{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Processing Example" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from fibermagic.IO.NeurophotometricsIO import read_project_rawdata, read_project_logs\n", "from pathlib import Path\n", "import plotly.express as px\n", "from fibermagic.core.demodulate import zdFF_airPLS, add_zdFF\n", "from fibermagic.core.perievents import perievents\n", "from fibermagic.utils.download_dataset import download" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading started\n", "Downloading Completed, Extracting...\n" ] } ], "source": [ "download('arena-data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data was taken in two batches from different mice. Batch 1 consists of one recording per mouse (n=2). Batch 2 includes two recordings per mouse (n=7). Every mouse was recorded individually (no multiple mice per recording).\n", "\n", "The experimental environment was a 1m x 1m sized arena. The mouse was able to move freely. Food pellets, a running wheel, and a novel object was placed in different locations of the arena. Sessions were 15 minutes long.\n", "\n", "For behavioral evaluation, a video was recorded using a IR-camera. The video was used to determing the region in which the mouse was." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data\\Batch 1\\Day1\\FP\\B9295\\arena.csv\n", "data\\Batch 1\\Day1\\FP\\B9296\\arena.csv\n", "data\\Batch 2\\Day1\\FP\\B9443\\arena.csv\n", "data\\Batch 2\\Day1\\FP\\B9670\\arena.csv\n", "data\\Batch 2\\Day1\\FP\\B9671\\arena.csv\n", "data\\Batch 2\\Day1\\FP\\B9888\\arena.csv\n", "data\\Batch 2\\Day1\\FP\\B9889\\arena.csv\n", "data\\Batch 2\\Day1\\FP\\B9890\\arena.csv\n", "data\\Batch 2\\Day1\\FP\\B9891\\arena.csv\n", "data\\Batch 2\\Day2\\FP\\B9443\\arena.csv\n", "data\\Batch 2\\Day2\\FP\\B9670\\arena.csv\n", "data\\Batch 2\\Day2\\FP\\B9671\\arena.csv\n", "data\\Batch 2\\Day2\\FP\\B9888\\arena.csv\n", "data\\Batch 2\\Day2\\FP\\B9889\\arena.csv\n", "data\\Batch 2\\Day2\\FP\\B9890\\arena.csv\n", "data\\Batch 2\\Day2\\FP\\B9891\\arena.csv\n" ] } ], "source": [ "PROJECT_PATH = Path(r'data')\n", "df_idx = read_project_rawdata(PROJECT_PATH,\n", " ['Batch', 'Day', 'FP', 'Mouse_'], 'arena.csv', ignore_dirs=['meta', 'processed', 'processed.zip'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A first spike into to investigate the raw data.\n", "\n", "By changing values to different mice and days, you can see that mice B9671 and B9889 had bad signals." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['B9295', 'B9296', 'B9443', 'B9670', 'B9671', 'B9888', 'B9889',\n", " 'B9890', 'B9891'], dtype=object)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df_idx.reset_index()\n", "df.Mouse.unique() # list recorded mice" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "variable=Signal
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390.8, 391.2, 391.6, 392, 392.4, 392.8, 393.2, 393.6, 394, 394.4, 394.8, 395.2, 395.6, 396, 396.4, 396.8, 397.2, 397.6, 398, 398.4, 398.8, 399.2, 399.6, 400, 400.4, 400.8, 401.2, 401.6, 402, 402.4, 402.8, 403.2, 403.6, 404, 404.4, 404.8, 405.2, 405.6, 406, 406.4, 406.8, 407.2, 407.6, 408, 408.4, 408.8, 409.2, 409.6, 410, 410.4, 410.8, 411.2, 411.6, 412, 412.4, 412.8, 413.2, 413.6, 414, 414.4, 414.8, 415.2, 415.6, 416, 416.4, 416.8, 417.2, 417.6, 418, 418.4, 418.8, 419.2, 419.6, 420, 420.4, 420.8, 421.2, 421.6, 422, 422.4, 422.8, 423.2, 423.6, 424, 424.4, 424.8, 425.2, 425.6, 426, 426.4, 426.8, 427.2, 427.6, 428, 428.4, 428.8, 429.2, 429.6, 430, 430.4, 430.8, 431.2, 431.6, 432, 432.4, 432.8, 433.2, 433.6, 434, 434.4, 434.8, 435.2, 435.6, 436, 436.4, 436.8, 437.2, 437.6, 438, 438.4, 438.8, 439.2, 439.6, 440, 440.4, 440.8, 441.2, 441.6, 442, 442.4, 442.8, 443.2, 443.6, 444, 444.4, 444.8, 445.2, 445.6, 446, 446.4, 446.8, 447.2, 447.6, 448, 448.4, 448.8, 449.2, 449.6, 450, 450.4, 450.8, 451.2, 451.6, 452, 452.4, 452.8, 453.2, 453.6, 454, 454.4, 454.8, 455.2, 