7GueNGJE2niYMwGr+uq9L1vNvL5yyrJx94NUQwqHcaLV1hzG5+b41Xkr7f4JaXMgs7N/5Mhk+jVPZBqE611PY5uodDZlsbV/FGPFeDSHruYsXtq+r+o2Q/kSdu0r19PasLssHNLyN0wUFk1vtv/+8jjVdguKFA7jBDcrpRXj7UZVCNQ6u3dF5eyj5+EjR86Z0JEaSSFqDh2N6WsOXc1ZvNU3CiCe4o5dzTkMjBZBKfXNFbn0tnW++6spd905ekEHTlyarjlJ5JDZ5Z4TSbaljQMpHMYJ3kfi4Fn+DUqmKopC6m4rOdkRBeJ4aA5iob04MtOnNWdRMigG87qvmfBRVo76HfPaK7SMtAMibvvbE1I9nptD50ye531ii64pDHfIHbs43Rh/yfgiOkU76ux6FgXeohIA3hR6O9QL95v0j1T2IXhp+158+46XMZgv4e9OWYY7v3Si7YDlfcIPSTGUdyKwcJplVnrPQRPXEc2RmsM4sXrVfLTkNHz0qP3P7r4/M96aw4EzW+y/zz9+YeT344l1n7v+Waw4oBMnLeu2uxh+7vo12MPKZfB8Dt7k6pPHHoAj53Uk2txnIqIqBE9e+t5xMSmGRQqHcSKnqfjrFROr0JYkecTEs85xEA6nHzEbq/6pCzPa4qnfxUNR3+wZwZs9I7h5zXa89+AZUBViCwagrCF//J0LMLujAZ85YWHq1QAmCrPbJ17vBi/2z6sjkfz/9s49Rq6qjuOfr91SoS1F2oKtUNcHigXlVR5VUB5pBCLBiAEaQgtGK4oP/hOMBgyQCAHCSy0NlOALK6KxCLbBhqLgC6oVWFoebRpbJECxQkuDkfDzj3MWhp3dndndO/fO7H4/yc3cOefc3/ecuTPzu+fcc3+nIiTx3ukT2fjCK0yp4OpRUmGOAXhj7edaDrh4JSfmdTvOP+59nD5n3zeGn045aGZL1kc3xdM2zkHSicB1wDjg5oj4bsVVMqYlLFs0l/vWP1/6A3itYKAZZyt60lz+hXO7C3VGpjza4oa0pHHA94CTgNnAfEmzq62VMa1h+uQJnH74vlVXozBqQ8z3Lua06/hxvHP3t9sxdDDt0nM4Ang6IjYCSPoZcCrweKW1MsY0ZPlXj+a5l1+le+pEXns92LJtJ5u27nxjrWzTmbSLc3gXsLnm/RbgyL6FJC0CFgHMmjWrnJoZYwZl2qQJbwkgN2XXKRwwc0qFNTJF0BbDSkB/T8LUrQATEUsiYk5EzJk+vf3nCRtjTKfSLs5hC1A7CLsP8K+K6mKMMWOednEODwH7SXqPpF2AM4HlFdfJGGPGLG1xzyEiXpP0FWAlaSrr0ojoqbhaxhgzZmkL5wAQEfcA91RdD2OMMe0zrGSMMaaNsHMwxhhTh52DMcaYOhRR9zhBRyBpO/BETdIUYLD1CkeSPw3Y2iLbzeRbv3X6jY5tpD/StjVTpsr2j+Zz3+n6w7E9DZgYEY0fFIuIjtyAh/u8X9Kg/LDz+2oVabvJfOu37twOemwj/ZG2rd3bP5rPfafrD8d2o/bUbqNpWOmuFue3q7b1R2a/6rZXXYeq22/96rQHpZOHlR6OiDmjTcv61m8n/bHc9tGoPxR7ndxzWDJKtaxv/XbSH8ttH436Tdvr2J6DMcaY1tHJPQdjjDEtYkw6B0n7SrpP0jpJPZK+ntP3lHSvpKfy6zty+tRcfoekGwewuVzSY2XrS1ot6QlJa/O2V8n6u0haIulJSeslnVaWvqTJNe1eK2mrpGtLbv98SY9KekTSCknTStY/I2v3SLqyBdrzJK3JbVwj6fgaW4fl9KclXS+pv9D7rdS/XNJmSTsa6RatL2k3SXfn73yPpKaWNS64/Ssk/SPbWay0omZxNDutaTRtwAzg0Lw/GXiStDzplcCFOf1C4Iq8PxE4GjgPuLEfe58Bfgo8VrY+sBqYU1X7ge8Al+X9twHTyv78a+yuAT5elj4pNtnzvW3Ox19Sov5U4J/A9Pz+NuCEgrUPAWbm/QOBZ2ps/RWYS1qP5bfASS1o+2D6R2V7O1r43e9XH9gNOC7v7wL8oYL2755fBdwJnDmU/4GGdS3SWKduwK+BeaSH6mbUnMQn+pQ7h/o/x0nAA/kEN+UcCtZfzRCdQ8H6m0kP1VSiX5O3X66LytIHxgMvAO/OP9DFwKIS9Q8Hflfz/mzg+63QzukCXgQm5DLra/LmAze1qu199fukN+0cWqGf864DvlBR+8eTpq2eMdzPob9tTA4r1SKpm+Sd/wLsHRHPAuTXhkM0wKXA1cDOivQBbs3DKt9upmtflL6kPfLupZL+JukOSXuXpd+H+cCyyL+WMvQj4n/Al4BHSYtTzQZuKUsfeBrYX1K3pC7g07x10ayitU8D/h4R/yUt7bulJm9LTmuaEeqPmKL08+/gFGBV2fqSVpJ6r9uBXwxFvxFj2jlImkTqjl0QES8P4/iDgfdHxK+q0M+cFREfBo7J29kl6neRVu17MCIOBf4EXFWifi1nArcP5YACzv94knM4BJgJPAJcVJZ+RGzL+stIwxqbgNdaoS3pAOAK4Iu9Sf1VqRntgvRHRFH62SnfDlwfERvL1o+IT5J6GhOA4/s5dNiMWeeQf9h3Aj+JiF/m5Ockzcj5M0geeTDmAodJ2kQaWvqAnZzV0QAAA3RJREFUpNUl6hMRz+TX7aT7HkeUqP8iqcfU6xzvAA4tUb/X1kFAV0SsaaZ8gfoHA0TEhtxj+Tnw0RL1iYi7IuLIiJhLGpp4qmhtSfuQzvGCiNiQk7eQLgx6aXpp34L0h03B+kuApyKi4USIFukTEa+SVs48tdk6NMOYdA556OUWYF1EXFOTtRxYmPcXksYDByQifhARMyOim3TD8MmIOLYsfUldyrNj8hfuU0DDGVMFtj9IY53H5qQTgMfL0q9hPkPoNRSo/wwwW1JvELN5wLoS9VGenZZnt3wZuLlI7TxkcjdwUUQ82Fs4D31sl3RUtrmgyfoWoj9citSXdBkpuN0FZetLmlTjTLqAk4H1zdajKYq8gdEpG+mPPEjDAGvzdjJp9scq0tXXKmDPmmM2Af8GdpCummb3sdlN87OVCtEnzWJZk+30kG6KjSuz/aSbsb/PtlYBs8r+/IGNwP5VnH/SDKJ12dZdwNSS9W8nOeTHaWK2ylC1gW8Br9SUXQvslfPmkC5GNgA30sRkgIL1r8yfxev59ZKy9Ek9pcjnvjf98yXq7w08xJu//RtIvefC/if9hLQxxpg6xuSwkjHGmMGxczDGGFOHnYMxxpg67ByMMcbUYedgjDGmDjsHY1qApPMkLRhC+W41GdXXmDLoqroCxow2JHVFxOKq62HMSLBzMKYfclC0FaSgaIeQQisvAD4EXEOKxrsVOCcins1hU/4IfAxYLmkyKVroVTkG12JSmOcNwOciYpukw4ClpBAkD5TXOmMa42ElYwbmg8CSiPgI8DJwPulJ1M9GRO8f++U15feIiE9ExNV97PwQ+Ea28yhwcU6/FfhapLhIxrQV7jkYMzCb4814Nj8GvklacOXeHBl9HPBsTfllfQ1ImkJyGvfnpNuAO/pJ/xFwUvFNMGZ42DkYMzB9Y8tsB3oGudJ/ZQi21Y99Y9oGDysZMzCzJPU6gvnAn4HpvWmSxuc4+wMSES8B2yQdk5POBu6PiP8AL0k6OqefVXz1jRk+7jkYMzDrgIWSbiJFy7wBWAlcn4eFuoBrSVExB2MhsFjSbqQIsufm9HOBpZJ2ZrvGtA2OympMP+TZSr+JiAMrrooxleBhJWOMMXW452CMMaYO9xyMMcbUYedgjDGmDjsHY4wxddg5GGOMqcPOwRhjTB12DsYYY+r4P4Af5oyTpjPaAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-500:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour cet exercice, nous définissons la période de référence en septembre, ce qui correspond aussi à un creux dans l'incidence.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence : à la place du 1er août de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail : les données commencent à la semaine 49 de 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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": 20, + "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": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "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": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\n", + "2021 376290\n", + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\n", + "2019 584066\n", + "2015 604382\n", + "2000 617597\n", + "2001 619041\n", + "2012 624573\n", + "2005 628464\n", + "2006 632833\n", + "2022 641328\n", + "2011 642368\n", + "1993 643387\n", + "1995 652478\n", + "1994 661409\n", + "1998 677775\n", + "1997 683434\n", + "2014 685769\n", + "2013 698332\n", + "2007 717352\n", + "2008 749478\n", + "1999 756456\n", + "2003 758363\n", + "2004 777388\n", + "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "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": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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