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1816 lines
7.2 MiB
1816 lines
7.2 MiB
2 years ago
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{
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"cells": [
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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": "stdout",
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"output_type": "stream",
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"text": [
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"stim_data.pkl\n",
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"dict_keys(['stim_val', 'trial_stim_id', 'key_list', 'num_trial', 'trial_pair_id', 'pair_val', 'pair_trial_id', 'stim_id_trial', 'num_stim'])\n",
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"spike_data.pkl\n",
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"dict_keys(['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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"corr_data.pkl\n",
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"dict_keys(['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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"# load data\n",
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"\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.stats as sts\n",
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"import pickle\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')\n",
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"\n",
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"# load and process stimuli\n",
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"execfile('Stimulus.py')\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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"stim = pd.read_csv(data_folder+stim_file)\n",
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"num_trial = len(stim)\n",
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"\n",
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"# load and process spikes\n",
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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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"\n",
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"\n",
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"# sort by firing rate\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]]\n",
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"execfile('load.py')\n",
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"\n",
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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)\n",
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"\n",
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"# binary spike and stimulus trains\n",
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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)))\n",
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"\n",
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"# histogram error bars: num spikes\n",
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"# spike_count = np.zeros((num_unit, 2))\n",
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"# for unit_id in range(num_unit):\n",
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"# # print(\"unit: %d\"%unit_id)\n",
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"# a = np.zeros(len(tau_id_range))\n",
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"# for tau_id in tau_id_range:\n",
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"# a[tau_id] = np.sum(B_spike[unit_id].col >= tau_id)\n",
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" \n",
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"# spike_count[unit_id] = [np.mean(a), np.std(a)]\n",
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"\n",
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"key_symbol = {'pair':'$(\\\\theta,\\phi)$', 'orientation':'$\\\\theta$', 'phase':'$\\phi$'}\n",
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"\n",
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"# 2D tuning\n",
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"avg_firing_rate_pair = np.array([\n",
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" sts.zscore(stim_hist['pair'][unit_id]).reshape((len(tau_id_range), num_stim['orientation'], num_stim['phase'])) \n",
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" for unit_id in range(num_unit)])\n",
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"\n",
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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]\n",
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"\n"
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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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"# # tuning score\n",
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"\n",
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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]\n",
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" \n",
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"# from scipy.stats import zscore\n",
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"\n",
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"# var_tuning = {}\n",
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"# stim_hist_KL_div = {}\n",
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"# for key in key_list:\n",
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"# var_tuning[key] = np.zeros((num_unit, len(tau_id_range)))\n",
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"# stim_hist_KL_div[key] = np.zeros((num_unit, len(tau_id_range)))\n",
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"\n",
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"# for unit_id in range(num_unit):\n",
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"# var_tuning[key][unit_id] = zscore(np.var(stim_hist[key][unit_id], axis=1))\n",
