def test_genenerated_method_pickle_fail(self):
cls_dct = {
'add_sub_study_specs': [
SubStudySpec('ss1', BasicTestClass),
SubStudySpec('ss2', BasicTestClass)
],
'default_fileset_pipeline':
MultiStudy.translate('ss1', 'pipeline')
}
MultiGeneratedClass = MultiStudyMetaClass('MultiGeneratedClass',
(MultiStudy, ), cls_dct)
study = self.create_study(MultiGeneratedClass,
'multi_gen_cls',
inputs=[
FilesetSelector('ss1_fileset',
text_format, 'fileset'),
FilesetSelector('ss2_fileset',
text_format, 'fileset')
])
pkl_path = os.path.join(self.work_dir, 'multi_gen_cls.pkl')
with open(pkl_path, 'w') as f:
self.assertRaises(ArcanaCantPickleStudyError, pkl.dump, study, f)
def test_multi_study_generated_cls_pickle(self):
cls_dct = {
'add_sub_study_specs': [
SubStudySpec('ss1', BasicTestClass),
SubStudySpec('ss2', BasicTestClass)
]
}
MultiGeneratedClass = MultiStudyMetaClass('MultiGeneratedClass',
(MultiStudy, ), cls_dct)
study = self.create_study(MultiGeneratedClass,
'multi_gen_cls',
inputs=[
FilesetSelector('ss1_fileset',
text_format, 'fileset'),
FilesetSelector('ss2_fileset',
text_format, 'fileset')
])
pkl_path = os.path.join(self.work_dir, 'multi_gen_cls.pkl')
with open(pkl_path, 'wb') as f:
pkl.dump(study, f)
del MultiGeneratedClass
with open(pkl_path, 'rb') as f:
regen = pkl.load(f)
self.assertContentsEqual(regen.data('ss2_out_fileset'), 'foo')
def create_fmri_study_class(name,
t1,
epis,
epi_number,
echo_spacing,
fm_mag=None,
fm_phase=None,
run_regression=False):
inputs = []
dct = {}
data_specs = []
parameter_specs = []
output_files = []
distortion_correction = False
if fm_mag and fm_phase:
logger.info('Both magnitude and phase field map images provided. EPI '
'ditortion correction will be performed.')
distortion_correction = True
elif fm_mag or fm_phase:
logger.info(
'In order to perform EPI ditortion correction both magnitude '
'and phase field map images must be provided.')
else:
logger.info(
'No field map image provided. Distortion correction will not be'
'performed.')
study_specs = [SubStudySpec('t1', T1Study)]
ref_spec = {'t1_brain': 'coreg_ref_brain'}
inputs.append(
DatasetMatch('t1_primary', dicom_format, t1, is_regex=True, order=0))
epi_refspec = ref_spec.copy()
epi_refspec.update({
't1_wm_seg': 'coreg_ref_wmseg',
't1_preproc': 'coreg_ref_preproc',
'train_data': 'train_data'
})
study_specs.append(SubStudySpec('epi_0', FunctionalMRIStudy, epi_refspec))
if epi_number > 1:
epi_refspec.update({
't1_wm_seg': 'coreg_ref_wmseg',
't1_preproc': 'coreg_ref_preproc',
'train_data': 'train_data',
'epi_0_coreg_to_atlas_warp': 'coreg_to_atlas_warp',
'epi_0_coreg_to_atlas_mat': 'coreg_to_atlas_mat'
})
study_specs.extend(
SubStudySpec('epi_{}'.format(i), FunctionalMRIStudy, epi_refspec)
for i in range(1, epi_number))
for i in range(epi_number):
inputs.append(
DatasetMatch('epi_{}_primary'.format(i),
dicom_format,
epis,
order=i,
is_regex=True))
parameter_specs.append(
ParameterSpec('epi_{}_fugue_echo_spacing'.format(i), echo_spacing))
