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spike lfp analysis
2019-10-17 17:44:01 +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": [
"19:14:03 [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-spike-lfp-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": [
"lfp_action = actions['stimulus-spike-lfp-response']\n",
"lfp_results = pd.read_csv(lfp_action.data_path('results'))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# lfp_results has old unit id's but correct on (action, unit_name, channel_group)\n",
"lfp_results = lfp_results.drop('unit_id', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": 9,
"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(lfp_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 = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
"labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
"queries = ['baseline and Hz11', 'frequency==11', 'baseline and Hz30', '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",
"sessions_above_threshold = data.query(grid_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": 16,
"metadata": {},
"outputs": [],
"source": [
"gridcell_sessions = data[data.unit_day.isin(sessions_above_threshold.unit_day.values)]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
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" <td>10.840470</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.289100</td>\n",
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" <tr>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <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",
" </tr>\n",
" <tr>\n",
" <th>1202</th>\n",
" <td>1833-260619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</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",
" </tr>\n",
" <tr>\n",
" <th>1204</th>\n",
" <td>1833-260619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</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",
" </tr>\n",
" <tr>\n",
" <th>1208</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.293775</td>\n",
" <td>0.657679</td>\n",
" <td>7.071948</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1214</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</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",
" </tr>\n",
" <tr>\n",
" <th>1215</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <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",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1217</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</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",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1218</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.304748</td>\n",
" <td>0.641151</td>\n",
" <td>3.102590</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1219</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.277708</td>\n",
" <td>0.585645</td>\n",
" <td>6.900656</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1220</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.294291</td>\n",
" <td>0.639177</td>\n",
" <td>22.458685</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1221</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.258204</td>\n",
" <td>0.608094</td>\n",
" <td>3.767155</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1223</th>\n",
" <td>1833-200619-3</td>\n",
" <td>True</td>\n",
" <td>1833</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>baseline ii</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.276894</td>\n",
" <td>0.623636</td>\n",
" <td>12.778706</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1255</th>\n",
" <td>1833-010719-2</td>\n",
" <td>False</td>\n",
" <td>1833</td>\n",
