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{
"cells": [
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"13:03:48 [I] klustakwik KlustaKwik2 version 0.2.6\n",
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"/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",
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"execution_count": 4,
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"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",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
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" <td>28.810794</td>\n",
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" </tr>\n",
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" </tr>\n",
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"<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",
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"0 NaN False baseline ii ... 0.398230 \n",
"1 NaN False baseline ii ... 0.138014 \n",
"2 NaN False baseline ii ... 0.373986 \n",
"3 NaN False baseline ii ... 0.087413 \n",
"4 NaN False baseline ii ... 0.248771 \n",
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"\n",
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" bursty_spike_ratio gridness border_score information_rate \\\n",
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"0 0.678064 -0.466923 0.029328 1.009215 \n",
"1 0.263173 -0.666792 0.308146 0.192524 \n",
"2 0.659259 -0.572566 0.143252 4.745836 \n",
"3 0.179245 -0.437492 0.268948 0.157394 \n",
"4 0.463596 -0.085938 0.218744 0.519153 \n",
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"\n",
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" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
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"0 0.317256 5.438033 0.040874 0.628784 \n",
"1 0.033447 1.951740 0.017289 0.789388 \n",
"2 0.393704 4.439721 0.124731 0.555402 \n",
"3 0.073553 6.215195 0.101911 0.492250 \n",
"4 0.032683 1.531481 0.053810 0.559905 \n",
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"\n",
" orientation \n",
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"0 20.224859 \n",
"1 27.897271 \n",
"2 28.810794 \n",
"3 9.462322 \n",
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"4 0.000000 \n",
"\n",
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"[5 rows x 39 columns]"
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]
},
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"execution_count": 5,
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"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",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
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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",
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"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 "
]
},
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"execution_count": 6,
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"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",
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"execution_count": 7,
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"metadata": {},
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" <td>0.344280</td>\n",
" <td>0.215829</td>\n",
" <td>6.033364</td>\n",
" <td>0.110495</td>\n",
" <td>0.239996</td>\n",
" <td>0.054074</td>\n",
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" <td>0.269461</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</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",
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" <td>0.053810</td>\n",
" <td>0.559905</td>\n",
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" <td>0.000000</td>\n",
" <td>0.342799</td>\n",
" <td>0.218967</td>\n",
" <td>5.768170</td>\n",
" <td>0.054762</td>\n",
" <td>0.524990</td>\n",
" <td>0.144702</td>\n",
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" <td>0.133410</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
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"<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",
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"0 NaN False baseline ii ... 0.040874 0.628784 \n",
"1 NaN False baseline ii ... 0.017289 0.789388 \n",
"2 NaN False baseline ii ... 0.124731 0.555402 \n",
"3 NaN False baseline ii ... 0.101911 0.492250 \n",
"4 NaN False baseline ii ... 0.053810 0.559905 \n",
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"\n",
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" orientation border_score_threshold gridness_threshold \\\n",
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"0 20.224859 0.332548 0.229073 \n",
"1 27.897271 0.354830 0.089333 \n",
"2 28.810794 0.264610 -0.121081 \n",
"3 9.462322 0.344280 0.215829 \n",
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"4 0.000000 0.342799 0.218967 \n",
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"\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",
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" information_rate_threshold speed_score_threshold specificity \n",
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"0 1.115825 0.066736 0.451741 \n",
"1 0.223237 0.052594 0.098517 \n",
"2 4.964984 0.027120 0.400770 \n",
"3 0.239996 0.054074 0.269461 \n",
"4 0.524990 0.144702 0.133410 \n",
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"\n",
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"[5 rows x 46 columns]"
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]
},
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"execution_count": 7,
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"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",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulated\n",
"False 624\n",
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"True 660\n",
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"Name: action, dtype: int64"
]
},
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"execution_count": 8,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('stimulated').count()['action']"
]
},
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{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [],
"source": [
"data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find all cells with gridness above threshold"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Number of sessions above threshold 194\n",
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"Number of animals 4\n"
]
}
],
"source": [
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"query = (\n",
" 'gridness > gridness_threshold and '\n",
" 'information_rate > information_rate_threshold and '\n",
" 'gridness > .2 and '\n",
" 'average_rate < 25'\n",
")\n",
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"sessions_above_threshold = data.query(query)\n",
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"print(\"Number of sessions above threshold\", len(sessions_above_threshold))\n",
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"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [],
"source": [
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"gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
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]
},
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{
"cell_type": "code",
2019-12-13 10:43:57 +00:00
"execution_count": 12,
2019-10-22 10:22:00 +00:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells 139\n",
"Number of gridcell recordings 231\n",
"Number of animals 4\n"
]
}
],
"source": [
"print(\"Number of gridcells\", gridcell_sessions.unit_idnum.nunique())\n",
"print(\"Number of gridcell recordings\", len(gridcell_sessions))\n",
"print(\"Number of animals\", len(gridcell_sessions.groupby(['entity'])))"
]
},
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{
"cell_type": "code",
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"execution_count": 13,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Number of gridcells in baseline i sessions 66\n",
"Number of gridcells in stimulated 11Hz ms sessions 61\n",
"Number of gridcells in baseline ii sessions 56\n",
"Number of gridcells in stimulated 30Hz ms sessions 40\n"
2019-10-16 05:28:13 +00:00
]
}
],
"source": [
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"baseline_i = gridcell_sessions.query('baseline and Hz11')\n",
"stimulated_11 = gridcell_sessions.query('frequency==11 and stim_location==\"ms\"')\n",
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"\n",
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"baseline_ii = gridcell_sessions.query('baseline and Hz30')\n",
"stimulated_30 = gridcell_sessions.query('frequency==30 and stim_location==\"ms\"')\n",
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"\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",
2019-12-13 10:43:57 +00:00
"execution_count": 14,
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"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",
2019-12-13 10:43:57 +00:00
"execution_count": 15,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Number of gridcells in baseline i sessions 63\n",
"Number of gridcells in stimulated 11Hz ms sessions 58\n",
"Number of gridcells in baseline ii sessions 52\n",
"Number of gridcells in stimulated 30Hz ms sessions 38\n"
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",
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"execution_count": 16,
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"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",
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"execution_count": 17,
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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",
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" }\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",
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" <td>8.904501</td>\n",
" <td>0.521371</td>\n",
" <td>0.618384</td>\n",
" <td>5.934539</td>\n",
" <td>0.234632</td>\n",
" <td>37.437808</td>\n",
" <td>1.246546</td>\n",
" <td>2.404647</td>\n",
" <td>14.717635</td>\n",
" <td>6.346875</td>\n",
" <td>0.211840</td>\n",
" <td>0.478775</td>\n",
" <td>0.135495</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
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" <td>8.392252</td>\n",
" <td>0.440296</td>\n",
" <td>0.655698</td>\n",
" <td>5.977408</td>\n",
" <td>0.215736</td>\n",
" <td>33.716478</td>\n",
" <td>0.964787</td>\n",
" <td>2.223636</td>\n",
" <td>12.936021</td>\n",
" <td>6.122228</td>\n",
" <td>0.197264</td>\n",
" <td>0.455878</td>\n",
" <td>0.104697</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"stimulated \n",
2019-10-22 10:22:00 +00:00
"False 8.904501 0.521371 0.618384 5.934539 \n",
"True 8.392252 0.440296 0.655698 5.977408 \n",
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"\n",
" information_specificity max_rate information_rate \\\n",
"stimulated \n",
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"False 0.234632 37.437808 1.246546 \n",
"True 0.215736 33.716478 0.964787 \n",
