{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence of influenza-like illness in France" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "data_url = 'incidence-PAY-3.csv'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the documentation of the data from [the download site](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Column name | Description |\n", "|--------------|---------------------------------------------------------------------------------------------------------------------------|\n", "| `week` | ISO8601 Yearweek number as numeric (year times 100 + week nubmer) |\n", "| `indicator` | Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json |\n", "| `inc` | Estimated incidence value for the time step, in the geographic level |\n", "| `inc_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", "| `inc_up` | Upper bound of the estimated incidence 95% Confidence Interval |\n", "| `inc100` | Estimated rate incidence per 100,000 inhabitants |\n", "| `inc100_low` | Lower bound of the estimated incidence 95% Confidence Interval |\n", "| `inc100_up` | Upper bound of the estimated rate incidence 95% Confidence Interval |\n", "| `geo_insee` | Identifier of the geographic area, from INSEE https://www.insee.fr |\n", "| `geo_name` | Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading |\n", "\n", "The first line of the CSV file is a comment, which we ignore with `skip=1`." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204732119116469.025913.03225.039.0FRFrance
120204632510320716.029490.03831.045.0FRFrance
220204534251636857.048175.06556.074.0FRFrance
320204434456738521.050613.06859.077.0FRFrance
420204334373737523.049951.06657.075.0FRFrance
520204233514529812.040478.05345.061.0FRFrance
620204132787723206.032548.04235.049.0FRFrance
720204032044316381.024505.03125.037.0FRFrance
820203931981015900.023720.03024.036.0FRFrance
920203832556221142.029982.03932.046.0FRFrance
1020203731848514649.022321.02822.034.0FRFrance
112020363103907646.013134.01612.020.0FRFrance
12202035399186842.012994.01510.020.0FRFrance
13202034360843090.09078.094.014.0FRFrance
14202033361063411.08801.095.013.0FRFrance
15202032359183330.08506.095.013.0FRFrance
16202031343512269.06433.074.010.0FRFrance
17202030381795442.010916.0128.016.0FRFrance
18202029386875860.011514.0139.017.0FRFrance
19202028383405701.010979.0139.017.0FRFrance
20202027340662406.05726.063.09.0FRFrance
21202026340392389.05689.063.09.0FRFrance
22202025328531488.04218.042.06.0FRFrance
23202024330581690.04426.053.07.0FRFrance
24202023341682468.05868.063.09.0FRFrance
25202022335801947.05213.053.07.0FRFrance
26202021361144026.08202.096.012.0FRFrance
27202020393156775.011855.01410.018.0FRFrance
282020193116798722.014636.01814.022.0FRFrance
2920201831639812851.019945.02520.030.0FRFrance
.................................
185219852132609619621.032571.04735.059.0FRFrance
185319852032789620885.034907.05138.064.0FRFrance
185419851934315432821.053487.07859.097.0FRFrance
185519851834055529935.051175.07455.093.0FRFrance
185619851733405324366.043740.06244.080.0FRFrance
185719851635036236451.064273.09166.0116.0FRFrance
185819851536388145538.082224.011683.0149.0FRFrance
18591985143134545114400.0154690.0244207.0281.0FRFrance
18601985133197206176080.0218332.0357319.0395.0FRFrance
18611985123245240223304.0267176.0445405.0485.0FRFrance
18621985113276205252399.0300011.0501458.0544.0FRFrance
18631985103353231326279.0380183.0640591.0689.0FRFrance
18641985093369895341109.0398681.0670618.0722.0FRFrance
18651985083389886359529.0420243.0707652.0762.0FRFrance
18661985073471852432599.0511105.0855784.0926.0FRFrance
18671985063565825518011.0613639.01026939.01113.0FRFrance
18681985053637302592795.0681809.011551074.01236.0FRFrance
18691985043424937390794.0459080.0770708.0832.0FRFrance
18701985033213901174689.0253113.0388317.0459.0FRFrance
187119850239758680949.0114223.0177147.0207.0FRFrance
187219850138548965918.0105060.0155120.0190.0FRFrance
187319845238483060602.0109058.0154110.0198.0FRFrance
1874198451310172680242.0123210.0185146.0224.0FRFrance
18751984503123680101401.0145959.0225184.0266.0FRFrance
1876198449310107381684.0120462.0184149.0219.0FRFrance
187719844837862060634.096606.0143110.0176.0FRFrance
187819844737202954274.089784.013199.0163.0FRFrance
187919844638733067686.0106974.0159123.0195.0FRFrance
18801984453135223101414.0169032.0246184.0308.0FRFrance
188119844436842220056.0116788.012537.0213.0FRFrance
\n", "

1882 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202047 3 21191 16469.0 25913.0 32 25.0 \n", "1 202046 3 25103 20716.0 29490.0 38 31.0 \n", "2 202045 3 42516 36857.0 48175.0 65 56.0 \n", "3 202044 3 44567 38521.0 50613.0 68 59.0 \n", "4 202043 3 43737 37523.0 49951.0 66 57.0 \n", "5 202042 3 35145 29812.0 40478.0 53 45.0 \n", "6 202041 3 27877 23206.0 32548.0 42 35.0 \n", "7 202040 3 20443 16381.0 24505.0 31 25.0 \n", "8 202039 3 19810 15900.0 23720.0 30 24.0 \n", "9 202038 3 25562 21142.0 29982.0 39 32.0 \n", "10 202037 3 18485 14649.0 22321.0 28 22.0 \n", "11 202036 3 10390 7646.0 13134.0 16 12.0 \n", "12 202035 3 9918 6842.0 12994.0 15 10.0 \n", "13 202034 3 6084 3090.0 9078.0 9 4.0 \n", "14 202033 3 6106 3411.0 8801.0 9 5.0 \n", "15 202032 3 5918 3330.0 8506.0 9 5.0 \n", "16 202031 3 4351 2269.0 6433.0 7 4.0 \n", "17 202030 3 8179 5442.0 10916.0 12 8.0 \n", "18 202029 3 8687 5860.0 11514.0 13 9.0 \n", "19 202028 3 8340 5701.0 10979.0 13 9.0 \n", "20 202027 3 4066 2406.0 5726.0 6 3.0 \n", "21 202026 3 4039 2389.0 5689.0 6 3.0 \n", "22 202025 3 2853 1488.0 4218.0 4 2.0 \n", "23 202024 3 3058 1690.0 4426.0 5 3.0 \n", "24 202023 3 4168 2468.0 5868.0 6 3.0 \n", "25 202022 3 3580 1947.0 5213.0 5 3.0 \n", "26 202021 3 6114 4026.0 8202.0 9 6.0 \n", "27 202020 3 9315 6775.0 11855.0 14 10.0 \n", "28 202019 3 11679 8722.0 14636.0 18 14.0 \n", "29 202018 3 16398 12851.0 19945.0 25 20.0 \n", "... ... ... ... ... ... ... ... \n", "1852 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1853 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1854 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1855 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1856 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1857 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1858 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1859 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1860 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1861 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1862 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1863 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1864 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1865 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1866 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1867 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1868 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1869 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1870 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1871 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1872 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1873 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1874 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1875 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1876 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1877 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1878 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1879 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1880 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1881 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 39.0 FR France \n", "1 45.0 FR France \n", "2 74.0 FR France \n", "3 77.0 FR France \n", "4 75.0 FR France \n", "5 61.0 FR France \n", "6 49.0 FR France \n", "7 37.0 FR France \n", "8 36.0 FR France \n", "9 46.0 FR France \n", "10 34.0 FR France \n", "11 20.0 FR France \n", "12 20.0 FR France \n", "13 14.0 FR France \n", "14 13.0 FR France \n", "15 13.0 FR France \n", "16 10.0 FR France \n", "17 16.0 FR France \n", "18 17.0 FR France \n", "19 17.0 FR France \n", "20 9.0 FR France \n", "21 9.0 FR France \n", "22 6.0 FR France \n", "23 7.0 FR France \n", "24 9.0 FR France \n", "25 7.0 FR France \n", "26 12.0 FR France \n", "27 18.0 FR France \n", "28 22.0 FR France \n", "29 30.0 FR France \n", "... ... ... ... \n", "1852 59.0 FR France \n", "1853 64.0 FR France \n", "1854 97.0 FR France \n", "1855 93.0 FR France \n", "1856 80.0 FR France \n", "1857 116.0 FR France \n", "1858 149.0 FR France \n", "1859 281.0 FR France \n", "1860 395.0 FR France \n", "1861 485.0 FR France \n", "1862 544.0 FR France \n", "1863 689.0 FR France \n", "1864 722.0 FR France \n", "1865 762.0 FR France \n", "1866 926.0 FR France \n", "1867 1113.0 FR France \n", "1868 1236.0 FR France \n", "1869 832.0 FR France \n", "1870 459.0 FR France \n", "1871 207.0 FR France \n", "1872 190.0 FR France \n", "1873 198.0 FR France \n", "1874 224.0 FR France \n", "1875 266.0 FR France \n", "1876 219.0 FR France \n", "1877 176.0 FR France \n", "1878 163.0 FR France \n", "1879 195.0 FR France \n", "1880 308.0 FR France \n", "1881 213.0 FR France \n", "\n", "[1882 rows x 10 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are there missing data points? Yes, week 19 of year 1989 does not have any observed values." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
164519891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1645 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1645 FR France " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We delete this point, which does not have big consequence for our rather simple analysis." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204732119116469.025913.03225.039.0FRFrance
120204632510320716.029490.03831.045.0FRFrance
220204534251636857.048175.06556.074.0FRFrance
320204434456738521.050613.06859.077.0FRFrance
420204334373737523.049951.06657.075.0FRFrance
520204233514529812.040478.05345.061.0FRFrance
620204132787723206.032548.04235.049.0FRFrance
720204032044316381.024505.03125.037.0FRFrance
820203931981015900.023720.03024.036.0FRFrance
920203832556221142.029982.03932.046.0FRFrance
1020203731848514649.022321.02822.034.0FRFrance
112020363103907646.013134.01612.020.0FRFrance
12202035399186842.012994.01510.020.0FRFrance
13202034360843090.09078.094.014.0FRFrance
14202033361063411.08801.095.013.0FRFrance
15202032359183330.08506.095.013.0FRFrance
16202031343512269.06433.074.010.0FRFrance
17202030381795442.010916.0128.016.0FRFrance
18202029386875860.011514.0139.017.0FRFrance
19202028383405701.010979.0139.017.0FRFrance
20202027340662406.05726.063.09.0FRFrance
21202026340392389.05689.063.09.0FRFrance
22202025328531488.04218.042.06.0FRFrance
23202024330581690.04426.053.07.0FRFrance
24202023341682468.05868.063.09.0FRFrance
25202022335801947.05213.053.07.0FRFrance
26202021361144026.08202.096.012.0FRFrance
27202020393156775.011855.01410.018.0FRFrance
282020193116798722.014636.01814.022.0FRFrance
2920201831639812851.019945.02520.030.0FRFrance
.................................
