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from __future__ import division, print_function, absolute_import
import math
import numpy as np
import numpy.linalg as npl
# epsilon for testing whether a number is close to zero
_EPS = np.finfo(float).eps * 4.0
# axis sequences for Euler angles
_NEXT_AXIS = [1, 2, 0, 1]
# map axes strings to/from tuples of inner axis, parity, repetition, frame
_AXES2TUPLE = {
'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0),
'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0),
'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0),
'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0),
'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1),
'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1),
'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1),
'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)}
_TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items())
def sphere2cart(r, theta, phi):
''' Spherical to Cartesian coordinates
This is the standard physics convention where `theta` is the
inclination (polar) angle, and `phi` is the azimuth angle.
Imagine a sphere with center (0,0,0). Orient it with the z axis
running south-north, the y axis running west-east and the x axis
from posterior to anterior. `theta` (the inclination angle) is the
angle to rotate from the z-axis (the zenith) around the y-axis,
towards the x axis. Thus the rotation is counter-clockwise from the
point of view of positive y. `phi` (azimuth) gives the angle of
rotation around the z-axis towards the y axis. The rotation is
counter-clockwise from the point of view of positive z.
Equivalently, given a point P on the sphere, with coordinates x, y,
z, `theta` is the angle between P and the z-axis, and `phi` is
the angle between the projection of P onto the XY plane, and the X
axis.
Geographical nomenclature designates theta as 'co-latitude', and phi
as 'longitude'
Parameters
------------
r : array_like
radius
theta : array_like
inclination or polar angle
phi : array_like
azimuth angle
Returns
---------
x : array
x coordinate(s) in Cartesion space
y : array
y coordinate(s) in Cartesian space
z : array
z coordinate
Notes
--------
See these pages:
* http://en.wikipedia.org/wiki/Spherical_coordinate_system
* http://mathworld.wolfram.com/SphericalCoordinates.html
for excellent discussion of the many different conventions
possible. Here we use the physics conventions, used in the
wikipedia page.
Derivations of the formulae are simple. Consider a vector x, y, z of
length r (norm of x, y, z). The inclination angle (theta) can be
found from: cos(theta) == z / r -> z == r * cos(theta). This gives
the hypotenuse of the projection onto the XY plane, which we will
call Q. Q == r*sin(theta). Now x / Q == cos(phi) -> x == r *
sin(theta) * cos(phi) and so on.
We have deliberately named this function ``sphere2cart`` rather than
``sph2cart`` to distinguish it from the Matlab function of that
name, because the Matlab function uses an unusual convention for the
angles that we did not want to replicate. The Matlab function is
trivial to implement with the formulae given in the Matlab help.
'''
sin_theta = np.sin(theta)
x = r * np.cos(phi) * sin_theta
y = r * np.sin(phi) * sin_theta
z = r * np.cos(theta)
x, y, z = np.broadcast_arrays(x, y, z)
return x, y, z
def cart2sphere(x, y, z):
r''' Return angles for Cartesian 3D coordinates `x`, `y`, and `z`
See doc for ``sphere2cart`` for angle conventions and derivation
of the formulae.
$0\le\theta\mathrm{(theta)}\le\pi$ and $-\pi\le\phi\mathrm{(phi)}\le\pi$
Parameters
------------
x : array_like
x coordinate in Cartesian space
y : array_like
y coordinate in Cartesian space
z : array_like
z coordinate
Returns
---------
r : array
radius
theta : array
inclination (polar) angle
phi : array
azimuth angle
'''
r = np.sqrt(x * x + y * y + z * z)
theta = np.arccos(z / r)
theta = np.where(r > 0, theta, 0.)
phi = np.arctan2(y, x)
r, theta, phi = np.broadcast_arrays(r, theta, phi)
return r, theta, phi
def sph2latlon(theta, phi):
"""Convert spherical coordinates to latitude and longitude.
Returns
-------
lat, lon : ndarray
Latitude and longitude.
