core
Core objects
test ([label, verbose, extra_argv, doctests, ...])
|
Run tests for module using nose. |
Module: core.benchmarks.bench_sphere
Benchmarks for sphere
Run all benchmarks with:
import dipy.core as dipycore
dipycore.bench()
With Pytest, Run this benchmark with:
pytest -svv -c bench.ini /path/to/bench_sphere.py
Module: core.geometry
Utility functions for algebra etc
cart2sphere (x, y, z)
|
Return angles for Cartesian 3D coordinates x, y, and z |
cart_distance (pts1, pts2)
|
Cartesian distance between pts1 and pts2 |
circumradius (a, b, c)
|
a, b and c are 3-dimensional vectors which are the vertices of a triangle. |
compose_matrix ([scale, shear, angles, ...])
|
Return 4x4 transformation matrix from sequence of transformations. |
compose_transformations (*mats)
|
Compose multiple 4x4 affine transformations in one 4x4 matrix |
decompose_matrix (matrix)
|
Return sequence of transformations from transformation matrix. |
dist_to_corner (affine)
|
Calculate the maximal distance from the center to a corner of a voxel, given an affine |
euler_matrix (ai, aj, ak[, axes])
|
Return homogeneous rotation matrix from Euler angles and axis sequence. |
is_hemispherical (vecs)
|
Test whether all points on a unit sphere lie in the same hemisphere. |
lambert_equal_area_projection_cart (x, y, z)
|
Lambert Equal Area Projection from cartesian vector to plane |
lambert_equal_area_projection_polar (theta, phi)
|
Lambert Equal Area Projection from polar sphere to plane |
nearest_pos_semi_def (B)
|
Least squares positive semi-definite tensor estimation |
normalized_vector (vec[, axis])
|
Return vector divided by its Euclidean (L2) norm |
perpendicular_directions (v[, num, half])
|
Computes n evenly spaced perpendicular directions relative to a given vector v |
rodrigues_axis_rotation (r, theta)
|
Rodrigues formula |
sph2latlon (theta, phi)
|
Convert spherical coordinates to latitude and longitude. |
sphere2cart (r, theta, phi)
|
Spherical to Cartesian coordinates |
sphere_distance (pts1, pts2[, radius, ...])
|
Distance across sphere surface between pts1 and pts2 |
vec2vec_rotmat (u, v)
|
rotation matrix from 2 unit vectors |
vector_cosine (vecs1, vecs2)
|
Cosine of angle between two (sets of) vectors |
vector_norm (vec[, axis, keepdims])
|
Return vector Euclidean (L2) norm |
Module: core.gradients
GradientTable (gradients[, big_delta, ...])
|
Diffusion gradient information |
HemiSphere ([x, y, z, theta, phi, xyz, ...])
|
Points on the unit sphere. |
auto_attr (func)
|
Decorator to create OneTimeProperty attributes. |
btens_to_params (btens[, ztol])
|
Compute trace, anisotropy and assymetry parameters from b-tensors |
check_multi_b (gtab, n_bvals[, non_zero, bmag])
|
Check if you have enough different b-values in your gradient table |
deprecate_with_version (message[, since, ...])
|
Return decorator function function for deprecation warning / error. |
disperse_charges (hemi, iters[, const])
|
Models electrostatic repulsion on the unit sphere |
generate_bvecs (N[, iters])
|
Generates N bvectors. |
get_bval_indices (bvals, bval[, tol])
|
Get indices where the b-value is bval |
gradient_table (bvals[, bvecs, big_delta, ...])
|
A general function for creating diffusion MR gradients. |
gradient_table_from_bvals_bvecs (bvals, bvecs)
|
Creates a GradientTable from a bvals array and a bvecs array |
gradient_table_from_gradient_strength_bvecs (...)
|
A general function for creating diffusion MR gradients. |
gradient_table_from_qvals_bvecs (qvals, ...)
|
A general function for creating diffusion MR gradients. |
inv (a[, overwrite_a, check_finite])
|
Compute the inverse of a matrix. |
params_to_btens (bval, bdelta, b_eta)
|
Compute b-tensor from trace, anisotropy and assymetry parameters |
polar (a[, side])
|
Compute the polar decomposition. |
reorient_bvecs (gtab, affines[, atol])
|
Reorient the directions in a GradientTable. |
round_bvals (bvals[, bmag])
|
"This function rounds the b-values |
unique_bvals (bvals[, bmag, rbvals])
|
This function gives the unique rounded b-values of the data |
unique_bvals_magnitude (bvals[, bmag, rbvals])
|
This function gives the unique rounded b-values of the data |
unique_bvals_tolerance (bvals[, tol])
|
Gives the unique b-values of the data, within a tolerance gap |
vec2vec_rotmat (u, v)
|
rotation matrix from 2 unit vectors |
vector_norm (vec[, axis, keepdims])
|
Return vector Euclidean (L2) norm |
warn (/, message[, category, stacklevel, source])
|
Issue a warning, or maybe ignore it or raise an exception. |
Module: core.graph
A simple graph class
Graph ()
|
A simple graph class |
Module: core.histeq
histeq (arr[, num_bins])
|
Performs an histogram equalization on arr . |
Module: core.interpolation
Module: core.ndindex
as_strided (x[, shape, strides, subok, writeable])
|
Create a view into the array with the given shape and strides. |
ndindex (shape)
|
An N-dimensional iterator object to index arrays. |
Module: core.onetime
Descriptor support for NIPY.
Copyright (c) 2006-2011, NIPY Developers
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
- Neither the name of the NIPY Developers nor the names of any
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
“AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Utilities to support special Python descriptors [1,2], in particular the use of
a useful pattern for properties we call ‘one time properties’. These are
object attributes which are declared as properties, but become regular
attributes once they’ve been read the first time. They can thus be evaluated
later in the object’s life cycle, but once evaluated they become normal, static
attributes with no function call overhead on access or any other constraints.
A special ResetMixin class is provided to add a .reset() method to users who
may want to have their objects capable of resetting these computed properties
to their ‘untriggered’ state.
OneTimeProperty (func)
|
A descriptor to make special properties that become normal attributes. |
ResetMixin ()
|
A Mixin class to add a .reset() method to users of OneTimeProperty. |
auto_attr (func)
|
Decorator to create OneTimeProperty attributes. |
Module: core.optimize
A unified interface for performing and debugging optimization problems.
NonNegativeLeastSquares (*args, **kwargs)
|
A sklearn-like interface to scipy.optimize.nnls |
Optimizer (fun, x0[, args, method, jac, ...])
|
- Attributes
|
SKLearnLinearSolver (*args, **kwargs)
|
Provide a sklearn-like uniform interface to algorithms that solve problems of the form: \(y = Ax\) for \(x\) |
minimize (fun, x0[, args, method, jac, hess, ...])
|
Minimization of scalar function of one or more variables. |
sparse_nnls (y, X[, momentum, step_size, ...])
|
Solve y=Xh for h, using gradient descent, with X a sparse matrix. |
spdot (A, B)
|
The same as np.dot(A, B), except it works even if A or B or both are sparse matrices. |
Module: core.profile
Class for profiling cython code
Profiler ([call])
|
Profile python/cython files or functions |
optional_package (name[, trip_msg])
|
Return package-like thing and module setup for package name |
Module: core.rng
Random number generation utilities.
