def compute_tensor_model(dir_src, dir_out, verbose=False):
fbval = pjoin(dir_src, 'bvals_' + par_b_tag)
fbvec = pjoin(dir_src, 'bvecs_' + par_b_tag)
fdwi = pjoin(dir_src, 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
fmask = pjoin(dir_src, 'nodif_brain_mask_' + par_dim_tag + '.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=par_b0_threshold)
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
ten_model = TensorModel(gtab)
ten_fit = ten_model.fit(data, mask)
FA = ten_fit.fa
MD = ten_fit.md
EV = ten_fit.evecs.astype(np.float32)
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
save_nifti(pjoin(dir_out, fa_name), FA, affine)
md_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_MD.nii.gz'
save_nifti(pjoin(dir_out, md_name), MD, affine)
ev_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
save_nifti(pjoin(dir_out, ev_name), EV, affine)
def compute_tensor_model(dir_src, dir_out, verbose=False):
fbval = pjoin(dir_src, 'bvals_' + par_b_tag)
fbvec = pjoin(dir_src, 'bvecs_' + par_b_tag)
fdwi = pjoin(dir_src, 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
fmask = pjoin(dir_src, 'nodif_brain_mask_' + par_dim_tag + '.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=par_b0_threshold)
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
ten_model = TensorModel(gtab)
ten_fit = ten_model.fit(data, mask)
FA = ten_fit.fa
MD = ten_fit.md
EV = ten_fit.evecs.astype(np.float32)
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
save_nifti(pjoin(dir_out, fa_name), FA, affine)
md_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_MD.nii.gz'
save_nifti(pjoin(dir_out, md_name), MD, affine)
ev_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
save_nifti(pjoin(dir_out, ev_name), EV, affine)
def tracking_prob(dir_src, dir_out, verbose=False):
wm_name = 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
wm_mask, affine = load_nifti(pjoin(dir_src, wm_name), verbose)
sh_name = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
sh, _ = load_nifti(pjoin(dir_src, sh_name), verbose)
sphere = get_sphere('symmetric724')
classifier = ThresholdTissueClassifier(wm_mask.astype('f8'), .5)
classifier = BinaryTissueClassifier(wm_mask)
max_dg = ProbabilisticDirectionGetter.from_shcoeff(sh, max_angle=par_trk_max_angle, sphere=sphere)
seeds = utils.seeds_from_mask(wm_mask, density=2, affine=affine)
streamlines = LocalTracking(max_dg, classifier, seeds, affine, step_size=par_trk_step_size)
streamlines = list(streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_trk_prob_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
save_trk(trk_out, streamlines, affine, wm_mask.shape)
dpy_out = trk_out.replace('.trk', '.dpy')
dpy = Dpy(dpy_out, 'w')
dpy.write_tracks(streamlines)
dpy.close()
def resample_data_resolution(dir_src, dir_out, verbose=False):
fmask = pjoin(dir_src, 'nodif_brain_mask.nii.gz')
fdwi = pjoin(dir_out, 'data_' + par_b_tag + '.nii.gz')
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
data2, affine2 = reslice(data, affine, (1.25,) * 3, (par_dim_vox,) * 3)
mask2, _ = reslice(mask, affine, (1.25,) * 3, (par_dim_vox,) * 3, order=0)
fname = 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
fname = 'nodif_brain_mask_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), mask2, affine2)
fwmparc = pjoin(dir_src, '../wmparc.nii.gz')
data, affine = load_nifti(fwmparc, verbose)
data2, affine2 = reslice(data, affine, (0.7,) * 3, (par_dim_vox,) * 3, order=0)
fname = 'wmparc_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
ft1w = pjoin(dir_src, '../T1w_acpc_dc_restore_brain.nii.gz')
data, affine = load_nifti(ft1w, verbose)
data2, affine2 = reslice(data, affine, (0.7,) * 3, (par_dim_vox,) * 3, order=0, mode='constant')
fname = 't1w_acpc_dc_restore_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
def tracking_eudx(dir_src, dir_out, verbose=False):
# Loading FA and evecs data
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
FA, affine = load_nifti(pjoin(dir_src, fa_name), verbose)
evecs_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
evecs, _ = load_nifti(pjoin(dir_src, evecs_name), verbose)
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices= sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = (par_dim_vox,) * 3
