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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": [
"12:51:51 [I] klustakwik KlustaKwik2 version 0.2.6\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"import os\n",
"import pathlib\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"import shutil\n",
"import pandas as pd\n",
"import scipy.stats\n",
"\n",
"import exdir\n",
"import expipe\n",
"from distutils.dir_util import copy_tree\n",
"import septum_mec\n",
"import spatial_maps as sp\n",
"import head_direction.head as head\n",
"import septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n",
"from septum_mec.analysis.plotting import violinplot\n",
"\n",
"from spike_statistics.core import permutation_resampling"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"project_path = dp.project_path()\n",
"project = expipe.get_project(project_path)\n",
"actions = project.actions\n",
"\n",
"output_path = pathlib.Path(\"output\") / \"comparisons-gridcells\"\n",
"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load cell statistics and shuffling quantiles"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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" <th>entity</th>\n",
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" <th>i</th>\n",
" <th>ii</th>\n",
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" <th>bursty_spike_ratio</th>\n",
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" <th>border_score</th>\n",
" <th>information_rate</th>\n",
" <th>information_specificity</th>\n",
" <th>head_mean_ang</th>\n",
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" <th>orientation</th>\n",
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" </thead>\n",
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"</div>"
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"text/plain": [
" action baseline entity frequency i ii session \\\n",
"0 1849-060319-3 True 1849 NaN False True 3 \n",
"1 1849-060319-3 True 1849 NaN False True 3 \n",
"2 1849-060319-3 True 1849 NaN False True 3 \n",
"3 1849-060319-3 True 1849 NaN False True 3 \n",
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
" stim_location stimulated tag ... burst_event_ratio \\\n",
"0 NaN False baseline ii ... 0.398230 \n",
"1 NaN False baseline ii ... 0.138014 \n",
"2 NaN False baseline ii ... 0.373986 \n",
"3 NaN False baseline ii ... 0.087413 \n",
"4 NaN False baseline ii ... 0.248771 \n",
"\n",
" bursty_spike_ratio gridness border_score information_rate \\\n",
"0 0.678064 -0.466923 0.029328 1.009215 \n",
"1 0.263173 -0.666792 0.308146 0.192524 \n",
"2 0.659259 -0.572566 0.143252 4.745836 \n",
"3 0.179245 -0.437492 0.268948 0.157394 \n",
"4 0.463596 -0.085938 0.218744 0.519153 \n",
"\n",
" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
"0 0.317256 5.438033 0.040874 0.628784 \n",
"1 0.033447 1.951740 0.017289 0.789388 \n",
"2 0.393704 4.439721 0.124731 0.555402 \n",
"3 0.073553 6.215195 0.101911 0.492250 \n",
"4 0.032683 1.531481 0.053810 0.559905 \n",
"\n",
" orientation \n",
"0 20.224859 \n",
"1 27.897271 \n",
"2 28.810794 \n",
"3 9.462322 \n",
"4 0.000000 \n",
"\n",
"[5 rows x 39 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"statistics_action = actions['calculate-statistics']\n",
"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')\n",
"statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
"statistics = pd.merge(session_units, statistics_results, how='left')\n",
"statistics.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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" <td>225.0</td>\n",
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"text/plain": [
" border_score gridness head_mean_ang head_mean_vec_len information_rate \\\n",
"0 0.348023 0.275109 3.012689 0.086792 0.707197 \n",
"1 0.362380 0.166475 3.133138 0.037271 0.482486 \n",
"2 0.367498 0.266865 5.586395 0.182843 0.271188 \n",
"3 0.331942 0.312155 5.955767 0.090786 0.354018 \n",
"4 0.325842 0.180495 5.262721 0.103584 0.210427 \n",
"\n",
" speed_score action channel_group unit_name \n",
"0 0.149071 1833-010719-1 0.0 127.0 \n",
"1 0.132212 1833-010719-1 0.0 161.0 \n",
"2 0.062821 1833-010719-1 0.0 191.0 \n",
"3 0.052009 1833-010719-1 0.0 223.0 \n",
"4 0.094041 1833-010719-1 0.0 225.0 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shuffling = actions['shuffling']\n",
"quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
"quantiles_95.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
" action baseline entity frequency i ii session \\\n",
"0 1849-060319-3 True 1849 NaN False True 3 \n",
"1 1849-060319-3 True 1849 NaN False True 3 \n",
"2 1849-060319-3 True 1849 NaN False True 3 \n",
"3 1849-060319-3 True 1849 NaN False True 3 \n",
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
" stim_location stimulated tag ... head_mean_vec_len spacing \\\n",
"0 NaN False baseline ii ... 0.040874 0.628784 \n",
"1 NaN False baseline ii ... 0.017289 0.789388 \n",
"2 NaN False baseline ii ... 0.124731 0.555402 \n",
"3 NaN False baseline ii ... 0.101911 0.492250 \n",
"4 NaN False baseline ii ... 0.053810 0.559905 \n",
"\n",
" orientation border_score_threshold gridness_threshold \\\n",
"0 20.224859 0.332548 0.229073 \n",
"1 27.897271 0.354830 0.089333 \n",
"2 28.810794 0.264610 -0.121081 \n",
"3 9.462322 0.344280 0.215829 \n",
"4 0.000000 0.342799 0.218967 \n",
"\n",
" head_mean_ang_threshold head_mean_vec_len_threshold \\\n",
"0 6.029431 0.205362 \n",
"1 6.120055 0.073566 \n",
"2 5.759406 0.150827 \n",
"3 6.033364 0.110495 \n",
"4 5.768170 0.054762 \n",
"\n",
" information_rate_threshold speed_score_threshold specificity \n",
"0 1.115825 0.066736 0.451741 \n",
"1 0.223237 0.052594 0.098517 \n",
"2 4.964984 0.027120 0.400770 \n",
"3 0.239996 0.054074 0.269461 \n",
"4 0.524990 0.144702 0.133410 \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"action_columns = ['action', 'channel_group', 'unit_name']\n",