if distortion_correction:
inputs.extend(
DatasetMatch('epi_{}_field_map_mag'.format(i),
dicom_format,
fm_mag,
dicom_tags={IMAGE_TYPE_TAG: MAG_IMAGE_TYPE},
is_regex=True,
order=0) for i in range(epi_number))
inputs.extend(
DatasetMatch('epi_{}_field_map_phase'.format(i),
dicom_format,
fm_phase,
dicom_tags={IMAGE_TYPE_TAG: PHASE_IMAGE_TYPE},
is_regex=True,
order=0) for i in range(epi_number))
if run_regression:
output_files.extend('epi_{}_smoothed_ts'.format(i)
for i in range(epi_number))
else:
output_files.extend('epi_{}_fix_dir'.format(i)
for i in range(epi_number))
dct['add_sub_study_specs'] = study_specs
dct['add_data_specs'] = data_specs
dct['add_parameter_specs'] = parameter_specs
dct['__metaclass__'] = MultiStudyMetaClass
return (MultiStudyMetaClass(name, (FunctionalMRIMixin, ),
dct), inputs, output_files)
def create_motion_detection_class(name,
ref=None,
ref_type=None,
t1s=None,
t2s=None,
dwis=None,
epis=None,
pet_data_dir=None):
inputs = []
dct = {}
data_specs = []
run_pipeline = False
param_specs = [ParamSpec('ref_resampled_resolution', [1])]
if pet_data_dir is not None:
inputs.append(
InputFilesets('pet_data_dir', 'pet_data_dir', directory_format))
if not ref:
raise Exception('A reference image must be provided!')
if ref_type == 't1':
ref_study = T1Study
elif ref_type == 't2':
ref_study = T2Study
else:
raise Exception('{} is not a recognized ref_type!The available '
'ref_types are t1 or t2.'.format(ref_type))
study_specs = [SubStudySpec('ref', ref_study)]
ref_spec = {'coreg_ref_brain': 'ref_brain'}
inputs.append(InputFilesets('ref_magnitude', ref, dicom_format))
if t1s:
study_specs.extend([
SubStudySpec('t1_{}'.format(i), T1Study, ref_spec)
for i in range(len(t1s))
])
inputs.extend(
InputFilesets('t1_{}_magnitude'.format(i), t1_scan, dicom_format)
for i, t1_scan in enumerate(t1s))
run_pipeline = True
if t2s:
study_specs.extend([
SubStudySpec('t2_{}'.format(i), T2Study, ref_spec)
for i in range(len(t2s))
])
inputs.extend(
InputFilesets('t2_{}_magnitude'.format(i), t2_scan, dicom_format)
for i, t2_scan in enumerate(t2s))
run_pipeline = True
if epis:
epi_refspec = ref_spec.copy()
epi_refspec.update({
'coreg_ref_wmseg': 'ref_wm_seg',
'coreg_ref': 'ref_mag_preproc'
})
study_specs.extend(
SubStudySpec('epi_{}'.format(i), EpiSeriesStudy, epi_refspec)
for i in range(len(epis)))
inputs.extend(
InputFilesets('epi_{}_series'.format(i), epi_scan, dicom_format)
for i, epi_scan in enumerate(epis))
run_pipeline = True
if dwis:
unused_dwi = []
dwis_main = [x for x in dwis if x[-1] == '0']
dwis_ref = [x for x in dwis if x[-1] == '1']
dwis_opposite = [x for x in dwis if x[-1] == '-1']
b0_refspec = ref_spec.copy()
b0_refspec.update({
'coreg_ref_wmseg': 'ref_wm_seg',
'coreg_ref': 'ref_mag_preproc'
})
if dwis_main and not dwis_opposite:
logger.warning(
'No opposite phase encoding direction b0 provided. DWI '
'motion correction will be performed without distortion '
'correction. THIS IS SUB-OPTIMAL!')
