Science
/
amgen
1
1
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Topology in neuroscience
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
20 Commits
1 Branch
0 Tags
2.3 GiB
Tree: b01ba9aba1
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'b01ba9aba1'
${ noResults }
amgen/exp/ProcessExp.ipynb

2599 lines
1.6 MiB
Raw Normal View History Unescape

Processing experimental data for flashed gratings
2 years ago
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Experiment: Flashed gratings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import scipy\n",
"import scipy.stats as sts\n",
"import pickle as pkl\n",
"import ipywidgets as widgets\n",
"from mpl_toolkits import mplot3d\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from scipy import sparse\n",
Exp processing: PCA and stuff
2 years ago
"import sys\n",
Processing experimental data for flashed gratings
2 years ago
"from matplotlib import cm\n",
Exp processing: PCA and stuff
2 years ago
"sys.path.insert(0, '../model/')\n",
Processing experimental data for flashed gratings
2 years ago
"\n",
"# color maps\n",
"cmap_hot = cm.get_cmap('hot')\n",
"cmap_viridis = cm.get_cmap('viridis')\n",
"cmap_jet = cm.get_cmap('jet')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\n",
"data_folder = \"../data/\"\n",
"stim_file = \"Stiminfo_PVCre_2021_0012_s06_e14.csv\"\n",
"spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n",
"\n",
"key_symbol = {'pair':'$(\\\\theta,\\phi)$', 'orientation':'$\\\\theta$', 'phase':'$\\phi$'}"
]
},
Exp processing: PCA and stuff
2 years ago
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 3,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
Some beautiful UMAP results
2 years ago
"dict_keys(['data_folder', 'stim_file', 'spike_times_file', 'key_symbol', 'stim', 'num_trial', 'stim_val', 'trial_stim_id', 'key_list', 'stim_id_trial', 'num_stim', 'key', 'pair_id', 'trial_pair_id', 'pair_val', 'pair_trial_id', 'spike_times', 'num_unit', 'num_spike', 'spike_count_rate', 'avg_firing_rate', 'firing_rate', 'stim_num_trial', 'stim_hist', 'optimal_avg_firing_rate', 'latest_spike_time', 'dt', 'exp_time', 'B_stim', 'trials', 't_on', 't_off', 'B_spike', 'trial_range', 'units', 'orientation', 'phase', 'trial_spike_times', 'delta_t_on', 'delta_t_off', 'subseq_trials', 'subseq_spike_times', 'subseq_spike_times_locked', 'time_range', 'unit_id_2', 'p1', 'p2', 'delta_t', 'time_range_smoothing', 'period_1_dist', 'period_2_dist', 'period_3_dist', 'num_units', 'max_p_1', 'max_p_2', 'max_p_3', 'delta_2', 'delta_3', 't_p1', 't_p2', 'subseq_spike_times_locked_2', 'B_spike_fixed', 'B_stim_original', 'B_spike_smooth', 'B_stim_total', 'R', 'R_pair_smoothed', 'Rp', 'firing_rates_noise_red', 'firing_rates_flat', 'firing_rates_in_trials', 'avg_firing_rates', 'avg_fr_blur', 'avg_firing_rates_pca', 'param_corr', 'avg_firing_rates_pca_', 'embed'])\n"
Exp processing: PCA and stuff
2 years ago
]
}
],
"source": [
"file = open(\"dump.pkl\", 'rb')\n",
"pair_id = None\n",
"data = pkl.load(file)\n",
"print(data.keys())\n",
"for key, value in data.items():\n",
" globals()[key] = value\n",
"file.close()"
]
},
Some beautiful UMAP results
2 years ago
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"with open('dump.pkl', 'wb') as f:\n",
" pickle.dump({k: globals()[k] for k in [\n",
" 'data_folder', 'stim_file', 'spike_times_file', 'key_symbol', \n",
" 'stim', 'num_trial', 'stim_val', 'trial_stim_id', 'key_list', 'stim_id_trial', 'num_stim', 'key', 'pair_id', 'trial_pair_id', 'pair_val', 'pair_trial_id',\n",
" 'spike_times', 'num_unit', 'num_spike', 'spike_count_rate', 'avg_firing_rate', 'firing_rate', 'stim_num_trial', 'stim_hist', 'optimal_avg_firing_rate',\n",
" 'latest_spike_time', 'dt', 'exp_time', 'B_stim', 'trials', 't_on', 't_off', 'B_spike', 'trial_range', 'units', 'orientation', 'phase', 'trial_spike_times',\n",
" 'delta_t_on', 'delta_t_off', 'subseq_trials', 'subseq_spike_times', 'subseq_spike_times_locked', 'time_range', 'unit_id_2', 'p1', 'p2', 'delta_t', 'time_range_smoothing',\n",
" 'period_1_dist', 'period_2_dist', 'period_3_dist', 'num_units', 'max_p_1', 'max_p_2', 'max_p_3', 'delta_2', 'delta_3', 't_p1', 't_p2', 'subseq_spike_times_locked_2',\n",
" 'B_spike_fixed', 'B_stim_original', 'B_spike_smooth', 'B_stim_total', 'R', 'R_pair_smoothed', 'Rp', 'firing_rates_noise_red', 'firing_rates_flat', 'firing_rates_in_trials', 'avg_firing_rates',\n",
" 'avg_fr_blur', 'avg_firing_rates', 'avg_firing_rates_pca', 'param_corr', 'avg_firing_rates_pca_', 'embed'\n",
" ]}, f)"
]
},
Processing experimental data for flashed gratings
2 years ago
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load the stimulus data\n",
"**Stimulus data layout:**\n",
"```\n",
"stim_val = {'pair': [(or1, ph1), (or2, ph2), ...], 'orientation': [...], 'phase': [...]}\n",
"stim_id_trial = {'pair': [id1, id2, ...], 'phase': [..], ...} # corresponding trials\n",
"pair_trial_id[orientation_id][phase_id] = [Trial ids]\n",
"```"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 4,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [],
"source": [
"# Stimulus DataFrame\n",
"stim = pd.read_csv(data_folder + stim_file)\n",
"num_trial = len(stim)\n",
"stim_val = {}\n",
"trial_stim_id = {}\n",
"# 50 trials per orientation-phase pair\n",
"stim_val['pair'], trial_stim_id['pair'] = np.unique(stim[['grat_orientation', 'grat_phase']], return_inverse=True, axis=0) \n",
"# 1000 trials per orientation\n",
"stim_val['orientation'], trial_stim_id['orientation'] = np.unique(stim['grat_orientation'], return_inverse=True) \n",
"# 1000 trials per phase\n",
"stim_val['phase'], trial_stim_id['phase'] = np.unique(stim['grat_phase'], return_inverse=True) \n",
"key_list = ['pair', 'orientation', 'phase']\n",
"stim_id_trial = {}\n",
"num_stim = {}\n",
"for key in key_list:\n",
" stim_id_trial[key] = [np.where(trial_stim_id[key] == i)[0] for i in range(len(stim_val[key]))]\n",
" num_stim[key] = len(stim_val[key])\n",
"\n",
"def pair_id(i):\n",
" return (int(i//num_stim['orientation']), i%num_stim['orientation'])\n",
"\n",
"trial_pair_id = np.array([pair_id(i) for i in trial_stim_id['pair']], dtype=object)\n",
"pair_val = stim_val['pair'].reshape(num_stim['orientation'], num_stim['phase'],2)\n",
"\n",
"# for each (orientation_id = i, phase_id = j) find the trial indices\n",
"pair_trial_id = np.ndarray((num_stim['orientation'], num_stim['phase']), dtype=object)\n",
"for i in range(num_stim['pair']):\n",
" pair_trial_id[pair_id(i)] = stim_id_trial['pair'][i]"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 5,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>grat_orientation</th>\n",
" <th>grat_phase</th>\n",
" <th>stimvals</th>\n",
" <th>stim_ontime</th>\n",
" <th>stim_offtime</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>45.0</td>\n",
" <td>36.0</td>\n",
" <td>102</td>\n",
" <td>2.5005</td>\n",
" <td>2.5839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>1</td>\n",
" <td>2.6195</td>\n",
" <td>2.7028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>162.0</td>\n",
" <td>288.0</td>\n",
" <td>376</td>\n",
" <td>2.7030</td>\n",
" <td>2.7863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>54.0</td>\n",
" <td>3</td>\n",
" <td>2.7865</td>\n",
" <td>2.8699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>36.0</td>\n",
" <td>242</td>\n",
" <td>2.8700</td>\n",
" <td>2.9534</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 grat_orientation grat_phase stimvals stim_ontime \\\n",
"0 0 45.0 36.0 102 2.5005 \n",
