{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeu de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Code de l'indicateur de surveillance |\n", "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020231038650576141.096869.0130114.0146.0FRFrance
120230936204354707.069379.09382.0104.0FRFrance
220230837639168065.084717.0115102.0128.0FRFrance
320230738985180397.099305.0135121.0149.0FRFrance
420230639736887636.0107100.0146131.0161.0FRFrance
520230539546986268.0104670.0144130.0158.0FRFrance
620230437490166916.082886.0113101.0125.0FRFrance
720230336957061893.077247.010593.0117.0FRFrance
820230237826070090.086430.0118106.0130.0FRFrance
92023013121773111024.0132522.0183167.0199.0FRFrance
102022523155383142015.0168751.0234214.0254.0FRFrance
112022513248311232120.0264502.0374350.0398.0FRFrance
122022503234279219533.0249025.0353331.0375.0FRFrance
132022493163421151727.0175115.0246228.0264.0FRFrance
142022483121884111932.0131836.0184169.0199.0FRFrance
1520224739644787259.0105635.0145131.0159.0FRFrance
1620224636773560075.075395.010290.0114.0FRFrance
1720224534530638909.051703.06858.078.0FRFrance
1820224433471328880.040546.05243.061.0FRFrance
1920224334476936884.052654.06856.080.0FRFrance
2020224234746240773.054151.07262.082.0FRFrance
2120224134858342388.054778.07364.082.0FRFrance
2220224034192736115.047739.06354.072.0FRFrance
2320223933990234168.045636.06051.069.0FRFrance
2420223832878123733.033829.04335.051.0FRFrance
2520223732139517076.025714.03225.039.0FRFrance
2620223631412010487.017753.02116.026.0FRFrance
27202235392836485.012081.01410.018.0FRFrance
28202234374984731.010265.0117.015.0FRFrance
29202233375864442.010730.0116.016.0FRFrance
.................................
197219852132609619621.032571.04735.059.0FRFrance
197319852032789620885.034907.05138.064.0FRFrance
197419851934315432821.053487.07859.097.0FRFrance
197519851834055529935.051175.07455.093.0FRFrance
197619851733405324366.043740.06244.080.0FRFrance
197719851635036236451.064273.09166.0116.0FRFrance
197819851536388145538.082224.011683.0149.0FRFrance
19791985143134545114400.0154690.0244207.0281.0FRFrance
19801985133197206176080.0218332.0357319.0395.0FRFrance
19811985123245240223304.0267176.0445405.0485.0FRFrance
19821985113276205252399.0300011.0501458.0544.0FRFrance
19831985103353231326279.0380183.0640591.0689.0FRFrance
19841985093369895341109.0398681.0670618.0722.0FRFrance
19851985083389886359529.0420243.0707652.0762.0FRFrance
19861985073471852432599.0511105.0855784.0926.0FRFrance
19871985063565825518011.0613639.01026939.01113.0FRFrance
19881985053637302592795.0681809.011551074.01236.0FRFrance
19891985043424937390794.0459080.0770708.0832.0FRFrance
19901985033213901174689.0253113.0388317.0459.0FRFrance
199119850239758680949.0114223.0177147.0207.0FRFrance
199219850138548965918.0105060.0155120.0190.0FRFrance
199319845238483060602.0109058.0154110.0198.0FRFrance
1994198451310172680242.0123210.0185146.0224.0FRFrance
19951984503123680101401.0145959.0225184.0266.0FRFrance
1996198449310107381684.0120462.0184149.0219.0FRFrance
199719844837862060634.096606.0143110.0176.0FRFrance
199819844737202954274.089784.013199.0163.0FRFrance
199919844638733067686.0106974.0159123.0195.0FRFrance
20001984453135223101414.0169032.0246184.0308.0FRFrance
200119844436842220056.0116788.012537.0213.0FRFrance
\n", "

2002 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202310 3 86505 76141.0 96869.0 130 114.0 \n", "1 202309 3 62043 54707.0 69379.0 93 82.0 \n", "2 202308 3 76391 68065.0 84717.0 115 102.0 \n", "3 202307 3 89851 80397.0 99305.0 135 121.0 \n", "4 202306 3 97368 87636.0 107100.0 146 131.0 \n", "5 202305 3 95469 86268.0 104670.0 144 130.0 \n", "6 202304 3 74901 66916.0 82886.0 113 101.0 \n", "7 202303 3 69570 61893.0 77247.0 105 93.0 \n", "8 202302 3 78260 70090.0 86430.0 118 106.0 \n", "9 202301 3 121773 111024.0 132522.0 183 167.0 \n", "10 202252 3 155383 142015.0 168751.0 234 214.0 \n", "11 202251 3 248311 232120.0 264502.0 374 350.0 \n", "12 202250 3 234279 219533.0 249025.0 353 331.0 \n", "13 202249 3 163421 151727.0 175115.0 246 228.0 \n", "14 202248 3 121884 111932.0 131836.0 184 169.0 \n", "15 202247 3 96447 87259.0 105635.0 145 131.0 \n", "16 202246 3 67735 60075.0 75395.0 102 90.0 \n", "17 202245 3 45306 38909.0 51703.0 68 58.0 \n", "18 202244 3 34713 28880.0 40546.0 52 43.0 \n", "19 202243 3 44769 36884.0 52654.0 68 56.0 \n", "20 202242 3 47462 40773.0 54151.0 72 62.0 \n", "21 202241 3 48583 42388.0 54778.0 73 64.0 \n", "22 202240 3 41927 36115.0 47739.0 63 54.0 \n", "23 202239 3 39902 34168.0 45636.0 60 51.0 \n", "24 202238 3 28781 23733.0 33829.0 43 35.0 \n", "25 202237 3 21395 17076.0 25714.0 32 25.0 \n", "26 202236 3 14120 10487.0 17753.0 21 16.0 \n", "27 202235 3 9283 6485.0 12081.0 14 10.0 \n", "28 202234 3 7498 4731.0 10265.0 11 7.0 \n", "29 202233 3 7586 4442.0 10730.0 11 6.0 \n", "... ... ... ... ... ... ... ... \n", "1972 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1973 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1974 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1975 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1976 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1977 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1978 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1979 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1980 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1981 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1982 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1983 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1984 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1985 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1986 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1987 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1988 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1989 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1990 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1991 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1992 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1993 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1994 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1995 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1996 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1997 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1998 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1999 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2000 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2001 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 146.0 FR France \n", "1 104.0 FR France \n", "2 128.0 FR France \n", "3 149.0 FR France \n", "4 161.0 FR France \n", "5 158.0 FR France \n", "6 125.0 FR France \n", "7 117.0 FR France \n", "8 130.0 FR France \n", "9 199.0 FR France \n", "10 254.0 FR France \n", "11 398.0 FR France \n", "12 375.0 FR France \n", "13 264.0 FR France \n", "14 199.0 FR France \n", "15 159.0 FR France \n", "16 114.0 FR France \n", "17 78.0 FR France \n", "18 61.0 FR France \n", "19 80.0 FR France \n", "20 82.0 FR France \n", "21 82.0 FR France \n", "22 72.0 FR France \n", "23 69.0 FR France \n", "24 51.0 FR France \n", "25 39.0 FR France \n", "26 26.0 FR France \n", "27 18.0 FR France \n", "28 15.0 FR France \n", "29 16.0 FR France \n", "... ... ... ... \n", "1972 59.0 FR France \n", "1973 64.0 FR France \n", "1974 97.0 FR France \n", "1975 93.0 FR France \n", "1976 80.0 FR France \n", "1977 116.0 FR France \n", "1978 149.0 FR France \n", "1979 281.0 FR France \n", "1980 395.0 FR France \n", "1981 485.0 FR France \n", "1982 544.0 FR France \n", "1983 689.0 FR France \n", "1984 722.0 FR France \n", "1985 762.0 FR France \n", "1986 926.0 FR France \n", "1987 1113.0 FR France \n", "1988 1236.0 FR France \n", "1989 832.0 FR France \n", "1990 459.0 FR France \n", "1991 207.0 FR France \n", "1992 190.0 FR France \n", "1993 198.0 FR France \n", "1994 224.0 FR France \n", "1995 266.0 FR France \n", "1996 219.0 FR France \n", "1997 176.0 FR France \n", "1998 163.0 FR France \n", "1999 195.0 FR France \n", "2000 308.0 FR France \n", "2001 213.0 FR France \n", "\n", "[2002 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, 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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
176519891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1765 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1765 FR France " ] }, "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020231038650576141.096869.0130114.0146.0FRFrance
120230936204354707.069379.09382.0104.0FRFrance
220230837639168065.084717.0115102.0128.0FRFrance
320230738985180397.099305.0135121.0149.0FRFrance
420230639736887636.0107100.0146131.0161.0FRFrance
520230539546986268.0104670.0144130.0158.0FRFrance
620230437490166916.082886.0113101.0125.0FRFrance
720230336957061893.077247.010593.0117.0FRFrance
820230237826070090.086430.0118106.0130.0FRFrance
92023013121773111024.0132522.0183167.0199.0FRFrance
102022523155383142015.0168751.0234214.0254.0FRFrance
112022513248311232120.0264502.0374350.0398.0FRFrance
122022503234279219533.0249025.0353331.0375.0FRFrance
132022493163421151727.0175115.0246228.0264.0FRFrance
142022483121884111932.0131836.0184169.0199.0FRFrance
1520224739644787259.0105635.0145131.0159.0FRFrance
1620224636773560075.075395.010290.0114.0FRFrance
1720224534530638909.051703.06858.078.0FRFrance
1820224433471328880.040546.05243.061.0FRFrance
1920224334476936884.052654.06856.080.0FRFrance
2020224234746240773.054151.07262.082.0FRFrance
2120224134858342388.054778.07364.082.0FRFrance
2220224034192736115.047739.06354.072.0FRFrance
2320223933990234168.045636.06051.069.0FRFrance
2420223832878123733.033829.04335.051.0FRFrance
2520223732139517076.025714.03225.039.0FRFrance
2620223631412010487.017753.02116.026.0FRFrance
27202235392836485.012081.01410.018.0FRFrance
28202234374984731.010265.0117.015.0FRFrance
29202233375864442.010730.0116.016.0FRFrance
.................................
