cut_trial_block.segments = cut_segment_by_epoch(
data_segment, epoch, reset_time=True)
# =============================================================================
# Plot data
# =============================================================================
# Determine the first existing trial ID i from the Event object containing all
# start events. Then, by calling the filter() function of the Neo Block
# "data_cut_to_analysis_epochs" containing the data cut into the analysis
# epochs, we ask to return all Segments annotated by the behavioral trial ID i.
# In this case this call should return one matching analysis epoch around TS-ON
# belonging to behavioral trial ID i. For monkey N, this is trial ID 1, for
# monkey L this is trial ID 2 since trial ID 1 is not a correct trial.
trial_id = int(np.min(start_event.annotations['trial_id']))
trial_segments = cut_trial_block.filter(
targdict={"trial_id": trial_id}, objects=Segment)
assert len(trial_segments) == 1
trial_segment = trial_segments[0]
# Create figure
fig = plt.figure(facecolor='w')
time_unit = pq.CompoundUnit('1./30000*s')
amplitude_unit = pq.microvolt
nsx_colors = ['b', 'k', 'r']
# Loop through all analog signals and plot the signal in a color corresponding
# to its sampling frequency (i.e., originating from the ns2/ns5 or ns2/ns6).
for i, anasig in enumerate(trial_segment.analogsignals):
plt.plot(
anasig.times.rescale(time_unit),
anasig.squeeze().rescale(amplitude_unit),
stlist = [st - st.t_start for st in seg.spiketrains]
plt.subplot(len(block.segments), 1, seg.index + 1)
count, bins = np.histogram(stlist)
plt.bar(bins[:-1], count, width=bins[1] - bins[0])
plt.title(f"PSTH in segment {seg.index}")
plt.show()
# ..by neuron
plt.figure()
for i, group in enumerate(block.groups):
stlist = [st - st.t_start for st in group.spiketrains]
plt.subplot(len(block.groups), 1, i + 1)
count, bins = np.histogram(stlist)
plt.bar(bins[:-1], count, width=bins[1] - bins[0])
plt.title(f"PSTH of unit {group.name}")
plt.show()
# ..by tetrode
plt.figure()
for i, tetrode_id in enumerate(block.annotations["tetrode_ids"]):
stlist = []
for unit in block.filter(objects=Group, tetrode_id=tetrode_id):
stlist.extend([st - st.t_start for st in unit.spiketrains])
plt.subplot(2, 1, i + 1)
count, bins = np.histogram(stlist)
plt.bar(bins[:-1], count, width=bins[1] - bins[0])
plt.title(f"PSTH blend of tetrode {tetrode_id}")
plt.show()