"1 1 0.0 18.0 1 2.6195 \n",
"2 2 162.0 288.0 376 2.7030 \n",
"3 3 0.0 54.0 3 2.7865 \n",
"4 4 108.0 36.0 242 2.8700 \n",
"\n",
" stim_offtime \n",
"0 2.5839 \n",
"1 2.7028 \n",
"2 2.7863 \n",
"3 2.8699 \n",
"4 2.9534 "
]
},
Exp processing: PCA and stuff
2 years ago
"execution_count": 5,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stim.head()"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 6,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pair, orientation, phase\n",
"(400, 2)\n",
"(20,) (20,)\n"
]
}
],
"source": [
"print(', '.join(stim_val.keys()))\n",
"print(stim_val['pair'].shape)\n",
"print(stim_val['orientation'].shape, stim_val['phase'].shape)"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 7,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 82 482 1021 1421 1821 2183 2645 3045 3445 3643 4357 4757\n",
" 5014 5493 5893 6293 6693 6965 7365 7652 8052 8513 9116 9203\n",
" 9603 10003 10628 10849 11512 11912 12312 12535 12947 13347 13666 14248\n",
" 14682 14857 15257 15657 16393 16793 17193 17262 17906 18146 18546 19050\n",
" 19450 19904]\n"
]
}
],
"source": [
"print(stim_id_trial['pair'][0])"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 8,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 82, 482, 1021, 1421, 1821, 2183, 2645, 3045, 3445,\n",
" 3643, 4357, 4757, 5014, 5493, 5893, 6293, 6693, 6965,\n",
" 7365, 7652, 8052, 8513, 9116, 9203, 9603, 10003, 10628,\n",
" 10849, 11512, 11912, 12312, 12535, 12947, 13347, 13666, 14248,\n",
" 14682, 14857, 15257, 15657, 16393, 16793, 17193, 17262, 17906,\n",
" 18146, 18546, 19050, 19450, 19904])"
]
},
Exp processing: PCA and stuff
2 years ago
"execution_count": 8,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pair_trial_id[0][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Stimulus data layout:**\n",
"```\n",
"stim_val = {'pair': [(or1, ph1), (or2, ph2), ...], 'orientation': [...], 'phase': [...]}\n",
"stim_id_trial = {'pair': [id1, id2, ...], 'phase': [..], ...} # corresponding trials\n",
"pair_trial_id[orientation_id][phase_id] = [Trial ids]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load the spike data and correlations\n",
"```\n",
"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",
"Loaded: corr_stim_unit, optimal_avg_firing_rate, stim_hist, stim_hist_caution\n",
"```"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 9,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(40,)"
]
},
Exp processing: PCA and stuff
2 years ago
"execution_count": 9,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spike_times_file = \"Spiketimes_PVCre_2021_0012_s06_e14.npy\"\n",
"spike_times = np.load(data_folder+spike_times_file, allow_pickle=True)\n",
"active = [len(spike_times[i]) > 0 for i in range(len(spike_times))]\n",
"spike_times = spike_times[np.where(active)]\n",
"num_unit = len(spike_times)\n",
"spike_times.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Sort spikes by firing rate**"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 10,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [],
"source": [
"\n",
"num_spike = list(map(len, spike_times))\n",
"spike_times = spike_times[np.argsort(num_spike)[::-1]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Load a lot of variables out of a `.pkl` file**"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 11,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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",
"Loaded: corr_stim_unit, optimal_avg_firing_rate, stim_hist, stim_hist_caution\n"
]
}
],
"source": [
"# yeah...\n",
"files = ['spike_data.pkl', 'corr_data.pkl']\n",
"for file_name in files:\n",
" file = open(file_name, 'rb')\n",
" data = pkl.load(file)\n",
" print('Loaded:', ', '.join(data.keys()))\n",
" for key, value in data.items():\n",
" globals()[key] = value\n",
"\n",
" file.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Spike and stimulus preprocessing\n",
"\n",
"```\n",
"B_stim = {'pair': [mat_stim_1, mat_stim_2, ..], 'orientation': .., ...}\n",
"```\n",
"\n",
"`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",
"\n",
"```\n",
"B_spike = [unit_1_spikes, unit_2_spikes, ..]\n",
"unit_1_spikes[t] = 1 if there was a spike, 0 if not\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Read the data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# reverse correlation time offset range \n",
"max_delay = 300 # dt\n",
"tau_id_range = np.arange(max_delay)\n",
"\n",
"# experiment duration\n",
"latest_spike_time = max([np.max(s) for s in spike_times if len(s)])\n",
"latest_stim_offtime = list(stim['stim_offtime'])[-1]\n",
"experiment_dur = max([latest_spike_time, latest_stim_offtime])\n",
"\n",
"dt = 0.001 # 1 ms\n",
"exp_time = np.arange(0, experiment_dur, dt)\n",
"M = len(exp_time)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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 = stim['stim_ontime'][trial_id], 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))\n",
"s = spike_times//dt\n",
"B_spike = []\n",
"for unit_id in range(num_unit):\n",
" 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)))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"sorted_spike_num = np.sort(num_spike)[::-1]\n",
"cutoff_num_spike = 1000\n",
"num_unit = np.sum(sorted_spike_num > cutoff_num_spike)\n",
"for key in key_list:\n",
" stim_hist[key] = stim_hist[key][:num_unit]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Some plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This graph shows for unit 0 at a particular moment:\n",
"- One stimulus duration (blue)\n",
"- The second stimulus (red)\n",
"\n",
"And spike events"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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": {
"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()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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 = [\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",
" ]\n",
" 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",
"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…"
]
},
"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",
Exp processing: PCA and stuff
2 years ago
"execution_count": 6,
Processing experimental data for flashed gratings
2 years ago
"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": {
"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",
"\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",
Exp processing: PCA and stuff
2 years ago
"execution_count": 5,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [],
"source": [
"get_trial_index = lambda t: fix_time(stim['stim_ontime'][t])//dt"
]
},
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 43,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [],
"source": [
"B_spike_smooth = list()\n",
Exp processing: PCA and stuff
2 years ago
"for unit_id in range(len(B_spike)):\n",
Processing experimental data for flashed gratings
2 years ago
" 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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
"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"
]
},
Exp processing: PCA and stuff
2 years ago
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Reverse correlation\n",
"\n",
"TL;DR: We find the reverse correlation matrices. When we plot them they look meh, but if we plot them smoothed, they look awesome. However, we shouldn't use this smoothing when decoding parameters because it uses our (parameter-defined) notion of distance between points."
