From e2c2e6ec81f63c55933516fb301074bf9326b7ec Mon Sep 17 00:00:00 2001 From: 7c8b98dd05a86d272dc44ef63cc416c0 <7c8b98dd05a86d272dc44ef63cc416c0@app-learninglab.inria.fr> Date: Mon, 9 Aug 2021 13:07:18 +0000 Subject: [PATCH] Retry --- module3/exo2/.ipynb | 2486 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2486 insertions(+) create mode 100644 module3/exo2/.ipynb diff --git a/module3/exo2/.ipynb b/module3/exo2/.ipynb new file mode 100644 index 0000000..ec37ece --- /dev/null +++ b/module3/exo2/.ipynb @@ -0,0 +1,2486 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Incidence du syndrôme grippal" + ] + }, + { + "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) :" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| 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)|" + ] + }, + { + "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": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2320210732239318303.026483.03428.040.0FRFrance
2420210632318319134.027232.03529.041.0FRFrance
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.................................
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60634.0 96606.0 143 110.0 \n", + "1914 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1915 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1916 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1917 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 29.0 FR France \n", + "1 27.0 FR France \n", + "2 18.0 FR France \n", + "3 22.0 FR France \n", + "4 16.0 FR France \n", + "5 16.0 FR France \n", + "6 10.0 FR France \n", + "7 13.0 FR France \n", + "8 16.0 FR France \n", + "9 16.0 FR France \n", + "10 20.0 FR France \n", + "11 18.0 FR France \n", + "12 22.0 FR France \n", + "13 23.0 FR France \n", + "14 31.0 FR France \n", + "15 35.0 FR France \n", + "16 38.0 FR France \n", + "17 47.0 FR France \n", + "18 53.0 FR France \n", + "19 44.0 FR France \n", + "20 35.0 FR France \n", + "21 33.0 FR France \n", + "22 38.0 FR France \n", + "23 40.0 FR France \n", + "24 41.0 FR France \n", + "25 40.0 FR France \n", + "26 46.0 FR France \n", + "27 39.0 FR France \n", + "28 31.0 FR France \n", + "29 39.0 FR France \n", + "... ... ... ... \n", + "1888 59.0 FR France \n", + "1889 64.0 FR France \n", + "1890 97.0 FR France \n", + "1891 93.0 FR France \n", + "1892 80.0 FR France \n", + "1893 116.0 FR France \n", + "1894 149.0 FR France \n", + "1895 281.0 FR France \n", + "1896 395.0 FR France \n", + "1897 485.0 FR France \n", + "1898 544.0 FR France \n", + "1899 689.0 FR France \n", + "1900 722.0 FR France \n", + "1901 762.0 FR France \n", + "1902 926.0 FR France \n", + "1903 1113.0 FR France \n", + "1904 1236.0 FR France \n", + "1905 832.0 FR France \n", + "1906 459.0 FR France \n", + "1907 207.0 FR France \n", + "1908 190.0 FR France \n", + "1909 198.0 FR France \n", + "1910 224.0 FR France \n", + "1911 266.0 FR France \n", + "1912 219.0 FR France \n", + "1913 176.0 FR France \n", + "1914 163.0 FR France \n", + "1915 195.0 FR France \n", + "1916 308.0 FR France \n", + "1917 213.0 FR France \n", + "\n", + "[1918 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, 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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
168119891930NaNNaN0NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1681 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1681 FR France " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " raw_data[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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1917 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202130 3 14239 9839.0 18639.0 22 15.0 \n", + "1 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "2 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "3 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "4 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "5 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "6 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "7 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "8 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "9 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "10 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "11 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "12 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "13 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "14 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "15 202115 3 19306 15398.0 23214.0 29 23.0 \n", + "16 202114 3 21073 17099.0 25047.0 32 26.0 \n", + "17 202113 3 26413 22094.0 30732.0 40 33.0 \n", + "18 202112 3 30658 25919.0 35397.0 46 39.0 \n", + "19 202111 3 24988 20718.0 29258.0 38 32.0 \n", + "20 202110 3 19539 15951.0 23127.0 30 25.0 \n", + "21 202109 3 17572 13926.0 21218.0 27 21.0 \n", + "22 202108 3 20882 16907.0 24857.0 32 26.0 \n", + "23 202107 3 22393 18303.0 26483.0 34 28.0 \n", + "24 202106 3 23183 19134.0 27232.0 35 29.0 \n", + "25 202105 3 22426 18445.0 26407.0 34 28.0 \n", + "26 202104 3 25804 21491.0 30117.0 39 32.0 \n", + "27 202103 3 21810 17894.0 