def test_subtract(self):
tmp_dir = tempfile.mkdtemp()
in_file = op.join(tmp_dir, 'in_file.nii.gz')
with open(in_file, 'w') as f:
f.write('test')
out_file = op.join(tmp_dir, 'out_file.nii.gz')
unwrap = Node(UnwrapPhase(), name='unwrap')
unwrap.inputs.in_file = in_file
unwrap.inputs.voxelsize = [2.0, 2.0, 2.0]
# unwrap.inputs.out_file = out_file
unwrap.run()
self.assertTrue(op.exists(out_file))
def __call__(self, **kwargs):
kwargs = modify_paths(kwargs, relative=False)
interface = self.interface()
# Set the inputs early to get some argument checking
interface.inputs.set(**kwargs)
# Make a name for our node
inputs = interface.inputs.get_hashval()
hasher = hashlib.new('md5')
hasher.update(pickle.dumps(inputs))
dir_name = '%s-%s' % (interface.__class__.__module__.replace(
'.', '-'), interface.__class__.__name__)
job_name = hasher.hexdigest()
node = Node(interface, name=job_name)
node.base_dir = os.path.join(self.base_dir, dir_name)
cwd = os.getcwd()
try:
out = node.run()
finally:
# node.run() changes to the node directory - if something goes wrong
# before it cds back you would end up in strange places
os.chdir(cwd)
if self.callback is not None:
self.callback(dir_name, job_name)
return out
def __call__(self, **kwargs):
kwargs = modify_paths(kwargs, relative=False)
interface = self.interface()
# Set the inputs early to get some argument checking
interface.inputs.set(**kwargs)
# Make a name for our node
inputs = interface.inputs.get_hashval()
hasher = hashlib.new('md5')
hasher.update(pickle.dumps(inputs))
dir_name = '%s-%s' % (interface.__class__.__module__.replace('.', '-'),
interface.__class__.__name__)
job_name = hasher.hexdigest()
node = Node(interface, name=job_name)
node.base_dir = os.path.join(self.base_dir, dir_name)
cwd = os.getcwd()
try:
out = node.run()
finally:
# node.run() changes to the node directory - if something goes wrong
# before it cds back you would end up in strange places
os.chdir(cwd)
if self.callback is not None:
self.callback(dir_name, job_name)
return out
def _merge_nii(file_list, out_filename):
from nipype.pipeline.engine import Node, Workflow
import nipype.interfaces.fsl as fsl
merge = Node(fsl.Merge(dimension='t'), name='merge')
merge.base_dir = os.getcwd()
merge.inputs.in_files = file_list
merge.inputs.merged_file = out_filename
result = merge.run()
return result.outputs.merged_file
def __call__(self, **kwargs):
kwargs = modify_paths(kwargs, relative=False)
interface = self.interface()
# Set the inputs early to get some argument checking
interface.inputs.set(**kwargs)
# Make a name for our node
inputs = interface.inputs.get_hashval()
hasher = hashlib.new('md5')
hasher.update(pickle.dumps(inputs))
dir_name = '%s-%s' % (interface.__class__.__module__.replace(
'.', '-'), interface.__class__.__name__)
job_name = hasher.hexdigest()
node = Node(interface, name=job_name)
node.base_dir = os.path.join(self.base_dir, dir_name)
out = node.run()
if self.callback is not None:
self.callback(dir_name, job_name)
return out
def __call__(self, **kwargs):
kwargs = modify_paths(kwargs, relative=False)
interface = self.interface()
# Set the inputs early to get some argument checking
interface.inputs.set(**kwargs)
# Make a name for our node
inputs = interface.inputs.get_hashval()
hasher = hashlib.new('md5')
hasher.update(pickle.dumps(inputs))
dir_name = '%s-%s' % (interface.__class__.__module__.replace('.', '-'),
interface.__class__.__name__)
job_name = hasher.hexdigest()
node = Node(interface, name=job_name)
node.base_dir = os.path.join(self.base_dir, dir_name)
out = node.run()
if self.callback is not None:
self.callback(dir_name, job_name)
return out
def psacnn_workflow(input_file,
output_dir,
use_preprocess=True,
model_file=None,
contrast='t1w',
use_gpu=True,
gpu_id=0,
save_label_image=False,
save_prob_image=False,
patch_size=96,
batch_size=4,
sample_rate=20000):
subprocess.call(['mkdir', '-p', output_dir])
if use_gpu == False:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ""
gpu_id = -1
batch_size = 16
sample_rate = 40000
else:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
batch_size = 4
sample_rate = 20000
if use_preprocess == True:
preprocess_flow = Workflow(name='preprocess', base_dir=output_dir)
conform = Node(MRIConvert(conform=True,
out_type='niigz',
out_file='conformed.nii.gz'),
name='conform')
n4 = Node(N4BiasFieldCorrection(dimension=3,
bspline_fitting_distance=300,
shrink_factor=3,
n_iterations=[50, 50, 30, 20],
output_image='n4.nii.gz'),
name='n4')
robex = Node(ROBEX(seed=1729, stripped_image='brain.nii.gz'),
name='robex')
psacnn = Node(PSACNN(output_dir=output_dir,
contrast=contrast,
patch_size=patch_size,
batch_size=batch_size,
save_label_image=save_label_image,
save_prob_image=save_prob_image,
sample_rate=sample_rate),
name='psacnn')
preprocess_flow.connect([
(conform, n4, [('out_file', 'input_image')]),
(n4, robex, [('output_image', 'input_image')]),
(robex, psacnn, [('stripped_image', 'input_image')])
])
preprocess_flow.write_graph(graph2use='orig')
conform.inputs.in_file = input_file
preprocess_flow.run('MultiProc', plugin_args={'n_procs': 16})
else:
psacnn = PSACNN(input_image=input_file,
output_dir=output_dir,
contrast=contrast,
patch_size=patch_size,
batch_size=batch_size,
save_label_image=save_label_image,
save_prob_image=save_prob_image,
sample_rate=sample_rate)
# psacnn.inputs.input_image = input_file
# psacnn.inputs.output_dir = output_dir
# psacnn.inputs.contrast = contrast
# psacnn.inputs.patch_size = patch_size
# psacnn.inputs.batch_size = batch_size
# psacnn.inputs.save_label_image = save_label_image
# psacnn.inputs.save_prob_image = save_prob_image
# psacnn.inputs.sample_rate = sample_rate
psacnn.run()
home = '/Volumes/iang/active/BABIES/BABIES_MAMA'
t1w = home + '/' + subject + '/t1w'
dest = home + '/' + subject + '/anat'
func1 = home + '/' + subject + '/func/run1'
func2 = home + '/' + subject + '/func/run2'
copyfile((t1w + '/t1w_raw.nii.gz'), (dest + '/t1w_raw.nii.gz'))
# In[10]:
reorient = Node(fsl.Reorient2Std(in_file=(dest + '/t1w_raw.nii.gz'),
out_file=(dest + '/spgrorient.nii.gz'),
output_type='NIFTI_GZ'),
name='reorient')
reorient.run()
# In[11]:
skullstrip = Node(fsl.BET(in_file=(dest + '/spgrorient.nii.gz'),
out_file=(dest + '/spgrbrain.nii.gz'),
robust=True,
frac=0.5,
vertical_gradient=0),
name='skullstrip')
skullstrip.run()
# In[13]:
#Preprocess functional run1
func_reorient1 = Node(fsl.Reorient2Std(
