def test_reduce_rois():
roi1 = np.zeros((4, 4, 4), dtype=np.bool)
roi2 = np.zeros((4, 4, 4), dtype=np.bool)
roi1[1, 1, 1] = 1
roi2[2, 2, 2] = 1
include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, True])
npt.assert_equal(include_roi, roi1 + roi2)
npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))
include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, False])
npt.assert_equal(include_roi, roi1)
npt.assert_equal(exclude_roi, roi2)
# Array input:
include_roi, exclude_roi = reduce_rois(np.array([roi1, roi2]),
[True, True])
npt.assert_equal(include_roi, roi1 + roi2)
npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))
def test_reduce_rois():
roi1 = np.zeros((4, 4, 4), dtype=np.bool)
roi2 = np.zeros((4, 4, 4), dtype=np.bool)
roi1[1, 1, 1] = 1
roi2[2, 2, 2] = 1
include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, True])
npt.assert_equal(include_roi, roi1 + roi2)
npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))
include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, False])
npt.assert_equal(include_roi, roi1)
npt.assert_equal(exclude_roi, roi2)
# Array input:
include_roi, exclude_roi = reduce_rois(np.array([roi1, roi2]),
[True, True])
npt.assert_equal(include_roi, roi1 + roi2)
npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))
def test_reduce_rois():
roi1 = np.zeros((4, 4, 4), dtype=bool)
roi2 = np.zeros((4, 4, 4), dtype=bool)
roi1[1, 1, 1] = 1
roi2[2, 2, 2] = 1
include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, True])
npt.assert_equal(include_roi, roi1 + roi2)
npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))
include_roi, exclude_roi = reduce_rois([roi1, roi2], [True, False])
npt.assert_equal(include_roi, roi1)
npt.assert_equal(exclude_roi, roi2)
# Array input:
include_roi, exclude_roi = reduce_rois(np.array([roi1, roi2]),
[True, True])
npt.assert_equal(include_roi, roi1 + roi2)
npt.assert_equal(exclude_roi, np.zeros((4, 4, 4)))
# Int and float input
roi1 = np.zeros((4, 4, 4), dtype=int)
roi2 = np.zeros((4, 4, 4), dtype=float)
npt.assert_warns(UserWarning, reduce_rois, [roi1], [True])
npt.assert_warns(UserWarning, reduce_rois, [roi2], [True])
def select_by_rois(streamlines, rois, include, mode=None, affine=None,
tol=None):
"""Select streamlines based on logical relations with several regions of
interest (ROIs). For example, select streamlines that pass near ROI1,
but only if they do not pass near ROI2.
Parameters
----------
streamlines : list
A list of candidate streamlines for selection
rois : list or ndarray
A list of 3D arrays, each with shape (x, y, z) corresponding to the
shape of the brain volume, or a 4D array with shape (n_rois, x, y,
z). Non-zeros in each volume are considered to be within the region
include : array or list
A list or 1D array of boolean values marking inclusion or exclusion
criteria. If a streamline is near any of the inclusion ROIs, it
should evaluate to True, unless it is also near any of the exclusion
ROIs.
mode : string, optional
One of {"any", "all", "either_end", "both_end"}, where a streamline is
associated with an ROI if:
"any" : any point is within tol from ROI. Default.
"all" : all points are within tol from ROI.
"either_end" : either of the end-points is within tol from ROI
"both_end" : both end points are within tol from ROI.
affine : ndarray
Affine transformation from voxels to streamlines. Default: identity.
tol : float
Distance (in the units of the streamlines, usually mm). If any
coordinate in the streamline is within this distance from the center
of any voxel in the ROI, the filtering criterion is set to True for
this streamline, otherwise False. Defaults to the distance between
the center of each voxel and the corner of the voxel.
Notes
-----
The only operation currently possible is "(A or B or ...) and not (X or Y
or ...)", where A, B are inclusion regions and X, Y are exclusion regions.
Returns
-------
generator
Generates the streamlines to be included based on these criteria.
See also
--------
:func:`dipy.tracking.utils.near_roi`
:func:`dipy.tracking.utils.reduce_rois`
Examples
--------
>>> streamlines = [np.array([[0, 0., 0.9],
... [1.9, 0., 0.]]),
... np.array([[0., 0., 0],
... [0, 1., 1.],
... [0, 2., 2.]]),
... np.array([[2, 2, 2],
... [3, 3, 3]])]
>>> mask1 = np.zeros((4, 4, 4), dtype=bool)
>>> mask2 = np.zeros_like(mask1)
>>> mask1[0, 0, 0] = True
>>> mask2[1, 0, 0] = True
>>> selection = select_by_rois(streamlines, [mask1, mask2],
... [True, True],
... tol=1)
>>> list(selection) # The result is a generator
[array([[ 0. , 0. , 0.9],
[ 1.9, 0. , 0. ]]), array([[ 0., 0., 0.],
[ 0., 1., 1.],
[ 0., 2., 2.]])]
>>> selection = select_by_rois(streamlines, [mask1, mask2],
... [True, False],
... tol=0.87)
>>> list(selection)
[array([[ 0., 0., 0.],
[ 0., 1., 1.],
[ 0., 2., 2.]])]
>>> selection = select_by_rois(streamlines, [mask1, mask2],
... [True, True],
... mode="both_end",
... tol=1.0)
>>> list(selection)
[array([[ 0. , 0. , 0.9],
[ 1.9, 0. , 0. ]])]
>>> mask2[0, 2, 2] = True
>>> selection = select_by_rois(streamlines, [mask1, mask2],
... [True, True],
... mode="both_end",
... tol=1.0)
>>> list(selection)
[array([[ 0. , 0. , 0.9],
[ 1.9, 0. , 0. ]]), array([[ 0., 0., 0.],
[ 0., 1., 1.],
[ 0., 2., 2.]])]
