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septum-mec/actions/comparisons-gridcells/data/20_comparisons_gridcells.ipynb

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comparing stats on grid cells
2019-10-16 05:28:13 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"15:56:22 [I] klustakwik KlustaKwik2 version 0.2.6\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n",
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"import os\n",
"import pathlib\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"import shutil\n",
"import pandas as pd\n",
"import scipy.stats\n",
"\n",
"import exdir\n",
"import expipe\n",
"from distutils.dir_util import copy_tree\n",
"import septum_mec\n",
"import spatial_maps as sp\n",
"import head_direction.head as head\n",
"import septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n",
"from septum_mec.analysis.plotting import violinplot\n",
"\n",
"from spike_statistics.core import permutation_resampling"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"project_path = dp.project_path()\n",
"project = expipe.get_project(project_path)\n",
"actions = project.actions\n",
"\n",
"output_path = pathlib.Path(\"output\") / \"comparisons-gridcells\"\n",
"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load cell statistics and shuffling quantiles"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"text/plain": [
" action baseline entity frequency i ii session \\\n",
"0 1849-060319-3 True 1849 NaN False True 3 \n",
"1 1849-060319-3 True 1849 NaN False True 3 \n",
"2 1849-060319-3 True 1849 NaN False True 3 \n",
"3 1849-060319-3 True 1849 NaN False True 3 \n",
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
" stim_location stimulated tag ... burst_event_ratio \\\n",
"0 NaN False baseline ii ... 0.397921 \n",
"1 NaN False baseline ii ... 0.146481 \n",
"2 NaN False baseline ii ... 0.373466 \n",
"3 NaN False baseline ii ... 0.097464 \n",
"4 NaN False baseline ii ... 0.248036 \n",
"\n",
" bursty_spike_ratio gridness border_score information_rate \\\n",
"0 0.676486 -0.459487 0.078474 0.965845 \n",
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"3 0.196189 -0.641543 0.274989 0.153740 \n",
"4 0.461250 -0.085292 0.198676 0.526720 \n",
"\n",
" information_specificity head_mean_ang head_mean_vec_len spacing \\\n",
"0 0.309723 5.788704 0.043321 0.624971 \n",
"1 0.032266 1.821598 0.014624 0.753333 \n",
"2 0.336590 4.407614 0.121115 0.542877 \n",
"3 0.068626 6.128601 0.099223 0.484916 \n",
"4 0.033667 1.602362 0.051825 0.646571 \n",
"\n",
" orientation \n",
"0 22.067900 \n",
"1 0.000000 \n",
"2 27.758541 \n",
"3 11.309932 \n",
"4 0.000000 \n",
"\n",
"[5 rows x 34 columns]"
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"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"statistics_action = actions['calculate-statistics']\n",
"identification_action = actions['identify-neurons']\n",
"sessions = pd.read_csv(identification_action.data_path('sessions'))\n",
"units = pd.read_csv(identification_action.data_path('units'))\n",
"session_units = pd.merge(sessions, units, on='action')\n",
"statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
"statistics = pd.merge(session_units, statistics_results, how='left')\n",
"statistics.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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"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 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shuffling = actions['shuffling']\n",
"quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
"quantiles_95.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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"text/plain": [
" action baseline entity frequency i ii session \\\n",
"0 1849-060319-3 True 1849 NaN False True 3 \n",
"1 1849-060319-3 True 1849 NaN False True 3 \n",
"2 1849-060319-3 True 1849 NaN False True 3 \n",
"3 1849-060319-3 True 1849 NaN False True 3 \n",
"4 1849-060319-3 True 1849 NaN False True 3 \n",
"\n",
" stim_location stimulated tag ... head_mean_vec_len spacing \\\n",
"0 NaN False baseline ii ... 0.043321 0.624971 \n",
"1 NaN False baseline ii ... 0.014624 0.753333 \n",
"2 NaN False baseline ii ... 0.121115 0.542877 \n",
"3 NaN False baseline ii ... 0.099223 0.484916 \n",
"4 NaN False baseline ii ... 0.051825 0.646571 \n",
"\n",
" orientation border_score_threshold gridness_threshold \\\n",
"0 22.067900 0.332548 0.229073 \n",
"1 0.000000 0.354830 0.089333 \n",
"2 27.758541 0.264610 -0.121081 \n",
"3 11.309932 0.344280 0.215829 \n",
"4 0.000000 0.342799 0.218967 \n",
"\n",
" head_mean_ang_threshold head_mean_vec_len_threshold \\\n",
"0 6.029431 0.205362 \n",
"1 6.120055 0.073566 \n",
"2 5.759406 0.150827 \n",
"3 6.033364 0.110495 \n",
"4 5.768170 0.054762 \n",
"\n",
" information_rate_threshold speed_score_threshold specificity \n",
"0 1.115825 0.066736 0.445206 \n",
"1 0.223237 0.052594 0.097485 \n",
"2 4.964984 0.027120 0.393687 \n",
"3 0.239996 0.054074 0.262612 \n",
"4 0.524990 0.144702 0.133677 \n",
"\n",
"[5 rows x 41 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"action_columns = ['action', 'channel_group', 'unit_name']\n",
"data = pd.merge(statistics, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))\n",
"\n",
"data['specificity'] = np.log10(data['in_field_mean_rate'] / data['out_field_mean_rate'])\n",
"\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statistics about all cell-sessions"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"stimulated\n",
"False 624\n",
"True 674\n",
"Name: action, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('stimulated').count()['action']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find all cells with gridness above threshold"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells 226\n",
"Number of animals 4\n"
]
}
],
"source": [
"query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n",
"sessions_above_threshold = data.query(query)\n",
"print(\"Number of gridcells\", len(sessions_above_threshold))\n",
"print(\"Number of animals\", len(sessions_above_threshold.groupby(['entity'])))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"baseline = sessions_above_threshold.query('baseline')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"gridcell_in_baseline = data[data.unit_id.isin(baseline.unit_id)]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells in baseline i sessions 78\n",
"Number of gridcells in stimulated 11Hz ms sessions 35\n",
"Number of gridcells in baseline ii sessions 66\n",
"Number of gridcells in stimulated 30Hz ms sessions 33\n"
]
}
],
"source": [
"baseline_i = gridcell_in_baseline.query('baseline and i')\n",
"stimulated_11 = gridcell_in_baseline.query('frequency==11 and stim_location==\"ms\" and i')\n",
"\n",
"baseline_ii = gridcell_in_baseline.query('baseline and ii')\n",
"stimulated_30 = gridcell_in_baseline.query('frequency==30 and stim_location==\"ms\" and ii')\n",
