diff --git a/module3/exo2/analyse-syndrome-grippal-jupyter.ipynb b/module3/exo2/analyse-syndrome-grippal-jupyter.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9bcc54c50daadf87a288babb7d48241ca63b9359 --- /dev/null +++ b/module3/exo2/analyse-syndrome-grippal-jupyter.ipynb @@ -0,0 +1,2529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "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": { + "collapsed": true + }, + "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": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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2201840374094717.010101.0117.015.0FRFrance
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720183531506239.02773.020.04.0FRFrance
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142018283165452.03256.031.05.0FRFrance
15201827332691145.05393.052.08.0FRFrance
16201826337581493.06023.063.09.0FRFrance
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18201824332231351.05095.052.08.0FRFrance
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20201822332021330.05074.052.08.0FRFrance
2120182132537763.04311.041.07.0FRFrance
2220182032694967.04421.041.07.0FRFrance
23201819310250.02098.020.04.0FRFrance
24201818335411416.05666.052.08.0FRFrance
25201817325731003.04143.042.06.0FRFrance
26201816348182724.06912.074.010.0FRFrance
2720181531631112168.020454.02519.031.0FRFrance
2820181432266618092.027240.03528.042.0FRFrance
2920181333268025536.039824.05039.061.0FRFrance
.................................
174319852132609619621.032571.04735.059.0FRFrance
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1773 rows × 10 columns

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pd.read_csv(data_url, encoding = 'iso-8859-1', 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": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1536 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1536 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": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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1772 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 201842 3 7832 5145.0 10519.0 12 8.0 \n", + "1 201841 3 8048 5098.0 10998.0 12 8.0 \n", + "2 201840 3 7409 4717.0 10101.0 11 7.0 \n", + "3 201839 3 7174 4235.0 10113.0 11 7.0 \n", + "4 201838 3 6127 3482.0 8772.0 9 5.0 \n", + "5 201837 3 4644 2200.0 7088.0 7 3.0 \n", + "6 201836 3 3215 1349.0 5081.0 5 2.0 \n", + "7 201835 3 1506 239.0 2773.0 2 0.0 \n", + "8 201834 3 1368 116.0 2620.0 2 0.0 \n", + "9 201833 3 1962 5.0 3919.0 3 0.0 \n", + "10 201832 3 1839 183.0 3495.0 3 0.0 \n", + "11 201831 3 2048 242.0 3854.0 3 0.0 \n", + "12 201830 3 1951 202.0 3700.0 3 0.0 \n", + "13 201829 3 1951 252.0 3650.0 3 0.0 \n", + "14 201828 3 1654 52.0 3256.0 3 1.0 \n", + "15 201827 3 3269 1145.0 5393.0 5 2.0 \n", + "16 201826 3 3758 1493.0 6023.0 6 3.0 \n", + "17 201825 3 4580 2220.0 6940.0 7 3.0 \n", + "18 201824 3 3223 1351.0 5095.0 5 2.0 \n", + "19 201823 3 1207 136.0 2278.0 2 0.0 \n", + "20 201822 3 3202 1330.0 5074.0 5 2.0 \n", + "21 201821 3 2537 763.0 4311.0 4 1.0 \n", + "22 201820 3 2694 967.0 4421.0 4 1.0 \n", + "23 201819 3 1025 0.0 2098.0 2 0.0 \n", + "24 201818 3 3541 1416.0 5666.0 5 2.0 \n", + "25 201817 3 2573 1003.0 4143.0 4 2.0 \n", + "26 201816 3 4818 2724.0 6912.0 