{ "cells": [ { "cell_type": "code", "execution_count": 3, "id": "c37dce82", "metadata": { "scrolled": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# include modules to the path\n", "import sys, os\n", "parent_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir))\n", "sys.path.append(parent_dir)\n", "sys.path.append(os.path.join(parent_dir, 'session'))\n", "\n", "from pack import pack, write_units, write_spiking_metrics, write_spatial_metrics,\\\n", " write_best_match_rotation\n", "\n", "import os\n", "import numpy as np\n", "import h5py, json\n", "import matplotlib.pyplot as plt\n", "import scipy.ndimage as ndi\n", "from scipy import signal\n", "from session.utils import get_sessions_list, get_sampling_rate, cleaned_epochs\n", "from session.adapters import load_clu_res, H5NAMES, create_dataset\n", "from head_direction import head_direction\n", "#from spatial import place_field_2D, map_stats, get_field_patches\n", "#from spatial import bins2meters, cart2pol, pol2cart\n", "from spiketrain import instantaneous_rate, spike_idxs\n", "from spiking_metrics import mean_firing_rate, isi_cv, isi_fano\n", "\n", "from session.lists import processed_008228, processed_008229\n", "from session.sessions import selected_009265" ] }, { "cell_type": "code", "execution_count": 4, "id": "a8ea2991", "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}" ] }, { "cell_type": "markdown", "id": "87fb14f0", "metadata": {}, "source": [ "## Set session paths and steps to perform" ] }, { "cell_type": "code", "execution_count": 7, "id": "3891b792", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['009265_hippoSIT_2023-02-24_09-53-26',\n", " '009265_hippoSIT_2023-02-24_17-22-46',\n", " '009265_hippoSIT_2023-02-27_10-18-32',\n", " '009265_hippoSIT_2023-02-27_15-33-46',\n", " '009265_hippoSIT_2023-02-28_09-16-50',\n", " '009265_hippoSIT_2023-02-28_13-16-10',\n", " '009265_hippoSIT_2023-02-28_20-45-04']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "EPOCH_NAMES = ('Original', 'Conflict', 'Control', 'All')\n", "\n", "#source = '/home/sobolev/nevermind/Andrey/data'\n", "#source = '/home/sobolev/nevermind/Michael/FreeBehaving/SIT_sessions/'\n", "# GPU-XXL\n", "source = '/mnt/nevermind.data-share/ag-grothe/Andrey/data'\n", "\n", "# test session\n", "#filebase = '008229_hippoSIT_2022-05-16_20-36-44'\n", "#filebase = '64_prSIT_2023-02-27_15-06-02'\n", "#filebase = '009266_hippoSIT_2023-04-24_16-56-55'\n", "#animal = filebase.split('_')[0]\n", "#sessionpath = os.path.join(source, animal, filebase)\n", "#h5name = os.path.join(source, animal, filebase, filebase + '.h5')\n", "#sessionpath = os.path.join(source, filebase)\n", "#h5name = os.path.join(source, filebase, filebase + '.h5')\n", "\n", "#sessions = get_sessions_list(os.path.join(source, animal), animal)\n", "#sessions = get_sessions_list(source, animal)\n", "#sessions = [filebase]\n", "#sessions = processed_008229\n", "sessions = [\n", "#'009266_hippoSIT_2023-04-17_17-04-17', # ch17, 20 + 55 correction, 5067 events. Showcase for N2 / N3 mod in target\n", "#'009266_hippoSIT_2023-04-18_10-10-37', # ch17, 10 + 55 correction, 5682 events\n", "#'009266_hippoSIT_2023-04-18_17-03-10', # ch17, 6 + 55 correction, 5494 events: FIXME very weird 1-2nd in target, find out\n", "#'009266_hippoSIT_2023-04-19_10-33-51', # ch17, 4 + 55 correction, 6424 events: very weird 1-2nd in target, find out\n", "#'009266_hippoSIT_2023-04-20_08-57-39', # ch1, 1 + 55 correction, 6424 events. Showcase for N2 / N3 mod in target\n", "#'009266_hippoSIT_2023-04-24_16-56-55', # ch17, 