"\n",
"print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
"\n",
"print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
"print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# slice unique units"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"baseline_i = baseline_i.drop_duplicates('unit_id')\n",
"stimulated_11 = stimulated_11.drop_duplicates('unit_id')\n",
"baseline_ii = baseline_ii.drop_duplicates('unit_id')\n",
"stimulated_30 = stimulated_30.drop_duplicates('unit_id')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells in baseline i sessions 68\n",
"Number of gridcells in stimulated 11Hz ms sessions 32\n",
"Number of gridcells in baseline ii sessions 58\n",
"Number of gridcells in stimulated 30Hz ms sessions 28\n"
]
}
],
"source": [
"print(\"Number of gridcells in baseline i sessions\", len(baseline_i))\n",
"print(\"Number of gridcells in stimulated 11Hz ms sessions\", len(stimulated_11))\n",
"\n",
"print(\"Number of gridcells in baseline ii sessions\", len(baseline_ii))\n",
"print(\"Number of gridcells in stimulated 30Hz ms sessions\", len(stimulated_30))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate statistics"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"columns = [\n",
" 'average_rate', 'gridness', 'sparsity', 'selectivity', 'information_specificity',\n",
" 'max_rate', 'information_rate', 'interspike_interval_cv', \n",
" 'in_field_mean_rate', 'out_field_mean_rate', \n",
" 'burst_event_ratio', 'specificity', 'speed_score'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>average_rate</th>\n",
" <th>gridness</th>\n",
" <th>sparsity</th>\n",
" <th>selectivity</th>\n",
" <th>information_specificity</th>\n",
" <th>max_rate</th>\n",
" <th>information_rate</th>\n",
" <th>interspike_interval_cv</th>\n",
" <th>in_field_mean_rate</th>\n",
" <th>out_field_mean_rate</th>\n",
" <th>burst_event_ratio</th>\n",
" <th>specificity</th>\n",
" <th>speed_score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>stimulated</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>9.928801</td>\n",
" <td>0.562122</td>\n",
" <td>0.656634</td>\n",
" <td>5.320886</td>\n",
" <td>0.200475</td>\n",
" <td>37.440262</td>\n",
" <td>1.178277</td>\n",
" <td>2.348004</td>\n",
" <td>15.711336</td>\n",
" <td>7.319828</td>\n",
" <td>0.219627</td>\n",
" <td>0.444021</td>\n",
" <td>0.136236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>9.554733</td>\n",
" <td>0.365628</td>\n",
" <td>0.666120</td>\n",
" <td>6.232196</td>\n",
" <td>0.194542</td>\n",
" <td>39.864795</td>\n",
" <td>1.063930</td>\n",
" <td>2.328150</td>\n",
" <td>14.582445</td>\n",
" <td>7.121023</td>\n",
" <td>0.205369</td>\n",
" <td>0.445532</td>\n",
" <td>0.102438</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"stimulated \n",
"False 9.928801 0.562122 0.656634 5.320886 \n",
"True 9.554733 0.365628 0.666120 6.232196 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
"stimulated \n",
"False 0.200475 37.440262 1.178277 \n",
"True 0.194542 39.864795 1.063930 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"stimulated \n",
"False 2.348004 15.711336 7.319828 \n",
"True 2.328150 14.582445 7.121023 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
"stimulated \n",
"False 0.219627 0.444021 0.136236 \n",
"True 0.205369 0.445532 0.102438 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gridcell_in_baseline.groupby('stimulated')[columns].mean()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>average_rate</th>\n",
" <th>gridness</th>\n",
" <th>sparsity</th>\n",
" <th>selectivity</th>\n",
" <th>information_specificity</th>\n",
" <th>max_rate</th>\n",
" <th>information_rate</th>\n",
" <th>interspike_interval_cv</th>\n",
" <th>in_field_mean_rate</th>\n",
" <th>out_field_mean_rate</th>\n",
" <th>burst_event_ratio</th>\n",
" <th>specificity</th>\n",
" <th>speed_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" <td>144.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>9.928801</td>\n",
" <td>0.562122</td>\n",
" <td>0.656634</td>\n",
" <td>5.320886</td>\n",
" <td>0.200475</td>\n",
" <td>37.440262</td>\n",
" <td>1.178277</td>\n",
" <td>2.348004</td>\n",
" <td>15.711336</td>\n",
" <td>7.319828</td>\n",
" <td>0.219627</td>\n",
" <td>0.444021</td>\n",
" <td>0.136236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.727249</td>\n",
" <td>0.338826</td>\n",
" <td>0.186070</td>\n",
" <td>2.885443</td>\n",
" <td>0.175036</td>\n",
" <td>16.512138</td>\n",
" <td>0.570617</td>\n",
" <td>0.743517</td>\n",
" <td>9.798591</td>\n",
" <td>6.760978</td>\n",
" <td>0.082774</td>\n",
" <td>0.206192</td>\n",
" <td>0.075267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.516375</td>\n",
" <td>-0.360777</td>\n",
" <td>0.261912</td>\n",
" <td>1.842905</td>\n",
" <td>0.011661</td>\n",
" <td>3.013150</td>\n",
" <td>0.122324</td>\n",
" <td>1.361275</td>\n",
" <td>0.993877</td>\n",
" <td>0.257364</td>\n",
" <td>0.027228</td>\n",
" <td>0.128469</td>\n",
" <td>-0.023795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.833480</td>\n",
" <td>0.350175</td>\n",
" <td>0.517566</td>\n",
" <td>3.108402</td>\n",
" <td>0.072654</td>\n",
" <td>25.189028</td>\n",
" <td>0.748273</td>\n",
" <td>1.772429</td>\n",
" <td>7.649203</td>\n",
" <td>1.863476</td>\n",
" <td>0.162862</td>\n",
" <td>0.289491</td>\n",
" <td>0.082031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.101159</td>\n",
" <td>0.595244</td>\n",
" <td>0.701089</td>\n",
" <td>4.682344</td>\n",
" <td>0.139185</td>\n",
" <td>34.014566</td>\n",
" <td>1.064148</td>\n",
" <td>2.170671</td>\n",
" <td>12.863627</td>\n",
" <td>4.773814</td>\n",
" <td>0.213065</td>\n",
" <td>0.383049</td>\n",
" <td>0.130958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>15.349392</td>\n",
" <td>0.802880</td>\n",
" <td>0.820432</td>\n",
" <td>6.619374</td>\n",
" <td>0.261063</td>\n",
" <td>45.689916</td>\n",
" <td>1.562027</td>\n",
" <td>2.688595</td>\n",
" <td>23.123564</td>\n",
" <td>10.952948</td>\n",
" <td>0.280340</td>\n",
" <td>0.570619</td>\n",
" <td>0.188830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>31.367451</td>\n",
" <td>1.174288</td>\n",
" <td>0.954505</td>\n",
" <td>17.011330</td>\n",
" <td>0.918520</td>\n",
" <td>90.839266</td>\n",
" <td>3.540663</td>\n",
" <td>5.240845</td>\n",
" <td>45.349506</td>\n",
" <td>28.721619</td>\n",
" <td>0.400014</td>\n",
" <td>0.975050</td>\n",
" <td>0.323278</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"count 144.000000 144.000000 144.000000 144.000000 \n",
"mean 9.928801 0.562122 0.656634 5.320886 \n",
"std 7.727249 0.338826 0.186070 2.885443 \n",
"min 0.516375 -0.360777 0.261912 1.842905 \n",
"25% 3.833480 0.350175 0.517566 3.108402 \n",
"50% 7.101159 0.595244 0.701089 4.682344 \n",
"75% 15.349392 0.802880 0.820432 6.619374 \n",
"max 31.367451 1.174288 0.954505 17.011330 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
"count 144.000000 144.000000 144.000000 \n",
"mean 0.200475 37.440262 1.178277 \n",
