{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Extracellular Electrophysiology Data\n", "\n", "At the Allen Institute for Brain Science we carry out in vivo extracellular electrophysiology (ecephys) experiments in awake animals using high-density Neuropixels probes. The data from these experiments are organized into *sessions*, where each session is a distinct continuous recording period. During a session we collect:\n", "\n", "- spike times and characteristics (such as mean waveforms) from up to 6 neuropixels probes\n", "- local field potentials\n", "- behavioral data, such as running speed and eye position\n", "- visual stimuli which were presented during the session\n", "- cell-type specific optogenetic stimuli that were applied during the session\n", "\n", "The AllenSDK contains code for accessing across-session (project-level) metadata as well as code for accessing detailed within-session data. The standard workflow is to use project-level tools, such as `EcephysProjectCache` to identify and access sessions of interest, then delve into those sessions' data using `EcephysSession`.\n", "\n", "\n", "Project-level\n", "------------------\n", "The `EcephysProjectCache` class in `allensdk.brain_observatory.ecephys.ecephys_project_cache` accesses and stores data pertaining to many sessions. You can use this class to run queries that span all collected sessions and to download data for individual sessions.\n", "* Obtaining an `EcephysProjectCache`\n", "* Querying sessions\n", "* Querying probes\n", "* Querying units\n", "* Surveying metadata\n", "\n", "\n", "Session-level\n", "-------------------\n", "The `EcephysSession` class in `allensdk.brain_observatory.ecephys.ecephys_session` provides an interface to all of the data for a single session, aligned to a common clock. This notebook will show you how to use the `EcephysSession` class to extract these data.\n", "* Obtaining an `EcephysSession`\n", "* Stimulus information\n", "* Spike data\n", "* Spike histograms\n", "* Running speed\n", "* Optogenetic stimulation\n", "* Local Field Potential\n", "* Current source density\n", "* Unitwise mean waveforms\n", "* Suggested exercises\n", "* Eye tracking ellipse fits and estimated screen gaze location" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# first we need a bit of import boilerplate\n", "import os\n", "\n", "import numpy as np\n", "import xarray as xr\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from scipy.ndimage.filters import gaussian_filter\n", "\n", "from allensdk.brain_observatory.ecephys.ecephys_project_cache import EcephysProjectCache\n", "from allensdk.brain_observatory.ecephys.ecephys_session import (\n", " EcephysSession, \n", " removed_unused_stimulus_presentation_columns\n", ")\n", "from allensdk.brain_observatory.ecephys.visualization import plot_mean_waveforms, plot_spike_counts, raster_plot\n", "from allensdk.brain_observatory.visualization import plot_running_speed\n", "\n", "# tell pandas to show all columns when we display a DataFrame\n", "pd.set_option(\"display.max_columns\", None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obtaining an `EcephysProjectCache`\n", "\n", "In order to create an `EcephysProjectCache` object, you need to specify two things:\n", "1. A remote source for the object to fetch data from. We will instantiate our cache using `EcephysProjectCache.from_warehouse()` to point the cache at the Allen Institute's public web API.\n", "2. A path to a manifest json, which designates filesystem locations for downloaded data. The cache will try to read data from these locations before going to download those data from its remote source, preventing repeated downloads." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Example cache directory path, it determines where downloaded data will be stored\n", "manifest_path = os.path.join(\"/local1/ecephys_cache_dir/\", \"manifest.json\")\n", "\n", "cache = EcephysProjectCache.from_warehouse(manifest=manifest_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Querying across sessions\n", "\n", "Using your `EcephysProjectCache`, you can download a table listing metadata for all sessions." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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published_atspecimen_idsession_typeage_in_dayssexfull_genotypeunit_countchannel_countprobe_countecephys_structure_acronyms
id
7150937032019-10-03T00:00:00Z699733581brain_observatory_1.1118.0MSst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt88422196[CA1, VISrl, nan, PO, LP, LGd, CA3, DG, VISl, ...
7191615302019-10-03T00:00:00Z703279284brain_observatory_1.1122.0MSst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt75522146[TH, Eth, APN, POL, LP, DG, CA1, VISpm, nan, N...
7211238222019-10-03T00:00:00Z707296982brain_observatory_1.1125.0MPvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt44422296[MB, SCig, PPT, NOT, DG, CA1, VISam, nan, LP, ...
7325921052019-10-03T00:00:00Z717038288brain_observatory_1.1100.0Mwt/wt82418475[grey, VISpm, nan, VISp, VISl, VISal, VISrl]
7375810202019-10-03T00:00:00Z718643567brain_observatory_1.1108.0Mwt/wt56822186[grey, VISmma, nan, VISpm, VISp, VISl, VISrl]
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" ], "text/plain": [ " published_at specimen_id session_type \\\n", "id \n", "715093703 2019-10-03T00:00:00Z 699733581 brain_observatory_1.1 \n", "719161530 2019-10-03T00:00:00Z 703279284 brain_observatory_1.1 \n", "721123822 2019-10-03T00:00:00Z 707296982 brain_observatory_1.1 \n", "732592105 2019-10-03T00:00:00Z 717038288 brain_observatory_1.1 \n", "737581020 2019-10-03T00:00:00Z 718643567 brain_observatory_1.1 \n", "\n", " age_in_days sex full_genotype \\\n", "id \n", "715093703 118.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n", "719161530 122.0 M Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n", "721123822 125.0 M Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt \n", "732592105 100.0 M wt/wt \n", "737581020 108.0 M wt/wt \n", "\n", " unit_count channel_count probe_count \\\n", "id \n", "715093703 884 2219 6 \n", "719161530 755 2214 6 \n", "721123822 444 2229 6 \n", "732592105 824 1847 5 \n", "737581020 568 2218 6 \n", "\n", " ecephys_structure_acronyms \n", "id \n", "715093703 [CA1, VISrl, nan, PO, LP, LGd, CA3, DG, VISl, ... \n", "719161530 [TH, Eth, APN, POL, LP, DG, CA1, VISpm, nan, N... \n", "721123822 [MB, SCig, PPT, NOT, DG, CA1, VISam, nan, LP, ... \n", "732592105 [grey, VISpm, nan, VISp, VISl, VISal, VISrl] \n", "737581020 [grey, VISmma, nan, VISpm, VISp, VISl, VISrl] " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cache.get_session_table().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Querying across probes\n", "\n", "... or for all probes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ecephys_session_idlfp_sampling_ratenamephasesampling_ratehas_lfp_dataunit_countchannel_countecephys_structure_acronyms
id
7294456487191615301249.998642probeA3a29999.967418True87374[APN, LP, MB, DG, CA1, VISam, nan]
7294456507191615301249.996620probeB3a29999.918880True202368[TH, Eth, APN, POL, LP, DG, CA1, VISpm, nan]
7294456527191615301249.999897probeC3a29999.997521True207373[APN, NOT, MB, DG, SUB, VISp, nan]
7294456547191615301249.996707probeD3a29999.920963True93358[grey, VL, CA3, CA2, CA1, VISl, nan]
7294456567191615301249.999979probeE3a29999.999500True138370[PO, VPM, TH, LP, LGd, CA3, DG, CA1, VISal, nan]
\n", "
" ], "text/plain": [ " ecephys_session_id lfp_sampling_rate name phase sampling_rate \\\n", "id \n", "729445648 719161530 1249.998642 probeA 3a 29999.967418 \n", "729445650 719161530 1249.996620 probeB 3a 29999.918880 \n", "729445652 719161530 1249.999897 probeC 3a 29999.997521 \n", "729445654 719161530 1249.996707 probeD 3a 29999.920963 \n", "729445656 719161530 1249.999979 probeE 3a 29999.999500 \n", "\n", " has_lfp_data unit_count channel_count \\\n", "id \n", "729445648 True 87 374 \n", "729445650 True 202 368 \n", "729445652 True 207 373 \n", "729445654 True 93 358 \n", "729445656 True 138 370 \n", "\n", " ecephys_structure_acronyms \n", "id \n", "729445648 [APN, LP, MB, DG, CA1, VISam, nan] \n", "729445650 [TH, Eth, APN, POL, LP, DG, CA1, VISpm, nan] \n", "729445652 [APN, NOT, MB, DG, SUB, VISp, nan] \n", "729445654 [grey, VL, CA3, CA2, CA1, VISl, nan] \n", "729445656 [PO, VPM, TH, LP, LGd, CA3, DG, CA1, VISal, nan] " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cache.get_probes().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Querying across channels\n", "\n", "... or across channels." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ecephys_probe_idlocal_indexprobe_horizontal_positionprobe_vertical_positionanterior_posterior_ccf_coordinatedorsal_ventral_ccf_coordinateleft_right_ccf_coordinateecephys_structure_idecephys_structure_acronymecephys_session_idlfp_sampling_ratephasesampling_ratehas_lfp_dataunit_count
id
849705558792645504111208165.03314.06862.0215.0APN7798394711250.0014793a30000.035489True0
849705560792645504259408162.03307.06866.0215.0APN7798394711250.0014793a30000.035489True0
849705562792645504327408160.03301.06871.0215.0APN7798394711250.0014793a30000.035489True0
849705564792645504443608157.03295.06875.0215.0APN7798394711250.0014793a30000.035489True0
849705566792645504511608155.03288.06879.0215.0APN7798394711250.0014793a30000.035489True0
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" ], "text/plain": [ " ecephys_probe_id local_index probe_horizontal_position \\\n", "id \n", "849705558 792645504 1 11 \n", "849705560 792645504 2 59 \n", "849705562 792645504 3 27 \n", "849705564 792645504 4 43 \n", "849705566 792645504 5 11 \n", "\n", " probe_vertical_position anterior_posterior_ccf_coordinate \\\n", "id \n", "849705558 20 8165.0 \n", "849705560 40 8162.0 \n", "849705562 40 8160.0 \n", "849705564 60 8157.0 \n", "849705566 60 8155.0 \n", "\n", " dorsal_ventral_ccf_coordinate left_right_ccf_coordinate \\\n", "id \n", "849705558 3314.0 6862.0 \n", "849705560 3307.0 6866.0 \n", "849705562 3301.0 6871.0 \n", "849705564 3295.0 6875.0 \n", "849705566 3288.0 6879.0 \n", "\n", " ecephys_structure_id ecephys_structure_acronym ecephys_session_id \\\n", "id \n", "849705558 215.0 APN 779839471 \n", "849705560 215.0 APN 779839471 \n", "849705562 215.0 APN 779839471 \n", "849705564 215.0 APN 779839471 \n", "849705566 215.0 APN 779839471 \n", "\n", " lfp_sampling_rate phase sampling_rate has_lfp_data unit_count \n", "id \n", "849705558 1250.001479 3a 30000.035489 True 0 \n", "849705560 1250.001479 3a 30000.035489 True 0 \n", "849705562 1250.001479 3a 30000.035489 True 0 \n", "849705564 1250.001479 3a 30000.035489 True 0 \n", "849705566 1250.001479 3a 30000.035489 True 0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cache.get_channels().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Querying across units\n", "\n", "... as well as for sorted units." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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waveform_PT_ratiowaveform_amplitudeamplitude_cutoffcumulative_driftd_primewaveform_durationecephys_channel_idfiring_ratewaveform_halfwidthisi_violationsisolation_distanceL_ratiomax_driftnn_hit_ratenn_miss_ratepresence_ratiowaveform_recovery_slopewaveform_repolarization_slopesilhouette_scoresnrwaveform_spreadwaveform_velocity_abovewaveform_velocity_belowecephys_probe_idlocal_indexprobe_horizontal_positionprobe_vertical_positionanterior_posterior_ccf_coordinatedorsal_ventral_ccf_coordinateleft_right_ccf_coordinateecephys_structure_idecephys_structure_acronymecephys_session_idlfp_sampling_ratenamephasesampling_ratehas_lfp_datadate_of_acquisitionpublished_atspecimen_idsession_typeage_in_dayssexgenotype