" <td>11.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>ms</td>\n",
" <td>True</td>\n",
" <td>stim i</td>\n",
" <td>...</td>\n",
" <td>11.016969</td>\n",
" <td>11.215476</td>\n",
" <td>0.198507</td>\n",
" <td>0.058616</td>\n",
" <td>0.277537</td>\n",
" <td>0.570597</td>\n",
" <td>5.734302</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1257</th>\n",
" <td>1833-010719-2</td>\n",
" <td>False</td>\n",
" <td>1833</td>\n",
" <td>11.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>ms</td>\n",
" <td>True</td>\n",
" <td>stim i</td>\n",
" <td>...</td>\n",
" <td>11.023725</td>\n",
" <td>11.224735</td>\n",
" <td>0.201010</td>\n",
" <td>0.038708</td>\n",
" <td>0.248774</td>\n",
" <td>0.604394</td>\n",
" <td>2.814742</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1263</th>\n",
" <td>1833-010719-2</td>\n",
" <td>False</td>\n",
" <td>1833</td>\n",
" <td>11.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>ms</td>\n",
" <td>True</td>\n",
" <td>stim i</td>\n",
" <td>...</td>\n",
" <td>10.994052</td>\n",
" <td>11.225703</td>\n",
" <td>0.231651</td>\n",
" <td>0.045162</td>\n",
" <td>0.280033</td>\n",
" <td>0.560729</td>\n",
" <td>4.760330</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1264</th>\n",
" <td>1833-010719-2</td>\n",
" <td>False</td>\n",
" <td>1833</td>\n",
" <td>11.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>ms</td>\n",
" <td>True</td>\n",
" <td>stim i</td>\n",
" <td>...</td>\n",
" <td>11.000518</td>\n",
" <td>11.216176</td>\n",
" <td>0.215657</td>\n",
" <td>0.037637</td>\n",
" <td>0.281934</td>\n",
" <td>0.627089</td>\n",
" <td>15.890929</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1268</th>\n",
" <td>1833-010719-2</td>\n",
" <td>False</td>\n",
" <td>1833</td>\n",
" <td>11.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>ms</td>\n",
" <td>True</td>\n",
" <td>stim i</td>\n",
" <td>...</td>\n",
" <td>11.034662</td>\n",
" <td>11.197408</td>\n",
" <td>0.162746</td>\n",
" <td>0.021146</td>\n",
" <td>0.266512</td>\n",
" <td>0.594033</td>\n",
" <td>2.704037</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1275</th>\n",
" <td>1833-010719-2</td>\n",
" <td>False</td>\n",
" <td>1833</td>\n",
" <td>11.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>ms</td>\n",
" <td>True</td>\n",
" <td>stim i</td>\n",
" <td>...</td>\n",
" <td>11.016058</td>\n",
" <td>11.203307</td>\n",
" <td>0.187249</td>\n",
" <td>0.050767</td>\n",
" <td>0.257098</td>\n",
" <td>0.545188</td>\n",
" <td>5.292658</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>271 rows × 67 columns</p>\n",
"</div>"
],
"text/plain": [
" action baseline entity frequency i ii session \\\n",
"17 1839-120619-4 False 1839 30.0 False True 4 \n",
"19 1839-120619-4 False 1839 30.0 False True 4 \n",
"21 1839-120619-4 False 1839 30.0 False True 4 \n",
"29 1839-120619-4 False 1839 30.0 False True 4 \n",
"30 1839-120619-4 False 1839 30.0 False True 4 \n",
"31 1839-120619-4 False 1839 30.0 False True 4 \n",
"33 1833-260619-1 True 1833 NaN True False 1 \n",
"34 1833-260619-1 True 1833 NaN True False 1 \n",
"35 1833-260619-1 True 1833 NaN True False 1 \n",
"39 1833-260619-1 True 1833 NaN True False 1 \n",
"40 1833-260619-1 True 1833 NaN True False 1 \n",
"42 1833-260619-1 True 1833 NaN True False 1 \n",
"44 1833-260619-1 True 1833 NaN True False 1 \n",
"46 1833-260619-1 True 1833 NaN True False 1 \n",
"47 1833-260619-1 True 1833 NaN True False 1 \n",
"49 1833-260619-1 True 1833 NaN True False 1 \n",
"54 1839-060619-3 False 1839 11.0 True False 3 \n",
"57 1834-150319-3 True 1834 NaN False True 3 \n",
"76 1834-120319-4 False 1834 30.0 False True 4 \n",
"87 1849-280219-4 False 1849 30.0 False True 4 \n",
"106 1849-110319-2 False 1849 11.0 True False 2 \n",