"std 0.175036 16.512138 0.570617 \n",
"min 0.011661 3.013150 0.122324 \n",
"25% 0.072654 25.189028 0.748273 \n",
"50% 0.139185 34.014566 1.064148 \n",
"75% 0.261063 45.689916 1.562027 \n",
"max 0.918520 90.839266 3.540663 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"count 144.000000 144.000000 144.000000 \n",
"mean 2.348004 15.711336 7.319828 \n",
"std 0.743517 9.798591 6.760978 \n",
"min 1.361275 0.993877 0.257364 \n",
"25% 1.772429 7.649203 1.863476 \n",
"50% 2.170671 12.863627 4.773814 \n",
"75% 2.688595 23.123564 10.952948 \n",
"max 5.240845 45.349506 28.721619 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
"count 144.000000 144.000000 144.000000 \n",
"mean 0.219627 0.444021 0.136236 \n",
"std 0.082774 0.206192 0.075267 \n",
"min 0.027228 0.128469 -0.023795 \n",
"25% 0.162862 0.289491 0.082031 \n",
"50% 0.213065 0.383049 0.130958 \n",
"75% 0.280340 0.570619 0.188830 \n",
"max 0.400014 0.975050 0.323278 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gridcell_in_baseline.query('baseline')[columns].describe()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>average_rate</th>\n",
" <th>gridness</th>\n",
" <th>sparsity</th>\n",
" <th>selectivity</th>\n",
" <th>information_specificity</th>\n",
" <th>max_rate</th>\n",
" <th>information_rate</th>\n",
" <th>interspike_interval_cv</th>\n",
" <th>in_field_mean_rate</th>\n",
" <th>out_field_mean_rate</th>\n",
" <th>burst_event_ratio</th>\n",
" <th>specificity</th>\n",
" <th>speed_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>9.554733</td>\n",
" <td>0.365628</td>\n",
" <td>0.666120</td>\n",
" <td>6.232196</td>\n",
" <td>0.194542</td>\n",
" <td>39.864795</td>\n",
" <td>1.063930</td>\n",
" <td>2.328150</td>\n",
" <td>14.582445</td>\n",
" <td>7.121023</td>\n",
" <td>0.205369</td>\n",
" <td>0.445532</td>\n",
" <td>0.102438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.334232</td>\n",
" <td>0.397430</td>\n",
" <td>0.194908</td>\n",
" <td>5.760291</td>\n",
" <td>0.161491</td>\n",
" <td>25.342874</td>\n",
" <td>0.478339</td>\n",
" <td>0.731921</td>\n",
" <td>8.551638</td>\n",
" <td>6.482068</td>\n",
" <td>0.075895</td>\n",
" <td>0.230698</td>\n",
" <td>0.077154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.371102</td>\n",
" <td>-0.482293</td>\n",
" <td>0.297108</td>\n",
" <td>1.920211</td>\n",
" <td>0.020735</td>\n",
" <td>10.492070</td>\n",
" <td>0.292174</td>\n",
" <td>1.332239</td>\n",
" <td>3.531824</td>\n",
" <td>0.573040</td>\n",
" <td>0.042956</td>\n",
" <td>0.137978</td>\n",
" <td>-0.072000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.596484</td>\n",
" <td>0.052315</td>\n",
" <td>0.466494</td>\n",
" <td>3.531741</td>\n",
" <td>0.072324</td>\n",
" <td>25.324427</td>\n",
" <td>0.707876</td>\n",
" <td>1.742983</td>\n",
" <td>8.127398</td>\n",
" <td>1.813572</td>\n",
" <td>0.159265</td>\n",
" <td>0.248962</td>\n",
" <td>0.052617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.237246</td>\n",
" <td>0.290593</td>\n",
" <td>0.729540</td>\n",
" <td>4.476625</td>\n",
" <td>0.113483</td>\n",
" <td>33.048050</td>\n",
" <td>0.993926</td>\n",
" <td>2.212266</td>\n",
" <td>12.308800</td>\n",
" <td>4.675997</td>\n",
" <td>0.199137</td>\n",
" <td>0.365960</td>\n",
" <td>0.094618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>14.029394</td>\n",
" <td>0.679854</td>\n",
" <td>0.853552</td>\n",
" <td>7.867471</td>\n",
" <td>0.307852</td>\n",
" <td>46.159854</td>\n",
" <td>1.242135</td>\n",
" <td>2.822726</td>\n",
" <td>19.752448</td>\n",
" <td>10.723556</td>\n",
" <td>0.261186</td>\n",
" <td>0.670842</td>\n",
" <td>0.139564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>31.800150</td>\n",
" <td>1.110681</td>\n",
" <td>0.925871</td>\n",
" <td>45.427380</td>\n",
" <td>0.678935</td>\n",
" <td>199.999821</td>\n",
" <td>2.918984</td>\n",
" <td>4.604317</td>\n",
" <td>39.093347</td>\n",
" <td>25.836762</td>\n",
" <td>0.406678</td>\n",
" <td>0.966010</td>\n",
" <td>0.336072</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_rate gridness sparsity selectivity \\\n",
"count 73.000000 73.000000 73.000000 73.000000 \n",
"mean 9.554733 0.365628 0.666120 6.232196 \n",
"std 7.334232 0.397430 0.194908 5.760291 \n",
"min 1.371102 -0.482293 0.297108 1.920211 \n",
"25% 3.596484 0.052315 0.466494 3.531741 \n",
"50% 7.237246 0.290593 0.729540 4.476625 \n",
"75% 14.029394 0.679854 0.853552 7.867471 \n",
"max 31.800150 1.110681 0.925871 45.427380 \n",
"\n",
" information_specificity max_rate information_rate \\\n",
"count 73.000000 73.000000 73.000000 \n",
"mean 0.194542 39.864795 1.063930 \n",
"std 0.161491 25.342874 0.478339 \n",
"min 0.020735 10.492070 0.292174 \n",
"25% 0.072324 25.324427 0.707876 \n",
"50% 0.113483 33.048050 0.993926 \n",
"75% 0.307852 46.159854 1.242135 \n",
"max 0.678935 199.999821 2.918984 \n",
"\n",
" interspike_interval_cv in_field_mean_rate out_field_mean_rate \\\n",
"count 73.000000 73.000000 73.000000 \n",
"mean 2.328150 14.582445 7.121023 \n",
"std 0.731921 8.551638 6.482068 \n",
"min 1.332239 3.531824 0.573040 \n",
"25% 1.742983 8.127398 1.813572 \n",
"50% 2.212266 12.308800 4.675997 \n",
"75% 2.822726 19.752448 10.723556 \n",
"max 4.604317 39.093347 25.836762 \n",
"\n",
" burst_event_ratio specificity speed_score \n",
"count 73.000000 73.000000 73.000000 \n",
"mean 0.205369 0.445532 0.102438 \n",
"std 0.075895 0.230698 0.077154 \n",
"min 0.042956 0.137978 -0.072000 \n",
"25% 0.159265 0.248962 0.052617 \n",
"50% 0.199137 0.365960 0.094618 \n",
"75% 0.261186 0.670842 0.139564 \n",
"max 0.406678 0.966010 0.336072 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gridcell_in_baseline.query(\"stimulated\")[columns].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create nice table"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"def summarize(data):\n",
" return \"{:.2f} ± {:.2f} ({})\".format(data.mean(), data.sem(), sum(~np.isnan(data)))\n",
"\n",
"\n",
"def MWU(column, stim, base):\n",
" '''\n",
" Mann Whitney U\n",
" '''\n",
" Uvalue, pvalue = scipy.stats.mannwhitneyu(\n",
" stim[column].dropna(), \n",
" base[column].dropna(),\n",
" alternative='two-sided')\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(Uvalue, pvalue)\n",
"\n",
"\n",
"def PRS(column, stim, base):\n",
" '''\n",
" Permutation ReSampling\n",
" '''\n",
" pvalue, observed_diff, diffs = permutation_resampling(\n",
" stim[column].dropna(), \n",
" base[column].dropna(), statistic=np.median)\n",
"\n",
" return \"{:.2f}, {:.3f}\".format(observed_diff, pvalue)\n",
"\n",
"\n",
"def rename(name):\n",
" return name.replace(\"_field\", \"-field\").replace(\"_\", \" \").capitalize()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Stimulated</th>\n",
" <th>Baseline</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>9.55 ± 0.86 (73)</td>\n",
" <td>9.93 ± 0.64 (144)</td>\n",
" <td>5120.00, 0.757</td>\n",
" <td>0.14, 0.868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.37 ± 0.05 (73)</td>\n",