5 + 55* correction, 6165 events, frequency\n", "#'009266_hippoSIT_2023-04-26_08-20-17', # ch17, 12 + 55* correction, 6095 events, duration - showcase for N2 \n", "#'009266_hippoSIT_2023-05-02_12-22-14', # ch20, 10 + 55 correction, 5976 events, FIXME very weird 1-2nd in target, find out\n", "#'009266_hippoSIT_2023-05-04_09-11-06', # ch17, 5 + 55* correction, 4487 events, coma session with baseline AEPs\n", "#'009266_hippoSIT_2023-05-04_19-47-15',\n", " # PPC\n", "#'009266_hippoSIT_2023-04-20_15-24-14', # A1 ch20, PPC ch32, 60 + 55 correction, 5612 events\n", "#'009266_hippoSIT_2023-04-21_08-43-00', # A1 ch20, PPC ch32, 72 + 55 correction, 6282 events\n", "#'009266_hippoSIT_2023-04-21_13-12-31', # A1 ch20, PPC ch32, 72 + 55 correction, 6041 events\n", "#'009266_hippoSIT_2023-04-24_10-08-11', # A1 ch20, PPC ch40, 80 + 55 correction, 5579 events\n", " # HPC\n", "# '009266_hippoSIT_2023-05-22_09-27-22',\n", "# '009266_hippoSIT_2023-05-23_09-18-05',\n", "# '009266_hippoSIT_2023-05-25_15-55-57',\n", "# '009266_hippoSIT_2023-06-14_08-21-23',\n", "# '009266_hippoSIT_2023-06-19_08-58-35',\n", " \n", "'009265_hippoSIT_2023-02-28_20-45-04'\n", "]\n", "\n", "sessions = selected_009265\n", "\n", "# FIXME move occupancy outside units\n", "# FIXME Move 'processed' into H5NAMES\n", "\n", "steps = {\n", " 'write_trajectory': True, # Packing to HDF5 (trajectory, trials, sound events)\n", " 'write_units': True, # Loading units (spikes times, trajectory indices, instantaneous rate)\n", " 'write_spiking_metrics': True, # Spiking metrics (mean firing rate, ISI metrics etc.)\n", " 'write_spatial_metrics': False, # Spatial metrics\n", " 'write_best_mat_rot': False, # Best match rotation\n", "}\n", "\n", "sessions" ] }, { "cell_type": "code", "execution_count": 21, "id": "347fcf3d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['009266_hippoSIT_2023-02-28_19-53-56',\n", " '009266_hippoSIT_2023-03-01_17-53-11',\n", " '009266_hippoSIT_2023-03-06_15-10-36']" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_sessions_list(os.path.join(source, animal), animal)[:3]" ] }, { "cell_type": "code", "execution_count": 8, "id": "24cd2f6e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "session 009265_hippoSIT_2023-02-24_09-53-26 done\n", "session 009265_hippoSIT_2023-02-24_17-22-46 done\n", "session 009265_hippoSIT_2023-02-27_10-18-32 done\n", "session 009265_hippoSIT_2023-02-27_15-33-46 done\n", "session 009265_hippoSIT_2023-02-28_09-16-50 done\n", "session 009265_hippoSIT_2023-02-28_13-16-10 done\n", "session 009265_hippoSIT_2023-02-28_20-45-04 done\n" ] } ], "source": [ "# execute\n", "for session in sessions:\n", " animal = session.split('_')[0]\n", " sessionpath = os.path.join(source, animal, session)\n", " \n", " if steps['write_trajectory']:\n", " pack(sessionpath)\n", " if steps['write_units']:\n", " write_units(sessionpath)\n", " if steps['write_spiking_metrics']:\n", " write_spiking_metrics(sessionpath)\n", " if steps['write_spatial_metrics']:\n", " write_spatial_metrics(sessionpath)\n", " if steps['write_best_mat_rot']:\n", " write_best_match_rotation(sessionpath)\n", " \n", " print('session %s done' % session)" ] }, { "cell_type": "markdown", "id": "b1c468b5", "metadata": {}, "source": [ "## Playground" ] }, { "cell_type": "code", "execution_count": 4, "id": "6126ef98", "metadata": {}, "outputs": [], "source": [ "import h5py\n", "import os\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 6, "id": "4485e614", "metadata": {}, "outputs": [], "source": [ "source = '/home/sobolev/nevermind/Andrey/data'\n", "session = '009266_hippoSIT_2023-04-24_16-56-55'\n", "\n", "animal = session.split('_')[0]\n", "sessionpath = os.path.join(source, animal, session)\n", "dlc_file = os.path.join(sessionpath, 'dlc.csv')\n", "\n", "#with h5py.File(dlc_file, 'r') as f:\n", "# aeps = np.array(f['aeps'])\n", "# aeps_events = np.array(f['aeps_events'])" ] }, { "cell_type": "code", "execution_count": 7, "id": "433b7b01", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/sobolev/projects/pySIT/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3441: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27) have mixed types.Specify dtype option on import or set low_memory=False.\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n" ] } ], "source": [ "df = pd.read_csv(dlc_file)" ] }, { "cell_type": "code", "execution_count": 8, "id": "b4be27ef", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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scorerDLC_resnet50_timeSITOct21shuffle4_700000DLC_resnet50_timeSITOct21shuffle4_700000.1DLC_resnet50_timeSITOct21shuffle4_700000.2DLC_resnet50_timeSITOct21shuffle4_700000.3DLC_resnet50_timeSITOct21shuffle4_700000.4DLC_resnet50_timeSITOct21shuffle4_700000.5DLC_resnet50_timeSITOct21shuffle4_700000.6DLC_resnet50_timeSITOct21shuffle4_700000.7DLC_resnet50_timeSITOct21shuffle4_700000.8...DLC_resnet50_timeSITOct21shuffle4_700000.17DLC_resnet50_timeSITOct21shuffle4_700000.18DLC_resnet50_timeSITOct21shuffle4_700000.19DLC_resnet50_timeSITOct21shuffle4_700000.20DLC_resnet50_timeSITOct21shuffle4_700000.21DLC_resnet50_timeSITOct21shuffle4_700000.22DLC_resnet50_timeSITOct21shuffle4_700000.23DLC_resnet50_timeSITOct21shuffle4_700000.24DLC_resnet50_timeSITOct21shuffle4_700000.25DLC_resnet50_timeSITOct21shuffle4_700000.26
0bodypartsleft_eyeleft_eyeleft_eyeright_eyeright_eyeright_eyeleft_earleft_earleft_ear...red_dottail_basetail_basetail_baselower_spinelower_spinelower_spineneckneckneck
1coordsxylikelihoodxylikelihoodxylikelihood...likelihoodxylikelihoodxylikelihoodxylikelihood
20651.7044067382812581.3098144531250.9999692440032959641.3778686523438588.93847656250.9999958276748657649.8087768554688572.65472412109380.9999592304229736...0.9999963045120239625.6045532226562528.59509277343750.9999996423721313629.8932495117188544.0197753906250.9999817609786987637.4949951171875568.98535156250.9994956254959106
31651.7330322265625581.32531738281250.9999746084213257641.5486450195312588.94354248046880.9999963045120239649.8545532226562572.67218017578120.999961256980896...0.9999960660934448625.6072387695312528.63659667968750.9999997615814209629.9107666015625544.25164794921880.9999829530715942637.5068969726562569.10070800781250.9992895126342773
42651.7330932617188581.4855957031250.9999744892120361641.5501098632812588.95697021484380.9999963045120239650.0103149414062572.76373291015620.9999610185623169...0.9999960660934448625.6411743164062528.65228271484380.9999997615814209629.9863891601562544.25488281250.9999831914901733637.69287109375569.10290527343750.9992813467979431
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5 rows × 28 columns