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"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"stimulated \n",
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"False 2.404647 14.717635 6.346875 \n",
"True 2.223636 12.936021 6.122228 \n",
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"\n",
" burst_event_ratio specificity speed_score \n",
"stimulated \n",
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"False 0.211840 0.478775 0.135495 \n",
"True 0.197264 0.455878 0.104697 "
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]
},
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"execution_count": 17,
2019-10-16 05:28:13 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"gridcell_sessions.groupby('stimulated')[columns].mean()"
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]
},
{
"cell_type": "code",
2019-12-13 10:43:57 +00:00
"execution_count": 18,
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\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",
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" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
" <td>129.000000</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
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" <td>8.904501</td>\n",
" <td>0.521371</td>\n",
" <td>0.618384</td>\n",
" <td>5.934539</td>\n",
" <td>0.234632</td>\n",
" <td>37.437808</td>\n",
" <td>1.246546</td>\n",
" <td>2.404647</td>\n",
" <td>14.717635</td>\n",
" <td>6.346875</td>\n",
" <td>0.211840</td>\n",
" <td>0.478775</td>\n",
" <td>0.135495</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
2019-10-22 10:22:00 +00:00
" <td>7.605598</td>\n",
" <td>0.337607</td>\n",
" <td>0.187934</td>\n",
" <td>3.217366</td>\n",
" <td>0.200726</td>\n",
" <td>16.300117</td>\n",
" <td>0.605971</td>\n",
" <td>0.756407</td>\n",
" <td>9.267522</td>\n",
" <td>6.805499</td>\n",
" <td>0.080143</td>\n",
" <td>0.209531</td>\n",
" <td>0.072831</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
2019-10-22 10:22:00 +00:00
" <td>0.478349</td>\n",
" <td>-0.684924</td>\n",
" <td>0.200066</td>\n",
" <td>1.533216</td>\n",
" <td>0.007807</td>\n",
" <td>3.346027</td>\n",
" <td>0.117638</td>\n",
" <td>1.304387</td>\n",
" <td>0.924066</td>\n",
" <td>0.159076</td>\n",
" <td>0.025000</td>\n",
" <td>0.071681</td>\n",
" <td>-0.025629</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
2019-10-22 10:22:00 +00:00
" <td>3.518392</td>\n",
" <td>0.316326</td>\n",
" <td>0.437499</td>\n",
" <td>3.729863</td>\n",
" <td>0.093252</td>\n",
" <td>26.948843</td>\n",
" <td>0.786753</td>\n",
" <td>1.872991</td>\n",
" <td>7.701156</td>\n",
" <td>1.669844</td>\n",
" <td>0.160795</td>\n",
" <td>0.310822</td>\n",
" <td>0.084280</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
2019-10-22 10:22:00 +00:00
" <td>6.456882</td>\n",
" <td>0.529243</td>\n",
" <td>0.642167</td>\n",
" <td>4.794970</td>\n",
" <td>0.180286</td>\n",
" <td>35.064991</td>\n",
" <td>1.156087</td>\n",
" <td>2.221185</td>\n",
" <td>12.212289</td>\n",
" <td>4.314913</td>\n",
" <td>0.210240</td>\n",
" <td>0.436340</td>\n",
" <td>0.128603</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
2019-10-22 10:22:00 +00:00
" <td>12.721755</td>\n",
" <td>0.783682</td>\n",
" <td>0.758097</td>\n",
" <td>7.439464</td>\n",
" <td>0.312487</td>\n",
" <td>44.324873</td>\n",
" <td>1.592948</td>\n",
" <td>2.770624</td>\n",
" <td>20.974026</td>\n",
" <td>9.121505</td>\n",
" <td>0.267568</td>\n",
" <td>0.624834</td>\n",
" <td>0.188948</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
2019-10-22 10:22:00 +00:00
" <td>59.365312</td>\n",
" <td>1.148979</td>\n",
" <td>0.976157</td>\n",
" <td>18.975875</td>\n",
" <td>1.243307</td>\n",
" <td>90.160158</td>\n",
" <td>3.456796</td>\n",
" <td>5.671362</td>\n",
" <td>66.350754</td>\n",
" <td>56.255544</td>\n",
" <td>0.393306</td>\n",
" <td>1.066391</td>\n",
" <td>0.297548</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
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"count 129.000000 129.000000 129.000000 129.000000 \n",
"mean 8.904501 0.521371 0.618384 5.934539 \n",
"std 7.605598 0.337607 0.187934 3.217366 \n",
"min 0.478349 -0.684924 0.200066 1.533216 \n",
"25% 3.518392 0.316326 0.437499 3.729863 \n",
"50% 6.456882 0.529243 0.642167 4.794970 \n",
"75% 12.721755 0.783682 0.758097 7.439464 \n",
"max 59.365312 1.148979 0.976157 18.975875 \n",
2019-10-16 05:28:13 +00:00
"\n",
" information_specificity max_rate information_rate \\\n",
2019-10-22 10:22:00 +00:00
"count 129.000000 129.000000 129.000000 \n",
"mean 0.234632 37.437808 1.246546 \n",
"std 0.200726 16.300117 0.605971 \n",
"min 0.007807 3.346027 0.117638 \n",
"25% 0.093252 26.948843 0.786753 \n",
"50% 0.180286 35.064991 1.156087 \n",
"75% 0.312487 44.324873 1.592948 \n",
"max 1.243307 90.160158 3.456796 \n",
2019-10-16 05:28:13 +00:00
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
2019-10-22 10:22:00 +00:00
"count 129.000000 129.000000 129.000000 \n",
"mean 2.404647 14.717635 6.346875 \n",
"std 0.756407 9.267522 6.805499 \n",
"min 1.304387 0.924066 0.159076 \n",
"25% 1.872991 7.701156 1.669844 \n",
"50% 2.221185 12.212289 4.314913 \n",
"75% 2.770624 20.974026 9.121505 \n",
"max 5.671362 66.350754 56.255544 \n",
2019-10-16 05:28:13 +00:00
"\n",
" burst_event_ratio specificity speed_score \n",
2019-10-22 10:22:00 +00:00
"count 129.000000 129.000000 129.000000 \n",
"mean 0.211840 0.478775 0.135495 \n",
"std 0.080143 0.209531 0.072831 \n",
"min 0.025000 0.071681 -0.025629 \n",
"25% 0.160795 0.310822 0.084280 \n",
"50% 0.210240 0.436340 0.128603 \n",
"75% 0.267568 0.624834 0.188948 \n",
"max 0.393306 1.066391 0.297548 "
2019-10-16 05:28:13 +00:00
]
},
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"execution_count": 18,
2019-10-16 05:28:13 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
2019-10-17 17:51:12 +00:00
"gridcell_sessions.query('baseline')[columns].describe()"
2019-10-16 05:28:13 +00:00
]
},
{
"cell_type": "code",
2019-12-13 10:43:57 +00:00
"execution_count": 19,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"data": {
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"<div>\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",
2019-10-22 10:22:00 +00:00
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
" <td>102.000000</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
2019-10-22 10:22:00 +00:00
" <td>8.392252</td>\n",
" <td>0.440296</td>\n",
" <td>0.655698</td>\n",
" <td>5.977408</td>\n",
" <td>0.215736</td>\n",
" <td>33.716478</td>\n",
" <td>0.964787</td>\n",
" <td>2.223636</td>\n",
" <td>12.936021</td>\n",
" <td>6.122228</td>\n",
" <td>0.197264</td>\n",
" <td>0.455878</td>\n",
" <td>0.104697</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
2019-10-22 10:22:00 +00:00
" <td>6.057001</td>\n",
" <td>0.357038</td>\n",
" <td>0.211704</td>\n",
" <td>3.702400</td>\n",
" <td>0.235916</td>\n",
" <td>13.249312</td>\n",
" <td>0.572972</td>\n",
" <td>0.819734</td>\n",
" <td>7.211895</td>\n",
" <td>5.366332</td>\n",
" <td>0.082164</td>\n",
" <td>0.236777</td>\n",
" <td>0.081989</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
2019-10-22 10:22:00 +00:00
" <td>0.198337</td>\n",
" <td>-0.516914</td>\n",
" <td>0.172684</td>\n",
" <td>1.930026</td>\n",
" <td>0.013088</td>\n",
" <td>2.846281</td>\n",
" <td>0.063173</td>\n",
" <td>1.110672</td>\n",
" <td>0.524639</td>\n",
" <td>0.099060</td>\n",
" <td>0.008475</td>\n",
" <td>0.097718</td>\n",
" <td>-0.138128</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
2019-10-22 10:22:00 +00:00
" <td>3.579184</td>\n",
" <td>0.265949</td>\n",
" <td>0.458493</td>\n",
" <td>3.044303</td>\n",
" <td>0.066656</td>\n",
" <td>25.555110</td>\n",
" <td>0.564279</td>\n",
" <td>1.620472</td>\n",
" <td>7.555760</td>\n",
" <td>1.733624</td>\n",
" <td>0.146755</td>\n",
" <td>0.248057</td>\n",
" <td>0.056903</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
2019-10-22 10:22:00 +00:00
" <td>6.838561</td>\n",
" <td>0.399053</td>\n",
" <td>0.699561</td>\n",
" <td>4.891855</td>\n",
" <td>0.128562</td>\n",
" <td>31.402558</td>\n",
" <td>0.862413</td>\n",
" <td>2.084020</td>\n",
" <td>11.451560</td>\n",
" <td>4.234871</td>\n",
" <td>0.192948</td>\n",
" <td>0.376143</td>\n",
" <td>0.106314</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
2019-10-22 10:22:00 +00:00
" <td>11.934599</td>\n",
" <td>0.749561</td>\n",
" <td>0.842332</td>\n",
" <td>8.001587</td>\n",
" <td>0.300713</td>\n",
" <td>42.334786</td>\n",
" <td>1.190324</td>\n",
" <td>2.673991</td>\n",
" <td>17.335356</td>\n",
" <td>8.583415</td>\n",
" <td>0.247405</td>\n",
" <td>0.684623</td>\n",
" <td>0.149313</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
2019-10-22 10:22:00 +00:00
" <td>24.858738</td>\n",
" <td>1.155123</td>\n",
" <td>0.967003</td>\n",
" <td>19.911477</td>\n",
" <td>1.359164</td>\n",
" <td>65.990793</td>\n",
" <td>3.182285</td>\n",
" <td>6.526960</td>\n",
" <td>34.489913</td>\n",
" <td>21.696265</td>\n",
" <td>0.393037</td>\n",
" <td>1.091064</td>\n",
" <td>0.390079</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
2019-10-17 17:51:12 +00:00
" average_rate gridness sparsity selectivity \\\n",
2019-10-22 10:22:00 +00:00
"count 102.000000 102.000000 102.000000 102.000000 \n",
"mean 8.392252 0.440296 0.655698 5.977408 \n",
"std 6.057001 0.357038 0.211704 3.702400 \n",
"min 0.198337 -0.516914 0.172684 1.930026 \n",
"25% 3.579184 0.265949 0.458493 3.044303 \n",
"50% 6.838561 0.399053 0.699561 4.891855 \n",
"75% 11.934599 0.749561 0.842332 8.001587 \n",
"max 24.858738 1.155123 0.967003 19.911477 \n",
2019-10-16 05:28:13 +00:00
"\n",
" information_specificity max_rate information_rate \\\n",
2019-10-22 10:22:00 +00:00
"count 102.000000 102.000000 102.000000 \n",
"mean 0.215736 33.716478 0.964787 \n",
"std 0.235916 13.249312 0.572972 \n",
"min 0.013088 2.846281 0.063173 \n",
"25% 0.066656 25.555110 0.564279 \n",
"50% 0.128562 31.402558 0.862413 \n",
"75% 0.300713 42.334786 1.190324 \n",
"max 1.359164 65.990793 3.182285 \n",
2019-10-16 05:28:13 +00:00
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
2019-10-22 10:22:00 +00:00
"count 102.000000 102.000000 102.000000 \n",
"mean 2.223636 12.936021 6.122228 \n",
"std 0.819734 7.211895 5.366332 \n",
"min 1.110672 0.524639 0.099060 \n",
"25% 1.620472 7.555760 1.733624 \n",
"50% 2.084020 11.451560 4.234871 \n",
"75% 2.673991 17.335356 8.583415 \n",
"max 6.526960 34.489913 21.696265 \n",
2019-10-16 05:28:13 +00:00
"\n",
" burst_event_ratio specificity speed_score \n",
2019-10-22 10:22:00 +00:00
"count 102.000000 102.000000 102.000000 \n",
"mean 0.197264 0.455878 0.104697 \n",
"std 0.082164 0.236777 0.081989 \n",
"min 0.008475 0.097718 -0.138128 \n",
"25% 0.146755 0.248057 0.056903 \n",
"50% 0.192948 0.376143 0.106314 \n",
"75% 0.247405 0.684623 0.149313 \n",
"max 0.393037 1.091064 0.390079 "
2019-10-16 05:28:13 +00:00
]
},
2019-12-13 10:43:57 +00:00
"execution_count": 19,
2019-10-16 05:28:13 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
2019-10-17 17:51:12 +00:00
"gridcell_sessions.query(\"stimulated\")[columns].describe()"
2019-10-16 05:28:13 +00:00
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create nice table"
]
},
{
