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1205 lines
342 KiB
1205 lines
342 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"# Experiment: Flashed gratings" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"### Initialization" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"#### Imports" |
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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": 1, |
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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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"import numpy as np\n", |
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"import matplotlib.pyplot as plt\n", |
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"import pandas as pd\n", |
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"import scipy\n", |
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"import scipy.stats as sts\n", |
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"import pickle as pkl\n", |
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"import ipywidgets as widgets\n", |
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"from mpl_toolkits import mplot3d\n", |
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"from mpl_toolkits.mplot3d import Axes3D\n", |
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"from scipy import sparse\n", |
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"from matplotlib import cm\n", |
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"\n", |
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"# color maps\n", |
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"cmap_hot = cm.get_cmap('hot')\n", |
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"cmap_viridis = cm.get_cmap('viridis')\n", |
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"cmap_jet = cm.get_cmap('jet')" |
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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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"source": [ |
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"\n", |
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"data_folder = \"../data/\"\n", |
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"stim_file = \"Stiminfo_PVCre_2021_0012_s06_e14.csv\"\n", |
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"spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n", |
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"\n", |
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"key_symbol = {'pair':'$(\\\\theta,\\phi)$', 'orientation':'$\\\\theta$', 'phase':'$\\phi$'}" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"#### Load the stimulus data\n", |
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"**Stimulus data layout:**\n", |
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"```\n", |
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"stim_val = {'pair': [(or1, ph1), (or2, ph2), ...], 'orientation': [...], 'phase': [...]}\n", |
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"stim_id_trial = {'pair': [id1, id2, ...], 'phase': [..], ...} # corresponding trials\n", |
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"pair_trial_id[orientation_id][phase_id] = [Trial ids]\n", |
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"```" |
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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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"# Stimulus DataFrame\n", |
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"stim = pd.read_csv(data_folder + stim_file)\n", |
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"num_trial = len(stim)\n", |
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"stim_val = {}\n", |
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"trial_stim_id = {}\n", |
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"# 50 trials per orientation-phase pair\n", |
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"stim_val['pair'], trial_stim_id['pair'] = np.unique(stim[['grat_orientation', 'grat_phase']], return_inverse=True, axis=0) \n", |
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"# 1000 trials per orientation\n", |
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"stim_val['orientation'], trial_stim_id['orientation'] = np.unique(stim['grat_orientation'], return_inverse=True) \n", |
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"# 1000 trials per phase\n", |
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"stim_val['phase'], trial_stim_id['phase'] = np.unique(stim['grat_phase'], return_inverse=True) \n", |
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"key_list = ['pair', 'orientation', 'phase']\n", |
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"stim_id_trial = {}\n", |
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"num_stim = {}\n", |
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"for key in key_list:\n", |
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" stim_id_trial[key] = [np.where(trial_stim_id[key] == i)[0] for i in range(len(stim_val[key]))]\n", |
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" num_stim[key] = len(stim_val[key])\n", |
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"\n", |
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"def pair_id(i):\n", |
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" return (int(i//num_stim['orientation']), i%num_stim['orientation'])\n", |
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"\n", |
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"trial_pair_id = np.array([pair_id(i) for i in trial_stim_id['pair']], dtype=object)\n", |
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"pair_val = stim_val['pair'].reshape(num_stim['orientation'], num_stim['phase'],2)\n", |
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"\n", |