id
9159562820.611816164.8787400.072728309.713.9108730.5356788502294196.5194320.1648240.10491030.5469000.01386527.100.8981260.0015990.99-0.0875450.4809150.1023691.91183930.00.000000NaN73374464732740NaNNaNNaN8.0grey7325921051249.996475probeB3a29999.915391True2019-01-09T00:26:20Z2019-10-03T00:00:00Z717038288brain_observatory_1.1100.0Mwt/wt
9159563400.439372247.2543450.000881160.245.5190240.5631498502294199.6605540.2060300.00682559.6131820.0004107.790.9876540.0009030.99-0.1041960.7045220.1974583.35790830.00.000000NaN73374464732740NaNNaNNaN8.0grey7325921051249.996475probeB3a29999.915391True2019-01-09T00:26:20Z2019-10-03T00:00:00Z717038288brain_observatory_1.1100.0Mwt/wt
9159563450.500520251.2758300.001703129.363.5599110.52194385022941912.6984300.1922950.04493647.8057140.00828111.560.9300000.0049560.99-0.1531270.7812960.1388273.36219830.00.343384NaN73374464732740NaNNaNNaN8.0grey7325921051249.996475probeB3a29999.915391True2019-01-09T00:26:20Z2019-10-03T00:00:00Z717038288brain_observatory_1.1100.0Mwt/wt
9159563490.424620177.1153800.096378169.292.9739590.50820885022941916.1924130.1922950.12071554.6355150.01040614.870.8746670.0216360.99-0.0860220.5533930.1369012.68463640.00.206030NaN73374464732740NaNNaNNaN8.0grey7325921051249.996475probeB3a29999.915391True2019-01-09T00:26:20Z2019-10-03T00:00:00Z717038288brain_observatory_1.1100.0Mwt/wt
9159563560.512847214.9545450.054706263.012.9368510.5494148502294192.1931130.2335010.43042718.1363020.06134518.370.6373630.0006730.99-0.1060510.6329770.1088672.60540860.0-0.451304NaN73374464732740NaNNaNNaN8.0grey7325921051249.996475probeB3a29999.915391True2019-01-09T00:26:20Z2019-10-03T00:00:00Z717038288brain_observatory_1.1100.0Mwt/wt
\n", "
" ], "text/plain": [ " waveform_PT_ratio waveform_amplitude amplitude_cutoff \\\n", "id \n", "915956282 0.611816 164.878740 0.072728 \n", "915956340 0.439372 247.254345 0.000881 \n", "915956345 0.500520 251.275830 0.001703 \n", "915956349 0.424620 177.115380 0.096378 \n", "915956356 0.512847 214.954545 0.054706 \n", "\n", " cumulative_drift d_prime waveform_duration ecephys_channel_id \\\n", "id \n", "915956282 309.71 3.910873 0.535678 850229419 \n", "915956340 160.24 5.519024 0.563149 850229419 \n", "915956345 129.36 3.559911 0.521943 850229419 \n", "915956349 169.29 2.973959 0.508208 850229419 \n", "915956356 263.01 2.936851 0.549414 850229419 \n", "\n", " firing_rate waveform_halfwidth isi_violations \\\n", "id \n", "915956282 6.519432 0.164824 0.104910 \n", "915956340 9.660554 0.206030 0.006825 \n", "915956345 12.698430 0.192295 0.044936 \n", "915956349 16.192413 0.192295 0.120715 \n", "915956356 2.193113 0.233501 0.430427 \n", "\n", " isolation_distance L_ratio max_drift nn_hit_rate nn_miss_rate \\\n", "id \n", "915956282 30.546900 0.013865 27.10 0.898126 0.001599 \n", "915956340 59.613182 0.000410 7.79 0.987654 0.000903 \n", "915956345 47.805714 0.008281 11.56 0.930000 0.004956 \n", "915956349 54.635515 0.010406 14.87 0.874667 0.021636 \n", "915956356 18.136302 0.061345 18.37 0.637363 0.000673 \n", "\n", " presence_ratio waveform_recovery_slope \\\n", "id \n", "915956282 0.99 -0.087545 \n", "915956340 0.99 -0.104196 \n", "915956345 0.99 -0.153127 \n", "915956349 0.99 -0.086022 \n", "915956356 0.99 -0.106051 \n", "\n", " waveform_repolarization_slope silhouette_score snr \\\n", "id \n", "915956282 0.480915 0.102369 1.911839 \n", "915956340 0.704522 0.197458 3.357908 \n", "915956345 0.781296 0.138827 3.362198 \n", "915956349 0.553393 0.136901 2.684636 \n", "915956356 0.632977 0.108867 2.605408 \n", "\n", " waveform_spread waveform_velocity_above waveform_velocity_below \\\n", "id \n", "915956282 30.0 0.000000 NaN \n", "915956340 30.0 0.000000 NaN \n", "915956345 30.0 0.343384 NaN \n", "915956349 40.0 0.206030 NaN \n", "915956356 60.0 -0.451304 NaN \n", "\n", " ecephys_probe_id local_index probe_horizontal_position \\\n", "id \n", "915956282 733744647 3 27 \n", "915956340 733744647 3 27 \n", "915956345 733744647 3 27 \n", "915956349 733744647 3 27 \n", "915956356 733744647 3 27 \n", "\n", " probe_vertical_position anterior_posterior_ccf_coordinate \\\n", "id \n", "915956282 40 NaN \n", "915956340 40 NaN \n", "915956345 40 NaN \n", "915956349 40 NaN \n", "915956356 40 NaN \n", "\n", " dorsal_ventral_ccf_coordinate left_right_ccf_coordinate \\\n", "id \n", "915956282 NaN NaN \n", "915956340 NaN NaN \n", "915956345 NaN NaN \n", "915956349 NaN NaN \n", "915956356 NaN NaN \n", "\n", " ecephys_structure_id ecephys_structure_acronym ecephys_session_id \\\n", "id \n", "915956282 8.0 grey 732592105 \n", "915956340 8.0 grey 732592105 \n", "915956345 8.0 grey 732592105 \n", "915956349 8.0 grey 732592105 \n", "915956356 8.0 grey 732592105 \n", "\n", " lfp_sampling_rate name phase sampling_rate has_lfp_data \\\n", "id \n", "915956282 1249.996475 probeB 3a 29999.915391 True \n", "915956340 1249.996475 probeB 3a 29999.915391 True \n", "915956345 1249.996475 probeB 3a 29999.915391 True \n", "915956349 1249.996475 probeB 3a 29999.915391 True \n", "915956356 1249.996475 probeB 3a 29999.915391 True \n", "\n", " date_of_acquisition published_at specimen_id \\\n", "id \n", "915956282 2019-01-09T00:26:20Z 2019-10-03T00:00:00Z 717038288 \n", "915956340 2019-01-09T00:26:20Z 2019-10-03T00:00:00Z 717038288 \n", "915956345 2019-01-09T00:26:20Z 2019-10-03T00:00:00Z 717038288 \n", "915956349 2019-01-09T00:26:20Z 2019-10-03T00:00:00Z 717038288 \n", "915956356 2019-01-09T00:26:20Z 2019-10-03T00:00:00Z 717038288 \n", "\n", " session_type age_in_days sex genotype \n", "id \n", "915956282 brain_observatory_1.1 100.0 M wt/wt \n", "915956340 brain_observatory_1.1 100.0 M wt/wt \n", "915956345 brain_observatory_1.1 100.0 M wt/wt \n", "915956349 brain_observatory_1.1 100.0 M wt/wt \n", "915956356 brain_observatory_1.1 100.0 M wt/wt " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "units = cache.get_units()\n", "units.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "40010\n" ] } ], "source": [ "# There are quite a few of these\n", "print(units.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Surveying metadata\n", "\n", "You can answer questions like: \"what mouse genotypes were used in this dataset?\" using your `EcephysProjectCache`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stimulus sets: ['brain_observatory_1.1', 'functional_connectivity']\n", "genotypes: ['Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt', 'Pvalb-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt', 'wt/wt', 'Vip-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt']\n", "structures: ['APN', 'LP', 'MB', 'DG', 'CA1', 'VISrl', nan, 'TH', 'LGd', 'CA3', 'VIS', 'CA2', 'ProS', 'VISp', 'POL', 'VISpm', 'PPT', 'OP', 'NOT', 'HPF', 'SUB', 'VISam', 'ZI', 'LGv', 'VISal', 'VISl', 'SGN', 'SCig', 'MGm', 'MGv', 'VPM', 'grey', 'Eth', 'VPL', 'IGL', 'PP', 'PIL', 'PO', 'VISmma', 'POST', 'SCop', 'SCsg', 'SCzo', 'COApm', 'OLF', 'BMAa', 'SCiw', 'COAa', 'IntG', 'MGd', 'MRN', 'LD', 'VISmmp', 'CP', 'VISli', 'PRE', 'RPF', 'LT', 'PF', 'PoT', 'VL', 'RT']\n" ] } ], "source": [ "print(f\"stimulus sets: {cache.get_all_session_types()}\")\n", "print(f\"genotypes: {cache.get_all_full_genotypes()}\")\n", "print(f\"structures: {cache.get_structure_acronyms()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to look up a brain structure acronym, you can use our [online atlas viewer](http://atlas.brain-map.org/atlas?atlas=602630314). The AllenSDK additionally supports programmatic access to structure annotations. For more information, see the [reference space](https://allensdk.readthedocs.io/en/latest/reference_space.html) and [mouse connectivity](https://allensdk.readthedocs.io/en/latest/connectivity.html) documentation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obtaining an `EcephysSession`\n", "\n", "We package each session's data into a Neurodata Without Borders 2.0 (NWB) file. Calling `get_session_data` on your `EcephysProjectCache` will download such a file and return an `EcephysSession` object.\n", "\n", "`EcephysSession` objects contain methods and properties that access the data within an ecephys NWB file and cache it in memory." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:downloading a 2.46e+03mb file from http://api.brain-map.org//api/v2/well_known_file_download/1026124216\n" ] } ], "source": [ "session_id = 756029989 # for example\n", "session = cache.get_session_data(session_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This session object has some important metadata, such as the date and time at which the recording session started:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "session 756029989 was acquired in 2018-10-26 12:59:18-07:00\n" ] } ], "source": [ "print(f\"session {session.ecephys_session_id} was acquired in {session.session_start_time}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now jump in to accessing our session's data. If you ever want a complete documented list of the attributes and methods defined on `EcephysSession`, you can run `help(EcephysSession)` (or in a jupyter notebook: `EcephysSession?`)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sorted units\n", "\n", "Units are putative neurons, clustered from raw voltage traces using Kilosort 2. Each unit is associated with a single *peak channel* on a single probe, though its spikes might be picked up with some attenuation on multiple nearby channels. Each unit is assigned a unique integer identifier (\"unit_id\") which can be used to look up its spike times and its mean waveform.\n", "\n", "The units for a session are recorded in an attribute called, fittingly, `units`. This is a `pandas.DataFrame` whose index is the unit id and whose columns contain summary information about the unit, its peak channel, and its associated probe." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/nicholas.mei/miniconda3/envs/allensdk/lib/python3.7/site-packages/allensdk/brain_observatory/ecephys/ecephys_project_api/ecephys_project_warehouse_api.py:291: FutureWarning: Conversion of the second argument of issubdtype from `bool` to `np.generic` is deprecated. In future, it will be treated as `np.bool_ == np.dtype(bool).type`.\n", " pv_is_bool = np.issubdtype(output[\"p_value_rf\"].values[0], np.bool)\n" ] }, { "data": { "text/html": [ "
