def get_binned_spikes(spike_times, spike_clusters, cluster_id, epoch_time,
pre_time=0.2,post_time=0.5, bin_size=0.025, smoothing=0.025, return_fr=True):
binned_firing_rate = calculate_peths(
spike_times, spike_clusters, cluster_id, epoch_time,
pre_time=pre_time,post_time=post_time, bin_size=bin_size,
smoothing=smoothing, return_fr=return_fr)[1]
return binned_firing_rate
def compute_peth(self, trial_type, clust, trials_id):
peths, bin = calculate_peths(self.spikes.times, self.spikes.clusters,
[self.clust_ids[clust]],
self.trials[trial_type][trials_id],
self.t_before, self.t_after)
peth_mean = peths.means[0, :]
peth_std = peths.stds[0, :] / np.sqrt(len(trials_id))
t_peth = peths.tscale
return t_peth, peth_mean, peth_std
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 = calculate_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)
import matplotlib.pyplot as plt
import numpy as np
import alf.io
from brainbox.singlecell import calculate_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 = calculate_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 = calculate_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):
def peri_event_time_histogram(
spike_times,
spike_clusters,
events,
cluster_id, # Everything you need for a basic plot
t_before=0.2,
t_after=0.5,
bin_size=0.025,
smoothing=0.025,
as_rate=True,
include_raster=False,
n_rasters=None,
error_bars='std',
ax=None,
pethline_kwargs={
'color': 'blue',
'lw': 2
},
errbar_kwargs={
'color': 'blue',
'alpha': 0.5
},
eventline_kwargs={
'color': 'black',
'alpha': 0.5
},
raster_kwargs={
'color': 'black',
'lw': 0.5
},
**kwargs):
"""
Plot peri-event time histograms, with the meaning firing rate of units centered on a given
series of events. Can optionally add a raster underneath the PETH plot of individual spike
trains about the events.
Parameters
----------
spike_times : array_like
Spike times (in seconds)
spike_clusters : array-like
Cluster identities for each element of spikes
events : array-like
Times to align the histogram(s) to
cluster_id : int
Identity of the cluster for which to plot a PETH
t_before : float, optional
Time before event to plot (default: 0.2s)
t_after : float, optional
Time after event to plot (default: 0.5s)
bin_size :float, optional
Width of bin for histograms (default: 0.025s)
smoothing : float, optional
Sigma of gaussian smoothing to use in histograms. (default: 0.025s)
as_rate : bool, optional
Whether to use spike counts or rates in the plot (default: `True`, uses rates)
include_raster : bool, optional
Whether to put a raster below the PETH of individual spike trains (default: `False`)
n_rasters : int, optional
If include_raster is True, the number of rasters to include. If `None`
will default to plotting rasters around all provided events. (default: `None`)
error_bars : {'std', 'sem', 'none'}, optional
Defines which type of error bars to plot. Options are:
-- `'std'` for 1 standard deviation
-- `'sem'` for standard error of the mean
-- `'none'` for only plotting the mean value
(default: `'std'`)
ax : matplotlib axes, optional
If passed, the function will plot on the passed axes. Note: current
behavior causes whatever was on the axes to be cleared before plotting!
(default: `None`)
pethline_kwargs : dict, optional
Dict containing line properties to define PETH plot line. Default
is a blue line with weight of 2. Needs to have color. See matplotlib plot documentation
for more options.
(default: `{'color': 'blue', 'lw': 2}`)
errbar_kwargs : dict, optional
Dict containing fill-between properties to define PETH error bars.
Default is a blue fill with 50 percent opacity.. Needs to have color. See matplotlib
fill_between documentation for more options.
(default: `{'color': 'blue', 'alpha': 0.5}`)
eventline_kwargs : dict, optional
Dict containing fill-between properties to define line at event.
Default is a black line with 50 percent opacity.. Needs to have color. See matplotlib
vlines documentation for more options.
(default: `{'color': 'black', 'alpha': 0.5}`)
raster_kwargs : dict, optional
Dict containing properties defining lines in the raster plot.
Default is black lines with line width of 0.5. See matplotlib vlines for more options.
(default: `{'color': 'black', 'lw': 0.5}`)
Returns
-------
ax : matplotlib axes
Axes with all of the plots requested.