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"# for each (orientation_id = i, phase_id = j) find the trial indices\n", |
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"pair_trial_id = np.ndarray((num_stim['orientation'], num_stim['phase']), dtype=object)\n", |
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"for i in range(num_stim['pair']):\n", |
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" pair_trial_id[pair_id(i)] = stim_id_trial['pair'][i]" |
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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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{ |
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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>Unnamed: 0</th>\n", |
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" <th>grat_orientation</th>\n", |
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" <th>grat_phase</th>\n", |
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" <th>stimvals</th>\n", |
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" <th>stim_ontime</th>\n", |
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" <th>stim_offtime</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>0</th>\n", |
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" <td>0</td>\n", |
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" <td>45.0</td>\n", |
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" <td>36.0</td>\n", |
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" <td>102</td>\n", |
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" <td>2.5005</td>\n", |
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" <td>2.5839</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>1</th>\n", |
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" <td>1</td>\n", |
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" <td>0.0</td>\n", |
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" <td>18.0</td>\n", |
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" <td>1</td>\n", |
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" <td>2.6195</td>\n", |
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" <td>2.7028</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>2</th>\n", |
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" <td>2</td>\n", |
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" <td>162.0</td>\n", |
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" <td>288.0</td>\n", |
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" <td>376</td>\n", |
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" <td>2.7030</td>\n", |
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" <td>2.7863</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>3</th>\n", |
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" <td>3</td>\n", |
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" <td>0.0</td>\n", |
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" <td>54.0</td>\n", |
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" <td>3</td>\n", |
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" <td>2.7865</td>\n", |
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" <td>2.8699</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>4</th>\n", |
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" <td>4</td>\n", |
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" <td>108.0</td>\n", |
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" <td>36.0</td>\n", |
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" <td>242</td>\n", |
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" <td>2.8700</td>\n", |
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" <td>2.9534</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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" Unnamed: 0 grat_orientation grat_phase stimvals stim_ontime \\\n", |
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"0 0 45.0 36.0 102 2.5005 \n", |
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"1 1 0.0 18.0 1 2.6195 \n", |
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"2 2 162.0 288.0 376 2.7030 \n", |
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"3 3 0.0 54.0 3 2.7865 \n", |
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"4 4 108.0 36.0 242 2.8700 \n", |
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"\n", |
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" stim_offtime \n", |
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"0 2.5839 \n", |
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"1 2.7028 \n", |
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"2 2.7863 \n", |
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"3 2.8699 \n", |
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"4 2.9534 " |
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] |
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}, |
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"execution_count": 4, |
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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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"stim.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": 5, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"pair, orientation, phase\n", |
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"(400, 2)\n", |
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"(20,) (20,)\n" |
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] |
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} |
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], |
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"source": [ |
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"print(', '.join(stim_val.keys()))\n", |
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"print(stim_val['pair'].shape)\n", |
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"print(stim_val['orientation'].shape, stim_val['phase'].shape)" |