197219852132609619621.032571.04735.059.0FRFrance
197319852032789620885.034907.05138.064.0FRFrance
197419851934315432821.053487.07859.097.0FRFrance
197519851834055529935.051175.07455.093.0FRFrance
197619851733405324366.043740.06244.080.0FRFrance
197719851635036236451.064273.09166.0116.0FRFrance
197819851536388145538.082224.011683.0149.0FRFrance
19791985143134545114400.0154690.0244207.0281.0FRFrance
19801985133197206176080.0218332.0357319.0395.0FRFrance
19811985123245240223304.0267176.0445405.0485.0FRFrance
19821985113276205252399.0300011.0501458.0544.0FRFrance
19831985103353231326279.0380183.0640591.0689.0FRFrance
19841985093369895341109.0398681.0670618.0722.0FRFrance
19851985083389886359529.0420243.0707652.0762.0FRFrance
19861985073471852432599.0511105.0855784.0926.0FRFrance
19871985063565825518011.0613639.01026939.01113.0FRFrance
19881985053637302592795.0681809.011551074.01236.0FRFrance
19891985043424937390794.0459080.0770708.0832.0FRFrance
19901985033213901174689.0253113.0388317.0459.0FRFrance
199119850239758680949.0114223.0177147.0207.0FRFrance
199219850138548965918.0105060.0155120.0190.0FRFrance
199319845238483060602.0109058.0154110.0198.0FRFrance
1994198451310172680242.0123210.0185146.0224.0FRFrance
19951984503123680101401.0145959.0225184.0266.0FRFrance
1996198449310107381684.0120462.0184149.0219.0FRFrance
199719844837862060634.096606.0143110.0176.0FRFrance
199819844737202954274.089784.013199.0163.0FRFrance
199919844638733067686.0106974.0159123.0195.0FRFrance
20001984453135223101414.0169032.0246184.0308.0FRFrance
200119844436842220056.0116788.012537.0213.0FRFrance
\n", "

2001 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202310 3 86505 76141.0 96869.0 130 114.0 \n", "1 202309 3 62043 54707.0 69379.0 93 82.0 \n", "2 202308 3 76391 68065.0 84717.0 115 102.0 \n", "3 202307 3 89851 80397.0 99305.0 135 121.0 \n", "4 202306 3 97368 87636.0 107100.0 146 131.0 \n", "5 202305 3 95469 86268.0 104670.0 144 130.0 \n", "6 202304 3 74901 66916.0 82886.0 113 101.0 \n", "7 202303 3 69570 61893.0 77247.0 105 93.0 \n", "8 202302 3 78260 70090.0 86430.0 118 106.0 \n", "9 202301 3 121773 111024.0 132522.0 183 167.0 \n", "10 202252 3 155383 142015.0 168751.0 234 214.0 \n", "11 202251 3 248311 232120.0 264502.0 374 350.0 \n", "12 202250 3 234279 219533.0 249025.0 353 331.0 \n", "13 202249 3 163421 151727.0 175115.0 246 228.0 \n", "14 202248 3 121884 111932.0 131836.0 184 169.0 \n", "15 202247 3 96447 87259.0 105635.0 145 131.0 \n", "16 202246 3 67735 60075.0 75395.0 102 90.0 \n", "17 202245 3 45306 38909.0 51703.0 68 58.0 \n", "18 202244 3 34713 28880.0 40546.0 52 43.0 \n", "19 202243 3 44769 36884.0 52654.0 68 56.0 \n", "20 202242 3 47462 40773.0 54151.0 72 62.0 \n", "21 202241 3 48583 42388.0 54778.0 73 64.0 \n", "22 202240 3 41927 36115.0 47739.0 63 54.0 \n", "23 202239 3 39902 34168.0 45636.0 60 51.0 \n", "24 202238 3 28781 23733.0 33829.0 43 35.0 \n", "25 202237 3 21395 17076.0 25714.0 32 25.0 \n", "26 202236 3 14120 10487.0 17753.0 21 16.0 \n", "27 202235 3 9283 6485.0 12081.0 14 10.0 \n", "28 202234 3 7498 4731.0 10265.0 11 7.0 \n", "29 202233 3 7586 4442.0 10730.0 11 6.0 \n", "... ... ... ... ... ... ... ... \n", "1972 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1973 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1974 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1975 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1976 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1977 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1978 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1979 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1980 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1981 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1982 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1983 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1984 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1985 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1986 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1987 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1988 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1989 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1990 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1991 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1992 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1993 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1994 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1995 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1996 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1997 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1998 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1999 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2000 