Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

adding separate example output directories to avoid name collisions #490

Merged
merged 1 commit into from
Oct 6, 2020
Merged

adding separate example output directories to avoid name collisions #490

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
34 changes: 20 additions & 14 deletions examples/plot_recobundles.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,9 @@
import AFQ.segmentation as seg
import AFQ.api as api

# Target directory for this example's output files
working_dir = "./recobundles"

dpd.fetch_stanford_hardi()

hardi_dir = op.join(fetcher.dipy_home, "stanford_hardi")
Expand All @@ -44,32 +47,33 @@
img = nib.load(hardi_fdata)

print("Calculating DTI...")
if not op.exists('./dti_FA.nii.gz'):
if not op.exists(op.join(working_dir, 'dti_FA.nii.gz')):
dti_params = dti.fit_dti(hardi_fdata, hardi_fbval, hardi_fbvec,
out_dir='.')
out_dir=working_dir)
else:
dti_params = {'FA': './dti_FA.nii.gz',
'params': './dti_params.nii.gz'}
dti_params = {'FA': op.join(working_dir, 'dti_FA.nii.gz'),
'params': op.join(working_dir, 'dti_params.nii.gz')}

FA_img = nib.load(dti_params['FA'])
FA_data = FA_img.get_fdata()

print("Registering to template...")
MNI_T2_img = afd.read_mni_template()
if not op.exists('mapping.nii.gz'):
if not op.exists(op.join(working_dir, 'mapping.nii.gz')):
import dipy.core.gradients as dpg
gtab = dpg.gradient_table(hardi_fbval, hardi_fbvec)
warped_hardi, mapping = reg.syn_register_dwi(hardi_fdata, gtab,
template=MNI_T2_img)
reg.write_mapping(mapping, './mapping.nii.gz')
reg.write_mapping(mapping, op.join(working_dir, 'mapping.nii.gz'))
else:
mapping = reg.read_mapping('./mapping.nii.gz', img, MNI_T2_img)
mapping = reg.read_mapping(op.join(working_dir, 'mapping.nii.gz'), img,
MNI_T2_img)

bundle_names = ["CST", "UF", "CC_ForcepsMajor", "CC_ForcepsMinor", "OR", "VOF"]
bundles = api.make_bundle_dict(bundle_names=bundle_names, seg_algo="reco80")

print("Tracking...")
if not op.exists('dti_streamlines_reco.trk'):
if not op.exists(op.join(working_dir, 'dti_streamlines_reco.trk')):
seed_roi = np.zeros(img.shape[:-1])
for bundle in bundles:
if bundle != 'whole_brain':
Expand All @@ -86,7 +90,7 @@
np.linalg.inv(MNI_T2_img.affine)))

sft = StatefulTractogram(sl_xform, img, Space.RASMM)
save_tractogram(sft, f'./{bundle}_atlas.trk')
save_tractogram(sft, op.join(working_dir, f'{bundle}_atlas.trk'))

sl_xform = dts.Streamlines(
dtu.transform_tracking_output(sl_xform,
Expand All @@ -98,15 +102,17 @@
sl_as_idx[:, 1],
sl_as_idx[:, 2]] = 1

nib.save(nib.Nifti1Image(seed_roi, img.affine), 'seed_roi.nii.gz')
nib.save(nib.Nifti1Image(seed_roi, img.affine),
op.join(working_dir, 'seed_roi.nii.gz'))
sft = aft.track(dti_params['params'], seed_mask=seed_roi,
directions='det', stop_mask=FA_data,
stop_threshold=0.1)
print(len(sft.streamlines))
save_tractogram(sft, './dti_streamlines_reco.trk',
save_tractogram(sft, op.join(working_dir, 'dti_streamlines_reco.trk'),
bbox_valid_check=False)
else:
sft = load_tractogram('./dti_streamlines_reco.trk', img)
sft = load_tractogram(op.join(working_dir, 'dti_streamlines_reco.trk'),
img)

print("Segmenting fiber groups...")
segmentation = seg.Segmentation(seg_algo='reco80',
Expand All @@ -127,7 +133,7 @@
sft = StatefulTractogram(fiber_groups[kk].streamlines,
img,
Space.RASMM)
save_tractogram(sft, './%s_reco.trk' % kk,
save_tractogram(sft, op.join(working_dir, '%s_reco.trk' % kk),
bbox_valid_check=False)


