def create_fsl_recon_flow(config):
flow = pe.Workflow(name="reconstruction")
inputnode = pe.Node(interface=util.IdentityInterface(fields=["diffusion_resampled","wm_mask_resampled"]),name="inputnode")
outputnode = pe.Node(interface=util.IdentityInterface(fields=["phsamples","fsamples","thsamples"],mandatory_inputs=True),name="outputnode")
# Flip gradient table
flip_table = pe.Node(interface=flipTable(),name='flip_table')
flip_table.inputs.table = config.b_vectors
flip_table.inputs.flipping_axis = config.flip_table_axis
flip_table.inputs.delimiter = ' '
flip_table.inputs.header_lines = 0
flip_table.inputs.orientation = 'h'
fsl_node = pe.Node(interface=fsl.BEDPOSTX(),name='BEDPOSTX')
fsl_node.inputs.bvals = config.b_values
fsl_node.inputs.burn_period = config.burn_period
fsl_node.inputs.fibres = config.fibres_per_voxel
fsl_node.inputs.jumps = config.jumps
fsl_node.inputs.sampling = config.sampling
fsl_node.inputs.weight = config.weight
flow.connect([
(inputnode,fsl_node,[("diffusion_resampled","dwi")]),
(inputnode,fsl_node,[("wm_mask_resampled","mask")]),
(flip_table,fsl_node,[("table","bvecs")]),
(fsl_node,outputnode,[("merged_fsamples","fsamples")]),
(fsl_node,outputnode,[("merged_phsamples","phsamples")]),
(fsl_node,outputnode,[("merged_thsamples","thsamples")]),
])
return flow
def test_create_bedpostx_pipeline():
fsl_course_dir = os.path.abspath(os.environ['FSL_COURSE_DATA'])
mask_file = os.path.join(fsl_course_dir,
"fdt2/subj1.bedpostX/nodif_brain_mask.nii.gz")
bvecs_file = os.path.join(fsl_course_dir, "fdt2/subj1/bvecs")
bvals_file = os.path.join(fsl_course_dir, "fdt2/subj1/bvals")
dwi_file = os.path.join(fsl_course_dir, "fdt2/subj1/data.nii.gz")
z_min = 62
z_size = 2
slice_mask = pe.Node(fsl.ExtractROI(x_min=0,
x_size=-1,
y_min=0,
y_size=-1,
z_min=z_min,
z_size=z_size),
name="slice_mask")
slice_mask.inputs.in_file = mask_file
slice_dwi = pe.Node(fsl.ExtractROI(x_min=0,
x_size=-1,
y_min=0,
y_size=-1,
z_min=z_min,
z_size=z_size),
name="slice_dwi")
slice_dwi.inputs.in_file = dwi_file
nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
nipype_bedpostx.inputs.inputnode.bvecs = bvecs_file
nipype_bedpostx.inputs.inputnode.bvals = bvals_file
nipype_bedpostx.inputs.xfibres.n_fibres = 1
nipype_bedpostx.inputs.xfibres.fudge = 1
nipype_bedpostx.inputs.xfibres.burn_in = 0
nipype_bedpostx.inputs.xfibres.n_jumps = 1
nipype_bedpostx.inputs.xfibres.sample_every = 1
nipype_bedpostx.inputs.xfibres.cnlinear = True
nipype_bedpostx.inputs.xfibres.seed = 0
with warnings.catch_warnings():
warnings.simplefilter("ignore")
original_bedpostx = pe.Node(interface=fsl.BEDPOSTX(),
name="original_bedpostx")
original_bedpostx.inputs.bvecs = bvecs_file
original_bedpostx.inputs.bvals = bvals_file
original_bedpostx.inputs.environ['FSLPARALLEL'] = ""
original_bedpostx.inputs.n_fibres = 1
original_bedpostx.inputs.fudge = 1
original_bedpostx.inputs.burn_in = 0
original_bedpostx.inputs.n_jumps = 1
original_bedpostx.inputs.sample_every = 1
original_bedpostx.inputs.seed = 0
test_f1 = pe.Node(util.AssertEqual(), name="mean_f1_test")
pipeline = pe.Workflow(name="test_bedpostx")
pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_bedpostx_")
pipeline.connect([
(slice_mask, original_bedpostx, [("roi_file", "mask")]),
(slice_mask, nipype_bedpostx, [("roi_file", "inputnode.mask")]),
