def test_orient_by_rois():
streamlines = [np.array([[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]),
np.array([[2, 0., 0.],
[1, 0., 0],
[0, 0, 0.]])]
# Make two ROIs:
mask1_vol = np.zeros((4, 4, 4), dtype=bool)
mask2_vol = np.zeros_like(mask1_vol)
mask1_vol[0, 0, 0] = True
mask2_vol[1, 0, 0] = True
mask1_coords = np.array(np.where(mask1_vol)).T
mask2_coords = np.array(np.where(mask2_vol)).T
# If there is an affine, we'll use it:
affine = np.eye(4)
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
x_streamlines = [sl + affine[:3, 3] for sl in streamlines]
for copy in [True, False]:
for sl, affine in zip([streamlines, x_streamlines], [None, affine]):
for mask1, mask2 in \
zip([mask1_vol, mask1_coords], [mask2_vol, mask2_coords]):
new_streamlines = orient_by_rois(sl, mask1, mask2,
affine=affine, copy=copy)
if copy:
flipped_sl = [sl[0], sl[1][::-1]]
else:
flipped_sl = [np.array([[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]),
np.array([[0, 0., 0.],
[1, 0., 0],
[2, 0, 0.]])]
if affine is not None:
flipped_sl = [s + affine[:3, 3] for s in flipped_sl]
npt.assert_equal(new_streamlines, flipped_sl)
def test_orient_by_rois():
streamlines = [np.array([[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]),
np.array([[2, 0., 0.],
[1, 0., 0],
[0, 0, 0.]])]
# Make two ROIs:
mask1_vol = np.zeros((4, 4, 4), dtype=bool)
mask2_vol = np.zeros_like(mask1_vol)
mask1_vol[0, 0, 0] = True
mask2_vol[1, 0, 0] = True
mask1_coords = np.array(np.where(mask1_vol)).T
mask2_coords = np.array(np.where(mask2_vol)).T
# If there is an affine, we'll use it:
affine = np.eye(4)
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
x_streamlines = [sl + affine[:3, 3] for sl in streamlines]
for copy in [True, False]:
for sl, affine in zip([streamlines, x_streamlines], [None, affine]):
for mask1, mask2 in \
zip([mask1_vol, mask1_coords], [mask2_vol, mask2_coords]):
new_streamlines = orient_by_rois(sl, mask1, mask2,
affine=affine, copy=copy)
if copy:
flipped_sl = [sl[0], sl[1][::-1]]
else:
flipped_sl = [np.array([[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]),
np.array([[0, 0., 0.],
[1, 0., 0],
[2, 0, 0.]])]
if affine is not None:
flipped_sl = [s + affine[:3, 3] for s in flipped_sl]
npt.assert_equal(new_streamlines, flipped_sl)
def test_orient_by_rois():
streamlines = [np.array([[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]),
np.array([[2, 0., 0.],
[1, 0., 0],
[0, 0, 0.]])]
# Make two ROIs:
mask1_vol = np.zeros((4, 4, 4), dtype=bool)
mask2_vol = np.zeros_like(mask1_vol)
mask1_vol[0, 0, 0] = True
mask2_vol[1, 0, 0] = True
mask1_coords = np.array(np.where(mask1_vol)).T
mask2_coords = np.array(np.where(mask2_vol)).T
# If there is an affine, we'll use it:
affine = np.eye(4)
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
x_streamlines = [sl + affine[:3, 3] for sl in streamlines]
# After reorientation, this should be the answer:
flipped_sl = [streamlines[0], streamlines[1][::-1]]
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=False)
npt.assert_equal(new_streamlines, flipped_sl)
npt.assert_(new_streamlines is not streamlines)
# Test with affine:
x_flipped_sl = [s + affine[:3, 3] for s in flipped_sl]
new_streamlines = orient_by_rois(x_streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=affine,
as_generator=False)
npt.assert_equal(new_streamlines, x_flipped_sl)
npt.assert_(new_streamlines is not x_streamlines)
# Test providing coord ROIs instead of vol ROIs:
new_streamlines = orient_by_rois(x_streamlines,
mask1_coords,
mask2_coords,
in_place=False,
affine=affine,
as_generator=False)
npt.assert_equal(new_streamlines, x_flipped_sl)
# Test with as_generator set to True
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = list(new_streamlines)
npt.assert_equal(ll, flipped_sl)
# Test with as_generator set to True and with the affine
new_streamlines = orient_by_rois(x_streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=affine,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = list(new_streamlines)
npt.assert_equal(ll, x_flipped_sl)
# Test with generator input:
new_streamlines = orient_by_rois(generate_sl(streamlines),
