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1110 lines
35 KiB
1110 lines
35 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"\n",
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"path_root = os.path.dirname(os.path.abspath('.'))\n",
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"path_yeday = os.path.join(path_root, '20240716')\n",
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"path_today = os.path.join(path_root, '20240717')\n",
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"\n",
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"TM_FA_CRSRD_y = pd.read_csv(os.path.join(path_yeday, 'TM_FA_CRSRD.csv'))\n",
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"TM_FA_CRSRD_t = pd.read_csv(os.path.join(path_today, 'TM_FA_CRSRD.csv'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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" 591,\n",
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" 592,\n",
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" 593,\n",
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" 594,\n",
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" 595,\n",
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" 596,\n",
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" 597,\n",
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" 598,\n",
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" 599,\n",
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" 600,\n",
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" 601,\n",
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" 602,\n",
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" 603,\n",
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" 604,\n",
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" 605,\n",
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" 606,\n",
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" 607,\n",
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" 608,\n",
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" 609,\n",
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" 610,\n",
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" 611,\n",
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" 612,\n",
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" 613,\n",
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" 614,\n",
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" 615,\n",
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" 616,\n",
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" 617,\n",
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" 618,\n",
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" 619,\n",
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" 620,\n",
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" 621,\n",
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" 622,\n",
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" 623,\n",
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" 624,\n",
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" 625,\n",
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" 626,\n",
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" 627,\n",
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" 628,\n",
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" 629,\n",
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" 630,\n",
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" 631,\n",
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" 632,\n",
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" 633,\n",
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" 634,\n",
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" 635,\n",
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" 636,\n",
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" 637,\n",
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" 638,\n",
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" 639,\n",
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" 640,\n",
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" 641,\n",
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" 642,\n",
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" 643,\n",
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" 644,\n",
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" 645,\n",
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" 646,\n",
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" 648,\n",
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" 649,\n",
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" 650,\n",
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" 651,\n",
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" 652,\n",
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" 653,\n",
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" 654,\n",
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" 655,\n",
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" 656,\n",
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" 657,\n",
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" 658,\n",
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" 659,\n",
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" 660,\n",
|
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" 661,\n",
|
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" 662,\n",
|
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" 663,\n",
|
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" 664,\n",
|
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" 665,\n",
|
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" 666,\n",
|
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" 667,\n",
|
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" 668,\n",
|
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" 669,\n",
|
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" 670,\n",
|
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" 671,\n",
|
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" 672,\n",
|
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" 673,\n",
|
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" 674,\n",
|
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" 675,\n",
|
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" 676,\n",
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" 677,\n",
|
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" 678,\n",
|
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" 679,\n",
|
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" 680,\n",
|
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" 681,\n",
|
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" 682,\n",
|
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" 683,\n",
|
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" 684,\n",
|
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" 685,\n",
|
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" 686,\n",
|
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" 687,\n",
|
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" 688,\n",
|
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" 689,\n",
|
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" 690,\n",
|
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" 691,\n",
|
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" 692,\n",
|
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" 693,\n",
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" 694,\n",
|
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" 695,\n",
|
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" 696,\n",
|
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" 697,\n",
|
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" 698,\n",
|
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" 699,\n",
|
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" 700,\n",
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" 701,\n",
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" 702,\n",
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" 703,\n",
|
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" 704,\n",
|
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" 705,\n",
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" 706,\n",
|
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" 707,\n",
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" 708,\n",
|
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" 709,\n",
|
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" 710,\n",
|
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" 711,\n",
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" 712,\n",
|
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" 713,\n",
|
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" 714,\n",
|
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" 715,\n",
|
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" 716,\n",
|
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" 717,\n",
|
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" 718,\n",
|
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" 719,\n",
|
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" 720,\n",
|
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" 721,\n",
|
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" 722,\n",
|
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" 723,\n",
|
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" 724,\n",
|
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" 725,\n",
|
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" 726,\n",
|
|
" 727,\n",
|
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" 728,\n",
|
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" 729,\n",
|
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" 730,\n",
|
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" 731,\n",
|
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" 732,\n",
|
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" 733,\n",
|
|
" 734,\n",
|
|
" 735,\n",
|
|
" 736,\n",
|
|
" 737,\n",
|
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" 738,\n",
|
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" 739,\n",
|
|
" 740,\n",
|
|
" 741,\n",
|
|
" 742,\n",
|
|
" 743,\n",
|
|
" 744,\n",
|
|
" 745,\n",
|
|
" 746,\n",
|
|
" 747,\n",
|
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" 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": [
|
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"[2,\n",
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" 5,\n",
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" 6,\n",
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" 9,\n",
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" 350,\n",
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" 354,\n",
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" 358,\n",
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" 361,\n",
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" 364,\n",
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" 365,\n",
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" 367,\n",
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" 369,\n",
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" 371,\n",
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" 373,\n",
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" 375,\n",
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" 376,\n",
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" 377,\n",
|
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" 378,\n",
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" 379,\n",
|
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" 405,\n",
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" 409,\n",
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" 410,\n",
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" 411,\n",
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" 412,\n",
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" 414,\n",
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" 415,\n",
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" 416,\n",
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" 417,\n",
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" 420,\n",
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" 424,\n",
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" 425,\n",
|
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" 437,\n",
|
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" 443,\n",
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" 445,\n",
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" 448,\n",
|
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" 456,\n",
|
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" 457,\n",
|
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" 459,\n",
|
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" 461,\n",
|
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" 462,\n",
|
|
" 463,\n",
|
|
" 466,\n",
|
|
" 467,\n",
|
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" 476,\n",
|
|
" 481,\n",
|
|
" 482,\n",
|
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" 484,\n",
|
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" 486,\n",
|
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" 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": [
|
|
"<matplotlib_venn._common.VennDiagram at 0x150b0b0d370>"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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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
|
|
}
|