{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "2a06e6dd-0d31-4d64-8498-051a26ffa896",
"metadata": {},
"outputs": [],
"source": [
"import os.path as osp\n",
"from glob import glob\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.metrics import confusion_matrix\n",
"import seaborn as sn\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ce10f877-908c-4f47-86b0-35a215ca0bee",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sequence0\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence0.csv : 0.7677419354838709\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence0.csv : 0.7451612903225806\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence0.csv : 0.7645161290322581\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence0.csv : 0.7580645161290323\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence0.csv : 0.7516129032258064\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence0.csv : 0.7806451612903226\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence0.csv : 0.7419354838709677\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence0.csv : 0.7290322580645161\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence0.csv : 0.7806451612903226\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence0.csv : 0.7516129032258064\n",
"(array([ 0, 29, 38, 48, 50, 67, 73, 104, 120, 122, 152, 153, 156,\n",
" 168, 171, 172, 174, 176, 180, 183, 186, 187, 192, 193, 195, 197,\n",
" 199, 201, 203, 204, 206, 215, 217, 219, 222, 226, 228, 229, 234,\n",
" 239, 242, 243, 245, 248, 249, 250, 252, 253, 257, 258, 260, 262,\n",
" 264, 265, 268, 269, 270, 272, 279, 283, 284, 286, 291, 294, 295,\n",
" 296, 297, 300, 302, 304], dtype=int64),)\n",
"ensemble : 0.7741935483870968\n",
"\n",
"sequence1\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence1.csv : 0.8373493975903614\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence1.csv : 0.7530120481927711\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence1.csv : 0.7931726907630522\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence1.csv : 0.7811244979919679\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence1.csv : 0.8102409638554217\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence1.csv : 0.7951807228915663\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence1.csv : 0.7700803212851406\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence1.csv : 0.7259036144578314\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence1.csv : 0.7941767068273092\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence1.csv : 0.8152610441767069\n",
"(array([ 36, 43, 60, 66, 67, 84, 89, 93, 133, 159, 165, 178, 199,\n",
" 217, 280, 283, 287, 288, 307, 323, 332, 335, 405, 416, 417, 419,\n",
" 420, 423, 428, 454, 465, 475, 479, 486, 493, 498, 499, 502, 504,\n",
" 505, 507, 509, 517, 518, 520, 522, 523, 528, 531, 533, 534, 535,\n",
" 540, 544, 545, 551, 554, 556, 561, 562, 563, 566, 577, 578, 579,\n",
" 580, 586, 592, 596, 597, 605, 608, 610, 612, 614, 626, 630, 635,\n",
" 637, 638, 641, 647, 648, 658, 659, 664, 670, 685, 686, 691, 693,\n",
" 695, 698, 699, 701, 702, 703, 711, 713, 715, 721, 726, 729, 731,\n",
" 732, 735, 744, 747, 748, 751, 753, 759, 761, 764, 774, 778, 782,\n",
" 783, 786, 787, 788, 797, 802, 803, 809, 812, 813, 815, 819, 822,\n",
" 828, 834, 842, 844, 845, 851, 858, 861, 863, 870, 872, 880, 891,\n",
" 896, 902, 903, 907, 911, 915, 918, 922, 923, 925, 930, 933, 937,\n",
" 941, 942, 952, 956, 973, 976, 979, 980, 982, 983, 987, 993, 995],\n",
" dtype=int64),)\n",
"ensemble : 0.8303212851405622\n",
"\n",
"sequence2\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence2.csv : 0.9230769230769231\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence2.csv : 0.9020979020979021\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence2.csv : 0.8758741258741258\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence2.csv : 0.9108391608391608\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence2.csv : 0.9073426573426573\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence2.csv : 0.8986013986013986\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence2.csv : 0.8898601398601399\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence2.csv : 0.8863636363636364\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence2.csv : 0.9195804195804196\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence2.csv : 0.8968531468531469\n",
"(array([ 19, 45, 57, 168, 177, 219, 230, 289, 294, 303, 315, 325, 326,\n",
" 330, 347, 352, 367, 383, 388, 409, 413, 432, 434, 441, 459, 460,\n",
