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{
"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": [
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"08:27:49 [I] klustakwik KlustaKwik2 version 0.2.6\n"
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]
}
],
"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",
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"# import seaborn as sns\n",
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"from distutils.dir_util import copy_tree\n",
"from neo import SpikeTrain\n",
"import scipy\n",
"\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
"tqdm.pandas()\n",
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"\n",
"from spike_statistics.core import permutation_resampling\n",
"\n",
"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
"\n",
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"from septum_mec.analysis.plotting import violinplot, despine"
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]
},
{
"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",
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"execution_count": 7,
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"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",
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"execution_count": 8,
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"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",
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"execution_count": 9,
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"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": {},
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"outputs": [],
"source": [
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"data.loc[data.eval('t_i_peak == t_i_peak and not bs'), 'ns_inhibited'] = True\n",
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"data.ns_inhibited.fillna(False, inplace=True)\n",
"\n",
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"data.loc[data.eval('t_i_peak != t_i_peak and not bs'), 'ns_not_inhibited'] = True\n",
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"data.ns_not_inhibited.fillna(False, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Number of sessions above threshold 194\n",
"Number of animals 4\n"
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]
}
],
"source": [
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"query = (\n",
" 'gridness > gridness_threshold and '\n",
" 'information_rate > information_rate_threshold and '\n",
" 'gridness > .2 and '\n",
" 'average_rate < 25'\n",
")\n",
"sessions_above_threshold = data.query(query)\n",
"print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
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]
},
{
"cell_type": "code",
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"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells 139\n",
"Number of gridcell recordings 231\n",
"Number of animals 4\n"
]
}
],
"source": [
"gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]\n",
"print(\"Number of gridcells\", gridcell_sessions.unit_idnum.nunique())\n",
"print(\"Number of gridcell recordings\", len(gridcell_sessions))\n",
"print(\"Number of animals\", len(gridcell_sessions.groupby(['entity'])))"
]
},
{
"cell_type": "code",
"execution_count": 18,
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"metadata": {},
"outputs": [],
"source": [
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"data.loc[:,'gridcell'] = np.nan\n",
"data['gridcell'] = data.isin(gridcell_sessions)\n",
"\n",
"data.loc[data.eval('not gridcell and bs'), 'bs_not_gridcell'] = True\n",
"data.bs_not_gridcell.fillna(False, inplace=True)"
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]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
"outputs": [
{
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>action</th>\n",
