drift_model='cosine',
hfcut=128,
hrf_model=hrf_model,
add_regs=motion,
add_reg_names=add_reg_names)
########################################
# Create ROIs
########################################
positions = np.array([[60, -30, 5], [50, 27, 5]])
# in mm (here in the MNI space)
radii = np.array([8, 6])
domain = grid_domain_from_image(mask)
my_roi = mroi.subdomain_from_balls(domain, positions, radii)
# to save an image of the ROIs
save(my_roi.to_image(), path.join(write_dir, "roi.nii"))
#######################################
# Get the FMRI data
#######################################
fmri_data = surrogate_4d_dataset(mask=mask, dmtx=X)[0]
Y = fmri_data.get_data()[mask_array]
# artificially added signal in ROIs to make the example more meaningful
activation = 30 * (X.T[1] + .5 * X.T[0])
for (position, radius) in zip(positions, radii):
Y[((domain.coord - position)**2).sum(1) < radius**2 + 1] += activation
# compute the constrast image related to it
zvals = glm.contrast(contrast).z_score()
zmap = mask_array.astype(np.float)
zmap[mask_array] = zvals
########################################
# Create ROIs
########################################
positions = np.array([[60, -30, 5], [50, 27, 5]])
# in mm (here in the MNI space)
radii = np.array([8, 6])
domain = grid_domain_from_image(mask)
my_roi = mroi.subdomain_from_balls(domain, positions, radii)
# to save an image of the ROIs
save(my_roi.to_image(), op.join(write_dir, "roi.nii"))
# exact the time courses with ROIs
thresholded_fmri = fmri_data.get_data()[mask_array]
signal_feature = [thresholded_fmri[my_roi.select_id(id, roi=False)]
for id in my_roi.get_id()]
my_roi.set_feature('signal', signal_feature)
# ROI average time courses
my_roi.set_roi_feature('signal_avg', my_roi.representative_feature('signal'))
# roi-level contrast average
thresholded_contrast = zvals
# paths
data_dir = os.path.expanduser(os.path.join('~', '.nipy', 'tests', 'data'))
input_image = os.path.join(data_dir,'spmT_0029.nii.gz')
mask_image = os.path.join(data_dir,'mask.nii.gz')
#get_data_light.get_it()
# write dir
swd = tempfile.mkdtemp()
# -----------------------------------------------------
# example 1: create the ROI froma a given position
# -----------------------------------------------------
position = np.array([[0, 0, 0]])
domain = grid_domain_from_image(mask_image)
roi = mroi.subdomain_from_balls(domain, position, np.array([5.0]))
roi_domain = domain.mask(roi.label>-1)
roi_domain.to_image(os.path.join(swd, "myroi.nii"))
print 'Wrote an ROI mask image in %s' %os.path.join(swd, "myroi.nii")
# fixme: pot roi feature ...
# ----------------------------------------------------
# ---- example 2: create ROIs from a blob image ------
# ----------------------------------------------------
# --- 2.a create the blob image
# parameters
threshold = 3.0 # blob-forming threshold
smin = 5 # size threshold on bblobs
if (not path.exists(input_image)) or (not path.exists(mask_image)):
get_second_level_dataset()
# write directory
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
# -----------------------------------------------------
# example 1: create the ROI from a given position
# -----------------------------------------------------
position = np.array([[0, 0, 0]])
domain = grid_domain_from_image(mask_image)
roi = mroi.subdomain_from_balls(domain, position, np.array([5.0]))
roi_domain = domain.mask(roi.label > -1)
dom_img = roi_domain.to_image()
save(dom_img, path.join(write_dir, "myroi.nii"))
print('Wrote an ROI mask image in %s' % path.join(write_dir, "myroi.nii"))
# ----------------------------------------------------
# ---- example 2: create ROIs from a blob image ------
# ----------------------------------------------------
# --- 2.a create the blob image
# parameters
threshold = 3.0 # blob-forming threshold
smin = 10 # size threshold on bblobs