LEcuyer ([s1, s2])
|
Return a LEcuyer random number generator. |
WichmannHill1982 ([ix, iy, iz])
|
Algorithm AS 183 Appl. |
WichmannHill2006 ([ix, iy, iz, it])
|
Wichmann Hill (2006) random number generator. |
architecture ([executable, bits, linkage])
|
Queries the given executable (defaults to the Python interpreter binary) for various architecture information. |
floor (x, /)
|
Return the floor of x as an Integral. |
Module: core.sphere
HemiSphere ([x, y, z, theta, phi, xyz, ...])
|
Points on the unit sphere. |
Sphere ([x, y, z, theta, phi, xyz, faces, edges])
|
Points on the unit sphere. |
auto_attr (func)
|
Decorator to create OneTimeProperty attributes. |
cart2sphere (x, y, z)
|
Return angles for Cartesian 3D coordinates x, y, and z |
disperse_charges (hemi, iters[, const])
|
Models electrostatic repulsion on the unit sphere |
disperse_charges_alt (init_pointset, iters[, tol])
|
Reimplementation of disperse_charges making use of scipy.optimize.fmin_slsqp. |
euler_characteristic_check (sphere[, chi])
|
Checks the euler characteristic of a sphere |
faces_from_sphere_vertices (vertices)
|
Triangulate a set of vertices on the sphere. |
remove_similar_vertices
|
Remove vertices that are less than theta degrees from any other |
sphere2cart (r, theta, phi)
|
Spherical to Cartesian coordinates |
unique_edges (faces[, return_mapping])
|
Extract all unique edges from given triangular faces. |
unique_sets (sets[, return_inverse])
|
Remove duplicate sets. |
vector_norm (vec[, axis, keepdims])
|
Return vector Euclidean (L2) norm |
Module: core.sphere_stats
Statistics on spheres
permutations (iterable[, r])
|
Return successive r-length permutations of elements in the iterable. |
angular_similarity (S, T)
|
Computes the cosine distance of the best match between points of two sets of vectors S and T |
compare_orientation_sets (S, T)
|
Computes the mean cosine distance of the best match between points of two sets of vectors S and T (angular similarity) |
eigenstats (points[, alpha])
|
Principal direction and confidence ellipse |
random_uniform_on_sphere ([n, coords])
|
Random unit vectors from a uniform distribution on the sphere. |
Module: core.subdivide_octahedron
Create a unit sphere by subdividing all triangles of an octahedron
recursively.
The unit sphere has a radius of 1, which also means that all points in this
sphere (assumed to have centre at [0, 0, 0]) have an absolute value (modulus)
of 1. Another feature of the unit sphere is that the unit normals of this
sphere are exactly the same as the vertices.
This recursive method will avoid the common problem of the polar singularity,
produced by 2d (lon-lat) parameterization methods.
HemiSphere ([x, y, z, theta, phi, xyz, ...])
|
Points on the unit sphere. |
create_unit_hemisphere ([recursion_level])
|
Creates a unit sphere by subdividing a unit octahedron, returns half the sphere. |
create_unit_sphere ([recursion_level])
|
Creates a unit sphere by subdividing a unit octahedron. |
Module: core.wavelet
afb3D (x, af1[, af2, af3])
|
3D Analysis Filter Bank |
afb3D_A (x, af, d)
|
3D Analysis Filter Bank |
cshift3D (x, m, d)
|
3D Circular Shift |
dwt3D (x, J, af)
|
3-D Discrete Wavelet Transform |
idwt3D (w, J, sf)
|
Inverse 3-D Discrete Wavelet Transform |
permutationinverse (perm)
|
Function generating inverse of the permutation |
sfb3D (lo, hi, sf1[, sf2, sf3])
|
3D Synthesis Filter Bank |
sfb3D_A (lo, hi, sf, d)
|
3D Synthesis Filter Bank |
test
-
dipy.core.test(label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None, timer=False)
Run tests for module using nose.
- Parameters
- label{‘fast’, ‘full’, ‘’, attribute identifier}, optional
Identifies the tests to run. This can be a string to pass to
the nosetests executable with the ‘-A’ option, or one of several
special values. Special values are:
‘fast’ - the default - which corresponds to the nosetests -A
option of ‘not slow’.
‘full’ - fast (as above) and slow tests as in the
‘no -A’ option to nosetests - this is the same as ‘’.
None or ‘’ - run all tests.
attribute_identifier - string passed directly to nosetests as ‘-A’.
- verboseint, optional
Verbosity value for test outputs, in the range 1-10. Default is 1.
- extra_argvlist, optional
List with any extra arguments to pass to nosetests.
- doctestsbool, optional
If True, run doctests in module. Default is False.
- coveragebool, optional
If True, report coverage of NumPy code. Default is False.
(This requires the
coverage module).
- raise_warningsNone, str or sequence of warnings, optional
This specifies which warnings to configure as ‘raise’ instead
of being shown once during the test execution. Valid strings are:
“develop” : equals (Warning,)
“release” : equals ()
, do not raise on any warnings.
- timerbool or int, optional
Timing of individual tests with nose-timer
(which needs to be
installed). If True, time tests and report on all of them.
If an integer (say N
), report timing results for N
slowest
tests.
- Returns
- resultobject
Returns the result of running the tests as a
nose.result.TextTestResult
object.
Notes
Each NumPy module exposes test in its namespace to run all tests for it.
For example, to run all tests for numpy.lib:
Examples
>>> result = np.lib.test()
Running unit tests for numpy.lib
...
Ran 976 tests in 3.933s
OK
>>> result.errors
[]
>>> result.knownfail
[]
-
class dipy.core.benchmarks.bench_sphere.Timer
Bases: object
Methods
-
__init__(*args, **kwargs)
-
duration_in_seconds()
bench_disperse_charges_alt
-
dipy.core.benchmarks.bench_sphere.bench_disperse_charges_alt()
func_minimize_adhoc
-
dipy.core.benchmarks.bench_sphere.func_minimize_adhoc(init_hemisphere, num_iterations)
func_minimize_scipy
-
dipy.core.benchmarks.bench_sphere.func_minimize_scipy(init_pointset, num_iterations)
cart2sphere
-
dipy.core.geometry.cart2sphere(x, y, z)
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
- xarray_like
x coordinate in Cartesian space
- yarray_like
y coordinate in Cartesian space
- zarray_like
z coordinate
- Returns
- rarray
radius
- thetaarray
inclination (polar) angle
- phiarray
azimuth angle
cart_distance
-
dipy.core.geometry.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
Examples
>>> cart_distance([0,0,0], [0,0,3])
3.0
circumradius
-
dipy.core.geometry.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
- circumradiusfloat
the desired circumradius
compose_matrix
-
dipy.core.geometry.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.
- sheararray_like
Shear factors for x-y, x-z, y-z axes.
- anglesarray_like
Euler angles about static x, y, z axes.
- translatearray_like
Translation vector along x, y, z axes.
- perspectivearray_like
Perspective partition of matrix.
- Returns
- matrix4x4 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)
decompose_matrix
-
dipy.core.geometry.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
- matrixarray_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.
