def test_label_matcher_find_op():
with pytest.raises(AssertionError) as e:
fa.define_analysis('afq',
lambda **kwargs: {},
label_matcher=lambda val: val.startswith('afq '))
assert 'does not detect this operation' in str(e)
fa.define_analysis('afq',
lambda **kwargs: {},
label_matcher=lambda val: val.startswith('afq'))
def define_analysis(gear_name, acquisition_label, create_inputs):
'''Light wrapper around define_analysis that lets us pass in an acquisition_label
in to create_inputs.
'''
return fa.define_analysis(
gear_name,
lambda **kwargs: create_inputs(acquisition_label, **kwargs),
label=analysis_label(gear_name, acquisition_label))
def dtiinit_inputs(acquisitions, **kwargs):
diffusion = fa.find(acquisitions, label='Diffusion')
return dict(
bvec=diffusion.find_file('*1000.bvec'),
bval=diffusion.find_file('*1000.bval'),
dwi=diffusion.find_file('*1000.nii.gz'),
), dict(
# Here you can add any configuration. So for instance to add robust tensor, do:
# fitMethod='rt'
)
def afq_inputs(analyses, **kwargs):
dtiinit = fa.find(analyses, label=dtiinit_label)
return dict(dtiInit_Archive=dtiinit.find_file('dtiInit_*.zip'), )
if __name__ == '__main__':
with fa.installed_client(client):
ops = [
fa.define_analysis('dtiinit', dtiinit_inputs, label=dtiinit_label),
fa.define_analysis('afq', afq_inputs, label=afq_label),
]
fa.run(ops,
project=fa.find_project(label='HCP_preproc'),
session_limit=1)
from scitran_client import ScitranClient
import scitran_client.flywheel_analyzer as fa
client = ScitranClient('https://flywheel.scitran.stanford.edu')
with fa.installed_client(client):
D99 = fa.find(client.request(
'sessions/588bd1ac449f9800159305c2/acquisitions').json(),
label='atlas')
def anatomical_warp_inputs(acquisitions, **kwargs):
anatomical = fa.find(acquisitions, label='anatomical_mdeft')
return dict(
native=anatomical.find_file('*.nii.gz'),
standard=D99.find_file('D99_template.nii.gz'),
warp_target=D99.find_file('D99_atlas_1.2a.nii.gz'),
)
if __name__ == '__main__':
with fa.installed_client(client):
fa.run([
fa.define_analysis('afni-brain-warp',
anatomical_warp_inputs,
label='anatomical warp'),
],
project=fa.find_project(label='showdes'),
max_workers=2)
dtiinit_matcher = prefix_matcher('dtiinit')
afq_matcher = prefix_matcher('afq')
def dtiinit_inputs(acquisitions, **kwargs):
diffusion = fa.find(acquisitions, label='DTI 2mm b1250 84dir(axial)')
return dict(
bvec=diffusion.find_file('*.bvec'),
bval=diffusion.find_file('*.bval'),
nifti=diffusion.find_file('*.nii.gz'),
)
def afq_inputs(analyses, **kwargs):
dtiinit = fa.find(analyses, label=dtiinit_matcher)
return dict(dtiInit_Archive=dtiinit.find_file('dtiInit_*.zip'), )
if __name__ == '__main__':
with fa.installed_client(client):
fa.run([
fa.define_analysis(
'dtiinit', dtiinit_inputs, label_matcher=dtiinit_matcher),
fa.define_analysis('afq', afq_inputs, label_matcher=afq_matcher),
],
project=fa.find_project(label='ENGAGE'))