" 492, 499, 501, 518, 521, 527, 530, 531, 537, 541, 544, 563, 567,\n",
" 568], dtype=int64),)\n",
"ensemble : 0.9300699300699301\n",
"\n",
"sequence3\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence3.csv : 0.8541666666666666\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence3.csv : 0.8472222222222222\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence3.csv : 0.8038194444444444\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence3.csv : 0.7899305555555556\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence3.csv : 0.8246527777777778\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence3.csv : 0.8090277777777778\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence3.csv : 0.7934027777777778\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence3.csv : 0.7586805555555556\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence3.csv : 0.8246527777777778\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence3.csv : 0.8368055555555556\n",
"(array([ 6, 26, 32, 37, 44, 61, 66, 69, 81, 105, 107, 132, 143,\n",
" 151, 166, 235, 244, 253, 289, 297, 298, 300, 301, 313, 316, 319,\n",
" 323, 327, 328, 331, 335, 345, 349, 351, 352, 354, 361, 362, 381,\n",
" 385, 390, 395, 396, 398, 403, 406, 407, 413, 415, 418, 419, 420,\n",
" 424, 432, 438, 443, 448, 455, 458, 460, 469, 471, 472, 474, 475,\n",
" 477, 481, 489, 492, 497, 500, 502, 506, 507, 509, 514, 515, 516,\n",
" 521, 532, 540, 541, 546, 554, 555, 561, 563, 565], dtype=int64),)\n",
"ensemble : 0.8472222222222222\n",
"\n",
"sequence4\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence4.csv : 0.8662182361733931\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence4.csv : 0.8878923766816144\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence4.csv : 0.7899850523168909\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence4.csv : 0.8243647234678625\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence4.csv : 0.827354260089686\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence4.csv : 0.827354260089686\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence4.csv : 0.827354260089686\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence4.csv : 0.7167414050822123\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence4.csv : 0.8497757847533632\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence4.csv : 0.8124065769805681\n",
"(array([ 3, 7, 23, 25, 30, 31, 35, 40, 46, 53, 54,\n",
" 57, 66, 88, 92, 98, 99, 113, 117, 156, 169, 171,\n",
" 187, 211, 231, 234, 242, 244, 263, 270, 274, 275, 279,\n",
" 286, 290, 294, 297, 312, 318, 325, 327, 346, 364, 367,\n",
" 380, 394, 397, 422, 435, 436, 439, 444, 448, 456, 480,\n",
" 495, 498, 501, 514, 522, 523, 545, 547, 571, 584, 586,\n",
" 599, 617, 651, 654, 678, 681, 686, 696, 702, 709, 711,\n",
" 714, 726, 731, 743, 751, 752, 755, 767, 772, 779, 786,\n",
" 791, 792, 799, 806, 807, 808, 811, 813, 816, 820, 823,\n",
" 827, 835, 837, 839, 841, 842, 858, 859, 862, 872, 886,\n",
" 891, 895, 902, 903, 908, 909, 910, 915, 917, 923, 926,\n",
" 938, 958, 967, 969, 971, 978, 983, 992, 993, 1019, 1030,\n",
" 1031, 1043, 1045, 1046, 1049, 1050, 1060, 1061, 1072, 1083, 1084,\n",
" 1086, 1096, 1100, 1110, 1120, 1139, 1155, 1156, 1159, 1163, 1175,\n",
" 1180, 1181, 1188, 1189, 1197, 1199, 1200, 1205, 1209, 1210, 1214,\n",
" 1221, 1225, 1228, 1230, 1241, 1252, 1255, 1258, 1266, 1280, 1300,\n",
" 1304, 1312, 1317, 1322, 1324, 1331, 1335], dtype=int64),)\n",
"ensemble : 0.8632286995515696\n",
"\n",
"sequence5\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence5.csv : 0.8782608695652174\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence5.csv : 0.9043478260869565\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence5.csv : 0.8521739130434782\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence5.csv : 0.8608695652173913\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence5.csv : 0.8826086956521739\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence5.csv : 0.8782608695652174\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence5.csv : 0.8869565217391304\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence5.csv : 0.7739130434782608\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence5.csv : 0.8913043478260869\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence5.csv : 0.8826086956521739\n",
"(array([117, 125, 126, 130, 131, 135, 141, 142, 145, 158, 161, 162, 168,\n",
" 171, 178, 183, 201, 203, 206, 218, 226], dtype=int64),)\n",
"ensemble : 0.908695652173913\n",
"\n",