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9518148760.0017850.994.4454140.206030539.1005578501263820.1089080.518633129.6865052.5897170.00000040.680.0037561.0000000.00000050.00.0972860.3159130.652514325.2185.1787500.004799-0.1437624NaN900.03.4175730.7047620.6726900.8039801.0030550.1377090.0176750.0152480.02733472.2220.6784730.7950.042.00.3265870.2315950.0621570.0685480.068853315.0150.00.0000306.157503e-070.3535660.6741850.47287045.55650210.0043.39030144.70984836.82028844.88508435.195889NaN0.1535FalseFalseFalseFalseFalse0.214051False0.0018120.0000030.0023660.0043080.00294360437606400834215.0APN8162.03487.06737.0probeASee electrode locations29999.9496111249.9979True
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9518153140.0096660.994.1986120.1236183423.9022358501264200.1111060.740222173.4757053.360324-0.27470742.800.0176000.968000-0.06867790.00.0721290.1785590.459025420.0576.9163340.048075-0.17150331NaN200.09.5211381.8155622.6851613.0298165.8379140.3530140.0214650.0331740.06038790.0001.9610570.1150.041.0-0.381593-0.1104150.051182-0.007519-0.375085135.00.00.0093572.297652e-010.8190530.9776420.117368-25.00050210.5036.37073735.51741814.13204236.25675314.647080NaN0.2055FalseFalseFalseFalseFalse0.009373False0.0066690.0253390.0230170.0287780.0279932402776064008323215.0APN8088.03333.06792.0probeASee electrode locations29999.9496111249.9979True
\n", "
" ], "text/plain": [ " L_ratio presence_ratio d_prime waveform_halfwidth cluster_id \\\n", "unit_id \n", "951814884 0.024771 0.99 3.555518 0.096147 6 \n", "951814876 0.001785 0.99 4.445414 0.206030 5 \n", "951815032 0.035654 0.99 3.848256 0.096147 17 \n", "951815275 0.016783 0.99 3.065938 0.096147 30 \n", "951815314 0.009666 0.99 4.198612 0.123618 34 \n", "\n", " firing_rate peak_channel_id silhouette_score \\\n", "unit_id \n", "951814884 9.492176 850126382 0.033776 \n", "951814876 39.100557 850126382 0.108908 \n", "951815032 28.383277 850126398 0.096715 \n", "951815275 5.709358 850126416 0.144249 \n", "951815314 23.902235 850126420 0.111106 \n", "\n", " waveform_repolarization_slope waveform_amplitude snr \\\n", "unit_id \n", "951814884 0.673650 187.434780 3.342535 \n", "951814876 0.518633 129.686505 2.589717 \n", "951815032 0.766347 207.380940 3.811566 \n", "951815275 0.628944 158.158650 2.918134 \n", "951815314 0.740222 173.475705 3.360324 \n", "\n", " waveform_velocity_below max_drift nn_miss_rate nn_hit_rate \\\n", "unit_id \n", "951814884 0.000000 45.64 0.016202 0.727333 \n", "951814876 0.000000 40.68 0.003756 1.000000 \n", "951815032 -0.686767 40.01 0.014673 0.986000 \n", "951815275 0.000000 33.32 0.003683 0.883598 \n", "951815314 -0.274707 42.80 0.017600 0.968000 \n", "\n", " waveform_velocity_above waveform_spread amplitude_cutoff \\\n", "unit_id \n", "951814884 0.000000 40.0 0.042404 \n", "951814876 0.000000 50.0 0.097286 \n", "951815032 -0.206030 80.0 0.015482 \n", "951815275 0.686767 60.0 0.063807 \n", "951815314 -0.068677 90.0 0.072129 \n", "\n", " waveform_duration waveform_PT_ratio cumulative_drift \\\n", "unit_id \n", "951814884 0.151089 0.522713 463.86 \n", "951814876 0.315913 0.652514 325.21 \n", "951815032 0.164824 0.484297 396.28 \n", "951815275 0.178559 0.600600 374.82 \n", "951815314 0.178559 0.459025 420.05 \n", "\n", " isolation_distance isi_violations waveform_recovery_slope \\\n", "unit_id \n", "951814884 46.750473 0.181638 -0.249885 \n", "951814876 85.178750 0.004799 -0.143762 \n", "951815032 89.608836 0.007099 -0.255492 \n", "951815275 48.114336 0.032317 -0.206676 \n", "951815314 76.916334 0.048075 -0.171503 \n", "\n", " local_index_unit c50_dg area_rf fano_dg fano_fl fano_ns \\\n", "unit_id \n", "951814884 5 NaN 2600.0 2.370440 1.765128 3.983333 \n", "951814876 4 NaN 900.0 3.417573 0.704762 0.672690 \n", "951815032 15 NaN 400.0 2.301810 1.408866 2.711028 \n", "951815275 27 NaN 200.0 3.264583 1.145098 2.160000 \n", "951815314 31 NaN 200.0 9.521138 1.815562 2.685161 \n", "\n", " fano_rf fano_sg f1_f0_dg g_dsi_dg g_osi_dg g_osi_sg \\\n", "unit_id \n", "951814884 1.866667 5.186364 0.244796 0.053998 0.033134 0.023705 \n", "951814876 0.803980 1.003055 0.137709 0.017675 0.015248 0.027334 \n", "951815032 1.714790 2.055258 0.173597 0.013665 0.007886 0.051909 \n", "951815275 1.039456 2.604037 0.380119 0.018499 0.036959 0.072179 \n", "951815314 3.029816 5.837914 0.353014 0.021465 0.033174 0.060387 \n", "\n", " azimuth_rf mod_idx_dg p_value_rf pref_sf_sg pref_tf_dg \\\n", "unit_id \n", "951814884 61.923 0.612162 0.358 0.04 15.0 \n", "951814876 72.222 0.678473 0.795 0.04 2.0 \n", "951815032 85.000 0.768989 0.395 0.04 15.0 \n", "951815275 25.000 0.013071 0.786 0.04 15.0 \n", "951815314 90.000 1.961057 0.115 0.04 1.0 \n", "\n", " run_mod_dg run_mod_fl run_mod_ns run_mod_rf run_mod_sg \\\n", "unit_id \n", "951814884 -0.154286 -0.005676 -0.228819 -0.212121 -0.310345 \n", "951814876 0.326587 0.231595 0.062157 0.068548 0.068853 \n", "951815032 -0.026107 -0.220335 -0.345271 0.043011 -0.157445 \n", "951815275 -0.397810 -0.582707 -0.274725 -0.200000 0.068966 \n", "951815314 -0.381593 -0.110415 0.051182 -0.007519 -0.375085 \n", "\n", " pref_ori_dg pref_ori_sg run_pval_dg run_pval_fl run_pval_ns \\\n", "unit_id \n", "951814884 180.0 150.0 0.619716 9.763807e-01 0.507405 \n", "951814876 315.0 150.0 0.000030 6.157503e-07 0.353566 \n", "951815032 135.0 120.0 0.827195 1.179300e-02 0.006750 \n", "951815275 135.0 150.0 0.008270 6.722051e-07 0.466116 \n", "951815314 135.0 0.0 0.009357 2.297652e-01 0.819053 \n", "\n", " run_pval_rf run_pval_sg elevation_rf pref_image_ns \\\n", "unit_id \n", "951814884 0.469507 0.447263 -5.000 4929 \n", "951814876 0.674185 0.472870 45.556 5021 \n", "951815032 0.867140 0.229617 5.000 4990 \n", "951815275 0.492931 0.872051 50.000 4938 \n", "951815314 0.977642 0.117368 -25.000 5021 \n", "\n", " pref_phase_sg firing_rate_dg firing_rate_fl firing_rate_ns \\\n", "unit_id \n", "951814884 0.75 17.571944 11.057561 5.845194 \n", "951814876 0.00 43.390301 44.709848 36.820288 \n", "951815032 0.25 29.464485 28.829592 28.252666 \n", "951815275 0.50 10.510549 8.766114 1.972803 \n", "951815314 0.50 36.370737 35.517418 14.132042 \n", "\n", " firing_rate_rf firing_rate_sg on_off_ratio_fl time_to_peak_ns \\\n", "unit_id \n", "951814884 8.805963 6.974832 NaN 0.0385 \n", "951814876 44.885084 35.195889 NaN 0.1535 \n", "951815032 28.025354 30.002900 NaN 0.0495 \n", "951815275 8.492365 3.180672 NaN 0.0495 \n", "951815314 36.256753 14.647080 NaN 0.2055 \n", "\n", " pref_sf_multi_sg pref_tf_multi_dg pref_ori_multi_dg \\\n", "unit_id \n", "951814884 False False False \n", "951814876 False False False \n", "951815032 False False False \n", "951815275 False False False \n", "951815314 False False False \n", "\n", " pref_ori_multi_sg pref_phase_multi_sg image_selectivity_ns \\\n", "unit_id \n", "951814884 False False 0.042644 \n", "951814876 False False 0.214051 \n", "951815032 False False -0.005102 \n", "951815275 False False 0.298085 \n", "951815314 False False 0.009373 \n", "\n", " pref_image_multi_ns lifetime_sparseness_dg \\\n", "unit_id \n", "951814884 False 0.011829 \n", "951814876 False 0.001812 \n", "951815032 False 0.004121 \n", "951815275 False 0.009918 \n", "951815314 False 0.006669 \n", "\n", " lifetime_sparseness_fl lifetime_sparseness_ns \\\n", "unit_id \n", "951814884 0.000012 0.062616 \n", "951814876 0.000003 0.002366 \n", "951815032 0.006973 0.006743 \n", "951815275 0.002233 0.073080 \n", "951815314 0.025339 0.023017 \n", "\n", " lifetime_sparseness_rf lifetime_sparseness_sg \\\n", "unit_id \n", "951814884 0.058606 0.039197 \n", "951814876 0.004308 0.002943 \n", "951815032 0.018808 0.007783 \n", "951815275 0.035606 0.045548 \n", "951815314 0.028778 0.027993 \n", "\n", " probe_vertical_position probe_horizontal_position probe_id \\\n", "unit_id \n", "951814884 60 43 760640083 \n", "951814876 60 43 760640083 \n", "951815032 140 43 760640083 \n", "951815275 220 11 760640083 \n", "951815314 240 27 760640083 \n", "\n", " channel_local_index ecephys_structure_id \\\n", "unit_id \n", "951814884 4 215.0 \n", "951814876 4 215.0 \n", "951815032 12 215.0 \n", "951815275 21 215.0 \n", "951815314 23 215.0 \n", "\n", " ecephys_structure_acronym anterior_posterior_ccf_coordinate \\\n", "unit_id \n", "951814884 APN 8162.0 \n", "951814876 APN 8162.0 \n", "951815032 APN 8129.0 \n", "951815275 APN 8095.0 \n", "951815314 APN 8088.0 \n", "\n", " dorsal_ventral_ccf_coordinate left_right_ccf_coordinate \\\n", "unit_id \n", "951814884 3487.0 6737.0 \n", "951814876 3487.0 6737.0 \n", "951815032 3419.0 6762.0 \n", "951815275 3349.0 6787.0 \n", "951815314 3333.0 6792.0 \n", "\n", " probe_description location probe_sampling_rate \\\n", "unit_id \n", "951814884 probeA See electrode locations 29999.949611 \n", "951814876 probeA See electrode locations 29999.949611 \n", "951815032 probeA See electrode locations 29999.949611 \n", "951815275 probeA See electrode locations 29999.949611 \n", "951815314 probeA See electrode locations 29999.949611 \n", "\n", " probe_lfp_sampling_rate probe_has_lfp_data \n", "unit_id \n", "951814884 1249.9979 True \n", "951814876 1249.9979 True \n", "951815032 1249.9979 True \n", "951815275 1249.9979 True \n", "951815314 1249.9979 True " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "session.units.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a `pandas.DataFrame` the units table supports many straightforward filtering operations:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "684 units total\n", "81 units have snr > 4\n" ] } ], "source": [ "# how many units have signal to noise ratios that are greater than 4?\n", "print(f'{session.units.shape[0]} units total')\n", "units_with_very_high_snr = session.units[session.units['snr'] > 4]\n", "print(f'{units_with_very_high_snr.shape[0]} units have snr > 4')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... as well as some more advanced (and very useful!) operations. For more information, please see the pandas documentation. The following topics might be particularly handy:\n", "\n", "- [selecting data](http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)\n", "- [merging multiple dataframes](http://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)\n", "- [grouping rows within a dataframe](http://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html)\n", "- [pivot tables](http://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stimulus presentations\n", "\n", "During the course of a session, visual stimuli are presented on a monitor to the subject. We call intervals of time where a specific stimulus is presented (and its parameters held constant!) a *stimulus presentation*.\n", "\n", "You can find information about the stimulus presentations that were displayed during a session by accessing the `stimulus_presentations` attribute on your `EcephysSession` object. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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colorcontrastframeorientationphasesizespatial_frequencystart_timestimulus_blockstimulus_namestop_timetemporal_frequencyx_positiony_positiondurationstimulus_condition_id