" <td>0.56 ± 0.03 (144)</td>\n",
" <td>3718.00, 0.000</td>\n",
" <td>0.30, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.67 ± 0.02 (73)</td>\n",
" <td>0.66 ± 0.02 (144)</td>\n",
" <td>5515.00, 0.554</td>\n",
" <td>0.03, 0.413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>6.23 ± 0.67 (73)</td>\n",
" <td>5.32 ± 0.24 (144)</td>\n",
" <td>5482.00, 0.606</td>\n",
" <td>0.21, 0.718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.19 ± 0.02 (73)</td>\n",
" <td>0.20 ± 0.01 (144)</td>\n",
" <td>5094.00, 0.712</td>\n",
" <td>0.03, 0.501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>39.86 ± 2.97 (73)</td>\n",
" <td>37.44 ± 1.38 (144)</td>\n",
" <td>5256.00, 0.999</td>\n",
" <td>0.97, 0.592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.06 ± 0.06 (73)</td>\n",
" <td>1.18 ± 0.05 (144)</td>\n",
" <td>4681.00, 0.189</td>\n",
" <td>0.07, 0.426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.33 ± 0.09 (73)</td>\n",
" <td>2.35 ± 0.06 (144)</td>\n",
" <td>5197.00, 0.894</td>\n",
" <td>0.04, 0.715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>14.58 ± 1.00 (73)</td>\n",
" <td>15.71 ± 0.82 (144)</td>\n",
" <td>5000.00, 0.559</td>\n",
" <td>0.55, 0.751</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>7.12 ± 0.76 (73)</td>\n",
" <td>7.32 ± 0.56 (144)</td>\n",
" <td>5166.00, 0.838</td>\n",
" <td>0.10, 0.934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.21 ± 0.01 (73)</td>\n",
" <td>0.22 ± 0.01 (144)</td>\n",
" <td>4677.00, 0.186</td>\n",
" <td>0.01, 0.212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.45 ± 0.03 (73)</td>\n",
" <td>0.44 ± 0.02 (144)</td>\n",
" <td>5076.00, 0.681</td>\n",
" <td>0.02, 0.547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.10 ± 0.01 (73)</td>\n",
" <td>0.14 ± 0.01 (144)</td>\n",
" <td>3978.00, 0.003</td>\n",
" <td>0.04, 0.008</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Stimulated Baseline \\\n",
"Average rate 9.55 ± 0.86 (73) 9.93 ± 0.64 (144) \n",
"Gridness 0.37 ± 0.05 (73) 0.56 ± 0.03 (144) \n",
"Sparsity 0.67 ± 0.02 (73) 0.66 ± 0.02 (144) \n",
"Selectivity 6.23 ± 0.67 (73) 5.32 ± 0.24 (144) \n",
"Information specificity 0.19 ± 0.02 (73) 0.20 ± 0.01 (144) \n",
"Max rate 39.86 ± 2.97 (73) 37.44 ± 1.38 (144) \n",
"Information rate 1.06 ± 0.06 (73) 1.18 ± 0.05 (144) \n",
"Interspike interval cv 2.33 ± 0.09 (73) 2.35 ± 0.06 (144) \n",
"In-field mean rate 14.58 ± 1.00 (73) 15.71 ± 0.82 (144) \n",
"Out-field mean rate 7.12 ± 0.76 (73) 7.32 ± 0.56 (144) \n",
"Burst event ratio 0.21 ± 0.01 (73) 0.22 ± 0.01 (144) \n",
"Specificity 0.45 ± 0.03 (73) 0.44 ± 0.02 (144) \n",
"Speed score 0.10 ± 0.01 (73) 0.14 ± 0.01 (144) \n",
"\n",
" MWU PRS \n",
"Average rate 5120.00, 0.757 0.14, 0.868 \n",
"Gridness 3718.00, 0.000 0.30, 0.000 \n",
"Sparsity 5515.00, 0.554 0.03, 0.413 \n",
"Selectivity 5482.00, 0.606 0.21, 0.718 \n",
"Information specificity 5094.00, 0.712 0.03, 0.501 \n",
"Max rate 5256.00, 0.999 0.97, 0.592 \n",
"Information rate 4681.00, 0.189 0.07, 0.426 \n",
"Interspike interval cv 5197.00, 0.894 0.04, 0.715 \n",
"In-field mean rate 5000.00, 0.559 0.55, 0.751 \n",
"Out-field mean rate 5166.00, 0.838 0.10, 0.934 \n",
"Burst event ratio 4677.00, 0.186 0.01, 0.212 \n",
"Specificity 5076.00, 0.681 0.02, 0.547 \n",
"Speed score 3978.00, 0.003 0.04, 0.008 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = gridcell_in_baseline.query('stimulated')\n",
"_base_data = gridcell_in_baseline.query('baseline')\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline'] = _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",
"result.to_latex(output_path / \"statistics\" / \"statistics.tex\")\n",
"result.to_latex(output_path / \"statistics\" / \"statistics.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Stimulated</th>\n",
" <th>Baseline</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>9.06 ± 1.21 (32)</td>\n",
" <td>9.65 ± 0.90 (68)</td>\n",
" <td>1044.00, 0.748</td>\n",
" <td>0.02, 0.997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.34 ± 0.06 (32)</td>\n",
" <td>0.58 ± 0.04 (68)</td>\n",
" <td>676.00, 0.002</td>\n",
" <td>0.27, 0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.67 ± 0.03 (32)</td>\n",
" <td>0.65 ± 0.02 (68)</td>\n",
" <td>1154.00, 0.628</td>\n",
" <td>0.06, 0.319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>5.43 ± 0.47 (32)</td>\n",
" <td>5.22 ± 0.35 (68)</td>\n",
" <td>1140.00, 0.704</td>\n",
" <td>0.29, 0.705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.19 ± 0.03 (32)</td>\n",
" <td>0.21 ± 0.02 (68)</td>\n",
" <td>1005.00, 0.542</td>\n",
" <td>0.05, 0.095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>35.53 ± 2.50 (32)</td>\n",
" <td>36.19 ± 1.79 (68)</td>\n",
" <td>1063.00, 0.856</td>\n",
" <td>0.04, 0.972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.04 ± 0.10 (32)</td>\n",
" <td>1.21 ± 0.06 (68)</td>\n",
" <td>867.00, 0.103</td>\n",
" <td>0.12, 0.225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.29 ± 0.12 (32)</td>\n",
" <td>2.38 ± 0.10 (68)</td>\n",
" <td>1053.00, 0.799</td>\n",
" <td>0.04, 0.891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>13.87 ± 1.42 (32)</td>\n",
" <td>15.27 ± 1.12 (68)</td>\n",
" <td>1024.00, 0.639</td>\n",
" <td>0.10, 0.948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>6.52 ± 1.04 (32)</td>\n",
" <td>6.98 ± 0.76 (68)</td>\n",
" <td>1037.00, 0.709</td>\n",
" <td>0.35, 0.905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.23 ± 0.01 (32)</td>\n",
" <td>0.23 ± 0.01 (68)</td>\n",
" <td>1158.00, 0.608</td>\n",
" <td>0.01, 0.478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.45 ± 0.04 (32)</td>\n",
" <td>0.45 ± 0.02 (68)</td>\n",
" <td>1060.00, 0.839</td>\n",
" <td>0.01, 0.852</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.09 ± 0.01 (32)</td>\n",
" <td>0.14 ± 0.01 (68)</td>\n",
" <td>736.00, 0.009</td>\n",
" <td>0.05, 0.011</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Stimulated Baseline MWU \\\n",
"Average rate 9.06 ± 1.21 (32) 9.65 ± 0.90 (68) 1044.00, 0.748 \n",
"Gridness 0.34 ± 0.06 (32) 0.58 ± 0.04 (68) 676.00, 0.002 \n",
"Sparsity 0.67 ± 0.03 (32) 0.65 ± 0.02 (68) 1154.00, 0.628 \n",
"Selectivity 5.43 ± 0.47 (32) 5.22 ± 0.35 (68) 1140.00, 0.704 \n",
"Information specificity 0.19 ± 0.03 (32) 0.21 ± 0.02 (68) 1005.00, 0.542 \n",
"Max rate 35.53 ± 2.50 (32) 36.19 ± 1.79 (68) 1063.00, 0.856 \n",
"Information rate 1.04 ± 0.10 (32) 1.21 ± 0.06 (68) 867.00, 0.103 \n",
"Interspike interval cv 2.29 ± 0.12 (32) 2.38 ± 0.10 (68) 1053.00, 0.799 \n",
"In-field mean rate 13.87 ± 1.42 (32) 15.27 ± 1.12 (68) 1024.00, 0.639 \n",
"Out-field mean rate 6.52 ± 1.04 (32) 6.98 ± 0.76 (68) 1037.00, 0.709 \n",
"Burst event ratio 0.23 ± 0.01 (32) 0.23 ± 0.01 (68) 1158.00, 0.608 \n",
"Specificity 0.45 ± 0.04 (32) 0.45 ± 0.02 (68) 1060.00, 0.839 \n",
"Speed score 0.09 ± 0.01 (32) 0.14 ± 0.01 (68) 736.00, 0.009 \n",
"\n",
" PRS \n",
"Average rate 0.02, 0.997 \n",
"Gridness 0.27, 0.003 \n",
"Sparsity 0.06, 0.319 \n",
