{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "19:18:12 [I] klustakwik KlustaKwik2 version 0.2.6\n" ] } ], "source": [ "import os\n", "import expipe\n", "import pathlib\n", "import numpy as np\n", "import spatial_maps.stats as stats\n", "import septum_mec\n", "import septum_mec.analysis.data_processing as dp\n", "import septum_mec.analysis.registration\n", "import head_direction.head as head\n", "import spatial_maps as sp\n", "import speed_cells.speed as spd\n", "import re\n", "import joblib\n", "import multiprocessing\n", "import shutil\n", "import psutil\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import seaborn as sns\n", "from distutils.dir_util import copy_tree\n", "from neo import SpikeTrain\n", "import scipy\n", "\n", "from tqdm import tqdm_notebook as tqdm\n", "from tqdm._tqdm_notebook import tqdm_notebook\n", "tqdm_notebook.pandas()\n", "\n", "from spike_statistics.core import permutation_resampling\n", "\n", "from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n", "\n", "from septum_mec.analysis.plotting import violinplot" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.rc('axes', titlesize=12)\n", "plt.rcParams.update({\n", " 'font.size': 12, \n", " 'figure.figsize': (6, 4), \n", " 'figure.dpi': 150\n", "})\n", "\n", "output_path = pathlib.Path(\"output\") / \"stimulus-response\"\n", "(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n", "(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n", "output_path.mkdir(exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_loader = dp.Data()\n", "actions = data_loader.actions\n", "project = data_loader.project" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "identification_action = actions['identify-neurons']\n", "sessions = pd.read_csv(identification_action.data_path('sessions'))\n", "units = pd.read_csv(identification_action.data_path('units'))\n", "session_units = pd.merge(sessions, units, on='action')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "stim_action = actions['stimulus-response']\n", "stim_results = pd.read_csv(stim_action.data_path('results'))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# lfp_results has old unit id's but correct on (action, unit_name, channel_group)\n", "stim_results = stim_results.drop('unit_id', axis=1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "statistics_action = actions['calculate-statistics']\n", "shuffling = actions['shuffling']\n", "\n", "statistics_results = pd.read_csv(statistics_action.data_path('results'))\n", "statistics_results = session_units.merge(statistics_results, how='left')\n", "quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n", "action_columns = ['action', 'channel_group', 'unit_name']\n", "data = pd.merge(statistics_results, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "data = data.merge(stim_results, how='left')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "waveform_action = actions['waveform-analysis']\n", "waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "data = data.merge(waveform_results, how='left')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "colors = ['#d95f02','#e7298a']\n", "labels = ['11 Hz', '30 HZ']\n", "queries = ['frequency==11', 'frequency==30']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data.bs = data.bs.astype(bool)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of gridcells 225\n" ] } ], "source": [ "grid_query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n", "gridcell_sessions = data.query(grid_query)\n", "print(\"Number of gridcells\", len(gridcell_sessions))\n", "# print(\"Number of animals\", len(gridcell_sessions.groupby(['entity'])))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "data['gridcell'] = data.isin(data.query(grid_query))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1151 | \n", "1833-200619-1 | \n", "4 | \n", "165 | \n", "4.093726 | \n", "0.112030 | \n", "1.560769 | \n", "9.952907 | \n", "16.871964 | \n", "34.400735 | \n", "0.371794 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.272936 | \n", "0.784972 | \n", "4.093056 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1152 | \n", "1833-200619-1 | \n", "6 | \n", "163 | \n", "17.705502 | \n", "0.202908 | \n", "14.631392 | \n", "24.895637 | \n", "34.144570 | \n", "51.462522 | \n", "0.877996 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.303012 | \n", "0.661133 | \n", "17.702603 