{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "\n", "path_root = os.path.dirname(os.path.abspath('.'))\n", "path_yeday = os.path.join(path_root, '20240716')\n", "path_today = os.path.join(path_root, '20240717')\n", "\n", "TM_FA_CRSRD_y = pd.read_csv(os.path.join(path_yeday, 'TM_FA_CRSRD.csv'))\n", "TM_FA_CRSRD_t = pd.read_csv(os.path.join(path_today, 'TM_FA_CRSRD.csv'))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0,\n", " 2,\n", " 4,\n", " 5,\n", " 6,\n", " 7,\n", " 8,\n", " 9,\n", " 10,\n", " 11,\n", " 12,\n", " 13,\n", " 14,\n", " 15,\n", " 16,\n", " 17,\n", " 18,\n", " 19,\n", " 20,\n", " 21,\n", " 22,\n", " 23,\n", " 24,\n", " 25,\n", " 26,\n", " 27,\n", " 28,\n", " 29,\n", " 30,\n", " 31,\n", " 32,\n", " 33,\n", " 34,\n", " 35,\n", " 36,\n", " 37,\n", " 38,\n", " 39,\n", " 40,\n", " 41,\n", " 42,\n", " 43,\n", " 44,\n", " 45,\n", " 46,\n", " 47,\n", " 48,\n", " 49,\n", " 50,\n", " 51,\n", " 52,\n", " 53,\n", " 54,\n", " 55,\n", " 56,\n", " 57,\n", " 58,\n", " 59,\n", " 60,\n", " 61,\n", " 62,\n", " 63,\n", " 64,\n", " 65,\n", " 66,\n", " 67,\n", " 68,\n", " 69,\n", " 70,\n", " 71,\n", " 72,\n", " 73,\n", " 74,\n", " 75,\n", " 76,\n", " 77,\n", " 78,\n", " 79,\n", " 80,\n", " 81,\n", " 82,\n", " 83,\n", " 84,\n", " 85,\n", " 86,\n", " 87,\n", " 88,\n", " 89,\n", " 90,\n", " 91,\n", " 92,\n", " 93,\n", " 94,\n", " 95,\n", " 96,\n", " 97,\n", " 98,\n", " 99,\n", " 100,\n", " 101,\n", " 102,\n", " 103,\n", " 104,\n", " 105,\n", " 106,\n", " 107,\n", " 108,\n", " 109,\n", " 110,\n", " 111,\n", " 112,\n", " 113,\n", " 114,\n", " 115,\n", " 116,\n", " 117,\n", " 118,\n", " 119,\n", " 120,\n", " 121,\n", " 122,\n", " 123,\n", " 124,\n", " 125,\n", " 126,\n", " 127,\n", " 128,\n", " 129,\n", " 130,\n", " 131,\n", " 132,\n", " 133,\n", " 134,\n", " 135,\n", " 136,\n", " 137,\n", " 138,\n", " 139,\n", " 140,\n", " 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688,\n", " 689,\n", " 690,\n", " 691,\n", " 692,\n", " 693,\n", " 694,\n", " 695,\n", " 696,\n", " 697,\n", " 698,\n", " 699,\n", " 700,\n", " 701,\n", " 702,\n", " 703,\n", " 704,\n", " 705,\n", " 706,\n", " 707,\n", " 708,\n", " 