654 lines
228 KiB
Plaintext
654 lines
228 KiB
Plaintext
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"10:47:08 [I] klustakwik KlustaKwik2 version 0.2.6\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import expipe\n",
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"import pathlib\n",
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"import numpy as np\n",
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"import spatial_maps.stats as stats\n",
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"import septum_mec\n",
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"import septum_mec.analysis.data_processing as dp\n",
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"import septum_mec.analysis.registration\n",
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"import head_direction.head as head\n",
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"import spatial_maps as sp\n",
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"import speed_cells.speed as spd\n",
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"import re\n",
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"import joblib\n",
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"import multiprocessing\n",
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"import shutil\n",
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"import psutil\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"from distutils.dir_util import copy_tree\n",
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"from neo import SpikeTrain\n",
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"import scipy\n",
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"import seaborn as sns\n",
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"from tqdm.notebook import tqdm_notebook as tqdm\n",
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"tqdm.pandas()\n",
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"\n",
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"from spike_statistics.core import permutation_resampling\n",
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"\n",
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"from spikewaveform.core import calculate_waveform_features_from_template, cluster_waveform_features\n",
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"\n",
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"from septum_mec.analysis.plotting import violinplot, despine"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"plt.rc('axes', titlesize=12)\n",
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"plt.rcParams.update({\n",
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" 'font.size': 12, \n",
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" 'figure.figsize': (6, 4), \n",
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" 'figure.dpi': 150\n",
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"})\n",
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"\n",
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"output_path = pathlib.Path(\"output\") / \"stimulus-lfp-response-mec\"\n",
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"(output_path / \"statistics\").mkdir(exist_ok=True, parents=True)\n",
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"(output_path / \"figures\").mkdir(exist_ok=True, parents=True)\n",
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"output_path.mkdir(exist_ok=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_loader = dp.Data()\n",
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"actions = data_loader.actions\n",
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"project = data_loader.project"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"identify_neurons = actions['identify-neurons']\n",
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"sessions = pd.read_csv(identify_neurons.data_path('sessions'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_action = actions['stimulus-lfp-response']\n",
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"lfp_results = pd.read_csv(lfp_action.data_path('results'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_results = pd.merge(sessions, lfp_results, how='left')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def action_group(row):\n",
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" a = int(row.channel_group in [0,1,2,3])\n",
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" return f'{row.action}-{a}'\n",
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"lfp_results['action_side_a'] = lfp_results.apply(action_group, axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_results['stim_strength'] = lfp_results['stim_p_max'] / lfp_results['theta_energy']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"lfp_results = lfp_results.query('stim_location!=\"ms\"')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>action_side_a</th>\n",
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" <th>channel_group</th>\n",
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" <th>signal_to_noise</th>\n",
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" <th>stim_strength</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>68</th>\n",
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" <td>1833-010719-1-0</td>\n",
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" <td>4</td>\n",
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" <td>0.006686</td>\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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" <th>66</th>\n",
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" <td>1833-010719-1-1</td>\n",
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" <td>2</td>\n",
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" <td>0.034550</td>\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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" <th>580</th>\n",
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" <td>1833-020719-1-0</td>\n",
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" <td>4</td>\n",
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" <td>0.008427</td>\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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" <th>578</th>\n",
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" <td>1833-020719-1-1</td>\n",
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" <td>2</td>\n",
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" <td>0.019033</td>\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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" <th>372</th>\n",
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" <td>1833-020719-3-0</td>\n",
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" <td>4</td>\n",
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" <td>0.001076</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" action_side_a channel_group signal_to_noise stim_strength\n",
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"68 1833-010719-1-0 4 0.006686 NaN\n",
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"66 1833-010719-1-1 2 0.034550 NaN\n",