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1153 | \n", "1833-200619-1 | \n", "6 | \n", "171 | \n", "4.061107 | \n", "0.058014 | \n", "1.879235 | \n", "7.260758 | \n", "11.252257 | \n", "20.574695 | \n", "0.561983 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.310060 | \n", "0.632763 | \n", "4.060442 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1154 | \n", "1833-200619-1 | \n", "6 | \n", "206 | \n", "3.982277 | \n", "0.150630 | \n", "2.316705 | \n", "7.168058 | \n", "9.286450 | \n", "19.626376 | \n", "0.618229 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.290294 | \n", "0.618949 | \n", "3.981625 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1155 | \n", "1833-200619-1 | \n", "6 | \n", "240 | \n", "4.089649 | \n", "0.098818 | \n", "1.539874 | \n", "10.560745 | \n", "15.374288 | \n", "32.783007 | \n", "0.358157 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.263375 | \n", "0.622896 | \n", "4.088979 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1156 | \n", "1833-200619-1 | \n", "7 | \n", "143 | \n", "9.300587 | \n", "0.218310 | \n", "6.750717 | \n", "13.150023 | \n", "13.197378 | \n", "25.067697 | \n", "0.825910 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.289628 | \n", "0.650032 | \n", "9.299064 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1162 | \n", "1839-120619-3 | \n", "5 | \n", "131 | \n", "17.773050 | \n", "0.076020 | \n", "9.779864 | \n", "29.707618 | \n", "42.215165 | \n", "77.486029 | \n", "0.651012 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.245031 | \n", "0.528413 | \n", "17.770859 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1165 | \n", "1839-120619-3 | \n", "6 | \n", "133 | \n", "2.612293 | \n", "0.053873 | \n", "1.055067 | \n", "6.992168 | \n", "9.603099 | \n", "17.060484 | \n", "0.372375 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.239301 | \n", "0.531126 | \n", "2.611971 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1167 | \n", "1839-120619-3 | \n", "7 | \n", "119 | \n", "4.950355 | \n", "0.132893 | \n", "3.636504 | \n", "7.175598 | \n", "7.291281 | \n", "14.571674 | \n", "0.836244 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.284221 | \n", "0.610068 | \n", "4.949745 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1168 | \n", "1839-120619-3 | \n", "7 | \n", "127 | \n", "5.407801 | \n", "0.091931 | \n", "3.251329 | \n", "15.356306 | \n", "18.617758 | \n", "37.590469 | \n", "0.414271 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.273572 | \n", "0.611548 | \n", "5.407135 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1199 | \n", "1833-260619-3 | \n", "0 | \n", "140 | \n", "3.564682 | \n", "0.063184 | \n", "2.498756 | \n", "5.782665 | \n", "8.770230 | \n", "17.134986 | \n", "0.720704 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.189559 | \n", "0.248665 | \n", "3.564358 | \n", "False | \n", "NaN | \n", "0.0 | \n", "True | \n", "
1200 | \n", "1833-260619-3 | \n", "0 | \n", "141 | \n", "2.694224 | \n", "0.094154 | \n", "1.691471 | \n", "5.502054 | \n", "10.395725 | \n", "20.328752 | \n", "0.519950 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.225575 | \n", "0.277528 | \n", "2.693978 | \n", "False | \n", "NaN | \n", "0.0 | \n", "True | \n", "
1202 | \n", "1833-260619-3 | \n", "0 | \n", "182 | \n", "5.289030 | \n", "0.148720 | \n", "3.342163 | \n", "10.892485 | \n", "16.803801 | \n", "30.523793 | \n", "0.544679 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.275930 | \n", "0.594526 | \n", "5.288548 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1203 | \n", "1833-260619-3 | \n", "0 | \n", "194 | \n", "6.485358 | \n", "0.096207 | \n", "3.706339 | \n", "12.069498 | \n", "18.212336 | \n", "29.243464 | \n", "0.590584 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.222604 | \n", "0.576271 | \n", "6.484767 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1205 | \n", "1833-260619-3 | \n", "0 | \n", "209 | \n", "3.425497 | \n", "0.085117 | \n", "1.306754 | \n", "8.551145 | \n", "11.161798 | \n", "29.652423 | \n", "0.378044 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.244049 | \n", "0.571337 | \n", "3.425185 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1207 | \n", "1833-260619-3 | \n", "1 | \n", "170 | \n", "26.841716 | \n", "0.218178 | \n", "22.328079 | \n", "38.090240 | \n", "50.981983 | \n", "74.601637 | \n", "0.857579 