- perspectivendarray
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)
dist_to_corner
-
dipy.core.geometry.dist_to_corner(affine)
Calculate the maximal distance from the center to a corner of a voxel,
given an affine
- Parameters
- affine4 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.
euler_matrix
-
dipy.core.geometry.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, akEuler’s roll, pitch and yaw angles
- axesOne of 24 axis sequences as string or encoded tuple
- Returns
- matrixndarray (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():
... _ = euler_matrix(ai, aj, ak, axes)
>>> for axes in _TUPLE2AXES.keys():
... _ = euler_matrix(ai, aj, ak, axes)
is_hemispherical
-
dipy.core.geometry.is_hemispherical(vecs)
Test whether all points on a unit sphere lie in the same hemisphere.
- Parameters
- vecsnumpy.ndarray
2D numpy array with shape (N, 3) where N is the number of points.
All points must lie on the unit sphere.
- Returns
- is_hemibool
If True, one can find a hemisphere that contains all the points.
If False, then the points do not lie in any hemisphere
- polenumpy.ndarray
If is_hemi == True, then pole is the “central” pole of the
input vectors. Otherwise, pole is the zero vector.
References
https://rstudio-pubs-static.s3.amazonaws.com/27121_a22e51b47c544980bad594d5e0bb2d04.html # noqa
lambert_equal_area_projection_cart
-
dipy.core.geometry.lambert_equal_area_projection_cart(x, y, z)
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
- xarray_like
x coordinate in Cartesion space
- yarray_like
y coordinate in Cartesian space
- zarray_like
z coordinate
- Returns
- y(N,2) array
planar coordinates of points following mapping by Lambert’s EAP.
lambert_equal_area_projection_polar
-
dipy.core.geometry.lambert_equal_area_projection_polar(theta, phi)
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
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
- thetaarray_like
theta spherical coordinates
- phiarray_like
phi spherical coordinates
- Returns
- y(N,2) array
planar coordinates of points following mapping by Lambert’s EAP.
nearest_pos_semi_def
-
dipy.core.geometry.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.
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.
Examples
>>> B = np.diag([1, 1, -1])
>>> nearest_pos_semi_def(B)
array([[ 0.75, 0. , 0. ],
[ 0. , 0.75, 0. ],
[ 0. , 0. , 0. ]])
normalized_vector
-
dipy.core.geometry.normalized_vector(vec, axis=- 1)
Return vector divided by its Euclidean (L2) norm
See unit vector and Euclidean norm
- Parameters
- vecarray_like shape (3,)
- Returns
- nvecarray 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)
True
>>> normalized_vector(vec).shape == (1, 3)
True
perpendicular_directions
-
dipy.core.geometry.perpendicular_directions(v, num=30, half=False)
Computes n evenly spaced perpendicular directions relative to a given
vector v
- Parameters
- varray (3,)
Array containing the three cartesian coordinates of vector v
- numint, optional
Number of perpendicular directions to generate
- halfbool, 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
- psamplesarray (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:
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:
\[\left [ -\sin(a_{i}) \sqrt{{v_{y}}^{2}+{v_{z}}^{2}}
\; , \;
\frac{v_{x}v_{y}\sin(a_{i})-v_{z}\cos(a_{i})}
{\sqrt{{v_{y}}^{2}+{v_{z}}^{2}}}
\; , \;
\frac{v_{x}v_{z}\sin(a_{i})-v_{y}\cos(a_{i})}
{\sqrt{{v_{y}}^{2}+{v_{z}}^{2}}} \right ]\]
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>`_.
rodrigues_axis_rotation
-
dipy.core.geometry.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
- rarray_like shape (3,), axis
- thetafloat, angle in degrees
- Returns
- Rarray, 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
sph2latlon
-
dipy.core.geometry.sph2latlon(theta, phi)
Convert spherical coordinates to latitude and longitude.
- Returns
- lat, lonndarray
Latitude and longitude.
sphere2cart
-
dipy.core.geometry.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
- rarray_like
radius
- thetaarray_like
inclination or polar angle
- phiarray_like
azimuth angle
- Returns
- xarray
x coordinate(s) in Cartesion space
- yarray
y coordinate(s) in Cartesian space
- zarray
z coordinate
Notes
See these pages:
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.
sphere_distance
-
dipy.core.geometry.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
- radiusNone or float, optional
Radius of sphere. Default is to work out radius from mean of the
length of each point vector
- check_radiusbool, 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
Examples
>>> print('%.4f' % sphere_distance([0,1],[1,0]))
1.5708
>>> print('%.4f' % sphere_distance([0,3],[3,0]))
4.7124
vec2vec_rotmat
-
dipy.core.geometry.vec2vec_rotmat(u, v)
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
- uarray, shape(3,)
- varray, shape(3,)
- Returns
- Rarray, 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.])
vector_cosine
-
dipy.core.geometry.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.
vector_norm
-
dipy.core.geometry.vector_norm(vec, axis=- 1, keepdims=False)
Return vector Euclidean (L2) norm
See unit vector and Euclidean norm
- Parameters
- vecarray_like
Vectors to norm.
- axisint
Axis over which to norm. By default norm over last axis. If axis is
None, vec is flattened then normed.
- keepdimsbool
If True, the output will have the same number of dimensions as vec,
with shape 1 on axis.
- Returns
- normarray
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.])
-
class dipy.core.gradients.GradientTable(gradients, big_delta=None, small_delta=None, b0_threshold=50, btens=None)
Bases: object
Diffusion gradient information
- Parameters
- gradientsarray_like (N, 3)
Diffusion gradients. The direction of each of these vectors corresponds
to the b-vector, and the length corresponds to the b-value.
- b0_thresholdfloat
Gradients with b-value less than or equal to b0_threshold are
considered as b0s i.e. without diffusion weighting.
Notes
The GradientTable object is immutable. Do NOT assign attributes.
If you have your gradient table in a bval & bvec format, we recommend
using the factory function gradient_table
- Attributes
- gradients(N,3) ndarray
diffusion gradients
- bvals(N,) ndarray
The b-value, or magnitude, of each gradient direction.
- qvals: (N,) ndarray
The q-value for each gradient direction. Needs big and small
delta.
- bvecs(N,3) ndarray
The direction, represented as a unit vector, of each gradient.
- b0s_mask(N,) ndarray
Boolean array indicating which gradients have no diffusion
weighting, ie b-value is close to 0.
- b0_thresholdfloat
Gradients with b-value less than or equal to b0_threshold are
considered to not have diffusion weighting.
- btens(N,3,3) ndarray
The b-tensor of each gradient direction.
Methods
b0s_mask |
|
bvals |
|
bvecs |
|
gradient_strength |
|
qvals |
|
tau |
|
-
__init__(gradients, big_delta=None, small_delta=None, b0_threshold=50, btens=None)
Constructor for GradientTable class
-
b0s_mask()
-
bvals()
-
bvecs()
-
gradient_strength()
-
property info
-
qvals()
-
tau()
-
class dipy.core.gradients.HemiSphere(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)
Bases: dipy.core.sphere.Sphere
Points on the unit sphere.
