class RequirementsStudy(Study):
__metaclass__ = StudyMetaClass
add_data_specs = [
DatasetSpec('ones', nifti_gz_format),
DatasetSpec('twos', nifti_gz_format, 'pipeline')
]
def pipeline(self):
pipeline = self.create_pipeline(
name='pipeline',
inputs=[DatasetSpec('ones', nifti_gz_format)],
outputs=[DatasetSpec('twos', nifti_gz_format)],
desc=("A pipeline that tests loading of requirements"),
version=1,
citations=[],
)
# Convert from DICOM to NIfTI.gz format on input
input_merge = pipeline.create_node(Merge(2), "input_merge")
maths = pipeline.create_node(MRMath(),
"maths",
requirements=[(dummy1_req, dummy2_req,
mrtrix3_req),
dcm2niix1_req])
pipeline.connect_input('ones', input_merge, 'in1')
pipeline.connect_input('ones', input_merge, 'in2')
pipeline.connect(input_merge, 'out', maths, 'in_files')
maths.inputs.operation = 'sum'
pipeline.connect_output('twos', maths, 'out_file')
pipeline.assert_connected()
return pipeline
def combined_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='combined',
inputs=[DatasetSpec('ss1_z', mrtrix_format),
DatasetSpec('full_e', mrtrix_format),
DatasetSpec('partial_ss2_z', mrtrix_format)],
outputs=[DatasetSpec('g', mrtrix_format)],
desc=(
"A dummy pipeline used to test MultiMultiStudy class"),
version=1,
citations=[],
**kwargs)
merge = pipeline.create_node(Merge(3), name="merge")
mrmath = pipeline.create_node(MRMath(), name="mrmath",
requirements=[mrtrix3_req])
mrmath.inputs.operation = pipeline.option('combined_op')
# Connect inputs
pipeline.connect_input('ss1_z', merge, 'in1')
pipeline.connect_input('full_e', merge, 'in2')
pipeline.connect_input('partial_ss2_z', merge, 'in3')
# Connect nodes
pipeline.connect(merge, 'out', mrmath, 'in_files')
# Connect outputs
pipeline.connect_output('g', mrmath, 'out_file')
return pipeline
def pipeline1(self, **kwargs):
pipeline = self.create_pipeline(
name='pipeline1',
inputs=[DatasetSpec('start', nifti_gz_format)],
outputs=[
DatasetSpec('derived1_1', nifti_gz_format),
DatasetSpec('derived1_2', nifti_gz_format)
],
desc="A dummy pipeline used to test 'run_pipeline' method",
version=1,
citations=[],
**kwargs)
if not pipeline.option('pipeline_option'):
raise Exception("Pipeline option was not cascaded down to "
"pipeline1")
mrconvert = pipeline.create_node(MRConvert(),
name="convert1",
requirements=[mrtrix3_req])
mrconvert2 = pipeline.create_node(MRConvert(),
name="convert2",
requirements=[mrtrix3_req])
# Connect inputs
pipeline.connect_input('start', mrconvert, 'in_file')
pipeline.connect_input('start', mrconvert2, 'in_file')
# Connect outputs
pipeline.connect_output('derived1_1', mrconvert, 'out_file')
pipeline.connect_output('derived1_2', mrconvert2, 'out_file')
return pipeline
def pipeline2(self, **kwargs):
pipeline = self.create_pipeline(
name='pipeline2',
inputs=[
DatasetSpec('start', nifti_gz_format),
DatasetSpec('derived1_1', nifti_gz_format)
],
outputs=[DatasetSpec('derived2', nifti_gz_format)],
desc="A dummy pipeline used to test 'run_pipeline' method",
version=1,
citations=[],
**kwargs)
if not pipeline.option('pipeline_option'):
raise Exception("Pipeline option was not cascaded down to "
"pipeline2")
mrmath = pipeline.create_node(MRCat(),
name="mrcat",
requirements=[mrtrix3_req])
mrmath.inputs.axis = 0
# Connect inputs
pipeline.connect_input('start', mrmath, 'first_scan')
pipeline.connect_input('derived1_1', mrmath, 'second_scan')
# Connect outputs
pipeline.connect_output('derived2', mrmath, 'out_file')
return pipeline
class TestStudy(Study):
__metaclass__ = StudyMetaClass
add_data_specs = [
DatasetSpec('dataset1', nifti_gz_format),
DatasetSpec('dataset2', nifti_gz_format),
DatasetSpec('dataset3', nifti_gz_format),
DatasetSpec('dataset5', nifti_gz_format)
]
def pipeline(self):
pipeline = self.create_pipeline(
name='pipeline',
inputs=[DatasetSpec('mrtrix', mrtrix_format),
DatasetSpec('nifti_gz', nifti_gz_format),
