{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Analyse de l'incidence de la varicelle**" ] }, { "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": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_url= \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204931892814421.023435.02922.036.0FRFrance
120204831423610977.017495.02217.027.0FRFrance
220204731908515285.022885.02923.035.0FRFrance
320204632480120503.029099.03831.045.0FRFrance
420204534251636857.048175.06556.074.0FRFrance
520204434456738521.050613.06859.077.0FRFrance
620204334373737523.049951.06657.075.0FRFrance
720204233514529812.040478.05345.061.0FRFrance
820204132787723206.032548.04235.049.0FRFrance
920204032044316381.024505.03125.037.0FRFrance
1020203931981015900.023720.03024.036.0FRFrance
1120203832556221142.029982.03932.046.0FRFrance
1220203731848514649.022321.02822.034.0FRFrance
132020363103907646.013134.01612.020.0FRFrance
14202035399186842.012994.01510.020.0FRFrance
15202034360843090.09078.094.014.0FRFrance
16202033361063411.08801.095.013.0FRFrance
17202032359183330.08506.095.013.0FRFrance
18202031343512269.06433.074.010.0FRFrance
19202030381795442.010916.0128.016.0FRFrance
20202029386875860.011514.0139.017.0FRFrance
21202028383405701.010979.0139.017.0FRFrance
22202027340662406.05726.063.09.0FRFrance
23202026340392389.05689.063.09.0FRFrance
24202025328531488.04218.042.06.0FRFrance
25202024330581690.04426.053.07.0FRFrance
26202023341682468.05868.063.09.0FRFrance
27202022335801947.05213.053.07.0FRFrance
28202021361144026.08202.096.012.0FRFrance
29202020393156775.011855.01410.018.0FRFrance
.................................
185419852132609619621.032571.04735.059.0FRFrance
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185819851733405324366.043740.06244.080.0FRFrance
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186019851536388145538.082224.011683.0149.0FRFrance
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18701985053637302592795.0681809.011551074.01236.0FRFrance
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188319844436842220056.0116788.012537.0213.0FRFrance
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1884 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202049 3 18928 14421.0 23435.0 29 22.0 \n", "1 202048 3 14236 10977.0 17495.0 22 17.0 \n", "2 202047 3 19085 15285.0 22885.0 29 23.0 \n", "3 202046 3 24801 20503.0 29099.0 38 31.0 \n", "4 202045 3 42516 36857.0 48175.0 65 56.0 \n", "5 202044 3 44567 38521.0 50613.0 68 59.0 \n", "6 202043 3 43737 37523.0 49951.0 66 57.0 \n", "7 202042 3 35145 29812.0 40478.0 53 45.0 \n", "8 202041 3 27877 23206.0 32548.0 42 35.0 \n", "9 202040 3 20443 16381.0 24505.0 31 25.0 \n", "10 202039 3 19810 15900.0 23720.0 30 24.0 \n", "11 202038 3 25562 21142.0 29982.0 39 32.0 \n", "12 202037 3 18485 14649.0 22321.0 28 22.0 \n", "13 202036 3 10390 7646.0 13134.0 16 12.0 \n", "14 202035 3 9918 6842.0 12994.0 15 10.0 \n", "15 202034 3 6084 3090.0 9078.0 9 4.0 \n", "16 202033 3 6106 3411.0 8801.0 9 5.0 \n", "17 202032 3 5918 3330.0 8506.0 9 5.0 \n", "18 202031 3 4351 2269.0 6433.0 7 4.0 \n", "19 202030 3 8179 5442.0 10916.0 12 8.0 \n", "20 202029 3 8687 5860.0 11514.0 13 9.0 \n", "21 202028 3 8340 5701.0 10979.0 13 9.0 \n", "22 202027 3 4066 2406.0 5726.0 6 3.0 \n", "23 202026 3 4039 2389.0 5689.0 6 3.0 \n", "24 202025 3 2853 1488.0 4218.0 4 2.0 \n", "25 202024 3 3058 1690.0 4426.0 5 3.0 \n", "26 202023 3 4168 2468.0 5868.0 6 3.0 \n", "27 202022 3 3580 1947.0 5213.0 5 3.0 \n", "28 202021 3 6114 4026.0 8202.0 9 6.0 \n", "29 202020 3 9315 6775.0 11855.0 14 10.0 \n", "... ... ... ... ... ... ... ... \n", "1854 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1855 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1856 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1857 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1858 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1859 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1860 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1861 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1862 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1863 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1864 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1865 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1866 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1867 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1868 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1869 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1870 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1871 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1872 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1873 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1874 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1875 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1876 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1877 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1878 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1879 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1880 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1881 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1882 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1883 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 36.0 FR France \n", "1 27.0 FR France \n", "2 35.0 FR France \n", "3 45.0 FR France \n", "4 74.0 FR France \n", "5 77.0 FR France \n", "6 75.0 FR France \n", "7 61.0 FR France \n", "8 49.0 FR France \n", "9 37.0 FR France \n", "10 36.0 FR France \n", "11 46.0 FR France \n", "12 34.0 FR France \n", "13 20.0 FR France \n", "14 20.0 FR France \n", "15 14.0 FR France \n", "16 13.0 FR France \n", "17 13.0 FR France \n", "18 10.0 FR France \n", "19 16.0 FR France \n", "20 17.0 FR France \n", "21 17.0 FR France \n", "22 9.0 FR France \n", "23 9.0 FR France \n", "24 6.0 FR France \n", "25 7.0 FR France \n", "26 9.0 FR France \n", "27 7.0 FR France \n", "28 12.0 FR France \n", "29 18.0 FR France \n", "... ... ... ... \n", "1854 59.0 FR France \n", "1855 64.0 FR France \n", "1856 97.0 FR France \n", "1857 93.0 FR France \n", "1858 80.0 FR France \n", "1859 116.0 FR France \n", "1860 149.0 FR France \n", "1861 281.0 FR France \n", "1862 395.0 FR France \n", "1863 485.0 FR France \n", "1864 544.0 FR France \n", "1865 689.0 FR France \n", "1866 722.0 FR France \n", "1867 762.0 FR France \n", "1868 926.0 FR France \n", "1869 1113.0 FR France \n", "1870 1236.0 FR France \n", "1871 832.0 FR France \n", "1872 459.0 FR France \n", "1873 207.0 FR France \n", "1874 190.0 FR France \n", "1875 198.0 FR France \n", "1876 224.0 FR France \n", "1877 266.0 FR France \n", "1878 219.0 FR France \n", "1879 176.0 FR France \n", "1880 163.0 FR France \n", "1881 195.0 FR France \n", "1882 308.0 FR France \n", "1883 213.0 FR France \n", "\n", "[1884 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
164719891930NaNNaN0NaNNaNFRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1647 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1647 FR France " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204931892814421.023435.02922.036.0FRFrance
120204831423610977.017495.02217.027.0FRFrance
220204731908515285.022885.02923.035.0FRFrance
320204632480120503.029099.03831.045.0FRFrance
420204534251636857.048175.06556.074.0FRFrance
520204434456738521.050613.06859.077.0FRFrance
620204334373737523.049951.06657.075.0FRFrance
720204233514529812.040478.05345.061.0FRFrance
820204132787723206.032548.04235.049.0FRFrance
920204032044316381.024505.03125.037.0FRFrance
1020203931981015900.023720.03024.036.0FRFrance
1120203832556221142.029982.03932.046.0FRFrance
1220203731848514649.022321.02822.034.0FRFrance
132020363103907646.013134.01612.020.0FRFrance
14202035399186842.012994.01510.020.0FRFrance
15202034360843090.09078.094.014.0FRFrance
16202033361063411.08801.095.013.0FRFrance
17202032359183330.08506.095.013.0FRFrance
18202031343512269.06433.074.010.0FRFrance
19202030381795442.010916.0128.016.0FRFrance
20202029386875860.011514.0139.017.0FRFrance
21202028383405701.010979.0139.017.0FRFrance
22202027340662406.05726.063.09.0FRFrance
23202026340392389.05689.063.09.0FRFrance
24202025328531488.04218.042.06.0FRFrance
25202024330581690.04426.053.07.0FRFrance
26202023341682468.05868.063.09.0FRFrance
27202022335801947.05213.053.07.0FRFrance
28202021361144026.08202.096.012.0FRFrance
29202020393156775.011855.01410.018.0FRFrance
.................................
