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amgen/exp/ProcessExp.ipynb

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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",
"from matplotlib import cm\n",
"\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':'$\\phi$'}"
]
},
{
"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",
"execution_count": 3,
"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",
"execution_count": 4,
"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 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stim.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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",
"execution_count": 6,
"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",
"execution_count": 7,
"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])"
]
},
"execution_count": 7,
"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",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(40,)"
]
},
"execution_count": 8,
"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",
"execution_count": 9,
"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",
"execution_count": 10,
"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": {
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"text/plain": [
"<Figure size 1440x720 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"unit_id = 1\n",
"unit_id_2 = 3\n",
"\n",
"fig, ax = plt.subplots(1,3, figsize=(20,10))\n",
"ax[0].plot(np.diff(stim['stim_ontime'], n=2), np.arange(20000-2))\n",
"ax[0].invert_yaxis()\n",
"ax[0].set_xlabel('$\\Delta^2[t_{on}]=\\Delta$ trial duration / s', size=20)\n",
"ax[0].set(ylabel='trials')\n",
"ax[1].eventplot(subseq_spike_times_locked['ontime'][unit_id], lineoffsets=1, linelengths=0.8)\n",
"ax[1].set(title='unit %d'%unit_id, yticks=[], xlabel='t/s (locked to trial onsets)')\n",
"ax[1].invert_yaxis()\n",
"ax[2].eventplot(subseq_spike_times_locked['ontime'][unit_id_2], lineoffsets=1, linelengths=0.8)\n",
"ax[2].set(title='unit %d'%(unit_id_2), yticks=[], xlabel='t/s (locked to trial ends)')\n",
"ax[2].invert_yaxis()\n",
"plt.subplots_adjust(wspace=0)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Whaaat?_\n",
"\n",
"Ok, we've gotta fix it.\n",
"\n",
"Let's take the \"bad\" times, identify the periods we want to calibrate - and align everything"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2280 2286 2291 2292 2295 2298 2301 2307 2377 2378 2380 2383 2386 2464 2467 2469 2472 2475 2478 15300 15306 15312 15315 15316 15318 15327 15329 15333 15336 15338 15342 15344 15348 15349 15402 15403 15405 15408\n"
]
}
],
"source": [
"stim_2diffs = np.diff(stim['stim_ontime'], n=2)\n",
"print(*[i for i in range(len(stim_2diffs)) if stim_2diffs[i] > 0.01])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Periods:\n",
"$[0, 2350)$, $[2350, 15350)$, $[15350, 20000]$"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_4135643/175591453.py:20: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
" period_1_dist = scipy.ndimage.filters.gaussian_filter1d(period_1_dist, sigma=10)\n",
"/tmp/ipykernel_4135643/175591453.py:21: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
" period_2_dist = scipy.ndimage.filters.gaussian_filter1d(period_2_dist, sigma=10)\n",
"/tmp/ipykernel_4135643/175591453.py:22: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
" period_3_dist = scipy.ndimage.filters.gaussian_filter1d(period_3_dist, sigma=10)\n"
]
}
],
"source": [
"p1, p2 = 2350, 15350\n",
"delta_t = 0.002\n",
"time_range_smoothing = np.arange(0, 0.8, delta_t)\n",
"period_1_dist = np.zeros(len(time_range_smoothing) - 1)\n",
"period_2_dist = np.zeros(len(time_range_smoothing) - 1)\n",
"period_3_dist = np.zeros(len(time_range_smoothing) - 1)\n",
"num_units = subseq_spike_times_locked['ontime'].shape[0]\n",
"for i in range(num_units):\n",
" for j, row in enumerate(subseq_spike_times_locked['ontime'][i]):\n",
" hist = np.histogram(row, bins=time_range_smoothing)[0]\n",
" if j < p1:\n",
" period_1_dist += hist\n",
" elif j < p2:\n",
" period_2_dist += hist\n",
" else:\n",
" period_3_dist += hist\n",
"period_1_dist /= num_units\n",
"period_2_dist /= num_units\n",
"period_3_dist /= num_units\n",
"period_1_dist = scipy.ndimage.filters.gaussian_filter1d(period_1_dist, sigma=10)\n",
"period_2_dist = scipy.ndimage.filters.gaussian_filter1d(period_2_dist, sigma=10)\n",
"period_3_dist = scipy.ndimage.filters.gaussian_filter1d(period_3_dist, sigma=10)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.collections.EventCollection at 0x7f1b53e02f80>]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x1440 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(3, 1, figsize=(10,20))\n",
"ax[0].plot(time_range_smoothing[:-1], period_1_dist, label='period 1')\n",