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" ], "text/plain": [ " scorer DLC_resnet50_timeSITOct21shuffle4_700000 \\\n", "0 bodyparts left_eye \n", "1 coords x \n", "2 0 651.7044067382812 \n", "3 1 651.7330322265625 \n", "4 2 651.7330932617188 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.1 \\\n", "0 left_eye \n", "1 y \n", "2 581.309814453125 \n", "3 581.3253173828125 \n", "4 581.485595703125 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.2 \\\n", "0 left_eye \n", "1 likelihood \n", "2 0.9999692440032959 \n", "3 0.9999746084213257 \n", "4 0.9999744892120361 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.3 \\\n", "0 right_eye \n", "1 x \n", "2 641.3778686523438 \n", "3 641.5486450195312 \n", "4 641.5501098632812 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.4 \\\n", "0 right_eye \n", "1 y \n", "2 588.9384765625 \n", "3 588.9435424804688 \n", "4 588.9569702148438 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.5 \\\n", "0 right_eye \n", "1 likelihood \n", "2 0.9999958276748657 \n", "3 0.9999963045120239 \n", "4 0.9999963045120239 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.6 \\\n", "0 left_ear \n", "1 x \n", "2 649.8087768554688 \n", "3 649.8545532226562 \n", "4 650.0103149414062 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.7 \\\n", "0 left_ear \n", "1 y \n", "2 572.6547241210938 \n", "3 572.6721801757812 \n", "4 572.7637329101562 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.8 ... \\\n", "0 left_ear ... \n", "1 likelihood ... \n", "2 0.9999592304229736 ... \n", "3 0.999961256980896 ... \n", "4 0.9999610185623169 ... \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.17 \\\n", "0 red_dot \n", "1 likelihood \n", "2 0.9999963045120239 \n", "3 0.9999960660934448 \n", "4 0.9999960660934448 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.18 \\\n", "0 tail_base \n", "1 x \n", "2 625.6045532226562 \n", "3 625.6072387695312 \n", "4 625.6411743164062 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.19 \\\n", "0 tail_base \n", "1 y \n", "2 528.5950927734375 \n", "3 528.6365966796875 \n", "4 528.6522827148438 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.20 \\\n", "0 tail_base \n", "1 likelihood \n", "2 0.9999996423721313 \n", "3 0.9999997615814209 \n", "4 0.9999997615814209 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.21 \\\n", "0 lower_spine \n", "1 x \n", "2 629.8932495117188 \n", "3 629.9107666015625 \n", "4 629.9863891601562 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.22 \\\n", "0 lower_spine \n", "1 y \n", "2 544.019775390625 \n", "3 544.2516479492188 \n", "4 544.2548828125 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.23 \\\n", "0 lower_spine \n", "1 likelihood \n", "2 0.9999817609786987 \n", "3 0.9999829530715942 \n", "4 0.9999831914901733 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.24 \\\n", "0 neck \n", "1 x \n", "2 637.4949951171875 \n", "3 637.5068969726562 \n", "4 637.69287109375 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.25 \\\n", "0 neck \n", "1 y \n", "2 568.9853515625 \n", "3 569.1007080078125 \n", "4 569.1029052734375 \n", "\n", " DLC_resnet50_timeSITOct21shuffle4_700000.26 \n", "0 neck \n", "1 likelihood \n", "2 0.9994956254959106 \n", "3 0.9992895126342773 \n", "4 0.9992813467979431 \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "7f0d30bb", "metadata": {}, "source": [ "## Read unit names from H5" ] }, { "cell_type": "code", "execution_count": 34, "id": "bb04623f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 009266_hippoSIT_2023-02-28_19-53-56\n", "1 009266_hippoSIT_2023-03-01_17-53-11\n", "2 009266_hippoSIT_2023-03-06_15-10-36\n", "3 009266_hippoSIT_2023-03-06_20-43-19\n", "4 009266_hippoSIT_2023-03-08_17-06-45\n", "5 009266_hippoSIT_2023-03-09_09-37-07\n", "6 009266_hippoSIT_2023-03-09_19-12-22\n", "7 009266_hippoSIT_2023-04-12_15-49-49\n", "8 009266_hippoSIT_2023-04-13_08-57-46\n", "9 009266_hippoSIT_2023-04-14_09-17-34\n", "10 