185419852132609619621.032571.04735.059.0FRFrance
185519852032789620885.034907.05138.064.0FRFrance
185619851934315432821.053487.07859.097.0FRFrance
185719851834055529935.051175.07455.093.0FRFrance
185819851733405324366.043740.06244.080.0FRFrance
185919851635036236451.064273.09166.0116.0FRFrance
186019851536388145538.082224.011683.0149.0FRFrance
18611985143134545114400.0154690.0244207.0281.0FRFrance
18621985133197206176080.0218332.0357319.0395.0FRFrance
18631985123245240223304.0267176.0445405.0485.0FRFrance
18641985113276205252399.0300011.0501458.0544.0FRFrance
18651985103353231326279.0380183.0640591.0689.0FRFrance
18661985093369895341109.0398681.0670618.0722.0FRFrance
18671985083389886359529.0420243.0707652.0762.0FRFrance
18681985073471852432599.0511105.0855784.0926.0FRFrance
18691985063565825518011.0613639.01026939.01113.0FRFrance
18701985053637302592795.0681809.011551074.01236.0FRFrance
18711985043424937390794.0459080.0770708.0832.0FRFrance
18721985033213901174689.0253113.0388317.0459.0FRFrance
187319850239758680949.0114223.0177147.0207.0FRFrance
187419850138548965918.0105060.0155120.0190.0FRFrance
187519845238483060602.0109058.0154110.0198.0FRFrance
1876198451310172680242.0123210.0185146.0224.0FRFrance
18771984503123680101401.0145959.0225184.0266.0FRFrance
1878198449310107381684.0120462.0184149.0219.0FRFrance
187919844837862060634.096606.0143110.0176.0FRFrance
188019844737202954274.089784.013199.0163.0FRFrance
188119844638733067686.0106974.0159123.0195.0FRFrance
18821984453135223101414.0169032.0246184.0308.0FRFrance
188319844436842220056.0116788.012537.0213.0FRFrance
\n", "

1883 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202049 3 18928 14421.0 23435.0 29 22.0 \n", "1 202048 3 14236 10977.0 17495.0 22 17.0 \n", "2 202047 3 19085 15285.0 22885.0 29 23.0 \n", "3 202046 3 24801 20503.0 29099.0 38 31.0 \n", "4 202045 3 42516 36857.0 48175.0 65 56.0 \n", "5 202044 3 44567 38521.0 50613.0 68 59.0 \n", "6 202043 3 43737 37523.0 49951.0 66 57.0 \n", "7 202042 3 35145 29812.0 40478.0 53 45.0 \n", "8 202041 3 27877 23206.0 32548.0 42 35.0 \n", "9 202040 3 20443 16381.0 24505.0 31 25.0 \n", "10 202039 3 19810 15900.0 23720.0 30 24.0 \n", "11 202038 3 25562 21142.0 29982.0 39 32.0 \n", "12 202037 3 18485 14649.0 22321.0 28 22.0 \n", "13 202036 3 10390 7646.0 13134.0 16 12.0 \n", "14 202035 3 9918 6842.0 12994.0 15 10.0 \n", "15 202034 3 6084 3090.0 9078.0 9 4.0 \n", "16 202033 3 6106 3411.0 8801.0 9 5.0 \n", "17 202032 3 5918 3330.0 8506.0 9 5.0 \n", "18 202031 3 4351 2269.0 6433.0 7 4.0 \n", "19 202030 3 8179 5442.0 10916.0 12 8.0 \n", "20 202029 3 8687 5860.0 11514.0 13 9.0 \n", "21 202028 3 8340 5701.0 10979.0 13 9.0 \n", "22 202027 3 4066 2406.0 5726.0 6 3.0 \n", "23 202026 3 4039 2389.0 5689.0 6 3.0 \n", "24 202025 3 2853 1488.0 4218.0 4 2.0 \n", "25 202024 3 3058 1690.0 4426.0 5 3.0 \n", "26 202023 3 4168 2468.0 5868.0 6 3.0 \n", "27 202022 3 3580 1947.0 5213.0 5 3.0 \n", "28 202021 3 6114 4026.0 8202.0 9 6.0 \n", "29 202020 3 9315 6775.0 11855.0 14 10.0 \n", "... ... ... ... ... ... ... ... \n", "1854 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1855 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1856 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1857 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1858 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1859 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1860 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1861 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1862 