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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": [
2019-10-17 17:51:12 +00:00
"19:21:26 [I] klustakwik KlustaKwik2 version 0.2.6\n",
2019-10-16 05:28:13 +00:00
"/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": [
{
"data": {
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" <th></th>\n",
" <th>action</th>\n",
" <th>baseline</th>\n",
" <th>entity</th>\n",
" <th>frequency</th>\n",
" <th>i</th>\n",
" <th>ii</th>\n",
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" <th>bursty_spike_ratio</th>\n",
" <th>gridness</th>\n",
" <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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" <td>-0.615405</td>\n",
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" <td>0.032266</td>\n",
" <td>1.821598</td>\n",
" <td>0.014624</td>\n",
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" <td>0.658748</td>\n",
" <td>-0.527711</td>\n",
" <td>0.131660</td>\n",
" <td>3.833587</td>\n",
" <td>0.336590</td>\n",
" <td>4.407614</td>\n",
" <td>0.121115</td>\n",
" <td>0.542877</td>\n",
" <td>27.758541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1849-060319-3</td>\n",
" <td>True</td>\n",
" <td>1849</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>0.097464</td>\n",
" <td>0.196189</td>\n",
" <td>-0.641543</td>\n",
" <td>0.274989</td>\n",
" <td>0.153740</td>\n",
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" <td>0.526720</td>\n",
" <td>0.033667</td>\n",
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2019-10-17 17:51:12 +00:00
"<p>5 rows ×
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"</div>"
],
"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 ... 0.397921 \n",
"1 NaN False baseline ii ... 0.146481 \n",
"2 NaN False baseline ii ... 0.373466 \n",
"3 NaN False baseline ii ... 0.097464 \n",
"4 NaN False baseline ii ... 0.248036 \n",
"\n",
2019-10-17 17:51:12 +00:00
" bursty_spike_ratio gridness border_score information_rate \\\n",
"0 0.676486 -0.459487 0.078474 0.965845 \n",
"1 0.277121 -0.615405 0.311180 0.191375 \n",
"2 0.658748 -0.527711 0.131660 3.833587 \n",
"3 0.196189 -0.641543 0.274989 0.153740 \n",
"4 0.461250 -0.085292 0.198676 0.526720 \n",
2019-10-16 05:28:13 +00:00
"\n",
2019-10-17 17:51:12 +00:00
" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
"0 0.309723 5.788704 0.043321 0.624971 \n",
"1 0.032266 1.821598 0.014624 0.753333 \n",
"2 0.336590 4.407614 0.121115 0.542877 \n",
"3 0.068626 6.128601 0.099223 0.484916 \n",
"4 0.033667 1.602362 0.051825 0.646571 \n",
2019-10-16 05:28:13 +00:00
"\n",
" orientation \n",
"0 22.067900 \n",
"1 0.000000 \n",
"2 27.758541 \n",
"3 11.309932 \n",
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"\n",
2019-10-17 17:51:12 +00:00
"[5 rows x 39 columns]"
2019-10-16 05:28:13 +00:00
]
},
"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": [
{
"data": {
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" <th>4</th>\n",
" <td>0.325842</td>\n",
" <td>0.180495</td>\n",
" <td>5.262721</td>\n",
" <td>0.103584</td>\n",
" <td>0.210427</td>\n",
" <td>0.094041</td>\n",
" <td>1833-010719-1</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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2019-10-17 17:51:12 +00:00
"<p>5 rows ×
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"</div>"
],
"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 ... 0.043321 0.624971 \n",
"1 NaN False baseline ii ... 0.014624 0.753333 \n",
"2 NaN False baseline ii ... 0.121115 0.542877 \n",
"3 NaN False baseline ii ... 0.099223 0.484916 \n",
"4 NaN False baseline ii ... 0.051825 0.646571 \n",
"\n",
2019-10-17 17:51:12 +00:00
" orientation border_score_threshold gridness_threshold \\\n",
"0 22.067900 0.332548 0.229073 \n",
"1 0.000000 0.354830 0.089333 \n",
"2 27.758541 0.264610 -0.121081 \n",
"3 11.309932 0.344280 0.215829 \n",
"4 0.000000 0.342799 0.218967 \n",
2019-10-16 05:28:13 +00:00
"\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",
2019-10-17 17:51:12 +00:00
" information_rate_threshold speed_score_threshold specificity \n",
"0 1.115825 0.066736 0.445206 \n",
"1 0.223237 0.052594 0.097485 \n",
"2 4.964984 0.027120 0.393687 \n",
"3 0.239996 0.054074 0.262612 \n",
"4 0.524990 0.144702 0.133677 \n",
2019-10-16 05:28:13 +00:00
"\n",
2019-10-17 17:51:12 +00:00
"[5 rows x 46 columns]"
2019-10-16 05:28:13 +00:00
]
},
"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",
2019-10-17 17:51:12 +00:00
"execution_count": 7,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulated\n",
"False 624\n",
2019-10-17 17:51:12 +00:00
"True 660\n",
2019-10-16 05:28:13 +00:00
"Name: action, dtype: int64"
]
},
2019-10-17 17:51:12 +00:00
"execution_count": 7,
2019-10-16 05:28:13 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('stimulated').count()['action']"
]
},
2019-10-17 17:51:12 +00:00
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
]
},
2019-10-16 05:28:13 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find all cells with gridness above threshold"
]
},
{
"cell_type": "code",
2019-10-17 17:51:12 +00:00
"execution_count": 10,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
2019-10-17 17:51:12 +00:00
"Number of gridcells 225\n",
2019-10-16 05:28:13 +00:00
"Number of animals 4\n"
]
}
],
"source": [
"query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n",
"sessions_above_threshold = data.query(query)\n",
"print(\"Number of gridcells\", len(sessions_above_threshold))\n",
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
]
},
{
"cell_type": "code",
2019-10-17 17:51:12 +00:00
"execution_count": 11,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [],
"source": [
2019-10-17 17:51:12 +00:00
"gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
2019-10-16 05:28:13 +00:00
]
},
{
"cell_type": "code",
2019-10-17 17:51:12 +00:00
"execution_count": 12,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
2019-10-17 17:51:12 +00:00
"Number of gridcells in baseline i sessions 76\n",
"Number of gridcells in stimulated 11Hz ms sessions 68\n",
"Number of gridcells in baseline ii sessions 64\n",
"Number of gridcells in stimulated 30Hz ms sessions 52\n"
2019-10-16 05:28:13 +00:00
]
}
],
"source": [
2019-10-17 17:51:12 +00:00
"baseline_i = gridcell_sessions.query('baseline and Hz11')\n",
"stimulated_11 = gridcell_sessions.query('frequency==11 and stim_location==\"ms\"')\n",
2019-10-16 05:28:13 +00:00
"\n",
2019-10-17 17:51:12 +00:00
"baseline_ii = gridcell_sessions.query('baseline and Hz30')\n",
"stimulated_30 = gridcell_sessions.query('frequency==30 and stim_location==\"ms\"')\n",
2019-10-16 05:28:13 +00:00
"\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",
2019-10-17 17:51:12 +00:00
"execution_count": 13,
2019-10-16 05:28:13 +00:00
"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",
2019-10-17 17:51:12 +00:00
"execution_count": 14,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
2019-10-17 17:51:12 +00:00
"Number of gridcells in baseline i sessions 70\n",
"Number of gridcells in stimulated 11Hz ms sessions 65\n",
"Number of gridcells in baseline ii sessions 61\n",
"Number of gridcells in stimulated 30Hz ms sessions 49\n"
2019-10-16 05:28:13 +00:00
]
}
],
"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",
2019-10-17 17:51:12 +00:00
"execution_count": 15,
2019-10-16 05:28:13 +00:00
"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",
2019-10-17 17:51:12 +00:00
"execution_count": 16,
2019-10-16 05:28:13 +00:00
"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",
" <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",
2019-10-17 17:51:12 +00:00
" <td>10.046219</td>\n",
" <td>0.537204</td>\n",
" <td>0.656641</td>\n",
" <td>5.347833</td>\n",
" <td>0.205817</td>\n",
" <td>37.735779</td>\n",
" <td>1.175931</td>\n",
" <td>2.344483</td>\n",
" <td>15.790391</td>\n",
" <td>7.405761</td>\n",
" <td>0.219892</td>\n",
" <td>0.445701</td>\n",
" <td>0.132422</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
2019-10-17 17:51:12 +00:00
" <td>9.814609</td>\n",
" <td>0.433530</td>\n",
" <td>0.692547</td>\n",
" <td>5.280295</td>\n",
" <td>0.182564</td>\n",
" <td>34.650917</td>\n",
" <td>0.933478</td>\n",
" <td>2.247505</td>\n",
" <td>14.455320</td>\n",
" <td>7.429762</td>\n",
" <td>0.213281</td>\n",
" <td>0.419822</td>\n",
" <td>0.111848</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"stimulated \n",
2019-10-17 17:51:12 +00:00
"False 10.046219 0.537204 0.656641 5.347833 \n",
"True 9.814609 0.433530 0.692547 5.280295 \n",
2019-10-16 05:28:13 +00:00
"\n",
" information_specificity max_rate information_rate \\\n",
"stimulated \n",
2019-10-17 17:51:12 +00:00
"False 0.205817 37.735779 1.175931 \n",
"True 0.182564 34.650917 0.933478 \n",
2019-10-16 05:28:13 +00:00
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"stimulated \n",
2019-10-17 17:51:12 +00:00
"False 2.344483 15.790391 7.405761 \n",
"True 2.247505 14.455320 7.429762 \n",
2019-10-16 05:28:13 +00:00
"\n",
" burst_event_ratio specificity speed_score \n",
"stimulated \n",
2019-10-17 17:51:12 +00:00
"False 0.219892 0.445701 0.132422 \n",
"True 0.213281 0.419822 0.111848 "
2019-10-16 05:28:13 +00:00
]
},
2019-10-17 17:51:12 +00:00
"execution_count": 16,
2019-10-16 05:28:13 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