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" <th>baseline</th>\n",
" <th>entity</th>\n",
" <th>frequency</th>\n",
" <th>i</th>\n",
" <th>ii</th>\n",
" <th>session</th>\n",
" <th>stim_location</th>\n",
" <th>stimulated</th>\n",
" <th>tag</th>\n",
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" <th>...</th>\n",
" <th>half_width</th>\n",
" <th>peak_to_trough</th>\n",
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" <th>bs</th>\n",
" <th>bs_stim</th>\n",
" <th>bs_ctrl</th>\n",
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" <td>False</td>\n",
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" </tr>\n",
" <tr>\n",
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" <th>35</th>\n",
" <td>1833-260619-1</td>\n",
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" <td>True</td>\n",
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" <td>1833</td>\n",
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"<p>5 rows ×
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"</div>"
],
"text/plain": [
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" action baseline entity frequency i ii session \\\n",
"33 1833-260619-1 True 1833 NaN True False 1 \n",
"34 1833-260619-1 True 1833 NaN True False 1 \n",
"35 1833-260619-1 True 1833 NaN True False 1 \n",
"39 1833-260619-1 True 1833 NaN True False 1 \n",
"40 1833-260619-1 True 1833 NaN True False 1 \n",
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"\n",
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" stim_location stimulated tag ... half_width peak_to_trough \\\n",
"33 NaN False baseline i ... 0.272875 0.602667 \n",
"34 NaN False baseline i ... 0.226452 0.274814 \n",
"35 NaN False baseline i ... 0.247266 0.570104 \n",
"39 NaN False baseline i ... 0.284542 0.644111 \n",
"40 NaN False baseline i ... 0.259920 0.581698 \n",
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"\n",
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" average_firing_rate bs bs_stim bs_ctrl ns_inhibited \\\n",
"33 5.945508 True NaN 1.0 False \n",
"34 2.860048 False NaN 0.0 False \n",
"35 3.365674 True NaN 1.0 False \n",
"39 17.471520 True NaN 1.0 False \n",
"40 5.891739 True NaN 1.0 False \n",
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"\n",
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" ns_not_inhibited gridcell bs_not_gridcell \n",
"33 False True False \n",
"34 True True False \n",
"35 False True False \n",
"39 False True False \n",
"40 False True False \n",
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"\n",
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"[5 rows x 60 columns]"
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]
},
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"execution_count": 19,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"data.query('baseline and Hz11 and gridcell').head()"
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]
},
{
"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
"outputs": [],
"source": [
"entity_date_ = data.query('stim_location==\"ms\"').entity_date.unique()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"data = data.query('stim_location!=\"mecl\" and stim_location!=\"mecr\"')"
]
},
{
"cell_type": "code",
"execution_count": 22,
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"metadata": {
"scrolled": false
},
"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/histograms.py:898: RuntimeWarning: invalid value encountered in true_divide\n",
" return n/db/n.sum(), bin_edges\n"
]
},
{
"data": {
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2019-10-22 10:22:00 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-22 10:22:00 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-22 10:22:00 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-22 10:22:00 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-22 10:22:00 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-22 10:22:00 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-22 10:22:00 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