DatasetSpec('dicom', nifti_gz_format),
DatasetSpec('directory', directory_format),
DatasetSpec('zip', directory_format)],
outputs=[DatasetSpec('nifti_gz_from_dicom', nifti_gz_format),
DatasetSpec('mrtrix_from_nifti_gz', nifti_gz_format),
DatasetSpec('nifti_from_mrtrix', mrtrix_format),
DatasetSpec('directory_from_zip', directory_format),
DatasetSpec('zip_from_directory', directory_format)],
desc=("A pipeline that tests out various data format "
"conversions"),
version=1,
citations=[],)
# Convert from DICOM to NIfTI.gz format on input
nifti_gz_from_dicom = pipeline.create_node(
IdentityInterface(fields=['file']), "nifti_gz_from_dicom")
pipeline.connect_input('dicom', nifti_gz_from_dicom,
'file')
pipeline.connect_output('nifti_gz_from_dicom', nifti_gz_from_dicom,
'file')
# Convert from NIfTI.gz to MRtrix format on output
mrtrix_from_nifti_gz = pipeline.create_node(
IdentityInterface(fields=['file']), name='mrtrix_from_nifti_gz')
pipeline.connect_input('nifti_gz', mrtrix_from_nifti_gz,
'file')
pipeline.connect_output('mrtrix_from_nifti_gz', mrtrix_from_nifti_gz,
'file')
# Convert from MRtrix to NIfTI format on output
nifti_from_mrtrix = pipeline.create_node(
IdentityInterface(fields=['file']), 'nifti_from_mrtrix')
pipeline.connect_input('mrtrix', nifti_from_mrtrix,
'file')
pipeline.connect_output('nifti_from_mrtrix', nifti_from_mrtrix,
'file')
# Convert from zip file to directory format on input
directory_from_zip = pipeline.create_node(
IdentityInterface(fields=['file']), 'directory_from_zip')
pipeline.connect_input('zip', directory_from_zip,
'file')
pipeline.connect_output('directory_from_zip', directory_from_zip,
'file')
# Convert from NIfTI.gz to MRtrix format on output
zip_from_directory = pipeline.create_node(
IdentityInterface(fields=['file']), 'zip_from_directory')
pipeline.connect_input('directory', zip_from_directory,
'file')
pipeline.connect_output('zip_from_directory', zip_from_directory,
'file')
pipeline.assert_connected()
return pipeline
class StudyB(Study):
__metaclass__ = StudyMetaClass
add_data_specs = [
DatasetSpec('w', mrtrix_format),
DatasetSpec('x', mrtrix_format),
DatasetSpec('y', mrtrix_format, 'pipeline_beta'),
DatasetSpec('z', mrtrix_format, 'pipeline_beta')]
add_option_specs = [
OptionSpec('o1', 10),
OptionSpec('o2', '20'),
OptionSpec('o3', 30.0),
OptionSpec('product_op', 'not-specified')] # Needs to be set to 'product' @IgnorePep8
def pipeline_beta(self, **kwargs): # @UnusedVariable
pipeline = self.create_pipeline(
name='pipeline_beta',
inputs=[DatasetSpec('w', mrtrix_format),
DatasetSpec('x', mrtrix_format)],
outputs=[DatasetSpec('y', mrtrix_format),
DatasetSpec('z', mrtrix_format)],
desc="A dummy pipeline used to test MultiStudy class",
version=1,
citations=[],
**kwargs)
merge1 = pipeline.create_node(Merge(2), name='merge1')
merge2 = pipeline.create_node(Merge(2), name='merge2')
merge3 = pipeline.create_node(Merge(2), name='merge3')
mrsum1 = pipeline.create_node(MRMath(), name="mrsum1",
requirements=[mrtrix3_req])
mrsum1.inputs.operation = 'sum'
mrsum2 = pipeline.create_node(MRMath(), name="mrsum2",
requirements=[mrtrix3_req])
mrsum2.inputs.operation = 'sum'
mrproduct = pipeline.create_node(MRMath(), name="mrproduct",
requirements=[mrtrix3_req])
mrproduct.inputs.operation = pipeline.option('product_op')
# Connect inputs
pipeline.connect_input('w', merge1, 'in1')
pipeline.connect_input('x', merge1, 'in2')
pipeline.connect_input('x', merge2, 'in1')
# Connect nodes
pipeline.connect(merge1, 'out', mrsum1, 'in_files')
pipeline.connect(mrsum1, 'out_file', merge2, 'in2')
pipeline.connect(merge2, 'out', mrsum2, 'in_files')
pipeline.connect(mrsum1, 'out_file', merge3, 'in1')
pipeline.connect(mrsum2, 'out_file', merge3, 'in2')
pipeline.connect(merge3, 'out', mrproduct, 'in_files')
# Connect outputs
pipeline.connect_output('y', mrsum2, 'out_file')
pipeline.connect_output('z', mrproduct, 'out_file')
