From f0e81bdabfb7082616b389e4e8c49de4583ae0ae Mon Sep 17 00:00:00 2001 From: 360e3aaec2d14a21ec01fe80c5870bf2 <360e3aaec2d14a21ec01fe80c5870bf2@app-learninglab.inria.fr> Date: Sat, 9 Aug 2025 14:46:38 +0000 Subject: [PATCH] Remplacement du "-" par un "0" pour garder le reste du code. --- module3/exo1/analyse-syndrome-grippal.ipynb | 1149 +++---------------- 1 file changed, 155 insertions(+), 994 deletions(-) diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 3938e55..6c8cafe 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -1146,977 +1146,13 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020253132066413890.027438.03121.041.0FRFrance
120253031907514191.023959.02821.035.0FRFrance
220252931867313815.023531.02821.035.0FRFrance
320252832328518131.028439.03527.043.0FRFrance
420252732145317129.025777.03226.038.0FRFrance
520252632194517422.026468.03326.040.0FRFrance
620252532332318546.028100.03528.042.0FRFrance
720252432315418577.027731.03528.042.0FRFrance
820252332439119307.029475.03628.044.0FRFrance
920252231875514333.023177.02821.035.0FRFrance
1020252132376018671.028849.03527.043.0FRFrance
1120252032026515814.024716.03023.037.0FRFrance
1220251931626412394.020134.02418.030.0FRFrance
1320251831811513975.022255.02721.033.0FRFrance
1420251732215017291.027009.03326.040.0FRFrance
1520251632856422550.034578.04334.052.0FRFrance
1620251533572129592.041850.05344.062.0FRFrance
1720251433757931232.043926.05647.065.0FRFrance
1820251333967333686.045660.05950.068.0FRFrance
1920251235254345627.059459.07868.088.0FRFrance
2020251135946952154.066784.08978.0100.0FRFrance
2120251036033453048.067620.09079.0101.0FRFrance
2220250938453174994.094068.0126112.0140.0FRFrance
232025083136020124824.0147216.0203186.0220.0FRFrance
242025073208952195988.0221916.0312293.0331.0FRFrance
252025063273519258159.0288879.0408385.0431.0FRFrance
262025053334395318416.0350374.0499475.0523.0FRFrance
272025043350043332885.0367201.0522496.0548.0FRFrance
282025033252772238917.0266627.0377356.0398.0FRFrance
292025023257247242991.0271503.0384363.0405.0FRFrance
.................................
209719852132609619621.032571.04735.059.0FRFrance
209819852032789620885.034907.05138.064.0FRFrance
209919851934315432821.053487.07859.097.0FRFrance
210019851834055529935.051175.07455.093.0FRFrance
210119851733405324366.043740.06244.080.0FRFrance
210219851635036236451.064273.09166.0116.0FRFrance
210319851536388145538.082224.011683.0149.0FRFrance
21041985143134545114400.0154690.0244207.0281.0FRFrance
21051985133197206176080.0218332.0357319.0395.0FRFrance
21061985123245240223304.0267176.0445405.0485.0FRFrance
21071985113276205252399.0300011.0501458.0544.0FRFrance
21081985103353231326279.0380183.0640591.0689.0FRFrance
21091985093369895341109.0398681.0670618.0722.0FRFrance
21101985083389886359529.0420243.0707652.0762.0FRFrance
21111985073471852432599.0511105.0855784.0926.0FRFrance
21121985063565825518011.0613639.01026939.01113.0FRFrance
21131985053637302592795.0681809.011551074.01236.0FRFrance