"124 1833-010719-1 True 1833 NaN True False 1 \n",
"125 1833-010719-1 True 1833 NaN True False 1 \n",
"126 1833-010719-1 True 1833 NaN True False 1 \n",
"128 1833-010719-1 True 1833 NaN True False 1 \n",
"129 1833-010719-1 True 1833 NaN True False 1 \n",
"131 1833-010719-1 True 1833 NaN True False 1 \n",
"132 1833-010719-1 True 1833 NaN True False 1 \n",
"134 1833-010719-1 True 1833 NaN True False 1 \n",
"135 1833-010719-1 True 1833 NaN True False 1 \n",
"... ... ... ... ... ... ... ... \n",
"1154 1839-120619-3 True 1839 NaN False True 3 \n",
"1155 1834-110319-5 False 1834 11.0 True False 5 \n",
"1156 1834-110319-5 False 1834 11.0 True False 5 \n",
"1174 1839-200619-2 False 1839 11.0 True False 2 \n",
"1184 1833-260619-3 True 1833 NaN False True 3 \n",
"1185 1833-260619-3 True 1833 NaN False True 3 \n",
"1186 1833-260619-3 True 1833 NaN False True 3 \n",
"1189 1833-260619-3 True 1833 NaN False True 3 \n",
"1191 1833-260619-3 True 1833 NaN False True 3 \n",
"1193 1833-260619-3 True 1833 NaN False True 3 \n",
"1194 1833-260619-3 True 1833 NaN False True 3 \n",
"1197 1833-260619-3 True 1833 NaN False True 3 \n",
"1199 1833-260619-3 True 1833 NaN False True 3 \n",
"1202 1833-260619-3 True 1833 NaN False True 3 \n",
"1204 1833-260619-3 True 1833 NaN False True 3 \n",
"1208 1833-200619-3 True 1833 NaN False True 3 \n",
"1214 1833-200619-3 True 1833 NaN False True 3 \n",
"1215 1833-200619-3 True 1833 NaN False True 3 \n",
"1217 1833-200619-3 True 1833 NaN False True 3 \n",
"1218 1833-200619-3 True 1833 NaN False True 3 \n",
"1219 1833-200619-3 True 1833 NaN False True 3 \n",
"1220 1833-200619-3 True 1833 NaN False True 3 \n",
"1221 1833-200619-3 True 1833 NaN False True 3 \n",
"1223 1833-200619-3 True 1833 NaN False True 3 \n",
"1255 1833-010719-2 False 1833 11.0 True False 2 \n",
"1257 1833-010719-2 False 1833 11.0 True False 2 \n",
"1263 1833-010719-2 False 1833 11.0 True False 2 \n",
"1264 1833-010719-2 False 1833 11.0 True False 2 \n",
"1268 1833-010719-2 False 1833 11.0 True False 2 \n",
"1275 1833-010719-2 False 1833 11.0 True False 2 \n",
"\n",
" stim_location stimulated tag ... stim_half_f1 stim_half_f2 \\\n",
"17 ms True stim ii ... 30.173035 30.443743 \n",
"19 ms True stim ii ... 30.128480 30.460705 \n",
"21 ms True stim ii ... 30.188571 30.437126 \n",
"29 ms True stim ii ... 30.155404 30.445467 \n",
"30 ms True stim ii ... 30.195374 30.437554 \n",
"31 ms True stim ii ... 30.195640 30.437892 \n",
"33 NaN False baseline i ... NaN NaN \n",
"34 NaN False baseline i ... NaN NaN \n",
"35 NaN False baseline i ... NaN NaN \n",
"39 NaN False baseline i ... NaN NaN \n",
"40 NaN False baseline i ... NaN NaN \n",
"42 NaN False baseline i ... NaN NaN \n",
"44 NaN False baseline i ... NaN NaN \n",
"46 NaN False baseline i ... NaN NaN \n",
"47 NaN False baseline i ... NaN NaN \n",
"49 NaN False baseline i ... NaN NaN \n",
"54 ms True stim i ... 10.995334 11.259054 \n",
"57 NaN False baseline ii ... NaN NaN \n",
"76 ms True stim ii ... 30.136513 30.450445 \n",
"87 ms True stim ii ... 30.178205 30.439063 \n",
"106 ms True stim i ... 10.998382 11.225055 \n",
"124 NaN False baseline i ... NaN NaN \n",
"125 NaN False baseline i ... NaN NaN \n",
"126 NaN False baseline i ... NaN NaN \n",
"128 NaN False baseline i ... NaN NaN \n",
"129 NaN False baseline i ... NaN NaN \n",
"131 NaN False baseline i ... NaN NaN \n",
"132 NaN False baseline i ... NaN NaN \n",
"134 NaN False baseline i ... NaN NaN \n",
"135 NaN False baseline i ... NaN NaN \n",
"... ... ... ... ... ... ... \n",
"1154 NaN False baseline ii ... NaN NaN \n",
"1155 mecl True stim i ... 10.981120 11.242957 \n",
"1156 mecl True stim i ... 11.010395 11.204906 \n",