return pipeline
def pipeline1(self):
pipeline = self.create_pipeline(
'pipeline1',
inputs=[DatasetSpec('required', text_format)],
outputs=[DatasetSpec('derivable', text_format)],
desc="",
citations=[],
version=1)
identity = pipeline.create_node(IdentityInterface(['a']), 'identity')
pipeline.connect_input('required', identity, 'a')
pipeline.connect_output('derivable', identity, 'a')
return pipeline
def pipeline4(self, **kwargs):
pipeline = self.create_pipeline(
'pipeline4',
inputs=[DatasetSpec('wrong_option', text_format)],
outputs=[DatasetSpec('wrong_option2', text_format)],
desc="",
citations=[],
version=1,
**kwargs)
identity = pipeline.create_node(IdentityInterface(['a']), 'identity')
pipeline.connect_input('wrong_option', identity, 'a')
pipeline.connect_output('wrong_option2', identity, 'a')
return pipeline
class TestMatchStudy(Study):
__metaclass__ = StudyMetaClass
add_data_specs = [
DatasetSpec('gre_phase', dicom_format),
DatasetSpec('gre_mag', dicom_format)
]
def dummy_pipeline1(self):
pass
def dummy_pipeline2(self):
pass
def pipeline(self):
pipeline = self.create_pipeline(
name='pipeline',
inputs=[DatasetSpec('input_dataset', nifti_gz_format)],
outputs=[DatasetSpec('output_dataset', nifti_gz_format)],
desc=("A dummy pipeline used to test dicom-to-nifti "
"conversion method"),
version=1,
citations=[])
identity = pipeline.create_node(IdentityInterface(['field']),
name='identity')
# Connect inputs
pipeline.connect_input('input_dataset', identity, 'field')
# Connect outputs
pipeline.connect_output('output_dataset', identity, 'field')
return pipeline
def pipeline3(self, **kwargs):
pipeline = self.create_pipeline(
name='pipeline3',
inputs=[DatasetSpec('derived2', nifti_gz_format)],
outputs=[DatasetSpec('derived3', nifti_gz_format)],
desc="A dummy pipeline used to test 'run_pipeline' method",
version=1,
citations=[],
**kwargs)
mrconvert = pipeline.create_node(MRConvert(),
name="convert",
requirements=[mrtrix3_req])
# Connect inputs
pipeline.connect_input('derived2', mrconvert, 'in_file')
# Connect outputs
pipeline.connect_output('derived3', mrconvert, 'out_file')
return pipeline
def pipeline2(self):
pipeline = self.create_pipeline(
'pipeline2',
inputs=[
DatasetSpec('required', text_format),
DatasetSpec('optional', text_format)
],
outputs=[DatasetSpec('missing_input', text_format)],
desc="",
citations=[],
version=1)
identity = pipeline.create_node(IdentityInterface(['a', 'b']),
'identity')
pipeline.connect_input('required', identity, 'a')
pipeline.connect_input('optional', identity, 'b')
pipeline.connect_output('missing_input', identity, 'a')
return pipeline
class ExistingPrereqStudy(Study):
__metaclass__ = StudyMetaClass
add_data_specs = [
DatasetSpec('start', mrtrix_format),
DatasetSpec('tens', mrtrix_format, 'tens_pipeline'),
DatasetSpec('hundreds', mrtrix_format, 'hundreds_pipeline'),
DatasetSpec('thousands', mrtrix_format, 'thousands_pipeline')
]
def pipeline_factory(self, incr, input, output): # @ReservedAssignment
pipeline = self.create_pipeline(
name=output,
inputs=[DatasetSpec(input, mrtrix_format)],
outputs=[DatasetSpec(output, mrtrix_format)],
desc=("A dummy pipeline used to test 'partial-complete' method"),
version=1,
citations=[])
# Nodes
operands = pipeline.create_node(Merge(2), name='merge')
mult = pipeline.create_node(MRCalc(),
name="convert1",
requirements=[mrtrix3_req])
operands.inputs.in2 = incr
mult.inputs.operation = 'add'
# Connect inputs
pipeline.connect_input(input, operands, 'in1')
# Connect inter-nodes
pipeline.connect(operands, 'out', mult, 'operands')
# Connect outputs
pipeline.connect_output(output, mult, 'out_file')
return pipeline
def tens_pipeline(self, **kwargs): # @UnusedVariable
return self.pipeline_factory(10, 'start', 'tens')
def hundreds_pipeline(self, **kwargs): # @UnusedVariable
return self.pipeline_factory(100, 'tens', 'hundreds')