stimulus_presentation_id
0nullnullnullnullnullnullnull24.429348nullspontaneous84.496188nullnullnull60.0668400
1null0.8null45[3644.93333333, 3644.93333333][20.0, 20.0]0.0884.4961880gabors84.729704440300.2335161
2null0.8null45[3644.93333333, 3644.93333333][20.0, 20.0]0.0884.7297040gabors84.9799004-30100.2501962
3null0.8null90[3644.93333333, 3644.93333333][20.0, 20.0]0.0884.9799000gabors85.230095410-100.2501963
4null0.8null90[3644.93333333, 3644.93333333][20.0, 20.0]0.0885.2300950gabors85.480291430400.2501964
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" ], "text/plain": [ " color contrast frame orientation \\\n", "stimulus_presentation_id \n", "0 null null null null \n", "1 null 0.8 null 45 \n", "2 null 0.8 null 45 \n", "3 null 0.8 null 90 \n", "4 null 0.8 null 90 \n", "\n", " phase size \\\n", "stimulus_presentation_id \n", "0 null null \n", "1 [3644.93333333, 3644.93333333] [20.0, 20.0] \n", "2 [3644.93333333, 3644.93333333] [20.0, 20.0] \n", "3 [3644.93333333, 3644.93333333] [20.0, 20.0] \n", "4 [3644.93333333, 3644.93333333] [20.0, 20.0] \n", "\n", " spatial_frequency start_time stimulus_block \\\n", "stimulus_presentation_id \n", "0 null 24.429348 null \n", "1 0.08 84.496188 0 \n", "2 0.08 84.729704 0 \n", "3 0.08 84.979900 0 \n", "4 0.08 85.230095 0 \n", "\n", " stimulus_name stop_time temporal_frequency \\\n", "stimulus_presentation_id \n", "0 spontaneous 84.496188 null \n", "1 gabors 84.729704 4 \n", "2 gabors 84.979900 4 \n", "3 gabors 85.230095 4 \n", "4 gabors 85.480291 4 \n", "\n", " x_position y_position duration \\\n", "stimulus_presentation_id \n", "0 null null 60.066840 \n", "1 40 30 0.233516 \n", "2 -30 10 0.250196 \n", "3 10 -10 0.250196 \n", "4 30 40 0.250196 \n", "\n", " stimulus_condition_id \n", "stimulus_presentation_id \n", "0 0 \n", "1 1 \n", "2 2 \n", "3 3 \n", "4 4 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "session.stimulus_presentations.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like the units table, this is a `pandas.DataFrame`. Each row corresponds to a stimulus presentation and lists the time (on the session's master clock, in seconds) when that presentation began and ended as well as the kind of stimulus that was presented (the \"stimulus_name\" column) and the parameter values that were used for that presentation. Many of these parameter values don't overlap between stimulus classes, so the stimulus_presentations table uses the string `\"null\"` to indicate an inapplicable parameter. The index is named \"stimulus_presentation_id\" and many methods on `EcephysSession` use these ids.\n", "\n", "Some of the columns bear a bit of explanation:\n", "- stimulus_block : A block consists of multiple presentations of the same stimulus class presented with (probably) different parameter values. If during a session stimuli were presented in the following order:\n", " - drifting gratings\n", " - static gratings\n", " - drifting gratings\n", " then the blocks for that session would be [0, 1, 2]. The gray period stimulus (just a blank gray screen) never gets a block.\n", "- duration : this is just stop_time - start_time, precalculated for convenience.\n", "\n", "What kinds of stimuli were presented during this session? Pandas makes it easy to find out:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['spontaneous',\n", " 'gabors',\n", " 'flashes',\n", " 'drifting_gratings',\n", " 'natural_movie_three',\n", " 'natural_movie_one',\n", " 'static_gratings',\n", " 'natural_scenes']" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "session.stimulus_names # just the unique values from the 'stimulus_name' column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also obtain the `stimulus epochs` - blocks of time for which a particular kind of stimulus was presented - for this session." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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start_timestop_timedurationstimulus_namestimulus_block
024.42934884.49618860.066840spontaneousnull
184.496188996.491813911.995625gabors0
2996.4918131285.483398288.991585spontaneousnull
31285.4833981583.982946298.499548flashes1
41583.9829461585.7344181.751472spontaneousnull
51585.7344182185.235561599.501143drifting_gratings2
62185.2355612216.26149831.025937spontaneousnull
72216.2614982816.763498600.502000natural_movie_three3
82816.7634982846.78859830.025100spontaneousnull
92846.7885983147.039578300.250980natural_movie_one4
103147.0395783177.06468830.025110spontaneousnull
113177.0646883776.565851599.501163drifting_gratings5
123776.5658514077.834348301.268497spontaneousnull
134077.8343484678.336348600.502000natural_movie_three6
144678.3363484708.36143830.025090spontaneousnull
154708.3614385397.937871689.576433drifting_gratings7
165397.9378715398.9387181.000847spontaneousnull
175398.9387185879.340268480.401550static_gratings8
185879.3402685909.36539830.025130spontaneousnull
195909.3653986389.766968480.401570natural_scenes9
206389.7669686690.017948300.250980spontaneousnull
216690.0179487170.419568480.401620natural_scenes10
227170.4195687200.44462830.025060spontaneousnull
237200.4446287680.846188480.401560static_gratings11
247680.8461887710.87134830.025160spontaneousnull
257710.8713488011.122288300.250940natural_movie_one12
268011.1222888041.14740830.025120spontaneousnull
278041.1474088569.088694527.941286natural_scenes13
288569.0886948611.62424842.535554spontaneousnull
298611.6242489152.076028540.451780static_gratings14
\n", "
" ], "text/plain": [ " start_time stop_time duration stimulus_name stimulus_block\n", "0 24.429348 84.496188 60.066840 spontaneous null\n", "1 84.496188 996.491813 911.995625 gabors 0\n", "2 996.491813 1285.483398 288.991585 spontaneous null\n", "3 1285.483398 1583.982946 298.499548 flashes 1\n", "4 1583.982946 1585.734418 1.751472 spontaneous null\n", "5 1585.734418 2185.235561 599.501143 drifting_gratings 2\n", "6 2185.235561 2216.261498 31.025937 spontaneous null\n", "7 2216.261498 2816.763498 600.502000 natural_movie_three 3\n", "8 2816.763498 2846.788598 30.025100 spontaneous null\n", "9 2846.788598 3147.039578 300.250980 natural_movie_one 4\n", "10 3147.039578 3177.064688 30.025110 spontaneous null\n", "11 3177.064688 3776.565851 599.501163 drifting_gratings 5\n", "12 3776.565851 4077.834348 301.268497 spontaneous null\n", "13 4077.834348 4678.336348 600.502000 natural_movie_three 6\n", "14 4678.336348 4708.361438 30.025090 spontaneous null\n", "15 4708.361438 5397.937871 689.576433 drifting_gratings 7\n", "16 5397.937871 5398.938718 1.000847 spontaneous null\n", "17 5398.938718 5879.340268 480.401550 static_gratings 8\n", "18 5879.340268 5909.365398 30.025130 spontaneous null\n", "19 5909.365398 6389.766968 480.401570 natural_scenes 9\n", "20 6389.766968 6690.017948 300.250980 spontaneous null\n", "21 6690.017948 7170.419568 480.401620 natural_scenes 10\n", "22 7170.419568 7200.444628 30.025060 spontaneous null\n", "23 7200.444628 7680.846188 480.401560 static_gratings 11\n", "24 7680.846188 7710.871348 30.025160 spontaneous null\n", "25 7710.871348 8011.122288 300.250940 natural_movie_one 12\n", "26 8011.122288 8041.147408 30.025120 spontaneous null\n", "27 8041.147408 8569.088694 527.941286 natural_scenes 13\n", "28 8569.088694 8611.624248 42.535554 spontaneous null\n", "29 8611.624248 9152.076028 540.451780 static_gratings 14" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "session.get_stimulus_epochs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are only interested in a subset of stimuli, you can either filter using pandas or using the `get_stimulus_table` convience method:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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contrastorientationphasesizespatial_frequencystart_timestimulus_blockstimulus_namestop_timetemporal_frequencydurationstimulus_condition_id
stimulus_presentation_id
37980.8180[42471.86666667, 42471.86666667][250.0, 250.0]0.041585.7344182drifting_gratings1587.73609822.00168246
37990.8135[42471.86666667, 42471.86666667][250.0, 250.0]0.041588.7368912drifting_gratings1590.73857122.00168247
38000.8180[42471.86666667, 42471.86666667][250.0, 250.0]0.041591.7393982drifting_gratings1593.74107822.00168246
38010.8270[42471.86666667, 42471.86666667][250.0, 250.0]0.041594.7419212drifting_gratings1596.74359122.00167248
38020.8135[42471.86666667, 42471.86666667][250.0, 250.0]0.041597.7444582drifting_gratings1599.74608842.00163249
\n", "