2019-10-17 17:51:12 +00:00
"gridcell_sessions.groupby('stimulated')[columns].mean()"
2019-10-16 05:28:13 +00:00
]
},
{
"cell_type": "code",
2019-10-17 17:51:12 +00:00
"execution_count": 17,
2019-10-16 05:28:13 +00:00
"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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"</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",
2019-10-17 17:51:12 +00:00
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
" <td>147.000000</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
2019-10-17 17:51:12 +00:00
" <td>10.046219</td>\n",
" <td>0.537204</td>\n",
" <td>0.656641</td>\n",
" <td>5.347833</td>\n",
" <td>0.205817</td>\n",
" <td>37.735779</td>\n",
" <td>1.175931</td>\n",
" <td>2.344483</td>\n",
" <td>15.790391</td>\n",
" <td>7.405761</td>\n",
" <td>0.219892</td>\n",
" <td>0.445701</td>\n",
" <td>0.132422</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
2019-10-17 17:51:12 +00:00
" <td>7.913344</td>\n",
" <td>0.372942</td>\n",
" <td>0.190573</td>\n",
" <td>2.938819</td>\n",
" <td>0.192815</td>\n",
" <td>16.976912</td>\n",
" <td>0.582747</td>\n",
" <td>0.748791</td>\n",
" <td>9.952409</td>\n",
" <td>6.971963</td>\n",
" <td>0.083408</td>\n",
" <td>0.211635</td>\n",
" <td>0.075334</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.516375</td>\n",
2019-10-17 17:51:12 +00:00
" <td>-0.599569</td>\n",
" <td>0.220235</td>\n",
" <td>1.762785</td>\n",
" <td>0.005947</td>\n",
2019-10-16 05:28:13 +00:00
" <td>3.013150</td>\n",
2019-10-17 17:51:12 +00:00
" <td>0.102101</td>\n",
" <td>1.067244</td>\n",
2019-10-16 05:28:13 +00:00
" <td>0.993877</td>\n",
2019-10-17 17:51:12 +00:00
" <td>0.185332</td>\n",
2019-10-16 05:28:13 +00:00
" <td>0.027228</td>\n",
2019-10-17 17:51:12 +00:00
" <td>0.072063</td>\n",
2019-10-16 05:28:13 +00:00
" <td>-0.023795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
2019-10-17 17:51:12 +00:00
" <td>3.811514</td>\n",
" <td>0.324174</td>\n",
" <td>0.515183</td>\n",
" <td>3.107181</td>\n",
" <td>0.071747</td>\n",
" <td>25.148584</td>\n",
" <td>0.737153</td>\n",
" <td>1.749688</td>\n",
" <td>7.628858</td>\n",
" <td>1.800796</td>\n",
" <td>0.162830</td>\n",
" <td>0.289405</td>\n",
" <td>0.078827</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
2019-10-17 17:51:12 +00:00
" <td>7.129568</td>\n",
" <td>0.579600</td>\n",
" <td>0.698596</td>\n",
" <td>4.675862</td>\n",
" <td>0.141391</td>\n",
" <td>34.348592</td>\n",
" <td>1.055340</td>\n",
" <td>2.173263</td>\n",
" <td>13.000207</td>\n",
" <td>4.835608</td>\n",
" <td>0.213831</td>\n",
" <td>0.390758</td>\n",
" <td>0.124640</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
2019-10-17 17:51:12 +00:00
" <td>15.685084</td>\n",
" <td>0.798542</td>\n",
" <td>0.823981</td>\n",
" <td>6.646175</td>\n",
" <td>0.265521</td>\n",
" <td>47.346567</td>\n",
" <td>1.570106</td>\n",
" <td>2.691555</td>\n",
" <td>22.415152</td>\n",
" <td>10.981344</td>\n",
" <td>0.282480</td>\n",
" <td>0.572782</td>\n",
" <td>0.183005</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
2019-10-17 17:51:12 +00:00
" <td>35.560173</td>\n",
2019-10-16 05:28:13 +00:00
" <td>1.174288</td>\n",
2019-10-17 17:51:12 +00:00
" <td>0.980148</td>\n",
2019-10-16 05:28:13 +00:00
" <td>17.011330</td>\n",
2019-10-17 17:51:12 +00:00
" <td>1.202862</td>\n",
2019-10-16 05:28:13 +00:00
" <td>90.839266</td>\n",
" <td>3.540663</td>\n",
" <td>5.240845</td>\n",
" <td>45.349506</td>\n",
2019-10-17 17:51:12 +00:00
" <td>32.997789</td>\n",
2019-10-16 05:28:13 +00:00
" <td>0.400014</td>\n",
" <td>0.975050</td>\n",
2019-10-17 17:51:12 +00:00
" <td>0.333463</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
2019-10-17 17:51:12 +00:00
"count 147.000000 147.000000 147.000000 147.000000 \n",
"mean 10.046219 0.537204 0.656641 5.347833 \n",
"std 7.913344 0.372942 0.190573 2.938819 \n",
"min 0.516375 -0.599569 0.220235 1.762785 \n",
"25% 3.811514 0.324174 0.515183 3.107181 \n",
"50% 7.129568 0.579600 0.698596 4.675862 \n",
"75% 15.685084 0.798542 0.823981 6.646175 \n",
"max 35.560173 1.174288 0.980148 17.011330 \n",
2019-10-16 05:28:13 +00:00
"\n",
" information_specificity max_rate information_rate \\\n",
2019-10-17 17:51:12 +00:00
"count 147.000000 147.000000 147.000000 \n",
"mean 0.205817 37.735779 1.175931 \n",
"std 0.192815 16.976912 0.582747 \n",
"min 0.005947 3.013150 0.102101 \n",
"25% 0.071747 25.148584 0.737153 \n",
"50% 0.141391 34.348592 1.055340 \n",
"75% 0.265521 47.346567 1.570106 \n",
"max 1.202862 90.839266 3.540663 \n",
2019-10-16 05:28:13 +00:00
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
2019-10-17 17:51:12 +00:00
"count 147.000000 147.000000 147.000000 \n",
"mean 2.344483 15.790391 7.405761 \n",
"std 0.748791 9.952409 6.971963 \n",
"min 1.067244 0.993877 0.185332 \n",
"25% 1.749688 7.628858 1.800796 \n",
"50% 2.173263 13.000207 4.835608 \n",
"75% 2.691555 22.415152 10.981344 \n",
"max 5.240845 45.349506 32.997789 \n",
2019-10-16 05:28:13 +00:00
"\n",
" burst_event_ratio specificity speed_score \n",
2019-10-17 17:51:12 +00:00
"count 147.000000 147.000000 147.000000 \n",
"mean 0.219892 0.445701 0.132422 \n",
"std 0.083408 0.211635 0.075334 \n",
"min 0.027228 0.072063 -0.023795 \n",
"25% 0.162830 0.289405 0.078827 \n",
"50% 0.213831 0.390758 0.124640 \n",
"75% 0.282480 0.572782 0.183005 \n",
"max 0.400014 0.975050 0.333463 "
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]
},
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"execution_count": 17,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"gridcell_sessions.query('baseline')[columns].describe()"
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]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [
{
"data": {
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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",
" <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",
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" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
" <td>124.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
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" <td>9.814609</td>\n",
" <td>0.433530</td>\n",
" <td>0.692547</td>\n",
" <td>5.280295</td>\n",
" <td>0.182564</td>\n",
" <td>34.650917</td>\n",
" <td>0.933478</td>\n",
" <td>2.247505</td>\n",
" <td>14.455320</td>\n",
" <td>7.429762</td>\n",
" <td>0.213281</td>\n",
" <td>0.419822</td>\n",
" <td>0.111848</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
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" <td>7.676536</td>\n",
" <td>0.387343</td>\n",
" <td>0.197445</td>\n",
" <td>3.520949</td>\n",
" <td>0.208775</td>\n",
" <td>14.511629</td>\n",
" <td>0.492383</td>\n",
" <td>0.750923</td>\n",
" <td>8.796338</td>\n",
" <td>6.881408</td>\n",
" <td>0.077978</td>\n",
" <td>0.231655</td>\n",
" <td>0.076247</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
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" <td>0.571675</td>\n",
" <td>-0.509346</td>\n",
" <td>0.161197</td>\n",
" <td>1.502176</td>\n",
" <td>0.005851</td>\n",
" <td>8.703201</td>\n",
" <td>0.096607</td>\n",
" <td>1.060662</td>\n",
" <td>2.327366</td>\n",
" <td>0.212979</td>\n",
" <td>0.041561</td>\n",
" <td>0.075519</td>\n",
" <td>-0.073931</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
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" <td>3.835569</td>\n",
" <td>0.194332</td>\n",
" <td>0.552817</td>\n",
" <td>2.819310</td>\n",
" <td>0.062615</td>\n",
" <td>24.286536</td>\n",
" <td>0.552133</td>\n",
" <td>1.671374</td>\n",
" <td>8.097415</td>\n",
" <td>2.038374</td>\n",
" <td>0.160874</td>\n",
" <td>0.243125</td>\n",
" <td>0.065039</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
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" <td>7.690325</td>\n",
" <td>0.413583</td>\n",
" <td>0.733832</td>\n",
" <td>4.446917</td>\n",
" <td>0.109036</td>\n",
" <td>32.040628</td>\n",
" <td>0.879876</td>\n",
" <td>2.098479</td>\n",
" <td>12.325347</td>\n",
" <td>5.718993</td>\n",
" <td>0.204469</td>\n",
" <td>0.361016</td>\n",
" <td>0.105714</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
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" <td>14.035706</td>\n",
" <td>0.723850</td>\n",
" <td>0.861439</td>\n",
" <td>6.438574</td>\n",
" <td>0.219362</td>\n",
" <td>42.320860</td>\n",
" <td>1.196084</td>\n",
" <td>2.651945</td>\n",
" <td>19.237536</td>\n",
" <td>10.972856</td>\n",
" <td>0.266557</td>\n",
" <td>0.561412</td>\n",
" <td>0.159393</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
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" <td>34.844930</td>\n",
" <td>1.230658</td>\n",
" <td>0.983263</td>\n",
" <td>25.599598</td>\n",
" <td>1.296616</td>\n",
" <td>76.146357</td>\n",
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" <td>2.918984</td>\n",
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" <td>5.324055</td>\n",
" <td>42.803943</td>\n",
" <td>31.519482</td>\n",
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" <td>0.406678</td>\n",
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" <td>1.077313</td>\n",
" <td>0.349283</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" average_rate gridness sparsity selectivity \\\n",
"count 124.000000 124.000000 124.000000 124.000000 \n",