A HemiSphere is similar to a Sphere but it takes antipodal symmetry into
account. Antipodal symmetry means that point v on a HemiSphere is the same
as the point -v. Duplicate points are discarded when constructing a
HemiSphere (including antipodal duplicates). edges and faces are
remapped to the remaining points as closely as possible.
The HemiSphere can be constructed using one of three conventions:
HemiSphere(x, y, z)
HemiSphere(xyz=xyz)
HemiSphere(theta=theta, phi=phi)
- Parameters
- x, y, z1-D array_like
Vertices as x-y-z coordinates.
- theta, phi1-D array_like
Vertices as spherical coordinates. Theta and phi are the inclination
and azimuth angles respectively.
- xyz(N, 3) ndarray
Vertices as x-y-z coordinates.
- faces(N, 3) ndarray
Indices into vertices that form triangular faces. If unspecified,
the faces are computed using a Delaunay triangulation.
- edges(N, 2) ndarray
Edges between vertices. If unspecified, the edges are
derived from the faces.
- tolfloat
Angle in degrees. Vertices that are less than tol degrees apart are
treated as duplicates.
- Attributes
- x
- y
- z
Methods
find_closest (xyz)
|
Find the index of the vertex in the Sphere closest to the input vector, taking into account antipodal symmetry |
from_sphere (sphere[, tol])
|
Create instance from a Sphere |
mirror ()
|
Create a full Sphere from a HemiSphere |
subdivide ([n])
|
Create a more subdivided HemiSphere |
-
__init__(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)
Create a HemiSphere from points
-
faces()
-
find_closest(xyz)
Find the index of the vertex in the Sphere closest to the input vector,
taking into account antipodal symmetry
- Parameters
- xyzarray-like, 3 elements
A unit vector
- Returns
- idxint
The index into the Sphere.vertices array that gives the closest
vertex (in angle).
-
classmethod from_sphere(sphere, tol=1e-05)
Create instance from a Sphere
-
mirror()
Create a full Sphere from a HemiSphere
-
subdivide(n=1)
Create a more subdivided HemiSphere
See Sphere.subdivide for full documentation.
auto_attr
-
dipy.core.gradients.auto_attr(func)
Decorator to create OneTimeProperty attributes.
- Parameters
- funcmethod
The method that will be called the first time to compute a value.
Afterwards, the method’s name will be a standard attribute holding the
value of this computation.
Examples
>>> class MagicProp(object):
... @auto_attr
... def a(self):
... return 99
...
>>> x = MagicProp()
>>> 'a' in x.__dict__
False
>>> x.a
99
>>> 'a' in x.__dict__
True
btens_to_params
-
dipy.core.gradients.btens_to_params(btens, ztol=1e-10)
Compute trace, anisotropy and assymetry parameters from b-tensors
- Parameters
- btens(3, 3) OR (N, 3, 3) numpy.ndarray
input b-tensor, or b-tensors, where N = number of b-tensors
- ztolfloat
Any parameters smaller than this value are considered to be 0
- Returns
- bval: numpy.ndarray
b-value(s) (trace(s))
- bdelta: numpy.ndarray
normalized tensor anisotropy(s)
- b_eta: numpy.ndarray
tensor assymetry(s)
Notes
This function can be used to get b-tensor parameters directly from the
GradientTable btens attribute.
Examples
>>> lte = np.array([[1, 0, 0], [0, 0, 0], [0, 0, 0]])
>>> bval, bdelta, b_eta = btens_to_params(lte)
>>> print("bval={}; bdelta={}; b_eta={}".format(bdelta, bval, b_eta))
bval=[ 1.]; bdelta=[ 1.]; b_eta=[ 0.]
check_multi_b
-
dipy.core.gradients.check_multi_b(gtab, n_bvals, non_zero=True, bmag=None)
Check if you have enough different b-values in your gradient table
- Parameters
- gtabGradientTable class instance.
- n_bvalsint
The number of different b-values you are checking for.
- non_zerobool
Whether to check only non-zero bvalues. In this case, we will require
at least n_bvals non-zero b-values (where non-zero is defined
depending on the gtab object’s b0_threshold attribute)
- bmagint
The order of magnitude of the b-values used. The function will
normalize the b-values relative \(10^{bmag}\). Default: derive this
value from the maximal b-value provided:
\(bmag=log_{10}(max(bvals)) - 1\).
- Returns
- boolWhether there are at least n_bvals different b-values in the
- gradient table used.
deprecate_with_version
-
dipy.core.gradients.deprecate_with_version(message, since='', until='', version_comparator=<function cmp_pkg_version>, warn_class=<class 'DeprecationWarning'>, error_class=<class 'dipy.utils.deprecator.ExpiredDeprecationError'>)
Return decorator function function for deprecation warning / error.
The decorated function / method will:
Raise the given warning_class warning when the function / method gets
called, up to (and including) version until (if specified);
Raise the given error_class error when the function / method gets
called, when the package version is greater than version until (if
specified).
- Parameters
- messagestr
Message explaining deprecation, giving possible alternatives.
- sincestr, optional
Released version at which object was first deprecated.
- untilstr, optional
Last released version at which this function will still raise a
deprecation warning. Versions higher than this will raise an
error.
- version_comparatorcallable
Callable accepting string as argument, and return 1 if string
represents a higher version than encoded in the version_comparator, 0
if the version is equal, and -1 if the version is lower. For example,
the version_comparator may compare the input version string to the
current package version string.
- warn_classclass, optional
Class of warning to generate for deprecation.
- error_classclass, optional
Class of error to generate when version_comparator returns 1 for a
given argument of until
.
- Returns
- deprecatorfunc
Function returning a decorator.
disperse_charges
-
dipy.core.gradients.disperse_charges(hemi, iters, const=0.2)
Models electrostatic repulsion on the unit sphere
Places charges on a sphere and simulates the repulsive forces felt by each
one. Allows the charges to move for some number of iterations and returns
their final location as well as the total potential of the system at each
step.
- Parameters
- hemiHemiSphere
Points on a unit sphere.
- itersint
Number of iterations to run.
- constfloat
Using a smaller const could provide a more accurate result, but will
need more iterations to converge.
- Returns
- hemiHemiSphere
Distributed points on a unit sphere.
- potentialndarray
The electrostatic potential at each iteration. This can be useful to
check if the repulsion converged to a minimum.
Notes
This function is meant to be used with diffusion imaging so antipodal
symmetry is assumed. Therefor each charge must not only be unique, but if
there is a charge at +x, there cannot be a charge at -x. These are treated
as the same location and because the distance between the two charges will
be zero, the result will be unstable.
generate_bvecs
-
dipy.core.gradients.generate_bvecs(N, iters=5000)
Generates N bvectors.
Uses dipy.core.sphere.disperse_charges to model electrostatic repulsion on
a unit sphere.
- Parameters
- Nint
The number of bvectors to generate. This should be equal to the number
of bvals used.
- itersint
Number of iterations to run.
- Returns
- bvecs(N,3) ndarray
The generated directions, represented as a unit vector, of each
gradient.
get_bval_indices
-
dipy.core.gradients.get_bval_indices(bvals, bval, tol=20)
Get indices where the b-value is bval
- Parameters
- bvals: ndarray
Array containing the b-values
- bval: float or int
b-value to extract indices
- tol: int
The tolerated gap between the b-values to extract
and the actual b-values.