709,\n", " 710,\n", " 711,\n", " 712,\n", " 713,\n", " 714,\n", " 715,\n", " 716,\n", " 717,\n", " 718,\n", " 719,\n", " 720,\n", " 721,\n", " 722,\n", " 723,\n", " 724,\n", " 725,\n", " 726,\n", " 727,\n", " 728,\n", " 729,\n", " 730,\n", " 731,\n", " 732,\n", " 733,\n", " 734,\n", " 735,\n", " 736,\n", " 737,\n", " 738,\n", " 739,\n", " 740,\n", " 741,\n", " 742,\n", " 743,\n", " 744,\n", " 745,\n", " 746,\n", " 747,\n", " 748,\n", " 749,\n", " 750,\n", " 751,\n", " 752,\n", " 753,\n", " 754]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(TM_FA_CRSRD_y.NODE_ID)\n", "# display(TM_FA_CRSRD_y[:60])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2,\n", " 5,\n", " 6,\n", " 9,\n", " 14,\n", " 15,\n", " 16,\n", " 18,\n", " 19,\n", " 24,\n", " 25,\n", " 27,\n", " 28,\n", " 33,\n", " 38,\n", " 40,\n", " 44,\n", " 46,\n", " 49,\n", " 50,\n", " 53,\n", " 57,\n", " 64,\n", " 72,\n", " 78,\n", " 79,\n", " 83,\n", " 89,\n", " 91,\n", " 95,\n", " 96,\n", " 100,\n", " 102,\n", " 103,\n", " 104,\n", " 106,\n", " 119,\n", " 123,\n", " 131,\n", " 134,\n", " 135,\n", " 144,\n", " 145,\n", " 151,\n", " 154,\n", " 155,\n", " 156,\n", " 163,\n", " 167,\n", " 169,\n", " 172,\n", " 175,\n", " 178,\n", " 180,\n", " 183,\n", " 185,\n", " 191,\n", " 193,\n", " 201,\n", " 208,\n", " 214,\n", " 215,\n", " 217,\n", " 220,\n", " 222,\n", " 226,\n", " 231,\n", " 232,\n", " 240,\n", " 243,\n", " 245,\n", " 247,\n", " 249,\n", " 250,\n", " 254,\n", " 256,\n", " 259,\n", " 260,\n", " 263,\n", " 267,\n", " 270,\n", " 272,\n", " 273,\n", " 279,\n", " 281,\n", " 284,\n", " 287,\n", " 291,\n", " 293,\n", " 297,\n", " 298,\n", " 299,\n", " 300,\n", " 302,\n", " 305,\n", " 307,\n", " 320,\n", " 321,\n", " 325,\n", " 350,\n", " 354,\n", " 355,\n", " 358,\n", " 361,\n", " 364,\n", " 365,\n", " 367,\n", " 369,\n", " 371,\n", " 373,\n", " 375,\n", " 376,\n", " 377,\n", " 378,\n", " 379,\n", " 405,\n", " 409,\n", " 410,\n", " 411,\n", " 412,\n", " 414,\n", " 415,\n", " 416,\n", " 417,\n", " 420,\n", " 424,\n", " 425,\n", " 437,\n", " 443,\n", " 445,\n", " 448,\n", " 456,\n", " 457,\n", " 459,\n", " 461,\n", " 462,\n", " 463,\n", " 