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"# for tau_id in tau_id_range:\n",
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"# p_non = (1-np.sum(stim_hist[key][unit_id, tau_id]))\n",
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"# s_non = 0\n",
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"# if p_non > 0:\n",
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"# s_non = -p_non * np.log10(p_non)\n",
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" \n",
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"# nonzero_id = np.where(stim_hist[key][unit_id, tau_id] != 0)\n",
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"# p_stim = stim_hist[key][unit_id, tau_id, nonzero_id]\n",
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"# if np.any(p_stim<=0):\n",
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"# print('p_stim')\n",
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"\n",
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"# if p_non >1 or np.any(p_stim>1):\n",
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"# print('wrong')\n",
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" \n",
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"# s_stim = -p_stim * np.log10(p_stim)\n",
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"# ent = np.sum(s_stim) + s_non\n",
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"# if ent<0:\n",
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"# print('negative stim_hist_KL_div')\n",
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"# # if kl < 0:\n",
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"# # print(unit_id, tau_id, stim_id)\n",
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"# stim_hist_KL_div[key][unit_id, tau_id] = np.log10(num_stim[key]+1) - ent # KL div\n",
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"\n",
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"# # normalize KL div for each unit\n",
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"\n",
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"# stim_hist_KL_div_norm = {}\n",
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"# for key in key_list:\n",
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"# stim_hist_KL_div_norm[key] = zscore(stim_hist_KL_div[key], axis=1)\n",
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" \n",
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"# tuning_score = {}\n",
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"# for key in key_list:\n",
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"# tuning_score[key] = np.max(stim_hist_KL_div[key], axis=1)\n",
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" \n",
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"# unit_tuning_order = np.argsort(tuning_score['pair'])[::-1]\n",
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"# unit_tuning2rate_order = np.argsort(unit_tuning_order)\n",
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"\n",
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"# snr = {}\n",
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"# for key in key_list:\n",
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"# snr[key] = np.mean(stim_hist[key], axis=2)/np.std(stim_hist['phase'], axis=2)\n"
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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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"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.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 504x504 with 7 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from matplotlib import gridspec\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(3,1,figsize=(7,7))\n",
|
||
|
|
"gs0 = gridspec.GridSpec(3, 3, figure=fig, hspace=0, wspace=0)\n",
|
||
|
|
"\n",
|
||
|
|
"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",
|
||
|
|
"\n",
|
||
|
|
"ax0 = fig.add_subplot(gs0[0, :-1])\n",
|
||
|
|
"ax1 = fig.add_subplot(gs0[1, :-1])\n",
|
||
|
|
"ax2 = fig.add_subplot(gs0[2, :-1])\n",
|
||
|
|
"ax3 = fig.add_subplot(gs0[:, -1])\n",
|
||
|
|
"\n",
|
||
|
|
"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",
|
||
|
|
"\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",
|
||
|
|
"\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()\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# # subspace reverese correlation\n",
|
||
|
|
"# fig, ax = plt.subplots(2,1,figsize=(7,7))\n",
|
||
|
|
"# stim_id = 13\n",
|
||
|
|
"# trial_id = stim_id_trial['phase'][stim_id][0]-3\n",
|
||
|
|
"# stim_id = trial_stim_id['phase'][trial_id]\n",
|
||
|
|
"# unit_id = 0\n",
|
||
|
|
"# t_on, t_off = stim['stim_ontime'][trial_id], stim['stim_offtime'][trial_id]\n",
|
||
|
|
"# t0, t1 = t_off-0.5, t_off+0.5\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((t_off-0.5 < spike_times[unit_id]) & (spike_times[unit_id] < t_off+0.2))]\n",
|
||
|
|
"# ax[0].axvspan(t_on, t_off, color=cmap_jet(1-np.abs(stim_id-10)/10), alpha=0.3)\n",
|
||
|
|
"# ax[0].axvspan(t_prev_on, t_prev_off, color=cmap_jet(1-np.abs(stim_prev_id-10)/10), alpha=0.3)\n",
|
||
|
|
"# ax[0].eventplot(spikes)\n",
|
||
|
|
"# ax[0].set_xlim([t0, t1])\n",
|
||
|
|
"# ax[1].plot(np.arange(t0, t1, dt), step_stim[int(t0//dt): int(t1//dt)])\n",
|
||
|
|
"# # ax[1].plot(np.arange(t0, t1), step_stim[int(t0//dt): int(t1//dt)])\n",
|
||
|
|
"\n",
|
||
|
|
"# ax[1].set(ylabel='phase', xlabel='time')\n",
|
||
|
|
"# plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 504x504 with 3 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# subspace reverese correlation\n",
|
||
|
|
"stim_id = 13\n",
|
||
|
|
"ref_trial_id = stim_id_trial['phase'][stim_id][0]\n",
|
||
|
|
"stim_prev_id = trial_stim_id['phase'][ref_trial_id-1]\n",
|
||
|
|
"past_trials = 3\n",
|
||
|
|
"future_trials = 10\n",
|
||
|
|
"trial_range = np.arange(-past_trials,future_trials+1) + ref_trial_id\n",
|
||
|
|
"\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 = [spike_times[unit_id][np.where((t_on[0] < spike_times[unit_id]) & (spike_times[unit_id] < t_on[-1]))] for unit_id in units]\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(2,1,figsize=(7,7))\n",