study_specs.extend(
SubStudySpec('dwi_{}'.format(i), DwiStudy, ref_spec)
for i in range(len(dwis_main)))
inputs.extend(
InputFilesets('dwi_{}_series'.format(i), dwis_main_scan[0],
dicom_format)
for i, dwis_main_scan in enumerate(dwis_main))
if dwis_main and dwis_opposite:
study_specs.extend(
SubStudySpec('dwi_{}'.format(i), DwiStudy, ref_spec)
for i in range(len(dwis_main)))
inputs.extend(
InputFilesets('dwi_{}_series'.format(i), dwis_main[i][0],
dicom_format) for i in range(len(dwis_main)))
if len(dwis_main) <= len(dwis_opposite):
inputs.extend(
InputFilesets('dwi_{}_magnitude'.format(i),
dwis_opposite[i][0], dicom_format)
for i in range(len(dwis_main)))
else:
inputs.extend(
InputFilesets('dwi_{}_magnitude'.format(i),
dwis_opposite[0][0], dicom_format)
for i in range(len(dwis_main)))
if dwis_opposite and dwis_main and not dwis_ref:
study_specs.extend(
SubStudySpec('b0_{}'.format(i), DwiRefStudy, b0_refspec)
for i in range(len(dwis_opposite)))
inputs.extend(
InputFilesets('b0_{}_magnitude'.format(i), dwis_opposite[i][0],
dicom_format) for i in range(len(dwis_opposite)))
if len(dwis_opposite) <= len(dwis_main):
inputs.extend(
InputFilesets('b0_{}_reverse_phase'.format(i), dwis_main[i]
[0], dicom_format)
for i in range(len(dwis_opposite)))
else:
inputs.extend(
InputFilesets('b0_{}_reverse_phase'.format(i), dwis_main[0]
[0], dicom_format)
for i in range(len(dwis_opposite)))
elif dwis_opposite and dwis_ref:
min_index = min(len(dwis_opposite), len(dwis_ref))
study_specs.extend(
SubStudySpec('b0_{}'.format(i), DwiRefStudy, b0_refspec)
for i in range(min_index * 2))
inputs.extend(
InputFilesets('b0_{}_magnitude'.format(i), scan[0],
dicom_format)
for i, scan in enumerate(dwis_opposite[:min_index] +
dwis_ref[:min_index]))
inputs.extend(
InputFilesets('b0_{}_reverse_phase'.format(i), scan[0],
dicom_format)
for i, scan in enumerate(dwis_ref[:min_index] +
dwis_opposite[:min_index]))
unused_dwi = [
scan
for scan in dwis_ref[min_index:] + dwis_opposite[min_index:]
]
elif dwis_opposite or dwis_ref:
unused_dwi = [scan for scan in dwis_ref + dwis_opposite]
if unused_dwi:
logger.info(
'The following scans:\n{}\nwere not assigned during the DWI '
'motion detection initialization (probably a different number '
'of main DWI scans and b0 images was provided). They will be '
'processed os "other" scans.'.format('\n'.join(
s[0] for s in unused_dwi)))
study_specs.extend(
SubStudySpec('t2_{}'.format(i), T2Study, ref_spec)
for i in range(len(t2s),
len(t2s) + len(unused_dwi)))
inputs.extend(
InputFilesets('t2_{}_magnitude'.format(i), scan[0],
dicom_format)
for i, scan in enumerate(unused_dwi, start=len(t2s)))
run_pipeline = True
if not run_pipeline:
raise Exception('At least one scan, other than the reference, must be '
'provided!')
dct['add_substudy_specs'] = study_specs
dct['add_data_specs'] = data_specs
dct['__metaclass__'] = MultiStudyMetaClass
dct['add_param_specs'] = param_specs
return MultiStudyMetaClass(name, (MotionDetectionMixin, ), dct), inputs
def create_motion_correction_class(name,
ref=None,
ref_type=None,
t1s=None,
t2s=None,
dwis=None,
epis=None,
umap=None,
dynamic=False,
umap_ref=None,
pet_data_dir=None,
pet_recon_dir=None,
struct2align=None):
inputs = []
dct = {}
data_specs = []
run_pipeline = False
param_specs = [ParamSpec('ref_resampled_resolution', [1])]
switch_specs = []
if struct2align is not None:
struct_image = struct2align.split('/')[-1].split('.')[0]
if pet_data_dir is not None:
inputs.append(
InputFilesets('pet_data_dir', 'pet_data_dir', directory_format))
if pet_recon_dir is not None:
inputs.append(
InputFilesets('pet_data_reconstructed', 'pet_data_reconstructed',
directory_format))
if struct2align is not None:
inputs.append(
InputFilesets('struct2align', struct_image, nifti_gz_format))
if pet_data_dir is not None and pet_recon_dir is not None and dynamic:
output_data = 'dynamic_motion_correction_results'
param_specs.append(ParamSpec('dynamic_pet_mc', True))
if struct2align is not None:
inputs.append(
InputFilesets('struct2align', struct_image, nifti_gz_format))
elif (pet_recon_dir is not None and not dynamic):
output_data = 'static_motion_correction_results'
else:
output_data = 'motion_detection_output'
if not ref:
raise Exception('A reference image must be provided!')