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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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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"[ 82 482 1021 1421 1821 2183 2645 3045 3445 3643 4357 4757\n", |
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" 5014 5493 5893 6293 6693 6965 7365 7652 8052 8513 9116 9203\n", |
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" 9603 10003 10628 10849 11512 11912 12312 12535 12947 13347 13666 14248\n", |
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" 14682 14857 15257 15657 16393 16793 17193 17262 17906 18146 18546 19050\n", |
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" 19450 19904]\n" |
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] |
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} |
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], |
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"source": [ |
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"print(stim_id_trial['pair'][0])" |
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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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{ |
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"data": { |
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"text/plain": [ |
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"array([ 82, 482, 1021, 1421, 1821, 2183, 2645, 3045, 3445,\n", |
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" 3643, 4357, 4757, 5014, 5493, 5893, 6293, 6693, 6965,\n", |
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" 7365, 7652, 8052, 8513, 9116, 9203, 9603, 10003, 10628,\n", |
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" 10849, 11512, 11912, 12312, 12535, 12947, 13347, 13666, 14248,\n", |
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" 14682, 14857, 15257, 15657, 16393, 16793, 17193, 17262, 17906,\n", |
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" 18146, 18546, 19050, 19450, 19904])" |
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] |
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}, |
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"execution_count": 7, |
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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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"pair_trial_id[0][0]" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"**Stimulus data layout:**\n", |
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"```\n", |
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"stim_val = {'pair': [(or1, ph1), (or2, ph2), ...], 'orientation': [...], 'phase': [...]}\n", |
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"stim_id_trial = {'pair': [id1, id2, ...], 'phase': [..], ...} # corresponding trials\n", |
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"pair_trial_id[orientation_id][phase_id] = [Trial ids]\n", |
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"```" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"#### Load the spike data and correlations\n", |
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"```\n", |
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"Loaded: spike_count_rate, avg_firing_rate, sem_firing_rate, firing_rate, stim_num_trial, C_r_fphi_theta, theta_hist, phase_hist, pair_hist\n", |
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"Loaded: corr_stim_unit, optimal_avg_firing_rate, stim_hist, stim_hist_caution\n", |
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"```" |
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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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{ |
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"data": { |
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"text/plain": [ |
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"(40,)" |
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] |
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}, |
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"execution_count": 8, |
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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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"spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n", |
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"spike_times = np.load(data_folder+spike_times_file, allow_pickle=True)\n", |
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"active = [len(spike_times[i]) > 0 for i in range(len(spike_times))]\n", |
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"spike_times = spike_times[np.where(active)]\n", |
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"num_unit = len(spike_times)\n", |
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"spike_times.shape" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"**Sort spikes by firing rate**" |
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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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"\n", |
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"num_spike = list(map(len, spike_times))\n", |
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"spike_times = spike_times[np.argsort(num_spike)[::-1]]" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"**Load a lot of variables out of a `.pkl` file**" |
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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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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Loaded: spike_count_rate, avg_firing_rate, sem_firing_rate, firing_rate, stim_num_trial, C_r_fphi_theta, theta_hist, phase_hist, pair_hist\n", |
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"Loaded: corr_stim_unit, optimal_avg_firing_rate, stim_hist, stim_hist_caution\n" |
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] |