" ], "text/plain": [ " contrast orientation \\\n", "stimulus_presentation_id \n", "3798 0.8 180 \n", "3799 0.8 135 \n", "3800 0.8 180 \n", "3801 0.8 270 \n", "3802 0.8 135 \n", "\n", " phase size \\\n", "stimulus_presentation_id \n", "3798 [42471.86666667, 42471.86666667] [250.0, 250.0] \n", "3799 [42471.86666667, 42471.86666667] [250.0, 250.0] \n", "3800 [42471.86666667, 42471.86666667] [250.0, 250.0] \n", "3801 [42471.86666667, 42471.86666667] [250.0, 250.0] \n", "3802 [42471.86666667, 42471.86666667] [250.0, 250.0] \n", "\n", " spatial_frequency start_time stimulus_block \\\n", "stimulus_presentation_id \n", "3798 0.04 1585.734418 2 \n", "3799 0.04 1588.736891 2 \n", "3800 0.04 1591.739398 2 \n", "3801 0.04 1594.741921 2 \n", "3802 0.04 1597.744458 2 \n", "\n", " stimulus_name stop_time temporal_frequency \\\n", "stimulus_presentation_id \n", "3798 drifting_gratings 1587.736098 2 \n", "3799 drifting_gratings 1590.738571 2 \n", "3800 drifting_gratings 1593.741078 2 \n", "3801 drifting_gratings 1596.743591 2 \n", "3802 drifting_gratings 1599.746088 4 \n", "\n", " duration stimulus_condition_id \n", "stimulus_presentation_id \n", "3798 2.00168 246 \n", "3799 2.00168 247 \n", "3800 2.00168 246 \n", "3801 2.00167 248 \n", "3802 2.00163 249 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "session.get_stimulus_table(['drifting_gratings']).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We might also want to know what the total set of available parameters is. The `get_stimulus_parameter_values` method provides a dictionary mapping stimulus parameters to the set of values that were applied to those parameters:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "color: [-1.0 1.0]\n", "contrast: [0.8 1.0]\n", "frame: [-1.0 0.0 1.0 ... 3597.0 3598.0 3599.0]\n", "orientation: [0.0 30.0 45.0 60.0 90.0 120.0 135.0 150.0 180.0 225.0 270.0 315.0]\n", "phase: ['0.0' '0.25' '0.5' '0.75' '[0.0, 0.0]' '[3644.93333333, 3644.93333333]'\n", " '[42471.86666667, 42471.86666667]']\n", "size: ['[1920.0, 1080.0]' '[20.0, 20.0]' '[250.0, 250.0]' '[300.0, 300.0]']\n", "spatial_frequency: ['0.02' '0.04' '0.08' '0.16' '0.32' '[0.0, 0.0]']\n", "temporal_frequency: [1.0 2.0 4.0 8.0 15.0]\n", "x_position: [-40.0 -30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 40.0]\n", "y_position: [-40.0 -30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 40.0]\n" ] } ], "source": [ "for key, values in session.get_stimulus_parameter_values().items():\n", " print(f'{key}: {values}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each distinct state of the monitor is called a \"stimulus condition\". Each presentation in the stimulus presentations table exemplifies such a condition. This is encoded in its stimulus_condition_id field.\n", "\n", "To get the full list of conditions presented in a session, use the stimulus_conditions attribute:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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colorcontrastframemaskopacityorientationphasesizespatial_frequencystimulus_nametemporal_frequencyunitsx_positiony_positioncolor_triplet
stimulus_condition_id
0nullnullnullnullnullnullnullnullnullspontaneousnullnullnullnullnull
1null0.8nullcircle145[3644.93333333, 3644.93333333][20.0, 20.0]0.08gabors4deg4030[1.0, 1.0, 1.0]
2null0.8nullcircle145[3644.93333333, 3644.93333333][20.0, 20.0]0.08gabors4deg-3010[1.0, 1.0, 1.0]
3null0.8nullcircle190[3644.93333333, 3644.93333333][20.0, 20.0]0.08gabors4deg10-10[1.0, 1.0, 1.0]
4null0.8nullcircle190[3644.93333333, 3644.93333333][20.0, 20.0]0.08gabors4deg3040[1.0, 1.0, 1.0]
\n", "
" ], "text/plain": [ " color contrast frame mask opacity orientation \\\n", "stimulus_condition_id \n", "0 null null null null null null \n", "1 null 0.8 null circle 1 45 \n", "2 null 0.8 null circle 1 45 \n", "3 null 0.8 null circle 1 90 \n", "4 null 0.8 null circle 1 90 \n", "\n", " phase size \\\n", "stimulus_condition_id \n", "0 null null \n", "1 [3644.93333333, 3644.93333333] [20.0, 20.0] \n", "2 [3644.93333333, 3644.93333333] [20.0, 20.0] \n", "3 [3644.93333333, 3644.93333333] [20.0, 20.0] \n", "4 [3644.93333333, 3644.93333333] [20.0, 20.0] \n", "\n", " spatial_frequency stimulus_name temporal_frequency \\\n", "stimulus_condition_id \n", "0 null spontaneous null \n", "1 0.08 gabors 4 \n", "2 0.08 gabors 4 \n", "3 0.08 gabors 4 \n", "4 0.08 gabors 4 \n", "\n", " units x_position y_position color_triplet \n", "stimulus_condition_id \n", "0 null null null null \n", "1 deg 40 30 [1.0, 1.0, 1.0] \n", "2 deg -30 10 [1.0, 1.0, 1.0] \n", "3 deg 10 -10 [1.0, 1.0, 1.0] \n", "4 deg 30 40 [1.0, 1.0, 1.0] " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "session.stimulus_conditions.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spike data\n", "\n", "The `EcephysSession` object holds spike times (in seconds on the session master clock) for each unit. These are stored in a dictionary, which maps unit ids (the index values of the units table) to arrays of spike times." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/nicholas.mei/miniconda3/envs/allensdk/lib/python3.7/site-packages/allensdk/brain_observatory/ecephys/ecephys_session.py:1093: UserWarning: Session includes invalid time intervals that could be accessed with the attribute 'invalid_times',Spikes within these intervals are invalid and may need to be excluded from the analysis.\n", " warnings.warn(\"Session includes invalid time intervals that could be accessed with the attribute 'invalid_times',\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "236169 spikes were detected for unit 951816951 at times:\n" ] }, { "data": { "text/plain": [ "array([3.81328401e+00, 4.20301799e+00, 4.30151816e+00, ...,\n", " 9.96615988e+03, 9.96617945e+03, 9.96619655e+03])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ " # grab an arbitrary (though high-snr!) unit (we made units_with_high_snr above)\n", "high_snr_unit_ids = units_with_very_high_snr.index.values\n", "unit_id = high_snr_unit_ids[0]\n", "\n", "print(f\"{len(session.spike_times[unit_id])} spikes were detected for unit {unit_id} at times:\")\n", "session.spike_times[unit_id]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also obtain spikes tagged with the stimulus presentation during which they occurred:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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stimulus_presentation_idunit_idtime_since_stimulus_presentation_onset
spike_time
1585.73484137989518179270.000423
1585.73686237989518127420.002444
1585.73859137989518054270.004173
1585.73894137989518169510.004523
1585.73899537989518205100.004577
\n", "
" ], "text/plain": [ " stimulus_presentation_id unit_id \\\n", "spike_time \n", "1585.734841 3798 951817927 \n", "1585.736862 3798 951812742 \n", "1585.738591 3798 951805427 \n", "1585.738941 3798 951816951 \n", "1585.738995 3798 951820510 \n", "\n", " time_since_stimulus_presentation_onset \n", "spike_time \n", "1585.734841 0.000423 \n", "1585.736862 0.002444 \n", "1585.738591 0.004173 \n", "1585.738941 0.004523 \n", "1585.738995 0.004577 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get spike times from the first block of drifting gratings presentations \n", "drifting_gratings_presentation_ids = session.stimulus_presentations.loc[\n", " (session.stimulus_presentations['stimulus_name'] == 'drifting_gratings')\n", "].index.values\n", "\n", "times = session.presentationwise_spike_times(\n", " stimulus_presentation_ids=drifting_gratings_presentation_ids,\n", " unit_ids=high_snr_unit_ids\n", ")\n", "\n", "times.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can make raster plots of these data:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "color null\n", "contrast 0.8\n", "frame null\n", "orientation 180\n", "phase [42471.86666667, 42471.86666667]\n", "size [250.0, 250.0]\n", "spatial_frequency 0.04\n", "start_time 1585.73\n", "stimulus_block 2\n", "stimulus_name drifting_gratings\n", "stop_time 1587.74\n", "temporal_frequency 2\n", "x_position null\n", "y_position null\n", "duration 2.00168\n", "stimulus_condition_id 246\n", "Name: 3798, dtype: object" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_drifting_grating_presentation_id = times['stimulus_presentation_id'].values[0]\n", "plot_times = times[times['stimulus_presentation_id'] == first_drifting_grating_presentation_id]\n", "\n", "fig = raster_plot(plot_times, title=f'spike raster for stimulus presentation {first_drifting_grating_presentation_id}')\n", "plt.show()\n", "\n", "# also print out this presentation\n", "session.stimulus_presentations.loc[first_drifting_grating_presentation_id]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can access summary spike statistics for stimulus conditions and unit" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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spike_countstimulus_presentation_countspike_meanspike_stdspike_semcolorcontrastframemaskopacityorientationphasesizespatial_frequencystimulus_nametemporal_frequencyunitsx_positiony_positioncolor_triplet
unit_idstimulus_condition_id
95179933624613150.8666671.9952320.515167null0.8nullNone1180[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings2degnullnull[1.0, 1.0, 1.0]
95180097724626151.7333332.7377430.706882null0.8nullNone1180[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings2degnullnull[1.0, 1.0, 1.0]
951801127246103156.8666677.4149141.914523null0.8nullNone1180[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings2degnullnull[1.0, 1.0, 1.0]
9518011872464150.2666670.5936170.153271null0.8nullNone1180[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings2degnullnull[1.0, 1.0, 1.0]
95180146224683155.5333332.5875160.668094null0.8nullNone1180[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings2degnullnull[1.0, 1.0, 1.0]
\n", "
" ], "text/plain": [ " spike_count stimulus_presentation_count \\\n", "unit_id stimulus_condition_id \n", "951799336 246 13 15 \n", "951800977 246 26 15 \n", "951801127 246 103 15 \n", "951801187 246 4 15 \n", "951801462 246 83 15 \n", "\n", " spike_mean spike_std spike_sem color \\\n", "unit_id stimulus_condition_id \n", "951799336 246 0.866667 1.995232 0.515167 null \n", "951800977 246 1.733333 2.737743 0.706882 null \n", "951801127 246 6.866667 7.414914 1.914523 null \n", "951801187 246 0.266667 0.593617 0.153271 null \n", "951801462 246 5.533333 2.587516 0.668094 null \n", "\n", " contrast frame mask opacity orientation \\\n", "unit_id stimulus_condition_id \n", "951799336 246 0.8 null None 1 180 \n", "951800977 246 0.8 null None 1 180 \n", "951801127 246 0.8 null None 1 180 \n", "951801187 246 0.8 null None 1 180 \n", "951801462 246 0.8 null None 1 180 \n", "\n", " phase \\\n", "unit_id stimulus_condition_id \n", "951799336 246 [42471.86666667, 42471.86666667] \n", "951800977 246 [42471.86666667, 42471.86666667] \n", "951801127 246 [42471.86666667, 42471.86666667] \n", "951801187 246 [42471.86666667, 42471.86666667] \n", "951801462 246 [42471.86666667, 42471.86666667] \n", "\n", " size spatial_frequency \\\n", "unit_id stimulus_condition_id \n", "951799336 246 [250.0, 250.0] 0.04 \n", "951800977 246 [250.0, 250.0] 0.04 \n", "951801127 246 [250.0, 250.0] 0.04 \n", "951801187 246 [250.0, 250.0] 0.04 \n", "951801462 246 [250.0, 250.0] 0.04 \n", "\n", " stimulus_name temporal_frequency units \\\n", "unit_id stimulus_condition_id \n", "951799336 246 drifting_gratings 2 deg \n", "951800977 246 drifting_gratings 2 deg \n", "951801127 246 drifting_gratings 2 deg \n", "951801187 246 drifting_gratings 2 deg \n", "951801462 246 drifting_gratings 2 deg \n", "\n", " x_position y_position color_triplet \n", "unit_id stimulus_condition_id \n", "951799336 246 null null [1.0, 1.0, 1.0] \n", "951800977 246 null null [1.0, 1.0, 1.0] \n", "951801127 246 null null [1.0, 1.0, 1.0] \n", "951801187 246 null null [1.0, 1.0, 1.0] \n", "951801462 246 null null [1.0, 1.0, 1.0] " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats = session.conditionwise_spike_statistics(\n", " stimulus_presentation_ids=drifting_gratings_presentation_ids,\n", " unit_ids=high_snr_unit_ids\n", ")\n", "\n", "# display the parameters associated with each condition\n", "stats = pd.merge(stats, session.stimulus_conditions, left_on=\"stimulus_condition_id\", right_index=True)\n", "\n", "stats.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using these data, we can ask for each unit: which stimulus condition evoked the most activity on average?" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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spike_countstimulus_presentation_countspike_meanspike_stdspike_semcolorcontrastframemaskopacityorientationphasesizespatial_frequencystimulus_nametemporal_frequencyunitsx_positiony_positioncolor_triplet
