def _get_direction_getter(args, mask_data):
sh_data = nib.load(args.sh_file).get_data().astype('float64')
sphere = HemiSphere.from_sphere(get_sphere(args.sphere))
theta = get_theta(args.theta, args.algo)
if args.algo in ['det', 'prob']:
if args.algo == 'det':
dg_class = DeterministicMaximumDirectionGetter
else:
dg_class = ProbabilisticDirectionGetter
return dg_class.from_shcoeff(shcoeff=sh_data,
max_angle=theta,
sphere=sphere,
basis_type=args.sh_basis,
relative_peak_threshold=args.sf_threshold)
# Code for type EUDX. We don't use peaks_from_model
# because we want the peaks from the provided sh.
sh_shape_3d = sh_data.shape[:-1]
npeaks = 5
peak_dirs = np.zeros((sh_shape_3d + (npeaks, 3)))
peak_values = np.zeros((sh_shape_3d + (npeaks, )))
peak_indices = np.full((sh_shape_3d + (npeaks, )), -1, dtype='int')
b_matrix = get_b_matrix(find_order_from_nb_coeff(sh_data), sphere,
args.sh_basis)
for idx in np.ndindex(sh_shape_3d):
if not mask_data[idx]:
continue
directions, values, indices = get_maximas(sh_data[idx], sphere,
b_matrix, args.sf_threshold,
0)
if values.shape[0] != 0:
n = min(npeaks, values.shape[0])
peak_dirs[idx][:n] = directions[:n]
peak_values[idx][:n] = values[:n]
peak_indices[idx][:n] = indices[:n]
dg = PeaksAndMetrics()
dg.sphere = sphere
dg.peak_dirs = peak_dirs
dg.peak_values = peak_values
dg.peak_indices = peak_indices
dg.ang_thr = theta
dg.qa_thr = args.sf_threshold
return dg
def test_io_save_peaks_error():
with InTemporaryDirectory():
fname = 'test.pam5'
pam = PeaksAndMetrics()
npt.assert_raises(IOError, save_peaks, 'test.pam', pam)
npt.assert_raises(ValueError, save_peaks, fname, pam)
pam.affine = np.eye(4)
pam.peak_dirs = np.random.rand(10, 10, 10, 5, 3)
pam.peak_values = np.zeros((10, 10, 10, 5))
pam.peak_indices = np.zeros((10, 10, 10, 5))
pam.shm_coeff = np.zeros((10, 10, 10, 45))
pam.sphere = default_sphere
pam.B = np.zeros((45, default_sphere.vertices.shape[0]))
pam.total_weight = 0.5
pam.ang_thr = 60
pam.gfa = np.zeros((10, 10, 10))
pam.qa = np.zeros((10, 10, 10, 5))
pam.odf = np.zeros((10, 10, 10, default_sphere.vertices.shape[0]))
def test_io_peaks():
with InTemporaryDirectory():
fname = 'test.pam5'
pam = PeaksAndMetrics()
pam.affine = np.eye(4)
pam.peak_dirs = np.random.rand(10, 10, 10, 5, 3)
pam.peak_values = np.zeros((10, 10, 10, 5))
pam.peak_indices = np.zeros((10, 10, 10, 5))
pam.shm_coeff = np.zeros((10, 10, 10, 45))
pam.sphere = default_sphere
pam.B = np.zeros((45, default_sphere.vertices.shape[0]))
pam.total_weight = 0.5
pam.ang_thr = 60
pam.gfa = np.zeros((10, 10, 10))
pam.qa = np.zeros((10, 10, 10, 5))
pam.odf = np.zeros((10, 10, 10, default_sphere.vertices.shape[0]))
save_peaks(fname, pam)
pam2 = load_peaks(fname, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2.peak_dirs)
pam2.affine = None
fname2 = 'test2.pam5'
save_peaks(fname2, pam2, np.eye(4))
pam2_res = load_peaks(fname2, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2_res.peak_dirs)
pam3 = load_peaks(fname2, verbose=False)
for attr in [
'peak_dirs', 'peak_values', 'peak_indices', 'gfa', 'qa',
'shm_coeff', 'B', 'odf'
]:
npt.assert_array_equal(getattr(pam3, attr), getattr(pam, attr))
