septum-mec/actions/stimulus-response/data/20_stimulus-spike-response....

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2019-10-17 17:50:31 +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": [
"19:18:12 [I] klustakwik KlustaKwik2 version 0.2.6\n"
]
}
],
"source": [
"import os\n",
"import expipe\n",
"import pathlib\n",
"import numpy as np\n",
"import spatial_maps.stats as stats\n",
"import septum_mec\n",
"import septum_mec.analysis.data_processing as dp\n",
"import septum_mec.analysis.registration\n",
"import head_direction.head as head\n",
"import spatial_maps as sp\n",
"import speed_cells.speed as spd\n",
"import re\n",
"import joblib\n",
"import multiprocessing\n",
"import shutil\n",
"import psutil\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import seaborn as sns\n",
"from distutils.dir_util import copy_tree\n",
"from neo import SpikeTrain\n",
"import scipy\n",
"\n",
"from tqdm import tqdm_notebook as tqdm\n",
"from tqdm._tqdm_notebook import tqdm_notebook\n",
"tqdm_notebook.pandas()\n",
"\n",
"from spike_statistics.core import permutation_resampling\n",
"\n",
"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
"\n",
"from septum_mec.analysis.plotting import violinplot"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"plt.rc('axes', titlesize=12)\n",
"plt.rcParams.update({\n",
" 'font.size': 12, \n",
" 'figure.figsize': (6, 4), \n",
" 'figure.dpi': 150\n",
"})\n",
"\n",
"output_path = pathlib.Path(\"output\") / \"stimulus-response\"\n",
"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
"output_path.mkdir(exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data_loader = dp.Data()\n",
"actions = data_loader.actions\n",
"project = data_loader.project"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"identification_action = actions['identify-neurons']\n",
"sessions = pd.read_csv(identification_action.data_path('sessions'))\n",
"units = pd.read_csv(identification_action.data_path('units'))\n",
"session_units = pd.merge(sessions, units, on='action')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"stim_action = actions['stimulus-response']\n",
"stim_results = pd.read_csv(stim_action.data_path('results'))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# lfp_results has old unit id's but correct on (action, unit_name, channel_group)\n",
"stim_results = stim_results.drop('unit_id', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"statistics_action = actions['calculate-statistics']\n",
"shuffling = actions['shuffling']\n",
"\n",
"statistics_results = pd.read_csv(statistics_action.data_path('results'))\n",
"statistics_results = session_units.merge(statistics_results, how='left')\n",
"quantiles_95 = pd.read_csv(shuffling.data_path('quantiles_95'))\n",
"action_columns = ['action', 'channel_group', 'unit_name']\n",
"data = pd.merge(statistics_results, quantiles_95, on=action_columns, suffixes=(\"\", \"_threshold\"))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"data['unit_day'] = data.apply(lambda x: str(x.unit_idnum) + '_' + x.action.split('-')[1], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"data = data.merge(stim_results, how='left')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"waveform_action = actions['waveform-analysis']\n",