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} |
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], |
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"source": [ |
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"# yeah...\n", |
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"files = ['spike_data.pkl', 'corr_data.pkl']\n", |
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"for file_name in files:\n", |
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" file = open(file_name, 'rb')\n", |
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" data = pkl.load(file)\n", |
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" print('Loaded:', ', '.join(data.keys()))\n", |
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" for key, value in data.items():\n", |
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" globals()[key] = value\n", |
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"\n", |
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" file.close()" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"#### Spike and stimulus preprocessing\n", |
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"\n", |
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"```\n", |
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"B_stim = {'pair': [mat_stim_1, mat_stim_2, ..], 'orientation': .., ...}\n", |
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"```\n", |
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"\n", |
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"`mat_stim_i` is a matrix of shape `(1, M)` such that `mat[0][t] = 1` if there was stimuli at time $t$ and $0$ if not\n", |
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"\n", |
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"```\n", |
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"B_spike = [unit_1_spikes, unit_2_spikes, ..]\n", |
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"unit_1_spikes[t] = 1 if there was a spike, 0 if not\n", |
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"```" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"##### Read the data" |
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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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"source": [ |
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"# reverse correlation time offset range \n", |
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"max_delay = 300 # dt\n", |
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"tau_id_range = np.arange(max_delay)\n", |
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"\n", |
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"# experiment duration\n", |
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"latest_spike_time = max([np.max(s) for s in spike_times if len(s)])\n", |
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"latest_stim_offtime = list(stim['stim_offtime'])[-1]\n", |
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"experiment_dur = max([latest_spike_time, latest_stim_offtime])\n", |
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"\n", |
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"dt = 0.001 # 1 ms\n", |
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"exp_time = np.arange(0, experiment_dur, dt)\n", |
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"M = len(exp_time)" |
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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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"B_stim = {}\n", |
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"for key in key_list:\n", |
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" B_stim[key] = []\n", |
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" for stim_id, trials in enumerate(stim_id_trial[key]):\n", |
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" B_stim[key].append([])\n", |
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" s = []\n", |
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" for trial_id in trials:\n", |
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" t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n", |
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" s += list(np.arange(int(t_on//dt), int(t_off//dt)))\n", |
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"\n", |
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" B_stim[key][stim_id] = sparse.coo_matrix((np.ones(len(s)), (np.zeros(len(s), dtype=int), s)), shape=(1, M))\n", |
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"s = spike_times//dt\n", |
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"B_spike = []\n", |
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"for unit_id in range(num_unit):\n", |
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" B_spike.append(sparse.coo_matrix((np.ones(len(s[unit_id])), (np.zeros(len(s[unit_id]), dtype=int), np.int0(s[unit_id]))), shape=(1, M)))" |
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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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"outputs": [], |
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"source": [ |
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"sorted_spike_num = np.sort(num_spike)[::-1]\n", |
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"cutoff_num_spike = 1000\n", |
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"num_unit = np.sum(sorted_spike_num > cutoff_num_spike)\n", |
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"for key in key_list:\n", |
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" stim_hist[key] = stim_hist[key][:num_unit]" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"##### Some plots" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"This graph shows for unit 0 at a particular moment:\n", |
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"- One stimulus duration (blue)\n", |