25726.0 33 27.0 \n", + "28 202102 3 17320 13906.0 20734.0 26 21.0 \n", + "29 202101 3 21799 17778.0 25820.0 33 27.0 \n", + "... ... ... ... ... ... ... ... \n", + "1888 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1889 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1890 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1891 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1892 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1893 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1894 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1895 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1896 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1897 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1898 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1899 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1900 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1901 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1902 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1903 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1904 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1905 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1906 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1907 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1908 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1909 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1910 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1911 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1912 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1913 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1914 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1915 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1916 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1917 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 29.0 FR France \n", + "1 27.0 FR France \n", + "2 18.0 FR France \n", + "3 22.0 FR France \n", + "4 16.0 FR France \n", + "5 16.0 FR France \n", + "6 10.0 FR France \n", + "7 13.0 FR France \n", + "8 16.0 FR France \n", + "9 16.0 FR France \n", + "10 20.0 FR France \n", + "11 18.0 FR France \n", + "12 22.0 FR France \n", + "13 23.0 FR France \n", + "14 31.0 FR France \n", + "15 35.0 FR France \n", + "16 38.0 FR France \n", + "17 47.0 FR France \n", + "18 53.0 FR France \n", + "19 44.0 FR France \n", + "20 35.0 FR France \n", + "21 33.0 FR France \n", + "22 38.0 FR France \n", + "23 40.0 FR France \n", + "24 41.0 FR France \n", + "25 40.0 FR France \n", + "26 46.0 FR France \n", + "27 39.0 FR France \n", + "28 31.0 FR France \n", + "29 39.0 FR France \n", + "... ... ... ... \n", + "1888 59.0 FR France \n", + "1889 64.0 FR France \n", + "1890 97.0 FR France \n", + "1891 93.0 FR France \n", + "1892 80.0 FR France \n", + "1893 116.0 FR France \n", + "1894 149.0 FR France \n", + "1895 281.0 FR France \n", + "1896 395.0 FR France \n", + "1897 485.0 FR France \n", + "1898 544.0 FR France \n", + "1899 689.0 FR France \n", + "1900 722.0 FR France \n", + "1901 762.0 FR France \n", + "1902 926.0 FR France \n", + "1903 1113.0 FR France \n", + "1904 1236.0 FR France \n", + "1905 832.0 FR France \n", + "1906 459.0 FR France \n", + "1907 207.0 FR France \n", + "1908 190.0 FR France \n", + "1909 198.0 FR France \n", + "1910 224.0 FR France \n", + "1911 266.0 FR France \n", + "1912 219.0 FR France \n", + "1913 176.0 FR France \n", + "1914 163.0 FR France \n", + "1915 195.0 FR France \n", + "1916 308.0 FR France \n", + "1917 213.0 FR France \n", + "\n", + "[1917 rows x 10 columns]" + ] + }, + "execution_count": 5, + "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 semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont 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 reste deux petites modifications à faire.\n", + "\n", + "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", + "\n", + "Deuxièmement, nous trions les points par période, dans le sens chronologique." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations 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", + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Étude de l'incidence annuelle\n", + "Étant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er août de l'année $N$ au 1er août de l'année $N+1$.\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 août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.Encore un petit détail: les données commencent an octobre 1984, ce qui 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)]" + ] + }, + { + "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": {}, + "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": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2053781\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", + "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", + "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 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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\n", 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