"waveform_results = pd.read_csv(waveform_action.data_path('results')).drop('template', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data = data.merge(waveform_results, how='left')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"colors = ['#d95f02','#e7298a']\n",
"labels = ['11 Hz', '30 HZ']\n",
"queries = ['frequency==11', 'frequency==30']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"data.bs = data.bs.astype(bool)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of gridcells 225\n"
]
}
],
"source": [
"grid_query = 'gridness > gridness_threshold and information_rate > information_rate_threshold'\n",
"gridcell_sessions = data.query(grid_query)\n",
"print(\"Number of gridcells\", len(gridcell_sessions))\n",
"# print(\"Number of animals\", len(gridcell_sessions.groupby(['entity'])))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"data['gridcell'] = data.isin(data.query(grid_query))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
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" <td>NaN</td>\n",
" <td>0.273572</td>\n",
" <td>0.611548</td>\n",
" <td>5.407135</td>\n",
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" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1199</th>\n",
" <td>1833-260619-3</td>\n",
" <td>0</td>\n",
" <td>140</td>\n",
" <td>3.564682</td>\n",
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" <td>2.498756</td>\n",
" <td>5.782665</td>\n",
" <td>8.770230</td>\n",
" <td>17.134986</td>\n",
" <td>0.720704</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.189559</td>\n",
" <td>0.248665</td>\n",
" <td>3.564358</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>1833-260619-3</td>\n",
" <td>0</td>\n",
" <td>141</td>\n",
" <td>2.694224</td>\n",
" <td>0.094154</td>\n",
" <td>1.691471</td>\n",
" <td>5.502054</td>\n",
" <td>10.395725</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
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" <td>1833-260619-3</td>\n",
" <td>0</td>\n",
" <td>182</td>\n",
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" <td>0.148720</td>\n",
" <td>3.342163</td>\n",
" <td>10.892485</td>\n",
" <td>16.803801</td>\n",
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" <tr>\n",
" <th>1203</th>\n",
" <td>1833-260619-3</td>\n",
" <td>0</td>\n",
" <td>194</td>\n",
" <td>6.485358</td>\n",
" <td>0.096207</td>\n",
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" <td>6.484767</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1205</th>\n",
" <td>1833-260619-3</td>\n",
" <td>0</td>\n",
" <td>209</td>\n",
" <td>3.425497</td>\n",
" <td>0.085117</td>\n",
" <td>1.306754</td>\n",
" <td>8.551145</td>\n",
" <td>11.161798</td>\n",
" <td>29.652423</td>\n",
" <td>0.378044</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.244049</td>\n",
" <td>0.571337</td>\n",
" <td>3.425185</td>\n",
" <td>True</td>\n",
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" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1207</th>\n",
" <td>1833-260619-3</td>\n",
" <td>1</td>\n",
" <td>170</td>\n",
" <td>26.841716</td>\n",
" <td>0.218178</td>\n",
" <td>22.328079</td>\n",
" <td>38.090240</td>\n",
" <td>50.981983</td>\n",
" <td>74.601637</td>\n",
" <td>0.857579</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.257469</td>\n",
" <td>0.636957</td>\n",
" <td>26.839270</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1208</th>\n",