7 4.0 \n", + "27 201815 3 16311 12168.0 20454.0 25 19.0 \n", + "28 201814 3 22666 18092.0 27240.0 35 28.0 \n", + "29 201813 3 32680 25536.0 39824.0 50 39.0 \n", + "... ... ... ... ... ... ... ... \n", + "1743 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1744 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1745 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1746 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1747 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1748 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1749 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1750 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1751 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1752 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1753 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1754 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1755 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1756 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1757 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1758 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1759 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1760 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1761 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1762 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1763 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1764 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1765 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1766 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1767 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1768 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1769 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1770 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1771 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1772 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 16.0 FR France \n", + "1 16.0 FR France \n", + "2 15.0 FR France \n", + "3 15.0 FR France \n", + "4 13.0 FR France \n", + "5 11.0 FR France \n", + "6 8.0 FR France \n", + "7 4.0 FR France \n", + "8 4.0 FR France \n", + "9 6.0 FR France \n", + "10 6.0 FR France \n", + "11 6.0 FR France \n", + "12 6.0 FR France \n", + "13 6.0 FR France \n", + "14 5.0 FR France \n", + "15 8.0 FR France \n", + "16 9.0 FR France \n", + "17 11.0 FR France \n", + "18 8.0 FR France \n", + "19 4.0 FR France \n", + "20 8.0 FR France \n", + "21 7.0 FR France \n", + "22 7.0 FR France \n", + "23 4.0 FR France \n", + "24 8.0 FR France \n", + "25 6.0 FR France \n", + "26 10.0 FR France \n", + "27 31.0 FR France \n", + "28 42.0 FR France \n", + "29 61.0 FR France \n", + "... ... ... ... \n", + "1743 59.0 FR France \n", + "1744 64.0 FR France \n", + "1745 97.0 FR France \n", + "1746 93.0 FR France \n", + "1747 80.0 FR France \n", + "1748 116.0 FR France \n", + "1749 149.0 FR France \n", + "1750 281.0 FR France \n", + "1751 395.0 FR France \n", + "1752 485.0 FR France \n", + "1753 544.0 FR France \n", + "1754 689.0 FR France \n", + "1755 722.0 FR France \n", + "1756 762.0 FR France \n", + "1757 926.0 FR France \n", + "1758 1113.0 FR France \n", + "1759 1236.0 FR France \n", + "1760 832.0 FR France \n", + "1761 459.0 FR France \n", + "1762 207.0 FR France \n", + "1763 