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"- The second stimulus (red)\n", |
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"\n", |
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"And spike events" |
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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": 14, |
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"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"image/png": 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", |
|
"text/plain": [ |
|
"<Figure size 432x288 with 1 Axes>" |
|
] |
|
}, |
|
"metadata": { |
|
"needs_background": "light" |
|
}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"stim_id = 10\n", |
|
"trial_id = stim_id_trial['phase'][stim_id][0]\n", |
|
"unit_id = 0\n", |
|
"t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n", |
|
"t0, t1 = t_off-0.2, t_off+0.1\n", |
|
"stim_prev_id = trial_stim_id['phase'][trial_id-1]\n", |
|
"t_prev_on, t_prev_off = stim['stim_ontime'][trial_id-1], stim['stim_offtime'][trial_id-1]\n", |
|
"spikes = spike_times[unit_id][np.where((t0 < spike_times[unit_id]) & (spike_times[unit_id] < t1))]\n", |
|
"plt.axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n", |
|
"plt.axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n", |
|
"plt.eventplot(spikes)\n", |
|
"plt.xlim([t0, t1])\n", |
|
"plt.title('Spikes for unit 0')\n", |
|
"plt.xlabel('Time')\n", |
|
"plt.show()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 15, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
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"image/png": 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", |
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"text/plain": [ |
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"<Figure size 504x504 with 7 Axes>" |
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] |
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}, |
|
"metadata": { |
|
"needs_background": "light" |
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}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
|
"from matplotlib import gridspec\n", |
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"\n", |
|
"fig, ax = plt.subplots(3,1,figsize=(7,7))\n", |
|
"gs0 = gridspec.GridSpec(3, 3, figure=fig, hspace=0, wspace=0)\n", |
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"\n", |
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"stim_id = 10\n", |
|
"trial_id = stim_id_trial['phase'][stim_id][0]\n", |
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"unit_id = 0\n", |
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"t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n", |
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"t0, t1 = t_off-0.2, t_off+0.1\n", |
|
"stim_prev_id = trial_stim_id['phase'][trial_id-1]\n", |
|
"t_prev_on, t_prev_off = stim['stim_ontime'][trial_id-1], stim['stim_offtime'][trial_id-1]\n", |
|
"spikes = spike_times[unit_id][np.where((t0 < spike_times[unit_id]) & (spike_times[unit_id] < t1))]\n", |
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"\n", |
|
"ax0 = fig.add_subplot(gs0[0, :-1])\n", |
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"ax1 = fig.add_subplot(gs0[1, :-1])\n", |
|
"ax2 = fig.add_subplot(gs0[2, :-1])\n", |
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"ax3 = fig.add_subplot(gs0[:, -1])\n", |
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"\n", |
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"ax0.axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3, label='preferred')\n", |
|
"ax0.axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3, label='non-preferred')\n", |
|
"ax0.eventplot(spikes)\n", |
|
"ax0.set_xlim([t0, t1])\n", |
|
"ax0.legend(loc='upper left')\n", |
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"\n", |
|
"ax1.axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n", |
|
"ax1.axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n", |
|
"ax1.eventplot(spikes-0.05)\n", |
|
"ax1.set_xlim([t0, t1])\n", |
|
"\n", |
|
"ax2.axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n", |
|
"ax2.axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n", |
|
"ax2.eventplot(spikes-0.15)\n", |
|
"ax2.set_xlim([t0, t1])\n", |
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"\n", |
|
"ax3.plot(np.sin(np.pi*tau_id_range/300), np.arange(300))\n", |
|
"ax3.set_xticks([])\n", |
|
"ax3.set_yticks([])\n", |
|
"ax3.set_title('reverse correlation')\n", |
|
"# ax2.legend()\n", |
|
"for ax in fig.get_axes():\n", |
|
" ax.tick_params(bottom=False, labelbottom=False, left=False, labelleft=False)\n", |
|
"\n", |
|
"ax2.tick_params(bottom=True, labelbottom=True)\n", |
|
"ax2.set_xlabel('t/s')\n", |
|
"plt.tight_layout()\n", |
|
"fig.suptitle('subspace reverse correlation', size=16, y=1.05)\n", |
|
"plt.show()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 16, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"image/png": 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", |
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"text/plain": [ |
|
"<Figure size 504x504 with 3 Axes>" |
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] |
|
}, |
|
"metadata": { |
|
"needs_background": "light" |
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}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
|
"# subspace reverese correlation\n", |
|
"stim_id = 13\n", |
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"ref_trial_id = stim_id_trial['phase'][stim_id][0]\n", |
|
"stim_prev_id = trial_stim_id['phase'][ref_trial_id-1]\n", |
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"past_trials = 3\n", |
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"future_trials = 10\n", |
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"trial_range = np.arange(-past_trials, future_trials+1) + ref_trial_id\n", |
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"\n", |
|