" <td>1833-260619-3</td>\n",
" <td>1</td>\n",
" <td>207</td>\n",
" <td>4.589791</td>\n",
" <td>0.088439</td>\n",
" <td>2.309667</td>\n",
" <td>8.938164</td>\n",
" <td>10.731362</td>\n",
" <td>25.229471</td>\n",
" <td>0.538208</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.252255</td>\n",
" <td>0.587372</td>\n",
" <td>4.589373</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1211</th>\n",
" <td>1833-260619-3</td>\n",
" <td>3</td>\n",
" <td>176</td>\n",
" <td>7.407735</td>\n",
" <td>0.156101</td>\n",
" <td>5.622472</td>\n",
" <td>11.694017</td>\n",
" <td>16.474141</td>\n",
" <td>32.870310</td>\n",
" <td>0.757528</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.261129</td>\n",
" <td>0.592306</td>\n",
" <td>7.407060</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1213</th>\n",
" <td>1833-260619-3</td>\n",
" <td>5</td>\n",
" <td>111</td>\n",
" <td>9.222663</td>\n",
" <td>0.179913</td>\n",
" <td>6.341652</td>\n",
" <td>14.990045</td>\n",
" <td>17.803066</td>\n",
" <td>32.423819</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.277189</td>\n",
" <td>0.615988</td>\n",
" <td>9.221822</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1216</th>\n",
" <td>1833-260619-3</td>\n",
" <td>6</td>\n",
" <td>142</td>\n",
" <td>9.359639</td>\n",
" <td>0.129023</td>\n",
" <td>6.738758</td>\n",
" <td>14.564994</td>\n",
" <td>20.758052</td>\n",
" <td>44.189302</td>\n",
" <td>0.773930</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.300175</td>\n",
" <td>0.610068</td>\n",
" <td>9.358786</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1218</th>\n",
" <td>1833-260619-3</td>\n",
" <td>6</td>\n",
" <td>192</td>\n",
" <td>7.836336</td>\n",
" <td>0.170862</td>\n",
" <td>4.889011</td>\n",
" <td>13.019928</td>\n",
" <td>17.648343</td>\n",
" <td>34.791219</td>\n",
" <td>0.715811</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.287132</td>\n",
" <td>0.616235</td>\n",
" <td>7.835622</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1222</th>\n",
" <td>1833-200619-3</td>\n",
" <td>0</td>\n",
" <td>91</td>\n",
" <td>7.072750</td>\n",
" <td>0.074100</td>\n",
" <td>4.679924</td>\n",
" <td>11.282597</td>\n",
" <td>18.578196</td>\n",
" <td>35.109099</td>\n",
" <td>0.713088</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
" <td>0.293775</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1228</th>\n",
" <td>1833-200619-3</td>\n",
" <td>3</td>\n",
" <td>82</td>\n",
" <td>15.697615</td>\n",
" <td>0.127761</td>\n",
" <td>12.267443</td>\n",
" <td>21.346293</td>\n",
" <td>27.567344</td>\n",
" <td>38.706425</td>\n",
" <td>0.874674</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.252895</td>\n",
" <td>0.600200</td>\n",
" <td>15.695836</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1229</th>\n",
" <td>1833-200619-3</td>\n",
" <td>4</td>\n",
" <td>113</td>\n",
" <td>11.770313</td>\n",
" <td>0.136640</td>\n",
" <td>6.835310</td>\n",
" <td>20.280536</td>\n",
" <td>22.248766</td>\n",
" <td>44.143227</td>\n",
" <td>0.676058</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.271023</td>\n",
" <td>0.699617</td>\n",
" <td>11.768979</td>\n",
" <td>True</td>\n",
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" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1231</th>\n",
" <td>1833-200619-3</td>\n",
" <td>5</td>\n",
" <td>59</td>\n",
