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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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"12:13:51 [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",
"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",
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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": [
"data.loc[data.eval('not t_i_peak.isnull() and not bs'), 'ns_inhibited'] = True\n",
"data.ns_inhibited.fillna(False, inplace=True)\n",
"\n",
"data.loc[data.eval('t_i_peak.isnull() and not bs'), 'ns_not_inhibited'] = True\n",
"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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" <th>ns_inhibited</th>\n",
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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": {
"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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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"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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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
2019-10-17 17:50:31 +00:00
{
"data": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAFGCAYAAACsWHzVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XmYHFW5x/HvZAgkIUA2IDFAgAReNlGURQQuURFBEFHAjaABQQEV9cqm4CV4EQS8iijceFEEI8iiILuCyBZ2FZDNlzUBQUhIwhKSEDIz949zKtPp9D7V01U9v8/zzFNdXVWnTtd099tnrY6enh5EREQkHwa1OgMiIiJSOwVuERGRHFHgFhERyREFbhERkRxR4BYREckRBW4REZEcUeAWERHJEQVuERGRHFHgFhERyREFbhERkRxR4BYREckRBW4REZEcUeAWERHJEQVuERGRHFHgzhkz27rVeZDqzGyqmfXEv8+0Oj9ZY2bTCq7P+5qQfpL2Hxs8fnJBGscXbetz3s3s1nj8kgr7tPyzXvA6L2h1XqTXKq3OgNTGzNYCvgd8Bf3fRNqWPutSjd4U+fEj4JBWZ0JEmk6fdalIgTs/OludAZG0uPs0YFqLs1GWu98KdDQx/ckVNuuzLhWpjVtERCRHFLhFRERyRFXlGWdm04CTip7riQ9vq1Ll1ug5hwFHAPsCmwFrAvOAvwIXA5e5e3fa543nvgD4AuDuvpmZvQf4DrAzMAp4CfgL8EN3f6RKWoOBqcABwNbx+FeBh4DfAb9y96VV0tgZODCe/x2Ea/EG8C/gNuAcd/9nA69zNHArsFV86rvufkodx88CJgA/cfdvmNkHgSOBHYExwHxgJnCuu99SIZ1tgS8DuwLrA93AXOBu4BLganfvKXd8o4re1zu6+z0F226N+bnK3fc1s/cCRwGTgXWB14H7gV+4+5U1nm8fwuvcjvA/fIlw/X/i7g+U2H8ykFy3b7v7DyqkPQH4LrAH4drPJbw3znX3u8ock7zGt9x9SHxuGnV+1s3sI8DBhP/7usBi4CngeuCn7v5KuXzH4ycC3wA+CGxIeG/fAZzp7vdVOlZaRyVuWYGZbQc8AfyQEKzGAKsC44CPAb8F7jCzcf2QlwMIAWQ/wpfSYEJw+QLwgJlNqXDsJEKA/j/gwwXHrw3sBkwHHjIzK3P8UDP7HeFL7HBCgB1F+LE7Engn8FXgYTM7sM7XtSbwJxoM2iXSOx24mXCd3kH4f40F9gf+Ymb/Xea4Y4H7gEOBTYAhwDDCD4LPAH8AbjSz1RvNW1+Z2deAe4DPAxsAqxH+hx8FrjCzX5tZpbbowWZ2EXBVPGbtmMYEwvvoXjP7Qh+yuAvwMPBFYHxMez3Cj707zezMPqRdlpmtbmZXAH8EPk3vtRkBbAv8F/C0mX2sQhoHAY8T3sdbEP736xLeN3eZ2eHNyLv0nQJ39k0HtgGuKXhum/h3aJonMrMtCaWM8YRf3qcDuwM7AJ8ifJEDvJ/mf6GPBS4kBNtfEoLvLsCpwBJCAP21mX20xOsYSwi4mwNLgXOAvYDtgY/HdLsItQm3lPkRMp0QCCHUNHyRUOL7D0IpPilJrQL8PJagq4rX7HrgvfGpPgVtwpf2scDzwDcJ/5vdgHOBpLR2YvF4YzP7D+AHhA5YDxFKbe8n/Fg7FHgs7robYWhSK7wP+AmhhP1dwrXflfAeSGpKDiK8N8v5IPA54BlCqX0X4BOEGhcI76//NbMNG8zj6cAawEXAnoRreAyhxgPgaDP7Vo1p1fRZN7NBwNWE1wFwLeF9sAPh/3Uq8BqhZuFKM/tQ8YliQE8+X28CJxOuzQeAM4BlhM+NZJCqyjPO3V8CXjKz+QXPPdik0/0GWJ1QDTzZ3Z8u2HYfcLmZHUn4QG9F+DI9fqVU0rFWXE5x94sKnp9pZtcRqstXA35iZje5+9sF+0wnBP7Xgd3c/f6itK+OpemrCTUJPyaUMAEws00IAQHgXmBXd3+r4Pg7gAvN7HJC6WR1wpf2byq9IDMbQij57RSf6mvQhvA6HwV2cfcFBc/fbGazCYEFQunynoLtBxOC9jzC63utYNud8bX9HZgIfNHMjnX3rj7mtV7rAv8G3ufuzxU8f7uZPQRcGtenFjwu5S5gT3d/veC5P5jZdEL1+VBCc0ojpeMO4Cvufm7Bc3eb2ZXAnfE1/LeZXVit2rqOz/pRhB8kAIe7+8+Ltt9sZr+M5x8L/MrMJiafETNbDTgr5n0h4b1TeJ5bzewqQi3OkEp5ltZQiVsAMLMPA++Oq98qCtrLxS+oW+PqEbEduVl+XRS0kzzcRe+X7CRCKQMAM9sU2Ceufr9E0E7SuJZQ4gA4wMzeUbB5K+BJQsn+1KKgXagwb+MrvZB4nX4HJKWfNIJ24tiioJ04j95S97uKto2NyzlFQRuAGOROJIwpPoHWfYGfUhS0E5cDyWsufm3FvlwUtBOFJcqtSmyvxXVFQRuA+Pk5Lq4OJfy46LNY2v7PuHpDiaCdnP8Zen9Urw98smDzh4GN4+MflPpxED9jZ6SRZ0mfArck9ip4fFOVfa+PyzUJnX2apVJV3fkFjwvb8T5K7/jbWl/HIEI1OADufqW7G6HN75oSxyVeKni8WoX9Ogmd+pJrnGbQXkaofVhJDOZJcFujaHPSoW5zMzsvdrAqPv4Sd/+Wu5/j7m+mlN963Vjqydhh7tm4WvzaCj1foRPjkwWPRzWQNwh9KMq5FEh+9O3eYPrFtiYEYqj+/r6h4HFhdXlh89LFFY4/v8I2aSFVlUtim4LH88v02SplY3rbe9O0BPhbuY3u/qyZLSB0FNusYFPh6/h7na+j+BzLe1Ob2bpxn0mEjjzbEtozE5V+BJ9C6LGbmFdrpmrwsruXne+aUBWadKordA6h3X4NQvvpoWb2KCEY/Bm4tYXButCsCtsWxmWl77F/ldvg7ksK3h+NfhfeWyX9xwk1We9sMP1ihe/vH5nZj2o8rvD9nXxeXnP3Z0vtDODus81sDrBOnXmUJlPglsSYBo8bmWoues2poU31lXj+sQXPpfY64lCbIwkdotZa6YgwdKoWG8blW4SS+elmdq27P99APostrLI9+fGxQs9rd38qvr7z6f0i3zL+fQN4y8xuAqa7+3Up5LMRb7n7sgrbS762Im/UeK5GZ0mbW2V70q7daIm+WBrv73Xjcn6pHYu8jAJ35ihwSyJ5L7xCaAOr1QtNyAuEKuBqkqkhC8diF76ntwcKO61VsvwLOA4v+j9W7rX/PKGK+R+EWoZXCR14avE94GlCu/oawM9Zscqy37n73XEkwWRCG+gehM5oEH5g7A3sbWa/Ab7QrLH7TZb6GPQ6009+EJTrJ1Gvwvf3kYThkrVYXPC4lh88iVo/P9KPFLglkfz6Hg78IwNf0rWUUJLSx78LnissRbzg7i82cO7D6Q3aTxLGxN7o7iuUUOIkHbU4y91Pisd8gdAjeE8zO8jdZzSQv9TE//Nf4h9xWNSHCP0G9iJ8R0whtDW3NK8ZNYrKTR9JaTWt5pHC9+BrDY4w+TehZmWMmXVUmWAnrZoCSZECtyQeIYybHUJok/t7uR3NbA/CB38WYUanisNcGjTCzCa4++wyeTBC5zgI45AThR2R3gdcUe4EZrYDobQ5C7jT3ZP20K/EZRfwkQrtgBtUegEFCttBDyeU2IcAPzazP7n7nBrTSY2ZrUGoIp8XeyAD4O6zCOPmfxlnG7sqbtobBe5StgRuL7UhXuPN42rZz1Odit/fZTuXmdnahFL5LOBBd08+J48SRmIMJ7wHHi9z/AjCRDWSMepVnh/NLgH/qeDxkeV2MrNOwuQePyQMb2rmJCwHVdhWeNvDwuBc+DqOqJL+mYRJSC5hxc47k+JyQaXOO4TZsRI1/Qh29ycJndUARgM/q+W4NJnZ+oQx7vdR+Q5df6L3fafxvKV9usK2L9D7vvhjHWlW+qzfT2+p+3Px3t3lfI3w/72AMPFQ4vcFj79Y4fiDaOId0qRxCtz5sbyNzMyGNyH9PxBmlwI4pMI0nj8GNoqPrypXIk7Jt+M81Ss