185219852132609619621.032571.04735.059.0FRFrance
185319852032789620885.034907.05138.064.0FRFrance
185419851934315432821.053487.07859.097.0FRFrance
185519851834055529935.051175.07455.093.0FRFrance
185619851733405324366.043740.06244.080.0FRFrance
185719851635036236451.064273.09166.0116.0FRFrance
185819851536388145538.082224.011683.0149.0FRFrance
18591985143134545114400.0154690.0244207.0281.0FRFrance
18601985133197206176080.0218332.0357319.0395.0FRFrance
18611985123245240223304.0267176.0445405.0485.0FRFrance
18621985113276205252399.0300011.0501458.0544.0FRFrance
18631985103353231326279.0380183.0640591.0689.0FRFrance
18641985093369895341109.0398681.0670618.0722.0FRFrance
18651985083389886359529.0420243.0707652.0762.0FRFrance
18661985073471852432599.0511105.0855784.0926.0FRFrance
18671985063565825518011.0613639.01026939.01113.0FRFrance
18681985053637302592795.0681809.011551074.01236.0FRFrance
18691985043424937390794.0459080.0770708.0832.0FRFrance
18701985033213901174689.0253113.0388317.0459.0FRFrance
187119850239758680949.0114223.0177147.0207.0FRFrance
187219850138548965918.0105060.0155120.0190.0FRFrance
187319845238483060602.0109058.0154110.0198.0FRFrance
1874198451310172680242.0123210.0185146.0224.0FRFrance
18751984503123680101401.0145959.0225184.0266.0FRFrance
1876198449310107381684.0120462.0184149.0219.0FRFrance
187719844837862060634.096606.0143110.0176.0FRFrance
187819844737202954274.089784.013199.0163.0FRFrance
187919844638733067686.0106974.0159123.0195.0FRFrance
18801984453135223101414.0169032.0246184.0308.0FRFrance
188119844436842220056.0116788.012537.0213.0FRFrance
\n", "

1881 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202047 3 21191 16469.0 25913.0 32 25.0 \n", "1 202046 3 25103 20716.0 29490.0 38 31.0 \n", "2 202045 3 42516 36857.0 48175.0 65 56.0 \n", "3 202044 3 44567 38521.0 50613.0 68 59.0 \n", "4 202043 3 43737 37523.0 49951.0 66 57.0 \n", "5 202042 3 35145 29812.0 40478.0 53 45.0 \n", "6 202041 3 27877 23206.0 32548.0 42 35.0 \n", "7 202040 3 20443 16381.0 24505.0 31 25.0 \n", "8 202039 3 19810 15900.0 23720.0 30 24.0 \n", "9 202038 3 25562 21142.0 29982.0 39 32.0 \n", "10 202037 3 18485 14649.0 22321.0 28 22.0 \n", "11 202036 3 10390 7646.0 13134.0 16 12.0 \n", "12 202035 3 9918 6842.0 12994.0 15 10.0 \n", "13 202034 3 6084 3090.0 9078.0 9 4.0 \n", "14 202033 3 6106 3411.0 8801.0 9 5.0 \n", "15 202032 3 5918 3330.0 8506.0 9 5.0 \n", "16 202031 3 4351 2269.0 6433.0 7 4.0 \n", "17 202030 3 8179 5442.0 10916.0 12 8.0 \n", "18 202029 3 8687 5860.0 11514.0 13 9.0 \n", "19 202028 3 8340 5701.0 10979.0 13 9.0 \n", "20 202027 3 4066 2406.0 5726.0 6 3.0 \n", "21 202026 3 4039 2389.0 5689.0 6 3.0 \n", "22 202025 3 2853 1488.0 4218.0 4 2.0 \n", "23 202024 3 3058 1690.0 4426.0 5 3.0 \n", "24 202023 3 4168 2468.0 5868.0 6 3.0 \n", "25 202022 3 3580 1947.0 5213.0 5 3.0 \n", "26 202021 3 6114 4026.0 8202.0 9 6.0 \n", "27 202020 3 9315 6775.0 11855.0 14 10.0 \n", "28 202019 3 11679 8722.0 14636.0 18 14.0 \n", "29 202018 3 16398 12851.0 19945.0 25 20.0 \n", "... ... ... ... ... ... ... ... \n", "1852 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1853 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1854 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1855 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1856 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1857 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1858 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1859 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1860 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1861 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1862 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1863 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1864 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1865 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1866 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1867 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1868 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1869 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1870 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1871 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1872 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1873 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1874 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1875 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1876 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1877 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1878 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1879 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1880 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1881 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 39.0 FR France \n", "1 45.0 FR France \n", "2 74.0 FR France \n", "3 77.0 FR France \n", "4 75.0 FR France \n", "5 61.0 FR France \n", "6 49.0 FR France \n", "7 37.0 FR France \n", "8 36.0 FR France \n", "9 46.0 FR France \n", "10 34.0 FR France \n", "11 20.0 FR France \n", "12 20.0 FR France \n", "13 14.0 FR France \n", "14 13.0 FR France \n", "15 13.0 FR France \n", "16 10.0 FR France \n", "17 16.0 FR France \n", "18 17.0 FR France \n", "19 17.0 FR France \n", "20 9.0 FR France \n", "21 9.0 FR France \n", "22 6.0 FR France \n", "23 7.0 FR France \n", "24 9.0 FR France \n", "25 7.0 FR France \n", "26 12.0 FR France \n", "27 18.0 FR France \n", "28 22.0 FR France \n", "29 30.0 FR France \n", "... ... ... ... \n", "1852 59.0 FR France \n", "1853 64.0 FR France \n", "1854 97.0 FR France \n", "1855 93.0 FR France \n", "1856 80.0 FR France \n", "1857 116.0 FR France \n", "1858 149.0 FR France \n", "1859 281.0 FR France \n", "1860 395.0 FR France \n", "1861 485.0 FR France \n", "1862 544.0 FR France \n", "1863 689.0 FR France \n", "1864 722.0 FR France \n", "1865 762.0 FR France \n", "1866 926.0 FR France \n", "1867 1113.0 FR France \n", "1868 1236.0 FR France \n", "1869 832.0 FR France \n", "1870 459.0 FR France \n", "1871 207.0 FR France \n", "1872 190.0 FR France \n", "1873 198.0 FR France \n", "1874 224.0 FR France \n", "1875 266.0 FR France \n", "1876 219.0 FR France \n", "1877 176.0 FR France \n", "1878 163.0 FR France \n", "1879 195.0 FR France \n", "1880 308.0 FR France \n", "1881 213.0 FR France \n", "\n", "[1881 rows x 10 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our dataset uses an uncommon encoding; the week number is attached\n", "to the year number, leaving the impression of a six-digit integer.\n", "That is how Pandas interprets it.\n", "\n", "A second problem is that Pandas does not know about week numbers.\n", "It needs to be given the dates of the beginning and end of the week.\n", "We use the library `isoweek` for that.\n", "\n", "Since the conversion is a bit lengthy, we write a small Python \n", "function for doing it. Then we apply it to all points in our dataset. \n", "The results go into a new column 'period'." ] }, { "cell_type": "code", "execution_count": 25, "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": [ "There are two more small changes to make.\n", "\n", "First, we define the observation periods as the new index of\n", "our dataset. That turns it into a time series, which will be\n", "convenient later on.\n", "\n", "Second, we sort the points chronologically." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check the consistency of the data. Between the end of a period and\n", "the beginning of the next one, the difference should be zero, or very small.\n", "We tolerate an error of one second.\n", "\n", "This is OK except for one pair of consecutive periods between which\n", "a whole week is missing.\n", "\n", "We recognize the dates: it's the week without observations that we\n", "have deleted earlier!" ] }, { "cell_type": "code", "execution_count": 27, "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": [ "A first look at the data!" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qRJ2M+3eACdkxhu3MK0nY93ooaaiVRAnFOh5D1hUsUEqgZhpKiGxzPpQazkOoWthwNOQEiv/noDqVcoIxhBCYt2xH2Y79sHOgatkBHoxL4bCADgcLlCJQGnytJjYWA0d5hcfmRFaCZiBd+oM98voWXHXPQvx2wcaS0wCKmYdSellX7uh2ndHl0GjtwI9Kh6QfbJS7H8ohRYwIWSFqFj5azMj/YPiYbUT1qSsNJWEweSkNpZyZ3zu6nTkZm/f0lpwGEH6kXI5AufC25wEA62/9cEn3u6s1HBILADE6rKEUQYyU6cP8ofx+0WYsWFe1vb6M1HS14ZrlXBr9FpNXIgKTV5yULb46Jq9aDijqZVC/s6sPJ33rKSzb1lXrohwysEApgpisrSAN5WsPvYW//3n4RfyKIUwn4S5fX8ulV6q1p3xE6fT0OxE8KqInn0QEJi81GCnX/1YNk1dU1LoIz63oQHdfGne9uK7kNOpENjYMLFCKwO0Uav2lhICdiOE5MOCEA7ek/C3AUTjlY7FoBErYItTWKV8f3XBTsvxwb/6KioMFShFENcosFd4PxZ9y+y+lobQk/T8HlXw5rz3mao5lCpSwPpQ6iHM1lWBT5wHs6x2seP5NCUfj7BssfSCg6pvHZ+FggVIEtQ7JDZfroRc3X+7rUD4xkw8FEbx3peWUq93a7q8Hk6fCVIZz/vtZ/EOFzMI6LdKE2VWO8KqDemwkWKAUQU5DqXFBAqiPLYCrk09Oc6jsqD+KPWZiFI2gD6t51NKHEtQGe6V5cfn27oqXQ/nENnT2lJxGPfiiGgkWKEXgzkOpmckrfL411VCqtcFWRNmFratyFpl2BUq5PhTL7VEJ2XIIWv5nIOTillGgqqCcqlD1zSHQ4WCBUgSxiEI/K4lbtDouY+SU+ahKUJucyRTBe1fWtOpFeZWVTTT4lKGa304UgSkc3FIcLFCKgELMQ6kkIWcgAKhth1Lt0Vy5+YU1eZVTp6rtlK9N2XwoMp+6MHkVUk2BktMuyk+DCQcLlCKIh5iHUknCfIvqmtruKd5Y+VVlwcWI6iR0lFctw4YDzlWzWFEMbNQ7Z0UlHCxQiqDWYcNhEBGMyhqNqHwopo6wnjYtawQfisJPAFfX5OX9W1Ia0RTlkIEFShHYJjZWXnOxpy9ck9ehs/RKVFFepvkshAh8ZxHN9avGWl7lEjQvqJrFytVB6ZnyPJTiYIFSBO5I1SA46mEGfQTfUARlqLIPJSKnvI0oOulKV029z0OpZrmiyKsetNJGggVKEdg0lEqbwor5QOrB5BEVtg6/0iavXDlKzyOqxUjCFqFew4ar65QvX7vgKK/iYIFSBDGLhlIPnXg9RA1HnbXpWdyPPTKTV3C3Xw/v1wbV0UoJtY7yiiKnnE+yDiq0AShboBBRnIjeIKI/y/+PJKK5RLRK/h2hXXs9Ea0mohVEdJF2/DQiekeeu53kl01ETUT0gDw+n4imaPfMkXmsIqI55T5HGGzL11c6nDhM6rkor4oWJVQZnN+VL0hUGoqJKJ3yZXdMIW+vBx9K7U1eUkMpJw0WJEURhYbyJQDLtP9fB2CeEGIagHny/yCiEwDMBnAigFkAfkpEar3wnwG4GsA0+W+WPH4VgD1CiKMB3AbgOzKtkQBuAHA6gJkAbtAFV6WwrRhbaad8qLBhCM/fWhNFB2JLIiofit3kVR91GkhEi1BGgV8bbDgfSh0vs1SPlCVQiGgigA8D+F/t8CUA7pG/7wFwqXb8fiFEvxBiHYDVAGYS0XgAQ4UQrwjnK7g37x6V1sMAzpfay0UA5gohOoUQewDMRU4IVQzX5FUjH0ooorEClVmE6OL/83/rRDGDHcgNBGyrFkdRp0H+hTCErdt6WL7er75qMrGxjDw5yqs4ytVQfgDg3wDocnycEGIbAMi/Y+XxCQA2addtlscmyN/5xz33CCHSAPYBGBWQVkWxLQ5Zcad8qLBhh1+9vB6dPQMVLU8YSq0Rj9nMeE00H7vd5BXdkjvlCtuwRajl8vVBIrMWTvlyYDlSHCULFCL6CICdQohFYW/xOSYCjpd6jzdToquJaCERLezo6AhVUBO2jqU+woZzZfjh0ytrVAjtZ4l1ot9lS6J8H0pQc4tm6ZVqU69lrWa5IjG31sE3DQA7u/vwk2dX1015TJSjoZwN4GNEtB7A/QDOI6JfA9ghzViQf3fK6zcDmKTdPxHAVnl8os9xzz1ElAAwDEBnQFoFCCHuFELMEELMGDNmTGlPKrGtNlwPYcP10NyE4XcxFDO6LPcjCz3qLyOfqOYH2YoQxVL7ubwq0ZqqGOUVgVM+ivXAouArD7yJ7z61Au9s2VfjkgRTskARQlwvhJgohJgCx9n+jBDiUwAeA6CiruYAeFT+fgzAbBm5NRWO832BNIt1E9EZ0j9yZd49Kq1PyDwEgKcAXEhEI6Qz/kJ5rKJQrQVKmGtq3fLzKLU8XpNXbU2JFIGju/oLZpZPqe3ZlZ2+S6+UUaASy1FWGnXyPe3vc3YVrQs/bQD+m2iXx60AHiSiqwBsBHA5AAghlhDRgwCWAkgDuFYIoTZ7vgbArwC0AHhS/gOAuwDcR0Sr4Wgms2VanUT0bQCvyetuFEJ0VuBZfKlnp7xeglL39t6+rw9Lt+3DeceNK60MEQgD/T6rySsiH4qtusp5vVE1jbDJROE/CCrz5j0HMHFEq++5IMHbaBMbG2HuUT0RiUARQjwH4Dn5ezeA8w3X3QTgJp/jCwFM9zneBymQfM7dDeDuUstcCqptmeabVLrxVct+evnPX8amzl6su+XikoRSMcLAmEYR90W2llcF86lWxxTlnBlTmR9/exuu/e3ruO+qmThnmtmM7Hd3o4UN5ybPlp/WoQDPlC8Cd9JgjSY2hiEKobOpsxdAfTwPUHmnfHgfSuXzsKcTLqFoNBT/NF7fuAcAsHyb/za+QVlzlFdpuGEjJVodqgULlCJQL7XfsI1pvZm8SiUhow9Mz2ktg/D/XWoaUV7rR/h5KOU45aNpG7ZU1DyXKPIzNWfb6szu5Fqf+2uhoZQ1D6UOvmmd+hYnLFCKQjXM/f1p3/N1Mas2gvaflDuJlbr/tzfKKwIfiiWN8ndsdP7aJh2WZ/Jy/lare4qiLZqeVx2OGSRK0PpX1V3LK8oor/oSLPUKC5QS6DEIlHSFJUoxS6+UQyrhNIv+dMZypZ1S+w99YGhcHLLMPHJ5hTUjlZ5HZBqKJZmcDyUCc49lAm88FiyAa62huJ9jOe8tkpIcOrBAKQL1MfQO+ne0FXfKh5kpXw8air5sSolliCKNYvOy1W8kGkqVetQoLDXGjeTk8ZhBoIi8v373VoMocqqXiYR1UgwrLFCKQHU45nko1SyNP1E0PClPSnbKe30opZq87Gm4M9jL7D1znb2lTHUx0g1OKcqJjabO3xUoJh9KoFO+3FKFxw0bjiKNOunQ69wnzwKlGFSjMgmUejN5ldr4lG08Codk6RpK5fNQqE7D9rjl1Ie7onGVeoRKhg2rZh43+VACnfLV9MpHkESdCBJFvZUnHxYoRaDepXEeykGioahuIoq1yUpOQvj+9L0kqomNth6ovImNqpMtr7B2H0p0C1ma2nPG1VCCnfK+58otVBFUenJnNVFCut4nWrJAKQLVGRhNXhWf2FhYlkrgdkpRCMgSi1nUTPmyV/ANNmu484/KChsu+VZvOiGvq+Q8FFvYcI7C+6sZhhvp8vVRFCgCWKAcRNg0lIzWA1eiww+Ton5NqXtvlBspFMXSK54qrqBvw8krePSX04TKd8pXmpwPpfy0Sg8bNmde3bW86rvzLYbcoKa25bDBAqUYXB+KaWJj7ne5L35HV19wUSyjaaD0D6psgRLJ0itaGobnKGZVjLc27cWX73/Dd4RsmyMiXIETIiMDUY10w8/qr5zJS6VtChvOTSj0OVfFTj6KuT/1NrGxHiZPB8ECpQhcDSVj0lByx8v5oF9evQun3zwPf1m8zZt/qFDa8htcbiOx2jXeYrSxMJrDZ+9diD++uRW79vcXnLNF8tg0mLqiCmt5qXZhnikv//o65csvV2gi9KHUS/hwvbdBFihFYPWhRCRQlm7rAgC8tn5P0ffq2ZZs8pJ/S+2UvFpSBGlYOvowVd0v5w6pOTZ+eZl9Bt6/pRDVSDf8FsDl52dKwZZ2cNhwuHLdNnclPvqjF0Nda87LXh4b9WY2q3N5UpHl6w9a1Ls0Od/14+W8eLWWVjpvYovHrSAE/Fb2iaK9lRspVFjOUtIIoY25Jg17HmpdMr8rbc8ZRYRWtfqBqsxDkc3SNLDKLXliNi/a+OG8VU4aQpQcah3lema2lD57z0JMHd2Kb3z4hLLz9MM2ZaFeYA2lCGwvVR+Fmq7p6O7Hyh3+q7QqlG06aGKhzd4PlD4PJaoJg0AZHalHQ7F0XGE0FClQ/DpJm6YTRZRXVKaK8D6UKPIKHjjZ8ohiHsqgwbwchijrwJbW08t24BcvrCspj1ueWIYfPr0q1LVs8jqIUB2YyYeSDmHymvWD53Hhbc8H5hOPOa+lQCiFaEvRaCjO31LDoEUEmpow/PbmE3zeD3+BYj6nH49ig61y+wPb7VHOQzELWFkfJg0l4L0UW6yBMpafyAVClKFZVsGH8vPn1+K2p1eGupYFykFEMRqKqfPZ3TMQmAYAJOL+GkqY6Kko2lss5DyUJVv3YW3H/sIyeH6XVqAwH04py2IEjZor6pSvsmM/Gqd88HF7mLXfvUVqKJb15B5etNm4+ncU1EtAhsq9HiZPB8ECpQhUmzLOQ/GMzIMb4IEB80eglrTI96GEQc+11EU+woYNf/2Rd/CdvywPX6AiCOOUL2X06CfI1TGT8FMfcTl9SmQaiiUB9e6iGFGb6kO1f5MGG7TYZrGCbjDgG3hj4x78y0Nv4Zt/eMf3fDRbAMu/ddKRV3rydLmwQCkB0zyUdAgNxT0f0EClxasgjWLnmJTa9NywYUvj7R3M4MBA4crLkUR5eX5bRsJFpBts8jKUJcQo9cVVu3DNrxcZO/Jqj3Sj8H+Z2qga6JiycAV0BD4UtTukH6rt7egqDAXX8y+nJuptyZN6CV82wQKlCGxhw2Gc8oqghSTDhPva7NtRYEsrnRVGf1IujQjytmgotrru07YbCDTDlGjiAYAr756PJxdvN2qvrsmiwh1CuSHfOjYNxSS01PsIEt5heWb5Tus1Rs2ylBFHftoRaZZRUedBXixQikG9yzDzUGydcdDo3zZhLCylm7zUxMbg6zJZYTBJaPVQ4tccRsuxCXjFN/+42P3t18nl5EmwKTMoG7JMBq32MuiVdMrnNJTg78CmoQR9I6cdMQIAMGF4q/EaW/uOYg5JNkA4hmXh+k48v7KjrHKEbeu1huehFIHVh1KEyStMw8i/IkybjqLDyo1yLRpKRmDQ5znC+D+iwCbgFUu3drm//S61RXHlnPYBgwBbWSxmtbDY6lMJtijq3fT+XQ3FJHACOmH9SCYr3ACUfHL1WbrzIoo6CKNZ2gaPn7jjFQDA+ls/XHZ56sX0ZoI1lCKwbrClvWzbiw8lUAIbseF4FEuvKB9OCLOdLQqn1NJkPSNZQ9pq1Gapa33NqSAzjNn/If8GPKrSKo1bG4QQSmGIYqb8n97aiu6+QXtehiRck5btWS3lCmpe6p0GbvLmBiD4n1blK+ebCKNZljNXplhYoBxE6BqKX8egCwmbwAg6b5oZXOwWwKU2PeXDsXXUJpOXflfJM+U9z+GfhquhWD5oj0DxXRzS3AHq54M+ZrfOjJ2sN61KYfOhrN/Vgy/87g18+f43rWnZVhu2aTB+714vV1BdqOvKiWqKoqZtzwoE+0NL3UbbRL1Em5lggVIEepPyNZ0UsZZX4Cx4SwfnnAv+2PN/F0PMDRsOvi6dFb7PUWwZbn1yOa797eveNAzp+eVj63SScV1D8Usn2ISTs6MHZgPA3LnkOiZ7GkHYTV7OX5upaOXO4NUaAHuHbNxzPqQPJVCgqNDkgMGCEuK2gUA5MjzMTPnBtPlkX7owCrIcOGz4IEJ/l34dR7oIDSXInOTmIwzHA/CugVVa46OQWwCnMyKSEdgdf1uDx98ufmVl1WHY6tpq8lKPYBmRB2pbSghblnyvtIaintXqsjPjAAAgAElEQVRkhlHn9/fZJwOanpcspqZAH4p2KOi9qXsDTV4WoggbDqNZBs3mj0pDCdUG6wAWKEWhja582one6Oxhw+bz5kX3tN+WUTtQ+lyEsBMb09msweSlCYMS238xt5UtUCIxeTmYNRT7aHnfgUH8+JlVgc9jq0913jQh0O2oQ9j9bXnZoh397g/tQ1EaSghN3hxWbja9hSXMenFhTV5RLlZZr7BAKQKbhuLxoZThlDetQRRuPxTt+hDX+GGNWNLOW01eUYQNWzQHu1M+18z9Ozn112DCCWGuypmaggcDQfVx8xPL8L2/rsTTy3aYM7KgsjeNjIPmiJjSysc24MjlEZxmGC09cOBleQZ1tpxOOIyGEmTy0gV7FCG/9R42zAKlCPRXGbSEh+m86dqCfIT3b/7xIMJoMTbChJ4KITCYEb5RXmEitGyEWWDSjbqzjLYTFg0lirW8lD3fFuUV5NpQnU9XrzkCK4Re4UmrsBzO3zC2eKtT3qqhmOvaVgZ37k/Ad+I6/40TG/U2VOrAxv7uw5q8yjHf5ZenXmGBUgT6y/RrHJ55KLZJgSE+pvxLQn0g+giwxMan+t+gMqpHHfR5UBFBGcLc5e7LYclD3/s8aB6KzYwY6EJRYcMG4RYmWqgpGQfgndlfmE7ws7oaiqEcYQRbLq/gPEz9Y25ttOA0wzjlgzphm0nXG1EWeKmRMMEUQeuN9adZQ2EMCOQihnw1lJCjL9P9CtvIUJXFv4zCeg0A7OzqK3CEK2whsEDO5GeLwS9ZoIToeGzzghS6huJ3rd3kVYyZyJSG8zcoiZRhleliUALH1MkVY/IyzsuxpBHklPfM1QoQau4ClAEX5cKTDee1Oii1HYaZPxTkjyrGDB6uPGUnUVFKFihENImIniWiZUS0hIi+JI+PJKK5RLRK/h2h3XM9Ea0mohVEdJF2/DQiekeeu52kzYWImojoAXl8PhFN0e6ZI/NYRURzSn2OYhACSJj2KkFxJq+gRuh2Pvk+lJBl9Pudz6fumo9rf/u676rHYVas1Z2m+SNFrzCwl9kP/dlNabg+FJtTXgsb9p8bEU4ohumUjD6UMGazMKZGS/6u5liiQCkqus4igP1Oh90mW11XjoaiD3ZKFyjev34Embx0X6vNNBuuPPUtUcrRUNIAviaEOB7AGQCuJaITAFwHYJ4QYhqAefL/kOdmAzgRwCwAPyWiuEzrZwCuBjBN/pslj18FYI8Q4mgAtwH4jkxrJIAbAJwOYCaAG3TBVSmEEMEaSjFhwyHU/UIfivaxmzpZw/X5bOrsNaZjczAD3o813+zljeQpX0OxObqtAsHTiZnzKscpn7s2WLusdIdg01BsJhyvmah4gQEEayhhIyHDaFI5H4rpvB5h5X/Nva+sx5TrHke/Yb5ImPf25qa95jJq34lN8wwTlRlUjvsXbMTqnYX7E1WTkgWKEGKbEOJ1+bsbwDIAEwBcAuAeedk9AC6Vvy8BcL8Qol8IsQ7AagAziWg8gKFCiFeE8zXcm3ePSuthAOdL7eUiAHOFEJ1CiD0A5iInhCqGEEAy7lSZX+OIKmw4jMnLFipZcH3+dQHLciuTV5iwTqDQ7KX/r9SZvWH8Req4LQRWd4wGdXL+wsZeDh3b4pBhhFLgJZb7VREHDJFHNrNLuCVvZFqmZw2pVQRqKCHebZi15tz0DGVSW+/uMwRC2HanBIBv/3mp8VxRVotQAsV87rpH3sHFt79gTaOSROJDkaaoUwDMBzBOCLENcIQOgLHysgkANmm3bZbHJsjf+cc99wgh0gD2ARgVkJZf2a4mooVEtLCjo7wVP7NCuALFz7abLkLFDvzo5Kn8K7xmoOBRu60MQZ1Cbj+WoNFh7vnzI73CzoYOQq9e80ZOwecVR45pCyxP0ATJYh27Vh+KPYmyUOnbTF4mwpikbCZA1Tb8fSh6OvZyBPrxMkpT8r8mjMlLfc8mX2C5s+31xVNt7TRoPovNX6j6k6iXeimWsgUKEbUD+D2ALwshuoIu9TkmAo6Xeo/3oBB3CiFmCCFmjBkzJqB4drLCvD0vkOeUL0dDMZi89FtsnWz+9QXXqXR8LsppKPaPGSg0eYUVakGEcd4GPYMJv+Ko2/0+6LDmO5spKTdBzpyGadsCv3RMqDKaOhbb+wjjg3NNgNawYZ97Q47YlUO9nHko+vs0JZNMOJXeb4isU7cF1Vsqbu5GM0X4UPoH7cLAKDzrZJGvsgQKESXhCJPfCCEekYd3SDMW5F+1Q85mAJO02ycC2CqPT/Q57rmHiBIAhgHoDEiroggIt/H4jmaLGI0ERa+EMXmFsbcGdj4hRvdB+6EEmrw8nXBwGU33FTNSto+6C+/xy9fvgw87szuXV/C7CxUpFpCR7XZ1vmQNxeOUt2kohjRCRnkFCVdb5J2ej+kKfdBjyisp1XG/nUe95TAWAx84zhmo6isy+JXBd8CiJXwgIFxcYfom6yWcuJwoLwJwF4BlQojva6ceAzBH/p4D4FHt+GwZuTUVjvN9gTSLdRPRGTLNK/PuUWl9AsAz0s/yFIALiWiEdMZfKI9VFF1D8XuBni2Ai+jk/PJxEHnH7QJLGP+Tf525Mw6zwKD+rOlMvskr97sYDcXkELYJ2GICIILMWva9XUoXBjlbvPlepRkGRQ1Z81caimXpFdv9gLms7sKNhrSCZ8qHG3SpzjdsSK4fAxm7hqLaum2pmqB665brovldk7YMjPRjvQahBtgHJNVcQj+IcjbYOhvApwG8Q0RqLeyvA7gVwINEdBWAjQAuBwAhxBIiehDAUjgRYtcKIVQNXgPgVwBaADwp/wGOwLqPiFbD0Uxmy7Q6iejbAF6T190ohOgs41lCITQfis0pb3OwBdlLw9htzRs52Tti/TJfH0qIHRt1YZP/MeopFrOeWDqbRTwWLyiXLRTX1rHYAhWCNJ2wYa7u9RYfSnDYsPM3vxMtZskddd5saw++X39fprxcf5BJoKjjfnXtGXSZyxHGhxJkWgO8E0Tt69KZvicE5gEA+/vT7jVCCM/2E56Bl8VM3hWwR43Vb1XGICRKShYoQogXYd6F83zDPTcBuMnn+EIA032O90EKJJ9zdwO4O2x5oyAroDnlLZ1PEaPmgnOWDtS5xv9eYfhtyj/4ozaf1EdE+RFFYf04hfn5d962EFdbZ2Hb+Mz1ofh8lGEn4rnXW6K8goqqrskfbJRiNgyzCZx/GQrLU3hNcNtRAtHv/rUdPdb0AX0eirnSbSYvfcRvykt1/uVoKF7nP6BvQql/Q37alv54q3fux6mT/Wc/2DSUKJZ1iQKeKV8ETpSXdMr7NI5MVvOx2EZEISY2Bh0P45QPKoI7kg1Qw59dYY6Ky3hGXtHMQ0kbRq+2js0a328RcEGdcKbIyXG26LswnWiQTyqsD8VYZ0VEedl2sLRObPQ598gbW3zz0hFCaHmEK6sfvZqGYqs30/cYRqBkPM5/73W2yE/92IF+85YC7tpmhmIELf9STVigFIEQuZnyvg7HbPDER51ADcXwQeq3mCf7hesAcyYv81pc72zZZ7xf78QLTF4hRrl+mIIazBpb8PncdeE+aptJIigXd16PSXMM6GQVSpAU+KQC7snHFqhgj/KyCy/brP+giY1hyuI1dwZoKK5pzT8dXaDYoy5L3xgtaB+kYkxeQU75nFZoqPM68aGwQAmJ+oiSCbMPJSNy50u22cLcKYR1VI9qSwEA3jNlZGAZnDzM+Qehd3oFJi/LvjHGNA0mQ7PGFlJD0crgN+pWH6Ntwc9QExuNmoH8G2Kkm18Oj9ZpES85TciUh0VDCWFqtK0EHLRjo+e6EOabcpzyehiurU2b56Hkfht9Rp424j1n23TPE+XVHyBQLO0nyDRYTVighES995Srgfjth5J1TV62EUOYxSHzG4/+v6Dw1IkjWgD4hzGGKUeYWerekVc0GorJEV9ulJetk1TRQJmsKHjeTAjBpmPUpsJs1CTbTGGQg11rcM4J+yz2InwoJuEVFMUFBO8pb8rLL/3836brTFcMZLJIqQGeob91N0YzrhJtf/9BZq1MxuJD0Q6ZQpf1dE2WrXqJ8mKBEhL1QpMBAiOTFbkGbHMUBwoUwzXC/qEJaIsMBpbAnI5+yDT6985DiUageEZzYbSxkKsN28KGBwKWGA8b5dUnR8PlRHmp5y+M8jLe4psHEOCULyLKy9bh28KGbcUOEzhQTpRX/2AGLXJLALuG4l8xtrDf/DLm14ntfv1ev4Va868zR3mxQGko1ItMBE5shDbxsTAN08S9wnT8O8owEThCCHc/kzA9kV+n4Nn3xdBQvT6UfE0q3Ii6oCz6SC/EKNXmHPYrg1+99QdsghRGQ3nindw2AOWsNuxuCRAQ5BD0pMVMBjVRjHZqMwGV6kfU20HwxNvgfAYyWVegmNKxzUMJU6d6mxFZ8znbVIP7X9tUcD53XXAZDoqZ8ocKmzoPYN4yZ8J/UvbWfg00nc2p2P7RU7nfpZm8ghungsKs4RFQDv2QqaHqPpQgDaWYGbyepb5DOIdtjkq/MvilFbSrXpiZ3U8vzW3ZazPPBe4B4pq8zKPcIMKYiorRnI31rkbLllnb+WX4zK+caWPjhzUH3h/ah2IJQBhIZ9GcdL5Hm/nNVMdhViwOivKyBRiE1eDt81DqQ0MpZ2LjIcPFt7/gzoYNmimfETmTmF8npzfOwDWK1E6EAc7ZoE7UVVCMV+h5BY+awmko0YQNmzpE23Im9rBhgUSMkM4K3/IMpLPu+fzlV8KMUD0zsk2zy8MEOrgmL28inncQNGIPUe/59ZrvZwujBbubX5nykMfz1xN7ZrkzKGu2aA02E2X+OT+HdDqTRVZoeRl9KOReH5RHfrk8eWWd6M7BjCh4Ju+e8sHpA853HfPxfdr9VqyhNAxKmAA2k1dw2LBnBd2AD0U1jvy+PIyGI0S4RQaD0vH4UEJ8aIHL1xcxcDLZm4PMe0CYsOFcgILfpQOZLFpScVkG7/N6J6v6p+/xwRj9PTKNED6UQg0lXGcRRggH+b6AEKsKeDQYQx6y/P2GBSqbLH5GVQ9KyJvICZTCa1Te6r2WGuXlbZPmcihTd9Eaijx0zLh2AECfYV8WDhs+SHFNXr4aSM4p76/BhBt5uXH8+RqK1lWbOy4Rauc/hd+HpncUfutb6WUEfDqmEKNc97xRK7GnoS63aSjO/CB/zTGTFchkBVpThUu+6P8nCrBfezSU4LIGChR3hV2zhhL0pGH8Tnr2W/f2FpbBMiK3hcECmoZiGIzkIq+CBVJTImYJs1Yanb/WCSDnQ7G0EZPQDq2hJJRpzXtOF1RBPpS2JsdYZFrPy77tMmsoDUnCMBIBgGXbutx1sPx8D2HDIdNaGKuO1+Tlf68QcJ3ypW4I5TE5mEZuug8lfz+UkJMrAW89lToPJcymUUnDtgOq42lNJXzPq/pJxmNmG7quOVq0qaAoK1OUVxi/hn5/KhGzziEBgO1dfYVpWISXxxxqGWyYltDPLc0SXMZUIha4RpWqFz9NSAkzZfIy1ZtqqyYNRT+e73DXy2FakintMXmZB5ntUqDYVj3msOGDjJwPxXv85TW75N/dAIBBnx3zgsILdUwzjT1RYgH26zDzT/zKpNCFldEpnzV3LN5lU4Lz1z8Ek3PTNtq2RxPlRsX5Izm19avSUPI7c/VsyRiZNSWDIPSWQQkUcwepOp98jU///+6eAeP9Ko9UPBbK5LW/rzBMddDjhA7WUIx7rlgEiqpzcxmd+1IJ83PoZfEz3RVoKDYt19AhBzncAaeOgkxeYSc2tskBTZ9htrwqvzHyLsRS/dWABUqRqP0T8hvonp7cSqEx8m/k4TUU4XtNGKe8o6GUOw9FExbGD03rWAKWCrFG1xgmfoWah2KJ8tGvM80fUh1Pm6uh+AcYJOJm80uY9+rOxg8YSboaSv4oV/v/7fNWGe/PaVMUyuHd7SNQvB2TTx4Z83t37hE5DcUwnFZzdsxhx87fpkQ81EZ0ft+a0lqaLSYvk5nRPW8xeal0UwaTly0QRx1qs2goblsPYfL6ygNv+l5TDVigFIka/WcKOtHci07EY2WZvEwTx8I45TNZJ6IJCOdD8Z+HkjObGSd8BcwADjvfBvB2Ol4HuF2g6EkHOeYzQriO4EINxeu8NflQgkxe+oQ0m3kuaK+TnNAJiPIKQC+rbTdFwN9UpOftl4Ref34aiLonGSdkssLXZNUj68vWOaYsPhT1jfmVQ2lBLangsGFVB2aTl60+ckIc8DN5BX8L6lh7k9P+ek0aimu18D3tKf8f36z4XoNGWKAUSW6eife43l5T8ZivyStoRraOasSlOOX9QkGDMGkoqYA1y/KPB+6HYukLTU7LMNFVemezrzdoLwkglVBRXHkaSkb5UPzP66N+U+emRpf5ZdIJMs/krnHO5Y9Si43ySiVixvaht8F+n4giPQjD71n6PPebB03K1KQL0LFDmgAAc86c4rnWlEZTIhaooehrsOV/K2Gd8uo6k6+mdyCrbThn11Dy62wwY9nlVflQmi1OeeG9Ph+9/KdOHu57TTVggRICvYNuU2GIBZ19jmScfDuBgUyusYTZ5yG/Uwhn8hKRmLxya5LZo18KTF4hzFUKk9MylKMbwOh2ZyHMtbt6fK9R5UkZth0YKEJDMfVtI9tSGCo7BFsnmRUB5hd5vDPPTxJ6YqPmQxHCf1SuCzS/kb3+Pvxy7ZXaRSJGvve7AkXWp37NkOYELj7pMFx6ygQnfcNj6QJFCHNb9zxLXhscyDN5maowp6GYBEoaI1udNubn30hr7cPJJ09DyWbRlLQLFDfKyyePMD46NRCYMqrVfeZawAIlBHFtYofb8WgN546/rcEXf/eG+/9EPBZo1wWCo30GXA3Fe9w0k1wnI4QbOFBqlFc6K9xGaTIFqGdJxWOBa0/ZyqDXkyn81pSGEALjhjqzrrsCNBQ9CidfQPa7UV7qef3noSQCNJSBdBaj253Rt219K788FKoe9xwY8Dx/KSav/Dzdsmpp+WkYgxbnrtKehrcm/QWKvEdFzeVr5cl4zDWn2oSvTUvWy5pfp/lRXkHvDjBt/yxwYDCD4a1JAP5zRPI1Mr9VDlQZglazDoryCjMnK+1q2gnj/J9qwAIlBHoncdZRo51jWuO4+8V17u/7rprpmLwCYuOd+80vfc+BgYI8AG/kmPljzDnlw+A/ShXGiX6Knv40EjFCa1M80ORlW4xQX/reE9nl8cP435sVcD/2IJNXOuMIWb+JcmpTI+WUN2koqQAfSn86m+u4QvgubOtGZQWwV3uesCYvd1TuM+jRr0nFY0jEyNfkpdoe4K9BqA5vWEvS937ltFcdrN65DWYEErGY2z5t5kFlpgwz9yL/e1NL17cZBgqA866U4PHTxPvTWQjhaKBALpjArww5gZLv/8rmJnIGmbwCNJQw34IumEzRddWABUoI1Mu6+KTD3BGx3jgSmkns2HFDkIiTbwO+9cnlWpr+eWWzAou3dDnX5Ntjs97Rnh+iyLDhfkM0WnPCP4xW0dOfRltTAkkfbcxjmitCQzH6UAIiloZLc0SQQBnMZpGMx5CIFwqU//vYEgC5Ea0pwipIQ+lPZ4w+GL/nMdXpYCbrptPZ02+93u9+AGgOWLJ9UC7p3pSIefYLUVz/yDvub7/n7XU1lJRFQyn0oaiAkVgsWKCob6vJqqGYzXcq32FKu/B5Vr1sfnWsnnWEbGN+/g31Xpt9THwq3SANRR3KTWwsjLyz7eejP0tbU9xX0FcLFihFcML4obkoL+3FxrVNpJuScSR9zEAAMH9dp/vbpKH8WVu5Nn9E44zwgneEzAjhMdHZyJ+UCDijLrWonmk03TOQQVsq7quNpS1zGUzXevaVCCFQhACGt9g1FNWRJWOF72Vj5wEAZg3FDRuOmSOO+tPO0i1NiZjRqRpWQ1EDli17+zzHw6DeQ9BaWQPpLJJxQlMyHhhxBgRrKCNak773uyN2nw42nc0iESe3fZqy1ydoAjBOrg00ecl8h8n20TtY2FF7tCefOlY7KI6Sfjo/k5eyGrT6BCGodJvc1TPMPifV/noHfMyQIUzdfQMZEAFDmv1NkdWCBUoI3n/MGADANece7dp/9Q6PoAmUhGNOCPpYDx/W7DtiAoDl2xztZGhzwld9ttmE9SgvU1+ul92/UxBoChhVATkNJRGngk7Uu+GQfxnc/DWTl3E/FJMTW9ZHczKG/QH7cQ9mBBLxGOI+wRL/56wpAIAPHDemoAz6/1MGp7wQAm9s3IuuvjTamxLGcqQtde6UM4vjDhsCINcO1HFFkPKZc0SbfShKQ0nF/TWUEXJED/i3MRUiPawlhcFMYXSVql4/k1daaSjKhxLgBwSAprh/qLf+LAqTU35Yi9IufDQUvWw+70RpC0oL7vcxR6lAGxWlVTC40kxevhqKpgE3JWI44Cf4PDtPFpx2yir3fmlKxNiHUu/cfNlJ+Nu/not4jEBESMbJ49zUHWnNyThSiUIzkPq4v3LBMdi6rw9/WbK9oBEPpLP46XNrAABXnD7ZE6IJOI1Vjbj2G7YLFSIXlWbacU9vcL5mi4zulDdrKK1NCbQ3JQoirNIhTHMK72qs/k55k8VnMOv4RoI6csD5qJNxQiJWqE31p7MY0pxAIubvtNc/eD9ta8lWp+N/a9NetDeby6EL+qDJouOHtYDIEdhu+WUZzjturLv0jx+qLlsC/DkDGcf815SM+ZpGJo9qc8NO/fp7ZeNXgie/81LvvtVPQ5GCXWkeftoxkNNIgraCALwmqHzhqJ5N+dj8fBN61Jafz1N917koL3OgjfKB+Jm8UomYsxacIQAGcL7ZllTcV8PV35NJ43cFSpIFSt0zYXgLjhjV5v6/vSnhmcw2RI5Orv3AUQDkKql5DVRN5lIfGgDsOeA10/zbw2+5v5sTcQyks55GOJjJYmRbCjEC9hiW4Mhk7XNI9A/JT6AMZrNoSQZvZdzTn0ZbKo6jx7Zjf7/3OcLscpe71t+HopsgTB/RYMZxMLemEp4OuDAPxxmciFGB2aFvMINmaaZUafo9i2lOhF609qaE73ImKl2T49a9JiuQTBBak3H0aB2LyrdNOlxtdnSbycv1ofi8+96BtGvP9xuQqE5Wmea6+7zvPhc2XNjBprPONgFNiULtRUevcz3NfHoG0q5gyzdH9eebvHx8E119wYEPrg+lzRw27K600KQ0lEIBm5RBEH7tx9UqE3G0Jv0Fii7ITHXRO+Bo66l4nE1ejUZ7s7fjWCdH6F/94LEAnLDNfBV8r1yaZbhmUtAjaoDcDNcPnjDOjV3X0+mTo5Cpo9vwzpZ9vmXLCE278JlcCeSEW376bhpaqKOpAXd092N0exPamhKBJi9b5PKAIXJNRSMBwJa9vZhy3eN47K2tnmuFcHwbbU0J9Bg0NsD5yBMxcpzyeQJS1WmbnKmcn47qRNqbk77CNaH5z9bv6sG85Tt9Bcb+/jRGtDnv3q/ODwyk3X1ZWlLeAYtqayatQKHeg+rc/EbESgg3JeL+AmUwF2Dg9+qXSo1s7FAnTHpvr79AUdFVeiesou2UoDA5j9VxZUYydZAH+jO5CKy8NqjquL0pgXiMfDUU/Rv2e7euD0Xm4ZeGXaAoMx/5fkvqHTQlY2hOxd08vdfkjpkGaH2DGcePZ9A8qwULlBJoTSbw/KoOAN7RszJpDGlOFsyL2C2jdka1p/Dbz57uHNufEyg3Pb7U/d07kHGjrNQH+Zv5GzB/XSf29Q5i+oRhWNOxv6BcL6zqcDsl03piAPDe7zzr/s7/WIUQGMzkorz8lpARQmB7Vx/GD2tGi8+oyjO3xBLyqnec+U7WtqY42lJxzF/rBDP8+tUNBXk4Jq94sIYSEDbcO5hBczLmdgj5JivdpOEnCPT4B6VV3KWFkQPArv39ODCQwai2Jrc8+XxLRpslYjEcPrwZmzpzS8vv7HYc9BOGtzhlMvjfvizXcFKjcj8NZTDjzAVJJWK+HXXvQAZDm5OynIXnlVBXHfmf3/Iu85GvwSgtQK3xFY/FEIuR48MxCAr17Rwm0/DryPceGMDungF34JN/TU9/GjFyfF/OyL8wL7WWWWuqMPQd0CPa7JFiaumU/GdS/jtHO/YTKE4eTYkYhjT5a9pqkBMj/zIAOZOXCpLRBxMPLdyEl1fv8r0valiglMCKHd3YtX8AS7buww2yIzjrqFHu+UkjW7B2V4+ns3x2hSOARrU1YaSMGtE1lF+8kOuEYjFyPxTVQL/xh8Vu3iNaUwVRTZmswKfvWuDcT+Qbzgt41Xw9fYX6MIe2yMUSfTq/NR37MZDOYtxQKVAGM9596LXG7LcAoc6qnTnBqJujlGlmeGsKW/c5nasyGel5JOPkaCg+Jg1FX9oxayXisQLTRt+gY4pqNwoUpz6GyCCJ/HXKPvbjlwA47/y2f3gXABRE2X3iZy8DAMbJUX1Hdz/yeXDhZjfNkW0pz3va2d2PUW0p1/SS/w7zUZpM/rvbe2AAzyzfie6+QWnyKuyoewcyGNqSRCJGxoUKgdzg6fZnVnuOq3sOk9v8qkGTaldK+wmKiOuSbWasEig+16m5X8qHlS9Qlm3rxtFj2xGLOe3Dr866pal2RGvK1xy1Zud+EAFHjpGbX/kItvXSOjFpRCsA/3koyTghbjB5uRpKIl5g+VC8sXEPAGDiiNYCE6OidyDnQwFygm4gncW/Pvw2Pvm/833vixoWKGXQO5DBva84o+ZL3z3BPf6eKSMxkM5ibYfT2Lbv63NXiR3VnnKdfGop8nybeJxy9uP8RvzVDx6DIdL5q49Cdu/PdVLNSSeKx29EvXH3Ac//9+aZ3dSyH2qE6dfpPLXE2UP9gyeMQ0sqjqzIi+aR+Q5tTmDvAXPnJ4TAz2QQAuANblDO4xFtSTcN3f+k8lAmL5Mz/PZ5q7D3wKB0vFOB87VvMIMmGR2TiFFBOn3uBLkEhDxv9VIAABscSURBVPCa5bbt63VH+Td+bDo+evLhBc8BAOtlnU8d7XRMy7d3wYRa9kbXHnZ29WPMkCaMkWth7fQRSPp7UrP284W5aqvrdx/w9aGomeGtqThaUvGC59Cf/eixzrPoJlwgF0wwYUQLRrensExGq6myKH/j5FGtWLmj27cOunoH0ZyMaf6PwjaoNpH7/t87Qjx/5L5+d49bxvHDm303E1NlGtGW9B18bd3Xi9HtTRjWkkRTIuYbNqz8oMrHmh9o0JfOoCkRR9ygoajvuykRQ3tTwncA9qg0hU8b2+57vncgg1fW7kYsBtdMrN7tDp89byoJC5QSOPtoRxvRHaf6h3XEKGe0ouY4fO+vK9xz44e1uCNN5Vj/09u5uSeAo2EoDUV9KNPGtuP848biC+cdjSHNTuemj8r1zZJaUwkkfSLNAOdD03ly8Xb3txAC1/3emdg2aWQrUomYxywHAC+v3oXvPrUCI9tSmDSy1dUaPI5+2WmPbm8yLonSn864EW2AI3z0QINBGR2jJpUBXg1FdbjJRAztBqd832AG35+7EoAz58FPa+sZSKM1FQcRob3Zm86bm/Zi6dYuNCW0yCRNIOlbFiTihETccXabtKXR7SmMH9bsDjT8GNaS9Lw7IQSeXrYDW/b0Yky70nAKO4mV23Oa3hBpstJHs/3pjCsszzxylONDKYiMcmaGNyfjaE3FPRo2kGtv37j4eIwd0owpo1rxvmljPNeoAcmI1hQOG9bsauHfe2qFp2wnTxzm7h2UT1ffIIY2J90BRL720dU3iB/KAdrMqSP9r+lNuyHDI1tTvgObxVv2IRWPYeyQZt8or+37+lytsjkZL/DTAMADr20CkJt3k59O74AjoOMx/6AOZWkY0pzAkOak78Bo2rh2tKXiOHJMm69AeXiRU4ZX13a64f5qgMECpQG46dKTAADvbN7rHlOTn4DcaGWDHJlukJ34yROHIR5zzFFDmhNYvr0Lr6zZjVfXej+sWKzQcdndl8bIthSISOswNIGyL9dw2psSSMYJew4MFmg/qkwnHj7UPaZGTq+t34MXpa11ZFsKY9qbCkbDSnVWHYe6d72m+agIsBFtKezt9Y9Ge/ztbfiu7GQ+efpkjGxLoVP76HsHnJGdighS9aJQzuDhLUmjU17/ODt7+jG8NemOKIUQ+MXza7F4S5fr22hLeU0Ol/7kJTy9bAdSiZi7PLnuE8oPqgCcTvnO59e