diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 36554b96c7ead82e9723cf5faf6f19b978901435..34f6eb6fc24815b6f72b4990e1172aa63ea1b009 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -29,10 +29,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -62,9 +60,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020212931890813107.024709.02920.038.0FRFrance
1202128386985462.011934.0138.018.0FRFrance
22021273106936838.014548.01610.022.0FRFrance
3202126370864109.010063.0116.016.0FRFrance
4202125379425540.010344.0128.016.0FRFrance
5202124348553011.06699.074.010.0FRFrance
6202123367104455.08965.0107.013.0FRFrance
7202122378795495.010263.0128.016.0FRFrance
8202121378275403.010251.0128.016.0FRFrance
92021203102787540.013016.01612.020.0FRFrance
10202119395396860.012218.01410.018.0FRFrance
112021183121359165.015105.01814.022.0FRFrance
122021173120588891.015225.01813.023.0FRFrance
1320211631650512735.020275.02519.031.0FRFrance
1420211531930615398.023214.02923.035.0FRFrance
1520211432107317099.025047.03226.038.0FRFrance
1620211332641322094.030732.04033.047.0FRFrance
1720211233065825919.035397.04639.053.0FRFrance
1820211132498820718.029258.03832.044.0FRFrance
1920211031953915951.023127.03025.035.0FRFrance
2020210931757213926.021218.02721.033.0FRFrance
2120210832088216907.024857.03226.038.0FRFrance
2220210732239318303.026483.03428.040.0FRFrance
2320210632318319134.027232.03529.041.0FRFrance
2420210532242618445.026407.03428.040.0FRFrance
2520210432580421491.030117.03932.046.0FRFrance
2620210332181017894.025726.03327.039.0FRFrance
2720210231732013906.020734.02621.031.0FRFrance
2820210132179917778.025820.03327.039.0FRFrance
2920205332122016498.025942.03225.039.0FRFrance
.................................
188719852132609619621.032571.04735.059.0FRFrance
188819852032789620885.034907.05138.064.0FRFrance
188919851934315432821.053487.07859.097.0FRFrance
189019851834055529935.051175.07455.093.0FRFrance
189119851733405324366.043740.06244.080.0FRFrance
189219851635036236451.064273.09166.0116.0FRFrance
189319851536388145538.082224.011683.0149.0FRFrance
18941985143134545114400.0154690.0244207.0281.0FRFrance
18951985133197206176080.0218332.0357319.0395.0FRFrance
18961985123245240223304.0267176.0445405.0485.0FRFrance
18971985113276205252399.0300011.0501458.0544.0FRFrance
18981985103353231326279.0380183.0640591.0689.0FRFrance
18991985093369895341109.0398681.0670618.0722.0FRFrance
19001985083389886359529.0420243.0707652.0762.0FRFrance
19011985073471852432599.0511105.0855784.0926.0FRFrance
19021985063565825518011.0613639.01026939.01113.0FRFrance
19031985053637302592795.0681809.011551074.01236.0FRFrance
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19051985033213901174689.0253113.0388317.0459.0FRFrance
190619850239758680949.0114223.0177147.0207.0FRFrance
190719850138548965918.0105060.0155120.0190.0FRFrance
190819845238483060602.0109058.0154110.0198.0FRFrance
1909198451310172680242.0123210.0185146.0224.0FRFrance
19101984503123680101401.0145959.0225184.0266.0FRFrance
1911198449310107381684.0120462.0184149.0219.0FRFrance
191219844837862060634.096606.0143110.0176.0FRFrance
191319844737202954274.089784.013199.0163.0FRFrance
191419844638733067686.0106974.0159123.0195.0FRFrance
19151984453135223101414.0169032.0246184.0308.0FRFrance
191619844436842220056.0116788.012537.0213.0FRFrance
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1917 rows × 10 columns

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207.0 \n", + "1895 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1896 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1897 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1898 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1899 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1900 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1901 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1902 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1903 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1904 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1905 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1906 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1907 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1908 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1909 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1910 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1911 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1912 198448 3 78620 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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202129 3 18908 13107.0 24709.0 29 20.0 \n", + "1 202128 3 8698 5462.0 11934.0 13 8.0 \n", + "2 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "3 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "4 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "5 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "6 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "7 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "8 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "9 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "10 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "11 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "12 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "13 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "14 202115 3 19306 15398.0 23214.0 29 23.0 \n", + "15 202114 3 21073 17099.0 25047.0 32 26.0 \n", + "16 202113 3 26413 22094.0 30732.0 40 33.0 \n", + "17 202112 3 30658 25919.0 35397.0 46 39.0 \n", + "18 202111 3 24988 20718.0 29258.0 38 32.0 \n", + "19 202110 3 19539 15951.0 23127.0 30 25.0 \n", + "20 202109 3 17572 13926.0 21218.0 27 21.0 \n", + "21 202108 3 20882 16907.0 24857.0 32 26.0 \n", + "22 202107 3 22393 18303.0 26483.0 34 28.0 \n", + "23 202106 3 23183 19134.0 27232.0 35 29.0 \n", + "24 202105 3 22426 18445.0 26407.0 34 28.0 \n", + "25 202104 3 25804 21491.0 30117.0 39 32.0 \n", + "26 202103 3 21810 17894.0 25726.0 33 27.0 \n", + "27 202102 3 17320 13906.0 20734.0 26 21.0 \n", + "28 202101 3 21799 17778.0 25820.0 33 27.0 \n", + "29 202053 3 21220 16498.0 25942.0 32 25.0 \n", + "... ... ... ... ... ... ... ... \n", + "1887 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1888 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1889 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1890 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1891 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1892 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1893 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1894 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1895 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1896 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1897 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1898 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1899 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1900 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1901 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1902 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1903 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1904 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1905 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1906 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1907 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1908 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1909 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1910 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1911 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1912 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1913 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1914 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1915 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1916 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 38.0 FR France \n", + "1 18.0 FR France \n", + "2 22.0 FR France \n", + "3 16.0 FR France \n", + "4 16.0 FR France \n", + "5 10.0 FR France \n", + "6 13.0 FR France \n", + "7 16.0 FR France \n", + "8 16.0 FR France \n", + "9 20.0 FR France \n", + "10 18.0 FR France \n", + "11 22.0 FR France \n", + "12 23.0 FR France \n", + "13 31.0 FR France \n", + "14 35.0 FR France \n", + "15 38.0 FR France \n", + "16 47.0 FR France \n", + "17 53.0 FR France \n", + "18 44.0 FR France \n", + "19 35.0 FR France \n", + "20 33.0 FR France \n", + "21 38.0 FR France \n", + "22 40.0 FR France \n", + "23 41.0 FR France \n", + "24 40.0 FR France \n", + "25 46.0 FR France \n", + "26 39.0 FR France \n", + "27 31.0 FR France \n", + "28 39.0 FR France \n", + "29 39.0 FR France \n", + "... ... ... ... \n", + "1887 59.0 FR France \n", + "1888 64.0 FR France \n", + "1889 97.0 FR France \n", + "1890 93.0 FR France \n", + "1891 80.0 FR France \n", + "1892 116.0 FR France \n", + "1893 149.0 FR France \n", + "1894 281.0 FR France \n", + "1895 395.0 FR France \n", + "1896 485.0 FR France \n", + "1897 544.0 FR France \n", + "1898 689.0 FR France \n", + "1899 722.0 FR France \n", + "1900 762.0 FR France \n", + "1901 926.0 FR France \n", + "1902 1113.0 FR France \n", + "1903 1236.0 FR France \n", + "1904 832.0 FR France \n", + "1905 459.0 FR France \n", + "1906 207.0 FR France \n", + "1907 190.0 FR France \n", + "1908 198.0 FR France \n", + "1909 224.0 FR France \n", + "1910 266.0 FR France \n", + "1911 219.0 FR France \n", + "1912 176.0 FR France \n", + "1913 163.0 FR France \n", + "1914 195.0 FR France \n", + "1915 308.0 FR France \n", + "1916 213.0 FR France \n", + "\n", + "[1916 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -126,7 +2122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -156,10 +2152,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -183,9 +2177,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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", @@ -203,9 +2205,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.plot(style='*')" ] @@ -318,9 +2387,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "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": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -335,9 +2450,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] @@ -345,9 +2483,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } 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/module3_travail_pratique.ipynb b/module3/exo3/module3_travail_pratique.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4c2498d0e2dad6a6fa03967befcafd8e38650011 --- /dev/null +++ b/module3/exo3/module3_travail_pratique.ipynb @@ -0,0 +1,1066 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sujet 1 : Concentration de CO2 dans l'atmosphère depuis 1958" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import os.path\n", + "import pandas as pd\n", + "import datetime\n", + "import scipy.signal as sps\n", + "import scipy.optimize as spo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Récupération des données" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On va commencer par mettre les données dans un dataframe pandas. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = r'https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv'\n", + "data_file = r'weekly_in_situ_co2_mlo.csv'\n", + "\n", + "if not os.path.exists(data_file):\n", + " print('please downlad the csv file of the following url', data_url, '\\n', 'and put it in the jupyter repository, at:', \n", + " 'https://app-learninglab.inria.fr/moocrr/jupyter/user/your_unique_identifier/edit/work/module3/exo3/weekly_in_situ_co2_mlo.csv')\n", + "else: \n", + " data = pd.read_csv(data_file, skiprows=44, names=['week', 'CO2'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "J'ai eu des problèmes pour télécharger automatiquement les données (problème de certificat: cf [ce post sur StackOverflow](https://stackoverflow.com/questions/32400867/pandas-read-csv-from-url)), et puis ensuite pour y accéder depuis Jupyter. \n", + "La solution la plus simple que j'ai trouvée est de télécharger à la main les données, puis de les réenvoyer dans le dossier Jupyter en ligne (cf [ce post](https://stackoverflow.com/questions/46972225/how-to-open-local-file-on-jupyter))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Nettoyage et mise en forme des données" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On visualise ensuite rapidement les données:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " week CO2\n", + "0 1958-03-29 316.19\n", + "1 1958-04-05 317.31\n", + "2 1958-04-12 317.69\n", + "3 1958-04-19 317.58\n", + "4 1958-04-26 316.48" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3229, 2)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On a deux colonnes et 3229 lignes (une par semaine). " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "week object\n", + "CO2 float64\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On voit que la première colonne est du type \"objet\", il nous faut le transformer en un format \"date\":" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data['week'] = data['week'].astype('datetime64[ns]')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "week datetime64[ns]\n", + "CO2 float64\n", + "dtype: object" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On vérifie qu'il n'y a pas de données manquantes en regardant si les semaines se suivent bien." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1958-05-03 00:00:00 1958-05-17 00:00:00\n", + "1958-05-24 00:00:00 1958-07-05 00:00:00\n", + "1958-08-16 00:00:00 1958-08-30 00:00:00\n", + "1958-09-06 00:00:00 1958-11-08 00:00:00\n", + "1959-01-31 00:00:00 1959-02-14 00:00:00\n", + "1959-03-07 00:00:00 1959-03-21 00:00:00\n", + "1959-05-23 00:00:00 1959-06-06 00:00:00\n", + "1959-08-08 00:00:00 1959-08-22 00:00:00\n", + "1962-08-18 00:00:00 1962-09-15 00:00:00\n", + "1962-12-22 00:00:00 1963-01-05 00:00:00\n", + "1963-02-09 00:00:00 1963-02-23 00:00:00\n", + "1963-04-27 00:00:00 1963-05-11 00:00:00\n", + "1963-11-16 00:00:00 1963-11-30 00:00:00\n", + "1964-01-18 00:00:00 1964-05-30 00:00:00\n", + "1964-06-06 00:00:00 1964-06-27 00:00:00\n", + "1964-08-01 00:00:00 1964-08-15 00:00:00\n", + "1966-07-09 00:00:00 1966-08-06 00:00:00\n", + "1966-10-29 00:00:00 1966-11-12 00:00:00\n", + "1967-01-14 00:00:00 1967-02-04 00:00:00\n", + "1976-06-19 00:00:00 1976-07-03 00:00:00\n", + "1984-03-24 00:00:00 1984-04-28 00:00:00\n", + "1985-07-27 00:00:00 1985-08-10 00:00:00\n", + "2003-06-07 00:00:00 2003-06-21 00:00:00\n", + "2003-10-04 00:00:00 2003-10-25 00:00:00\n", + "2005-02-19 00:00:00 2005-03-26 00:00:00\n", + "2006-02-04 00:00:00 2006-02-25 00:00:00\n", + "2007-01-20 00:00:00 2007-02-03 00:00:00\n", + "2012-09-29 00:00:00 2012-10-20 00:00:00\n", + "2020-01-11 00:00:00 2020-01-25 00:00:00\n" + ] + } + ], + "source": [ + "for p1, p2 in zip(data['week'][:-1], data['week'][1:]):\n", + " delta = p2-p1\n", + " if delta > 7*pd.Timedelta('1day'):\n", + " print(p1,p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On constate qu'il manque quelques données. Il faudra prendre cela en compte par la suite. Pour faciliter le traitement des données, on va temporairement indexer le dataframe avec la première colonne, celle des semaines. Puis on va remplir les lignes manquantes avec des 'NaN'. Cela facilitera grandement l'analyse de la période. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "data.set_index('week', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.resample('W').sum()\n", + "data.replace(0, np.nan, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On vérifie à nouveau s'il manque des lignes: cette fois, on a des NaN à la place." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "for p1, p2 in zip(data.index[:-1], data.index[1:]):\n", + " delta = p2-p1\n", + " if delta > 7*pd.Timedelta('1day'):\n", + " print(p1,p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1958-04-27316.48
1958-05-04316.95
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1958-08-10315.10
1958-08-17315.09
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1958-08-31314.14
1958-09-07313.54
1958-09-14NaN
1958-09-21NaN
1958-09-28NaN
1958-10-05NaN
1958-10-12NaN
1958-10-19NaN
......