"cell_type": "code",
2019-10-17 17:51:12 +00:00
"execution_count": 19,
2019-10-16 05:28:13 +00:00
"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",
2019-10-22 10:22:00 +00:00
"execution_count": 20,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<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",
2019-10-17 17:51:12 +00:00
" <th>Stimulated</th>\n",
2019-10-16 05:28:13 +00:00
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
2019-12-13 10:43:57 +00:00
" <td>8.61 ± 0.75 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>8.39 ± 0.60 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3599.00, 0.947</td>\n",
" <td>0.55, 0.764</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.51 ± 0.04 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>0.44 ± 0.04 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3208.00, 0.203</td>\n",
" <td>0.13, 0.141</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.61 ± 0.02 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>0.66 ± 0.02 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>4170.00, 0.091</td>\n",
" <td>0.06, 0.179</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
2019-12-13 10:43:57 +00:00
" <td>5.91 ± 0.37 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>5.98 ± 0.37 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3460.00, 0.620</td>\n",
" <td>0.10, 0.869</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
2019-12-13 10:43:57 +00:00
" <td>0.25 ± 0.03 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>0.22 ± 0.02 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>2944.00, 0.037</td>\n",
" <td>0.05, 0.033</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
2019-12-13 10:43:57 +00:00
" <td>36.55 ± 1.78 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>33.72 ± 1.31 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3291.00, 0.309</td>\n",
" <td>3.19, 0.195</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>1.30 ± 0.07 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>0.96 ± 0.06 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>2385.00, 0.000</td>\n",
" <td>0.32, 0.000</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
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" <td>2.42 ± 0.10 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>2.22 ± 0.08 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3034.00, 0.070</td>\n",
" <td>0.12, 0.392</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
2019-12-13 10:43:57 +00:00
" <td>14.43 ± 1.00 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>12.94 ± 0.71 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3368.00, 0.436</td>\n",
" <td>0.39, 0.812</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
2019-12-13 10:43:57 +00:00
" <td>6.05 ± 0.62 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>6.12 ± 0.53 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3600.00, 0.950</td>\n",
" <td>0.08, 0.946</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
2019-12-13 10:43:57 +00:00
" <td>0.22 ± 0.01 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>0.20 ± 0.01 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3090.00, 0.102</td>\n",
" <td>0.02, 0.124</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
2019-12-13 10:43:57 +00:00
" <td>0.48 ± 0.03 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>0.46 ± 0.02 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>3268.00, 0.277</td>\n",
" <td>0.06, 0.356</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
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" <td>0.14 ± 0.01 (71)</td>\n",
2019-10-22 10:22:00 +00:00
" <td>0.10 ± 0.01 (102)</td>\n",
2019-12-13 10:43:57 +00:00
" <td>2546.00, 0.001</td>\n",
" <td>0.05, 0.000</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Baseline I Stimulated \\\n",
"Average rate 8.61 ± 0.75 (71) 8.39 ± 0.60 (102) \n",
"Gridness 0.51 ± 0.04 (71) 0.44 ± 0.04 (102) \n",
"Sparsity 0.61 ± 0.02 (71) 0.66 ± 0.02 (102) \n",
"Selectivity 5.91 ± 0.37 (71) 5.98 ± 0.37 (102) \n",
"Information specificity 0.25 ± 0.03 (71) 0.22 ± 0.02 (102) \n",
"Max rate 36.55 ± 1.78 (71) 33.72 ± 1.31 (102) \n",
"Information rate 1.30 ± 0.07 (71) 0.96 ± 0.06 (102) \n",
"Interspike interval cv 2.42 ± 0.10 (71) 2.22 ± 0.08 (102) \n",
"In-field mean rate 14.43 ± 1.00 (71) 12.94 ± 0.71 (102) \n",
"Out-field mean rate 6.05 ± 0.62 (71) 6.12 ± 0.53 (102) \n",
"Burst event ratio 0.22 ± 0.01 (71) 0.20 ± 0.01 (102) \n",
"Specificity 0.48 ± 0.03 (71) 0.46 ± 0.02 (102) \n",
"Speed score 0.14 ± 0.01 (71) 0.10 ± 0.01 (102) \n",
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"\n",
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" MWU PRS \n",
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"Average rate 3599.00, 0.947 0.55, 0.764 \n",
"Gridness 3208.00, 0.203 0.13, 0.141 \n",
"Sparsity 4170.00, 0.091 0.06, 0.179 \n",
"Selectivity 3460.00, 0.620 0.10, 0.869 \n",
"Information specificity 2944.00, 0.037 0.05, 0.033 \n",
"Max rate 3291.00, 0.309 3.19, 0.195 \n",
"Information rate 2385.00, 0.000 0.32, 0.000 \n",
"Interspike interval cv 3034.00, 0.070 0.12, 0.392 \n",
"In-field mean rate 3368.00, 0.436 0.39, 0.812 \n",
"Out-field mean rate 3600.00, 0.950 0.08, 0.946 \n",
"Burst event ratio 3090.00, 0.102 0.02, 0.124 \n",
"Specificity 3268.00, 0.277 0.06, 0.356 \n",
"Speed score 2546.00, 0.001 0.05, 0.000 "
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]
},
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"execution_count": 20,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"_stim_data = gridcell_sessions.query('stimulated')\n",
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"_base_data = gridcell_sessions.query('baseline and i')\n",
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"\n",
"result = pd.DataFrame()\n",
"\n",
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"result['Baseline I'] = _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",
2019-10-22 10:22:00 +00:00
"execution_count": 21,
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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",
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" <th>Baseline I</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>8.96 ± 0.80 (63)</td>\n",
" <td>8.80 ± 0.85 (58)</td>\n",
" <td>1781.00, 0.813</td>\n",
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" <td>0.04, 0.972</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.53 ± 0.05 (63)</td>\n",
" <td>0.41 ± 0.05 (58)</td>\n",
" <td>1459.00, 0.057</td>\n",
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" <td>0.21, 0.043</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.63 ± 0.02 (63)</td>\n",
" <td>0.67 ± 0.03 (58)</td>\n",
" <td>2138.00, 0.107</td>\n",
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" <td>0.07, 0.119</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
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" <td>5.76 ± 0.40 (63)</td>\n",
" <td>5.69 ± 0.50 (58)</td>\n",
" <td>1687.00, 0.469</td>\n",
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" <td>0.00, 0.982</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
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" <td>0.24 ± 0.03 (63)</td>\n",
" <td>0.21 ± 0.03 (58)</td>\n",
" <td>1452.00, 0.052</td>\n",
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" <td>0.06, 0.032</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
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" <td>37.39 ± 1.91 (63)</td>\n",
" <td>33.11 ± 1.85 (58)</td>\n",
" <td>1538.00, 0.134</td>\n",
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" <td>4.06, 0.122</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>1.31 ± 0.08 (63)</td>\n",
" <td>0.94 ± 0.08 (58)</td>\n",
" <td>1143.00, 0.000</td>\n",
" <td>0.32, 0.003</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
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" <td>2.39 ± 0.10 (63)</td>\n",
" <td>2.19 ± 0.12 (58)</td>\n",
" <td>1462.00, 0.059</td>\n",
" <td>0.18, 0.135</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
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" <td>14.88 ± 1.05 (63)</td>\n",
" <td>13.27 ± 1.04 (58)</td>\n",
" <td>1633.00, 0.315</td>\n",
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" <td>0.77, 0.688</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
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" <td>6.37 ± 0.67 (63)</td>\n",
" <td>6.57 ± 0.77 (58)</td>\n",
" <td>1795.00, 0.870</td>\n",
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" <td>0.47, 0.724</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 (63)</td>\n",
" <td>0.22 ± 0.01 (58)</td>\n",
" <td>1897.00, 0.718</td>\n",
" <td>0.00, 0.824</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
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" <td>0.47 ± 0.03 (63)</td>\n",
" <td>0.44 ± 0.03 (58)</td>\n",
" <td>1605.00, 0.250</td>\n",
" <td>0.06, 0.398</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
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" <td>0.14 ± 0.01 (63)</td>\n",
" <td>0.11 ± 0.01 (58)</td>\n",
" <td>1378.00, 0.020</td>\n",
" <td>0.04, 0.023</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Baseline I 11 Hz MWU \\\n",
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"Average rate 8.96 ± 0.80 (63) 8.80 ± 0.85 (58) 1781.00, 0.813 \n",
"Gridness 0.53 ± 0.05 (63) 0.41 ± 0.05 (58) 1459.00, 0.057 \n",
"Sparsity 0.63 ± 0.02 (63) 0.67 ± 0.03 (58) 2138.00, 0.107 \n",
"Selectivity 5.76 ± 0.40 (63) 5.69 ± 0.50 (58) 1687.00, 0.469 \n",
"Information specificity 0.24 ± 0.03 (63) 0.21 ± 0.03 (58) 1452.00, 0.052 \n",
"Max rate 37.39 ± 1.91 (63) 33.11 ± 1.85 (58) 1538.00, 0.134 \n",
"Information rate 1.31 ± 0.08 (63) 0.94 ± 0.08 (58) 1143.00, 0.000 \n",
"Interspike interval cv 2.39 ± 0.10 (63) 2.19 ± 0.12 (58) 1462.00, 0.059 \n",
"In-field mean rate 14.88 ± 1.05 (63) 13.27 ± 1.04 (58) 1633.00, 0.315 \n",
"Out-field mean rate 6.37 ± 0.67 (63) 6.57 ± 0.77 (58) 1795.00, 0.870 \n",
"Burst event ratio 0.22 ± 0.01 (63) 0.22 ± 0.01 (58) 1897.00, 0.718 \n",
"Specificity 0.47 ± 0.03 (63) 0.44 ± 0.03 (58) 1605.00, 0.250 \n",
"Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (58) 1378.00, 0.020 \n",
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"\n",
" PRS \n",
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"Average rate 0.04, 0.972 \n",
"Gridness 0.21, 0.043 \n",
"Sparsity 0.07, 0.119 \n",
"Selectivity 0.00, 0.982 \n",
"Information specificity 0.06, 0.032 \n",
"Max rate 4.06, 0.122 \n",
2019-10-22 10:22:00 +00:00
"Information rate 0.32, 0.003 \n",
"Interspike interval cv 0.18, 0.135 \n",
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"In-field mean rate 0.77, 0.688 \n",
"Out-field mean rate 0.47, 0.724 \n",
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"Burst event ratio 0.00, 0.824 \n",
"Specificity 0.06, 0.398 \n",
"Speed score 0.04, 0.023 "
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]
},
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"execution_count": 21,
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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",