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = list(new_streamlines)
npt.assert_equal(ll, flipped_sl)
# Generator output cannot take a True `in_place` kwarg:
npt.assert_raises(ValueError, orient_by_rois, *[generate_sl(streamlines),
mask1_vol,
mask2_vol],
**dict(in_place=True,
affine=None,
as_generator=True))
# But you can input a generator and get a non-generator as output:
new_streamlines = orient_by_rois(generate_sl(streamlines),
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=False)
npt.assert_(not isinstance(new_streamlines, types.GeneratorType))
npt.assert_equal(new_streamlines, flipped_sl)
# Modify in-place:
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=True,
affine=None,
as_generator=False)
npt.assert_equal(new_streamlines, flipped_sl)
# The two objects are one and the same:
npt.assert_(new_streamlines is streamlines)
def test_orient_by_rois():
streamlines = [
np.array([[0, 0., 0], [1, 0., 0.], [2, 0., 0.]]),
np.array([[2, 0., 0.], [1, 0., 0], [0, 0, 0.]])
]
# Make two ROIs:
mask1_vol = np.zeros((4, 4, 4), dtype=bool)
mask2_vol = np.zeros_like(mask1_vol)
mask1_vol[0, 0, 0] = True
mask2_vol[1, 0, 0] = True
mask1_coords = np.array(np.where(mask1_vol)).T
mask2_coords = np.array(np.where(mask2_vol)).T
# If there is an affine, we'll use it:
affine = np.eye(4)
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
x_streamlines = [sl + affine[:3, 3] for sl in streamlines]
# After reorientation, this should be the answer:
flipped_sl = [streamlines[0], streamlines[1][::-1]]
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=False)
npt.assert_equal(new_streamlines, flipped_sl)
npt.assert_(new_streamlines is not streamlines)
# Test with affine:
x_flipped_sl = [s + affine[:3, 3] for s in flipped_sl]
new_streamlines = orient_by_rois(x_streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=affine,
as_generator=False)
npt.assert_equal(new_streamlines, x_flipped_sl)
npt.assert_(new_streamlines is not x_streamlines)
# Test providing coord ROIs instead of vol ROIs:
new_streamlines = orient_by_rois(x_streamlines,
mask1_coords,
mask2_coords,
in_place=False,
affine=affine,
as_generator=False)
npt.assert_equal(new_streamlines, x_flipped_sl)
# Test with as_generator set to True
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = list(new_streamlines)
npt.assert_equal(ll, flipped_sl)
# Test with as_generator set to True and with the affine
new_streamlines = orient_by_rois(x_streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=affine,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = list(new_streamlines)
npt.assert_equal(ll, x_flipped_sl)
# Test with generator input:
new_streamlines = orient_by_rois(generate_sl(streamlines),
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = list(new_streamlines)
npt.assert_equal(ll, flipped_sl)
# Generator output cannot take a True `in_place` kwarg:
npt.assert_raises(ValueError, orient_by_rois,
*[generate_sl(streamlines), mask1_vol, mask2_vol],
**dict(in_place=True, affine=None, as_generator=True))
# But you can input a generator and get a non-generator as output:
new_streamlines = orient_by_rois(generate_sl(streamlines),
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=False)
npt.assert_(not isinstance(new_streamlines, types.GeneratorType))
npt.assert_equal(new_streamlines, flipped_sl)
# Modify in-place:
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=True,
affine=None,
as_generator=False)
npt.assert_equal(new_streamlines, flipped_sl)
# The two objects are one and the same:
npt.assert_(new_streamlines is streamlines)
def segment(fdata,
fbval,
fbvec,
streamlines,
bundles,
reg_template=None,
mapping=None,
as_generator=True,
clip_to_roi=True,
**reg_kwargs):
"""
Segment streamlines into bundles.
Parameters
----------
fdata, fbval, fbvec : str
Full path to data, bvals, bvecs
streamlines : list of 2D arrays
Each array is a streamline, shape (3, N).
bundles: dict
The format is something like::
{'name': {'ROIs':[img, img], 'rules':[True, True]}}
reg_template : str or nib.Nifti1Image, optional.