2020-12-06413.00
2020-12-13413.60
2020-12-20414.34
2020-12-27414.64
2021-01-03415.19
2021-01-10414.83
2021-01-17414.84
2021-01-24415.46
2021-01-31415.68
2021-02-07416.91
2021-02-14416.46
2021-02-21416.16
2021-02-28416.43
2021-03-07417.56
2021-03-14416.54
2021-03-21417.93
2021-03-28416.43
2021-04-04417.69
2021-04-11419.02
2021-04-18417.66
2021-04-25418.54
2021-05-02419.65
2021-05-09418.16
2021-05-16418.90
2021-05-23417.94
2021-05-30419.49
2021-06-06419.54
2021-06-13418.93
2021-06-20418.49
2021-06-27417.82
\n", + "

3301 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " CO2\n", + "week \n", + "1958-03-30 316.19\n", + "1958-04-06 317.31\n", + "1958-04-13 317.69\n", + "1958-04-20 317.58\n", + "1958-04-27 316.48\n", + "1958-05-04 316.95\n", + "1958-05-11 NaN\n", + "1958-05-18 317.56\n", + "1958-05-25 317.99\n", + "1958-06-01 NaN\n", + "1958-06-08 NaN\n", + "1958-06-15 NaN\n", + "1958-06-22 NaN\n", + "1958-06-29 NaN\n", + "1958-07-06 315.85\n", + "1958-07-13 315.85\n", + "1958-07-20 315.46\n", + "1958-07-27 315.59\n", + "1958-08-03 315.64\n", + "1958-08-10 315.10\n", + "1958-08-17 315.09\n", + "1958-08-24 NaN\n", + "1958-08-31 314.14\n", + "1958-09-07 313.54\n", + "1958-09-14 NaN\n", + "1958-09-21 NaN\n", + "1958-09-28 NaN\n", + "1958-10-05 NaN\n", + "1958-10-12 NaN\n", + "1958-10-19 NaN\n", + "... ...\n", + "2020-12-06 413.00\n", + "2020-12-13 413.60\n", + "2020-12-20 414.34\n", + "2020-12-27 414.64\n", + "2021-01-03 415.19\n", + "2021-01-10 414.83\n", + "2021-01-17 414.84\n", + "2021-01-24 415.46\n", + "2021-01-31 415.68\n", + "2021-02-07 416.91\n", + "2021-02-14 416.46\n", + "2021-02-21 416.16\n", + "2021-02-28 416.43\n", + "2021-03-07 417.56\n", + "2021-03-14 416.54\n", + "2021-03-21 417.93\n", + "2021-03-28 416.43\n", + "2021-04-04 417.69\n", + "2021-04-11 419.02\n", + "2021-04-18 417.66\n", + "2021-04-25 418.54\n", + "2021-05-02 419.65\n", + "2021-05-09 418.16\n", + "2021-05-16 418.90\n", + "2021-05-23 417.94\n", + "2021-05-30 419.49\n", + "2021-06-06 419.54\n", + "2021-06-13 418.93\n", + "2021-06-20 418.49\n", + "2021-06-27 417.82\n", + "\n", + "[3301 rows x 1 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualisation des données" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut maintenant tracer le graphique. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(data['CO2'])\n", + "plt.title('C02 concentration in the atmosphere at Mauna Loa observatory')\n", + "plt.ylabel('CO2 concentration (ppm)')\n", + "plt.xlabel('year')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On va maintenant réaliser une moyenne glissante sur les données, sur 1 an, pour s'affranchir de la périodicité annuelle. On travaille directement avec le temps,(cf [la doc de pandas sur la fonction rolling](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html))." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "data['CO2_smooth'] = data['CO2'].rolling('31536000s').mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On va maintenant superposer les deux jeux de données: " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(data['CO2'], label='weekly C02 concentration')\n", + "plt.plot(data['CO2_smooth'], label='C02 concentration smoothed over a year')\n", + "plt.title('C02 concentration in the atmosphere at Mauna Loa observatory')\n", + "plt.ylabel('CO2 concentration (ppm)')\n", + "plt.xlabel('year')\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Etude de l'oscillation périodique\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour faciliter le traitement de l'oscillation périodique, on repasse l'index en numérique plutôt qu'en temps. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.reset_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour caractériser l'oscillation périodique, on va soustraire à la concentration brute l'évolution de la concentration moyennée sur 1 an. Dans ce cas simple, il suffit de zoomer un peu sur la courbe pour voir que l'oscillation est annuelle. \n", + "Mais pour aller un peu plus loin, essayons de mieux caractériser cette oscillation. Elle s'apparente à un signal triangle, de période 1 an, et d'amplitude 4ppm. On va donc superposer un tel signal, créé numériquement. On est obligés de le tracer en fonction d'un index recréé, car sawtooth ne peut pas gérer le format datetime. Mais puisque il ne manque pas de dates, on a une parfaite correspondance entre les deux. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "data['CO2-CO2_smooth'] = data['CO2'] - data['CO2_smooth']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i, j = 0,300\n", + "plt.plot(data['week'][i:j], data['CO2-CO2_smooth'][i:j], label='C02 concentration corrected from yearly change', color='g')\n", + "plt.plot(data['week'][i:j],4*sps.sawtooth(2*np.pi*data.index*1/52.1429-10, width=0.5)[i:j], label='analytical signal')\n", + "plt.ylabel('CO2 concentration (ppm)')\n", + "plt.xlabel('year')\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En zoomant sur quelques endroits de la courbe avec i et j (entre 0 et 3301), on peut voir que ce signal triangle analytique se superpose bien aux données.Si on voulait être beaucoup plus précis, on pourrait faire de l'optimisation de ces paramètres pour que les deux signaux soient les plus proches possibles, mais je considère que cela n'apporterait pas beaucoup plus de précision. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Etude de la variation de la concentration en C02 à échelle anuelle\n", + "On va regarder l'évolution de la concentration en C02 corrigée de la variation saisonnière." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(data['week'],data['CO2_smooth'], label='C02 concentration smoothed over a year', color='orange')\n", + "plt.title('C02 concentration in the atmosphere at Mauna Loa observatory')\n", + "plt.ylabel('CO2 concentration (ppm)')\n", + "plt.xlabel('year')\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On va essayer de fitter deux modèles sur l'évolution de la concentration en C02: un modèle linéaire et un modèle exponentiel. Pour cela, on utilise les fonctions d'optimisation de la bibliothèque scipy.optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: overflow encountered in exp\n", + " \n" + ] + }, + { + "data": { + "image/png": 