def thousands_pipeline(self, **kwargs): # @UnusedVariable
return self.pipeline_factory(1000, 'hundreds', 'thousands')
def pipeline3(self, **kwargs):
outputs = [DatasetSpec('another_derivable', text_format)]
switch = self.pre_option('switch', 'pipeline3', **kwargs)
if switch:
outputs.append(DatasetSpec('wrong_option', text_format))
pipeline = self.create_pipeline(
'pipeline3',
inputs=[DatasetSpec('required', text_format)],
outputs=outputs,
desc="",
citations=[],
version=1)
identity = pipeline.create_node(IdentityInterface(['a', 'b']),
'identity')
pipeline.connect_input('required', identity, 'a')
pipeline.connect_input('required', identity, 'b')
pipeline.connect_output('another_derivable', identity, 'a')
if switch:
pipeline.connect_output('wrong_option', identity, 'b')
return pipeline
class PartialMultiStudy(MultiStudy):
__metaclass__ = MultiStudyMetaClass
add_sub_study_specs = [
SubStudySpec('ss1', StudyA,
{'a': 'x', 'b': 'y', 'p1': 'o1'}),
SubStudySpec('ss2', StudyB,
{'b': 'w', 'c': 'x', 'p1': 'o1'})]
add_data_specs = [
DatasetSpec('a', mrtrix_format),
DatasetSpec('b', mrtrix_format),
DatasetSpec('c', mrtrix_format)]
pipeline_alpha_trans = MultiStudy.translate(
'ss1', 'pipeline_alpha')
add_option_specs = [
OptionSpec('p1', 1000)]
def visit_summary_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name="visit_summary",
inputs=[DatasetSpec('ones_slice', mrtrix_format)],
outputs=[DatasetSpec('visit_summary', mrtrix_format)],
desc=("Test of project summary variables"),
version=1,
citations=[],
**kwargs)
mrmath = pipeline.create_join_visits_node(MRMath(),
'in_files',
'mrmath',
requirements=[mrtrix3_req])
mrmath.inputs.operation = 'sum'
# Connect inputs
pipeline.connect_input('ones_slice', mrmath, 'in_files')
# Connect outputs
pipeline.connect_output('visit_summary', mrmath, 'out_file')
pipeline.assert_connected()
return pipeline
def test_dataset_and_field(self):
objs = [
DatasetSpec('a', nifti_gz_format, 'dummy_pipeline1'),
FieldSpec('b', int, 'dummy_pipeline2')
]
for i, obj in enumerate(objs):
fname = os.path.join(self.pkl_dir, '{}.pkl'.format(i))
with open(fname, 'w') as f:
pkl.dump(obj, f)
with open(fname) as f:
re_obj = pkl.load(f)
self.assertEqual(obj, re_obj)
def pipeline4(self, **kwargs):
pipeline = self.create_pipeline(
name='pipeline4',
inputs=[
DatasetSpec('derived1_2', nifti_gz_format),
DatasetSpec('derived3', nifti_gz_format)
],
outputs=[DatasetSpec('derived4', nifti_gz_format)],
desc="A dummy pipeline used to test 'run_pipeline' method",
version=1,
citations=[],
**kwargs)
mrmath = pipeline.create_node(MRCat(),
name="mrcat",
requirements=[mrtrix3_req])
mrmath.inputs.axis = 0
# Connect inputs
pipeline.connect_input('derived1_2', mrmath, 'first_scan')
pipeline.connect_input('derived3', mrmath, 'second_scan')
# Connect outputs
pipeline.connect_output('derived4', mrmath, 'out_file')
return pipeline
def pipeline_factory(self, incr, input, output): # @ReservedAssignment
pipeline = self.create_pipeline(
name=output,
inputs=[DatasetSpec(input, mrtrix_format)],
outputs=[DatasetSpec(output, mrtrix_format)],
desc=("A dummy pipeline used to test 'partial-complete' method"),
version=1,
citations=[])
# Nodes
operands = pipeline.create_node(Merge(2), name='merge')
mult = pipeline.create_node(MRCalc(),
name="convert1",
requirements=[mrtrix3_req])
operands.inputs.in2 = incr
mult.inputs.operation = 'add'
# Connect inputs
pipeline.connect_input(input, operands, 'in1')
# Connect inter-nodes
pipeline.connect(operands, 'out', mult, 'operands')
# Connect outputs
pipeline.connect_output(output, mult, 'out_file')
return pipeline
def pipeline_alpha(self, **kwargs): # @UnusedVariable
pipeline = self.create_pipeline(
name='pipeline_alpha',
inputs=[DatasetSpec('x', mrtrix_format),
DatasetSpec('y', mrtrix_format)],
outputs=[DatasetSpec('z', mrtrix_format)],
desc="A dummy pipeline used to test MultiStudy class",