"""
if affine is None:
affine = np.eye(4)
# This calculates the maximal distance to a corner of the voxel:
dtc = dist_to_corner(affine)
if tol is None:
tol = dtc
elif tol < dtc:
w_s = "Tolerance input provided would create gaps in your"
w_s += " inclusion ROI. Setting to: %s" % dist_to_corner
warn(w_s)
tol = dtc
include_roi, exclude_roi = ut.reduce_rois(rois, include)
include_roi_coords = np.array(np.where(include_roi)).T
x_include_roi_coords = apply_affine(affine, include_roi_coords)
exclude_roi_coords = np.array(np.where(exclude_roi)).T
x_exclude_roi_coords = apply_affine(affine, exclude_roi_coords)
if mode is None:
mode = "any"
for sl in streamlines:
include = streamline_near_roi(sl, x_include_roi_coords, tol=tol,
mode=mode)
exclude = streamline_near_roi(sl, x_exclude_roi_coords, tol=tol,
mode=mode)
if include & ~exclude:
yield sl
def select_by_rois(streamlines,
rois,
include,
mode=None,
affine=None,
tol=None):
"""Select streamlines based on logical relations with several regions of
interest (ROIs). For example, select streamlines that pass near ROI1,
but only if they do not pass near ROI2.
Parameters
----------
streamlines : list
A list of candidate streamlines for selection
rois : list or ndarray
A list of 3D arrays, each with shape (x, y, z) corresponding to the
shape of the brain volume, or a 4D array with shape (n_rois, x, y,
z). Non-zeros in each volume are considered to be within the region
include : array or list
A list or 1D array of boolean values marking inclusion or exclusion
criteria. If a streamline is near any of the inclusion ROIs, it
should evaluate to True, unless it is also near any of the exclusion
ROIs.
mode : string, optional
One of {"any", "all", "either_end", "both_end"}, where a streamline is
associated with an ROI if:
"any" : any point is within tol from ROI. Default.
"all" : all points are within tol from ROI.
"either_end" : either of the end-points is within tol from ROI
"both_end" : both end points are within tol from ROI.
affine : ndarray
Affine transformation from voxels to streamlines. Default: identity.
tol : float
Distance (in the units of the streamlines, usually mm). If any
coordinate in the streamline is within this distance from the center
of any voxel in the ROI, the filtering criterion is set to True for
this streamline, otherwise False. Defaults to the distance between
the center of each voxel and the corner of the voxel.
Notes
-----
The only operation currently possible is "(A or B or ...) and not (X or Y
or ...)", where A, B are inclusion regions and X, Y are exclusion regions.
Returns
-------
generator
Generates the streamlines to be included based on these criteria.
See also
--------
:func:`dipy.tracking.utils.near_roi`
:func:`dipy.tracking.utils.reduce_rois`
Examples
--------
>>> streamlines = [np.array([[0, 0., 0.9],
... [1.9, 0., 0.]]),
... np.array([[0., 0., 0],
... [0, 1., 1.],
... [0, 2., 2.]]),
... np.array([[2, 2, 2],
... [3, 3, 3]])]
>>> mask1 = np.zeros((4, 4, 4), dtype=bool)
>>> mask2 = np.zeros_like(mask1)
>>> mask1[0, 0, 0] = True
>>> mask2[1, 0, 0] = True
>>> selection = select_by_rois(streamlines, [mask1, mask2],
... [True, True],
... tol=1)
>>> list(selection) # The result is a generator
[array([[ 0. , 0. , 0.9],
[ 1.9, 0. , 0. ]]), array([[ 0., 0., 0.],
[ 0., 1., 1.],
[ 0., 2., 2.]])]
>>> selection = select_by_rois(streamlines, [mask1, mask2],
... [True, False],
... tol=0.87)
>>> list(selection)
[array([[ 0., 0., 0.],
[ 0., 1., 1.],
[ 0., 2., 2.]])]
>>> selection = select_by_rois(streamlines, [mask1, mask2],
... [True, True],
... mode="both_end",
... tol=1.0)
>>> list(selection)
[array([[ 0. , 0. , 0.9],
[ 1.9, 0. , 0. ]])]
>>> mask2[0, 2, 2] = True
>>> selection = select_by_rois(streamlines, [mask1, mask2],
... [True, True],
... mode="both_end",
... tol=1.0)
>>> list(selection)
[array([[ 0. , 0. , 0.9],
[ 1.9, 0. , 0. ]]), array([[ 0., 0., 0.],
[ 0., 1., 1.],
[ 0., 2., 2.]])]
"""
if affine is None:
affine = np.eye(4)
# This calculates the maximal distance to a corner of the voxel:
dtc = dist_to_corner(affine)
if tol is None:
tol = dtc
elif tol < dtc:
w_s = "Tolerance input provided would create gaps in your"
w_s += " inclusion ROI. Setting to: %s" % dist_to_corner
warn(w_s)
tol = dtc
include_roi, exclude_roi = ut.reduce_rois(rois, include)
include_roi_coords = np.array(np.where(include_roi)).T
x_include_roi_coords = apply_affine(affine, include_roi_coords)
exclude_roi_coords = np.array(np.where(exclude_roi)).T
x_exclude_roi_coords = apply_affine(affine, exclude_roi_coords)
if mode is None:
mode = "any"
for sl in streamlines:
include = streamline_near_roi(sl,
x_include_roi_coords,
tol=tol,
mode=mode)
exclude = streamline_near_roi(sl,
x_exclude_roi_coords,
tol=tol,
mode=mode)
if include & ~exclude:
yield sl