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1863 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1864 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1865 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1866 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1867 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1868 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1869 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1870 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1871 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1872 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1873 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1874 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1875 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1876 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1877 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1878 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1879 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1880 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1881 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1882 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1883 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 36.0 FR France \n", "1 27.0 FR France \n", "2 35.0 FR France \n", "3 45.0 FR France \n", "4 74.0 FR France \n", "5 77.0 FR France \n", "6 75.0 FR France \n", "7 61.0 FR France \n", "8 49.0 FR France \n", "9 37.0 FR France \n", "10 36.0 FR France \n", "11 46.0 FR France \n", "12 34.0 FR France \n", "13 20.0 FR France \n", "14 20.0 FR France \n", "15 14.0 FR France \n", "16 13.0 FR France \n", "17 13.0 FR France \n", "18 10.0 FR France \n", "19 16.0 FR France \n", "20 17.0 FR France \n", "21 17.0 FR France \n", "22 9.0 FR France \n", "23 9.0 FR France \n", "24 6.0 FR France \n", "25 7.0 FR France \n", "26 9.0 FR France \n", "27 7.0 FR France \n", "28 12.0 FR France \n", "29 18.0 FR France \n", "... ... ... ... \n", "1854 59.0 FR France \n", "1855 64.0 FR France \n", "1856 97.0 FR France \n", "1857 93.0 FR France \n", "1858 80.0 FR France \n", "1859 116.0 FR France \n", "1860 149.0 FR France \n", "1861 281.0 FR France \n", "1862 395.0 FR France \n", "1863 485.0 FR France \n", "1864 544.0 FR France \n", "1865 689.0 FR France \n", "1866 722.0 FR France \n", "1867 762.0 FR France \n", "1868 926.0 FR France \n", "1869 1113.0 FR France \n", "1870 1236.0 FR France \n", "1871 832.0 FR France \n", "1872 459.0 FR France \n", "1873 207.0 FR France \n", "1874 190.0 FR France \n", "1875 198.0 FR France \n", "1876 224.0 FR France \n", "1877 266.0 FR France \n", "1878 219.0 FR France \n", "1879 176.0 FR France \n", "1880 163.0 FR France \n", "1881 195.0 FR France \n", "1882 308.0 FR France \n", "1883 213.0 FR France \n", "\n", "[1883 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 11, "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": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 