"1174 ms True stim i ... 11.023967 11.199671 \n",
"1184 NaN False baseline ii ... NaN NaN \n",
"1185 NaN False baseline ii ... NaN NaN \n",
"1186 NaN False baseline ii ... NaN NaN \n",
"1189 NaN False baseline ii ... NaN NaN \n",
"1191 NaN False baseline ii ... NaN NaN \n",
"1193 NaN False baseline ii ... NaN NaN \n",
"1194 NaN False baseline ii ... NaN NaN \n",
"1197 NaN False baseline ii ... NaN NaN \n",
"1199 NaN False baseline ii ... NaN NaN \n",
"1202 NaN False baseline ii ... NaN NaN \n",
"1204 NaN False baseline ii ... NaN NaN \n",
"1208 NaN False baseline ii ... NaN NaN \n",
"1214 NaN False baseline ii ... NaN NaN \n",
"1215 NaN False baseline ii ... NaN NaN \n",
"1217 NaN False baseline ii ... NaN NaN \n",
"1218 NaN False baseline ii ... NaN NaN \n",
"1219 NaN False baseline ii ... NaN NaN \n",
"1220 NaN False baseline ii ... NaN NaN \n",
"1221 NaN False baseline ii ... NaN NaN \n",
"1223 NaN False baseline ii ... NaN NaN \n",
"1255 ms True stim i ... 11.016969 11.215476 \n",
"1257 ms True stim i ... 11.023725 11.224735 \n",
"1263 ms True stim i ... 10.994052 11.225703 \n",
"1264 ms True stim i ... 11.000518 11.216176 \n",
"1268 ms True stim i ... 11.034662 11.197408 \n",
"1275 ms True stim i ... 11.016058 11.203307 \n",
"\n",
" stim_half_width stim_energy half_width peak_to_trough \\\n",
"17 0.270708 0.129917 0.283497 0.606614 \n",
"19 0.332225 0.258594 0.261815 0.633750 \n",
"21 0.248556 0.080818 0.242524 0.534827 \n",
"29 0.290063 0.160690 0.279806 0.598967 \n",
"30 0.242181 0.091302 0.265158 0.581451 \n",
"31 0.242252 0.098635 0.246920 0.570844 \n",
"33 NaN NaN 0.272875 0.602667 \n",
"34 NaN NaN 0.226452 0.274814 \n",
"35 NaN NaN 0.247266 0.570104 \n",
"39 NaN NaN 0.284542 0.644111 \n",
"40 NaN NaN 0.259920 0.581698 \n",
"42 NaN NaN 0.263630 0.596746 \n",
"44 NaN NaN 0.281399 0.607354 \n",
"46 NaN NaN 0.285816 0.603160 \n",
"47 NaN NaN 0.279177 0.585152 \n",
"49 NaN NaN 0.282336 0.711705 \n",
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"... ... ... ... ... \n",
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"\n",
" average_firing_rate bs bs_stim bs_ctrl \n",
"17 9.779867 True 1.0 NaN \n",
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"40 5.891739 True NaN 1.0 \n",
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"46 7.914246 True NaN 1.0 \n",
"47 10.840470 True NaN 1.0 \n",
"49 5.890705 True NaN 1.0 \n",
"54 14.025342 True 1.0 NaN \n",
"57 17.162446 True NaN 1.0 \n",
"76 34.841257 True 1.0 NaN \n",
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"106 2.339767 True 1.0 NaN \n",
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"125 6.912052 True NaN 1.0 \n",
"126 4.229867 True NaN 1.0 \n",
"128 16.735961 True NaN 1.0 \n",
"129 25.974728 True NaN 1.0 \n",
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"132 18.657578 True NaN 1.0 \n",
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"... ... ... ... ... \n",
"1154 5.407135 True NaN 1.0 \n",
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"\n",
"[271 rows x 67 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gridcell_sessions"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"data.loc[:,'gridcell'] = False\n",
"data['gridcell'] = data.isin(gridcell_sessions)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
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"text/plain": [
" action baseline entity frequency i ii session \\\n",
"33 1833-260619-1 True 1833 NaN True False 1 \n",
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"35 1833-260619-1 True 1833 NaN True False 1 \n",
"39 1833-260619-1 True 1833 NaN True False 1 \n",
"40 1833-260619-1 True 1833 NaN True False 1 \n",
"\n",
" stim_location stimulated tag ... stim_half_f2 stim_half_width \\\n",