"""
return np.rad2deg(theta - np.pi / 2), np.rad2deg(phi - np.pi)
def normalized_vector(vec, axis=-1):
''' Return vector divided by its Euclidean (L2) norm
See :term:`unit vector` and :term:`Euclidean norm`
Parameters
------------
vec : array_like shape (3,)
Returns
----------
nvec : array shape (3,)
vector divided by L2 norm
Examples
-----------
>>> vec = [1, 2, 3]
>>> l2n = np.sqrt(np.dot(vec, vec))
>>> nvec = normalized_vector(vec)
>>> np.allclose(np.array(vec) / l2n, nvec)
True
>>> vec = np.array([[1, 2, 3]])
>>> vec.shape
(1, 3)
>>> normalized_vector(vec).shape
(1, 3)
'''
return vec / vector_norm(vec, axis, keepdims=True)
def vector_norm(vec, axis=-1, keepdims=False):
''' Return vector Euclidean (L2) norm
See :term:`unit vector` and :term:`Euclidean norm`
Parameters
-------------
vec : array_like
Vectors to norm.
axis : int
Axis over which to norm. By default norm over last axis. If `axis` is
None, `vec` is flattened then normed.
keepdims : bool
If True, the output will have the same number of dimensions as `vec`,
with shape 1 on `axis`.
Returns
---------
norm : array
Euclidean norms of vectors.
Examples
--------
>>> import numpy as np
>>> vec = [[8, 15, 0], [0, 36, 77]]
>>> vector_norm(vec)
array([ 17., 85.])
>>> vector_norm(vec, keepdims=True)
array([[ 17.],
[ 85.]])
>>> vector_norm(vec, axis=0)
array([ 8., 39., 77.])
'''
vec = np.asarray(vec)
vec_norm = np.sqrt((vec * vec).sum(axis))
if keepdims:
if axis is None:
shape = [1] * vec.ndim
else:
shape = list(vec.shape)
shape[axis] = 1
vec_norm = vec_norm.reshape(shape)
return vec_norm
def rodrigues_axis_rotation(r, theta):
""" Rodrigues formula
Rotation matrix for rotation around axis r for angle theta.
The rotation matrix is given by the Rodrigues formula:
R = Id + sin(theta)*Sn + (1-cos(theta))*Sn^2
with::
0 -nz ny
Sn = nz 0 -nx
-ny nx 0
where n = r / ||r||
In case the angle ||r|| is very small, the above formula may lead
to numerical instabilities. We instead use a Taylor expansion
around theta=0:
R = I + sin(theta)/tetha Sr + (1-cos(theta))/teta2 Sr^2
leading to:
R = I + (1-theta2/6)*Sr + (1/2-theta2/24)*Sr^2
Parameters
-----------
r : array_like shape (3,), axis
theta : float, angle in degrees
Returns
----------
R : array, shape (3,3), rotation matrix
Examples
---------
>>> import numpy as np
>>> from dipy.core.geometry import rodrigues_axis_rotation
>>> v=np.array([0,0,1])
>>> u=np.array([1,0,0])
>>> R=rodrigues_axis_rotation(v,40)
>>> ur=np.dot(R,u)
>>> np.round(np.rad2deg(np.arccos(np.dot(ur,u))))
40.0
"""
theta = np.deg2rad(theta)
if theta > 1e-30:
n = r / np.linalg.norm(r)
Sn = np.array([[0, -n[2], n[1]], [n[2], 0, -n[0]], [-n[1], n[0], 0]])
R = np.eye(3) + np.sin(theta) * Sn + \
(1 - np.cos(theta)) * np.dot(Sn, Sn)
else:
Sr = np.array([[0, -r[2], r[1]], [r[2], 0, -r[0]], [-r[1], r[0], 0]])
theta2 = theta * theta
R = np.eye(3) + (1 - theta2 / 6.) * \
Sr + (.5 - theta2 / 24.) * np.dot(Sr, Sr)
return R
def nearest_pos_semi_def(B):
''' Least squares positive semi-definite tensor estimation
Parameters
------------
B : (3,3) array_like
B matrix - symmetric. We do not check the symmetry.