21141985043424937390794.0459080.0770708.0832.0FRFrance
21151985033213901174689.0253113.0388317.0459.0FRFrance
211619850239758680949.0114223.0177147.0207.0FRFrance
211719850138548965918.0105060.0155120.0190.0FRFrance
211819845238483060602.0109058.0154110.0198.0FRFrance
2119198451310172680242.0123210.0185146.0224.0FRFrance
21201984503123680101401.0145959.0225184.0266.0FRFrance
2121198449310107381684.0120462.0184149.0219.0FRFrance
212219844837862060634.096606.0143110.0176.0FRFrance
212319844737202954274.089784.013199.0163.0FRFrance
212419844638733067686.0106974.0159123.0195.0FRFrance
21251984453135223101414.0169032.0246184.0308.0FRFrance
212619844436842220056.0116788.012537.0213.0FRFrance
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2126 rows × 10 columns

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" - ], - "text/plain": [ - " week indicator inc inc_low inc_up inc100 inc100_low \\\n", - "0 202531 3 20664 13890.0 27438.0 31 21.0 \n", - "1 202530 3 19075 14191.0 23959.0 28 21.0 \n", - "2 202529 3 18673 13815.0 23531.0 28 21.0 \n", - "3 202528 3 23285 18131.0 28439.0 35 27.0 \n", - "4 202527 3 21453 17129.0 25777.0 32 26.0 \n", - "5 202526 3 21945 17422.0 26468.0 33 26.0 \n", - "6 202525 3 23323 18546.0 28100.0 35 28.0 \n", - "7 202524 3 23154 18577.0 27731.0 35 28.0 \n", - "8 202523 3 24391 19307.0 29475.0 36 28.0 \n", - "9 202522 3 18755 14333.0 23177.0 28 21.0 \n", - "10 202521 3 23760 18671.0 28849.0 35 27.0 \n", - "11 202520 3 20265 15814.0 24716.0 30 23.0 \n", - "12 202519 3 16264 12394.0 20134.0 24 18.0 \n", - "13 202518 3 18115 13975.0 22255.0 27 21.0 \n", - "14 202517 3 22150 17291.0 27009.0 33 26.0 \n", - "15 202516 3 28564 22550.0 34578.0 43 34.0 \n", - "16 202515 3 35721 29592.0 41850.0 53 44.0 \n", - "17 202514 3 37579 31232.0 43926.0 56 47.0 \n", - "18 202513 3 39673 33686.0 45660.0 59 50.0 \n", - "19 202512 3 52543 45627.0 59459.0 78 68.0 \n", - "20 202511 3 59469 52154.0 66784.0 89 78.0 \n", - "21 202510 3 60334 53048.0 67620.0 90 79.0 \n", - "22 202509 3 84531 74994.0 94068.0 126 112.0 \n", - "23 202508 3 136020 124824.0 147216.0 203 186.0 \n", - "24 202507 3 208952 195988.0 221916.0 312 293.0 \n", - "25 202506 3 273519 258159.0 288879.0 408 385.0 \n", - "26 202505 3 334395 318416.0 350374.0 499 475.0 \n", - "27 202504 3 350043 332885.0 367201.0 522 496.0 \n", - "28 202503 3 252772 238917.0 266627.0 377 356.0 \n", - "29 202502 3 257247 242991.0 271503.0 384 363.0 \n", - "... ... ... ... ... ... ... ... \n", - "2097 198521 3 26096 19621.0 32571.0 47 35.0 \n", - "2098 198520 3 27896 20885.0 34907.0 51 38.0 \n", - "2099 198519 3 43154 32821.0 53487.0 78 59.0 \n", - "2100 198518 3 40555 29935.0 51175.0 74 55.0 \n", - "2101 198517 3 34053 24366.0 43740.0 62 44.0 \n", - "2102 198516 3 50362 36451.0 64273.0 91 66.0 \n", - "2103 198515 3 63881 45538.0 82224.0 116 83.0 \n", - "2104 198514 3 134545 114400.0 154690.0 244 207.0 \n", - "2105 198513 3 197206 176080.0 218332.0 357 319.0 \n", - "2106 198512 3 