455.6, 456, 456.4, 456.8, 457.2, 457.6, 458, 458.4, 458.8, 459.2, 459.6, 460, 460.4, 460.8, 461.2, 461.6, 462, 462.4, 462.8, 463.2, 463.6, 464, 464.4, 464.8, 465.2, 465.6, 466, 466.4, 466.8, 467.2, 467.6, 468, 468.4, 468.8, 469.2, 469.6, 470, 470.4, 470.8, 471.2, 471.6, 472, 472.4, 472.8, 473.2, 473.6, 474, 474.4, 474.8, 475.2, 475.6, 476, 476.4, 476.8, 477.2, 477.6, 478, 478.4, 478.8, 479.2, 479.6, 480, 480.4, 480.8, 481.2, 481.6, 482, 482.4, 482.8, 483.2, 483.6, 484, 484.4, 484.8, 485.2, 485.6, 486, 486.4, 486.8, 487.2, 487.6, 488, 488.4, 488.8, 489.2, 489.6, 490, 490.4, 490.8, 491.2, 491.6, 492, 492.4, 492.8, 493.2, 493.6, 494, 494.4, 494.8, 495.2, 495.6, 496, 496.4, 496.8, 497.2, 497.6, 498, 498.4, 498.8, 499.2, 499.6, 500, 500.4, 500.8, 501.2, 501.6, 502, 502.4, 502.8, 503.2, 503.6, 504, 504.4, 504.8, 505.2, 505.6, 506, 506.4, 506.8, 507.2, 507.6, 508, 508.4, 508.8, 509.2, 509.6, 510, 510.4, 510.8, 511.2, 511.6, 512, 512.4, 512.8, 513.2, 513.6, 514, 514.4, 514.8, 515.2, 515.6, 516, 516.4, 516.8, 517.2, 517.6, 518, 518.4, 518.8, 519.2, 519.6, 520, 520.4, 520.8, 521.2, 521.6, 522, 522.4, 522.8, 523.2, 523.6, 524, 524.4, 524.8, 525.2, 525.6, 526, 526.4, 526.8, 527.2, 527.6, 528, 528.4, 528.8, 529.2, 529.6, 530, 530.4, 530.8, 531.2, 531.6, 532, 532.4, 532.8, 533.2, 533.6, 534, 534.4, 534.8, 535.2, 535.6, 536, 536.4, 536.8, 537.2, 537.6, 538, 538.4, 538.8, 539.2, 539.6, 540, 540.4, 540.8, 541.2, 541.6, 542, 542.4, 542.8, 543.2, 543.6, 544, 544.4, 544.8, 545.2, 545.6, 546, 546.4, 546.8, 547.2, 547.6, 548, 548.4, 548.8, 549.2, 549.6, 550, 550.4, 550.8, 551.2, 551.6, 552, 552.4, 552.8, 553.2, 553.6, 554, 554.4, 554.8, 555.2, 555.6, 556, 556.4, 556.8, 557.2, 557.6, 558, 558.4, 558.8, 559.2, 559.6, 560, 560.4, 560.8, 561.2, 561.6, 562, 562.4, 562.8, 563.2, 563.6, 564, 564.4, 564.8, 565.2, 565.6, 566, 566.4, 566.8, 567.2, 567.6, 568, 568.4, 568.8, 569.2, 569.6, 570, 570.4, 570.8, 571.2, 571.6, 572, 572.4, 572.8, 573.2, 573.6, 574, 574.4, 574.8, 575.2, 575.6, 576, 576.4, 576.8, 577.2, 577.6, 578, 578.4, 578.8, 579.2, 579.6, 580, 580.4, 580.8, 581.2, 581.6, 582, 582.4, 582.8, 583.2, 583.6, 584, 584.4, 584.8, 585.2, 585.6, 586, 586.4, 586.8, 587.2, 587.6, 588, 588.4, 588.8, 589.2, 589.6, 590, 590.4, 590.8, 591.2, 591.6, 592, 592.4, 592.8, 593.2, 593.6, 594, 594.4, 594.8, 595.2, 595.6, 596, 596.4, 596.8, 597.2, 597.6, 598, 598.4, 598.8, 599.2, 599.6, 600, 600.4, 600.8, 601.2, 601.6, 602, 602.4, 602.8, 603.2, 603.6, 604, 604.4, 604.8, 605.2, 605.6, 606, 606.4, 606.8, 607.2, 607.6, 608, 608.4, 608.8, 609.2, 609.6, 610, 610.4, 610.8, 611.2, 611.6, 612, 612.4, 612.8, 613.2, 613.6, 614, 614.4, 614.8, 615.2, 615.6, 616, 616.4, 616.8, 617.2, 617.6, 618, 618.4, 618.8, 619.2, 619.6, 620, 620.4, 620.8, 621.2, 621.6, 622, 622.4, 622.8, 623.2, 623.6, 624, 624.4, 624.8, 625.2, 625.6, 626, 626.4, 626.8, 627.2, 627.6, 628, 628.4, 628.8, 629.2, 629.6, 630, 630.4, 630.8, 631.2, 631.6, 632, 632.4, 632.8, 633.2, 633.6, 634, 634.4, 634.8, 635.2, 635.6, 636, 636.4, 636.8, 637.2, 637.6, 638, 638.4, 638.8, 639.2, 639.6, 640, 640.4, 640.8, 641.2, 641.6, 642, 642.4, 642.8, 643.2, 643.6, 644, 644.4, 644.8, 645.2, 645.6, 646, 646.4, 646.8, 647.2, 647.6, 648, 648.4, 648.8, 649.2, 649.6, 650, 650.4, 650.8, 651.2, 651.6, 652, 652.4, 652.8, 653.2, 653.6, 654, 654.4, 654.8, 655.2, 655.6, 656, 656.4, 656.8, 657.2, 657.6, 658, 658.4, 658.8, 659.2, 659.6, 660, 660.4, 660.8, 661.2, 661.6, 662, 662.4, 662.8, 663.2, 663.6, 664, 664.4, 664.8, 665.2, 665.6, 666, 666.4, 666.8, 667.2, 667.6, 668, 668.4, 668.8, 669.2, 669.6, 670, 670.4, 670.8, 671.2, 671.6, 672, 672.4, 672.8, 673.2, 673.6, 674, 674.4, 674.8, 675.2, 675.6, 676, 676.4, 676.8, 677.2, 677.6, 678, 678.4, 678.8, 679.2, 679.6, 680, 680.4, 680.8, 681.2, 681.6, 682, 682.4, 682.8, 683.2, 683.6, 684, 684.4, 684.8, 685.2, 685.6, 686, 686.4, 686.8, 687.2, 687.6, 688, 688.4, 688.8, 689.2, 689.6, 690, 690.4, 690.8, 691.2, 691.6, 692, 692.4, 692.8, 693.2, 693.6, 694, 