"image/png": "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
2019-10-22 10:22:00 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
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{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-17 17:50:31 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-17 17:50:31 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-17 17:50:31 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
"image/png": "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
2019-10-17 17:50:31 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
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2019-10-17 17:50:31 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-12-13 10:43:57 +00:00
"image/png": "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
2019-10-17 17:50:31 +00:00
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"density = True\n",
"cumulative = True\n",
"histtype = 'step'\n",
"lw = 2\n",
"bins = {\n",
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" 't_i_peak': np.arange(0, 0.02, 0.0005),\n",
" 't_e_peak': np.arange(0, 0.03, 0.0005),\n",
" 'p_i_peak': np.arange(0, 0.04, 0.0005),\n",
" 'p_e_peak': np.arange(0, 0.04, 0.0005),\n",
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"}\n",
"xlabel = {\n",
" 't_i_peak': 's',\n",
" 't_e_peak': 's',\n",
" 'p_i_peak': 'prob',\n",
" 'p_e_peak': 'prob',\n",
"}\n",
"\n",
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"for cell_type in ['gridcell', 'bs_not_gridcell', 'ns_inhibited', 'ns_not_inhibited']:\n",
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" for key in bins:\n",
" fig = plt.figure(figsize=(3.5,2.2))\n",
" plt.suptitle(key + ' ' + cell_type)\n",
" legend_lines = []\n",
" for color, query, label in zip(colors, queries, labels):\n",
" data.query(query + ' and ' + cell_type)[key].hist(\n",
" bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
" histtype=histtype, color=color)\n",
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n",
" plt.xlabel(xlabel[key])\n",
" plt.legend(\n",
" handles=legend_lines,\n",
" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
" plt.tight_layout()\n",
" plt.grid(False)\n",
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" plt.xlim(-0.001, bins[key].max() - bins[key].max()*0.02)\n",
" despine()\n",
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" figname = f'histogram-{key}-{cell_type}'.replace(' ', '-')\n",
" fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
" fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
"outputs": [],
"source": [
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [],
"source": [
"psth = pd.read_feather(output_path / 'data' / 'psth.feather')\n",
"times = pd.read_feather(output_path / 'data' / 'times.feather')"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [],
"source": [
"times = times.T.iloc[0].values"
]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 750x300 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
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"cs = ['#993404', '#980043', '#d95f02', '#e7298a']\n",
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"lb = ['GC 11 Hz', 'GC 30 Hz', 'NSI 11 Hz', 'NSI 30 Hz']\n",
"\n",
"fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
"ii = 0\n",
"for cell_type, ls in zip(['gridcell', 'ns_inhibited'], ['-', '--']):\n",
" for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
" selection = [\n",
" f'{r.action}_{r.channel_group}_{r.unit_name}' \n",
" for i, r in data.query(query + ' and ' + cell_type).iterrows()]\n",
" values = psth.loc[:, selection].dropna(axis=1).to_numpy()\n",
"\n",
" plot_bootstrap_timeseries(times*1000, values, ax=ax, lw=2, label=lb[ii], color=cs[ii], ls=ls)\n",