|
||
|
|
"plt.subplots_adjust(hspace=0)\n",
|
||
|
|
"s = 1\n",
|
||
|
|
"ax[0].eventplot(spikes, linelength=s, lineoffsets=s)\n",
|
||
|
|
"\n",
|
||
|
|
"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",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Phase reference changes with orientation"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from tqdm import trange\n",
|
||
|
|
"import sys\n",
|
||
|
|
"sys.path.insert(0, './Persistent_Homology')\n",
|
||
|
|
"sys.path.insert(0, '../numerical')\n",
|
||
|
|
"\n",
|
||
|
|
"import gratings\n",
|
||
|
|
"import decorator"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "7dc1b6ee06bf4540a417ab8eadea52f6",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(FloatSlider(value=0.0, description='stim_theta', max=180.0, step=5.0), FloatSlider(value…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"@widgets.interact(\n",
|
||
|
|
" stim_theta = widgets.FloatSlider(0, min=0, max=180, step=5),\n",
|
||
|
|
" stim_phi = widgets.FloatSlider(90, min=0, max=360, step=10),\n",
|
||
|
|
" stim_freq = widgets.FloatSlider(1.5, min=0, max=10, step=0.5),\n",
|
||
|
|
" RF_theta = widgets.FloatSlider(0, min=0, max=180, step=5),\n",
|
||
|
|
" RF_phi = widgets.FloatSlider(90, min=0, max=360, step=10),\n",
|
||
|
|
" RF_freq = widgets.FloatSlider(1.5, min=0, max=10, step=0.5),\n",
|
||
|
|
" RF_radius = widgets.FloatSlider(0.5, min=0, max=1, step=0.1), \n",
|
||
|
|
" RF_x = widgets.FloatSlider(0.5, min=-1, max=1, step=0.1),\n",
|
||
|
|
" RF_y = widgets.FloatSlider(-0.5, min=-1, max=1, step=0.1)\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
"def plot_grating(stim_theta, stim_phi, stim_freq, RF_theta, RF_phi, RF_freq, RF_radius, RF_x, RF_y):\n",
|
||
|
|
" stim_grating = (stim_theta/180*np.pi, stim_freq, stim_phi/180*np.pi, 1)\n",
|
||
|
|
" RF_grating = (RF_theta/180*np.pi, RF_freq, RF_phi/180*np.pi, 1)\n",
|
||
|
|
" stim_grat_image = gratings.grating_image(grating=stim_grating, plot=False)\n",
|
||
|
|
" RF_grat_image = gratings.grating_image(grating=RF_grating, gabor=True, rf_sigma=RF_radius, center=(RF_y, RF_x), plot=False)\n",
|
||
|
|
" \n",
|
||
|
|
" fig, ax = plt.subplots(1,5, figsize=(25,6))\n",
|
||
|
|
"\n",
|
||
|
|
" ax[0].imshow(stim_grat_image,\"gray\", vmin = -1, vmax = 1, extent=[-1,1,1,-1]) \n",
|
||
|
|
" receptive_field = plt.Circle((RF_x, RF_y), RF_radius, color='b', fill=False)\n",
|
||
|
|
" ax[0].add_patch(receptive_field)\n",
|
||
|
|
" ax[0].legend([receptive_field], ['receptive field'])\n",
|
||
|
|
" ax[0].invert_yaxis()\n",
|
||
|
|
"\n",
|
||
|
|
" ax[1].imshow(RF_grat_image,\"gray\", vmin = -1, vmax = 1, extent=[-1,1,1,-1]) \n",
|
||
|
|
" receptive_field = plt.Circle((RF_x, RF_y), RF_radius, color='b', fill=False)\n",
|
||
|
|
" ax[1].add_patch(receptive_field)\n",
|
||
|
|
" ax[1].legend([receptive_field], ['receptive field']) \n",
|
||
|
|
" ax[1].invert_yaxis()\n",
|
||
|
|
"\n",
|
||
|
|
" response = gratings.response(RF_grating, stim_grating, rf_sigma=RF_radius, center=(RF_y, RF_x))\n",
|
||
|
|
" ax[2].axhline(response)\n",
|
||
|
|
" ax[2].set(ylim=[0,1])\n",
|
||
|
|
"\n",
|
||
|
|
" # Nstim = (20, 1, 20, 1)\n",
|
||
|
|
" # stim_list = gratings.get_locations(Nstim)\n",
|
||
|
|
" # rates = np.zeros([len(stim_list)])\n",
|
||
|
|
" # iterator = trange(0, len(stim_list), position=0, leave=True)\n",
|
||
|
|
" # iterator.set_description(\"Simulating data stim_list\")\n",
|
||
|
|
" # for i in iterator:\n",
|
||
|
|
" # rates[i] = response(stim_list[i], RF_grating, rf_sigma=RF_radius, center=(RF_y, RF_x))\n",
|
||
|
|
"\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Stimulus pair sequence (History or change dependence)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "SyntaxError",
|
||
|
|
"evalue": "incomplete input (619742353.py, line 2)",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;36m Input \u001b[0;32mIn [9]\u001b[0;36m\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"def coarse_grain(stim_range):\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# # sort by tuning score\n",
|
||
|
|
"# for key in key_list:\n",
|
||
|
|
"\n",
|
||
|
|
"# stim_hist[key] = stim_hist[key][unit_tuning_order]\n",
|
||
|
|
"# stim_hist_KL_div[key] = stim_hist_KL_div[key][unit_tuning_order]\n",
|
||
|
|
"# stim_hist_KL_div_norm[key] = stim_hist_KL_div_norm[key][unit_tuning_order]\n",
|
||
|
|
"# tuning_score[key] = tuning_score[key][unit_tuning_order]\n",
|
||
|
|
"# var_tuning[key] = var_tuning[key][unit_tuning_order]\n",
|
||
|
|
"# snr[key] = snr[key][unit_tuning_order]\n",
|
||
|
|
"\n",
|
||
|
|
"# s = s[:num_unit][unit_tuning_order]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "dd669e607c4e4619b78a3e04b7d0599f",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(IntSlider(value=0, description='delay/ms', max=299), IntSlider(value=0, description='uni…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"@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": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1728x2016 with 28 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"tau_id = 100\n",
|
||
|
|
"key = 'orientation'\n",
|
||
|
|
"fig, ax = plt.subplots(7,4,figsize=(6*4, 7*4))\n",
|
||
|
|
"X, Y = np.meshgrid(stim_val[key], tau_id_range)\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].errorbar(stim_val[key], stim_hist[key][unit_id, tau_id], yerr=np.sqrt(stim_hist[key][unit_id, tau_id]/sorted_spike_num[unit_id]))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ymin = np.min(stim_hist[key][unit_id])*0.95\n",
|
||
|
|
" ymax = np.max(stim_hist[key][unit_id])*1.05\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('$\\mathcal{P}($'+key_symbol[key]+'$|\\\\tau)$')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key_symbol[key])\n",
|
||
|
|
" # ax[unit_id//4, unit_id%4].set_ylim([ymin, ymax])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylim([0, 0.1])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_title('unit %d'%unit_id + ', spike num: %d'%sorted_spike_num[unit_id])\n",