- Returns
- Array of indices where the b-value is bval
gradient_table
-
dipy.core.gradients.gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None, b0_threshold=50, atol=0.01, btens=None)
A general function for creating diffusion MR gradients.
It reads, loads and prepares scanner parameters like the b-values and
b-vectors so that they can be useful during the reconstruction process.
- Parameters
- bvalscan be any of the four options
an array of shape (N,) or (1, N) or (N, 1) with the b-values.
a path for the file which contains an array like the above (1).
an array of shape (N, 4) or (4, N). Then this parameter is
considered to be a b-table which contains both bvals and bvecs. In
this case the next parameter is skipped.
a path for the file which contains an array like the one at (3).
- bvecscan be any of two options
an array of shape (N, 3) or (3, N) with the b-vectors.
a path for the file which contains an array like the previous.
- big_deltafloat
acquisition pulse separation time in seconds (default None)
- small_deltafloat
acquisition pulse duration time in seconds (default None)
- b0_thresholdfloat
All b-values with values less than or equal to bo_threshold are
considered as b0s i.e. without diffusion weighting.
- atolfloat
All b-vectors need to be unit vectors up to a tolerance.
- btenscan be any of three options
a string specifying the shape of the encoding tensor for all volumes
in data. Options: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to
linear, planar, spherical, and “cigar-shaped” tensor encoding.
Tensors are rotated so that linear and cigar tensors are aligned
with the corresponding gradient direction and the planar tensor’s
normal is aligned with the corresponding gradient direction.
Magnitude is scaled to match the b-value.
an array of strings of shape (N,), (N, 1), or (1, N) specifying
encoding tensor shape for each volume separately. N corresponds to
the number volumes in data. Options for elements in array: ‘LTE’,
‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and
“cigar-shaped” tensor encoding. Tensors are rotated so that linear
and cigar tensors are aligned with the corresponding gradient
direction and the planar tensor’s normal is aligned with the
corresponding gradient direction. Magnitude is scaled to match the
b-value.
an array of shape (N,3,3) specifying the b-tensor of each volume
exactly. N corresponds to the number volumes in data. No rotation or
scaling is performed.
- Returns
- gradientsGradientTable
A GradientTable with all the gradient information.
Notes
Often b0s (b-values which correspond to images without diffusion
weighting) have 0 values however in some cases the scanner cannot
provide b0s of an exact 0 value and it gives a bit higher values
e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
We assume that the minimum number of b-values is 7.
B-vectors should be unit vectors.
Examples
>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
... [1, 0, 0],
... [0, 1, 0],
... [0, 0, 1],
... [sq2, sq2, 0],
... [sq2, 0, sq2],
... [0, sq2, sq2]])
>>> gt = gradient_table(bvals, bvecs)
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt = gradient_table(bvals, bvecs.T)
>>> gt.bvecs.shape == bvecs.T.shape
False
gradient_table_from_bvals_bvecs
-
dipy.core.gradients.gradient_table_from_bvals_bvecs(bvals, bvecs, b0_threshold=50, atol=0.01, btens=None, **kwargs)
Creates a GradientTable from a bvals array and a bvecs array
- Parameters
- bvalsarray_like (N,)
The b-value, or magnitude, of each gradient direction.
- bvecsarray_like (N, 3)
The direction, represented as a unit vector, of each gradient.
- b0_thresholdfloat
Gradients with b-value less than or equal to bo_threshold are
considered to not have diffusion weighting.
- atolfloat
Each vector in bvecs must be a unit vectors up to a tolerance of
atol.
- btenscan be any of three options
a string specifying the shape of the encoding tensor for all volumes
in data. Options: ‘LTE’, ‘PTE’, ‘STE’, ‘CTE’ corresponding to
linear, planar, spherical, and “cigar-shaped” tensor encoding.
Tensors are rotated so that linear and cigar tensors are aligned
with the corresponding gradient direction and the planar tensor’s
normal is aligned with the corresponding gradient direction.
Magnitude is scaled to match the b-value.
an array of strings of shape (N,), (N, 1), or (1, N) specifying
encoding tensor shape for each volume separately. N corresponds to
the number volumes in data. Options for elements in array: ‘LTE’,
‘PTE’, ‘STE’, ‘CTE’ corresponding to linear, planar, spherical, and
“cigar-shaped” tensor encoding. Tensors are rotated so that linear
and cigar tensors are aligned with the corresponding gradient
direction and the planar tensor’s normal is aligned with the
corresponding gradient direction. Magnitude is scaled to match the
b-value.
an array of shape (N,3,3) specifying the b-tensor of each volume
exactly. N corresponds to the number volumes in data. No rotation or
scaling is performed.
- Returns
- gradientsGradientTable
A GradientTable with all the gradient information.
- Other Parameters
- **kwargsdict
Other keyword inputs are passed to GradientTable.
gradient_table_from_gradient_strength_bvecs
-
dipy.core.gradients.gradient_table_from_gradient_strength_bvecs(gradient_strength, bvecs, big_delta, small_delta, b0_threshold=50, atol=0.01)
A general function for creating diffusion MR gradients.
It reads, loads and prepares scanner parameters like the b-values and
b-vectors so that they can be useful during the reconstruction process.
- Parameters
- gradient_strengthan array of shape (N,),
gradient strength given in T/mm
- bvecscan be any of two options
an array of shape (N, 3) or (3, N) with the b-vectors.
a path for the file which contains an array like the previous.
- big_deltafloat or array of shape (N,)
acquisition pulse separation time in seconds
- small_deltafloat
acquisition pulse duration time in seconds
- b0_thresholdfloat
All b-values with values less than or equal to bo_threshold are
considered as b0s i.e. without diffusion weighting.
- atolfloat
All b-vectors need to be unit vectors up to a tolerance.
- Returns
- gradientsGradientTable
A GradientTable with all the gradient information.
Notes
Often b0s (b-values which correspond to images without diffusion
weighting) have 0 values however in some cases the scanner cannot
provide b0s of an exact 0 value and it gives a bit higher values
e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
We assume that the minimum number of b-values is 7.
B-vectors should be unit vectors.
Examples
>>> from dipy.core.gradients import (
... gradient_table_from_gradient_strength_bvecs)
>>> gradient_strength = .03e-3 * np.ones(7) # clinical strength at 30 mT/m
>>> big_delta = .03 # pulse separation of 30ms
>>> small_delta = 0.01 # pulse duration of 10ms
>>> gradient_strength[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
... [1, 0, 0],
... [0, 1, 0],
... [0, 0, 1],
... [sq2, sq2, 0],
... [sq2, 0, sq2],
... [0, sq2, sq2]])
>>> gt = gradient_table_from_gradient_strength_bvecs(
... gradient_strength, bvecs, big_delta, small_delta)
gradient_table_from_qvals_bvecs
-
dipy.core.gradients.gradient_table_from_qvals_bvecs(qvals, bvecs, big_delta, small_delta, b0_threshold=50, atol=0.01)
A general function for creating diffusion MR gradients.
It reads, loads and prepares scanner parameters like the b-values and
b-vectors so that they can be useful during the reconstruction process.