"mean 9.814609 0.433530 0.692547 5.280295 \n",
"std 7.676536 0.387343 0.197445 3.520949 \n",
"min 0.571675 -0.509346 0.161197 1.502176 \n",
"25% 3.835569 0.194332 0.552817 2.819310 \n",
"50% 7.690325 0.413583 0.733832 4.446917 \n",
"75% 14.035706 0.723850 0.861439 6.438574 \n",
"max 34.844930 1.230658 0.983263 25.599598 \n",
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"\n",
" information_specificity max_rate information_rate \\\n",
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"count 124.000000 124.000000 124.000000 \n",
"mean 0.182564 34.650917 0.933478 \n",
"std 0.208775 14.511629 0.492383 \n",
"min 0.005851 8.703201 0.096607 \n",
"25% 0.062615 24.286536 0.552133 \n",
"50% 0.109036 32.040628 0.879876 \n",
"75% 0.219362 42.320860 1.196084 \n",
"max 1.296616 76.146357 2.918984 \n",
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"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
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"count 124.000000 124.000000 124.000000 \n",
"mean 2.247505 14.455320 7.429762 \n",
"std 0.750923 8.796338 6.881408 \n",
"min 1.060662 2.327366 0.212979 \n",
"25% 1.671374 8.097415 2.038374 \n",
"50% 2.098479 12.325347 5.718993 \n",
"75% 2.651945 19.237536 10.972856 \n",
"max 5.324055 42.803943 31.519482 \n",
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"\n",
" burst_event_ratio specificity speed_score \n",
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"count 124.000000 124.000000 124.000000 \n",
"mean 0.213281 0.419822 0.111848 \n",
"std 0.077978 0.231655 0.076247 \n",
"min 0.041561 0.075519 -0.073931 \n",
"25% 0.160874 0.243125 0.065039 \n",
"50% 0.204469 0.361016 0.105714 \n",
"75% 0.266557 0.561412 0.159393 \n",
"max 0.406678 1.077313 0.349283 "
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]
},
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"execution_count": 18,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"gridcell_sessions.query(\"stimulated\")[columns].describe()"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create nice table"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"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",
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"execution_count": 49,
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"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Baseline</th>\n",
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" <th>Stimulated</th>\n",
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" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
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" <td>10.05 ± 0.65 (147)</td>\n",
" <td>9.81 ± 0.69 (124)</td>\n",
" <td>9040.00, 0.909</td>\n",
" <td>0.56, 0.717</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.54 ± 0.03 (147)</td>\n",
" <td>0.43 ± 0.03 (124)</td>\n",
" <td>7516.00, 0.013</td>\n",
" <td>0.17, 0.004</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.66 ± 0.02 (147)</td>\n",
" <td>0.69 ± 0.02 (124)</td>\n",
" <td>10275.00, 0.071</td>\n",
" <td>0.04, 0.161</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
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" <td>5.35 ± 0.24 (147)</td>\n",
" <td>5.28 ± 0.32 (124)</td>\n",
" <td>8488.00, 0.330</td>\n",
" <td>0.23, 0.450</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
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" <td>0.21 ± 0.02 (147)</td>\n",
" <td>0.18 ± 0.02 (124)</td>\n",
" <td>7883.00, 0.056</td>\n",
" <td>0.03, 0.103</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
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" <td>37.74 ± 1.40 (147)</td>\n",
" <td>34.65 ± 1.30 (124)</td>\n",
" <td>8165.00, 0.140</td>\n",
" <td>2.31, 0.108</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>1.18 ± 0.05 (147)</td>\n",
" <td>0.93 ± 0.04 (124)</td>\n",
" <td>6772.00, 0.000</td>\n",
" <td>0.18, 0.008</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
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" <td>2.34 ± 0.06 (147)</td>\n",
" <td>2.25 ± 0.07 (124)</td>\n",
" <td>8361.00, 0.242</td>\n",
" <td>0.07, 0.500</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
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" <td>15.79 ± 0.82 (147)</td>\n",
" <td>14.46 ± 0.79 (124)</td>\n",
" <td>8526.00, 0.361</td>\n",
" <td>0.67, 0.638</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
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" <td>7.41 ± 0.58 (147)</td>\n",
" <td>7.43 ± 0.62 (124)</td>\n",
" <td>9193.00, 0.903</td>\n",
" <td>0.88, 0.456</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
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" <td>0.22 ± 0.01 (147)</td>\n",
" <td>0.21 ± 0.01 (124)</td>\n",
" <td>8548.00, 0.379</td>\n",
" <td>0.01, 0.370</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
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" <td>0.45 ± 0.02 (147)</td>\n",
" <td>0.42 ± 0.02 (124)</td>\n",
" <td>8221.00, 0.165</td>\n",
" <td>0.03, 0.167</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
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" <td>0.13 ± 0.01 (147)</td>\n",
" <td>0.11 ± 0.01 (124)</td>\n",
" <td>7793.00, 0.040</td>\n",
" <td>0.02, 0.046</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
2019-10-17 17:51:12 +00:00
" Baseline Stimulated \\\n",
"Average rate 10.05 ± 0.65 (147) 9.81 ± 0.69 (124) \n",
"Gridness 0.54 ± 0.03 (147) 0.43 ± 0.03 (124) \n",
"Sparsity 0.66 ± 0.02 (147) 0.69 ± 0.02 (124) \n",
"Selectivity 5.35 ± 0.24 (147) 5.28 ± 0.32 (124) \n",
"Information specificity 0.21 ± 0.02 (147) 0.18 ± 0.02 (124) \n",
"Max rate 37.74 ± 1.40 (147) 34.65 ± 1.30 (124) \n",
"Information rate 1.18 ± 0.05 (147) 0.93 ± 0.04 (124) \n",
"Interspike interval cv 2.34 ± 0.06 (147) 2.25 ± 0.07 (124) \n",
"In-field mean rate 15.79 ± 0.82 (147) 14.46 ± 0.79 (124) \n",
"Out-field mean rate 7.41 ± 0.58 (147) 7.43 ± 0.62 (124) \n",
"Burst event ratio 0.22 ± 0.01 (147) 0.21 ± 0.01 (124) \n",
"Specificity 0.45 ± 0.02 (147) 0.42 ± 0.02 (124) \n",
"Speed score 0.13 ± 0.01 (147) 0.11 ± 0.01 (124) \n",
2019-10-16 05:28:13 +00:00
"\n",
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" MWU PRS \n",
"Average rate 9040.00, 0.909 0.56, 0.717 \n",
"Gridness 7516.00, 0.013 0.17, 0.004 \n",
"Sparsity 10275.00, 0.071 0.04, 0.161 \n",
"Selectivity 8488.00, 0.330 0.23, 0.450 \n",
"Information specificity 7883.00, 0.056 0.03, 0.103 \n",
"Max rate 8165.00, 0.140 2.31, 0.108 \n",
"Information rate 6772.00, 0.000 0.18, 0.008 \n",
"Interspike interval cv 8361.00, 0.242 0.07, 0.500 \n",
"In-field mean rate 8526.00, 0.361 0.67, 0.638 \n",
"Out-field mean rate 9193.00, 0.903 0.88, 0.456 \n",
"Burst event ratio 8548.00, 0.379 0.01, 0.370 \n",
"Specificity 8221.00, 0.165 0.03, 0.167 \n",
"Speed score 7793.00, 0.040 0.02, 0.046 "
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]
},
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"execution_count": 49,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"_stim_data = gridcell_sessions.query('stimulated')\n",
"_base_data = gridcell_sessions.query('baseline')\n",
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"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline'] = _base_data[columns].agg(summarize)\n",
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"result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
"\n",
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"\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_latex(output_path / \"statistics\" / \"statistics.csv\")\n",
"result"
]
},
{
"cell_type": "code",
2019-10-17 17:51:12 +00:00
"execution_count": 47,
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"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Baseline</th>\n",
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" <th>11 Hz</th>\n",
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" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
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" <td>9.82 ± 0.91 (70)</td>\n",
" <td>9.28 ± 0.90 (65)</td>\n",
" <td>2175.00, 0.661</td>\n",
" <td>0.18, 0.933</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.54 ± 0.05 (70)</td>\n",
" <td>0.42 ± 0.05 (65)</td>\n",
" <td>1822.00, 0.046</td>\n",
" <td>0.17, 0.052</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.65 ± 0.02 (70)</td>\n",
" <td>0.69 ± 0.02 (65)</td>\n",
" <td>2578.00, 0.183</td>\n",
" <td>0.06, 0.147</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
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" <td>5.25 ± 0.35 (70)</td>\n",
" <td>5.43 ± 0.48 (65)</td>\n",
" <td>2214.00, 0.790</td>\n",
" <td>0.05, 0.961</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
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" <td>0.22 ± 0.03 (70)</td>\n",
" <td>0.19 ± 0.03 (65)</td>\n",
" <td>1888.00, 0.089</td>\n",
" <td>0.05, 0.020</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
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" <td>36.77 ± 1.96 (70)</td>\n",
" <td>33.16 ± 1.79 (65)</td>\n",
" <td>1971.00, 0.181</td>\n",
" <td>3.18, 0.250</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>1.22 ± 0.06 (70)</td>\n",
" <td>0.89 ± 0.06 (65)</td>\n",
" <td>1431.00, 0.000</td>\n",
" <td>0.20, 0.006</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