190.0 FR France \n", + "1764 198.0 FR France \n", + "1765 224.0 FR France \n", + "1766 266.0 FR France \n", + "1767 219.0 FR France \n", + "1768 176.0 FR France \n", + "1769 163.0 FR France \n", + "1770 195.0 FR France \n", + "1771 308.0 FR France \n", + "1772 213.0 FR France \n", + "\n", + "[1772 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\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": { + "collapsed": false + }, + "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": { + "collapsed": true + }, + "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": 8, + "metadata": { + "collapsed": false + }, + "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": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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BLI0U4pZG1iyy6Jv7irA0io5peEI6jbcwkkSjbEsjr3slxNIoKqaRx9Io647w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\nnUXRaOD2lkZHR/H3afiYQFG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "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": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "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": 11, + "metadata": { + "collapsed": true + }, + "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": 12, + "metadata": { + "collapsed": false + }, + "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": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "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": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2012 2175217\n", + "2003 2234584\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\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": 14, + "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": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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--- /dev/null +++ "b/module3/exo3/Sujet 1 Concentration de CO2 dans l'atmosph\303\250re depuis 1958.ipynb" @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "File b'C:\\\\Users\\\\t-gen\\\\Downloads\\\\monthly_in_situ_co2_mlo.csv' does not exist", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr\"C:\\Users\\t-gen\\Downloads\\monthly_in_situ_co2_mlo.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mdata2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'monthly_in_situ_co2_mlo.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, 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\u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 816\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 818\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 820\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1047\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1048\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1050\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1051\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1693\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1695\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1697\u001b[0m \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: File b'C:\\\\Users\\\\t-gen\\\\Downloads\\\\monthly_in_situ_co2_mlo.csv' does not exist" + ] + } + ], + "source": [ + "import numpy as np \n", + "import pandas as pd\n", + "data = pd.read_csv(r\"C:\\Users\\t-gen\\Downloads\\monthly_in_situ_co2_mlo.csv\")\n", + "data2 = pd.read_csv('monthly_in_situ_co2_mlo.csv')\n", + "print(data)\n", + "print(data2)" + ] + } + ], + "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/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb deleted file mode 100644 index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0000000000000000000000000000000000000000 --- a/module3/exo3/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/exo3/monthly_in_situ_co2_mlo.csv b/module3/exo3/monthly_in_situ_co2_mlo.csv new file mode 100644 index 0000000000000000000000000000000000000000..d16e63ae522d9c6265954ba7502d63cca9cb8145 --- /dev/null +++ b/module3/exo3/monthly_in_situ_co2_mlo.csv @@ -0,0 +1,837 @@ +"-------------------------------------------------------------------------------------------" +" Atmospheric CO2 concentrations (ppm) derived from in situ air measurements " +" at Mauna Loa, Observatory, Hawaii: Latitude 19.5°N Longitude 155.6°W Elevation 3397m " +" " +" Source: R. F. Keeling, S. J. Walker, S. C. Piper and A. F. Bollenbacher " +" Scripps CO2 Program ( http://scrippsco2.ucsd.edu ) " +" Scripps Institution of Oceanography (SIO) " +" University of California " +" La Jolla, California USA 92093-0244 " +" " +" Status of data and correspondence: " +" " +" These data are subject to revision based on recalibration of standard gases. Questions " +" about the data should be directed to Dr. Ralph Keeling (rkeeling@ucsd.edu), Stephen Walker" +" (sjwalker@ucsd.edu) and Stephen Piper (scpiper@ucsd.edu), Scripps CO2 Program. " +" " +" Baseline data in this file through 09-Aug-2022 from archive dated 10-Aug-2022 17:36:17 " +" " +"-------------------------------------------------------------------------------------------" +" " +" Please cite as: " +" " +" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and " +" H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and " +" oceans from 1978 to 2000. I. Global aspects, SIO Reference Series, No. 01-06, Scripps " +" Institution of Oceanography, San Diego, 88 pages, 2001. " +" " +" If it is necessary to cite a peer-reviewed article, please cite as: " +" " +" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and " +" H. A. Meijer, Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and " +" oceans from 1978 to 2000: observations and carbon cycle implications, pages 83-113, " +" in "A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems", " +" editors, Ehleringer, J.R., T. E. Cerling, M. D. Dearing, Springer Verlag, " +" New York, 2005. " +" " +"-------------------------------------------------------------------------------------------" +" " +" The data file below contains 10 columns. Columns 1-4 give the dates in several redundant " +" formats. Column 5 below gives monthly Mauna Loa CO2 concentrations in micro-mol CO2 per " +" mole (ppm), reported on the 2012 SIO manometric mole fraction scale. This is the " +" standard version of the data most often sought. The monthly values have been adjusted " +" to 24:00 hours on the 15th of each month. Column 6 gives the same data after a seasonal " +" adjustment to remove the quasi-regular seasonal cycle. The adjustment involves " +" subtracting from the data a 4-harmonic fit with a linear gain factor. Column 7 is a " +" smoothed version of the