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2001 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 146.0 FR France \n", "1 104.0 FR France \n", "2 128.0 FR France \n", "3 149.0 FR France \n", "4 161.0 FR France \n", "5 158.0 FR France \n", "6 125.0 FR France \n", "7 117.0 FR France \n", "8 130.0 FR France \n", "9 199.0 FR France \n", "10 254.0 FR France \n", "11 398.0 FR France \n", "12 375.0 FR France \n", "13 264.0 FR France \n", "14 199.0 FR France \n", "15 159.0 FR France \n", "16 114.0 FR France \n", "17 78.0 FR France \n", "18 61.0 FR France \n", "19 80.0 FR France \n", "20 82.0 FR France \n", "21 82.0 FR France \n", "22 72.0 FR France \n", "23 69.0 FR France \n", "24 51.0 FR France \n", "25 39.0 FR France \n", "26 26.0 FR France \n", "27 18.0 FR France \n", "28 15.0 FR France \n", "29 16.0 FR France \n", "... ... ... ... \n", "1972 59.0 FR France \n", "1973 64.0 FR France \n", "1974 97.0 FR France \n", "1975 93.0 FR France \n", "1976 80.0 FR France \n", "1977 116.0 FR France \n", "1978 149.0 FR France \n", "1979 281.0 FR France \n", "1980 395.0 FR France \n", "1981 485.0 FR France \n", "1982 544.0 FR France \n", "1983 689.0 FR France \n", "1984 722.0 FR France \n", "1985 762.0 FR France \n", "1986 926.0 FR France \n", "1987 1113.0 FR France \n", "1988 1236.0 FR France \n", "1989 832.0 FR France \n", "1990 459.0 FR France \n", "1991 207.0 FR France \n", "1992 190.0 FR France \n", "1993 198.0 FR France \n", "1994 224.0 FR France \n", "1995 266.0 FR France \n", "1996 219.0 FR France \n", "1997 176.0 FR France \n", "1998 163.0 FR France \n", "1999 195.0 FR France \n", "2000 308.0 FR France \n", "2001 213.0 FR France \n", "\n", "[2001 rows x 10 columns]" ] }, "execution_count": 7, "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": 8, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation\n", "comme nouvel index de notre jeux de données. Ceci en fait\n", "une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans\n", "le sens chronologique." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", "le début de la période qui suit, la différence temporelle doit être\n", "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", "entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations\n", "que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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j7/hpr6UWUyAEmw5jZW+kJWV9BsPZQnUK8KPK69BVgpGc9VySTbq00PcfXIoZVzxQ72owYIETiUaLKGoUc0zcTrPa0Xjrege0x1vSUuDk8trz1aTYhTfGO9SRzVsCp1HaWbnc9LQVkJHL12FAwbhggROBfK01nEYe/jqIPA8npKCJ8xgeX7YFp//4CTz05qaScy0pax/Aemg4tg+ngV9xNm9VrsHGWaFJyGc8kK39gIJxwwInAoUGGyjVu7OKGzSgqOY2DG+st/wQSzftLjmXUSa1OoyA1T03w0rYzTLw8dKesea39w/n6lwThgVOBGqt4QTRKLWpVlh0Mf/oeefkKD2VLG3yKTkEzudr/ySLWy/UvOiyaXYNJ1eH98u4YYETgYbz4TSYAIxKNbeazkq1NJUs7dmVwMnFeK8vv7cD2/uGy06XaAKTmqJZfTjV3iCQCQ8LnAgUaixw9valbYI6smLocPQ7zeastBmNhpNUGk4MW+mnbnwef/2L58tOp/YAqvag4al3erBxZ7zJpc3eYcdpP0xlYIETgYYzqTVIdWJHqVWmGlpySsNJlGo4iQpoOACw1hAF50etNJx58xfh4z97NlYejdLOosICp/6wwImAScNZvnkPbnl2dcXLCzZl1PdDorhbfqrUpomfAefDoEJ7dT4cVUA9TKXq2dWiM+ztj7dSQL067D+8sh4f/smTsfNpMEv4PglvTxABk4bz8Z89g2xe4JIPHFjR8pplYBa/ngE+nBgCbXDEColtSyeN+cbVcOLQDK+4XnX8xu9ehxDWoCGtGzCEphme8t4NazgRMI2E1XyFWo+U6y2QbD9ExPRh6x/nPgflHIz2TKnAUa6bemg4zbS5XK19lwq1EsRgxHk0RS2yUjViosICJwJBvuVchSfqNE3QQAOvNDAoJ3UmNT6cosmu9k9SdYLNGgG2eyhrXMGhUqiJuUMj8SZusg+n/rDAiUBQ0ECt4/0b5TuK2mmG3X4gjkDI2cuzVDbfuOnVM2uUd+iHrsM++9qncPqPn6hqua1y6aFdg9lI6e0oxwabsL0vwgInAkGml0oLnOCQgcYIGmhkDcevjLj5xjHVqPo0w+hbV8Utu8ufe1Qu07vaAQDvbY+nSTXDM97bYYETgaCGW2mTWhDN/h2F3WCuEvep00bs/CuYZ+i0dh6RswguI2bmtYyk06FMalHL54mfjQMLnAgEC5wKazhN8qXErWU1w7/9zHZxHfexXrcstJqdeaWyrncrjPtZsYZTf1jgRMBkUlP+6GyVFoE0zlNpkO8ovi9Ef1yNUGOZrnx8JdUXlH5pK1OHMGVExV6ctU7trGiyjVcBFjj1hwVOBEwWM9UxVjq8ttF9OJUiOGigEmWUZlKIbVKLUR9bu6reO4zb0RZNUvVtZ/E1nMrUg4kOC5wImKLUhG0eqWVtGknDqW66WL4S2zkfvfygvKOlrX6U2t5jUouvizL1hQVOBEwaTMHu1CrcsJvkO4m9xXTQatGxcjeXIYonI+UZ532rlNXcniC2hlOhfOISOWjATl+5ujDRYIETgaCGX2nTg+rITZ1SvTWcakcBVUIL8Eta7X18fNM2USdYPx9OZdpXvVZKYIqwwIlAkI+mWu3avLhlY3xI1Z+HE/8+tUEDPhFsYaiIhoPqqThx30u9w6Ljali8tE3jwAInAkEjpUp/mM0yCo7sdK92AY60uncTd7Z/JXw41SS+Sa0x5rHEFRj1DnpgKiBwiChJRK8S0Z/l/7uIaAERrZB/xzmuvZKIVhLRciI613H8RCJ6U567gaQOTUQtRHS3PL6QiGY40syTZawgonlx76Mcgpa2qfUSGk3/HdkaRvV8OL5h0Y3QkVXRhxO7diFWkqhmZx5fw4ofVs9UhkpoOF8FsNTx/ysAPCaEmAXgMfl/ENERAOYCOBLAHAC/ICK1dO+NAC4FMEv+myOPXwJghxDiYADXAfiRzKsLwFUATgFwMoCrnIKt2gSb1GodFt0YVGseTtjzYdLqsoi7gGYl6lVNKiUM/J5PLTpznofT/MQSOEQ0FcB5AG52HD4fwO3y9+0APuk4fpcQYlgIsRrASgAnE9FkAKOFEC8Iq0Xd4Umj8roXwFlS+zkXwAIhRK8QYgeABSgKqaoTHDRQo4rY5dX3Q7InBsbMpzY+HI1JrY4dmR0QEqsG/sQVBmGivGrRmfNKA81PXA3npwC+BcBpRJokhNgEAPLvRHl8CoB1juvWy2NT5G/vcVcaIUQOwC4A433yqglBCwnU2ofjPP3Esq0VLbssovpAQiasxChal4U6FtmHE7UyMcosr5AKZeNT2VrsJRT3u2J5U38iCxwi+jiArUKIl8Mm0RwTPsejpnEXSnQpES0mosU9PT2hKhpErU1qQTiLu/i2l2patqsecefhBJ2vyCKZOidO5GwBVGoeThWj1GLeYJgor2o2+UotrVOvaM6n3ump70CwgYij