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" <td>2.37 ± 0.09 (70)</td>\n",
" <td>2.24 ± 0.09 (65)</td>\n",
" <td>2022.00, 0.266</td>\n",
" <td>0.12, 0.520</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
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" <td>15.52 ± 1.15 (70)</td>\n",
" <td>13.80 ± 1.06 (65)</td>\n",
" <td>2064.00, 0.354</td>\n",
" <td>0.63, 0.738</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
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" <td>7.09 ± 0.77 (70)</td>\n",
" <td>7.00 ± 0.80 (65)</td>\n",
" <td>2236.00, 0.865</td>\n",
" <td>0.01, 0.979</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
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" <td>0.23 ± 0.01 (70)</td>\n",
" <td>0.23 ± 0.01 (65)</td>\n",
" <td>2307.00, 0.890</td>\n",
" <td>0.01, 0.732</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
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" <td>0.45 ± 0.03 (70)</td>\n",
" <td>0.42 ± 0.03 (65)</td>\n",
" <td>2049.00, 0.321</td>\n",
" <td>0.01, 0.476</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
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" <td>0.14 ± 0.01 (70)</td>\n",
" <td>0.12 ± 0.01 (65)</td>\n",
" <td>1939.00, 0.140</td>\n",
" <td>0.03, 0.069</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Baseline 11 Hz MWU \\\n",
"Average rate 9.82 ± 0.91 (70) 9.28 ± 0.90 (65) 2175.00, 0.661 \n",
"Gridness 0.54 ± 0.05 (70) 0.42 ± 0.05 (65) 1822.00, 0.046 \n",
"Sparsity 0.65 ± 0.02 (70) 0.69 ± 0.02 (65) 2578.00, 0.183 \n",
"Selectivity 5.25 ± 0.35 (70) 5.43 ± 0.48 (65) 2214.00, 0.790 \n",
"Information specificity 0.22 ± 0.03 (70) 0.19 ± 0.03 (65) 1888.00, 0.089 \n",
"Max rate 36.77 ± 1.96 (70) 33.16 ± 1.79 (65) 1971.00, 0.181 \n",
"Information rate 1.22 ± 0.06 (70) 0.89 ± 0.06 (65) 1431.00, 0.000 \n",
"Interspike interval cv 2.37 ± 0.09 (70) 2.24 ± 0.09 (65) 2022.00, 0.266 \n",
"In-field mean rate 15.52 ± 1.15 (70) 13.80 ± 1.06 (65) 2064.00, 0.354 \n",
"Out-field mean rate 7.09 ± 0.77 (70) 7.00 ± 0.80 (65) 2236.00, 0.865 \n",
"Burst event ratio 0.23 ± 0.01 (70) 0.23 ± 0.01 (65) 2307.00, 0.890 \n",
"Specificity 0.45 ± 0.03 (70) 0.42 ± 0.03 (65) 2049.00, 0.321 \n",
"Speed score 0.14 ± 0.01 (70) 0.12 ± 0.01 (65) 1939.00, 0.140 \n",
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"\n",
" PRS \n",
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"Average rate 0.18, 0.933 \n",
"Gridness 0.17, 0.052 \n",
"Sparsity 0.06, 0.147 \n",
"Selectivity 0.05, 0.961 \n",
"Information specificity 0.05, 0.020 \n",
"Max rate 3.18, 0.250 \n",
"Information rate 0.20, 0.006 \n",
"Interspike interval cv 0.12, 0.520 \n",
"In-field mean rate 0.63, 0.738 \n",
"Out-field mean rate 0.01, 0.979 \n",
"Burst event ratio 0.01, 0.732 \n",
"Specificity 0.01, 0.476 \n",
"Speed score 0.03, 0.069 "
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]
},
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"execution_count": 47,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_11\n",
"_base_data = baseline_i\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline'] = _base_data[columns].agg(summarize)\n",
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"result['11 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
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"\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_latex(output_path / \"statistics\" / \"statistics_11.csv\")\n",
"result"
]
},
{
"cell_type": "code",
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"execution_count": 48,
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Baseline</th>\n",
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" <th>30 Hz</th>\n",
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" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
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" <td>10.08 ± 1.05 (61)</td>\n",
" <td>9.94 ± 1.17 (49)</td>\n",
" <td>1491.00, 0.986</td>\n",
" <td>0.24, 0.763</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.53 ± 0.05 (61)</td>\n",
" <td>0.46 ± 0.06 (49)</td>\n",
" <td>1342.00, 0.361</td>\n",
" <td>0.08, 0.289</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.67 ± 0.02 (61)</td>\n",
" <td>0.69 ± 0.03 (49)</td>\n",
" <td>1622.00, 0.445</td>\n",
" <td>0.03, 0.466</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
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" <td>5.34 ± 0.38 (61)</td>\n",
" <td>5.21 ± 0.46 (49)</td>\n",
" <td>1372.00, 0.463</td>\n",
" <td>0.37, 0.420</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
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" <td>0.19 ± 0.02 (61)</td>\n",
" <td>0.18 ± 0.03 (49)</td>\n",
" <td>1380.00, 0.493</td>\n",
" <td>0.01, 0.725</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
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" <td>37.61 ± 2.31 (61)</td>\n",
" <td>34.42 ± 1.99 (49)</td>\n",
" <td>1342.00, 0.361</td>\n",
" <td>2.37, 0.351</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>1.08 ± 0.08 (61)</td>\n",
" <td>0.95 ± 0.07 (49)</td>\n",
" <td>1321.00, 0.298</td>\n",
" <td>0.14, 0.413</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
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" <td>2.28 ± 0.09 (61)</td>\n",
" <td>2.24 ± 0.11 (49)</td>\n",
" <td>1419.00, 0.652</td>\n",
" <td>0.06, 0.740</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
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" <td>15.61 ± 1.32 (61)</td>\n",
" <td>14.54 ± 1.29 (49)</td>\n",
" <td>1418.00, 0.648</td>\n",
" <td>0.64, 0.675</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
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" <td>7.65 ± 0.96 (61)</td>\n",
" <td>7.54 ± 1.06 (49)</td>\n",
" <td>1487.00, 0.966</td>\n",
" <td>0.37, 0.789</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
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" <td>0.21 ± 0.01 (61)</td>\n",
" <td>0.19 ± 0.01 (49)</td>\n",
" <td>1241.00, 0.128</td>\n",
" <td>0.04, 0.037</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
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" <td>0.42 ± 0.03 (61)</td>\n",
" <td>0.42 ± 0.03 (49)</td>\n",
" <td>1429.00, 0.696</td>\n",
" <td>0.03, 0.495</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
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" <td>0.12 ± 0.01 (61)</td>\n",
" <td>0.11 ± 0.01 (49)</td>\n",
" <td>1335.00, 0.339</td>\n",
" <td>0.01, 0.545</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
2019-10-17 17:51:12 +00:00
" Baseline 30 Hz MWU \\\n",
"Average rate 10.08 ± 1.05 (61) 9.94 ± 1.17 (49) 1491.00, 0.986 \n",
"Gridness 0.53 ± 0.05 (61) 0.46 ± 0.06 (49) 1342.00, 0.361 \n",
"Sparsity 0.67 ± 0.02 (61) 0.69 ± 0.03 (49) 1622.00, 0.445 \n",
"Selectivity 5.34 ± 0.38 (61) 5.21 ± 0.46 (49) 1372.00, 0.463 \n",
"Information specificity 0.19 ± 0.02 (61) 0.18 ± 0.03 (49) 1380.00, 0.493 \n",
"Max rate 37.61 ± 2.31 (61) 34.42 ± 1.99 (49) 1342.00, 0.361 \n",
"Information rate 1.08 ± 0.08 (61) 0.95 ± 0.07 (49) 1321.00, 0.298 \n",
"Interspike interval cv 2.28 ± 0.09 (61) 2.24 ± 0.11 (49) 1419.00, 0.652 \n",
"In-field mean rate 15.61 ± 1.32 (61) 14.54 ± 1.29 (49) 1418.00, 0.648 \n",
"Out-field mean rate 7.65 ± 0.96 (61) 7.54 ± 1.06 (49) 1487.00, 0.966 \n",
"Burst event ratio 0.21 ± 0.01 (61) 0.19 ± 0.01 (49) 1241.00, 0.128 \n",
"Specificity 0.42 ± 0.03 (61) 0.42 ± 0.03 (49) 1429.00, 0.696 \n",
"Speed score 0.12 ± 0.01 (61) 0.11 ± 0.01 (49) 1335.00, 0.339 \n",
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"\n",
" PRS \n",
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"Average rate 0.24, 0.763 \n",
"Gridness 0.08, 0.289 \n",
"Sparsity 0.03, 0.466 \n",
"Selectivity 0.37, 0.420 \n",
"Information specificity 0.01, 0.725 \n",
"Max rate 2.37, 0.351 \n",
"Information rate 0.14, 0.413 \n",
"Interspike interval cv 0.06, 0.740 \n",
"In-field mean rate 0.64, 0.675 \n",
"Out-field mean rate 0.37, 0.789 \n",
"Burst event ratio 0.04, 0.037 \n",
"Specificity 0.03, 0.495 \n",
"Speed score 0.01, 0.545 "
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]
},
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"execution_count": 48,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_30\n",
"_base_data = baseline_ii\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline'] = _base_data[columns].agg(summarize)\n",
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"result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
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"\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_latex(output_path / \"statistics\" / \"statistics_30.csv\")\n",
"result"
]
},
{
"cell_type": "code",
2019-10-17 17:51:12 +00:00
"execution_count": 45,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>11 Hz</th>\n",
" <th>30 Hz</th>\n",
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" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
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" <td>9.28 ± 0.90 (65)</td>\n",
" <td>9.94 ± 1.17 (49)</td>\n",
" <td>1641.00, 0.784</td>\n",
" <td>0.09, 0.925</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.42 ± 0.05 (65)</td>\n",
" <td>0.46 ± 0.06 (49)</td>\n",
" <td>1739.00, 0.403</td>\n",