13, "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": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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FDjPBZRQ4RKQIL2j8UFV/AqCqR1U1pqpx4LvASrd5MzA3afdG4JArb0xRPmAfEQkA1UDrEMcaQFXvUtUVqrqivr4+k7dkMtQbPtXGoQp9EftSy4V4XInGlaB/cK8qn1VVmQkvk15VAtwN7FDVryeVz0ra7MPAVvf4MWCN6ym1AFgMvKSqh4FOEbnEHfMG4NGkfRI9pq4FnnXtIE8Bq0SkxlWFrXJlZpx0h1xVVdD7grN2jtxIVEed3jhuAwDNxJdJr6q3Ax8DtojIZlf2j8D1InIBXtXRXuATAKq6TUQeBrbj9ci62fWoArgJ+AFQiteb6glXfjdwv4g04WUaa9yxWkXkS8BGt90XVbV1ZG/VjERvxFVVFXsfFa9LbnF+T2oSCEVTB46A32dTjpgJb9jAoarPk7qt4fEh9rkNuC1F+SZgeYryPuC6NMe6B7hnuPM0uReJxYnElLIiP2VB76NiU6vnRiI4BAc1jgf9PhsvYyY8Gzlu0kr0ovIyDr8rsy+1XAhFvWtbXHT6yPFo3No4zMRmgcOk1esCR3lxoD/jsDaO3EhUVZWcFjisqspMfBY4TFqJ7KIs6Ke6tAiAjt5IPk9p0uiLuIzDRo6bAmSBw6TVX1VV5Ke2PAhAa3c4n6c0aSQyjtMDh40cNxOfBQ6TViJwlAUDTCstwicWOHIlFElfVWUZh5noLHCYtBLVKSVFPnw+oaYsyAkLHDnRF01TVRWwAYBm4rPAYdIa3IBbWx6ktcsCRy4kMo7iQetxBC3jMAXAAodJKzyoHr62PGhVVTlyqjvuoAGAtnSsKQAWOExa/V9ugVMZx4nuUD5PadJI28ZhCzmZAmCBw6Q1eFqM2vIgbT3WHTcXQunaONwkhzYLsZnILHCYtEKDxhrUlQdp6wkTs5HNo5auO25iChIbPW4mMgscJq3+L7eiUxmHKrT3WDvHaJ3qsXZ6d1zAqqvMhGaBw6TVX1XlvsxqK7xZca2BfPRC0Tg+8RrDkwUSgSNqGYeZuCxwmLTC0TgBn/R/mdW50eM2lmP0QtE4xQE/3tI0pySqqmz5WDORWeAwaYWisQHrRdSU2bQjuRKKxE7rigunqqqicQscZuKywGHS8u6KT31E6ios48iVvkickkGD/yCpjcOqqjLWE47y+pGT+T6NKSWTpWPnishzIrJDRLaJyKddea2IrBeRXe53TdI+t4hIk4i8ISKrk8ovEpEt7rk73RKyuGVmH3LlG0RkftI+a91r7BKRtZhxE4rEB4xsTmQcbRY4Ri0UTZNxuEBtVVWZ+95v9vChb/y2v8OBGXuZZBxR4DOquhS4BLhZRM4BPg88o6qLgWfc37jn1gDLgCuBb4pI4tvnW8A6vHXIF7vnAW4E2lR1EXAHcLs7Vi1wK3AxsBK4NTlAmbE1+MstGPBRWRKwqqocGJzNJRS5xnLrVZW57YdOEo7GaW7ryfepTBnDBg5VPayqr7jHncAOYA5wNXCv2+xe4Br3+GrgQVUNqeoeoAlYKSKzgCpVfUG90U33DdoncaxHgCtcNrIaWK+qraraBqznVLAxYywcO/3Lra7cJjrMhb5I7LSuuGDdcUeiqaULgP2tFjjGS1ZtHK4K6UJgA9CgqofBCy7ADLfZHOBA0m7NrmyOezy4fMA+qhoFOoC6IY5lxkEoEh/QOA6J+aps2pHRSptxBBKBw9o4MhGOxtl7vBuA/ScscIyXjAOHiFQAPwb+RlWHaomSFGU6RPlI90k+t3UisklENrW0tAxxaiYbiS6jyWrLizlhM+SOWqprC95CTmAZR6b2nejuH2W/v7U3z2czdWQUOESkCC9o/FBVf+KKj7rqJ9zvY668GZibtHsjcMiVN6YoH7CPiASAaqB1iGMNoKp3qeoKVV1RX1+fyVsyGQhFY6fdFc+tLWXfiR7iNiXGqPRFTr+2cGqwpQWOzDQd86qpgn6fVVWNo0x6VQlwN7BDVb+e9NRjQKKX01rg0aTyNa6n1AK8RvCXXHVWp4hc4o55w6B9Ese6FnjWtYM8BawSkRrXKL7KlZlxkKo6ZWF9Bb2RGEdO9uXprCaHUDSeso0jYIEjK7tc4HjrghoOWOAYN4EMtnk78DFgi4hsdmX/CHwZeFhEbgT2A9cBqOo2EXkY2I7XI+tmVU30k7sJ+AFQCjzhfsALTPeLSBNeprHGHatVRL4EbHTbfVFVW0f4Xk2WBnfHBTizvhyAN1u6mT2tNB+nNSmkyubgVFVV2MZxZKTpWBeNNaUsaaji9/v3o6qnjcY3uTds4FDV50nd1gBwRZp9bgNuS1G+CVieorwPF3hSPHcPcM9w52lyLxw7vXF8UX0FALtburhs8fR8nNakEIrEU47jCNrI8azsOtbFohkVzKstpScc40R3mOluTjUzdmzkuEkrlKIevr6ymIriAG+6LpBmZLw2DuuOOxrxuLK7pYtF9RXMqysDrEvueLHAYdIKRU+/KxYRFtaXs7ulO09nNTmkuraQ1B3XqqqG1ROJEY7GmVFVzNwaL3BYO8f4sMBh0krXZfTM+grLOEZBVdN3x/XZ7LiZ6glFASgLBmh0gcPGcowPCxwmrXQNuAvryznU0UdPOJqHsyp86Vb/A6uqykZ32OtzU17spzTop6asiGOdNjh1PFjgMCnF40okpqc1joPXJRe8nlUme4nAkXLKEXe9ozZyfFjdLuMoLfL6+NSUBWm11SnHhQUOk1KiqiRVdcriBi9wPLjR6/5oshOKDlzLPVmRLeSUsZ6kjAOgpjxoyxqPEwscJqVQJH11ysL6Cj7+9vn814v7+cLPto/3qRW8oa5tkc+qqjLVHT7VxgEu4+iO5POUpgwLHCal/rviFD1/RIT/9wfn8OEL5/DAS/uJ2fQjWTl1bU/P5nw+we8TCxwZ6B2ccZQVWcYxTixwmJQS9fCJAWmDiQhvW1hHKBq3vvNZ6nMZR0mKjAO86qpw1ALHcBJtHOUu4/Bmbg5b9ek4sMBhUhrqrjhhycxKAN6wZTuzMty1LSny9wduk16ijaMs6F3HaWVBQtE4vbYS4JizwGFSGqrLaMKiGRWIwBtHbExHNoZq4wAoCfhtGdQMJNo4yosTGUcRAG091s4x1ixwmJQyCRxlwQDzasvYebRzvE5rUhiqO65X7qM3YhnHcHpCMXxy6jNaUxYEoM1WqBxzFjhMSqfuitNXVQEsaajkDQscWRmqOy54AcUyjuF1h6OUBwP9s+HWlLvAYQ3kY84Ch0kp8eWWagBgsiUzK9lzvLt/ezO8vuGqqixwZKQnFKOs+NSNTSLjaLWMY8xZ4DApZVJVBXBWQyWxuLL7mI0iz9TwjeO+/ozPpNcTifX3qAKvOy5YVdV4sMBhUgr318MPn3EAvHHUelZlqr+NY4iMw3oGDa8nFKU0eCr4VpcWIWKN4+Mhk6Vj7xGRYyKyNansn0XkoIhsdj9XJT13i4g0icgbIrI6qfwiEdninrvTLR+LW2L2IVe+QUTmJ+2zVkR2uZ/E0rJmHJzKOIZu45hfV07AJ+w6aj2rMpWohkqXcZRaVVVGEm0cCQG/j+rSImvjGAeZZBw/AK5MUX6Hql7gfh4HEJFz8JZ9Xeb2+aaIJP53fAtYh7cG+eKkY94ItKnqIuAO4HZ3rFrgVuBiYCVwq1t33IyD4RpwE4IBH2fUldF0zAJHpobtjlvkp8/ajIbVEx7YxgFeO4dlHGNv2MChqr/GWwc8E1cDD6pqSFX3AE3AShGZBVSp6gvqDeu8D7gmaZ973eNHgCtcNrIaWK+qraraBqwndQAzYyDx5TZc4zh44zmabH2OjIWicfw+6Z9CfbCSIl9/A7pJrzs0MOMAr53D2jjG3mjaOD4pIq+5qqxEJjAHOJC0TbMrm+MeDy4fsI+qRoEOoG6IY5lxkGlVFXiBY9+JHpsmI0N9KZbkTVYc8NMXtoxjOD3hWP+o8QQv47DAMdZGGji+BSwELgAOA19z5ZJiWx2ifKT7DCAi60Rkk4hsamlpGeq8TYYSQSDTjCMWV/adsJ5VmfBW/0t/XUuDVlWVie5QtH/UeEJNedAyjnEwosChqkdVNaaqceC7eG0Q4GUFc5M2bQQOufLGFOUD9hGRAFCNVzWW7lipzucuVV2hqivq6+tH8pbMIKFojCK/N1PrcBbVez2rrJ0jM17GkT6TKwn4icTUZh0eRqqMo7bcFnMaDyMKHK7NIuHDQKLH1WPAGtdTagFeI/hLqnoY6BSRS1z7xQ3Ao0n7JHpMXQs869pBngJWiUiNqwpb5crMOEi3JnYqC2eUAxY4MtUTOf0LL1miC7T1rEovHI0TjetpGce0siL6IvH+KdfN2AgMt4GIPAC8G5guIs14PZ3eLSIX4FUd7QU+AaCq20TkYWA7EAVuVtXEv+BNeD20SoEn3A/A3cD9ItKEl2msccdqFZEvARvddl9U1Uwb6c0ohaKxjKqpwJuzas60UnYd62L99qOsnF9LtRuMZU7XG44NGH8wWGIOq95I7LQvRuPp6V/EaeB1nF5eDMCJ7hCNwbJxP6+pYthPpapen6L47iG2vw24LUX5JmB5ivI+4Lo0x7oHuGe4czS5F4oMXQ8/2MIZFfzstUM89uohbrj0DL549Wn/1MbpCUeHzDhKXeCwjCO97sQiToN6VU2v9KYdaekM0VhjgWOs2Mhxk9JwDbiDXTB3Gn4R5teV8cTWI8Stfj4tL+NIf89W3F9VZb3U0ulxizgNHsdRX1ECwPEua+cYSxY4TErhLNo4AG6+fCEv/uMV/N2qJbR0hti0r20Mz66w9YRjlA2xQFaJZRzDyiTjMGPHAodJKRSNpVxvPJ3igJ/pFcW85+wZBAM+Ht9yeAzPrrD1Dts4boFjOImMY3BbUZ1r4zjeZYFjLFngMCmFovG0640PpaI4wLvOqufJrUds7ec0hmscP9XGYVVV6aTLOIIBH9PKiizjGGMWOExKoWg8q4wj2aVn1nHkZB8nbCBWSqnGHySz7rjD6+9VVXz6dayvKLbAMcYscJiU+iIxSrJo40g2r9brzXKgtSeXpzQpxONKb2ToxvH+qiobPZ5WT5qMA2B6RbFVVY0xCxwmJW/m0ZGNIZjrAsd+CxynSQSDITMOF7BtEFt63Wl6VQHUVxbTYoFjTFngMCl5M4+OLOOYW1sKQHNbby5PaVJI3CkPGTiCrqrKJo1Mq/86puidVl9ZzHGrqhpTFjhMSl49/MgyjrJggOkVQauqSiGRRZRk0B03ZG0caXWHogQDPgIpOnBMryimOxzrz0pM7lngMKdRVW91tRTVAJmaW1tmVVUpZJRxBKw77nBO9kWoLk09rU19pXXJHWsWOMxp+iJxVBlxxgEwt6aMA20WOAZLN8dSsiK/4BNs3fEhdPRGmJYmcEyv8AYBWuAYOxY4zGkSX26jyTjm1ZZxqL2PaMzq6ZMlqqpKi9IHZRFx647btUunvWf4jMO65I4dCxzmND39X26jqaoqJRZXDnf05eq0JoVEFjFUxgFu3XHLONJq74kwLc0MzPUVLnDYfFVjxgKHOU13f8YxuqoqsLEcg2XSxgGJwGEZRzodvRGqS4Mpn6stDyJiGcdYssBhTtMdyuzLbSg2liO1/qqqYa5tcZHPMo4hdPSmzzgCfh/1FcW8fvjkOJ/V1GGBw5ymJwcZx6zqEgI+YZ8FjgFONY4PfW1LAlZVlU4kFqcrFE3bxgHwRyvm8ovtR9l6sAPweqjd/fwePvfIqzxhE3CO2rCBQ0TuEZFjIrI1qaxWRNaLyC73uybpuVtEpElE3hCR1UnlF4nIFvfcnW4JWdwysw+58g0iMj9pn7XuNXaJSGJ5WTPGcpFxBPw+FjdUsu2Q3fUl68mwjaM06LcpR9Lo6I0ApM04ANa960ymlRXxlafeAOA/nt3Fl/53O4+83Mw9v90zLuc5mWWScfwAuHJQ2eeBZ1R1MfCM+xsROQdv6ddlbp9vikjif8i3gHV465AvTjrmjUCbqi4C7gBud8eqxVum9mJgJXBrcoAyY6c/4xhFd1yA8+ZUs6W53WbJTdIbjiHCsItklRT5rI0jjfYeL3AMlXFUlRTxycsX8eudLXz5ide5+/k9XHPBbN6/fJZNvpkDwwYOVf013lrgya4G7nWP7wWuSSp/UFVDqrrbZpPeAAAgAElEQVQHaAJWisgsoEpVX1DvW+S+QfskjvUIcIXLRlYD61W1VVXbgPWcHsDMGOhvwB1Fd1yAcxuraeuJ2NQjSRKLOLmEO62SgN/mqkrjVMaRunE84eNvX8BV587k27/aTTwOn1m1hLqKIK0WOEZtpLeUDap6GEBVD4vIDFc+B3gxabtmVxZxjweXJ/Y54I4VFZEOoC65PMU+ZgzlLONorAZgy8GO/sbyqa5nmLU4EkqKrKoqnY5e74t/qIwDwO8T7vjoBZQFt7J0VhVza8uoKy+mvSdCJBanaATrzRhPrq9cqtsoHaJ8pPsMfFGRdSKySUQ2tbS0ZHSiJr1EG8doxnEALJlZSZFfeK25IxenNSn0hqMZB46QVVWllKiqSjdyPFlxwM9XrzufGy9bAECtG1XeZlnHqIw0cBx11U+438dceTMwN2m7RuCQK29MUT5gHxEJANV4VWPpjnUaVb1LVVeo6or6+voRviWT0BOOUlrkx+cbujplOMUBP2fPrGLLwfYcnVnh86qqhs/kSqw7blr9gWOIxvF0ppd7gcPaOUZnpIHjMSDRy2kt8GhS+RrXU2oBXiP4S65aq1NELnHtFzcM2idxrGuBZ107yFPAKhGpcY3iq1zZmOjoifBP/7OVDW+eGKuXKBjd4diophtJdm5jNa81d1gDueMt4pRZxmFzVaXW3htBBCpLsg8ctYnAYaPKRyWT7rgPAC8AS0SkWURuBL4MvE9EdgHvc3+jqtuAh4HtwJPAzaqa+PTfBHwPr8F8N/CEK78bqBORJuDvcD20VLUV+BKw0f180ZWNCfHB/S/us2oVoCcUHdUEh8mWzqqisy/KMRvFC3i9qjLp5pzIOCzgnu5kb4SqkiL8I8iI69x0JCe67fM4GsN+O6jq9WmeuiLN9rcBt6Uo3wQsT1HeB1yX5lj3APcMd465UFkcIBjw2YyaeBnHaMZwJJtVVQLAkY4+GtzjqawnHMuoiqW0yE9cIRJTgoHRVRlONu094WEbxtNJzJxrGcfoWLcCR0S8Re4tcNAbjo1q1HiymdVesLDJDj3DrTeekFjMyaqrTtc+xHQjw0lkKpZxjI4FjiTTK4IctzsRusPRnGUcicBx9KQFDvA6HqRa7nSwKndHfdKNWTCnDDWl+nB8PqG23MZyjJYFjiTTK2ytYoCeUO6qqmrLggT9Pss4nEzHcdS6wW32BXe6k72RYQf/DaWu3G4QR8sCR5LpFcXWxoGXcYx28F+CzyfMqCrmSIeNHofMG8drXO+f1h77ghusvTdCdenIP582enz0LHAkmV4Z5ER3mHh8avdk6QnHRj3dSLJZ1SUcsaoqwtE40bhmFDgS3UZtoNpA8bjS3hNmWpq1ODJRV17MCbtBHBULHEmmVxQTiyvtU7xeuTuUu4wDoKGqhCNWVdU/91RJBm0cVlWVWlc4SlyHn25kKLXlQetVNUoWOJIk+nhP5eqqWFwJReM5G8cBpzKOqT4moSeS2VocAFWlAfw+oc2qqgZI3IA0VI+8a/f0iiCdoSghmwtsxCxwJEn08Z7KDeSnFnHKXVVVQ1UJfZF4/6ymU1Xi/VdlUD8vItSUBWntntrXbLDmNm9hsDnTSkd8jNpy7wbRsrmRs8CR5NQi91M5cCQWGsplxuH9J5/q7RwH3fTyszP80qstL6LVxhsMkLiGc2tGHjjqbBDgqFngSDK9v6pq6n6gukO5zzhmVnvXdap3yT3Y7n3pNWYYOGrKgrRZxjFAc3svQb+v///qSCRqFqbyDeJoWeBIUl1aRMAnU7qNI5FxjHZK9WSJqUaOTvXA0Zbdl15dRdC64w7S3NbLnJrSUc3cPKPS+zy2nJy6/89HywJHEp9PqKsITuk2jlMZR+6qqmZUliBiGcfB9l5mTyvJ+EvPyzgscCQ72NY7qvYNgPpKL3DbbAYjZ4FjkKk+CPBUG0fuMo5gwMeMymL2nejO2TEL0cF27245U7XlQdp6bFxRsuYcBI6SIj/TyopsxuZRsMAxiBc4pu5dXqL752j6yadyfuM0Xtk/tRd0yvZuuaYsSFzhZJ+1cwD0RWIc7wrROIqG8YQZlcWWcYyCBY5B6iqCU3pU6aH27Hr+ZOqt82vZ39rDsc6p+Z81FI1xrDPEnGmZr71ea6vVDZDoXJBN1pZOQ1WJZRyjYIFjkPpKb2r1qTpY7WB7H3XlwYxGN2fjovk1ALy8ty2nxy0Uh9u9gJltVRXYtCMJia64o62qAu//+THLOEZsVIFDRPaKyBYR2Swim1xZrYisF5Fd7ndN0va3iEiTiLwhIquTyi9yx2kSkTvd8rK4JWgfcuUbRGT+aM43Ew2VJURiSlvP1KweONzRy6xpuV9wafnsaooDPjZO0cDRf7ecxZdeInDYQDVPf3fm2syztnQaqkpo6QpZ+9EI5SLjuFxVL1DVFe7vzwPPqOpi4Bn3NyJyDrAGWAZcCXxTRBK3td8C1uGtUb7YPQ9wI9CmqouAO4Dbc3C+Q+rvOjpF70YOtfcyuzq31VTgNZCfP3caL+8bs9V/J7TE3XI29fOJGXJt2hFPc1sPfp/QUDnyMRwJMyqL3Q2iXduRGIuqqquBe93je4FrksofVNWQqu7BW3t8pYjMAqpU9QX16ofuG7RP4liPAFckspGx0lA1tbvqHWrvy3n7RsJb59ew9dDJ/mlNppLm9l58cmphq0ycmuhwama/g+093sOcaaUE/KP/2krcIFo7x8iM9l9AgV+IyMsiss6VNajqYQD3e4YrnwMcSNq32ZXNcY8Hlw/YR1WjQAdQN8pzHlL/B2oKDg462RehKxTNSR1yKuc1TiMWV3Yd7RqT409kB9t6aagqoSiLL73SoJ/SIr9NO+LsOHKSs2dW5uRYM2wsx6iMNnC8XVXfArwfuFlE3jnEtqkyBR2ifKh9Bh5YZJ2IbBKRTS0tLcOd85Cm8uCgRI+qsWjjAFg0owKApmNTL3C81tzOkhF86c2oKuZQ+9T7LA7WG46x93g3S2dV5eR4idHjlnGMzKgCh6oecr+PAT8FVgJHXfUT7vcxt3kzMDdp90bgkCtvTFE+YB8RCQDVwGmV5Kp6l6quUNUV9fX1o3lLU3pw0Fh1xU2YV1tGwCfsbplageNEV4hdx7q4eEH2yfLiGRXsOtY5BmdVWHYe7SSu5C5wuCpp61k1MiMOHCJSLiKVicfAKmAr8Biw1m22FnjUPX4MWON6Si3AawR/yVVndYrIJa794oZB+ySOdS3wrI5DP9mGypIpmXEcTHQZHaPAUeT3cUZd2ZQLHC/t8e51Lj6zNut9FzdUsud4N5FYPNenVVB2HD4JwNJZuamqKinyU1USKJgbxJ+9eog/+s4LXHb7s/z45ea8DxcYzYREDcBPXVt1APhvVX1SRDYCD4vIjcB+4DoAVd0mIg8D24EocLOqJlZSuQn4AVAKPOF+AO4G7heRJrxMY80ozjdjM6qKOVogH6hcOtzeS5Ff+qeXHwsL6yumXFXVhj2tlBb5OXdOddb7ntVQQSSm7D3ezeKG3HxpFqIdh09SHvQzt2b0XXETGqoK4waxrTvM3z60mbm1ZdSWB/nMj16lua2XT793cd7OacSBQ1XfBM5PUX4CuCLNPrcBt6Uo3wQsT1Hehws846mhqoSmY8fH+2Xz7lC714A7mplHh7NwRgXPvn6MSCyeVUNxIWrpDNEdivLimye46IyaEb3fxTO8YLHzaNfUDhxHOlkyszKnn82Z1SUFMfHm41sPE40r/3H9hSydVcVHv/MCz75+NK+BY3L/zx2hhqpijnVOvcFBY9kVN2FRfQXRuLK/tWdMX2ci+NQDr/Dur/6S1490cvGC7KupwOtQ4BOvjn+qUlV2HD6Zs/aNhIX1Few62jXh/58/+vtDLJpRwbLZVfh9wnmN09iZ5/O2wJFCQ1UJsbhOqTmCVJXdLV2ckYNRuUNZOEV6VnX2Rdi4t40L503j/MZqrjpv1oiOU1LkZ15t2ZRuIN93oofOvmjOA8c5s6rojcQm9E1Mc1sPL+1t5ZoLZpMYwrZkZgW9kRjNblBpPuRu0YVJJNFV7+jJvv7uuZPd7pZuTnSHeev8kd0ZZ+rM+nL3epM7cLy0p5VYXPn7VUt426LpozrW4oZKdk6hsS/3v7iP9u4wn7rCq4r5+ZbDALx7yeh6TA52tmtof/3ISeZPL8/psXPlp68cBOBD58/pLzvLVVm+cbSTeXVje6OXjmUcKSRGj0+lmVwTPX9WjrBKJVNVJUXMqCxm55HJfQf9fNNxigM+3nJGzfAbD+Oshgr2Hu8mHJ38PatC0RhffeoNvrZ+J09vPwp4PYpWnFFDYw4bxsFrP/IJbD88MT+L0Vic/35pP5ctmj4gQCTauvJZfWmBI4VT81VNnZ5VL+05wYzKYs4YhzuYi8+s4ze7jhOb4HXLo/HbpuOsXFCbk1mGz5lVTTSuvNY8+dcz+eUbLXT0RqgpK+LzP9nCE1sO8/qRTj50weycv1Zp0M/86eW87rr6TjTPvH6Mwx19/OklZwworygO0FhTyht5vPmywJFCfWUxAZ9wYALXfeaSqrJhTysrF9QyxlOBAfDepTM40R1m84HJ+UV47GQfO4928fZRVlElvPOs6QT9Pp7ceiQnx5vIHt18kOkVQe6/8WJC0Rg3/fAV/D7hqnNH1kY0nKUzq3h9gma///XiPmZVl/DepTNOe25JQ6VlHBNNkd/HohkV/YOOJrvmtl4Od/SNuOdPtt591gwCPuGZHUfH5fXG25PbvC/4d52Vmzr5ypIiLls8nSe2Hsn7wK+x1NEb4ekdx/iD82azfE41v/zsu7nxsgV86j2LmD5GY4uWzqpkf2sPXaH8Trx59GQfv2s6NQTgzZYufrPrOH+8cl7KSR3PmlnJ7pauvA0MtcCRxtJZVeyYoHWfubbBtW+8dZwCR3VZESsX1PL0JA0cP9rUzNJZVTntBXTl8pkcbO9ly8GOnB1zIglH4/z1A78nGotz7UXeDER1FcX80x+cw9+896wxe92zZ3r/Rvm+Sfy//7OVP717A7tcFvHDDfsJ+ISPrpybcvslDZVEYpq3z4MFjjTOmVXFkZN9U2IRnd81HaeuPMhZM8ZvgNl7lzaw82jXpOuW+/qRk2w52MF1FzUOv3EW3re0Ab9P+nsYTSaqyj/8+DV+tbOF2z58LstHMMJ+pC46o4bSIj8PbNg/bq852IHWHp7ecZS4wtd+sZPecIwfbTrAlctn9vfwHOzys2dQXVrEvz+9a5zP1mOBI43E3WK+70TGmqryfNNxLl1YN6Yjxgf74PmzKQ74uOvXu8ftNcdSKBrjp79v5vYnXqfIL1xz4Zzhd8pCTXmQ9y1t4L9f3D/pbma++cvd/PT3B/nsqrO4fuW8cX3tmvIgH7v0DP5n88G8dRG/74W9+ES4fuU8ntx2hOu/+yIn+6J8bFCjeLLq0iJuvnwhv9rZwgu7TwDe6p2dfeOzdosFjjQSk6ltPzS5A8fuli6OdYa4LEcNuZmqryxmzVvn8pNXDvYvCVqo4nHl7x56lb996FWee6OFP7ywsX/Z11z6zKqz6A5H+c/nmnJ+7Hx54KX9fPUXb3D1BbO5+fJFeTmHde88k2DAxxd/tp32cV4RsDcc46GNB3j/8pn8nw8sZUlDJX2RGH/9nkXDdo2/4dL5zKou4bbHtxOJeVV91337hXEZUW6BI426imIaqooLMuOIxzXjPv+/bfLuVnLVAygb6961EIDv/ebNcX/tXLrj6Z38fMthPnflEnZ88Upuv/a8MXmdxQ2VXHtRI/e/sI+mSTCS/M5ndnHLT7bwzsX13P6R88alR18q0yuK+eyqJfx6Vwvv+v9+Oa7LGz+57TAn+6L8ycVnUFEc4Km/fSdP/s07+btVS4a9HiVFfv7PB5ay9eBJ/uS7G9i4t40bL1swLjUHFjiGcM6sKrYXWOCIxuKsu38Tl/7rMzz3+rG02zUd6+Sf/mcrD286wLzaMuaO8VQjqcyZVsoVS2fwVAH3Ftq0t5VvPNfEH61o5KZ3LaQ0OPpxG0P5zKolVJUGWHf/y+NWLTEWdrd08W9P7+RD58/m7rUrcjLeZTT+4h1n8vhfv4PKkgCf/dFr9EViw++UA4+83Mzc2tIR92j8wLmzuHxJPS/tbeUdi6f3dywYaxY4hnD+3Gm8fqSTP/v+SwVxhxeLK//06Fae3nGMkiI/H//BRlb8y9P8+Q82DhgFr6r840+2cv+L+9h26CTvOfv0fuLj5R2L6znU0cfeE4U1ZqYrFGVLcwef+/FrzK4u5dYPLhuXO+aGqhK+8cdvYd+JHm59dNuYv95Y+bend1FS5Of/ffCcnKwhngtLZ1Xx5T88jz3Hu/n6+p1j/nrNbT38bvcJrn3L3BFnCSLCbR8+l4+8pZEvj2PWZnNVDWHdO88E4Ae/28uffX8jP//UO6guK8rzWaW29WAHn/3Rq7x+pJO/evdC/vqKxdz/wj52t3Tx6OZDfPg/f8f31q5g6awqfrH9KC/tbeVfrlnOFUtnjEl9fKYSbSvPNx1nwQSdL2iwE10hrrrzN/0zC9z35yspLx6//0qXnFnHjZct4Hu/eZO/fd9ZeckWs9XWHWbXsS5WnFHDhj2t/OzVQ9x8+cIxG58xUpctns71K+dy16/fpKMnwheuXpbzbCgUjfGNZ5t47o1jqMIfvmV0HSlmTyvla3902goXY0oKtYognRUrVuimTZtyesxX9rfx0e+8wPy6ctp6IsysLuaGS+dz3UWNeauXTaaqvO+OX3OyN8KtH1zGVefOHHBeWw92cOO9G+nqi/Lxty/gRy8foLKkiCc//Y683+2pKpfd/hznzqnm2x+7KK/n0tETYduhDkqDfnrC3qyp2w514BfhjLpyPnTBbOrKg/zVD1/hmR3HuP3aczl3TjWLxrEbc8Lhjl7ecftzfOzSM7j1g8vG/fXTicTiPLr5EA9vPMDihgpWLZvJluZ27vr1m5zsi7Kwvpx9J3porCnl0Zsvm5A3YrG4csf6nXzjuSYuX1LPdz62gmBg6P8nqsqXn3idp7Ydob03wrzaMv545TzWpOgldstPtvDAS/tZNruKD50/m0+4tr58E5GXVXVFJtsWRMYhIlcC/w74ge+p6pfH8/XfMq+GWz+4jDvW7+TShXXsbunmc4+8RkNVSc5GByf0RWK8fqST6tIi5taUZvTF/qudLTQd6+Lrf3Q+H0gxfffyOdU8evNl/MV9G/nGc02cO6eaf7lmed6DBnip9tsX1fHk1iPE4op/HLsEJ9t8oJ2/vP9ljgxaEa6yJIAAJ/ui/OsTO6gpC3KsM8Q/XHk2H75wfOqTU5lVXcoHz5/NQxsP8BfvOHPMlvvNxu6WLj794O/ZevAk8+vK2HygnR+68RHvWDydVctm8v3n97B62Uz+9SPnUlUy8YIGgN8nfHb1EubUlHLLT7bwqQde4d/XXDhk5vHwpgN859dv8o7F03lbbRmvHmjnlp9uoaY8yMneCFsOdhCOxmntDvOL7Ue56d0L+Ycrzx7Hd5VbEz7jEBE/sBN4H9AMbASuV9XtqbYfi4xjsHA0zju/8hwLppfzwLpLUm7TF4mxaW8bC+rLh/1PHYnFefHNEzzfdJwfbWru76c/s6qEay9qZNnsKhbUlzO/rjzlh/djd2/gjSOdPP8P7xnyzqjPzeG/yK2JMVE8uvkgn35wM3+/egnXrWhkx2Fv4aPk9xqNxTnY3ktjTRl+n/D6kZN899d72Haog4vOqOH8udM4r7G6fyRwpjp6IvzHs7u494W9NFSVcOsHl+H3QVkwwKzqEubVliEiNB3r4pGXm2nrDjO3tpSb3r0ob0EuYefRTq75z9/i9wk3X76IS8+s47zGakSEvce78fuE+sriMW94PtkX4e7f7OE7v95NSZGf2645l6vOnUlLV4jXD3dy9sxKZlSlHsg20X3/t3v4ws+2c/7caaw4o4auvihXLp/JuY3VtHWH+envD3LkZB9PbDnChfOm8V83XozPJ/RFYlz77d+x9aDXuaaqJEBp0E8w4OOSBXV8+SPn5f3zM1g2GUchBI5LgX9W1dXu71sAVPVfU20/HoEDvC6k//LzHfz4prfxlnnT2Huih9/tPs7m/e30ReO8sPs4x7u8AHDunGr+fvUS6iqCHG7v67+rnV4RpLGmjP/7P1vZfKAdv0+4fMkMPnzhHLpDUX722iF+s2vgErazq0uoKAnQHYrRG4nRHYoSisb57Kqz+OR78reU5GiEojE+/cDm/jmeAC6YO40rl