694.4, 694.8, 695.2, 695.6, 696, 696.4, 696.8, 697.2, 697.6, 698, 698.4, 698.8, 699.2, 699.6, 700, 700.4, 700.8, 701.2, 701.6, 702, 702.4, 702.8, 703.2, 703.6, 704, 704.4, 704.8, 705.2, 705.6, 706, 706.4, 706.8, 707.2, 707.6, 708, 708.4, 708.8, 709.2, 709.6, 710, 710.4, 710.8, 711.2, 711.6, 712, 712.4, 712.8, 713.2, 713.6, 714, 714.4, 714.8, 715.2, 715.6, 716, 716.4, 716.8, 717.2, 717.6, 718, 718.4, 718.8, 719.2, 719.6, 720, 720.4, 720.8, 721.2, 721.6, 722, 722.4, 722.8, 723.2, 723.6, 724, 724.4, 724.8, 725.2, 725.6, 726, 726.4, 726.8, 727.2, 727.6, 728, 728.4, 728.8, 729.2, 729.6, 730, 730.4, 730.8, 731.2, 731.6, 732, 732.4, 732.8, 733.2, 733.6, 734, 734.4, 734.8, 735.2, 735.6, 736, 736.4, 736.8, 737.2, 737.6, 738, 738.4, 738.8, 739.2, 739.6, 740, 740.4, 740.8, 741.2, 741.6, 742, 742.4, 742.8, 743.2, 743.6, 744, 744.4, 744.8, 745.2, 745.6, 746, 746.4, 746.8, 747.2, 747.6, 748, 748.4, 748.8, 749.2, 749.6, 750, 750.4, 750.8, 751.2, 751.6, 752, 752.4, 752.8, 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330.4, 330.8, 331.2, 331.6, 332, 332.4, 332.8, 333.2, 333.6, 334, 334.4, 334.8, 335.2, 335.6, 336, 336.4, 336.8, 337.2, 337.6, 338, 338.4, 338.8, 339.2, 339.6, 340, 340.4, 340.8, 341.2, 341.6, 342, 342.4, 342.8, 343.2, 343.6, 344, 344.4, 344.8, 345.2, 345.6, 346, 346.4, 346.8, 347.2, 347.6, 348, 348.4, 348.8, 349.2, 349.6, 350, 350.4, 350.8, 351.2, 351.6, 352, 352.4, 352.8, 353.2, 353.6, 354, 354.4, 354.8, 355.2, 355.6, 356, 356.4, 356.8, 357.2, 357.6, 358, 358.4, 358.8, 359.2, 359.6, 360, 360.4, 360.8, 361.2, 361.6, 362, 362.4, 362.8, 363.2, 363.6, 364, 364.4, 364.8, 365.2, 365.6, 366, 366.4, 366.8, 367.2, 367.6, 368, 368.4, 368.8, 369.2, 369.6, 370, 370.4, 370.8, 371.2, 371.6, 372, 372.4, 372.8, 373.2, 373.6, 374, 374.4, 374.8, 375.2, 375.6, 376, 376.4, 376.8, 377.2, 377.6, 378, 378.4, 378.8, 379.2, 379.6, 380, 380.4, 380.8, 381.2, 381.6, 382, 382.4, 382.8, 383.2, 383.6, 384, 384.4, 384.8, 385.2, 385.6, 386, 386.4, 386.8, 387.2, 387.6, 388, 388.4, 388.8, 389.2, 389.6, 390, 390.4, 390.8, 391.2, 391.6, 392, 392.4, 392.8, 393.2, 393.6, 394, 394.4, 394.8, 395.2, 395.6, 396, 396.4, 396.8, 397.2, 397.6, 398, 398.4, 398.8, 399.2, 399.6, 400, 400.4, 400.8, 401.2, 401.6, 402, 402.4, 402.8, 403.2, 403.6, 404, 404.4, 404.8, 405.2, 405.6, 406, 406.4, 406.8, 407.2, 407.6, 408, 408.4, 408.8, 409.2, 409.6, 410, 410.4, 410.8, 411.2, 411.6, 412, 412.4, 412.8, 413.2, 413.6, 414, 414.4, 414.8, 415.2, 415.6, 416, 416.4, 416.8, 417.2, 417.6, 418, 418.4, 418.8, 419.2, 419.6, 420, 420.4, 420.8, 421.2, 421.6, 422, 422.4, 422.8, 423.2, 423.6, 424, 424.4, 424.8, 425.2, 425.6, 426, 426.4, 426.8, 427.2, 427.6, 428, 428.4, 428.8, 429.2, 429.6, 430, 430.4, 430.8, 431.2, 431.6, 432, 432.4, 432.8, 433.2, 433.6, 434, 434.4, 434.8, 435.2, 435.6, 436, 436.4, 436.8, 437.2, 437.6, 438, 438.4, 438.8, 439.2, 439.6, 440, 440.4, 440.8, 441.2, 441.6, 442, 442.4, 442.8, 443.2, 443.6, 444, 444.4, 444.8, 445.2, 445.6, 446, 446.4, 446.8, 447.2, 447.6, 448, 448.4, 448.8, 449.2, 449.6, 450, 450.4, 450.8, 451.2, 451.6, 452, 452.4, 452.8, 453.2, 453.6, 454, 454.4, 454.8, 455.2, 455.6, 456, 456.4, 456.8, 457.2, 457.6, 458, 458.4, 458.8, 459.2, 459.6, 460, 460.4, 460.8, 461.2, 461.6, 462, 462.4, 462.8, 463.2, 463.6, 464, 464.4, 464.8, 465.2, 465.6, 466, 466.4, 466.8, 467.2, 467.6, 468, 468.4, 468.8, 469.2, 469.6, 470, 470.4, 470.8, 471.2, 471.6, 472, 472.4, 472.8, 473.2, 473.6, 474, 474.4, 474.8, 475.2, 475.6, 476, 476.4, 476.8, 477.2, 477.6, 478, 478.4, 478.8, 479.2, 479.6, 480, 480.4, 480.8, 481.2, 481.6, 482, 482.4, 482.8, 483.2, 483.6, 484, 484.4, 484.8, 485.2, 485.6, 486, 486.4, 486.8, 487.2, 487.6, 488, 488.4, 488.8, 489.2, 489.6, 490, 490.4, 490.8, 491.2, 491.6, 492, 492.4, 492.8, 493.2, 493.6, 494, 494.4, 494.8, 495.2, 495.6, 496, 496.4, 496.8, 497.2, 497.6, 498, 498.4, 498.8, 499.2, 499.6, 500, 500.4, 500.8, 501.2, 501.6, 502, 502.4, 502.8, 503.2, 503.6, 504, 504.4, 504.8, 505.2, 505.6, 506, 506.4, 506.8, 