version=1,
citations=[],
**kwargs)
merge = pipeline.create_node(Merge(2), name="merge")
mrmath = pipeline.create_node(MRMath(), name="mrmath",
requirements=[mrtrix3_req])
mrmath.inputs.operation = 'sum'
# Connect inputs
pipeline.connect_input('x', merge, 'in1')
pipeline.connect_input('y', merge, 'in2')
# Connect nodes
pipeline.connect(merge, 'out', mrmath, 'in_files')
# Connect outputs
pipeline.connect_output('z', mrmath, 'out_file')
return pipeline
def pipeline_beta(self, **kwargs): # @UnusedVariable
pipeline = self.create_pipeline(
name='pipeline_beta',
inputs=[DatasetSpec('w', mrtrix_format),
DatasetSpec('x', mrtrix_format)],
outputs=[DatasetSpec('y', mrtrix_format),
DatasetSpec('z', mrtrix_format)],
desc="A dummy pipeline used to test MultiStudy class",
version=1,
citations=[],
**kwargs)
merge1 = pipeline.create_node(Merge(2), name='merge1')
merge2 = pipeline.create_node(Merge(2), name='merge2')
merge3 = pipeline.create_node(Merge(2), name='merge3')
mrsum1 = pipeline.create_node(MRMath(), name="mrsum1",
requirements=[mrtrix3_req])
mrsum1.inputs.operation = 'sum'
mrsum2 = pipeline.create_node(MRMath(), name="mrsum2",
requirements=[mrtrix3_req])
mrsum2.inputs.operation = 'sum'
mrproduct = pipeline.create_node(MRMath(), name="mrproduct",
requirements=[mrtrix3_req])
mrproduct.inputs.operation = pipeline.option('product_op')
# Connect inputs
pipeline.connect_input('w', merge1, 'in1')
pipeline.connect_input('x', merge1, 'in2')
pipeline.connect_input('x', merge2, 'in1')
# Connect nodes
pipeline.connect(merge1, 'out', mrsum1, 'in_files')
pipeline.connect(mrsum1, 'out_file', merge2, 'in2')
pipeline.connect(merge2, 'out', mrsum2, 'in_files')
pipeline.connect(mrsum1, 'out_file', merge3, 'in1')
pipeline.connect(mrsum2, 'out_file', merge3, 'in2')
pipeline.connect(merge3, 'out', mrproduct, 'in_files')
# Connect outputs
pipeline.connect_output('y', mrsum2, 'out_file')
pipeline.connect_output('z', mrproduct, 'out_file')
return pipeline
def subject_ids_access_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='subject_ids_access',
inputs=[],
outputs=[DatasetSpec('subject_ids', text_format)],
desc=("A dummy pipeline used to test access to 'subject' IDs"),
version=1,
citations=[],
**kwargs)
subjects_to_file = pipeline.create_join_subjects_node(
IteratorToFile(), name='subjects_to_file', joinfield='ids')
pipeline.connect_subject_id(subjects_to_file, 'ids')
pipeline.connect_output('subject_ids', subjects_to_file, 'out_file')
return pipeline
class MultiMultiStudy(MultiStudy):
__metaclass__ = MultiStudyMetaClass
add_sub_study_specs = [
SubStudySpec('ss1', StudyA, {}),
SubStudySpec('full', FullMultiStudy),
SubStudySpec('partial', PartialMultiStudy)]
add_data_specs = [
DatasetSpec('g', mrtrix_format, 'combined_pipeline')]
add_option_specs = [
OptionSpec('combined_op', 'sum')]
def combined_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='combined',
inputs=[DatasetSpec('ss1_z', mrtrix_format),
DatasetSpec('full_e', mrtrix_format),
DatasetSpec('partial_ss2_z', mrtrix_format)],
outputs=[DatasetSpec('g', mrtrix_format)],
desc=(
"A dummy pipeline used to test MultiMultiStudy class"),
version=1,
citations=[],
**kwargs)
merge = pipeline.create_node(Merge(3), name="merge")
mrmath = pipeline.create_node(MRMath(), name="mrmath",
requirements=[mrtrix3_req])
mrmath.inputs.operation = pipeline.option('combined_op')
# Connect inputs
pipeline.connect_input('ss1_z', merge, 'in1')
pipeline.connect_input('full_e', merge, 'in2')
pipeline.connect_input('partial_ss2_z', merge, 'in3')
# Connect nodes
pipeline.connect(merge, 'out', mrmath, 'in_files')
# Connect outputs
pipeline.connect_output('g', mrmath, 'out_file')
return pipeline
class TestLocalInterfacePickle(TestCase):
datasets = [DatasetSpec('a', nifti_gz_format)]
fields = [FieldSpec('b', int)]
def setUp(self):
self.tmp_dir = tempfile.mkdtemp()