"data = pd.merge(statistics, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))\n",
"\n",
"data['specificity'] = np.log10(data['in_field_mean_rate'] / data['out_field_mean_rate'])\n",
"\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statistics about all cell-sessions"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulated\n",
"False 624\n",
"True 660\n",
"Name: action, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('stimulated').count()['action']"
]
},
{
"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": "markdown",
"metadata": {},
"source": [
"# Find all cells with gridness above threshold"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of sessions above threshold 194\n",
"Number of animals 4\n"
]
}
],
"source": [
"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'])))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": [
"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": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells in baseline i sessions 66\n",
"Number of gridcells in stimulated 11Hz ms sessions 61\n",
"Number of gridcells in baseline ii sessions 56\n",
"Number of gridcells in stimulated 30Hz ms sessions 40\n"
]
}
],
"source": [
"baseline_i = gridcell_sessions.query('baseline and Hz11')\n",
"stimulated_11 = gridcell_sessions.query('frequency==11 and stim_location==\"ms\"')\n",
"\n",
"baseline_ii = gridcell_sessions.query('baseline and Hz30')\n",
"stimulated_30 = gridcell_sessions.query('frequency==30 and stim_location==\"ms\"')\n",
"\n",
"print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
"\n",
"print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
"print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# slice unique units"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"baseline_i = baseline_i.drop_duplicates('unit_id')\n",
"stimulated_11 = stimulated_11.drop_duplicates('unit_id')\n",
"baseline_ii = baseline_ii.drop_duplicates('unit_id')\n",
"stimulated_30 = stimulated_30.drop_duplicates('unit_id')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells in baseline i sessions 63\n",
"Number of gridcells in stimulated 11Hz ms sessions 58\n",
"Number of gridcells in baseline ii sessions 52\n",
"Number of gridcells in stimulated 30Hz ms sessions 38\n"
]
}
],
"source": [
"print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
"\n",
"print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
"print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate statistics"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"columns = [\n",
" 'average_rate', 'gridness', 'sparsity', 'selectivity', 'information_specificity',\n",
" 'max_rate', 'information_rate', 'interspike_interval_cv', \n",
" 'in_field_mean_rate', 'out_field_mean_rate', \n",
" 'burst_event_ratio', 'specificity', 'speed_score'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>average_rate</th>\n",
" <th>gridness</th>\n",
" <th>sparsity</th>\n",
" <th>selectivity</th>\n",
" <th>information_specificity</th>\n",
" <th>max_rate</th>\n",
" <th>information_rate</th>\n",
" <th>interspike_interval_cv</th>\n",
" <th>in_field_mean_rate</th>\n",
" <th>out_field_mean_rate</th>\n",
" <th>burst_event_ratio</th>\n",
" <th>specificity</th>\n",
" <th>speed_score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>stimulated</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>8.904501</td>\n",
" <td>0.521371</td>\n",
" <td>0.618384</td>\n",
" <td>5.934539</td>\n",
" <td>0.234632</td>\n",
" <td>37.437808</td>\n",
" <td>1.246546</td>\n",
" <td>2.404647</td>\n",
" <td>14.717635</td>\n",
" <td>6.346875</td>\n",
" <td>0.211840</td>\n",
" <td>0.478775</td>\n",
" <td>0.135495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>8.392252</td>\n",
" <td>0.440296</td>\n",
" <td>0.655698</td>\n",
" <td>5.977408</td>\n",
" <td>0.215736</td>\n",
" <td>33.716478</td>\n",
" <td>0.964787</td>\n",
" <td>2.223636</td>\n",
" <td>12.936021</td>\n",
" <td>6.122228</td>\n",
" <td>0.197264</td>\n",
" <td>0.455878</td>\n",
" <td>0.104697</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"stimulated \n",
"False 8.904501 0.521371 0.618384 5.934539 \n",
"True 8.392252 0.440296 0.655698 5.977408 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
"stimulated \n",
"False 0.234632 37.437808 1.246546 \n",
"True 0.215736 33.716478 0.964787 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"stimulated \n",
"False 2.404647 14.717635 6.346875 \n",
"True 2.223636 12.936021 6.122228 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
"stimulated \n",
"False 0.211840 0.478775 0.135495 \n",
"True 0.197264 0.455878 0.104697 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gridcell_sessions.groupby('stimulated')[columns].mean()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>average_rate</th>\n",
" <th>gridness</th>\n",
" <th>sparsity</th>\n",
" <th>selectivity</th>\n",
" <th>information_specificity</th>\n",
" <th>max_rate</th>\n",
" <th>information_rate</th>\n",
" <th>interspike_interval_cv</th>\n",
" <th>in_field_mean_rate</th>\n",
" <th>out_field_mean_rate</th>\n",
" <th>burst_event_ratio</th>\n",
" <th>specificity</th>\n",
" <th>speed_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8.904501</td>\n",
" <td>0.521371</td>\n",
" <td>0.618384</td>\n",
" <td>5.934539</td>\n",
" <td>0.234632</td>\n",
" <td>37.437808</td>\n",
" <td>1.246546</td>\n",
" <td>2.404647</td>\n",
" <td>14.717635</td>\n",
" <td>6.346875</td>\n",
" <td>0.211840</td>\n",
" <td>0.478775</td>\n",
" <td>0.135495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.605598</td>\n",
" <td>0.337607</td>\n",
" <td>0.187934</td>\n",
" <td>3.217366</td>\n",
" <td>0.200726</td>\n",
" <td>16.300117</td>\n",
" <td>0.605971</td>\n",
" <td>0.756407</td>\n",
" <td>9.267522</td>\n",
" <td>6.805499</td>\n",
" <td>0.080143</td>\n",
" <td>0.209531</td>\n",