dims = FA.shape[:3]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_rec_tag + '_' + par_eudx_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
nib.trackvis.write(trk_out, strm, hdr, points_space='voxel')
dpy_out = trk_out.replace('.trk', '.dpy')
dpy = Dpy(dpy_out, 'w')
dpy.write_tracks(streamlines)
dpy.close()
def tracking_eudx(dir_src, dir_out, verbose=False):
# Loading FA and evecs data
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
FA, affine = load_nifti(pjoin(dir_src, fa_name), verbose)
evecs_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
evecs, _ = load_nifti(pjoin(dir_src, evecs_name), verbose)
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices=sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = (par_dim_vox, ) * 3
dims = FA.shape[:3]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_rec_tag + '_' + par_eudx_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
nib.trackvis.write(trk_out, strm, hdr, points_space='voxel')
def constrained_spherical_deconvolution(dir_src, dir_out, verbose=False):
# Load data
fbval = pjoin(dir_src, 'bvals_' + par_b_tag)
fbvec = pjoin(dir_src, 'bvecs_' + par_b_tag)
fdwi = pjoin(dir_src, 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
#fmask = pjoin(dir_src, 'nodif_brain_mask_' + par_dim_tag + '.nii.gz')
fmask = pjoin(dir_src, 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=par_b0_threshold)
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
sphere = get_sphere('symmetric724')
response, ratio = auto_response(gtab, data, roi_radius=par_ar_radius,
fa_thr=par_ar_fa_th)
# print('Response function', response)
# Model fitting
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd_model.fit(data, mask=mask)
# Saving Spherical Harmonic Coefficient
out_peaks = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, out_peaks), csd_fit.shm_coeff, affine)
def resample_data_resolution(dir_src, dir_out, verbose=False):
fmask = pjoin(dir_src, 'nodif_brain_mask.nii.gz')
fdwi = pjoin(dir_out, 'data_' + par_b_tag + '.nii.gz')
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
data2, affine2 = reslice(data, affine, (1.25, ) * 3, (par_dim_vox, ) * 3)
mask2, _ = reslice(mask,
affine, (1.25, ) * 3, (par_dim_vox, ) * 3,
order=0)
fname = 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
fname = 'nodif_brain_mask_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), mask2, affine2)
fwmparc = pjoin(dir_src, '../wmparc.nii.gz')
data, affine = load_nifti(fwmparc, verbose)
data2, affine2 = reslice(data,
affine, (0.7, ) * 3, (par_dim_vox, ) * 3,
order=0)
fname = 'wmparc_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
ft1w = pjoin(dir_src, '../T1w_acpc_dc_restore_brain.nii.gz')
data, affine = load_nifti(ft1w, verbose)
data2, affine2 = reslice(data,
affine, (0.7, ) * 3, (par_dim_vox, ) * 3,
order=0,
mode='constant')
fname = 't1w_acpc_dc_restore_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), data2, affine2)
def constrained_spherical_deconvolution(dir_src, dir_out, verbose=False):
# Load data
fbval = pjoin(dir_src, 'bvals_' + par_b_tag)
fbvec = pjoin(dir_src, 'bvecs_' + par_b_tag)
fdwi = pjoin(dir_src, 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
#fmask = pjoin(dir_src, 'nodif_brain_mask_' + par_dim_tag + '.nii.gz')
fmask = pjoin(dir_src,
'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=par_b0_threshold)
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
sphere = get_sphere('symmetric724')
response, ratio = auto_response(gtab,
data,
roi_radius=par_ar_radius,
fa_thr=par_ar_fa_th)
# print('Response function', response)
# Model fitting
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd_model.fit(data, mask=mask)
# Saving Spherical Harmonic Coefficient
out_peaks = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, out_peaks), csd_fit.shm_coeff, affine)
def white_matter_mask_FA(dir_src, dir_out, verbose=False):
src_fa = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
src_md = 'data_' + par_b_tag + '_' + par_dim_tag + '_MD.nii.gz'
FA, affine = load_nifti(pjoin(dir_src, src_fa), verbose)
MD, _ = load_nifti(pjoin(dir_src, src_md), verbose)
wm_mask = (np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011/2.))))