npt.assert_equal(pam3.total_weight, pam.total_weight)
npt.assert_equal(pam3.ang_thr, pam.ang_thr)
npt.assert_array_almost_equal(pam3.sphere.vertices,
pam.sphere.vertices)
fname3 = 'test3.pam5'
pam4 = PeaksAndMetrics()
npt.assert_raises(ValueError, save_peaks, fname3, pam4)
fname4 = 'test4.pam5'
del pam.affine
save_peaks(fname4, pam, affine=None)
fname5 = 'test5.pkm'
npt.assert_raises(IOError, save_peaks, fname5, pam)
pam.affine = np.eye(4)
fname6 = 'test6.pam5'
save_peaks(fname6, pam, verbose=True)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=False)
pam.shm_coeff = np.zeros((10, 10, 10, 45))
del pam.odf
save_peaks(fname6, pam)
pam_tmp = load_peaks(fname6, True)
npt.assert_equal(pam_tmp.odf, None)
fname7 = 'test7.paw'
npt.assert_raises(IOError, load_peaks, fname7)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=True)
fname_shm = 'shm.nii.gz'
fname_dirs = 'dirs.nii.gz'
fname_values = 'values.nii.gz'
fname_indices = 'indices.nii.gz'
fname_gfa = 'gfa.nii.gz'
pam.shm_coeff = np.ones((10, 10, 10, 45))
peaks_to_niftis(pam,
fname_shm,
fname_dirs,
fname_values,
fname_indices,
fname_gfa,
reshape_dirs=False)
os.path.isfile(fname_shm)
os.path.isfile(fname_dirs)
os.path.isfile(fname_values)
os.path.isfile(fname_indices)
os.path.isfile(fname_gfa)
def load_peaks(fname, verbose=False):
""" Load a PeaksAndMetrics HDF5 file (PAM5)
Parameters
----------
fname : string
Filename of PAM5 file.
verbose : bool
Print summary information about the loaded file.
Returns
-------
pam : PeaksAndMetrics object
"""
if os.path.splitext(fname)[1].lower() != '.pam5':
raise IOError('This function supports only PAM5 (HDF5) files')
f = h5py.File(fname, 'r')
pam = PeaksAndMetrics()
pamh = f['pam']
version = f.attrs['version']
if version != '0.0.1':
raise IOError('Incorrect PAM5 file version {0}'.format(version,))
try:
affine = pamh['affine'][:]
except KeyError:
affine = None
peak_dirs = pamh['peak_dirs'][:]
peak_values = pamh['peak_values'][:]
peak_indices = pamh['peak_indices'][:]
try:
shm_coeff = pamh['shm_coeff'][:]
except KeyError:
shm_coeff = None
sphere_vertices = pamh['sphere_vertices'][:]
try:
odf = pamh['odf'][:]
except KeyError:
odf = None
pam.affine = affine
pam.peak_dirs = peak_dirs
pam.peak_values = peak_values
pam.peak_indices = peak_indices
pam.shm_coeff = shm_coeff
pam.sphere = Sphere(xyz=sphere_vertices)
pam.B = pamh['B'][:]
pam.total_weight = pamh['total_weight'][:][0]
pam.ang_thr = pamh['ang_thr'][:][0]
pam.gfa = pamh['gfa'][:]
pam.qa = pamh['qa'][:]
pam.odf = odf
f.close()
if verbose:
print('PAM5 version')
print(version)
print('Affine')
print(pam.affine)
print('Dirs shape')
print(pam.peak_dirs.shape)
print('SH shape')
if pam.shm_coeff is not None:
print(pam.shm_coeff.shape)
else:
print('None')
print('ODF shape')
if pam.odf is not None:
print(pam.odf.shape)
else:
print('None')
print('Total weight')
print(pam.total_weight)
print('Angular threshold')
print(pam.ang_thr)
print('Sphere vertices shape')
print(pam.sphere.vertices.shape)
return pam
def load_peaks(fname, verbose=False):
""" Load a PeaksAndMetrics HDF5 file (PAM5)
Parameters
----------
fname : string
Filename of PAM5 file.
verbose : bool
Print summary information about the loaded file.