" <td>0.09, 0.420</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.69 ± 0.02 (65)</td>\n",
" <td>0.69 ± 0.03 (49)</td>\n",
" <td>1618.00, 0.886</td>\n",
" <td>0.01, 0.660</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
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" <td>5.43 ± 0.48 (65)</td>\n",
" <td>5.21 ± 0.46 (49)</td>\n",
" <td>1548.00, 0.801</td>\n",
" <td>0.17, 0.835</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
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" <td>0.19 ± 0.03 (65)</td>\n",
" <td>0.18 ± 0.03 (49)</td>\n",
" <td>1569.00, 0.895</td>\n",
" <td>0.01, 0.783</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
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" <td>33.16 ± 1.79 (65)</td>\n",
" <td>34.42 ± 1.99 (49)</td>\n",
" <td>1681.00, 0.614</td>\n",
" <td>1.38, 0.740</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>0.89 ± 0.06 (65)</td>\n",
" <td>0.95 ± 0.07 (49)</td>\n",
" <td>1701.00, 0.536</td>\n",
" <td>0.07, 0.480</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
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" <td>2.24 ± 0.09 (65)</td>\n",
" <td>2.24 ± 0.11 (49)</td>\n",
" <td>1583.00, 0.959</td>\n",
" <td>0.05, 0.814</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
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" <td>13.80 ± 1.06 (65)</td>\n",
" <td>14.54 ± 1.29 (49)</td>\n",
" <td>1658.00, 0.710</td>\n",
" <td>0.88, 0.678</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
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" <td>7.00 ± 0.80 (65)</td>\n",
" <td>7.54 ± 1.06 (49)</td>\n",
" <td>1631.00, 0.828</td>\n",
" <td>0.38, 0.923</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
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" <td>0.23 ± 0.01 (65)</td>\n",
" <td>0.19 ± 0.01 (49)</td>\n",
" <td>1093.00, 0.004</td>\n",
" <td>0.05, 0.004</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
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" <td>0.42 ± 0.03 (65)</td>\n",
" <td>0.42 ± 0.03 (49)</td>\n",
" <td>1559.00, 0.850</td>\n",
" <td>0.01, 0.597</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
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" <td>0.12 ± 0.01 (65)</td>\n",
" <td>0.11 ± 0.01 (49)</td>\n",
" <td>1459.00, 0.446</td>\n",
" <td>0.01, 0.397</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" 11 Hz 30 Hz MWU \\\n",
"Average rate 9.28 ± 0.90 (65) 9.94 ± 1.17 (49) 1641.00, 0.784 \n",
"Gridness 0.42 ± 0.05 (65) 0.46 ± 0.06 (49) 1739.00, 0.403 \n",
"Sparsity 0.69 ± 0.02 (65) 0.69 ± 0.03 (49) 1618.00, 0.886 \n",
"Selectivity 5.43 ± 0.48 (65) 5.21 ± 0.46 (49) 1548.00, 0.801 \n",
"Information specificity 0.19 ± 0.03 (65) 0.18 ± 0.03 (49) 1569.00, 0.895 \n",
"Max rate 33.16 ± 1.79 (65) 34.42 ± 1.99 (49) 1681.00, 0.614 \n",
"Information rate 0.89 ± 0.06 (65) 0.95 ± 0.07 (49) 1701.00, 0.536 \n",
"Interspike interval cv 2.24 ± 0.09 (65) 2.24 ± 0.11 (49) 1583.00, 0.959 \n",
"In-field mean rate 13.80 ± 1.06 (65) 14.54 ± 1.29 (49) 1658.00, 0.710 \n",
"Out-field mean rate 7.00 ± 0.80 (65) 7.54 ± 1.06 (49) 1631.00, 0.828 \n",
"Burst event ratio 0.23 ± 0.01 (65) 0.19 ± 0.01 (49) 1093.00, 0.004 \n",
"Specificity 0.42 ± 0.03 (65) 0.42 ± 0.03 (49) 1559.00, 0.850 \n",
"Speed score 0.12 ± 0.01 (65) 0.11 ± 0.01 (49) 1459.00, 0.446 \n",
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"\n",
" PRS \n",
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"Average rate 0.09, 0.925 \n",
"Gridness 0.09, 0.420 \n",
"Sparsity 0.01, 0.660 \n",
"Selectivity 0.17, 0.835 \n",
"Information specificity 0.01, 0.783 \n",
"Max rate 1.38, 0.740 \n",
"Information rate 0.07, 0.480 \n",
"Interspike interval cv 0.05, 0.814 \n",
"In-field mean rate 0.88, 0.678 \n",
"Out-field mean rate 0.38, 0.923 \n",
"Burst event ratio 0.05, 0.004 \n",
"Specificity 0.01, 0.597 \n",
"Speed score 0.01, 0.397 "
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]
},
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"execution_count": 45,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_30\n",
"_base_data = stimulated_11\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
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"result['11 Hz'] = _base_data[columns].agg(summarize)\n",
"result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
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"\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_latex(output_path / \"statistics\" / \"statistics_11_vs_30.csv\")\n",
"result"
]
},
{
"cell_type": "code",
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"execution_count": 46,
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"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",
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" <th>Baseline I</th>\n",
" <th>Baseline II</th>\n",
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" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
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" <td>9.82 ± 0.91 (70)</td>\n",
" <td>10.08 ± 1.05 (61)</td>\n",
" <td>2166.00, 0.888</td>\n",
" <td>0.15, 0.852</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.54 ± 0.05 (70)</td>\n",
" <td>0.53 ± 0.05 (61)</td>\n",
" <td>2158.00, 0.917</td>\n",
" <td>0.00, 0.983</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.65 ± 0.02 (70)</td>\n",
" <td>0.67 ± 0.02 (61)</td>\n",
" <td>2001.00, 0.538</td>\n",
" <td>0.04, 0.361</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
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" <td>5.25 ± 0.35 (70)</td>\n",
" <td>5.34 ± 0.38 (61)</td>\n",
" <td>2062.00, 0.738</td>\n",
" <td>0.25, 0.594</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
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" <td>0.22 ± 0.03 (70)</td>\n",
" <td>0.19 ± 0.02 (61)</td>\n",
" <td>2329.00, 0.372</td>\n",
" <td>0.05, 0.143</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
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" <td>36.77 ± 1.96 (70)</td>\n",
" <td>37.61 ± 2.31 (61)</td>\n",
" <td>2088.00, 0.830</td>\n",
" <td>0.58, 0.784</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>1.22 ± 0.06 (70)</td>\n",
" <td>1.08 ± 0.08 (61)</td>\n",
" <td>2501.00, 0.092</td>\n",
" <td>0.14, 0.151</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
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" <td>2.37 ± 0.09 (70)</td>\n",
" <td>2.28 ± 0.09 (61)</td>\n",
" <td>2257.00, 0.575</td>\n",
" <td>0.01, 0.928</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
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" <td>15.52 ± 1.15 (70)</td>\n",
" <td>15.61 ± 1.32 (61)</td>\n",
" <td>2162.00, 0.903</td>\n",
" <td>0.87, 0.724</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
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" <td>7.09 ± 0.77 (70)</td>\n",
" <td>7.65 ± 0.96 (61)</td>\n",
" <td>2115.00, 0.928</td>\n",
" <td>0.02, 0.986</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
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" <td>0.23 ± 0.01 (70)</td>\n",
" <td>0.21 ± 0.01 (61)</td>\n",
" <td>2299.00, 0.451</td>\n",
" <td>0.00, 0.830</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
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" <td>0.45 ± 0.03 (70)</td>\n",
" <td>0.42 ± 0.03 (61)</td>\n",
" <td>2245.00, 0.613</td>\n",
" <td>0.01, 0.921</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
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" <td>0.14 ± 0.01 (70)</td>\n",
" <td>0.12 ± 0.01 (61)</td>\n",
" <td>2423.00, 0.185</td>\n",
" <td>0.04, 0.042</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Baseline I Baseline II MWU \\\n",
"Average rate 9.82 ± 0.91 (70) 10.08 ± 1.05 (61) 2166.00, 0.888 \n",
"Gridness 0.54 ± 0.05 (70) 0.53 ± 0.05 (61) 2158.00, 0.917 \n",
"Sparsity 0.65 ± 0.02 (70) 0.67 ± 0.02 (61) 2001.00, 0.538 \n",
"Selectivity 5.25 ± 0.35 (70) 5.34 ± 0.38 (61) 2062.00, 0.738 \n",
"Information specificity 0.22 ± 0.03 (70) 0.19 ± 0.02 (61) 2329.00, 0.372 \n",
"Max rate 36.77 ± 1.96 (70) 37.61 ± 2.31 (61) 2088.00, 0.830 \n",
"Information rate 1.22 ± 0.06 (70) 1.08 ± 0.08 (61) 2501.00, 0.092 \n",
"Interspike interval cv 2.37 ± 0.09 (70) 2.28 ± 0.09 (61) 2257.00, 0.575 \n",
"In-field mean rate 15.52 ± 1.15 (70) 15.61 ± 1.32 (61) 2162.00, 0.903 \n",
"Out-field mean rate 7.09 ± 0.77 (70) 7.65 ± 0.96 (61) 2115.00, 0.928 \n",
"Burst event ratio 0.23 ± 0.01 (70) 0.21 ± 0.01 (61) 2299.00, 0.451 \n",
"Specificity 0.45 ± 0.03 (70) 0.42 ± 0.03 (61) 2245.00, 0.613 \n",
"Speed score 0.14 ± 0.01 (70) 0.12 ± 0.01 (61) 2423.00, 0.185 \n",
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"\n",
" PRS \n",
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"Average rate 0.15, 0.852 \n",
"Gridness 0.00, 0.983 \n",
"Sparsity 0.04, 0.361 \n",
"Selectivity 0.25, 0.594 \n",
"Information specificity 0.05, 0.143 \n",
"Max rate 0.58, 0.784 \n",
"Information rate 0.14, 0.151 \n",
"Interspike interval cv 0.01, 0.928 \n",
"In-field mean rate 0.87, 0.724 \n",
"Out-field mean rate 0.02, 0.986 \n",
"Burst event ratio 0.00, 0.830 \n",
"Specificity 0.01, 0.921 \n",
"Speed score 0.04, 0.042 "
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]
},
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"execution_count": 46,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = baseline_i\n",