8/kvt/tJRxTesJResIxzmus5sK507jvxX2UFvm5YO40Xj3QTnfY6wVz+ZJ6LphbQ1tPmHiKz7bfJyyeUUl9ZTG7W7r49q9209Eb4bqLGrnl/UupyWN7z0jsPe5lvy/t9bqQXryglrqKII9vOXUtq0uLaKgqpqGqhBmVJbxtYR0r5tew4/BJDrX30dzWy9aDHcytLeOKpTN4ZV8b7b0RivzCofY+KkoCXDh3GsVFfuJxRcRbE6KlM8SDG/ez4c1WonHlA+fN4p8+cA4zqwszSKTz5NYjfObhzUTjSjDgo7Pv1LxWfp8ws6qEhqpivvknFw14781tPXztFzv5wLmzuGLpjAlRrT2UyRY4rgWuVNW/cH9/DLhYVT+ZavvxChzdoShvv/1ZOnojTCstoq3H6xpZVx6ksiTAwvoKrlsxl+a2Hr7/271DDnKrKA7whQ8tY/XymVQMamTtCkXZe7ybPe7nzZYu+iJxyor9lAX9lAcDVJUW8Wdvmz+uDbS5pqr89PcHOdjWS11FMf/y8+30hGO8bWEdZ9SVURzwM3taCd/51Zuc6A7zJxfP43Orz6a6rIhoLM6Btl6e2naEb/3SCwRVJYGUd3ShaJye8Kmulpctms4/XrWUc2bndnW58Xb0ZB9PbTvCHet30hOO8Yl3LaRxWinHOvs4ejLU/7u5rZfjXQOXCygO+Fg6q4pdRzvpDscIBnxMLw8SjsVpqCqhtTucdm3uebVlXHXuLK46dybnNU4bj7eaF32RGAGfEFd4vqmFA629+HzClctmTprF3iZb4LgOWD0ocKxU1U8lbbMOWAcwb968i/bt2zcu57braCePbznCgbYezp87jbctrOPM6eWn3Vn0RWI8te0IQb+PWdNKmVlVgk/gYHsvWw+d5J2Lp3NGXWH0KBovb7Z0cbwrfNro2fYe70ss3QSCkVgcVdJW2akqB1p76eiNeO1ItaUT/k4wGz3hKJGYUl2auv1AVdm0r403jnSybHYV8+vKqS4twucTOvsibD90knMbqykLBgbsc6Lby+D8IkTjyvZDJyny+3jbOE9VY8bOZAscE7KqyhhjJpNsAkf+u9UMbyOwWEQWiEgQWAM8ludzMsaYKWvCV4qralREPgk8hdcd9x5VLdwhs8YYU+AmfOAAUNXHgcfzfR7GGGMKo6rKGGPMBGKBwxhjTFYscBhjjMmKBQ5jjDFZscBhjDEmKxN+AGC2RKQTeCPFU9OB4ynKR6oa6JhCx0vI1XUshPeb62PaZ3B0JvL1K/RrNx0oV9XMpvtW1Un1A2zKpnwUr3PXVDperq9jIbzfMThH+wxO0utX6Ncu22trVVUj97MpdrxcK4T3a9dwYh0v13J5flPq2k3GqqpNmmK+lXTlJjt2HUfOrt3o2PUbueGuXbbXdjJmHHdlWW6yY9dx5OzajY5dv5Eb7tpldW0nXcZhjDFmbE3GjMMYY8wYKtjAISJzReQ5EdkhIttE5NOuvFZE1ovILve7xpXXue27ROQbScepFJHNST/HReTf8vW+xluurqN77noR2SIir4nIkyIyPR/vabzk+Np91F23bSLylXy8n/E2guv3PhF52X3GXhaR9yQd6yJX3iQid8pkWp0rhRxfu9tE5ICIdGV8Arns8jWeP8As4C3ucSWwEzgH+ArweVf+eeB297gcuAz4S+AbQxz3ZeCd+X5/hXYd8WZaPgZMd39/BW8Brry/xwK4dnXAfqDe/X0vcEW+398EvH4XArPd4+XAwaRjvQRcCgjwBPD+fL+/Arp2l7jjdWX6+gWbcajqYVV9xT3uBHYAc4Cr8f7j4X5f47bpVtXngdSLJwMishiYAfxmDE99QsnhdRT3U+7u9qqAQ2P/DvInh9fuTGCnqra4v58GPjLGp593I7h+v1fVxGdqG1AiIsUiMguoUtUX1PsmvC+xz2SVq2vnnntRVQ9n8/oFGziSich8vIi6AWhIXAT3e0YWh7oeeMh9+Kac0VxHVY0ANwFb8ALGOcDdY3i6E8ooP4NNwNkiMl9EAnj/2eeO3dlOPCO4fh8Bfq+qIbwvzOak55pd2ZQwyms3IgUfOESkAvgx8DeqenKUh1sDPDD6syo8o72OIlKEFzguBGYDrwG35PQkJ6jRXjtVbcO7dg/hZbt7gWguz3Eiy/b6icgy4HbgE4miFJtNiZu/HFy7ESnowOG+rH4M/FBVf+KKj7rUFff7WIbHOh8IqOrLY3KyE1iOruMFAKq622VsDwNvG6NTnjBy9RlU1Z+p6sWqeineXGu7xuqcJ5Jsr5+INAI/BW5Q1d2uuBloTDpsI5O8mhRydu1GpGADh6tHvxvYoapfT3rqMWCte7wWeDTDQ17PFMw2cngdDwLniEhikrT34dW7Tlq5/AyKyAz3uwb4K+B7uT3biSfb6yci04CfA7eo6m8TG7sqmU4RucQd8wYy/39fkHJ17UYs370DRvqD1ztF8apENrufq/B6qDyDd8f2DFCbtM9eoBXowrtLOSfpuTeBs/P9vgr5OuL1FtrhjvUzoC7f76+Art0DwHb3sybf720iXj/g/wLdSdtuBma451YAW4HdwDdwg5sn60+Or91X3Gcx7n7/83CvbyPHjTHGZKVgq6qMMcbkhwUOY4wxWbHAYYwxJisWOIwxxmTFAocxxpisWOAwZpyJyF+KyA1ZbD9fRH2QTtAAAAGfSURBVLaO5TkZk41Avk/AmKlERAKq+u18n4cxo2GBw5gsuUnlnsSbVO5CvCmtbwCWAl8HKoDjwJ+p6mER+SXwO+DtwGMiUok3hfVXReQC4NtAGd7gtT9X1TYRuQi4B+gBnh+/d2fM8KyqypiRWQLcparnASeBm4H/AK5V1cSX/m1J209T1Xep6tcGHec+4B/ccbYAt7ry7wN/rd7cVcZMKJZxGDMyB/TUnD//Bfwj3gI5693ic34geY2DhwYfQESq8QLKr1zRvcCPUpTfD7w/92/BmJGxwGHMyAyeq6cT2DZEhtCdxbElxfGNmTCsqsqYkZknIokgcT3wIlCfKBORIrf2QVqq2gG0icg7XNHHgF+pajvQISKXufI/yf3pGzNylnEYMzI7gLUi8h28mUj/A3gKuNNVNQWAf8NbpnMoa4Fvi0gZ3gzNH3flHwfuEZEed1xjJgybHdeYLLleVf+rqsvzfCrG5IVVVRljjMmKZRzGGGOyYhmHMcaYrFjgMMYYkxULHMYYY7JigcMYY0xWLHAYY4zJigUOY4wxWfn/AXWMk4g634noAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_september_week[:-1],\n", " first_september_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": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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