"Selectivity 0.29, 0.705 \n",
"Information specificity 0.05, 0.095 \n",
"Max rate 0.04, 0.972 \n",
"Information rate 0.12, 0.225 \n",
"Interspike interval cv 0.04, 0.891 \n",
"In-field mean rate 0.10, 0.948 \n",
"Out-field mean rate 0.35, 0.905 \n",
"Burst event ratio 0.01, 0.478 \n",
"Specificity 0.01, 0.852 \n",
"Speed score 0.05, 0.011 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_11\n",
"_base_data = baseline_i\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline'] = _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_11.tex\")\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_11.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"\n",
" .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>Stimulated</th>\n",
" <th>Baseline</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>10.11 ± 1.51 (28)</td>\n",
" <td>10.01 ± 1.06 (58)</td>\n",
" <td>808.00, 0.974</td>\n",
" <td>0.07, 0.968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.28 ± 0.08 (28)</td>\n",
" <td>0.57 ± 0.05 (58)</td>\n",
" <td>493.00, 0.003</td>\n",
" <td>0.46, 0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.68 ± 0.04 (28)</td>\n",
" <td>0.66 ± 0.02 (58)</td>\n",
" <td>881.00, 0.528</td>\n",
" <td>0.04, 0.328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>7.47 ± 1.63 (28)</td>\n",
" <td>5.53 ± 0.40 (58)</td>\n",
" <td>809.00, 0.982</td>\n",
" <td>0.30, 0.638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.20 ± 0.03 (28)</td>\n",
" <td>0.19 ± 0.02 (58)</td>\n",
" <td>812.00, 0.996</td>\n",
" <td>0.01, 0.588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>45.33 ± 6.85 (28)</td>\n",
" <td>38.95 ± 2.48 (58)</td>\n",
" <td>797.00, 0.894</td>\n",
" <td>2.09, 0.451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.04 ± 0.08 (28)</td>\n",
" <td>1.12 ± 0.09 (58)</td>\n",
" <td>799.00, 0.908</td>\n",
" <td>0.03, 0.858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.28 ± 0.16 (28)</td>\n",
" <td>2.32 ± 0.09 (58)</td>\n",
" <td>745.00, 0.540</td>\n",
" <td>0.16, 0.463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>14.95 ± 1.71 (28)</td>\n",
" <td>15.81 ± 1.38 (58)</td>\n",
" <td>779.00, 0.765</td>\n",
" <td>0.98, 0.712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>7.80 ± 1.35 (28)</td>\n",
" <td>7.58 ± 0.96 (58)</td>\n",
" <td>827.00, 0.894</td>\n",
" <td>0.10, 0.927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.18 ± 0.01 (28)</td>\n",
" <td>0.21 ± 0.01 (58)</td>\n",
" <td>641.00, 0.116</td>\n",
" <td>0.03, 0.099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.43 ± 0.05 (28)</td>\n",
" <td>0.43 ± 0.03 (58)</td>\n",
" <td>749.00, 0.565</td>\n",
" <td>0.02, 0.657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.10 ± 0.02 (28)</td>\n",
" <td>0.12 ± 0.01 (58)</td>\n",
" <td>617.00, 0.073</td>\n",
" <td>0.02, 0.116</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Stimulated Baseline MWU \\\n",
"Average rate 10.11 ± 1.51 (28) 10.01 ± 1.06 (58) 808.00, 0.974 \n",
"Gridness 0.28 ± 0.08 (28) 0.57 ± 0.05 (58) 493.00, 0.003 \n",
"Sparsity 0.68 ± 0.04 (28) 0.66 ± 0.02 (58) 881.00, 0.528 \n",
"Selectivity 7.47 ± 1.63 (28) 5.53 ± 0.40 (58) 809.00, 0.982 \n",
"Information specificity 0.20 ± 0.03 (28) 0.19 ± 0.02 (58) 812.00, 0.996 \n",
"Max rate 45.33 ± 6.85 (28) 38.95 ± 2.48 (58) 797.00, 0.894 \n",
"Information rate 1.04 ± 0.08 (28) 1.12 ± 0.09 (58) 799.00, 0.908 \n",
"Interspike interval cv 2.28 ± 0.16 (28) 2.32 ± 0.09 (58) 745.00, 0.540 \n",
"In-field mean rate 14.95 ± 1.71 (28) 15.81 ± 1.38 (58) 779.00, 0.765 \n",
"Out-field mean rate 7.80 ± 1.35 (28) 7.58 ± 0.96 (58) 827.00, 0.894 \n",
"Burst event ratio 0.18 ± 0.01 (28) 0.21 ± 0.01 (58) 641.00, 0.116 \n",
"Specificity 0.43 ± 0.05 (28) 0.43 ± 0.03 (58) 749.00, 0.565 \n",
"Speed score 0.10 ± 0.02 (28) 0.12 ± 0.01 (58) 617.00, 0.073 \n",
"\n",
" PRS \n",
"Average rate 0.07, 0.968 \n",
"Gridness 0.46, 0.000 \n",
"Sparsity 0.04, 0.328 \n",
"Selectivity 0.30, 0.638 \n",
"Information specificity 0.01, 0.588 \n",
"Max rate 2.09, 0.451 \n",
"Information rate 0.03, 0.858 \n",
"Interspike interval cv 0.16, 0.463 \n",
"In-field mean rate 0.98, 0.712 \n",
"Out-field mean rate 0.10, 0.927 \n",
"Burst event ratio 0.03, 0.099 \n",
"Specificity 0.02, 0.657 \n",
"Speed score 0.02, 0.116 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_30\n",
"_base_data = baseline_ii\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Stimulated'] = _stim_data[columns].agg(summarize)\n",
"result['Baseline'] = _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_30.tex\")\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_30.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Stimulated 30Hz</th>\n",
" <th>Stimulated 11Hz</th>\n",
" <th>MWU</th>\n",
" <th>PRS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Average rate</th>\n",
" <td>10.11 ± 1.51 (28)</td>\n",
" <td>9.06 ± 1.21 (32)</td>\n",
" <td>463.00, 0.830</td>\n",
" <td>0.12, 0.978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.28 ± 0.08 (28)</td>\n",
" <td>0.34 ± 0.06 (32)</td>\n",
" <td>402.00, 0.500</td>\n",
" <td>0.15, 0.330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.68 ± 0.04 (28)</td>\n",
" <td>0.67 ± 0.03 (32)</td>\n",
" <td>479.00, 0.651</td>\n",
" <td>0.03, 0.493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>7.47 ± 1.63 (28)</td>\n",
" <td>5.43 ± 0.47 (32)</td>\n",
" <td>449.00, 0.994</td>\n",
" <td>0.00, 0.999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.20 ± 0.03 (28)</td>\n",
" <td>0.19 ± 0.03 (32)</td>\n",
" <td>440.00, 0.912</td>\n",
" <td>0.01, 0.768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>45.33 ± 6.85 (28)</td>\n",
" <td>35.53 ± 2.50 (32)</td>\n",
" <td>488.00, 0.558</td>\n",
" <td>1.22, 0.682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.04 ± 0.08 (28)</td>\n",
" <td>1.04 ± 0.10 (32)</td>\n",
" <td>475.00, 0.695</td>\n",
" <td>0.02, 0.775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.28 ± 0.16 (28)</td>\n",
" <td>2.29 ± 0.12 (32)</td>\n",
" <td>411.00, 0.589</td>\n",
" <td>0.14, 0.659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>14.95 ± 1.71 (28)</td>\n",
" <td>13.87 ± 1.42 (32)</td>\n",
" <td>473.00, 0.717</td>\n",
" <td>1.02, 0.794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>7.80 ± 1.35 (28)</td>\n",
" <td>6.52 ± 1.04 (32)</td>\n",
" <td>489.00, 0.548</td>\n",
" <td>0.17, 0.940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.18 ± 0.01 (28)</td>\n",
" <td>0.23 ± 0.01 (32)</td>\n",
" <td>273.00, 0.010</td>\n",
" <td>0.05, 0.028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.43 ± 0.05 (28)</td>\n",
" <td>0.45 ± 0.04 (32)</td>\n",
" <td>400.00, 0.482</td>\n",
" <td>0.02, 0.570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.10 ± 0.02 (28)</td>\n",
" <td>0.09 ± 0.01 (32)</td>\n",
" <td>446.00, 0.982</td>\n",
" <td>0.01, 0.480</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Stimulated 30Hz Stimulated 11Hz MWU \\\n",
"Average rate 10.11 ± 1.51 (28) 9.06 ± 1.21 (32) 463.00, 0.830 \n",