unit_id
95179933681155.4000009.2874722.398015null0.8nullNone1.00[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings4degnullnull[1.0, 1.0, 1.0]
95180097741152.7333332.8401880.733333null0.8nullNone1.045[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings1degnullnull[1.0, 1.0, 1.0]
9518011272091513.9333339.7282112.511813null0.8nullNone1.090[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings1degnullnull[1.0, 1.0, 1.0]
95180118753153.5333335.9023801.523988null0.8nullNone1.0270[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings4degnullnull[1.0, 1.0, 1.0]
951801462136159.0666675.1612111.332619null0.8nullNone1.045[42471.86666667, 42471.86666667][250.0, 250.0]0.04drifting_gratings2degnullnull[1.0, 1.0, 1.0]
\n", "
" ], "text/plain": [ " spike_count stimulus_presentation_count spike_mean spike_std \\\n", "unit_id \n", "951799336 81 15 5.400000 9.287472 \n", "951800977 41 15 2.733333 2.840188 \n", "951801127 209 15 13.933333 9.728211 \n", "951801187 53 15 3.533333 5.902380 \n", "951801462 136 15 9.066667 5.161211 \n", "\n", " spike_sem color contrast frame mask opacity orientation \\\n", "unit_id \n", "951799336 2.398015 null 0.8 null None 1.0 0 \n", "951800977 0.733333 null 0.8 null None 1.0 45 \n", "951801127 2.511813 null 0.8 null None 1.0 90 \n", "951801187 1.523988 null 0.8 null None 1.0 270 \n", "951801462 1.332619 null 0.8 null None 1.0 45 \n", "\n", " phase size spatial_frequency \\\n", "unit_id \n", "951799336 [42471.86666667, 42471.86666667] [250.0, 250.0] 0.04 \n", "951800977 [42471.86666667, 42471.86666667] [250.0, 250.0] 0.04 \n", "951801127 [42471.86666667, 42471.86666667] [250.0, 250.0] 0.04 \n", "951801187 [42471.86666667, 42471.86666667] [250.0, 250.0] 0.04 \n", "951801462 [42471.86666667, 42471.86666667] [250.0, 250.0] 0.04 \n", "\n", " stimulus_name temporal_frequency units x_position y_position \\\n", "unit_id \n", "951799336 drifting_gratings 4 deg null null \n", "951800977 drifting_gratings 1 deg null null \n", "951801127 drifting_gratings 1 deg null null \n", "951801187 drifting_gratings 4 deg null null \n", "951801462 drifting_gratings 2 deg null null \n", "\n", " color_triplet \n", "unit_id \n", "951799336 [1.0, 1.0, 1.0] \n", "951800977 [1.0, 1.0, 1.0] \n", "951801127 [1.0, 1.0, 1.0] \n", "951801187 [1.0, 1.0, 1.0] \n", "951801462 [1.0, 1.0, 1.0] " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with_repeats = stats[stats[\"stimulus_presentation_count\"] >= 5]\n", "\n", "highest_mean_rate = lambda df: df.loc[df['spike_mean'].idxmax()]\n", "max_rate_conditions = with_repeats.groupby('unit_id').apply(highest_mean_rate)\n", "max_rate_conditions.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spike histograms\n", "\n", "It is commonly useful to compare spike data from across units and stimulus presentations, all relative to the onset of a stimulus presentation. We can do this using the `presentationwise_spike_counts` method. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "Show/Hide data repr\n", "\n", "\n", "\n", "\n", "\n", "Show/Hide attributes\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
xarray.DataArray
'spike_counts'
  • stimulus_presentation_id: 150
  • time_relative_to_stimulus_onset: 199
  • unit_id: 631
  • 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
    array([[[0, 1, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 1, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]],\n",
       "\n",
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       "\n",
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       "\n",
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       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint16)
    • stimulus_presentation_id
      (stimulus_presentation_id)
      int64
      3647 3648 3649 ... 3794 3795 3796
      array([3647, 3648, 3649, 3650, 3651, 3652, 3653, 3654, 3655, 3656, 3657, 3658,\n",
       "       3659, 3660, 3661, 3662, 3663, 3664, 3665, 3666, 3667, 3668, 3669, 3670,\n",
       "       3671, 3672, 3673, 3674, 3675, 3676, 3677, 3678, 3679, 3680, 3681, 3682,\n",
       "       3683, 3684, 3685, 3686, 3687, 3688, 3689, 3690, 3691, 3692, 3693, 3694,\n",
       "       3695, 3696, 3697, 3698, 3699, 3700, 3701, 3702, 3703, 3704, 3705, 3706,\n",
       "       3707, 3708, 3709, 3710, 3711, 3712, 3713, 3714, 3715, 3716, 3717, 3718,\n",
       "       3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727, 3728, 3729, 3730,\n",
       "       3731, 3732, 3733, 3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741, 3742,\n",
       "       3743, 3744, 3745, 3746, 3747, 3748, 3749, 3750, 3751, 3752, 3753, 3754,\n",
       "       3755, 3756, 3757, 3758, 3759, 3760, 3761, 3762, 3763, 3764, 3765, 3766,\n",
       "       3767, 3768, 3769, 3770, 3771, 3772, 3773, 3774, 3775, 3776, 3777, 3778,\n",
       "       3779, 3780, 3781, 3782, 3783, 3784, 3785, 3786, 3787, 3788, 3789, 3790,\n",
       "       3791, 3792, 3793, 3794, 3795, 3796])
    • time_relative_to_stimulus_onset
      (time_relative_to_stimulus_onset)
      float64
      -0.00897 -0.00691 ... 0.3969 0.399
      array([-0.00897 , -0.00691 , -0.004849, -0.002789, -0.000729,  0.001332,\n",
       "        0.003392,  0.005452,  0.007513,  0.009573,  0.011633,  0.013693,\n",
       "        0.015754,  0.017814,  0.019874,  0.021935,  0.023995,  0.026055,\n",
       "        0.028116,  0.030176,  0.032236,  0.034296,  0.036357,  0.038417,\n",
       "        0.040477,  0.042538,  0.044598,  0.046658,  0.048719,  0.050779,\n",
       "        0.052839,  0.054899,  0.05696 ,  0.05902 ,  0.06108 ,  0.063141,\n",
       "        0.065201,  0.067261,  0.069322,  0.071382,  0.073442,  0.075503,\n",
       "        0.077563,  0.079623,  0.081683,  0.083744,  0.085804,  0.087864,\n",
       "        0.089925,  0.091985,  0.094045,  0.096106,  0.098166,  0.100226,\n",
       "        0.102286,  0.104347,  0.106407,  0.108467,  0.110528,  0.112588,\n",
       "        0.114648,  0.116709,  0.118769,  0.120829,  0.122889,  0.12495 ,\n",
       "        0.12701 ,  0.12907 ,  0.131131,  0.133191,  0.135251,  0.137312,\n",
       "        0.139372,  0.141432,  0.143492,  0.145553,  0.147613,  0.149673,\n",
       "        0.151734,  0.153794,  0.155854,  0.157915,  0.159975,  0.162035,\n",
       "        0.164095,  0.166156,  0.168216,  0.170276,  0.172337,  0.174397,\n",
       "        0.176457,  0.178518,  0.180578,  0.182638,  0.184698,  0.186759,\n",
       "        0.188819,  0.190879,  0.19294 ,  0.195   ,  0.19706 ,  0.199121,\n",
       "        0.201181,  0.203241,  0.205302,  0.207362,  0.209422,  0.211482,\n",
       "        0.213543,  0.215603,  0.217663,  0.219724,  0.221784,  0.223844,\n",
       "        0.225905,  0.227965,  0.230025,  0.232085,  0.234146,  0.236206,\n",
       "        0.238266,  0.240327,  0.242387,  0.244447,  0.246508,  0.248568,\n",
       "        0.250628,  0.252688,  0.254749,  0.256809,  0.258869,  0.26093 ,\n",
       "        0.26299 ,  0.26505 ,  0.267111,  0.269171,  0.271231,  0.273291,\n",
       "        0.275352,  0.277412,  0.279472,  0.281533,  0.283593,  0.285653,\n",
       "        0.287714,  0.289774,  0.291834,  0.293894,  0.295955,  0.298015,\n",
       "        0.300075,  0.302136,  0.304196,  0.306256,  0.308317,  0.310377,\n",
       "        0.312437,  0.314497,  0.316558,  0.318618,  0.320678,  0.322739,\n",
       "        0.324799,  0.326859,  0.32892 ,  0.33098 ,  0.33304 ,  0.335101,\n",
       "        0.337161,  0.339221,  0.341281,  0.343342,  0.345402,  0.347462,\n",
       "        0.349523,  0.351583,  0.353643,  0.355704,  0.357764,  0.359824,\n",
       "        0.361884,  0.363945,  0.366005,  0.368065,  0.370126,  0.372186,\n",
       "        0.374246,  0.376307,  0.378367,  0.380427,  0.382487,  0.384548,\n",
       "        0.386608,  0.388668,  0.390729,  0.392789,  0.394849,  0.39691 ,\n",
       "        0.39897 ])
    • unit_id
      (unit_id)
      int64
      951814884 951814876 ... 951814312
      array([951814884, 951814876, 951815032, ..., 951814089, 951814199, 951814312])