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.257469 | \n", "0.636957 | \n", "26.839270 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1208 | \n", "1833-260619-3 | \n", "1 | \n", "207 | \n", "4.589791 | \n", "0.088439 | \n", "2.309667 | \n", "8.938164 | \n", "10.731362 | \n", "25.229471 | \n", "0.538208 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.252255 | \n", "0.587372 | \n", "4.589373 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1211 | \n", "1833-260619-3 | \n", "3 | \n", "176 | \n", "7.407735 | \n", "0.156101 | \n", "5.622472 | \n", "11.694017 | \n", "16.474141 | \n", "32.870310 | \n", "0.757528 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.261129 | \n", "0.592306 | \n", "7.407060 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1213 | \n", "1833-260619-3 | \n", "5 | \n", "111 | \n", "9.222663 | \n", "0.179913 | \n", "6.341652 | \n", "14.990045 | \n", "17.803066 | \n", "32.423819 | \n", "0.732917 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.277189 | \n", "0.615988 | \n", "9.221822 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1216 | \n", "1833-260619-3 | \n", "6 | \n", "142 | \n", "9.359639 | \n", "0.129023 | \n", "6.738758 | \n", "14.564994 | \n", "20.758052 | \n", "44.189302 | \n", "0.773930 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.300175 | \n", "0.610068 | \n", "9.358786 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1218 | \n", "1833-260619-3 | \n", "6 | \n", "192 | \n", "7.836336 | \n", "0.170862 | \n", "4.889011 | \n", "13.019928 | \n", "17.648343 | \n", "34.791219 | \n", "0.715811 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.287132 | \n", "0.616235 | \n", "7.835622 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1222 | \n", "1833-200619-3 | \n", "0 | \n", "91 | \n", "7.072750 | \n", "0.074100 | \n", "4.679924 | \n", "11.282597 | \n", "18.578196 | \n", "35.109099 | \n", "0.713088 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.293775 | \n", "0.657679 | \n", "7.071948 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1228 | \n", "1833-200619-3 | \n", "3 | \n", "82 | \n", "15.697615 | \n", "0.127761 | \n", "12.267443 | \n", "21.346293 | \n", "27.567344 | \n", "38.706425 | \n", "0.874674 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.252895 | \n", "0.600200 | \n", "15.695836 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1229 | \n", "1833-200619-3 | \n", "4 | \n", "113 | \n", "11.770313 | \n", "0.136640 | \n", "6.835310 | \n", "20.280536 | \n", "22.248766 | \n", "44.143227 | \n", "0.676058 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.271023 | \n", "0.699617 | \n", "11.768979 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1231 | \n", "1833-200619-3 | \n", "5 | \n", "59 | \n", "4.442527 | \n", "0.110165 | \n", "2.926793 | \n", "7.344323 | \n", "8.786494 | \n", "20.320606 | \n", "0.722984 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.343906 | \n", "0.698383 | \n", "4.442023 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1232 | \n", "1833-200619-3 | \n", "6 | \n", "120 | \n", "22.461229 | \n", "0.268466 | \n", "18.182326 | \n", "32.115585 | \n", "33.640870 | \n", "62.235139 | \n", "0.833921 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.294291 | \n", "0.639177 | \n", "22.458685 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1233 | \n", "1833-200619-3 | \n", "6 | \n", "126 | \n", "3.102942 | \n", "0.090727 | \n", "1.447857 | \n", "6.981766 | \n", "9.945472 | \n", "21.048478 | \n", "0.436204 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.304748 | \n", "0.641151 | \n", "3.102590 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1234 | \n", "1833-200619-3 | \n", "6 | \n", "132 | \n", "6.901437 | \n", "0.072648 | \n", "4.231220 | \n", "14.073295 | \n", "20.697950 | \n", "36.231604 | \n", "0.612146 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.277708 | \n", "0.585645 | \n", "6.900656 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
1235 | \n", "1833-200619-3 | \n", "6 | \n", "150 | \n", "3.767582 | \n", "0.114920 | \n", "1.422876 | \n", "10.607271 | \n", "13.651769 | \n", "34.348592 | \n", "0.332963 | \n", "... | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0.258204 | \n", "0.608094 | \n", "3.767155 | \n", "True | \n", "NaN | \n", "1.0 | \n", "True | \n", "
130 rows × 51 columns
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