"Gridness 0.28 ± 0.08 (28) 0.34 ± 0.06 (32) 402.00, 0.500 \n",
"Sparsity 0.68 ± 0.04 (28) 0.67 ± 0.03 (32) 479.00, 0.651 \n",
"Selectivity 7.47 ± 1.63 (28) 5.43 ± 0.47 (32) 449.00, 0.994 \n",
"Information specificity 0.20 ± 0.03 (28) 0.19 ± 0.03 (32) 440.00, 0.912 \n",
"Max rate 45.33 ± 6.85 (28) 35.53 ± 2.50 (32) 488.00, 0.558 \n",
"Information rate 1.04 ± 0.08 (28) 1.04 ± 0.10 (32) 475.00, 0.695 \n",
"Interspike interval cv 2.28 ± 0.16 (28) 2.29 ± 0.12 (32) 411.00, 0.589 \n",
"In-field mean rate 14.95 ± 1.71 (28) 13.87 ± 1.42 (32) 473.00, 0.717 \n",
"Out-field mean rate 7.80 ± 1.35 (28) 6.52 ± 1.04 (32) 489.00, 0.548 \n",
"Burst event ratio 0.18 ± 0.01 (28) 0.23 ± 0.01 (32) 273.00, 0.010 \n",
"Specificity 0.43 ± 0.05 (28) 0.45 ± 0.04 (32) 400.00, 0.482 \n",
"Speed score 0.10 ± 0.02 (28) 0.09 ± 0.01 (32) 446.00, 0.982 \n",
"\n",
" PRS \n",
"Average rate 0.12, 0.978 \n",
"Gridness 0.15, 0.330 \n",
"Sparsity 0.03, 0.493 \n",
"Selectivity 0.00, 0.999 \n",
"Information specificity 0.01, 0.768 \n",
"Max rate 1.22, 0.682 \n",
"Information rate 0.02, 0.775 \n",
"Interspike interval cv 0.14, 0.659 \n",
"In-field mean rate 1.02, 0.794 \n",
"Out-field mean rate 0.17, 0.940 \n",
"Burst event ratio 0.05, 0.028 \n",
"Specificity 0.02, 0.570 \n",
"Speed score 0.01, 0.480 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_stim_data = stimulated_30\n",
"_base_data = stimulated_11\n",
"\n",
"result = pd.DataFrame()\n",
"\n",
"result['Stimulated 30Hz'] = _stim_data[columns].agg(summarize)\n",
"result['Stimulated 11Hz'] = _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_11_vs_30.tex\")\n",
"result.to_latex(output_path / \"statistics\" / \"statistics_11_vs_30.csv\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Baseline i</th>\n",
" <th>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>9.65 ± 0.90 (68)</td>\n",
" <td>10.01 ± 1.06 (58)</td>\n",
" <td>1979.00, 0.975</td>\n",
" <td>0.20, 0.935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gridness</th>\n",
" <td>0.58 ± 0.04 (68)</td>\n",
" <td>0.57 ± 0.05 (58)</td>\n",
" <td>1946.00, 0.901</td>\n",
" <td>0.04, 0.479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sparsity</th>\n",
" <td>0.65 ± 0.02 (68)</td>\n",
" <td>0.66 ± 0.02 (58)</td>\n",
" <td>1870.00, 0.619</td>\n",
" <td>0.05, 0.253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Selectivity</th>\n",
" <td>5.22 ± 0.35 (68)</td>\n",
" <td>5.53 ± 0.40 (58)</td>\n",
" <td>1833.00, 0.498</td>\n",
" <td>0.01, 0.973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information specificity</th>\n",
" <td>0.21 ± 0.02 (68)</td>\n",
" <td>0.19 ± 0.02 (58)</td>\n",
" <td>2135.00, 0.426</td>\n",
" <td>0.05, 0.136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Max rate</th>\n",
" <td>36.19 ± 1.79 (68)</td>\n",
" <td>38.95 ± 2.48 (58)</td>\n",
" <td>1824.00, 0.470</td>\n",
" <td>0.84, 0.675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information rate</th>\n",
" <td>1.21 ± 0.06 (68)</td>\n",
" <td>1.12 ± 0.09 (58)</td>\n",
" <td>2246.00, 0.181</td>\n",
" <td>0.13, 0.169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interspike interval cv</th>\n",
" <td>2.38 ± 0.10 (68)</td>\n",
" <td>2.32 ± 0.09 (58)</td>\n",
" <td>2055.00, 0.686</td>\n",
" <td>0.02, 0.805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In-field mean rate</th>\n",
" <td>15.27 ± 1.12 (68)</td>\n",
" <td>15.81 ± 1.38 (58)</td>\n",
" <td>1926.00, 0.824</td>\n",
" <td>0.15, 0.931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Out-field mean rate</th>\n",
" <td>6.98 ± 0.76 (68)</td>\n",
" <td>7.58 ± 0.96 (58)</td>\n",
" <td>1946.00, 0.901</td>\n",
" <td>0.62, 0.650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burst event ratio</th>\n",
" <td>0.23 ± 0.01 (68)</td>\n",
" <td>0.21 ± 0.01 (58)</td>\n",
" <td>2112.00, 0.495</td>\n",
" <td>0.00, 0.743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specificity</th>\n",
" <td>0.45 ± 0.02 (68)</td>\n",
" <td>0.43 ± 0.03 (58)</td>\n",
" <td>2035.00, 0.760</td>\n",
" <td>0.01, 0.834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed score</th>\n",
" <td>0.14 ± 0.01 (68)</td>\n",
" <td>0.12 ± 0.01 (58)</td>\n",
" <td>2267.00, 0.149</td>\n",
" <td>0.05, 0.014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Baseline i Baseline ii MWU \\\n",
"Average rate 9.65 ± 0.90 (68) 10.01 ± 1.06 (58) 1979.00, 0.975 \n",
"Gridness 0.58 ± 0.04 (68) 0.57 ± 0.05 (58) 1946.00, 0.901 \n",
"Sparsity 0.65 ± 0.02 (68) 0.66 ± 0.02 (58) 1870.00, 0.619 \n",
"Selectivity 5.22 ± 0.35 (68) 5.53 ± 0.40 (58) 1833.00, 0.498 \n",
"Information specificity 0.21 ± 0.02 (68) 0.19 ± 0.02 (58) 2135.00, 0.426 \n",
"Max rate 36.19 ± 1.79 (68) 38.95 ± 2.48 (58) 1824.00, 0.470 \n",
"Information rate 1.21 ± 0.06 (68) 1.12 ± 0.09 (58) 2246.00, 0.181 \n",
"Interspike interval cv 2.38 ± 0.10 (68) 2.32 ± 0.09 (58) 2055.00, 0.686 \n",
"In-field mean rate 15.27 ± 1.12 (68) 15.81 ± 1.38 (58) 1926.00, 0.824 \n",
"Out-field mean rate 6.98 ± 0.76 (68) 7.58 ± 0.96 (58) 1946.00, 0.901 \n",
"Burst event ratio 0.23 ± 0.01 (68) 0.21 ± 0.01 (58) 2112.00, 0.495 \n",
"Specificity 0.45 ± 0.02 (68) 0.43 ± 0.03 (58) 2035.00, 0.760 \n",
"Speed score 0.14 ± 0.01 (68) 0.12 ± 0.01 (58) 2267.00, 0.149 \n",
"\n",
" PRS \n",
"Average rate 0.20, 0.935 \n",
"Gridness 0.04, 0.479 \n",
"Sparsity 0.05, 0.253 \n",
"Selectivity 0.01, 0.973 \n",
"Information specificity 0.05, 0.136 \n",
"Max rate 0.84, 0.675 \n",
"Information rate 0.13, 0.169 \n",
"Interspike interval cv 0.02, 0.805 \n",
"In-field mean rate 0.15, 0.931 \n",
"Out-field mean rate 0.62, 0.650 \n",
"Burst event ratio 0.00, 0.743 \n",
"Specificity 0.01, 0.834 \n",
"Speed score 0.05, 0.014 "
]
},
"execution_count": 30,
"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": 47,
"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": 48,
"metadata": {},
"outputs": [],
"source": [
"stuff = {\n",
" '': {\n",
" 'base': gridcell_in_baseline.query('baseline'),\n",
" 'stim': gridcell_in_baseline.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",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Information rate"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"U-test: U value 5831.0 p value 0.18862797777215656\n",
"_11\n",
"U-test: U value 1309.0 p value 0.10324315446274247\n",
"_30\n",
"U-test: U value 825.0 p value 0.9082875409541091\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"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['information_rate'].to_numpy()\n",
" stimulated = data['stim']['information_rate'].to_numpy()\n",
" print(key)\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Spatial information\")\n",
" plt.ylabel(\"bits/s\")\n",
" plt.ylim(-0.2, 4)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"spatial_information{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"spatial_information{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5436.0 p value 0.6812512952522969\n",