" <td>4.442527</td>\n",
" <td>0.110165</td>\n",
" <td>2.926793</td>\n",
" <td>7.344323</td>\n",
" <td>8.786494</td>\n",
" <td>20.320606</td>\n",
" <td>0.722984</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.343906</td>\n",
" <td>0.698383</td>\n",
" <td>4.442023</td>\n",
" <td>True</td>\n",
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" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1232</th>\n",
" <td>1833-200619-3</td>\n",
" <td>6</td>\n",
" <td>120</td>\n",
" <td>22.461229</td>\n",
" <td>0.268466</td>\n",
" <td>18.182326</td>\n",
" <td>32.115585</td>\n",
" <td>33.640870</td>\n",
" <td>62.235139</td>\n",
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" <td>NaN</td>\n",
" <td>0.294291</td>\n",
" <td>0.639177</td>\n",
" <td>22.458685</td>\n",
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" <td>1.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1233</th>\n",
" <td>1833-200619-3</td>\n",
" <td>6</td>\n",
" <td>126</td>\n",
" <td>3.102942</td>\n",
" <td>0.090727</td>\n",
" <td>1.447857</td>\n",
" <td>6.981766</td>\n",
" <td>9.945472</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1234</th>\n",
" <td>1833-200619-3</td>\n",
" <td>6</td>\n",
" <td>132</td>\n",
" <td>6.901437</td>\n",
" <td>0.072648</td>\n",
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" <td>14.073295</td>\n",
" <td>20.697950</td>\n",
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" <td>0.277708</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1235</th>\n",
" <td>1833-200619-3</td>\n",
" <td>6</td>\n",
" <td>150</td>\n",
" <td>3.767582</td>\n",
" <td>0.114920</td>\n",
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" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>130 rows × 51 columns</p>\n",
"</div>"
],
"text/plain": [
" action channel_group unit_name average_rate speed_score \\\n",
"32 1833-260619-1 0 118 5.946164 0.169495 \n",
"34 1833-260619-1 0 130 2.860363 0.081075 \n",
"35 1833-260619-1 0 132 3.366046 0.072301 \n",
"39 1833-260619-1 1 116 17.473449 0.193373 \n",
"40 1833-260619-1 1 126 5.892390 0.183633 \n",
"42 1833-260619-1 3 114 13.438331 0.224642 \n",
"43 1833-260619-1 5 100 17.448630 0.144593 \n",
"45 1833-260619-1 6 102 10.841667 0.235736 \n",
"48 1833-260619-1 6 112 5.891356 0.226892 \n",
"49 1833-260619-1 6 124 7.915120 0.182376 \n",
"57 1834-150319-3 3 61 17.163920 0.021890 \n",
"124 1833-010719-1 1 219 2.868256 0.170572 \n",
"125 1833-010719-1 1 221 6.912671 0.090486 \n",
"126 1833-010719-1 1 229 4.230245 0.018811 \n",
"128 1833-010719-1 1 8 16.737459 0.254297 \n",
"129 1833-010719-1 2 202 25.977054 0.226032 \n",
"131 1833-010719-1 3 171 14.687550 0.163959 \n",
"132 1833-010719-1 3 198 18.659249 0.282318 \n",
"134 1833-010719-1 3 240 3.107182 0.076765 \n",
"135 1833-010719-1 5 134 6.214363 0.168450 \n",
"136 1833-010719-1 5 144 2.226506 0.119543 \n",
"209 1833-050619-1 2 99 3.350056 0.095012 \n",
"214 1833-050619-1 6 60 7.177620 0.259306 \n",
"215 1833-050619-1 6 64 16.944449 0.243525 \n",
"216 1833-050619-1 6 91 3.325889 0.155904 \n",
"221 1833-060619-1 4 172 2.654829 0.119661 \n",
"223 1833-060619-1 5 164 3.083686 0.021853 \n",
"227 1833-060619-1 6 170 3.080462 0.155454 \n",
"262 1834-150319-1 3 95 19.609185 0.063354 \n",
"274 1839-120619-1 5 158 12.579822 0.285708 \n",
"... ... ... ... ... ... \n",