" # ax.set_title(titles[i])\n",
" ax.set_xlabel('Time (ms)')\n",
" ax.legend(frameon=False)\n",
" ii += 1\n",
" axs[0].set_ylabel('Probability density')\n",
" despine()\n",
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" plt.xlim(-5, 30)\n",
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" \n",
"figname = f'response-probability-gc-ns'\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
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]
},
{
"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAq0AAAFFCAYAAADGo9TZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdeZQV1bX48W/dqeeZeVRATjAiEUkcIA6YCHnOT9Sfoj7yfCYaBRxQf5KoiUJUgryoGPitJD4VlWcSFAc6aYcoTkRBIArSZWRsoOl5uvO9VfX7o243fem+Pd4ewP1Zq1d3V51TddqF1btP7bOPZlkWQgghhBBC9GeOvh6AEEIIIYQQ7ZGgVQghhBBC9HsStAohhBBCiH5PglYhhBBCCNHvSdAqhBBCCCH6PQlahRBCCCFEvydBqxBCCCGE6PckaBVCCCGEEP2eBK1CCCGEEKLfk6BVCCGEEEL0exK0CiGEEEKIfk+CViGEEEII0e9J0CqEEEIIIfo9CVp7mFLqNaXUa309DiGE6A3yzBNC9BRXXw/gG2DsuHHjTgSsvh6IEKJf0vp6AEkmzzwhRFu6/MyTmVYhhBBCCNHvSdAqhBBCCCH6PQlahRBCCCFEvydBqxBCCCGE6PckaBVCCCGEEP2eBK1CCCGEEKLfk6BVCCGEEEL0exK0CiGEEEKIfk+CViGaiVTswb/jXSzT7OuhCCGEEKIZ2RFLiBjLsqj78FmsaAhHahapx0/p6yEJIYQQIkZmWoUAzEADNesexTIiRGsOEqna19dDEkIIIUQzMtMqBBDct5VIzUGsSBDTX0O05mBfD0kIIYQQzSQ1aFVKbQeeBp7Xdb0smdcWoidFyndi1B3C8FYCEK0rw/DV4MzI6+ORCSGEEAKSnx4wAVgClCilXlVKXaqUktlc0a8ZgXoiVXsxfDV4RpyMIzUH01dN+NBXfT00IYQQQsQkO6C8DvgPYDpwEXAhUKWUeh54Rtf1z5N8P5RSe4DRHWy+Xtf1c2L9PIAXcLfRvk7X9dzujE/0f4Hi9USrD+BIycSZkYcZ8mJ4q4gc+oq0saf19fCEEEIIQZKDVl3XXwBeUEoNA67HDmInALcB85VSW4D/AV7Udb0mSbfdCOxv43wmMCn29b+aHZ+AHbD6gS0J+jZ0e3SiX7NMk/CBL4nW7Mc9eDwAzvQ8wjUHiNYcwLIsNE3r41EKIYQQokde3eu6fhB4BHhEKTUFmANcBUyOfSxVSr0GPAP8Tdd1qxv3uqKt80qpl7CD1s3AvGanGgPZ93Vd/1FX7y+ObkbdIQx/LVY0giPdnlTXUjKwomGMYAOmrxpnZkEfj1IIIYQQPV7yStf1Tbqu3woMAc4DngAiwCzgDez81weVUkOTfW+l1I3AldhpAFfquh5odroxaP0i2fcVR49I5W4Mfw2O9NymGVVN03CkZmEGGqSKgBBCCNFP9Gad1tOAC7BzXTMADTCBYcAvgJ1KqXuSdbNYELws9u3/1XV95xFNGoPWbcm6pzj6RGsPYQYacKRlxx3XUjMxgw1EayVoFUIIIfqDHl3Zr5Q6ATuv9VoOL5bSgM+xc1ufB4YCPwFuAn6tlDJ1Xf9NEm7/a+x81k3A71o5LzOtAsNbiRX248zIjzvuSMnE9FURrTvURyMTQgghRHNJD1qVUvnA/8EOVr8XO6wBdcBq4I+6rn/WrEsVME8pVQwsB24FuhW0KqUmY1cxALj9yJzZ2EKxAYABNCilfo49E5wO7AZe1nX9r90Zg+j/LMvC8FZjhgNonvS4c47UTKJV+zDqy2UxlhBCCNEPJHtzgbXATOxV+RpgAe9ibzjwsq7rwTa6v4EdtA5MwlDujd3/77quf9jK+cZZVgt7pjX1iPP/pZRaB1yt67pUEDhGWUEvVjgA0TCaJ/6fgObJwIqGMAL1mMEGnEekDwghhBCidyV7pvXi2Od92JUB/kfX9b0d7JsPfAV81J0BKKWOAy6LfbsoQbPGoNUFvAI8CmwHcoDLsSsfXAC8BPxbB++7PcGpsR3pL3qf4a3CDPvR3KloWnx6t+ZwoHnSsEI+jLqybgWt+/fv57zzzgPg0ksv5dFHH22z/SeffML1118PgK7rrbYpLi7mL3/5Cxs2bKCsrIxQKER+fj4nnHAC55xzDrNmzSI19ci/xWzTp0/nwIED3HrrrcydO7fLPxfAHXfcwbp163jzzTcZPbqj5ZJh3rx5FBUV8c477zBixIhO3fPjjz/mxz/+MQDr169nyJAhbbZfsGABr7/+OmeccQbPPPNMp+4lEpNnnmjLsfTc27BhA88++yxbtmzB5/MxaNAgpk2bxpw5cxgzZkzCfl999RUrVqzgk08+ob6+nkGDBnHWWWdx8803M3jw4E6N4cknn2T58uUMHz6cv//97+22T+Zzvj9JdtD6v9izqu90toyVrutbgW8lYQy3Ak7gH7quv5ugzefACuCQrusPNjseBH6nlPoceB/4kVLqR5IqcGyK1h7EDHnRUjJaPa+507DCAQx/skoKw9q1a5k5cybnnntul6/xxBNPsGLFCkzTJDMzk1GjRuF2u6moqOCDDz7ggw8+4A9/+ANPPfUU3/72t5M29iOtXr2adevWdbrf888/T1FRUQ+MSAjRHx3Nz73ly5fz5JNPApCbm8sJJ5xASUkJL730EmvXruU3v/kNM2bMaNFv06ZN/Od//iehUIi8vDzGjx/P7t27Wb16NYWFhTz77LNMmDAhaeP8pkh20LoSexepdgNWpdT5wDhd11tbJNUdV8Y+P5Ooga7rhUBhG+c/VEq9DfwQe9a23aBV1/VW/y+JzUac2F5/0fsilXsw/XU40nNaPa95UjEjQUxf8oJWgPvuu49169aRk9P6fduyZs0annrqKdLT03n44Yf54Q9/iNPpbDq/c+dOFi5cyNatW7nhhhsoLCwkPz+/jSt2zTPPPMMjjzzS6X5PP/00S5YsSfp4RO+TZ57ojKPxubdhw4amgPXuu+9mzpw5OJ1OwuEwS5cu5dlnn+Wuu+5i0qRJcW98amtr+dnPfkYoFOLGG2/ktttuw+Vy4fV6WbhwIUVFRcydO5fCwkI8Hk+3x/lNkuySV+9h12HtiEXA4mTePLaRwUjsOrB/7ublGnfJOr6b1xH9kGWaRKtKMP21ONNa36nX4UrFigQx/bVJu6+maVRUVLBoUaLMlbatXLkSsB+gM2fOjHtwA4wdO5YVK1ZQUFBATU0Nzz33XLfH3Fx5eTnz5s3j4YcfxrI6/jKlrKyMW2+9lUcffbRT/YQQR7+j9bn3hz/8AYALL7yQG264oem+Ho+He++9l7FjxxIKhXjllVfi+q1atYq6ujq+853vsGDBAlwue34wMzOTpUuXMnLkSEpKSnj11VeTMs5vki4HrUqpLKXUmOYfsVNpRx4/4mOsUmo6