" ], "text/plain": [ "\n", "array([[[0, 1, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 1, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 1, 0, ..., 0, 0, 0]],\n", "\n", " ...,\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [1, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]]], dtype=uint16)\n", "Coordinates:\n", " * stimulus_presentation_id (stimulus_presentation_id) int64 3647 ... 3796\n", " * time_relative_to_stimulus_onset (time_relative_to_stimulus_onset) float64 -0.00897 ... 0.399\n", " * unit_id (unit_id) int64 951814884 ... 951814312" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We're going to build an array of spike counts surrounding stimulus presentation onset\n", "# To do that, we will need to specify some bins (in seconds, relative to stimulus onset)\n", "time_bin_edges = np.linspace(-0.01, 0.4, 200)\n", "\n", "# look at responses to the flash stimulus\n", "flash_250_ms_stimulus_presentation_ids = session.stimulus_presentations[\n", " session.stimulus_presentations['stimulus_name'] == 'flashes'\n", "].index.values\n", "\n", "# and get a set of units with only decent snr\n", "decent_snr_unit_ids = session.units[\n", " session.units['snr'] >= 1.5\n", "].index.values\n", "\n", "spike_counts_da = session.presentationwise_spike_counts(\n", " bin_edges=time_bin_edges,\n", " stimulus_presentation_ids=flash_250_ms_stimulus_presentation_ids,\n", " unit_ids=decent_snr_unit_ids\n", ")\n", "spike_counts_da" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This has returned a new (to this notebook) data structure, the `xarray.DataArray`. You can think of this as similar to a 3+D `pandas.DataFrame`, or as a `numpy.ndarray` with labeled axes and indices. See the [xarray documentation](http://xarray.pydata.org/en/stable/index.html) for more information. In the mean time, the salient features are:\n", "\n", "- Dimensions : Each axis on each data variable is associated with a named dimension. This lets us see unambiguously what the axes of our array mean.\n", "- Coordinates : Arrays of labels for each sample on each dimension.\n", "\n", "xarray is nice because it forces code to be explicit about dimensions and coordinates, improving readability and avoiding bugs. However, you can always convert to numpy or pandas data structures as follows:\n", "- to pandas: `spike_counts_ds.to_dataframe()` produces a multiindexed dataframe\n", "- to numpy: `spike_counts_ds.values` gives you access to the underlying numpy array\n", "\n", "We can now plot spike counts for a particular presentation:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "presentation_id = 3796 # chosen arbitrarily\n", "plot_spike_counts(\n", " spike_counts_da.loc[{'stimulus_presentation_id': presentation_id}], \n", " spike_counts_da['time_relative_to_stimulus_onset'],\n", " 'spike count', \n", " f'unitwise spike counts on presentation {presentation_id}'\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also average across all presentations, adding a new data array to the dataset. Notice that this one no longer has a stimulus_presentation_id dimension, as we have collapsed it by averaging." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "Show/Hide data repr\n", "\n", "\n", "\n", "\n", "\n", "Show/Hide attributes\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
xarray.DataArray
'spike_counts'
  • time_relative_to_stimulus_onset: 199
  • unit_id: 631
  • 0.02667 0.1 0.08 0.02667 0.04667 ... 0.006667 0.006667 0.0 0.0 0.0
    array([[0.02666667, 0.1       , 0.08      , ..., 0.00666667, 0.        ,\n",
       "        0.00666667],\n",
       "       [0.02666667, 0.06666667, 0.04666667, ..., 0.02      , 0.        ,\n",
       "        0.        ],\n",
       "       [0.02      , 0.11333333, 0.03333333, ..., 0.03333333, 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.02666667, 0.09333333, 0.05333333, ..., 0.00666667, 0.        ,\n",
       "        0.        ],\n",
       "       [0.02666667, 0.06666667, 0.02666667, ..., 0.00666667, 0.02      ,\n",
       "        0.        ],\n",
       "       [0.02666667, 0.12      , 0.02      , ..., 0.        , 0.        ,\n",
       "        0.        ]])
    • time_relative_to_stimulus_onset
      (time_relative_to_stimulus_onset)
      float64
      -0.00897 -0.00691 ... 0.3969 0.399
      array([-0.00897 , -0.00691 , -0.004849, -0.002789, -0.000729,  0.001332,\n",
       "        0.003392,  0.005452,  0.007513,  0.009573,  0.011633,  0.013693,\n",
       "        0.015754,  0.017814,  0.019874,  0.021935,  0.023995,  0.026055,\n",
       "        0.028116,  0.030176,  0.032236,  0.034296,  0.036357,  0.038417,\n",
       "        0.040477,  0.042538,  0.044598,  0.046658,  0.048719,  0.050779,\n",
       "        0.052839,  0.054899,  0.05696 ,  0.05902 ,  0.06108 ,  0.063141,\n",
       "        0.065201,  0.067261,  0.069322,  0.071382,  0.073442,  0.075503,\n",
       "        0.077563,  0.079623,  0.081683,  0.083744,  0.085804,  0.087864,\n",
       "        0.089925,  0.091985,  0.094045,  0.096106,  0.098166,  0.100226,\n",
       "        0.102286,  0.104347,  0.106407,  0.108467,  0.110528,  0.112588,\n",
       "        0.114648,  0.116709,  0.118769,  0.120829,  0.122889,  0.12495 ,\n",
       "        0.12701 ,  0.12907 ,  0.131131,  0.133191,  0.135251,  0.137312,\n",
       "        0.139372,  0.141432,  0.143492,  0.145553,  0.147613,  0.149673,\n",
       "        0.151734,  0.153794,  0.155854,  0.157915,  0.159975,  0.162035,\n",
       "        0.164095,  0.166156,  0.168216,  0.170276,  0.172337,  0.174397,\n",
       "        0.176457,  0.178518,  0.180578,  0.182638,  0.184698,  0.186759,\n",
       "        0.188819,  0.190879,  0.19294 ,  0.195   ,  0.19706 ,  0.199121,\n",
       "        0.201181,  0.203241,  0.205302,  0.207362,  0.209422,  0.211482,\n",
       "        0.213543,  0.215603,  0.217663,  0.219724,  0.221784,  0.223844,\n",
       "        0.225905,  0.227965,  0.230025,  0.232085,  0.234146,  0.236206,\n",
       "        0.238266,  0.240327,  0.242387,  0.244447,  0.246508,  0.248568,\n",
       "        0.250628,  0.252688,  0.254749,  0.256809,  0.258869,  0.26093 ,\n",
       "        0.26299 ,  0.26505 ,  0.267111,  0.269171,  0.271231,  0.273291,\n",
       "        0.275352,  0.277412,  0.279472,  0.281533,  0.283593,  0.285653,\n",
       "        0.287714,  0.289774,  0.291834,  0.293894,  0.295955,  0.298015,\n",
       "        0.300075,  0.302136,  0.304196,  0.306256,  0.308317,  0.310377,\n",
       "        0.312437,  0.314497,  0.316558,  0.318618,  0.320678,  0.322739,\n",
       "        0.324799,  0.326859,  0.32892 ,  0.33098 ,  0.33304 ,  0.335101,\n",
       "        0.337161,  0.339221,  0.341281,  0.343342,  0.345402,  0.347462,\n",
       "        0.349523,  0.351583,  0.353643,  0.355704,  0.357764,  0.359824,\n",
       "        0.361884,  0.363945,  0.366005,  0.368065,  0.370126,  0.372186,\n",
       "        0.374246,  0.376307,  0.378367,  0.380427,  0.382487,  0.384548,\n",
       "        0.386608,  0.388668,  0.390729,  0.392789,  0.394849,  0.39691 ,\n",
       "        0.39897 ])
    • unit_id
      (unit_id)
      int64
      951814884 951814876 ... 951814312
      array([951814884, 951814876, 951815032, ..., 951814089, 951814199, 951814312])
" ], "text/plain": [ "\n", "array([[0.02666667, 0.1 , 0.08 , ..., 0.00666667, 0. ,\n", " 0.00666667],\n", " [0.02666667, 0.06666667, 0.04666667, ..., 0.02 , 0. ,\n", " 0. ],\n", " [0.02 , 0.11333333, 0.03333333, ..., 0.03333333, 0. ,\n", " 0. ],\n", " ...,\n", " [0.02666667, 0.09333333, 0.05333333, ..., 0.00666667, 0. ,\n", " 0. ],\n", " [0.02666667, 0.06666667, 0.02666667, ..., 0.00666667, 0.02 ,\n", " 0. ],\n", " [0.02666667, 0.12 , 0.02 , ..., 0. , 0. ,\n", " 0. ]])\n", "Coordinates:\n", " * time_relative_to_stimulus_onset (time_relative_to_stimulus_onset) float64 -0.00897 ... 0.399\n", " * unit_id (unit_id) int64 951814884 ... 951814312" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_spike_counts = spike_counts_da.mean(dim='stimulus_presentation_id')\n", "mean_spike_counts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and plot the mean spike counts" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_spike_counts(\n", " mean_spike_counts, \n", " mean_spike_counts['time_relative_to_stimulus_onset'],\n", " 'mean spike count', \n", " 'mean spike counts on flash_250_ms presentations'\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Waveforms\n", "\n", "We store precomputed mean waveforms for each unit in the `mean_waveforms` attribute on the `EcephysSession` object. This is a dictionary which maps unit ids to xarray [DataArrays](http://xarray.pydata.org/en/stable/generated/xarray.DataArray.html). These have `channel` and `time` (seconds, aligned to the detected event times) dimensions. The data values are in microvolts, as measured at the recording site." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "units_of_interest = high_snr_unit_ids[:35]\n", "\n", "waveforms = {uid: session.mean_waveforms[uid] for uid in units_of_interest}\n", "peak_channels = {uid: session.units.loc[uid, 'peak_channel_id'] for uid in units_of_interest}\n", "\n", "# plot the mean waveform on each unit's peak channel/\n", "plot_mean_waveforms(waveforms, units_of_interest, peak_channels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since neuropixels probes are densely populated with channels, spikes are typically detected on several channels. We can see this by plotting mean waveforms on channels surrounding a unit's peak channel:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "uid = units_of_interest[12]\n", "unit_waveforms = waveforms[uid]\n", "peak_channel = peak_channels[uid]\n", "peak_channel_idx = np.where(unit_waveforms[\"channel_id\"] == peak_channel)[0][0]\n", "\n", "ch_min = max(peak_channel_idx - 10, 0)\n", "ch_max = min(peak_channel_idx + 10, len(unit_waveforms[\"channel_id\"]) - 1)\n", "surrounding_channels = unit_waveforms[\"channel_id\"][np.arange(ch_min, ch_max, 2)]\n", "\n", "fig, ax = plt.subplots()\n", "ax.imshow(unit_waveforms.loc[{\"channel_id\": surrounding_channels}])\n", "\n", "ax.yaxis.set_major_locator(plt.NullLocator())\n", "ax.set_ylabel(\"channel\", fontsize=16)\n", "\n", "ax.set_xticks(np.arange(0, len(unit_waveforms['time']), 20))\n", "ax.set_xticklabels([f'{float(ii):1.4f}' for ii in unit_waveforms['time'][::20]], rotation=45)\n", "ax.set_xlabel(\"time (s)\", fontsize=16)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running speed\n", "\n", "We can obtain the velocity at which the experimental subject ran as a function of time by accessing the `running_speed` attribute. This returns a pandas dataframe whose rows are intervals of time (defined by \"start_time\" and \"end_time\" columns), and whose \"velocity\" column contains mean running speeds within those intervals.\n", "\n", "Here we'll plot the running speed trace for an arbitrary chunk of time." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "running_speed_midpoints = session.running_speed[\"start_time\"] + \\\n", " (session.running_speed[\"end_time\"] - session.running_speed[\"start_time\"]) / 2\n", "plot_running_speed(\n", " running_speed_midpoints, \n", " session.running_speed[\"velocity\"], \n", " start_index=5000,\n", " stop_index=5100\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optogenetic stimulation" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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start_timeconditionlevelstop_timestimulus_nameduration
id
09224.65339half-period of a cosine wave1.09225.65339raised_cosine1.000
19226.82361half-period of a cosine wave2.59227.82361raised_cosine1.000
29228.64353a single square pulse1.09228.64853pulse0.005
39230.65374a single square pulse4.09230.66374pulse0.010
49232.423752.5 ms pulses at 10 Hz4.09233.42375fast_pulses1.000
.....................