]
},
Processing experimental data for flashed gratings
2 years ago
{
"cell_type": "code",
Exp processing: PCA and stuff
2 years ago
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"B_stim_total = np.sum(B_stim['phase'])"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"R = dict()\n",
"for key in key_list:\n",
" R[key] = list()\n",
" for i in range(len(B_stim[key])):\n",
" item = []\n",
" for u in range(len(B_spike_smooth)):\n",
" item.append(B_stim[key][i].dot(np.roll(B_spike_smooth[u], 80)))\n",
" R[key].append(item)\n",
" R[key] = np.array(R[key])"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"R['pair'] = R['pair'].reshape((20, 20, 40))\n",
"Rp = R['pair']"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"R_pair_smoothed = np.array([scipy.ndimage.gaussian_filter(R['pair'][:, :, i], sigma=2) for i in range(40)])"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 864x936 with 16 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(4, 4, figsize=(12, 13))\n",
"fig.tight_layout(w_pad=2)\n",
"for i, ax in enumerate(axs.flatten()):\n",
" ax.imshow(R['pair'][:, :, i], extent=(0, 360, 0, 180), aspect=2)\n",
" ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 864x936 with 16 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(4, 4, figsize=(12, 13))\n",
"fig.tight_layout(w_pad=2)\n",
"for i, ax in enumerate(axs.flatten()):\n",
" ax.imshow(R_pair_smoothed[i], extent=(0, 360, 0, 180), aspect=2)\n",
" ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Topological noise reduction: 100%|██████████| 100/100 [01:57<00:00, 1.18s/it]\n"
]
}
],
"source": [
"res = top_noise_reduction(pd.DataFrame(Rp.reshape((400, 40)))).to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"128.81601674915015"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res[0][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Intermission: save variables to a file"
]
},
{
"cell_type": "code",
"execution_count": 132,
Processing experimental data for flashed gratings
2 years ago
"metadata": {},
"outputs": [],
Exp processing: PCA and stuff
2 years ago
"source": [
"import pickle\n",
"with open('dump.pkl', 'wb') as f:\n",
" pickle.dump({k: globals()[k] for k in [\n",
" 'data_folder', 'stim_file', 'spike_times_file', 'key_symbol', \n",
" 'stim', 'num_trial', 'stim_val', 'trial_stim_id', 'key_list', 'stim_id_trial', 'num_stim', 'key', 'pair_id', 'trial_pair_id', 'pair_val', 'pair_trial_id',\n",
" 'spike_times', 'num_unit', 'num_spike', 'spike_count_rate', 'avg_firing_rate', 'firing_rate', 'stim_num_trial', 'stim_hist', 'optimal_avg_firing_rate',\n",
" 'latest_spike_time', 'dt', 'exp_time', 'B_stim', 'trials', 't_on', 't_off', 'B_spike', 'trial_range', 'units', 'orientation', 'phase', 'trial_spike_times',\n",
" 'delta_t_on', 'delta_t_off', 'subseq_trials', 'subseq_spike_times', 'subseq_spike_times_locked', 'time_range', 'unit_id_2', 'p1', 'p2', 'delta_t', 'time_range_smoothing',\n",
" 'period_1_dist', 'period_2_dist', 'period_3_dist', 'num_units', 'max_p_1', 'max_p_2', 'max_p_3', 'delta_2', 'delta_3', 't_p1', 't_p2', 'subseq_spike_times_locked_2',\n",
" 'B_spike_fixed', 'B_stim_original', 'B_spike_smooth', 'B_stim_total', 'R', 'R_pair_smoothed', 'Rp'\n",
" ]}, f)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['data_folder', 'stim_file', 'spike_times_file', 'key_symbol', 'stim', 'num_trial', 'stim_val', 'trial_stim_id', 'key_list', 'stim_id_trial', 'num_stim', 'key', 'pair_id', 'trial_pair_id', 'pair_val', 'pair_trial_id', 'spike_times', 'num_unit', 'num_spike', 'spike_count_rate', 'avg_firing_rate', 'firing_rate', 'stim_num_trial', 'stim_hist', 'optimal_avg_firing_rate', 'latest_spike_time', 'dt', 'exp_time', 'B_stim', 'trials', 't_on', 't_off', 'B_spike', 'trial_range', 'units', 'orientation', 'phase', 'trial_spike_times', 'delta_t_on', 'delta_t_off', 'subseq_trials', 'subseq_spike_times', 'subseq_spike_times_locked', 'time_range', 'unit_id_2', 'p1', 'p2', 'delta_t', 'time_range_smoothing', 'period_1_dist', 'period_2_dist', 'period_3_dist', 'num_units', 'max_p_1', 'max_p_2', 'max_p_3', 'delta_2', 'delta_3', 't_p1', 't_p2', 'subseq_spike_times_locked_2', 'B_spike_fixed', 'B_stim_original', 'B_spike_smooth', 'B_stim_total', 'R', 'R_pair_smoothed', 'Rp', 'firing_rates_noise_red', 'firing_rates_flat', 'firing_rates_in_trials', 'avg_firing_rates'])\n"
]
}
],
"source": [
"file = open(\"dump.pkl\", 'rb')\n",
"data = pkl.load(file)\n",
"print(data.keys())\n",
"for key, value in data.items():\n",
" globals()[key] = value\n",
"file.close()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"__name__ __doc__ __package__ __loader__ __spec__ __builtin__ __builtins__ _ih _oh _dh In Out get_ipython exit quit _ __ ___ os sys __vsc_ipynb_file__ _i _ii _iii _i1 np plt pd scipy sts pkl widgets mplot3d Axes3D sparse cm cmap_hot cmap_viridis cmap_jet _i2 data_folder stim_file spike_times_file key_symbol _i3 stim num_trial stim_val trial_stim_id key_list stim_id_trial num_stim key pair_id trial_pair_id pair_val pair_trial_id i _i4 _4 _i5 _i6 _i7 _7 _i8 spike_times active num_unit _8 _i9 num_spike _i10 files file_name file data value spike_count_rate avg_firing_rate sem_firing_rate firing_rate stim_num_trial C_r_fphi_theta theta_hist phase_hist pair_hist corr_stim_unit optimal_avg_firing_rate stim_hist stim_hist_caution _i11 max_delay tau_id_range latest_spike_time latest_stim_offtime experiment_dur dt exp_time M _i12 B_stim stim_id trials s trial_id t_on t_off B_spike unit_id _i13 sorted_spike_num cutoff_num_spike _i14 t0 t1 stim_prev_id t_prev_on t_prev_off spikes _i15 gridspec fig ax gs0 ax0 ax1 ax2 ax3 _i16 ref_trial_id past_trials future_trials trial_range units orientation phase ref_id _i17 PSSH _i18 trial_spike_times delta_t_on delta_t_off subseq_trials subseq_spike_times subseq_spike_times_locked _i19 time_range _i20 subseq_spike_times_len chain _20 _i21 unit_id_2 _i22 stim_2diffs _i23 p1 p2 delta_t time_range_smoothing period_1_dist period_2_dist period_3_dist num_units j row hist _i24 _24 _i25 _25 _i26 max_p_1 max_p_2 max_p_3 delta_2 delta_3 _i27 t_p1 t_p2 fix_time _i28 _28 _i29 subseq_spike_times_locked_2 _i30 _i31 B_spike_fixed _i32 deepcopy B_stim_original _i33 _i34 get_trial_index _i35 B_spike_smooth _i36 _36 _i37 _37 _i38 _38 _i39 _39 _i40 _40 _i41 _41 _i42 B_stim_total _i43 _i44 R _i45 item u _i46 _i47 _i48 _i49 _i50 _i51 _51 _i52 _52 _i53 _53 _i54 _54 _i55 _i56 _56 _i57 _57 _i58 _58 _i59 _59 _i60 _60 _i61 _61 _i62 _62 _i63 _63 _i64 _64 _i65 _65 _i66 _66 _i67 R_pair_smoothed _i68 axs _i69 _i70 _i71 _i72 _i73 _i74 _i75 _i76 _i77 _i78 _i79 _i80 _i81 _i82 _i83 _i84 _i85 _i86 _i87 _i88 _i89 _i90 _i91 _i92 _i93 _i94 _i95 _i96 _i97 _i98 _i99 _i100 R1 item1 _i101 _i102 _i103 _i104 Rp _i105 _i106 _i107 _i108 _i109 top_noise_reduction _i110 _i111 _i112 _i113 res _i114 _114 _i115 _115 _i116 _116 _i117 _117 _i118 _118 _i119 _i120 _120 _i121 _121 _i122 pickle _i123 f _i124 _i125 _i126 _i127 _i128 _128 _i129 _129 _i130 _130 _i131\n"
]
}
],
"source": [
"print(*globals().keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Forward correlation"