466,\n", " 467,\n", " 476,\n", " 481,\n", " 482,\n", " 484,\n", " 486,\n", " 491,\n", " 492,\n", " 496,\n", " 503,\n", " 506,\n", " 507,\n", " 514,\n", " 517,\n", " 520,\n", " 522,\n", " 523,\n", " 532,\n", " 534,\n", " 536,\n", " 541,\n", " 570,\n", " 583,\n", " 592,\n", " 596,\n", " 622,\n", " 623,\n", " 624,\n", " 629,\n", " 633,\n", " 634,\n", " 636,\n", " 637,\n", " 638,\n", " 639,\n", " 645,\n", " 646,\n", " 648,\n", " 650,\n", " 656,\n", " 658,\n", " 660,\n", " 661,\n", " 676,\n", " 682,\n", " 683,\n", " 684,\n", " 691,\n", " 698,\n", " 699,\n", " 701,\n", " 703,\n", " 706,\n", " 707,\n", " 713,\n", " 722,\n", " 724,\n", " 727,\n", " 738,\n", " 739,\n", " 740,\n", " 741,\n", " 742,\n", " 743,\n", " 745,\n", " 747,\n", " 749,\n", " 751,\n", " 752,\n", " 2001,\n", " 2002,\n", " 2003,\n", " 2004,\n", " 2005,\n", " 2006,\n", " 2007,\n", " 2008,\n", " 2010,\n", " 2101,\n", " 2102,\n", " 2103,\n", " 2104,\n", " 2105,\n", " 2201]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(TM_FA_CRSRD_t.NODE_ID)\n", "# display(TM_FA_CRSRD_t[:60])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n", "545\n", "207\n" ] } ], "source": [ "print(len(set(TM_FA_CRSRD_t.NODE_ID) - set(TM_FA_CRSRD_y.NODE_ID)))\n", "print(len(set(TM_FA_CRSRD_y.NODE_ID) - set(TM_FA_CRSRD_t.NODE_ID)))\n", "print(len(set(TM_FA_CRSRD_t.NODE_ID) & set(TM_FA_CRSRD_y.NODE_ID)))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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43He4iNUUn3uD5eRJ2RUQzWqoKcxpxT4WTSZn7A6btOfsHvRN9BWxouJyb7CcOiW7AqIZTdRFMB7kYL3ftczSHQYAWTOL5049h0TGmzMF3Rks/f3AyIjsKoimSUc0DJZzXIWAuuTch7WNp8bxfMfzRaqmuNwZLGytkAOZAAYaAzAwe/86+Uc0mZqzOwwAzo2e8+Q5LgwWIouMNUa5BT5N0WJMXyx5pTfOv+G5LjH3BcvAgNjNmMhBMmENw1GOq9BUV+sOA4BEJuG5M1zcFyxcaU8ONNiocWoxTRNNJRE0p6/Ev9LR/qPoGe8pQkXF4b5g6eqSXQHRFLGaMOLcB4xmYgKN5uyzwy63+8xuGKY3xufcFSyGAVy4ILsKootMVcFQpfe36KD81Wfn9zI7GB/0zPkt7gqWvj4gzQOSyDnGGiLIgMFCsytPzf81a3/XfkykJmyspjjcFSwcXyEHMTQVo1HOAqO5BdMZlF92suRc0kYae87tsbki+7krWDi+Qg4ysiCELK4+64eo0QjO+7anhk6hP9ZvYzX2c0+wZDJAj3dmTZC7ZUMBjIU4YE/zU53ObVD+rQtv2VRJcbgnWHp6gCzfHZIzjDTonF5M81aWTE87snguncOdGIy7d/d29wQLx1fIIbJBFeNBtlZo/hTDQDXmN+