4bwfwF8R0RoAdwE4k4h+DWCLNJNB/lVPej2AaY70UwFslMenao670hBRCsAYAL0+eZUghLhJCDFbCDG7u7s72p16COpgKu/DaeyhWeW2J6hi0IDPXJ6Cz7lweUetVbHsxjapBRtNgwJpKkHUAYctMOu0H868+YvqOhBsJCILHCHElUKIqUKIGbCCAR4XQvwdgPsBqKixeQDuk7/vBzBXRp4dCCs4YJE0u+0holOlf+YiTxqV1wWyDAHgEQDnENE4GSxwjjxWE+o1D8dMYwik2D6cKhYgPH/DnguVdwWCBqq50kDlticwX1NNrb4Si7da6RvjO9mXqcY8nB8COJuIVgA4W/4fQoglAO4B8DaAhwFcLoRQe8ZeBivwYCWAdwE8JI/fAmA8Ea0E8HXIiDchRC+AqwG8JP99Vx6rCYEaTsW3J4h3Pgz3vbYB/cO5SGnjajih11Kr0sTPuB1Ro3dkYWq3ZfdQ4CrnfrdZi1n8vLRN85OqRCZCiCcBPCl/bwdwluG6awBcozm+GMBRmuNDAC405DUfwPyodY5DrTWcaodFv7VhF75612s4/7j9cf3c4yPnE1sg1ECw6jqt2BMK46StiUnNv4Z7hrI45fuP4bOnTMf3//roSPlUszOvlMCodzQnwysNRCJI4NS6YcctbihrKZrreuNt4Vstp27cKDJnWm0WMTv9OKP7cu8pUtsKSNInNdvHl+od22Gc9rXQ8mL7cFje1B0WOBEYzvmbHiresKu8WGg6aTWDbL6+X2Q54d9l5+1TSD07ojBRas73G+VVB92fOh/kR/Kd+FnFh1iptdwa3fS5L8ACpwySckvPwZG873XN1rBTSeu+4u5UGtnpHva6CjxXXQ717MjCJHVeE6WswCWDAjS8MKs110Joc9BA88MCpwza0tZKPP0j/s71ZlvaJmNrONEEDoVZbMsHO2Q56LpIuduFANCPxOP7BmKkDbHSgLM9Ralr2KATk5YVxqRW3bBoFaUWrwyWN/WHBU4ZqA9vMOuv4dR+aZt46VVHE9ekFjssOrBjjKFJeP5WKl+gBhpOzLLCLsVkNKmFMGnVIkqtWSd+MkVY4ESg0bYniP8hydF/nUeQpvuoxExzO2igCmHRFfEt+V0j9L9DlxHow7EuSAQ4cfyyqe48HFlGRKGmNPB6TfxkirDAKQPV3HP12oCt7BPhKPh0xmEozsOJaFILeT5ex24229U13DaEOdFtUqt841LmMJO8CbMBGvtwmDCwwCkD1bHUfHuCKi75YuUfrpzAfCpUj6jnw5Wh8+H4d7jBecaoT5nXV8Okptpz4O37Bg1UvzOPu+Mny5v6wwKnDFR7rfU8HJWbqUOIW1z8HSErU49gTacCUWqaLOI/v+hpwwh75/uJUlTQ/an2bDKpFZeWqY8Pp1IDItZw6g8LnDJQ7bXx1lKLh++kyHLyqVRFop0OlVYntOJ2lnEEYZi0Lh9OBD9EUAl2ezaZ1EJoCNVt88rHGC+XZvsu90ZY4JSB6hyCfTjRW/aO/hEM59xRcNUOGvDbfrkWhF9LzZ+dAyP4wm0vYVvfsLEMfdBAefUoSR/DGR1G2Mf14cQ1qYXx4VRzPxyVdfy11Fji1BsWOGWg2mvQiDjOx3f81Qswb/4iffkB9YqK3+i/vHyq6wMKyv/XL76Hx5dtxa3PrS6rjEoJ7Ghpy7sm0sTPgCRqABW0J09YoVhp1HvntdSaHxY4ZaCaa5CGE7ddv7jKvfB1sG8jHrYgrfP3aHpuYZ+nei/JRGmzVln4Ld4Z9farFa6tyz/axM+YGo690kA4P5OXuTe9gGseeNu/kj4UNdC4PpxYyZkKwAKnHEL7cGrbsuNrFpWpb9RqqGSB0Xghnd+pRGnX6bcBW/FctBuIJ3DC+HCE9nfoMgLOBwUNFMs2n/P7JF5c1YtfPWPWOoPwGyyUlY9P+n/941v4x/95JVb+TDAscMpAdcxRgwbWbh+ItCJztcOi4/owlFc5suAKWXBQ/kUNp7z45rg7flZii2m/HOJrOP7nbQ0ncB6OOY9amNSizxML3sDtzhffw5/f2FR23n98dQO+fOfL0Sq2D1KR/XD2FVSDjxo08MH/eAIAsOaH51W0XnGJO8Iv5hOzHobjYbcI9tNw7DJ8TGpRO804tx3mmbvDoqsQNFABgV+LsOhGnPj5tbtfq3ieezOs4ZSBaq55Tc8X1+wRiwp19EHZPL9yG7ZrIsCCd7wPWY+ADILyz+XNGo5fp6U6oqgdUiXWUvNrM+6ggehlmFDt2bh4Zw3Coh9busWo/StBV++ll5j4sMApA3ulAU3DdZrZau2crFR0mW+nJwQ+e/NCfOZXL/pcE6sasaPUVMfpr+GYj0X2QVXApOaHKyw6QuMKNEXmlQ8nfD28xA2LvuT2xfjo9c/oy5XjO5742fywwCkDPw3HaZaIbJoxpAsc+cft6FWH63ON6lDe2dJnziei4AsWNOGus304SV2UmnD9daI6cb/3dvMzq3D9oyt86xeFUPfmOBmlrKA0QYt3hpElYYRB0D5SaufRkrx54udeAwucMrB9OBoVxymDojZsY1hw4AZa0cpThAkaCPJbVQJTp5WwfTj+dXhh1XYApig1918nRR+OOe/vPbAU1z36jvZcrKVtwqw04CorgoYTkCQXEDQQxuQYxg+0apt5sOJH7ImfFdox1I9abM+wN8ACJwK6huv84KKq/vVwWgPhTGphTCbV+p7VyNuvU3tz/S6s6ukHYPDhqL/aoAGz9hOGWCa1EA60uCsNBN1X0Dwc1Zl6ixauehnKdpkDg6/RX+BfRliivqd7Fq/D+h3+0aXV3YBu74EFTgR0o32XDyfil2H+aP3TVWqGv18ufhpO1FWWS+phKMIOa/WpQ+/AiP1bp+EUhYquXDmCj7hETSUGt35CIW5YdFCa4lpq+hepknvzcddLX4jzuzB1ymHntdVj4ufOgRF869438MXbF/teZ7oHXt3ADQuckDgbjq5xOTvDqB1QvZyadrk+xYfTcKJqCP7nlUvGT+glHZ2lrt8MY1KLvp9PHA0nOK27Xeivf3HVdqzq0ZusgsrIBWg4pki6MMEMzsM63ycQrB0UBV7MgVWE5Ot6BwEAuwazvteZ2iZb2tzwPJyQCNeHU9qKci6BU1mTWlBusdt0sLxBLsTwP36UmsmHI01qfgLHodXohYrZbBbXKR0raCBEHmE0nLk3WdGDujleQfelhIUpSs00MbYQol4F10DNVH5A/US896OrS1g27LQEzqhW/64yb9ie3TWHSojA9er2dljDCYmzOWk1nAANKAz16PAAf/+GQhcooShnHs7Hf/YMfrtorad8fYemSITYj8UpcPzm2vgHDVRHQ4ub1nmN33vwycH3bNDinSZh7fy/qc07n6lp0BI0mBEVej9RNFi1cntLKul7neke6jpdQgh88fbFeHbFttoW7AMLnJAI14dT2nLC2KoVZvODyQ6sfhhr51teEH7+DUWlggbe2rAbV/7hTX36gLSmETLgFTg6k6c6pyk35gg63tI2/sLWeQ0QTtMsSR+k4Qh/k5opis+Zr6kzd5vUDNcE3FLcpW3ivN+RnFW5RMAkpTACt9Ym86FsAY8u3YIv3vFSTcv1gwVOSJxNRScwygkaMAkk0+ZaqsMxaeMVm4fjk49/0EC8tdT+5jF9AAAgAElEQVSC6q8Wfw6r4eg6P7vT0s7DMacLQ5hUb23YhT++ukFTrxD5O67J+kldA0EdrdKazO1L3+G7zMxhggYMFam2DydO+mzISbGme3CbHWsrcPL2QKJxzHjswwmJy6wRaFLzzytfEEhrNHSjIJKHjR2Cf3GBhAkLNjl8gfgj0GI++uMqICCMWQ8wmdRUIbpzwRqeH2E6ko//7FkAwCePn+I6XqyWOQ9n/tkIJrXQ2xMETPz03qd79B5ctkngBGltcX04Qem9y1I5n4MS8MkA34upbboDK0JVt2LkQwrLWsIaTkiC7NVlmdSMoyGTwKnuyMju9CJqOIEWP0mQ5mfqdCnEPJwg00VxpQFdWnO6UMR4PaE0HMfvKBpOoKlS+HdMJoHsdYhr8w6h4QSb1ErLK4cgH45TiHurGNukVoFgoqhk5YMN2nailrDACUlQlFo5612ZQyj9NZwwdYuEcP3R4qddhHXKRt24rjhPxk/g6H97j+nyCJO/H+V0JENZ7/Iuwdqh87lGM6mF1HAMphfT+3X+L4y5LKpJLW7YetCAwqlhea8ZyatO27+MMGHRNTep2fOralqsLyxwIqAPGnD8jhg0YOxw5V9jhxByiL1002688O720vrYPYo5rV/QgDoT9EHFtcH7Pdeg1bqLPhxzvaI7pfX10DGcdQuMMGYWZ5YjuQiVDEgStB+Orh5AuLlnhRDfRfCW7YWSvMrD36Tm1nDcFwUJY+91fsdrbVJTg5MGkjfRBQ4RTSOiJ4hoKREtIaKvyuNdRLSAiFbIv+Mcaa4kopVEtJyIznUcP5GI3pTnbiBpQyGiFiK6Wx5fSEQzHGnmyTJWENG8qPcRFmc71HWcrlFShTUcO4ooZtDAR69/Rrvac1HemDPy1U5C2thNH2XQPBtVPz+hFxS0URwll6aNa1IL48tQeP0VfqY+XZ6RotQCzoddJ6/Uh+P4HcI/Y/bhBHwvedW+4mk4puROrdF7japb8OZ/+vfiXv6nthqOvQp4Azlx4mg4OQDfEEIcDuBUAJcT0REArgDwmBBiFoDH5P8hz80FcCSAOQB+QUTKdX4jgEsBzJL/5sjjlwDYIYQ4GMB1AH4k8+oCcBWAUwCcDOAqp2CrBq7QVI1ZwzWSC1qqI8RoyFV2gGZcIYuar+CqhIZjGuEGLa6ojvvVIdikZu604jqlncmCnoG3cw3nw6m2Sa0Q6jrvafccE5P2or/elI+OrF0//fmX39uBGVc8gJVb/VdaMNVx084hY13CalfmsGjnb/01jyzZjMVrev0LiEDQChL1ILLAEUJsEkK8In/vAbAUwBQA5wO4XV52O4BPyt/nA7hLCDEshFgNYCWAk4loMoDRQogXhNUy7vCkUXndC+Asqf2cC2CBEKJXCLEDwAIUhVRVCOrQXLbqiB1vkEnNlGul9gnxy8VvZB3WJBVkSjRHEVl/fYVewEiyuABl6bkwGp4fbnOe/7VegRFG2DvPZSOY1II1z3DXeU+LEG3e2W7K1eztdHn/L+BPr28EADz9To8hf5naUM73HnjbWBdV58BvOoR/yvR8v3Tny7jgly/45h+F3N4aNCBNXccDWAhgkhBiE2AJJQAT5WVTAKxzJFsvj02Rv73HXWmEEDkAuwCM98lLV7dLiWgxES3u6dE3yDCoxpogfedbzjycMCGUurKrpZGHcfr7d/bB1/idV8eDnptfx+RMqrtMHfMLaY+s4YQYxSq87z7MO3XmOVJFDSdo4OI9H6YzDRNME9Ru1DszXZZOWh2qSfsLer/OZ+q9RoUWB5r9GjBKrTi/ai8SOETUCeD3AL4mhNjtd6nmmPA5HjWN+6AQNwkhZgshZnd3d/tUzx/VbtLJhFa9LgSM9sLYcgNH+FVqsH4OdYVvWLStoUTT7IL2W1H186tDUFi0n1kujMD1I0jYOTH5cPyevjNPnTk3iGCnvPobJHA8+bru2yRMir9N7y9Q4OT9TX4pubqrsX0EtM8DJ3QULzVoOEHPUIVPe6nnSgN20EDjyJt4AoeI0rCEzW+EEH+Qh7dIMxnk363y+HoA0xzJpwLYKI9P1Rx3pSGiFIAxAHp98qoaqiGmk4lADUfXJ4Saj2DqcOHf6OO24zBmHdPihGHTA2Y7eNAI1C+k2ZuHKR91LK6GozXJIXynUlJ+qDKLv3UTP4M1Q//88z4+Er8ReiFEm/YLOQ5Ka+eR938/aekUD+r0TemnjG1zXKuvm0mYZaSwM+1mGmSKrxS/eHIl/mehe41CVfcGihmIFaVGAG4BsFQIca3j1P0A5snf8wDc5zg+V0aeHQgrOGCRNLvtIaJTZZ4XedKovC4A8Lj08zwC4BwiGieDBc6Rx6qGaiypJKEgSjse14epC5sWwR9n0BbTRt9PzLCBMALLX8Px11CKefgvcBgkcP2DBsJpOH4RbGFGoPo5WM66+lNiUlN/fRI636/OpBZktgorBHXtz7WxoF+5psGCU8MxDFqCNPdsgMmvuLSSnuIz1l+xZ6i4tbX3/QZpOJmUFDgl86ssypmfF4cfP7wc3/5f9xqFxWV5GkfixFna5v0APg/gTSJ6TR77NoAfAriHiC4BsBbAhQAghFhCRPcAeBtWhNvlQgj1li4DcBuANgAPyX+AJdDuJKKVsDSbuTKvXiK6GoBale67QojKh3k4cGo4gNUwU8nii3R2yLoPKMx8BOPy7ULVASVLb6jjcYja0XrLD5yHY9zxUZ03aX6yDn4+HEfefoEBfhqO6fnq6uLOO7yGUxI0EMKc6SxTu8qFo8xsoYCWhHvdpLC+MW0wjI+GE8ZvGVfDEUJgKBsuis5EkMm2f7gocEr8VLL+psFSazqBvmFg95B+v5y6+nAKjTcPJ7LAEUI8C/O9nGVIcw2AazTHFwM4SnN8CFJgac7NBzA/bH3jYvtwpH6aKwg4VyzPB4xknOfLDhrwLKvjFHRA5UxqfvhFqYXdT8YoaANMHkECyUrrP9r205Kcx4Twt3lrl81xajgBLpaSsGj/y2X+/tqx28cj0OL5qr2+Fq9AVe0xzLNxMpQNFibu7QnKFzjDDjNZ1ImTxQGR/rx7Lyv9OVPaCZ0t2NY3gtVye3MvtTKp6QjadqIe8EoDIVEfvXJQ+tqzdaaJECMd4wjIOcLV+hDiEWbplDAaTthFIk3H4wUNOH/rtBjIPPxNUkHPMnBZo7I1HPU3+r0566QbzARFkxUFvn9abx2//+BS7XVOnPUJs96YF+dSQKZna/enASZp0yP22zyx6MPxl3YDBpNaGN9ttQhaBbwesMAJiWoqSrvwdn5BDSuMg9X0QVR7xVlnub96ZpX2Guf9mtbUCrs3fcnxAIETZBLx1kkfNGAexed8ljYx5eMu2/HbN7XZh+OPf/28JrXS+pnfHVB8JrpbL7jeu/vcu44trU2PLe5aamG0qCCKJlPTgMdchh2wEBDwYg4a8H/21SRnrwPXOBKHBU5IVMNRUSneUVnQKDfMhxdm8c6gkOvxHRltHn44TXY7+ke01zjr7I2UCrvBVdBsbLNJ0T+9t2xdQIefDycf8O7ceenKDp/eew9hfDiu968pXwQ45oPMjX6TG/3MTa4tIYzaS/F3lKVt1I6bfunVOmemXNRx4+KdzgGH5/nmAnw4qkoDIznteWeRQVGAlaZoUqtaEWXDAickzig1QKfhWH+Joms4Rs0n4Bp1ZNLoFhw+ebQ2Dz+c35KKuvHivN/SuSQyn6gmtQCBpZ6d3xQUP6HhjOwKejdBg9Agk2bZJjX/4mSe/vm7NBzNQ3Ie0m9AZ9YgXc/Gb78kU2ceYi01v07XGeocfev2gPbl03byAW1P1X3AoOEEDWbCrmMXhb12pYF9AduHkyhGqTlRDSudTGgbZ1AUG2AepQvD8eJB60+CKFKItDNFJqnfuz3vuCnv8ipBNnI7j4AOxzjPyE4fvAkcUNqxOAVOsI9D0ynk/c065SxtY5qH45cu0Fwb4JgvBNSvGBatKdvnuToHJ2E68zC7gnpxBg1EWRrHqd0aNRwfH46t/Rk1HOt86bYTpfkFBWVUmiz7cJoX1W4ySYPAkQ0yk0wEOnbDzMMxBRnoNRzZsHzvoIi3Y3eW29GiFzjOj9LrJwg7Dyc4Ss30XNR15rz9RqlBo2T3sy7N+6r7lxTPB0SJBQkcrwYSxi8R9P6dddKtRBAkEIsapE7YOtO6z59/XHE1KWNYdJigAZ9noJ5XSyoRaBkIMpca/Uw+7z/v82yc+b+0Zof2fNCz1/ncKkVxa4XGgQVOCNb1DuCxpVsAFE1qpQLH+psxfBhBnYZ1jf63qxwfpzURhQqR9ubh/G+HN6ZWpfGJhFL/Cxqsme38SuAY0imB5OfDcc3DcZ8bcY2SA9bB0zzA3zhmcGvnWLnem/9D8D47NQr1SxVUP2f5upUInDLIz6zjN2HWW4+X39uB6x9bAQBIJihU5GUUH456d22ZZGB6nbANEx3qJ1T9QsYBd7vT+XECn71P2WExC3u1tE3jiJw4Ez/3Gc657ml7JnEyoRc46qWnk/qPL0zDN13jMhf5DIjCtqt8QSDtUGTCLM3i0nAMob1RfTh20IBR8/NP7y3bm082wIfjN9fEVFdX/cpa2sb97PzMhPY1ZQSkRAn79otScz4bp+BWAzDA+ibMPpxggeOdQOrcv0WZQ9vTPgInrxz7ZmEKhPvuvPdhazghBOr2vhG0d6WM5/VBGe626Z1jFwaTllRcaaDsLKsGazghcC5bYa804G2YLh+O/0jRuMSH4eN0ZufntCYKNwm0NFLKfE53vNTxrTqsAIET8MEHjZLDzAWyrnefGwnwA5QTuuq3NI63Hjq85fstK6MrU+cfLLjejaZ9BAksH7ORS+A4Cm/PFEcsCZ92V+4agt42ot5dayZp1ISKGo6m/q6Qd0MdfQZ0SiD41X10qyVk+jUajjsgxV/D0b27MJj6k+JKA40jcVjglElKDhe8HY9qkBmDwAlSrQHPaMc1KjWP3oHixx42aMD7UQeZZKy6Ce1vq04y3wCbmmkwH7jSgOc6bd4+napyPJsiCIN8OKZrixUMryF5OwdTZ2GsX4DA3LxrqOS8ew6VOf8g7ckpuNsyxZF8kshnWSJzu7GP+/h5VJntviY1c+iy85jp3YSZ+GkeiBXN0Lq5OEFmcue9R9l6AjBP1uaw6L0Ak4azbLO1M0MmldA7L306bN01plDdqgQNBOxmapXriFIz7jviX65J9VcjP/PGdMEajp9ZyWmWCXo3USZ+BmmgTrzPLkyUUpBZxpnH1j2lAsdtMjQLlaCgAWeH2JZ2ajg+JrW8vk078fPzqDLb0knjXBhVht5/Fay95n2EksqzIPTpC0IUBY4mUi1o8U7nPZlWuw4ia3jGKj8Oi25ikraG4z7+6xctx3JLOqnfgrpM56lpZKRfGNQ6lkiYgwb81uNypgkyWwDmiZ9B5qggU6Ipve3j8dVwrL86LSYb4Hgua+JnoGNd8+59BhuqwzFtj2zlqc+rWH7xd99QqVkn63JMl+av6hS0ErWzQ2xNF7sOv3an0hP5DbRK6+Itsy2TMs6FUdfovrswPpxcXtibuHm/r6B5RAUh0CHNi7rQ6KCdgJ31u2j+Im39gnAOYrIaAVbrRUP9YIFTJrZJzfASO1uSGAkYaYVyXroad5BJxfqbDLnKcWmUmlPDMdQt77zGNPHTWDwAvWYkhHAEDejThdFw1DWZZKLkHuxRckY/SvaLcPMStNeRdpQtzM9OdTibNKYwXfoggdc3rIuU8p9HpM7rRtimoAHn8YTBVAkU7880XQDwdMpGDSdhDLAY9gkaCGMutYJo9Gsk+pmSVdqiSa38kHRnW126yW//SjMmk6QyJVdzrk+5sMApE1NYtKI9k9KHZ4bo1E2jMZdJzcekkkyQ0YPjWibeUz1huM5Ut1INp7TOOnQCJ4w5Sl3jG6Xm6NhMo+T2dEq7kVx5UWr+6XXv1s8xHMaH414