"sequence6\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence6.csv : 0.9191176470588235\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence6.csv : 0.9191176470588235\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence6.csv : 0.8970588235294118\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence6.csv : 0.8970588235294118\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence6.csv : 0.8970588235294118\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence6.csv : 0.9044117647058824\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence6.csv : 0.8455882352941176\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence6.csv : 0.7867647058823529\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence6.csv : 0.9044117647058824\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence6.csv : 0.9117647058823529\n",
"(array([ 15, 35, 72, 79, 89, 91, 103, 110, 117, 118, 119, 120, 133],\n",
" dtype=int64),)\n",
"ensemble : 0.9044117647058824\n",
"\n",
"sequence7\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\0\\bagging0, sequence7.csv : 0.8901098901098901\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence7.csv : 0.8956043956043956\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence7.csv : 0.8186813186813187\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence7.csv : 0.8461538461538461\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence7.csv : 0.8516483516483516\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence7.csv : 0.8461538461538461\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence7.csv : 0.8186813186813187\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence7.csv : 0.8296703296703297\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence7.csv : 0.8736263736263736\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence7.csv : 0.8461538461538461\n",
"(array([ 14, 22, 55, 61, 92, 94, 101, 102, 105, 106, 110, 113, 117,\n",
" 122, 125, 141, 144, 156, 159, 161, 166, 177], dtype=int64),)\n",
"ensemble : 0.8791208791208791\n",
"\n",
"sequence8\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\1\\bagging1, sequence8.csv : 0.7816091954022989\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\2\\bagging2, sequence8.csv : 0.7701149425287356\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\3\\bagging3, sequence8.csv : 0.8448275862068966\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\4\\bagging4, sequence8.csv : 0.8505747126436781\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\5\\bagging5, sequence8.csv : 0.8103448275862069\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\6\\bagging6, sequence8.csv : 0.7126436781609196\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\7\\bagging7, sequence8.csv : 0.7298850574712644\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\8\\bagging8, sequence8.csv : 0.9022988505747126\n",
"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\\9\\bagging9, sequence8.csv : 0.8735632183908046\n",
"(array([ 0, 2, 6, 19, 32, 46, 48, 58, 71, 86, 95, 101, 107,\n",
" 113, 117, 118, 130, 138, 140, 143, 147, 151, 155, 161], dtype=int64),)\n",
"ensemble : 0.8620689655172413\n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"base_path = r\"D:\\Deep_Learning\\WasteNet\\logs\\pred_csv\"\n",
"save_confusion_matrix = True\n",
"\n",
"for target_sequence in range(9):\n",
" print(f\"sequence{target_sequence}\")\n",
" target_csvs = glob(osp.join(base_path, \"*\", f\"bagging*, sequence{target_sequence}.csv\"))\n",
"\n",
" for i, target_csv in enumerate(target_csvs):\n",
" preds = pd.read_csv(target_csv, index_col=0)\n",
"\n",
" if i == 0:\n",
" out = preds[[\"pred0\", \"pred2\"]].values\n",
" else:\n",
" out = out + preds[[\"pred0\", \"pred2\"]].values\n",
"\n",
" label = preds[\"label\"].values\n",
" pred_index = np.argmax(preds[[\"pred0\", \"pred2\"]].values, axis=1)\n",
"\n",
" print(f\"{target_csv} : {np.sum(pred_index == label) / label.size}\")\n",
" ens_out = np.argmax(out, axis=1)\n",
" print(np.where(np.logical_not(ens_out == label)))\n",
" if save_confusion_matrix:\n",
" cf_matrix = confusion_matrix(list(label), list(ens_out))\n",
" df_cm = pd.DataFrame(cf_matrix, index=[\"bad data(label)\", \"good data(label)\"], columns=[\"bad data(predicted)\", \"good data(predicted)\"])\n",
" plt.figure(figsize=(12, 7))\n",
" show_fig = sn.heatmap(df_cm, annot=True).get_figure()\n",
" plt.savefig(f\"sequence{target_sequence}.png\")\n",
" print(f\"ensemble : {np.sum(ens_out == label) / label.size}\")\n",
" print(\"\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