"units = np.arange(num_unit)\n", |
|
"orientation, phase, t_on = np.array(stim[['grat_orientation', 'grat_phase','stim_ontime']].iloc[trial_range]).T\n", |
|
"spikes = [\n", |
|
" spike_times[unit_id][\n", |
|
" # Where the spike occurs during the stimulus period in one of the right trials\n", |
|
" np.where((t_on[0] < spike_times[unit_id]) & (spike_times[unit_id] < t_on[-1]))\n", |
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" ]\n", |
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" for unit_id in units]\n", |
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"\n", |
|
"fig, ax = plt.subplots(2,1,figsize=(7,7))\n", |
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"plt.subplots_adjust(hspace=0)\n", |
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"s = 1\n", |
|
"ax[0].eventplot(spikes, linelength=s, lineoffsets=s)\n", |
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"\n", |
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"ref_id = list(trial_range).index(ref_trial_id)\n", |
|
"# ax[0].axvspan(t_on[ref_id], t_on[ref_id]+0.08, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n", |
|
"# ax[0].axvspan(t_on[ref_id-1], t_on[ref_id-1]+0.08, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n", |
|
"ax[0].set(ylabel='unit')\n", |
|
"ax[0].invert_yaxis()\n", |
|
"ax[1].step(t_on, phase, 'b', where='post', alpha=0.5, label='phase')\n", |
|
"ax[1].set(xlabel='t / s', xlim=[t_on[0], t_on[-1]], ylim=[0, 360], ylabel='phase')\n", |
|
"ax[1].legend(loc='upper left')\n", |
|
"ax2 = ax[1].twinx()\n", |
|
"ax2.step(t_on, orientation, 'r', where='post', alpha=0.5, label='orientation')\n", |
|
"ax2.set(ylim=[0, 180], ylabel='orientation')\n", |
|
"ax2.legend()\n", |
|
"ax[0].set_title('Spikes in units')\n", |
|
"ax[1].set_title('Phase and orientation')\n", |
|
"\n", |
|
"plt.show()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 17, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"application/vnd.jupyter.widget-view+json": { |
|
"model_id": "afc70624cbfe4a5f8838bce9ae0c0ebc", |
|
"version_major": 2, |
|
"version_minor": 0 |
|
}, |
|
"text/plain": [ |
|
"interactive(children=(IntSlider(value=0, description='delay/ms', max=299), IntSlider(value=0, description='uni…" |
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] |
|
}, |
|
"metadata": {}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"@widgets.interact(key=widgets.RadioButtons(options=key_list[1:],\n", |
|
" description='stimulus:',\n", |
|
" disabled=False), tau_id=widgets.IntSlider(0, min=0, max=max_delay-1, description='delay/ms'), unit_id=widgets.IntSlider(0, min=0, max=num_unit-1))\n", |
|
"\n", |
|
"def PSSH(tau_id, unit_id, key):\n", |
|
" fig, ax = plt.subplots(1, 1, figsize=(7,6))\n", |
|
" prob = stim_hist[key][unit_id, tau_id]\n", |
|
" n = sorted_spike_num[unit_id]\n", |
|
" confidence_interval = np.sqrt(prob/n)\n", |
|
" plt.errorbar(stim_val[key], stim_hist[key][unit_id, tau_id], yerr=confidence_interval)\n", |
|
" ymin = np.min(stim_hist[key][unit_id])*0.95\n", |
|
" ymax = np.max(stim_hist[key][unit_id])*1.05\n", |
|
" ax.set_ylabel('$\\mathcal{P}($'+key_symbol[key]+'$|\\\\tau)$')\n", |
|
" ax.set_xlabel(key_symbol[key])\n", |
|
" plt.ylim([ymin, ymax])\n", |
|
" plt.suptitle('Distribution of stimulus at time $\\\\tau$ before a spike of unit: %d'%unit_id)\n", |
|
" plt.show()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"metadata": {}, |
|
"source": [ |
|
"##### Problem with the data\n", |
|
"\n", |
|
"The picture a couple of cells below clearly shows that there is a shift in the stimulus presentation times. Let's fix it!" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 18, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stderr", |
|
"output_type": "stream", |
|
"text": [ |
|
"/usr/lib/python3.10/site-packages/pandas/core/roperator.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", |
|
" return right - left\n" |
|
] |
|
} |
|
], |
|
"source": [ |
|
"trial_spike_times = np.ndarray((num_unit, num_trial), dtype=object)\n", |
|
"delta_t_on = 0.2 # 200ms\n", |
|
"delta_t_off = 0.3\n", |
|
"for trial_id in range(num_trial):\n", |
|
" t_on, t_off = stim['stim_ontime'][trial_id]-delta_t_on, stim['stim_offtime'][trial_id]+delta_t_off\n", |
|
" for unit_id in range(num_unit):\n", |
|
" trial_spike_times[unit_id, trial_id] = spike_times[unit_id][np.where((spike_times[unit_id] < t_off) & (spike_times[unit_id] > t_on))]\n", |
|
"subseq_trials = 5\n", |
|
"subseq_spike_times = np.ndarray(num_unit, dtype=object)\n", |
|
"for unit_id in range(num_unit):\n", |
|
" subseq_spike_times[unit_id] = [np.concatenate(trial_spike_times[unit_id][i:i+subseq_trials]) for i in range(num_trial-subseq_trials)]\n", |
|
"\n", |
|
"subseq_spike_times_locked = {}\n", |
|
"subseq_spike_times_locked['ontime'] = [subseq_spike_times[unit_id] - stim['stim_ontime'][:-subseq_trials] for unit_id in range(num_unit)]\n", |
|
"subseq_spike_times_locked['offtime'] = [subseq_spike_times[unit_id] - stim['stim_offtime'][:-subseq_trials] for unit_id in range(num_unit)]" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 19, |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"time_range = np.arange(0, stim['stim_ontime'].max(), np.array(sorted(np.diff(stim['stim_ontime'], n=1))[10:-10]).mean())" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 20, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"text/plain": [ |
|
"((28, 19995), (28, 19995), 0.7671829999999886)" |
|
] |
|
}, |
|
"execution_count": 20, |
|
"metadata": {}, |
|
"output_type": "execute_result" |
|
} |
|
], |
|
"source": [ |
|
"subseq_spike_times_len = len(subseq_spike_times[0])\n", |
|
"subseq_spike_times_locked['ontime'] = np.array(subseq_spike_times_locked['ontime'])\n", |
|
"subseq_spike_times_locked['offtime'] = np.array(subseq_spike_times_locked['offtime'])\n", |
|
"from itertools import chain\n", |
|