]
},
{
"cell_type": "markdown",
"metadata": {},
Processing experimental data for flashed gratings
2 years ago
"source": []
Exp processing: PCA and stuff
2 years ago
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"firing_rates_in_trials = [] # by pair, then by trial, then by neuron\n",
"firing_rates_flat = []\n",
"\n",
"for trial_ids in stim_id_trial['pair']:\n",
" row = []\n",
" for trial_id in trial_ids:\n",
" ind = round(get_trial_index(trial_id))\n",
" rr = []\n",
" for u in range(len(B_spike_smooth)):\n",
" rr.append(np.mean(B_spike_smooth[u][ind + 50:ind + 90]))\n",
" row.append(np.array(rr))\n",
" firing_rates_flat.append(np.array(rr))\n",
" firing_rates_in_trials.append(np.array(row))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(400, (50, 40))"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firing_rates_flat = np.array(firing_rates_flat)\n",
"len(firing_rates_in_trials), firing_rates_in_trials[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"avg_firing_rates = np.array([np.mean(fr_pair, axis=0) for fr_pair in firing_rates_in_trials]).reshape((20, 20, 40))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 864x936 with 16 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(4, 4, figsize=(12, 13))\n",
"fig.tight_layout(w_pad=2)\n",
"for i, ax in enumerate(axs.flatten()):\n",
" ax.imshow(avg_firing_rates[:, :, i], extent=(0, 360, 0, 180), aspect=2)\n",
" ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"G_PAD = 5\n",
"\n",
"def gauss(img):\n",
" img_vpad = np.concatenate((img[-G_PAD:][::-1, ::-1], img, img[:G_PAD][::-1, ::-1]))\n",
" # pad horizontally too\n",
" img_vhpad = np.concatenate((img_vpad[:, :G_PAD][::-1], img_vpad, img_vpad[:, -G_PAD:][::-1]), axis=1)\n",
" return scipy.ndimage.gaussian_filter(img_vhpad, sigma=2, mode='wrap')[G_PAD:-G_PAD, G_PAD:-G_PAD]"
]
},
{
"cell_type": "code",
"execution_count": 8,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
Some beautiful UMAP results
2 years ago
"(20, 20)"
Exp processing: PCA and stuff
2 years ago
]
},
Some beautiful UMAP results
2 years ago
"execution_count": 8,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
Some beautiful UMAP results
2 years ago
"output_type": "execute_result"
}
],
"source": [
"avg_fr_blur = np.array([gauss(avg_firing_rates[:, :, i]) for i in range(40)])\n",
"gauss(avg_firing_rates[:, :, 0]).shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 864x936 with 16 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
Exp processing: PCA and stuff
2 years ago
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(4, 4, figsize=(12, 13))\n",
"fig.tight_layout(w_pad=2)\n",
"for i, ax in enumerate(axs.flatten()):\n",
" ax.imshow(avg_fr_blur[i], extent=(0, 360, 0, 180), aspect=2)\n",
" ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")\n",
Some beautiful UMAP results
2 years ago
"avg_fr_blur = avg_fr_blur.swapaxes(0, 2).reshape((400, 40))"
Exp processing: PCA and stuff
2 years ago
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Topological noise reduction (not used)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '../model')\n",
"from noisereduction import top_noise_reduction"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Topological noise reduction: 100%|██████████| 100/100 [31:33<00:00, 18.93s/it]\n"
]
}
],
"source": [
"firing_rates_noise_red = top_noise_reduction(pd.DataFrame(firing_rates_flat[::5]))"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(400, 40)"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firing_rates_noise_red.shape"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Topological noise reduction: 100%|██████████| 100/100 [3:44:17<00:00, 134.57s/it] \n"
]
}
],
"source": [
"firing_rates_noise_red = top_noise_reduction(pd.DataFrame(firing_rates_flat))"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2000, 40)"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firing_rates_noise_red = firing_rates_noise_red.to_numpy()\n",
"firing_rates_noise_red.shape"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 167,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firing_rates_noise_red[0] in firing_rates_flat"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [],
"source": [
"from decoding import cohomological_parameterization"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decoding... done\n"
]
}
],
"source": [
"param = cohomological_parameterization(pd.DataFrame(firing_rates_noise_red[::5, ::5]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### PCA"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 6,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 19,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
Some beautiful UMAP results
2 years ago
"execution_count": 19,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fr_df = pd.DataFrame(firing_rates_flat)\n",
"sns.heatmap(fr_df.corr())"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 20,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
Some beautiful UMAP results
2 years ago
{
"data": {
"text/plain": [
"(400, 40)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg_fr_blur.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400, 7)\n"
]
},
Exp processing: PCA and stuff
2 years ago
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
Some beautiful UMAP results
2 years ago
" <td>-0.000578</td>\n",
" <td>-0.000424</td>\n",
" <td>-0.000689</td>\n",
" <td>0.000034</td>\n",
" <td>-0.000342</td>\n",
" <td>-0.000145</td>\n",
" <td>-0.000016</td>\n",
Exp processing: PCA and stuff
2 years ago
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
Some beautiful UMAP results
2 years ago
" <td>-0.000181</td>\n",
" <td>-0.000554</td>\n",
" <td>-0.000777</td>\n",
" <td>0.000153</td>\n",
" <td>-0.000264</td>\n",
" <td>-0.000231</td>\n",
" <td>0.000029</td>\n",
Exp processing: PCA and stuff
2 years ago
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
Some beautiful UMAP results
2 years ago
" <td>0.000059</td>\n",
" <td>-0.000686</td>\n",
" <td>-0.000693</td>\n",
" <td>0.000247</td>\n",
" <td>-0.000197</td>\n",
" <td>-0.000266</td>\n",
" <td>0.000044</td>\n",
Exp processing: PCA and stuff
2 years ago
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
Some beautiful UMAP results
2 years ago
" <td>0.000138</td>\n",
" <td>-0.000856</td>\n",
" <td>-0.000464</td>\n",
" <td>0.000301</td>\n",
" <td>-0.000142</td>\n",
" <td>-0.000259</td>\n",
" <td>0.000039</td>\n",
Exp processing: PCA and stuff
2 years ago
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
Some beautiful UMAP results
2 years ago
" <td>0.000134</td>\n",
" <td>-0.001063</td>\n",
" <td>-0.000163</td>\n",
" <td>0.000324</td>\n",
" <td>-0.000106</td>\n",
" <td>-0.000209</td>\n",
" <td>0.000031</td>\n",
Exp processing: PCA and stuff
2 years ago
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6\n",
Some beautiful UMAP results
2 years ago
"0 -0.000578 -0.000424 -0.000689 0.000034 -0.000342 -0.000145 -0.000016\n",
"1 -0.000181 -0.000554 -0.000777 0.000153 -0.000264 -0.000231 0.000029\n",