|
||
|
|
"\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1728x2016 with 28 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'phase'\n",
|
||
|
|
"fig, ax = plt.subplots(7,4,figsize=(6*4, 7*4))\n",
|
||
|
|
"X, Y = np.meshgrid(stim_val[key], tau_id_range)\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"tau_id = 60\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].errorbar(stim_val[key], stim_hist[key][unit_id, tau_id], yerr=np.sqrt(stim_hist[key][unit_id, tau_id]/sorted_spike_num[unit_id]))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ymin = np.min(stim_hist[key][unit_id])*0.95\n",
|
||
|
|
" ymax = np.max(stim_hist[key][unit_id])*1.05\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('$\\mathcal{P}($'+key_symbol[key]+'$|\\\\tau)$')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel(key_symbol[key])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylim([0, 0.1])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_title('unit %d'%unit_id + ', spike num: %d'%sorted_spike_num[unit_id])\n",
|
||
|
|
"\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"# @widgets.interact(key=widgets.RadioButtons(options=key_list[1:],\n",
|
||
|
|
"# description='stimulus:',\n",
|
||
|
|
"# disabled=False), unit_id=widgets.IntSlider(0, min=0, max=num_unit-1))\n",
|
||
|
|
"\n",
|
||
|
|
"# def tuning_curve_time_evolution(unit_id, key):\n",
|
||
|
|
"# fig, ax = plt.subplots(1,1)\n",
|
||
|
|
"# from matplotlib import cm\n",
|
||
|
|
"# cmap = cm.get_cmap('hot')\n",
|
||
|
|
"\n",
|
||
|
|
"# for tau_id in tau_id_range:\n",
|
||
|
|
"# ax.plot(stim_val[key], stim_hist[key][unit_id, tau_id], c=cmap(tau_id))\n",
|
||
|
|
"# ax.set_xlabel(key_symbol[key])\n",
|
||
|
|
"# ax.set_ylabel('$P($'+key_symbol[key]+'$|\\\\tau)$')\n",
|
||
|
|
"\n",
|
||
|
|
"# plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "7ce2a8b3870146d5bd0e02729e847d48",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(IntSlider(value=0, description='delay/ms', max=299), IntSlider(value=0, description='uni…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"@widgets.interact(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 TwoDimTuning(tau_id, unit_id):\n",
|
||
|
|
" min_2d = np.min(avg_firing_rate_pair[unit_id])\n",
|
||
|
|
" max_2d = np.max(avg_firing_rate_pair[unit_id])\n",
|
||
|
|
" fig, ax = plt.subplots(1, 1, figsize=(6,6))\n",
|
||
|
|
" im = ax.imshow(avg_firing_rate_pair[unit_id, tau_id], interpolation='None', cmap='jet', vmin=min_2d, vmax=max_2d)\n",
|
||
|
|
"\n",
|
||
|
|
" ax.set_xticks(np.arange(20)[::5])\n",
|
||
|
|
" ax.set_xticklabels(stim_val['phase'][::5])\n",
|
||
|
|
" ax.set_yticks(np.arange(20)[::5])\n",
|
||
|
|
" ax.set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax.set_xlabel('phase')\n",
|
||
|
|
" ax.set_ylabel('orientation')\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "ef75a02b1b8049439d355eb5d141b772",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(IntSlider(value=0, description='stim_id', max=19), IntSlider(value=0, description='unit_…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"@widgets.interact(key=widgets.RadioButtons(\n",
|
||
|
|
" options=key_list[1:],\n",
|
||
|
|
" description='stimulus:',\n",
|
||
|
|
" disabled=False),\n",
|
||
|
|
" stim_id=widgets.IntSlider(0, min=0, max=19), \n",
|
||
|
|
" unit_id=widgets.IntSlider(0, min=0, max=num_unit-1))\n",
|
||
|
|
"\n",
|
||
|
|
"def PSSH(stim_id, unit_id, key):\n",
|
||
|
|
" ymin = np.min(stim_hist[key][unit_id])*0.95\n",
|
||
|
|
" ymax = np.max(stim_hist[key][unit_id])*1.05\n",
|
||
|
|
"\n",
|
||
|
|
" fig, ax = plt.subplots(1, 1, figsize=(7,6))\n",
|
||
|
|
" ax.errorbar(tau_id_range, stim_hist[key][unit_id, :, stim_id], yerr=np.sqrt(stim_hist[key][unit_id, :, stim_id]/sorted_spike_num[unit_id]), \n",
|
||
|
|
" label=key_symbol[key]+'=%d'%stim_val[key][stim_id]+'$^\\circ$')\n",
|
||
|
|
" ax.set_ylim([ymin, ymax])\n",
|
||
|
|
" ax.set_ylabel('$\\mathcal{P}($'+key_symbol[key]+'$|\\\\tau)$')\n",
|
||
|
|
" ax.set_xlabel('$\\\\tau/ms$')\n",
|
||
|
|
" ax.legend()\n",
|
||
|
|
" plt.show()\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "d181fa482d184dfab8e3f309e9d36bae",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(IntSlider(value=0, description='phi', max=19), IntSlider(value=0, description='theta', m…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'pair'\n",
|
||
|
|
"@widgets.interact(phi=widgets.IntSlider(0, min=0, max=19), theta=widgets.IntSlider(0, min=0, max=19), unit_id=widgets.IntSlider(0, min=0, max=num_unit-1))\n",
|
||
|
|
"\n",
|
||
|
|
"def PSSH(phi, theta, unit_id):\n",
|
||
|
|
" ymin = np.min(stim_hist[key][unit_id])*0.95\n",
|
||
|
|
" ymax = np.max(stim_hist[key][unit_id])*1.05\n",
|
||
|
|
"\n",
|
||
|
|
" stim_id = int(20*theta + phi)\n",
|
||
|
|
" fig, ax = plt.subplots(1, 1, figsize=(7,6))\n",
|
||
|
|
" ax.errorbar(tau_id_range, stim_hist[key][unit_id, :, stim_id], yerr=np.sqrt(stim_hist[key][unit_id, :, stim_id]/sorted_spike_num[unit_id]),\n",
|
||
|
|
" label=key_symbol[key]+'=(%d'%stim_val['orientation'][theta]+'$^\\circ$,%d'%stim_val['phase'][phi]+'$^\\circ)$')\n",
|
||
|
|
" ax.set_ylim([ymin, ymax])\n",
|
||
|
|
" ax.set_ylabel('$\\mathcal{P}((\\\\theta, \\phi)|\\\\tau)$')\n",
|
||
|
|
" ax.set_xlabel('$\\\\tau/ms$')\n",
|
||
|
|
" ax.legend()\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x1512 with 28 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# %matplotlib inline\n",
|
||
|
|
"# %matplotlib widget\n",
|
||
|
|
"# @widgets.interact(key=widgets.RadioButtons(\n",
|
||
|
|
"# options=key_list[1:],\n",
|
||
|
|
"# description='stimulus:',\n",
|
||
|
|
"# disabled=False),\n",
|
||
|
|
"# unit_id=widgets.IntSlider(0, min=0, max=num_unit-1))\n",
|
||
|
|
"\n",
|
||
|
|
"# def contour_plot(key, unit_id):\n",
|
||
|
|
"# fig = plt.figure(figsize=(8,8))\n",
|
||
|
|
"# # ax = plt.axes(projection='3d', [0,0,0])\n",
|
||
|
|
"# ax = plt.axes()\n",
|
||
|
|
"# X, Y = np.meshgrid(np.arange(20), tau_id_range)\n",
|
||
|
|
"# ax.contour(X, Y, stim_hist[key][unit_id], cmap='jet')\n",