"1130 1834-010319-4 0 7 18.428099 0.073675 \n",
"1151 1833-200619-1 4 165 4.093726 0.112030 \n",
"1152 1833-200619-1 6 163 17.705502 0.202908 \n",
"1153 1833-200619-1 6 171 4.061107 0.058014 \n",
"1154 1833-200619-1 6 206 3.982277 0.150630 \n",
"1155 1833-200619-1 6 240 4.089649 0.098818 \n",
"1156 1833-200619-1 7 143 9.300587 0.218310 \n",
"1162 1839-120619-3 5 131 17.773050 0.076020 \n",
"1165 1839-120619-3 6 133 2.612293 0.053873 \n",
"1167 1839-120619-3 7 119 4.950355 0.132893 \n",
"1168 1839-120619-3 7 127 5.407801 0.091931 \n",
"1199 1833-260619-3 0 140 3.564682 0.063184 \n",
"1200 1833-260619-3 0 141 2.694224 0.094154 \n",
"1202 1833-260619-3 0 182 5.289030 0.148720 \n",
"1203 1833-260619-3 0 194 6.485358 0.096207 \n",
"1205 1833-260619-3 0 209 3.425497 0.085117 \n",
"1207 1833-260619-3 1 170 26.841716 0.218178 \n",
"1208 1833-260619-3 1 207 4.589791 0.088439 \n",
"1211 1833-260619-3 3 176 7.407735 0.156101 \n",
"1213 1833-260619-3 5 111 9.222663 0.179913 \n",
"1216 1833-260619-3 6 142 9.359639 0.129023 \n",
"1218 1833-260619-3 6 192 7.836336 0.170862 \n",
"1222 1833-200619-3 0 91 7.072750 0.074100 \n",
"1228 1833-200619-3 3 82 15.697615 0.127761 \n",
"1229 1833-200619-3 4 113 11.770313 0.136640 \n",
"1231 1833-200619-3 5 59 4.442527 0.110165 \n",
"1232 1833-200619-3 6 120 22.461229 0.268466 \n",
"1233 1833-200619-3 6 126 3.102942 0.090727 \n",
"1234 1833-200619-3 6 132 6.901437 0.072648 \n",
"1235 1833-200619-3 6 150 3.767582 0.114920 \n",
"\n",
" out_field_mean_rate in_field_mean_rate max_field_mean_rate max_rate \\\n",
"32 4.138169 10.175750 16.836097 29.863371 \n",
"34 1.362852 6.837975 10.333063 21.846576 \n",
"35 1.204876 8.320200 11.903539 24.820419 \n",
"39 12.435315 25.886509 35.066123 58.438209 \n",
"40 4.008668 10.376607 11.424828 22.616252 \n",
"42 10.451118 18.904366 20.482248 37.829102 \n",
"43 12.651420 25.885399 31.780144 50.983827 \n",
"45 7.896926 16.159949 15.994156 37.844022 \n",
"48 4.028409 10.441355 13.169649 24.406383 \n",
"49 4.543545 14.013583 17.035745 30.787249 \n",
"57 12.070353 23.188083 24.427655 44.829894 \n",
"124 1.391229 6.759410 8.941986 21.915347 \n",
"125 4.070879 11.915337 24.220877 32.274461 \n",
"126 1.546702 8.504585 15.581766 33.782863 \n",
"128 12.420895 25.377508 23.273238 52.301684 \n",
"129 21.598716 37.463629 43.547728 66.169116 \n",
"131 11.038136 20.488701 21.342234 45.144706 \n",
"132 15.427596 26.715844 33.932272 51.441681 \n",
"134 1.059941 7.228602 12.831970 33.059125 \n",
"135 4.835608 9.832902 18.534635 33.761835 \n",
"136 1.188425 5.927293 13.273928 26.877971 \n",
"209 1.224499 7.669547 14.470606 29.613931 \n",
"214 5.263129 11.558126 13.097257 24.533320 \n",
"215 13.371230 26.025889 33.591762 60.449939 \n",
"216 2.039584 7.702821 9.078369 21.975777 \n",
"221 1.666324 6.169001 7.323174 22.931784 \n",
"223 1.755081 5.101697 8.325821 21.146134 \n",
"227 1.816201 6.197439 8.744690 18.172981 \n",
"262 14.334866 25.933220 29.106613 53.460587 \n",
"274 9.656518 23.105339 25.311402 59.566964 \n",
"... ... ... ... ... \n",
"1130 13.995565 25.034061 27.551569 45.574876 \n",
"1151 1.560769 9.952907 16.871964 34.400735 \n",
"1152 14.631392 24.895637 34.144570 51.462522 \n",
"1153 1.879235 7.260758 11.252257 20.574695 \n",
"1154 2.316705 7.168058 9.286450 19.626376 \n",
"1155 1.539874 10.560745 15.374288 32.783007 \n",