"2 0.000059 -0.000686 -0.000693 0.000247 -0.000197 -0.000266 0.000044\n",
"3 0.000138 -0.000856 -0.000464 0.000301 -0.000142 -0.000259 0.000039\n",
"4 0.000134 -0.001063 -0.000163 0.000324 -0.000106 -0.000209 0.000031"
Exp processing: PCA and stuff
2 years ago
]
},
Some beautiful UMAP results
2 years ago
"execution_count": 10,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N_COMP = 7\n",
"pca = PCA(n_components=N_COMP)\n",
"pca.fit(avg_fr_blur)\n",
"data_pca = pca.transform(avg_fr_blur)\n",
"data_pca = pd.DataFrame(data_pca)\n",
Some beautiful UMAP results
2 years ago
"print(data_pca.shape)\n",
Exp processing: PCA and stuff
2 years ago
"data_pca.head()"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 22,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(400, 50, 7)"
]
},
Some beautiful UMAP results
2 years ago
"execution_count": 22,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firing_rates_in_trials_pca = []\n",
"for trials in firing_rates_in_trials:\n",
" firing_rates_in_trials_pca.append(pca.transform(pd.DataFrame(trials)))\n",
"firing_rates_in_trials_pca = np.array(firing_rates_in_trials_pca)\n",
"firing_rates_in_trials_pca.shape"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 11,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [],
"source": [
Some beautiful UMAP results
2 years ago
"avg_firing_rates_pca = data_pca.to_numpy().reshape((20, 20, N_COMP))# np.mean(firing_rates_in_trials_pca, axis=1).reshape((20, 20, N_COMP))"
Exp processing: PCA and stuff
2 years ago
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 24,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
Some beautiful UMAP results
2 years ago
"image/png": "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
Exp processing: PCA and stuff
2 years ago
"text/plain": [
Some beautiful UMAP results
2 years ago
"<Figure size 864x864 with 6 Axes>"
Exp processing: PCA and stuff
2 years ago
]
},
Some beautiful UMAP results
2 years ago
"metadata": {
"needs_background": "light"
},
Exp processing: PCA and stuff
2 years ago
"output_type": "display_data"
}
],
"source": [
"\n",
"fig, axs = plt.subplots(2, 3, figsize=(12, 12))\n",
"fig.tight_layout(w_pad=2)\n",
"for i, ax in enumerate(axs.flatten()[:7]):\n",
" ax.imshow(avg_firing_rates_pca[:, :, i], extent=(0, 360, 0, 180), aspect=2)\n",
" ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 12,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
Some beautiful UMAP results
2 years ago
"image/png": "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
Exp processing: PCA and stuff
2 years ago
"text/plain": [
Some beautiful UMAP results
2 years ago
"<Figure size 864x936 with 6 Axes>"
Exp processing: PCA and stuff
2 years ago
]
},
Some beautiful UMAP results
2 years ago
"metadata": {
"needs_background": "light"
},
Exp processing: PCA and stuff
2 years ago
"output_type": "display_data"
}
],
"source": [
"\n",
"fig, axs = plt.subplots(2, 3, figsize=(12, 13))\n",
"fig.tight_layout(w_pad=2)\n",
"for i, ax in enumerate(axs.flatten()):\n",
" ax.imshow(scipy.ndimage.gaussian_filter(avg_firing_rates_pca[:, :, i], sigma=2), extent=(0, 360, 0, 180), aspect=2)\n",
" ax.set(xlabel=\"phase\", ylabel=\"orientation\", title=f\"Unit {i}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Point Cloud Simplification"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"def radial_distance(X, eps, random_state=None):\n",
" \"\"\"\n",
" point cloud simplification using radial distance (euclidean metric). \n",
" Start with the first point in in X and mark it as a key point. All consecutive points that have a distance less than a predetermined distance eps to the key point are removed. The first point that have a distance greater than eps to the key point is marked as the new key point. The process repeates itself from this new key point, and continues until it reaches the end of the point cloud.\n",
" \n",
" Parameters\n",
" ----------\n",
" X: pandas DataFrame (n_datapoints, n_features):\n",
"\n",
" eps: max radial distance - cutoff distance\n",
"\n",
" random_state: seed of random generator used for choosing the inital point\n",
"\n",
" Returns\n",
" -------\n",
" X.iloc[ind_reduced]: dataframe with chosen datapoints \n",
" \"\"\"\n",
" if random_state is not None:\n",
" np.random.seed(random_state)\n",
" \n",
" ix0 = np.random.choice(X.shape[0])\n",
" x0 = X.iloc[ix0]\n",
" xt = x0\n",
" ixt = ix0\n",
" X_temp = X\n",
" ind_reduced = [ix0]\n",
"\n",
" while True:\n",
" dist = np.linalg.norm(X_temp.to_numpy() - xt.to_numpy(), axis=1)\n",
" cond = dist < eps\n",
"\n",
" X_temp = X_temp.drop(X_temp.index[np.where(cond)])\n",
" if len(X_temp)==0:\n",
" break\n",
"\n",
" where_not_cond = np.where(np.logical_not(cond))\n",
" w = np.argmin(dist[where_not_cond]) \n",
" ixt = X_temp.index[w]\n",
" xt = X.iloc[ixt]\n",
" ind_reduced.append(ixt)\n",
"\n",
" return X.iloc[ind_reduced], ind_reduced \n"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"from tqdm import trange\n",
"from noisereduction import compute_gradient\n",
"def top_noise_reduction(X, n=100, omega=0.2, fraction=0.1, plot=False):\n",
" \"\"\"\n",
" Topological denoising algorithm as in arxiv:0910.5947\n",
" \n",
" Parameters\n",
" ----------\n",
" X: dataframe(n_datapoints, n_features):\n",
" Dataframe containing the data\n",
" n: int, optional, default 100\n",
" Number of iterations\n",
" omega: float, optional, default 0.2\n",
" Strength of the repulsive force between datapoints\n",
" fraction: float between 0 and 1, optional, default 0.1\n",
" The fraction of datapoints from which the denoised dataset is\n",
" constructed\n",
" plot: bool, optional, default False\n",
" When true plot the dataset and homology each iteration\n",
" \"\"\"\n",
" N = X.shape[0]\n",
" inds = np.random.choice(N, round(fraction*N), replace=False)\n",
" S = X.iloc[inds]\n",
" sigma = X.stack().std()\n",
" c = 0.02*np.max(scipy.spatial.distance.cdist(X, X, metric='euclidean'))\n",
" \n",
" iterator = trange(0, n, position=0, leave=True)\n",
" iterator.set_description(\"Topological noise reduction\")\n",
" for i in iterator:\n",
" gradF = compute_gradient(S.to_numpy(), X.to_numpy(), sigma, omega)\n",
" \n",
" if i == 0:\n",
" maxgradF = np.max(np.sqrt(np.square(gradF).sum(axis=1)))\n",
" S = S + c* gradF/maxgradF\n",
" \n",
" if plot:\n",
" fig = pyplot.figure()\n",
" ax = Axes3D(fig)\n",
" ax.scatter(X[0],X[1],X[2],alpha=0.1)\n",
" ax.scatter(S[0],S[1],S[2])\n",
" pyplot.show()\n",
" return S, inds"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 127,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
Some beautiful UMAP results
2 years ago
"data": {
"text/plain": [
"(414, 7)"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
Exp processing: PCA and stuff
2 years ago
}
],
"source": [
"fr_pca_simpl, inds_simpl = radial_distance(pd.DataFrame(firing_rates_in_trials_pca.reshape((400 * 50, -1))), 0.025)\n",
"fr_pca_simpl.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
Some beautiful UMAP results
2 years ago