|
||
|
|
"# ax.set_xlabel(key_symbol[key])\n",
|
||
|
|
"# ax.set_ylabel('$\\\\tau$')\n",
|
||
|
|
"# # ax.set_zlabel('$\\mathcal{P}($'+key_symbol[key]+'$|\\\\tau)$')\n",
|
||
|
|
"# plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'orientation'\n",
|
||
|
|
"fig, ax = plt.subplots(7,4,figsize=(4*5, 7*3))\n",
|
||
|
|
"X, Y = np.meshgrid(stim_val[key], -tau_id_range)\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].contour(Y, X, stim_hist[key][unit_id])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_title('unit %d'%unit_id + ', spike num: %d'%sorted_spike_num[unit_id])\n",
|
||
|
|
"\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x22095f8cee0>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 75,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plt.imshow((np.log10(stim_hist['pair'][unit_id]*num_stim[key])[60]).reshape(20,20))\n",
|
||
|
|
"plt.plot(np.log10(stim_hist['phase'][unit_id]*num_stim[key])[60])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x22096143a60>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 76,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(-np.mean(np.log10(stim_hist['phase'][unit_id]*num_stim[key]), axis=1))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 360x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'phase'\n",
|
||
|
|
"unit_id = 5\n",
|
||
|
|
"fig, ax = plt.subplots(1,1,figsize=(5, 4))\n",
|
||
|
|
"X, Y = np.meshgrid(stim_val[key], -tau_id_range)\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"im = ax.contour(Y, X, np.log10(stim_hist[key][unit_id]*num_stim[key]))\n",
|
||
|
|
"ax.set_ylabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
"ax.set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
"fig.colorbar(im)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2016 with 28 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'orientation'\n",
|
||
|
|
"fig, ax = plt.subplots(7,4,figsize=(5*4, 7*4))\n",
|
||
|
|
"X, Y = np.meshgrid(stim_val[key], -tau_id_range)\n",
|
||
|
|
"plt.set_cmap('jet')\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].contour(Y, X, stim_hist[key][unit_id])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel(key_symbol[key]+'/degrees')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"var_tuning = {}\n",
|
||
|
|
"for key in key_list:\n",
|
||
|
|
" var_tuning[key] = np.zeros((num_unit, len(tau_id_range)))\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" var_tuning[key][unit_id] = np.var(stim_hist[key][unit_id], axis=1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2016 with 28 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"key = 'phase'\n",
|
||
|
|
"fig, ax = plt.subplots(7,4,figsize=(5*4, 7*4))\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('variance')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
"\n",
|
||
|
|
"fig.suptitle('variance of phase tuning curve as a function of delay')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1440x2016 with 28 Axes>"
|
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|
|
]
|
||
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|
},
|
||
|
|
"metadata": {
|
||
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|
"needs_background": "light"
|
||
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|
},
|
||
|
|
"output_type": "display_data"
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||
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|
}
|
||
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|
],
|
||
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"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
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|
"\n",
|
||
|
|
"key = 'orientation'\n",
|
||
|
|
"fig, ax = plt.subplots(7,4,figsize=(5*4, 7*4))\n",
|
||
|
|
"for unit_id in range(num_unit):\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(var_tuning[key][unit_id])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('variance')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('$\\\\tau/(ms)$')\n",
|
||
|
|
"\n",
|
||
|
|
"fig.suptitle('variance of orientation tuning curve as a function of delay')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "cd2dd687bbc2413ba4501eaf2ed7ae31",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(IntSlider(value=0, description='delay/ms', max=299), Output()), _dom_classes=('widget-in…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"@widgets.interact(tau_id = widgets.IntSlider(0, min=0, max=max_delay-1, description='delay/ms'))\n",
|
||
|
|
"\n",
|
||
|
|
"def TwoDimTuning(tau_id):\n",
|
||
|
|
" unit_selection = [0,1,2,3]\n",
|
||
|
|
"\n",
|
||
|
|
" fig, ax = plt.subplots(1, 4, figsize=(5*4, 1*4))\n",
|
||
|
|
" for i, unit_id in enumerate(unit_selection):\n",
|
||
|
|
" ax[i].imshow(avg_firing_rate_pair[unit_id, tau_id], interpolation='None', cmap='jet')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i].set_xticks(np.arange(20)[::5])\n",
|
||
|
|
" ax[i].set_xticklabels(stim_val['phase'][::5])\n",
|
||
|
|
" ax[i].set_yticks(np.arange(20)[::5])\n",
|
||
|
|
" ax[i].set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax[i].set_xlabel('phase')\n",
|
||
|
|
" ax[i].set_ylabel('orientation')\n",
|
||
|
|
" ax[i].set_title('delay:%d'%tau_id +' unit %d'%unit_id + ', spike num: %d'%sorted_spike_num[unit_id])\n",
|
||
|
|
" \n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Klein bottle symmetry: Continuity at parametrization boundary?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "c8d97450e868410781bdc602a3922fbb",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(IntSlider(value=0, description='delay/ms', max=299), IntSlider(value=10, description='ph…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# klein bottle symmetry\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"\n",
|
||
|
|
"@widgets.interact(tau_id=widgets.IntSlider(0, min=0, max=len(tau_id_range)-1, description='delay/ms'), phase_ref = widgets.IntSlider(10, min=0, max=19, description='phase reference'))\n",
|
||
|
|
"\n",
|
||
|
|
"def klein_bottle(tau_id, phase_ref):\n",
|
||
|
|
