def expand_mask(mask, custom_mask=None):
"""expand a mask with multiple integers into separate binary masks
Args:
mask: nibabel or Brain_Data instance
custom_mask: nibabel instance or string to file path; optional
Returns:
out: Brain_Data instance of multiple binary masks
"""
from nltools.data import Brain_Data
if isinstance(mask, nib.Nifti1Image):
mask = Brain_Data(mask, mask=custom_mask)
if not isinstance(mask, Brain_Data):
raise ValueError("Make sure mask is a nibabel or Brain_Data instance.")
mask.data = np.round(mask.data).astype(int)
tmp = []
for i in np.nonzero(np.unique(mask.data))[0]:
tmp.append((mask.data == i) * 1)
out = mask.empty()
out.data = np.array(tmp)
return out
def collapse_mask(mask, auto_label=True, custom_mask=None):
"""collapse separate masks into one mask with multiple integers
overlapping areas are ignored
Args:
mask: nibabel or Brain_Data instance
custom_mask: nibabel instance or string to file path; optional
Returns:
out: Brain_Data instance of a mask with different integers indicating
different masks
"""
from nltools.data import Brain_Data
if not isinstance(mask, Brain_Data):
if isinstance(mask, nib.Nifti1Image):
mask = Brain_Data(mask, mask=custom_mask)
else:
raise ValueError("Make sure mask is a nibabel or Brain_Data "
"instance.")
if len(mask.shape()) > 1:
if len(mask) > 1:
out = mask.empty()
# Create list of masks and find any overlaps
m_list = []
for x in range(len(mask)):
m_list.append(mask[x].to_nifti())
intersect = intersect_masks(m_list, threshold=1, connected=False)
intersect = Brain_Data(
nib.Nifti1Image(np.abs(intersect.get_data() - 1),
intersect.get_affine()),
mask=custom_mask,
)
merge = []
if auto_label:
# Combine all masks into sequential order
# ignoring any areas of overlap
for i in range(len(m_list)):
merge.append(
np.multiply(
Brain_Data(m_list[i], mask=custom_mask).data,
intersect.data) * (i + 1))
out.data = np.sum(np.array(merge).T, 1).astype(int)
else:
# Collapse masks using value as label
for i in range(len(m_list)):
merge.append(
np.multiply(
Brain_Data(m_list[i], mask=custom_mask).data,
intersect.data))
out.data = np.sum(np.array(merge).T, 1)
return out
else:
warnings.warn("Doesn't need to be collapased")
def expand_mask(mask):
""" expand a mask with multiple integers into separate binary masks
Args:
mask: nibabel or Brain_Data instance
Returns:
out: Brain_Data instance of multiple binary masks
"""
from nltools.data import Brain_Data
if isinstance(mask,nib.Nifti1Image):
mask = Brain_Data(mask)
if not isinstance(mask,Brain_Data):
raise ValueError('Make sure mask is a nibabel or Brain_Data instance.')
mask.data = mask.data.astype(int)
tmp = []
for i in np.unique(mask.data):
tmp.append((mask.data==i)*1)
out = mask.empty()
out.data = np.array(tmp)
return out
def expand_mask(mask):
""" expand a mask with multiple integers into separate binary masks
Args:
mask: nibabel or Brain_Data instance
Returns:
out: Brain_Data instance of multiple binary masks
"""
from nltools.data import Brain_Data
if isinstance(mask, nib.Nifti1Image):
mask = Brain_Data(mask)
if not isinstance(mask, Brain_Data):
raise ValueError('Make sure mask is a nibabel or Brain_Data instance.')
mask.data = mask.data.astype(int)
tmp = []
for i in np.unique(mask.data):
tmp.append((mask.data == i) * 1)
out = mask.empty()
out.data = np.array(tmp)
return out
print 'Load Data: Elapsed: %.2f seconds' % (time() - tic) #Stop timer
###############################################################################
# Run Regression separately for each subject
tic = time() #Start Timer
start = 0
stop = n_trials
dat.X = pd.DataFrame(
{
'Intercept': np.ones(len(dat.Y)),
'X1': np.array(dat.Y).flatten()
},
index=None)
all = dat.empty()
for i in xrange(n_subs):
sub_out = dat[start:stop].regress()
start = start + n_trials
stop = stop + n_trials
tmp = sub_out['beta'].empty(data=False)[1]
all = all.append(tmp)
print 'Regression: Elapsed: %.2f seconds' % (time() - tic) # Stop timer
###############################################################################
# Run One sample t-test
tic = time() #Start Timer
l2 = all.ttest(threshold_dict={'fdr': .05})
l2['thr_t'].plot()