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"580 1833-020719-1-0 4 0.008427 NaN\n",
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"578 1833-020719-1-1 2 0.019033 NaN\n",
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"372 1833-020719-3-0 4 0.001076 NaN"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lfp_results_hemisphere = lfp_results.sort_values(\n",
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" by=['action_side_a', 'stim_strength', 'signal_to_noise'], ascending=[True, False, False]\n",
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").drop_duplicates(subset='action_side_a', keep='first')\n",
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"lfp_results_hemisphere.loc[:,['action_side_a','channel_group', 'signal_to_noise', 'stim_strength']].head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"colors = ['#1b9e77','#d95f02','#7570b3','#e7298a']\n",
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"labels = ['Baseline I', '11 Hz', 'Baseline II', '30 Hz']\n",
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"queries = ['baseline and Hz11', 'frequency==11', 'baseline and Hz30', 'frequency==30']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/lib/histograms.py:898: RuntimeWarning: invalid value encountered in true_divide\n",
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" return n/db/n.sum(), bin_edges\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 525x300 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 525x300 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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||
|
|
"text/plain": [
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"<Figure size 525x300 with 1 Axes>"
|
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|
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]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 525x300 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': 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 energy (dB)',\n",
|
||
|
|
" 'theta_peak': 'Peak PSD (dB/Hz)',\n",
|
||
|
|
" 'theta_freq': '(Hz)',\n",
|
||
|
|
" 'theta_half_width': '(Hz)',\n",
|
||
|
|
"}\n",
|
||
|
|
"# key = 'theta_energy'\n",
|
||
|
|
"# key = 'theta_peak'\n",
|
||
|
|
"for key in bins:\n",
|
||
|
|
" fig = plt.figure(figsize=(3.5,2))\n",
|
||
|
|
" plt.suptitle(key)\n",
|
||
|
|
" legend_lines = []\n",
|
||
|
|
" for color, query, label in zip(colors, queries, labels):\n",
|
||
|
|
" lfp_results_hemisphere.query(query)[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",
|
||
|
|
" \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()*0.025)\n",
|
||
|
|
" despine()\n",
|
||
|
|
" plt.xlabel(xlabel[key])\n",
|
||
|
|
" figname = f'lfp-psd-histogram-{key}'\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": 14,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
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"text/plain": [
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 480x300 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 480x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 480x300 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, .7, .01),\n",
|
||
|
|
" 'stim_half_width': np.arange(0, 10, .5),\n",
|
||
|
|
" 'stim_p_max': np.arange(0, 4, .01),\n",
|
||
|
|
" 'stim_strength': np.arange(0, 160, 1)\n",
|
||
|
|
"}\n",
|
||
|
|
"xlabel = {\n",
|
||
|
|
" 'stim_energy': 'Energy (dB)',\n",
|
||
|
|
" 'stim_half_width': '(Hz)',\n",
|
||
|
|
" 'stim_p_max': 'Peak PSD (dB/Hz)',\n",
|
||
|
|
" 'stim_strength': 'Ratio',\n",
|
||
|
|
"}\n",
|
||
|
|
"# key = 'theta_energy'\n",
|
||
|
|
"# key = 'theta_peak'\n",
|
||
|
|
"for key in bins:\n",
|
||
|
|
" fig = plt.figure(figsize=(3.2,2))\n",
|
||
|
|
" plt.suptitle(key)\n",
|
||
|
|
" legend_lines = []\n",
|
||
|
|
" for color, query, label in zip(colors[1::2], queries[1::2], labels[1::2]):\n",
|
||
|
|
" lfp_results_hemisphere.query(query)[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",
|
||
|
|
" \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",
|
||
|
|
" despine()\n",
|
||
|
|
" plt.xlabel(xlabel[key])\n",
|
||
|
|
" figname = f'lfp-psd-histogram-{key}'\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": [
|
||
|
|
"# Plot PSD"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"psd = pd.read_feather(pathlib.Path(\"output\") / \"stimulus-lfp-response\" / 'data' / 'psd.feather')\n",
|
||
|
|
"freqs = pd.read_feather(pathlib.Path(\"output\") / \"stimulus-lfp-response\" / 'data' / 'freqs.feather')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from septum_mec.analysis.plotting import plot_bootstrap_timeseries"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"freq = freqs.T.iloc[0].values\n",
|
||
|
|
"\n",
|
||
|
|
"mask = (freq < 100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
|
||
|
|
" out=out, **kwargs)\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:154: RuntimeWarning: invalid value encountered in true_divide\n",
|
||
|
|
" ret, rcount, out=ret, casting='unsafe', subok=False)\n",
|
||
|
|
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
|
||
|
|
" ret = ret.dtype.type(ret / rcount)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 750x300 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\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}' \n",
|
||
|
|
" for i, r in lfp_results_hemisphere.query(query).iterrows()]\n",
|
||
|
|
" values = psd.loc[mask, selection].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('PSD (dB/Hz)')\n",
|
||
|
|
" \n",
|
||
|
|
"despine()\n",
|
||
|
|
"\n",
|
||
|
|
"figname = 'lfp-psd'\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": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"action = project.require_action(\"stimulus-lfp-response-mec\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"['/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_energy.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_strength.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_peak.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_p_max.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_freq.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_energy.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_freq.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_half_width.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_half_width.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_half_width.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_energy.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_peak.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_p_max.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-theta_half_width.png',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_energy.svg',\n",
|
||
|
|
" '/media/storage/expipe/septum-mec/actions/stimulus-lfp-response-mec/data/figures/lfp-psd-histogram-stim_strength.svg']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"copy_tree(output_path, str(action.data_path()))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"septum_mec.analysis.registration.store_notebook(action, \"20_stimulus-lfp-response-mec.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
|
||
|
|
}
|