" unit_selection = [0,1,2,3]\n",
|
||
|
|
" klein_bottle_rate = np.zeros((num_unit, 40, 40))\n",
|
||
|
|
"\n",
|
||
|
|
" fig, ax = plt.subplots(1, 4, figsize=(5*4, 1*4))\n",
|
||
|
|
" for i, unit_id in enumerate(unit_selection):\n",
|
||
|
|
" x = np.hstack((avg_firing_rate_pair[unit_id,tau_id], avg_firing_rate_pair[unit_id,tau_id]))\n",
|
||
|
|
" y = np.hstack((x[:,phase_ref:], x[:, :phase_ref]))\n",
|
||
|
|
" klein_bottle_rate[unit_id] = np.vstack((x, y[:,::-1]))\n",
|
||
|
|
" ax[i].imshow(klein_bottle_rate[unit_id], interpolation='None', cmap='jet')\n",
|
||
|
|
" ax[i].set_xticks([])\n",
|
||
|
|
" ax[i].set_yticks([])\n",
|
||
|
|
" ax[i].plot(np.arange(40), 19.5*np.ones(40), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[i].plot(19.5*np.ones(40), np.arange(40), 'r', linewidth=0.5)\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i].set_xlabel('phase')\n",
|
||
|
|
" ax[i].set_ylabel('orientation')\n",
|
||
|
|
" \n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"stim_val_2cycle = {}\n",
|
||
|
|
"x = np.copy(stim_val['orientation'])\n",
|
||
|
|
"y = np.concatenate((x, x+180))\n",
|
||
|
|
"stim_val_2cycle['orientation'] = np.concatenate((y,y))\n",
|
||
|
|
"\n",
|
||
|
|
"x = np.copy(stim_val['phase'])\n",
|
||
|
|
"stim_val_2cycle['phase'] = np.concatenate((x, x))\n",
|
||
|
|
"\n",
|
||
|
|
"for key in key_list[1:]:\n",
|
||
|
|
" stim_val_2cycle[key] = list(map(int, stim_val_2cycle[key]))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "36851f4d840c4816981f92436e99252e",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"interactive(children=(RadioButtons(description='grid', options=('ON', 'OFF'), value='ON'), RadioButtons(descri…"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# klein bottle symmetry\n",
|
||
|
|
"\n",
|
||
|
|
" \n",
|
||
|
|
"@widgets.interact(grid=widgets.RadioButtons(options=['ON', 'OFF'], disabled=False), tau_id=widgets.IntSlider(60, min=0, max=len(tau_id_range)-1, description='delay/ms'), \n",
|
||
|
|
"phase_ref = widgets.IntSlider(10, min=0, max=19, description='phase reference'),\n",
|
||
|
|
"ticks = widgets.RadioButtons(options=['ON', 'OFF'], disabled=False))\n",
|
||
|
|
"\n",
|
||
|
|
"def klein_bottle(grid, ticks, phase_ref, tau_id):\n",
|
||
|
|
" klein_bottle_rate = np.zeros((num_unit, 80, 40))\n",
|
||
|
|
"\n",
|
||
|
|
" fig, ax = plt.subplots(10, 4, figsize=(5*4, 10*4))\n",
|
||
|
|
" for unit_id in range(num_unit):\n",
|
||
|
|
" x = np.hstack((avg_firing_rate_pair[unit_id,tau_id], avg_firing_rate_pair[unit_id,tau_id]))\n",
|
||
|
|
" y = np.hstack((x[:,phase_ref:], x[:, :phase_ref]))\n",
|
||
|
|
" z = np.vstack((x, y[:,::-1]))\n",
|
||
|
|
" klein_bottle_rate[unit_id] = np.vstack((z,z))\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].imshow(klein_bottle_rate[unit_id], interpolation='None', cmap='jet', aspect=0.5)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticks([])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticks([])\n",
|
||
|
|
" \n",
|
||
|
|
" if grid=='ON':\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(np.arange(40), 19.5*np.ones(40), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(np.arange(40), (19.5 + 20)*np.ones(40), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(np.arange(40), (19.5 + 40)*np.ones(40), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot(19.5*np.ones(80), np.arange(80), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot((19.5 + 10)*np.ones(80), np.arange(80), 'r', linewidth=0.5)\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].plot((19.5 - 10)*np.ones(80), np.arange(80), 'r', linewidth=0.5)\n",
|
||
|
|
"\n",
|
||
|
|
" if ticks=='ON':\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticks(np.arange(40)[::5])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticks(np.arange(80)[::10])\n",
|
||
|
|
" \n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_yticklabels(stim_val_2cycle['orientation'][::10])\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xticklabels(stim_val_2cycle['phase'][::5])\n",
|
||
|
|
"\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_xlabel('phase')\n",
|
||
|
|
" ax[unit_id//4, unit_id%4].set_ylabel('orientation')\n",
|
||
|
|
" \n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"So we see that a phase reference shift of 180 is necessary.\n",
|
||
|
|
"\n",
|
||
|
|
"$\\theta \\rightarrow \\theta + \\pi$\n",
|
||
|
|
"\n",
|
||
|
|
"$\\phi \\rightarrow \\pi -\\phi$\n",
|
||
|
|
"\n",
|
||
|
|
"or equivalently\n",
|
||
|
|
"\n",
|
||
|
|
"$\\phi - \\frac{\\pi}{2} \\rightarrow \\frac{\\pi}{2} - \\phi$\n",
|
||
|
|
"\n",
|
||
|
|
"which means that the proper phase reference that is maintained is at $\\pi/2$\n",
|
||
|
|
"\n",
|
||
|
|
"i.e. at $\\phi=\\pi/2$ the orientation loop is of period $180$ degrees instead of $360$ degrees. So at $\\phi=0$ the center of rotation is on the border (exactly in the middle of contrast extrema) between black and white stripes.\n",
|
||
|
|
"\n",
|
||
|
|
"Another transformation (?)\n",
|
||
|
|
"\n",
|
||
|
|
"$\\theta \\rightarrow \\pi - \\theta$ (But this is clearly wrong)\n",
|
||
|
|
"\n",
|
||
|
|
"$\\phi \\rightarrow \\phi - \\pi$\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# # relabelling\n",
|
||
|
|
"# param = np.arange(400).reshape(20,20)\n",
|
||
|
|
"# phase_ref = 10\n",
|
||
|
|
"# x = param[:, :phase_ref]\n",
|
||
|
|
"# y = param[:, phase_ref:]\n",
|
||
|
|
"# reparam = np.vstack((x, y[:,::-1]))\n",
|
||
|
|
"# stim_val_reparam = {}\n",
|
||
|
|
"# stim_val_reparam['phase'] = stim_val['phase'][:phase_ref]/2\n",
|
||
|
|
"# stim_val_reparam['orientation'] = np.concatenate((stim_val['orientation'], stim_val['orientation']+180))\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# stim_val_reparam['pair'] = stim_val['pair'][reparam]\n",
|
||
|
|
"\n",
|
||
|
|
"# stim_val_reparam['pair'] = stim_val['pair']\n",
|
||
|
|
"# for x in stim_val['pair']:\n",
|
||
|
|
"# if x[1]>= 180:\n",
|
||
|
|
"# x[0] += 180\n",
|
||
|
|
"# x[1] = phase_ref - x[1]\n",
|
||
|
|
"\n",
|
||
|
|
"# stim_val['pair'][reparam]\n",
|
||
|
|
"# stim_val['pair'].shape\n",
