def discrete_to_roi_features(self, fid, method='average'):
"""
Compute an ROI-level feature given the discrete features
Parameters
----------
fid(string) the discrete feature under consideration
method='average' the assessment method
Results
------
the computed roi-feature is returned
"""
if method not in['min','max','average','cumulated_average']:
raise ValueError, 'unknown method'
if method=='cumulated_average':
df = self.discrete_features[fid]
data = self.discrete_features[fid]
d0 = data[0]
if np.size(d0) == np.shape(d0)[0]:
np.reshape(d0,(np.size(d0),1))
fdim = d0.shape[1]
ldata = np.zeros((self.k,fdim))
for k in range(self.k):
dk = self.get_descendents(k)
card = np.sum(self.get_size()[dk])
for ch in dk:
ldata[k] += np.sum(data[ch],0)
ldata[k]/=card
self.set_roi_feature(fid,ldata)
else:
ldata = MultipleROI.discrete_to_roi_features(self, fid, method)
return ldata
roiPath2 = os.path.join(swd, "roi_blob_1.nii")
roi.make_image(roiPath2)
# --- 2.c take the blob closest to 'position as an ROI'
roiPath3 = os.path.join(swd, "blob_closest_to_%d_%d_%d.nii")%\
(position[0][0], position[0][1], position[0][2])
roi.from_position_and_image(blobPath, np.array(position))
roi.make_image(roiPath3)
# --- 2.d make a set of ROIs from all the blobs
mroi = MultipleROI( affine=affine, shape=shape)
mroi.from_labelled_image(blobPath)
roiPath4 = os.path.join(swd, "roi_all_blobs.nii")
mroi.make_image(roiPath4)
mroi.set_discrete_feature_from_image('activ', input_image)
mroi.discrete_to_roi_features('activ')
mroi.plot_roi_feature('activ')
# ---- 2.e the same, a bit more complex
mroi = MultipleROI( affine=affine, shape=shape)
mroi.as_multiple_balls(np.array([[-10.,0.,10.]]),np.array([7.0]))
mroi.from_labelled_image(blobPath,np.arange(1,20))
mroi.from_labelled_image(blobPath,np.arange(31,50))
roiPath5 = os.path.join(swd,"roi_some_blobs.nii")
mroi.set_discrete_feature_from_image('activ',input_image)
mroi.discrete_to_roi_features('activ')
valid = mroi.get_roi_feature('activ')>4.0
mroi.clean(valid)
mroi.make_image(roiPath5)
print "Wrote ROI mask images in %s, \n %s \n %s \n and %s" %\
########################################
positions = np.array([[60, -30, 5],[50, 27, 5]])
# in mm (here in the MNI space)
radii = np.array([8,6])
mroi = MultipleROI( affine=mask.get_affine(), shape=mask.get_shape())
mroi.as_multiple_balls(positions, radii)
# to save an image of the ROIs
mroi.make_image((op.join(swd, "roi.nii")))
# exact the time courses with ROIs
mroi.set_discrete_feature_from_image('signal', image=fmri_data)
# ROI average time courses
mroi.discrete_to_roi_features('signal')
# roi-level contrast average
mroi.set_discrete_feature_from_image('contrast', image=contrast_image)
mroi.discrete_to_roi_features('contrast')
########################################
# GLM analysis on the ROI average time courses
########################################
nreg = len(names)
ROI_tc = mroi.get_roi_feature('signal')
glm.fit(ROI_tc.T, X, method=method, model=model)
mp.figure()
