def test_mask(fsl_output_type=None):
prev_type = set_output_type(fsl_output_type)
files, testdir, origdir, out_ext = create_files_in_directory()
# Get the command
masker = fsl.ApplyMask(in_file="a.nii", out_file="c.nii")
# Test the underlying command
yield assert_equal, masker.cmd, "fslmaths"
# Test that the mask image is mandatory
yield assert_raises, ValueError, masker.run
# Test setting the mask image
masker.inputs.mask_file = "b.nii"
yield assert_equal, masker.cmdline, "fslmaths a.nii -mas b.nii c.nii"
# Test auto name generation
masker = fsl.ApplyMask(in_file="a.nii", mask_file="b.nii")
yield assert_equal, masker.cmdline, "fslmaths a.nii -mas b.nii " + os.path.join(
testdir, "a_masked%s" % out_ext)
# Clean up our mess
clean_directory(testdir, origdir)
set_output_type(prev_type)
def mask_img(img, mask):
msk = math.ApplyMask()
msk.inputs.in_file = img
msk.inputs.mask_file = mask
msk.inputs.out_file = img
msk.inputs.output_type = 'NIFTI_GZ'
msk.inputs.ignore_exception = True
msk.run()
def test_mask(create_files_in_directory):
files, testdir, out_ext = create_files_in_directory
# Get the command
masker = fsl.ApplyMask(in_file="a.nii", out_file="c.nii")
# Test the underlying command
assert masker.cmd == "fslmaths"
# Test that the mask image is mandatory
with pytest.raises(ValueError):
masker.run()
# Test setting the mask image
masker.inputs.mask_file = "b.nii"
assert masker.cmdline == "fslmaths a.nii -mas b.nii c.nii"
# Test auto name generation
masker = fsl.ApplyMask(in_file="a.nii", mask_file="b.nii")
assert masker.cmdline == "fslmaths a.nii -mas b.nii " + os.path.join(testdir, "a_masked%s" % out_ext)
# define working dir for smoothing
work_dir_smooth = os.path.join(os.getcwd(), 'smooth')
if not os.path.exists(work_dir_smooth):
os.makedirs(work_dir_smooth)
os.chdir(work_dir_smooth)
# Step#1 get a brain mask for func2mni image
from nipype.interfaces import afni as afni
automask = afni.Automask()
automask.inputs.in_file = rest_mni
automask.inputs.outputtype = "NIFTI_GZ"
automask.run()
# Step#2 smooth func2mni image
from nipype.interfaces.fsl import maths
smooth = maths.IsotropicSmooth()
smooth.inputs.in_file = rest_mni
smooth.inputs.fwhm = 6
smooth.run()
## Step#3 mask the smoothed image
from nipype.interfaces.fsl import maths
maskApp = maths.ApplyMask()
maskApp.inputs.in_file = 'rest_preprocessed2mni_smooth.nii.gz'
maskApp.inputs.mask_file = 'rest_preprocessed2mni_mask.nii.gz'
maskApp.inputs.out_file = os.path.join(work_dir,
'rest_preprocessed2mni_sm.nii.gz')
maskApp.inputs.output_type = 'NIFTI_GZ'
maskApp.run()
# START PIPELINE
# inputmask = Node(IdentityInterface(fields=['mask_file']), name='inputmask')
if args.prep_pipeline.startswith("fsl"):
masker = Node(ApplyWarp(
in_file = cf_files['bold'],
field_file=cf_files['warpfile'],
ref_file=cf_files['standard'],
out_file = cf_files['masked'],
mask_file = cf_files['standard_mask']
), name = 'masker')
#inputmask.inputs.mask_file = cf_files['standard_mask']
else:
masker = Node(maths.ApplyMask(
in_file=cf_files['bold'],
out_file=cf_files['masked'],
mask_file=cf_files['standard_mask']
), name='masker')
bim = Node(afni.BlurInMask(
out_file=cf_files['smoothed'],
mask = cf_files['standard_mask'],
fwhm=5.0
), name='bim')
l1 = Node(SpecifyModel(
event_files=EVfiles,
realignment_parameters=confoundsfile,
input_units='secs',
time_repetition=2,