|
||
|
|
"# plt.imshow(reparam%20)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Dimensionality Reduction"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import sys\n",
|
||
|
|
"sys.path.insert(0, './Persistent_Homology')\n",
|
||
|
|
"\n",
|
||
|
|
"from gratings import grating_model\n",
|
||
|
|
"from plotting import plot_data, plot_mean_against_index, show_feature\n",
|
||
|
|
"from persistence import persistence\n",
|
||
|
|
"from decoding import cohomological_parameterization, remove_feature\n",
|
||
|
|
"from noisereduction import PCA_reduction, z_cutoff, density_filtration\n",
|
||
|
|
"from sklearn.decomposition import PCA \n",
|
||
|
|
"\n",
|
||
|
|
"# relabelling\n",
|
||
|
|
"param = np.arange(400).reshape(20,20)\n",
|
||
|
|
"phase_ref = 10\n",
|
||
|
|
"x = param[:phase_ref]\n",
|
||
|
|
"reparam = np.vstack((x, x[:,::-1]))\n",
|
||
|
|
"stim_val_reparam = {}\n",
|
||
|
|
"stim_val_reparam['phase'] = stim_val['phase'][:phase_ref]\n",
|
||
|
|
"stim_val_reparam['orientation'] = np.concatenate((stim_val['orientation'], stim_val['orientation']+180))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"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"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "c54a07c1ee6b4cf9ae61f78044c1ddc0",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"image/png": "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"\n",
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" <div style=\"display: inline-block;\">\n",
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" Figure\n",
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" </div>\n",
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" <img src='data:image/png;base64,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
|
||
|
|
" </div>\n",
|
||
|
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" "
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"tau_id = 60\n",
|
||
|
|
"Z = sts.zscore(stim_hist['pair'][:, tau_id, :].T, axis=0)\n",
|
||
|
|
"# Z = Z[:, np.logical_not(np.isnan(Z[0]))]\n",
|
||
|
|
"# n_components = len(Z[0])\n",
|
||
|
|
"n_components = 20\n",
|
||
|
|
"pca = PCA(n_components)\n",
|
||
|
|
"X = pca.fit_transform(Z)\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"PC_values = np.arange(n_components)\n",
|
||
|
|
"plt.plot(PC_values, pca.explained_variance_ratio_, 'o-', linewidth=2, color='blue')\n",
|
||
|
|
"plt.title('Scree Plot')\n",
|
||
|
|
"plt.xticks(np.arange(20))\n",
|
||
|
|
"plt.xlabel('Principal Component')\n",
|
||
|
|
"plt.ylabel('Variance Explained')\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"from mpl_toolkits import mplot3d\n",
|
||
|
|
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"%matplotlib widget\n",
|
||
|
|
"fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
|
||
|
|
"ax[0] = fig.add_subplot(1, 2, 1, projection='3d')\n",
|
||
|
|
"x, y, z = X[:,0], X[:,1], X[:,2]\n",
|
||
|
|
"ax[0].scatter3D(x, y, z, c=stim_val['pair'][:,0])\n",
|
||
|
|
"ax[0].set_title(\"orientation\", pad=25, size=15)\n",
|
||
|
|
"ax[0].set_xlabel(\"X\") \n",
|
||
|
|
"ax[0].set_ylabel(\"Y\") \n",
|
||
|
|
"ax[0].set_zlabel(\"Z\")\n",
|
||
|
|
"\n",
|
||
|
|
"ax[1] = fig.add_subplot(1, 2, 2, projection='3d')\n",
|
||
|
|
"x, y, z = X[:,0], X[:,1], X[:,2]\n",
|
||
|
|
"ax[1].scatter3D(x, y, z, c=stim_val['pair'][:,1])\n",
|
||
|
|
"ax[1].set_title(\"PCA - color code: phase label\", pad=25, size=15)\n",
|
||
|
|
"ax[1].set_xlabel(\"X\") \n",
|
||
|
|
"ax[1].set_ylabel(\"Y\") \n",
|
||
|
|
"ax[1].set_zlabel(\"Z\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# PCA"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 25 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"ncomp = 5\n",
|
||
|
|
"X_reduced = X[:,:ncomp]\n",
|
||
|
|
"# xmin, xmax = -2*np.std(X_reduced)+np.mean(X_reduced), 2*np.std(X_reduced)+np.mean(X_reduced)\n",
|
||
|
|
"fig = plt.figure(figsize=(12,12))\n",
|
||
|
|
"for i in range(ncomp):\n",
|
||
|
|
" for j in range(ncomp):\n",
|
||
|
|
" ax = plt.subplot(ncomp,ncomp, j + ncomp*i + 1)\n",
|
||
|
|
" if i==j:\n",
|
||
|
|
" ax.imshow(X_reduced[:,i].reshape(20,20), interpolation='None', cmap='jet')\n",
|
||
|
|
" ax.set_xticks(np.arange(20)[::5])\n",
|
||
|
|
" ax.set_xticklabels(stim_val['phase'][::5])\n",
|
||
|
|
" ax.set_yticks(np.arange(20)[::5])\n",
|
||
|
|
" ax.set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax.set_xlabel('phase')\n",
|
||
|
|
" ax.set_ylabel('orientation')\n",
|
||
|
|
"\n",
|
||
|
|
" else:\n",
|
||
|
|
" ax.scatter(X_reduced[:,j], X_reduced[:,i], s=1, c=stim_val['pair'][:,1], cmap='jet')\n",
|
||
|
|
" # ax.set_xlim([xmin, xmax])\n",
|
||
|
|
" ax.set_xticks([])\n",
|
||
|
|
"\n",
|
||
|
|
" # ax.set_ylim([xmin, xmax])\n",
|
||
|
|
" ax.set_yticks([])\n",
|
||
|
|
" \n",
|
||
|
|
"# fig.suptitle('n_neighbors: %d'%n_neighbors)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"embed = {5:np.load('embed3d_n_neighbors5.npy'), 20:np.load('embed3d_n_neighbors20.npy'), 50:np.load('embed3d_n_neighbors50.npy')}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def show_umap(embed, n_neighbors):\n",
|
||
|
|
" %matplotlib inline\n",
|
||
|
|
" ncomp = embed.shape[1]\n",
|
||
|
|
" # xmin, xmax = -2*np.std(embed)+np.mean(embed), 2*np.std(embed)+np.mean(embed)\n",
|
||
|
|
" fig = plt.figure(figsize=(12,12))\n",
|
||
|
|
" for i in range(ncomp):\n",
|
||
|
|
" for j in range(ncomp):\n",
|
||
|
|
" ax = plt.subplot(ncomp,ncomp, j + ncomp*i + 1)\n",
|
||
|
|
" if i==j:\n",
|
||
|
|
" ax.imshow(embed[:,i].reshape(20,20), interpolation='None', cmap='jet')\n",
|
||
|
|
" ax.set_xticks(np.arange(20)[::5])\n",
|
||
|
|
" ax.set_xticklabels(stim_val['phase'][::5])\n",
|
||
|
|
" ax.set_yticks(np.arange(20)[::5])\n",
|
||
|
|
" ax.set_yticklabels(stim_val['orientation'][::5])\n",
|
||
|
|
" ax.set_xlabel('phase')\n",
|
||
|
|
" ax.set_ylabel('orientation')\n",
|
||
|
|
"\n",
|
||
|
|
" else:\n",
|
||
|
|
" ax.scatter(embed[:,j], embed[:,i], s=1, c=stim_val['pair'][:,1], cmap='jet')\n",
|
||
|
|
" # ax.set_xlim([xmin, xmax])\n",
|
||
|
|
" ax.set_xticks([])\n",