"_base_data = baseline_ii\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
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"result['Baseline I'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline II'] = _base_data[columns].agg(summarize)\n",
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"\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_latex(output_path / \"statistics\" / \"statistics_base_i_vs_base_ii.csv\")\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Violinplot"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"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",
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"execution_count": 51,
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"metadata": {},
"outputs": [],
"source": [
"stuff = {\n",
" '': {\n",
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" 'base': gridcell_sessions.query('baseline'),\n",
" 'stim': gridcell_sessions.query('stimulated')\n",
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" },\n",
" '_11': {\n",
" 'base': baseline_i,\n",
" 'stim': stimulated_11\n",
" },\n",
" '_30': {\n",
" 'base': baseline_ii,\n",
" 'stim': stimulated_30\n",
" }\n",
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"}\n",
"\n",
"label = {\n",
" '': ['Baseline ', ' Stimulated'],\n",
" '_11': ['Baseline I ', ' 11 Hz'],\n",
" '_30': ['Baseline II ', ' 30 Hz']\n",
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"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Information rate"
]
},
{
"cell_type": "code",
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"execution_count": 67,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
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"U-test: U value 10345.0 p value 0.0555771740141912\n",
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"_11\n",
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"U-test: U value 2662.0 p value 0.08875139162540739\n",
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"_30\n",
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"U-test: U value 1609.0 p value 0.49296516393290757\n"
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]
},
{
"data": {
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:51:12 +00:00
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:51:12 +00:00
"image/png": "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
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['information_specificity'].to_numpy()\n",
" stimulated = data['stim']['information_specificity'].to_numpy()\n",
" print(key)\n",
" plt.figure()\n",
" violinplot(baseline, stimulated, xticks=label[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": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"U-test: U value 11456.0 p value 0.0002697279841506103\n",
"_11\n",
"U-test: U value 3119.0 p value 0.00020360452883018144\n",
"_30\n",
"U-test: U value 1668.0 p value 0.29814082297055944\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAS8AAAG1CAYAAACoI3L9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXe4JFd1r/1WVYeTz+Q80kia0UYZFFAiSGBMEBgBIlgGIXIwGGywsS9cA74GjPl8He5nm4t9EfjBOHBtoowkkhDK0iiNRqM9I03OMyd27gr7/rGrz+k5OrFPd1dX936fZ6ZPd1dXrQ71q7XWXnttSymFwWAwxA07agMMBoOhFox4GQyGWGLEy2AwxBIjXgaDIZYY8TIYDLHEiJfBYIglRrwMBkMsMeJlMBhiiREvg8EQS4x4GQyGWGLEy2AwxBIjXgaDIZYY8TIYDLHEiJfBYIgliagNiAtCiAuB9wIvB9YDXcAJ4EngVuD/SCkL0Vl4KkKI86SU26c8thc4HXiflPIfF7n/m4FbgENSyg3zfM2dwEuBL0gpP7OY41ft8ybg94CzgTJwv5TyVfXYdxwQQiSAM6WUO6seuwb4RXg3KaX0orCt0RjPax4IIT4PPAp8FNgA7AYeAwLgVcD/AqQQ4uLIjAwRQqwVQnwbuC1qWxqNEOJNwDeBi4BRYBf6u+kIhBC/jr543hS1LVFgPK85EEK8C/hjIAfcDHxXSulXPX8O8HXgCuB2IcS5UsoTUdga8krgN4FD0zz3ciAJHGmqRZPcBPQAJ+u0v7eEt3cD17arhzEL/w0Q0zz+IHAOQDt/Jka85ubT4e0npZT/d+qTUsodQojfQF8BVwG/A/z3Jto3b6SUz0Z8/P113uWK8Pbudj5JF4qUMg88HbUdjcaEjbMghFgKnBXefWCm7UJP63vh3csbbZdhAie8LUVqhSESjOc1O27V369F571m4rPAXwPHqh8UQnwDeCfwu+g81JeAlwAp4BngG8D/llIWp+5QCNENvBu4HrgQWAoUgf3A7cBfSCkPV21fvSDB+sp9KaUVPr+XGRL2QojnAx8GXszkgMQIsBX4+nRe50KZLmEvhNgE7EF/bmvD9/t+4NzwZU8CXwO+IaVU4Wu+gf5MK3xWCPHZ6vcabrcU7QlfD2xBX6z3oQdY/kJKeUr4XDUI8W/oPObfosOvIeArUsq/rPqMk8AN4f4vADzgIeCzUsr7hBC96LDurcBGdE7uduAPq7+zqmNvBj4CXIv+jnqBMeAJ4F/Q34E/xc4KnxZCfBr4ppTy5rkS9kKIS4GPob+LNUA2PM4/hfvwp2x/Z7jtq9Eph0+H95eg0xPfB77Y7HSJ8bxmQUqZBe4J735eCPENIcRLhBDONNselVI+JaUcmmF3F6FzEa8HDgMHgecDfwX8RAgxWL2xEGIl2tv7/9G5qnHgcXTu7Tz0CNujQojqkb570Elr0CNv91TZPyNCiA+hRep9wGq0qD4LDKJ/sN8RQnxhrv0sEgudfP9H9MjhTrQgXIHOKX6patud6Pc1Ht4/wJT3Go4OPwl8Di38ewEJbAY+AWwPT/LpeB5aaDYB29EXje1TtvlLtKhsRn9eaeAVwC+EENeiv7s/Qp9ju9AphXcAdwkheqp3JIR4fWjrx4Az0QL7NPoCci1avP+p6iXHZnj/O5kDIcQfhLa9Hf39Po4WyZcC/wf46dTfYhWvRgv0G9B5ywPAGcDHgXuFEP1zHb+eGPGam4+ir0wW+mr/S2BYCHGrEOIPhRCXCyHm8zneDAwDF0spL5BSngNchf4hvgj48ynbfwV9RX8GEFLKLVLKy6SUa9EjnHn0CfGxyguklC8CvhjePSGlfFH42IwIIbagPUYb+AywWkp5cWjfOuDfw01/P/RkGsUq4Eb0+1khpbwE7Yl9K3z+E6GgI6X8Yvi+Kp7w16vfa3gS/Ti0/z5gi5TyfCnlC9CjxT9EC9L3hBBnTGPLRcA24HQp5cXha34yZZuPAH8IrAv3ezb6opQGfooemLhcSnmmlPJ84NcBH52GeHNlJ+Fnekv4ur9Hf/4XSSkvQF9I/ibc9EYhxHnh+//xlPf/T+H7r3z30xKOzn4Z/V3/D2BV+Js6A3gZ+rd4DacKZTW/A9wBnCalPE9KuRl9MfbRIv6e2Y5fb4x4zYGU8lF0HuvuqocHgNegvYH7gSNCiD+dekWdQgC8Xkr5WNW+72NymPu9Qoh1AEKIJDq0VMDvSil3Ve9ISnk78K/h3QtqfW8hv472cLZKKb8gpZwIlaWUw8Anw7tJph/Zqid/J6X8m0rYEobSH0d/DgnghfPcz2+jhesYcJ2UcqJ8Qkp5DB3ubUN7Hp+edg/waSnlWPiaoUrIWsVtUsovV0IyKeVBtOcI+rx6j5Tyoarj/gT4VXi3uqTmxejP9ijwO2GyvfKaHNpLLIcPLfa7rnjPX5NS/rGUciJXKKX8BdqjAvgNIcR0F73jwA3V4baU8gfAf4V3r16kfQvCiNc8CMPBFwMvAD4P3Mup+bBV6JPgiSlhXDU/l1I+Ps2+70DnfGzgdeFjrpTyTPTV+9aprxFCWOjwkXCbmpFS/q2Usgd9Ek1HvurvRR1rHvxw6gNhGF7JpSyZ535eF95+U0o5Ms0+y+icFugT1ZqySYD22GbjOd8LOjQF/Zn9cprnK7mugSpbfiCl7EcXmk43YtqF9thhEZ9/6GFXLj5/Nd024cX03vDu9dNs8tPpcrPAjvB2vt9PXTAJ+wUQek2PAZ8Lvayr0XVV70AL2FnAd4Arp3n5g7Ps+gl07uDsKccrCiFWCyEuD587A52PeQE67IH6XYDKQogXAuej38dZ6Cv986q2afTFbrraNIDKzIX5/l4rNm+dZZvKcyuBZeikfIXRecyWODDNYxUPaUhKGUzzfOWCN1UskVIWhBAXoUPWM9Gf/3no7yMZbraYz7/ymeSllDtm2W4rOp0xnZddr++nLhjxqpHQvf8JOtn+39FJ5bcBVwghLpZSPjLlJcNT91FFNryduHIJIdagcyC/wak/2jxaCBPoXNmiEUK8Ex0Cr53y1B50Evd99TjOPCjP8fxzTvoZqHg2Y7NsM171dz+nitd8pnnlZnluOuGaESHEa9B5x81TnjqMzjm+hsmLVa1UPpPxWbeafH665Hu9vp+6YMLGWRBCfFUIsSschp6R8Cr9fia/3OmuWr2z7KIyunM8PG4X8HO06z4K/Gn4twAGpJTXAnfN933MRihc30AL123AB9Ae5bIwdP3tehynyWTC25lGzeBUMcjMuFWDCUcmf4gWrvvRA0TXoBP366WUb2d+YjoXlfc4MOtWk59LZJ/JfDGe1+x0o39U1zOZ7JwWKWVGCJFFhyDT1bucP8vLLwpvK8Px16PrizzgiqkJ+5B5TYaeB38U3v6TlPKd0zxfr+M0k6fRofslTI6WTuXS8HZklvKWZvAptBPxc+DXp6mxSjM5k2AxVCrue4QQ58wSOlY+l+l+cy2F8bxmpzJMf2lYGDgj4STZZejw8P5pNrkuDAWnvu616CLGEpMJ68rwfWY64RJCrEYXzcJzL0CVkGW+LnzlWDPlh95b9XdcLnaVz/Gd05V3CCFS6IJc0CUVUVL5/B+fKlwhN6ELmmER37XUXSdkePfj020jhLiKyRHdqD+XOTHiNQvh8PZ/hHf/UQjxV2FF+ARCiK5w8nblCv/psLh1Kj3A94UQG6teew2TldJ/VhmaZ/IquVQI8bHq0TAhxBXoOqJlVfutpnLsZfMsGqwc6wNCiPVVxxkQQnwOXctU/R7iwN+jk8urgVura7mEEKvQgyrno0Ojz0VhYBWVz/83w0n+wMTv6iNM1nnBzN/16fM8VmXO7fuFEJ8PvbrK8a5h8rd+G/o31tLE5UoaJTeiK5xvQhdQfkwIsR9dQ9SNnnaSRue7/khK+dUZ9rMLXVG/WwixDegLXwu6Uru6wPAH6CHrq9DD2p8SQhxC56XWo+uefgr8GrBOCGFV1SE9gb4id6Hb9BwGXjlLaPRp9PSOc0PbKlfnLeE+dofHO4uYhJBSylEhxOv
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAASQAAAG1CAYAAABQw4kKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXecJFd1779V1WHybF6FVV7popwRCIEEmByETZZBCBkMNmAZhydsgY15zjxsP96z4YGfQDwHDMYGhEASSAgJoWTlsLqrlbRabZzcYTpVen/c6t3e0czshO6u6p7z/Xz2M9vd1XVPp1+dc+6551phGCIIgpAE7LgNEARBqCOCJAhCYhBBEgQhMYggCYKQGESQBEFIDCJIgiAkBhEkQRASgwiSIAiJQQRJEITEIIIkCEJiEEESBCExiCAJgpAYRJAEQUgMIkiCICSGVNwGdApKqTOADwGvBo4EeoBR4DHgBuD/aq3L8Vl4MEqpU7XWj8+4bztwDPBhrfU/LvP8VwBfA3ZprTct8Dm3ARcDf6a1/vRyxm845+XA7wAnATXgbq3165tx7k5AKZUCjtdab2247xLgp9HNtNbai8O2pSAe0gJQSv0J8CDwCWAT8AzwEBAArwf+F6CVUufEZmSEUupwpdS/ADfGbUurUUq9HbgOOBOYAp7CfDYrAqXUazEXxMvjtqVZiId0CJRSHwT+CJgGrgD+U2vtNzx+MnAt8BLgJqXUKVrr0ThsjXgd8F5g1yyPvRpIA3vaatEBLgf6gLEmne9d0d+fA6/sJE+gSfwhoGa5/17gZIBOe09EkA7NNdHf39Na//vMB7XWW5RSb8VcqTYAvwV8po32LRit9dMxj7+jyadcF/39eaf98FqJ1roEPBm3HUtBQrZ5UEqtBk6Ibt4z13GRR/Td6OYFrbZL2I8T/a3GaoXQNMRDmh+34f9vxuSR5uKPgf8J7Gu8Uyn1deADwCcxeZ2/AF4BZIBtwNeB/6O1rsw8oVKqF7gSeBtwBrAaqAA7gJuAL2itdzcc39gg/cj6ba21FT2+nTmS2kqps4DfBF7OgaT9JHA/cO1s3uFimS2prZQ6FngW874dHr3eXwdOiZ72GPAV4Ota6zB6ztcx72mdP1ZK/XHja42OW43xWN8GnIi5AD+HmYT4gtb6oNC1IVH/b5i84N9jQp9x4PNa679teI/TwDui858OeMB9wB9rre9SSvVjQqp3A0dhclw3AZ9q/Mwaxt4MfBx4JeYz6gdywCPAv2I+A3+GnXWuUUpdA1yntb7iUEltpdR5wFWYz+IwoBiN843oHP6M42+Ljn0DJty/Jrq9CpMa+B7w581IVYiHNA9a6yJwZ3TzT5RSX1dKvUIp5cxy7F6t9RNa6/E5TncmJra/FNgN7ATOAv4O+LFSarjxYKXUeoxX9r8xuZ888DAml3UqZmbpQaVU4wzXnZjELpgZpzsb7J8TpdRvYITnw8BGjFA+DQxjvoTfVkr92aHOs0wsTIL6HzEzZlsxP/KXYHJ0f9Fw7FbM68pHt59nxmuNZkUfAz6LEfPtgAY2A78LPB79cGfjRRjxOBZ4HHMheHzGMX+LEYrNmPcrC7wG+KlS6pWYz+4PML+xpzDh/PuB25VSfY0nUkpdGtl6FXA8RjSfxFwUXokR5G80PGXfHK9/K4dAKfXfItveh/l8H8YI38XA/wV+MvO72MAbMKL7y5g84PPAccBvA79QSg0eavxDIYJ0aD6BuYJYmKvyz4AJpdQNSqlPKaUuUEot5H28ApgAztFan661Phm4EPPlugj46xnHfx5z5d0GKK31iVrr87XWh2Nm9kqYL/lV9SdorS8C/jy6Oaq1vii6b06UUidiPDsb+DSwUWt9TmTfEcC3okN/P/I4WsUG4DLM61mntT4X4zH9U/T470Yijdb6z6PXVfdYr218rdEP40eR/XcBJ2qtT9Nan42ZJb0eIzLfVUodN4stZwKPAsdorc+JnvPjGcd8HPgUcER03pMwF5os8BNM8v4CrfXxWuvTgNcCPiYF8M76SaL39GvR876Eef/P1Fqfjrk4fDE69DKl1KnR6//RjNf/jej11z/7WYlmJf8K81n/d2BD9J06DngV5rt4CQeLXyO/BdwMHK21PlVrvRlzgfUxwvxr842/EESQDoHW+kFMXujnDXcPAW/EXLXvBvYopf505pVvBgFwqdb6oYZz38WBKdsPKaWOAFBKpTFhXQh8Umv9VOOJtNY3Ad+Mbp6+1NcW8VqMJ3K/1vrPtNb7w1St9QTwe9HNNLPP6DSTf9Baf7EeMkRh7G9j3ocU8OIFnudjGDHaB7xJa72/FEBrvQ8Taj2K8RCumfUMcI3WOhc9Z7weLjZwo9b6r+rhkNZ6J8bDA/O7+jWt9X0N4/4YuCO62Vge8nLMe7sX+K0oIV1/zjTGm6tFdy33s657uV/RWv+R1np/7k1r/VOM5wPwVqXUbBeyEeAdjaGu1vr7wA+jmy9bpn0iSAshCsVeDpwN/AnwCw7OL23AfLEfmRFCNXKr1vrhWc59MyaHYgNvie5ztdbHY66yN8x8jlLKwoRuRMcsGa3132ut+zA/jNkoNfx/WWMtgOtn3hGFwPXcxKoFnuct0d/rtNaTs5yzhskRgfnxWTMOCTCe1Xy84HPBhIVg3rOfzfJ4PXc01GDL97XWg5jixtlmCnswnjUs4/2PPOH6BeXvZjsmukD+Irr5tlkO+clsuU5gS/R3oZ/PnEhSexFE3s1DwGcjb+hlmLqf92NE6QTg28BLZ3n6vfOc+hFMLH7SjPEqSqmNSqkLoseOw+Q3zsaEHNC8i0pNKfVi4DTM6zgBc0V+UcMxrb6AzVY7BVCvgF/o97Vu8/3zHFN/bD2wBpO4rjO1gKr752e5r+7JjGutg1ker1/EZgogWuuyUupMTLh4POb9PxXzeaSjw5bz/tffk5LWess8x92PSSXM5g036/OZExGkJRK51j/GJKQ/g0m8vgd4iVLqHK31AzOeMjHzHA0Uo7/7rzBKqcMwOYW3cvAXsYQRtxQm97RslFIfwISfh8946FlMovPDzRhnAdQO8fgLfshzUPdAcvMck2/4/yAHC9JClgBNz/PYbGI0J0qpN2LyeJtnPLQbk8N7IwcuQEul/p7k5z3qwOOzJaib9fnMiYRs86CU+rJS6qloSnVOoqvpr3PgA5vt6tI/zynqsxoj0bg9wK0Yt3kK+NPo/woY0lq/Erh9oa9jPiIx+jpGjG4EPoLx/NZEYePHmjFOmylEf+eaLYKDf+CFOY9qMdGM3PUYMbobM4lyCSa5faTW+n0sTCAPRf01Ds171IH3JZb3RDyk+enFfFHexoGE4KxorQtKqSLG/Z+tHuO0eZ5+ZvS3PrX8Nkz9iwe8ZGZSO2JBC1oXwB9Ef7+htf7ALI83a5x28iQmbD6XA7OEMzkv+js5T6lGO7ga4xjcCrx2lhqgLAcq0pdDvXK7Tyl18jxhW/19me0713LEQ5qf+pTzeVEx2pxECx3XYEKzu2c55E1RGDbzeW/GFM5VOZDUrU9FF2YTI6XURkyhJrzwolIPFxbqPtfHmivf8qGG/3fKBaz+Pn5gtlIFpVQGUwQKpjwgTurv/8MzxSjickwRLSzjs9amG4CObv72bMcopS7kwExmLO+LCNI8RFO134lu/qNS6u+iyuL9KKV6ogW49SvxNVFB5Uz6gO8ppY5qeO4lHKi4/cv6NDMHrmarlVJXNc4CKaVegqlzWdNw3kbqY69ZYKFafayPKKWObBhnSCn1WUytTeNr6AS+hEnAbgRuaKw1UkptwEw8nIYJSz4bh4EN1N//90YLtYH936uPc6AOCeb+rI9Z4Fj1NZa/rpT6k8j7qo93CQe+6zdivmNtp1OueHFyGaZS9nJM0d5VSqkdmBqXXsyShCwmf/QHWusvz3GepzCV2c8opR4FBqLngqn4bSxq+z5m+vVCzBTt1UqpXZg8z5GYupyfAL8EHKGUshrqZB7BXDl7MC1RdgOvmycsuQZT+n9KZFv9KnpidI5novFOoEPCN631lFLqLZip+ZcC25R
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['information_rate'].to_numpy()\n",
" stimulated = data['stim']['information_rate'].to_numpy()\n",
" print(key)\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 53,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 10007.0 p value 0.1649858159393146\n",
"U-test: U value 2501.0 p value 0.32069572732432705\n",
"U-test: U value 1560.0 p value 0.6958619307501573\n"
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]
},
{
"data": {
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:51:12 +00:00
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['specificity'].to_numpy()\n",
" stimulated = data['stim']['specificity'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 54,
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"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 9188.0 p value 0.908962887905669\n",
"U-test: U value 2375.0 p value 0.6612660438729908\n",
"U-test: U value 1498.0 p value 0.985605256484472\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 255x450 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 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['average_rate'].to_numpy()\n",
" stimulated = data['stim']['average_rate'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 55,
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"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 10063.0 p value 0.1400464227005638\n",
"U-test: U value 2579.0 p value 0.18138067068099561\n",
"U-test: U value 1647.0 p value 0.3606465475361048\n"
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]
},
{
"data": {
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:51:12 +00:00
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['max_rate'].to_numpy()\n",
" stimulated = data['stim']['max_rate'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 56,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 9867.0 p value 0.24172204410041476\n",
"U-test: U value 2528.0 p value 0.2661689034616067\n",
"U-test: U value 1570.0 p value 0.6519514527439945\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 255x450 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 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['interspike_interval_cv'].to_numpy()\n",
" stimulated = data['stim']['interspike_interval_cv'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 57,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 9702.0 p value 0.3607186855527281\n",
"U-test: U value 2486.0 p value 0.35394001431515965\n",
"U-test: U value 1571.0 p value 0.6476229630232442\n"
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]
},
{
"data": {
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:51:12 +00:00
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['in_field_mean_rate'].to_numpy()\n",
" stimulated = data['stim']['in_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 58,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 9035.0 p value 0.902799635194732\n",
"U-test: U value 2314.0 p value 0.8653706776265208\n",
"U-test: U value 1502.0 p value 0.9664203618429744\n"
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]
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAT0AAAG1CAYAAACLeCMoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXmcZGdV//++t5ZeZiYzISFBEkggCU9YAwhiZIsQQAFXVmUxoMiOoogKfGX5KRhRgyIYQQREVBCBAAFZAoSsZE8mk8wzM5l9732p7W7P74/n3u6anqrqruq6tZ53Mq/bVXc7Xd330+c55zzncYwxCIIgDAtutw0QBEHoJCJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFSJ6giAMFdluGyCsDaXUCPBa4FeAJwIPBiJgH3A98Dmt9Y0p3v+xWuttaV2/xv0U8DfAM4ANwHHgRVrrrWs4dxPw18CvA6cDM8D74mv9DvBFrfWr12nfucCe+OUFWutdazzvEuBH8cuc1jpYjx3tQCmVBR6ptd7RbVs6gYheH6CUej7wr8DD4rcmgW3AZuB84NHAG5RSXwFer7VeaOO9HwX8I7ARKxqpo5TaCPwQeChQAu7FCt+eRudV8Z/AiwETnwuwlw7Z30/Ev1v/CHwF+4dh4BHR63GUUq8F/g3IANcB7wWu11qbeP8m4A3AXwAvBR6rlHqO1vpom0z4beAFwA1tut5aeCZW8Azws1rr+9d6olJqA/Ci+OWbtdb/UrVvG9YDnGujrf3OewDVbSM6iYheD6OUejLwKazgXQm8VWsdVR8Te3V/r5T6LvA9rNf3b8ALO2xuOzk93h5rRvBiHgQ48dc/rt6htT4CHFmfaUK/I4mM3uajwAhwK/C2lYJXTRxve2P88peVUq/pgH1pkYm3lXWc2+r5woDjyBoZvYlS6rEsx6NerrX+nzWedyNwMXCr1vrnqt7fC5wDvEFr/a81zvscNsj/ea31ZSsC9dXs01qfu+ZvxF77ucBbgF8ATsMOL28DPq21/mrVcZewHORfyeu01p9b5T71fpmT7+lz1ElkKKUywKvj/U/ExhAPA98HPqq13rni+HNpkMiIv5c/Ap6Kjb3eC1yB9TSbSmQopT4AvB+4HLg53p4bX+vPtNb/HR93PvA24BexP+sN2M/6HuC/gH/TWofxsZcBn61xu89rrS+ruvcpwB8Av4mNH7vAbuCrwBVa69nV7O81xNPrXS6NtxHw3SbOS0TkKUqpM9Zx/zI2jncgfj0fv761mYsopT4O/AD70OSAuwAP+CXgf5VSX1JK5eLD5+J7JAJTiV/fABxbw+1uwIppwm3xew2zknHi5P+Az2EFowhsxQr0G4C7lVK/uYb7J9f7U2wi5lewz9g24FHYBMv713qdGjwbm3A4FbgfG/e8M77nr2GF9Q+AR2Kz+tuB0fh7+hTw71XXOob9bObj1wdY8VkppS4E7gY+BDwe+0dgJ3AhNoZ8V3xMXyGi17s8Md7u1VrPNzzyRO6Mtw72F7UltNZHtdbPwMYHAbZqrZ+htX7ZWq+hlPpjrOcRAG8FztBa/5zW+izg5cBivP3b+J53xvf8cHyJo/E9n6G1/s4abH4GUG3fy+JzP1zvnJhPY//IbAOeprU+W2v9FOCM2JYx4ItKqcet4Xt+OjZZAvAu4Ge01k8FHgJ8HLhktWs04OeBbwAP01o/EThLa62VUqdivbYR4J+BM7XWF2mtHw+cic3OAvx2PIJAa/2d+PNKfl/+vfqzihNC38R6lFcBD9daq/i+DwOuxnqT31BKja3je+o4Inq9SxLMn2ryvGqP6MFtsqVplFKjLJdA/IXW+pPVMcl4uP6G+OVb4+Fix1FKXQS8EuvdvUBrfUuyT2td1lq/F/gy1mP6f2u45Hvj7ee11n+XfM/xtd6B9QDXw7u01pX4mhPxe8/EetFHgXdorYtV30MB+GOsdw1r/0P4e9jh7B3AS7TWh6uueRT7x2UfcAFwWavfTDcQ0etdRuOt1/Cok6mOETl1j0qfZwJbsPZ8os4xXwIOYZMPL+6QXSv5jXj7Y631oTrHfCHe/nIc+6uJUmoceE788nN1DruyaQuXOaK13r3yTa31N7TWm7AFxrVihKPAdPz1+BrvlXwu/53EAVfcs4QdaoMdxvcNUrLSuyQe3oOaPO/0qq8n6h6VPkmsZ2e94bnW2iil7gDOonu1YsmQ9SlKqevrHJMM3zZhbd1f57hzsENMWE5CreSupi1cpmG5jda6FHuuF2HjeucBj8V+j0ncdK2OTvK5vCGOF9biIfG2r+J6Inq9y93AK4DzlVLj1UOWVXhS1derTtlqFaXU/wA/U2PXkTjud0r8erVC4EQQN63hnh/nxO9viTg+1Qqb4+0Z8b/V2EJ90Tu16uvFOsfMrNGuWpTq7VBKvRD4B+yQtJrD2OH5C1fYtxrJ53JB/K8RW5q4btcR0etdvoUNouewMwzWVLKCnW8KcLvWulbGs96Qd0Nz5vFUrGezkn3xNpkKt7nGMdUkD+Japs49Hnj6Go5rhkK8/Tut9bvWea3q+Osp1Pa02x70V0r9Ijbp4GJLWr6I/YN3v9b6eHzMIZoTvQL2Z/crWutvtdfi7iKi16NorbcqpX6ELTd4r1LqKq11w/ieUupZLMeU/mnF7iTWM0JtHtqkfeeucsj2eHuBUuqUWkNcpZTLsue2c+X+Gve8pBkb14iOt4+td4BS6jTsEO4AcCCZAliD/dhSn1Hs9/W9GsfUvc86+FOs4P0QeP7KGFzcrOL0Wic2QAM/h7W3pugppS7ACuO+qqRKzyOJjN7m97F/cS8C/mWVIPp5wH/EL78PfH7FIZPx9qT4i1LqbOApdS6dZFybTYpcjx3KZbHlKrV4JXaIbLB1ct3gm/H2UqXUo+sc89fY7+fHNPgc4uD+t+OXb65z2BvqvL8eHhFv766VdMB258nHX690dOr9fJPP5fdqlaTEnVmuwtZt/m3TFncREb0eJq7yfz22SPcy4IdKqYurj1FKbVBK/T52WPMw4AHgtTW8kSRI/ztxLVly/gXA11l+KFaSxKbOin/R12p7EfhI/PJDSqm3xJ5dct+XAEkzgE91q62R1vp6bPF3Fvi2UuoXkn1KqRGl1Puw5RsAlzeaChjzfmzG/deVUpcrpfLxtXJKqQ9hi7TbTeJV/1a1cCulRpVSb2O5Tg9Ozt4mP9+VoYpPYBMn52Nr8R5edd0HY+OEj8Z+r30lejK87XG01l9WSu3D/pI9C7hRKXUcO5Qaw/5SJkPWL2OnmdXKln4MO83qTOB6pdR98fsXYj2yK4B31jgvKV49B9iplDoCPL3BEK+av8V6IW/GPkQfVErtBs5meTj9FeAP13CtNHkVttj2acANSqk92BKP81gO0l9R3bGlHlrre5VSr8cWdb8bm/3chc2mngZ8jeVykHbxIeB52GzqVqXUDuwfyvOxLcEmsMPVi7CffTV3YktOXqWUegJwndb6rVrrGaXUr2I9vkuBPfHvTITNtI9gQya/tZYeh72EeHp9gNb6p9hpTL+HHT6FLJclPICt/bpYa/2KBuUhB7FD2CuxsakkHvMZ4AnAfXXO+xHwJ9gExVlYETtzjXYbrfVbgOdjvcmQ5RjeN4Ff11q/TGtdXsv10kJrPYWtK3wjcC1W6C4CfOzn/Wta6z9q4npfxArof2GLnp+ALRx+O3aaWFvRWt8e2/sf2J/TefG/B7DJsMdhM7sAL1JKVQ9lL8f2apzC/o49oeq6t8Xnfgg7f/dcrHd3DDul7Wer5073C9JwQBCEoUI8PUEQhgoRPUEQhgpJZAh9TVVfuC8Bvwv8GbZzyznYcp8bgb/RWl+34rwLsYmG57C8FsdubAzvH5KiXmHwEE9PGBS2YMt23ofNWN6HnWXyYuBHSqlk3Qzisp/bgNex3ODzEHbGx3uAO5RSD0MYSET0hEHhBdhZBy/QWp+ltX4yNtN8D7aLy19VHXsFVhD/EXi
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"text/plain": [
"<Figure size 255x450 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 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['out_field_mean_rate'].to_numpy()\n",
" stimulated = data['stim']['out_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 59,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 9680.0 p value 0.37898378591633064\n",
