for c, t in zip(this_cond_mat, ttls):
assert np.array_equal(decimals_to_binary(c, n_digits), t)
# this maps the sequence of 4 (single-digit) ints into a single int.
# the sequence of experiment variables corresponding to each order of
# 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