- Parameters
- qvalsan array of shape (N,),
q-value given in 1/mm
- bvecscan be any of two options
an array of shape (N, 3) or (3, N) with the b-vectors.
a path for the file which contains an array like the previous.
- big_deltafloat or array of shape (N,)
acquisition pulse separation time in seconds
- small_deltafloat
acquisition pulse duration time in seconds
- b0_thresholdfloat
All b-values with values less than or equal to bo_threshold are
considered as b0s i.e. without diffusion weighting.
- atolfloat
All b-vectors need to be unit vectors up to a tolerance.
- Returns
- gradientsGradientTable
A GradientTable with all the gradient information.
Notes
Often b0s (b-values which correspond to images without diffusion
weighting) have 0 values however in some cases the scanner cannot
provide b0s of an exact 0 value and it gives a bit higher values
e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
We assume that the minimum number of b-values is 7.
B-vectors should be unit vectors.
Examples
>>> from dipy.core.gradients import gradient_table_from_qvals_bvecs
>>> qvals = 30. * np.ones(7)
>>> big_delta = .03 # pulse separation of 30ms
>>> small_delta = 0.01 # pulse duration of 10ms
>>> qvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
... [1, 0, 0],
... [0, 1, 0],
... [0, 0, 1],
... [sq2, sq2, 0],
... [sq2, 0, sq2],
... [0, sq2, sq2]])
>>> gt = gradient_table_from_qvals_bvecs(qvals, bvecs,
... big_delta, small_delta)
inv
-
dipy.core.gradients.inv(a, overwrite_a=False, check_finite=True)
Compute the inverse of a matrix.
- Parameters
- aarray_like
Square matrix to be inverted.
- overwrite_abool, optional
Discard data in a (may improve performance). Default is False.
- check_finitebool, optional
Whether to check that the input matrix contains only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.
- Returns
- ainvndarray
Inverse of the matrix a.
- Raises
- LinAlgError
If a is singular.
- ValueError
If a is not square, or not 2D.
Examples
>>> from scipy import linalg
>>> a = np.array([[1., 2.], [3., 4.]])
>>> linalg.inv(a)
array([[-2. , 1. ],
[ 1.5, -0.5]])
>>> np.dot(a, linalg.inv(a))
array([[ 1., 0.],
[ 0., 1.]])
params_to_btens
-
dipy.core.gradients.params_to_btens(bval, bdelta, b_eta)
Compute b-tensor from trace, anisotropy and assymetry parameters
- Parameters
- bval: int or float
b-value (>= 0)
- bdelta: int or float
normalized tensor anisotropy (>= -0.5 and <= 1)
- b_eta: int or float
tensor assymetry (>= 0 and <= 1)
- Returns
- (3, 3) numpy.ndarray
output b-tensor
Notes
Implements eq. 7.11. p. 231 in [1].
References
- 1
Topgaard, NMR methods for studying microscopic diffusion
anisotropy, in: R. Valiullin (Ed.), Diffusion NMR of Confined Systems: Fluid
Transport in Porous Solids and Heterogeneous Materials, Royal Society of
Chemistry, Cambridge, UK, 2016.
polar
-
dipy.core.gradients.polar(a, side='right')
Compute the polar decomposition.
Returns the factors of the polar decomposition [1] u and p such
that a = up
(if side is “right”) or a = pu
(if side is
“left”), where p is positive semidefinite. Depending on the shape
of a, either the rows or columns of u are orthonormal. When a
is a square array, u is a square unitary array. When a is not
square, the “canonical polar decomposition” [2] is computed.
- Parameters
- a(m, n) array_like
The array to be factored.
- side{‘left’, ‘right’}, optional
Determines whether a right or left polar decomposition is computed.
If side is “right”, then a = up
. If side is “left”, then
a = pu
. The default is “right”.
- Returns
- u(m, n) ndarray
If a is square, then u is unitary. If m > n, then the columns
of a are orthonormal, and if m < n, then the rows of u are
orthonormal.
- pndarray
p is Hermitian positive semidefinite. If a is nonsingular, p
is positive definite. The shape of p is (n, n) or (m, m), depending
on whether side is “right” or “left”, respectively.
References
- 1
R. A. Horn and C. R. Johnson, “Matrix Analysis”, Cambridge
University Press, 1985.
- 2
N. J. Higham, “Functions of Matrices: Theory and Computation”,
SIAM, 2008.
Examples
>>> from scipy.linalg import polar
>>> a = np.array([[1, -1], [2, 4]])
>>> u, p = polar(a)
>>> u
array([[ 0.85749293, -0.51449576],
[ 0.51449576, 0.85749293]])
>>> p
array([[ 1.88648444, 1.2004901 ],
[ 1.2004901 , 3.94446746]])
A non-square example, with m < n:
>>> b = np.array([[0.5, 1, 2], [1.5, 3, 4]])
>>> u, p = polar(b)
>>> u
array([[-0.21196618, -0.42393237, 0.88054056],
[ 0.39378971, 0.78757942, 0.4739708 ]])
>>> p
array([[ 0.48470147, 0.96940295, 1.15122648],
[ 0.96940295, 1.9388059 , 2.30245295],
[ 1.15122648, 2.30245295, 3.65696431]])
>>> u.dot(p) # Verify the decomposition.
array([[ 0.5, 1. , 2. ],
[ 1.5, 3. , 4. ]])
>>> u.dot(u.T) # The rows of u are orthonormal.
array([[ 1.00000000e+00, -2.07353665e-17],
[ -2.07353665e-17, 1.00000000e+00]])
Another non-square example, with m > n:
>>> c = b.T
>>> u, p = polar(c)
>>> u
array([[-0.21196618, 0.39378971],
[-0.42393237, 0.78757942],
[ 0.88054056, 0.4739708 ]])
>>> p
array([[ 1.23116567, 1.93241587],
[ 1.93241587, 4.84930602]])
>>> u.dot(p) # Verify the decomposition.
array([[ 0.5, 1.5],
[ 1. , 3. ],
[ 2. , 4. ]])
>>> u.T.dot(u) # The columns of u are orthonormal.
array([[ 1.00000000e+00, -1.26363763e-16],
[ -1.26363763e-16, 1.00000000e+00]])
reorient_bvecs
-
dipy.core.gradients.reorient_bvecs(gtab, affines, atol=0.01)
Reorient the directions in a GradientTable.
When correcting for motion, rotation of the diffusion-weighted volumes
might cause systematic bias in rotationally invariant measures, such as FA
and MD, and also cause characteristic biases in tractography, unless the
gradient directions are appropriately reoriented to compensate for this
effect [Leemans2009].
- Parameters
- gtabGradientTable
The nominal gradient table with which the data were acquired.
- affineslist or ndarray of shape (n, 4, 4) or (n, 3, 3)
Each entry in this list or array contain either an affine
transformation (4,4) or a rotation matrix (3, 3).
In both cases, the transformations encode the rotation that was applied
to the image corresponding to one of the non-zero gradient directions
(ordered according to their order in gtab.bvecs[~gtab.b0s_mask])
- atol: see gradient_table()
- Returns
- gtaba GradientTable class instance with the reoriented directions
References
- Leemans2009
The B-Matrix Must Be Rotated When Correcting for
Subject Motion in DTI Data. Leemans, A. and Jones, D.K. (2009).
