diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb deleted file mode 100644 index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0000000000000000000000000000000000000000 --- a/module3/exo2/exercice.ipynb +++ /dev/null @@ -1,25 +0,0 @@ -{ - "cells": [], - "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": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} - diff --git a/module3/exo2/incidence_varicelle.ipynb b/module3/exo2/incidence_varicelle.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..51492324768eec3b8c179bc6cebd1c58cbbe1dfb --- /dev/null +++ b/module3/exo2/incidence_varicelle.ipynb @@ -0,0 +1,2404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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 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 1990 et se termine avec une semaine récente." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202438740201138102FRFrance
12024377933271839102FRFrance
2202436722358703600315FRFrance
3202435716202852955204FRFrance
4202434725606224498417FRFrance
5202433719715363406315FRFrance
620243274399194468547311FRFrance
720243174500221367877410FRFrance
8202430770044278973011715FRFrance
920242979270630312237141018FRFrance
1020242879364649812230141018FRFrance
11202427710247709013404151020FRFrance
122024267143681039918337221628FRFrance
13202425711174803914309171222FRFrance
14202424712621935715885191424FRFrance
152024237146571133917975221727FRFrance
16202422711628836114895171222FRFrance
1720242179701685112551151119FRFrance
182024207136611020917113201525FRFrance
1920241971008364131375315921FRFrance
20202418713438951417362201426FRFrance
212024177153031121919387231729FRFrance
222024167181381354022736272034FRFrance
232024157249291731532543372648FRFrance
242024147161811254419818241929FRFrance
252024137183221420622438272133FRFrance
26202412712818912816508191325FRFrance
272024117159731240019546241929FRFrance
282024107143011076117841211626FRFrance
292024097143371087117803211626FRFrance
.................................
17341991267176081130423912312042FRFrance
17351991257161691070021638281838FRFrance
17361991247161711007122271281739FRFrance
1737199123711947767116223211329FRFrance
1738199122715452995320951271737FRFrance
1739199121714903897520831261636FRFrance
17401991207190531274225364342345FRFrance
17411991197167391124622232291939FRFrance
17421991187213851388228888382551FRFrance
1743199117713462887718047241632FRFrance
17441991167148571006819646261834FRFrance
1745199115713975978118169251832FRFrance
1746199114712265768416846221430FRFrance
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1751199109713741878018702241533FRFrance
