def test_epochs_deconv():
"""Test epochs deconvolution"""
tmin, tmax = -0.5, 1.5
event_dict = dict(foo=999)
events = np.array([np.arange(0, 21000, 1000, int),
999 * np.ones(21, int)]).T
for fi, fname in enumerate(fnames):
if fi == 0:
n_jobs = 1
else:
n_jobs = 0
raw = read_raw(fname)
epochs = Epochs(raw, events, event_dict, tmin, tmax)
a = raw.info['sample_fields']
b = epochs.info['data_cols']
assert_equal(len(a), len(b))
assert_true(all(aa == bb for aa, bb in zip(a, b)))
data = epochs.get_data('ps')
assert_raises(RuntimeError, Epochs, raw, events, 'test', tmin, tmax)
fit, times = epochs.deconvolve()
assert_array_equal(data, epochs.get_data('ps'))
assert_equal(fit.shape, (len(epochs), len(times)))
fit, times = epochs.deconvolve(spacing=[-0.1, 0.4, 1.0],
bounds=(0, np.inf),
n_jobs=n_jobs)
assert_equal(fit.shape, (len(epochs), len(times)))
assert_equal(len(times), 3)
if fi == 0:
if _has_joblib():
assert_raises(ValueError, epochs.deconvolve, n_jobs=-1000)
def test_epochs_deconv():
"""Test epochs deconvolution"""
tmin, tmax = -0.5, 1.5
event_dict = dict(foo=999)
events = np.array([np.arange(0, 21000, 1000, int),
999 * np.ones(21, int)]).T
for fi, fname in enumerate(fnames):
if fi == 0:
n_jobs = 1
else:
n_jobs = 0
raw = read_raw(fname)
epochs = Epochs(raw, events, event_dict,
tmin, tmax)
a = raw.info['sample_fields']
b = epochs.info['data_cols']
assert_equal(len(a), len(b))
assert_true(all(aa == bb for aa, bb in zip(a, b)))
data = epochs.get_data('ps')
assert_raises(RuntimeError, Epochs, raw, events, 'test', tmin, tmax)
fit, times = epochs.deconvolve()
assert_array_equal(data, epochs.get_data('ps'))
assert_equal(fit.shape, (len(epochs), len(times)))
fit, times = epochs.deconvolve(spacing=[-0.1, 0.4, 1.0],
bounds=(0, np.inf), n_jobs=n_jobs)
assert_equal(fit.shape, (len(epochs), len(times)))
assert_equal(len(times), 3)
if fi == 0:
if _has_joblib():
assert_raises(ValueError, epochs.deconvolve, n_jobs=-1000)
def test_epochs_deconv():
"""Test epochs deconvolution"""
tmin, tmax = -0.5, 1.5
event_dict = dict(foo=999, bar=77)
events_a = np.array([[12000, 77], [1000, 999]])
for fname in fnames:
raw = Raw(fname)
epochs = Epochs([raw] * 2, [events_a] * 2, event_dict,
tmin, tmax)
assert_raises(RuntimeError, Epochs, raw, events_a, 'test', tmin, tmax)
fit, times = epochs.deconvolve()
assert_equal(fit.shape, (len(epochs), len(times)))
fit, times = epochs.deconvolve(spacing=[-0.1, 0.4, 1.0],
bounds=(0, np.inf))
assert_equal(fit.shape, (len(epochs), len(times)))
assert_equal(len(times), 3)
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]
zscore = zscore[order]
fit = fit[order]
# add this subject's data to global container
fits.append(fit)
zscores.append(zscore)
gaze_angles.append(angles)
print(' Done: {} sec.'.format(str(round(time.time() - t0, 1))))
# convert to arrays
band_num = run_inds[ri][:, 1]
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)
band_num = run_inds[ri][:, 1]
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)