Returns
-------
pam : PeaksAndMetrics object
"""
if os.path.splitext(fname)[1] != '.pam5':
raise IOError('This function supports only PAM5 (HDF5) files')
if TABLES_LESS_3_0:
func_open_file = tables.openFile
else:
func_open_file = tables.open_file
f = func_open_file(fname, 'r')
pam = PeaksAndMetrics()
pamh = f.root.pam
version = f.root.version[0].decode()
if version != '0.0.1':
raise IOError('Incorrect PAM5 file version')
try:
affine = pamh.affine[:]
except tables.NoSuchNodeError:
affine = None
peak_dirs = pamh.peak_dirs[:]
peak_values = pamh.peak_values[:]
peak_indices = pamh.peak_indices[:]
try:
shm_coeff = pamh.shm_coeff[:]
except tables.NoSuchNodeError:
shm_coeff = None
sphere_vertices = pamh.sphere_vertices[:]
try:
odf = pamh.odf[:]
except tables.NoSuchNodeError:
odf = None
pam.affine = affine
pam.peak_dirs = peak_dirs
pam.peak_values = peak_values
pam.peak_indices = peak_indices
pam.shm_coeff = shm_coeff
pam.sphere = Sphere(xyz=sphere_vertices)
pam.B = pamh.B[:]
pam.total_weight = pamh.total_weight[:][0]
pam.ang_thr = pamh.ang_thr[:][0]
pam.gfa = pamh.gfa[:]
pam.qa = pamh.qa[:]
pam.odf = odf
f.close()
if verbose:
print('PAM5 version')
print(version)
print('Affine')
print(pam.affine)
print('Dirs shape')
print(pam.peak_dirs.shape)
print('SH shape')
if pam.shm_coeff is not None:
print(pam.shm_coeff.shape)
else:
print('None')
print('ODF shape')
if pam.odf is not None:
print(pam.odf.shape)
else:
print('None')
print('Total weight')
print(pam.total_weight)
print('Angular threshold')
print(pam.ang_thr)
print('Sphere vertices shape')
print(pam.sphere.vertices.shape)
return pam
def _get_direction_getter(args):
odf_data = nib.load(args.in_odf).get_fdata(dtype=np.float32)
sphere = HemiSphere.from_sphere(get_sphere(args.sphere))
theta = get_theta(args.theta, args.algo)
non_zeros_count = np.count_nonzero(np.sum(odf_data, axis=-1))
non_first_val_count = np.count_nonzero(np.argmax(odf_data, axis=-1))
if args.algo in ['det', 'prob']:
if non_first_val_count / non_zeros_count > 0.5:
logging.warning('Input detected as peaks. Input should be'
'fodf for det/prob, verify input just in case.')
if args.algo == 'det':
dg_class = DeterministicMaximumDirectionGetter
else:
dg_class = ProbabilisticDirectionGetter
return dg_class.from_shcoeff(
shcoeff=odf_data, max_angle=theta, sphere=sphere,
basis_type=args.sh_basis,
relative_peak_threshold=args.sf_threshold)
elif args.algo == 'eudx':
# Code for type EUDX. We don't use peaks_from_model
# because we want the peaks from the provided sh.
odf_shape_3d = odf_data.shape[:-1]
dg = PeaksAndMetrics()
dg.sphere = sphere
dg.ang_thr = theta
dg.qa_thr = args.sf_threshold
# Heuristic to find out if the input are peaks or fodf
# fodf are always around 0.15 and peaks around 0.75
if non_first_val_count / non_zeros_count > 0.5:
logging.info('Input detected as peaks.')
nb_peaks = odf_data.shape[-1] // 3
slices = np.arange(0, 15+1, 3)
peak_values = np.zeros(odf_shape_3d+(nb_peaks,))
peak_indices = np.zeros(odf_shape_3d+(nb_peaks,))
for idx in np.argwhere(np.sum(odf_data, axis=-1)):
idx = tuple(idx)
for i in range(nb_peaks):
peak_values[idx][i] = np.linalg.norm(
odf_data[idx][slices[i]:slices[i+1]], axis=-1)
peak_indices[idx][i] = sphere.find_closest(
odf_data[idx][slices[i]:slices[i+1]])
dg.peak_dirs = odf_data
else:
logging.info('Input detected as fodf.')
npeaks = 5
peak_dirs = np.zeros((odf_shape_3d + (npeaks, 3)))
peak_values = np.zeros((odf_shape_3d + (npeaks, )))
peak_indices = np.full((odf_shape_3d + (npeaks, )), -1, dtype='int')
b_matrix = get_b_matrix(
find_order_from_nb_coeff(odf_data), sphere, args.sh_basis)
for idx in np.argwhere(np.sum(odf_data, axis=-1)):
idx = tuple(idx)
directions, values, indices = get_maximas(odf_data[idx],
sphere, b_matrix,
args.sf_threshold, 0)
if values.shape[0] != 0:
n = min(npeaks, values.shape[0])
peak_dirs[idx][:n] = directions[:n]
peak_values[idx][:n] = values[:n]
peak_indices[idx][:n] = indices[:n]
dg.peak_dirs = peak_dirs
dg.peak_values = peak_values
dg.peak_indices = peak_indices
return dg