"U-test: U value 1116.0 p value 0.8389752457478267\n",
"U-test: U value 875.0 p value 0.5646191993775383\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATMAAAG1CAYAAACVsRObAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXmYJFWV9/+JiMystbtZGhDZofEqiooiyiCKOm6joo464oKio4iKOsy4/nQEfVV0FsGZUfEdQWYU94HXGXFcQGRttmanm9tr9b7Vnnusvz9uRFV2kbVlZWZERt7P89QTFRVxI05mVnzz3HPPPdcIggCNRqPpdMy4DdBoNJpmoMVMo9GkAi1mGo0mFWgx02g0qUCLmUajSQVazDQaTSrQYqbRaFKBFjONRpMKtJhpNJpUoMVMo9GkAi1mGo0mFWgx02g0qUCLmUajSQVazDQaTSrIxG1AGhFCPBv4APAK4CigF9gPPAbcCFwtpSzHZ+GBCCGeKaV8fMbfhoDjgA9KKb+/xOtfAPwA2CmlPHop1+pkhBBRva1XSilvqvm7AVwCXAgcDxSBG4A7aML7JoQ4B7gl3M1KKd0Zx48GJqWUk43eIwloz6zJCCG+BDwIfAw4GtgMPAT4wGuAfwWkEOJ5sRkZIoQ4UgjxY+C3cdvS5fwt8M+AAHYDQ8CWVt9UCJETQnwZWA8c3ur7tRrtmTURIcT7gC+ivlkvAG6QUno1x58BXAO8CPidEOIUKeX+OGwNeTXwDmBnnWOvALKoh0vTHJ4RbrfN+PtfhdufSinfEf1RCLECuBtwlnjfe6N7z/DKngr8/RKvnRi0mDWXz4fbT0opfznzoJRynRDiXFR383Dg4yT0n0lKuSluG9KGlPKJWQ6tDLd/mnH+BDDRhPuWgNnunRp0N7NJCCEOBk4Kd++Z7bzQE/t/4e4LW22XpiOwwm01Vis6HO2ZNY/arsDrUXGz2bgU+Bawt/aPQohrgfeigsG/BS4HXgLkgI3AtcD3pJSVmRcUQvQB7wfeBDwbOBiooLo0vwP+WUq5q+b82sUfjor2pZRGeHyIWQYAhBDPBT4CnM30AMcYsAa4pp5X2ghCiFcDH0V1yw9GeSmPAT8Hvi+ltGvOPQcV5H4Y9SXxBeBdqK7UbuAm4BtSyo2z3OsI4O9Qn93xgIfyZn4KfLveex62Oym08S+AYwE3tPEHqIEev+bcAwYAhBB/Al5ac7kfCCF+AGyVUh4/38CJEOJs4MPAWcBTgElUt/RKKeXNdd4bCAcA6tx7gxAC4GXAh4DzgP+RUp47y+t+F/Aj4Akp5TPqndNutGfWJKSUBeDOcPdLQohrhRAvEUJYdc7dI6VcK6UcmeVyz0HFOd4I7AJ2AM8FrgT+EMZSphBCHIbyBv8NFeuaRD3UReCZqADzg+GoVcSdwIbwdzvcv5N5EEJ8GCVaHwSOQInsJmAF8FrgF0KIr853nQXc5+MoQX8D6oviISCPegC/jYo5Pum9RcX5bkSJWR9KWI5AjS6vEUK8rM69zgIeBz4FrEIN2mwDng/8E3C3EOIpddq9ObTrEpTwr0ONWp8J/F/gP8ORytl4FPWeRx7ZhnD/vjnaRPf+GnArKuY5CDyCGmR6PXCTEOLCeS7xKHB/zf6a8N4TqLguwGuEECtnNgx5b7j9wXy2tgstZs3lY0ABMFAf9q3AqBDiRiHEZ4UQLxRCLOQ9vwAYBZ4npTw1/Ob7M5Qn92LgH2ac/4/AqShhEVLKk6WUL5BSHokaQS2hYnSfiBpIKV8MfC3c3S+lfHH4t1kRQpyM8ihNlFgcIaV8XmjfU1EeE8Cnwm53QwghDgK+Ee6+Q0p5VPh6TkANWpSBc4C31Wl+CvBylIAfJaU8HeU9/gpYDvy09stACBEdOxT4d+BwKeWzpJTPBE5GfUk8p+a1Re1OQnkmg8B/AE+RUj5fSnkSStTLKM/wA7O9Tinlx8L3fE/4p6+Fn0O911V77/OAz6HE629Cm1+A+gy+EJ72nXDAadZ7c+D7d1547weBm1FinkV5aDPvfxTqS9MDfjiXre1Ei1kTCf8RXojKD4pYjuqCXI7qAuwWQnxFCNE/x6V84I1Syodqrr0aeE+4+wEhxFMBhBBZVFc0AC6RUm6ovZCU8neorhIowVsKr0J1o9ZIKb8qpZzqWkspR4FPhrtZVJpBowimu64/qz0gpfw96r38JcqjrMcVUsoroi6elHIM9VBuQYn6h2vO/SRKyP5bSnmhlHK85l6bUN7xJHC2EOK1M9r1oz7T94fB+qjdb4GvhLvvX8TrXiiXhtt/llJ+Kxoxl1J6UsqvAn9AxeHeM9sF5iJ8364Nd8+vc8r5KO34rZQyMaPdWsyaTNh9PBs4DfgScBcHxtMOR416PjKj21fLH6WUD9e59u9RD6SJ6n4hpXSklCeiHqwbZ7YJuznFcHcuAZ0XKeW3pZT9qFhZPUo1vy/lXltQonkwcK0Q4jkz7Pg/Usq3SSmvn6X9N2f+IYx5XRvuvrHm0JvD7Y/qXUhKuRclDhC+5zN+//fauFgN/wY8C+VBNg0hxCrg6eHu92Y57a9Rg1Gfn+X4QvgB6gvyDBEG02p4T805iUEPALSI0Kt6CLgs9MLOQnWRzkcJ2knAL1DxlZncO8elHwFOAJ42434VIcQRQogXhsdOQP3Tn4YSBWjel5cthDgD9bCeFP6cyvRDtqR7SSn3CSG+gXoY3wO8RwixB9X9+T3wGynl8CzNd0kp6+XNgXrvIHzvhBCDqFgXwBeFEJ+o20oNCED4+oQQvaiuK6jYZL3XMImKwzWbVeG2IKXcPMu9ty/1JlLKISHELagu+/mE3VchxAtQOWsjwP8s9T7NRItZGwjzfP6ACt7/PSrAeh7wIiHE86SUD8xoMjrH5Qrh9qDoD2Fw+rvAuRwoIiWUMGZQsbYlI4R4L6qbd+SMQ1uAq1EDA0tGSvkFIcQa4GJUN/opqBjUuwBXCPFT4OLa7l3IQt67FTO2oIR5PqL3/NA612wX0b3bcd9rUGL2biHE30spA6YD/9fVjiYnAS1mTUIIcRUqKHptGLeoi5SyHI40/SUq5UIAM8VsYI5bRQ/gvvC+vcAfUd+Wo8B3UKNU64BNUkovHF1cspiFQnZtuPtb1PzBx4B1UsqxMH7XFDEDkFLeANwghFiOGsU8BxV/fDrwbtR7MTN1YCHvXTTrolhz7FQp5WMLNK223bIFtmkW0b0H23Cv61Gjm8cBZwkh7mZ6QCBRXUzQYtZM+lBdgDcBc6YmSCnzQogCcAjTD1Ytc3kJUfwo6sK8CSVkLvCimQMAIc2a3P25cPufUsr31jnelPuEOXMnA4aU8uGwy/Y/4c/fCSE+i/IO3yCEWDHDOztWCLFMSpmvc+nnhtvHAaSU40KIvajUjWeihLmePaeiAupbpZRjYbt9qHDBszgwxSFqcyRK7IeAD9XxIBtlfbgdFEIcL6UcqnPvc1GjufdKKT/d6I3CL96fABcx/eV7KPBQ7eBUUtADAM0jCiCfHiY7zooQ4lUoIRtFjYbN5HWz5DW9HjgGlZcUxStOCLf5ekIWJoO+Ptyd+eUVBa7nyoWqJbrXmlmO16YhLOWL8kJULOpHs+Rp/aHm95m5ZhYqteUAwrhlFLj+Rc2hX4fbi+ulzYRpHLegkqD/pubQ/4bb2UYr34Ya2X5RE4UMlMc9NM+9L0B5svNNHq8duJjtf+DqcPtmpgdOEueVgRazpiGl/APwX+Hu94UQVwohjq89RwjRG05Gj3KWPh8m286kH/iVEOKYmrbnMP1P9PWaBySac3ewEOITtQ+/EOJFqMz3Q2quW0t070OEEAvpLkX3+lCYaxTdZ7kQ4jLgszNeQ6P8HJV28SzgCiHEVNcxTOKMPN97wpSQmXw9TGiN2hyGSuU4FpBMd5UBvo56H16MEs+VNe2OQ40QH4rqbn27pt0/oL5UzhZC/Fttqo0Q4jU1Ns7MCVwSYdzq/4S7nxVC/HX0mQshrNBrfTPKU3/SqO4Mav/3jqt3gpTyftTAyfEo8bSB6xp+AS1Ei1lzeScqgdJEJah
"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"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['specificity'].to_numpy()\n",
" stimulated = data['stim']['specificity'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Spatial specificity\")\n",
" plt.ylabel(\"\")\n",
" plt.ylim(-0.02, 1.25)\n",
" plt.savefig(output_path / \"figures\" / f\"specificity{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"specificity{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5392.0 p value 0.756508134572621\n",
"U-test: U value 1132.0 p value 0.7478826789327293\n",
"U-test: U value 816.0 p value 0.9742681632988652\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"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['average_rate'].to_numpy()\n",
" stimulated = data['stim']['average_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Average rate\")\n",
" plt.ylabel(\"spikes/s\")\n",
" plt.ylim(-0.2, 40)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"average_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"average_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5256.0 p value 0.9990870864121681\n",
"U-test: U value 1113.0 p value 0.8563387857160952\n",
"U-test: U value 827.0 p value 0.8936946693232326\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"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['max_rate'].to_numpy()\n",
" stimulated = data['stim']['max_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Max rate\")\n",
" plt.ylabel(\"spikes/s\")\n",
" # plt.ylim(-0.2, 45)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"max_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"max_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5315.0 p value 0.8935072494346303\n",
"U-test: U value 1123.0 p value 0.7987777816762969\n",
"U-test: U value 879.0 p value 0.5399704510090971\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"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['interspike_interval_cv'].to_numpy()\n",
" stimulated = data['stim']['interspike_interval_cv'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"ISI CV\")\n",
" plt.ylabel(\"Coefficient of variation\")\n",
" # plt.ylim(0.9, 5)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"isi_cv{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"isi_cv{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5512.0 p value 0.5587698160186371\n",
"U-test: U value 1152.0 p value 0.6389141666384814\n",
"U-test: U value 845.0 p value 0.764545672323149\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"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['in_field_mean_rate'].to_numpy()\n",
" stimulated = data['stim']['in_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"In-field rate\")\n",
" plt.ylabel(\"spikes/s\")\n",