" <td>0.072831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.478349</td>\n",
" <td>-0.684924</td>\n",
" <td>0.200066</td>\n",
" <td>1.533216</td>\n",
" <td>0.007807</td>\n",
" <td>3.346027</td>\n",
" <td>0.117638</td>\n",
" <td>1.304387</td>\n",
" <td>0.924066</td>\n",
" <td>0.159076</td>\n",
" <td>0.025000</td>\n",
" <td>0.071681</td>\n",
" <td>-0.025629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.518392</td>\n",
" <td>0.316326</td>\n",
" <td>0.437499</td>\n",
" <td>3.729863</td>\n",
" <td>0.093252</td>\n",
" <td>26.948843</td>\n",
" <td>0.786753</td>\n",
" <td>1.872991</td>\n",
" <td>7.701156</td>\n",
" <td>1.669844</td>\n",
" <td>0.160795</td>\n",
" <td>0.310822</td>\n",
" <td>0.084280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.456882</td>\n",
" <td>0.529243</td>\n",
" <td>0.642167</td>\n",
" <td>4.794970</td>\n",
" <td>0.180286</td>\n",
" <td>35.064991</td>\n",
" <td>1.156087</td>\n",
" <td>2.221185</td>\n",
" <td>12.212289</td>\n",
" <td>4.314913</td>\n",
" <td>0.210240</td>\n",
" <td>0.436340</td>\n",
" <td>0.128603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>12.721755</td>\n",
" <td>0.783682</td>\n",
" <td>0.758097</td>\n",
" <td>7.439464</td>\n",
" <td>0.312487</td>\n",
" <td>44.324873</td>\n",
" <td>1.592948</td>\n",
" <td>2.770624</td>\n",
" <td>20.974026</td>\n",
" <td>9.121505</td>\n",
" <td>0.267568</td>\n",
" <td>0.624834</td>\n",
" <td>0.188948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>59.365312</td>\n",
" <td>1.148979</td>\n",
" <td>0.976157</td>\n",
" <td>18.975875</td>\n",
" <td>1.243307</td>\n",
" <td>90.160158</td>\n",
" <td>3.456796</td>\n",
" <td>5.671362</td>\n",
" <td>66.350754</td>\n",
" <td>56.255544</td>\n",
" <td>0.393306</td>\n",
" <td>1.066391</td>\n",
" <td>0.297548</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"count 129.000000 129.000000 129.000000 129.000000 \n",
"mean 8.904501 0.521371 0.618384 5.934539 \n",
"std 7.605598 0.337607 0.187934 3.217366 \n",
"min 0.478349 -0.684924 0.200066 1.533216 \n",
"25% 3.518392 0.316326 0.437499 3.729863 \n",
"50% 6.456882 0.529243 0.642167 4.794970 \n",
"75% 12.721755 0.783682 0.758097 7.439464 \n",
"max 59.365312 1.148979 0.976157 18.975875 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
"count 129.000000 129.000000 129.000000 \n",
"mean 0.234632 37.437808 1.246546 \n",
"std 0.200726 16.300117 0.605971 \n",
"min 0.007807 3.346027 0.117638 \n",
"25% 0.093252 26.948843 0.786753 \n",
"50% 0.180286 35.064991 1.156087 \n",
"75% 0.312487 44.324873 1.592948 \n",
"max 1.243307 90.160158 3.456796 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"count 129.000000 129.000000 129.000000 \n",
"mean 2.404647 14.717635 6.346875 \n",
"std 0.756407 9.267522 6.805499 \n",
"min 1.304387 0.924066 0.159076 \n",
"25% 1.872991 7.701156 1.669844 \n",
"50% 2.221185 12.212289 4.314913 \n",
"75% 2.770624 20.974026 9.121505 \n",
"max 5.671362 66.350754 56.255544 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
"count 129.000000 129.000000 129.000000 \n",
"mean 0.211840 0.478775 0.135495 \n",
"std 0.080143 0.209531 0.072831 \n",
"min 0.025000 0.071681 -0.025629 \n",
"25% 0.160795 0.310822 0.084280 \n",
"50% 0.210240 0.436340 0.128603 \n",
"75% 0.267568 0.624834 0.188948 \n",
"max 0.393306 1.066391 0.297548 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gridcell_sessions.query('baseline')[columns].describe()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>average_rate</th>\n",
" <th>gridness</th>\n",
" <th>sparsity</th>\n",
" <th>selectivity</th>\n",
" <th>information_specificity</th>\n",
" <th>max_rate</th>\n",
" <th>information_rate</th>\n",
" <th>interspike_interval_cv</th>\n",
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" <th>out_field_mean_rate</th>\n",
" <th>burst_event_ratio</th>\n",
" <th>specificity</th>\n",
" <th>speed_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8.392252</td>\n",
" <td>0.440296</td>\n",
" <td>0.655698</td>\n",
" <td>5.977408</td>\n",
" <td>0.215736</td>\n",
" <td>33.716478</td>\n",
" <td>0.964787</td>\n",
" <td>2.223636</td>\n",
" <td>12.936021</td>\n",
" <td>6.122228</td>\n",
" <td>0.197264</td>\n",
" <td>0.455878</td>\n",
" <td>0.104697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.057001</td>\n",
" <td>0.357038</td>\n",
" <td>0.211704</td>\n",
" <td>3.702400</td>\n",
" <td>0.235916</td>\n",
" <td>13.249312</td>\n",
" <td>0.572972</td>\n",
" <td>0.819734</td>\n",
" <td>7.211895</td>\n",
" <td>5.366332</td>\n",
" <td>0.082164</td>\n",
" <td>0.236777</td>\n",
" <td>0.081989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.198337</td>\n",
" <td>-0.516914</td>\n",
" <td>0.172684</td>\n",
" <td>1.930026</td>\n",
" <td>0.013088</td>\n",
" <td>2.846281</td>\n",
" <td>0.063173</td>\n",
" <td>1.110672</td>\n",
" <td>0.524639</td>\n",
" <td>0.099060</td>\n",
" <td>0.008475</td>\n",
" <td>0.097718</td>\n",
" <td>-0.138128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.579184</td>\n",
" <td>0.265949</td>\n",
" <td>0.458493</td>\n",
" <td>3.044303</td>\n",
" <td>0.066656</td>\n",
" <td>25.555110</td>\n",
" <td>0.564279</td>\n",
" <td>1.620472</td>\n",
" <td>7.555760</td>\n",
" <td>1.733624</td>\n",
" <td>0.146755</td>\n",
" <td>0.248057</td>\n",
" <td>0.056903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.838561</td>\n",
" <td>0.399053</td>\n",
" <td>0.699561</td>\n",
" <td>4.891855</td>\n",
" <td>0.128562</td>\n",
" <td>31.402558</td>\n",
" <td>0.862413</td>\n",
" <td>2.084020</td>\n",
" <td>11.451560</td>\n",
" <td>4.234871</td>\n",
" <td>0.192948</td>\n",
" <td>0.376143</td>\n",
" <td>0.106314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>11.934599</td>\n",
" <td>0.749561</td>\n",
" <td>0.842332</td>\n",
" <td>8.001587</td>\n",
" <td>0.300713</td>\n",
" <td>42.334786</td>\n",
" <td>1.190324</td>\n",
" <td>2.673991</td>\n",
" <td>17.335356</td>\n",
" <td>8.583415</td>\n",