MRM, 61: 1336-1349
round_bvals
-
dipy.core.gradients.round_bvals(bvals, bmag=None)
“This function rounds the b-values
- Parameters
- bvalsndarray
Array containing the b-values
- bmagint
The order of magnitude to round the b-values. If not given b-values
will be rounded relative to the order of magnitude
\(bmag = (bmagmax - 1)\), where bmaxmag is the magnitude order of the
larger b-value.
- Returns
- rbvalsndarray
Array containing the rounded b-values
unique_bvals
-
dipy.core.gradients.unique_bvals(bvals, bmag=None, rbvals=False)
This function gives the unique rounded b-values of the data
dipy.core.gradients.unique_bvals is deprecated, Please use dipy.core.gradients.unique_bvals_magnitude instead
- Parameters
- bvalsndarray
Array containing the b-values
- bmagint
The order of magnitude that the bvalues have to differ to be
considered an unique b-value. B-values are also rounded up to
this order of magnitude. Default: derive this value from the
maximal b-value provided: \(bmag=log_{10}(max(bvals)) - 1\).
- rbvalsbool, optional
If True function also returns all individual rounded b-values.
Default: False
- Returns
- ubvalsndarray
Array containing the rounded unique b-values
unique_bvals_magnitude
-
dipy.core.gradients.unique_bvals_magnitude(bvals, bmag=None, rbvals=False)
This function gives the unique rounded b-values of the data
- Parameters
- bvalsndarray
Array containing the b-values
- bmagint
The order of magnitude that the bvalues have to differ to be
considered an unique b-value. B-values are also rounded up to
this order of magnitude. Default: derive this value from the
maximal b-value provided: \(bmag=log_{10}(max(bvals)) - 1\).
- rbvalsbool, optional
If True function also returns all individual rounded b-values.
Default: False
- Returns
- ubvalsndarray
Array containing the rounded unique b-values
unique_bvals_tolerance
-
dipy.core.gradients.unique_bvals_tolerance(bvals, tol=20)
Gives the unique b-values of the data, within a tolerance gap
The b-values must be regrouped in clusters easily separated by a
distance greater than the tolerance gap. If all the b-values of a
cluster fit within the tolerance gap, the highest b-value is kept.
- Parameters
- bvalsndarray
Array containing the b-values
- tolint
The tolerated gap between the b-values to extract
and the actual b-values.
- Returns
- ubvalsndarray
Array containing the unique b-values using the median value
for each cluster
vec2vec_rotmat
-
dipy.core.gradients.vec2vec_rotmat(u, v)
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
- uarray, shape(3,)
- varray, shape(3,)
- Returns
- Rarray, 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.])
vector_norm
-
dipy.core.gradients.vector_norm(vec, axis=- 1, keepdims=False)
Return vector Euclidean (L2) norm
See unit vector and Euclidean norm
- Parameters
- vecarray_like
Vectors to norm.
- axisint
Axis over which to norm. By default norm over last axis. If axis is
None, vec is flattened then normed.
- keepdimsbool
If True, the output will have the same number of dimensions as vec,
with shape 1 on axis.
- Returns
- normarray
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.])
warn
-
dipy.core.gradients.warn(/, message, category=None, stacklevel=1, source=None)
Issue a warning, or maybe ignore it or raise an exception.
-
class dipy.core.graph.Graph
Bases: object
A simple graph class
Methods
add_edge |
|
add_node |
|
all_paths |
|
children |
|
del_node |
|
del_node_and_edges |
|
down |
|
down_short |
|
parents |
|
shortest_path |
|
up |
|
up_short |
|
-
__init__()
A graph class with nodes and edges :-)
This class allows us to:
find the shortest path
find all paths
add/delete nodes and edges
get parent & children nodes
Examples
>>> from dipy.core.graph import Graph
>>> g=Graph()
>>> g.add_node('a',5)
>>> g.add_node('b',6)
>>> g.add_node('c',10)
>>> g.add_node('d',11)
>>> g.add_edge('a','b')
>>> g.add_edge('b','c')
>>> g.add_edge('c','d')
>>> g.add_edge('b','d')
>>> g.up_short('d')
['d', 'b', 'a']
-
add_edge(n, m, ws=True, wp=True)
-
add_node(n, attr=None)
-
all_paths(graph, start, end=None, path=[])
-
children(n)
-
del_node(n)
-
del_node_and_edges(n)
-
down(n)
-
down_short(n)
-
parents(n)
-
shortest_path(graph, start, end=None, path=[])
-
up(n)
-
up_short(n)
-
class dipy.core.interpolation.Interpolator(data, voxel_size)
Bases: object
Class to be subclassed by different interpolator types
-
__init__(data, voxel_size)
-
class dipy.core.interpolation.NearestNeighborInterpolator(data, voxel_size)
Bases: dipy.core.interpolation.Interpolator
Interpolates data using nearest neighbor interpolation
-
__init__(data, voxel_size)
-
class dipy.core.interpolation.OutsideImage
Bases: Exception
- Attributes
- args
Methods
with_traceback
|
Exception.with_traceback(tb) -- set self.__traceback__ to tb and return self. |
-
__init__(*args, **kwargs)
-
class dipy.core.interpolation.TriLinearInterpolator(data, voxel_size)
Bases: dipy.core.interpolation.Interpolator
Interpolates data using trilinear interpolation
interpolate 4d diffusion volume using 3 indices, ie data[x, y, z]
-
__init__(data, voxel_size)
interp_rbf
-
dipy.core.interpolation.interp_rbf()
Interpolate data on the sphere, using radial basis functions.
- Parameters
- data(N,) ndarray
Function values on the unit sphere.
- sphere_originSphere
Positions of data values.
- sphere_targetSphere
M target positions for which to interpolate.
- function{‘multiquadric’, ‘inverse’, ‘gaussian’}
Radial basis function.
- epsilonfloat
Radial basis function spread parameter. Defaults to approximate average
distance between nodes.
- a good start
- smoothfloat
values greater than zero increase the smoothness of the
approximation with 0 as pure interpolation. Default: 0.1
- normstr
A string indicating the function that returns the
“distance” between two points.
‘angle’ - The angle between two vectors
‘euclidean_norm’ - The Euclidean distance
- Returns
- v(M,) ndarray
Interpolated values.