507.2, 507.6, 508, 508.4, 508.8, 509.2, 509.6, 510, 510.4, 510.8, 511.2, 511.6, 512, 512.4, 512.8, 513.2, 513.6, 514, 514.4, 514.8, 515.2, 515.6, 516, 516.4, 516.8, 517.2, 517.6, 518, 518.4, 518.8, 519.2, 519.6, 520, 520.4, 520.8, 521.2, 521.6, 522, 522.4, 522.8, 523.2, 523.6, 524, 524.4, 524.8, 525.2, 525.6, 526, 526.4, 526.8, 527.2, 527.6, 528, 528.4, 528.8, 529.2, 529.6, 530, 530.4, 530.8, 531.2, 531.6, 532, 532.4, 532.8, 533.2, 533.6, 534, 534.4, 534.8, 535.2, 535.6, 536, 536.4, 536.8, 537.2, 537.6, 538, 538.4, 538.8, 539.2, 539.6, 540, 540.4, 540.8, 541.2, 541.6, 542, 542.4, 542.8, 543.2, 543.6, 544, 544.4, 544.8, 545.2, 545.6, 546, 546.4, 546.8, 547.2, 547.6, 548, 548.4, 548.8, 549.2, 549.6, 550, 550.4, 550.8, 551.2, 551.6, 552, 552.4, 552.8, 553.2, 553.6, 554, 554.4, 554.8, 555.2, 555.6, 556, 556.4, 556.8, 557.2, 557.6, 558, 558.4, 558.8, 559.2, 559.6, 560, 560.4, 560.8, 561.2, 561.6, 562, 562.4, 562.8, 563.2, 563.6, 564, 564.4, 564.8, 565.2, 565.6, 566, 566.4, 566.8, 567.2, 567.6, 568, 568.4, 568.8, 569.2, 569.6, 570, 570.4, 570.8, 571.2, 571.6, 572, 572.4, 572.8, 573.2, 573.6, 574, 574.4, 574.8, 575.2, 575.6, 576, 576.4, 576.8, 577.2, 577.6, 578, 578.4, 578.8, 579.2, 579.6, 580, 580.4, 580.8, 581.2, 581.6, 582, 582.4, 582.8, 583.2, 583.6, 584, 584.4, 584.8, 585.2, 585.6, 586, 586.4, 586.8, 587.2, 587.6, 588, 588.4, 588.8, 589.2, 589.6, 590, 590.4, 590.8, 591.2, 591.6, 592, 592.4, 592.8, 593.2, 593.6, 594, 594.4, 594.8, 595.2, 595.6, 596, 596.4, 596.8, 597.2, 597.6, 598, 598.4, 598.8, 599.2, 599.6, 600, 600.4, 600.8, 601.2, 601.6, 602, 602.4, 602.8, 603.2, 603.6, 604, 604.4, 604.8, 605.2, 605.6, 606, 606.4, 606.8, 607.2, 607.6, 608, 608.4, 608.8, 609.2, 609.6, 610, 610.4, 610.8, 611.2, 611.6, 612, 612.4, 612.8, 613.2, 613.6, 614, 614.4, 614.8, 615.2, 615.6, 616, 616.4, 616.8, 617.2, 617.6, 618, 618.4, 618.8, 619.2, 619.6, 620, 620.4, 620.8, 621.2, 621.6, 622, 622.4, 622.8, 623.2, 623.6, 624, 624.4, 624.8, 625.2, 625.6, 626, 626.4, 626.8, 627.2, 627.6, 628, 628.4, 628.8, 629.2, 629.6, 630, 630.4, 630.8, 631.2, 631.6, 632, 632.4, 632.8, 633.2, 633.6, 634, 634.4, 634.8, 635.2, 635.6, 636, 636.4, 636.8, 637.2, 637.6, 638, 638.4, 638.8, 639.2, 639.6, 640, 640.4, 640.8, 641.2, 641.6, 642, 642.4, 642.8, 643.2, 643.6, 644, 644.4, 644.8, 645.2, 645.6, 646, 646.4, 646.8, 647.2, 647.6, 648, 648.4, 648.8, 649.2, 649.6, 650, 650.4, 650.8, 651.2, 651.6, 652, 652.4, 652.8, 653.2, 653.6, 654, 654.4, 654.8, 655.2, 655.6, 656, 656.4, 656.8, 657.2, 657.6, 658, 658.4, 658.8, 659.2, 659.6, 660, 660.4, 660.8, 661.2, 661.6, 662, 662.4, 662.8, 663.2, 663.6, 664, 664.4, 664.8, 665.2, 665.6, 666, 666.4, 666.8, 667.2, 667.6, 668, 668.4, 668.8, 669.2, 669.6, 670, 670.4, 670.8, 671.2, 671.6, 672, 672.4, 672.8, 673.2, 673.6, 674, 674.4, 674.8, 675.2, 675.6, 676, 676.4, 676.8, 677.2, 677.6, 678, 678.4, 678.8, 679.2, 679.6, 680, 680.4, 680.8, 681.2, 681.6, 682, 682.4, 682.8, 683.2, 683.6, 684, 684.4, 684.8, 685.2, 685.6, 686, 686.4, 686.8, 687.2, 687.6, 688, 688.4, 688.8, 689.2, 689.6, 690, 690.4, 690.8, 691.2, 691.6, 692, 692.4, 692.8, 693.2, 693.6, 694, 694.4, 694.8, 695.2, 695.6, 696, 696.4, 696.8, 697.2, 697.6, 698, 698.4, 698.8, 699.2, 699.6, 700, 700.4, 700.8, 701.2, 701.6, 702, 702.4, 702.8, 703.2, 703.6, 704, 704.4, 704.8, 705.2, 705.6, 706, 706.4, 706.8, 707.2, 707.6, 708, 708.4, 708.8, 709.2, 709.6, 710, 710.4, 710.8, 711.2, 711.6, 712, 712.4, 712.8, 713.2, 713.6, 714, 714.4, 714.8, 715.2, 715.6, 716, 716.4, 716.8, 717.2, 717.6, 718, 718.4, 718.8, 719.2, 719.6, 720, 720.4, 720.8, 721.2, 721.6, 722, 722.4, 722.8, 723.2, 723.6, 724, 724.4, 724.8, 725.2, 725.6, 726, 726.4, 726.8, 727.2, 727.6, 728, 728.4, 728.8, 729.2, 729.6, 730, 730.4, 730.8, 731.2, 731.6, 732, 732.4, 732.8, 733.2, 733.6, 734, 734.4, 734.8, 735.2, 735.6, 736, 736.4, 736.8, 737.2, 737.6, 738, 738.4, 738.8, 739.2, 739.6, 740, 740.4, 740.8, 741.2, 741.6, 742, 742.4, 742.8, 743.2, 743.6, 744, 744.4, 744.8, 745.2, 745.6, 