diqAOyk/xWGXxD4X6bpe3c74nUqptmaaG//bRJIyMtGvhEuLiTZUYpkGWmpmq200TxpWJICRxJnW2bNnA/Daa6/xzjvvdKpvXV0d+/btA2DSpEkJ2+Xn5/ODH/wAgM8//7yLI23prbfeYsaMGRQVFZGTk8P999/foX5/+9vfmDFjBm+99Ra5ubkd7ieEODYcrc+9yZMnM336dC6//PIW5zRNY/x4e+vvgwfj63k3BrGzZs1q0c/j8TRd74033kjKOL9JupMekE3LlfcWcCrwrw5eY0M37t+aabHPb7fVSCm1BxgFLODwBgRHmhz7/GUyBnasi9ZXdLmvIzUThyet1XNGQ2WXZ+YcKek4EuSr+nf8nUj51zgy8rHCfqymPofba+5UrEgI01/Xpfu35rrrrqO4uJhNmzbxwAMPcOqpp5Kb2/pM75Ea/1oHePfddznxxMRvYOfOncv1119PQUHytqAtLi4mEAhw0UUXcc899xAKhTrUb8eOHQSDQS655BLuvvtufD5f0sbUVc0Xc7VnyZIlXHLJJe03FN84ZrW/y321dDdaauvzNmaNH7r6QiLVhSO99VfOZm0AzJYXduSnt9I6eY7W594tt9yS8JxhGOzYsQMgbhFqeXk5Bw4
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"text/plain": [
"<Figure size 750x300 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"cs = ['#d95f02', '#e7298a', '#993404', '#980043']\n",
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"lb = ['NSI 11 Hz', 'NSI 30 Hz', 'NS 11 Hz', 'NS 30 Hz']\n",
"\n",
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"fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
"ii = 0\n",
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"for cell_type, ls in zip(['ns_inhibited', 'ns_not_inhibited'], ['--', '-']):\n",
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" for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
" selection = [\n",
" f'{r.action}_{r.channel_group}_{r.unit_name}' \n",
" for i, r in data.query(query + ' and ' + cell_type).iterrows()]\n",
" values = psth.loc[:, selection].dropna(axis=1).to_numpy()\n",
"\n",
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" plot_bootstrap_timeseries(times*1000, values, ax=ax, lw=2, label=lb[ii], color=cs[ii], ls=ls)\n",
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" # ax.set_title(titles[i])\n",
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" ax.set_xlabel('Time (ms)')\n",
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" ax.legend(frameon=False)\n",
" ii += 1\n",
" axs[0].set_ylabel('Probability density')\n",
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" despine()\n",
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" plt.xlim(-5, 30)\n",
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" \n",
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"figname = f'response-probability-nsi-ns'\n",
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"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Store results in Expipe action"
]
},
{
"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"stimulus-response\")"
]
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},
{
"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/media/storage/expipe/septum-mec/actions/stimulus-response/data/data/times.feather',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/data/psth.feather',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-gridcell-stim-mec.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_inhibited-stim-mec.png',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_inhibited.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-bs_not_gridcell.svg',\n",
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2019-10-22 10:22:00 +00:00
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]
},
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"execution_count": 29,
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"metadata": {},
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}
],
"source": [
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]
},
{
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"execution_count": 30,
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"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-spike-response.ipynb\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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}
],
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