14kptbLe4JFo6vkEOM14fZWqGc1Rhz7xt2pY6hDtceZeyOYEmlxFRjIslMVcFYmDPBKHeVOa59NGHiYM9Be4qxmTuC5cIFnm1PjjBRF+ZMMMpLSSr39U7tA+1IZtz3RsYdwdLdLbsCIgDAaCkXQ1J+9HQaITO3l9yMkXHlMcbuCJahIdkVECFRGUIa3PmB8leL+a9nmcRgscuge6fdkXdMVLjj6ULOlesAPgBMpCfQPe6uXhvnP1PSaWB8XHYV5HOGqiCmua+vm5ylLMeFkpNODJ6wuBJ7OT9Y2FohB4jXhHnkMBUsnOcmuh1DHTBdNIHJ+cHC8RVygPFS9zypybm0bBZ6Hi+78UwcXWPuWcvn/GBhi4Uky4YCSPAgL7JIpTm/nY6vdHLopMWV2IfBQnQV8YrcZ/IQzabcyO9lt2OowzUnTDo/WNgVRpLFS2RXQF5SluNalknJbBLnRs9ZXI09nB0siQQQj8uugnzMVICEmpJdBnlINJN/q+PU0CkLK7GPs4OF3WAkWbIixNlgZKlwJv/dG86MnLGwEvs4O1jYDUaSxctyX9BGNBctk0HQVPL62UQmgZHEiMUVWc/ZwcIWC0kW17k3GFmvTMlvZhgA9Ew4/yRdZwfLiPOTmbzLCChIg+MrZL1onjPDAKB3otfCSuzh7GDhwD1JlCrL7cQ/ovmKIL+uMADoGWeLpTAMFpIoGeX4CtkjkueUYwAYjA8inXX2LtvODRbTBJLc9I/kSYU4G4zsEcrmv0WQCdPx3WHODZZEgqdGklRJ1dnvCsm9QkZhr21OH8B3drAQSZLVAzyCmGwTzBb22GKLJV8cXyGJ0pH8p4MSXY1WYLD0x/otqsQeDBaiGWRCzn1qkPuphgGtgJlh8XTc0RtSOvfZw2AhiTJ6/k96ovnI51yWSSZMxNIxC6uxlnODhWMsJFFac+67QfKGQoIFACZSExZVYj3nBgtbLCRRJsBgIXvlu1/YpIk0gyV3DBaSiDPCyG7BAsZYAGA8NW5RJdZzbrCwK4wkYrCQ3QoNFnaF5YPBQpIYmnOfFuQdBQcLu8LyYLCPm+Qwgs59WpB3FDo/xMldYc5dBcZgmeLrTz2Fb+zcOeW6FQ0NOPrQQ1OuM00TH/n+9/HLd9/Fz++/Hx9dv/7i95Q//uNp9/voZz+L39u0yZaa3SqrcarxD37yDJ595W2cPNuNcEjHxlVL8KU/+h0sbV5w8TaJVBrffuRxPPXCPqTSGbzvhlX45p99EnVV5QCAx599DV98+F9nvP99jz+M2vdu51eT61ja32rHrv+9C2eOnMFI/wju/5v7sf7W9Rdv96Ov/wh7du6Z8rOrtq7Cl//nl4tZbk6cGyzcJ2ya1QsX4ld//ucXv9YC03ff/dtf/xqKMvsL4//69Kdx5+rVF7+ujEYtrdELDC0AwN/7hL1+sB2/f++tuG7FYmSyWTz8wyex47/8HZ774dcRjYQAAN/8x8fwwuvv4B+/+h9RVhLBV7//KD739Ufwf//uQQDA3bfegFs2rZ5yvw/8jx8hmcr4PlSAS91FqXgKTdc0Yfs92/HIFx+Z8bart63Gp7/66Ytfa7rm6AWSzg0Wtlim0VQVCyoqZv3+gbNn8Z3nnsO+v/xLND744Iy3qYxG57wPAkzulo8f/7c/m/L13zz4GVz/uw/gneOnceO65Rgdj+OxX76Kv/vLP8S2DdcCAB7+4mdw+3/4Gt46fAobVy1BOKQjHLp0ps3A8Bj2HDiG//6FHUX9WxzrvTfPa7avwZrta+a8qRbUUFE79XnLYMkHWyzTHO/txcIHH0Q4GMTWJUvwX3/7t9FSXQ0AiKVS+OQPf4gffOITcwbHf3r0UXz2xz/Gkro6fO5978MfbNs2ZwvHj8wCB1W9aGxCTP+vLCsBABw6fhrpTBbbN668eJtlLQuwqL76YrBc6d+e24twSMdH3rexOEU7nJrD46x9fzseuOMBRMuiWLFpBe69/17oNc49iM65wcIWyxQ3trXhR5/5DFY0NODCyAi+sXMnbn74YRz62tdQFg7j8489hm1LluDey8ZUrvTQPffgthUrENV17Dp8GH/yk59gPJnEn952W/H+EDdgrkxhGAYe+sfHcMPqpVjRtggA0Dc4Cj2ooaJ0aldqbVU5+oZmPlL8Z8+8intv2zylFeNnJub35nn11tXY8P4NqF1Ui75zfXjyB0/i+3/6fXz5XznGQgX68JpLTeV1TU24sa0NrX/xF3hs3z7UlZXh+WPH8PaX536gfeWuuy5+vqGlBROpFB7etYvBQnP6yt8/imOdXXjib7+Y933sP3wSJ85cwPe+9AcWVuZuyjzfwWz60KXJNYuWLcKiZYvwVx/9KxzddxS4wa7qCuPcYFE55XMuldEoljc04ERfH945fx4n+/pQ+fnPT7nNxx55BDdfcw1e/MIXZryPG9va8M2nn0YynUYoGCxG2e7AXtiLvvr9R/H86+/gse8+gMa6qovX11WXI5XOYGQ8NqXV0j80irqq6V2xP/v3V7FqaTPWLm8tSt1eVtdUh9LKUvSd7ZNdyqycGyzs95/TeCKBk319+P0tW3Df9dfjszfdNOX7ax96CN+77z7cvW7drPdx4OxZVEWjDJUrKEwWmKaJr/3DT/HsKwfw0+/8ZzQ31k75/pprWhHUAnjtraP48HtjJifPduN87+C08ZWJeAJPv7QPD/7hbxetfjfI91E21DOEiZEJVF0W9E7DYHGJB554AnevW4fW6mp0jYzga089hYCq4hObNqGurGzGAfuW6mq01YoXhKd+8xv0jI1hS1sbwsEgnjtyBH/9zDN44I47iv2nOJ7C4T185e8fxf97/g3880N/gpJoGL2DYtykvCSCcEhHeWkE9925Hd965HFUlJegLBrG1/7hp9i4asm0YNn54j5ksgY+evuNMv4Ux5rcgzIRS0xpffSf78fZY2dRUlGCaHkUO/95JzbethHlNeXoO9eHf/v7f0Ndcx3WbZ/9TaNszg0WdoVNcW5oCJ/4l3/BwMQE6kpLcdOyZdj7pS+hrqxsXj8fDATwgxdfxOcfewwmgGV1dfjuxz+OP7qipUOAmmWL5f889RIA4Pe+8J0p1z/8xU/j4x/aBgD4yp/cB1VVcP83Hrm0QPJPPzntvn72zKu486YN0wb6/W7yUXb68Gl893PfvXj94997HACw9be24pNf+iTOHz+PvTv3IjYWQ2VdJVZuWYl7P3cvQqGQhKrnRzFNh87rffRRYGxMdhXkQ+mohq7GjOwyyOPOlEVxJJD/YV01kRp8bNXHLKzIOs5tFrDFQpKoafaFkf0ySmHv6Uv0EosqsZ5zX711znUnORgsVAzpAoeRS4IMltxFIrIrIJ9SAKgOfmqQN6RR2BuYUr3Uokqs59xnTzgsuwLysQC4YRjZq9BRPHaF5YMtFpJIMxgsZK9UgS0WdoXlg8FCEmkZrqMie6U5eC8Bu8JIoqC/j2OhIii0xcIxlnywxUISaUlnLu8ibzAVpaBgCQVC0FQHr2+XXcCsGCwkkZbgAkmyT2aG019zURF29mF9zg0WdoWRRFo8M+9tzYlylQ4U9tLbUNJgUSX2cG6wsMVCEikAQiYX6ZI9UgW2WBpKGSz5CQS4+p6k0tOcckz2SBbYGGaLpRDsDiOJQnFu7UL2SATyT5aSYImjpxoDTg8WdoeRRPoY5xyTPRIFHPrj9G4wwOnBMs+zRojsoCWzCDj4yCJyr1ghweLwbjDA6cFS5dyjN8kfwlke20zWG0c2759li6VQ1dWyKyCfi0zIroC8xlBVxPIMloASQG201uKKrOfsYGGLhSQLDydll0Aekwzm3wpuKG2Aqjj7ZRtwerCUlwMa+7hJnkDa4HoWslRcy/9ld2nVUgsrsY+zgwVgq4WkiyT55oasM67mN9VYVVS0VbVZXI09GCxEVxEZ4b5hZJ1RNb8ZYYvKFiGsuWNtn/ODhQP4JJk+nkIQnB1G1hhGfuujlla7oxsMYLAQzUtJnMFChTNVFRNK7jPCAkoAiysXW1+QTZwfLOwKIwcoGUzJLoE8IBnMb7yuqbwJesA9k0icHywlJdyMkqTTEhmETXf0b5NzjeUZLG7qBgPcECwAu8PIEUrGeT4LFWY4kPvJpJqqobWi1YZq7OOOYGF3GDlAtD8B1SVPGXKmPuTepbqkagmCAXeN8bnjWVJfL7sCIqiGidIUu8MoP9lAAGN5DNyva1hnQzX2ckewLFwouwIiAEBZb5IHFlNeYnrurY6m8iZUR9w3FOCOYCkr4xb65AhaMotolucEUe5GtNzfkqytX2tDJfZzR7AAwKJFsisgAgCU9+e/5Tn511COK+6rI9Vormi2qRp7uSdY2B1GDqGPpzj1mHKW68D9xsaNNlViP/cEC1ss5CAVA7lPGyX/SoR0pJX5P2aqwlVoq3THhpMzcU+wRCKcdkyOER5JImKw1ULzM6TntjByQ+MGKIp7p4m4J1gAtlrIUSp7OdZC89Ojzv+xUhmudM25K7NxV7BwnIUcRJ9IozTDGWI0N0NV0Yv5n0R6U8tNrm6tAG4MFpf/g5O3VHSnoHBlC81hPKTDnOdDZFn1Miwsc/8baHcFi64DtbWyqyC6SEtmUZ5kq4Vm1z/PdZF6QMfWpq32FlMk7goWgN1h5DgVXXFo4PHFNLML6vxOIN20cBMiQW+8SXFfsHAAnxxGMUxUDzJYaLpUMIhxXD1YaqO1WFW3qggVFYf7gqWxkeezkONEhhIo4VYvdIWh0NX7wRQouLnlZtcP2F/OfcESCACt7jqbgPyh6nwSAQRkl0EOci5w9fPtV9atRF1JXRGqKR73BQsALFkiuwKiaQJpA9XD7jo3g+yTDmroV+YOlpJgCTYv2lykiorHncHS3MzuMHKk6EACZWl2iREwGJr7NUpVVNy+5HZXnWU/X+4MFlUFFi+WXQXRjKrOJaDDey8WlJtz2tyD9luatqChtKFI1RSXO4MFYHcYOZZimKi9YPIYYx9LBzX0z7Gb8dKqpVhTv6aIFRWXex/5TU1AmJsAkjMFY2lUjYVkl0GSzNUNVhWuwi2LbyliNcXn3mBRVWCpuzdqI28r7Y1zvMWnZusGC6pB3LH0Dmiqt9c9uTdYAGD5ctkVEM2p6kyc2+v7TFLXZ+0Gu2XxLagMVxa3IAncHSx1dUBlpewqiGalAKg9k0SQg/m+0R2euTWytn4tllT5Y2zY3cECsNVCjqdmTdSfN7h40gdMRUGHkph2/ZKqJdjStEVCRXK4P1iuuYZb6ZPjaYkM6voC3GLf40YiYSQVY8p1iysX47a22zy1ZcvVuD9YSkq4MSW5Qmg0hfoBneHiYaf1qaHSXN6M25fcDlVx/0ttLrzx165dK7sConkJDydRN8hw8aJUMIjuy06KXFi2EHcsvcN3oQJ4JViam4GqKtlVEM1LZCiJusEQw8VjeiKX9olbULoAdy670/PTimfjjWABgHXrZFdANG+RoQTqhhguXmEqCk6pYtC+Llrn61ABvBQs11wDRKOyqyCat8hgAnWDOrd+8YChSBgJGKiN1uIj13zEkxtL5sI7j2hVBVavll0FUU4iQ0k09AQQ4NHGrnZSz6ClogX3rLgHIY1b+XgnWABg1SpA4xOU3EUfT2PBOZOLKF1qIhzGwvoV+NDSD/m6++ty3gqWUAhYsUJ2FUQ505JZLDidQdjku123qV68Eduat/lqncrVeCtYADH1mP/B5EJqxkB9RxJlKW5c6QYqVNSjEQsWeXf7+3x5L1jKy3kIGLmWYgLVZ+OoHYlwUN/BgtCxoEtFZAlDZSbefORy6jG5XEl/HAsuBDju4kBl6SgaO9IIhkqBtjbZ5TiSN4OloUFciFwsGEtjQUcapRl2jTlBAAHUD4VRfSYGxTCBDRtkl+RY3gwWALjuOtkVEBVMNUzUnI6jfigMjVOSpYkYYTSeFWuPAABlZTxocA7eDZbFi9lqIc+IDCbQ2JlFKU+kLKoAAqgZj6C+I4FAKnvpG9ddJ9bO0Yy8/S+zdavsCogso2ZN1JyJo2EgxNZLEZRmImg8Y6K0Jz71G5WVwLXXSqnJLbwdLPX1wBJ/nNhG/hEeTmJhRxaV8ShnjtkgCB0N/SHUnI4jkDam32DLFrZWrsL7/zo33sgHAXmOYpio6Iph4VmFg/sWCSCAqlgUjadSCI8kZ75RUxPQ0lLcwlzI+6+4ZWXAGs41J28KpLKoOR1HY3cQESMsuxxXUqGiMh7Fwg4D5RdiUMxZbqgo7F6fJ+8HCwBs3Ci2eyHyKH0ijfqOBBb0hxAx+FifDxUqKpJRLDoNVHTFoBqzJcp7Vq7kuU/z5I9g0XXg+utlV0Fku9BIEvUdSTT26ijJsAUzkwA00UI5A1Sei0HNzDCOciVdB264wf7iPMI/U0tWrQIOHQJGR2VXQmQ7fSyF2jGgMqxhrE7HhJ5EFtmr/6CHhcwQykZVRAfiUMxMbj+8cSMQZlDPl2Ka5lXafx7S0QE895zsKoiKzlSAeE0Y42VA/L2TDv1AhYpoJoTSoSxCo6n87qS8HLjvPk4CyoF/WiyA2NdnwQKgu1t2JURFpZhAtD+BaD+QCQUwURvCRCiNtJKWXZrlFCiIZMMoGTMQGUxCMeNX/6G5cGZpzvzVYgGA3l7gySdlV0HkCOmIhniljngki6SShFtfDFSoCGd1RGIKogMJqFmL/pLGRuDuu625Lx/xV4sFEIsmr70WOHpUdiVE0gXjGQTjGZQDyAZVJCpDSESBpJZBGs5tzSgAdDOESEJFeDwLfTQFBRZ38XF6cd78FyyAWDl77hwwPi67EiLHCKQNlPTFUfLe19mgilSZjkSJilQwi5SShoF5zKCyQRBB6FkNegLQ4wb0sRRUY5ZFjFZZuRKorbX3d3iU/7rCJp0/Dzz9tOwqiFwlG1SRjmpIh1VkdAVpzURWNZBFFgYMmHl2pikQ04ADZgCaoULLKNBSJoJJA8HxlHVdW/NVXg787u8Cmj/fexfKv8ECAK+8Ahw+LLsKIs8wNBXZoApDD8BUIGJGUQAFMBUTgALFANSsATVjQslkoWbN4gfHXBQFuOce7o5eAH/H8Y03AmfPAmNjsish8gQ1Y4gFh/Ec14k4yXXXMVQK5O85dMEgcMstsqsgIqeoruYKewv4O1gAYOFCblJJRGKtyvvfzzUrFuC/IABs3iwG64jIv66/HqipkV2FJzBYADHz49ZbxaAdEflPfT2wfr3sKjyDwTJpwQJg7VrZVRBRsWma6ALjG0vLMFgut2mTOM+aiPzjxhuBigrZVXgKg+VygQBw223iIxF536JFwOrVsqvwHAbLlWprgZtukl0FEdktGhVjq2Q5BstMVqwQG1USkTcFAsAHPwiUlFz9tpQzBststm8H6upkV0FEdrj5ZjETjGzBYJlNIADccQePIyXymrVrgeXLZVfhaQyWuZSWAh/4AFfiEnlFU5M4NoNsxVfMq1m0iA9EIou8dvIkvv7UUxiOxYr/yysqxBtFrlexHYNlPtasEYf+EFFBXjt5Et/YubP4waLrwIc+BIRCxf29PsVgma/t28X510TkLooi1qdx8XPR+Pugr1wlEsDPf87zW4jy8PWnnsI3du6cdn3Ht7+NxXYeAbx5M/cBKzJ/H/SVq3AYuPNO4Be/AJI2n7dN5DG/s2ED2nt68Oibb+J7H/84aktLAQB1ZWX2/dJlyxgqEjBYclVVBXz4w8DTTwPptOxqiFxjXVMTNra04NE338RH16+3t5UCiHUq73ufvb+DZsQxlnzU14uWi8ZcJnKk6mrxBpDPUSkYLPlqbBQLKLnGhchZKiqAu+7iDDCJ+KpYiOZm4PbbGS5ETlFaKkIlEpFdia/xFbFQixfz9EmieVLsfJ5EIiJU3psUQPIwWKywbJnY1I6I5lSi6wCA4Xjc2jsOh0Wo8MAuR+DIllWuvRbIZIDXXpNdCZFjXd/aCgD48pNP4vc2bUIwEMDd69ahpJDxkHAY+K3fEgP25AhcIGm1t98G3nxTdhVEjvWtp5/GIy+/jAsjIzBMs7AFkgwVR2Kw2OGNN4ADB2RXQeRtk91fNTWyK6ErcIzFDps3Axs3yq6CyLsYKo7GFoudjh4FXnkFMAzZlRB5R1mZWPzITSUdi8FitzNngF/9SgzsE1Fh6urErhdcp+JoDJZi6OsDfvlLwOoplkR+0toqDuriNi2Ox2ApltFR4JlngJER2ZUQuc/q1cC2bVyI7BIMlmJKJIBnnwV6emRXQuQOigLceCOwbp3sSigHDJZiy2SA558HOjtlV0LkbJoGvP/9QFub7EooRwwWGUxTrNB/913ZlRA50+ShevX1siuhPDBYZPrNb4DXX5ddBZGzVFSI6cTl5bIroTwxWGQ7exZ44QUx/kLkd5PnHIXDsiuhAjBYnCAWA379a+DCBdmVEMmhKMCGDcD113PmlwcwWJzCNIH9+8UmlvwvIT+JRoHbbgMWLpRdCVmEweI058+LrrFYTHYlRPZraREH5bHry1MYLE4Uj4spyefPy66EyB6qCmzZAqxZI7sSsgGDxalMU2y9v28fu8bIWyoqxNYs+Z7BQo7HYHG67m4xsD8xIbsSosItXw5s3w4Eg7IrIRsxWNwgkQBefFHslEzkRsEgcPPNwLJlsiuhImCwuEl7O7B3L9e8kLssWCAG6Lng0TcYLG6TSIjtYE6ckF0J0dzCYbGB5IoVsiuhImOwuNXZs8Du3cD4uOxKiKZbuVIc0R0Kya6EJGCwuFk6LRZVHjrE44/JGWprgZtu4uaRPsdg8YLBQeDVV7klDMmj68ANN4gDubgli+8xWLzkxAkxuM9V+1RMy5aJxY7RqOxKyCEYLF6TTotFle++y+4xsldlpej24h5fdAUGi1eNjQFvvQUcP86AIWuFw8D69WI7FlWVXQ05EIPF60ZHLwUM/6upELouzp5fu5Yr52lODBa/GBkRM8hOnmTAUG40TbROrruO04dpXhgsfjM8LMZgTp2SXQk5naYBq1aJQIlEZFdDLsJg8avBQdGC6eiQXQk5ja6LFsqaNTwnhfLCYPG7gQERMJ2dsish2SIRMX6yapUIF6I8MVhIGB0Fjh4Fjh0TB42Rf9TUiC1Yli8X3V9EBWKw0FSGIbrHjhwBurpkV0N2CQaBpUuBa6/l9itkOQYLzW54WARMezuQTMquhqxQVyfCZNkyThkm2zBY6OqyWTFN+cgRoKdHdjWUK10XQbJypej2IrIZg4VyMzgIHD4s9iVLpWRXQ3NpaBBhsmQJx06oqBgslB/DELspnz4tZpTxXBhnqKsDWluBtjagqkp2NeRTDBayRn//pZAZGJBdjX9oGrBokQiTlhbuMEyOwGAh642Pi4Dp7AS6u7kJptVKSkSItLaKnYXZzUUOw2AheyWTwJkzojXT1QUkErIrcqfaWhEkra3icyIHY7BQcQ0Pi1bM5GV0VHZFzqPrYm1JXZ34WF/PvbrIVRgsJFcsBvT2ikt/P9DX5681M4GAmAJ8eZBUVMiuiqggDBZyntFRETB9fWK7/9FRcXBZJiO7ssJoGlBeLrqyJoOkpoaHZZHnMFjIPWIxETBjYyJsJgNnbAyYmJB/zoyqillZJSVAaakIkYoK8bG8nDO2yDcYLOQNhnEpZOJxIJ0WLZx0+tJlrq8NQ7QocrkEg1ODJBIBFEX2vwSRdAwWIiKyFDt3iYjIUgwWIiKyFIOFiIgsxWAhIiJLMViIiMhSDBYiIrIUg4WIiCzFYCEiIksxWIiIyFIMFiIishSDhYiILMVgISIiSzFYiIjIUgwWIiKyFIOFiIgsxWAhIiJLMVjI81544QUoioKf//zn0773k5/8BIqiYM+ePRIqI/ImniBJnmeaJlpbW7F582Y88cQTU75311134dixYzhx4oSk6oi8hy0W8jxFUfCpT30KO3fuxMjIyMXr+/r6sGvXLnzqU5+SWB2R9zBYyBd27NiBZDI5pcXys5/9DJlMhsFCZDF2hZFvbN68GaWlpXj++ecBAFu3bgUAjq8QWYwtFvKNHTt24KWXXsK5c+dw8uRJ7N27l60VIhuwxUK+0d/fj4ULF+Lb3/424vE4vvWtb6Grqwu1tbWySyPyFAYL+cq9996Lzs5OJBIJrFixAr/4xS9kl0TkOewKI1/ZsWMHDh48iPb2dnaDEdmELRbylVQqhQULFsAwDHR3dyMcDssuichzNNkFEBWTqqrQNA133303Q4XIJuwKI1958skn0dfXhx07dsguhciz2BVGvvD666/j4MGD+OY3v4na2lq89dZbsksi8iy2WMgX/umf/gn3338/6uvr8eMf/1h2OUSexhYLERFZii0WIiKyFIOFiIgsxWAhIiJLMViIiMhSDBYiIrIUg4WIiCzFYCEiIksxWIiIyFL/H9x9tdwDsrW7AAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from matplotlib_venn import venn2\n", "\n", "venn2([set(TM_FA_CRSRD_y.NODE_ID), set(TM_FA_CRSRD_t.NODE_ID)], ('y', 't'))" ] } ], "metadata": { "kernelspec": { "display_name": "siggen", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 2 }