Returns
---------
npds : (3,3) array
Estimated nearest positive semi-definite array to matrix `B`.
Examples
----------
>>> B = np.diag([1, 1, -1])
>>> nearest_pos_semi_def(B)
array([[ 0.75, 0. , 0. ],
[ 0. , 0.75, 0. ],
[ 0. , 0. , 0. ]])
References
----------
.. [1] Niethammer M, San Jose Estepar R, Bouix S, Shenton M, Westin CF.
On diffusion tensor estimation. Conf Proc IEEE Eng Med Biol Soc.
2006;1:2622-5. PubMed PMID: 17946125; PubMed Central PMCID:
PMC2791793.
'''
B = np.asarray(B)
vals, vecs = npl.eigh(B)
# indices of eigenvalues in descending order
inds = np.argsort(vals)[::-1]
vals = vals[inds]
cardneg = np.sum(vals < 0)
if cardneg == 0:
return B
if cardneg == 3:
return np.zeros((3, 3))
lam1a, lam2a, lam3a = vals
scalers = np.zeros((3,))
if cardneg == 2:
b112 = np.max([0, lam1a + (lam2a + lam3a) / 3.])
scalers[0] = b112
elif cardneg == 1:
lam1b = lam1a + 0.25 * lam3a
lam2b = lam2a + 0.25 * lam3a
if lam1b >= 0 and lam2b >= 0:
scalers[:2] = lam1b, lam2b
else: # one of the lam1b, lam2b is < 0
if lam2b < 0:
b111 = np.max([0, lam1a + (lam2a + lam3a) / 3.])
scalers[0] = b111
if lam1b < 0:
b221 = np.max([0, lam2a + (lam1a + lam3a) / 3.])
scalers[1] = b221
# resort the scalers to match the original vecs
scalers = scalers[np.argsort(inds)]
return np.dot(vecs, np.dot(np.diag(scalers), vecs.T))
def sphere_distance(pts1, pts2, radius=None, check_radius=True):
""" Distance across sphere surface between `pts1` and `pts2`
Parameters
------------
pts1 : (N,R) or (R,) array_like
where N is the number of points and R is the number of
coordinates defining a point (``R==3`` for 3D)
pts2 : (N,R) or (R,) array_like
where N is the number of points and R is the number of
coordinates defining a point (``R==3`` for 3D). It should be
possible to broadcast `pts1` against `pts2`
radius : None or float, optional
Radius of sphere. Default is to work out radius from mean of the
length of each point vector
check_radius : bool, optional
If True, check if the points are on the sphere surface - i.e
check if the vector lengths in `pts1` and `pts2` are close to
`radius`. Default is True.
Returns
---------
d : (N,) or (0,) array
Distances between corresponding points in `pts1` and `pts2`
across the spherical surface, i.e. the great circle distance
See also
----------
cart_distance : cartesian distance between points
vector_cosine : cosine of angle between vectors
Examples
----------
>>> print('%.4f' % sphere_distance([0,1],[1,0]))
1.5708
>>> print('%.4f' % sphere_distance([0,3],[3,0]))
4.7124
"""
pts1 = np.asarray(pts1)
pts2 = np.asarray(pts2)
lens1 = np.sqrt(np.sum(pts1 ** 2, axis=-1))
lens2 = np.sqrt(np.sum(pts2 ** 2, axis=-1))
if radius is None:
radius = (np.mean(lens1) + np.mean(lens2)) / 2.0
if check_radius:
if not (np.allclose(radius, lens1) and
np.allclose(radius, lens2)):
raise ValueError('Radii do not match sphere surface')
# Get angle with vector cosine
dots = np.inner(pts1, pts2)
lens = lens1 * lens2
angle_cos = np.arccos(dots / lens)
return angle_cos * radius
def cart_distance(pts1, pts2):
''' Cartesian distance between `pts1` and `pts2`
If either of `pts1` or `pts2` is 2D, then we take the first
dimension to index points, and the second indexes coordinate. More
generally, we take the last dimension to be the coordinate
dimension.