|
||
|
|
"\n",
|
||
|
|
" # ax.set_ylim([xmin, xmax])\n",
|
||
|
|
" ax.set_yticks([])\n",
|
||
|
|
" \n",
|
||
|
|
" fig.suptitle('n_neighbors: %d'%n_neighbors)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"n_neighbors. This parameter controls how UMAP balances local versus global structure in the data. It does this by constraining the size of the local neighborhood UMAP will look at when attempting to learn the manifold structure of the data."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 25 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"n_neighbors = 5\n",
|
||
|
|
"show_umap(embed[n_neighbors], n_neighbors=n_neighbors)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 25 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"n_neighbors = 20\n",
|
||
|
|
"show_umap(embed[n_neighbors], n_neighbors=n_neighbors)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 25 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"n_neighbors = 50\n",
|
||
|
|
"show_umap(embed[n_neighbors], n_neighbors=n_neighbors)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Cohomological Decoding on UMAP"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"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"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"persistence(pd.DataFrame(embed[5]),homdim=1,coeff=2)\n",
|
||
|
|
"persistence(pd.DataFrame(embed[20]),homdim=1,coeff=2)\n",
|
||
|
|
"persistence(pd.DataFrame(embed[50]),homdim=1,coeff=2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"file = open('decoding50.pkl', 'rb')\n",
|
||
|
|
"decoding50 = pickle.load(file)\n",
|
||
|
|
"file.close()\n",
|
||
|
|
"\n",
|
||
|
|
"decoding50['phase'] = stim_val['pair'][decoding50.index][:,1]\n",
|
||
|
|
"decoding50['orientation'] = stim_val['pair'][decoding50.index][:,0]\n",
|
||
|
|
"def phasor_avg(x, period):\n",
|
||
|
|
" return np.angle(np.sum(np.exp(2*np.pi*1j*x/period)))*180/np.pi + 180\n",
|
||
|
|
"\n",
|
||
|
|
"statistic, bin_edges, bin_number = sts.binned_statistic(decoding50['decoding'], decoding50['phase'], lambda x:phasor_avg(x, period=360), 20)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([137.14126279, 220.09508264, 175.41479907, 153. ,\n",
|
||
|
|
" 162. , 112.86104702, 95.45882883, 66.83662297,\n",
|
||
|
|
" 29.00746987, 354.46203037, 12.11351122, 5.80639544,\n",
|
||
|
|
" 354.02211424, 234. , 342. , 314.12816312,\n",
|
||
|
|
" 272.63321064, 229.74777269, 207.23795075, 212.06151078])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 113,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"statistic"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([0, 3, 6, 9])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 114,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"np.arange(10)[::3]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 0., 54., 108., 162., 216., 270., 324.])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 115,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"stim_val['phase'][::3]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x360 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(1,2, figsize=(12,5))\n",
|
||
|
|
"ax[0].scatter(decoding50['decoding'], stim_val['pair'][decoding50.index][:,1])\n",
|
||
|
|
"ax[0].set_xlabel('decoded parameter')\n",
|
||
|
|
"ax[0].set_ylabel('actual parameter')\n",
|
||
|
|
"\n",
|
||
|
|
"ax[1].scatter(bin_edges[:-1], statistic)\n",
|
||
|
|
"ax[1].set_ylabel('avg. actual phase')\n",
|
||
|
|
"ax[1].set_xlabel('decoded parameter')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"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": [
|
||
|
|
"hist2d, xbin, ybin = np.histogram2d(decoding50['decoding'], stim_val['pair'][decoding50.index][:,1], bins=20)\n",
|
||
|
|
"fig, ax = plt.subplots(1,1)\n",
|
||
|
|
"ax.imshow(hist2d)\n",
|
||
|
|
"ax.set_xlabel('phase label')\n",
|
||
|
|
"ax.set_ylabel('decoded parameter')\n",
|
||
|
|
"# ax.set_xticklabels\n",
|
||
|
|
"# ax.set_yticks(xbin[:-1])\n",
|
||
|
|
"ax.invert_yaxis()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## UMAP fixed $\\phi$"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# fix phase at 90\n",
|
||
|
|
"phase_id = 5\n",
|
||
|
|
"phase_sec_id = stim_val['pair'][:,1] == stim_val['phase'][phase_id]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## UMAP fixed $\\phi^\\prime$"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
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|
"kernelspec": {
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||
|
|
"display_name": "Python 3.10.5 64-bit",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
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||
|
|
},
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||
|
|
"language_info": {
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||
|
|
"codemirror_mode": {
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||
|
|
"name": "ipython",
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||
|
|
"version": 3
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||
|
|
},
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|
|
"file_extension": ".py",
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||
|
|
"mimetype": "text/x-python",
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||
|
|
"name": "python",
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||
|
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"nbconvert_exporter": "python",
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||
|
|
"pygments_lexer": "ipython3",
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"version": "3.10.5"
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|
},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
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|
}
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|
}
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||
|
|
},
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||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
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||
|
|
}
|