Template to use for registration (defaults to the MNI T2)
mapping : DiffeomorphicMap object, str or nib.Nifti1Image, optional
A mapping between DWI space and a template. Defaults to generate this.
as_generator : bool, optional
Whether to generate the streamlines here, or return generators.
Default: True.
clip_to_roi : bool, optional
Whether to clip the streamlines between the ROIs
"""
img, data, gtab, mask = ut.prepare_data(fdata, fbval, fbvec)
xform_sl = [
s for s in dtu.move_streamlines(streamlines, np.linalg.inv(img.affine))
]
if reg_template is None:
reg_template = dpd.read_mni_template()
if mapping is None:
mapping = reg.syn_register_dwi(fdata,
gtab,
template=reg_template,
**reg_kwargs)
if isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
mapping = reg.read_mapping(mapping, img, reg_template)
fiber_groups = {}
for bundle in bundles:
select_sl = xform_sl
for ROI, rule in zip(bundles[bundle]['ROIs'],
bundles[bundle]['rules']):
data = ROI.get_data()
warped_ROI = patch_up_roi(
mapping.transform_inverse(data, interpolation='nearest'))
# This function requires lists as inputs:
select_sl = dts.select_by_rois(select_sl,
[warped_ROI.astype(bool)], [rule])
# Next, we reorient each streamline according to an ARBITRARY, but
# CONSISTENT order. To do this, we use the first ROI for which the rule
# is True as the first one to pass through, and the last ROI for which
# the rule is True as the last one to pass through:
# Indices where the 'rule' is True:
idx = np.where(bundles[bundle]['rules'])
orient_ROIs = [
bundles[bundle]['ROIs'][idx[0][0]],
bundles[bundle]['ROIs'][idx[0][-1]]
]
select_sl = dts.orient_by_rois(select_sl,
orient_ROIs[0].get_data(),
orient_ROIs[1].get_data(),
as_generator=True)
# XXX Implement clipping to the ROIs
# if clip_to_roi:
# dts.clip()
if as_generator:
fiber_groups[bundle] = select_sl
else:
fiber_groups[bundle] = list(select_sl)
return fiber_groups
def segment(fdata, fbval, fbvec, streamlines, bundles,
reg_template=None, mapping=None, as_generator=True, **reg_kwargs):
"""
generate : bool
Whether to generate the streamlines here, or return generators.
reg_template : template to use for registration (defaults to the MNI T2)
bundles: dict
The format is something like::
{'name': {'ROIs':[img, img], 'rules':[True, True]}}
"""
img, data, gtab, mask = ut.prepare_data(fdata, fbval, fbvec)
xform_sl = [s for s in dtu.move_streamlines(streamlines,
np.linalg.inv(img.affine))]
if reg_template is None:
reg_template = dpd.read_mni_template()
if mapping is None:
mapping = reg.syn_register_dwi(fdata, gtab, template=reg_template,
**reg_kwargs)
if isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
mapping = reg.read_mapping(mapping, img, reg_template)
fiber_groups = {}
for bundle in bundles:
select_sl = xform_sl
for ROI, rule in zip(bundles[bundle]['ROIs'],
bundles[bundle]['rules']):
data = ROI.get_data()
warped_ROI = patch_up_roi(mapping.transform_inverse(
data,
interpolation='nearest'))
# This function requires lists as inputs:
select_sl = dts.select_by_rois(select_sl,
[warped_ROI.astype(bool)],
[rule])
# Next, we reorient each streamline according to an ARBITRARY, but
# CONSISTENT order. To do this, we use the first ROI for which the rule
# is True as the first one to pass through, and the last ROI for which
# the rule is True as the last one to pass through:
# Indices where the 'rule' is True:
idx = np.where(bundles[bundle]['rules'])
orient_ROIs = [bundles[bundle]['ROIs'][idx[0][0]],
bundles[bundle]['ROIs'][idx[0][-1]]]
select_sl = dts.orient_by_rois(select_sl,
orient_ROIs[0].get_data(),
orient_ROIs[1].get_data(),
in_place=True)
if as_generator:
fiber_groups[bundle] = select_sl
else:
fiber_groups[bundle] = list(select_sl)
return fiber_groups