Expand All @@ -137,7 +143,7 @@

fig, ax = plt.subplots(1)
sft = load_tractogram(
f'./{bundle}_reco.trk',
op.join(working_dir, f'{bundle}_reco.trk'),
img,
to_space=Space.VOX,
bbox_valid_check=False)
Expand Down
34 changes: 19 additions & 15 deletions examples/plot_tract_profile.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,8 @@
import logging
logging.basicConfig(level=logging.INFO)

# Target directory for this example's output files
working_dir = "./tract_profile"

##########################################################################
# Get example data:
Expand All @@ -48,12 +50,12 @@
# -------------------------

print("Calculating DTI...")
if not op.exists('./dti_FA.nii.gz'):
if not op.exists(op.join(working_dir, 'dti_FA.nii.gz')):
dti_params = dti.fit_dti(hardi_fdata, hardi_fbval, hardi_fbvec,
out_dir='.')
out_dir=working_dir)
else:
dti_params = {'FA': './dti_FA.nii.gz',
'params': './dti_params.nii.gz'}
dti_params = {'FA': op.join(working_dir, 'dti_FA.nii.gz'),
'params': op.join(working_dir, 'dti_params.nii.gz')}

FA_img = nib.load(dti_params['FA'])
FA_data = FA_img.get_fdata()
Expand All @@ -75,14 +77,15 @@
#
print("Registering to template...")
MNI_T2_img = afd.read_mni_template()
if not op.exists('mapping.nii.gz'):
if not op.exists(op.join(working_dir, 'mapping.nii.gz')):
import dipy.core.gradients as dpg
gtab = dpg.gradient_table(hardi_fbval, hardi_fbvec)

warped_hardi, mapping = reg.syn_register_dwi(hardi_fdata, gtab)
reg.write_mapping(mapping, './mapping.nii.gz')
reg.write_mapping(mapping, op.join(working_dir, 'mapping.nii.gz'))
else:
mapping = reg.read_mapping('./mapping.nii.gz', img, MNI_T2_img)
mapping = reg.read_mapping(op.join(working_dir, 'mapping.nii.gz'),
img, MNI_T2_img)


##########################################################################
Expand All @@ -104,7 +107,7 @@
# algorithm. For speed, we seed only within the waypoint ROIs for each bundle.

print("Tracking...")
if not op.exists('dti_streamlines.trk'):
if not op.exists(op.join(working_dir, 'dti_streamlines.trk')):
seed_roi = np.zeros(img.shape[:-1])
for bundle in bundles:
for idx, roi in enumerate(bundles[bundle]['ROIs']):
Expand All @@ -116,17 +119,17 @@
bundle_name=bundle)

nib.save(nib.Nifti1Image(warped_roi.astype(float), img.affine),
f"{bundle}_{idx+1}.nii.gz")
op.join(working_dir, f"{bundle}_{idx+1}.nii.gz"))
# Add voxels that aren't there yet:
seed_roi = np.logical_or(seed_roi, warped_roi)
nib.save(nib.Nifti1Image(seed_roi.astype(float), img.affine),
'seed_roi.nii.gz')
op.join(working_dir, 'seed_roi.nii.gz'))
sft = aft.track(dti_params['params'], seed_mask=seed_roi,
stop_mask=FA_data, stop_threshold=0.1)
save_tractogram(sft, './dti_streamlines.trk',
save_tractogram(sft, op.join(working_dir, 'dti_streamlines.trk'),
bbox_valid_check=False)
else:
sft = load_tractogram('./dti_streamlines.trk', img)
sft = load_tractogram(op.join(working_dir, 'dti_streamlines.trk'), img)

sft.to_vox()

Expand Down Expand Up @@ -167,12 +170,12 @@
print(f"Afer cleaning: {len(new_fibers)} streamlines")

idx_in_global = fiber_groups[bundle]['idx'][idx_in_bundle]
np.save(f'{bundle}_idx.npy', idx_in_global)
np.save(op.join(working_dir, f'{bundle}_idx.npy'), idx_in_global)
sft = StatefulTractogram(new_fibers.streamlines,
img,
Space.VOX)
sft.to_rasmm()
save_tractogram(sft, f'./{bundle}_afq.trk',
save_tractogram(sft, op.join(working_dir, f'{bundle}_afq.trk'),
bbox_valid_check=False)


Expand All @@ -186,7 +189,8 @@

print("Extracting tract profiles...")
for bundle in bundles:
sft = load_tractogram(f'./{bundle}_afq.trk', img, to_space=Space.VOX)
sft = load_tractogram(op.join(working_dir, f'{bundle}_afq.trk'),
img, to_space=Space.VOX)
fig, ax = plt.subplots(1)
weights = gaussian_weights(sft.streamlines)
profile = afq_profile(FA_data, sft.streamlines,
Expand Down