data generated from a stiff cubic spline function plus 4-harmonic " +" functions with linear gain. Column 8 is the same smoothed version with the seasonal " +" cycle removed. Column 9 is identical to Column 5 except that the missing values from " +" Column 5 have been filled with values from Column 7. Column 10 is identical to Column 6 " +" except missing values have been filled with values from Column 8. Missing values are " +" denoted by -99.99 " +" " +" CO2 concentrations are measured on the '12' calibration scale " +" " + Yr, Mn, Date, Date, CO2,seasonally, fit, seasonally, CO2, seasonally + , , , , , adjusted, ,adjusted fit, filled,adjusted filled + , , Excel, , [ppm], [ppm] , [ppm], [ppm], [ppm], [ppm] +1958, 01, 21200, 1958.0411, -99.99, -99.99, -99.99, -99.99, -99.99, -99.99 +1958, 02, 21231, 1958.1260, -99.99, -99.99, -99.99, -99.99, -99.99, -99.99 +1958, 03, 21259, 1958.2027, 315.71, 314.44, 316.20, 314.91, 315.71, 314.44 +1958, 04, 21290, 1958.2877, 317.45, 315.16, 317.30, 314.99, 317.45, 315.16 +1958, 05, 21320, 1958.3699, 317.51, 314.70, 317.88, 315.07, 317.51, 314.70 +1958, 06, 21351, 1958.4548, -99.99, -99.99, 317.26, 315.15, 317.26, 315.15 +1958, 07, 21381, 1958.5370, 315.87, 315.20, 315.86, 315.22, 315.87, 315.20 +1958, 08, 21412, 1958.6219, 314.93, 316.21, 313.98, 315.29, 314.93, 316.21 +1958, 09, 21443, 1958.7068, 313.21, 316.10, 312.45, 315.36, 313.21, 316.10 +1958, 10, 21473, 1958.7890, -99.99, -99.99, 312.43, 315.41, 312.43, 315.41 +1958, 11, 21504, 1958.8740, 313.33, 315.21, 313.61, 315.46, 313.33, 315.21 +1958, 12, 21534, 1958.9562, 314.67, 315.43, 314.77, 315.52, 314.67, 315.43 +1959, 01, 21565, 1959.0411, 315.58, 315.52, 315.64, 315.58, 315.58, 315.52 +1959, 02, 21596, 1959.1260, 316.49, 315.84, 316.29, 315.64, 316.49, 315.84 +1959, 03, 21624, 1959.2027, 316.65, 315.37, 316.99, 315.70, 316.65, 315.37 +1959, 04, 21655, 1959.2877, 317.72, 315.42, 318.09, 315.77, 317.72, 315.42 +1959, 05, 21685, 1959.3699, 318.29, 315.48, 318.67, 315.85, 318.29, 315.48 +1959, 06, 21716, 1959.4548, 318.15, 316.01, 318.06, 315.94, 318.15, 316.01 +1959, 07, 21746, 1959.5370, 316.54, 315.87, 316.67, 316.03, 316.54, 315.87 +1959, 08, 21777, 1959.6219, 314.80, 316.08, 314.81, 316.13, 314.80, 316.08 +1959, 09, 21808, 1959.7068, 313.84, 316.74, 313.30, 316.22, 313.84, 316.74 +1959, 10, 21838, 1959.7890, 313.33, 316.33, 313.32, 316.31, 313.33, 316.33 +1959, 11, 21869, 1959.8740, 314.81, 316.69, 314.54, 316.40, 314.81, 316.69 +1959, 12, 21899, 1959.9562, 315.58, 316.35, 315.73, 316.48, 315.58, 316.35 +1960, 01, 21930, 1960.0410, 316.43, 316.37, 316.63, 316.56, 316.43, 316.37 +1960, 02, 21961, 1960.1257, 316.98, 316.33, 317.30, 316.64, 316.98, 316.33 +1960, 03, 21990, 1960.2049, 317.58, 316.27, 318.04, 316.72, 317.58, 316.27 +1960, 04, 22021, 1960.2896, 319.03, 316.70, 319.14, 316.80, 319.03, 316.70 +1960, 05, 22051, 1960.3716, 320.03, 317.21, 319.69, 316.87, 320.03, 317.21 +1960, 06, 22082, 1960.4563, 319.59, 317.46, 319.03, 316.93, 319.59, 317.46 +1960, 07, 22112, 1960.5383, 318.18, 317.53, 317.60, 316.98, 318.18, 317.53 +1960, 08, 22143, 1960.6230, 315.90, 317.22, 315.67, 317.02, 315.90, 317.22 +1960, 09, 22174, 1960.7077, 314.17, 317.09, 314.11, 317.05, 314.17, 317.09 +1960, 10, 22204, 1960.7896, 313.83, 316.84, 314.09, 317.08, 313.83, 316.84 +1960, 