"subseq_spike_times_locked['ontime'].shape, subseq_spike_times_locked['offtime'].shape, max(chain(*subseq_spike_times_locked['ontime'].flatten()))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 21, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"image/png": 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", |
|
"text/plain": [ |
|
"<Figure size 1440x720 with 3 Axes>" |
|
] |
|
}, |
|
"metadata": { |
|
"needs_background": "light" |
|
}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"unit_id = 1\n", |
|
"unit_id_2 = 3\n", |
|
"\n", |
|
"fig, ax = plt.subplots(1,3, figsize=(20,10))\n", |
|
"ax[0].plot(np.diff(stim['stim_ontime'], n=2), np.arange(20000-2))\n", |
|
"ax[0].invert_yaxis()\n", |
|
"ax[0].set_xlabel('$\\Delta^2[t_{on}]=\\Delta$ trial duration / s', size=20)\n", |
|
"ax[0].set(ylabel='trials')\n", |
|
"ax[1].eventplot(subseq_spike_times_locked['ontime'][unit_id], lineoffsets=1, linelengths=0.8)\n", |
|
"ax[1].set(title='unit %d'%unit_id, yticks=[], xlabel='t/s (locked to trial onsets)')\n", |
|
"ax[1].invert_yaxis()\n", |
|
"ax[2].eventplot(subseq_spike_times_locked['ontime'][unit_id_2], lineoffsets=1, linelengths=0.8)\n", |
|
"ax[2].set(title='unit %d'%(unit_id_2), yticks=[], xlabel='t/s (locked to trial ends)')\n", |
|
"ax[2].invert_yaxis()\n", |
|
"plt.subplots_adjust(wspace=0)\n", |
|
"plt.show()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"metadata": {}, |
|
"source": [ |
|
"_Whaaat?_\n", |
|
"\n", |
|
"Ok, we've gotta fix it.\n", |
|
"\n", |
|
"Let's take the \"bad\" times, identify the periods we want to calibrate - and align everything" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 22, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stdout", |
|
"output_type": "stream", |
|
"text": [ |
|
"2280 2286 2291 2292 2295 2298 2301 2307 2377 2378 2380 2383 2386 2464 2467 2469 2472 2475 2478 15300 15306 15312 15315 15316 15318 15327 15329 15333 15336 15338 15342 15344 15348 15349 15402 15403 15405 15408\n" |
|
] |
|
} |
|
], |
|
"source": [ |
|
"stim_2diffs = np.diff(stim['stim_ontime'], n=2)\n", |
|
"print(*[i for i in range(len(stim_2diffs)) if stim_2diffs[i] > 0.01])" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"metadata": {}, |
|
"source": [ |
|
"Periods:\n", |
|
"$[0, 2350)$, $[2350, 15350)$, $[15350, 20000]$" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 23, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stderr", |
|
"output_type": "stream", |
|
"text": [ |
|
"/tmp/ipykernel_4135643/175591453.py:20: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n", |
|
" period_1_dist = scipy.ndimage.filters.gaussian_filter1d(period_1_dist, sigma=10)\n", |
|
"/tmp/ipykernel_4135643/175591453.py:21: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n", |
|
" period_2_dist = scipy.ndimage.filters.gaussian_filter1d(period_2_dist, sigma=10)\n", |
|
"/tmp/ipykernel_4135643/175591453.py:22: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n", |
|
" period_3_dist = scipy.ndimage.filters.gaussian_filter1d(period_3_dist, sigma=10)\n" |
|
] |
|
} |
|
], |
|
"source": [ |
|
"p1, p2 = 2350, 15350\n", |
|
"delta_t = 0.002\n", |
|
"time_range_smoothing = np.arange(0, 0.8, delta_t)\n", |
|
"period_1_dist = np.zeros(len(time_range_smoothing) - 1)\n", |
|
"period_2_dist = np.zeros(len(time_range_smoothing) - 1)\n", |
|
"period_3_dist = np.zeros(len(time_range_smoothing) - 1)\n", |
|
"num_units = subseq_spike_times_locked['ontime'].shape[0]\n", |
|
"for i in range(num_units):\n", |
|
" for j, row in enumerate(subseq_spike_times_locked['ontime'][i]):\n", |
|
" hist = np.histogram(row, bins=time_range_smoothing)[0]\n", |
|
" if j < p1:\n", |
|
" period_1_dist += hist\n", |
|
" elif j < p2:\n", |
|
" period_2_dist += hist\n", |
|
" else:\n", |
|
" period_3_dist += hist\n", |
|
"period_1_dist /= num_units\n", |
|
"period_2_dist /= num_units\n", |
|
"period_3_dist /= num_units\n", |
|
"period_1_dist = scipy.ndimage.filters.gaussian_filter1d(period_1_dist, sigma=10)\n", |
|
"period_2_dist = scipy.ndimage.filters.gaussian_filter1d(period_2_dist, sigma=10)\n", |
|
"period_3_dist = scipy.ndimage.filters.gaussian_filter1d(period_3_dist, sigma=10)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 24, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"text/plain": [ |
|
"[<matplotlib.collections.EventCollection at 0x7f1b53e02f80>]" |
|
] |
|
}, |
|
"execution_count": 24, |
|
"metadata": {}, |
|
"output_type": "execute_result" |
|
}, |
|
{ |
|
"data": { |
|
"image/png": 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", |
|
"text/plain": [ |
|
"<Figure size 720x1440 with 3 Axes>" |
|
] |
|
}, |
|
"metadata": { |
|
"needs_background": "light" |
|
}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"fig, ax = plt.subplots(3, 1, figsize=(10,20))\n", |
|
"ax[0].plot(time_range_smoothing[:-1], period_1_dist, label='period 1')\n", |
|
"ax[1].plot(time_range_smoothing[:-1], period_2_dist, label='period 2')\n", |
|
"ax[2].plot(time_range_smoothing[:-1], period_3_dist, label='period 3')\n", |
|
"ax[0].set(xlabel='t/s', ylabel='spike count', title='1st part')\n", |
|
"ax[1].set(xlabel='t/s', ylabel='spike count', title='2nd part')\n", |
|
"ax[2].set(xlabel='t/s', ylabel='spike count', title='3rd part')\n", |
|
"ax[0].eventplot([0.2], linelengths=0.8)\n", |
|
"ax[1].eventplot([0.2], linelengths=0.8)\n", |
|
"ax[2].eventplot([0.2], linelengths=0.8)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 25, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"text/plain": [ |
|
"100" |
|
] |
|
}, |
|
"execution_count": 25, |
|
"metadata": {}, |
|
"output_type": "execute_result" |
|
} |
|
], |
|
"source": [ |
|
"list(time_range_smoothing).index(0.2)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 26, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stdout", |
|
"output_type": "stream", |
|
"text": [ |
|
"0.2 0.162 0.194\n" |
|
] |
|
} |
|
], |
|
"source": [ |
|
"max_p_1 = time_range_smoothing[np.argmax(period_1_dist[:120])]\n", |
|
"max_p_2 = time_range_smoothing[np.argmax(period_2_dist[:100])]\n", |
|
"max_p_3 = time_range_smoothing[np.argmax(period_3_dist[:120])]\n", |