self.pkl_dir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.tmp_dir)
shutil.rmtree(self.pkl_dir)
def test_source(self):
source = LocalSource('a_study', self.datasets, self.fields,
base_dir=self.tmp_dir)
fname = os.path.join(self.pkl_dir, 'source.pkl')
with open(fname, 'w') as f:
pkl.dump(source, f)
with open(fname) as f:
re_source = pkl.load(f)
self.assertEqual(source, re_source)
class FullMultiStudy(MultiStudy):
__metaclass__ = MultiStudyMetaClass
add_sub_study_specs = [
SubStudySpec('ss1', StudyA,
{'a': 'x',
'b': 'y',
'd': 'z',
'p1': 'o1',
'p2': 'o2',
'p3': 'o3'}),
SubStudySpec('ss2', StudyB,
{'b': 'w',
'c': 'x',
'e': 'y',
'f': 'z',
'q1': 'o1',
'q2': 'o2',
'p3': 'o3',
'required_op': 'product_op'})]
add_data_specs = [
DatasetSpec('a', mrtrix_format),
DatasetSpec('b', mrtrix_format),
DatasetSpec('c', mrtrix_format),
DatasetSpec('d', mrtrix_format, 'pipeline_alpha_trans'),
DatasetSpec('e', mrtrix_format, 'pipeline_beta_trans'),
DatasetSpec('f', mrtrix_format, 'pipeline_beta_trans')]
add_option_specs = [
OptionSpec('p1', 100),
OptionSpec('p2', '200'),
OptionSpec('p3', 300.0),
OptionSpec('q1', 150),
OptionSpec('q2', '250'),
OptionSpec('required_op', 'still-not-specified')]
pipeline_alpha_trans = MultiStudy.translate(
'ss1', 'pipeline_alpha')
pipeline_beta_trans = MultiStudy.translate(
'ss2', 'pipeline_beta')
class DummyStudy(Study):
__metaclass__ = StudyMetaClass
add_data_specs = [
DatasetSpec('source1', nifti_gz_format),
DatasetSpec('source2', nifti_gz_format),
DatasetSpec('source3', nifti_gz_format),
DatasetSpec('source4', nifti_gz_format,
optional=True),
DatasetSpec('sink1', nifti_gz_format, 'dummy_pipeline'),
DatasetSpec('sink3', nifti_gz_format, 'dummy_pipeline'),
DatasetSpec('sink4', nifti_gz_format, 'dummy_pipeline'),
DatasetSpec('subject_sink', nifti_gz_format, 'dummy_pipeline',
frequency='per_subject'),
DatasetSpec('visit_sink', nifti_gz_format, 'dummy_pipeline',
frequency='per_visit'),
DatasetSpec('project_sink', nifti_gz_format, 'dummy_pipeline',
frequency='per_project'),
DatasetSpec('resink1', nifti_gz_format, 'dummy_pipeline'),
DatasetSpec('resink2', nifti_gz_format, 'dummy_pipeline'),
DatasetSpec('resink3', nifti_gz_format, 'dummy_pipeline')]
def dummy_pipeline(self):
pass
class TestDerivableStudy(Study):
__metaclass__ = StudyMetaClass
add_data_specs = [
DatasetSpec('required', text_format),
DatasetSpec('optional', text_format, optional=True),
DatasetSpec('derivable', text_format, 'pipeline1'),
DatasetSpec('missing_input', text_format, 'pipeline2'),
DatasetSpec('another_derivable', text_format, 'pipeline3'),
DatasetSpec('wrong_option', text_format, 'pipeline3'),
DatasetSpec('wrong_option2', text_format, 'pipeline4')
]
add_option_specs = [OptionSpec('switch', 0)]
def pipeline1(self):
pipeline = self.create_pipeline(
'pipeline1',
inputs=[DatasetSpec('required', text_format)],
outputs=[DatasetSpec('derivable', text_format)],
desc="",
citations=[],
version=1)
identity = pipeline.create_node(IdentityInterface(['a']), 'identity')
pipeline.connect_input('required', identity, 'a')
pipeline.connect_output('derivable', identity, 'a')
return pipeline
def pipeline2(self):
pipeline = self.create_pipeline(
'pipeline2',
inputs=[
DatasetSpec('required', text_format),
DatasetSpec('optional', text_format)
],
outputs=[DatasetSpec('missing_input', text_format)],
desc="",
citations=[],
version=1)
identity = pipeline.create_node(IdentityInterface(['a', 'b']),
'identity')
pipeline.connect_input('required', identity, 'a')
pipeline.connect_input('optional', identity, 'b')
pipeline.connect_output('missing_input', identity, 'a')
return pipeline
def pipeline3(self, **kwargs):
outputs = [DatasetSpec('another_derivable', text_format)]
switch = self.pre_option('switch', 'pipeline3', **kwargs)
if switch:
outputs.append(DatasetSpec('wrong_option', text_format))
pipeline = self.create_pipeline(
'pipeline3',