1752199108713289881317765231531FRFrance
1753199107712337807716597221529FRFrance
1754199106710877701314741191226FRFrance
1755199105710442654414340181125FRFrance
17561991047791345631126314820FRFrance
17571991037153871048420290271836FRFrance
17581991027162771104621508292038FRFrance
17591991017155651027120859271836FRFrance
17601990527193751329525455342345FRFrance
17611990517190801380724353342543FRFrance
1762199050711079666015498201228FRFrance
17631990497114302610205FRFrance
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1764 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202438 7 402 0 1138 1 0 \n", + "1 202437 7 933 27 1839 1 0 \n", + "2 202436 7 2235 870 3600 3 1 \n", + "3 202435 7 1620 285 2955 2 0 \n", + "4 202434 7 2560 622 4498 4 1 \n", + "5 202433 7 1971 536 3406 3 1 \n", + "6 202432 7 4399 1944 6854 7 3 \n", + "7 202431 7 4500 2213 6787 7 4 \n", + "8 202430 7 7004 4278 9730 11 7 \n", + "9 202429 7 9270 6303 12237 14 10 \n", + "10 202428 7 9364 6498 12230 14 10 \n", + "11 202427 7 10247 7090 13404 15 10 \n", + "12 202426 7 14368 10399 18337 22 16 \n", + "13 202425 7 11174 8039 14309 17 12 \n", + "14 202424 7 12621 9357 15885 19 14 \n", + "15 202423 7 14657 11339 17975 22 17 \n", + "16 202422 7 11628 8361 14895 17 12 \n", + "17 202421 7 9701 6851 12551 15 11 \n", + "18 202420 7 13661 10209 17113 20 15 \n", + "19 202419 7 10083 6413 13753 15 9 \n", + "20 202418 7 13438 9514 17362 20 14 \n", + "21 202417 7 15303 11219 19387 23 17 \n", + "22 202416 7 18138 13540 22736 27 20 \n", + "23 202415 7 24929 17315 32543 37 26 \n", + "24 202414 7 16181 12544 19818 24 19 \n", + "25 202413 7 18322 14206 22438 27 21 \n", + "26 202412 7 12818 9128 16508 19 13 \n", + "27 202411 7 15973 12400 19546 24 19 \n", + "28 202410 7 14301 10761 17841 21 16 \n", + "29 202409 7 14337 10871 17803 21 16 \n", + "... ... ... ... ... ... ... ... \n", + "1734 199126 7 17608 11304 23912 31 20 \n", + "1735 199125 7 16169 10700 21638 28 18 \n", + "1736 199124 7 16171 10071 22271 28 17 \n", + "1737 199123 7 11947 7671 16223 21 13 \n", + "1738 199122 7 15452 9953 20951 27 17 \n", + "1739 199121 7 14903 8975 20831 26 16 \n", + "1740 199120 7 19053 12742 25364 34 23 \n", + "1741 199119 7 16739 11246 22232 29 19 \n", + "1742 199118 7 21385 13882 28888 38 25 \n", + "1743 199117 7 13462 8877 18047 24 16 \n", + "1744 199116 7 14857 10068 19646 26 18 \n", + "1745 199115 7 13975 9781 18169 25 18 \n", + "1746 199114 7 12265 7684 16846 22 14 \n", + "1747 199113 7 9567 6041 13093 17 11 \n", + "1748 199112 7 10864 7331 14397 19 13 \n", + "1749 199111 7 15574 11184 19964 27 19 \n", + "1750 199110 7 16643 11372 