def correlation_brain_image(outside_brain_value):
"""
Uses a mask to plot only the areas that are the brain itself and saves
that image into the figures folder.
Parameters
----------
outside_brain_values : int
Returns
-------
None
"""
if outside_brain_value < 0:
title = 'light'
else:
title = 'dark'
correlations_3d = np.reshape(
get_correlations('mean',
nan=False),
VOXEL_DIMENSIONS)
image = correlations_3d[:, :, 24]
image += image.min()
outside_brain_mask = np.logical_not(bm.get_brain_mask())[:, :, 24]
image[outside_brain_mask] = outside_brain_value
hot_mask = image >= np.percentile(get_correlations('mean', nan=True), 95)
hot_pixels = np.zeros_like(image, dtype=np.float)
hot_pixels[hot_mask] = image[hot_mask]
hot_pixels[~hot_mask] = np.nan
plt.imshow(image, interpolation='nearest', cmap='gray')
plt.imshow(hot_pixels, interpolation='nearest', cmap='Blues')
plot_path = '{0}/figures/correlated_brain_{1}.png'.format(REPO_HOME_PATH,
title)
plt.savefig(plot_path)
print('Saved {0}'.format(plot_path))
def correlation_brain_image(outside_brain_value):
"""
Uses a mask to plot only the areas that are the brain itself and saves
that image into the figures folder.
Parameters
----------
outside_brain_values : int
Returns
-------
None
"""
if outside_brain_value < 0:
title = 'light'
else:
title = 'dark'
correlations_3d = np.reshape(get_correlations('mean', nan=False),
VOXEL_DIMENSIONS)
image = correlations_3d[:, :, 24]
image += image.min()
outside_brain_mask = np.logical_not(bm.get_brain_mask())[:, :, 24]
image[outside_brain_mask] = outside_brain_value
hot_mask = image >= np.percentile(get_correlations('mean', nan=True), 95)
hot_pixels = np.zeros_like(image, dtype=np.float)
hot_pixels[hot_mask] = image[hot_mask]
hot_pixels[~hot_mask] = np.nan
plt.imshow(image, interpolation='nearest', cmap='gray')
plt.imshow(hot_pixels, interpolation='nearest', cmap='Blues')
plot_path = '{0}/figures/correlated_brain_{1}.png'.format(
REPO_HOME_PATH, title)
plt.savefig(plot_path)
print('Saved {0}'.format(plot_path))
def get_pairwise_correlations(only_brain = True):
"""
Finds and returns the paths to the correlations of all possible pairs of
subjects (if the paths exist)
Parameters
----------
None
Returns
-------
paths : string array
"""
subject_pairs = itertools.combinations(SUBJECTS, 2)
brain_mask = np.ravel(bm.get_brain_mask())
correlations = [np.load(dp.get_correlation_path(subj_a, subj_b))
for subj_a, subj_b in subject_pairs]
if only_brain:
return [c[brain_mask] for c in correlations]
return correlations
def get_pairwise_correlations(only_brain=True):
"""
Finds and returns the paths to the correlations of all possible pairs of
subjects (if the paths exist)
Parameters
----------
None
Returns
-------
paths : string array
"""
subject_pairs = itertools.combinations(SUBJECTS, 2)
brain_mask = np.ravel(bm.get_brain_mask())
correlations = [
np.load(dp.get_correlation_path(subj_a, subj_b))
for subj_a, subj_b in subject_pairs
]
if only_brain:
return [c[brain_mask] for c in correlations]
return correlations
