def test_opt_params():
# adression issue #104
subject_data = SubjectData()
for deleteorient in [True, False]:
for niigz2nii in [True, False]:
# this shouldn't crash
subject_data.sanitize(deleteorient=deleteorient,
niigz2nii=niigz2nii)
subject_data.output_dir = "/tmp/toto"
subject_data.sanitize()
assert_true(os.path.isdir(subject_data.output_dir))
subject_data._delete_orientation()
def _spm_auditory_subject_data():
""" Fetching auditory example into SubjectData Structure
"""
subject_data = fetch_spm_auditory()
subject_data['func'] = None
base_dir = os.path.dirname(subject_data['anat'])
subject_data.output_dir = os.path.join(base_dir, OUTPUT_DIR)
return SubjectData(**subject_data)
def test_opt_params():
# adression issue #104
subject_data = SubjectData()
for deleteorient in [True, False]:
for niigz2nii in [True, False]:
# this shouldn't crash
subject_data.sanitize(deleteorient=deleteorient,
niigz2nii=niigz2nii)
subject_data.output_dir = "/tmp/toto"
subject_data.sanitize()
assert_true(os.path.isdir(subject_data.output_dir))
subject_data._delete_orientation()
def _make_sd(func_filenames=None,
anat_filename=None,
ext=".nii.gz",
n_sessions=1,
make_sess_dirs=False,
func_ndim=4,
unique_func_names=False,
output_dir="/tmp/titi"):
if not func_filenames is None:
n_sessions = len(func_filenames)
func = [create_random_image(ndim=func_ndim) for _ in range(n_sessions)]
anat = create_random_image(ndim=3)
if anat_filename is None:
anat_filename = '%s/anat%s' % (DATA_DIR, ext)
_save_img(anat, anat_filename)
if not func_filenames is None:
for sess_func, filename in zip(func, func_filenames):
if isinstance(filename, str):
_save_img(sess_func, filename)
else:
vols = nibabel.four_to_three(sess_func)
for x, y in zip(vols, filename):
assert isinstance(y, str), type(y)
_save_img(x, y)
else:
func_filenames = []
for sess in range(n_sessions):
sess_dir = DATA_DIR if not make_sess_dirs else os.path.join(
DATA_DIR, "session%i" % sess)
if not os.path.exists(sess_dir):
os.makedirs(sess_dir)
func_filename = '%s/func%s%s' % (sess_dir, "_sess_%i_" % sess if (
n_sessions > 1 and unique_func_names) else "", ext)
_save_img(func[sess], func_filename)
func_filenames.append(func_filename)
sd = SubjectData(anat=anat_filename,
func=func_filenames,
output_dir=output_dir)
return sd
def do_preproc(funcfile, anatfile, subject, decimate=False):
""" Function that performs the preprocessing.
Parameters
----------
funcfile: str
the functional volume.
anatfile: str
the anatomical volume.
subject: str
the subject identifier.
decimate: bool, default False
if set reduce the input functional volume size (loose information).
Returns
-------
subject_data: object
a structure that contains the output results.
"""
# Grab the data
splitpath = anatfile.split(os.sep)
outdir = os.path.join(BIDS_DATA_DIR, "derivatives",
"spmpreproc_{0}".format(splitpath[-3]), subject)
if not os.path.isdir(outdir):
os.makedirs(outdir)
local_funcfile = os.path.join(outdir, os.path.basename(funcfile))
if not os.path.isfile(local_funcfile):
shutil.copy2(funcfile, local_funcfile)
if decimate:
im = nibabel.load(local_funcfile)
dec_im = nibabel.Nifti1Image(im.get_data()[..., :3], im.affine)
nibabel.save(dec_im, local_funcfile)
local_anatfile = os.path.join(outdir, os.path.basename(anatfile))
if not os.path.isfile(local_anatfile):
shutil.copy2(anatfile, local_anatfile)
#cwd = os.getcwd()
os.chdir(outdir)
subject_data = SubjectData(subject_id=subject,
func=local_funcfile,
anat=local_anatfile,
output_dir=outdir,
caching=True)
# Start processing
print('Starting pre-processing...')
subject_data = do_subject_preproc(subject_data,
deleteorient=False,
slice_timing=False,
ref_slice=0,
TR=2.5,
TA=None,
realign=True,
realign_reslice=True,
register_to_mean=True,
realign_software="spm",
coregister=True,
coregister_reslice=True,
coreg_anat_to_func=False,
coregister_software="spm",
segment=False,
normalize=True,
fwhm=0,
anat_fwhm=0,
func_write_voxel_sizes=[3, 3, 3],
anat_write_voxel_sizes=[1, 1, 1],
hardlink_output=True,
report=False,
tsdiffana=True,
parent_results_gallery=None,
last_stage=True,
preproc_undergone=None,
prepreproc_undergone="",
caching=True)
return subject_data
def test_init():
sd = SubjectData(anat='/tmp/anat.nii.gz', func='/tmp/func.nii.gz')
assert_equal(sd.anat, "/tmp/anat.nii.gz")
assert_equal(sd.func, "/tmp/func.nii.gz")
dataset_dir = sys.argv[1]
else:
dataset_dir = os.path.join(this_dir, "spm_multimodal_faces")
# fetch spm multimodal_faces data
subject_data = fetch_spm_multimodal_fmri()
dataset_dir = os.path.dirname(
os.path.dirname(os.path.dirname(subject_data.anat)))
# preprocess the data
subject_id = "sub001"
subject_data = SubjectData(output_dir=os.path.join(dataset_dir,
"pypreprocess_output",
subject_id),
subject_id=subject_id,
func=[subject_data.func1, subject_data.func2],
anat=subject_data.anat,
trials_ses1=subject_data.trials_ses1,
trials_ses2=subject_data.trials_ses2,
session_ids=["Session1", "Session2"])
subject_data = do_subject_preproc(subject_data,
realign=True,
coregister=True,
segment=True,
normalize=True)
# experimental paradigm meta-params
stats_start_time = time.ctime()
tr = 2.
drift_model = 'Cosine'
hrf_model = 'spm + derivative'