Parameters
----------
pts1 : (N,R) or (R,) array_like
where N is the number of points and R is the number of
coordinates defining a point (``R==3`` for 3D)
pts2 : (N,R) or (R,) array_like
where N is the number of points and R is the number of
coordinates defining a point (``R==3`` for 3D). It should be
possible to broadcast `pts1` against `pts2`
Returns
-------
d : (N,) or (0,) array
Cartesian distances between corresponding points in `pts1` and
`pts2`
See also
--------
sphere_distance : distance between points on sphere surface
Examples
----------
>>> cart_distance([0,0,0], [0,0,3])
3.0
'''
sqs = np.subtract(pts1, pts2) ** 2
return np.sqrt(np.sum(sqs, axis=-1))
def vector_cosine(vecs1, vecs2):
""" Cosine of angle between two (sets of) vectors
The cosine of the angle between two vectors ``v1`` and ``v2`` is
given by the inner product of ``v1`` and ``v2`` divided by the
product of the vector lengths::
v_cos = np.inner(v1, v2) / (np.sqrt(np.sum(v1**2)) *
np.sqrt(np.sum(v2**2)))
Parameters
-------------
vecs1 : (N, R) or (R,) array_like
N vectors (as rows) or single vector. Vectors have R elements.
vecs1 : (N, R) or (R,) array_like
N vectors (as rows) or single vector. Vectors have R elements.
It should be possible to broadcast `vecs1` against `vecs2`
Returns
----------
vcos : (N,) or (0,) array
Vector cosines. To get the angles you will need ``np.arccos``
Notes
--------
The vector cosine will be the same as the correlation only if all
the input vectors have zero mean.
"""
vecs1 = np.asarray(vecs1)
vecs2 = np.asarray(vecs2)
lens1 = np.sqrt(np.sum(vecs1 ** 2, axis=-1))
lens2 = np.sqrt(np.sum(vecs2 ** 2, axis=-1))
dots = np.inner(vecs1, vecs2)
lens = lens1 * lens2
return dots / lens
def lambert_equal_area_projection_polar(theta, phi):
r""" Lambert Equal Area Projection from polar sphere to plane
Return positions in (y1,y2) plane corresponding to the points
with polar coordinates (theta, phi) on the unit sphere, under the
Lambert Equal Area Projection mapping (see Mardia and Jupp (2000),
Directional Statistics, p. 161).
See doc for ``sphere2cart`` for angle conventions
- $0 \le \theta \le \pi$ and $0 \le \phi \le 2 \pi$
- $|(y_1,y_2)| \le 2$
The Lambert EAP maps the upper hemisphere to the planar disc of radius 1
and the lower hemisphere to the planar annulus between radii 1 and 2,
and *vice versa*.
Parameters
----------
theta : array_like
theta spherical coordinates
phi : array_like
phi spherical coordinates
Returns
---------
y : (N,2) array
planar coordinates of points following mapping by Lambert's EAP.
"""
return 2 * np.repeat(np.sin(theta / 2), 2).reshape((theta.shape[0], 2)) * \
np.column_stack((np.cos(phi), np.sin(phi)))
def lambert_equal_area_projection_cart(x, y, z):
r''' Lambert Equal Area Projection from cartesian vector to plane
Return positions in $(y_1,y_2)$ plane corresponding to the
directions of the vectors with cartesian coordinates xyz under the
Lambert Equal Area Projection mapping (see Mardia and Jupp (2000),
Directional Statistics, p. 161).
The Lambert EAP maps the upper hemisphere to the planar disc of radius 1
and the lower hemisphere to the planar annulus between radii 1 and 2,
The Lambert EAP maps the upper hemisphere to the planar disc of radius 1
and the lower hemisphere to the planar annulus between radii 1 and 2.
and *vice versa*.