out_wm = 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, out_wm), wm_mask.astype('f4'), affine)
def white_matter_mask_FA(dir_src, dir_out, verbose=False):
src_fa = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
src_md = 'data_' + par_b_tag + '_' + par_dim_tag + '_MD.nii.gz'
FA, affine = load_nifti(pjoin(dir_src, src_fa), verbose)
MD, _ = load_nifti(pjoin(dir_src, src_md), verbose)
wm_mask = (np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011 / 2.))))
out_wm = 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
save_nifti(pjoin(dir_out, out_wm), wm_mask.astype('f4'), affine)
def tracking_eudx4csd(dir_src, dir_out, verbose=False):
# Load data
fbval = pjoin(dir_src, 'bvals_' + par_b_tag)
fbvec = pjoin(dir_src, 'bvecs_' + par_b_tag)
fdwi = pjoin(dir_src, 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
#fmask = pjoin(dir_src, 'nodif_brain_mask_' + par_dim_tag + '.nii.gz')
fmask = pjoin(dir_src,
'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=par_b0_threshold)
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
sphere = get_sphere('symmetric724')
response, ratio = auto_response(gtab,
data,
roi_radius=par_ar_radius,
fa_thr=par_ar_fa_th)
# print('Response function', response)
# Model fitting
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_peaks = peaks_from_model(csd_model,
data,
sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
# Computation of streamlines
streamlines = EuDX(csd_peaks.peak_values,
csd_peaks.peak_indices,
seeds=par_eudx_seeds,
odf_vertices=sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = (par_dim_vox, ) * 3
dims = mask.shape[:3]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_csd_tag + '_' + par_eudx_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
nib.trackvis.write(trk_out, strm, hdr, points_space='voxel')
def tracking_eudx4csd(dir_src, dir_out, verbose=False):
# Load data
fbval = pjoin(dir_src, 'bvals_' + par_b_tag)
fbvec = pjoin(dir_src, 'bvecs_' + par_b_tag)
fdwi = pjoin(dir_src, 'data_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
#fmask = pjoin(dir_src, 'nodif_brain_mask_' + par_dim_tag + '.nii.gz')
fmask = pjoin(dir_src, 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=par_b0_threshold)
data, affine = load_nifti(fdwi, verbose)
mask, _ = load_nifti(fmask, verbose)
sphere = get_sphere('symmetric724')
response, ratio = auto_response(gtab, data, roi_radius=par_ar_radius,
fa_thr=par_ar_fa_th)
# print('Response function', response)
# Model fitting
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_peaks = peaks_from_model(csd_model,
data,
sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
# Computation of streamlines
streamlines = EuDX(csd_peaks.peak_values,
csd_peaks.peak_indices,
seeds=par_eudx_seeds,
odf_vertices= sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = (par_dim_vox,) * 3
dims = mask.shape[:3]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_csd_tag + '_' + par_eudx_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
nib.trackvis.write(trk_out, strm, hdr, points_space='voxel')
def single_shell_extraction(dir_src, dir_out, verbose=False):
fbval = pjoin(dir_src, 'bvals')
fbvec = pjoin(dir_src, 'bvecs')
fmask = pjoin(dir_src, 'nodif_brain_mask.nii.gz')
fdwi = pjoin(dir_src, 'data.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
data, affine = load_nifti(fdwi, verbose)
if par_b_shell == 1000:
sind = (bvals < 10) | ((bvals < 1100) & (bvals > 900))
elif par_b_shell == 2000:
sind = (bvals < 10) | ((bvals < 2100) & (bvals > 1900))
elif par_b_shell == 3000:
sind = (bvals < 10) | ((bvals < 3100) & (bvals > 2900))