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"result['Baseline I'] = _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",
2019-10-22 10:22:00 +00:00
"execution_count": 22,
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>Baseline II</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>8.29 ± 0.87 (52)</td>\n",
" <td>7.61 ± 0.87 (38)</td>\n",
" <td>958.00, 0.810</td>\n",
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" <td>0.27, 0.800</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.54 ± 0.04 (52)</td>\n",
" <td>0.48 ± 0.06 (38)</td>\n",
" <td>914.00, 0.548</td>\n",
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" <td>0.04, 0.601</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.63 ± 0.03 (52)</td>\n",
" <td>0.64 ± 0.03 (38)</td>\n",
" <td>1040.00, 0.674</td>\n",
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" <td>0.06, 0.393</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
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" <td>5.96 ± 0.46 (52)</td>\n",
" <td>6.42 ± 0.60 (38)</td>\n",
" <td>1019.00, 0.803</td>\n",
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" <td>0.20, 0.847</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
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" <td>0.21 ± 0.02 (52)</td>\n",
" <td>0.22 ± 0.03 (38)</td>\n",
" <td>950.00, 0.759</td>\n",
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" <td>0.04, 0.502</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
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" <td>36.27 ± 2.34 (52)</td>\n",
" <td>33.49 ± 1.89 (38)</td>\n",
" <td>943.00, 0.716</td>\n",
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" <td>2.90, 0.555</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>1.13 ± 0.08 (52)</td>\n",
" <td>0.98 ± 0.09 (38)</td>\n",
" <td>827.00, 0.190</td>\n",
2019-12-13 10:43:57 +00:00
" <td>0.07, 0.334</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
2019-10-22 10:22:00 +00:00
" <td>2.37 ± 0.09 (52)</td>\n",
" <td>2.23 ± 0.11 (38)</td>\n",
" <td>869.00, 0.333</td>\n",
2019-12-13 10:43:57 +00:00
" <td>0.17, 0.476</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
2019-10-22 10:22:00 +00:00
" <td>13.79 ± 1.12 (52)</td>\n",
" <td>12.21 ± 0.98 (38)</td>\n",
" <td>912.00, 0.537</td>\n",
2019-12-13 10:43:57 +00:00
" <td>1.06, 0.455</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
2019-10-22 10:22:00 +00:00
" <td>5.80 ± 0.72 (52)</td>\n",
" <td>5.36 ± 0.73 (38)</td>\n",
" <td>959.00, 0.816</td>\n",
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" <td>0.13, 0.912</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
2019-10-22 10:22:00 +00:00
" <td>0.20 ± 0.01 (52)</td>\n",
" <td>0.16 ± 0.01 (38)</td>\n",
" <td>676.00, 0.011</td>\n",
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" <td>0.05, 0.006</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
2019-10-22 10:22:00 +00:00
" <td>0.47 ± 0.03 (52)</td>\n",
" <td>0.48 ± 0.04 (38)</td>\n",
" <td>976.00, 0.925</td>\n",
2019-12-13 10:43:57 +00:00
" <td>0.00, 0.987</td>\n",
2019-10-16 05:28:13 +00:00
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
2019-10-22 10:22:00 +00:00
" <td>0.12 ± 0.01 (52)</td>\n",
" <td>0.11 ± 0.01 (38)</td>\n",
" <td>784.00, 0.096</td>\n",
2019-12-13 10:43:57 +00:00
" <td>0.01, 0.230</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
2019-12-13 10:43:57 +00:00
" Baseline II 30 Hz MWU \\\n",
2019-10-22 10:22:00 +00:00
"Average rate 8.29 ± 0.87 (52) 7.61 ± 0.87 (38) 958.00, 0.810 \n",
"Gridness 0.54 ± 0.04 (52) 0.48 ± 0.06 (38) 914.00, 0.548 \n",
"Sparsity 0.63 ± 0.03 (52) 0.64 ± 0.03 (38) 1040.00, 0.674 \n",
"Selectivity 5.96 ± 0.46 (52) 6.42 ± 0.60 (38) 1019.00, 0.803 \n",
"Information specificity 0.21 ± 0.02 (52) 0.22 ± 0.03 (38) 950.00, 0.759 \n",
"Max rate 36.27 ± 2.34 (52) 33.49 ± 1.89 (38) 943.00, 0.716 \n",
"Information rate 1.13 ± 0.08 (52) 0.98 ± 0.09 (38) 827.00, 0.190 \n",
"Interspike interval cv 2.37 ± 0.09 (52) 2.23 ± 0.11 (38) 869.00, 0.333 \n",
"In-field mean rate 13.79 ± 1.12 (52) 12.21 ± 0.98 (38) 912.00, 0.537 \n",
"Out-field mean rate 5.80 ± 0.72 (52) 5.36 ± 0.73 (38) 959.00, 0.816 \n",
"Burst event ratio 0.20 ± 0.01 (52) 0.16 ± 0.01 (38) 676.00, 0.011 \n",
"Specificity 0.47 ± 0.03 (52) 0.48 ± 0.04 (38) 976.00, 0.925 \n",
"Speed score 0.12 ± 0.01 (52) 0.11 ± 0.01 (38) 784.00, 0.096 \n",
2019-10-16 05:28:13 +00:00
"\n",
" PRS \n",
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"Average rate 0.27, 0.800 \n",
"Gridness 0.04, 0.601 \n",
"Sparsity 0.06, 0.393 \n",
"Selectivity 0.20, 0.847 \n",
"Information specificity 0.04, 0.502 \n",
"Max rate 2.90, 0.555 \n",
"Information rate 0.07, 0.334 \n",
"Interspike interval cv 0.17, 0.476 \n",
"In-field mean rate 1.06, 0.455 \n",
"Out-field mean rate 0.13, 0.912 \n",
"Burst event ratio 0.05, 0.006 \n",
"Specificity 0.00, 0.987 \n",
"Speed score 0.01, 0.230 "
2019-10-16 05:28:13 +00:00
]
},
2019-10-22 10:22:00 +00:00
"execution_count": 22,
2019-10-16 05:28:13 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_30\n",
"_base_data = baseline_ii\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
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"result['Baseline II'] = _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",
2019-10-22 10:22:00 +00:00
"execution_count": 23,
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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",
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" <th>Baseline I</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>8.96 ± 0.80 (63)</td>\n",
" <td>7.61 ± 0.87 (38)</td>\n",
" <td>1081.00, 0.418</td>\n",
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" <td>0.27, 0.806</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
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" <td>0.53 ± 0.05 (63)</td>\n",
" <td>0.48 ± 0.06 (38)</td>\n",
" <td>1094.00, 0.472</td>\n",
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" <td>0.08, 0.361</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
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" <td>0.63 ± 0.02 (63)</td>\n",
" <td>0.64 ± 0.03 (38)</td>\n",
" <td>1261.00, 0.656</td>\n",
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" <td>0.03, 0.638</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
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" <td>5.76 ± 0.40 (63)</td>\n",
" <td>6.42 ± 0.60 (38)</td>\n",
" <td>1276.00, 0.582</td>\n",
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" <td>0.86, 0.293</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
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" <td>0.24 ± 0.03 (63)</td>\n",
" <td>0.22 ± 0.03 (38)</td>\n",
" <td>1076.00, 0.398</td>\n",
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" <td>0.05, 0.165</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
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" <td>37.39 ± 1.91 (63)</td>\n",
" <td>33.49 ± 1.89 (38)</td>\n",
" <td>1027.00, 0.235</td>\n",
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" <td>3.99, 0.188</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
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" <td>1.31 ± 0.08 (63)</td>\n",
" <td>0.98 ± 0.09 (38)</td>\n",
" <td>797.00, 0.005</td>\n",
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" <td>0.32, 0.047</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
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" <td>2.39 ± 0.10 (63)</td>\n",
" <td>2.23 ± 0.11 (38)</td>\n",
" <td>1100.00, 0.499</td>\n",
" <td>0.01, 0.993</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
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" <td>14.88 ± 1.05 (63)</td>\n",
" <td>12.21 ± 0.98 (38)</td>\n",
" <td>1018.00, 0.211</td>\n",
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" <td>1.74, 0.276</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
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" <td>6.37 ± 0.67 (63)</td>\n",
" <td>5.36 ± 0.73 (38)</td>\n",
" <td>1079.00, 0.410</td>\n",
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" <td>0.51, 0.631</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 (63)</td>\n",
" <td>0.16 ± 0.01 (38)</td>\n",
" <td>675.00, 0.000</td>\n",
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" <td>0.05, 0.003</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
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" <td>0.47 ± 0.03 (63)</td>\n",
" <td>0.48 ± 0.04 (38)</td>\n",
" <td>1206.00, 0.952</td>\n",
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" <td>0.01, 0.873</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
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" <td>0.14 ± 0.01 (63)</td>\n",
" <td>0.11 ± 0.01 (38)</td>\n",
" <td>835.00, 0.011</td>\n",
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" <td>0.06, 0.004</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Baseline I 30 Hz MWU \\\n",
"Average rate 8.96 ± 0.80 (63) 7.61 ± 0.87 (38) 1081.00, 0.418 \n",
"Gridness 0.53 ± 0.05 (63) 0.48 ± 0.06 (38) 1094.00, 0.472 \n",
"Sparsity 0.63 ± 0.02 (63) 0.64 ± 0.03 (38) 1261.00, 0.656 \n",
"Selectivity 5.76 ± 0.40 (63) 6.42 ± 0.60 (38) 1276.00, 0.582 \n",
"Information specificity 0.24 ± 0.03 (63) 0.22 ± 0.03 (38) 1076.00, 0.398 \n",
"Max rate 37.39 ± 1.91 (63) 33.49 ± 1.89 (38) 1027.00, 0.235 \n",
"Information rate 1.31 ± 0.08 (63) 0.98 ± 0.09 (38) 797.00, 0.005 \n",
"Interspike interval cv 2.39 ± 0.10 (63) 2.23 ± 0.11 (38) 1100.00, 0.499 \n",
"In-field mean rate 14.88 ± 1.05 (63) 12.21 ± 0.98 (38) 1018.00, 0.211 \n",
"Out-field mean rate 6.37 ± 0.67 (63) 5.36 ± 0.73 (38) 1079.00, 0.410 \n",
"Burst event ratio 0.22 ± 0.01 (63) 0.16 ± 0.01 (38) 675.00, 0.000 \n",
"Specificity 0.47 ± 0.03 (63) 0.48 ± 0.04 (38) 1206.00, 0.952 \n",
"Speed score 0.14 ± 0.01 (63) 0.11 ± 0.01 (38) 835.00, 0.011 \n",
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"\n",
" PRS \n",
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"Average rate 0.27, 0.806 \n",
"Gridness 0.08, 0.361 \n",
"Sparsity 0.03, 0.638 \n",
"Selectivity 0.86, 0.293 \n",
"Information specificity 0.05, 0.165 \n",
"Max rate 3.99, 0.188 \n",
"Information rate 0.32, 0.047 \n",
2019-10-22 10:22:00 +00:00
"Interspike interval cv 0.01, 0.993 \n",
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"In-field mean rate 1.74, 0.276 \n",
"Out-field mean rate 0.51, 0.631 \n",
"Burst event ratio 0.05, 0.003 \n",
"Specificity 0.01, 0.873 \n",
"Speed score 0.06, 0.004 "
2019-10-16 05:28:13 +00:00
]
},
2019-10-22 10:22:00 +00:00
"execution_count": 23,
2019-10-16 05:28:13 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
2019-10-22 10:22:00 +00:00
"source": [
"_stim_data = stimulated_30\n",
"_base_data = baseline_i\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline I'] = _base_data[columns].agg(summarize)\n",
"result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
"result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
"result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
"\n",
"\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_b_i_30.tex\")\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_b_i_30.csv\")\n",
"result"