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Y0hdpsTD2XczWJ6uc/QU3q2p/Vd0tIosxzqKiMA6/4u8HliyRkZGcOHGCiIhk9/GzWCzpDC8vL0qWLImnp2dai2JJAzL01kH+/v4a36jhyJEj5MmTh0KFCnHthroWiyU9o6qcO3eOixcvUq5cubQW545HRLarqn9qnjPT7dQQERFhlZHFkgEREQoVKmRHN+5gMp1CAqwyslgyKPbZvbPJlArJYrFY7mSiI6OJ3PMBRF9Ja1FuCKuQUoBTp07RrVs3ypcvT9WqVWnTpg379+8HYN68eVSoUIEKFSowb968uDzdu3enUqVKeHt706dPHyIjk3KImz5Yt24dGzfeuI+/HTt28P3338cdL1++nMmTr3MCm64JCgri88/jXMwwd+5cnnnmmZsub926dbRr1+52iGa507kaytZZg+n5ZB5igtzxA5l+sArpNqOqPPTQQwQEBHDo0CH27NnDa6+9xunTpzl//jwTJ05ky5YtbN26lYkTJxISEgIYhbR371527txJeHg4H374YRrXJHmSUkhRUVGJ5ouvkDp06MDIkSNvu3wpSXyFlJFI6t6kNulJlsyAXj4NqwOoX2Q2Y8Zlw+OeBH2YplusQrrNrF27Fk9PT/r37x8X5ufnR6NGjVi5ciUtW7akYMGCFChQgJYtW7JihXFw2aZNG0QEEaFOnTqcOHHiurKjo6MZOnQoPj4++Pr6MmPGDADWrFlDjRo18PHxoU+fPly5YrrpZcuWZfz48dSsWRMfHx/27jVOSsPCwujdu3dcOV9++SUAP/74I/Xr16dmzZp06dKFsLCwRMsJCgpi1qxZTJs2DT8/P3755ReeeOIJXnjhBZo2bcqIESPYunUrDRo0oEaNGjRo0IB9+/Zx9epVxo0bx6JFi/Dz82PRokXX9C6OHj1K8+bN8fX1pXnz5hw7dgyAJ554gueee44GDRpwzz338MUXX1x3fS5dukTbtm2pXr063t7eLFq0KE7+0aNHU79+ffz9/fntt99o3bo15cuXZ9asWYD5kBg2bBje3t74+PjE5U0sfOTIkfzyyy/4+fkxbdo0AE6ePMn9999PhQoVGD58eJxciV3XFStWULlyZRo2bMhXX8V31mqIiIiIu1c1atRg7dq1ANStW5fdu3fHpQsICGD79u1cunSJPn36ULt2bWrUqMGyZcsA04Pr0qUL7du3p1WrVted58EHH6RWrVpUq1aN2bNnXxe/Zs0aHnroobjjVatW0alTpyTr9/LLL1O7dm28vb3p168fsRa9AQEBjB49miZNmvDOO+8kWG/LjXM5+DgdAnbz7bry0ORbvB/oBBltTk5VM+yvVq1aGp89e/b8dxA4WHVVk9v7Cxx83Tldeeedd3TIkCEJxk2dOlVfeeWVuOOXX35Zp06dek2aq1evao0aNXT9+vXX5Z85c6Z26tRJIyMjVVX13LlzGh4eriVLltR9+/apqmrPnj112rRpqqpapkwZnT59uqqqvvfee/rkk0+qqurw4cN18OD/6nH+/HkNDg7WRo0aaVhYmKqqTp48WSdOnJhkOePHj79G/l69emnbtm01KipKVVUvXLgQJ+uqVau0U6dOqqo6Z84cHTRoUFw+1+N27drp3LlzVVX1o48+0o4dO8aV3blzZ42Ojtbdu3dr+fLlr7s+X3zxhfbt2zfuODQ0NE7+mTNnqqrqkCFD1MfHR//99189c+aMFilSJC5vixYtNCoqSk+dOqWlSpXSkydPJhq+du1abdu27TV1KFeunIaGhmp4eLiWLl1ajx07luh1jb1v+/fv15iYGO3Spcs15cXy5ptv6hNPPKGqqn/99ZeWKlVKw8PD9a233tJx48apqurJkye1QoUKqqo6atQonT9/vqqqhoSEaIUKFTQsLEznzJmjJUqU0HPnzl13DlWNC798+bJWq1ZNz549e018TEyMVqpUSc+cOaOqqo8++qguX748yXbjeq4ePXro8uXLVVW1SZMmOmDAgATlUI33DFvcIjRolzasskU9JEo/fufAbSkTCNRUfqfbHlIqogms+YpvVTRw4EAaN25Mo0aNrku7evVq+vfvT9asZj1zwYIF2bdvH+XKlaNixYoA9OrVi/Xr18flif2KrVWrFkFBQXHlDBo0KC5NgQIF2Lx5M3v27OG+++7Dz8+PefPmcfTo0STLSYguXbqQJYvxGnLhwgW6dOmCt7c3zz///DVf9ImxadMmHnvsMQB69uzJhg0b4uIefPBBPDw8qFq1KqdPn74ur4+PD6tXr2bEiBH88ssv5MuXLy6uQ4cOcWnq1q1Lnjx5KFKkCF5eXoSGhrJhwwYeffRRsmTJwl133UWTJk3Ytm1bouEJ0bx5c/Lly4eXlxdVq1bl6NGjiV7XvXv3Uq5cOSpUqICI0KNHjwTL3LBhAz179gSgcuXKlClThv379/PII4+wZImZH1i8eDFdunQBTG9l8uTJ+Pn5ERAQQERERFwvM7Z3nhDTp0+nevXq1KtXj+PHj3PgwIFr4kWEnj178umnnxIaGsqmTZt44IEHkmw3a9eupW7duvj4+PDTTz9dc/+7du2aoByWG+fs3q00axrJ5v01WPDhcXo/d29ai3TTZG4X5rXeTvVTVqtWLcHhJICSJUuybt26uOMTJ04QEBAQdzxx4kSCg4N5//33E8yvqtcpsISUnCvZs2cHIEuWLHHj9YmV07JlSxYsWOB2OQmRK1euuP/Hjh1L06ZNWbp0KUFBQdfU1V1c5YyVIVbe+FSsWJHt27fz/fffM2rUKFq1asW4ceOuyevh4XFNOR4eHkRFRSV6HZO7vq64lht7nRK7rjt27HDLxDmx85coUYJChQrx559/smjRorg2o6p8+eWXVKpU6Zr0W7ZsuebeuLJu3TpWr17Npk2byJkzZ5wii0/v3r1p3749Xl5edOnShaxZsyZav4iICAYOHEhgYCClSpViwoQJ15SZmCyWGyN071qatLyLw2fuYdniENp0KpvWIt0Stod0m2nWrBlXrlzhgw8+iAvbtm0bP//8M61bt+bHH38kJCSEkJAQfvzxR1q3bg3Ahx9+yMqVK1mwYAEeHgnfllatWjFr1qw4hXD+/HkqV65MUFAQBw8eBGD+/Pk0adIkSRlbtWrFu+++G3ccEhJCvXr1+PXXX+PKuXz5cpxlYGLkyZOHixcvJhp/4cIFSpQwHkTmzp3rVr4GDRqwcOFCAD777DMaNmyYpAyunDx5kpw5c9KjRw+GDh3Kb7/95nbexo0bs2jRIqKjowkODmb9+vXUqVMn0fDk6h5LYte1cuXKHDlyhEOHDgEk+iHQuHFjPvvMbIi/f/9+jh07FqdsunXrxpQpU7hw4QI+Pj4AtG7dmhkzZsQpst9//z1ZGS9cuECBAgXImTMne/fuZfPmzQmmK168OMWLF+fVV1/liSeeSLJ+scqncOHChIWFJfqRZrkFji8l3+/307buVlZ8c5k2nYqmtUS3jFVItxkRYenSpaxatYry5ctTrVo1JkyYQPHixSlYsCBjx46ldu3a1K5dm3HjxsUNofTv35/Tp09Tv359/Pz8ePnll68ru2/fvpQuXRpfX1+qV6/O559/jpeXF3PmzKFLly74+Pjg4eFxjUFFQowZM4aQkBC8vb2pXr06a9eupUiRIsydO5dHH30UX19f6tWrF2cEkRjt27dn6dKlcUYN8Rk+fDijRo3ivvvuIzr6v20JmzZtyp49e+KMGlyZPn06c+bMwdfXl/nz59/QpPfOnTupU6cOfn5+TJo0iTFjxrid96GHHoq7rs2aNWPKlCncfffdiYb7+vqSNWtWqlevHmfUkBCJXVcvLy9mz55N27ZtadiwIWXKlEkw/8CBA4mOjsbHx4euXbsyd+7cuJ5Y586dWbhwIY888khc+rFjxxIZGYmvry/e3t6MHTs22brff//9REVF4evry9ixY6lXr16iabt3706pUqWoWrVqkvXLnz8/Tz31FD4+Pjz44IPUrl07WTks7rNn5TL2LR6NFKzJlM870qRVwkOxGY1Mt5fdX3/9RZUqVdJIIoslc/PMM89Qo0YNnnzyyRQ7h32Gk+a3Zcto3bMB5YqfY8vOUohnygx/psVedpl7Dslisdw2atWqRa5cufi///u/tBbljmXDl+tp2zOAAnkj+PzrsoinV1qLdFuxCslisbjF9u0JOn+2pBIrvzjAQ939KVUkmNW/3EWpcplLGYFVSBaLxZLu0UsnefOVf6hYPJIf1xWmaJnMp4zAKiSLxWJJ11wNPUG2X1uzZEgoNF1B/jIZ35ouMayVncVisaRDNCaGsYO206L+UcLPnyJ/m8/JX9YnrcVKUaxCslgslnRGdOgh+nf4jldn1qJiuQt4tt8GdyW9vjAzYBVSCpA7d27ALNTs3Llzqp77ypUrtGjRIm6NT9++fdmzZw8Ar732WqrKYrFYbpyIQyt4pPVuZn/XntEDdvDBtw+QNf89aS1WqmDnkFKQ4sWLp/gK9aioqLi97cCszI+MjGTHjh3AtXuGvfbaa4wePTpF5bFYLDeJKvw1hX5PF+OrrY8z7Y3zDBnul9ZSpSq2h5SCBAUF4e3tDZitczp16nRD7gludPv+M2fO0KNHD3bs2IGfnx+HDh0iICCAwMBARo4cSXh4OH5+fnTvnrF8pFgsmZ6rF2Bjd9gxktH9d7Dg0ysMGX4Luy9ERIDjay0jkfkVUkDA9b+ZM03c5csJx8fuu3b27PVxt8COHTtYtGgRO3fuZNGiRRw/fpyzZ8/y6quvsnr1an777Tf8/f156623ALMqftu2bezatYvw8HC+/fbbuLJCQ0P5+eefefHFF+PCihYtyocffkijRo3YsWMH5cuXj4ubPHkyOXLkYMeOHXF7o1kslnRAyA4Oz+nAxGkVUZ9XqfzY/9Gte/bk8yWEKnz9NVSrBoMH3145UwE7ZJeKxLonAOLcE4SGhsZt3w9w9epV6tevD5jt+6dMmcLly5c5f/481apVo3379oDdvt9iyRT8e4AdHz3P/ZOWECkF6P2WJ6Vv1qeeKjz0ECxbBlWrguO2JCOR+RWSi7uH68iZM+n4woWTjr9BbsQ9gd2+32LJ5FwN5ce3J/Pw69+Qv1B21q7ypHTpmyjn338hTx7jHTYgAJo3hwEDIGvGe71n/iG7dE5qbt/v6elJZGTkLZdjsVhukZgoPh3/Pm1fnsU998DmLZ7c8H6y0dHwv//BPffA0qUmbMgQePbZDKmMwCqkNCc1t+/v168fvr6+1qjBYklrfnuBAlHrad7gNOs35sZxG+Y+69ZBzZowcCB4e0OFCikhZapj3U9YLJZ0RWZ+hqOiYMOi7wiQdlD5BbTG/+GG4+BrefZZePddKFMG3nwTHn6YGy8keaz7CYvFYsmkhIVB14cvsmLV/ez+uA+V/aa4r0cuXzbDcNmyQcOGULQoDB0KOXKkqMypTYoP2YlIFhH5XUS+dY4LisgqETng/C3gknaUiBwUkX0i0jqlZbNYLJbU4PRpCGgSxYrVOflfv+FU7joJPLIkn1EVFi6ESpVgxgwT1rUrjB2b6ZQRpM4c0mDgL5fjkcAaVa0ArHGOEZGqQDegGnA/MFNE3LhjFovFkn7Ztw/q14/hr92RLHuxM/0mdoAcdyef8bffoHFjePRRKFIEknAtn1lIUYUkIiWBtsCHLsEdgXnO//OAB13CF6rqFVU9AhwE6qSkfBaLxZLS/LD4IGHnQ1g3JoB2g7q5t0nqlCng72+02QcfwLZt4KxVzMykdA/pbWA4EOMSdpeq/gPg/I117lECOO6S7oQTdg0i0k9EAkUkMDg4OGWktlgsllvk3NkY2DWJweUrsXvGA9Tu8zKUSWJB+9WrZqIJjPIZMgT274e+fSHLnTFYlGIKSUTaAWdU1V2/xwlN711nAqiqs1XVX1X9ixQpcksyWiwWy+1GFd6YdJkK91ziwI9zkTJdKdLtJyiexLT4ihXg6wsjR5rj++6Dt96C/PlTR+h0Qkr2kO4DOohIELAQaCYinwKnRaQYgPP3jJP+BFDKJX9J4GQKymfhepcUDRo0SDZPrHuN+EyfPp0qVarQvXt3li9fzuTJkwH4+uuv41xgWCyZmatXoW+PM4wck5PW3t9TqtUwaPAZeCafRaPYAAAgAElEQVT8zHDgALRrBw88YBa6PvBA6gqc3lDVFP8BAcC3zv9TgZHO/yOBKc7/1YA/gOxAOeAwkCWpcmvVqqXx2bNnz3VhlsTJlSvXbctTqVIlPXz48HXhvXr10iVLltzweSx3Jhn1GT5/5pI2rbVXQXVc17c1Jnhr0hnmz1f19FTNk0d1yhTViIjUEdRNgEBNBf3g+kuLnRomAy1F5ADQ0jlGVXcDi4E9wApgkKpGp4F8t8ynn35KnTp18PPz4+mnnyY6OpqjR49SoUIFzp49S0xMDI0aNeLHH38kKCiIypUr06tXL3x9fencuTOXL18GYM2aNdSoUQMfHx/69OnDlStXAChbtizjx4+nZs2a+Pj4sHfvXgAuXbpEnz59qF27NjVq1GDZsmVA4q4vEnJJEdv7CQsLo3nz5nHniC0rMfr378/hw4fp0KED06ZNY+7cuTzzzDNs3LiR5cuXM2zYsDiXGBZLpiP8NG88+zUbdpTjk4mfMXF+H6RwArurxMT85xaiQQN4/HEzTzRsGGS/yR2+MxOprQFv58+dHlKTJtf/3nvPxF26lHD8nDkmPjj4+rjk2LNnj7Zr106vXr2qqqoDBgzQefPmqarqBx98oA8//LBOmTJF+/Xrp6qqR44cUUA3bNigqqq9e/fWqVOnanh4uJYsWVL37dunqqo9e/bUadOmqapqmTJldPr06aqq+t577+mTTz6pqqqjRo3S+fPnq6pqSEiIVqhQQcPCwnTOnDlarlw5DQ0N1fDwcC1durQeO3ZMVa/v7cQeR0ZG6oULF5zrEKzly5fXmJiYBPPEUqZMGQ0ODlZV1Tlz5uigQYNU1faQLDdGRushRZ4/oPp1GQ2fl1e3LVuReMKNG1X9/VXbtEk94W4B7pAeUqZmzZo1bN++ndq1a+Pn58eaNWs4fPgwAH379uXixYvMmjWLN998My5PqVKl4txP9OjRgw0bNrBv3z7KlStHxYoVAejVqxfr16+Py9OpUycAatWqRVBQEGAc/U2ePBk/Pz8CAgKIiIjg2LFjwH+uL7y8vOJcXySFqjJ69Gh8fX1p0aIFf//9N6dPn749F8liySR8NuU7avldIiQ0C15t1+DfIQHDhb//Nq4gGjSAkyfhsceM5YPlOjL91kGp7X1CVenVqxevv/76dXGXL1/mxIkTgBkSy5MnDwASb/8QEYmde0uUWFcWsW4sYs/95ZdfUqlSpWvSbtmyJUHXF0nx2WefERwczPbt2/H09KRs2bLXuL+wWO5kNEaZOGA1E2e3pUnVX9Em30OhStcnXLMGOnQwBgujR8OoUZCIUZDF7vZ922nevDlffPEFZ84Y48Hz58/H9UZGjBhB9+7defnll3nqqafi8hw7doxNmzYBsGDBAho2bEjlypUJCgqKc0sxf/58mjRJekFd69atmTFjRpwy+/3335OVNzGXFBcuXKBo0aJ4enqydu3aZHtUSZEnTx4uXrx40/ktlvRERAT07LCTibNb8kS7Tfy4vR4Fy7ooI1U4d8787+8PjzwCe/bApElWGSWDVUi3mapVq/Lqq6/SqlUrfH19admyJf/88w8///wz27Zti1NK2bJlY86cOQBUqVKFefPm4evry/nz5xkwYABeXl7MmTOHLl264OPjg4eHB/3790/y3GPHjiUyMhJfX1+8vb0ZO3ZssvIm5pKie/fuBAYG4u/vz2effUblypVv+pp069aNqVOnUqNGDWvUYMnwvNBnF59958tr/b/g42X1yOblsmh11y5o2dI4youKgnz5YM4c47PIkizW/UQaExQURLt27di1a1dai2KxpAvS6zOsCnJiKf988wxbQwbScdQI8HBmPc6fh/HjjcO8vHnhlVfg6aczrKM8SKfuJ0SkKGaRa3EgHNiFsb6ISTKjxWKxZBIWLYJF806xuHsPipX3pWPz5/9TRrt2QZMmEBpqXIdPnAiFCqWtwBmURIfsRKSpiKwEvgMeAIoBVYExwE4RmSgieVNHzMxL2bJlbe/IYkmnxMTA2DExdOsGZw4dJCxbTWjyDWTNCbF7aVaubJzk7dhhHOdZZXTTJNVDagM8parH4keISFagHWZh65cpJNtNo6rXWa5ZLJb0T3qaQggLg8d7RrP06yz0afIRMyf9Qfa6K+BEMAztD7/8Yha15ssHs2entbiZgkQVkqoOSyIuCvg6RSS6Rby8vDh37hyFChWySsliyUCoKufOncPLyyutRQHgkU4XWbkmJ2/3HMxzLxZA7n0dJk6GqVPN7tujRhkPrpbbhjtzSPmBx4GyrulV9bmUE+vmKVmyJCdOnMC6prBYMh5eXl6ULFkybYWIiYYDM5kYsJghdYvRakBvyFLTDM2dOGEWtr7xBqS1nJkQd0xAvgc2Azu51q9RusTT05Ny5cqltRgWiyWjocqHb2zl0PY/eP2h56hdvzWUGwPFfU18167w0EN3hKO8tMIdheSlqi+kuCQWi8WSRlyNiOKFnht474sAWteKIrL8h3jO3ggL6hkrunvuAZftviwpgzsKab6IPAV8C1yJDVTV8ykmlcVisaQSp/5RujxwhA1/BDC0xzpe9w4ka6tX4PJlGDzYWs2lIu4opKsYH0Yv8Z8HVwXs0mOLxZKhibx4loa1I/nnbHEWvPo53T6fBJ/uMY7ypk2DSgnsT2dJMdxRSC8A96rq2ZQWxmKxWFKNiGA8f27ElLa+3PtAO3w79gSPo1C9OrRpk9bS3ZG4o5B2A5dTWhCLxWJJDa5cgWcHRnBfjnfoFXSQTj8egscHg4gx5bakGe4opGhgh4is5do5pHRp9m2xWCyJ8fff8PCDV9gS6EXxbFkhMgb69IF7701r0Sy4p5C+Jp0ugrVYLBZ32fBzJJ07RxJ2PoYldKez718waxvUqpXWolkcklVIqjpPRLIBlTHGDPtU9WqKS2axWCy3icPrdtKsZWXKFj7Bmh5DqNYoAJ5cYobpLOkGd3ZqaAO8DxwCBCgnIk+r6g8pLZzFYrHcChoegUx4mnvemc8HTfrQ8Z2HyF/1O6uI0inuOOh7C2iqqgGq2gRoCkxLWbEsFovlFlBl/zvfUjvfHrZM2Qu1ctPrzU7kr9bWKqN0jDtzSGdU9aDL8WHgTArJY7FYLLfMF41ep8+vz5DN4yqXhrSFN9dDluxpLZYlGdwy+xaR74HFmDmkLsA2EekEoKpfpaB8FovF4h7nzxMZc5URA7cw7dfR1C0ayJJvLlGqzri0lsziJu4M2XkBp4EmQAAQDBQE2mN8IlksFkvaERUF770H95ZnTtupTFvSkece28L6wxUpVadJWktnuQHcsbLrnRqCWCwWyw3z008weDAXdwWRp1o4T3b+mPJDH6N5l7ppLZnlJki2hyQi94jINyISLCJnRGSZiFj/DhaLJW2ZMIGY5i149UgnKuXax8kx7cky4Head7HrijIq7gzZfY6ZPyoGFAeWAAtTUiiLxWJJkEuX4Px50BjOlwqjfbFvGXtpIk1bRJK37VeQu2xaS2i5BdxRSKKq81U1yvl9yn+7fieeScRLRLaKyB8isltEJjrhfiKyWUR2iEigiNRxyTNKRA6KyD4RaX3z1bJYLJkKVfjsM7P79vPPsGnWWGqMeJbVZ1sy8+0LfLq0DLnzWHPujI47VnZrRWQkplekQFfgOxEpCEn6RboCNFPVMBHxBDaIyA/Ay8BEVf3BWXQ7BQgQkapAN6Aapie2WkQqqmr0rVTQYrFkcAIDjV+ijRuhegWo+A3T5n1Mlhz52PB9VmrXyZfWElpuE+4opK7O36fjhfchCb9IqqpAmHPo6fzU+eV1wvMBJ53/OwILVfUKcEREDgJ1gE1uyGixWDIjH30ETz0FRYpwZmQ/wu9aQZmK5ZndwxvJn598VhdlKtyxsrtpAwYRyQJsB+4F3lPVLSIyBFgpIm9ihgwbOMlLAJtdsp9wwuKX2Q/oB1C6dOmbFc1isaRXrl6Fc+egWDFo3RqGDWNd2Wo8NroF95YezM+BZcjvmSutpbSkAInOIYlIw6QyikheEfFOKo2qRquqH1ASqOOkHwA8r6qlgOeBj2KLTKiIBMqcrar+qupfpEiRpE5vsVgyGt99B97e8NhjoEp0sZJMCO9K80HdyZtXmTG3ImKVUaYlKaOGh0Vko4iME5G2IlJHRBqLSB8RmQ98C+Rw5ySqGgqsA+4HegGxuzsswQzLgekRlXLJVpL/hvMsFktmZu9e46W1XTvw8IDhwwk+K7S47yQTZ9SkR4ufCNxZlOo13JllsGRUElVIqvo80Bb4B7Nd0CsYd+YVgPdVtbGqbkssv4gUEZH8zv85gBbAXoySiV0+3Qw44Py/HOgmItmddU4VgK23UDeLxZIRWLECfHzg11/hrbfgzz+hRUNyHniJS8EnmDvi/5j3/X3kzuuZ1pJaUpgkPzdUNQT4wPndKMWAec48kgewWFW/FZFQ4B0RyQpE4MwHqepuEVkM7AGigEHWws5iyaRERxv3raVLQ6NG8NxzMGIEkblz8vbY7QzwfpjcWf5h86dn8agz3W6MeocgxhguY+Lv76+BgYFpLYbFYrkRfv3VKKCLF2HXLsiWDa6c5+CXL/HY2N5sO1yHucNep9eQmlDcLkdMK0Rku6r6p+Y57YCsxWJJHU6cgOHDYcECKFkSpkwBT0/00knmjXmPZ/43Fc9sHiyZuYnO/UeAuLNu35KZsArJYrGkPH/8AQ0aQEwMjB0LI0ZArlxw8SCvPr2ccQsmEdAghE8WFqBUqfppLa0ljXBLIYlIA6Csa3pV/SSFZLJYLJkBVQgKgnLljNHCs89C//5QtiwAMef+wOPn1vRoWByvil14YWwpsmRJU4ktaUyyCskx8S4P7ABijQwUsArJYrEkzJ9/wpAhsGMHHDgAhQrB5MkAREbChGHH2bPxOF+N8qRc988Zlq9UMgVa7gTc6SH5A1U1I1s/WCyW1OHcORg3DmbNgvz5YdIkXPf3OXQIHuscytYdpejdYitXm/xK9nx2xxWLwR2FtAu4G7MeyWKxWBLm1CmoWhX+/RcGDYIJE6BgQcCM3n3yCTwzKJKsqiwePYIuY4eCl91txfIf7iikwsAeEdmK2cEbAFXtkGJSWSyWjMOhQ1C+PNx9N7z4InTsaLb/ceHiRRg57DI1S21j/tiZlO7yAXjmTaRAy52KOwppQkoLYbFYMiCHDxsF9O23sHMnVK4ML710TZJffoH69WLIe/wNfhn5EeV8K5MlYAlkdWvXMcsdRrKG/qr6M2bLnzzO7y8nzGKx3ImEhcHo0VClCqxaBS+/HGc5F8ulSzBwIDRuDLOGvg9/jObeurXJ0vRLq4wsieKOld0jwFTM5qgCzBCRYar6RQrLZrFY0hsREcaEOygIevQwlnMlrvUSs2kTPP64cugQvNh+Jn39XwL/96DCABDr1dWSOO4M2b0E1FbVM2A2TQVWA1YhWSx3CgcOQIUK4OUFw4ZBjRpQ//oFrO++C4MHK6WKnGbtS11p0vAK1NsE+aqkgdCWjIY7e3N4xCojh3Nu5rNYLBmdU6egd2+oVAnWrDFhAwcmqIy4fJK6Xi/Ru/FH/Dm1Hk26PwQtf7XKyOI27vSQVojISmCBc9wV+D7lRLJYLGnOlSswfTq88ooZphs2DGrXvi5ZdDS8PS2G4zt38na7xtTOfYXabw+Dqn9aKzrLDeOOC/NhIvIwcB9mDmm2qi5NccksFkvaoAoBAbB5s3GY99ZbZrguHkeOwBPd/2X9prw86H+YqB71yVrvXchzb+rLbMkUuLWXnap+CXyZwrJYLJa05OBBs+9clizw/POQNy/cf/91yWIun2XWG7sYPqUOHsCcQc/S68X6SNkfrNGC5ZZIVCGJyAZVbSgiFzF718VFAaqqtj9usWQGQkON6faMGfC//0HfvvDII9emiYmEk9/D4Xmc3Pk7w9/YyX3V/uCD/ztA6QavQrZ8CZdtsdwAiSokVW3o/M2TeuJYLJZUIzoaPv7YLGY9e9Yoog4JbMBy/Ctitgxi+ca6dLxvEyXr9yDw57+pVKc+ItZVhOX24dZu36raM7kwi8WSwejeHRYtgoYNYcUKqFnz+jRHF3Fo6Rj6fPQd63fV5IcWUdxfMyuVU19ayx2AO3NI1VwPRCQrUCtlxLFYLCnK8eNQoADkzg39+sGDD0LXrgnO/cQcWcy7EzYyctFOPLNn5+OPofUD1qenJeVIdD2RiIxy5o98ReRf53cROA0sSzUJLRbLrRMebuaJKlWK80tEs2bQrVvChgjnf+fRx4TBn7xDQNOs7N4t9O5tbRYsKUuiCklVX3fmj6aqal7nl0dVC6nqqFSU0WKx3CyqsGSJ2fh0/Hhjxv3UU4kmj46GqHN7YN0D9Gr+DXNm/8t332elZMlUlNlyx+LOOqRRIlIAqAB4uYSvT0nBLBbLbWDkSJgyBXx9Yd48s74oEXbvhr69L9Pu3q95qYsHbYaNhnzWmNaSerhj1NAXGAyUxLgxrwdsApqlrGgWi+WmOHsWoqKMf6KePc3aor59IWvCj/uVK8ax6+TJMeT1Cueehieg+TrIWzF15bbc8bizJ91goDZwVFWbAjWA4BSVymKx3DiRkWYtUYUK8MILJszbG/r3T1QZbdsGfn5mh6Cu9b/gr+lNeXTC81YZWdIEd0xmIlQ1QkQQkeyquldEKqW4ZBaLxX1Wr4bBg2HPHmjZEsaMST7PpaPIvqVEXujMihF9aV1/PzRbBXnKp7y8FksCuKOQTohIfuBrYJWIhAAnU1Ysi8XiNjNnwqBBcM898PXXZnFrEuZwyxf+zdbvf+XVB7rjn1XY+/FSshZvClWWgKddB29JO9wxanjI+XeCiKwF8gErUlQqi8WSNBcvmrmicuWgc2fjxfW554y/okQ4dewCz/U9ypJVvviWqcLoASPJ6duPrLlKpaLgFkviJDmHJCIeIrIr9lhVf1bV5ap6NbmCRcRLRLaKyB8isltEJrrEPSsi+5zwKS7ho0TkoBPX+mYrZbFkWmJi4JNPzHqi7t2NWXfRojB8eKLKSBU+fnM7Vaoqy9dVZNLTSwj8swg5678CVhlZ0hFJ9pBUNcZRKKVV9dgNln0FaKaqYSLiCWwQkR+AHEBHwFdVr4hIUQARqQp0w+wMURxYLSIVVTX6RitlsWRKtm41vaAtW6BOHeMWIrmVqjGRnF49iefGDKVWhf3M/tCLSnW7pI68FssN4s4cUjFgt4hsBS7FBqpqArsw/oeqKhDmHHo6PwUGAJNV9YqTLtYbbUdgoRN+REQOAnUwJuYWy53NN9+YuaG774a5c405t0fiAxyXL8OnH53jqYoduPvcRjbPzUbVh1/EwzN76slssdwg7iikicknSRgRyQJsB+4F3lPVLSJSEWgkIpOACGCoqm4DSgCbXbKfcMIsljuTK1eMF7zKlY3l3KRJ8OyzkCdpw4PvvlWeGRBG0IlCeL+cgwaPL8S7TNdUEtpiuXncWYfUxpk7ivsBbdwpXFWjVdUPs6i2joh4Y5RgAcwC22HAYhERjJ+l64qIHyAi/UQkUEQCg4PtcihLJkTV9Ii8vY2DvCtXzPzQ6NFJKqMTJ+DhTtG0ay/k1GP8/OazNHh+NlhlZMkguKOQWiYQ9sCNnERVQ4F1wP2Yns9XatgKxACFnXDXGdaSJGBerqqzVdVfVf2LFClyI2JYLOmfv/4ySqhDB/D0hPffh+zJD7PFxECrFpH88H0kr3cdye/LvqTx829D7ntSQWiL5faQlMfYAcBA4B4R+dMlKg+wMbmCRaQIEKmqoSKSA2gBvIGZV2oGrHOG77IBZ4HlwOci8hbGqKECsPWmamWxZER++80YK+TODW+/DQMHGqWUBNu2mW3qsof+zOzHp1Ei7yHKPfQ6lGiXSkJbLLePpOaQPgd+AF4HRrqEX1TV826UXQyY58wjeQCLVfVbEckGfOyYk18FejkGELtFZDGwB4gCBlkLO0umJzoadu2C6tWhRg147TXo3RuS6f2fP29G8GbPVqYOmMeL9/WmYbVy0GQZ5PdJJeEtltuLGF2QTCKjVO7CRYHdhBn4bcff318DAwPTWgyL5eZYv95s93PwIBw+nKwSAqO/PvwQXnpJCQ1VBj/wHhM6v06emk9DlWGQNWcqCG65ExCR7arqn5rndGe372eACRjHfDFOsAK+KSeWxZKJOXYMhg2DxYuhVCmjYQoXTjpPxFk4s46nnyvFR8vq0rhaIDOGPomvfwGo9yvkLpc6slssKYg7Zt9DgEqqei6lhbFYMj3//ANVqhgrhPHjzQ4LORPo1URfgdNr4cw6Tu/ZRtaLOyiU5zwD6taneYX6dGsViFQYCWW6gbhjm2SxpH/cUUjHgQspLYjFkmlRNQYLtWpBsWIwdarx3Fq69PXpzqyHg7Ph5LdEhl9m5ppnGffFMrq1PcD7716iVuF61PJw57G1WDIe7rTswxiLuO8w2wEBoKpvpZhUFktmYccOM0+0YQP8+SdUq2as5+Jz7AvYPQlCdkC2AqwNHsOz0/uze18eWrWCF16pAUVTX3yLJTVxp69/DFiFMc/O4/KzWCyJERxsHOPVqmV8g8+caXZciE9kGPz2ImzoAjFXoc5sph8/RbNBw7h0JQ9Ll8KKFWYvVYsls+OO+4mJACKSS1UvJZfeYrnjiYgwZtxnzpitfsaPhwIFrk1zNRSOLoJdEyH8HyJKP0tIuf+jWAlPHnwYQi8au4ccOdKmChZLWpBsD0lE6ovIHuAv57i6iMxMcckslozGtm1mHsjLC6ZMMUN0b799rTI6uwXWtoGvisK2/miOUiwJ30vlHtN5/AmzCLZ0aRg3ziojy52HO0N2bwOtgXMAqvoH0DglhbJYMhQHD0LHjmaXhRWO78oePaBqVfO/KgRvgl86w4/1IGQ7VBpCYNGdNJ60mUf6ViJ/frPQ1WK5k3HLXEdVj8u1flfsDgoWy8WLZgfuadMgWzaYPBmaNbs2zZlf4PehcG4rZM0DPhOh8gt8sSw3XboY33offGA2Z8iSJW2qYbGkF9wy+xaRBoA62/48hzN8Z7HcsahCkybw++/Qqxe8/rox6Y4l/BQEDoLjX0GOElB7FuF3PcbxU3mo6AmtW5uppRdegLx5064aFkt6Itmtg0SkMPAOZnNUAX4EBqeHhbJ26yBLqrN9u9nN1NMTvvvO7LBQt+61acIOw08tIfwfqDoKrfwii77MyYgRZg3srl22N2RJ/6TF1kHJziGp6llV7a6qd6lqUVXtkR6UkcWSqvzzDzzxBPj7m61+ANq2vV4ZnVoDK+vA1RBovpZtEWNpGJCTRx+FggXhf/+zyshiSQx3rOzmiUh+l+MCIvJxyoplsaQTrlyBN96AihVhwQIYOdIYLMRHFfbNgLWtwesuaLWFtTvrUqcOHDpkdFhgIAQEpHoNLJYMgztzSL6Ogz0AVDVERGqkoEwWS/qhWzf4+mtjRffmm3DvvQmn+2M07JnMudzd2Z1jNo3z5qRxY3jrLXjySTtPZLG4gzsKyUNECqhqCICIFHQzn8WSMdmzxxgoFChgNj8dOBBaJuQ4GYi+Cr89T/juj5mx/Utem/8Q2bMLx44ZR6/PP5+6olssGRl31iH9H7BRRF4RkVcw3mKnpKxYFksaEBJi9p3z9TUm3AD16yeujE6vI+b7Wsz/+F8qjTrJiHc7cd99wpo1bnkdt1gs8XBn66BPRGQ70BRjZddJVfekuGQWS2rxn9c744q1Xz8YOjTx9JdPwu8vwtGFbD7eicdnzadmTZj72fXLkCwWi/u4O/S2FwiJTS8ipdODx1iL5bYwdKjZ4qdxY3jnHfDzSzidxsChj/hz2Ty27veh76DxNHhkBKv9oWlT8LBuiSyWW8Idj7HPAuMxHmOjMb0k6zHWkrE5ehREzMZxAweaobkuXUxYfCL/hcNzCVr/FRPmP8EnG9ZTtEgMj03LSs6s0Lx5qktvsWRK3PmmG4zxGFtNVX1V1UdVrTKyZEwuXzZbJFSubLbTBqhQAR555HpldPkEBA7mzLwaPPucUHHgKhZu6cmLLwh/7c2aoKNXi8Vy81iPsZY7A1VYvNgooePHoWtXsyN3QoTuhgPvwaGPQGO4kGcwH64fRO8+HowdCyVLpq7oFsudgvUYa7kzeOcdY4Pt5weffQaNGl2fJiYStvbn0p6FzFg1hL/+Xce8RcWokLssJ1pAoUKpL7bFcifhjkI65vyyOT+LJWMQHGx+VavC449D7tyJb6sd8idXfx3AB1/48ep3/3DqbF7atoUrnpAdq4wsltTgRjzG5jGHGpbiUlkst0JkJLz3HkyYYLb82bLFbCTXt+/1aS8ehH3T2b5yM12mL+bImbI0agRLXoOGDVNdcovljsYdKztvYD5Q0Dk+CzyuqrtTWDaL5cZZuRKGDIG9e42Ph2nTEracC95EzB8TCD64k7vynaGc/0DKVinBzHkmW0JZLBZLyuLOkN1s4AVVXQsgIgHAB0CDFJTLYrlxvvoKHn7Y7Df3zTdmN+4ENIsGLWL5e18w4YupkK0Av/2elYI5i/FT6zSQ2WKxxOGO2XeuWGUEoKrrgFwpJpHFciNcvGi20QZo1874d9i1y/wfTxlpdBTfzphP7WblefDNJVz0qMbzL5VCvYolULDFYklt3LKyE5GxmGE7gB7AkZQTyWJxg5gY+OQTGDXKbJFw5IhxI96/f8Lpgzey5K2v6Dr5TcoVD2bOR1H0eDwrWe02wRZLusGdHlIfoAjwlfMrDPROLpOIeInIVhH5Q0R2i8jEePFDRUQdj7SxYaNE5KCI7BMRO4BiSZjNm6FePWMxV7ascQ+R7XoDUI04z8oPv+arMS/Cqvt4sOZXfDJtG/uOFOaJPlYZWSzpDXes7EKA526i7CtAM1UNExFPYIOI/KCqm0WkFNASY04OgIhUBboB1YDiwGoRqaiq0TdxbktmZft2s81PsWKmh9S9+3WbyNc3jPUAABhJSURBVOmZX1mzYD3jZwawcf+D1K9Sjk49SpKtfF96euZJI8EtFktyuOMxdlUCHmNXJpdPDbEm4p7OT53jacBwl2OAjsBCVb2iqkeAg0Ad96phydRERMCGDeb/mjXhgw9g/37o2fNaZfTvPrb+7wUaN46h5ZBRHAutzKy3jrNuR3Wo/DxYZWSxpGvcGbIrHN9jLFDUncJFJIuI7ADOAKtUdYuIdAD+VtU/4iUvgdmmKJYTTlj8MvuJSKCIBAYHB7sjhiWjomqG46pVg1atzCJXEbOeKHdukyYyDP3zFSKW1YfvqnLu2FEOh/jx3vQrHDxWgKefL5XQaJ7l/9u79zidqv2B458vhhl3uTUacs+dYZBO5TZJRZKKOJ1Sh1yK+nVFVEo35aWObkpyTqWopJA4MoqEKPfkGiLkPjJmhu/vj7Udk8xkzDyz9zzzfb9ez2v2s/blWV8PvrPWXnstYwLobBLSCRGpdPKNiFzIH1s26VLV46raCIgBmolIA2AIMOwMh5/pyY8/fY6qjlXVOFWNK1u27NlUw+RGq1e7JNS5M0RFwaefQtrv+0QKJ9a9xkeDB9Kk0zU8NvEuqDuU9sNeY9PWYvS7u5AtkmdMLnM2t3WH4O7/zPPeXw70zsyHqOoBEUnAdctVAZaLG5IbAywTkWa4FlHFNKfFADsy8zkmTOzc6brmCheGl16Cvn1JOwIhde9aPnhuIk+925U1v/ShRtWjNOzUGBq432osDxmTO53NoIaZItIYuBj37/1eVf3tr84TkbJAipeMooB44FlVLZfmmC1AnKr+JiKfAu+JyCjcoIYawOJzCcrkQsePQ0KCW1woOhrGj3ctpDJlTh2TkggrhnLv4GqMmTWcehcdYOJ7yo03RZ1xejpjTO5yVgNfvQQ0LZPXjgYmiEh+XNfgJFVN9xqqulpEJgFrgFSgv42wyyMSEmDgQFixwr3q14fu3f+3OykJxr20lbbF+1Gr+Az69hxG2zsOcu0NJW2VVmPCSMiexFDVFUDsXxxT+bT3I4ARoaqTCZgtW9z6RB9+CBdeCJMnQ716/9t96BCMfS2VF0Ye5dffKvFY11Y8Ovoe6pwfTx3/am2MCRF7NND4IykJmjWDxEQYPhzuv98NXvCMeOwwI0dFcPBwJG3qLmbiY4tp+c8+UKiUj5U2xoSSJSSTc1Rh1ix3bygyEsaNcwvmVXRjWX7+GSqV3YmsHsHeJdW5ovYFPNhzIU07XQXRg3yuvDEm1NLtgReR+iLyrYhsE5GxIlIqzT4bbGAyZ9kyuPxyaN8eZsxwZR07QsWKLF0KN3VJomrVEyQ8cxtseJ0Xhq1l8n9jadpnFERf4WvVjTE5I6Nbwq8CjwH1gZ9wQ7+refsiQlwvEy5274ZevSAuDtatc7MstG+PKsyeDfHxSlwcfPH5MR7sMJLazWtDx3VI89ehWHW/a2+MyUEZddkVVdWZ3vbzIrIUmCkit3CWD8aaPE4V4uNh7Vq4914YNgxKlADgWBLccssJ8qUe4Lmbn+bOv2+j+KVPQPEaPlfaGOOXjBKSiEgJVT0IoKpzRaQL8BHe6rHGnNGcOW7970KF4F//gvLlSYypxfjxMHUqzJwJkSkbmTX0YS4qNp1CTZ+AWs/ZMq3G5HEZddk9C9ROW+AN5W6LW4bCmD9av97dF4qPhzffBGB7tZY8NL4WMTEwYAD8/rvy61evwox6NCg3i0LxU6D2fZaMjDHpt5BU9b2T2yJS1BXpEVXdCvTKicqZXOLQIXjySRg92o2eGzkSevVi2TJo3tytpXdDlxPc2/VzLo4YCL9uhJjOEPcvKPyn+XONMXlUhsO+RaQvMAi3ZLmIyGHc9D+v5ETlTC7RowdMm8bxW29nWqvn2XuiFLcXhEZ1D/Fov1X8/ZK3qRwxFZJ2Q+FYaPkZVLjGWkXGmD9INyGJyCPAJUArVd3klVUFXhSR81T1yRyqowmihQuhenUoW5bEQSN4u9YYXvzkQjZMgMaNUugZO4x8G17lkeYHoXBNKN0OKt0IF3S0RGSMOaOMWki3AA1VNelkgapuEpGbgOWAJaS86Jdf4KGH4N134f77mdh4JP36NeDAAbi44U6eGjaOzjWfQtYmQaUboPaDUDrO71obY3KBDLvs0iajNGVHReRE6KpkAikpCUaNQkc8xfyUZlTo1oNqV26lyqbHuaJec+6Nf5wWNb6F4rUgug/U6GtDuI0xmZJRQtouIm1VdU7aQhFpA+wMbbVM0Px+z2Dee/0QY4osZHlKfe7e9xIvHRnFxeUWMumxeRBzM1R4B4pV++uLGWPMGWSUkAYAU0VkPrAU9zBsU+BvuIX2TLhbtQoKFWL4xBq8OPEp9hFJ/dJrGPvgLHr0vR7KDvC7hsaYMJLuc0iquhqoB3wFVAaqetv1vH0mTOnefSy84QW0QUMYPJg9G9bQutZ0Ep69k+VrStJrWDsKl43xu5rGmDCT0Si76kB5VX3rtPLLRGSHqm4Mee1MjjpyMJX/9FnAmMnlWH38PuZ3hr8N2M1LW+sit7WDyz6CiKJ+V9MYE6YymqlhNHD4DOVHvX0mTOzf76aau+D8VPq+35JCUfl5e+gqmvSeC9ueQ6reCq2mWTIyxoRURveQKntTBf2Bqn4nIpVDViOTI5KT3YKtNQtuIWrHPj74oDFXXS3cXfkZWnRchOz6Ag7lg6avQvU77dkhY0zIZZSQIjPYF5XBPhNgW7bA2LEw7k2lWOo+fjpSi8ia0Wx6/yoid78PyfvhUDWo8g+o8zAUrex3lY0xeURGCWmJiPRS1TfSForIHbhRdyYXWbjQTTf3+eeKoHSImE6f5FegRTLcvIXInROgYmeoejuc38bv6hpj8qCMEtI9wBQR6cGpBBQHFAQ6h7piJut27oSCBaF0qRR2/fAl3y+K45Fmb9Br0ctUjNkNj3SB1u9CqVgoWhXyF/S7ysaYPCyj2b53AZeISGvc8G+A6ar6ZY7UzJyTEyfcckSvvw5TpyqD+63m8SYd6bh9K9eMq09EmVhY2R3++SQUsIV/jTHBkeHUQeAW5gPm5kBdTBY98wy89hr8/DOULpXMPR3e45bNT8KbW8lfrCT5hy1yi+b9ze+aGmPMn2U07NsEXHIyJCScer9kCdSocoSJg4ez/bbijFzSm+qfbYTL28G8b1wyMsaYgPrLFpIJnh9/hHHjYMIE2LMH1q9LpXrxeXwwcAIFdrwPWwrCU8egZg2YPhquvtrvKhtjzF+yhJSLrFkDvXvDggVQoIBybbs9/PPKKVRZ+RTs30qBjVHQpQ90GgI15rvlxAvaQAVjTO5gCSnAVF03XHIyXNoimfKpszm8uxHP9Z3DP2KfpHyR9UBBWFQTxpeAxGPwf0Mhqix06eJ39Y0xJlNClpBEJBI3GWsh73M+VNVHRWQk0BFIBjYCPVX1gHfOIOAO4DgwQFW/CFX9guyXX+Cdd1yX3Nq10Kr5L8x9sDGlk3az/IkoiKoAZS6BPb1g+ERY9j1ccgm89BKULet39Y0x5pyEsoV0DGijqokiEgHMF5HPgdnAIFVNFZFngUHAQyJSB+gG1AUqAP8VkZqqejyEdQyc++6D0aPd8O2/NdzI2DvHcFPcW1C8ETQfD9HtIF8B2LEDWl4I5cu71Vtvvtmm9zHG5GohG2WnTqL3NsJ7qarOUtVUr/xb4OQ6Bp2A91X1mKpuBjYAzUJVvyBQha+/dveF9u93ZU0bHWZIr3msf7EB8x+qRa/umylx5dvQNgFKt4GPP3EHVqgAn3ziRjh0727JyBiT64X0HpKI5MfN8lAdeFlVF512yO3AB972BbgEddJ2ryzsbN4M//63e23aBEWKKDc3Gk3riq/RLWIzXJ4CMZ0h9mMoVt1lrilTXPNpyxZYuRLq1YNrrvE7FGOMyTYhTUhed1sjESmJm4aonqquAhCRIUAq8K53+Jl+xdfTC0SkN9AboFKlSiGpd7ZRhYOr4XgSWqQqcuAHtv2cQrUW7QChdWt4dMhRrj/vKooenQ8lOkLFLlC5B5Ss666xciUMHAhz57okNGeO+2mMMWEmR0bZqeoBEUkA2gOrRORWoAPQVlVPJp3tQMU0p8UAO85wrbHAWIC4uLg/JSzfHd0FO2bAri9J3jaXL5bE8u6CHkRGrOLtPj2pCLzxwMtc0fM6Kh17FTaNd+dc9jHEXHvatY5C69Yusb38suvbK2ADI40x4SmUo+zKAileMooC4oFnRaQ98BDQUlV/T3PKp8B7IjIKN6ihBrA4VPXLVseT4Of33Wv3Vyz5qS7jvr6LyYteYd+hYpQulcRt1y6H1rNgz9fcQX9Y1h8QN0ihzjtQvpW7VmoqTJ4MXbtCVBR8+CE0aADnnednhMYYE3Kh/HU7Gpjg3UfKB0xS1WkisgE3FHy2uBvx36pqH1VdLSKTgDW4rrz+IR1hpyfg6K+QryBElsn8+SmH4PB62D4V1r/Kqg3lqVlDKVjlVqYuGsx/FsRw3XVC9+7Qrl0kERHN3XnRV0DhSpByECpcAyVqnbrml1+67rlVq6B4cXePqFWrbAnXGGOCTk71mOU+cXFx+t13353byaufhuWDoVAZ6LQFChRJ/1hV2DUXjmyB/T/AnvlwYDlb91zAxIXdeXdxH1Zuqsy0z5RrOgj79rkJEoqe7YrfmzfD/ffDxx9DlSrwwgtw3XU2cs4Y4xsRWaqqcTn5mXn3hsS2j93PY7/Bikeh4QjIXwhOpMC+pVC4onsA9dfZsGIo7PV6D/MXZnf+9tzw/BS+XnYh4J5JHfN/0Pxil0Ay1bumCtde64bbPfmkG0kXmdFivcYYE57ybkLSVNdllnoYfnwBft8OVXtCQvtTxxQqDcf2si+1Lp/8MpdjkXXpO7AUZShA5FiXP7p3d42azH22wqRJbq65woXhrbcgOhpiYv76XGOMCVN5t8vu0HqXlAqWggXdYPe8U/tiR3Jw5WSmLorng2V9mb3gAlJShBYt4Jtvsljp776DAQPcmuJjxkD//lm8oDHGZD/rsstJxWuc2m71OWx8g8PHylC0ThekQCEeGHUfb7wpVKrkxhl07QpNmmTh83btgsGDYfx4N9/cuHFw221ZjcIYY8JG3m0heRITYfp0+OADmDHDLe3QpImb1PTgQWjePJvGFnToALNmuew2dKgbRWeMMQFlLaQctGOHyw3Tp7vnT6Oj4c47oWRJt7927Wz4kOnTITbWzTv3/POQLx/UrJkNFzbGmPCTZ5cwL1UKvv8eevaEefNg2zZ48UWoVi0bLr5unVultUMHN3U3QK1aloyMMSYDebaFFBUF69dn86M+Bw/C8OFuXaLChd3zRHfdlY0fYIwx4SvPJiQIwXOnQ4bAK6/AHXfAiBFQrlw2f4AxxoSvPJ2QssWCBVCihJuBe8gQ1weYpeF4xhiTN+XZe0hZtm2beyr20ktdNx24kRGWjIwx5pxYQsqso0fhiSfcIIUpU9wQ7vHj/a6VMcbketZll1kvvwzDhsENN8DIkVC5st81MsaYsGAJ6WysWOFG0F12GfTrB02bQsuWftfKGGPCinXZZWTvXjfXXGysm4Vb1Q3ntmRkjDHZzhLSmaSmuolPa9SA1193raKZM219ImOMCSHrsjuTTz6Bu++Gtm3dTAv16vldI2OMCXuWkE7atAnWrHHT/Vx/Pcye7RKStYqMMSZHWJddYqJbFqJ2bTe7anKymwQ1Pt6SkTHG5KC8m5BU4Z134KKL4Omn4aabYPFiKFjQ75oZY0yelHe77JYuhVtugbg4+PBDaNHC7xoZY0yelncTUlwczJkDrVq5LjpjjDG+yrsJCaBNG79rYIwxxmNNA2OMMYFgCckYY0wgWEIyxhgTCJaQjDHGBIIlJGOMMYEQsoQkIpEislhElovIahF53Cs/T0Rmi8h672epNOcMEpENIrJORK4MVd2MMcYETyhbSMeANqraEGgEtBeRi4GHgTmqWgOY471HROoA3YC6QHvgFRHJH8L6GWOMCZCQJSR1Er23Ed5LgU7ABK98AnCdt90JeF9Vj6nqZmAD0CxU9TPGGBMsIX0w1mvhLAWqAy+r6iIRKa+qOwFUdaeIlPMOvwD4Ns3p272y06/ZG+jtvU0UkXUhCyBnlAF+87sS2SAc4giHGCA84rAY/HdhTn9gSBOSqh4HGolISWCKiGS0sNCZptbWM1xzLDA2m6roOxH5TlXj/K5HVoVDHOEQA4RHHBZD3pQjo+xU9QCQgLs3tEtEogG8n7u9w7YDFdOcFgPsyIn6GWOM8V8oR9mV9VpGiEgUEA/8CHwK3Ooddisw1dv+FOgmIoVEpApQA1gcqvoZY4wJllB22UUDE7z7SPmASao6TUQWApNE5A5gK3AjgKquFpFJwBogFejvdfmFu3DpfgyHOMIhBgiPOCyGPEhU/3SbxhhjjMlxNlODMcaYQLCEZIwxJhAsIYWAiLwlIrtFZFWasoYislBEVorIZyJSPM2+Bt6+1d7+SK88wZtG6QfvVe5Mn+d3DCLSI00dfxCREyLSyO8YziGOCBGZ4JWvFZFBac7JLd9FQREZ75UvF5FWAYmhoojM9f5cV4vIQK8801OJ+RVHZmMQkdLe8YkiMua0a/n67yKwVNVe2fwCLgcaA6vSlC0BWnrbtwNPeNsFgBVAQ+99aSC/t50AxAU9htPOqw9sSvPetxjO4bvojpstBKAwsAWo7HccmYyhPzDe2y6HezA9XwBiiAYae9vFgJ+AOsBzwMNe+cPAs952HWA5UAioAmz0+9/FOcRQBLgU6AOMOe1avv67COrLWkghoKpfAftOK74I+Mrbng108bbbAStUdbl37l4NwOjCTMaQ1s3AxBBWLVMyGYcCRUSkABAFJAOHcqKeGclkDHVwc0SiqruBA4DvD2eq6k5VXeZtHwbW4mZiyTVTiWU2BlU9oqrzgSQfqpsrWULKOauAa73tGzn1EHBNQEXkCxFZJiIPnnbeeK9JP1REzjSbRU5KL4a0uvLnhBSkGCD9OD4EjgA7cY8kPK+qaRNBkOJIL4blQCcRKSDueb4m/PF78j0GEakMxAKLgD9MJYZr1YH7j35bmtNOn0rM1zjOMoa/4vt3ETSWkHLO7UB/EVmKa+4ne+UFcM36Ht7PziLS1tvXQ1XrA5d5r1tytsp/kl4MAIhIc+B3VV2VpjhoMUD6cTQDjgMVcN1E94lIVW9f0OJIL4a3cP95fweMBr7BPdcHAYhBRIoCHwH3qGpGrc+MphLzNY5MxJAR37+LILKElENU9UdVbaeqTXAtiI3eru3APFX9TVV/B2bg7hegqr94Pw8D7+F/l0V6MZzUjdNaR0GLwatLenF0B2aqaorX3bUAr7sraHGkF4OqpqrqvaraSFU7ASWB9d4+X2MQkQjcf+TvqurHXnGmpxLzM45MxpAuv7+LoLKElENOjqIRkXzAI8Br3q4vgAYiUti7d9ESWON1uZTxzokAOuC6aXyTQQwny24E3k9TFrgYvLqkF8dWoI04RYCLgR+DGEd6MXh/j4p421cAqarq+98nr0tqHLBWVUel2ZWpqcT8jOMcYkjvOoH7+xQYfo+qCMcX7jfWnUAK7je9O4CBuFE5PwHP4M2S4R3/d2A17i/lc15ZEdwIqRXevhfxRhkFNIZWwLenXcPXGDIbB1AUmOzVdQ3wQBDiyGQMlYF1uBvu/wUuDEgMl+K63FYAP3ivq3GjSufgWnFzgPPSnDME1/JbB1zldxznGMMW3ICURO+7q+P3dxHkl00dZIwxJhCsy84YY0wgWEIyxhgTCJaQjDHGBIIlJGOMMYFgCckYY0wgWEIyxhgTCJaQjAkYEcnvdx2M8YMlJGOyQESeOLkujvd+hIgMEJEHRGSJiKwQkcfT7P9ERJZ66+n0TlOeKCLDRWQR0CKHwzAmECwhGZM14/CmjfGm8ekG7MJNddMMaAQ0EZHLveNvVzf/XBwwQERKe+VFcOsdNVe3ZIExeU4BvytgTG6mqltEZK+IxALlge+Bprh1rr73DiuKS1Bf4ZJQZ6+8ole+FzfL+Ec5WXdjgsYSkjFZ9yZwG3A+bvmHtsDTqvp62oPELSceD7RQ1d9FJAGI9HYnaQAWZjTGT9ZlZ0zWTQHa41pGX3iv2711cxCRC7zZuUsA+71kVAs3m7gxxmMtJGOySFWTRWQucMBr5cwSkdrAQm8h0ETcjO4zgT4isgI3g/W3ftXZmCCy2b6NySJvMMMy4EZVXe93fYzJrazLzpgsEJE6wAZgjiUjY7LGWkjGGGMCwVpIxhhjAsESkjHGmECwhGSMMSYQLCEZY4wJBEtIxhhjAuH/AXvmXywewkP6AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def func_linear(xdata, a, b):\n", + " ydata = a*xdata+b\n", + " return ydata\n", + "\n", + "def func_exp(xdata, a, b, c):\n", + " ydata = a * np.exp(-b * xdata) + c\n", + " return ydata\n", + "\n", + "xdata = data.index\n", + "ydata = data['CO2_smooth']\n", + "popt_linear, pcov_linear = spo.curve_fit(func_linear, xdata, ydata)\n", + "popt_exp, pcov_exp = spo.curve_fit(func_exp, xdata, ydata)\n", + "\n", + "\n", + "plt.plot(data['week'],data['CO2_smooth'], label='C02 concentration smoothed over a year', color='orange')\n", + "plt.plot(data['week'],func_linear(xdata, *popt_linear), '--', label='linear fit', color='red')\n", + "plt.plot(data['week'],func_exp(xdata, *popt_exp), '--', label='exponential fit', color='blue')\n", + "\n", + "plt.title('C02 concentration in the atmosphere at Mauna Loa observatory')\n", + "plt.ylabel('CO2 concentration (ppm)')\n", + "plt.xlabel('year')\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le modèle exponentiel correspond très bien. C'est logique: tous les ans, l'humanité émet plus de CO2 que l'année précédente, et cela s'accumule donc de plus en plus vite dans l'atmosphère. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut facilement estimer la concentration en 2025 en étendant la prédiction:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "xdata_extended_dates = pd.date_range(start='1958-03-30', end='2025-12-01', dtype='datetime64[ns]', freq='W')\n", + "xdata_extended = np.arange(len(xdata_extended_dates))\n", + "plt.plot(data['week'],data['CO2_smooth'], label='C02 concentration smoothed over a year', color='orange')\n", + "plt.plot(xdata_extended_dates,func_exp(xdata_extended, *popt_exp), '--', label='exponential fit', color='blue')\n", + "plt.axhline(y=func_exp(xdata_extended[-1], *popt_exp), label='predicted concentration in december 2025', linestyle='--', color='r')\n", + "plt.xlim(np.datetime64('1950-01-01'), np.datetime64('2030-01-01'))\n", + "plt.ylim(300,500)\n", + "\n", + "plt.title('C02 concentration in the atmosphere at Mauna Loa observatory')\n", + "plt.ylabel('CO2 concentration (ppm)')\n", + "plt.xlabel('year')\n", + "plt.legend()\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La concentration en C02 dans l'atmosphère devrait donc être, en décembre 2025, de:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "427 ppm\n" + ] + } + ], + "source": [ + "print(int(func_exp(xdata_extended[-1], *popt_exp)), 'ppm')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PS: Ce qui a comme conséquence directe [ceci](https://www.francetvinfo.fr/monde/afrique/madagascar/madagascar-une-famine-causee-par-le-rechauffement-climatique_4683471.html)." + ] + } + ], + "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/weekly_in_situ_co2_mlo.csv b/module3/exo3/weekly_in_situ_co2_mlo.csv new file mode 100644 index 0000000000000000000000000000000000000000..81d9fefc5ca0c00643137f4d6002e98cdaf4c30a --- /dev/null +++ b/module3/exo3/weekly_in_situ_co2_mlo.csv @@ -0,0 +1,3273 @@ +"-------------------------------------------------------------------------------------------" +" 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 01-Jul-2021 from archive dated 02-Jul-2021 09:04:47 " +" " +"-------------------------------------------------------------------------------------------" +" " +" 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 2 columns indicaing the date and CO2 " +" concentrations in micro-mol CO2 per mole (ppm), reported on the 2012 " +" SIO manometric mole fraction scale. These weekly values have been " +" adjusted to 12:00 hours at middle day of each weekly period as " +" indicated by the date in the first column. " +1958-03-29, 316.19 +1958-04-05, 317.31 +1958-04-12, 317.69 +1958-04-19, 317.58 +1958-04-26, 316.48 +1958-05-03, 316.95 +1958-05-17, 317.56 +1958-05-24, 317.99 +1958-07-05, 315.85 +1958-07-12, 315.85 +1958-07-19, 315.46 +1958-07-26, 315.59 +1958-08-02, 315.64 +1958-08-09, 315.10 +1958-08-16, 315.09 +1958-08-30, 314.14 +1958-09-06, 313.54 +1958-11-08, 313.05 +1958-11-15, 313.26 +1958-11-22, 313.57 +1958-11-29, 314.01 +1958-12-06, 314.56 +1958-12-13, 314.41 +1958-12-20, 314.77 +1958-12-27, 315.21 +1959-01-03, 315.24 +1959-01-10, 315.50 +1959-01-17, 315.69 +1959-01-24, 315.86 +1959-01-31, 315.42 +1959-02-14, 316.94 +1959-02-21, 316.61 +1959-02-28, 316.62 +1959-03-07, 316.81 +1959-03-21, 316.73 +1959-03-28, 316.71 +1959-04-04, 317.72 +1959-04-11, 317.12 +1959-04-18, 317.64 +1959-04-25, 318.32 +1959-05-02, 318.27 +1959-05-09, 318.78 +1959-05-16, 318.05 +1959-05-23, 318.43 +1959-06-06, 318.53 +1959-06-13, 318.14 +1959-06-20, 317.88 +1959-06-27, 317.76 +1959-07-04, 316.86 +1959-07-11, 316.83 +1959-07-18, 316.45 +1959-07-25, 316.16 +1959-08-01, 315.62 +1959-08-08, 314.91 +1959-08-22, 315.00 +1959-08-29, 314.15 +1959-09-05, 314.45 +1959-09-12, 313.93 +1959-09-19, 313.57 +1959-09-26, 313.54 +1959-10-03, 313.04 +1959-10-10, 313.15 +1959-10-17, 313.43 +1959-10-24, 313.46 +1959-10-31, 314.12 +1959-11-07, 314.42 +1959-11-14, 314.88 +1959-11-21, 315.20 +1959-11-28, 315.11 +1959-12-05, 315.06 +1959-12-12, 315.64 +1959-12-19, 315.86 +1959-12-26, 315.77 +1960-01-02, 315.72 +1960-01-09, 316.40 +1960-01-16, 316.73 +1960-01-23, 316.57 +1960-01-30, 316.68 +1960-02-06, 316.61 +1960-02-13, 316.98 +1960-02-20, 317.42 +1960-02-27, 317.00 +1960-03-05, 316.96 +1960-03-12, 317.76 +1960-03-19, 318.07 +1960-03-26, 317.77 +1960-04-02, 318.61 +1960-04-09, 319.34 +1960-04-16, 319.01 +1960-04-23, 319.03 +1960-04-30, 319.77 +1960-05-07, 319.96 +1960-05-14, 319.82 +1960-05-21, 320.04 +1960-05-28, 320.06 +1960-06-04, 319.46 +1960-06-11, 320.08 +1960-06-18, 319.42 +1960-06-25, 319.09 +1960-07-02, 318.18 +1960-07-09, 318.64 +1960-07-16, 318.41 +1960-07-23, 317.91 +1960-07-30, 317.33 +1960-08-06, 316.62 +1960-08-13, 316.70 +1960-08-20, 315.10 +1960-08-27, 314.78 +1960-09-03, 314.72 +1960-09-10, 314.58 +1960-09-17, 314.28 +1960-09-24, 313.33 +1960-10-01, 313.66 +1960-10-08, 313.32 +1960-10-15, 313.92 +1960-10-22, 314.21 +1960-10-29, 314.29 +1960-11-05, 314.50 +1960-11-12, 315.10 +1960-11-19, 315.10 +1960-11-26, 315.41 +1960-12-03, 315.84 +1960-12-10, 316.07 +1960-12-17, 316.21 +1960-12-24, 316.40 +1960-12-31, 316.67 +1961-01-07, 316.73 +1961-01-14, 317.02 +1961-01-21, 317.03 +1961-01-28, 317.01 +1961-02-04, 317.06 +1961-02-11, 317.75 +1961-02-18, 317.94 +1961-02-25, 318.00 +1961-03-04, 318.04 +1961-03-11, 318.51 +1961-03-18, 318.92 +1961-03-25, 318.71 +1961-04-01, 319.41 +1961-04-08, 319.55 +1961-04-15, 318.90 +1961-04-22, 319.74 +1961-04-29, 319.67 +1961-05-06, 320.67 +1961-05-13, 320.46 +1961-05-20, 320.61 +1961-05-27, 320.36 +1961-06-03, 320.09 +1961-06-10, 320.02 +1961-06-17, 319.62 +1961-06-24, 319.49 +1961-07-01, 319.26 +1961-07-08, 319.07 +1961-07-15, 318.49 +1961-07-22, 317.16 +1961-07-29, 317.97 +1961-08-05, 317.91 +1961-08-12, 317.33 +1961-08-19, 316.76 +1961-08-26, 315.26 +1961-09-02, 315.28 +1961-09-09, 314.97 +1961-09-16, 314.53 +1961-09-23, 315.06 +1961-09-30, 315.61 +1961-10-07, 314.97 +1961-10-14, 315.43 +1961-10-21, 315.48 +1961-10-28, 315.72 +1961-11-04, 315.82 +1961-11-11, 315.70 +1961-11-18, 316.21 +1961-11-25, 316.55 +1961-12-02, 316.52 +1961-12-09, 317.02 +1961-12-16, 316.94 +1961-12-23, 317.18 +1961-12-30, 317.46 +1962-01-06, 317.99 +1962-01-13, 318.12 +1962-01-20, 318.07 +1962-01-27, 317.78 +1962-02-03, 318.08 +1962-02-10, 318.35 +1962-02-17, 318.92 +1962-02-24, 319.38 +1962-03-03, 319.28 +1962-03-10, 319.46 +1962-03-17, 319.95 +1962-03-24, 319.78 +1962-03-31, 320.27 +1962-04-07, 320.26 +1962-04-14, 321.03 +1962-04-21, 320.78 +1962-04-28, 320.36 +1962-05-05, 320.89 +1962-05-12, 320.78 +1962-05-19, 321.05 +1962-05-26, 321.16 +1962-06-02, 320.91 +1962-06-09, 320.87 +1962-06-16, 320.52 +1962-06-23, 320.15 +1962-06-30, 320.23 +1962-07-07, 320.10 +1962-07-14, 319.95 +1962-07-21, 319.01 +1962-07-28, 318.75 +1962-08-04, 318.68 +1962-08-11, 317.24 +1962-08-18, 317.48 +1962-09-15, 316.65 +1962-09-22, 315.72 +1962-09-29, 315.92 +1962-10-06, 315.10 +1962-10-13, 315.40 +1962-10-20, 315.72 +1962-10-27, 315.78 +1962-11-03, 315.94 +1962-11-10, 316.49 +1962-11-17, 316.92 +1962-11-24, 317.08 +1962-12-01, 317.19 +1962-12-08, 317.31 +1962-12-15, 317.66 +1962-12-22, 318.18 +1963-01-05, 318.54 +1963-01-12, 318.73 +1963-01-19, 318.90 +1963-01-26, 318.82 +1963-02-02, 318.77 +1963-02-09, 318.95 +1963-02-23, 319.37 +1963-03-02, 319.35 +1963-03-09, 319.70 +1963-03-16, 319.80 +1963-03-23, 320.34 +1963-03-30, 320.29 +1963-04-06, 320.38 +1963-04-13, 320.96 +1963-04-20, 322.05 +1963-04-27, 321.92 +1963-05-11, 321.97 +1963-05-18, 322.28 +1963-05-25, 322.27 +1963-06-01, 322.32 +1963-06-08, 322.09 +1963-06-15, 321.83 +1963-06-22, 320.88 +1963-06-29, 320.49 +1963-07-06, 320.12 +1963-07-13, 319.82 +1963-07-20, 319.56 +1963-07-27, 319.06 +1963-08-03, 318.60 +1963-08-10, 318.27 +1963-08-17, 317.73 +1963-08-24, 317.12 +1963-08-31, 316.44 +1963-09-07, 316.81 +1963-09-14, 315.99 +1963-09-21, 316.03 +1963-09-28, 315.93 +1963-10-05, 316.28 +1963-10-12, 315.65 +1963-10-19, 316.14 +1963-10-26, 316.36 +1963-11-02, 316.63 +1963-11-09, 316.83 +1963-11-16, 317.13 +1963-11-30, 317.56 +1963-12-07, 318.07 +1963-12-14, 318.27 +1963-12-21, 318.59 +1963-12-28, 318.74 +1964-01-04, 319.04 +1964-01-11, 319.49 +1964-01-18, 319.88 +1964-05-30, 322.07 +1964-06-06, 322.06 +1964-06-27, 321.57 +1964-07-04, 321.14 +1964-07-11, 319.95 +1964-07-18, 320.28 +1964-07-25, 320.02 +1964-08-01, 319.14 +1964-08-15, 318.61 +1964-08-22, 318.23 +1964-08-29, 318.11 +1964-09-05, 317.48 +1964-09-12, 316.57 +1964-09-19, 315.59 +1964-09-26, 317.01 +1964-10-03, 316.93 +1964-10-10, 316.51 +1964-10-17, 316.86 +1964-10-24, 317.05 +1964-10-31, 317.65 +1964-11-07, 317.77 +1964-11-14, 317.65 +1964-11-21, 317.52 +1964-11-28, 318.11 +1964-12-05, 318.42 +1964-12-12, 318.58 +1964-12-19, 318.93 +1964-12-26, 318.91 +1965-01-02, 