" # plt.ylim(-0.1, 18)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"in_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"in_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5346.0 p value 0.837723677011064\n",
"U-test: U value 1139.0 p value 0.7090314211265186\n",
"U-test: U value 797.0 p value 0.8936946693232326\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"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['out_field_mean_rate'].to_numpy()\n",
" stimulated = data['stim']['out_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Out-of-field rate\")\n",
" plt.ylabel(\"spikes/s\")\n",
" # plt.ylim(-0.2, 8)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"out_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"out_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5835.0 p value 0.18556878084894968\n",
"U-test: U value 1018.0 p value 0.6075645264212683\n",
"U-test: U value 983.0 p value 0.11611024526570707\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"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['burst_event_ratio'].to_numpy()\n",
" stimulated = data['stim']['burst_event_ratio'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Bursting ratio\")\n",
" plt.ylabel(\"\")\n",
" # plt.ylim(-0.02, 0.60)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"burst_event_ratio{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"burst_event_ratio{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5445.0 p value 0.6662129955034535\n",
"U-test: U value 1097.0 p value 0.9499188826627789\n",
"U-test: U value 889.0 p value 0.4807998283550271\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"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['max_field_mean_rate'].to_numpy()\n",
" stimulated = data['stim']['max_field_mean_rate'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Mean rate of max field\")\n",
" plt.ylabel(\"(spikes/s)\")\n",
" # plt.ylim(-0.5,25)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"max_field_mean_rate{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"max_field_mean_rate{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 5912.0 p value 0.13361338521771\n",
"U-test: U value 1033.0 p value 0.6871584526572116\n",
"U-test: U value 998.0 p value 0.08734905208437223\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"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['bursty_spike_ratio'].to_numpy()\n",
" stimulated = data['stim']['bursty_spike_ratio'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"ratio of spikes per burst\")\n",
" plt.ylabel(\"\")\n",
" # plt.ylim(-0.03,0.9)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"bursty_spike_ratio{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"bursty_spike_ratio{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 6794.0 p value 0.00043429512252855416\n",
"U-test: U value 1500.0 p value 0.002360466792504703\n",
"U-test: U value 1131.0 p value 0.0033326217809176804\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"
}
],
"source": [
"\n",
"for key, data in stuff.items():\n",
" baseline = data['base']['gridness'].to_numpy()\n",
" stimulated = data['stim']['gridness'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Gridness\")\n",
" plt.ylabel(\"Gridness\")\n",
" plt.ylim(-0.005, 1.5)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"gridness{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"gridness{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U-test: U value 6534.0 p value 0.003462885676336014\n",
"U-test: U value 1440.0 p value 0.00939537597606729\n",
"U-test: U value 1007.0 p value 0.07305394917377077\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": {
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"text/plain": [
"<Figure size 255x450 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for key, data in stuff.items():\n",
" baseline = data['base']['speed_score'].to_numpy()\n",
" stimulated = data['stim']['speed_score'].to_numpy()\n",
" plt.figure()\n",
" violinplot(baseline, stimulated)\n",
" plt.title(\"Speed score\")\n",
" plt.ylabel(\"Speed score\")\n",
" # plt.ylim(-0.1, 0.5)\n",
"\n",
" plt.savefig(output_path / \"figures\" / f\"speed_score{key}.svg\", bbox_inches=\"tight\")\n",
" plt.savefig(output_path / \"figures\" / f\"speed_score{key}.png\", dpi=600, bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register in Expipe"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"comparisons-gridcells\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_30.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_11.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_base_i_vs_base_ii.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_30.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_base_i_vs_base_ii.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_11_vs_30.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_11_vs_30.tex',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/statistics/statistics_11.csv',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/spatial_information.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/specificity_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/bursty_spike_ratio_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/burst_event_ratio.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/isi_cv.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/gridness_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/out_field_mean_rate_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_field_mean_rate_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/max_field_mean_rate_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/gridness_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/spatial_information_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/out_field_mean_rate_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/in_field_mean_rate_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/out_field_mean_rate_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/isi_cv_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/specificity_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/specificity_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/bursty_spike_ratio_11.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/specificity_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/burst_event_ratio_30.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/isi_cv_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/average_rate_30.png',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/burst_event_ratio_11.svg',\n",
" '/media/storage/expipe/septum-mec/actions/comparisons-gridcells/data/figures/speed_score.png',\n",
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]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_comparisons_gridcells.ipynb\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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