"""
# Check to make sure if we fail, we fail in an informative way
if not len(spike_times) == len(spike_clusters):
raise ValueError('Spike times and clusters are not of the same shape')
if len(events) == 1:
raise ValueError('Cannot make a PETH with only one event.')
if error_bars not in ('std', 'sem', 'none'):
raise ValueError('Invalid error bar type was passed.')
if not all(np.isfinite(events)):
raise ValueError(
'There are NaN or inf values in the list of events passed. '
' Please remove non-finite data points and try again.')
# Compute peths
peths, binned_spikes = singlecell.calculate_peths(
spike_times, spike_clusters, [cluster_id], events, t_before, t_after,
bin_size, smoothing, as_rate)
# Construct an axis object if none passed
if ax is None:
plt.figure()
ax = plt.gca()
# Plot the curve and add error bars
mean = peths.means[0, :]
ax.plot(peths.tscale, mean, **pethline_kwargs)
if error_bars == 'std':
bars = peths.stds[0, :]
elif error_bars == 'sem':
bars = peths.stds[0, :] / np.sqrt(len(events))
else:
bars = np.zeros_like(mean)
if error_bars != 'none':
ax.fill_between(peths.tscale, mean - bars, mean + bars,
**errbar_kwargs)
# Plot the event marker line. Extends to 5% higher than max value of means plus any error bar.
plot_edge = (mean.max() + bars[mean.argmax()]) * 1.05
ax.vlines(0., 0., plot_edge, **eventline_kwargs)
# Set the limits on the axes to t_before and t_after. Either set the ylim to the 0 and max
# values of the PETH, or if we want to plot a spike raster below, create an equal amount of
# blank space below the zero where the raster will go.
ax.set_xlim([-t_before, t_after])
ax.set_ylim([-plot_edge if include_raster else 0., plot_edge])
# Put y ticks only at min, max, and zero
if mean.min() != 0:
ax.set_yticks([0, mean.min(), mean.max()])
else:
ax.set_yticks([0., mean.max()])
# Move the x axis line from the bottom of the plotting space to zero if including a raster,
# Then plot the raster
if include_raster:
if n_rasters is None:
n_rasters = len(events)
if n_rasters > 60:
warn(
"Number of raster traces is greater than 60. This might look bad on the plot."
)
ax.axhline(0., color='black')
tickheight = plot_edge / len(
events[:n_rasters]) # How much space per trace
tickedges = np.arange(0., -plot_edge - 1e-5, -tickheight)
clu_spks = spike_times[spike_clusters == cluster_id]
for i, t in enumerate(events[:n_rasters]):
idx = np.bitwise_and(clu_spks >= t - t_before,
clu_spks <= t + t_after)
event_spks = clu_spks[idx]
ax.vlines(event_spks - t, tickedges[i + 1], tickedges[i],
**raster_kwargs)
ax.set_ylabel('Firing Rate' if as_rate else 'Number of spikes', y=0.75)
else:
ax.set_ylabel('Firing Rate' if as_rate else 'Number of spikes')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel('Time (s) after event')
return ax
def plot_grating_figures(
session_path, cluster_ids_summary, cluster_ids_selected, save_dir=None, format='png',
pre_time=0.5, post_time=2.5, bin_size=0.005, smoothing=0.025, n_rand_clusters=20,
plot_summary=True, plot_selected=True):
"""
Produces two summary figures for the oriented grating protocol; the first summary figure
contains plots that compare different measures during the first and second grating protocols,
such as orientation selectivity index (OSI), orientation preference, fraction of visual
clusters, PSTHs, firing rate histograms, etc. The second summary figure contains plots of polar
PSTHs and corresponding rasters for a random subset of visually responsive clusters.