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Plots"
]
},
Exp processing: PCA and stuff
2 years ago
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 12,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
Some beautiful UMAP results
2 years ago
"(400, 2)"
Exp processing: PCA and stuff
2 years ago
]
},
Some beautiful UMAP results
2 years ago
"execution_count": 12,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
Some beautiful UMAP results
2 years ago
"param_corr = stim_val['pair'] # [np.array(inds_simpl) // 50]\n",
Exp processing: PCA and stuff
2 years ago
"param_corr.shape"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 13,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [],
"source": [
"avg_firing_rates_pca_ = avg_firing_rates_pca.reshape((-1, N_COMP))"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 41,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
Some beautiful UMAP results
2 years ago
"image/png": "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
Exp processing: PCA and stuff
2 years ago
"text/plain": [
"<Figure size 576x576 with 15 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"ncomp_disp = 5\n",
"fig = plt.figure(figsize=(8,8))\n",
"for j in range(ncomp_disp):\n",
" for i in range(j+1):\n",
" ax = fig.add_subplot(ncomp_disp,ncomp_disp, j + ncomp_disp*i + 1)\n",
" if i == j:\n",
" ax.scatter(\n",
" # param_corr[:, 0],\n",
" stim_val['pair'].reshape((-1, 2))[:, 0],\n",
" avg_firing_rates_pca_[:, i], s=1)\n",
" ax.set(xlabel='stim', ylabel='PC%d'%i)\n",
" else:\n",
" ax.scatter(avg_firing_rates_pca_[:, j], avg_firing_rates_pca_[:, i], s=1, c=stim_val['pair'].reshape((-1, 2))[:, 0], cmap='hsv')\n",
" \n",
" ax.set(xticks=[], yticks=[])\n",
" \n",
" if i==0 and j>0:\n",
" ax.set(xlabel='PC%d'%j)\n",
" ax.xaxis.set_label_position('top') \n",
"\n",
" if j==ncomp_disp-1 and i<ncomp_disp-1:\n",
" ax.set(ylabel='PC%d'%i)\n",
" ax.yaxis.set_label_position('right') \n",
"\n",
"fig.set_facecolor('white')\n",
"fig.suptitle('PCA')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### UMAP"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 4,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [],
"source": [
"from umap import UMAP"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"ncomp_umap = 4\n",
"embed = UMAP(n_components=ncomp_umap, n_neighbors=30, metric='euclidean').fit_transform(avg_firing_rates_pca_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"param_corr[:, 0].max()"
]
},
{
"cell_type": "code",
"execution_count": 6,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
Some beautiful UMAP results
2 years ago
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "723e6a2e25e34392954a050ad7ba10a6",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
},
Exp processing: PCA and stuff
2 years ago
{
"name": "stderr",
"output_type": "stream",
"text": [
Some beautiful UMAP results
2 years ago
"Unhandled message type set_device_pixel_ratio. {'device_pixel_ratio': 0.8333333134651184, 'type': 'set_device_pixel_ratio'}\n",
"Unhandled message type set_device_pixel_ratio. {'device_pixel_ratio': 0.8333333134651184, 'type': 'set_device_pixel_ratio'}\n"
Exp processing: PCA and stuff
2 years ago
]
}
],
"source": [
Some beautiful UMAP results
2 years ago
"from mpl_toolkits import mplot3d\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"%matplotlib inline\n",
"%matplotlib widget\n",
"\n",
"x, y, z, u = embed[:,0], embed[:,1], embed[:,2], embed[:,3] \n",
"\n",
"fig, ax = plt.subplots(1, 1, figsize=(20, 10))\n",
"# ax.scatter3D(x, y, z, c=np.array([param_corr[:, 0] / 180, [0] * param_corr.shape[0], param_corr[:, 1] / 360]).swapaxes(0, 1))\n",
"ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
"ax.scatter3D(x, y, z, c=param_corr[:, 0], cmap=\"hsv\")\n",
"ax.set(xlabel=\"U0\", ylabel=\"U1\", zlabel=\"U2\", xticks=[], yticks=[], zticks=[])\n",
"ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
"ax.scatter3D(x, y, z, c=param_corr[:, 1], cmap=\"hsv\")\n",
"ax.set(xlabel=\"U0\", ylabel=\"U1\", zlabel=\"U2\", xticks=[], yticks=[], zticks=[])\n",
"# ax = fig.add_subplot(2, 2, 3, projection='3d')\n",
"# ax.scatter3D(x, y, u, c=param_corr[:, 0], cmap=\"hsv\")\n",
"# ax.set(xlabel=\"U0\", ylabel=\"U1\", zlabel=\"U3\", xticks=[], yticks=[], zticks=[])\n",
"# ax = fig.add_subplot(2, 2, 4, projection='3d')\n",
"# ax.scatter3D(x, y, u, c=param_corr[:, 1], cmap=\"hsv\")\n",
"# ax.set(xlabel=\"U0\", ylabel=\"U1\", zlabel=\"U3\", xticks=[], yticks=[], zticks=[])\n",
"fig.suptitle('UMAP 3D')\n",
"plt.show()"
Exp processing: PCA and stuff
2 years ago
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 7,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
Some beautiful UMAP results
2 years ago
"model_id": "cc2c2edbd8b8491ca20366c9839f92dc",
Exp processing: PCA and stuff
2 years ago
"version_major": 2,
"version_minor": 0
},
Some beautiful UMAP results
2 years ago
"image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAALQCAYAAABmJdeHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd3xkV3n/8c85905Rr9u77XXv3TRjTDMYYkOAYAglhBZIQhKSAEmAJOQXEhJSCIFQTYcQTDcYY2xccMEd2+u+9va+klajafee5/fHSNqm3ZV2Narfd14bLOnOzJ3RjOac75zzPM7MEBEREREREREREREZb36yT0BEREREREREREREZiYF0CIiIiIiIiIiIiJSFwqgRURERERERERERKQuFECLiIiIiIiIiIiISF0ogBYRERERERERERGRulAALSIiIiIiIiIiIiJ1oQBaREREREREREREROpCAbSIiIiIiIiIiIiI1IUCaBERERERERERERGpCwXQIiIiIiIiIiIiIlIXCqBFREREREREREREpC4UQIuIiIiIiIiIiIhIXSiAFhEREREREREREZG6UAAtIiIiIiIiIiIiInWhAFpEREREpj3nnDnnjtnnex92zn118L+fO3jMd/c55rTB79+wz/edc+5J59xDI9zWDc65knOu3zm3zTl3lXNuwQHO65+dc2udc33Ouaedcx/Y42fLB2+7f/DfZufcj5xzLziCh0JEREREZEpRAC0iIiIis8VW4ALnXNce33sj8OgIxz4HmAsc5Zw7Z4Sfv9vMmoFjgXbg3w5wm58HjjezVuAZwOucc6/Y55j2wes6DbgW+K5z7k2ju0siIiIiIlObAmgRERERmS0qwPeA3wFwzkXAa4CvjXDsG4HvA1cP/veIzGwH8B3g5AP8/BEzK+zxrQAcc4BjN5nZfwAfBv7JOaexuoiIiIhMexrUioiIiMhs8mXgDYP//SLgAWDDngc45xqB36YWTH8N+B3nXHakK3POdQOvBO450A06597nnOsH1gFNwNcPcY5XUVt9fdyh7oyIiIiIyFSnAFpEREREZg0z+xXQ6Zw7jloQ/eURDnsFUAZ+BvwYyAAv3eeY/3TO9QD3ARuBPz3IbX4UaAHOBL4C9B7iNIcC8c5DHCciIiIiMuUpgBYRERGRmSClFhTvKQNURzj2K8C7gYuA747w8zcC/2tmiZmVqJXY2LcMxx+ZWbuZLTKz15nZ1oOdnNXcAxSBvz3EfVk0+L87DnGciIiIiMiUF0/2CYiIiIiIjIM1wHJg1R7fW8HIDQa/AjwOfNnMBpxzwz9wzi0Gngec65x75eC3G4G8c67bzLYd4XnGwNGHOOZyYAvwyBHeloiIiIjIpNMKaBERERGZCb4F/LVzbrFzzjvnng+8DPi/fQ80s9XAhcBfjXA9v0sttD4OOH3w37HU6je/diwnNHgeb3fOdbiac4F3Adcd4Ph5zrl3Ax8C3m9mYSy3JyIiIiIyFWkFtIiIiIjMBH83+O9moAN4AnidmT0w0sFmdvMBrueNwCfNbNOe33TOfXrwZ58Y43ldDvwjkKVW2/kTI1xHj6stwy4AdwKvMrOfjvF2RERERESmJGdmk30OIiIiIiIiIiIiIjIDqQSHiIiIiIiIiIiIiNSFAmgRERERERERERERqQsF0CIiIiIiIiIiIiJSFwqgRURERERERERERKQuFECLiIiIiIiIiIiISF0ogBYRERERERERERGRulAALSIiIiIiIiIiIiJ1oQBaREREREREREREROpCAbSIiIiIiIiIiIiI1IUCaBERERERERERERGpCwXQIiIiIiIiIiIiIlIXCqBFREREREREREREpC4UQIuIiIiIiIiIiIhIXSiAFhEREREREREREZG6UAAtIiIiIiIiIiIiInWhAFpERERERERERERE6kIBtIiIiIiIiIiIiIjUhQJoEREREREREREREakLBdAiIiIiIiIiIiIiUhcKoEVERERERERERESkLhRAi4iIiIiIiIiIiEhdKIAWERERERERERERkbpQAC0iIiIiIiIiIiIidaEAWkRERERERERERETqQgG0iIiIiIiIiIiIiNSFAmgRERERERERERERqQsF0CIiIiIiIiIiIiJSFwqgRURERERERERERKQuFECLiIiIiIiIiIiISF0ogBYRERERERERERGRulAALSIiIiIiIiIiIiJ1oQBaREREREREREREROpCAbSIiIiIiIiIiIiI1IUCaBERERERERERERGpCwXQIiIiIiIiIiIiIlIXCqBFREREREREREREpC4UQIuIiIiIiIiIiIhIXSiAFhEREREREREREZG6UAAtIiIiIiIiIiIiInWhAFpERERERERERERE6kIBtIiIiIiIiIiIiIjUhQJoEREREREREREREakLBdAiIiIiIiIiIiIiUhcKoEVERERERERERESkLhRAi4iIiIiIiIiIiEhdKIAWERERERERERERkbpQAC0iIiIiIiIiIiIidaEAWkRERERERERERETqQgG0iIiIiIiIiIiIiNSFAmgRERERERERERERqQsF0CIiIiIiIiIiIiJSFwqgRURERERERERERKQuFECLiIiIiIiIiIiISF0ogBYRERERERERERGRulAALSIiIiIiIiIiIiJ1oQBaREREREREREREROpCAbSIiIiIiIiIiIiI1IUCaJkUzrkvOOe2OOceOMDPnXPuP51zjzvn7nfOnTnR5ygiIiIiIiIiIiJHRgG0TJYrgRcf5OeXACsH/70N+NQEnJOIiIiIiIiIiIiMIwXQMinM7EZgx0EO+S3gy1ZzG9DunFswMWcnIiIiIiIiIiIi40EBtExVi4C1e3y9bvB7IiIiIiIiIiIiMk3Ek30CIkfKOfc2amU6aGpqOuv444+f5DMSEREREREREZk57rrrrm1mNmeyz0OmJwXQMlWtB5bs8fXiwe/tx8w+A3wG4Oyzz7Y777yz/mcnIiIiIiIiIjJLOOeenuxzkOlLJThkqvoB8AZXcz7Qa2YbJ/ukREREREREREREZPS0AlomhXPuG8BzgW7n3DrgQ0AGwMw+DVwNvAR4HBgA3jw5ZyoiIiIiIiIiIiKHSwG0TAoze+0hfm7AuybodERERERERERERKQOVIJDREREREREREREROpCAbSIiIiIiIiIiIiI1IUCaBERERERERERERGpCwXQIiIiIiIiIiIiIlIXCqBFREREREREREREpC4UQIuIiIiIiIiIiIhIXSiAFhEREREREREREZG6UAAtIiIiIiIiIiIiInWhAFpERERERERERERE6kIBtIiIiIiIiIiIiIjUhQJoEREREREREREREakLBdAiIiIiIiIiIiIiUhcKoEVERERERERERESkLhRAi4iIiIiIiIiIiEhdODOb7HMQGU96QouIiIiIiIiIjCPn3F1mdvZkn4dMT1oBLSIiIiIiIiIiIiJ1oQBaREREREREREREROpCAbSIiIiIiIiIiIiI1IUCaBERERERERERERGpCwXQIiIiIiIiIiIiIlIXCqBFREREREREREREpC4UQIuIiIiIiIiIiIhIXSiAFhEREREREREREZG6UAAtIiIiIiIiIiIiInWhAFpERERERERERERE6kIBtIiIiIiIiIiIiIjUhQJoEREREREREREREakLBdAiIiIiIiIiIiIiUhcKoEVERERERERERESkLhRAi4iIiIiIiIiIiEhdKIAWERERERERERERkbpQAC0iIiIiIiIiIiIidaEAWkRERERERERERETqQgG0iIiIiIiIiIiIiNSFAmgRERERERERERERqQsF0CIiIiIiIiIiIiJSFwqgRURERERERERERKQuFECLiIiIiIiIiIiISF0ogBYRERERERERERGRulAALSIiIiIiIiIiIiJ1oQBaREREREREREREROpCAbSIiIiIiIiIiIiI1IUCaBERERERERERERGpCwXQIiIiIiIiIiIiIlIXCqBFREREREREREREpC4UQIuIiIiIiIiIiIhIXSiAFhEREREREREREZG6UAAtIiIiIiI
Exp processing: PCA and stuff
2 years ago
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
Some beautiful UMAP results
2 years ago
" <img src='data:image/png;base64,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
Exp processing: PCA and stuff
2 years ago
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
Some beautiful UMAP results
2 years ago
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Unhandled message type set_device_pixel_ratio. {'device_pixel_ratio': 0.8333333134651184, 'type': 'set_device_pixel_ratio'}\n",
"Unhandled message type set_device_pixel_ratio. {'device_pixel_ratio': 0.8333333134651184, 'type': 'set_device_pixel_ratio'}\n"
]
Exp processing: PCA and stuff
2 years ago
}
],
"source": [
Some beautiful UMAP results
2 years ago
"fig, ax = plt.subplots(1, 1, figsize=(20, 10))\n",
"# ax.scatter3D(x, y, z, c=np.array([param_corr[:, 0] / 180, [0] * param_corr.shape[0], param_corr[:, 1] / 360]).swapaxes(0, 1))\n",
"ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
"ax.scatter3D(x, y, u, c=param_corr[:, 0], cmap=\"hsv\")\n",
"ax.set(xlabel=\"U0\", ylabel=\"U1\", zlabel=\"U3\", xticks=[], yticks=[], zticks=[])\n",
"ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
"ax.scatter3D(x, y, u, c=param_corr[:, 1], cmap=\"hsv\")\n",
"ax.set(xlabel=\"U0\", ylabel=\"U1\", zlabel=\"U3\", xticks=[], yticks=[], zticks=[])\n",
Exp processing: PCA and stuff
2 years ago
"\n",
"fig.suptitle('UMAP 3D')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Decoding"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 8,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [],
"source": [
"import decoding"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 10,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
Some beautiful UMAP results
2 years ago
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decoding... done\n"
Exp processing: PCA and stuff
2 years ago
]
}
],
"source": [
"# param = decoding.cohomological_parameterization(pd.DataFrame(firing_rates_in_trials_pca.reshape((400*50, -1))[::41]))\n",
Some beautiful UMAP results
2 years ago
"param = decoding.cohomological_parameterization(pd.DataFrame(embed))\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(param)"
Exp processing: PCA and stuff
2 years ago
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(788,)"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"param_corr[:, 0].shape"
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 12,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"ename": "ValueError",
Some beautiful UMAP results
2 years ago
"evalue": "'c' argument must be a color, a sequence of colors, or a sequence of numbers, not decoding\n0 0.728825\n1 0.728807\n2 0.728802\n3 0.729053\n4 0.729382\n.. ...\n395 0.988458\n396 0.917262\n397 0.913100\n398 0.974053\n399 0.055629\n\n[400 rows x 1 columns]",
Exp processing: PCA and stuff
2 years ago
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4350\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m 4349\u001b[0m \u001b[39mtry\u001b[39;00m: \u001b[39m# Is 'c' acceptable as PathCollection facecolors?\u001b[39;00m\n\u001b[0;32m-> 4350\u001b[0m colors \u001b[39m=\u001b[39m mcolors\u001b[39m.\u001b[39;49mto_rgba_array(c)\n\u001b[1;32m 4351\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mTypeError\u001b[39;00m, \u001b[39mValueError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:385\u001b[0m, in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 385\u001b[0m rgba \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([to_rgba(cc) \u001b[39mfor\u001b[39;00m cc \u001b[39min\u001b[39;00m c])\n\u001b[1;32m 387\u001b[0m \u001b[39mif\u001b[39;00m alpha \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:385\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 385\u001b[0m rgba \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([to_rgba(cc) \u001b[39mfor\u001b[39;00m cc \u001b[39min\u001b[39;00m c])\n\u001b[1;32m 387\u001b[0m \u001b[39mif\u001b[39;00m alpha \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:206\u001b[0m, in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[39mif\u001b[39;00m rgba \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m: \u001b[39m# Suppress exception chaining of cache lookup failure.\u001b[39;00m\n\u001b[0;32m--> 206\u001b[0m rgba \u001b[39m=\u001b[39m _to_rgba_no_colorcycle(c, alpha)\n\u001b[1;32m 207\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/colors.py:277\u001b[0m, in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[39mreturn\u001b[39;00m c, c, c, alpha \u001b[39mif\u001b[39;00m alpha \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39m1.\u001b[39m\n\u001b[0;32m--> 277\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mInvalid RGBA argument: \u001b[39m\u001b[39m{\u001b[39;00morig_c\u001b[39m!r}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 278\u001b[0m \u001b[39m# turn 2-D array into 1-D array\u001b[39;00m\n",
"\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: 'decoding'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
Some beautiful UMAP results
2 years ago