"1156 6.750717 13.150023 13.197378 25.067697 \n",
"1162 9.779864 29.707618 42.215165 77.486029 \n",
"1165 1.055067 6.992168 9.603099 17.060484 \n",
"1167 3.636504 7.175598 7.291281 14.571674 \n",
"1168 3.251329 15.356306 18.617758 37.590469 \n",
"1199 2.498756 5.782665 8.770230 17.134986 \n",
"1200 1.691471 5.502054 10.395725 20.328752 \n",
"1202 3.342163 10.892485 16.803801 30.523793 \n",
"1203 3.706339 12.069498 18.212336 29.243464 \n",
"1205 1.306754 8.551145 11.161798 29.652423 \n",
"1207 22.328079 38.090240 50.981983 74.601637 \n",
"1208 2.309667 8.938164 10.731362 25.229471 \n",
"1211 5.622472 11.694017 16.474141 32.870310 \n",
"1213 6.341652 14.990045 17.803066 32.423819 \n",
"1216 6.738758 14.564994 20.758052 44.189302 \n",
"1218 4.889011 13.019928 17.648343 34.791219 \n",
"1222 4.679924 11.282597 18.578196 35.109099 \n",
"1228 12.267443 21.346293 27.567344 38.706425 \n",
"1229 6.835310 20.280536 22.248766 44.143227 \n",
"1231 2.926793 7.344323 8.786494 20.320606 \n",
"1232 18.182326 32.115585 33.640870 62.235139 \n",
"1233 1.447857 6.981766 9.945472 21.048478 \n",
"1234 4.231220 14.073295 20.697950 36.231604 \n",
"1235 1.422876 10.607271 13.651769 34.348592 \n",
"\n",
" sparsity ... p_e_peak t_i_peak p_i_peak half_width peak_to_trough \\\n",
"32 0.633240 ... NaN NaN NaN 0.272875 0.602667 \n",
"34 0.424446 ... NaN NaN NaN 0.226452 0.274814 \n",
"35 0.393028 ... NaN NaN NaN 0.247266 0.570104 \n",
"39 0.760804 ... NaN NaN NaN 0.284542 0.644111 \n",
"40 0.698596 ... NaN NaN NaN 0.259920 0.581698 \n",
"42 0.841781 ... NaN NaN NaN 0.263630 0.596746 \n",
"43 0.823859 ... NaN NaN NaN 0.281399 0.607354 \n",
"45 0.799767 ... NaN NaN NaN 0.279177 0.585152 \n",
"48 0.643995 ... NaN NaN NaN 0.282336 0.711705 \n",
"49 0.646322 ... NaN NaN NaN 0.285816 0.603160 \n",
"57 0.837844 ... NaN NaN NaN 0.277867 0.588852 \n",
"124 0.446442 ... NaN NaN NaN 0.271262 0.615002 \n",
"125 0.661683 ... NaN NaN NaN 0.307694 0.659653 \n",
"126 0.456739 ... NaN NaN NaN 0.267708 0.630543 \n",
"128 0.802417 ... NaN NaN NaN 0.289100 0.673221 \n",
"129 0.862176 ... NaN NaN NaN 0.290402 0.650772 \n",
"131 0.858017 ... NaN NaN NaN 0.272160 0.620429 \n",
"132 0.860475 ... NaN NaN NaN 0.241405 0.595513 \n",
"134 0.383354 ... NaN NaN NaN 0.269911 0.609574 \n",
"135 0.750893 ... NaN NaN NaN 0.273069 0.651265 \n",
"136 0.358918 ... NaN NaN NaN 0.263251 0.629310 \n",
"209 0.384212 ... NaN NaN NaN 0.251027 0.593786 \n",
"214 0.764622 ... NaN NaN NaN 0.296577 0.631283 \n",
"215 0.824103 ... NaN NaN NaN 0.295235 0.633010 \n",
"216 0.462461 ... NaN NaN NaN 0.268553 0.618949 \n",
"221 0.457083 ... NaN NaN NaN 0.263816 0.607601 \n",
"223 0.566049 ... NaN NaN NaN 0.313833 0.646825 \n",
"227 0.529688 ... NaN NaN NaN 0.261029 0.596500 \n",
"262 0.857509 ... NaN NaN NaN 0.282343 0.604147 \n",
"274 0.660943 ... NaN NaN NaN 0.265655 0.574791 \n",
"... ... ... ... ... ... ... ... \n",
"1130 0.864388 ... NaN NaN NaN 0.284016 0.615742 \n",
"1151 0.371794 ... NaN NaN NaN 0.272936 0.784972 \n",
"1152 0.877996 ... NaN NaN NaN 0.303012 0.661133 \n",
"1153 0.561983 ... NaN NaN NaN 0.310060 0.632763 \n",
"1154 0.618229 ... NaN NaN NaN 0.290294 0.618949 \n",
"1155 0.358157 ... NaN NaN NaN 0.263375 0.622896 \n",
"1156 0.825910 ... NaN NaN NaN 0.289628 0.650032 \n",
"1162 0.651012 ... NaN NaN NaN 0.245031 0.528413 \n",