" <td>0.247405</td>\n",
" <td>0.684623</td>\n",
" <td>0.149313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>24.858738</td>\n",
" <td>1.155123</td>\n",
" <td>0.967003</td>\n",
" <td>19.911477</td>\n",
" <td>1.359164</td>\n",
" <td>65.990793</td>\n",
" <td>3.182285</td>\n",
" <td>6.526960</td>\n",
" <td>34.489913</td>\n",
" <td>21.696265</td>\n",
" <td>0.393037</td>\n",
" <td>1.091064</td>\n",
" <td>0.390079</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"count 102.000000 102.000000 102.000000 102.000000 \n",
"mean 8.392252 0.440296 0.655698 5.977408 \n",
"std 6.057001 0.357038 0.211704 3.702400 \n",
"min 0.198337 -0.516914 0.172684 1.930026 \n",
"25% 3.579184 0.265949 0.458493 3.044303 \n",
"50% 6.838561 0.399053 0.699561 4.891855 \n",
"75% 11.934599 0.749561 0.842332 8.001587 \n",
"max 24.858738 1.155123 0.967003 19.911477 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
"count 102.000000 102.000000 102.000000 \n",
"mean 0.215736 33.716478 0.964787 \n",
"std 0.235916 13.249312 0.572972 \n",
"min 0.013088 2.846281 0.063173 \n",
"25% 0.066656 25.555110 0.564279 \n",
"50% 0.128562 31.402558 0.862413 \n",
"75% 0.300713 42.334786 1.190324 \n",
"max 1.359164 65.990793 3.182285 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"count 102.000000 102.000000 102.000000 \n",
"mean 2.223636 12.936021 6.122228 \n",
"std 0.819734 7.211895 5.366332 \n",
"min 1.110672 0.524639 0.099060 \n",
"25% 1.620472 7.555760 1.733624 \n",
"50% 2.084020 11.451560 4.234871 \n",
"75% 2.673991 17.335356 8.583415 \n",
"max 6.526960 34.489913 21.696265 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
"count 102.000000 102.000000 102.000000 \n",
"mean 0.197264 0.455878 0.104697 \n",
"std 0.082164 0.236777 0.081989 \n",
"min 0.008475 0.097718 -0.138128 \n",
"25% 0.146755 0.248057 0.056903 \n",
"50% 0.192948 0.376143 0.106314 \n",
"75% 0.247405 0.684623 0.149313 \n",
"max 0.393037 1.091064 0.390079 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gridcell_sessions.query(\"stimulated\")[columns].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create nice table"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
"\n",
"\n",
"def MWU(column, stim, base):\n",
" '''\n",
" Mann Whitney U\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
" stim[column].dropna(), \n",
" base[column].dropna(),\n",
" alternative='two-sided')\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
"\n",
"\n",
"def PRS(column, stim, base):\n",
" '''\n",
" Permutation ReSampling\n",
" '''\n",
" pvalue, observed_diff, diffs = permutation_resampling(\n",
" stim[column].dropna(), \n",
" base[column].dropna(), statistic=np.median)\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
"\n",
"\n",
"def rename(name):\n",
" return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Baseline I</th>\n",
" <th>Stimulated</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>8.61 ± 0.75 (71)</td>\n",
" <td>8.39 ± 0.60 (102)</td>\n",
" <td>3599.00, 0.947</td>\n",
" <td>0.55, 0.757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.51 ± 0.04 (71)</td>\n",
" <td>0.44 ± 0.04 (102)</td>\n",
" <td>3208.00, 0.203</td>\n",
" <td>0.13, 0.145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.61 ± 0.02 (71)</td>\n",
" <td>0.66 ± 0.02 (102)</td>\n",
" <td>4170.00, 0.091</td>\n",
" <td>0.06, 0.179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>5.91 ± 0.37 (71)</td>\n",
" <td>5.98 ± 0.37 (102)</td>\n",
" <td>3460.00, 0.620</td>\n",
" <td>0.10, 0.874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.25 ± 0.03 (71)</td>\n",
" <td>0.22 ± 0.02 (102)</td>\n",
" <td>2944.00, 0.037</td>\n",
" <td>0.05, 0.034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>36.55 ± 1.78 (71)</td>\n",
" <td>33.72 ± 1.31 (102)</td>\n",
" <td>3291.00, 0.309</td>\n",
" <td>3.19, 0.194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.30 ± 0.07 (71)</td>\n",
" <td>0.96 ± 0.06 (102)</td>\n",
" <td>2385.00, 0.000</td>\n",
" <td>0.32, 0.001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.42 ± 0.10 (71)</td>\n",
" <td>2.22 ± 0.08 (102)</td>\n",
" <td>3034.00, 0.070</td>\n",
" <td>0.12, 0.398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>14.43 ± 1.00 (71)</td>\n",
" <td>12.94 ± 0.71 (102)</td>\n",
" <td>3368.00, 0.436</td>\n",
" <td>0.39, 0.817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>6.05 ± 0.62 (71)</td>\n",
" <td>6.12 ± 0.53 (102)</td>\n",
" <td>3600.00, 0.950</td>\n",
" <td>0.08, 0.944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.22 ± 0.01 (71)</td>\n",
" <td>0.20 ± 0.01 (102)</td>\n",
" <td>3090.00, 0.102</td>\n",
" <td>0.02, 0.129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.48 ± 0.03 (71)</td>\n",
" <td>0.46 ± 0.02 (102)</td>\n",
" <td>3268.00, 0.277</td>\n",
" <td>0.06, 0.360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.14 ± 0.01 (71)</td>\n",
" <td>0.10 ± 0.01 (102)</td>\n",
" <td>2546.00, 0.001</td>\n",
" <td>0.05, 0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline I Stimulated \\\n",
"Average rate 8.61 ± 0.75 (71) 8.39 ± 0.60 (102) \n",
"Gridness 0.51 ± 0.04 (71) 0.44 ± 0.04 (102) \n",
"Sparsity 0.61 ± 0.02 (71) 0.66 ± 0.02 (102) \n",
"Selectivity 5.91 ± 0.37 (71) 5.98 ± 0.37 (102) \n",
"Information specificity 0.25 ± 0.03 (71) 0.22 ± 0.02 (102) \n",
"Max rate 36.55 ± 1.78 (71) 33.72 ± 1.31 (102) \n",
"Information rate 1.30 ± 0.07 (71) 0.96 ± 0.06 (102) \n",
"Interspike interval cv 2.42 ± 0.10 (71) 2.22 ± 0.08 (102) \n",
"In-field mean rate 14.43 ± 1.00 (71) 12.94 ± 0.71 (102) \n",
"Out-field mean rate 6.05 ± 0.62 (71) 6.12 ± 0.53 (102) \n",
"Burst event ratio 0.22 ± 0.01 (71) 0.20 ± 0.01 (102) \n",
"Specificity 0.48 ± 0.03 (71) 0.46 ± 0.02 (102) \n",
"Speed score 0.14 ± 0.01 (71) 0.10 ± 0.01 (102) \n",
"\n",
" MWU PRS \n",
"Average rate 3599.00, 0.947 0.55, 0.757 \n",
"Gridness 3208.00, 0.203 0.13, 0.145 \n",
"Sparsity 4170.00, 0.091 0.06, 0.179 \n",