746, 746.4, 746.8, 747.2, 747.6, 748, 748.4, 748.8, 749.2, 749.6, 750, 750.4, 750.8, 751.2, 751.6, 752, 752.4, 752.8, 753.2, 753.6, 754, 754.4, 754.8, 755.2, 755.6, 756, 756.4, 756.8, 757.2, 757.6, 758, 758.4, 758.8, 759.2, 759.6, 760, 760.4, 760.8, 761.2, 761.6, 762, 762.4, 762.8, 763.2, 763.6, 764, 764.4, 764.8, 765.2, 765.6, 766, 766.4, 766.8, 767.2, 767.6, 768, 768.4, 768.8, 769.2, 769.6, 770, 770.4, 770.8, 771.2, 771.6, 772, 772.4, 772.8, 773.2, 773.6, 774, 774.4, 774.8, 775.2, 775.6, 776, 776.4, 776.8, 777.2, 777.6, 778, 778.4, 778.8, 779.2, 779.6, 780, 780.4, 780.8, 781.2, 781.6, 782, 782.4, 782.8, 783.2, 783.6, 784, 784.4, 784.8, 785.2, 785.6, 786, 786.4, 786.8, 787.2, 787.6, 788, 788.4, 788.8, 789.2, 789.6, 790, 790.4, 790.8, 791.2, 791.6, 792, 792.4, 792.8, 793.2, 793.6, 794, 794.4, 794.8, 795.2, 795.6, 796, 796.4, 796.8, 797.2, 797.6, 798, 798.4, 798.8, 799.2, 799.6, 800, 800.4, 800.8, 801.2, 801.6, 802, 802.4, 802.8, 803.2, 803.6, 804, 804.4, 804.8, 805.2, 805.6, 806, 806.4, 806.8, 807.2, 807.6, 808, 808.4, 808.8, 809.2, 809.6, 810, 810.4, 810.8, 811.2, 811.6, 812, 812.4, 812.8, 813.2, 813.6, 814, 814.4, 814.8, 815.2, 815.6, 816, 816.4, 816.8, 817.2, 817.6, 818, 818.4, 818.8, 819.2, 819.6, 820, 820.4, 820.8, 821.2, 821.6, 822, 822.4, 822.8, 823.2, 823.6, 824, 824.4, 824.8, 825.2, 825.6, 826, 826.4, 826.8, 827.2, 827.6, 828, 828.4, 828.8, 829.2, 829.6, 830, 830.4, 830.8, 831.2, 831.6, 832, 832.4, 832.8, 833.2, 833.6, 834, 834.4, 834.8, 835.2, 835.6, 836, 836.4, 836.8, 837.2, 837.6, 838, 838.4, 838.8, 839.2, 839.6, 840, 840.4, 840.8, 841.2, 841.6, 842, 842.4, 842.8, 843.2, 843.6, 844, 844.4, 844.8, 845.2, 845.6, 846, 846.4, 846.8, 847.2, 847.6, 848, 848.4, 848.8, 849.2, 849.6, 850, 850.4, 850.8, 851.2, 851.6, 852, 852.4, 852.8, 853.2, 853.6, 854, 854.4, 854.8, 855.2, 855.6, 856, 856.4, 856.8, 857.2, 857.6, 858, 858.4, 858.8, 859.2, 859.6, 860, 860.4, 860.8, 861.2, 861.6, 862, 862.4, 862.8, 863.2, 863.6, 864, 864.4, 864.8, 865.2, 865.6, 866, 866.4, 866.8, 867.2, 867.6, 868, 868.4, 868.8, 869.2, 869.6, 870, 870.4, 870.8, 871.2, 871.6, 872, 872.4, 872.8, 873.2, 873.6, 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568.4, 568.8, 569.2, 569.6, 570, 570.4, 570.8, 571.2, 571.6, 572, 572.4, 572.8, 573.2, 573.6, 574, 574.4, 574.8, 575.2, 575.6, 576, 576.4, 576.8, 577.2, 577.6, 578, 578.4, 578.8, 579.2, 579.6, 580, 580.4, 580.8, 581.2, 581.6, 582, 582.4, 582.8, 583.2, 583.6, 584, 584.4, 584.8, 585.2, 585.6, 586, 586.4, 586.8, 587.2, 587.6, 588, 588.4, 588.8, 589.2, 589.6, 590, 590.4, 590.8, 591.2, 591.6, 592, 592.4, 592.8, 593.2, 593.6, 594, 594.4, 594.8, 595.2, 595.6, 596, 596.4, 596.8, 597.2, 597.6, 598, 598.4, 598.8, 599.2, 599.6, 600, 600.4, 600.8, 601.2, 601.6, 602, 602.4, 602.8, 603.2, 603.6, 604, 604.4, 604.8, 605.2, 605.6, 606, 606.4, 606.8, 607.2, 607.6, 608, 608.4, 608.8, 609.2, 609.6, 610, 610.4, 610.8, 611.2, 611.6, 612, 612.4, 612.8, 613.2, 613.6, 614, 614.4, 614.8, 615.2, 615.6, 616, 616.4, 616.8, 617.2, 617.6, 618, 618.4, 618.8, 619.2, 619.6, 620, 620.4, 620.8, 621.2, 621.6, 622, 622.4, 622.8, 623.2, 623.6, 624, 624.4, 624.8, 625.2, 625.6, 626, 626.4, 626.8, 627.2, 627.6, 628, 628.4, 628.8, 629.2, 629.6, 630, 630.4, 630.8, 631.2, 631.6, 632, 632.4, 632.8, 633.2, 633.6, 634, 634.4, 634.8, 635.2, 635.6, 636, 636.4, 636.8, 637.2, 637.6, 638, 638.4, 638.8, 639.2, 639.6, 640, 640.4, 640.8, 641.2, 641.6, 642, 642.4, 642.8, 643.2, 643.6, 644, 644.4, 644.8, 645.2, 645.6, 646, 646.4, 646.8, 647.2, 647.6, 648, 648.4, 648.8, 649.2, 649.6, 650, 650.4, 650.8, 651.2, 651.6, 652, 652.4, 652.8, 653.2, 653.6, 654, 654.4, 654.8, 655.2, 655.6, 656, 656.4, 656.8, 657.2, 657.6, 658, 658.4, 658.8, 659.2, 659.6, 660, 660.4, 660.8, 661.2, 661.6, 662, 662.4, 662.8, 663.2, 663.6, 664, 664.4, 664.8, 665.2, 665.6, 666, 666.4, 666.8, 667.2, 667.6, 668, 668.4, 668.8, 669.2, 669.6, 670, 670.4, 670.8, 671.2, 671.6, 672, 672.4, 672.8, 673.2, 673.6, 674, 674.4, 674.8, 675.2, 675.6, 