"ax[1].plot(time_range_smoothing[:-1], period_2_dist, label='period 2')\n",
"ax[2].plot(time_range_smoothing[:-1], period_3_dist, label='period 3')\n",
"ax[0].set(xlabel='t/s', ylabel='spike count', title='1st part')\n",
"ax[1].set(xlabel='t/s', ylabel='spike count', title='2nd part')\n",
"ax[2].set(xlabel='t/s', ylabel='spike count', title='3rd part')\n",
"ax[0].eventplot([0.2], linelengths=0.8)\n",
"ax[1].eventplot([0.2], linelengths=0.8)\n",
"ax[2].eventplot([0.2], linelengths=0.8)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(time_range_smoothing).index(0.2)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.2 0.162 0.194\n"
]
}
],
"source": [
"max_p_1 = time_range_smoothing[np.argmax(period_1_dist[:120])]\n",
"max_p_2 = time_range_smoothing[np.argmax(period_2_dist[:100])]\n",
"max_p_3 = time_range_smoothing[np.argmax(period_3_dist[:120])]\n",
"delta_2 = max_p_1 - max_p_2\n",
"delta_3 = max_p_1 - max_p_3\n",
"print(max_p_1, max_p_2, max_p_3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll fix the times"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"t_p1, t_p2 = stim['stim_offtime'][p1], stim['stim_offtime'][p2]\n",
"fix_time = lambda t: t - delta_2 * ((t < t_p2) & (t >= t_p1)) - delta_3 * ((t >= t_p2))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2.5839\n",
"1 2.7028\n",
"2 2.7863\n",
"3 2.8699\n",
"4 2.9534\n",
" ... \n",
"19990 1671.6389\n",
"19991 1671.7224\n",
"19992 1671.8059\n",
"19993 1671.8894\n",
"19994 1671.9730\n",
"Name: stim_offtime, Length: 19995, dtype: float64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fix_time(stim['stim_offtime'][:-subseq_trials])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.10/site-packages/pandas/core/roperator.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
" return right - left\n"
]
}
],
"source": [
"subseq_spike_times_locked_2 = {}\n",
"subseq_spike_times_locked_2['ontime'] = [subseq_spike_times[unit_id] - fix_time(stim['stim_ontime'][:-subseq_trials]) for unit_id in range(num_unit)]\n",
"subseq_spike_times_locked_2['offtime'] = [subseq_spike_times[unit_id] - fix_time(stim['stim_offtime'][:-subseq_trials]) for unit_id in range(num_unit)]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1440x720 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"unit_id = 1\n",
"\n",
"fig, ax = plt.subplots(1,3, figsize=(20,10))\n",
"ax[0].plot(np.diff(stim['stim_ontime'], n=2), np.arange(20000-2))\n",
"ax[0].invert_yaxis()\n",
"ax[0].set_xlabel('$\\Delta^2[t_{on}]=\\Delta$ trial duration / s', size=20)\n",
"ax[0].set(ylabel='trials')\n",
"ax[1].eventplot(subseq_spike_times_locked_2['ontime'][unit_id], lineoffsets=1, linelengths=0.8)\n",
"ax[1].set(title='unit %d'%unit_id, yticks=[], xlabel='t/s (locked to trial onsets)')\n",
"ax[1].invert_yaxis()\n",
"ax[2].eventplot(subseq_spike_times_locked_2['ontime'][unit_id_2], lineoffsets=1, linelengths=0.8)\n",
"ax[2].set(title='unit %d'%(unit_id_2), yticks=[], xlabel='t/s (locked to trial ends)')\n",
"ax[2].invert_yaxis()\n",
"plt.subplots_adjust(wspace=0)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"s = np.array([np.array(list(map(fix_time, spike_times[unit_id]))) for unit_id in range(len(spike_times))], dtype=object)//dt\n",
"B_spike_fixed = []\n",
"for unit_id in range(num_unit):\n",
" B_spike_fixed.append(sparse.coo_matrix((np.ones(len(s[unit_id])), (np.zeros(len(s[unit_id]), dtype=int), np.int0(s[unit_id]))), shape=(1, M)))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"from copy import deepcopy\n",
"B_stim_original = deepcopy(B_stim)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"B_stim = {}\n",
"for key in key_list:\n",
" B_stim[key] = []\n",
" for stim_id, trials in enumerate(stim_id_trial[key]):\n",
" B_stim[key].append([])\n",
" s = []\n",
" for trial_id in trials:\n",
" t_on, t_off = fix_time(stim['stim_ontime'][trial_id]), fix_time(stim['stim_offtime'][trial_id])\n",
" s += list(np.arange(int(t_on//dt), int(t_off//dt)))\n",
"\n",
" B_stim[key][stim_id] = sparse.coo_matrix((np.ones(len(s)), (np.zeros(len(s), dtype=int), s)), shape=(1, M))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Calculate some matrices"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"get_trial_index = lambda t: fix_time(stim['stim_ontime'][t])//dt"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"B_spike_smooth = list()\n",
"for unit_id in range(num_unit):\n",
" B_spike_smooth.append(scipy.ndimage.gaussian_filter(B_spike[unit_id].toarray()[0], sigma=50))\n",
"B_spike_smooth = np.array(B_spike_smooth)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f1b6ae5dcc0>]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(B_spike_smooth[0][:5000]) # ~10 trials"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.5 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}