YtvS8rpNx4tJwNPmrOozkC5rl+R0CxxDt5xsWrd6LwdRslW8OWy/6cFLG9EUNp/S8e/tos8BTAscUFm1KL4RVN8Cg4QSGpMefh+O8R+e7VssCVXM1g3JhgVMmKYMPR9Fq8BM4R4dmDUf/QbsisHRRSqIocEwSxy/008/ko0tvmrwYrOH4j871+9gUj+0c0O854iw7renYsi4NR2/WSAZoroogk5zOLOXsVIZy7k7J77l6r8kkE1oN15lOJ3CCtGvVNoXwX5Q26xIM1vFDJnWCyCxw1HUtKX3dgXBBA37zcNQ96zpvpxDwi8Asajil57z34j3fKSdLB/lwdOU7Q5ePmTpGW78gnPftFN7DcuHT1dv6cbNhUd5awwKnTEa3pgGYHaDpJGk7DvdcEH3eptFU0OKQLoFjwKU9lQQNOOsQrOGY1lIL9uEEdYalaZxZbt49FLiNsBUl6L5GdUhthrkcBSF8TaXHOjqCoPXGgu5xZ79baDpt7gPD/utxpZNk6LT8Hc+uYA3NM3auA+ZNr8pOkCe8XJb52/9zKhLkE4Ho0jwN80VcHaZB4PjMwxmRQlw3oHEKHJ0wVmVmDJYL57M1RQi2t5g1nCCTmiqvPZOKHjSQc36b+sHP9x5YikaABU6ZjGuXAsfReGZc8YD9O51IaAWO2/5t+PAMq9YGzWVQh/zCop0fsmkUS2QOi3au2hvZpKaJGAorTCd0Wqtg7zHtrKg65RSV1M8ZWmsapRZNKqV5D+cKxVXCNeeD7PzOY70D7tW4XeawvP+eKulUQqtZj+T0nYydPsCk5hyZlwgcWXZbOqmdz5RKJqywaKM5VAqclH6NQcBfw8nmCyDyX9pmxMdXofwqo1tTxvXO8gWBVhl1VxK2HvjtFYMGdGHRwSsNFDW4qGHRbpNaqYajWNVjDkypFSxwyuTgiZ0A9I1vXHsaqSTpbckhVr3d4eiM3HucmM1hzvz8NRxzWLPT7GGqm/NjKr0/4aqHiazLrOiuuym9OjK6zRL0uzVRWEDxHlpTpSNhZYozmWUKheIMb12nMJwroE12Kqb0Ct1gw/m8vNs/uMKOjSNw63hrKhlsUtN0qs6ABN39DWbzGCUnL5rCtq0OsfRdpZME8guLdvhwTAMtp1bo7XSH8wWkkwmkpG9Op0WrNLpnr4TpuI6MWcMpCLRIgVPybeQLxvUTAasdt6SSSCUomknN1nCSkTWcXF7ftwx7zLdq4eF6Uv8aNBFzT5qGTqk+q4bkbKCfPmka0km9hvPNe9+wf5v8JMs277F/u2znrm12NZ2yMqn5rKVmijZynmv1mevQP5K3Z1Sbl7bRl61w2ZoLKmSzNB8n6jmPUQJnUK/hqI5b50O7Ru5MaZrLkSsI+2PU3cNwNm/PrNfOVfEJqLDOF8v0ajjOc6YOUXX0LWl9pFfWp8MGgAfe3GT/9ibPFwRGcgX7+XrroMprTSdt05VV7+IgJ+kXFm0LpoRx8VXngMqrJYzkCmiRWpRVn9L0dtCATsORQmBsW9pHwymgTYZ5e59friDQkjL7bfMFgQRZbStwHo5OO1ZzxDIpow8vCGebcwcNuPNrSde/u69/DZqIQ/cbVbKWmrORZZIJpJOkbfjOcFVTp/Gn1zdqrwmaC6Daqd8Axm9Gs91Zp5JGk9rgSA5jpDmxZBQcch5OVjNCdgpQUxQQYHUYALDbYFJTH67fSLqjJWV0+qcDNBzVIes6Fb+wZ6B4r50tKWzZNWScE+U1gSiyDg1H93rU+c6WYD+A9/7sDlm+W1PbaPeYfNTxVCIRLiw6pIbj3VdmJFdAJpVw7ENlNlfrnv3QiLq/jGv3UHf5wtZgdSY11VGXLFqr/FsJQmsmqRVo7jlQpc9I1Wl0a3QfjvOb8JtI2mLY66qW1L8GTcAHD+kGAMw7bYa9sKDqZJyN7NGlWy3V3+BDUOg69S27hzzX6EMd9asVF0ebpi7fWc9siYZTQIIs/4ep4+gfztudrvfDsrfhDdBwdFFOQTuhquvGdSgfjtmklkoQUolSH87psyYAsD5q7VpqDpOa9/EOZfPY3j9iz7Xo18xzcZnUfO5hZncH+kfy2LBzsOQcUGoCsfNU64ml9ZFeSgPqbEkZtSSF9/6URqHerbetujUctx+SyGpzCZ/FO10ajqGBON/XYNb9fJXAMU24dtZZ912pFZzHtqeNz3ckX0BHRq+95/IFtKQsYWRa9iZBZGk4Gh/OiMaM7ER9S6Pb0pEEzqLVvfiH37ziqK9bw0k7FgNV91FPWOCE4Ad/czSe+uaHkEgQ0lKNUJ3AkGOy10//9jikE1Qyn0E1+m9/7DAAwH88srykjP9+ygpb/Pv3zQDg3XiqaEfWRTIVhACRtUifSctwju50ZoNUImEMeFD3sN/oVgDAe9sHXOdUmnLm4ShHdtBum2rU2tVuCRyjSU0KDV3HlkwQjpk6BslEAkKUmiXzQtjv1fv8fv3iewCs5fgB/cRKl0lN02moTmBaVzsAS3g7zx223ygAZpOacpybtr5QHVVHS6kfQMi2MWVsm/y/O63q8EwmNfXKWtNu7VcJeEBNttXXPS8jAJNkHsw4O2rv5MkR5cORZemWRxryCxqQ7X5ce0arQQohMJIv2KZy17ydgpA+moR9z06cZkWTSc21RbbBfwZY0a9RFu98da17Hx53iHweZx02CUdNsXYBNu3mW0vqX4MmYMrYNhwwvgNAcYMypfo751Ucut+o4lprjsbZL6/tbElr8985MII7Zcd2/PSxANzmlWxe2FFaOzUdbkEIJMmypZs+6iFXlFmpDyeZIKQMYbfqHrpHtWBCZ0tJp1tc/FGb1EYXKefUCPSmRutYV2eAhpO3hEYyUWrSHMrm0ZpK2lqMdwKjEMWP0ZvW2wHrNBz3fel8REWzCeDeNyVXKNjak2mEOyLvzbRmmZ9JbThXkJMTrXbrjWJU7XjiKGsw4V0aR9Xd69R2zl3KpBK+DvlEQNsazObtkbh3T5ls3tJwlFNfZ7ZS7VEnjAadGkS+oF10VwigvaU0aMBpDgRKByqqHbWkEmjNJG3h5sT5XPQmNVW/lHbibRDrdwy6/u+dBNySTuDXl5yCe750mm9QUa1ggVMmaiSkGrkKNfy3TxwBoDgx1Nlx7ZSO4q4OvcDZ1jds/546zhoFO0dGw7kCpnW1ozWdwJKNu0rS5wuWWp9OJrTL6gDuUaTOxJdKEJKJhH5yZkGgt38EXR0ZdLS4bdVCiFBL7KtyinWWAsdRl8FsHof960P4zv++aR9THd6YtjSIfHw4hQKSScuk5h1tD2WtD69dE76qnoV6ryY7veLf//R2Sdl9wzl7bTHd89u6x3q/o+QcLlV+Nl/AULZg+6dMnfbgSA5tmaQ1o98nSq2zNVWivap3pe7dm3yzNOVOktrrLs+ARg0C2qWPQj2PXL4YaNGSShiF5XDWcvpbbdOkPefRJU2mXi1hRIakK2HtHXAM5/K+Kw0MZfNoTSfsxUa9z1i9LyX0sx7LAoBiyLRX4OSKfsO2dML2F+muAfQDqiGHhgOUv2K0Gqgq1P1s3TOE97YPoCWVwNj2DE4+sKusfKsFC5wyUQ3zjfU7ARQ7INXW1axjp+lnh5zs19XRgq+ffQiAYkPc0T+Cj1z7tH2t6rjUh/HbRWvx+rqdGMrmcVB3J9Zs63fVp7d/BI8u3YJEAsikSjtbwGrUzoUBvUIpLztrK+ChNP32/hHkCwKTRreiNeU2HQStI+bEaZ9X5ajOMpNKYNdgFkPZgmuHTdWJZJIJjGpJGTWcrOwAdVqe1ekk7Xfn1NCUYLc7nJIN0nRluQ8++vYWjG3LyPqWJrj41pcAANOlSW299OH0yhDpqePaXHVxUigI3P7Ce9g1mEXCMN/FNqllUiVh0c4Rvq6MebJd7DemBQDwK8+MdGXi6u5sQa4gXLP61Yi5JZU0Csu+4RxGtabQlk5qnfZCCDy6dIstlL1BA/3DlsCwB3qe9+82T5bmPzCSQ1s6aX9XXg3J+ewAlJgNrfvTh0Wre25JJdCRSWnNrc49cnQmvaGs5T/tsDWs8jQcZREp1tkq4+o/W5GZ6rk2CixwykSNFO9ZvB4bdw7acxw+c/I0AMBU2aksdYQ4P7F8KwBLw1EjOaX1/PPvi+HSQNGxpxrzlX+wRvtLN+3B2PZ0yTyUr939GlZu7cNQtoCUwQfjdFIDpRrOwEgebemk0SSnFhWcOKq1xHTgHPX1DZuXngH0Yd9qRNfd2VISOAEUO/d0MoFRrWmjD2c4m0dLyhpJ60airemk3Wm5OgGl4bSWjnAHR/K47tF3AFi+NdVpOTXSl9b0Ys9wDpt3D4HIEs4mlB9FCc0e2RnMmNDhOu7EaTJJGSIg1SzyzpZSDUdpU0qDMQnsKWOtdvvaup2u48rENWmMlV4JycGRYqh4JpUwhhz3DeXQ2ZpCSzqhdarvHrTyV3LUe8172/txwPiOEsuCQpk4M8mENmBjzbYBTBnXZmspXsH4Xm+/vH/r3egi8TIGgePUcDpbU9oN2P73lQ32b51AGspa357y0Tq/zd+/vF673YQ7vT6qcPGaXgClJrd6wwKnTJz7g//kL0Xnv1rA74Rp4wAA78jOdeueIdzy7GoAluNynHR+q/kY73pm/6rRlHek+rWzZ6GzJVUywnvXsay9NQeo9KNb2+t28nvt5DsGshjbnkEmmdCOwp5c3oNMMoH3HTzeMh046ubsAHf5rHX29sbdeHVtsTNTI29V3+5RLYY5LNaxVJIwuq1U4ALAwlXb8YdXN6CzJWX5cDRmpZZU0aTm9MOo++3UjHCd7+Zzp0zHtZ8+DkCxkwTcncjB3Z1Y7hCqXka3uSOheqTgOmw/y6m7TTMa3TlYFGAdLaWjaGcHNbotXdKhquc8abSlwZje0aGTRmmPK43jmCnWSFrdX99wzhYC07vasXpbv1bD6B+xrrM0nFKBo9rmhSdORWs64dKehRDo6RvGpNGt9oDAKzCViVp2P7kAABo+SURBVHVMe1o7WNq4axDTu9rt78pbhx3yeaiADq/THShqP17/mcqrJWVpzzr/nlPD0An7m59djf6RPDJyoKne55bdQ/jG7163tWMd63cMlKww/e9/WoL/c8die2BQrk+o2rDAiUFaM/FlTHsao1pTWLfD+pBueGyFfa6rI2MvjaPMbO/2uE1kKuZfRd4cNWU0Tp81AZedcRA6W9LaUZJdH8M6bus9Auf/3bfE/r1s8248unQLxrSl0NWRwfZ+d6f39Ds9mP/cakwf347RrWm0ejoOpRGMaklhz3BO62PY0T+Cq//8tueYdf+qvt2jWrT3pD7AVCKBUa0p7dI2f3vTiwCA5Vv2IJUsDYvul6NxW8ORZpiBkRz++Jo1ArU1HIdJzenPSCWLAsv1DhxFzZjQYYzgA4Ax0uymntlNMjJx6rg2jGpNuTQnxZPLe+zfozwDDiEE3ttebD8t6VJfylDWreF4fWDvP3g8jp8+FmPa0/jgId04bprbRLNjYAQdmSSmSLPfzsERrOsdwENvbbY39jpi/9EYGMljjefeAauT7ZACRxfF9YmfPwsAOO+YyWjPpFyDoV2DWWTzAt2jWmwfh7f9L9tkCcApY9u0bb9nz7ClmaugA49J8dkV1vNVgxVnHkrYqlUYvAJNadujW9PoNJh7OzJJnH/c/trzzvetgiZU+atkv+C1Tjh5+K3NJcfWbB/Agre32G31+rnHG9PXAxY4Ebj6/CMBAAMGM8K0ce1YJzv5db1Wgzlu2lgQkT2f5MnlW+1rnNgmNZl331AOXR0ZEJGxw1WkkwnsHsqWfHhqFHnazPElaT56/TMArFDnCZ0t2NbnNgkp388WaTp8Z/MevLF+lz1yUmaEyWNbIQSwRyMQf/7ESrywajuAYmi4MikOeUbgQDFEFyh+1GPa0hjdqtdwnIxty9iCIl8QuOGxFdg1mJUBD+65NNf+5R1cu8AymXVqnMbO1alTCbLT3/b8GgDAI0s24+LbrBHory6ajQO62vFeb79rVKnqctSU0Tio2zKdKdOPijjcb0wrJo9pxaNLt7qf2+Mr7PrdecnJlobreL5X/3kpzr7O8v/99G+PQ1va8qU437/ywagoNO+K233DeTuYoSOTLNF+e/YMY+LoVozvzIDI2ihOafbLt1idvWqr3/jd6/CyZyhr+XAylsAxjbi7OjJoSyddPhxlcpzQmbHfj7f9/+7ldQCs78sbNPDm+l3YM5RD96gWu015NbxfPWNZH9ozSaST5BLY3jlKXu1ftcXRbSl7DpR3mZnBbB7Tu9rlu3OX3ePQfpTZTmmo6r662jN4c/0ufOG2l1yDVyEE/vL2FnRkkjjv6MnwsmMgi//7kUNw1JRoK1BXCxY4ETjz8EkAYK8MMFnatxXTutqwTtpOdw5mMWN8O35/2fsAwDap/ffTq3D6j58oyVv5CVRjdJouOltS2D2U82wnYDXQjxw+EV0d1mzqWd95yD7/y6fexa+eWY3D9huF3156Kk6d2WXP+wCKtvOhbB4TOluwazCrjTg6+wjrnjdKwaM6hhfetQSJChvv0dicN+0qjtI+cez+ACyTohACd75gRdmoDhGwnL+qY1Kd8tj2NFrSCSzdtNvVad33WtFGfvH7Z2B8ZwZ9wzk8/U4PXly13e6wuzoytmlEddrOMFGleTrt8M617QpCYIa8xz+9vhHPrdyGL935sn3+tIPGY9akTgxlCy6tVZlTv3rWISAiy9eQV5F3KZw0YxzSyQS2941gw85B3O9YbeInf3nH/n36rG50tqYwMJK3R9rzn1ttn59z1H44YLxlFlJaz5KNu/B3tywEYLXRjkzSpREtXLUdr6/biXY5+m/PpFxOeMAyCSkN4+DuTqzY0of9ZHv/xw8fDACYLp/L2u1ubf2WZ1fj3Z5+dHVk0JpOQgi3D+XTv3zB/n3IpFFozxQnT27rG7aF6f5j24oBH44Bx7reAby4yvJVTBrdisFs3m5rW3YP2dpT96iWoinb4WO7a1ExOKWrM2MNaAZL37/SDr3C2DkYUlqQUyte1zuAgrDa3ui2VImwf3ujZQ578Cunu0x+hYLAH6TvZ9W2fnzi58/i8WVb7bYMAL9euBaLVveifySPGz5zPJZ/bw68TB7bWnKs3jS1wCGiOUS0nIhWEtEVtSp3/zGtdgMDgIe/9kHX+Wnj2rFyax9WbNmDDTsGcOrM8XbnNtFjOuoe1YJPz55q/78llcT7Dx6PB9/aBCEEdg8VBY6aKzDz2w9i2ebdKBQEtveN4POnHoD/+twJ9vIkTn740DK7HACYPKYNyzbvsZ2RKs3N82ZjvJzrosxqQgiMaknh2Klj8INPHQ0A+Oa5hwIodsxqjbiTZ1hhl0+9s80ue8vuIZzy/Ufx4Jub0dWRwTvf+6gdNHHrc2uwZONu/OXtLQAsk8ro1hT2H9OK/pG87XxXYeDj2jPo7rTu4ar7LZNg33AOX73rNQDACdPH4qpPHGnPV7po/iK8sb4YQq5CuoFSUwkAdMm871q0Dr9bbI0unZ3H9K52dI9qwWdPmY5RLSl87uaFrufc2ZLCCdMt/91Hrn0Kz63chotvXYQbn3wXAHDk/pafJp0kZHPWfJAXV/Xa70UJwa/89lX72XtxRmp5z7emk5g5wVpYdvU2S6O94/liyOx+Y1oxZVybHQYNFE2Ryk/Unik1ey1a3Yvx8p1N72rH2t4Be5Ly5VLgnCFX4jjxgGLobaEgbDNqLi/ssGQlUFb19GGRdGy3Z5JoTSdtLQgAHlu6xc5r8phWZKQP7j8XvIOLb7W07i//uijw1WoSyk/oNG3uP6bN0bate13XO4ArZEDOBClsLA3favtD2Tw+f4tVzkkypNg5GHnqnR5boxvdmsbkMZbJ0blQ6ityUuasiaMwdVx7iS/1zQ270J5J4tD9Rtk+pNXb+o2h/0BR6D34hrU+XkKu9qBbRWCqDIRoJJpW4BBREsB/AfgogCMAfIaIjqhR2Xj2W2fa/3eagIDiMixnX/c0tvWN2PZjAPbSOIqePcM4UHYUitkHdGFd7yAOvPJBjOQKdid9uHQuA8Ccnz6DGx5fgVxB4KDuDrSkkvj8qQfY519a0+uyEV99/lEArI4ZAC648QX09o9g92AWXzlrFk48oMvW1L4nQyrXbB/AnuEcLpg9zW7QarWBq+5bgtWOEO1575uBqePa8OyKHuweymIom8eyzXuwZbdVh3+ec6g1gU/ms3Jrn8s+3T2qBW/827n43l9b9Xxrwy68vm4n/usJq8Me257Gl86YCQC4Q2pFyxwO005pFhrfURToP3p4mf37xAPG2X6a3v4RbN095NIglNa4cHUvvnnvG3hl7Q7b7PfgV063g0VOnD5OazYE3Fra525eiCcc/hf1bNsySdy9eB2eWWkJZiVE/+nMg+1rz/+v5/DUO8W0SpPYX3Ygj7y92RaabekkbvjM8fYzAoAH3tgIIYTd4QHWKL17VAtWbOnDWxt2ubTkfznP+mxGt6XQ2z+CPUNZbNw5aA9W1HuePr7dFWnY6lgM8q+O3R9Pv9OD4VweC97egtUObWdb3zAOkqusvyhNq29uKA4Gnr/C+pbaM0n0DeWQLwj8+Y3igqNKw1BayhPLe/DSml4skRrCfZe/H9PHF6PscvkCfvpo8d3OnjEO49otk+BmKRCcq308+vUzAADjOzPYJgXSQ28Vyz9QRhH+9NGiSWueY5rB6La0LdCUBtU3nMPX73kdRMCpM7swY7zl31MDhZVb+3Db82uQTlqh/AdP7EQqQXhrwy7brO31pwHAvS+vx+H/+rBtor77S6fZ50otLe0l6etNKviShuVkACuFEKsAgIjuAnA+gNKZeVVgTLs1EVFnkv67Uw9wNWhvrPzdl55qjy4B4OgpY7Do22fZndrfnjQN1zvstcqE8eHDJuLSD87ETU9bI0z1AXTLji6VTODGz52Ay37zCi785Qv2yPRfzjvcDr2de/J0/Ot9S7C2dwAnXL0AADBNOoQ/eEg3jp4yBg+8uQkPOPb4Of3gCfZvNeP/obc24yHptLz3y6chk0pgelc7nljeg2P+7S+u+/3KmQfjUycUtbh/+NBB+MWT7+LbcoS56Dtn2U7h02ZOQDJB+HtHdM4xU8egNZ20hR3g3oMIAC7/0EEAYH/4irHtaTz1zQ+7BgXXP7bC9XwBlEyM+5tfPG//PmL/oqBXZivFQd0duGXeSQDcGpOT1//fOfa7nTS6FUs27rY7LLVO3+UfPtgWgK+v22nf/20Xn4QPHToRQFFL+ta9b+BbUrP8yYXH4rxjJsvyrXv842sb0ZZJYYWMYLxKTkoe1ZLGc9u24+M/exZq3POdjx1u2/mVhna05/395MJjAQDvO2gCbn1uDQDg86ce4IrY/NCh3bj/9Y049F8eLrn/r599KA6VZtzLHOt+AdaOoWOlIJnQ2YI/v7EJB337Qfv86h98zC5nxoR2e5ByoTTHHTVlNI6ZOsa+5qanV9nfBwAsu3qOPeATwvIndrSkbNPlS9/5iF1+RyaFZ1Zsc7WtH19wjPQfpvDe9gH84KGleH7ldvv86bOs9qra10XzF+EfPnSQrbmfMH0cUskEZkxox2A2jx8+vAxHTB6Nr91taeZnHW6925ZUEsdNG4v/fnoV/lvW/+tnH4LX1u3EmYdNxNRxbTjuuwtcAT+3f+FknDSj2G4f+urp2NY3gn//0xK8vm5niQBqBJpWwwEwBcA6x//Xy2M1Y9nVc/DWv59bcnxMWxq/umi2/f9PHLO/6/wpM8fjP+VHfOkHZ+L9B4/HRDkCBayRrHKuj+/I4OOO9N/+2OF47BtnuPwPJx04zv594ozib2U+OPGA4rF0MoE//9MHXOsqHSJDYtPJBK6U5TpRwgrQBx4cK0diXz7joJJzAPCVs2bZKzAAljaUShC294/gmKlj7FE+YGkAc47az5X+/n/8AABLs1S+MCcrr/koTpH1OmL/0fjMydPtc399/BSXsPmoJ+9vnH0I1vzwPOw/tg1L/v1c25ej8P7f64T99seKwjyRIFsbUfzLeYfbq2wDwJc8z0hNBiUiPHfFmZjpeNaA1ckrpo0rHbE6tyUe25a2o5N+K/0T/zznMFz8/gMBAKcfUsxLKTgzux3v9qDSd/uVs2bZ93z2EZPwg785Gh8/ZjKu/uRRruv+5oSp+NjR+5Wkf/f7H8MR+49GMkH4gqyH4rxjJtvvFoD9DhVfOmOmS6gdtX+pA/x//+H9DmHuNld/a86hLuvCmYdZnbvSfL90xkxXdOTfv38GnAaI846ejE/PtubXfee8wwFYax4q7ewfPnSQ/Z1Pd2gTv3jyXazc2oc5R+6H//rsCQCAc47Yz07/1btegxDAFz9wIH5ywbF2uo8fU3T+T+jM4IQDxtnPf2x7Bv9xwTEAgDlH7ocHvvIB25SpGNuewcETO/HLvzsRT37zw65vrlGgRovTDgsRXQjgXCHEF+X/Pw/gZCHEP3muuxTApQAwffr0E997772SvKqBEAKPLNmMSaNbcfz0cdrzQ9nixl7lsmcoi8VrduCMQ7pLzHR/WbIZW/cM438WrsXfnDAFXzx9prb8W59bg2OnjXUJJMBy2qeSVoit2mDLy7a+YQyO5DF1XJvrfL4g8B+PLMe6HQP48KET8YljJ2vtyzv6R/A/i9biwtlTXaYoVf6i1b2477WN+NSJU0u0j5Vb+7CudwAvrenFZR86yB7Ze/NfuLoXZx8xySWchRDI5gU27hxEriDsDfWcZPMFnHvd0zhlZheu/NjhtvblrN8fXt2AN9fvwnfPP7Lk+Qzn8nh86Vb0Dozg07On2evrKdb1DmBlTx/W9w7g86fNKClfXUNUXOrImfeO/ix+/8p6nHFItzYKaeXWPfjItU/jfQeNx20Xn+waXGzdM4T7X9uIG598F58+aRq+cfYhro5p12AWtz+/BkdPHYPWVBInHjAu9KKPhYLAi6u3Y0xbGr9ZuBbfPOdQ27yszvcOjOCPr27AMVPH4qQZ40razjtb9lgLlSYTmNntfjcDIzk8s2IbhBDYOZDFR46YhAmOwYoQAjsGssgXBHYNjuCA8R0lz75vOIe3N+7Ggrc345IPzLStB05eXLUdm3YN4hPH7G8/m4GRHH755LvYPZTDmYdNxIzxHZjW5W77r6/biT+8sh4fPmwisnmBsw6b6Po2X35vB556pwc7B0Zw1P5j8KkTp7raZi5fwJ/e2IjpXe2YOq7dNiV6n7H3e68FRPSyEGJ28JUB+TSxwDkNwL8JIc6V/78SAIQQPzClmT17tli8eHGNasgwDLN3UCmB03g6V3heAjCLiA4kogyAuQDur3OdGIZhGANNGzQghMgR0T8CeARAEsB8IcSSgGQMwzBMnWhagQMAQogHATwYeCHDMAxTd5rZpMYwDMM0ESxwGIZhmJrAAodhGIapCSxwGIZhmJrAAodhGIapCU078TMKRDQIwC90egyAXT7npwNY63M+KH21z3P9zOcbuW5hznP9op9v5LoBzVG/NiGEflvYchBC7DP/APQEnL+pyumrfZ7r14R14/pV/d03bN2apX4AFvtdE/bfvmZS2xlw/k9VTl/t81y/6Gn52cU738j1a+S6Ac1fv9Dsaya1xSLGekBx01cbrl90GrluANcvDo1cN6A56gcAlahjU680EIGb6py+2nD9otPIdQO4fnFo5LoB+1D99ikNh2EYhqkf+5oPh2EYhqkT+7zAIaL5RLSViN5yHDuWiF4gojeJ6E9ENFoeTxPR7fL4UrUHjzz3JBEtJ6LX5L+JNa5bhohulcdfJ6IPOdKcKI+vJKIbSLejWn3rV41nN42InpDvaQkRfVUe7yKiBUS0Qv4d50hzpXxGy4noXMfxij+/Ctev7s+PiMbL6/uI6OeevCr6/Cpct0Z4dmcT0cvyGb1MRGc68qp72wuoX3nPrxKhbs38D8AHAZwA4C3HsZcAnCF/fwHA1fL3ZwHcJX+3A1gDYIb8/5MAZtexbpcDuFX+ngjgZQAJ+f9FAE4DQAAeAvDRBqtfNZ7dZAAnyN+jALwD4AgAPwZwhTx+BYAfyd9HAHgdQAuAAwG8CyBZredX4fo1wvPrAPABAF8G8HNPXhV9fhWuWyM8u+MB7C9/HwVgQ7WeXRXqV9bz2+c1HCHE0wB6PYcPBfC0/L0AwKfU5QA6iCgFoA3ACIDdDVK3IwA8JtNthRXKOJuIJgMYLYR4QVgt5A4An2yU+lWiHoa6bRJCvCJ/7wGwFMAUAOcDuF1edjuKz+J8WIOJYSHEagArAZxcredXqfrFrUel6ieE6BdCPAtgyJlPNZ5fpepWLSLU71UhxEZ5fAmAViJqaZS2Z6pflLL3eYFj4C0AfyV/Xwhgmvx9L4B+AJtgzQz+iRDC2eHeKtXKf62E6ltm3V4HcD4RpYjoQAAnynNTAKx3pF8vj1WLcuunqNqzI6IZsEZpCwFMEkJsAqwPD5a2BVjPZJ0jmXpOVX9+MeunqPfzM1HV5xezbopGenafAvCqEGIYjdP2TPVThH5+LHD0fAHA5UT0MiyVc0QePxlAHsD+sMwa3yCimfLc54QQRwM4Xf77fI3rNh9Wg1wM4KcAngeQg6WKe6lmaGK59QOq+OyIqBPA7wF8TQjhp42anlNVn18F6gc0xvMzZqE5VpHnV4G6AQ307IjoSAA/AvAldUhzWT3anql+QJnPjwWOBiHEMiHEOUKIEwH8Fpa9HLB8OA8LIbLSLPQcpFlICLFB/t0D4H9QJXOHqW5CiJwQ4v8KIY4TQpwPYCyAFbA6+amOLKYC2OjNt471q9qzI6I0rA/qN0KIP8jDW6SpQpl7tsrj6+HWuNRzqtrzq1D9GuX5majK86tQ3Rrm2RHRVAD/C+AiIYTqbxql7ZnqV/bzY4GjQUVaEFECwL8A+KU8tRbAmWTRAeBUAMukmWiCTJMG8HFYpqWa1Y2I2mWdQERnA8gJId6WqvEeIjpVqrsXAbivGnWLUr9qPTt5r7cAWCqEuNZx6n4A8+TveSg+i/sBzJW28wMBzAKwqFrPr1L1a6Dnp6Uaz69SdWuUZ0dEYwE8AOBKIcRz6uJGaXum+kV6ft4ogn3tH6xR+CYAWVgjiksAfBVW5MY7AH6I4gTZTgC/g+U4exvAN+XxDlhRV2/Ic9dDRhDVsG4zACyH5QB8FMABjnxmy4bwLoCfqzSNUL8qPrsPwDI/vAHgNfnvYwDGwwpeWCH/djnSfEc+o+VwRANV4/lVqn4N9vzWwAoi6ZPt4YhqPL9K1a1Rnh2sgVm/49rXAExslLZnql+U58crDTAMwzA1gU1qDMMwTE1ggcMwDMPUBBY4DMMwTE1ggcMwDMPUBBY4DMMwTE1ggcMwDQIRfZmILirj+hnkWKmbYRqdfW3HT4ZpSIgoJYT4ZfCVDNO8sMBhmAohF0J8GNZCiMfDmvx6EYDDAVwLa+LwNgB/L4TYRERPwlpT7v0A7ieiUQD6hBA/IaLjYK3S0A5r0t8XhBA7iOhEWOvSDQB4tnZ3xzDxYZMaw1SWQwHcJIQ4BtbWFZcD+BmAC4S1vtx8ANc4rh8rhDhDCPGfnnzuAPDPMp83AVwlj98K4CtCiNOqeRMMUw1Yw2GYyrJOFNeb+jWAb8PatGqBXLk9CWs5IMXd3gyIaAwsQfSUPHQ7gN9pjt8J4KOVvwWGqQ4scBimsnjXitoDYImPRtJfRt6kyZ9hmgY2qTFMZZlOREq4fAbAiwC61TEiSst9RYwIIXYB2EFEp8tDnwfwlBBiJ4BdRPQBefxzla8+w1QP1nAYprIsBTCPiP4b1qq7PwPwCIAbpEksBWsDuiUB+cwD8EsiagewCsDF8vjFAOYT0YDMl2GaBl4tmmEqhIxS+7MQ4qg6V4VhGhI2qTEMwzA1gTUchmEYpiawhsMwDMPUBBY4DMMwTE1ggcMwDMPUBBY4DMMwTE1ggcMwDMPUBBY4DMMwTE34/65OE7gWFiqlAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-500:].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": 14, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 743449\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2010315\n", "2022 2060360\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": 17, "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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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