]
},
{
"cell_type": "code",
2019-12-13 10:43:57 +00:00
"execution_count": 24,
2019-10-22 10:22:00 +00:00
"metadata": {},
2019-12-13 10:43:57 +00:00
"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>11 Hz</th>\n",
" <th>30 Hz</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>8.80 ± 0.85 (58)</td>\n",
" <td>7.61 ± 0.87 (38)</td>\n",
" <td>1010.00, 0.493</td>\n",
" <td>0.23, 0.893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.41 ± 0.05 (58)</td>\n",
" <td>0.48 ± 0.06 (38)</td>\n",
" <td>1259.00, 0.241</td>\n",
" <td>0.13, 0.094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.67 ± 0.03 (58)</td>\n",
" <td>0.64 ± 0.03 (38)</td>\n",
" <td>1002.00, 0.456</td>\n",
" <td>0.04, 0.561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>5.69 ± 0.50 (58)</td>\n",
" <td>6.42 ± 0.60 (38)</td>\n",
" <td>1260.00, 0.238</td>\n",
" <td>0.85, 0.335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.21 ± 0.03 (58)</td>\n",
" <td>0.22 ± 0.03 (38)</td>\n",
" <td>1231.00, 0.336</td>\n",
" <td>0.01, 0.732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>33.11 ± 1.85 (58)</td>\n",
" <td>33.49 ± 1.89 (38)</td>\n",
" <td>1136.00, 0.802</td>\n",
" <td>0.07, 0.993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>0.94 ± 0.08 (58)</td>\n",
" <td>0.98 ± 0.09 (38)</td>\n",
" <td>1171.00, 0.608</td>\n",
" <td>0.01, 0.789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.19 ± 0.12 (58)</td>\n",
" <td>2.23 ± 0.11 (38)</td>\n",
" <td>1228.00, 0.347</td>\n",
" <td>0.17, 0.328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>13.27 ± 1.04 (58)</td>\n",
" <td>12.21 ± 0.98 (38)</td>\n",
" <td>1058.00, 0.744</td>\n",
" <td>0.97, 0.637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>6.57 ± 0.77 (58)</td>\n",
" <td>5.36 ± 0.73 (38)</td>\n",
" <td>1019.00, 0.537</td>\n",
" <td>0.04, 0.958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.22 ± 0.01 (58)</td>\n",
" <td>0.16 ± 0.01 (38)</td>\n",
" <td>552.00, 0.000</td>\n",
" <td>0.06, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.44 ± 0.03 (58)</td>\n",
" <td>0.48 ± 0.04 (38)</td>\n",
" <td>1233.00, 0.328</td>\n",
" <td>0.07, 0.380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.11 ± 0.01 (58)</td>\n",
" <td>0.11 ± 0.01 (38)</td>\n",
" <td>1022.00, 0.551</td>\n",
" <td>0.02, 0.144</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 11 Hz 30 Hz MWU \\\n",
"Average rate 8.80 ± 0.85 (58) 7.61 ± 0.87 (38) 1010.00, 0.493 \n",
"Gridness 0.41 ± 0.05 (58) 0.48 ± 0.06 (38) 1259.00, 0.241 \n",
"Sparsity 0.67 ± 0.03 (58) 0.64 ± 0.03 (38) 1002.00, 0.456 \n",
"Selectivity 5.69 ± 0.50 (58) 6.42 ± 0.60 (38) 1260.00, 0.238 \n",
"Information specificity 0.21 ± 0.03 (58) 0.22 ± 0.03 (38) 1231.00, 0.336 \n",
"Max rate 33.11 ± 1.85 (58) 33.49 ± 1.89 (38) 1136.00, 0.802 \n",
"Information rate 0.94 ± 0.08 (58) 0.98 ± 0.09 (38) 1171.00, 0.608 \n",
"Interspike interval cv 2.19 ± 0.12 (58) 2.23 ± 0.11 (38) 1228.00, 0.347 \n",
"In-field mean rate 13.27 ± 1.04 (58) 12.21 ± 0.98 (38) 1058.00, 0.744 \n",
"Out-field mean rate 6.57 ± 0.77 (58) 5.36 ± 0.73 (38) 1019.00, 0.537 \n",
"Burst event ratio 0.22 ± 0.01 (58) 0.16 ± 0.01 (38) 552.00, 0.000 \n",
"Specificity 0.44 ± 0.03 (58) 0.48 ± 0.04 (38) 1233.00, 0.328 \n",
"Speed score 0.11 ± 0.01 (58) 0.11 ± 0.01 (38) 1022.00, 0.551 \n",
"\n",
" PRS \n",
"Average rate 0.23, 0.893 \n",
"Gridness 0.13, 0.094 \n",
"Sparsity 0.04, 0.561 \n",
"Selectivity 0.85, 0.335 \n",
"Information specificity 0.01, 0.732 \n",
"Max rate 0.07, 0.993 \n",
"Information rate 0.01, 0.789 \n",
"Interspike interval cv 0.17, 0.328 \n",
"In-field mean rate 0.97, 0.637 \n",
"Out-field mean rate 0.04, 0.958 \n",
"Burst event ratio 0.06, 0.000 \n",
"Specificity 0.07, 0.380 \n",
"Speed score 0.02, 0.144 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
2019-10-16 05:28:13 +00:00
"source": [
"_stim_data = stimulated_30\n",
"_base_data = stimulated_11\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
2019-10-17 17:51:12 +00:00
"result['11 Hz'] = _base_data[columns].agg(summarize)\n",
"result['30 Hz'] = _stim_data[columns].agg(summarize)\n",
"\n",
2019-10-16 05:28:13 +00:00
"\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",
2019-12-13 10:43:57 +00:00
"execution_count": 25,
2019-10-16 05:28:13 +00:00
"metadata": {},
2019-12-13 10:43:57 +00:00
"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 I</th>\n",
" <th>Baseline II</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>8.96 ± 0.80 (63)</td>\n",
" <td>8.29 ± 0.87 (52)</td>\n",
" <td>1756.00, 0.509</td>\n",
" <td>0.55, 0.674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.53 ± 0.05 (63)</td>\n",
" <td>0.54 ± 0.04 (52)</td>\n",
" <td>1664.00, 0.886</td>\n",
" <td>0.04, 0.550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.63 ± 0.02 (63)</td>\n",
" <td>0.63 ± 0.03 (52)</td>\n",
" <td>1652.00, 0.940</td>\n",
" <td>0.03, 0.648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>5.76 ± 0.40 (63)</td>\n",
" <td>5.96 ± 0.46 (52)</td>\n",
" <td>1542.00, 0.592</td>\n",
" <td>0.66, 0.360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.24 ± 0.03 (63)</td>\n",
" <td>0.21 ± 0.02 (52)</td>\n",
" <td>1718.00, 0.655</td>\n",
" <td>0.01, 0.812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>37.39 ± 1.91 (63)</td>\n",
" <td>36.27 ± 2.34 (52)</td>\n",
" <td>1757.00, 0.505</td>\n",
" <td>1.09, 0.610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.31 ± 0.08 (63)</td>\n",
" <td>1.13 ± 0.08 (52)</td>\n",
" <td>1929.00, 0.103</td>\n",
" <td>0.25, 0.140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.39 ± 0.10 (63)</td>\n",
" <td>2.37 ± 0.09 (52)</td>\n",
" <td>1572.00, 0.713</td>\n",
" <td>0.16, 0.479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>14.88 ± 1.05 (63)</td>\n",
" <td>13.79 ± 1.12 (52)</td>\n",
" <td>1763.00, 0.484</td>\n",
" <td>0.68, 0.690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>6.37 ± 0.67 (63)</td>\n",
" <td>5.80 ± 0.72 (52)</td>\n",
" <td>1737.00, 0.580</td>\n",
" <td>0.38, 0.586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.22 ± 0.01 (63)</td>\n",
" <td>0.20 ± 0.01 (52)</td>\n",
" <td>1834.00, 0.272</td>\n",
" <td>0.01, 0.681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.47 ± 0.03 (63)</td>\n",
" <td>0.47 ± 0.03 (52)</td>\n",
" <td>1588.00, 0.781</td>\n",
" <td>0.01, 0.765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.14 ± 0.01 (63)</td>\n",
" <td>0.12 ± 0.01 (52)</td>\n",
" <td>1943.00, 0.087</td>\n",
" <td>0.04, 0.017</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline I Baseline II MWU \\\n",
"Average rate 8.96 ± 0.80 (63) 8.29 ± 0.87 (52) 1756.00, 0.509 \n",
"Gridness 0.53 ± 0.05 (63) 0.54 ± 0.04 (52) 1664.00, 0.886 \n",
"Sparsity 0.63 ± 0.02 (63) 0.63 ± 0.03 (52) 1652.00, 0.940 \n",
"Selectivity 5.76 ± 0.40 (63) 5.96 ± 0.46 (52) 1542.00, 0.592 \n",
"Information specificity 0.24 ± 0.03 (63) 0.21 ± 0.02 (52) 1718.00, 0.655 \n",
"Max rate 37.39 ± 1.91 (63) 36.27 ± 2.34 (52) 1757.00, 0.505 \n",
"Information rate 1.31 ± 0.08 (63) 1.13 ± 0.08 (52) 1929.00, 0.103 \n",
"Interspike interval cv 2.39 ± 0.10 (63) 2.37 ± 0.09 (52) 1572.00, 0.713 \n",
"In-field mean rate 14.88 ± 1.05 (63) 13.79 ± 1.12 (52) 1763.00, 0.484 \n",
"Out-field mean rate 6.37 ± 0.67 (63) 5.80 ± 0.72 (52) 1737.00, 0.580 \n",
"Burst event ratio 0.22 ± 0.01 (63) 0.20 ± 0.01 (52) 1834.00, 0.272 \n",
"Specificity 0.47 ± 0.03 (63) 0.47 ± 0.03 (52) 1588.00, 0.781 \n",
"Speed score 0.14 ± 0.01 (63) 0.12 ± 0.01 (52) 1943.00, 0.087 \n",
"\n",
" PRS \n",
"Average rate 0.55, 0.674 \n",
"Gridness 0.04, 0.550 \n",
"Sparsity 0.03, 0.648 \n",
"Selectivity 0.66, 0.360 \n",
"Information specificity 0.01, 0.812 \n",
"Max rate 1.09, 0.610 \n",
"Information rate 0.25, 0.140 \n",
"Interspike interval cv 0.16, 0.479 \n",
"In-field mean rate 0.68, 0.690 \n",
"Out-field mean rate 0.38, 0.586 \n",
"Burst event ratio 0.01, 0.681 \n",
"Specificity 0.01, 0.765 \n",
"Speed score 0.04, 0.017 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = baseline_i\n",
"_base_data = baseline_ii\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Baseline I'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline II'] = _base_data[columns].agg(summarize)\n",
"\n",
"result.index = map(rename, result.index)\n",
"\n",
"result['MWU'] = list(map(lambda x: MWU(x, _stim_data, _base_data), columns))\n",
"result['PRS'] = list(map(lambda x: PRS(x, _stim_data, _base_data), columns))\n",
"\n",
"\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_base_i_vs_base_ii.tex\")\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_base_i_vs_base_ii.csv\")\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Violinplot"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (1.7, 3), \n",
" 'figure.dpi': 150\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
"# labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
"\n",
"stuff = {\n",
" '': {\n",
" 'base': gridcell_sessions.query('baseline and i'),\n",
" 'stim': gridcell_sessions.query('stimulated')\n",
" },\n",
" '_11': {\n",
" 'base': baseline_i,\n",
" 'stim': stimulated_11\n",
" },\n",
" '_30': {\n",
" 'base': baseline_ii,\n",
" 'stim': stimulated_30\n",
" }\n",
"}\n",
"\n",
"label = {\n",
" '': ['Baseline I ', ' Stimulated'],\n",
" '_11': ['Baseline I ', ' 11 Hz'],\n",
" '_30': ['Baseline II ', ' 30 Hz']\n",
"}\n",
"\n",
"colors = {\n",
" '': ['#1b9e77', '#b2182b'],\n",
" '_11': ['#1b9e77', '#d95f02'],\n",
" '_30': ['#7570b3', '#e7298a']\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Information rate"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"U-test: U value 4298.0 p value 0.036830785214103115\n",
"_11\n",
"U-test: U value 2202.0 p value 0.05201320820170774\n",
"_30\n",
"U-test: U value 1026.0 p value 0.7593436297175663\n"
]
},
{
"data": {
