def compute_DVARS(func_img, mean_img=None, mask=None, apply_mask=False):
"""Computes the DVARS for a given fMRI image."""
if mean_img is None:
mean_img = np.mean(func_img, axis=3)
if apply_mask:
if mask is None:
mask = mask_image(mean_img)
mean_img[np.logical_not(mask)] = 0.0
num_time_points = func_img.shape[3]
RMS_diff = lambda img2, img1: np.sqrt(np.mean(np.square(img2 - img1)))
DVARS_1_to_N = [
RMS_diff(func_img[:, :, :, t], func_img[:, :, :, t - 1])
for t in range(1, num_time_points)
]
DVARS = np.full(num_time_points, np.nan)
# dvars value at time point 0 is set to 0
DVARS[0] = 0.0
DVARS[1:] = DVARS_1_to_N
return DVARS
def compute_stats(self):
"""Computes the necessary stats to be displayed."""
mean_img_temporal, stdev_img_temporal = temporal_stats(
self.img_this_unit)
mean_signal_spatial, stdev_signal_spatial = spatial_stats(
self.img_this_unit)
dvars = compute_DVARS(self.img_this_unit)
for stat, sname in zip(
(mean_signal_spatial, stdev_signal_spatial, dvars),
('mean_signal_spatial', 'stdev_signal_spatial', 'dvars')):
if len(stat) != self.img_this_unit.shape[3]:
raise ValueError(
'ERROR: lengths of different stats do not match!')
if any(np.isnan(stat)):
raise ValueError(
'ERROR: invalid values in stat : {}'.format(sname))
mask = mask_image(mean_img_temporal,
update_factor=0.9,
init_percentile=5)
carpet = self.make_carpet(mask)
return carpet, mean_signal_spatial, stdev_signal_spatial, dvars
def _compute_background(self):
"""Computes the background image for the current image."""
if not hasattr(self, 'background_img'):
# need to scale the mask, as Collage class does NOT automatically rescale
self.foreground_mask = mask_image(self.current_img, out_dtype=bool)
temp_background_img = np.copy(self.current_img)
temp_background_img[self.foreground_mask] = 0.0
self.background_img = scale_0to1(temp_background_img,
exclude_outliers_below=1,
exclude_outliers_above=1)