Parameters
----------
session_path : str
absolute path to experimental session directory
cluster_ids_summary : list
the clusters for which to plot summary psths/rasters; if empty, all clusters with responses
during the grating presentations are used
cluster_ids_selected : list
the clusters for which to plot individual psths/rasters; if empty, `n_rand_clusters` are
randomly chosen
save_dir : str or NoneType
if NoneType, figures are displayed; else a string defining the absolute filepath to the
directory in which figures will be saved
format : str
file format, i.e. 'png' | 'pdf' | 'jpg'
pre_time : float
time (sec) to plot before grating presentation onset
post_time : float
time (sec) to plot after grating presentation onset (should include length of stimulus)
bin_size : float
size of bins for raster plots/psths
smoothing : float
size of smoothing kernel (sec)
n_rand_clusters : int
the number of random clusters to choose for which to plot psths/rasters if
`cluster_ids_slected` is empty
plot_summary : bool
a flag for plotting the summary figure
plot_selected : bool
a flag for plotting the selected units figure
Returns
-------
metrics : dict
- 'osi' (dict): keys 'beg', 'end' point to arrays of osis during these epochs
- 'orientation_pref' (dict): keys 'beg', 'end' point to arrays of orientation preference
- 'frac_resp_by_depth' (dict): fraction of responsive clusters by depth
fig_dict : dict
A dict whose values are handles to one or both figures generated.
"""
fig_dict = {}
cluster_ids = cluster_ids_summary
cluster_idxs = cluster_ids_selected
epochs = ['beg', 'end']
# -------------------------
# load required alf objects
# -------------------------
print('loading alf objects...', end='', flush=True)
spikes = ioalf.load_object(session_path, 'spikes')
clusters = ioalf.load_object(session_path, 'clusters')
gratings = ioalf.load_object(session_path, '_iblcertif_.odsgratings')
spontaneous = ioalf.load_object(session_path, '_iblcertif_.spontaneous')
grating_times = {
'beg': gratings['odsgratings.times.00'],
'end': gratings['odsgratings.times.01']}
grating_vals = {
'beg': gratings['odsgratings.stims.00'],
'end': gratings['odsgratings.stims.01']}
spont_times = {
'beg': spontaneous['spontaneous.times.00'],
'end': spontaneous['spontaneous.times.01']}
# --------------------------
# calculate relevant metrics
# --------------------------
print('calcuating mean responses to gratings...', end='', flush=True)
# calculate mean responses to gratings
mask_clust = np.isin(spikes.clusters, cluster_ids) # update mask for responsive clusters
mask_times = np.full(spikes.times.shape, fill_value=False)
for epoch in epochs:
mask_times |= (spikes.times >= grating_times[epoch].min()) & \
(spikes.times <= grating_times[epoch].max())
resp = {epoch: [] for epoch in epochs}
for epoch in epochs:
resp[epoch] = are_neurons_responsive(
spikes.times[mask_clust], spikes.clusters[mask_clust], grating_times[epoch],
grating_vals[epoch], spont_times[epoch])
responses = {epoch: [] for epoch in epochs}
for epoch in epochs:
responses[epoch] = bin_responses(
spikes.times[mask_clust], spikes.clusters[mask_clust], grating_times[epoch],
grating_vals[epoch])
responses_mean = {epoch: np.mean(responses[epoch], axis=2) for epoch in epochs}
# responses_se = {epoch: np.std(responses[epoch], axis=2) / np.sqrt(responses[epoch].shape[2])
# for epoch in responses.keys()}
print('done')
# calculate osi and orientation preference
print('calcuating osi/orientation preference...', end='', flush=True)
ori_pref = {epoch: [] for epoch in epochs}
osi = {epoch: [] for epoch in epochs}
for epoch in epochs:
osi[epoch], ori_pref[epoch] = compute_selectivity(
responses_mean[epoch], np.unique(grating_vals[epoch]), 'ori')
print('done')
# calculate depth vs osi ratio (osi_beg/osi_end)
print('calcuating osi ratio as a function of depth...', end='', flush=True)
depths = np.array([clusters.depths[c] for c in cluster_ids])
ratios = np.array([osi['beg'][c] / osi['end'][c] for c in range(len(cluster_ids))])