1759562.68831a single square pulse4.09562.69331pulse0.005
1769564.75842a single square pulse1.09564.76842pulse0.010
1779566.86855a single square pulse2.59566.87855pulse0.010
1789568.98860a single square pulse4.09568.99860pulse0.010
1799571.11868half-period of a cosine wave4.09572.11868raised_cosine1.000
\n", "

180 rows × 6 columns

\n", "
" ], "text/plain": [ " start_time condition level stop_time \\\n", "id \n", "0 9224.65339 half-period of a cosine wave 1.0 9225.65339 \n", "1 9226.82361 half-period of a cosine wave 2.5 9227.82361 \n", "2 9228.64353 a single square pulse 1.0 9228.64853 \n", "3 9230.65374 a single square pulse 4.0 9230.66374 \n", "4 9232.42375 2.5 ms pulses at 10 Hz 4.0 9233.42375 \n", ".. ... ... ... ... \n", "175 9562.68831 a single square pulse 4.0 9562.69331 \n", "176 9564.75842 a single square pulse 1.0 9564.76842 \n", "177 9566.86855 a single square pulse 2.5 9566.87855 \n", "178 9568.98860 a single square pulse 4.0 9568.99860 \n", "179 9571.11868 half-period of a cosine wave 4.0 9572.11868 \n", "\n", " stimulus_name duration \n", "id \n", "0 raised_cosine 1.000 \n", "1 raised_cosine 1.000 \n", "2 pulse 0.005 \n", "3 pulse 0.010 \n", "4 fast_pulses 1.000 \n", ".. ... ... \n", "175 pulse 0.005 \n", "176 pulse 0.010 \n", "177 pulse 0.010 \n", "178 pulse 0.010 \n", "179 raised_cosine 1.000 \n", "\n", "[180 rows x 6 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "session.optogenetic_stimulation_epochs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Eye tracking ellipse fits and estimated screen gaze location" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ecephys sessions may contain eye tracking data in the form of ellipse fits and estimated screen gaze location. Let's look at the ellipse fits first:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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corneal_reflection_center_xcorneal_reflection_center_ycorneal_reflection_heightcorneal_reflection_widthcorneal_reflection_phipupil_center_xpupil_center_ypupil_heightpupil_widthpupil_phieye_center_xeye_center_yeye_heighteye_widtheye_phi
Time (s)
3.20620321.221602215.48402111.09165812.801322-0.340513291.258341187.96547396.294414102.751684-0.759200310.242901207.161069273.535714323.2277260.099357
3.22948321.291022215.28212211.31821112.807982-0.243113290.703953188.65987294.239278103.384550-0.715791310.140402207.007975273.303505322.9191430.091675
3.23714321.294868215.05500011.13612212.423237-0.331511290.687532188.29475494.729755103.814759-0.673967310.224041206.700513273.481027323.4973560.090202
3.27028321.288914215.25099910.59605012.414694-0.245863289.154532188.52494393.87726499.315115-0.732209310.134502206.791105273.988038323.1477540.091154
3.30396320.819438215.9151439.36692712.8705960.109020290.206682187.41127190.221821103.856061-0.583017310.226498206.519467273.542559322.2339830.097546
................................................
9939.71240322.580849216.60034912.79914313.400555-0.525985NaNNaNNaNNaNNaN313.501794210.587410280.224381322.5330040.167348
9939.74576324.007748216.52087011.84317013.212379-0.403054NaNNaNNaNNaNNaN313.532165211.023852280.388767322.9420960.189335
9939.77918324.118835216.89887111.33694512.877454-0.254458NaNNaNNaNNaNNaN314.227284210.967838279.978946323.7517670.201428
9939.81257324.141869215.92982612.34329113.347376-0.186693NaNNaNNaNNaNNaN313.128124210.027071280.560120321.8328750.189717
9939.84647324.200410215.62195613.62957714.614380-0.270081NaNNaNNaNNaNNaN312.543325209.802086279.835031321.0550790.193363
\n", "

297838 rows × 15 columns

\n", "
" ], "text/plain": [ " corneal_reflection_center_x corneal_reflection_center_y \\\n", "Time (s) \n", "3.20620 321.221602 215.484021 \n", "3.22948 321.291022 215.282122 \n", "3.23714 321.294868 215.055000 \n", "3.27028 321.288914 215.250999 \n", "3.30396 320.819438 215.915143 \n", "... ... ... \n", "9939.71240 322.580849 216.600349 \n", "9939.74576 324.007748 216.520870 \n", "9939.77918 324.118835 216.898871 \n", "9939.81257 324.141869 215.929826 \n", "9939.84647 324.200410 215.621956 \n", "\n", " corneal_reflection_height corneal_reflection_width \\\n", "Time (s) \n", "3.20620 11.091658 12.801322 \n", "3.22948 11.318211 12.807982 \n", "3.23714 11.136122 12.423237 \n", "3.27028 10.596050 12.414694 \n", "3.30396 9.366927 12.870596 \n", "... ... ... \n", "9939.71240 12.799143 13.400555 \n", "9939.74576 11.843170 13.212379 \n", "9939.77918 11.336945 12.877454 \n", "9939.81257 12.343291 13.347376 \n", "9939.84647 13.629577 14.614380 \n", "\n", " corneal_reflection_phi pupil_center_x pupil_center_y \\\n", "Time (s) \n", "3.20620 -0.340513 291.258341 187.965473 \n", "3.22948 -0.243113 290.703953 188.659872 \n", "3.23714 -0.331511 290.687532 188.294754 \n", "3.27028 -0.245863 289.154532 188.524943 \n", "3.30396 0.109020 290.206682 187.411271 \n", "... ... ... ... \n", "9939.71240 -0.525985 NaN NaN \n", "9939.74576 -0.403054 NaN NaN \n", "9939.77918 -0.254458 NaN NaN \n", "9939.81257 -0.186693 NaN NaN \n", "9939.84647 -0.270081 NaN NaN \n", "\n", " pupil_height pupil_width pupil_phi eye_center_x eye_center_y \\\n", "Time (s) \n", "3.20620 96.294414 102.751684 -0.759200 310.242901 207.161069 \n", "3.22948 94.239278 103.384550 -0.715791 310.140402 207.007975 \n", "3.23714 94.729755 103.814759 -0.673967 310.224041 206.700513 \n", "3.27028 93.877264 99.315115 -0.732209 310.134502 206.791105 \n", "3.30396 90.221821 103.856061 -0.583017 310.226498 206.519467 \n", "... ... ... ... ... ... \n", "9939.71240 NaN NaN NaN 313.501794 210.587410 \n", "9939.74576 NaN NaN NaN 313.532165 211.023852 \n", "9939.77918 NaN NaN NaN 314.227284 210.967838 \n", "9939.81257 NaN NaN NaN 313.128124 210.027071 \n", "9939.84647 NaN NaN NaN 312.543325 209.802086 \n", "\n", " eye_height eye_width eye_phi \n", "Time (s) \n", "3.20620 273.535714 323.227726 0.099357 \n", "3.22948 273.303505 322.919143 0.091675 \n", "3.23714 273.481027 323.497356 0.090202 \n", "3.27028 273.988038 323.147754 0.091154 \n", "3.30396 273.542559 322.233983 0.097546 \n", "... ... ... ... \n", "9939.71240 280.224381 322.533004 0.167348 \n", "9939.74576 280.388767 322.942096 0.189335 \n", "9939.77918 279.978946 323.751767 0.201428 \n", "9939.81257 280.560120 321.832875 0.189717 \n", "9939.84647 279.835031 321.055079 0.193363 \n", "\n", "[297838 rows x 15 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pupil_data = session.get_pupil_data()\n", "pupil_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This particular session has eye tracking data, let's try plotting the ellipse fits over time." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "from matplotlib import animation\n", "from matplotlib.patches import Ellipse\n", "\n", "def plot_animated_ellipse_fits(pupil_data: pd.DataFrame, start_frame: int, end_frame: int):\n", "\n", " start_frame = 0 if (start_frame < 0) else start_frame\n", " end_frame = len(pupil_data) if (end_frame > len(pupil_data)) else end_frame\n", " \n", " frame_times = pupil_data.index.values[start_frame:end_frame]\n", " interval = np.average(np.diff(frame_times)) * 1000\n", "\n", " fig = plt.figure()\n", " ax = plt.axes(xlim=(0, 480), ylim=(0, 480))\n", "\n", " cr_ellipse = Ellipse((0, 0), width=0.0, height=0.0, angle=0, color='white')\n", " pupil_ellipse = Ellipse((0, 0), width=0.0, height=0.0, angle=0, color='black')\n", " eye_ellipse = Ellipse((0, 0), width=0.0, height=0.0, angle=0, color='grey')\n", "\n", " ax.add_patch(eye_ellipse)\n", " ax.add_patch(pupil_ellipse)\n", " ax.add_patch(cr_ellipse)\n", "\n", " def update_ellipse(ellipse_patch, ellipse_frame_vals: pd.DataFrame, prefix: str):\n", " ellipse_patch.center = tuple(ellipse_frame_vals[[f\"{prefix}_center_x\", f\"{prefix}_center_y\"]].values)\n", " ellipse_patch.width = ellipse_frame_vals[f\"{prefix}_width\"]\n", " ellipse_patch.height = ellipse_frame_vals[f\"{prefix}_height\"]\n", " ellipse_patch.angle = np.degrees(ellipse_frame_vals[f\"{prefix}_phi\"])\n", " \n", " def init():\n", " return [cr_ellipse, pupil_ellipse, eye_ellipse]\n", "\n", " def animate(i):\n", " ellipse_frame_vals = pupil_data.iloc[i]\n", " \n", " update_ellipse(cr_ellipse, ellipse_frame_vals, prefix=\"corneal_reflection\")\n", " update_ellipse(pupil_ellipse, ellipse_frame_vals, prefix=\"pupil\")\n", " update_ellipse(eye_ellipse, ellipse_frame_vals, prefix=\"eye\")\n", " \n", " return [cr_ellipse, pupil_ellipse, eye_ellipse]\n", " \n", " return animation.FuncAnimation(fig, animate, init_func=init, interval=interval, frames=range(start_frame, end_frame), blit=True)\n", "\n", "anim = plot_animated_ellipse_fits(pupil_data, 100, 600)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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