676, 676.4, 676.8, 677.2, 677.6, 678, 678.4, 678.8, 679.2, 679.6, 680, 680.4, 680.8, 681.2, 681.6, 682, 682.4, 682.8, 683.2, 683.6, 684, 684.4, 684.8, 685.2, 685.6, 686, 686.4, 686.8, 687.2, 687.6, 688, 688.4, 688.8, 689.2, 689.6, 690, 690.4, 690.8, 691.2, 691.6, 692, 692.4, 692.8, 693.2, 693.6, 694, 694.4, 694.8, 695.2, 695.6, 696, 696.4, 696.8, 697.2, 697.6, 698, 698.4, 698.8, 699.2, 699.6, 700, 700.4, 700.8, 701.2, 701.6, 702, 702.4, 702.8, 703.2, 703.6, 704, 704.4, 704.8, 705.2, 705.6, 706, 706.4, 706.8, 707.2, 707.6, 708, 708.4, 708.8, 709.2, 709.6, 710, 710.4, 710.8, 711.2, 711.6, 712, 712.4, 712.8, 713.2, 713.6, 714, 714.4, 714.8, 715.2, 715.6, 716, 716.4, 716.8, 717.2, 717.6, 718, 718.4, 718.8, 719.2, 719.6, 720, 720.4, 720.8, 721.2, 721.6, 722, 722.4, 722.8, 723.2, 723.6, 724, 724.4, 724.8, 725.2, 725.6, 726, 726.4, 726.8, 727.2, 727.6, 728, 728.4, 728.8, 729.2, 729.6, 730, 730.4, 730.8, 731.2, 731.6, 732, 732.4, 732.8, 733.2, 733.6, 734, 734.4, 734.8, 735.2, 735.6, 736, 736.4, 736.8, 737.2, 737.6, 738, 738.4, 738.8, 739.2, 739.6, 740, 740.4, 740.8, 741.2, 741.6, 742, 742.4, 742.8, 743.2, 743.6, 744, 744.4, 744.8, 745.2, 745.6, 746, 746.4, 746.8, 747.2, 747.6, 748, 748.4, 748.8, 749.2, 749.6, 750, 750.4, 750.8, 751.2, 751.6, 752, 752.4, 752.8, 753.2, 753.6, 754, 754.4, 754.8, 755.2, 755.6, 756, 756.4, 756.8, 757.2, 757.6, 758, 758.4, 758.8, 759.2, 759.6, 760, 760.4, 760.8, 761.2, 761.6, 762, 762.4, 762.8, 763.2, 763.6, 764, 764.4, 764.8, 765.2, 765.6, 766, 766.4, 766.8, 767.2, 767.6, 768, 768.4, 768.8, 769.2, 769.6, 770, 770.4, 770.8, 771.2, 771.6, 772, 772.4, 772.8, 773.2, 773.6, 774, 774.4, 774.8, 775.2, 775.6, 776, 776.4, 776.8, 777.2, 777.6, 778, 778.4, 778.8, 779.2, 779.6, 780, 780.4, 780.8, 781.2, 781.6, 782, 782.4, 782.8, 783.2, 783.6, 784, 784.4, 784.8, 785.2, 785.6, 786, 786.4, 786.8, 787.2, 787.6, 788, 788.4, 788.8, 789.2, 789.6, 790, 790.4, 790.8, 791.2, 791.6, 792, 792.4, 792.8, 793.2, 793.6, 794, 794.4, 794.8, 795.2, 795.6, 796, 796.4, 796.8, 797.2, 797.6, 798, 798.4, 798.8, 799.2, 799.6, 800, 800.4, 800.8, 801.2, 801.6, 802, 802.4, 802.8, 803.2, 803.6, 804, 804.4, 804.8, 805.2, 805.6, 806, 806.4, 806.8, 807.2, 807.6, 808, 808.4, 808.8, 809.2, 809.6, 810, 810.4, 810.8, 811.2, 811.6, 812, 812.4, 812.8, 813.2, 813.6, 814, 814.4, 814.8, 815.2, 815.6, 816, 816.4, 816.8, 817.2, 817.6, 818, 818.4, 818.8, 819.2, 819.6, 820, 820.4, 820.8, 821.2, 821.6, 822, 822.4, 822.8, 823.2, 823.6, 824, 824.4, 824.8, 825.2, 825.6, 826, 826.4, 826.8, 827.2, 827.6, 828, 828.4, 828.8, 829.2, 829.6, 830, 830.4, 830.8, 831.2, 831.6, 832, 832.4, 832.8, 833.2, 833.6, 834, 834.4, 834.8, 835.2, 835.6, 836, 836.4, 836.8, 837.2, 837.6, 838, 838.4, 838.8, 839.2, 839.6, 840, 840.4, 840.8, 841.2, 841.6, 842, 842.4, 842.8, 843.2, 843.6, 844, 844.4, 844.8, 845.2, 845.6, 846, 846.4, 846.8, 847.2, 847.6, 848, 848.4, 848.8, 849.2, 849.6, 850, 850.4, 850.8, 851.2, 851.6, 852, 852.4, 852.8, 853.2, 853.6, 854, 854.4, 854.8, 855.2, 855.6, 856, 856.4, 856.8, 857.2, 857.6, 858, 858.4, 858.8, 859.2, 859.6, 860, 860.4, 860.8, 861.2, 861.6, 862, 862.4, 862.8, 863.2, 863.6, 864, 864.4, 864.8, 865.2, 865.6, 866, 866.4, 866.8, 867.2, 867.6, 868, 868.4, 868.8, 869.2, 869.6, 870, 870.4, 870.8, 871.2, 871.6, 872, 872.4, 872.8, 873.2, 873.6, 874, 874.4, 874.8, 875.2, 875.6, 876, 876.4, 876.8, 877.2, 877.6, 878, 878.4, 878.8, 879.2, 879.6, 880, 880.4, 880.8, 881.2, 881.6, 882, 882.4, 882.8, 883.2, 883.6, 884, 884.4, 884.8, 885.2, 885.6, 886, 886.4, 886.8, 887.2, 887.6, 888, 888.4, 888.8, 889.2, 889.6, 890, 890.4, 890.8, 891.2, 891.6, 892, 892.4, 892.8, 893.2, 893.6, 894, 894.4, 894.8, 895.2, 895.6, 896, 896.4, 896.8, 897.2, 897.6, 898, 898.4, 898.8, 899.2, 899.6, 900, 900.4, 900.8, 901.2, 901.6, 902, 902.4, 902.8, 903.2, 903.6, 904, 904.4, 904.8, 905.2, 905.6, 906, 906.4, 906.8, 907.2, 907.6, 908, 908.4, 908.8, 909.2, 909.6, 910, 910.4, 910.8, 911.2, 911.6, 912, 912.4, 912.8, 913.2, 913.6, 914, 914.4, 914.8, 