print('done')
# calculate fraction of visual neurons by depth
print('calcuating fraction of visual clusters by depth...', end='', flush=True)
n_bins = 10
min_depth = np.min(clusters['depths'])
max_depth = np.max(clusters['depths'])
depth_limits = np.linspace(min_depth - 1, max_depth, n_bins + 1)
depth_avg = (depth_limits[:-1] + depth_limits[1:]) / 2
# aggregate clusters
clusters_binned = {epoch: [] for epoch in epochs}
frac_responsive = {epoch: [] for epoch in epochs}
cids = cluster_ids
for epoch in epochs:
# just look at responsive clusters during this epoch
cids_tmp = cids[resp[epoch]]
for d in range(n_bins):
lo_limit = depth_limits[d]
up_limit = depth_limits[d + 1]
# clusters.depth index is cluster id
cids_curr_depth = np.where(
(lo_limit < clusters.depths) & (clusters.depths <= up_limit))[0]
clusters_binned[epoch].append(cids_curr_depth)
frac_responsive[epoch].append(np.sum(
np.isin(cids_tmp, cids_curr_depth)) / len(cids_curr_depth))
# package for plotting
responsive = {'fraction': frac_responsive, 'depth': depth_avg}
print('done')
# calculate PSTH averaged over all clusters/orientations
print('calcuating average PSTH...', end='', flush=True)
peths = {epoch: [] for epoch in epochs}
peths_avg = {epoch: [] for epoch in epochs}
for epoch in epochs:
stim_ids = np.unique(grating_vals[epoch])
peths[epoch] = {i: None for i in range(len(stim_ids))}
peths_avg_tmp = []
for i, stim_id in enumerate(stim_ids):
curr_stim_idxs = np.where(grating_vals[epoch] == stim_id)
align_times = grating_times[epoch][curr_stim_idxs, 0][0]
peths[epoch][i], _ = calculate_peths(
spikes.times[mask_times], spikes.clusters[mask_times], cluster_ids,
align_times, pre_time=pre_time, post_time=post_time, bin_size=bin_size,
smoothing=smoothing, return_fr=True)
peths_avg_tmp.append(
np.mean(peths[epoch][i]['means'], axis=0, keepdims=True))
peths_avg_tmp = np.vstack(peths_avg_tmp)
peths_avg[epoch] = {
'mean': np.mean(peths_avg_tmp, axis=0),
'std': np.std(peths_avg_tmp, axis=0) / np.sqrt(peths_avg_tmp.shape[0])}
peths_avg['bin_size'] = bin_size
peths_avg['on_idx'] = int(pre_time / bin_size)
peths_avg['off_idx'] = peths_avg['on_idx'] + int(2 / bin_size)
print('done')
# compute rasters for entire orientation sequence at beg/end epoch
if plot_summary:
print('computing rasters for example stimulus sequences...', end='', flush=True)
r = {epoch: None for epoch in epochs}
r_times = {epoch: None for epoch in epochs}
r_clusters = {epoch: None for epoch in epochs}
for epoch in epochs:
# restrict activity to a single stim series; assumes each possible grating direction
# is displayed before repeating
n_stims = len(np.unique(grating_vals[epoch]))
mask_idxs_e = (spikes.times >= grating_times[epoch][:n_stims].min()) & \
(spikes.times <= grating_times[epoch][:n_stims].max())
r_tmp, r_times[epoch], r_clusters[epoch] = bincount2D(
spikes.times[mask_idxs_e], spikes.clusters[mask_idxs_e], bin_size)
# order activity by anatomical depth of neurons
d = dict(zip(spikes.clusters[mask_idxs_e], spikes.depths[mask_idxs_e]))
y = sorted([[i, d[i]] for i in d])
isort = np.argsort([x[1] for x in y])
r[epoch] = r_tmp[isort, :]
# package for plotting
rasters = {'spikes': r, 'times': r_times, 'clusters': r_clusters, 'bin_size': bin_size}
print('done')
# -------------------------------------------------
# compute psths and rasters for individual clusters
# -------------------------------------------------
if plot_selected:
print('computing psths and rasters for clusters...', end='', flush=True)
if len(cluster_ids_selected) == 0:
if (n_rand_clusters < len(cluster_ids)):
cluster_idxs = np.random.choice(cluster_ids, size=n_rand_clusters, replace=False)
else:
cluster_idxs = cluster_ids
else:
cluster_idxs = cluster_ids_selected
mean_responses = {cluster: {epoch: [] for epoch in epochs} for cluster in cluster_idxs}
osis = {cluster: {epoch: [] for epoch in epochs} for cluster in cluster_idxs}