See doc for ``sphere2cart`` for angle conventions
Parameters
------------
x : array_like
x coordinate in Cartesion space
y : array_like
y coordinate in Cartesian space
z : array_like
z coordinate
Returns
----------
y : (N,2) array
planar coordinates of points following mapping by Lambert's EAP.
'''
(r, theta, phi) = cart2sphere(x, y, z)
return lambert_equal_area_projection_polar(theta, phi)
def euler_matrix(ai, aj, ak, axes='sxyz'):
"""Return homogeneous rotation matrix from Euler angles and axis sequence.
Code modified from the work of Christoph Gohlke link provided here
http://www.lfd.uci.edu/~gohlke/code/transformations.py.html
Parameters
------------
ai, aj, ak : Euler's roll, pitch and yaw angles
axes : One of 24 axis sequences as string or encoded tuple
Returns
---------
matrix : ndarray (4, 4)
Code modified from the work of Christoph Gohlke link provided here
http://www.lfd.uci.edu/~gohlke/code/transformations.py.html
Examples
--------
>>> import numpy
>>> R = euler_matrix(1, 2, 3, 'syxz')
>>> numpy.allclose(numpy.sum(R[0]), -1.34786452)
True
>>> R = euler_matrix(1, 2, 3, (0, 1, 0, 1))
>>> numpy.allclose(numpy.sum(R[0]), -0.383436184)
True
>>> ai, aj, ak = (4.0*math.pi) * (numpy.random.random(3) - 0.5)
>>> for axes in _AXES2TUPLE.keys():
... R = euler_matrix(ai, aj, ak, axes)
>>> for axes in _TUPLE2AXES.keys():
... R = euler_matrix(ai, aj, ak, axes)
"""
try:
firstaxis, parity, repetition, frame = _AXES2TUPLE[axes]
except (AttributeError, KeyError):
firstaxis, parity, repetition, frame = axes
i = firstaxis
j = _NEXT_AXIS[i + parity]
k = _NEXT_AXIS[i - parity + 1]
if frame:
ai, ak = ak, ai
if parity:
ai, aj, ak = -ai, -aj, -ak
si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak)
ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak)
cc, cs = ci * ck, ci * sk
sc, ss = si * ck, si * sk
M = np.identity(4)
if repetition:
M[i, i] = cj
M[i, j] = sj * si
M[i, k] = sj * ci
M[j, i] = sj * sk
M[j, j] = -cj * ss + cc
M[j, k] = -cj * cs - sc
M[k, i] = -sj * ck
M[k, j] = cj * sc + cs
M[k, k] = cj * cc - ss
else:
M[i, i] = cj * ck
M[i, j] = sj * sc - cs
M[i, k] = sj * cc + ss
M[j, i] = cj * sk
M[j, j] = sj * ss + cc
M[j, k] = sj * cs - sc
M[k, i] = -sj
M[k, j] = cj * si
M[k, k] = cj * ci
return M
def compose_matrix(scale=None, shear=None, angles=None, translate=None,
perspective=None):
"""Return 4x4 transformation matrix from sequence of
transformations.
Code modified from the work of Christoph Gohlke link provided here
http://www.lfd.uci.edu/~gohlke/code/transformations.py.html
This is the inverse of the ``decompose_matrix`` function.
Parameters
-------------
scale : (3,) array_like
Scaling factors.
shear : array_like
Shear factors for x-y, x-z, y-z axes.
angles : array_like
Euler angles about static x, y, z axes.
translate : array_like
Translation vector along x, y, z axes.
perspective : array_like
Perspective partition of matrix.