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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +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": {
2019-10-22 10:22:00 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAFGCAYAAACsWHzVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xu8pXPd//HXnj2DOWCGMUYOg5l8UJRySHRTSdxJFKWMwo1UUv2KDriju0g6Km4diAYJyVlCyCGHupVTfRxniDTMDBmMmdl7//74fK/Za9as815rr3Wt/X4+HvtxrbWu6/qu77r2Wtfn+h6vnoGBAURERCQfRrU7AyIiIlI7BW4REZEcUeAWERHJEQVuERGRHFHgFhERyREFbhERkRxR4BYREckRBW4REZEcUeAWERHJEQVuERGRHFHgFhERyREFbhERkRxR4BYREckRBW4REZEcGd3uDOSZmW3p7ve2Ox9SmZkdCPw8Pf2wu1/QxuxIFa38XZlZdh/ja919tyanfTzw1fR0e3e/o4E0bgJ2Al5191WK1g0p72a2M3Bjevpld/9miW1WAjZ297/Xm36zFB3Hjdx9drvy0qlU4m6Ama1uZj8A/q/deRHpFvpdtZeZ7QLcC+zX7rxIZSpxN+a7wMHtzoRIl9Hvqk3MbH3gunbnQ2qjwN2Y3nZnQKQLtfx35e49rX6PoXD3nVuY9k1Auc+vc1qOqKpcREQkRxS4RUREckRV5XUo6u2YvZb19Ly5FdVcZjYO+ASwF7ApsBowD/gTcD5wobv3N/t903ufDXwMcHff1MzeBHwF2BFYA3gG+D3wbXe/v0paY4ADgX2BLdP+zwN/BS4Gfu7ui6uksSOwf3r/1xDH4kXgH8DNwGmN9IY1szWBm4DXp5eOc/ev17H/bGAa8AN3/6yZvQP4JLA9MBmYD9wKnO7uN1ZIZ2vg40Sv4vWBfuBZ4I/ABcDl7j5Qbv9GFXyvX3D3iWY2Ffh/wHuBDYAlwN+AXwH/6+6vVkhrZeCjwD7AG4FJxP/5AeBS4Cfu/kqZ9y98rSW/q3I9s4t6XG8FPAh8CvgQYMDKwGzgSuA77v6vGt5rLeALxHHcEFgEPAT8kjiOK3zfK/UqL9quh/g9HUZ8b/tT2hcDP3L3l0rsU/gZl/UqLzgmma+aWfb/eHuqYi9MZw3g08B7gBnAOGAucAfxO76mXL7T/qOBDwKHEOe0icCjwHnA9yrtK0El7g5mZtsQP8ZvE8FqMrASsA5xMvglcIuZrTMMedmXCCAfANYGxhDB5WPAPWY2s8K+M4gA/RPgXQX7rwXsApwB/NXMrMz+Y83sYuAW4HDiRLUGceE5CdgCOAK4z8z2r/NzrQZcS4NBu0R6JwM3EMfpNcT/ayoRyH5vZv9TZr+jgbuIk9lrgVWIE+I0opfvpcDvzGx8o3mrMf87EkH2KOKkOg5YHXgLcVK9K524S+27JYP/512BKQz+n3cGvg94ukDpZFOIC+PvAtsRgWUssBlxXP5uZm+uksaOgANHp/3GEt/V7YjjcFv67jViNHAhcBbxf5lAXMRuDXwT+JuZbdZg2hWZ2X8SQfZ4YBviM61MnAv2Ba42s8vMbNUy+69BXGSfB7ydOJeNJX5/JxG/gTVbkfduosBdnzOIq/ErCl7bKv0d0sw3MrPXEVfH6xKlypOJk+F2xNXqpWnTt9L6E/pU4BziJHwmEXzfBpxIlCJGA79IP+rizzGVCLibAYuB04gr9W2B96V0+4ggcWOZi5AziEAIcUL9LyIQ/AdR6rg9rRsN/DiVoKtKx+xqIDsJDyloE6Wzo4Engc8R/5tdgNOBrFRzrJm9pSgf/0GccHuIwHdQ2ndH4nv1YNp0F+BrQ8hfNWOBy4kg8L/AbikfRwBPp222BL5RvKOZvRa4niidQpRM9yW+r3sTgQbiBH+Tmb2+YPdh+13V6BziYvC3DH6G/YC70/qJxPe9Uke3U9J2s4iL7P8APgM8l9ZnQbYR7yAuBP9B1ApsT/yWrkzr1weuM7PVa0xvK+I3mfkxg8f/T9mLZvZO4vsxkShh/zcRfN8CHEDUWgHsCfzGzJbr8GZmo4BriO8UwB+Ic9lbgJnE8d2SqK2SClRVXgd3fwZ4xszmF7z2lxa93bnAeOLHubO7P1qw7i7gIjP7JBEIXw8cB3ypRXnJTgAz3f28gtdvNbOriOrylYEfmNl17r6kYJsziMD/b2AXd7+b5V2eStOXE1ff36NgHGkKCAekp3cCOxVV1d4CnGNmFxEns/HA7sTxK8vMVgEuA3ZILw01aEN8zgeAt7n7goLXbzCzOcTFF0QtReHkHAcRQXse8fleKFh3W/ps/wdMB/7LzI52974h5rWUlYjexXu4+28LXv+jmV0N3E+UwD9iZkcW/Z9PJ0rWAJ939+8WpX1pSuNs4n90npm90d0Hhvl3VYupwDfc/diC1+4ys98Qv703AJsTF593Vkjng+5+ccHzW8zsCuI7MhaYaWZHNNDU1UNczO3k7s8VvH65mZ1CVM+vCxxL1BBU5O5/MbPnC156pvj4m9lY4jfVC9wHvNPdny3Y5E7g3FTjdDTwTuBQ4vef+S/imEFc0HysoOnnTjP7FXGBt3e1PI90KnF3IDN7F9E+CHESfLTUdu5+OoNXuZ9I7cit8ouioJ3l4XaidAHR3rVLts7MNiGuviFOhMVBO0vjSqKUA7Cvmb2mYPXrgYeJkv2JFdpXC/O2bqUPko7TxcTJBZoTtDNHFwXtzE8ZLHW/oWjd1LScWxS0AXD3fxMn4e8CxxDV6K1ySVHQzvLwOIPjfFcDNsrWmdkbGfy/X1UiaGdpnEMEboiSVVNnLmuifxJVwctJbdJnF7xU/H8s9OuioJ2l8ThR6gRYlWgKacSBRUE782Xi9wJwcJoJrRlmMvg9PaQoaBc6BngkPf5M0bqPp+UC4JPF/TXcfSkxjn+F34AsT4G7MxVWW1WbFOHqtFyNaHNqldMqrDur4PF7Cx7/J4PjRmv9HKOIanAA3P037m5ESe+KEvtlnil4vHKF7XqJTn3ZMW5m0F5K1D6sIAXzLKAXt/9lHeo2M7OfmtkKJ3N3v8DdP+/up5XqeNREv6uwrvACsvAzvLvg8Y+rpH96wePda83UMPt9CiKllDsGxa6usO7hgscl+wtUcU+Fi+ClxPc7S7tZ/Qmy38sL7n5XuY3S+2ffoU2zi3AzWxt4U3r9CndfWGb/54HfNCfL3UtV5Z1pq4LH88v02SplYwbbe5tpEfDncivd/XEzW0B0VNm0YFXh5/i/Oj9H8XssuzpPJ4GNiRL+5sTJ6a0Fm1e6IP060cM3M6/WTNXgX+6+qML6hQx2qit0GlGNuCrRpnuImT1AXOxcD9zU4mBdaHaFdYUn28LPUNheXW1+7nuIvg4rEe3InWh2hXXljkGxf1RYV/gdaeQcXKl6HuIYZ7agOeeE7Le8eole6JVsTPSPMAYv4qs1g9xF9F2RMlTi7kyTG9xvUlNzMWhuDW2qWbXd1ILXmvY5zOzdqbfq80Tp+nbgF0S7/i7UXn28YVpmVe4np+kem6FkKaJAdsJbrlOTuz9ClFoLh7K9Dvgs0eFonpldYWaFNTGtUukzFJ6wCz9D1hmwn8HvQUmpRJZdLDVS2hwOjRyDYi/W+F6NzOQ2t8r6wv9Bs47xUH/Laxe8Vu1iuepQu5FOJe7OlP1fniN6cNfqqRbkBaIKuJqsB2nh2NTC79e2xHjgWixrP0s9d3/Cir2LnyQC3b1EEH+eGIZVi68RVZ7nEKXcHxPV+m3j7n9MIwl2Bt5PtP9OT6tXBvYA9jCzc4lOPS0Zu9+geoNP9l3ppM/QbE0fb1+Hwv9H2TH3dcp+y/cSnStr9Xha1nrBA7WfJ0YsBe7OlPWunQDc2wEn6Vqu2rMr8n8WvDa/4PFT7v409TucwaD9MDEE5XfuXph2NrlELb7v7l9N+3yMGFqzu5kd4O6zGshf06T/8+/TH2a2IdGB7r1EG+NoopPQ74heuZ0i+1+MIr4H5TouZR0Ds1LY/HLbSUXVfo9TCh43qyloPlGbNrb
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": {
2019-10-22 10:22:00 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFGCAYAAAB+CHaNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xe8FNX5x/HPpYsiCEZBBETQR7GAPQaNqEk09hq7sUejsfxIRBMT0agxlgTRxNhib0Ex9hhFYwIqVlSIPhoRUCyAFEWk398f5wx3Wbbetjvwfb9evGZ3Z87Zs4e7+8ycNjW1tbWIiIhIerWodAFERESkYRTMRUREUk7BXEREJOUUzEVERFJOwVxERCTlFMxFRERSTsFcREQk5RTMRUREUk7BXEREJOUUzEVERFJOwVxERCTlFMxFRERSTsFcREQk5RTMRUREUq5VpQsg9WdmtwE/jk+7uftnFSxO1TKz5D6/T7n7nhUtjBRkZmsA67j7xCbI+zaa6PtiZhsAH8anN7j7qfXI4zjg1vj0CHe/L2PfbTSw7GY2CegFuLtvkueYLd39rXLzbixZ9XiRuw+tVFnSRlfmIlIVzOww4F3gu5Uuy6rGzLqa2V3Aw5Uui9SPrsxFpOLMbGfgvqIHSlO5G9gNmFzpgkj9KJinmLsfBxxX4WKINIaWTf0G1f59cffbgNuaMP8NCuxu8vqXpqVmdhERkZRTMBcREUk5NbPXU8YI6fOB3wMnAScDmxDq9V1gBDDc3b9uojLcRtONzh0EPBef7ghMAM4DDgF6AvOBN4BbgHvcvTZHNpn5fRs4BdgFWA9YBEwC/kmooylF0q9LqN/dAQM6A4uBL4BXCP2tDxYrR568LyP8PwK8Cnzf3WeXmHYocCEwx907mVlX4P+AfQn1tAh4B7gfuN7dF+TJZy3gNGAfoB/QHphF+Dt6gjBCuqQylSNr9PCBwCOEpuhjgc2ADsBU4Cnganf/oEh+OxL+n3cGuhP+j6YAo4Dr3P29Au+fuNXMklHdvd19UvmfLGfZbiPP9yVjpPc17n62me0G/JTwt782MBMYDfzZ3Z+jCDOrAY4h1GV/wv/nVOAfwDB3/1+ONMeRZzR7jmO3JPzN7gp0Aj4Bnonln5AnTfIZl41mz6oTgF4Zv223x66J7M/1I+BIYFtC3XxF+Bt/GPiLu8/NV+6YR3/gHELd9iR8h58GLgdyfj+kOF2ZN1wL4G/AjcB2hB+/1YCtgMuA18ysZ+WK1yjWAV4GfglsDLQj/IDsCtwFjDSzdrkSmlkrM/sz8CJwPLBhTN8B2AIYDLxnZqfke3Mz+zHhB/+3wCCgG9AWWJ3wY3Aw4cTpYTMrq+/PzH5FPQN5jrx2Ipz0/IJwUtce6Ah8G/gj8LKZdc6RbsuY7lLCD1xHoDWh3r9L+JH7IAbKptSeEBBuIZx0rU2o5w0JJxoTzGyvXAnNrG0MDC8QAlgfwv/zGoSTk5/F9L9s2o/QcGb2e8LJx8GEE882QFfCieyzZvbbIll0IASn2wnfkc6EuugDnA68YWZ7NKCIPyL8rR4OrEv4P+pNONkdZ2ZnNSDvvMxsHeA/hBPn/airmy7ATsCVgBf6OzWz8wgXAT+m7rekO+FvZlzMV+pBwbzhfkb4kk8Dzga+A+wPPBn3G/AvM2tfmeI1ihsIwekN4ChCcDqOcDYOcADhhyuXmwmBAMKVzY9j+kGEk4PPCD9GN8SgvZx4hXQb4QRpJnAx8MOYx8HAn4CF8fB9CS0kJTGzs4FL4tMGBfJYvkeANYHrgT0JfwtnEK6aALYkBOzMMrQEHiCcoHwNDCW0PmxP+Du6Ox7aGRhhZqvVs3yluJoQfF4iXFXuQPi/fTrubwvcFueCZ36GFsA91F3hTQbOIvzADyK0XMwmtFhdGlszEp8QTnxPznjtwvjaVtTVXXM5DDgX+Ihw9fgd4HvAn4HkivWC2NKUz5GE/8O3CH+PAwkBOLmiXwO4w8w61LOMwwi/3X+K77Mz4UR3HqGOh5nZwSXm9RtCPb8Wn39KXd3/JjnIzFaP5R9IqIe7CC052xO+j9cSWuvWA/5pZptlv5GZnQH8DqgBZgA/J9TvDwi/MW0IJ71SD2pmb7iuhObind3944zXHzGz4YRg3xsYQviRSqOuhKbeA909CZxjzewBwpXct4EfmdlfMpsgzewA6n7gf+fu2Vdlz5vZzcC/CFdvfzKzx9z9i4xjLo7bxcAe7v5qVh4jzewfwKPx+aGEH4aCYktA8sPR0EAO4YeoJbCPu/8j4/UXzewJYDzhyvdIMzvT3RfF/TsBG8XHP3H3u1neI2b2CeFqvzuwF/BgA8pZSFfgTuA4d1+avGhmjwCPxff+FrA3odsgcQRwUHw8Bvihu3+Vsf95M7ud8P+8AfCb+P/8avx7GmdmnTKOn+Lu4xr1k5WuK6GVZGd3n5Xx+igzm0zoUoPwd/1SgXxGEprKk+8L8fvyOCH4rUM44RtRjzLWAge4+2MZr402s0eB5wknlsPN7JGMv7OcYvfWFDNLmsYX5qn7Swnf0cWE34HHsvb/w8zuiO+/BqF1Z9kJj5l9i7oT50+A77h75jS4p83saepXH4KuzBvLCVmBPDEYSFayOiX2N6XRLOCYzB8mgDgW4Fgg+eHPXvXqF3E7AfhVrozdfTqhbxJCs/myK+vYmtGWcEX+aI5AnuTxGOHKD0LAK8jMjiZcPUPjBPLEyKxAnpTvQ+qubtcknNwlumY8fj9PvtcANxG6A1boa21E84GzMwM5QByHcFPGS/2z0iX/zwuAw7MCeZLHZOqmhdVkpKlG52YF8sRN1F2dZ9dBpgXAKTm+L7XU/d0BbF7P8l2fI5ji7q9Qd7KxHqFlp8HiiVbScnJTrveO7/8qcEV8uoOZ7ZCx+zBC9xGE+l1hPru7P0g4mZR6UDBvuHfyDYiJZ8V3xaddCX3qaXSfu8/MtcPd3yc0nwPsaWatYNmArqTvbFSRgWmjCYNoIDQbJnnPc/ft3L0LoSujkGQwU9tCB8Xmx9sIf/uv0XiBHMJgvnwyB45lNq++m/H4VjPbLTZbL+PuU939FHe/3N3fbIyC5vFavv9n8pQ/DvhLAtvjeU5qAXD354H/xqc/yP6cVWIx8GyuHTHAJ0G+UBP5y1mtS5kyT9hWGD9RohsL7Mvs7vpBPfPPNojQqgR1J6X5PJHxePeMx8lYiwUUbln6a1klk2XUzN5wY4rsfz3j8SaEgWRpU8pn/C7hqnM9wujlAYQrMIAzzezMEt9rw1wvJleLse+uN2Ew0SaEQLIT0CMeWihAbEHo200GyS0EviyxXKWYVGBf5gjfZd87d3/TzJ4kNL32Iwy8+sLMRhG6MP6Z6yqmiUwqsC9n+Qkj3hOFmp0zj+lHGEDZg+pbcexzd59fYP9cQhAu9NuZ94SG0PqRqM/v7zxCl01O7j7JzGYT6neLeuSfy1YZj0eaWanpMr/LyVrwXqR+XyW0fqS1FbNiFMwbrtgAnekZj7vmPaq6lfsZpxBGQtfHWtkvmNn6hC6L/cgT7AlN/cWu9NaL2wWEK/gdCWMarqlXSVdUaEpOZstE9g/V4YTBTEfFfV0IA6Z+BGBmbxNOQq4rNu2ngepT/i4Zj6eV8B6fZzzuTPUF82L1m9RDoWCzQjdDHvUJWDOyu0FyHUMI5vW98s/WGN/ldeM2X4sFELruYv99fQcHrrIUzBtucZH9mVOlFuY9qrrV5zNm/m1dQumDtpZkPjGzPQmjvVfPeDmZ1zqBMMf8GeAhlr9KzGccof9uDOFH6tI4UCh7rnOzcfcvgWPM7DeEAXz7EAYPtY6HbEEYBfxTM9u12FzvZlZuQMr8WykWlNKq7LUOGlnyf9JYc7Yzv8sHUPoJ2JyMx6WcBCUKDtqT3BTMG67Y2e+3Mh5/2pQFaUL1+YyZfa/f1Gd0cuyPvY8QyBcRAtq9hKa62qxj11gxhxVMBHZ395lm9n/AHTHvmwjTjyoqnlBcAVwRP8/OhBHPhxGubHoQ+kt3z5tJ88v8f16nhOPXzXicr39e8luh5SqH5PtY8Cq4DJn/T9P
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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": 21,
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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": 22,
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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": 23,
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"metadata": {},
"outputs": [],
"source": [
"times = times.T.iloc[0].values"
]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
{
"data": {
"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 = ['#e7298a', '#d95f02', '#993404', '#980043']\n",
"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",
" plt.xlim(0, 29)\n",
" \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": 25,
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"metadata": {},
"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/indexing.py:1494: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
" return self._getitem_tuple(key)\n"
]
},
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{
"data": {
2019-10-22 10:22:00 +00:00
"image/png": "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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 = ['#993404', '#980043', '#d95f02', '#e7298a']\n",
"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",
" plt.xlim(0, 29)\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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Store results in Expipe action"
]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"stimulus-response\")"
]
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},
{
"cell_type": "code",
"execution_count": 27,
"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",
" '/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",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_not_inhibited.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-nsi-ns.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_inhibited.svg',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-bs_not_gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_inhibited.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_not_inhibited.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_inhibited.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-bs_not_gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-bs_not_gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-gc-ns.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-bs_not_gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_not_inhibited.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/response-probability-nsi-ns.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-gridcell.png',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-gridcell.svg',\n",
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" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-ns_not_inhibited.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-bs_not_gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-bs_not_gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_i_peak-bs_not_gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_inhibited.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_not_inhibited.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-p_e_peak-ns_not_inhibited.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_e_peak-ns_not_inhibited.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-response/data/figures/histogram-t_i_peak-ns_inhibited.png']"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-spike-response.ipynb\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
2019-10-17 17:50:31 +00:00
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.8"
}
},
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}