binned = {cluster: {epoch: [] for epoch in epochs} for cluster in cluster_idxs}
for cluster_idx in cluster_idxs:
cluster = np.where(cluster_ids == cluster_idx)[0]
for epoch in epochs:
mean_responses[cluster_idx][epoch] = responses_mean[epoch][cluster, :][0]
osis[cluster_idx][epoch] = osi[epoch][cluster]
stim_ids = np.unique(grating_vals[epoch])
binned[cluster_idx][epoch] = {j: None for j in range(len(stim_ids))}
for j, stim_id in enumerate(stim_ids):
curr_stim_idxs = np.where(grating_vals[epoch] == stim_id)
align_times = grating_times[epoch][curr_stim_idxs, 0][0]
_, binned[cluster_idx][epoch][j] = calculate_peths(
spikes.times[mask_times], spikes.clusters[mask_times], [cluster_idx],
align_times, pre_time=pre_time, post_time=post_time, bin_size=bin_size)
print('done')
# --------------
# output figures
# --------------
print('producing figures...', end='')
if plot_summary:
if save_dir is None:
save_file = None
else:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_file = os.path.join(save_dir, 'grating_summary_figure.' + format)
fig_gr_summary = plot_summary_figure(
ratios=ratios, depths=depths, responsive=responsive, peths_avg=peths_avg, osi=osi,
ori_pref=ori_pref, responses_mean=responses_mean, rasters=rasters, save_file=save_file)
fig_gr_summary.suptitle('Summary Grating Responses')
fig_dict['gr_summary'] = fig_gr_summary
if plot_selected:
if save_dir is None:
save_file = None
else:
save_file = os.path.join(save_dir, 'grating_random_responses.' + format)
fig_gr_selected = plot_psths_and_rasters(
mean_responses, binned, osis, grating_vals, on_idx=peths_avg['on_idx'],
off_idx=peths_avg['off_idx'], bin_size=bin_size, save_file=save_file)
fig_gr_selected.suptitle('Selected Units Grating Responses')
print('done')
fig_dict['gr_selected'] = fig_gr_selected
# -----------------------------
# package up and return metrics
# -----------------------------
metrics = {
'osi': osi,
'orientation_pref': ori_pref,
'frac_resp_by_depth': responsive,
}
return fig_dict, metrics
def make(self, key):
clusters_spk_depths, clusters_spk_times, clusters_ids = \
(ephys.DefaultCluster & key).fetch(
'cluster_spikes_depths', 'cluster_spikes_times', 'cluster_id')
spikes_depths = np.hstack(clusters_spk_depths)
spikes_times = np.hstack(clusters_spk_times)
spikes_clusters = np.hstack(
[[cluster_id]*len(cluster_spk_depths)
for (cluster_id, cluster_spk_depths) in zip(clusters_ids,
clusters_spk_depths)])
if key['event'] == 'movement':
q = behavior.TrialSet.Trial * wheel.MovementTimes & key & 'trial_feedback_type=1'
else:
q = behavior.TrialSet.Trial & key & 'trial_feedback_type=1'
trials = q.fetch()
bin_size_depth = 80
min_depth = np.nanmin(spikes_depths)
max_depth = np.nanmax(spikes_depths)
bin_edges = np.arange(min_depth, max_depth, bin_size_depth)
spk_bin_ids = np.digitize(spikes_depths, bin_edges)
edges = np.hstack([bin_edges, [bin_edges[-1]+bin_size_depth]])
key.update(trial_type='Correct All',
depth_bin_centers=(edges[:-1] + edges[1:])/2)
if key['event'] == 'feedback':
event_times = trials['trial_feedback_time']
elif key['event'] == 'stim on':
event_times = trials['trial_stim_on_time']
elif key['event'] == 'movement':
event_times = trials['movement_onset']
peth_list = []
baseline_list = []
for i in tqdm(np.arange(len(bin_edges)) + 1, position=0):
f = spk_bin_ids == i
spikes_ibin = spikes_times[f]
spike_clusters = spikes_clusters[f]
cluster_ids = np.unique(spike_clusters)
peths, binned_spikes = singlecell.calculate_peths(
spikes_ibin, spike_clusters, cluster_ids,
event_times, pre_time=0.3, post_time=1)
if len(peths.means):
time = peths.tscale
peth = np.sum(peths.means, axis=0)
baseline = peth[np.logical_and(time > -0.3, time < 0)]
mean_bsl = np.mean(baseline)
peth_list.append(peth)
baseline_list.append(mean_bsl)
else:
peth_list.append(np.zeros_like(peths.tscale))
baseline_list.append(0)
key.update(depth_peth=np.vstack(peth_list),
depth_baseline=np.array(baseline_list),
time_bin_centers=peths.tscale)
self.insert1(key, skip_duplicates=True)