def test_ApplyVolTransform_outputs():
output_map = dict(transformed_file=dict(), )
outputs = ApplyVolTransform.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 test_ApplyVolTransform_outputs():
output_map = dict(transformed_file=dict(),
)
outputs = ApplyVolTransform.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 plot_epi_T1_corregistration(
mean_epi_file,
reg_file,
fssubjects_dir,
subject_id,
similarity_distribution=None,
figsize=(11.7, 8.3),
):
fig = plt.figure(figsize=figsize)
if similarity_distribution:
ax = plt.subplot(2, 1, 1)
sns.distplot(similarity_distribution.values(), ax=ax)
ax.set_xlabel(
"EPI-T1 similarity after coregistration (over all subjects)")
cur_similarity = similarity_distribution[subject_id]
label = "similarity = %g" % cur_similarity
plot_vline(cur_similarity, label, ax=ax)
ax = plt.subplot(2, 1, 0)
else:
ax = plt.subplot(1, 1, 0)
res = ApplyVolTransform(source_file=mean_epi_file,
reg_file=reg_file,
fs_target=True,
subjects_dir=fssubjects_dir,
terminal_output="none").run()
func = nb.load(res.outputs.transformed_file).get_data()
func_affine = nb.load(res.outputs.transformed_file).get_affine()
ribbon_file = "%s/%s/mri/ribbon.mgz" % (fssubjects_dir, subject_id)
ribbon_nii = nb.load(ribbon_file)
ribbon_data = ribbon_nii.get_data()
ribbon_data[ribbon_data > 1] = 1
ribbon_affine = ribbon_nii.get_affine()
slicer = viz.plot_anat(
np.asarray(func),
np.asarray(func_affine),
black_bg=True,
cmap=cm.Greys_r, # @UndefinedVariable
cut_coords=(-6, 3, 32),
figure=fig,
axes=ax,
draw_cross=False)
slicer.contour_map(np.asarray(ribbon_data),
np.asarray(ribbon_affine),
levels=[.51],
colors=[
'r',
])
return fig
def plot_epi_T1_corregistration(mean_epi_file,
wm_file,
reg_file,
fssubjects_dir,
subject_id,
figsize=(11.7, 8.3)):
fig = plt.figure(figsize=figsize)
ax = plt.subplot(1, 1, 1)
print ax
res = ApplyVolTransform(source_file=mean_epi_file,
reg_file=reg_file,
fs_target=True,
subjects_dir=fssubjects_dir,
terminal_output="none").run()
func = nb.load(res.outputs.transformed_file).get_data()
func_affine = nb.load(res.outputs.transformed_file).get_affine()
# ribbon_file = "%s/%s/mri/ribbon.mgz"%(fssubjects_dir, subject_id)
# ribbon_nii = nb.load(ribbon_file)
# ribbon_data = ribbon_nii.get_data()
# ribbon_data[ribbon_data > 1] = 1
# ribbon_affine = ribbon_nii.get_affine()
wm_nii = nb.load(wm_file)
wm_data = wm_nii.get_data()
wm_data[wm_data > 1] = 1
wm_affine = wm_nii.get_affine()
slicer = viz.plot_anat(
np.asarray(func),
np.asarray(func_affine),
black_bg=True,
cmap=cm.Greys_r, # @UndefinedVariable
figure=fig,
axes=ax,
draw_cross=False)
slicer.contour_map(np.asarray(wm_data),
np.asarray(wm_affine),
linewidths=[0.1],
colors=[
'r',
])
fig.suptitle('coregistration', fontsize='14')
return fig
plt.show(fig)
def test_ApplyVolTransform_inputs():
input_map = dict(args=dict(argstr='%s',
),
environ=dict(nohash=True,
usedefault=True,
),
fs_target=dict(argstr='--fstarg',
mandatory=True,
requires=['reg_file'],
xor=('target_file', 'tal', 'fs_target'),
),
fsl_reg_file=dict(argstr='--fsl %s',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header', 'subject'),
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
interp=dict(argstr='--interp %s',
),
inverse=dict(argstr='--inv',
),
invert_morph=dict(argstr='--inv-morph',
requires=['m3z_file'],
),
m3z_file=dict(argstr='--m3z %s',
),
no_ded_m3z_path=dict(argstr='--noDefM3zPath',
requires=['m3z_file'],
),
no_resample=dict(argstr='--no-resample',
),
reg_file=dict(argstr='--reg %s',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header', 'subject'),
),
reg_header=dict(argstr='--regheader',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header', 'subject'),
),
source_file=dict(argstr='--mov %s',
copyfile=False,
mandatory=True,
),
subject=dict(argstr='--s %s',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header', 'subject'),
),
subjects_dir=dict(),
tal=dict(argstr='--tal',
mandatory=True,
xor=('target_file', 'tal', 'fs_target'),
),
tal_resolution=dict(argstr='--talres %.10f',
),
target_file=dict(argstr='--targ %s',
mandatory=True,
xor=('target_file', 'tal', 'fs_target'),
),
terminal_output=dict(nohash=True,
),
transformed_file=dict(argstr='--o %s',
genfile=True,
),
xfm_reg_file=dict(argstr='--xfm %s',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header', 'subject'),
),
)
inputs = ApplyVolTransform.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_ApplyVolTransform_inputs():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
fs_target=dict(
argstr='--fstarg',
mandatory=True,
requires=['reg_file'],
xor=('target_file', 'tal', 'fs_target'),
),
fsl_reg_file=dict(
argstr='--fsl %s',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header',
'subject'),
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
interp=dict(argstr='--interp %s', ),
inverse=dict(argstr='--inv', ),
invert_morph=dict(
argstr='--inv-morph',
requires=['m3z_file'],
),
m3z_file=dict(argstr='--m3z %s', ),
no_ded_m3z_path=dict(
argstr='--noDefM3zPath',
requires=['m3z_file'],
),
no_resample=dict(argstr='--no-resample', ),
reg_file=dict(
argstr='--reg %s',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header',
'subject'),
),
reg_header=dict(
argstr='--regheader',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header',
'subject'),
),
source_file=dict(
argstr='--mov %s',
copyfile=False,
mandatory=True,
),
subject=dict(
argstr='--s %s',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header',
'subject'),
),
subjects_dir=dict(),
tal=dict(
argstr='--tal',
mandatory=True,
xor=('target_file', 'tal', 'fs_target'),
),
tal_resolution=dict(argstr='--talres %.10f', ),
target_file=dict(
argstr='--targ %s',
mandatory=True,
xor=('target_file', 'tal', 'fs_target'),
),
terminal_output=dict(
mandatory=True,
nohash=True,
),
transformed_file=dict(
argstr='--o %s',
genfile=True,
),
xfm_reg_file=dict(
argstr='--xfm %s',
mandatory=True,
xor=('reg_file', 'fsl_reg_file', 'xfm_reg_file', 'reg_header',
'subject'),
),
)
inputs = ApplyVolTransform.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