shell_data = data[..., sind]
shell_gtab = gradient_table(bvals[sind], bvecs[sind, :],
b0_threshold=par_b0_threshold)
fname = 'data_' + par_b_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), shell_data, affine)
np.savetxt(pjoin(dir_out, 'bvals_' + par_b_tag), shell_gtab.bvals)
np.savetxt(pjoin(dir_out, 'bvecs_' + par_b_tag), shell_gtab.bvecs.T)
def single_shell_extraction(dir_src, dir_out, verbose=False):
fbval = pjoin(dir_src, 'bvals')
fbvec = pjoin(dir_src, 'bvecs')
fmask = pjoin(dir_src, 'nodif_brain_mask.nii.gz')
fdwi = pjoin(dir_src, 'data.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
data, affine = load_nifti(fdwi, verbose)
if par_b_shell == 1000:
sind = (bvals < 10) | ((bvals < 1100) & (bvals > 900))
elif par_b_shell == 2000:
sind = (bvals < 10) | ((bvals < 2100) & (bvals > 1900))
elif par_b_shell == 3000:
sind = (bvals < 10) | ((bvals < 3100) & (bvals > 2900))
shell_data = data[..., sind]
shell_gtab = gradient_table(bvals[sind],
bvecs[sind, :],
b0_threshold=par_b0_threshold)
fname = 'data_' + par_b_tag + '.nii.gz'
save_nifti(pjoin(dir_out, fname), shell_data, affine)
np.savetxt(pjoin(dir_out, 'bvals_' + par_b_tag), shell_gtab.bvals)
np.savetxt(pjoin(dir_out, 'bvecs_' + par_b_tag), shell_gtab.bvecs.T)
def tracking_maxodf(dir_src, dir_out, verbose=False):
wm_name = 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
wm_mask, affine = load_nifti(pjoin(dir_src, wm_name), verbose)
sh_name = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
sh, _ = load_nifti(pjoin(dir_src, sh_name), verbose)
sphere = get_sphere('symmetric724')
classifier = ThresholdTissueClassifier(wm_mask.astype('f8'), .5)
classifier = BinaryTissueClassifier(wm_mask)
max_dg = DeterministicMaximumDirectionGetter.from_shcoeff(sh, max_angle=par_trk_max_angle, sphere=sphere)
seeds = utils.seeds_from_mask(wm_mask, density=2, affine=affine)
streamlines = LocalTracking(max_dg, classifier, seeds, affine, step_size=par_trk_step_size)
streamlines = list(streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_trk_odf_tag + '.trk'
save_trk(pjoin(dir_out, trk_name), streamlines, affine, wm_mask.shape)
def white_matter_mask_wmparc(dir_src, dir_out, verbose=False):
fwmparc_src = pjoin(dir_src, 'wmparc.nii.gz')
fribbon_src = pjoin(dir_src, 'ribbon.nii.gz')
wmparc, affine = load_nifti(fwmparc_src)
ribbon, affine = load_nifti(fribbon_src)
mask = np.zeros_like(wmparc)
for label in ribbon_structures:
mask[ribbon == label] = 1
for label in wmparc_structures + wmparc_cc_structures:
mask[wmparc == label] = 1
for label in wmparc_del_structures + wmparc_del_structures2:
mask[wmparc == label] = 0
mask = mask.astype('f8')
mask2, affine2 = resample(mask, affine,
(0.7,) * 3, (par_dim_vox,) * 3, order=0)
wm_out = "wm_mask_%s_%s_%s.nii.gz" % (par_b_tag, par_dim_tag, par_wmp_tag)
save_nifti(pjoin(dir_out, wm_out), mask2, affine2)
def white_matter_mask_wmparc(dir_src, dir_out, verbose=False):
fwmparc_src = pjoin(dir_src, 'wmparc.nii.gz')
fribbon_src = pjoin(dir_src, 'ribbon.nii.gz')
wmparc, affine = load_nifti(fwmparc_src)
ribbon, affine = load_nifti(fribbon_src)
mask = np.zeros_like(wmparc)
for label in ribbon_structures:
mask[ribbon == label] = 1
for label in wmparc_structures + wmparc_cc_structures:
mask[wmparc == label] = 1
for label in wmparc_del_structures + wmparc_del_structures2:
mask[wmparc == label] = 0
mask = mask.astype('f8')
mask2, affine2 = resample(mask, affine,
(0.7,) * 3, (par_dim_vox,) * 3, order=0)
wm_out = "wm_mask_%s_%s_%s.nii.gz" % (par_b_tag, par_dim_tag, par_wmp_tag)
save_nifti(pjoin(dir_out, wm_out), mask2, affine2)