if ref_type == 't1':
ref_study = T1Study
elif ref_type == 't2':
ref_study = T2Study
else:
raise Exception('{} is not a recognized ref_type!The available '
'ref_types are t1 or t2.'.format(ref_type))
study_specs = [SubStudySpec('ref', ref_study)]
ref_spec = {'ref_brain': 'coreg_ref_brain'}
inputs.append(InputFilesets('ref_primary', ref, dicom_format))
if umap_ref and umap:
if umap_ref.endswith('/'):
umap_ref = umap_ref.split('/')[-2]
else:
umap_ref = umap_ref.split('/')[-1]
if umap_ref in t1s:
umap_ref_study = T1Study
t1s.remove(umap_ref)
elif umap_ref in t2s:
umap_ref_study = T2Study
t2s.remove(umap_ref)
else:
umap_ref = None
if t1s:
study_specs.extend([
SubStudySpec('t1_{}'.format(i), T1Study, ref_spec)
for i in range(len(t1s))
])
inputs.extend(
InputFilesets('t1_{}_primary'.format(i), dicom_format, t1_scan)
for i, t1_scan in enumerate(t1s))
run_pipeline = True
if t2s:
study_specs.extend([
SubStudySpec('t2_{}'.format(i), T2Study, ref_spec)
for i in range(len(t2s))
])
inputs.extend(
InputFilesets('t2_{}_primary'.format(i), t2_scan, dicom_format)
for i, t2_scan in enumerate(t2s))
run_pipeline = True
if umap_ref and not umap:
logger.info(
'Umap not provided. The umap realignment will not be '
'performed. Umap_ref will be trated as {}'.format(umap_ref_study))
elif umap_ref and umap:
logger.info('Umap will be realigned to match the head position in '
'each frame.')
if type(umap) == list and len(umap) > 1:
logger.info('More than one umap provided. Only the first one will '
'be used.')
umap = umap[0]
study_specs.append(SubStudySpec('umap_ref', umap_ref_study, ref_spec))
inputs.append(InputFilesets('umap_ref_primary', dicom_format,
umap_ref))
inputs.append(InputFilesets('umap', dicom_format, umap))
run_pipeline = True
elif not umap_ref and umap:
logger.warning('Umap provided without corresponding reference image. '
'Realignment cannot be performed without umap_ref. Umap'
' will be ignored.')
if epis:
epi_refspec = ref_spec.copy()
epi_refspec.update({
'ref_wm_seg': 'coreg_ref_wmseg',
'ref_preproc': 'coreg_ref'
})
study_specs.extend(
SubStudySpec('epi_{}'.format(i), EpiSeriesStudy, epi_refspec)
for i in range(len(epis)))
inputs.extend(
InputFilesets('epi_{}_primary'.format(i), epi_scan, dicom_format)
for i, epi_scan in enumerate(epis))
run_pipeline = True
if dwis:
unused_dwi = []
dwis_main = [x for x in dwis if x[-1] == '0']
dwis_ref = [x for x in dwis if x[-1] == '1']
dwis_opposite = [x for x in dwis if x[-1] == '-1']
dwi_refspec = ref_spec.copy()
dwi_refspec.update({
'ref_wm_seg': 'coreg_ref_wmseg',
'ref_preproc': 'coreg_ref'
})
if dwis_main:
switch_specs.extend(
SwitchSpec('dwi_{}_brain_extract_method'.format(i), 'fsl', (
'mrtrix', 'fsl')) for i in range(len(dwis_main)))
if dwis_main and not dwis_opposite:
logger.warning(
'No opposite phase encoding direction b0 provided. DWI '
'motion correction will be performed without distortion '
'correction. THIS IS SUB-OPTIMAL!')