"1165 0.372375 ... NaN NaN NaN 0.239301 0.531126 \n",
"1167 0.836244 ... NaN NaN NaN 0.284221 0.610068 \n",
"1168 0.414271 ... NaN NaN NaN 0.273572 0.611548 \n",
"1199 0.720704 ... NaN NaN NaN 0.189559 0.248665 \n",
"1200 0.519950 ... NaN NaN NaN 0.225575 0.277528 \n",
"1202 0.544679 ... NaN NaN NaN 0.275930 0.594526 \n",
"1203 0.590584 ... NaN NaN NaN 0.222604 0.576271 \n",
"1205 0.378044 ... NaN NaN NaN 0.244049 0.571337 \n",
"1207 0.857579 ... NaN NaN NaN 0.257469 0.636957 \n",
"1208 0.538208 ... NaN NaN NaN 0.252255 0.587372 \n",
"1211 0.757528 ... NaN NaN NaN 0.261129 0.592306 \n",
"1213 0.732917 ... NaN NaN NaN 0.277189 0.615988 \n",
"1216 0.773930 ... NaN NaN NaN 0.300175 0.610068 \n",
"1218 0.715811 ... NaN NaN NaN 0.287132 0.616235 \n",
"1222 0.713088 ... NaN NaN NaN 0.293775 0.657679 \n",
"1228 0.874674 ... NaN NaN NaN 0.252895 0.600200 \n",
"1229 0.676058 ... NaN NaN NaN 0.271023 0.699617 \n",
"1231 0.722984 ... NaN NaN NaN 0.343906 0.698383 \n",
"1232 0.833921 ... NaN NaN NaN 0.294291 0.639177 \n",
"1233 0.436204 ... NaN NaN NaN 0.304748 0.641151 \n",
"1234 0.612146 ... NaN NaN NaN 0.277708 0.585645 \n",
"1235 0.332963 ... NaN NaN NaN 0.258204 0.608094 \n",
"\n",
" average_firing_rate bs bs_stim bs_ctrl gridcell \n",
"32 5.945508 True NaN 1.0 True \n",
"34 2.860048 False NaN 0.0 True \n",
"35 3.365674 True NaN 1.0 True \n",
"39 17.471520 True NaN 1.0 True \n",
"40 5.891739 True NaN 1.0 True \n",
"42 13.436847 True NaN 1.0 True \n",
"43 17.446704 True NaN 1.0 True \n",
"45 10.840470 True NaN 1.0 True \n",
"48 5.890705 True NaN 1.0 True \n",
"49 7.914246 True NaN 1.0 True \n",
"57 17.162446 True NaN 1.0 True \n",
"124 2.868000 True NaN 1.0 True \n",
"125 6.912052 True NaN 1.0 True \n",
"126 4.229867 True NaN 1.0 True \n",
"128 16.735961 True NaN 1.0 True \n",
"129 25.974728 True NaN 1.0 True \n",
"131 14.686236 True NaN 1.0 True \n",
"132 18.657578 True NaN 1.0 True \n",
"134 3.106903 True NaN 1.0 True \n",
"135 6.213807 True NaN 1.0 True \n",
"136 2.226306 True NaN 1.0 True \n",
"209 3.347881 True NaN 1.0 True \n",
"214 7.172961 True NaN 1.0 True \n",
"215 16.933450 True NaN 1.0 True \n",
"216 3.323730 True NaN 1.0 True \n",
"221 2.654511 True NaN 1.0 True \n",
"223 3.083316 True NaN 1.0 True \n",
"227 3.080092 True NaN 1.0 True \n",
"262 19.498454 True NaN 1.0 True \n",
"274 12.578109 True NaN 1.0 True \n",
"... ... ... ... ... ... \n",
"1130 18.426477 True NaN 1.0 True \n",
"1151 4.093056 True NaN 1.0 True \n",
"1152 17.702603 True NaN 1.0 True \n",
"1153 4.060442 True NaN 1.0 True \n",
"1154 3.981625 True NaN 1.0 True \n",
"1155 4.088979 True NaN 1.0 True \n",
"1156 9.299064 True NaN 1.0 True \n",
"1162 17.770859 True NaN 1.0 True \n",
"1165 2.611971 True NaN 1.0 True \n",
"1167 4.949745 True NaN 1.0 True \n",
"1168 5.407135 True NaN 1.0 True \n",
"1199 3.564358 False NaN 0.0 True \n",
"1200 2.693978 False NaN 0.0 True \n",
"1202 5.288548 True NaN 1.0 True \n",
"1203 6.484767 True NaN 1.0 True \n",
"1205 3.425185 True NaN 1.0 True \n",
"1207 26.839270 True NaN 1.0 True \n",
"1208 4.589373 True NaN 1.0 True \n",
"1211 7.407060 True NaN 1.0 True \n",
"1213 9.221822 True NaN 1.0 True \n",
"1216 9.358786 True NaN 1.0 True \n",
"1218 7.835622 True NaN 1.0 True \n",
"1222 7.071948 True NaN 1.0 True \n",
"1228 15.695836 True NaN 1.0 True \n",
"1229 11.768979 True NaN 1.0 True \n",