inputs=[DatasetSpec('required', text_format)],
outputs=outputs,
desc="",
citations=[],
version=1)
identity = pipeline.create_node(IdentityInterface(['a', 'b']),
'identity')
pipeline.connect_input('required', identity, 'a')
pipeline.connect_input('required', identity, 'b')
pipeline.connect_output('another_derivable', identity, 'a')
if switch:
pipeline.connect_output('wrong_option', identity, 'b')
return pipeline
def pipeline4(self, **kwargs):
pipeline = self.create_pipeline(
'pipeline4',
inputs=[DatasetSpec('wrong_option', text_format)],
outputs=[DatasetSpec('wrong_option2', text_format)],
desc="",
citations=[],
version=1,
**kwargs)
identity = pipeline.create_node(IdentityInterface(['a']), 'identity')
pipeline.connect_input('wrong_option', identity, 'a')
pipeline.connect_output('wrong_option2', identity, 'a')
return pipeline
class TestStudy(Study):
__metaclass__ = StudyMetaClass
add_data_specs = [
DatasetSpec('start', nifti_gz_format),
DatasetSpec('ones_slice', mrtrix_format),
DatasetSpec('derived1_1', nifti_gz_format, 'pipeline1'),
DatasetSpec('derived1_2', nifti_gz_format, 'pipeline1'),
DatasetSpec('derived2', nifti_gz_format, 'pipeline2'),
DatasetSpec('derived3', nifti_gz_format, 'pipeline3'),
DatasetSpec('derived4', nifti_gz_format, 'pipeline4'),
DatasetSpec('subject_summary',
mrtrix_format,
'subject_summary_pipeline',
frequency='per_subject'),
DatasetSpec('visit_summary',
mrtrix_format,
'visit_summary_pipeline',
frequency='per_visit'),
DatasetSpec('project_summary',
mrtrix_format,
'project_summary_pipeline',
frequency='per_project'),
DatasetSpec('subject_ids',
text_format,
'subject_ids_access_pipeline',
frequency='per_visit'),
DatasetSpec('visit_ids',
text_format,
'visit_ids_access_pipeline',
frequency='per_subject')
]
add_option_specs = [OptionSpec('pipeline_option', False)]
def pipeline1(self, **kwargs):
pipeline = self.create_pipeline(
name='pipeline1',
inputs=[DatasetSpec('start', nifti_gz_format)],
outputs=[
DatasetSpec('derived1_1', nifti_gz_format),
DatasetSpec('derived1_2', nifti_gz_format)
],
desc="A dummy pipeline used to test 'run_pipeline' method",
version=1,
citations=[],
**kwargs)
if not pipeline.option('pipeline_option'):
raise Exception("Pipeline option was not cascaded down to "
"pipeline1")
mrconvert = pipeline.create_node(MRConvert(),
name="convert1",
requirements=[mrtrix3_req])
mrconvert2 = pipeline.create_node(MRConvert(),
name="convert2",
requirements=[mrtrix3_req])
# Connect inputs
pipeline.connect_input('start', mrconvert, 'in_file')
pipeline.connect_input('start', mrconvert2, 'in_file')
# Connect outputs
pipeline.connect_output('derived1_1', mrconvert, 'out_file')
pipeline.connect_output('derived1_2', mrconvert2, 'out_file')
return pipeline
def pipeline2(self, **kwargs):
pipeline = self.create_pipeline(
name='pipeline2',
inputs=[
DatasetSpec('start', nifti_gz_format),
DatasetSpec('derived1_1', nifti_gz_format)
],
outputs=[DatasetSpec('derived2', nifti_gz_format)],
desc="A dummy pipeline used to test 'run_pipeline' method",
version=1,
citations=[],
**kwargs)
if not pipeline.option('pipeline_option'):
raise Exception("Pipeline option was not cascaded down to "
"pipeline2")
mrmath = pipeline.create_node(MRCat(),
name="mrcat",
requirements=[mrtrix3_req])
mrmath.inputs.axis = 0
# Connect inputs
pipeline.connect_input('start', mrmath, 'first_scan')
pipeline.connect_input('derived1_1', mrmath, 'second_scan')
# Connect outputs
pipeline.connect_output('derived2', mrmath, 'out_file')
return pipeline
def pipeline3(self, **kwargs):
pipeline = self.create_pipeline(
name='pipeline3',
inputs=[DatasetSpec('derived2', nifti_gz_format)],
outputs=[DatasetSpec('derived3', nifti_gz_format)],
desc="A dummy pipeline used to test 'run_pipeline' method",
version=1,
citations=[],
**kwargs)
mrconvert = pipeline.create_node(MRConvert(),
name="convert",
requirements=[mrtrix3_req])
