def test_peths_synthetic(self):
n_spikes = 20000
n_clusters = 20
n_events = 200
record_length = 1654
cluster_sel = [1, 2, 3, 6, 15, 16]
np.random.seed(seed=42)
spike_times = np.sort(np.random.rand(n_spikes, ) * record_length)
spike_clusters = np.random.randint(0, n_clusters, n_spikes)
event_times = np.sort(np.random.rand(n_events, ) * record_length)
peth, fr = peths(spike_times,
spike_clusters,
cluster_ids=cluster_sel,
align_times=event_times)
self.assertTrue(peth.means.shape[0] == len(cluster_sel))
self.assertTrue(np.all(peth.means.shape == peth.stds.shape))
self.assertTrue(np.all(fr.shape == (n_events, len(cluster_sel), 28)))
self.assertTrue(peth.tscale.size == 28)
#ses_path = get_session_path(datasets)
ses_path = datasets[
0].local_path.parent #local path where the data has been downloaded
spikes = alf.io.load_object(ses_path, 'spikes')
trials = alf.io.load_object(ses_path, 'trials')
# check which neurons are responsive
#are_neurons_responsive(spike_times,spike_clusters,stimulus_intervals=None,spontaneous_period=None,p_value_threshold=.05):
# spontaenous period is just 1 interval! on and off time
responsive = are_neurons_responsive(
spikes.times, spikes.clusters,
np.vstack((trials.stimOn_times, trials.stimOn_times + 0.5)).T,
np.array([0, trials.stimOn_times[0] - 1]).reshape(-1), 0.001)
#peths(spike_times, spike_clusters, cluster_ids, align_times, pre_time=0.2,
# post_time=0.5, bin_size=0.025, smoothing=0.025, return_fr=True):
responsive_neuron = np.unique(spikes.clusters)[responsive]
peth, bs = peths(spikes.times, spikes.clusters, responsive_neuron,
trials.stimOn_times)
plt.plot(peth.tscale, peth.means.T)
#for m in np.arange(peth.means.shape[0]):
# plt.fill_between(peth.tscale,
# peth.means[m, :].T - peth.stds[m, :].T / 20,
# peth.means[m, :].T + peth.stds[m, :].T / 20,
# alpha=0.2, edgecolor='#1B2ACC', facecolor='#089FFF',
# linewidth=4, linestyle='dashdot', antialiased=True)
import matplotlib.pyplot as plt
import numpy as np
import alf.io
from brainbox.singlecell import peths
from oneibl.one import ONE
one = ONE()
eid = one.search(subject='KS004', date=['2019-09-25'], task_protocol='ephysChoiceWorld')[0]
datasets = one.load(eid, download_only=True)
ses_path = datasets[0].local_path.parent
spikes = alf.io.load_object(ses_path, 'spikes')
trials = alf.io.load_object(ses_path, '_ibl_trials')
peth, bs = peths(spikes.times, spikes.clusters, [225, 52], trials.goCue_times)
plt.plot(peth.tscale, peth.means.T)
for m in np.arange(peth.means.shape[0]):
plt.fill_between(peth.tscale,
peth.means[m, :].T - peth.stds[m, :].T / 20,
peth.means[m, :].T + peth.stds[m, :].T / 20,
alpha=0.2, edgecolor='#1B2ACC', facecolor='#089FFF',
linewidth=4, linestyle='dashdot', antialiased=True)
for i, align_event in enumerate(align_events):
if align_event == 'stimOff':
if choice == stim_side:
offset = 1.0
else:
offset = 2.0
align_times = trials['feedback_times'][trial_ids[d]] + offset
elif align_event == 'movement':
raise NotImplementedError
else:
align_times = trials[align_event + '_times'][trial_ids[d]]
peth_, bs = peths(spikes['times'],
spikes['clusters'],
cluster_ids,
align_times,
pre_time=PRE_TIME,
post_time=POST_TIME,
bin_size=BIN_SIZE,
smoothing=SMOOTH_SIZE)
peth_means[d][align_event] = peth_.means
peth_stds[d][align_event] = peth_.stds
binned[d][align_event] = bs
# plot peths for each cluster
n_trials, n_clusters, _ = binned[d][align_event].shape
n_rows = 4 # clusters per page
n_plots = int(np.ceil(n_clusters / n_rows))
# n_plots = 3 # for testing
for d in ['l', 'r']:
for p in range(n_plots):