def main():
arguments = docopt(__doc__)
input_dir = arguments['<input_dir>']
rest_files = arguments['<rest_file>']
user_output_dir = arguments['--output_dir']
debug = arguments['--debug']
if debug:
logger.setLevel(logging.DEBUG)
ciftify.utils.log_arguments(arguments)
verify_wb_available()
verify_FSL_available()
if user_output_dir and os.listdir(user_output_dir):
logger.debug(
"Outputs found at {}. No work to do.".format(user_output_dir))
return
with TempDir() as temp:
brainmask = get_brainmask(input_dir)
wm_mask, csf_mask = generate_masks(input_dir, temp)
for image in rest_files:
if not os.path.exists(image):
logger.error(
"Rest file {} does not exist. Skipping".format(image))
continue
resampled_wm = resample_mask(image, wm_mask, temp)
resampled_csf = resample_mask(image, csf_mask, temp)
resampled_brainmask = resample_mask(image, brainmask, temp)
image_name = get_image_name(image)
output_path = get_output_path(user_output_dir, image)
wm_csv = os.path.join(output_path, image_name + '_WM.csv')
csf_csv = os.path.join(output_path, image_name + '_CSF.csv')
global_signal_csv = os.path.join(output_path,
image_name + '_GS.csv')
ciftify_meants(image,
resampled_wm,
wm_csv,
mask=resampled_brainmask)
ciftify_meants(image,
resampled_csf,
csf_csv,
mask=resampled_brainmask)
ciftify_meants(image, resampled_brainmask, global_signal_csv)
def load_hemisphere_data(filename, wb_structure, suppress_echo=False):
'''loads data from one hemisphere of dscalar,nii file'''
with TempDir() as little_tempdir:
## separate the cifti file into left and right surfaces
data_gii = os.path.join(little_tempdir, 'data.func.gii')
run([
'wb_command', '-cifti-separate', filename, 'COLUMN', '-metric',
wb_structure, data_gii
], suppress_echo)
# loads label table as dict and data as numpy array
data = load_gii_data(data_gii)
return data
def __split(self, output_loc, scene_file, logging, width, height):
with TempDir() as tmp_dir:
tmp_img = os.path.join(tmp_dir, "scene{}.png".format(self.index))
self.__show_scene(tmp_img, scene_file, logging, width, height)
with Image.open(tmp_img) as img:
half_the_height = height // 2
img_top = img.crop((0, 0, width, half_the_height))
img_btm = img.crop((0, half_the_height, width, height))
im2 = Image.new('RGBA', (int(width * 2), half_the_height))
im2.paste(img_top, (0, 0))
im2.paste(img_btm, (width, 0))
im2.save(output_loc)
return output_loc
def make_image(self,
output_loc,
scene_file,
logging='WARNING',
width=600,
height=400):
montage_cmd = ['montage', '-mode', 'concatenate', '-tile', self.layout]
with TempDir() as tmp_dir:
for scene in self.scenes:
tmp_path = os.path.join(tmp_dir, "{}.png".format(scene.name))
scene.make_image(tmp_path, scene_file, logging, width, height)
montage_cmd.append(tmp_path)
montage_cmd.append(output_loc)
run(montage_cmd)
self.path = output_loc
def __split(self, output_loc, scene_file, logging, width, height):
with TempDir() as tmp_dir:
tmp_img = os.path.join(tmp_dir, "scene{}.png".format(self.index))
self.__show_scene(tmp_img, scene_file, logging, width, height)
tmp_top = os.path.join(tmp_dir, 'top.png')
tmp_bottom = os.path.join(tmp_dir, 'bottom.png')
run(['convert', tmp_img, '-crop', '100x50%+0+0', tmp_top])
run(['convert', tmp_img, '-crop', '100x50%+0+200', tmp_bottom])
run([
'montage', '-mode', 'concatenate', '-tile', '2x1', tmp_top,
tmp_bottom, output_loc
])
return output_loc
def load_hemisphere_labels(filename, wb_structure, map_number=1):
'''separates dlabel file into left and right and loads label data'''
with TempDir() as little_tempdir:
## separate the cifti file into left and right surfaces
labels_gii = os.path.join(little_tempdir, 'data.label.gii')
run([
'wb_command', '-cifti-separate', filename, 'COLUMN', '-label',
wb_structure, labels_gii
])
# loads label table as dict and data as numpy array
gifti_img = nibabel.gifti.giftiio.read(labels_gii)
atlas_data = gifti_img.getArraysFromIntent('NIFTI_INTENT_LABEL')[
map_number - 1].data
atlas_dict = gifti_img.get_labeltable().get_labels_as_dict()
return atlas_data, atlas_dict
def load_surfaces(filename, suppress_echo=False):
'''
separate a cifti file into surfaces,
then loads the surface data
'''
## separate the cifti file into left and right surfaces
with TempDir() as tempdir:
L_data_surf = os.path.join(tempdir, 'Ldata.func.gii')
R_data_surf = os.path.join(tempdir, 'Rdata.func.gii')
run([
'wb_command', '-cifti-separate', filename, 'COLUMN', '-metric',
'CORTEX_LEFT', L_data_surf, '-metric', 'CORTEX_RIGHT', R_data_surf
],
suppress_echo=suppress_echo)
## load both surfaces and concatenate them together
Ldata = load_gii_data(L_data_surf)
Rdata = load_gii_data(R_data_surf)
return Ldata, Rdata
def get_surf_distances(surf,
orig_vertex,
radius_search=100,
dryrun=False,
suppress_echo=False):
'''
uses wb_command -surface-geodesic-distance command to measure
distance between two vertices on the surface
'''
with TempDir() as tmpdir:
surf_distance = os.path.join(tmpdir, "distancecalc.shape.gii")
run([
'wb_command', '-surface-geodesic-distance', surf,
str(orig_vertex), surf_distance, '-limit',
str(radius_search)
],
dryrun=dryrun,
suppress_echo=suppress_echo)
distances = load_gii_data(surf_distance)
return (distances)
def load_cifti(filename):
"""
Usage:
cifti, affine, header, dims = load_cifti(filename)
Loads a Cifti file (6 dimensions).
Returns:
a 2D matrix of voxels x timepoints,
"""
logger = logging.getLogger(__name__)
# load everything in
try:
cifti = nib.load(filename)
except:
logger.error("Cannot read {}".format(filename))
sys.exit(1)
## separate the cifti file into left and right surfaces
with TempDir() as tempdir:
L_data_surf = os.path.join(tempdir, 'Ldata.func.gii')
R_data_surf = os.path.join(tempdir, 'Rdata.func.gii')
vol_data_nii = os.path.join(tempdir, 'vol.nii.gz')
run([
'wb_command', '-cifti-separate', filename, 'COLUMN', '-metric',
'CORTEX_LEFT', L_data_surf, '-metric', 'CORTEX_RIGHT', R_data_surf,
'-volume-all', vol_data_nii
])
## load both surfaces and concatenate them together
Ldata = load_gii_data(L_data_surf)
Rdata = load_gii_data(R_data_surf)
voldata, _, _, _ = load_nifti(vol_data_nii)
cifti_data = np.vstack((Ldata, Rdata, voldata))
return cifti_data