Returns
---------
matrix : 4x4 array
Examples
----------
>>> import math
>>> import numpy as np
>>> import dipy.core.geometry as gm
>>> scale = np.random.random(3) - 0.5
>>> shear = np.random.random(3) - 0.5
>>> angles = (np.random.random(3) - 0.5) * (2*math.pi)
>>> trans = np.random.random(3) - 0.5
>>> persp = np.random.random(4) - 0.5
>>> M0 = gm.compose_matrix(scale, shear, angles, trans, persp)
"""
M = np.identity(4)
if perspective is not None:
P = np.identity(4)
P[3, :] = perspective[:4]
M = np.dot(M, P)
if translate is not None:
T = np.identity(4)
T[:3, 3] = translate[:3]
M = np.dot(M, T)
if angles is not None:
R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz')
M = np.dot(M, R)
if shear is not None:
Z = np.identity(4)
Z[1, 2] = shear[2]
Z[0, 2] = shear[1]
Z[0, 1] = shear[0]
M = np.dot(M, Z)
if scale is not None:
S = np.identity(4)
S[0, 0] = scale[0]
S[1, 1] = scale[1]
S[2, 2] = scale[2]
M = np.dot(M, S)
M /= M[3, 3]
return M
def decompose_matrix(matrix):
"""Return sequence of transformations from transformation matrix.
Code modified from the excellent work of Christoph Gohlke link provided
here: http://www.lfd.uci.edu/~gohlke/code/transformations.py.html
Parameters
------------
matrix : array_like
Non-degenerative homogeneous transformation matrix
Returns
---------
scale : (3,) ndarray
Three scaling factors.
shear : (3,) ndarray
Shear factors for x-y, x-z, y-z axes.
angles : (3,) ndarray
Euler angles about static x, y, z axes.
translate : (3,) ndarray
Translation vector along x, y, z axes.
perspective : ndarray
Perspective partition of matrix.
Raises
------
ValueError
If matrix is of wrong type or degenerative.
Examples
-----------
>>> import numpy as np
>>> T0=np.diag([2,1,1,1])
>>> scale, shear, angles, trans, persp = decompose_matrix(T0)
"""
M = np.array(matrix, dtype=np.float64, copy=True).T
if abs(M[3, 3]) < _EPS:
raise ValueError("M[3, 3] is zero")
M /= M[3, 3]
P = M.copy()
P[:, 3] = 0, 0, 0, 1
if not np.linalg.det(P):
raise ValueError("matrix is singular")
scale = np.zeros((3, ), dtype=np.float64)
shear = [0, 0, 0]
angles = [0, 0, 0]
if any(abs(M[:3, 3]) > _EPS):
perspective = np.dot(M[:, 3], np.linalg.inv(P.T))
M[:, 3] = 0, 0, 0, 1
else:
perspective = np.array((0, 0, 0, 1), dtype=np.float64)
translate = M[3, :3].copy()
M[3, :3] = 0
row = M[:3, :3].copy()
scale[0] = vector_norm(row[0])
row[0] /= scale[0]
shear[0] = np.dot(row[0], row[1])
row[1] -= row[0] * shear[0]
scale[1] = vector_norm(row[1])
row[1] /= scale[1]
shear[0] /= scale[1]
shear[1] = np.dot(row[0], row[2])
row[2] -= row[0] * shear[1]
shear[2] = np.dot(row[1], row[2])
row[2] -= row[1] * shear[2]
scale[2] = vector_norm(row[2])
row[2] /= scale[2]
shear[1:] /= scale[2]
if np.dot(row[0], np.cross(row[1], row[2])) < 0:
scale *= -1
row *= -1
angles[1] = math.asin(-row[0, 2])
if math.cos(angles[1]):
angles[0] = math.atan2(row[1, 2], row[2, 2])
angles[2] = math.atan2(row[0, 1], row[0, 0])
else:
# angles[0] = math.atan2(row[1, 0], row[1, 1])
angles[0] = math.atan2(-row[2, 1], row[1, 1])
angles[2] = 0.0
return scale, shear, angles, translate, perspective
def circumradius(a, b, c):
''' a, b and c are 3-dimensional vectors which are the vertices of a
triangle. The function returns the circumradius of the triangle, i.e
the radius of the smallest circle that can contain the triangle. In
the degenerate case when the 3 points are collinear it returns
half the distance between the furthest apart points.