"33 NaN False baseline i ... NaN NaN \n",
"34 NaN False baseline i ... NaN NaN \n",
"35 NaN False baseline i ... NaN NaN \n",
"39 NaN False baseline i ... NaN NaN \n",
"40 NaN False baseline i ... NaN NaN \n",
"\n",
" stim_energy half_width peak_to_trough average_firing_rate bs \\\n",
"33 NaN 0.272875 0.602667 5.945508 True \n",
"34 NaN 0.226452 0.274814 2.860048 False \n",
"35 NaN 0.247266 0.570104 3.365674 True \n",
"39 NaN 0.284542 0.644111 17.471520 True \n",
"40 NaN 0.259920 0.581698 5.891739 True \n",
"\n",
" bs_stim bs_ctrl gridcell \n",
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"\n",
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"metadata": {},
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}
],
"source": [
"data.query('baseline and Hz11 and gridcell').head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": false
},
"outputs": [
{
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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": {
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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",
" 'theta_energy': None,#np.arange(0, .7, .03),\n",
" 'theta_peak': np.arange(0, .7, .03),\n",
" 'theta_freq': np.arange(4, 10, .5),\n",
" 'theta_half_width': np.arange(0, 15, .5)\n",
"}\n",
"xlabel = {\n",
" 'theta_energy': 'Theta coherence energy',\n",
" 'theta_peak': 'Theta peak coherence',\n",
" 'theta_freq': '(Hz)',\n",
" 'theta_half_width': '(Hz)',\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'spike-lfp-coherence-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": 22,
"metadata": {},
"outputs": [],
"source": [
"data['stim_strength'] = data.stim_p_max / data.theta_energy"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 480x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 480x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAFGCAYAAAASDiSIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XecnFXZ//HP7iZAGpAGAQIBErgApUkzCA/YEJSmgAVCCd2fgA9KU1BABRTUhyrw0EJHUESKSn/oRRGEELhCC1WEkAQIJCS7O78/zrmzk8n0ndmZnfv7fr32de/Mfe4zZ2Z255rT2zKZDCIiImnU3ugCiIiINIqCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpJaCoIiIpNaARhdAasPMNnD3p/PcPwXYN95cyd3f7tOCiVTBzGYA4wB393WquH5b4N54c7K7T6lV2Xqr0HMzs9WBV+LNC9390D4vXAopCPZzZrYc8DPge+j9FBGpiD40+7/fAvs3uhAiIv2RgmD/11HspLvvB+zXJyURqRF3X73RZZB00MAYERFJLQVBERFJLTWHNgkzM+D/AV8E1iB8QZkJPAHcCFzj7p1Z6U8CTszJIxN/vc/dt433TaHA6NCsUWq/cfejzOxrwGHAJsBQ4DXgj8Dp7v5+vGYj4IfA54HRsYz3AD939+m9fiFy5Izymwg8CxwH7A6sBswHngQuIbxGmTzZ1KIcM+ij18rM1gAOArYF1gRGEJ7nu8CjwBR3vzPnmk8D/wCWBj4BNnL35/Pk/QXgLqANmA58xt0/qvC1GA38ANg5lm8BMA2YAlwEHAOcBuDubTnXJu/PkcBtwLnAVsBC4EXgOHe/q5zRoWa2NXAE4T1YGXgbuCV57DKfyxeAA4BNCX9PCwEH/gyck7yXBa79LHAwsE18/IXADOAO4Gx3f63cckjjqCbYBMxsT+Bpwj/0p4DBwDLAWGAX4HLg72a2Yh3LcAFwK7A94QN7EGDAj4EHzGyImR0IPA5MAlYBliL8808C/mFmG9arfNEK8fF/DKxNeI2WJwSZq4AbzWyZOpehrq+Vmf2IEJx+RAj6KwIDgWGEgLMncIeZ/S77OnefCvwk3lwauMjMcgPQ8oS/pTbCB/aeVQTATej5IrIe4T1YFvgscAFwNzCkjKxWBR4CtiP8vS8HfIYQCEuVod3MzgXuJ3wZWoPwnMcRvphMBTYvkccwM/tDLO+e9Pw9DSMExJ8D0/K9T2Y2IL7+jwCTCe9Lcu36hC8+083s4FLPRRpPQbDBzGwCcCnhQ/IV4LuEb8YTCR+Yj8SkGwHZH3wXABsTvvkmNo4/B1ZYjH2BQwjfgA8CPgfsTfhWC+Ef+w/AhcB/CMF6IvBV4PaYZhhwVoWPW6kLgXUINb+9CB+8+wHPxfO7Ej7k66lur5WZTQZOJbTQvAEcSwgSE4FvEQJ9d0z+XTP7Sk4WvwEejL9vBeTOM/sd4YsVwAnu/kR5T3lR+VYn1MpHx3JcAnwF2JJQM/wPofZ6TBnZ/TcwCjgd2BrYAzjV3WeUce3phClBEF73Qwmv0Y7A9cBwitQG45eDm4Hd4l1PE/5nJgI7AdfE+1cGbjOz4TlZXEz4P4Xweu9L+FvclvBF6G1CUL7QzPZFmpqaQxtvL8I/TBfweXd/Nevco2Z2PfB/hA+ar5vZKHefGZs13zazWUlid3+qyjKMInwQbO3uH8T7HjazJwnfqiHUemYAW7j7O8mFZnY7oYluM+C/zGx5d59TZTlKGQP8Bfi6uy+I9z0Wv9HfRfgg+qaZXeDu9xbKpJfq8lrFD+afxaRzgP9y92TiNPG6683sMeCceN8e9ARW3L07fuj+i9BE+0szu9nd3zSzbwPfiUnvBX5dxXP/H0IAB9jL3a/LOveImV1LqJ2tVUZe7YSgd3zWfX8odZGZrQt8P96cSnidZmcluS2+Rr8pks0BhIAF8CfgW+6+MOv8rWY2HTiJUIs/jFAzxMx2pad74TR3/3FO3veZ2cWE/9n1gPPM7FZ3f6/Uc5PGUE2w8cbE41zgrdyT8Z/zROBsQj9Kvd6zE7I+1JPHfpaeWhbAydkf6jFNNz210TZgfJ3KBzAb2DsrACZl+AjYh55aUr1X2qjHazUOmAW8D1yWEwCzXZX1+yq5J939ZeCoeHNZ4EwzGwOcF++bBewTy1I2M1uNUNMGuDYnACaP/TaVzVk9v5IyRAfQ8+X90JwAmJTjt4RgXEjSTPkBYTWZhXnSnAIk7992WfcfHY/PAseTh7u/S+jfh9A0XGnLjPQhBcHGSwYvLAfcEL/pLsbd73L377v7WbkfrDXSTc/gk1xvZv1+d4E02WUaWpMS5Xedu8/Kd8LdX6CnKXB7M6tXK0ddXit3n+HuG7r78vQEsXzeB+bF35fOl8DdLwT+Fm/uHn8fEW8f5O5vFMm/kJ2yfr+sUCJ3f5AwSKaUN6ssx1fj8VV3f6hIukvz3Rm/EGwab95caOBLHIT2X8AYd986Xjuc0GQKcHeJQVgPAh/G379YJJ00mJpDG+8KwrfLVQiDYHYxs5eBOwlNfHfVsXkxMdPd5xY490nW7/8uI01bgTS1UOxDD+CfhA+uZQn9OfUYnVf31yqppZnZsoRBF+OBdQn9vVsRBuJA8S+xBxCaC4cDyeCOS9z9xiLXFLNR1u//KJH2MUJTYDGvV1oAM2snDGCB0ORbzOMF7l+Lntf9n8UycHfPuWujrGuPMLMjSpQhsWaZ6aQBFAQbzN1nm9mXCN+uPxvvXpMw+OIQoMvM7iN8s63XFIAPSydZ9O24kZZoLs7xbtbvY6hPEKzraxVbAn4A7ECe5k6grPff3d8ysxPoaQZdSHkDVgpJRiZ35WuCzFFOa8UHpZMsYSQ9KySV6mP7T4H7s0dYV9pPN6rC9IncgTXSRBQEm0CczzXRzLYgjFj7KmGqBIR/+i/En8lmtpO7z8ufU9UaHdzKVaqc2UvILSiYqr5lqFocHfq/LP5/OYvQ1ziVUMO6k9CEXnQaQqw1fSfrroGEIHhclcVbKh7bzaytxJexcgJ1NV/msq8p1eKQr58PeveZl33tLwjzQsvR1YvHlDpTEGwi7v4Y4YPumNh38QXCsO9dCU1gXyT0F/28YYVsrBElzo/O+r1Qc2RTMrP16QmAHxJGJt6YO2UgBrdBudfncRSh6RRCP+JywNFxtOjDVRRxZjy2EWpkM4ukrbbGVMp7hOA2kMXf63wK/a1k9ymPrPDxs6+d14vR2NJEFAQbzMySidYL4whDYNFIu2uAa8xsY0I/TDshKKY1CG5EWMmjkM3i8d+U1yTXTA6h5//xMHe/okC6sZQY0BZXj0mmW0wFvk1YeWhp4HIz29DdP66wfE8RpvNAmNR+R5G0m1SYd1ncPWNm0wh9nJuUqJFuVOD+7EE7RRd3MLNfEVpmXga+Sc8UGOjpuih07VKEBQ/eAKa5+yPF0kvjaHRoA8V/lPcIk78LDhd39yfpqdnkrohS0VD3fu47ZpZ31wwzWw/YIt68qV7Lp9XRhKzfi01in5T1+xJfYs1sIGGw1dKEv40D4per5IvTBOCMKsp3a9bvexVKFGu0G1eRf7mSJsgxwNeKpNsn351xRGoy4GVnM8vbrBznbe5MGJS0jrvPcfc3CVMjIIxAXqPI408i1OYvJiyqIE1KQbCB4ny35Bv1Vma2W750ZrYNPYMk/p5z+pOsdPWcntAMjDB/a/E7wyjKK+hZDuzcPi5XLWQ3L+6QL4GZfRX4adZd+aZI/JSeIHSWuyejJE8nTPKHsNrMlyopXOy3TqZd7G1mO+cp33KE9UPr6VJ6BtWcF+cv5pZjT+AbRfJIFhsYHvPI9zl4NGF1IgiBLJFMwh8IXBeXost9/LUJrzeEfsxzctNI81BzaOOdTPhGO4DwT3UFYWHhNwl9FtvQM/H2Y5b8Fp/d93VqvL4r1h5b0bFm9ilC/9k7wAaEwR7JMPRfuns589SazfX01LBONbOVCF+QPgBWJ8z
"text/plain": [
"<Figure size 480x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 480x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 480x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 480x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAFGCAYAAAASDiSIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xec3FW9//HXloSQAmk0kxAggQ/FUFRAEIWrVwWlKVhoQhAELsXLpaiIUlRA+MEVFQTpvQkXQRSkSRVQBKR+qEmQIoQU0gjJ7vz+OOebncxO3f3Ozux+38/HYx/fnfme75kzM7vzmdNbcrkcIiIiWdTa6AKIiIg0ioKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloKgiIhkloJgBpjZxiXuv9TMcvFn9b4ul0i9lPqbr+K6E/P+Jz6Zdrmk+bQ3ugBSP2a2MnAycCh6ryUDzGwy8CtgRWC7xpZG+gPVBAe2s4AjgLZGF0Skj9wBbN/oQkj/odrBwFY2+Ln7fsB+fVISkb6hL3xSE9UERUQksxQERUQks9Qc2g+YmQH/BXwOWJvw5WUm8DhwE3C1uy/NS38icEJBHrn4633uvl2871Jg33j/Gu7+dl76acBE4Ex3P9rMvgwcBnwcGA7MAG4ETnf3ufGaTYGjgP8AVollvAf4ibu/2OsXooCZbQfcG29uBTwLfB/YHVgT+AB4AriI8BrlimSTRjmm0UevlZmtDRxIGPSxDjCa8DzfBR4BLnX3Owuu+Sjwd2AFYDGwqbu/UCTvzwJ3AS3Ai8DH3H1Bla/BX4Btgd+7+65m9nFCf/R2wGrA+8DfgAvd/f8q5LUqcAjwJWA9YGh8fo8BVwE3Fb6XeY+f2Dbvb/4kdz+xmudRpCwTgR8R+hnHxnLcB5zr7g+XuW5UfA47AhvG5zAbeAH4I3C+u8/pSZkkXaoJNjkz2xP4J+EDZSPCP9MQYDywC3AZ8DczW62OZTgP+APhg2AVwsg7A44DHjCzYWZ2AOFDam9gHDAY+Ei8/Xcz26Re5YtWjY9/HOGDcwgwkhBkrgRuMrMhdS5DXV8rM/sBITj9gBD0VwMGASMIAXFP4M9mdm7+de7+DOGDHEIgvMDMWgryHkn4W2oBlgB7VhsAi5TzcEJA/hbhy8gK8bX4EuF9uLzw8fOu/Xp8jicCWxDew8GE1+krwO+Av5jZKj0pW40+DTwNfDs+/gqE/7u9gIfM7IxiF8XpGc8CPyO8TysT3qdVgc8ApwGvmNlW9X4CUpmCYBOLw70vJnwIvEb4ZrkN4R9rb+CvMemmQP4H33nAZsCtefdtFn8OqLEY+wIHAU6ogXwK2AeYFs9PIXwwnQ/8mxCstyJ84N0R04wAzq7xcWt1PrA+oea3F/BJwqCf5+P5XQkf8vVUt9fKzKYCpxBab/4FfA/4Qrz+G4RA3xmTH2JmXyzI4kzgwfj7NsDBBefPJXzAAxzv7o9X95S7+WQs//uEwPsZQg3tFODDmGYf4OuFF5rZV4FrCEFjSSzT9nS9l4/FpJ8B7jWz4XmXH0D4+34r3n6crr/583r4XH5OeD+uAnYAtgaOAWbF80eb2VEFz6GN8B6vASwgBPPPEQL6LjEvCDX4G8xsxR6WTVLSksvVpYVIUmBmJxD+iTqASe4+veD8IOAvhH/OHLCqu8/MO38psbnT3bt9866yORRCTfTT7v5+3vmNgGfyspsGbOnu7+SlaSXUCDaP5RudZhNQQXMohGamr7j7h3lphhGa+JKJz5919/xr0ijHNOr4WsVa0wxCkJpDaKZ8rUg5DiPMkQO4yN0PKDi/DvAUoYn2fWBDd3/DzL5JCD4QXs//dPdOalDQHPkW8El3n1GQ5uvAdfHm7e6+Q965lYBXgTHAQuCL7v5gwfWtwG8JNTOAX7n7EQVpphHei2XN/jU+jxNZvivhUHc/tyDNJOAhQk18EbBm8n9nZtsS/icB9nb3qyhgZqcTginA7u5+Y63llPSoJtjcklVc5gNvFp509yWEf9hfAkdSv/fz+PwP9fjYz9JVy4LQ7/JOQZpOumqjLcCkOpUPQn/LPvkBMJZhAaFZLvlQL6wBpa0er9VEQu1jLnBJsQAYXZn3+7jCk+7+KnB0vLkS8Iu4UtA58b5ZwLdqDYBF/LQwAEY3EN4ngMIm3/0JARDghMIACMteo/8iNJcCHGhmo3tZ1nJuKwyAsRyvEGriEJq798s7nb/y0ksl8j0buIDQrP1y74spvaEg2NySwQsrE5pONihM4O53uft33f3swg/WlHSyfG0r3xt5v99dIk1+mYaXSJOGa919VrET7v4SXU2B25tZvQaE1eW1cvdp7r6Ju4+kK4gVM5dQM4HQf9WNu58P3B5v7h5/TwLJge7+rzL5V+vPJR47R2jWh9DMmC9pvu0ALiyVcfySk5wfQn1XhfltmXPXEQYZQWiWTuQPOLrEzD4ba7DLuPsb7v4ddz/N3Z9KqazSQxod2twuJzSbjCP0J+xiZq8CdxKa+O7qgxFmM919folzi/N+f6uKNEUHQ6TkoQrn/0HoS1qJMAilWE2lt+r+WiW1tNh8uA6hxrgBoe9rG0LNBMp/wf02oXl2FF01sovc/aYy19RiWplzyetT+Nnz0Xj0Kv6mH8n7fQphhHQ9PFrqhLt/YGbPE/rjp+Td/5SZ/YnQh7gh4QvPe2Z2N+F/9s+F3RrSWAqCTczdZ5vZfwKX0NWntQ5h8MVBQIeZ3UcYPFOvKQDzqizr0sqp6qpbc3GBd/N+X536BMG6vlaxJeB/CB+w3Zo7CX2J1Tz+m2Z2PF3NoEuAY3tSpiIWV3h+SRkLg3zSFFpNa8a/836vZ3PouxXOJ/3vhWX4JuG13YvwPMcQBgJ9HcDMngauBn5d5kuT9BE1hzY5d3/B3bciBMEzCEOvE23AZwl9QXfWaaRZo4NbtSqVM385rQ9LpqpvGXosjg79J2EUZBIAZxFqwOcT+tTWJIxIrJRXK7BH3l2DSC8I9lQtrQT572Vv+y/LqfSlIilzfg0ed3/f3fch1NK/BzxA+KKRmAKcCjwXB9lIA6km2E+4+6OE5plj42CGzxIm4u5KaAL7HKG/6CcNK2RjVaoR5M8rK9Uc2ZTMbAqhf6qdUNs8kTBhfFpBula6mkPLOZrQdAqhH3Fl4Bgzu6XcBPA6m0Vopl61irT5c2KL9gOnZDTwXpnzSVmLpokDmE4HTo/TOT5NmPLxDcJzmEB4Xz+XVoGldqoJNjEzW9HMNo1D7Jdx97fd/Wp335MwFy35NrxjnxeyeWxa4fzm8fgW1TW5NZOD6PrCepi7n1UYAKPxVPifjqvHnBxvPkP4+1kcr7vMzIamUuLa/TMeLU7cLyd/n79uK9+kaKNSJ8xsBKEvFkJ/c3J/u5mtZ2afyk/v7vPd/U/u/t143avx1Gc1V7CxFASblJkNJnzDfAL4Tal07v4EXTWbwhVR6tlU1Gz2iBOVuzGzDYEt482b67V8Wh1Nzvu93CT2vfN+79bKE+eVXk4YOdoJfDtO30haDyYTmtwbIRlR2kbXPMBu4v/F/vHmUrrm5CXS/Jv/Rplz+9L1Gt+ed//thMUS7opzVLtx99l0LXQB3f9vpQ8pCDapOBQ8+WDYxsx2K5YuTs5N+oj+VnB6cV66ek5PaAZGWKZq+TvDKMrL6VoO7Nd9XK40zMz7fYdiCczsS8CP8+4qNkXix4RRpABnu3uyAsvpdNXEDomDsfraxYSFAABOKrakWGzuPQdYN951mbsXDl5J/ubT+Hs/yMy67U0Yl0X7abz5DsvPz/xDPA4h9Pt1E5c4TJpAX4lBURpEfYLN7STgy4T36Vozuxy4jTDnbAxhhY7/imkX0v1bfH7f1ynx+o5YexyIvhebjn9L+HDamLCg9jrx/Gnu/lyjCtcL1xNGGkJ4H9cgfEF6H1iLMN/vKyw/uGTl/AzMbAvC5GwIc/WOT865+xIz+zZh6kEbcLGZTUkW++4L7j43rqn6O2AYYX3Q3xI
"text/plain": [
"<Figure size 480x330 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 480x330 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",
" 'stim_energy': np.arange(0, .4, .01),\n",
" 'stim_half_width': np.arange(0, 10, .5),\n",
" 'stim_p_max': np.arange(0, 1, .01),\n",
" 'stim_strength': np.arange(0, 50, 1)\n",
"}\n",
"xlabel = {\n",
" 'stim_energy': 'Coherence energy',\n",
" 'stim_half_width': '(Hz)',\n",
" 'stim_p_max': 'Peak coherence',\n",
" 'stim_strength': 'Ratio',\n",
"}\n",
"# key = 'theta_energy'\n",
"# key = 'theta_peak'\n",
"for cell_type in ['gridcell', 'not bs']:\n",
" for key in bins:\n",
" fig = plt.figure(figsize=(3.2,2.2))\n",
" plt.suptitle(key + ' ' + cell_type)\n",
" legend_lines = []\n",
" for color, query, label in zip(colors[1::2], queries[1::2], labels[1::2]):\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'spike-lfp-coherence-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": 24,
"metadata": {},
"outputs": [],
"source": [
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"coher = pd.read_feather(output_path / 'data' / 'coherence.feather')\n",