"Selectivity 3460.00, 0.620 0.10, 0.874 \n",
"Information specificity 2944.00, 0.037 0.05, 0.034 \n",
"Max rate 3291.00, 0.309 3.19, 0.194 \n",
"Information rate 2385.00, 0.000 0.32, 0.001 \n",
"Interspike interval cv 3034.00, 0.070 0.12, 0.398 \n",
"In-field mean rate 3368.00, 0.436 0.39, 0.817 \n",
"Out-field mean rate 3600.00, 0.950 0.08, 0.944 \n",
"Burst event ratio 3090.00, 0.102 0.02, 0.129 \n",
"Specificity 3268.00, 0.277 0.06, 0.360 \n",
"Speed score 2546.00, 0.001 0.05, 0.000 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = gridcell_sessions.query('stimulated')\n",
"_base_data = gridcell_sessions.query('baseline and i')\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline I'] = _base_data[columns].agg(summarize)\n",
"result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
"\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
"result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
"result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
"\n",
"result.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n",
"result.to_csv(output_path / \"statistics\" / \"statistics.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Baseline I</th>\n",
" <th>11 Hz</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>8.96 ± 0.80 (63)</td>\n",
" <td>8.80 ± 0.85 (58)</td>\n",
" <td>1781.00, 0.813</td>\n",
" <td>0.04, 0.968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.53 ± 0.05 (63)</td>\n",
" <td>0.41 ± 0.05 (58)</td>\n",
" <td>1459.00, 0.057</td>\n",
" <td>0.21, 0.041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.63 ± 0.02 (63)</td>\n",
" <td>0.67 ± 0.03 (58)</td>\n",
" <td>2138.00, 0.107</td>\n",
" <td>0.07, 0.128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>5.76 ± 0.40 (63)</td>\n",
" <td>5.69 ± 0.50 (58)</td>\n",
" <td>1687.00, 0.469</td>\n",
" <td>0.00, 0.983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.24 ± 0.03 (63)</td>\n",
" <td>0.21 ± 0.03 (58)</td>\n",
" <td>1452.00, 0.052</td>\n",
" <td>0.06, 0.030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>37.39 ± 1.91 (63)</td>\n",
" <td>33.11 ± 1.85 (58)</td>\n",
" <td>1538.00, 0.134</td>\n",
" <td>4.06, 0.125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.31 ± 0.08 (63)</td>\n",
" <td>0.94 ± 0.08 (58)</td>\n",
" <td>1143.00, 0.000</td>\n",
" <td>0.32, 0.004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.39 ± 0.10 (63)</td>\n",
" <td>2.19 ± 0.12 (58)</td>\n",
" <td>1462.00, 0.059</td>\n",
" <td>0.18, 0.139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>14.88 ± 1.05 (63)</td>\n",
" <td>13.27 ± 1.04 (58)</td>\n",
" <td>1633.00, 0.315</td>\n",
" <td>0.77, 0.690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>6.37 ± 0.67 (63)</td>\n",
" <td>6.57 ± 0.77 (58)</td>\n",
" <td>1795.00, 0.870</td>\n",
" <td>0.47, 0.719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.22 ± 0.01 (63)</td>\n",
" <td>0.22 ± 0.01 (58)</td>\n",
" <td>1897.00, 0.718</td>\n",
" <td>0.00, 0.824</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.47 ± 0.03 (63)</td>\n",
" <td>0.44 ± 0.03 (58)</td>\n",
" <td>1605.00, 0.250</td>\n",
" <td>0.06, 0.414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.14 ± 0.01 (63)</td>\n",
" <td>0.11 ± 0.01 (58)</td>\n",
" <td>1378.00, 0.020</td>\n",
" <td>0.04, 0.022</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline I 11 Hz MWU \\\n",
"Average rate 8.96 ± 0.80 (63) 8.80 ± 0.85 (58) 1781.00, 0.813 \n",
"Gridness 0.53 ± 0.05 (63) 0.41 ± 0.05 (58) 1459.00, 0.057 \n",
"Sparsity 0.63 ± 0.02 (63) 0.67 ± 0.03 (58) 2138.00, 0.107 \n",
"Selectivity 5.76 ± 0.40 (63) 5.69 ± 0.50 (58) 1687.00, 0.469 \n",
"Information specificity 0.24 ± 0.03 (63) 0.21 ± 0.03 (58) 1452.00, 0.052 \n",
"Max rate 37.39 ± 1.91 (63) 33.11 ± 1.85 (58) 1538.00, 0.134 \n",
"Information rate 1.31 ± 0.08 (63) 0.94 ± 0.08 (58) 1143.00, 0.000 \n",
"Interspike interval cv 2.39 ± 0.10 (63) 2.19 ± 0.12 (58) 1462.00, 0.059 \n",
"In-field mean rate 14.88 ± 1.05 (63) 13.27 ± 1.04 (58) 1633.00, 0.315 \n",
"Out-field mean rate 6.37 ± 0.67 (63) 6.57 ± 0.77 (58) 1795.00, 0.870 \n",
"Burst event ratio 0.22 ± 0.01 (63) 0.22 ± 0.01 (58) 1897.00, 0.718 \n",
"Specificity 0.47 ± 0.03 (63) 0.44 ± 0.03 (58) 1605.00, 0.250 \n",
"Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (58) 1378.00, 0.020 \n",
"\n",
" PRS \n",
"Average rate 0.04, 0.968 \n",
"Gridness 0.21, 0.041 \n",
"Sparsity 0.07, 0.128 \n",
"Selectivity 0.00, 0.983 \n",
"Information specificity 0.06, 0.030 \n",
"Max rate 4.06, 0.125 \n",
"Information rate 0.32, 0.004 \n",
"Interspike interval cv 0.18, 0.139 \n",
"In-field mean rate 0.77, 0.690 \n",
"Out-field mean rate 0.47, 0.719 \n",
"Burst event ratio 0.00, 0.824 \n",
"Specificity 0.06, 0.414 \n",
"Speed score 0.04, 0.022 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_11\n",
"_base_data = baseline_i\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline I'] = _base_data[columns].agg(summarize)\n",
"result['11 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
"result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
"result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
"\n",
"\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_11.tex\")\n",
"result.to_csv(output_path / \"statistics\" / \"statistics_11.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Baseline II</th>\n",
" <th>30 Hz</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>8.29 ± 0.87 (52)</td>\n",
" <td>7.61 ± 0.87 (38)</td>\n",
" <td>958.00, 0.810</td>\n",
" <td>0.27, 0.808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.54 ± 0.04 (52)</td>\n",
" <td>0.48 ± 0.06 (38)</td>\n",
" <td>914.00, 0.548</td>\n",
" <td>0.04, 0.598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.63 ± 0.03 (52)</td>\n",
" <td>0.64 ± 0.03 (38)</td>\n",
" <td>1040.00, 0.674</td>\n",
" <td>0.06, 0.398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>5.96 ± 0.46 (52)</td>\n",