009266_hippoSIT_2023-04-17_09-06-10\n", "11 009266_hippoSIT_2023-04-17_17-04-17\n", "12 009266_hippoSIT_2023-04-18_10-10-37\n", "13 009266_hippoSIT_2023-04-18_17-03-10\n", "14 009266_hippoSIT_2023-04-19_10-33-51\n", "15 009266_hippoSIT_2023-04-19_11-21-37\n", "16 009266_hippoSIT_2023-04-19_17-12-48\n", "17 009266_hippoSIT_2023-04-20_08-57-39\n", "18 009266_hippoSIT_2023-04-20_15-24-14\n", "19 009266_hippoSIT_2023-04-21_08-43-00\n", "20 009266_hippoSIT_2023-04-21_13-12-31\n", "21 009266_hippoSIT_2023-04-24_10-08-11\n", "22 009266_hippoSIT_2023-04-24_16-56-55\n", "23 009266_hippoSIT_2023-04-25_09-02-56\n", "24 009266_hippoSIT_2023-04-25_17-27-51\n", "25 009266_hippoSIT_2023-04-26_08-20-17\n", "26 009266_hippoSIT_2023-04-27_08-50-53\n", "27 009266_hippoSIT_2023-04-27_21-04-41\n", "28 009266_hippoSIT_2023-04-28_09-04-09\n", "29 009266_hippoSIT_2023-04-28_16-40-08\n", "30 009266_hippoSIT_2023-05-02_12-22-14\n", "31 009266_hippoSIT_2023-05-02_17-20-39\n", "32 009266_hippoSIT_2023-05-03_08-22-14\n", "33 009266_hippoSIT_2023-05-04_09-11-06\n", "34 009266_hippoSIT_2023-05-04_19-47-15\n", "35 009266_hippoSIT_2023-05-05_08-32-22\n", "36 009266_hippoSIT_2023-05-05_15-06-54\n", "37 009266_hippoSIT_2023-05-18_16-22-42\n", "38 009266_hippoSIT_2023-05-19_10-22-53\n", "39 009266_hippoSIT_2023-05-21_10-56-38\n", "40 009266_hippoSIT_2023-05-22_09-27-22\n", "41 009266_hippoSIT_2023-05-22_21-54-39\n", "42 009266_hippoSIT_2023-05-23_09-18-05\n", "43 009266_hippoSIT_2023-05-23_17-48-12\n", "44 009266_hippoSIT_2023-05-25_09-56-32\n", "45 009266_hippoSIT_2023-05-25_15-55-57\n", "46 009266_hippoSIT_2023-06-13_08-49-11\n", "47 009266_hippoSIT_2023-06-14_08-21-23\n", "48 009266_hippoSIT_2023-06-15_09-25-15\n", "49 009266_hippoSIT_2023-06-15_17-30-45\n", "50 009266_hippoSIT_2023-06-16_08-49-13\n", "51 009266_hippoSIT_2023-06-19_08-58-35\n", "52 009266_hippoSIT_2023-06-20_08-26-29\n", "53 009266_hippoSIT_2023-06-21_08-15-10\n", "54 009266_hippoSIT_2023-06-21_20-39-34\n" ] } ], "source": [ "source = '/home/sobolev/nevermind/Andrey/data'\n", "animal = '009266'\n", "\n", "sessions = get_sessions_list(os.path.join(source, animal), animal)\n", "for i, session in enumerate(sessions):\n", " print(i, session)" ] }, { "cell_type": "code", "execution_count": 44, "id": "46e18f9a", "metadata": {}, "outputs": [], "source": [ "session = sessions[8]\n", "session = '009266_hippoSIT_2023-05-04_19-47-15'\n", "sessionpath = os.path.join(source, animal, session)\n", "h5_file = os.path.join(sessionpath, '%s.h5' % session)\n", "\n", "with h5py.File(h5_file, 'r') as f:\n", " if 'units' in f:\n", " units = [unit_name for unit_name in f['units']]\n", " else:\n", " units = []" ] }, { "cell_type": "markdown", "id": "f5c286d0", "metadata": {}, "source": [ "## Test sounds csv for delays" ] }, { "cell_type": "code", "execution_count": 5, "id": "d48b4b8c", "metadata": {}, "outputs": [], "source": [ "session = '009266_hippoSIT_2023-05-04_19-47-15'\n", "animal = '009266'\n", "sessionpath = os.path.join(source, animal, session)" ] }, { "cell_type": "code", "execution_count": 7, "id": "349972b8", "metadata": {}, "outputs": [], "source": [ "sounds = np.loadtxt(os.path.join(sessionpath, 'sounds.csv'), skiprows=1, delimiter=',')" ] }, { "cell_type": "code", "execution_count": 8, "id": "1d5e760b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(9599, 2)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sounds.shape" ] }, { "cell_type": "code", "execution_count": null, "id": "fadf4d9c", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 }