# Connect inputs
pipeline.connect_input('derived2', mrconvert, 'in_file')
# Connect outputs
pipeline.connect_output('derived3', mrconvert, 'out_file')
return pipeline
def pipeline4(self, **kwargs):
pipeline = self.create_pipeline(
name='pipeline4',
inputs=[
DatasetSpec('derived1_2', nifti_gz_format),
DatasetSpec('derived3', nifti_gz_format)
],
outputs=[DatasetSpec('derived4', nifti_gz_format)],
desc="A dummy pipeline used to test 'run_pipeline' method",
version=1,
citations=[],
**kwargs)
mrmath = pipeline.create_node(MRCat(),
name="mrcat",
requirements=[mrtrix3_req])
mrmath.inputs.axis = 0
# Connect inputs
pipeline.connect_input('derived1_2', mrmath, 'first_scan')
pipeline.connect_input('derived3', mrmath, 'second_scan')
# Connect outputs
pipeline.connect_output('derived4', mrmath, 'out_file')
return pipeline
def visit_ids_access_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='visit_ids_access',
inputs=[],
outputs=[DatasetSpec('visit_ids', text_format)],
desc=("A dummy pipeline used to test access to 'session' IDs"),
version=1,
citations=[],
**kwargs)
sessions_to_file = pipeline.create_join_visits_node(
IteratorToFile(), name='sess_to_file', joinfield='ids')
pipeline.connect_visit_id(sessions_to_file, 'ids')
pipeline.connect_output('visit_ids', sessions_to_file, 'out_file')
return pipeline
def subject_ids_access_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='subject_ids_access',
inputs=[],
outputs=[DatasetSpec('subject_ids', text_format)],
desc=("A dummy pipeline used to test access to 'subject' IDs"),
version=1,
citations=[],
**kwargs)
subjects_to_file = pipeline.create_join_subjects_node(
IteratorToFile(), name='subjects_to_file', joinfield='ids')
pipeline.connect_subject_id(subjects_to_file, 'ids')
pipeline.connect_output('subject_ids', subjects_to_file, 'out_file')
return pipeline
def subject_summary_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name="subject_summary",
inputs=[DatasetSpec('ones_slice', mrtrix_format)],
outputs=[DatasetSpec('subject_summary', mrtrix_format)],
desc=("Test of project summary variables"),
version=1,
citations=[],
**kwargs)
mrmath = pipeline.create_join_visits_node(MRMath(),
'in_files',
'mrmath',
requirements=[mrtrix3_req])
mrmath.inputs.operation = 'sum'
# Connect inputs
pipeline.connect_input('ones_slice', mrmath, 'in_files')
# Connect outputs
pipeline.connect_output('subject_summary', mrmath, 'out_file')
pipeline.assert_connected()
return pipeline
def visit_summary_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name="visit_summary",
inputs=[DatasetSpec('ones_slice', mrtrix_format)],
outputs=[DatasetSpec('visit_summary', mrtrix_format)],
desc=("Test of project summary variables"),
version=1,
citations=[],
**kwargs)
mrmath = pipeline.create_join_visits_node(MRMath(),
'in_files',
'mrmath',
requirements=[mrtrix3_req])
mrmath.inputs.operation = 'sum'
# Connect inputs
pipeline.connect_input('ones_slice', mrmath, 'in_files')
# Connect outputs
pipeline.connect_output('visit_summary', mrmath, 'out_file')
pipeline.assert_connected()
return pipeline
def project_summary_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name="project_summary",
inputs=[DatasetSpec('ones_slice', mrtrix_format)],
outputs=[DatasetSpec('project_summary', mrtrix_format)],
desc=("Test of project summary variables"),
version=1,
citations=[],
**kwargs)
mrmath1 = pipeline.create_join_visits_node(MRMath(),
'in_files',
'mrmath1',
requirements=[mrtrix3_req])
mrmath2 = pipeline.create_join_subjects_node(
MRMath(), 'in_files', 'mrmath2', requirements=[mrtrix3_req])
mrmath1.inputs.operation = 'sum'
mrmath2.inputs.operation = 'sum'
# Connect inputs
pipeline.connect_input('ones_slice', mrmath1, 'in_files')
pipeline.connect(mrmath1, 'out_file', mrmath2, 'in_files')
# Connect outputs
pipeline.connect_output('project_summary', mrmath2, 'out_file')
pipeline.assert_connected()
return pipeline