"U-test: U value 2243.0 p value 0.889674708636679\n",
"U-test: U value 1748.0 p value 0.12812146204516903\n"
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]
},
{
"data": {
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:51:12 +00:00
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['burst_event_ratio'].to_numpy()\n",
" stimulated = data['stim']['burst_event_ratio'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 60,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 10084.0 p value 0.13148065660819572\n",
"U-test: U value 2542.0 p value 0.2405633131179855\n",
"U-test: U value 1693.0 p value 0.23374014039208268\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 255x450 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 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['max_field_mean_rate'].to_numpy()\n",
" stimulated = data['stim']['max_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 61,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 9791.0 p value 0.2925898167186858\n",
"U-test: U value 2282.0 p value 0.9771645493355854\n",
"U-test: U value 1775.0 p value 0.09219506786209755\n"
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]
},
{
"data": {
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:51:12 +00:00
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['bursty_spike_ratio'].to_numpy()\n",
" stimulated = data['stim']['bursty_spike_ratio'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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",
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"execution_count": 62,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 10712.0 p value 0.012944498763933892\n",
"U-test: U value 2728.0 p value 0.046298262379147186\n",
"U-test: U value 1647.0 p value 0.3606465475361048\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 255x450 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 255x450 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 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['gridness'].to_numpy()\n",
" stimulated = data['stim']['gridness'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" plt.title(\"Gridness\")\n",
" plt.ylabel(\"Gridness\")\n",
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" plt.ylim(-0.6, 1.5)\n",
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"\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",
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"execution_count": 63,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"U-test: U value 10435.0 p value 0.03994154034871453\n",
"U-test: U value 2611.0 p value 0.13955942364664278\n",
"U-test: U value 1654.0 p value 0.338955005854493\n"
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]
},
{
"data": {
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
2019-10-17 17:51:12 +00:00
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2019-10-16 05:28:13 +00:00
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"for key, data in stuff.items(): #TODO narrow broad spiking\n",
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" baseline = data['base']['speed_score'].to_numpy()\n",
" stimulated = data['stim']['speed_score'].to_numpy()\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key])\n",
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" 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\")"
]
},
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{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# fig, (ax1, ax2) = plt.subplots(2,1, figsize=(6,6), sharey=True)\n",
"# for key, data in stuff.items():\n",
"# ax1.set_title('Baseline')\n",
"# peak_rate = data['base']['max_rate'].to_numpy()\n",
"# spacing = data['base']['spacing'].to_numpy()\n",
"# ax1.scatter(spacing, peak_rate)\n",
" \n",
"# ax2.set_title('Stim')\n",
"# peak_rate = data['stim']['max_rate'].to_numpy()\n",
"# spacing = data['stim']['spacing'].to_numpy()\n",
"# ax2.scatter(spacing, peak_rate, label=key)\n",
" \n",
"# ax2.legend()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register in Expipe"
]
},
{
"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"comparisons-gridcells\")"
]
},
{
"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
"outputs": [
{
"data": {
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" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_11.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_base_i_vs_base_ii.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_30.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_base_i_vs_base_ii.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_11_vs_30.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_11_vs_30.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_11.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/spatial_information.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/specificity_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/bursty_spike_ratio_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/burst_event_ratio.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score_11.svg',\n",
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" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/out_field_mean_rate_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_field_mean_rate_30.png',\n",
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" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/out_field_mean_rate_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/in_field_mean_rate_11.svg',\n",
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" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/specificity_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/bursty_spike_ratio_11.png',\n",
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" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/isi_cv_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/average_rate_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/burst_event_ratio_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_field_mean_rate_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/spatial_information.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_rate_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/in_field_mean_rate_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/average_rate_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_rate.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/bursty_spike_ratio.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/spatial_information_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/isi_cv_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/in_field_mean_rate.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/average_rate_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/average_rate_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/isi_cv.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/in_field_mean_rate_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/average_rate.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_rate.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_field_mean_rate.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/bursty_spike_ratio_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/in_field_mean_rate.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/out_field_mean_rate.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_rate_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/specificity.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/in_field_mean_rate_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/out_field_mean_rate_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/bursty_spike_ratio_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/burst_event_ratio_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/isi_cv_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/bursty_spike_ratio.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/out_field_mean_rate.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/gridness_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/average_rate.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/spatial_information_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_field_mean_rate_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_rate_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/burst_event_ratio.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/gridness.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/gridness_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/spatial_information_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_rate_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/gridness.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_field_mean_rate.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/specificity.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/burst_event_ratio_30.png']"
]
},
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"execution_count": 41,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
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"execution_count": 42,
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"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
}