|
"delta_2 = max_p_1 - max_p_2\n", |
|
"delta_3 = max_p_1 - max_p_3\n", |
|
"print(max_p_1, max_p_2, max_p_3)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"metadata": {}, |
|
"source": [ |
|
"Now we'll fix the times" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 27, |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"t_p1, t_p2 = stim['stim_offtime'][p1], stim['stim_offtime'][p2]\n", |
|
"fix_time = lambda t: t - delta_2 * ((t < t_p2) & (t >= t_p1)) - delta_3 * ((t >= t_p2))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 28, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"text/plain": [ |
|
"0 2.5839\n", |
|
"1 2.7028\n", |
|
"2 2.7863\n", |
|
"3 2.8699\n", |
|
"4 2.9534\n", |
|
" ... \n", |
|
"19990 1671.6389\n", |
|
"19991 1671.7224\n", |
|
"19992 1671.8059\n", |
|
"19993 1671.8894\n", |
|
"19994 1671.9730\n", |
|
"Name: stim_offtime, Length: 19995, dtype: float64" |
|
] |
|
}, |
|
"execution_count": 28, |
|
"metadata": {}, |
|
"output_type": "execute_result" |
|
} |
|
], |
|
"source": [ |
|
"fix_time(stim['stim_offtime'][:-subseq_trials])" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 29, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"name": "stderr", |
|
"output_type": "stream", |
|
"text": [ |
|
"/usr/lib/python3.10/site-packages/pandas/core/roperator.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", |
|
" return right - left\n" |
|
] |
|
} |
|
], |
|
"source": [ |
|
"subseq_spike_times_locked_2 = {}\n", |
|
"subseq_spike_times_locked_2['ontime'] = [subseq_spike_times[unit_id] - fix_time(stim['stim_ontime'][:-subseq_trials]) for unit_id in range(num_unit)]\n", |
|
"subseq_spike_times_locked_2['offtime'] = [subseq_spike_times[unit_id] - fix_time(stim['stim_offtime'][:-subseq_trials]) for unit_id in range(num_unit)]" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 30, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
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", |
|
"text/plain": [ |
|
"<Figure size 1440x720 with 3 Axes>" |
|
] |
|
}, |
|
"metadata": { |
|
"needs_background": "light" |
|
}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"unit_id = 1\n", |
|
"\n", |
|
"fig, ax = plt.subplots(1,3, figsize=(20,10))\n", |
|
"ax[0].plot(np.diff(stim['stim_ontime'], n=2), np.arange(20000-2))\n", |
|
"ax[0].invert_yaxis()\n", |
|
"ax[0].set_xlabel('$\\Delta^2[t_{on}]=\\Delta$ trial duration / s', size=20)\n", |
|
"ax[0].set(ylabel='trials')\n", |
|
"ax[1].eventplot(subseq_spike_times_locked_2['ontime'][unit_id], lineoffsets=1, linelengths=0.8)\n", |
|
"ax[1].set(title='unit %d'%unit_id, yticks=[], xlabel='t/s (locked to trial onsets)')\n", |
|
"ax[1].invert_yaxis()\n", |
|
"ax[2].eventplot(subseq_spike_times_locked_2['ontime'][unit_id_2], lineoffsets=1, linelengths=0.8)\n", |
|
"ax[2].set(title='unit %d'%(unit_id_2), yticks=[], xlabel='t/s (locked to trial ends)')\n", |
|
"ax[2].invert_yaxis()\n", |
|
"plt.subplots_adjust(wspace=0)\n", |
|
"plt.show()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 31, |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"s = np.array([np.array(list(map(fix_time, spike_times[unit_id]))) for unit_id in range(len(spike_times))], dtype=object)//dt\n", |
|
"B_spike_fixed = []\n", |
|
"for unit_id in range(num_unit):\n", |
|
" B_spike_fixed.append(sparse.coo_matrix((np.ones(len(s[unit_id])), (np.zeros(len(s[unit_id]), dtype=int), np.int0(s[unit_id]))), shape=(1, M)))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 32, |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"from copy import deepcopy\n", |
|
"B_stim_original = deepcopy(B_stim)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 33, |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"B_stim = {}\n", |
|
"for key in key_list:\n", |
|
" B_stim[key] = []\n", |
|
" for stim_id, trials in enumerate(stim_id_trial[key]):\n", |
|
" B_stim[key].append([])\n", |
|
" s = []\n", |
|
" for trial_id in trials:\n", |
|
" t_on, t_off = fix_time(stim['stim_ontime'][trial_id]), fix_time(stim['stim_offtime'][trial_id])\n", |
|
" s += list(np.arange(int(t_on//dt), int(t_off//dt)))\n", |
|
"\n", |
|
" B_stim[key][stim_id] = sparse.coo_matrix((np.ones(len(s)), (np.zeros(len(s), dtype=int), s)), shape=(1, M))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
"metadata": {}, |
|
"source": [ |
|
"#### Calculate some matrices" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 34, |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"get_trial_index = lambda t: fix_time(stim['stim_ontime'][t])//dt" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 35, |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"B_spike_smooth = list()\n", |
|
"for unit_id in range(num_unit):\n", |
|
" B_spike_smooth.append(scipy.ndimage.gaussian_filter(B_spike[unit_id].toarray()[0], sigma=50))\n", |
|
"B_spike_smooth = np.array(B_spike_smooth)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 36, |
|
"metadata": {}, |
|
"outputs": [ |
|
{ |
|
"data": { |
|
"text/plain": [ |
|
"[<matplotlib.lines.Line2D at 0x7f1b6ae5dcc0>]" |
|
] |
|
}, |
|
"execution_count": 36, |
|
"metadata": {}, |
|
"output_type": "execute_result" |
|
}, |
|
{ |
|
"data": { |
|
"image/png": 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", |
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"text/plain": [ |
|
"<Figure size 432x288 with 1 Axes>" |
|
] |
|
}, |
|
"metadata": { |
|
"needs_background": "light" |
|
}, |
|
"output_type": "display_data" |
|
} |
|
], |
|
"source": [ |
|
"plt.plot(B_spike_smooth[0][:5000]) # ~10 trials" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [] |
|
} |
|
], |
|
"metadata": { |
|
"kernelspec": { |
|
"display_name": "Python 3.10.5 64-bit", |
|
"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": "3.10.5" |
|
}, |
|
"orig_nbformat": 4, |
|
"vscode": { |
|
"interpreter": { |
|
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a" |
|
} |
|
} |
|
}, |
|
"nbformat": 4, |
|
"nbformat_minor": 2 |
|
}
|
|
|