def mean_bundle_weight(folder_name, bundle_file_name):
if not os.path.exists(bundle_file_name):
mean_bundle = np.nan
else:
bundle = load_trk(bundle_file_name, "same", bbox_valid_check=False)
bundle = bundle.streamlines
bvec_file = load_dwi_files(folder_name)[6]
mean_vols = weighting_streamlines(folder_name,
bundle,
bvec_file,
weight_by='AxPasi7')
mean_bundle = np.nanmean(mean_vols)
return mean_bundle
def calc_cc_part_val(streamlines, mask, affine, calc_type='mean'):
from dipy.tracking import utils
from dipy.tracking.streamline import Streamlines
from weighted_tracts import weighting_streamlines
masked_streamlines = utils.target(streamlines, affine, mask)
masked_streamlines = Streamlines(masked_streamlines)
weights = weighting_streamlines(dir_name,
masked_streamlines,
affine,
show=False,
weight_by='3_2_AxPasi7')
if 'mean' in calc_type:
val = np.nanmean(weights)
elif 'median' in calc_type:
val = np.nanmedian(weights)
return val
os.path.join(main_fec, '*/'))
all_subj_folders = []
for sfol in subj_folder[::]:
for folder_name in glob(os.path.join(sfol, '*/'))[::]:
dir_name = folder_name + 'streamlines'
gtab, data, affine, labels, white_matter, nii_file, bvec_file = load_dwi_files(
folder_name, small_delta=15)
#tract_path = f'{dir_name}{n}_wholebrain_5d_labmask_msmt.trk'
for tfiles in glob(os.path.join(dir_name, '*')):
if 'wholebrain_4d_labmask.trk' in tfiles:
tract_path = os.path.join(dir_name, tfiles)
continue
streamlines = load_ft(tract_path, nii_file)
mean_vol_per_tract = weighting_streamlines(folder_name,
streamlines,
bvec_file,
weight_by='3_2_AxPasi7')
vox_vol = vol_map(streamlines, affine, mean_vol_per_tract,
white_matter.shape)
empty_header = nib.Nifti1Header()
#vol_img = nib.Nifti1Image(vox_vol,affine,empty_header)
#vol_file_name = folder_name+f'{n}_ADD_along_streamlines.nii'
#nib.save(vol_img,vol_file_name)
vox_vol_masked = vox_vol * white_matter
masked_vol_img = nib.Nifti1Image(vox_vol_masked, affine,
empty_header)
masked_vol_file_name = os.path.join(
folder_name, 'ADD_along_streamlines_WMmasked.nii')
nib.save(masked_vol_img, masked_vol_file_name)
for subj in os.listdir(group):
folder_name = os.path.join(group, subj)
full_bund_name = f'{subj}_{file_bundle_name}'
bundle_file_name = rf'{folder_name}\streamlines\{full_bund_name}.trk'
if not os.path.exists(bundle_file_name):
print('Moving on!')
vec_mean.append(np.nan)
vec_median.append(np.nan)
continue
bundle = load_trk(bundle_file_name, "same", bbox_valid_check=False)
bundle = bundle.streamlines
bvec_file = load_dwi_files(folder_name)[6]
mean_vols = weighting_streamlines(
folder_name,
bundle,
bvec_file,
weight_by='ADD_along_streamlines_WMmasked')
vec_mean.append(np.nanmean(mean_vols))
vec_median.append(np.nanmedian(mean_vols))
val_dict[bundle_name + '_mean_' +
str.split(group, os.sep)[-2]] = vec_mean
val_dict[bundle_name + '_median_' +
str.split(group, os.sep)[-2]] = vec_median
table_vals = pd.DataFrame(val_dict, index=os.listdir(group))
table_vals.to_excel(os.path.join(main_folder, 'values_avaraged_volume.xlsx'))
from weighted_tracts import weighting_streamlines, load_ft, load_dwi_files
from all_subj import all_subj_names, all_subj_folders
subj = all_subj_folders
names = all_subj_names
masks = ['cc_cortex_cleaned', 'wholebrain']
weight_by = '1.5_2_AxPasi5'
for n, s in zip(names, subj):
folder_name = r'C:\Users\Admin\my_scripts\Ax3D_Pack\V6\after_file_prep' + s
dir_name = folder_name + '\streamlines'
gtab, data, affine, labels, white_matter, nii_file, bvec_file = load_dwi_files(
folder_name)
for m in masks:
tract_path = dir_name + n + '_' + m + '.trk'
streamlines = load_ft(tract_path, nii_file)
fig_name = m + '_example'
weighting_streamlines(folder_name,
streamlines,
bvec_file,
weight_by='1.5_2_AxPasi5',
hue=[0.0, 1.0],
saturation=[0.0, 1.0],
scale=[2, 7],
fig_type=fig_name)
if __name__ == "__main__":
main_folder = r'F:\Hila\balance\eo\after'
bundle_name = 'SCP'
file_bundle_name = bundle_name + r'_mct01rt20_4d'
for subj in os.listdir(main_folder):
folder_name = os.path.join(main_folder, subj)
full_bund_name = f'{subj}_{file_bundle_name}'
bundle_file_name = rf'{folder_name}\streamlines\{full_bund_name}.trk'
if not os.path.exists(bundle_file_name):
print('Moving on!')
continue
bundle = load_trk(bundle_file_name, "same", bbox_valid_check=False)
bundle = bundle.streamlines
bvec_file = load_dwi_files(folder_name)[6]
mean_vols = weighting_streamlines(folder_name,
bundle,
bvec_file,
weight_by='AxPasi7')
mean_bundle = np.nanmean(mean_vols)
median_bundle = np.nanmedian(mean_vols)
print(
f'{subj} mean value for {bundle_name} : {mean_bundle:.2f}\n {subj} median value for {bundle_name} : {median_bundle:.2f} \n'
)