11, 22235, 1960.8743, 315.00, 316.88, 315.25, 317.11, 315.00, 316.88 +1960, 12, 22265, 1960.9563, 316.19, 316.96, 316.40, 317.15, 316.19, 316.96 +1961, 01, 22296, 1961.0411, 316.89, 316.84, 317.27, 317.21, 316.89, 316.84 +1961, 02, 22327, 1961.1260, 317.70, 317.05, 317.93, 317.27, 317.70, 317.05 +1961, 03, 22355, 1961.2027, 318.54, 317.25, 318.64, 317.34, 318.54, 317.25 +1961, 04, 22386, 1961.2877, 319.48, 317.16, 319.75, 317.42, 319.48, 317.16 +1961, 05, 22416, 1961.3699, 320.58, 317.75, 320.34, 317.50, 320.58, 317.75 +1961, 06, 22447, 1961.4548, 319.77, 317.62, 319.73, 317.60, 319.77, 317.62 +1961, 07, 22477, 1961.5370, 318.56, 317.89, 318.33, 317.68, 318.56, 317.89 +1961, 08, 22508, 1961.6219, 316.79, 318.08, 316.44, 317.77, 316.79, 318.08 +1961, 09, 22539, 1961.7068, 314.99, 317.91, 314.91, 317.85, 314.99, 317.91 +1961, 10, 22569, 1961.7890, 315.31, 318.33, 314.92, 317.93, 315.31, 318.33 +1961, 11, 22600, 1961.8740, 316.10, 318.00, 316.12, 318.00, 316.10, 318.00 +1961, 12, 22630, 1961.9562, 317.01, 317.78, 317.30, 318.06, 317.01, 317.78 +1962, 01, 22661, 1962.0411, 317.94, 317.88, 318.20, 318.13, 317.94, 317.88 +1962, 02, 22692, 1962.1260, 318.55, 317.89, 318.86, 318.20, 318.55, 317.89 +1962, 03, 22720, 1962.2027, 319.68, 318.39, 319.56, 318.26, 319.68, 318.39 +1962, 04, 22751, 1962.2877, 320.57, 318.25, 320.66, 318.33, 320.57, 318.25 +1962, 05, 22781, 1962.3699, 321.02, 318.17, 321.23, 318.39, 321.02, 318.17 +1962, 06, 22812, 1962.4548, 320.62, 318.46, 320.59, 318.45, 320.62, 318.46 +1962, 07, 22842, 1962.5370, 319.61, 318.94, 319.16, 318.51, 319.61, 318.94 +1962, 08, 22873, 1962.6219, 317.40, 318.70, 317.23, 318.56, 317.40, 318.70 +1962, 09, 22904, 1962.7068, 316.24, 319.18, 315.66, 318.61, 316.24, 319.18 +1962, 10, 22934, 1962.7890, 315.42, 318.45, 315.63, 318.64, 315.42, 318.45 +1962, 11, 22965, 1962.8740, 316.69, 318.59, 316.80, 318.68, 316.69, 318.59 +1962, 12, 22995, 1962.9562, 317.70, 318.47, 317.96, 318.72, 317.70, 318.47 +1963, 01, 23026, 1963.0411, 318.74, 318.68, 318.83, 318.77, 318.74, 318.68 +1963, 02, 23057, 1963.1260, 319.07, 318.41, 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401.41, 398.88 +2014, 07, 41835, 2014.5370, 399.17, 398.37, 399.54, 398.78, 399.17, 398.37 +2014, 08, 41866, 2014.6219, 397.30, 398.82, 397.37, 398.94, 397.30, 398.82 +2014, 09, 41897, 2014.7068, 395.49, 398.93, 395.64, 399.10, 395.49, 398.93 +2014, 10, 41927, 2014.7890, 395.74, 399.29, 395.72, 399.26, 395.74, 399.29 +2014, 11, 41958, 2014.8740, 397.32, 399.55, 397.22, 399.43, 397.32, 399.55 +2014, 12, 41988, 2014.9562, 398.88, 399.80, 398.70, 399.59, 398.88, 399.80 +2015, 01, 42019, 2015.0411, 399.94, 399.88, 399.85, 399.76, 399.94, 399.88 +2015, 02, 42050, 2015.1260, 400.40, 399.63, 400.72, 399.94, 400.40, 399.63 +2015, 03, 42078, 2015.2027, 401.60, 400.08, 401.65, 400.11, 401.60, 400.08 +2015, 04, 42109, 2015.2877, 403.53, 400.79, 403.05, 400.30, 403.53, 400.79 +2015, 05, 42139, 2015.3699, 404.04, 400.69, 403.85, 400.50, 404.04, 400.69 +2015, 06, 42170, 2015.4548, 402.81, 400.27, 403.24, 400.72, 402.81, 400.27 +2015, 07, 42200, 2015.5370, 401.54, 400.74, 401.71, 400.95, 401.54, 400.74 +2015, 08, 42231, 2015.6219, 398.93, 400.46, 399.65, 401.21, 398.93, 400.46 +2015, 09, 42262, 2015.7068, 397.43, 400.88, 398.03, 401.50, 397.43, 400.88 +2015, 10, 42292, 2015.7890, 398.22, 401.78, 398.24, 401.79, 398.22, 401.78 +2015, 11, 42323, 2015.8740, 400.17, 402.41, 399.90, 402.11, 400.17, 402.41 +2015, 12, 42353, 2015.9562, 401.82, 402.73, 401.53, 402.42, 401.82, 402.73 +2016, 01, 42384, 2016.0410, 402.58, 402.51, 402.81, 402.74, 402.58, 402.51 +2016, 02, 42415, 2016.1257, 404.09, 403.32, 403.82, 403.04, 404.09, 403.32 +2016, 03, 42444, 2016.2049, 404.79, 403.23, 404.89, 403.32, 404.79, 403.23 +2016, 04, 42475, 2016.2896, 407.50, 404.73, 406.39, 403.60, 407.50, 404.73 +2016, 05, 42505, 2016.3716, 407.59, 404.23, 407.20, 403.85, 407.59, 404.23 +2016, 06, 42536, 2016.4563, 406.94, 404.42, 406.59, 404.09, 406.94, 404.42 +2016, 07, 42566, 2016.5383, 404.43, 403.67, 405.05, 404.31, 404.43, 403.67 +2016, 08, 42597, 2016.6230, 402.17, 403.73, 402.94, 404.54, 402.17, 403.73 +2016, 09, 42628, 2016.7077, 400.95, 404.42, 401.27, 404.76, 400.95, 404.42 +2016, 10, 42658, 2016.7896, 401.43, 405.00, 401.41, 404.97, 401.43, 405.00 +2016, 11, 42689, 2016.8743, 403.57, 405.81, 402.97, 405.18, 403.57, 405.81 +2016, 12, 42719, 2016.9563, 404.48, 405.39, 404.48, 405.37, 404.48, 405.39 +2017, 01, 42750, 2017.0411, 406.00, 405.94, 405.64, 405.56, 406.00, 405.94 +2017, 02, 42781, 2017.1260, 406.57, 405.80, 406.53, 405.75, 406.57, 405.80 +2017, 03, 42809, 2017.2027, 406.99, 405.46, 407.45, 405.90, 406.99, 405.46 +2017, 04, 42840, 2017.2877, 408.88, 406.13, 408.84, 406.07, 408.88, 406.13 +2017, 05, 42870, 2017.3699, 409.84, 406.48, 409.60, 406.23, 409.84, 406.48 +2017, 06, 42901, 2017.4548, 409.05, 406.50, 408.93, 406.39, 409.05, 406.50 +2017, 07, 42931, 2017.5370, 407.13, 406.33, 407.31, 406.55, 407.13, 406.33 +2017, 08, 42962, 2017.6219, 405.17, 406.71, 405.13, 406.71, 405.17, 406.71 +2017, 09, 42993, 2017.7068, 403.20, 406.67, 403.37, 406.86, 403.20, 406.67 +2017, 10, 43023, 2017.7890, 403.57, 407.15, 403.44, 407.01, 403.57, 407.15 +2017, 11, 43054, 2017.8740, 405.10, 407.35, 404.94, 407.17, 405.10, 407.35 +2017, 12, 43084, 2017.9562, 406.68, 407.60, 406.42, 407.32, 406.68, 407.60 +2018, 01, 43115, 2018.0411, 407.98, 407.91, 407.55, 407.47, 407.98, 407.91 +2018, 02, 43146, 2018.1260, 408.36, 407.58, 408.42, 407.63, 408.36, 407.58 +2018, 03, 43174, 2018.2027, 409.21, 407.68, 409.33, 407.78, 409.21, 407.68 +2018, 04, 43205, 2018.2877, 410.24, 407.48, 410.74, 407.96, 410.24, 407.48 +2018, 05, 43235, 2018.3699, 411.23, 407.86, 411.53, 408.15, 411.23, 407.86 +2018, 06, 43266, 2018.4548, 410.81, 408.25, 410.91, 408.37, 410.81, 408.25 +2018, 07, 43296, 2018.5370, 408.83, 408.03, 409.37, 408.60, 408.83, 408.03 +2018, 08, 43327, 2018.6219, 407.02, 408.56, 407.27, 408.86, 407.02, 408.56 +2018, 09, 43358, 2018.7068, 405.52, 409.00, 405.63, 409.12, 405.52, 409.00 +2018, 10, 43388, 2018.7890, 405.93, 409.52, 405.81, 409.39, 405.93, 409.52 +2018, 11, 43419, 2018.8740, 408.04, 410.30, 407.43, 409.66, 408.04, 410.30 +2018, 12, 43449, 2018.9562, 409.17, 410.08, 409.02, 409.92, 409.17, 410.08 +2019, 01, 43480, 2019.0411, 410.85, 410.78, 410.26, 410.18, 410.85, 410.78 +2019, 02, 43511, 2019.1260, 411.59, 410.81, 411.21, 410.42, 411.59, 410.81 +2019, 03, 43539, 2019.2027, 411.91, 410.37, 412.19, 410.63, 411.91, 410.37 +2019, 04, 43570, 2019.2877, 413.46, 410.70, 413.65, 410.86, 413.46, 410.70 +2019, 05, 43600, 2019.3699, 414.76, 