915.2, 915.6, 916, 916.4, 916.8, 917.2, 917.6, 918, 918.4, 918.8, 919.2, 919.6, 920, 920.4, 920.8, 921.2, 921.6, 922, 922.4, 922.8, 923.2, 923.6, 924, 924.4, 924.8, 925.2, 925.6, 926, 926.4, 926.8, 927.2, 927.6, 928, 928.4, 928.8, 929.2, 929.6, 930, 930.4, 930.8, 931.2, 931.6, 932, 932.4, 932.8, 933.2, 933.6, 934, 934.4, 934.8, 935.2, 935.6, 936, 936.4, 936.8, 937.2, 937.6, 938, 938.4, 938.8, 939.2, 939.6, 940, 940.4, 940.8, 941.2, 941.6, 942, 942.4, 942.8, 943.2, 943.6, 944, 944.4, 944.8, 945.2, 945.6, 946, 946.4, 946.8, 947.2, 947.6, 948, 948.4, 948.8, 949.2, 949.6, 950, 950.4, 950.8, 951.2, 951.6, 952, 952.4, 952.8, 953.2, 953.6, 954, 954.4, 954.8, 955.2, 955.6, 956, 956.4, 956.8, 957.2, 957.6, 958, 958.4, 958.8, 959.2, 959.6, 960, 960.4, 960.8, 961.2, 961.6, 962, 962.4, 962.8, 963.2, 963.6, 964, 964.4, 964.8, 965.2, 965.6, 966, 966.4, 966.8, 967.2, 967.6, 968, 968.4, 968.8, 969.2, 969.6, 970, 970.4, 970.8, 971.2, 971.6, 972, 972.4, 972.8, 973.2, 973.6, 974, 974.4, 974.8, 975.2, 975.6, 976, 976.4, 976.8, 977.2, 977.6, 978, 978.4, 978.8, 979.2, 979.6, 980, 980.4, 980.8, 981.2, 981.6, 982, 982.4, 982.8, 983.2, 983.6, 984, 984.4, 984.8, 985.2, 985.6, 986, 986.4, 986.8, 987.2, 987.6, 988, 988.4, 988.8, 989.2, 989.6, 990, 990.4, 990.8, 991.2, 991.6, 992, 992.4, 992.8, 993.2, 993.6, 994, 994.4, 994.8, 995.2, 995.6, 996, 996.4, 996.8, 997.2, 997.6, 998, 998.4, 998.8, 999.2, 999.6, 1000, 1000.4, 1000.8, 1001.2, 1001.6, 1002, 1002.4, 1002.8, 1003.2, 1003.6, 1004, 1004.4, 1004.8, 1005.2, 1005.6, 1006, 1006.4, 1006.8, 1007.2, 1007.6, 1008, 1008.4, 1008.8, 1009.2, 1009.6, 1010, 1010.4, 1010.8, 1011.2, 1011.6, 1012, 1012.4, 1012.8, 1013.2, 1013.6, 1014, 1014.4, 1014.8, 1015.2, 1015.6, 1016, 1016.4, 1016.8, 1017.2, 1017.6, 1018, 1018.4, 1018.8, 1019.2, 1019.6, 1020, 1020.4, 1020.8, 1021.2, 1021.6, 1022, 1022.4, 1022.8, 1023.2, 1023.6, 1024, 1024.4, 1024.8, 1025.2, 1025.6, 1026, 1026.4, 1026.8, 1027.2, 1027.6, 1028, 1028.4, 1028.8, 1029.2, 1029.6, 1030, 1030.4, 1030.8, 1031.2, 1031.6, 1032, 1032.4, 1032.8, 1033.2, 1033.6, 1034, 1034.4, 1034.8, 1035.2, 1035.6, 1036, 1036.4, 1036.8, 1037.2, 1037.6, 1038, 1038.4, 1038.8, 1039.2, 1039.6, 1040, 1040.4, 1040.8, 1041.2, 1041.6, 1042, 1042.4, 1042.8, 1043.2, 1043.6, 1044, 1044.4, 1044.8, 1045.2, 1045.6, 1046, 1046.4, 1046.8, 1047.2, 1047.6, 1048, 1048.4, 1048.8, 1049.2, 1049.6, 1050, 1050.4, 1050.8, 1051.2, 1051.6, 1052, 1052.4, 1052.8, 1053.2, 1053.6, 1054, 1054.4, 1054.8, 1055.2, 1055.6, 1056, 1056.4, 1056.8, 1057.2, 1057.6, 1058, 1058.4, 1058.8, 1059.2, 1059.6, 1060, 1060.4, 1060.8, 1061.2, 1061.6, 1062, 1062.4, 1062.8, 1063.2, 1063.6, 1064, 1064.4, 1064.8, 1065.2, 1065.6, 1066, 1066.4, 1066.8, 1067.2, 1067.6, 1068, 1068.4, 1068.8, 1069.2, 1069.6, 1070, 1070.4, 1070.8, 1071.2, 1071.6, 1072, 1072.4, 1072.8, 1073.2, 1073.6, 1074, 1074.4, 1074.8, 1075.2, 1075.6, 1076, 1076.4, 1076.8, 1077.2, 1077.6, 1078, 1078.4, 1078.8, 1079.2, 1079.6, 1080, 1080.4, 1080.8, 1081.2, 1081.6, 1082, 1082.4, 1082.8, 1083.2, 1083.6, 1084, 1084.4, 1084.8, 1085.2, 1085.6, 1086, 1086.4, 1086.8, 1087.2, 1087.6, 1088, 1088.4, 1088.8, 1089.2, 1089.6, 1090, 1090.4, 1090.8, 1091.2, 1091.6, 1092, 1092.4, 1092.8, 1093.2, 1093.6, 1094, 1094.4, 1094.8, 1095.2, 1095.6, 1096, 1096.4, 1096.8, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "food_intake_9670 = [195, 289, 324, 362, 739, 781, 1262, 1330, 1454, 2130, 2190, 2310, 2366, 3075, 4022, 4080, 4180, 4240, 4350, 4480, 5140, 7410, 8094, 8150]\n", "subset = df[(df.Mouse=='B9670') & (df.Day=='Day2') & (df.Channel==560) ]\n", "fig = px.line(x=(subset.FrameCounter - 307)/2.5, y=subset['zdFF (airPLS)'])\n", "for food in food_intake_9890:\n", " fig = fig.add_vline(x=food)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Synchronize photometry recording with behavioral events from video recording" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "def arena_sync(logs, path):\n", " ttl = pd.read_csv(path/'input1.csv')\n", " frame_times = pd.read_csv(path/'time.csv')\n", " first_ttl = ttl.loc[0, 'Item2']\n", " \n", " first_frame = ((first_ttl - frame_times.Item2).abs().idxmin())\n", " framerate = 30\n", " logs['Timestamp'] = logs.Frame / 30\n", " logs['FrameCounter'] = logs.Timestamp * 25 // 1\n", " logs['FrameCounter'] += first_frame\n", " logs['FrameCounter'] = logs.FrameCounter.astype(int)\n", " logs = logs.rename(columns={'ROI': 'Event'})\n", " \n", " # ignore events that are too close to start or end of recording\n", " logs = logs[(logs.Timestamp > 20) & (logs.Timestamp < max(logs.Timestamp) - 20)]\n", " return logs.set_index('FrameCounter')\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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BatchDayFPMouse_MouseFrameCounter
Batch 1Day1FPB9295B929525222717non_roi_out90.566667
25232718food_in90.600000
25722777food_out92.566667
25732778non_roi_in92.600000
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Batch 2Day2FPB9891B98912010423044non_roi_in768.133333
2011823061non_roi_out768.700000
2011923062wheel_in768.733333
2018323139wheel_out771.300000
2018423140non_roi_in771.333333
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3008 rows × 3 columns

\n", "
" ], "text/plain": [ " Frame Event Timestamp\n", "Batch Day FP Mouse_ Mouse FrameCounter \n", "Batch 1 Day1 FP B9295 B9295 2522 2717 non_roi_out 90.566667\n", " 2523 2718 food_in 90.600000\n", " 2572 2777 food_out 92.566667\n", " 2573 2778 non_roi_in 92.600000\n", " 2578 2784 non_roi_out 92.800000\n", "... ... ... ...\n", "Batch 2 Day2 FP B9891 B9891 20104 23044 non_roi_in 768.133333\n", " 20118 23061 non_roi_out 768.700000\n", " 20119 23062 wheel_in 768.733333\n", " 20183 23139 wheel_out 771.300000\n", " 20184 23140 non_roi_in 771.333333\n", "\n", "[3008 rows x 3 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "logs = read_project_logs(PROJECT_PATH,\n", " ['Batch', 'Day', 'FP', 'Mouse_'], sync_fun=arena_sync, ignore_dirs=['meta', 'processed', 'processed.zip'])\n", "logs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Select appropriate events and calculate perievents" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# use only entries if mouse was more than x seconds in region\n", "min_sec_in_region = 10\n", "\n", "idxin = None\n", "onset = 0\n", "to_drop = list()\n", "for index, row in logs.iterrows():\n", " if '_in' in row.Event:\n", " idxin = index\n", " onset = row.Timestamp\n", " elif '_out' in row.Event:\n", " if row.Timestamp - onset < min_sec_in_region:\n", " to_drop.append(idxin)\n", " to_drop.append(index)\n", "logs = logs.drop(index=to_drop)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\georg\\git\\fibermagic\\fibermagic\\core\\perievents.py:41: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "C:\\Users\\Georg\\anaconda3\\envs\\fibermagic\\lib\\site-packages\\pandas\\core\\frame.py:3678: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "c:\\users\\georg\\git\\fibermagic\\fibermagic\\core\\perievents.py:58: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n" ] } ], "source": [ "peri = perievents(df_idx, logs, 15, 25)\n", "peri" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Further Analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# save datastream, logs and perievents\n", "\n", "output_path = PROJECT_PATH / 'processed'\n", "\n", "# uncomment if you like to save the output files\n", "# df.to_csv(output_path / 'datastream.csv')\n", "# logs.to_csv(output_path / 'logs_time_criteria_synced.csv')\n", "# peri.to_csv(output_path / 'perievents_time_criteria_synced.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, you can use data analysis tools like Tableau or PowerBI or any data analysis technology like R, Matlab, Python (e.g. Seaborn) to visualize your data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (fibermagic)", "language": "python", "name": "fibermagic" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 1 }