21914 29 20 \n", + "1751 199109 7 13741 8780 18702 24 15 \n", + "1752 199108 7 13289 8813 17765 23 15 \n", + "1753 199107 7 12337 8077 16597 22 15 \n", + "1754 199106 7 10877 7013 14741 19 12 \n", + "1755 199105 7 10442 6544 14340 18 11 \n", + "1756 199104 7 7913 4563 11263 14 8 \n", + "1757 199103 7 15387 10484 20290 27 18 \n", + "1758 199102 7 16277 11046 21508 29 20 \n", + "1759 199101 7 15565 10271 20859 27 18 \n", + "1760 199052 7 19375 13295 25455 34 23 \n", + "1761 199051 7 19080 13807 24353 34 25 \n", + "1762 199050 7 11079 6660 15498 20 12 \n", + "1763 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 2 FR France \n", + "1 2 FR France \n", + "2 5 FR France \n", + "3 4 FR France \n", + "4 7 FR France \n", + "5 5 FR France \n", + "6 11 FR France \n", + "7 10 FR France \n", + "8 15 FR France \n", + "9 18 FR France \n", + "10 18 FR France \n", + "11 20 FR France \n", + "12 28 FR France \n", + "13 22 FR France \n", + "14 24 FR France \n", + "15 27 FR France \n", + "16 22 FR France \n", + "17 19 FR France \n", + "18 25 FR France \n", + "19 21 FR France \n", + "20 26 FR France \n", + "21 29 FR France \n", + "22 34 FR France \n", + "23 48 FR France \n", + "24 29 FR France \n", + "25 33 FR France \n", + "26 25 FR France \n", + "27 29 FR France \n", + "28 26 FR France \n", + "29 26 FR France \n", + "... ... ... ... \n", + "1734 42 FR France \n", + "1735 38 FR France \n", + "1736 39 FR France \n", + "1737 29 FR France \n", + "1738 37 FR France \n", + "1739 36 FR France \n", + "1740 45 FR France \n", + "1741 39 FR France \n", + "1742 51 FR France \n", + "1743 32 FR France \n", + "1744 34 FR France \n", + "1745 32 FR France \n", + "1746 30 FR France \n", + "1747 23 FR France \n", + "1748 25 FR France \n", + "1749 35 FR France \n", + "1750 38 FR France \n", + "1751 33 FR France \n", + "1752 31 FR France \n", + "1753 29 FR France \n", + "1754 26 FR France \n", + "1755 25 FR France \n", + "1756 20 FR France \n", + "1757 36 FR France \n", + "1758 38 FR France \n", + "1759 36 FR France \n", + "1760 45 FR France \n", + "1761 43 FR France \n", + "1762 28 FR France \n", + "1763 5 FR France \n", + "\n", + "[1764 rows x 10 columns]" + ] + }, + "execution_count": 8, + "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": 9, + "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": 11, + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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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\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": [ + "Nous définissons la période de référence 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", + "Encore un petit détail: les données commencent fin 1990, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1991." