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ucol_vals = [None]
ncols = len(ucol_vals)
fig, axs = plt.subplots(nrows, ncols, sharex=True, sharey=True, squeeze=False, figsize=(10, 10))
big_ax = create_big_ax(fig)
for i, row in enumerate(urow_vals):
for j, col in enumerate(ucol_vals):
ax = axs[i, j]
ax_trials_select = trials_select.copy()
if row is not None:
ax_trials_select &= row_vals == row
if col is not None:
ax_trials_select &= col_vals == col
ax_trials_select = np.where(ax_trials_select)[0]
if len(ax_trials_select):
data = align_by_time_intervals(units, index, time_intervals, align_by, align_by,
before, after, ax_trials_select)
show_psth_raster(data, before, after, ax=ax)
ax.set_xlabel('')
ax.set_ylabel('')
if ax.is_first_col():
ax.set_ylabel(row)
if ax.is_last_row():
ax.set_xlabel(col)
big_ax.set_xlabel(cols_label, labelpad=50)
big_ax.set_ylabel(rows_label, labelpad=60)
return fig
group_inds
labels
sigma_in_secs: float, optional
standard deviation of gaussian kernel
ntt:
Number of time points to use for smooth curve
Returns
-------
matplotlib.Figure
"""
if trials is None:
trials = units.get_ancestor('NWBFile').trials
data = align_by_time_intervals(units, index, trials, start_label, start_label, before, after, order,
progress_bar=progress_bar)
# expanded data so that gaussian smoother uses larger window than is viewed
expanded_data = align_by_time_intervals(units, index, trials, start_label, start_label,
before + sigma_in_secs * 4,
after + sigma_in_secs * 4,
order,
progress_bar=progress_bar)
fig, axs = plt.subplots(2, 1, figsize=(10, 10))
show_psth_raster(data, before, after, group_inds, labels, ax=axs[0], progress_bar=progress_bar)
axs[0].set_title('PSTH for unit {}'.format(index))
axs[0].set_xticks([])
axs[0].set_xlabel('')
standard deviation of gaussian kernel
ntt:
Number of time points to use for smooth curve
Returns
-------
matplotlib.Figure
"""
if trials is None:
trials = units.get_ancestor('NWBFile').trials
data = align_by_time_intervals(units, index, trials, start_label, start_label, before, after, order,
progress_bar=progress_bar)
# expanded data so that gaussian smoother uses larger window than is viewed
expanded_data = align_by_time_intervals(units, index, trials, start_label, start_label,
before + sigma_in_secs * 4,
after + sigma_in_secs * 4,
order,
progress_bar=progress_bar)
fig, axs = plt.subplots(2, 1, figsize=(10, 10))
show_psth_raster(data, before, after, group_inds, labels, ax=axs[0], progress_bar=progress_bar)
axs[0].set_title('PSTH for unit {}'.format(index))
axs[0].set_xticks([])
axs[0].set_xlabel('')
show_psth_smoothed(expanded_data, axs[1], before, after, group_inds,
sigma_in_secs=sigma_in_secs, ntt=ntt)
return fig