(slice_dwi, original_bedpostx, [("roi_file", "dwi")]),
(slice_dwi, nipype_bedpostx, [("roi_file", "inputnode.dwi")]),
(nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples",
list_to_filename), "volume1")]),
(original_bedpostx, test_f1, [("mean_fsamples", "volume2")]),
])
pipeline.run(plugin='Linear')
shutil.rmtree(pipeline.base_dir)
def test_create_bedpostx_pipeline():
fsl_course_dir = os.path.abspath('fsl_course_data')
mask_file = os.path.join(fsl_course_dir,
"fdt/subj1.bedpostX/nodif_brain_mask.nii.gz")
bvecs_file = os.path.join(fsl_course_dir, "fdt/subj1/bvecs")
bvals_file = os.path.join(fsl_course_dir, "fdt/subj1/bvals")
dwi_file = os.path.join(fsl_course_dir, "fdt/subj1/data.nii.gz")
nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
nipype_bedpostx.inputs.inputnode.dwi = dwi_file
nipype_bedpostx.inputs.inputnode.mask = mask_file
nipype_bedpostx.inputs.inputnode.bvecs = bvecs_file
nipype_bedpostx.inputs.inputnode.bvals = bvals_file
nipype_bedpostx.inputs.xfibres.n_fibres = 2
nipype_bedpostx.inputs.xfibres.fudge = 1
nipype_bedpostx.inputs.xfibres.burn_in = 1000
nipype_bedpostx.inputs.xfibres.n_jumps = 1250
nipype_bedpostx.inputs.xfibres.sample_every = 25
with warnings.catch_warnings():
warnings.simplefilter("ignore")
original_bedpostx = pe.Node(interface=fsl.BEDPOSTX(),
name="original_bedpostx")
original_bedpostx.inputs.dwi = dwi_file
original_bedpostx.inputs.mask = mask_file
original_bedpostx.inputs.bvecs = bvecs_file
original_bedpostx.inputs.bvals = bvals_file
original_bedpostx.inputs.environ['FSLPARALLEL'] = ""
original_bedpostx.inputs.fibres = 2
original_bedpostx.inputs.weight = 1
original_bedpostx.inputs.burn_period = 1000
original_bedpostx.inputs.jumps = 1250
original_bedpostx.inputs.sampling = 25
test_f1 = pe.Node(util.AssertEqual(), name="mean_f1_test")
test_f2 = pe.Node(util.AssertEqual(), name="mean_f2_test")
test_th1 = pe.Node(util.AssertEqual(), name="mean_th1_test")
test_th2 = pe.Node(util.AssertEqual(), name="mean_th2_test")
test_ph1 = pe.Node(util.AssertEqual(), name="mean_ph1_test")
test_ph2 = pe.Node(util.AssertEqual(), name="mean_ph2_test")
pipeline = pe.Workflow(name="test_bedpostx")
pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_bedpostx_")
def pickFirst(l):
return l[0]
def pickSecond(l):
return l[1]
pipeline.connect([
(nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples", pickFirst),
"volume1")]),
(nipype_bedpostx, test_f2, [(("outputnode.mean_fsamples", pickSecond),
"volume1")]),
(nipype_bedpostx, test_th1, [(("outputnode.mean_thsamples", pickFirst),
"volume1")]),
(nipype_bedpostx, test_th2, [(("outputnode.mean_thsamples",
pickSecond), "volume1")]),
(nipype_bedpostx, test_ph1, [(("outputnode.mean_phsamples", pickFirst),
"volume1")]),
(nipype_bedpostx, test_ph2, [(("outputnode.mean_phsamples",
pickSecond), "volume1")]),
(original_bedpostx, test_f1, [(("mean_fsamples", pickFirst), "volume2")
]),
(original_bedpostx, test_f2, [(("mean_fsamples", pickSecond),
"volume2")]),
(original_bedpostx, test_th1, [(("mean_thsamples", pickFirst),
"volume2")]),
(original_bedpostx, test_th2, [(("mean_thsamples", pickSecond),
"volume2")]),
(original_bedpostx, test_ph1, [(("mean_phsamples", pickFirst),
"volume2")]),
(original_bedpostx, test_ph2, [(("mean_phsamples", pickSecond),
"volume2")])
])
pipeline.run(plugin='Linear')
shutil.rmtree(pipeline.base_dir)