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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": 17, + "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": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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D44PKREQ/8D5wwQh1DSJpvaQOSR09PT0ZD8nMzIqVdcrt5yPiHUmzgHZJPxlhXw0TixHiYy1zJhCxBdgC+e6pEdpmZmbjkOlKIyLeSc/dwDPkxyveTV1OpOfutPsR4OKC4vOAd1J83jDxQWUkNQDnA70j1GVmZhUwatKQdJ6k6QPbwErgNWAHMDCbqQ14Nm3vAFrTjKgFwEJgX+rCOiFpeRqvuHFImYG6rgNeSuMezwMrJTWn2VkrU8zMzCogS/fURcAzaXZsA/BERPxvSfuBbZLWAYeB6wEi4qCkbcDrQD9wa0ScSnXdDDwCTAN2pgfAQ8BjkrrIX2G0prp6Jd0L7E/73RMRveM4XjMzGwcvjW5mZl4a3czMSs9Jw8zMMnPSMDOzzJw0zMwsMycNMzPLzEnDzMwyc9IwM7PMnDTMzCwzJw0zM8vMScPMzDJz0jAzs8ycNMzMLDMnDTMzy8xJw8zMMnPSMDOzzJw0zMwsMycNMzPLzEnDzMwyc9IwM7PMnDTMzCwzJw0zM8vMScPMzDLLnDQknSPpZUl/l17PlNQuqTM9Nxfsu1FSl6RDklYVxJdIejX9bLMkpXijpKdTfK+k+QVl2tLv6JTUVoqDNjOzsSnmSuM24I2C1xuAXRGxENiVXiNpEdAKXA6sBr4r6ZxU5gFgPbAwPVan+DrgWER8Evg2cH+qayZwN7AMWArcXZiczMysvDIlDUnzgC8B3ysIrwG2pu2twNqC+FMR0RcRbwJdwFJJs4EZEbEnIgJ4dEiZgbq2AyvSVcgqoD0ieiPiGNDOmURjZmZllvVK4y+A24HTBbGLIuIoQHqeleJzgbcL9juSYnPT9tD4oDIR0Q+8D1wwQl1mZlYBoyYNSX8AdEfEgYx1aphYjBAfa5nCNq6X1CGpo6enJ2MzzcysWFmuND4PXCPpLeAp4IuSHgfeTV1OpOfutP8R4OKC8vOAd1J83jDxQWUkNQDnA70j1DVIRGyJiFxE5FpaWjIckpmZjcWoSSMiNkbEvIiYT36A+6WI+CNgBzAwm6kNeDZt7wBa04yoBeQHvPelLqwTkpan8Yobh5QZqOu69DsCeB5YKak5DYCvTDEzM6uAhnGUvQ/YJmkdcBi4HiAiDkraBrwO9AO3RsSpVOZm4BFgGrAzPQAeAh6T1EX+CqM11dUr6V5gf9rvnojoHUebzcxsHJT/QF8/crlcdHR0VLoZZmY1RdKBiMiNtp/vCDczq4Du4yf58oN76D5xstJNKYqThplZBWze1cn+t3rZ/GJnpZtSlPGMaZiZWZEuu2snff1nbnl7fO9hHt97mMaGKRzadHUFW5aNrzTMzMpo9+1Xcc3iOTRNzf/32zR1CmsWz2H3HVeNq95ydXc5aZiZldGsGU1Mb2ygr/80jQ1T6Os/zfTGBmZNbxpXveXq7nL3lJlZmb33QR83LLuUry69hCf2HaZnHFcH5e7u8pRbM7Ma1n38JJuee4MXDv6Skx+epmnqFFZd/gnu/NKni7p68ZRbM7NJYKK6u87G3VNmZjWulN1do3H3lJmZuXvKzMxKz0nDzMwyc9IwM7PMnDTMzCwzJw0zM8vMScPMzDJz0jAzs8ycNMzMLDMnDTMzy8xJw8zMMnPSMDOzzJw0zMwsMycNMzPLbNSkIalJ0j5JP5Z0UNJ/TfGZktoldabn5oIyGyV1STokaVVBfImkV9PPNktSijdKejrF90qaX1CmLf2OTkltpTx4MzMrTpYrjT7gixHxW8BiYLWk5cAGYFdELAR2pddIWgS0ApcDq4HvSjon1fUAsB5YmB6rU3wdcCwiPgl8G7g/1TUTuBtYBiwF7i5MTmZmVl6jJo3I+yC9nJoeAawBtqb4VmBt2l4DPBURfRHxJtAFLJU0G5gREXsi/yUejw4pM1DXdmBFugpZBbRHRG9EHAPaOZNozMyszDKNaUg6R9IrQDf5/8T3AhdFxFGA9Dwr7T4XeLug+JEUm5u2h8YHlYmIfuB94IIR6jIzswrIlDQi4lRELAbmkb9quGKE3TVcFSPEx1rmzC+U1kvqkNTR09MzQtPMzGw8ipo9FRG/An5Avovo3dTlRHruTrsdAS4uKDYPeCfF5w0TH1RGUgNwPtA7Ql1D27UlInIRkWtpaSnmkMzMrAhZZk+1SPp42p4G/B7wE2AHMDCbqQ14Nm3vAFrTjKgF5Ae896UurBOSlqfxihuHlBmo6zrgpTTu8TywUlJzGgBfmWJmZlYBDRn2mQ1sTTOgpgDbIuLvJO0BtklaBxwGrgeIiIOStgGvA/3ArRFxKtV1M/AIMA3YmR4ADwGPSeoif4XRmurqlXQvsD/td09E9I7ngM3MbOyU/0BfP3K5XHR0dFS6GWZmNUXSgYjIjbaf7wg3M7PMnDTMzCwzJw0zM8vMScPMzDJz0jAzs8ycNMzMLDMnDTMzy8xJw8zMMnPSMDOzzJw0zMwsMycNMzPLzEnDzMwyc9IwM7PMnDTMzCwzJw0zM8vMScPMzDJz0jAzs8ycNMzMSqz7+Em+/OAeuk+crHRTSs5Jw8ysxDbv6mT/W71sfrGz0k0puYZKN8DMrF5cdtdO+vpP/+vrx/ce5vG9h2lsmMKhTVdXsGWl4ysNM7MS2X37VVyzeA5NU/P/tTZNncKaxXPYfcdVFW5Z6ThpmJmVyKwZTUxvbKCv/zSNDVPo6z/N9MYGZk1vqnTTSsbdU2ZmJfTeB33csOxSvrr0Ep7Yd5ieOhsMV0SMvIN0MfAo8AngNLAlIv5S0kzgaWA+8Bbw5Yg4lspsBNYBp4A/iYjnU3wJ8AgwDXgOuC0iQlJj+h1LgP8HfCUi3kpl2oC7UnM2RcTWkdqby+Wio6Mj+7+AmZkh6UBE5EbbL0v3VD/wnyPi08By4FZJi4ANwK6IWAjsSq9JP2sFLgdWA9+VdE6q6wFgPbAwPVan+DrgWER8Evg2cH+qayZwN7AMWArcLak5Q5vNzGwCjJo0IuJoRPwobZ8A3gDmAmuAgU/9W4G1aXsN8FRE9EXEm0AXsFTSbGBGROyJ/OXNo0PKDNS1HVghScAqoD0ietNVTDtnEo2ZmZVZUQPhkuYDVwJ7gYsi4ijkEwswK+02F3i7oNiRFJubtofGB5WJiH7gfeCCEeoyM7MKyJw0JP0G8DfANyPi+Ei7DhOLEeJjLVPYtvWSOiR19PT0jNA0MzMbj0xJQ9JU8gnj+xHxtyn8bupyIj13p/gR4OKC4vOAd1J83jDxQWUkNQDnA70j1DVIRGyJiFxE5FpaWrIckpmZjcGoSSONLTwEvBER/73gRzuAtrTdBjxbEG+V1ChpAfkB732pC+uEpOWpzhuHlBmo6zrgpTTu8TywUlJzGgBfmWJmZlYBWabcfgHYDbxKfsotwLfIj2tsAy4BDgPXR0RvKnMncBP5mVffjIidKZ7jzJTbncA30pTbJuAx8uMlvUBrRPwslbkp/T6AP4uIh0dpbw/w84zHXy0uBN6rdCPGycdQHXwM1aPWjuPSiBi1q2bUpGETT1JHlvnR1czHUB18DNWjXo5jKC8jYmZmmTlpmJlZZk4a1WFLpRtQAj6G6uBjqB71chyDeEzDzMwy85WGmZll5qQxAST9laRuSa8VxH5L0h5Jr0r6n5JmpPjHJD2c4j+W9LsFZX4g6ZCkV9Jj1jC/bqKO4WJJ/0fSG5IOSrotxWdKapfUmZ6bC8pslNSV2ryqIL4kHV+XpM3pPp1aO4aKnItij0HSBWn/DyR9Z0hdNXEeRjmGmnlPSPp9SQfSv/kBSV8sqKsi56IkIsKPEj+A3wE+B7xWENsP/Lu0fRNwb9q+FXg4bc8CDgBT0usfALkKHcNs4HNpezrwz8Ai4M+BDSm+Abg/bS8Cfgw0AguAnwLnpJ/tA36b/LIwO4Gra/AYKnIuxnAM5wFfAL4OfGdIXbVyHkY6hlp6T1wJzEnbVwC/qPS5KMXDVxoTICL+nvxNioUuA/4+bbcDf5i2F5FfWp6I6AZ+BVR8bneUZ3XjmjiGcrT1bIo9hoj4dUT8X2DQN//U0nk42zFU2hiO4+WIGFj26CDQpPxKGRU7F6XgpFE+rwHXpO3rObOm1o+BNZIalF92ZQmD19t6OF2G/5dKXcJq4lY3LptxHsOAip6LjMdwNrV0HkZTK++JQn8IvBwRfVTJuRgrJ43yuYn8F1gdIH9p+y8p/lfk/2g6gL8A/pH88isAN0TEZ4B/mx5fK2uLmfDVjcuiBMcAFT4XRRzDWasYJlat52EktfSeGNj/cvJfLPfHA6FhdquZaaxOGmUSET+JiJURsQR4knx/ORHRHxF/GhGLI2IN8HGgM/3sF+n5BPAEZe4q0cSvbjzhSnQMFT0XRR7D2dTSeTirGntPIGke8AxwY0T8NIUrei7Gy0mjTAZmeUiaQv47z/9Hen2upPPS9u8D/RHxeuquujDFpwJ/QL6Lq1ztLcfqxjVxDJU8F2M4hmHV2Hk4Wz019Z6Q9HHgfwEbI+IfBnau5LkoiUqPxNfjg/yVxFHgQ/KfKtYBt5GfbfHPwH2cubFyPnCI/KDai+RXmoT8DJIDwD+RH0T7S9JMnjIdwxfIXzL/E/BKevx78t+ouIv81dAuYGZBmTvJX0EdomA2CPmB/dfSz74zcOy1cgyVPBdjPIa3yE/E+CD9/S2qwfPwkWOotfcE+Q+Hvy7Y9xVgViXPRSkeviPczMwyc/eUmZll5qRhZmaZOWmYmVlmThpmZpaZk4aZmWXmpGFmZpk5aZiZWWZOGmZmltn/B3v28ZbrUIyxAAAAAElFTkSuQmCC\n", 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