# magnitude is taken from params['cond_readme']; in this case it is
# 1000s: attn, 100s: spatial, 10s: idents, 1s: gap
event[:, 1] = [ttl_to_id(c) for c in this_cond_mat]
for e in event[:, 1]:
assert e in rev_dict
events.append(event)
print('\n Epoching...')
epochs = Epochs(raws, events, event_dict, t_min, t_max)
if downsample:
print(' Downsampling...')
epochs.resample(fs_out, n_jobs=n_jobs)
# compute gaze angles (in degrees)
angles = get_gaze_angle(epochs, screenprops)
# zscore pupil sizes
zscore = epochs.pupil_zscores()
# init some containers
kernel_fits = list()
kernel_zscores = list()
print(' Deconvolving...')
deconv_kwargs = dict(kernel=kernel, n_jobs=n_jobs, acc=1e-3)
if deconv_time_pts is not None:
deconv_kwargs.update(dict(spacing=deconv_time_pts))
fit, time_pts = epochs.deconvolve(**deconv_kwargs)
if deconv_time_pts is None:
deconv_time_pts = time_pts
assert np.array_equal(deconv_time_pts, time_pts)
# order sequentially by stimulus ID
order = np.argsort(stim_nums)
angles = angles[order]
gap_num = 10 * small_mat[:, 1] # cf. line 251 of vocExperiment_v2.m
attn_num = 100 * small_mat[:, 0] # cf. line 250 of vocExperiment_v2.m
event[:, 1] = band_num + gap_num + attn_num
events.append(event)
print('\n Epoching...')
epochs = Epochs(raws, events, event_dict, t_min, t_max)
kernel_fits = list()
kernel_zscores = list()
kernel_fits_continuous = list()
for kk, kernel in enumerate(kernels):
kname = ['LABSN', 'Wierda'][kk]
print(' Deconvolving ({} kernel)...'.format(kname))
fit, time_pts = epochs.deconvolve(kernel=kernel, n_jobs=6)
zscore = epochs.pupil_zscores()
if deconv_time_pts is None:
deconv_time_pts = time_pts
assert np.array_equal(deconv_time_pts, time_pts)
# reorder fits to match big_mat (stim# in serial order, voc band, time)
order = np.array(run_inds)
band_idx = (order[:, :, 1] - 1).ravel()
stim_idx = (order[:, :, 0] - 1).ravel() # convert 1-based to 0-based
fits_ordered = np.inf * np.ones((len(big_mat), 2,
len(deconv_time_pts)))
zscores_ordered = np.inf * np.ones((len(big_mat), 2, epochs.n_times))
fits_ordered[stim_idx, band_idx, :] = fit
zscores_ordered[stim_idx, band_idx, :] = zscore
assert not np.any(fits_ordered == np.inf)
assert not np.any(zscores_ordered == np.inf)