study_specs.extend(
SubStudySpec('dwi_{}'.format(i), DwiStudy, dwi_refspec)
for i in range(len(dwis_main)))
inputs.extend(
InputFilesets('dwi_{}_primary'.format(i), dwis_main_scan[0],
dicom_format)
for i, dwis_main_scan in enumerate(dwis_main))
if dwis_main and dwis_opposite:
study_specs.extend(
SubStudySpec('dwi_{}'.format(i), DwiStudy, dwi_refspec)
for i in range(len(dwis_main)))
inputs.extend(
InputFilesets('dwi_{}_primary'.format(i), dwis_main[i][0],
dicom_format) for i in range(len(dwis_main)))
if len(dwis_main) <= len(dwis_opposite):
inputs.extend(
InputFilesets('dwi_{}_dwi_reference'.format(i),
dwis_opposite[i][0], dicom_format)
for i in range(len(dwis_main)))
else:
inputs.extend(
InputFilesets('dwi_{}_dwi_reference'.format(i),
dwis_opposite[0][0], dicom_format)
for i in range(len(dwis_main)))
if dwis_opposite and dwis_main and not dwis_ref:
study_specs.extend(
SubStudySpec('b0_{}'.format(i), EpiSeriesStudy, dwi_refspec)
for i in range(len(dwis_opposite)))
inputs.extend(
InputFilesets('b0_{}_primary'.format(i), dwis_opposite[i][0],
dicom_format) for i in range(len(dwis_opposite)))
if len(dwis_opposite) <= len(dwis_main):
inputs.extend(
InputFilesets('b0_{}_reverse_phase'.format(i), dwis_main[i]
[0], dicom_format)
for i in range(len(dwis_opposite)))
else:
inputs.extend(
InputFilesets('b0_{}_reverse_phase'.format(i), dwis_main[0]
[0], dicom_format)
for i in range(len(dwis_opposite)))
elif dwis_opposite and dwis_ref:
min_index = min(len(dwis_opposite), len(dwis_ref))
study_specs.extend(
SubStudySpec('b0_{}'.format(i), EpiSeriesStudy, dwi_refspec)
for i in range(min_index * 2))
inputs.extend(
InputFilesets('b0_{}_primary'.format(i), scan[0], dicom_format)
for i, scan in enumerate(dwis_opposite[:min_index] +
dwis_ref[:min_index]))
inputs.extend(
InputFilesets('b0_{}_reverse_phase'.format(i), scan[0],
dicom_format)
for i, scan in enumerate(dwis_ref[:min_index] +
dwis_opposite[:min_index]))
unused_dwi = [
scan
for scan in dwis_ref[min_index:] + dwis_opposite[min_index:]
]
elif dwis_opposite or dwis_ref:
unused_dwi = [scan for scan in dwis_ref + dwis_opposite]
if unused_dwi:
logger.info(
'The following scans:\n{}\nwere not assigned during the DWI '
'motion detection initialization (probably a different number '
'of main DWI scans and b0 images was provided). They will be '
'processed os "other" scans.'.format('\n'.join(
s[0] for s in unused_dwi)))
study_specs.extend(
SubStudySpec('t2_{}'.format(i), T2Study, ref_spec)
for i in range(len(t2s),
len(t2s) + len(unused_dwi)))
inputs.extend(
InputFilesets('t2_{}_primary'.format(i), scan[0], dicom_format)
for i, scan in enumerate(unused_dwi, start=len(t2s)))
run_pipeline = True
if not run_pipeline:
raise Exception('At least one scan, other than the reference, must be '
'provided!')
dct['add_substudy_specs'] = study_specs
dct['add_data_specs'] = data_specs
dct['__metaclass__'] = MultiStudyMetaClass
dct['add_param_specs'] = param_specs
dct['add_switch_specs'] = switch_specs
return (MultiStudyMetaClass(name, (MotionDetectionMixin, ),
dct), inputs, output_data)