Parameters
----------
a, b, c : (3,) array_like
the three vertices of the triangle
Returns
-------
circumradius : float
the desired circumradius
'''
x = a - c
xx = np.linalg.norm(x) ** 2
y = b - c
yy = np.linalg.norm(y) ** 2
z = np.cross(x, y)
# test for collinearity
if np.linalg.norm(z) == 0:
return np.sqrt(np.max(np.dot(x, x), np.dot(y, y),
np.dot(a - b, a - b))) / 2.
else:
m = np.vstack((x, y, z))
w = np.dot(np.linalg.inv(m.T), np.array([xx / 2., yy / 2., 0]))
return np.linalg.norm(w) / 2.
def vec2vec_rotmat(u, v):
r""" rotation matrix from 2 unit vectors
u, v being unit 3d vectors return a 3x3 rotation matrix R than aligns u to
v.
In general there are many rotations that will map u to v. If S is any
rotation using v as an axis then R.S will also map u to v since (S.R)u =
S(Ru) = Sv = v. The rotation R returned by vec2vec_rotmat leaves fixed the
perpendicular to the plane spanned by u and v.
The transpose of R will align v to u.
Parameters
-----------
u : array, shape(3,)
v : array, shape(3,)
Returns
---------
R : array, shape(3,3)
Examples
---------
>>> import numpy as np
>>> from dipy.core.geometry import vec2vec_rotmat
>>> u=np.array([1,0,0])
>>> v=np.array([0,1,0])
>>> R=vec2vec_rotmat(u,v)
>>> np.dot(R,u)
array([ 0., 1., 0.])
>>> np.dot(R.T,v)
array([ 1., 0., 0.])
"""
# Cross product is the first step to find R
# Rely on numpy instead of manual checking for failing
# cases
w = np.cross(u, v)
wn = np.linalg.norm(w)
# Check that cross product is OK and vectors
# u, v are not collinear (norm(w)>0.0)
if np.isnan(wn) or wn < np.finfo(float).eps:
norm_u_v = np.linalg.norm(u - v)
# This is the case of two antipodal vectors:
# ** former checking assumed norm(u) == norm(v)
if norm_u_v > np.linalg.norm(u):
return -np.eye(3)
return np.eye(3)
# if everything ok, normalize w
w = w / wn
# vp is in plane of u,v, perpendicular to u
vp = (v - (np.dot(u, v) * u))
vp = vp / np.linalg.norm(vp)
# (u vp w) is an orthonormal basis
P = np.array([u, vp, w])
Pt = P.T
cosa = np.dot(u, v)
sina = np.sqrt(1 - cosa ** 2)
R = np.array([[cosa, -sina, 0], [sina, cosa, 0], [0, 0, 1]])
Rp = np.dot(Pt, np.dot(R, P))
# make sure that you don't return any Nans
# check using the appropriate tool in numpy
if np.any(np.isnan(Rp)):
return np.eye(3)
return Rp
def compose_transformations(*mats):
""" Compose multiple 4x4 affine transformations in one 4x4 matrix
Parameters
-----------
mat1 : array, (4, 4)
mat2 : array, (4, 4)
...
matN : array, (4, 4)
Returns
-------
matN x ... x mat2 x mat1 : array, (4, 4)
"""
prev = mats[0]
if len(mats) < 2:
raise ValueError('At least two or more matrices are needed')
for mat in mats[1:]:
prev = np.dot(mat, prev)
return prev
def perpendicular_directions(v, num=30, half=False):
r""" Computes n evenly spaced perpendicular directions relative to a given
vector v
Parameters
-----------
v : array (3,)
Array containing the three cartesian coordinates of vector v
num : int, optional
Number of perpendicular directions to generate
half : bool, optional
If half is True, perpendicular directions are sampled on half of the
unit circumference perpendicular to v, otherwive perpendicular
directions are sampled on the full circumference. Default of half is
False
Returns
-------
psamples : array (n, 3)
array of vectors perpendicular to v
Notes
--------
Perpendicular directions are estimated using the following two step
procedure:
1) the perpendicular directions are first sampled in a unit
circumference parallel to the plane normal to the x-axis.