"freqs = pd.read_feather(output_path / 'data' / 'freqs.feather')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"freq = freqs.T.iloc[0].values\n",
"\n",
"mask = (freq < 100)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 750x300 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for cell_type in ['gridcell', 'not bs']:\n",
" fig, axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(5,2))\n",
" axs = axs.repeat(2)\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 = coher.loc[mask, selection].dropna(axis=1).to_numpy()\n",
" values = 10 * np.log10(values)\n",
" plot_bootstrap_timeseries(freq[mask], values, ax=ax, lw=1, label=labels[i], color=colors[i])\n",
" # ax.set_title(titles[i])\n",
" ax.set_xlabel('Frequency Hz')\n",
" ax.legend(frameon=False)\n",
" axs[0].set_ylabel('Coherence')\n",
" sns.despine()\n",
" figname = f'spike-lfp-coherence-{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": "markdown",
"metadata": {},
"source": [
"# Store results in Expipe action"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"action = project.require_action(\"stimulus-spike-lfp-response\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/data/freqs.feather',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/data/coherence.feather',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_strength-not-bs.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_energy-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_peak-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_energy-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-not-bs.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_energy-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_peak-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_strength-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_p_max-not-bs.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_peak-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_half_width-not-bs.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_half_width-not-bs.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_half_width-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_energy-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_freq-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_strength-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_freq-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_peak-not-bs.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_half_width-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_p_max-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_freq-not-bs.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_half_width-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_freq-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_strength-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_energy-not-bs.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_p_max-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_energy-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_half_width-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_energy-not-bs.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_p_max-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_half_width-gridcell.svg',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-theta_half_width-gridcell.png',\n",
" '/media/storage/expipe/septum-mec/actions/stimulus-spike-lfp-response/data/figures/spike-lfp-coherence-histogram-stim_energy-not-bs.svg']"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"copy_tree(output_path, str(action.data_path()))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-spike-lfp-response.ipynb\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}