" <td>6.42 ± 0.60 (38)</td>\n",
" <td>1019.00, 0.803</td>\n",
" <td>0.20, 0.845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.21 ± 0.02 (52)</td>\n",
" <td>0.22 ± 0.03 (38)</td>\n",
" <td>950.00, 0.759</td>\n",
" <td>0.04, 0.506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>36.27 ± 2.34 (52)</td>\n",
" <td>33.49 ± 1.89 (38)</td>\n",
" <td>943.00, 0.716</td>\n",
" <td>2.90, 0.565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.13 ± 0.08 (52)</td>\n",
" <td>0.98 ± 0.09 (38)</td>\n",
" <td>827.00, 0.190</td>\n",
" <td>0.07, 0.335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.37 ± 0.09 (52)</td>\n",
" <td>2.23 ± 0.11 (38)</td>\n",
" <td>869.00, 0.333</td>\n",
" <td>0.17, 0.482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>13.79 ± 1.12 (52)</td>\n",
" <td>12.21 ± 0.98 (38)</td>\n",
" <td>912.00, 0.537</td>\n",
" <td>1.06, 0.444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>5.80 ± 0.72 (52)</td>\n",
" <td>5.36 ± 0.73 (38)</td>\n",
" <td>959.00, 0.816</td>\n",
" <td>0.13, 0.912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.20 ± 0.01 (52)</td>\n",
" <td>0.16 ± 0.01 (38)</td>\n",
" <td>676.00, 0.011</td>\n",
" <td>0.05, 0.009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.47 ± 0.03 (52)</td>\n",
" <td>0.48 ± 0.04 (38)</td>\n",
" <td>976.00, 0.925</td>\n",
" <td>0.00, 0.988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.12 ± 0.01 (52)</td>\n",
" <td>0.11 ± 0.01 (38)</td>\n",
" <td>784.00, 0.096</td>\n",
" <td>0.01, 0.242</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline II 30 Hz MWU \\\n",
"Average rate 8.29 ± 0.87 (52) 7.61 ± 0.87 (38) 958.00, 0.810 \n",
"Gridness 0.54 ± 0.04 (52) 0.48 ± 0.06 (38) 914.00, 0.548 \n",
"Sparsity 0.63 ± 0.03 (52) 0.64 ± 0.03 (38) 1040.00, 0.674 \n",
"Selectivity 5.96 ± 0.46 (52) 6.42 ± 0.60 (38) 1019.00, 0.803 \n",
"Information specificity 0.21 ± 0.02 (52) 0.22 ± 0.03 (38) 950.00, 0.759 \n",
"Max rate 36.27 ± 2.34 (52) 33.49 ± 1.89 (38) 943.00, 0.716 \n",
"Information rate 1.13 ± 0.08 (52) 0.98 ± 0.09 (38) 827.00, 0.190 \n",
"Interspike interval cv 2.37 ± 0.09 (52) 2.23 ± 0.11 (38) 869.00, 0.333 \n",
"In-field mean rate 13.79 ± 1.12 (52) 12.21 ± 0.98 (38) 912.00, 0.537 \n",
"Out-field mean rate 5.80 ± 0.72 (52) 5.36 ± 0.73 (38) 959.00, 0.816 \n",
"Burst event ratio 0.20 ± 0.01 (52) 0.16 ± 0.01 (38) 676.00, 0.011 \n",
"Specificity 0.47 ± 0.03 (52) 0.48 ± 0.04 (38) 976.00, 0.925 \n",
"Speed score 0.12 ± 0.01 (52) 0.11 ± 0.01 (38) 784.00, 0.096 \n",
"\n",
" PRS \n",
"Average rate 0.27, 0.808 \n",
"Gridness 0.04, 0.598 \n",
"Sparsity 0.06, 0.398 \n",
"Selectivity 0.20, 0.845 \n",
"Information specificity 0.04, 0.506 \n",
"Max rate 2.90, 0.565 \n",
"Information rate 0.07, 0.335 \n",
"Interspike interval cv 0.17, 0.482 \n",
"In-field mean rate 1.06, 0.444 \n",
"Out-field mean rate 0.13, 0.912 \n",
"Burst event ratio 0.05, 0.009 \n",
"Specificity 0.00, 0.988 \n",
"Speed score 0.01, 0.242 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_30\n",
"_base_data = baseline_ii\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline II'] = _base_data[columns].agg(summarize)\n",
"result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
"result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
"result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
"\n",
"\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_30.tex\")\n",
"result.to_csv(output_path / \"statistics\" / \"statistics_30.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Baseline I</th>\n",
" <th>30 Hz</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>8.96 ± 0.80 (63)</td>\n",
" <td>7.61 ± 0.87 (38)</td>\n",
" <td>1081.00, 0.418</td>\n",
" <td>0.27, 0.804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.53 ± 0.05 (63)</td>\n",
" <td>0.48 ± 0.06 (38)</td>\n",
" <td>1094.00, 0.472</td>\n",
" <td>0.08, 0.354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.63 ± 0.02 (63)</td>\n",
" <td>0.64 ± 0.03 (38)</td>\n",
" <td>1261.00, 0.656</td>\n",
" <td>0.03, 0.648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>5.76 ± 0.40 (63)</td>\n",
" <td>6.42 ± 0.60 (38)</td>\n",
" <td>1276.00, 0.582</td>\n",
" <td>0.86, 0.292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.24 ± 0.03 (63)</td>\n",
" <td>0.22 ± 0.03 (38)</td>\n",
" <td>1076.00, 0.398</td>\n",
" <td>0.05, 0.159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>37.39 ± 1.91 (63)</td>\n",
" <td>33.49 ± 1.89 (38)</td>\n",
" <td>1027.00, 0.235</td>\n",
" <td>3.99, 0.191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.31 ± 0.08 (63)</td>\n",
" <td>0.98 ± 0.09 (38)</td>\n",
" <td>797.00, 0.005</td>\n",
" <td>0.32, 0.049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.39 ± 0.10 (63)</td>\n",
" <td>2.23 ± 0.11 (38)</td>\n",
" <td>1100.00, 0.499</td>\n",
" <td>0.01, 0.991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>14.88 ± 1.05 (63)</td>\n",
" <td>12.21 ± 0.98 (38)</td>\n",
" <td>1018.00, 0.211</td>\n",
" <td>1.74, 0.273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>6.37 ± 0.67 (63)</td>\n",
" <td>5.36 ± 0.73 (38)</td>\n",
" <td>1079.00, 0.410</td>\n",
" <td>0.51, 0.641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.22 ± 0.01 (63)</td>\n",
" <td>0.16 ± 0.01 (38)</td>\n",
" <td>675.00, 0.000</td>\n",
" <td>0.05, 0.004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.47 ± 0.03 (63)</td>\n",
" <td>0.48 ± 0.04 (38)</td>\n",
" <td>1206.00, 0.952</td>\n",
" <td>0.01, 0.875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.14 ± 0.01 (63)</td>\n",
" <td>0.11 ± 0.01 (38)</td>\n",
" <td>835.00, 0.011</td>\n",
" <td>0.06, 0.004</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline I 30 Hz MWU \\\n",
"Average rate 8.96 ± 0.80 (63) 7.61 ± 0.87 (38) 1081.00, 0.418 \n",
"Gridness 0.53 ± 0.05 (63) 0.48 ± 0.06 (38) 1094.00, 0.472 \n",
"Sparsity 0.63 ± 0.02 (63) 0.64 ± 0.03 (38) 1261.00, 0.656 \n",
"Selectivity 5.76 ± 0.40 (63) 6.42 ± 0.60 (38) 1276.00, 0.582 \n",