2) Samples are then rotated and aligned to the plane normal to vector
v. The rotational matrix for this rotation is constructed as reference
frame basis which axis are the following:
- The first axis is vector v
- The second axis is defined as the normalized vector given by the
cross product between vector v and the unit vector aligned to the
x-axis
- The third axis is defined as the cross product between the
previous computed vector and vector v.
Following this two steps, coordinates of the final perpendicular directions
are given as:
.. math::
\left [ -\sin(a_{i}) \sqrt{{v_{y}}^{2}+{v_{z}}^{2}}
\; , \;
\frac{v_{x}v_{y}\sin(a_{i})-v_{z}\cos(a_{i})}
{\sqrt{{v_{y}}^{2}+{v_{z}}^{2}}}
\; , \;
\frac{v_{x}v_{z}\sin(a_{i})-v_{y}\cos(a_{i})}
{\sqrt{{v_{y}}^{2}+{v_{z}}^{2}}} \right ]
This procedure has a singularity when vector v is aligned to the x-axis. To
solve this singularity, perpendicular directions in procedure's step 1 are
defined in the plane normal to y-axis and the second axis of the rotated
frame of reference is computed as the normalized vector given by the cross
product between vector v and the unit vector aligned to the y-axis.
Following this, the coordinates of the perpendicular directions are given
as:
\left [ -\frac{\left (v_{x}v_{y}\sin(a_{i})+v_{z}\cos(a_{i}) \right )}
{\sqrt{{v_{x}}^{2}+{v_{z}}^{2}}}
\; , \;
\sin(a_{i}) \sqrt{{v_{x}}^{2}+{v_{z}}^{2}}
\; , \;
\frac{v_{y}v_{z}\sin(a_{i})+v_{x}\cos(a_{i})}
{\sqrt{{v_{x}}^{2}+{v_{z}}^{2}}} \right ]
For more details on this calculation, see ` here <http://gsoc2015dipydki.blogspot.it/2015/07/rnh-post-8-computing-perpendicular.html>`_.
"""
v = np.array(v, dtype=float)
# Float error used for floats comparison
er = np.finfo(v[0]).eps * 1e3
# Define circumference or semi-circumference
if half is True:
a = np.linspace(0., math.pi, num=num, endpoint=False)
else:
a = np.linspace(0., 2 * math.pi, num=num, endpoint=False)
cosa = np.cos(a)
sina = np.sin(a)
# Check if vector is not aligned to the x axis
if abs(v[0] - 1.) > er:
sq = np.sqrt(v[1]**2 + v[2]**2)
psamples = np.array([- sq*sina, (v[0]*v[1]*sina - v[2]*cosa) / sq,
(v[0]*v[2]*sina + v[1]*cosa) / sq])
else:
sq = np.sqrt(v[0]**2 + v[2]**2)
psamples = np.array([- (v[2]*cosa + v[0]*v[1]*sina) / sq, sina*sq,
(v[0]*cosa - v[2]*v[1]*sina) / sq])
return psamples.T
def dist_to_corner(affine):
"""Calculate the maximal distance from the center to a corner of a voxel,
given an affine
Parameters
----------
affine : 4 by 4 array.
The spatial transformation from the measurement to the scanner space.
Returns
-------
dist: float
The maximal distance to the corner of a voxel, given voxel size encoded
in the affine.
"""
R = affine[0:3, 0:3]
vox_dim = np.diag(np.linalg.cholesky(R.T.dot(R)))
return np.sqrt(np.sum((vox_dim / 2) ** 2))
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