"1231 4.442023 True NaN 1.0 True \n",
"1232 22.458685 True NaN 1.0 True \n",
"1233 3.102590 True NaN 1.0 True \n",
"1234 6.900656 True NaN 1.0 True \n",
"1235 3.767155 True NaN 1.0 True \n",
"\n",
"[130 rows x 51 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.query('baseline and gridcell')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 525x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"density = True\n",
"cumulative = True\n",
"histtype = 'step'\n",
"lw = 2\n",
"bins = {\n",
" 't_i_peak': None,\n",
" 't_e_peak': None,\n",
" 'p_i_peak': None,\n",
" 'p_e_peak': None,\n",
"}\n",
"xlabel = {\n",
" 't_i_peak': 's',\n",
" 't_e_peak': 's',\n",
" 'p_i_peak': 'prob',\n",
" 'p_e_peak': 'prob',\n",
"}\n",
"\n",
"for cell_type in ['gridcell', 'not bs']:\n",
" for key in bins:\n",
" fig = plt.figure(figsize=(3.5,2.2))\n",
" plt.suptitle(key + ' ' + cell_type)\n",
" legend_lines = []\n",
" for color, query, label in zip(colors, queries, labels):\n",
" data.query(query + ' and ' + cell_type)[key].hist(\n",
" bins=bins[key], density=density, cumulative=cumulative, lw=lw, \n",
" histtype=histtype, color=color)\n",
" legend_lines.append(matplotlib.lines.Line2D([0], [0], color=color, lw=lw, label=label))\n",
" plt.xlabel(xlabel[key])\n",
" plt.legend(\n",
" handles=legend_lines,\n",
" bbox_to_anchor=(1.04,1), borderaxespad=0, frameon=False)\n",
" plt.tight_layout()\n",
" plt.grid(False)\n",
"# plt.xlim(-0.05, bins[key].max() - bins[key].max()*0.02)\n",
" sns.despine()\n",
" figname = f'histogram-{key}-{cell_type}'.replace(' ', '-')\n",
" fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
" fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"psth = pd.read_feather(output_path / 'data' / 'psth.feather')\n",
"times = pd.read_feather(output_path / 'data' / 'times.feather')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"times = times.T.iloc[0].values"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"cs = ['#d95f02', '#e7298a', '#993404', '#980043']\n",
"lb = ['GC 11 Hz', 'GC 30 Hz', 'NS 11 Hz', 'NS 30 Hz']"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 750x300 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
"ii = 0\n",
"for cell_type, ls in zip(['gridcell', 'not bs'], ['-', '--']):\n",
" for i, (ax, query) in enumerate(zip(axs.ravel(), queries)):\n",
" selection = [\n",
" f'{r.action}_{r.channel_group}_{r.unit_name}' \n",
" for i, r in data.query(query + ' and ' + cell_type).iterrows()]\n",
" values = psth.loc[:, selection].dropna(axis=1).to_numpy()\n",
"\n",
" plot_bootstrap_timeseries(times, values, ax=ax, lw=2, label=lb[ii], color=cs[ii], ls=ls)\n",
" # ax.set_title(titles[i])\n",
" ax.set_xlabel('Time (s)')\n",
" ax.legend(frameon=False)\n",
" ii += 1\n",
" axs[0].set_ylabel('Probability density')\n",
" sns.despine()\n",
" plt.xlim(0, 0.029)\n",
" \n",
"figname = f'response-probability'\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.png', \n",
" bbox_inches='tight', transparent=True)\n",
"fig.savefig(\n",
" output_path / 'figures' / f'{figname}.svg', \n",
" bbox_inches='tight', transparent=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Store results in Expipe action"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"stimulus-response\")"
]
}
],
"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
}