def test_ImageMeants_outputs():
output_map = dict(out_file=dict(),
)
outputs = ImageMeants.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def extract_eigenv_roi(bold_image, mask, csvpath, sub, run):
"""
Use fsl.ImageMeants to extract eigenvariate
"""
if not os.path.exists(csvpath):
os.makedirs(csvpath)
extract_eigv = ImageMeants(in_file=bold_image,
terminal_output='file',
eig=True,
mask=mask,
out_file=os.path.join(
csvpath, '{}_{}_{}.csv'.format(
sub, run,
mask.replace('_mask.nii.gz', '')[-4:])))
extract_eigv.run()
def test_ImageMeants_inputs():
input_map = dict(args=dict(argstr='%s',
),
eig=dict(argstr='--eig',
),
environ=dict(nohash=True,
usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(argstr='-i %s',
mandatory=True,
position=0,
),
mask=dict(argstr='-m %s',
),
nobin=dict(argstr='--no_bin',
),
order=dict(argstr='--order=%d',
usedefault=True,
),
out_file=dict(argstr='-o %s',
genfile=True,
hash_files=False,
),
output_type=dict(),
show_all=dict(argstr='--showall',
),
spatial_coord=dict(argstr='-c %s',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
transpose=dict(argstr='--transpose',
),
use_mm=dict(argstr='--usemm',
),
)
inputs = ImageMeants.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
# #"sub-184420", ¿?¿?¿? NO BOLD
# "sub-185225","sub-187232",
"sub-48296",
"sub-49664",
"sub-50000",
"sub-73417",
"sub-84766",
"sub-86143",
"sub-88604",
"sub-92889",
"sub-92918",
"sub-93338"
]
#extracting timesieries
extraction = ImageMeants()
for subject_id in list_subjs:
#defines WF
wf_reg = get_nuisance_regressors_wf(outdir=root_path +
'/nuisance_correction',
subject_id=subject_id,
timepoints=490)
#sets necessary inputs
wf_reg.inputs.input_node.realign_movpar_txt = root_path + '/fmri2standard/{subject_id}/realign_fmri2SBref/{subject_id}_ses-01_run-01_rest_bold_ap_roi_mcf.nii.gz.par'.format(
subject_id=subject_id)
wf_reg.inputs.input_node.rfmri_unwarped_imgs = root_path + '/fmri2standard/{subject_id}/spm_coregister2T1_bold/{subject_id}_ses-01_run-01_rest_bold_ap_roi_mcf_corrected_coregistered2T1.nii.gz'.format(
subject_id=subject_id)
#wf_reg.inputs.input_node.masks_imgs = root_path+'/nuisance_correction/{subject_id}/masks_csf_wm/wm_binmask.nii.gz'.format(subject_id=subject_id)
wf_reg.inputs.input_node.mask_wm = root_path + '/nuisance_correction/{subject_id}/masks_csf_wm/wm_binmask.nii.gz'.format(
selectfiles = Node(SelectFiles(templates), name='selectfiles')
# Datasink- where our select outputs will go
datasink = Node(DataSink(), name='datasink')
datasink.inputs.base_directory = output_dir
datasink.inputs.container = output_dir
substitutions = [('_subject_id_', ''),
('_ROIs_..home..camachocm2..Box_home..CARS_rest..ROIs..', '')]
datasink.inputs.substitutions = substitutions
# In[3]:
## Seed-based level 1
# Extract ROI timeseries
ROI_timeseries = Node(ImageMeants(), name='ROI_timeseries', iterfield='mask')
def converthex(orig):
from numpy import genfromtxt, savetxt
from os.path import abspath
orig = genfromtxt(orig, delimiter=' ', dtype=None, skip_header=0)
new = 'func_roi_ts.txt'
savetxt(new, orig, delimiter=' ')
new_file = abspath(new)
return (new_file)
converthex = Node(name='converthex',