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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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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAG1CAYAAAALCxQ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdd5wkZZ348U91mtmZ2V0WWHJm4REkiK6ipyJiQA8VPfFUFOU8AyqiGH4GUNDTQ/QUTMCZCAqcegcnHklyWHJOu9/NOc3OTurp6VRVvz+e6pne3p7YXd0zNd/367Wv2e6urno6feup7/Otpxzf91FKKTW9xZrdAKWUUrXTYK6UUhGgwVwppSJAg7lSSkWABnOllIoADeZKKRUBGsyVUioCNJgrpVQEaDBXSqkI0GCulFIRoMFcKaUiQIO5UkpFgAZzpZSKAA3mSikVAYlmN6AaY8wxwCeBtwD7Aq1AJ/ACcDPwOxEZbF4Ld2SMebmIvFhx32rgQOBTIvLbGtd/JnAlsEFE9hvnc+4F3gT8QETOr2X7Zev8GPBl4HAgDzwiIu+ox7qnA2NMAjhERJaW3XcicE9wMykixWa0bSYxxhwErApuHiYiy8seSwIXAB8B9gF6gcuChy8AFonIG2rY9pmM8ls0xhwBLBGRhs8tPuV65saY7wJPA18A9gNWAs8AHvAO4BeAGGNe2bRGBowxextjrgNua3ZbwmaMeT9wNXAs0AMsw342M4Ix5u3YzsTHmt0WNaqfAucBB2ED/gZgddgbNcbMMcb8EngOiIe9vWqmVM/cGPMvwHeAAeBM4EYRccsePwL4PfBa4HZjzJEi0tmMtgZOBj6M/cJUeguQBDY1tEXDPga0AdvqtL5/Dv4+CLx5BvZAvwWYKvc/BhwBMAPfk2bZQPCeA2sqHit9T38oIt8s3WmM2R34LyBT47ZvBB4BChX3vxL4fI3rrsmUCubYPSrAV0XkvysfFJHFxpj3YHtIewDnAN9uYPvGTURWNHn7a+u8yt2Dvw9q0BomIhlgSbPbMZOISIGR3/PS9/Teiudsow4dGxHpxaZuppwpk2YxxswDDg1uPjrSckFP/H+Dm8eH3S41pHTomGtqK5QaXSmmzbjvqTNVrgFqjOkA+oOb3xGRfxtl2b2AXYEtItJVdv9VwMeBc7F57IuAE4AUsBy4CvhPEclWWecs4BPAe4FjgHlAFlgL3A78REQ2li1f9Y0TESd4fDUjDIAaY14BfA54I8MDvN3Ak8DvK49K6jUAWjZwtAXYO3i9nwaODJ72AvBr4KrSAE7Zezriaw2Wm4c9UnovcBj2R7UGO2D9ExHZId1U9pr+hB0H+RX20LkL+LGIXFL2HieB04L1Hw0UgceBC0TkYWNMOzYN8kFgf2xO/3bgG+WfWdm2FwBnA2/Gfkbt2N7Wc8D12M/ArWhnpatF5MyxBkCNMQuBL2I/i72AdLCda4J1uBXL3xss+05siu684PYu2PTCX4F/n2h60RjzYezn/SqgA/sePQX8AbheRLyyZUuv+a/Yz/77wPuA3YD1wE3Yz2jzCNs6BPgq8HbsuFcWeB475nJl5Wsue96x2FRFqfBhMGjj5eW/iWoDoGW/t0r3iciJxpgLGWUA1BjzbmzRxauwR/3bgAeAH4nIk1Xem6Hf4ijbPhj7fXot8AsROWeE130e9j2+TUTeWW2Z8ZgyPXMRSQOLgpvfNcZcZYw5wRiz02CCiGwWkZfKA3mFY7G5zFOBjdgv4CuAS4E7jDFzyxc2xszHHg38EvtF6gOexebuX46t4HjaGFMeSBdhBwHBVnYsKmv/iIwxn8UG7U8Be2J3MiuAudgf8F+MMT8Yaz01crA/rN9iK1OWYgPka7FjEheVLbsU+7r6gtvrqHitQfXRC8CF2B3hakCABcBXgBeDoFfNy7CB9yDgRexO9MWKZS7B/igWYN+vFuBtwD3GmDdjP7tvYr/Py7A/xjOA+40xbeUrMsacGrT1i8Ah2B3OEuwO9c3Yndk1ZU/ZMsLrX8oYjDH/L2jbR7Gf77PYncabgN8Bd1Z+F8u8E7vDeh82sKzDBocvAQ8ZY2aPtf2ydvwUuA54K8Pf7QJ2zOeP2E5ONXOwYyRnAy6wGBu0vgw8aYw5usq2/gn7/n4WW02yBFuJ9gbgN9ixro4qz/tc8Ho/BcwP1pEGTmJ8v4nH2fH390Jw+/nRnmSMiRtjrsHuoN6DPQJ9DpiFzb8/YowZK8A+HmyvpPT7yGJ/TwAfCqqhqikNqlfrNIzblAnmgS9gP0AH2yO4D9hujLnZGPMNY8zxxpjxtPlMYDvwShE5WkSOAP4B+8N8A/CjiuV/jO3xLQeMiBwmIq8Wkb2xFTQZbID4YukJwd7934ObnSLyhrFKnowxhwE/w77v5wN7isgrg/btA/w5WPRrQU83LHsAp2Nfz+4i8ipsT/2PweNfCXZwiMi/B6/r6eCx35e/1iCo3Bq0/2FsT+koETkO2yv7GzZA/68x5uAqbTkW+4M7UEReGTznjoplzga+AewTrPdw7E66BbgTO9B7vIgcIiJHYXuELjZt94HSSoL39MrgeZdj3/9jReRo7I7158GipxtjXh68/lsrXv81wesvffZVBdU/F2M/638D9gi+UwdjA9QW4ER23HGUOwf4O3CAiLxcRBZgOycudqf2r6Ntv6wdR2CPVLPYgeuDgnbsi/2NecAZxpjXVnn6m7FHWaeLSOnzORj7Oe8DXF8eoIKe9XXYHeP3gd1E5BUichh2gHAZtrN0eUUb/wF7dJYEfhi8VwtFZH/s0YQHfMsY87aRXqeIfKDi9/eF4HP6whhv0dewO/4MtphhHxFZiP09XI4dV/zzaL9HEfkANnaVnBhsezPDg67zsbFkB8aY12G/z93YI6FJm1LBXESexubBHyy7ew7wj9je4iPAJmPM9yt7XBU84FQReaZs3Q8zvAf8pDFmHxiqSz0B8IFzRWRZ+YpE5HbsBwI24Nfi7dge8JMi8oNgIKe0ne3YQ1OwX+pqlRP1dJmI/Lx0yBuknr6EfR8SwGvGuZ7PY3/YW4BTRGSoXFFEtmDTI89je6bnVV0DnBcMLCEiXVVqdG8TkYtLKQwRWY89sgD7Hf5XEXm8bLt3YA+RwQaRkjdi39vNwDnB4GXpOQPYo4h8cFetn3WpJ/lrEfmOiAzlcEXkHmyPG+A9xphqnYCtwGnl6SkRuQm4Jbj5+nG245jhp8u95Q+IyDXYgHU9dgdXzddE5Pqy52zA7lR6sUetp5Ute2Gwnl+IyLel7FyQ4Lf9fuzO6CPGmCPLnvdt7Of4ZxH5ZnkaVESuxB7FgA3sdWOMSWE7CWCLLv6r9N0L2nA29gizg+EqmQkRkX6glCI6o8oipRTmdeXfkcmYUsEcIEifvBE4Dvgu8BA7lgHtgQ0Kz1WkPcrdLSLPVln337G5thjw7uC+gogcgu3d3Vz5HGOMg023ECwzaSLyKxFpwwaVasrLpmra1jj8rfKOIG1VysXuMs71vDv4e7WIdFdZZx7b6wIbuJyKRTxsT280O30uDNcOZ7BHcJVKufI5ZW25SURmY0/8qVaR04o9ooMa3v/gCKy0M7602jJB5+Kh4OZ7qyxyp1QZ28GmOmD8n0+pc3KsMeY/graVt+NsETldRKq9h2lsaoSK53QCNwQ3TwUwxrRgU0MwfIRX+bznseeMOMC7gue1YY9UAP5zhNdwPvb9rHeN/wnYTkaOKqkmseMIpwAHYNNvk1VKtbynPK0WvGcfDG7WlGKBqVeaOCToVT8DXBh84K/H5vjOwAb0Q4G/AK+r8vTHRln1c9hDxcMrtpc1xuxpjDk+eOxgbD73OGyaAOq388sbY14DHIV9HYdie4IvK1sm7B1ttdp4sINOMP7vRqnNT46yTOmx+diB6/Kxjh4Z+2zedVXuK/Wgu6Rs8K5MqQNQufNARAaDlMCx2Lz5odhe5lHYnjvU9v6X3pOMiCweZbknsem/akdhdfl8ROQpY8y12DMiv4JNoa0G7sKOVdwW9B6reW6EHQrY3xEM/44OY7h3f5kxZqReZmmgsPQ
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for key, dd in stuff.items():\n",
" baseline = dd['base']['information_specificity'].to_numpy()\n",
" stimulated = dd['stim']['information_specificity'].to_numpy()\n",
" print(key)\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[key])\n",
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" 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",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"U-test: U value 4857.0 p value 0.00013747228542563089\n",
"_11\n",
"U-test: U value 2511.0 p value 0.0003908085167909787\n",
"_30\n",
"U-test: U value 1149.0 p value 0.18980799435841422\n"
]
},
{
"data": {
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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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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAASQAAAG1CAYAAABQw4kKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOy9d5wkV3nv/a2qTpM3a4NWu6sNRxJCWQjllYQQIKLBBmOC4MqATc5guKTXgLlcLO71a+DFBOHX4MtrG5mgSJIACQlZEouQtGfjzIbZOLmnu6u6qs77x6menZ2dPB2qu8/389nPbHdXVz3dVf2r84TzHEsphcFgMMQBu9YGGAwGQwkjSAaDITYYQTIYDLHBCJLBYIgNRpAMBkNsMIJkMBhigxEkg8EQG4wgGQyG2GAEyWAwxAYjSAaDITYYQTIYDLHBCJLBYIgNRpAMBkNsMIJkMBhiQ6LWBtQLQojzgFuBG4A1QAY4BvwRuBP4ppQyXzsLT0YI8Swp5VMTnusG1gF/KaX8xgL3fwvwbeCglPL0Wb7nfuBa4LNSyo8v5Pjj9vkG4H3AFsADHpZSvqAc+64HhBAJ4Ewp5Y5xz20Ffhk9TEop/VrYNh/MCGkWCCE+DTwBvBM4HdgD/B4IgRcA/wBIIcRFNTMyQgixSgjxPeCeWttSaYQQrwS+A5wPDAI70eemKRBCPB99Q3xDrW0pF2aENANCiDcBnwBGgVuAO6SUwbjXzwa+BTwXuFcIcY6U8lgtbI24Cfhz4OAkr90AJIFDVbXoBG8AWoHjZdrfn0V/fwNcV08jgTLxN4CY5PnfAWcD1Nt3YgRpZj4W/f2AlPLfJ74opXxGCPFS9J1qBfAu4L9X0b5ZI6XcXePj7yvzLpdFf39Tbz+8SiKlzAHba23HfDAu2zQIIRYDG6OHj0y1XTQi+s/o4WWVtsswhhP9dWtqhaFsmBHS9BTH/f/F6DjSVHwS+F/AkfFPCiFuB94IvBcd1/k8cA2QAnYBtwP/j5SyMHGHQogW4M3Ay4HzgMVAAdgH3At8SUrZO2778Q3S15QeSymt6PVupghqCyEuAP4auJoTQfsB4DHgW5ONDufKZEFtIcR6YC/6e1sVfd63AOdEb/sj8HXgdimlit5zO/o7LfFJIcQnx3/WaLvF6BHry4HN6BtwDzoJ8SUp5Umu67hA/ffRccF/RLs+fcAXpZS3jfuOk8Crov0/G/CBR4FPSil/K4RoQ7tUrwbWomNc9wIfGX/Oxh17E/AO4Dr0OWoDhoA/AP+KPgfBBDtLfEwI8THgO1LKW2YKagshLgHejT4XK4FsdJx/jvYRTNj+/mjbF6Ld/Y9FjxehQwM/BD5XjlCFGSFNg5QyCzwYPfy0EOJ2IcQ1Qghnkm0PSymfllL2TbG789G+/cuAXuAAcAHwZeCnQoiu8RsLIZajR2X/Nzr2MwxsQ8eynoXOLD0hhBif4XoQHdgFnXF6cJz9UyKE+Cu08PwlcBpaKHcDXeiL8N+EEJ+daT8LxEIHqL+BzpjtQP/In4uO0X1+3LY70J9rOHq8nwmfNcqK/hH4FFrMuwEJbALeDzwV/XAn4yy0eKwHnkLfCJ6asM1taKHYhP6+0sCNwC+FENehz91H0b+xnWh3/vXAr4QQreN3JIR4WWTru4Ez0aK5HX1TuA4tyP887i1Hpvj8O5gBIcSHIttehz6/29DCdy3wTeBnE6/FcbwQLbqvQMcB9wMbgPcADwkhOmY6/kwYQZqZd6LvIBb6rvwA0C+EuFMI8REhxGVCiNl8j7cA/cBFUspnSynPBq5AX1xXAf9jwvZfRN95dwFCSrlZSnmplHIVOrOXQ1/k7y69QUp5FfC56OExKeVV0XNTIoTYjB7Z2cDHgdOklBdF9q0G/r9o0w9GI45KsQJ4LfrzLJNSXoweMf1L9Pr7I5FGSvm56HOVRqzfGv9Zox/G3ZH9vwU2SynPlVJeiM6S/hgtMv8phNgwiS3nA08C66SUF0Xv+emEbd4BfARYHe13C/pGkwZ+hg7eXyalPFNKeS7wfCBAhwD+tLST6Dv9dvS+r6K///OllM9G3xz+d7Tpa4UQz4o+/90TPv8/R5+/dO4nJcpKfgF9rv8vYEV0TW0Arkdfi1s5WfzG8y7gPuAMKeWzpJSb0DfYAC3M/226488GI0gzIKV8Ah0X+s24pzuBF6Hv2g8Dh4QQfzvxzjeBEHiZlPL34/b9W06kbG8VQqwGEEIk0W6dAt4rpdw5fkdSynuB/xM9fPZ8P1vE89EjkceklJ+VUo65qVLKfuAD0cMkk2d0yslXpJT/u+QyRG7se9DfQwJ4ziz383a0GB0BbpZSjpUCSCmPoF2tJ9EjhI9Nugf4mJRyKHpPX8ldHMc9UsovlNwhKeUB9AgP9O/qv0kpHx133J8Cv44eji8PuRr93R4G3hUFpEvvGUWP5rzoqYWe69Io9+tSyk9IKcdib1LKX6JHPgAvFUJMdiM7CrxqvKsrpfwRcFf08MoF2mcEaTZErtjVwIXAp4GHODm+tAJ9Yf9hggs1nl9IKbdNsu/70DEUG3hJ9FxRSnkm+i5758T3CCEstOtGtM28kVL+o5SyFf3DmIzcuP8v6Fiz4McTn4hc4FJsYtEs9/OS6O93pJQDk+zTQ8eIQP/4rAmbhOiR1XSccl7QbiHo7+yBSV4vxY46x9nyIyllB7q4cbJMYQY9soYFfP/RSLh0Q/nyZNtEN8iHoocvn2STn00W6wSeif7O9vxMiQlqz4FodPN74FPRaOhKdN3P69GitBH4N+DySd7+u2l2/Qe0L75lwvEKQojThBCXRa9tQMc3LkS7HFC+m4onhHgOcC76c2xE35HPGrdNpW9gk9VOAZQq4Gd7vZZsfmyabUqvLQeWoAPXJQZnUXW/f5LnSiOZPillOMnrpZvYRAFESpkXQpyPdhfPRH//z0Kfj2S02UK+/9J3kpNSPjPNdo+hQwmTjYbLdX6mxAjSPImG1j9FB6T/Ozrw+hrguUKIi6SUj094S//EfYwjG/0du8MIIVaiYwov5eQLMYcWtwQ69rRghBBvRLufqya8tBcd6PzLchxnFngzvH7KD3kKSiOQoWm2GR73/w5OFqTZTAEanea1ycRoSoQQL0LH8TZNeKkXHcN7ESduQPOl9J0MT7vVidcnC1CX6/xMiXHZpkEI8TUhxM4opTol0d30LZw4YZPdXdqm2UUpq3E0Om4G+AV62DwI/G30fwF0SimvA341288xHZEY3Y4Wo3uAt6JHfksit/Ht5ThOlRmJ/k6VLYKTf+AjU25VYaKM3I/RYvQwOomyFR3cXiOlfB2zE8iZKH3Gzmm3OvG91OQ7MSOk6WlBXygv50RAcFKklCNCiCx6+D9ZPca507z9/OhvKbX8cnT9iw88d2JQO2JWE1pnwUejv/8spXzjJK+X6zjVZDvabb6YE1nCiVwS/R2YplSjGnwYPTD4BfD8SWqA0pyoSF8IpcrtViHE2dO4baXvZbJrruKYEdL0lFLOl0TFaFMSTXRcgnbNHp5kk5sjN2zi+16MLpxzORHULaWiRyYTIyHEaehCTTj1plJyF2Y7fC4da6p4y63j/l8vN7DS9/jGyUoVhBApdBEo6PKAWlL6/rdNFKOIN6CLaGEB51rqbgAyevieybYRQlzBiUxmTb4XI0jTEKVq/yN6+A0hxJejyuIxhBCZaAJu6U78saigciKtwA+FEGvHvXcrJypu/66UZubE3WyxEOLd47NAQojnoutclozb73hKx14yy0K10rHeKoRYM+44nUKIT6FrbcZ/hnrgq+gA7GnAneNrjYQQK9CJh3PRbsmnamHgOErf/59HE7WBsevqHZyoQ4Kpz/W6WR6rNMfyLUKIT0ejr9LxtnLiWr8HfY1VnXq549WS16IrZd+ALtp7txBiH7rGpQU9JSGNjh99VEr5tSn2sxNdmb1HCPEk0B69F3TF7/iith+h069XoFO0HxZCHETHedag63J+BjwPWC2EsMbVyfwBfefMoFui9AI3TeOWfAxd+n9OZFvpLro52see6HgbqRP3TUo5KIR
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
"\n",
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['information_rate'].to_numpy()\n",
" stimulated = dd['stim']['information_rate'].to_numpy()\n",
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" print(key)\n",
" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 24,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 3974.0 p value 0.2766869426478231\n",
"U-test: U value 2049.0 p value 0.25046855806374924\n",
"U-test: U value 1000.0 p value 0.9251527394243083\n"
]
},
{
"data": {
"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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"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"
}
],
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"source": [
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['specificity'].to_numpy()\n",
" stimulated = dd['stim']['specificity'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 25,
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"metadata": {
"scrolled": false
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 3643.0 p value 0.9471010896899097\n",
"U-test: U value 1873.0 p value 0.8133796041365939\n",
"U-test: U value 1018.0 p value 0.8095631030980726\n"
]
},
{
"data": {
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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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"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"
}
],
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"source": [
"\n",
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['average_rate'].to_numpy()\n",
" stimulated = dd['stim']['average_rate'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 26,
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"metadata": {
"scrolled": false
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 3951.0 p value 0.30924120740340955\n",
"U-test: U value 2116.0 p value 0.13443770008325492\n",
"U-test: U value 1033.0 p value 0.7162113753904321\n"
]
},
{
"data": {
"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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"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"
}
],
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"source": [
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['max_rate'].to_numpy()\n",
" stimulated = dd['stim']['max_rate'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 27,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 4208.0 p value 0.0703111829950676\n",
"U-test: U value 2192.0 p value 0.05860549066385172\n",
"U-test: U value 1107.0 p value 0.3330227188913163\n"
]
},
{
"data": {
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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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"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"
}
],
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"source": [
"\n",
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['interspike_interval_cv'].to_numpy()\n",
" stimulated = dd['stim']['interspike_interval_cv'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 28,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 3874.0 p value 0.4358627795642821\n",
"U-test: U value 2021.0 p value 0.31540792775479376\n",
"U-test: U value 1064.0 p value 0.5373862857775564\n"
]
},
{
"data": {
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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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"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"
}
],