"Information specificity 0.24 ± 0.03 (63) 0.22 ± 0.03 (38) 1076.00, 0.398 \n",
"Max rate 37.39 ± 1.91 (63) 33.49 ± 1.89 (38) 1027.00, 0.235 \n",
"Information rate 1.31 ± 0.08 (63) 0.98 ± 0.09 (38) 797.00, 0.005 \n",
"Interspike interval cv 2.39 ± 0.10 (63) 2.23 ± 0.11 (38) 1100.00, 0.499 \n",
"In-field mean rate 14.88 ± 1.05 (63) 12.21 ± 0.98 (38) 1018.00, 0.211 \n",
"Out-field mean rate 6.37 ± 0.67 (63) 5.36 ± 0.73 (38) 1079.00, 0.410 \n",
"Burst event ratio 0.22 ± 0.01 (63) 0.16 ± 0.01 (38) 675.00, 0.000 \n",
"Specificity 0.47 ± 0.03 (63) 0.48 ± 0.04 (38) 1206.00, 0.952 \n",
"Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (38) 835.00, 0.011 \n",
"\n",
" PRS \n",
"Average rate 0.27, 0.804 \n",
"Gridness 0.08, 0.354 \n",
"Sparsity 0.03, 0.648 \n",
"Selectivity 0.86, 0.292 \n",
"Information specificity 0.05, 0.159 \n",
"Max rate 3.99, 0.191 \n",
"Information rate 0.32, 0.049 \n",
"Interspike interval cv 0.01, 0.991 \n",
"In-field mean rate 1.74, 0.273 \n",
"Out-field mean rate 0.51, 0.641 \n",
"Burst event ratio 0.05, 0.004 \n",
"Specificity 0.01, 0.875 \n",
"Speed score 0.06, 0.004 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_30\n",
"_base_data = baseline_i\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline I'] = _base_data[columns].agg(summarize)\n",
"result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
"result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
"result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
"\n",
"\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_b_i_30.tex\")\n",
"result.to_csv(output_path / \"statistics\" / \"statistics_b_i_30.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_stim_data = stimulated_30\n",
"_base_data = stimulated_11\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['11 Hz'] = _base_data[columns].agg(summarize)\n",
"result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
"result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
"result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
"\n",
"\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_11_vs_30.tex\")\n",
"result.to_csv(output_path / \"statistics\" / \"statistics_11_vs_30.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_stim_data = baseline_i\n",
"_base_data = baseline_ii\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline I'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline II'] = _base_data[columns].agg(summarize)\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
"result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
"result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
"\n",
"\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_base_i_vs_base_ii.tex\")\n",
"result.to_csv(output_path / \"statistics\" / \"statistics_base_i_vs_base_ii.csv\")\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Violinplot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (1.7, 3), \n",
" 'figure.dpi': 150\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
"# labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
"\n",
"stuff = {\n",
" '': {\n",
" 'base': gridcell_sessions.query('baseline and i'),\n",
" 'stim': gridcell_sessions.query('stimulated')\n",
" },\n",
" '_11': {\n",
" 'base': baseline_i,\n",
" 'stim': stimulated_11\n",
" },\n",
" '_30': {\n",
" 'base': baseline_ii,\n",
" 'stim': stimulated_30\n",
" }\n",
"}\n",
"\n",
"label = {\n",
" '': ['Baseline I ', ' Stimulated'],\n",
" '_11': ['Baseline I ', ' 11 Hz'],\n",
" '_30': ['Baseline II ', ' 30 Hz']\n",
"}\n",
"\n",
"colors = {\n",
" '': ['#1b9e77', '#b2182b'],\n",
" '_11': ['#1b9e77', '#d95f02'],\n",
" '_30': ['#7570b3', '#e7298a']\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Information rate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for key, dd in stuff.items():\n",
" baseline = dd['base']['information_specificity'].to_numpy()\n",
" stimulated = dd['stim']['information_specificity'].to_numpy()\n",
" print(key)\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Spatial information specificity\")\n",
" plt.ylabel(\"bits/spike\")\n",
" plt.ylim(-0.2, 1.6)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"information_specificity{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"information_specificity{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"for key, dd in stuff.items():\n",
" baseline = dd['base']['information_rate'].to_numpy()\n",
" stimulated = dd['stim']['information_rate'].to_numpy()\n",
" print(key)\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Spatial information\")\n",
" plt.ylabel(\"bits/s\")\n",
" plt.ylim(-0.2, 4)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"spatial_information{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"spatial_information{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for key, dd in stuff.items():\n",
" baseline = dd['base']['specificity'].to_numpy()\n",
" stimulated = dd['stim']['specificity'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Spatial specificity\")\n",
" plt.ylabel(\"\")\n",
" plt.ylim(-0.02, 1.25)\n",
" plt.savefig(output_path / \"figures\" / f\"specificity{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"specificity{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"\n",
"for key, dd in stuff.items():\n",
" baseline = dd['base']['average_rate'].to_numpy()\n",
" stimulated = dd['stim']['average_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Average rate\")\n",
" plt.ylabel(\"spikes/s\")\n",
" plt.ylim(-0.2, 40)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"average_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"average_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"for key, dd in stuff.items():\n",
" baseline = dd['base']['max_rate'].to_numpy()\n",
" stimulated = dd['stim']['max_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Max rate\")\n",