319.08 +1965-01-09, 319.17 +1965-01-16, 319.16 +1965-01-23, 319.79 +1965-01-30, 320.12 +1965-02-06, 320.20 +1965-02-13, 320.16 +1965-02-20, 320.70 +1965-02-27, 320.89 +1965-03-06, 321.07 +1965-03-13, 320.98 +1965-03-20, 320.49 +1965-03-27, 321.40 +1965-04-03, 321.95 +1965-04-10, 322.27 +1965-04-17, 322.17 +1965-04-24, 321.86 +1965-05-01, 322.46 +1965-05-08, 322.22 +1965-05-15, 321.94 +1965-05-22, 321.69 +1965-05-29, 322.26 +1965-06-05, 321.87 +1965-06-12, 321.77 +1965-06-19, 321.77 +1965-06-26, 321.99 +1965-07-03, 321.97 +1965-07-10, 321.85 +1965-07-17, 321.76 +1965-07-24, 320.47 +1965-07-31, 319.49 +1965-08-07, 319.47 +1965-08-14, 318.02 +1965-08-21, 318.82 +1965-08-28, 318.73 +1965-09-04, 318.39 +1965-09-11, 318.05 +1965-09-18, 318.26 +1965-09-25, 316.85 +1965-10-02, 316.60 +1965-10-09, 317.26 +1965-10-16, 317.86 +1965-10-23, 317.56 +1965-10-30, 317.63 +1965-11-06, 318.30 +1965-11-13, 319.26 +1965-11-20, 318.99 +1965-11-27, 319.17 +1965-12-04, 319.09 +1965-12-11, 319.37 +1965-12-18, 319.39 +1965-12-25, 319.70 +1966-01-01, 319.69 +1966-01-08, 320.49 +1966-01-15, 320.63 +1966-01-22, 321.09 +1966-01-29, 321.16 +1966-02-05, 321.26 +1966-02-12, 321.75 +1966-02-19, 321.67 +1966-02-26, 321.80 +1966-03-05, 322.10 +1966-03-12, 322.14 +1966-03-19, 322.57 +1966-03-26, 322.86 +1966-04-02, 323.55 +1966-04-09, 323.62 +1966-04-16, 323.88 +1966-04-23, 323.91 +1966-04-30, 323.53 +1966-05-07, 324.00 +1966-05-14, 324.10 +1966-05-21, 323.72 +1966-05-28, 324.31 +1966-06-04, 324.02 +1966-06-11, 323.88 +1966-06-18, 323.75 +1966-06-25, 323.32 +1966-07-02, 322.82 +1966-07-09, 322.90 +1966-08-06, 321.31 +1966-08-13, 320.85 +1966-08-20, 319.16 +1966-08-27, 319.64 +1966-09-03, 319.94 +1966-09-10, 318.22 +1966-09-17, 318.46 +1966-09-24, 318.31 +1966-10-01, 317.90 +1966-10-08, 317.90 +1966-10-15, 318.21 +1966-10-22, 318.61 +1966-10-29, 318.39 +1966-11-12, 319.59 +1966-11-19, 320.03 +1966-11-26, 320.24 +1966-12-03, 320.80 +1966-12-10, 320.87 +1966-12-17, 321.16 +1966-12-24, 321.31 +1966-12-31, 321.37 +1967-01-07, 321.92 +1967-01-14, 322.93 +1967-02-04, 322.19 +1967-02-11, 322.23 +1967-02-18, 322.86 +1967-02-25, 322.60 +1967-03-04, 322.94 +1967-03-11, 322.60 +1967-03-18, 323.33 +1967-03-25, 323.24 +1967-04-01, 323.15 +1967-04-08, 324.21 +1967-04-15, 324.41 +1967-04-22, 324.60 +1967-04-29, 325.16 +1967-05-06, 325.26 +1967-05-13, 324.78 +1967-05-20, 325.09 +1967-05-27, 324.94 +1967-06-03, 324.95 +1967-06-10, 324.65 +1967-06-17, 323.31 +1967-06-24, 323.50 +1967-07-01, 322.89 +1967-07-08, 322.85 +1967-07-15, 322.45 +1967-07-22, 322.40 +1967-07-29, 322.04 +1967-08-05, 321.65 +1967-08-12, 321.40 +1967-08-19, 320.48 +1967-08-26, 320.01 +1967-09-02, 320.07 +1967-09-09, 319.43 +1967-09-16, 319.35 +1967-09-23, 319.03 +1967-09-30, 318.81 +1967-10-07, 318.88 +1967-10-14, 319.86 +1967-10-21, 319.47 +1967-10-28, 319.86 +1967-11-04, 320.01 +1967-11-11, 320.34 +1967-11-18, 320.91 +1967-11-25, 321.54 +1967-12-02, 321.40 +1967-12-09, 321.89 +1967-12-16, 322.29 +1967-12-23, 321.98 +1967-12-30, 322.30 +1968-01-06, 322.46 +1968-01-13, 322.50 +1968-01-20, 322.59 +1968-01-27, 322.83 +1968-02-03, 322.66 +1968-02-10, 323.17 +1968-02-17, 323.46 +1968-02-24, 323.18 +1968-03-02, 323.15 +1968-03-09, 323.85 +1968-03-16, 323.85 +1968-03-23, 324.21 +1968-03-30, 324.77 +1968-04-06, 324.79 +1968-04-13, 325.04 +1968-04-20, 324.84 +1968-04-27, 325.45 +1968-05-04, 325.63 +1968-05-11, 325.09 +1968-05-18, 325.52 +1968-05-25, 325.86 +1968-06-01, 325.67 +1968-06-08, 325.71 +1968-06-15, 325.56 +1968-06-22, 324.99 +1968-06-29, 324.63 +1968-07-06, 324.70 +1968-07-13, 324.13 +1968-07-20, 323.94 +1968-07-27, 323.40 +1968-08-03, 322.49 +1968-08-10, 322.27 +1968-08-17, 322.02 +1968-08-24, 321.74 +1968-08-31, 321.42 +1968-09-07, 320.44 +1968-09-14, 321.18 +1968-09-21, 319.78 +1968-09-28, 319.89 +1968-10-05, 320.57 +1968-10-12, 320.52 +1968-10-19, 320.26 +1968-10-26, 319.92 +1968-11-02, 320.70 +1968-11-09, 320.89 +1968-11-16, 321.40 +1968-11-23, 321.78 +1968-11-30, 322.06 +1968-12-07, 322.44 +1968-12-14, 322.91 +1968-12-21, 323.33 +1968-12-28, 323.19 +1969-01-04, 323.39 +1969-01-11, 324.06 +1969-01-18, 324.36 +1969-01-25, 324.14 +1969-02-01, 324.16 +1969-02-08, 323.92 +1969-02-15, 324.37 +1969-02-22, 324.86 +1969-03-01, 325.14 +1969-03-08, 325.58 +1969-03-15, 325.72 +1969-03-22, 325.96 +1969-03-29, 325.89 +1969-04-05, 326.27 +1969-04-12, 327.13 +1969-04-19, 326.58 +1969-04-26, 326.57 +1969-05-03, 327.52 +1969-05-10, 327.82 +1969-05-17, 326.86 +1969-05-24, 326.92 +1969-05-31, 327.43 +1969-06-07, 326.51 +1969-06-14, 326.78 +1969-06-21, 326.73 +1969-06-28, 326.37 +1969-07-05, 326.16 +1969-07-12, 326.07 +1969-07-19, 325.72 +1969-07-26, 325.47 +1969-08-02, 324.50 +1969-08-09, 324.56 +1969-08-16, 323.14 +1969-08-23, 322.56 +1969-08-30, 323.14 +1969-09-06, 322.21 +1969-09-13, 322.79 +1969-09-20, 322.84 +1969-09-27, 321.96 +1969-10-04, 321.50 +1969-10-11, 321.82 +1969-10-18, 322.00 +1969-10-25, 321.93 +1969-11-01, 322.21 +1969-11-08, 322.64 +1969-11-15, 322.78 +1969-11-22, 323.10 +1969-11-29, 323.52 +1969-12-06, 323.91 +1969-12-13, 323.99 +1969-12-20, 324.22 +1969-12-27, 324.50 +1970-01-03, 324.70 +1970-01-10, 325.40 +1970-01-17, 325.05 +1970-01-24, 324.80 +1970-01-31, 325.55 +1970-02-07, 325.70 +1970-02-14, 326.02 +1970-02-21, 326.32 +1970-02-28, 326.17 +1970-03-07, 326.85 +1970-03-14, 326.80 +1970-03-21, 327.51 +1970-03-28, 326.95 +1970-04-04, 328.20 +1970-04-11, 327.84 +1970-04-18, 327.81 +1970-04-25, 328.55 +1970-05-02, 327.99 +1970-05-09, 327.56 +1970-05-16, 328.03 +1970-05-23, 328.33 +1970-05-30, 327.98 +1970-06-06, 327.55 +1970-06-13, 327.86 +1970-06-20, 327.71 +1970-06-27, 327.23 +1970-07-04, 327.29 +1970-07-11, 326.24 +1970-07-18, 326.28 +1970-07-25, 325.63 +1970-08-01, 325.24 +1970-08-08, 325.21 +1970-08-15, 324.86 +1970-08-22, 324.84 +1970-08-29, 323.33 +1970-09-05, 322.97 +1970-09-12, 323.58 +1970-09-19, 323.18 +1970-09-26, 323.15 +1970-10-03, 323.21 +1970-10-10, 323.56 +1970-10-17, 322.92 +1970-10-24, 323.02 +1970-10-31, 323.18 +1970-11-07, 323.69 +1970-11-14, 323.76 +1970-11-21, 324.23 +1970-11-28, 324.71 +1970-12-05, 324.73 +1970-12-12, 325.00 +1970-12-19, 325.29 +1970-12-26, 325.58 +1971-01-02, 325.77 +1971-01-09, 325.89 +1971-01-16, 325.90 +1971-01-23, 326.72 +1971-01-30, 326.68 +1971-02-06, 326.55 +1971-02-13, 326.49 +1971-02-20, 326.79 +1971-02-27, 327.05 +1971-03-06, 327.15 +1971-03-13, 327.34 +1971-03-20, 327.08 +1971-03-27, 327.45 +1971-04-03, 327.44 +1971-04-10, 327.10 +1971-04-17, 327.71 +1971-04-24, 328.56 +1971-05-01, 328.20 +1971-05-08, 329.07 +1971-05-15, 329.28 +1971-05-22, 328.92 +1971-05-29, 328.81 +1971-06-05, 328.56 +1971-06-12, 328.80 +1971-06-19, 328.18 +1971-06-26, 328.52 +1971-07-03, 327.92 +1971-07-10, 327.88 +1971-07-17, 327.55 +1971-07-24, 326.21 +1971-07-31, 326.74 +1971-08-07, 326.12 +1971-08-14, 325.61 +1971-08-21, 325.39 +1971-08-28, 324.04 +1971-09-04, 323.65 +1971-09-11, 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