411.38, 414.46, 411.08, 414.76, 411.38 +2019, 06, 43631, 2019.4548, 413.89, 411.32, 413.85, 411.30, 413.89, 411.32 +2019, 07, 43661, 2019.5370, 411.78, 410.98, 412.28, 411.51, 411.78, 410.98 +2019, 08, 43692, 2019.6219, 410.01, 411.56, 410.15, 411.74, 410.01, 411.56 +2019, 09, 43723, 2019.7068, 408.48, 411.97, 408.45, 411.96, 408.48, 411.97 +2019, 10, 43753, 2019.7890, 408.40, 412.00, 408.59, 412.18, 408.40, 412.00 +2019, 11, 43784, 2019.8740, 410.16, 412.43, 410.16, 412.40, 410.16, 412.43 +2019, 12, 43814, 2019.9562, 411.81, 412.73, 411.71, 412.61, 411.81, 412.73 +2020, 01, 43845, 2020.0410, 413.30, 413.24, 412.91, 412.83, 413.30, 413.24 +2020, 02, 43876, 2020.1257, 414.05, 413.27, 413.83, 413.04, 414.05, 413.27 +2020, 03, 43905, 2020.2049, 414.45, 412.88, 414.82, 413.23, 414.45, 412.88 +2020, 04, 43936, 2020.2896, 416.11, 413.31, 416.25, 413.43, 416.11, 413.31 +2020, 05, 43966, 2020.3716, 417.15, 413.76, 417.02, 413.63, 417.15, 413.76 +2020, 06, 43997, 2020.4563, 416.29, 413.74, 416.36, 413.83, 416.29, 413.74 +2020, 07, 44027, 2020.5383, 414.42, 413.64, 414.77, 414.03, 414.42, 413.64 +2020, 08, 44058, 2020.6230, 412.52, 414.10, 412.61, 414.23, 412.52, 414.10 +2020, 09, 44089, 2020.7077, 411.18, 414.69, 410.90, 414.43, 411.18, 414.69 +2020, 10, 44119, 2020.7896, 411.12, 414.73, 411.02, 414.62, 411.12, 414.73 +2020, 11, 44150, 2020.8743, 412.88, 415.15, 412.57, 414.81, 412.88, 415.15 +2020, 12, 44180, 2020.9563, 413.89, 414.81, 414.08, 414.99, 413.89, 414.81 +2021, 01, 44211, 2021.0411, 415.15, 415.08, 415.25, 415.16, 415.15, 415.08 +2021, 02, 44242, 2021.1260, 416.47, 415.69, 416.13, 415.34, 416.47, 415.69 +2021, 03, 44270, 2021.2027, 417.16, 415.62, 417.06, 415.50, 417.16, 415.62 +2021, 04, 44301, 2021.2877, 418.24, 415.46, 418.47, 415.67, 418.24, 415.46 +2021, 05, 44331, 2021.3699, 418.95, 415.55, 419.24, 415.84, 418.95, 415.55 +2021, 06, 44362, 2021.4548, 418.70, 416.12, 418.58, 416.02, 418.70, 416.12 +2021, 07, 44392, 2021.5370, 416.65, 415.84, 416.97, 416.20, 416.65, 415.84 +2021, 08, 44423, 2021.6219, 414.34, 415.89, 414.79, 416.39, 414.34, 415.89 +2021, 09, 44454, 2021.7068, 412.90, 416.41, 413.05, 416.58, 412.90, 416.41 +2021, 10, 44484, 2021.7890, 413.55, 417.17, 413.15, 416.76, 413.55, 417.17 +2021, 11, 44515, 2021.8740, 414.82, 417.09, 414.70, 416.94, 414.82, 417.09 +2021, 12, 44545, 2021.9562, 416.43, 417.36, 416.20, 417.11, 416.43, 417.36 +2022, 01, 44576, 2022.0411, 418.01, 417.94, 417.35, 417.26, 418.01, 417.94 +2022, 02, 44607, 2022.1260, 418.99, 418.20, 418.20, 417.40, 418.99, 418.20 +2022, 03, 44635, 2022.2027, 418.45, 416.90, 419.08, 417.51, 418.45, 416.90 +2022, 04, 44666, 2022.2877, 420.02, 417.23, 420.44, 417.63, 420.02, 417.23 +2022, 05, 44696, 2022.3699, 420.78, 417.36, 421.16, 417.75, 420.78, 417.36 +2022, 06, 44727, 2022.4548, 420.68, 418.09, 420.43, 417.86, 420.68, 418.09 +2022, 07, 44757, 2022.5370, 418.66, 417.85, 418.75, 417.98, 418.66, 417.85 +2022, 08, 44788, 2022.6219, -99.99, -99.99, -99.99, -99.99, -99.99, -99.99 +2022, 09, 44819, 2022.7068, -99.99, -99.99, -99.99, -99.99, -99.99, -99.99 +2022, 10, 44849, 2022.7890, -99.99, -99.99, -99.99, -99.99, -99.99, -99.99 +2022, 11, 44880, 2022.8740, -99.99, -99.99, -99.99, -99.99, -99.99, -99.99 +2022, 12, 44910, 2022.9562, -99.99, -99.99, -99.99, -99.99, -99.99, -99.99