" plt.ylabel(\"spikes/s\")\n",
" # plt.ylim(-0.2, 45)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"max_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"max_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"for key, dd in stuff.items():\n",
" baseline = dd['base']['interspike_interval_cv'].to_numpy()\n",
" stimulated = dd['stim']['interspike_interval_cv'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"ISI CV\")\n",
" plt.ylabel(\"Coefficient of variation\")\n",
" # plt.ylim(0.9, 5)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"isi_cv{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"isi_cv{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"for key, dd in stuff.items():\n",
" baseline = dd['base']['in_field_mean_rate'].to_numpy()\n",
" stimulated = dd['stim']['in_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"In-field rate\")\n",
" plt.ylabel(\"spikes/s\")\n",
" # plt.ylim(-0.1, 18)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"in_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"in_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"for key, dd in stuff.items():\n",
" baseline = dd['base']['out_field_mean_rate'].to_numpy()\n",
" stimulated = dd['stim']['out_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Out-of-field rate\")\n",
" plt.ylabel(\"spikes/s\")\n",
" # plt.ylim(-0.2, 8)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"out_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"out_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for key, dd in stuff.items():\n",
" baseline = dd['base']['burst_event_ratio'].to_numpy()\n",
" stimulated = dd['stim']['burst_event_ratio'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Bursting ratio\")\n",
" plt.ylabel(\"\")\n",
" # plt.ylim(-0.02, 0.60)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"burst_event_ratio{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"burst_event_ratio{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for key, dd in stuff.items():\n",
" baseline = dd['base']['max_field_mean_rate'].to_numpy()\n",
" stimulated = dd['stim']['max_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Mean rate of max field\")\n",
" plt.ylabel(\"(spikes/s)\")\n",
" # plt.ylim(-0.5,25)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"max_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"max_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for key, dd in stuff.items():\n",
" baseline = dd['base']['bursty_spike_ratio'].to_numpy()\n",
" stimulated = dd['stim']['bursty_spike_ratio'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"ratio of spikes per burst\")\n",
" plt.ylabel(\"\")\n",
" # plt.ylim(-0.03,0.9)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"bursty_spike_ratio{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"bursty_spike_ratio{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"for key, dd in stuff.items():\n",
" baseline = dd['base']['gridness'].to_numpy()\n",
" stimulated = dd['stim']['gridness'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Gridness\")\n",
" plt.ylabel(\"Gridness\")\n",
" plt.ylim(-0.6, 1.5)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"gridness{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"gridness{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for key, dd in stuff.items(): #TODO narrow broad spiking\n",
" baseline = dd['base']['speed_score'].to_numpy()\n",
" stimulated = dd['stim']['speed_score'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
" plt.title(\"Speed score\")\n",
" plt.ylabel(\"Speed score\")\n",
" # plt.ylim(-0.1, 0.5)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"speed_score{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"speed_score{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# inihibitory cells"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"stim_action = actions['stimulus-response']\n",
"stim_results = pd.read_csv(stim_action.data_path('results'))\n",
"# stim_results has old unit id's but correct on (action, unit_name, channel_group)\n",
"stim_results = stim_results.drop('unit_id', axis=1)\n",
"\n",
"data = data.merge(stim_results, how='left')\n",
"\n",
"waveform_action = actions['waveform-analysis']\n",
"waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)\n",
"\n",
"data = data.merge(waveform_results, how='left')\n",
"\n",
"data.bs = data.bs.astype(bool)\n",
"\n",
"data.loc[data.eval('t_i_peak == t_i_peak and not bs'), 'ns_inhibited'] = True\n",
"data.ns_inhibited.fillna(False, inplace=True)\n",
"\n",
"data.loc[data.eval('t_i_peak != t_i_peak and not bs'), 'ns_not_inhibited'] = True\n",
"data.ns_not_inhibited.fillna(False, inplace=True)\n",
"\n",
"# make baseline for inhibited vs not inhibited\n",
"data.loc[data.unit_id.isin(data.query('ns_inhibited').unit_id.values), 'ns_inhibited'] = True\n",
"data.loc[data.unit_id.isin(data.query('ns_not_inhibited').unit_id.values), 'ns_not_inhibited'] = True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"baseline = data.query('ns_inhibited and baseline and i')['speed_score'].to_numpy()\n",
"stimulated = data.query('ns_inhibited and stimulated')['speed_score'].to_numpy()\n",
"plt.figure()\n",
"violinplot(baseline, stimulated, xticks=label[''], colors=colors[''])\n",
"plt.title(\"Speed score\")\n",
"plt.ylabel(\"Speed score\")\n",
"# plt.ylim(-0.1, 0.5)\n",
"\n",
"plt.savefig(output_path / \"figures\" / f\"speed_score_ns_inhibited.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / f\"speed_score_ns_inhibited.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register in Expipe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"comparisons-gridcells\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_comparisons_gridcells.ipynb\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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