245240 223304.0 267176.0 445 405.0 \n", - "2107 198511 3 276205 252399.0 300011.0 501 458.0 \n", - "2108 198510 3 353231 326279.0 380183.0 640 591.0 \n", - "2109 198509 3 369895 341109.0 398681.0 670 618.0 \n", - "2110 198508 3 389886 359529.0 420243.0 707 652.0 \n", - "2111 198507 3 471852 432599.0 511105.0 855 784.0 \n", - "2112 198506 3 565825 518011.0 613639.0 1026 939.0 \n", - "2113 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", - "2114 198504 3 424937 390794.0 459080.0 770 708.0 \n", - "2115 198503 3 213901 174689.0 253113.0 388 317.0 \n", - "2116 198502 3 97586 80949.0 114223.0 177 147.0 \n", - "2117 198501 3 85489 65918.0 105060.0 155 120.0 \n", - "2118 198452 3 84830 60602.0 109058.0 154 110.0 \n", - "2119 198451 3 101726 80242.0 123210.0 185 146.0 \n", - "2120 198450 3 123680 101401.0 145959.0 225 184.0 \n", - "2121 198449 3 101073 81684.0 120462.0 184 149.0 \n", - "2122 198448 3 78620 60634.0 96606.0 143 110.0 \n", - "2123 198447 3 72029 54274.0 89784.0 131 99.0 \n", - "2124 198446 3 87330 67686.0 106974.0 159 123.0 \n", - "2125 198445 3 135223 101414.0 169032.0 246 184.0 \n", - "2126 198444 3 68422 20056.0 116788.0 125 37.0 \n", - "\n", - " inc100_up geo_insee geo_name \n", - "0 41.0 FR France \n", - "1 35.0 FR France \n", - "2 35.0 FR France \n", - "3 43.0 FR France \n", - "4 38.0 FR France \n", - "5 40.0 FR France \n", - "6 42.0 FR France \n", - "7 42.0 FR France \n", - "8 44.0 FR France \n", - "9 35.0 FR France \n", - "10 43.0 FR France \n", - "11 37.0 FR France \n", - "12 30.0 FR France \n", - "13 33.0 FR France \n", - "14 40.0 FR France \n", - "15 52.0 FR France \n", - "16 62.0 FR France \n", - "17 65.0 FR France \n", - "18 68.0 FR France \n", - "19 88.0 FR France \n", - "20 100.0 FR France \n", - "21 101.0 FR France \n", - "22 140.0 FR France \n", - "23 220.0 FR France \n", - "24 331.0 FR France \n", - "25 431.0 FR France \n", - "26 523.0 FR France \n", - "27 548.0 FR France \n", - "28 398.0 FR France \n", - "29 405.0 FR France \n", - "... ... ... ... \n", - "2097 59.0 FR France \n", - "2098 64.0 FR France \n", - "2099 97.0 FR France \n", - "2100 93.0 FR France \n", - "2101 80.0 FR France \n", - "2102 116.0 FR France \n", - "2103 149.0 FR France \n", - "2104 281.0 FR France \n", - "2105 395.0 FR France \n", - "2106 485.0 FR France \n", - "2107 544.0 FR France \n", - "2108 689.0 FR France \n", - "2109 722.0 FR France \n", - "2110 762.0 FR France \n", - "2111 926.0 FR France \n", - "2112 1113.0 FR France \n", - "2113 1236.0 FR France \n", - "2114 832.0 FR France \n", - "2115 459.0 FR France \n", - "2116 207.0 FR France \n", - "2117 190.0 FR France \n", - "2118 198.0 FR France \n", - "2119 224.0 FR France \n", - "2120 266.0 FR France \n", - "2121 219.0 FR France \n", - "2122 176.0 FR France \n", - "2123 163.0 FR France \n", - "2124 195.0 FR France \n", - "2125 308.0 FR France \n", - "2126 213.0 FR France \n", - "\n", - "[2126 rows x 10 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data = raw_data.dropna().copy()\n", - "data" + "raw_data_1 = raw_data.replace('-', 0)\n", + "raw_data_1 = raw_data_1.dropna().copy()\n", + "colonnes_numeriques = ['inc','inc100']\n", + "raw_data_1[colonnes_numeriques] = raw_data_1[colonnes_numeriques].astype(int)\n", + "data=raw_data_1" ] }, { @@ -2226,20 +1262,26 @@ "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "Empty 'DataFrame': no numeric data to plot", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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"1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -2369,9 +1507,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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09wAnNLfHgPcBhwNPAb7R8dzdKZteUAL8h5QrK+0LfAl4QMe80XatW6h/AeWDdh5laGhxM/3zwPHN7QcDpzWP708ZO1zQ0X+/bV5nCWXr5LXAJ4APAtu1XaP12wfWP/W/gV9Dj4i9KTsunwOcAryxeWg95bqnUL5Rvwu8ADgH2DUiHh0RW2UZL18fEU/MzHMpP6/5DuCLwBmZ+ZtNy8rmnZ+HDgOeDbwFOICyfwDKETqbtjiuBL4NPDMzL6SsrSwHyMwNwAXA/pm5HjiaMqx0DfDGzLx1juro1rDXD/bBsNcP9HYJulZExL7ArZl5aTPpscClmXlsRDwGODUixoBJ4OkRsW1m3hYRPwZeSDmW9HPAK4H3RcTtwFrgiub1Pgx8LjNvmbuqptaM9WVE7E/ZNPw2cHaWQyn/Erg8M8+NiCsoZ7Q+DVgDPC8iFmfm9RFxGXBrRDwIeD9wVETsQrna1A2UzU0yczWwes6LvBfDXj/YB8Ne/3QMzBp6RDwsIr5DGfN+c0S8tHnoTmBds7Z9EWVT6QDgNu465Ajgj5RNql0pa+E/bV7rPOD6zLwKylr4PA7zg4CPU/a4PwV4ezPLncClEbEoM6+g9MGjKWOBV1OOrYWyV34B5X0/i9IPRwL7AatyHh1P2ykiFjT1P4myCTxU9QM0tWWUsxOH8TOwwzDXP21tj/ls6R+wHXBAx/3DgPc2tx9H+fbcAziGspm1pHnscMp4+abHzmumb0MZblnc8Zr7Avdtu9Yt1L8t8Gru2prYCvg74DXN4zsDP2lqOIIyNri0o69WUa5wfhhlC2RHyj6Cr3XWDNyn7Vrv5f1/BeWPbiVlR9bQ1N/Rvu2BsylXBAM4cVj6oPkbOKb5uz1r2Orv5t+8XEOPiJOBy4GvRcRoM/nplGPDyXLq7Q+A4yjHkO5OOWYcylj5PpSjUz4F3BQRn6bs7PwF8P9jYZn5w8y8Y/YrmpmI2BX4KjAOfJqyE+f5lC2PjQCZeRNlp+3xlHHCXbjrkMvzKcfb35GZXwU+RjnD9QOUvfl/3LSsnIdrJM3xwudQ/vg+AjyNsv9jf8qaWNX1b2YR5XyJh0bEYsrnfAHU3QcRsRVl39fhwLsy8wXNQ/tumqfm+rvW9jfKFr6ZxymbSx8FVjbTXkcZL9s0z57Alc3tU4G3djx2IbBvc3tryiFL+7dd1wzqXwQ8ruP+sZSdPMcAP+iY/kDg6ub2ayinKu/cPP8rwIM65l08F23vYx/s1HH77yl/tEcOS/0d7T4GeBfwJuDllNPPLxyGPqAcmHDkZtOOAC4Yhvq76rO2G7CFN3LToURHcNeQyU7AzcA2HfNdSPnG3gn4AmUT6z8p38Bbt11HD/XHpn/N/cd09MMNlONnN837rU3hD/wT5YifG4B/aLuOPvTDDpT9HNcCb23u3wCM1l5/x3v/MsrQ2/OBzzbTrh+SPjiMcmLP6ZSDHN5MGUq9Edil9vq7+Tcvh1wy80/Nzf8Cto+IvTPzZsq4+as6Zr0I2L557DjKsMp/ACuy7PkeSNnomHQCZW0FyvjfiQBRfl/mV8CmQyvfQtmSWZKZ75ij5s6azPwdZWjt8ZSd2S+kDJu9Kopq6+94/w+hDDudA+wWEW+k7OxfAXV/BrIMlVxBCeejgUcBz6V8Bl5d+2egG/HnuTH/RMQHKePhr2+O8vhbSrDvTDlh6JCOL4DqRMRulPG/4zLz0ig/JraC8uFeAvwo5+MZa30WEftQvsy/Txkn3YtyqFm19UfECGW4ZWtKzX9FORnmZMqa+8Opvw+2zczbmtt7Uz7736Wcml/9Z2CmBiHQ96EctfJkyof6Nspp+LcDH87Mi1ts3qyL8vsxTwLeQBlPvYqyiXkE8PMsh2pWLyJ2p3yxvTgzb4iIo4CLM/OHLTdt1kS50Mq/UXbgnUE55O7kzHxa83j1fdApyi8dfhQ4IjNvHLb6p2MQAv1FlEP3bgPeRtnjXc9e6SlExHeBh1B+1e1q4C2Z+ZNWGzVHImJHyhf5Syg7wVcBH8jMP97rEyvVnAzzfGAiM69puz1zISK2plyrYNOQy4eAD2b5mWttZl4HekQ8mnIa/pmUHUID9ZO1vWoO3TqFMo74mUHeL9CNiFhIGWb5A6X+oXr/N4mIBcCdOZ//WGdRRLyKcrjqp4f1MzBd8zrQJUnTNy+PcpEkzZyBLkmVMNAlqRIGuiRVwkCXpEoY6JJUCQNdkirxfxo0Erf7i0deAAAAAElFTkSuQmCC\n", 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