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"source": [
"\n",
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['in_field_mean_rate'].to_numpy()\n",
" stimulated = dd['stim']['in_field_mean_rate'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 29,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 3642.0 p value 0.9495581436162017\n",
"U-test: U value 1859.0 p value 0.8701783404716995\n",
"U-test: U value 1017.0 p value 0.8159007523250839\n"
]
},
{
"data": {
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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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"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"
}
],
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"source": [
"\n",
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['out_field_mean_rate'].to_numpy()\n",
" stimulated = dd['stim']['out_field_mean_rate'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 30,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 4152.0 p value 0.10161200342185885\n",
"U-test: U value 1757.0 p value 0.718407479245363\n",
"U-test: U value 1300.0 p value 0.010937547927479946\n"
]
},
{
"data": {
"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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"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"
}
],
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"source": [
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['burst_event_ratio'].to_numpy()\n",
" stimulated = dd['stim']['burst_event_ratio'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 31,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 3934.0 p value 0.3348681711667292\n",
"U-test: U value 2022.0 p value 0.3129134904118731\n",
"U-test: U value 1069.0 p value 0.5107849438878747\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/linalg/linalg.py:2125: RuntimeWarning: invalid value encountered in det\n",
" r = _umath_linalg.det(a, signature=signature)\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUYAAAG1CAYAAACF0V1HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydd7hkVZW333Oq6qZOIBIkSGaByoCKOWHEHAAV44DZGYwoxvlExzBGzMM4o87o6IhZkRwUBJHcie5eNN00nbtvDpVP+P7Y59xbXX1TpVu3qvb7PPepW1Xn7FqVfrX2Xmuv5YRhiMVisVimcJttgMVisSw2rDBaLBZLGVYYLRaLpQwrjBaLxVKGFUaLxWIpwwqjxWKxlGGF0WKxWMqwwmixWCxlWGG0WCyWMqwwWiwWSxlWGC0Wi6UMK4wWi8VShhVGi8ViKcMKo8VisZSRbLYBiwEROQZ4qOSm76jq++dx3keAr0ZXd6jqkQ0wr20RkVOADaq6aGvfichTgC8CTwS6gD3AGao62FTDmoSIHAZ8DXghcAAwALwd+DjwHOALqvrpGh/jTODP0dWUqnrzPO984MfU4btoPcbpOUdEnHkc9/qGW9KGiMhyEfkusBpINNuemRCRo4CbgOcBDnA/0N/BougC1wBvAh4BrANGgC1NNKshWI9xfzzgcOAZwK0zHSQixwFnLJRRbcYTgH9uthHz4OVAHzAOnKCq/U22p9kIcFr0/8tV9drJO0TeinmtBpphWL2xwrg/NwEvAl7LLMLIlLd4H/D4RhtlaQqPjC7vt6IITL0eAH8pvUNVty6sKY3FTqX355fR5VzT6dcDQcnxlvYjnubnm2rF4mFy2UNV2/o1sR7j/vwV2AUcATwduK38ABGJpxQ3AbtnG0xETgM+DDwXOBSYAO4GfqCqv5nhnF7gbcCrgX8ADgRywFbgWuDrqrqz7JwtwNHAKdHjXAw8FViCCSz9Eviqqk7M/vT3GTMOihwGfD2yxwfuAV6kqp6IJIE3AK/DTJEPwixH7MS8Pt9Q1QemsTOmaF5OjlXVLSXHPRt4P2ZJ4yBgGLgd+Laq3jTf51D2fM4B3oFZAlkBDAJ/A75XOmbJIn7Mc0pei+eq6l/meJz42BRwbvQ8TsW8LncBn1HV20VkCfBJzI/sUZj1umuBj5e/v9G4JwAXYj5LR2Pe21HMWu3/AT9SVT869mBgDeazcIOqvrBsrF7M5/AxwHXAi2cKgk0TnCx9jp9V1UtE5C/MEHwRkW7gPcB50eN1YT7LV2I+k7ume9yZEJHXRK/DaUA35vP4xUrGmAvrMe5PAMSC9doZjomn0b+YbSAR+WfMm/ZWjLjdjxHGFwG/FpGfiUii7JyDgTuA7wLPB8aAVUAaeCxGZO8TkZmibu/ERPSeB2wH+oGTgf8HXBstoFfKb4A3ApuADLA7EsVezJfqJ5j1uALmyzgEnAi8G7hXREqXGu4C1pZcvy36y5W8Bv8G3Aycg/ngr8a8L68CbozunzcikhKR3wK/Bl4MFIGVGMfg7GjMb5ScsieyaVt0fazEztEKHvpSjGCdADwYPZcXAn8Wkedi3udPYL6HG4FDgLcAt4hIX9lzeBXmdfsAcBzwMLAB6MEI5Q8w7wMA0dT/7dHVF4jIO8ps+zpGpPYAb50jMyAXPffp3rdZp9Ai8ijg78A3gadgPhvrgEcDHwLWisgzZhujbLzvAb/FfL4zmNfgyZgflH+c7zhzYYVxeuaaTr8e8+Wa1uMDEJGXAN/BeFgfAA5Q1Seo6tHAC4C9GLG5pOzUr2K8iwcBUdUTVfVJqvoozJc6g/kCfWCGh/4w8N/AYap6GuYDGAc6ng68ciabZ+EM4MxovCMwv9YAH8N8KQeAJ6vqsZGtj8Z8WHdhvJpPxgOp6muB95WMfaaqPlNVdwOIyLujcUeAN6vqI1T1DExA7DzMD8THROTtzJ9vAK/B/Ci9VlUPV9UnYzzhCzGe3IdE5EORjVer6jOBH0Xn3xfZ+ExVva+Cx70Qk8ZyuKo+HjgJ40l3AzdgghVPUdXjVPVxmB9MHziekh9lETkQ48F2A/8OHKqqp6nqqRiP8NvRoW8UkcfG56nqlcBl0dWvicgR0XivAN4LhBhR3DPbk1DV3dHr8b6S2+LX40cznRd9d34NnI5Zrz8l+ow8EfPa/wgT3f59lAY0KyLyJuCfMD/Ab1LVR6vqkzCfjV8BZ841xnyxwjg9t2I+wEcCTyu9Q0ROxfzSXq+qQ7OM8UVMisfHVfXb8RQHQFVvBM6Prl4kIgdFY6eAZ2M+sB9S1Y2lA0ZRwNhLPXWGx10FvF1VR6NzQlX9PsaTAzM1rZRfquot0XhByfN+AcaT+6yq3lVm612YL/Fstu6DiHQBn42uvk1Vf1YyXqiqlwMfjW76bDSNn2vMIzHTOIB3qeqvS8b0VfV7wL9EN31GRJbOx9Z5co2qfjnOw1PV7cD/RPe5mPdp8nVT1esxSzlgliVinoWZlu8G3q+qmZJz0sBFGLGA/V/riwDFLB18X0QOBX4Y3fdVVb2utqc4K6/E/BjvxEzVtcTuUcyyxh2YoM6H5jHep6LLL6rqz0vGGgHejHmedcEK4zRE04pfRVfLp9NzTqOjNZnTo6v/O8NjXI3xtHoxU2ZUtaiqx2E8iSunGdfBeExEx0zHlTNMi9ZHlwfMZPcsTBudj7yIHqa8knLiL/BMtpbzdIwHNA78YYZjfoYR4yPYVzxm4iWYKfNu4PIZjvkORlhWUEevg2neQ6Zy/jKY5YJy4rXF5fENqvpHVV0GHDdDsnMPZooKZa91JKJvxsxwXomJJh8M3AnUlIg9D14TXf4+EvB9iD6n8ffjFbMNFKXHnRJd/e9pxiowJfg1Y4MvM/NLzHT1XBH5cInYvB6z5vL7Wc59XMn/v4uCC9PRE12eXHqjquZE5NBo18VJwLHRMY/HrFXCzD9qO2a4PRtdVvOez7g4rqpFETlQRJ4a2XpcdPl4jMjNZms58evWhVlnm+k4PxrzZMwXfDbi1/Y+VQ2mO0BV0yKiGG9LgD/N09652DbNbbFnNziDPcXocr8lHFXNRsG80zCv8/GYdefHYTxKmOa1VtW7ReSzwOcxr8cY8AZVLZYfW2fi9/MVkd3TEX+eTxIRZ5a1zvjDMK6qD89wzMpqjJwOK4wzczvmg30UJrp7u4g8AbOQ/htVHZ/l3BUl/89n6jrpxUVrLf+O+XUv/ZBnMCKQBJ45y1iFWe6Dab5w8yA73Y0isgz4FsYjSZXcVQDuxeR4vriCx4lft24qfN1mIfa85gqajEWXy+Yx5nzZz0sqYVqRngkReSnmtT6h7K6dmB/xlzIlMtPxB4wwglnfnjWbok7E7+dR0d9sJDCv/dgM98fPbbbXdHj+ps2OFcYZUNVQRH6NWft4LUYo5xWNZurNG1TVR856ZAki0oNJcTkFMzX6PialYj2wSVV9EfkCswvjQvIHTPAli5mO/h0Ted8YeZLvpDJhjF+3e6KASz2If8BWzHrU1Bdvth+8phBFsK/A/FD+HbOcsAZYr6p7o2N2MIMwRmu3ccQ6wIjr1zCBjEYSv5/vU9Xv1jhWvA1zth+u3hofYxIrjLPzS4wwniMiF2Fy9caZfu2olHgR+CAROSyOuJYjIs/EvOFbVDWLyRM8BRMlfWp58CViURSqiKbOz42uvkxV/zzNYZXaGr9uJ4lIcrr1tGid9UxMKtLD0drSbGyILh8vIu5001cRWY6Z/oNJm1lsfAwjijdh8kf90jujPMHZfoA/h1na2IlJ57oCeK+I/ElVr2qMyYB5P0/DTPenJdqPfgSwdbrczbKxAJaIyElakhtbwoyPUyk2+DILqvp3TJ5WnPJyDPDHSMRmO289Jt0G9k1NmSTK3forJqfrqdHNx0aX49OJYhRRfHl0tdk/aseW/H9P+Z1RHt4boqvltpaKU+nU/hbMlHcZcMEMj/tGjEBsYO7pGcDVmB+aw5i56MeFkY0zBUSaTfxaryoXxYi3YtZloey1jhLl40j+uyIh/E50/YciMu8ZTRVcEV2eJyKHzHDMjzC
"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"
}
],
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"source": [
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['max_field_mean_rate'].to_numpy()\n",
" stimulated = dd['stim']['max_field_mean_rate'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 32,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 4196.0 p value 0.07625033158277\n",
"U-test: U value 1789.0 p value 0.8457359656438074\n",
"U-test: U value 1312.0 p value 0.008224362184318894\n"
]
},
{
"data": {
"image/png": "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
"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"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['bursty_spike_ratio'].to_numpy()\n",
" stimulated = dd['stim']['bursty_spike_ratio'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 33,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 4034.0 p value 0.20303646513670592\n",
"U-test: U value 2195.0 p value 0.05655864319615998\n",
"U-test: U value 1062.0 p value 0.5482183981489739\n"
]
},
{
"data": {
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"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"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
"\n",
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"for key, dd in stuff.items():\n",
" baseline = dd['base']['gridness'].to_numpy()\n",
" stimulated = dd['stim']['gridness'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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": 34,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 4696.0 p value 0.0009136914115846572\n",
"U-test: U value 2276.0 p value 0.019967404084342423\n",
"U-test: U value 1192.0 p value 0.09642850317783999\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUgAAAG1CAYAAACbGG30AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOy9eZxcVZn//7639t7XLCQhCSScsENAVhlwwx0dFUERxWVwGbf5fXV01JnRcRn191WRUV+AqIDiBi44iiAgBMIS9i0hJwmQPem9qmuvW3Xv949zK2k6vfetvl1V5/169et21733nKdvd33qOed5znMMx3HQaDQazaGYfhug0Wg08xUtkBqNRjMOWiA1Go1mHLRAajQazThogdRoNJpx0AKp0Wg046AFUqPRaMZBC6RGo9GMgxZIjUajGQctkBqNRjMOWiA1Go1mHLRAajQazThogdRoNJpx0AKp0Wg04xD02wCNvwghTgA+BLwKWAJEgT7gWeAvwE+klFn/LKwsQoh7gHOBr0spv+SzOZp5hvYg6xghxFeAJ4BPAEuBF4AnARt4HfA/gBRCrPXNSI3GR7RA1ilCiPcD/wFkgQuBDinlWinlGVLK5cAxwEPAMuB2IUS3f9ZqNP6gBbJ++aJ7/IyU8mYpZWnkSSnlc8AFQC/QBXxyju3TaHxHC2QdIoRoB450f9ww3nVSyj7gj+6Pp1faLo1mvqGDNPWJNeL7N6HmIcfjP4HvAz0jXxRCXAe8D/gX4Dbgv4F/AMLANuA64GopZW6sRoUQ/4DySs8GOoEh4EHgSinl38czRgjxFuBy4GVAGyqgtA74v1LKx8e5p83t6yJgBTAM/Bn49wl+73ERQgRcGy4BjgdiQD/qw+YnUso/j3PfkcA/A28ADgeKqGDYz9z77DH6uQx4D3AS0ID6O6wDvjf69xVCrABedK9ZC/wYeCVqGuU2KeW7R1w7o+dfbxh60676RAixHvXmcIAbgJ8C948eak9w/3UogbwOeDvQBGxEfeiucS9bD7xJSpkYde83gc+5Pw6hgkNLgEXua9+SUn5+1D1Bt69L3Jd6gZ3AEUAHUAI+LaX8waj7Dgdud20qoQQpCghgL5ACjmKKUWwhhAHc5P7OoD4M4sByoDxP+1Up5X+Muu8fUc+5CcgBm1ACf4R7yY3ApVJKx72+BfgrcJZ7fjtKhI8CWlCBtM9IKb83oo8VKIGMA3vca59FifE1UsovuNdN+/nXK3qIXb98AiUOBkro1gGDQoi/CCE+L4Q4XQgxlf+Py4BBYK2U8ngp5dGoN3UP8HLg2yMvFkJ8GPXmjAPvkVJ2SClPBQ4DLgbSwOeEEB8c1c9XUeK4G3idlHKhlPJlwAKUJ+QAVwohXjPqvmtR4vg0cJSU8iQp5RrUlIGDEpHp8FqUOPYBJ0gpV7t2LAa+4F7zBSHE0hG/85HAL1DieD2wSEp5ipTySOD1KA/vElS6VZkbUc9xP/AKKeXKEb/vV1Hv3e8KId42ho1tKLE7SUq5FvVsv+naMtPnX5dogaxTpJRPoERi/YiXW1DDv/9GRbD3CSG+JoRomKApG3iLlPLJEW0/CLzX/fFDQojDAIQQYeAr7usfkFLeOOIeR0r5G+Cz7ktfcb1GhBALUUN53L5uH3FfSUr5P8B3UWL/tfI5IcRpwGtQnuPbpJQvjLjvYeDAkHManOgeH5BSPjPKjv9GeZe/BNpH3PMZ1PD4Iff3Toy477YRNn/AtfsM1NQHwNullPeMuD7veqdXuy99axw7fySl3OTeU5BSDs/0+dczWiDrGCnlJinlOcDJqDfOA7x0fnIBKtr99EiPaBR/l1I+NUbbf0MN90zgze7LZwELgSRwyzjt3YgS3SWoeTRQXlYE2DTePCPwc/d4mhBigft9WWTuk1I+P4aN96KGutNhq3t8oxDi34QQy0a1+U4p5XtHiicHf/8fj55ndPkBcBxw3qjrH5ZSPjCOHd9xj6uEEMeNcX79GK/N9PnXLXX/CaEB1/t7Eviy6y2ejRpKXooSySNRntGZY9z+8ARNPw2s5OAwtvxGDgP3CiHGu6+EEtY1bvvl+5a6c6djMfLDfg1qjrLcwbMT2PgkKudzqvwJNR1xLvAN4BtCiM3Anahg1V0jA1NCiChKbAAO+SABkFIOo+ZvR9oP8Nh4RkgptwohhlFev+DQ33HfGLfN9PnXLVogNS9BSpkB7gDuEEL8Oyp4czFwhhBi7Rge3OAEzaXcY5t7bHWPEZQIT8bo+1qmeV95mJsa70JUkGLKSCmLQojzgY+j5l+PRwnJGve1YSHEt4FvuAGXzhG3T2THSFrcY2LCq5Qn2AI0j3FurOWhM33+dYsWyDpECHEVau31dVLKr493nZQyK4S4HHgbyusQwGiBbJygq/Ibstc9pt3jY25gYKqU7/udlPId07hvwD22THBNbBrtAWpODzXn+V136uEV7tcbUEPYr6EE6rsctB3GFrKxSLrH1gmvOihgyQmvOshMn3/dogWyPokBq4C3AuMKJICUMimESKFSafrGuGSs+a8y5YBGefgo3eNRQoiglLI4+gY3jeY8VLR6hytG5fuOHa8jd2rgZcAu977SiPtOnsDGcdscp5921AfFHinlLinlbtT858+FECHgd6g5xEuB70op40KIXtRUxXHAo2O0uRj4AyqV58PAZvfUKRPYcTQHP5y2jnfdKGb6/OsWHaSpT37hHk8VQlw20YXucLIDNZR+aIxL3iiEWDT6RSHEm1DruPPA/7ov34saNjYD7x+ny3cDf0eJRDkAcitqXmzNGGk8Zf4FuAc1p1gWjt+5xzOEEIeIpFvJ6LRx2huPn6ISqj83+oSU0kLNTwIERpz6q3v8wDhtXojKKDjDjXCXn9dpQoizxrmnHNXfDTwzzjWjmenzr1u0QNYhUso7OCge1wohrnCTjA8ghIi6BS1+6770RSnlWHNoDcAtI6O5QojzUKtDAL5ZTmuRUqZRKUQA3xdCvH9krqW7SuYq98ffliPPUsodqFUhAL8SQrx5xD2mEOJDwJfdl37oBj1wI8m/QqX//F4IcdKI+451n4Ex9lMal3K0/MNCiEtdj2tkm+U167eOuOfbqA+Kc4QQPxiZNiWEeB0Hvfhvu3Y/OOL+37nPs3x9xK3C9E/uS58tJ5dPxkyffz2jV9LUKW5O3DWofMXym3wnKsE7BqxGTeYXgP+UUn5z1P3XoRLMt6JWkZgoT6bJvReUOF02cpjmCsrVHHyD96PSgZagkpVBpai81g0Yle+LoiLp5dSdvajVIis4uILlZuDikauBhBBrgOdGmL4RlSB+LCpZ+ikOptdcIqX85VjPy23rGtfuGzm4oqcP9dxaUdF+AxX5fbWUMjni3gtR4hpBBWs2o+Yryx8s16FyE8sraTpQnuTIlTR9qOF9C8qj/qKU8kAe5IiVNACrpZTbxvgdZvT86xXtQdYpbvLwZagh5ndQwZcIas3vUtR81f+PWi3yzfHaQYnB2ajI9ypU5Z91KKF69+g5LDch+XJUGtEfUOuRT0YN+x5CeWCvGv3mdFNnLkCtp74NFTQ6GTWPfjdKrC8qi6MQojzM3u4e/44afq9ACcHNqFSdkV5S3L23oewZup50ebhcDvp8GDVEXYd6D52IilavR0WyXz5SHF37b3Kvu9Zt5wSUqN4NvFNK+f6RnqCUchAl3JejhsZt7j39wE+Al40Ux6ky0+dfr2gPUjMjRniQN0op3+OzOS/BFbTy3NwPcQtnoDzH96ISs/+MEoSbUSL1M1TazitRUfvzpZQPCiE+gVpCeB3KO/ywe90/A6cCi6WU+Tn5xTRzjo5ia2qRBajKOmdysFxbOaiyA7VSpBPl/b6dg8sir3PPPc3BHMoSajj/GQ6+X/6ImlN8AOWNloe1mhpDe5CaGTGfPciRCCF+C7wDJWjnSynvG3W+FZV6cyRK6M6SUvaM0c4pqCF0BLWa5h1jpcloags9B6mpWYQQX0Sl0HwK5QXeJIRYOeJ8EPg1al7yIlTA5CYhRGRUO4uB37ttfAp4C2qZoabG0QKpqUmE2kP