"\u001b[1;32m/home/ennucore/dev/amgen/exp/ProcessExp.ipynb Cell 115\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/ennucore/dev/amgen/exp/ProcessExp.ipynb#ch0000097?line=0'>1</a>\u001b[0m plt\u001b[39m.\u001b[39;49mscatter(param_corr[:, \u001b[39m0\u001b[39;49m], param_corr[:, \u001b[39m1\u001b[39;49m], c\u001b[39m=\u001b[39;49mparam)\n",
Exp processing: PCA and stuff
2 years ago
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/pyplot.py:3068\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 3063\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mscatter)\n\u001b[1;32m 3064\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mscatter\u001b[39m(\n\u001b[1;32m 3065\u001b[0m x, y, s\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, c\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, marker\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, cmap\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, norm\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 3066\u001b[0m vmin\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, vmax\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, alpha\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, linewidths\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m,\n\u001b[1;32m 3067\u001b[0m edgecolors\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, plotnonfinite\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m-> 3068\u001b[0m __ret \u001b[39m=\u001b[39m gca()\u001b[39m.\u001b[39;49mscatter(\n\u001b[1;32m 3069\u001b[0m x, y, s\u001b[39m=\u001b[39;49ms, c\u001b[39m=\u001b[39;49mc, marker\u001b[39m=\u001b[39;49mmarker, cmap\u001b[39m=\u001b[39;49mcmap, norm\u001b[39m=\u001b[39;49mnorm,\n\u001b[1;32m 3070\u001b[0m vmin\u001b[39m=\u001b[39;49mvmin, vmax\u001b[39m=\u001b[39;49mvmax, alpha\u001b[39m=\u001b[39;49malpha, linewidths\u001b[39m=\u001b[39;49mlinewidths,\n\u001b[1;32m 3071\u001b[0m edgecolors\u001b[39m=\u001b[39;49medgecolors, plotnonfinite\u001b[39m=\u001b[39;49mplotnonfinite,\n\u001b[1;32m 3072\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m({\u001b[39m\"\u001b[39;49m\u001b[39mdata\u001b[39;49m\u001b[39m\"\u001b[39;49m: data} \u001b[39mif\u001b[39;49;00m data \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m {}), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 3073\u001b[0m sci(__ret)\n\u001b[1;32m 3074\u001b[0m \u001b[39mreturn\u001b[39;00m __ret\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/__init__.py:1361\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1358\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m 1359\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 1360\u001b[0m \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1361\u001b[0m \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1363\u001b[0m bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 1364\u001b[0m auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m 1365\u001b[0m \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4516\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4513\u001b[0m \u001b[39mif\u001b[39;00m edgecolors \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 4514\u001b[0m orig_edgecolor \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39medgecolor\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m 4515\u001b[0m c, colors, edgecolors \u001b[39m=\u001b[39m \\\n\u001b[0;32m-> 4516\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_scatter_color_args(\n\u001b[1;32m 4517\u001b[0m c, edgecolors, kwargs, x\u001b[39m.\u001b[39;49msize,\n\u001b[1;32m 4518\u001b[0m get_next_color_func\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_patches_for_fill\u001b[39m.\u001b[39;49mget_next_color)\n\u001b[1;32m 4520\u001b[0m \u001b[39mif\u001b[39;00m plotnonfinite \u001b[39mand\u001b[39;00m colors \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 4521\u001b[0m c \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mma\u001b[39m.\u001b[39mmasked_invalid(c)\n",
"File \u001b[0;32m/usr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4359\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m 4356\u001b[0m \u001b[39mraise\u001b[39;00m invalid_shape_exception(c\u001b[39m.\u001b[39msize, xsize) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 4357\u001b[0m \u001b[39m# Both the mapping *and* the RGBA conversion failed: pretty\u001b[39;00m\n\u001b[1;32m 4358\u001b[0m \u001b[39m# severe failure => one may appreciate a verbose feedback.\u001b[39;00m\n\u001b[0;32m-> 4359\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 4360\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mc\u001b[39m\u001b[39m'\u001b[39m\u001b[39m argument must be a color, a sequence of colors, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 4361\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mor a sequence of numbers, not \u001b[39m\u001b[39m{\u001b[39;00mc\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 4362\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 4363\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(colors) \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m (\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, xsize):\n\u001b[1;32m 4364\u001b[0m \u001b[39m# NB: remember that a single color is also acceptable.\u001b[39;00m\n\u001b[1;32m 4365\u001b[0m \u001b[39m# Besides *colors* will be an empty array if c == 'none'.\u001b[39;00m\n",
Some beautiful UMAP results
2 years ago
"\u001b[0;31mValueError\u001b[0m: 'c' argument must be a color, a sequence of colors, or a sequence of numbers, not decoding\n0 0.728825\n1 0.728807\n2 0.728802\n3 0.729053\n4 0.729382\n.. ...\n395 0.988458\n396 0.917262\n397 0.913100\n398 0.974053\n399 0.055629\n\n[400 rows x 1 columns]"
Exp processing: PCA and stuff
2 years ago
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
Some beautiful UMAP results
2 years ago
"plt.scatter(param_corr[:, 0], param_corr[:, 1], c=param)"
Exp processing: PCA and stuff
2 years ago
]
},
{
"cell_type": "code",
Some beautiful UMAP results
2 years ago
"execution_count": 14,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
Some beautiful UMAP results
2 years ago
"<matplotlib.collections.PathCollection at 0x7f35ae6ef310>"
Exp processing: PCA and stuff
2 years ago
]
},
Some beautiful UMAP results
2 years ago
"execution_count": 14,
Exp processing: PCA and stuff
2 years ago
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
Some beautiful UMAP results
2 years ago
"image/png": "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
Exp processing: PCA and stuff
2 years ago
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
Some beautiful UMAP results
2 years ago
"plt.scatter(param_corr[:, 0], param)"
Exp processing: PCA and stuff
2 years ago
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.10/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Density'>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(param.clip(0.885, 0.888))"
]
Processing experimental data for flashed gratings
2 years ago
}
],
"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
}