6/7/63oXu7zlnTcHU0W2+C1mquSP/eMYRHu1SdYQTRrRgvDQjzVu203Pvr1/dgI/++EUAwIemH+ZqAap9HBhI44yb57nluuczM9GSihd0XkoTaE3FMdrn3au6UeZQv+i6HVLYjRvahMOGNmPlDkfQPbTIMekpv8jcpTvks+woqIuu3jSGtiTdAUS+pqQPPNQcEb2zF0Kgq3fQ1XCGtiRd85ZiMJPFgws346SJw5wIvryBxvpdPVi/+wDGDWmW+SQLAhD60xlskZqPah/5VoGefkegmEyM+3oHMaQpgURcaSiFgq+zZwDTxg3B0OYkegczBYMiJac++q7D3bk7asD1pftze6NUY+MtFiglMGV0GyYMb8H3/rrSPTZpZKv7u70pgVFtKWzsdDqOvQcGceLhQ/GHfz7bvWZUWwpPvLMdV/ziVRw/3uncr5g5CYDjfGuVTr5tUlB09w26gkR1GHqHpo9Epoxuw46ufjz+9jb85NmcjfvuF9fhu0+twGFDm/H4F8/Bv8061k0b8O4xPlaaWPzs/UDOZ6Q0o79ITUcIgbteXIeegQxGt6c8gk5H13y+fcl0R6DIwIXVO/fj6WU70N4U9zzXeq0jVp3KyLaUawLcopU/ncnic/ctcv9/1XuPxJj2Jry1aS/e2rQXK3fsx01PLAMAjB7idEpDDPNIuvvS7mKHuhDX6z+/njZ1HsCP5q3CX2XHOWF4C5LxGE4YPxRLt3Z5Rv9/XbIdfYNZ/Pus49DelPBsf6A66C+cNw2jpIby0KLNHlPlN/+42P1962Un5wSK7ERfWr3bFaREzvyOKaPasHVfr1uO3fv7ccq35wJwNMFJI1qxqdNrHlX+wvampPxb6Lva2dWPIc0JtKYSOO2Ikdiyt9fTSaqBjGrz+draXxZvx+PvbMOQ5oQ76te1j8FMFu//7nMAnO9ELTOjhE7fYAZTr38CA5ksDpPaxZDmBLp6veVUGtOothRGtqWwdW+vK1CzWYFzv/cc1u3qcf04o9pS2KWZlfceGMCNf8oF0qTiMTQnY5730tHdj97BDIa3ptDWFMcBn0HPvgOD7vIwjuBLewSGEAKLNuzB6PaUe12+trV0axdaU3H84B/e7Qp7pT3r38TmPYVmv6hpaIFCRLOIaAURrSai66qZ92feO9X9fcXMyRgrRzKKkW0p/G7BJuzvT2Nj5wGceeQozxpb+sRE1RmdPNGZUBaPEU6dPALDW5P401tbkckK9Axk3I5Cjag/fPuLePztbRjMZLGjqx8xAhZ8/XzXDAAAj7yxxf1945+dD0B1oKrMf5KROkr9PmfaaEwa0YqxQ5o8JjIhBIY0J3DmkaNw15z3AAD+Sc40nzraEai/W7DJvf7kicOxpqPHIwh2dvfhrFvm4aYnliFGwNvfutB1WL60ejf60xl8SmpBr63fg1s/fhLiMcLU0W2esqyQ9vcRrSm8/1jH7HL2rc+4GtN/P7UCCzc4zsw7PnUqjj1sCEZLH8QlP3kJ89fltMLRSkMJ8MUo7eCXL67D01JI6CPlj0j/yUOfOxMAMOeXC/A/c3MDjiPHOFrrjCkjMZDJ4uRv/RWLt+zDvz38Fq6Wgu9dE4cBAJKJ3FpwKsJNmV4UX33wLfgxrDXpDjxUJ7pTM5HNfo8zaJk2rh1CAOt3OUJDRS0CjkN90sgWbN7T645q39y019UqVMhwa1O8QDN8Zc1uDJEBDqpNPCcDUj40/TA3mu6OT50GoHAey+d+7dTF25v35TQU7Z188w854XnPZ2YiEXd8LR37nWdcsT3nl1H1MLQ5iX29g5hy3eNY07Ef+3oH8XOprZ177Fh84Nix6BnIYMlWJxRf32ZZaTmj2ps8g6AfPL0Kv5m/EQDwmbOngog8AzAhBK665zUAjk+1NVUofL/71HI88sYW16w9YXgzhPBaG7bt68OBgQymjGpzHf8b8szWb2zag5lTRyIeI0wb56y0sGhDp8fH+i8XHuM67CtJwwoUIooD+AmADwE4AcAVRHRCtfK/ShMoylmqo/wnZ948D/3prBvZpThmXLv7+/Z5qzCqLeUuu/GuicPRnIzj5InD8ee3t+Gorz8BILf3h3I2AsC1v30dV9+7EA8t2oTR7U3uiOqV688D4JgYXl6zy/Ph/s/l7wYAvG+as3Lyfzy6BG9v3osdXX1IxAi//Kf3IBYjjB/WjM17evFHKZTWdPSguy+ND5883h09KiF3/2ubsGBdp9tR/9el03HhCeMAOOaNvsEM+gYzWL6tG1vlB/O1C491Q1SVie+pJTvczvR9x4zBiYcPw5qbL8Y/vGcSdu0fwOY9B7B0axf+76POaHlkWwqnTh7hPpvqUBZo66ZNGe105soHAcC9HwDOPnq0+yydPQMQQuCnz3mjl1SQwv++uA6fvXch1u/qcUf9D33uTNfHcsqk4UjGqWDkfdoRThlVZ5zOCvzLQ2+50V0AcOLhjkBpScaxu2cAf357q+u4ViN6FcH1zPKduOynL2GxNh/pwyePd+vEKetaZLPCFXzvP2YMvnzBMQByPr9nlu+AEAIL1u1x03n/MWMwcUQr+tNZ/PntbehPZ1wNFIBb38Nakli/qwfZrMD2fX145PXNWLEj935VvX9Bbu2ga3FtTQkcO24Inly8HdmswF+XbPcI80zWWfhzVFvKHRgAwAMLcwOWc6T/ZsLwFmzZ04tMVuD3r+fq891yxr9qowDw9NId+Mf/fdX9/xUzJ7nlfH3jHmSyAr9bsNFzHnC+8S17e2VYfRa/enm9rIvh+I+PHO9c096EDqnFLN7Shbc3O+/mtCNGYERrEi+syn2HnT0D+Mmzjg9xQJrCVDCFEmjpTNatuw+fPN4dlOht6yfPrsbKHftxrBQkR41px6SRLZi7bIe70Od/fuxEfP68aQWD3kqQsF9St8wEsFoIsRYAiOh+AJcAWBp4V4T8+JOn4PO/fcN3h7Q/Xns2PvTDF9CtOUJ1fjj7FMy5ewGWyiiYEycMwymTR+DJL53jNo7Z75mE51fmRo7KNzNmSBPuu2qmGyasQpJV6CvgOP8vP20iHlq0GS+v2Y0ZskO78ZITcawUXGOHNuO4w4Zg+fZuN/z1iFGt7vIeXzh/Gn4zfyO+/MCb7hwHADj32JwjVuX5xsa9+PufvwIAOHJ0Gz51xhGuXfqmJ5a55iXFtR84yiOU7/jUaTjtv57GF3/3BlpTcVwxcxJuuexk9/wFx4/FrU8uxzn//Szapflp/LBmNzz1slMn4JHXt+Di219w7N2ysx/SlMBxhzmdsRptKoa3JvHM1851O+DDhjbjuRUdmHr9E+41yTjhjCNHuR+zWwffe879/Z4pOY0w4bNY5hfPOxqf/8DRbh6K5dpo+vl//YBr0nA67HX4/G/fcO9RI/v5X78Ax3zzSQDA6xv34iM/cnwnP/nkqa5AUZrwps5eHPn13LPc85mZ7m8lyL/315Xo7kvjr0scgXHFzEkgIkwc4cx5+YK2zw8ALP/2LDf990wZiUff3OrJAwB+ONsZsBw1pt1zXGm1inOPHYOfP7+24H4A+Ok/nop4jDBr+mH4zfyN+PPbjxecd5+lJYFnV3S4Ay8AWPyfF7lt87jxOX/hLdqK37+4cgaICJNGOs968xPLcfMTufOvXH8exg9zzo1ub0J3XxrTvvEkZkshc+m7D8ctl53sRpy1NSXwwqpdmHJdrqyXnToB8RjhQyeNx7MrOvCRH72I044Y4Q7SAOCGj54IICfkL/3JS7j6fUdi5Y5uLNqwB/EY4V0Th7vrFlz3yNtokpGcyg95/vHj3PRmThklBaszuDtHDhyrQcNqKAAmANik/X+zPFY1PnLy4Xj9Pz6Ic48dW3Du+PFD8RU5GmxJxnGKNooGHKHw+2vOAuDYtW/+u+nufcr5fPFJ4/GpMyYDcBz6esM4Z9oYLLtxFj59xhHuMTVSUugdnRrl6ccA4PfXnIVPnDbR/b8SZoDzEX3twmMLnm3iiJy/KBGP4ZOnT/acVx9cIh7DZzWhofOVC47x7Cw3qr0JH3tXLux21vTxnuuPGtOO6ROGQgiguz+NeIzwyvXnu+dvuewkTJZ+LCVM/n3WcXj7Wxe615x3/Fh8/NTcs3781ImuMAGctbLyWfKfs3DfVaejORnHgm+cX3BejzxTfPgkb9m/fMExrpA+fHiL2/ErvvrBYzB5VK5OP3jCOM/58cNzQiiViOHRa892tVlT2T8q69KEruX+/Pm12N0zgP/vnKm4+e+cgJNT89qrQn9nF514WMH5y06dgEtkCH0yHsPCb16AmVNG4pF/PssVmIp/n3Wc+850lt04CxfLOrzm3KMKzt/xqVPd8wBw9lHeDvND0w/zDK5On+pt8wDw6TOOcOtZD/xQzDnzCFeYAMDfyV0m01mBX7/qaDBfvuAYV1MHgKvfd6QntP2Yce34/t87wvVy+Y1t2H0Aj7y+xdmWOh7DH/75LHxcnjt6TO6d3vn8Wjy3ogMzp47Enz7/XsRizlyWc6aNRlY4zvZrfvM6AOCxz5/tMXNfPiPXxr/6wWPceTTVgKrh+a8ERHQ5gIuEEJ+V//80gJlCiC/kXXc1gKsBYPLkyadt2LChIK1Kkc44JoPjxw91tYJ8Dgyk0ZKMu6OcUnh+ZQemTxjm6RwBx45736sbMLQ5iTv+tgZXnjmloPNXLFjXibUd+/HRdx3u2SNdPUdCzo1Ixsm3rJmswJKt+zD98GGeaCzAifq65cllONCfwazph+H9x47x/YgBx5+TjMcwa3phZ9U3mMH8dZ34y+Lt+PipEzAjTzh29gxg275ePL9yF44e217QMSt27e/Hog17cMHx4wr2jukbzCAZj+HF1bvw3qNH++4tc2AgjY/9+CWcd9xYXPuBows0H8Bxtn71wTfxzx84CqcdUdih9Q5k8PyqDnT2DOCyUycU1Mf+/jTW7+rBW5v34n3TxniCPnSU4zz/fCbrmGZeWr0L63cfwD+ePtl3a9iVO7px4W3P49TJw/Hrz57uBh8Ajn9jTUcPbn5iGQ4MpHHrZSdj+oRhnvt37+/Hw4s245TJI5DOZnHq5BFFbUGbzQq8uXkv2psSuPeV9fjiedNcs61ejnW7evDCql349JlHuNqVYjCTxZqO/YiT0zZ1YamYv3Y3EvEYlm/vwmFDmz0jesD5VvYcGEQmK9DVN4gJw1t8n2Pj7gN4cfUu9A5mPBq2zrJtXVi5oxsXHD/O8y09tWQ75q/txKj2FD568uFoa4q7gRaKRRv24E9vbcUFx4/Dvt5BXHDCWE/b6O4bxGNvbcWybV2YPLIV7540wiNMFEHfaikQ0SIhxAzrdQ0sUM4E8C0hxEXy/9cDgBDiFtM9M2bMEAsXLjSdZhiGYXwIK1Aa2eT1GoBpRDSViFIAZgN4rMZlYhiGOWRpWKe8ECJNRJ8H8BSAOIC7hRBLLLcxDMMwFaJhBQoACCGeAFAYIsIwDMNUnUY2eTEMwzB1BAsUhmEYJhJYoDAMwzCRwAKFYRiGiQQWKAzDMEwkNOzExlIgom4AKwIuGQZgX8D5yQA2BpwPk4btfFRp2MpajXI0SjnDXFONd98o5YwijUYpJ3DwtNFy8jhWCOG/3IeOEOKQ+QdgoeX8nZbzHSHysKUReD7CNALLWo1yNEo56+XdN0o5I3rWhihnmLI2ShstJw9b36n+scnLy58s5/dazodJw3Y+qjRsZa1GORqlnGGuqca7b5RyRpFGo5QTOHjaaBR5BHKombwWihDr0VTq/mrSKGXlckYLlzN6GqWslSxn2LQPNQ3lzhrfX00apaxczmjhckZPo5S1kuUMlfYhpaEwDMMwleNQ01AYhmGYCnHICxQiupuIdhLRYu3Yu4joFSJ6h4j+RERD5fEkEd0jjy9Te7DIc88R0QoielP+K9z+r3rlTBHRL+Xxt4joXO2e0+Tx1UR0O0W1A0/05ax0fU4iomfle1xCRF+Sx0cS0VwiWiX/jtDuuV7W2woiukg7XrE6jbicFavTYstJRKPk9fuJ6Md5aVW6jUZZ1nqq0w8S0SJZd4uI6DwtrYrWqUuYULCD+R+A9wE4FcBi7dhrAN4vf38GwLfl708CuF/+bgWwHsAU+f/nAMyok3JeC+CX8vdYAIsAxOT/FwA4EwABeBLAh+q0nJWuz/EATpW/hwBYCeAEAP8N4Dp5/DoA35G/TwDwFoAmAFMBrAEQr3SdRlzOitVpCeVsA/BeAJ8D8OO8tCrdRqMsaz3V6SkADpe/pwPYUq06Vf8OeQ1FCPE8gM68w8cCeF7+ngvg4+pyAG1ElADQAmAAQFcdlvMEAPPkfTvhhBPOIKLxAIYKIV4RTiu7F8Cl9VbOKMtjQgixTQjxuvzdDWAZgAkALgFwj7zsHuTq5xI4g4l+IcQ6AKsBzKx0nUZVzqjKE1U5hRA9QogXAfTp6VSpjUZS1kpTQjnfEEJslceXAGgmoqZq1KnikBcoBhYD+Jj8fTmASfL3wwB6AGyDMyP1e0IIvfP8pVR7/6NiKmW4cr4F4BIiShDRVACnyXMTAGzW7t8sj9VbORVVqU8imgJndDcfwDghxDbA+aDhaE6AU0+btNtU3VWtTsssp6LidRqynCaq2kbLLKuiHuv04wDeEEL0o4p1ygLFn88AuJaIFsFRNQfk8ZkAMgAOh2NO+BoRHSnP/aMQ4iQA58h/n65hOe+G02gWAvgBgJcBpOGou/lUI8yv2HICVapPImoH8HsAXxZCBGmbprqrSp1GUE6gCnVaRDmNSfgcq0gbjaCsQB3WKRGdCOA7AP5/dcjnsorUKQsUH4QQy4UQFwohTgPwOzh2aMDxofxFCDEoTTQvQZpohBBb5N9uAL9FdcwMvuUUQqSFEF8RQrxbCHEJgOEAVsHpvCdqSUwEsDU/3TooZ1Xqk4iScD7U3wghHpGHd0gTgTK/7JTHN8OrPam6q3idRlTOitdpkeU0UZU2GlFZ665OiWgigD8AuFIIofqtqn33LFB8UJEaRBQD8E0Ad8hTGwGcRw5tAM4AsFyabEbLe5IAPgLHzFOTchJRqywfiOiDANJCiKVSPe4mojOkan4lgEfrrZzVqE/5/HcBWCaE+L526jEAc+TvOcjVz2MAZkub9FQA0wAsqHSdRlXOStdpCeX0pRptNKqy1ludEtFwAI8DuF4I8ZK6uKrffdRe/kb7B2fEvA3AIBxJfhWAL8GJqFgJ4FbkJoC2A3gIjsNrKYB/FbkokEUA3pbnfggZWVOjck6Bs6ryMgBPAzhCS2cGnEa/BsCP1T31VM4q1ed74aj9bwN4U/67GMAoOIECq+Tfkdo935D1tgJalEwl6zSqcla6Tkss53o4ARz7ZVs5oUptNJKy1ludwhms9WjXvglgbDXqVP3jmfIMwzBMJLDJi2EYhokEFigMwzBMJLBAYRiGYSKBBQrDMAwTCSxQGIZhmEhggcIwdQIRfY6Irizi+imkrerMMLUmUesCMAzjTJITQtxhv5Jh6hcWKAwTEXIBv7/AWcDvFDgTOa8EcDyA78OZGLsLwD8JIbYR0XNw1i87G8BjRDQEwH4hxPeI6N1wVhRohTMZ7TNCiD1EdBqcNdAOAHixek/HMHbY5MUw0XIsgDuFECfD2drgWgA/AvAJ4axldjeAm7Trhwsh3i+E+J+8dO4F8O8ynXcA3CCP/xLAF4UQZ1byIRimFFhDYZho2SRy6yj9GsDX4Wx2NFeubB6HszSN4oH8BIhoGBxB8zd56B4AD/kcvw/Ah6J/BIYpDRYoDBMt+WsZdQNYEqBR9BSRNvmkzzB1A5u8GCZaJhOREh5XAHgVwBh1jIiScr8KI0KIfQD2ENE58tCnAfxNCLEXwL7/194dmyAQBFEA/QNG1mIJdmIgtqSJJlZhYiFiZhmmZ3CbCzJwBu+FGwyTfWZYdqtqO853/e3D70wo0OuZZF9Vl8yvwZ6S3JMcx8pqlfkzsceXOvsk56paJ3klOYzzQ5JrVb1HXfgbXhuGJuOW122aps3CrcAirLwAaGFCAaCFCQWAFgIFgBYCBYAWAgWAFgIFgBYCBYAWH+5g4dw8qLEhAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A zoom on the last few years shows more clearly that the peaks are situated in winter." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "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'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Study of the annual incidence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the peaks of the epidemic happen in winter, near the transition\n", "between calendar years, we define the reference period for the annual\n", "incidence from August 1st of year $N$ to August 1st of year $N+1$. We\n", "label this period as year $N+1$ because the peak is always located in\n", "year $N+1$. The very low incidence in summer ensures that the arbitrariness\n", "of the choice of reference period has no impact on our conclusions.\n", "\n", "Our task is a bit complicated by the fact that a year does not have an\n", "integer number of weeks. Therefore we modify our reference period a bit:\n", "instead of August 1st, we use the first day of the week containing August 1st.\n", "\n", "A final detail: the dataset starts in October 1984, the first peak is thus\n", "incomplete, We start the analysis with the first full peak." ] }, { "cell_type": "code", "execution_count": 30, "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": [ "Starting from this list of weeks that contain August 1st, we obtain intervals of approximately one year as the periods between two adjacent weeks in this list. We compute the sums of weekly incidences for all these periods.\n", "\n", "We also check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code." ] }, { "cell_type": "code", "execution_count": 31, "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": [ "And here are the annual incidences." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "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": [ "A sorted list makes it easier to find the highest values (at the end)." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\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": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, a histogram clearly shows the few very strong epidemics, which affect about 10% of the French population,\n", "but are rare: there were three of them in the course of 35 years. The typical epidemic affects only half as many people." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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