"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"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATQAAAG1CAYAAAB3bQjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeZwkVZWov4jIzFp6h2ZvoZHloKIIooi4juuMyszo6KCCAjrKOOrDeS7M4Cjuy5sRXAf3BRRnFHdxBQVZZOmFbhr6dLP0vlV315Z7ZkS8P25kVXZ1VXVlZmRlZeb9fr/q6Iy4ce/NrIqT59xz7jlOGIZYLBZLJ+C2egIWi8USF1agWSyWjsEKNIvF0jFYgWaxWDoGK9AsFkvHYAWaxWLpGKxAs1gsHYMVaBaLpWOwAs1isXQMVqBZLJaOwQo0i8XSMViBZrFYOgYr0CwWS8dgBZrFYukYEq2egKW1iMhTgLcALwSOA3qBAeAB4FfAN1Q117oZNhcR+RPwPODjqvqBFk/H0iBWQ+tiROTDwCrgncAy4FFgNRAALwO+AKiInNWySVosNWAFWpciIpcAHwRywGuAw1T1LFV9pqqeADwR+AvwOOC3InJE62ZrscwMK9C6lyuj43tU9Ueq6ldfVNWHgPOBPcBS4F2zPD+LpWasQOtCRGQJcFL08u6p2qnqAPDT6OU5zZ6XxdIo1inQnZSq/v8KzDraVHwI+Bywu/qkiHwbeBPwbuA3wCeB5wIp4GHg28BXVDU/Waci8lyM1ncecDgwCNwFfF5Vb5lqMiLyt8BbgacDizEOjFuB/1TVlVPcszga6x+B5cAI8EvgP6Z531MiIl40hzcATwb6gL2YL4dvqOovp7jvJOBfgL8BjgfKGOfLt6L7gknGuRi4EHgq0I/5PdwKXD3x/YrIcuCxqM1ZwNeAv8IsK/xGVV9f1bauz3+u49giKd2JiNyO+WMOge8C3wTumGh6TnP/tzEC7dvAq4H5wDrMl+RpUbPbgVeo6vCEez8FvD96OYhxRhwHHB2d+7SqXjHhnkQ01huiU3uALcDjgcMAH7hcVb844b7jgd9Gc/IxAqQXEGAHkAZOZYZeThFxgB9G7xmM8B4CTgAq64wfVdUPTrjv7zGf83wgDzyIEciPj5p8D7hIVcOo/ULg18CzouubMELzVGAhxnHzHlW9umqM5RiBNgRsj9o+gBGeX1XVf4/a1fz5twvW5Oxe3ol5mB2MYLoV2C8ivxKRK0TkHBGZyd/HxcB+4CxVfbKqPgHzEO4Gng18prqxiLwN8zANAReq6mGqejZwLHABkAHeLyJvnjDORzHCbBvwMlU9SlWfDhyJ0TRC4PMi8uIJ930dI8zWAKeq6lNV9TSMCR1iHvpaeClGmA0AT1HVU6J5HAP8e9Tm30VkWdV7Pgm4HiPMvgMcrapPU9WTgL/GaFBvwITPVPge5nPcBbxAVU+ser8fxTy7nxWRV00yx8UY4fRUVT0L89l+KppLvZ9/W2AFWpeiqqswD/XtVacXYsyhT2I8nDtF5GMi0j9NVwHwt6q6uqrvu4A3Ri/fIiLHAohICvhwdP5SVf1e1T2hqv4P8N7o1IcjrQwROQpj2hKN9duq+3xV/QLwWYxw/ljlmog8A3gxRjN7lao+WnXfPcCYCVYDZ0THO1V17YR5fBKjvX0fWFJ1z3sw5uJfovc9XHXfb6rmfGk072dilgIAXq2qf6pqX4i0v69Epz49xTy/rKoPRvcUVXWk3s+/nbACrYtR1QdV9TnAmZg/9Ds5cH3tSIw3dE21xjGBW1T1/kn6/h3G/HGBV0annwUcBYwCP5uiv+9hhORxmHUgMFpMD/DgVOtkwHXR8RkicmT0/4pQ+LOqPjLJHG/DmH61sDE6vlxE/k1EHjehz9eq6hurhR3j7/9rE9fJIr4InA48f0L7e1T1zinm8V/R8WQROX2S67dPcq7ez79taDsJbImfSLtaDVwVaWPnYUyrizBC7SSM5nHuJLffM03Xa4ATGTfrKg9eCrhNRKa6z8cIwtOi/iv3LYvW/iaj+sv5NMwaW2WAB6aZ42pMzN1M+TnGPH8e8AngEyKyHvgDxjlyc7UjRER6McIB4CDBD6CqI5j1x+r5A6yYahKqulFERjBatXDwe9w5yW31fv5tgxVolgNQ1Szwe+D3IvIfGGfBBcAzReSsSTSk/dN0l46Oi6PjoujYgxGah2LifQtrvK9i9qWnaohZFJ8xqloWkZcA78CsHz4Z8+CfFp0bEZHPAJ+IFvgPr7p9unlUszA6Dk/bymhaC4EFk1ybbLtavZ9/22AFWhciItdi9m5+W1U/PlU7Vc2JyFuBV2G+1QWYKNDmTTNU5QHaEx0z0XFFtBA9Uyr33aiq/1DDffui48Jp2vTV0B9g1qQwa3afjUzxF0Q/f4Mx6T6GESifZXzuMLngmYzR6Lho2lbjAmd02lbj1Pv5tw1WoHUnfcDJwN8BUwo0AFUdFZE0JjRiYJImk63fVKgsoFfMKY2Op4pIQlXLE2+IwiKej/Fmbo6ER+W+J001UGQqPx3YGt3nV9135jRznLLPKcZZghHs21V1q6puw6zfXSciSeBGzBrYRcBnVXVIRPZgTPfTgfsm6fMY4CeY0Iy3AeujS0+bZh5PYPzLZONU7SZQ7+ffNlinQHdyfXQ8W0Qunq5hZF4dhjEt/zJJk5eLyNETT4rIKzD7QAvAL6LTt2HMqAXAJVMM+XrgFsxDXVlwvwmzrnPaJGEZFd4N/AmzJlZ50G+Mjs8UkYOEWpRp5BlT9DcV38QEoL5/4gVVLWHW1wC8qku/jo6XTtHnazAe52dGHtDK5/UMEXnWFPdUvL7bgLVTtJlIvZ9/22AFWheiqr9n/GH/uohcEwVljiEivdEG9v+NTl2pqpOtAfUDP6v29onI8zHR7wCfqoQpqGoGExIC8DkRuaQ61i3aBXBt9PJ/K55JVd2MiXoHuEFEXll1jysibwGuik59KVpkJ/I03oAJ5/ixiDy16r4nRZ+BM/mnNCUVb+rbROSiSKOp7rOy5/Wmqns+gxHszxGRL1aHwYjIyxjXkj8TzfuuqvtvjD7PSvueKEvKP0Wn3lsJxj0U9X7+7YTdKdClRDFJX8XEi1Ueyi2YgNg+4BTM4nER+JCqfmrC/d/GBORuxETJuxhNYX50LxhhcnG12RIJgK8w/kDuxYR3HIcJ7gQTcvDSyEFRua8X42mthGLswETDL2c8Qv9HwAXVux2ibU+/ZHwRfB0moPZJmODS+zEm1ozzoYnIV6vmP4D53BZhvMEOxjP4IlUdrbrnNRhh2INxDqzHrLdVvgi+jYkNq+wUOAyjqVXvFBjAmLsLMRrrlao6FodWtVMA4BRVfXiSudf1+bcLVkPrUqJgy4sxJtd/YRb7ezB7Bpdh1lv+HyYa/lNT9YN5eM/DeEZPxmTmuBUjWF4/cQ0mCuB8KyYs5CeY/YxnYsygv2A0nBdOfJiiUIjzMfsxf4NxUpyJWQf+I0a4/uPErVuqOoRxgFyOMUeXYx7cH0XvvR4t5DKMyfZHzDN0BsabeTvG0/nsamEWzeOHUbuvY5wVT8EIwT8Cr1XVS6o1LVXdjxG0b8WYiouje/YC3wCeXi3MZkq9n3+7YDU0S11UaWjfU9ULWzwdiwWwGprFYukgrECzWCwdg41Ds3QsUUjKt4D/Ad4MXAG8FuPEyGD2rn5GVf884b7TgPdhcokdiwmSfRTjefycqu7BMiexGpqlG1iMWfD+AMYL+yAmVu0VwB9F5OWVhiJyLib49RLMov0DGG/qkzHpgVZO3JBumTtYgWapC1W9WFWdNnEIvBTjfX2pqh4X5Qg7EbN53uPA3RJXY4Td5zF5y85S1SdiNthvxIQ3XIllTmIFmqVb+JcopREAqrqT8dxgZ4jI/Mr/o+O3VLVQ1f5R4P9iYto2z8J8LXVg19As3YDP+Pajah6q+v8iTMDrRox5ea2IXAncFm1pQlV/wfi2JMscxAo0SzewXyev/l59rvIsvA8jtM7B5DhLi8htmMDhX6nqTDeCW1qANTkt3cBMMkY
"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
2019-12-13 10:43:57 +00:00
"for key, dd in stuff.items(): #TODO narrow broad spiking\n",
" baseline = dd['base']['speed_score'].to_numpy()\n",
" stimulated = dd['stim']['speed_score'].to_numpy()\n",
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" plt.figure()\n",
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" violinplot(baseline, stimulated, xticks=label[key], colors=colors[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\")"
]
},
2019-12-13 10:43:57 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# inihibitory cells"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "unhashable type: 'numpy.ndarray'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-35-d562b46ecc21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'not t_i_peak.isnull() and not bs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ns_inhibited'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mns_inhibited\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, expr, inplace, **kwargs)\u001b[0m\n\u001b[1;32m 3313\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"target\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3314\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresolvers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3315\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3317\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/eval.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0meng\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_engines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0meng_inst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meng\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed_expr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meng_inst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mparsed_expr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massigner\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/engines.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;31m# make sure no names in resolvers and locals/globals clash\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m return _reconstruct_object(\n\u001b[1;32m 72\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maligned_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/engines.py\u001b[0m in \u001b[0;36m_evaluate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0mscope\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_scope\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0mtruediv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscope\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"truediv\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m \u001b[0m_check_ne_builtin_clash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 119\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mne\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruediv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtruediv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/engines.py\u001b[0m in \u001b[0;36m_check_ne_builtin_clash\u001b[0;34m(expr)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mTerms\u001b[0m \u001b[0mcan\u001b[0m \u001b[0mcontain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \"\"\"\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0moverlap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0m_ne_builtins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/expipe/lib/python3.6/site-packages/pandas/core/computation/expr.py\u001b[0m in \u001b[0;36mnames\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 852\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_term\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 853\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 854\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mterm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
]
}
],
"source": [
"stim_action = actions['stimulus-response']\n",
"stim_results = pd.read_csv(stim_action.data_path('results'))\n",
"# stim_results has old unit id's but correct on (action, unit_name, channel_group)\n",
"stim_results = stim_results.drop('unit_id', axis=1)\n",
"\n",
"data = data.merge(stim_results, how='left')\n",
"\n",
"waveform_action = actions['waveform-analysis']\n",
"waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)\n",
"\n",
"data = data.merge(waveform_results, how='left')\n",
"\n",
"data.bs = data.bs.astype(bool)\n",
"\n",
"data.loc[data.eval('not t_i_peak.isnull() and not bs'), 'ns_inhibited'] = True\n",
"data.ns_inhibited.fillna(False, inplace=True)\n",
"\n",
"data.loc[data.eval('t_i_peak.isnull() and not bs'), 'ns_not_inhibited'] = True\n",
"data.ns_not_inhibited.fillna(False, inplace=True)\n",
"\n",
"# make baseline for inhibited vs not inhibited\n",
"data.loc[data.unit_id.isin(data.query('ns_inhibited').unit_id.values), 'ns_inhibited'] = True\n",
"data.loc[data.unit_id.isin(data.query('ns_not_inhibited').unit_id.values), 'ns_not_inhibited'] = True"
]
},
2019-10-17 17:51:12 +00:00
{
"cell_type": "code",
2019-10-22 10:22:00 +00:00
"execution_count": null,
2019-10-17 17:51:12 +00:00
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
2019-12-13 10:43:57 +00:00
"baseline = data.query('ns_inhibited and baseline and i')['speed_score'].to_numpy()\n",
"stimulated = data.query('ns_inhibited and stimulated')['speed_score'].to_numpy()\n",
"plt.figure()\n",
"violinplot(baseline, stimulated, xticks=label[''], colors=colors[''])\n",
"plt.title(\"Speed score\")\n",
"plt.ylabel(\"Speed score\")\n",
"# plt.ylim(-0.1, 0.5)\n",
"\n",
"plt.savefig(output_path / \"figures\" / f\"speed_score_ns_inhibited.svg\", bbox_inches=\"tight\")\n",
"plt.savefig(output_path / \"figures\" / f\"speed_score_ns_inhibited.png\", dpi=600, bbox_inches=\"tight\")"
2019-10-17 17:51:12 +00:00
]
},
2019-10-16 05:28:13 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register in Expipe"
]
},
{
"cell_type": "code",
2019-10-22 10:22:00 +00:00
"execution_count": null,
2019-10-16 05:28:13 +00:00
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"comparisons-gridcells\")"
]
},
{
"cell_type": "code",
2019-10-22 10:22:00 +00:00
"execution_count": null,
2019-10-16 05:28:13 +00:00
"metadata": {},
2019-10-22 10:22:00 +00:00
"outputs": [],
2019-10-16 05:28:13 +00:00
"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
2019-10-22 10:22:00 +00:00
"execution_count": null,
2019-10-16 05:28:13 +00:00
"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
}