See also
scipy.interpolate.Rbf
interpolate_scalar_2d
-
dipy.core.interpolation.interpolate_scalar_2d(image, locations)
Bilinear interpolation of a 2D scalar image
Interpolates the 2D image at the given locations. This function is
a wrapper for _interpolate_scalar_2d for testing purposes, it is
equivalent to scipy.ndimage.map_coordinates with
bilinear interpolation
- Parameters
- fieldarray, shape (S, R)
the 2D image to be interpolated
- locationsarray, shape (n, 2)
(locations[i,0], locations[i,1]), 0<=i<n must contain the row and
column coordinates to interpolate the image at
- Returns
- outarray, shape (n,)
out[i], 0<=i<n will be the interpolated scalar at coordinates
locations[i,:], or 0 if locations[i,:] is outside the image
- insidearray, (n,)
if locations[i:] is inside the image then inside[i]=1, else
inside[i]=0
interpolate_scalar_3d
-
dipy.core.interpolation.interpolate_scalar_3d(image, locations)
Trilinear interpolation of a 3D scalar image
Interpolates the 3D image at the given locations. This function is
a wrapper for _interpolate_scalar_3d for testing purposes, it is
equivalent to scipy.ndimage.map_coordinates with
trilinear interpolation
- Parameters
- fieldarray, shape (S, R, C)
the 3D image to be interpolated
- locationsarray, shape (n, 3)
(locations[i,0], locations[i,1], locations[i,2), 0<=i<n must contain
the coordinates to interpolate the image at
- Returns
- outarray, shape (n,)
out[i], 0<=i<n will be the interpolated scalar at coordinates
locations[i,:], or 0 if locations[i,:] is outside the image
- insidearray, (n,)
if locations[i,:] is inside the image then inside[i]=1, else
inside[i]=0
interpolate_scalar_nn_2d
-
dipy.core.interpolation.interpolate_scalar_nn_2d(image, locations)
Nearest neighbor interpolation of a 2D scalar image
Interpolates the 2D image at the given locations. This function is
a wrapper for _interpolate_scalar_nn_2d for testing purposes, it is
equivalent to scipy.ndimage.map_coordinates with
nearest neighbor interpolation
- Parameters
- imagearray, shape (S, R)
the 2D image to be interpolated
- locationsarray, shape (n, 2)
(locations[i,0], locations[i,1]), 0<=i<n must contain the row and
column coordinates to interpolate the image at
- Returns
- outarray, shape (n,)
out[i], 0<=i<n will be the interpolated scalar at coordinates
locations[i,:], or 0 if locations[i,:] is outside the image
- insidearray, (n,)
if locations[i:] is inside the image then inside[i]=1, else
inside[i]=0
interpolate_scalar_nn_3d
-
dipy.core.interpolation.interpolate_scalar_nn_3d(image, locations)
Nearest neighbor interpolation of a 3D scalar image
Interpolates the 3D image at the given locations. This function is
a wrapper for _interpolate_scalar_nn_3d for testing purposes, it is
equivalent to scipy.ndimage.map_coordinates with
nearest neighbor interpolation
- Parameters
- imagearray, shape (S, R, C)
the 3D image to be interpolated
- locationsarray, shape (n, 3)
(locations[i,0], locations[i,1], locations[i,2), 0<=i<n must contain
the coordinates to interpolate the image at
- Returns
- outarray, shape (n,)
out[i], 0<=i<n will be the interpolated scalar at coordinates
locations[i,:], or 0 if locations[i,:] is outside the image
- insidearray, (n,)
if locations[i,:] is inside the image then inside[i]=1, else
inside[i]=0
interpolate_vector_2d
-
dipy.core.interpolation.interpolate_vector_2d(field, locations)
Bilinear interpolation of a 2D vector field
Interpolates the 2D vector field at the given locations. This function is
a wrapper for _interpolate_vector_2d for testing purposes, it is
equivalent to using scipy.ndimage.map_coordinates with
bilinear interpolation at each vector component
- Parameters
- fieldarray, shape (S, R, 2)
the 2D vector field to be interpolated
- locationsarray, shape (n, 2)
(locations[i,0], locations[i,1]), 0<=i<n must contain the row and
column coordinates to interpolate the vector field at
- Returns
- outarray, shape (n, 2)
out[i,:], 0<=i<n will be the interpolated vector at coordinates
locations[i,:], or (0,0) if locations[i,:] is outside the field
- insidearray, (n,)
if (locations[i,0], locations[i,1]) is inside the vector field
then inside[i]=1, else inside[i]=0
interpolate_vector_3d
-
dipy.core.interpolation.interpolate_vector_3d(field, locations)
Trilinear interpolation of a 3D vector field
Interpolates the 3D vector field at the given locations. This function is
a wrapper for _interpolate_vector_3d for testing purposes, it is
equivalent to using scipy.ndimage.map_coordinates with
trilinear interpolation at each vector component
- Parameters
- fieldarray, shape (S, R, C, 3)
the 3D vector field to be interpolated
- locationsarray, shape (n, 3)
(locations[i,0], locations[i,1], locations[i,2), 0<=i<n must contain
the coordinates to interpolate the vector field at
- Returns
- outarray, shape (n, 3)
out[i,:], 0<=i<n will be the interpolated vector at coordinates
locations[i,:], or (0,0,0) if locations[i,:] is outside the field
- insidearray, (n,)
if locations[i,:] is inside the vector field then inside[i]=1, else
inside[i]=0
map_coordinates_trilinear_iso
-
dipy.core.interpolation.map_coordinates_trilinear_iso()
Trilinear interpolation (isotropic voxel size)
Has similar behavior to map_coordinates
from scipy.ndimage
- Parameters
- dataarray, float64 shape (X, Y, Z)
- pointsarray, float64 shape(N, 3)
- data_stridesarray npy_intp shape (3,)
Strides sequence for data array
- len_pointscnp.npy_intp
Number of points to interpolate
- resultarray, float64 shape(N)
The output array. This array should be initialized before you call
this function. On exit it will contain the interpolated values from
data at points given by points.
- Returns
- None
Notes
The output array result is filled in-place.
nearestneighbor_interpolate
-
dipy.core.interpolation.nearestneighbor_interpolate()
trilinear_interp
-
dipy.core.interpolation.trilinear_interp(data, index, voxel_size)
Interpolates vector from 4D data at 3D point given by index
Interpolates a vector of length T from a 4D volume of shape (I, J, K, T),
given point (x, y, z) where (x, y, z) are the coordinates of the point in
real units (not yet adjusted for voxel size).
trilinear_interpolate4d
-
dipy.core.interpolation.trilinear_interpolate4d()
Tri-linear interpolation along the last dimension of a 4d array
- Parameters
- point1d array (3,)
3 doubles representing a 3d point in space. If point has integer values
[i, j, k]
, the result will be the same as data[i, j, k]
.
- data4d array
Data to be interpolated.
- out1d array, optional
The output array for the result of the interpolation.
- Returns
- out1d array
The result of interpolation.
as_strided
-
dipy.core.ndindex.as_strided(x, shape=None, strides=None, subok=False, writeable=True)
Create a view into the array with the given shape and strides.
Warning
This function has to be used with extreme care, see notes.
- Parameters
- xndarray
Array to create a new.
- shapesequence of int, optional
The shape of the new array. Defaults to x.shape
.
- stridessequence of int, optional
The strides of the new array. Defaults to x.strides
.
- subokbool, optional
-
If True, subclasses are preserved.
- writeablebool, optional
-
If set to False, the returned array will always be readonly.
Otherwise it will be writable if the original array was. It
is advisable to set this to False if possible (see Notes).
- Returns
- viewndarray
See also
broadcast_to
broadcast an array to a given shape.
reshape
reshape an array.
lib.stride_tricks.sliding_window_view
userfriendly and safe function for the creation of sliding window views.
Notes
as_strided
creates a view into the array given the exact strides
and shape. This means it manipulates the internal data structure of
ndarray and, if done incorrectly, the array elements can point to
invalid memory and can corrupt results or crash your program.
It is advisable to always use the original x.strides
when
calculating new strides to avoid reliance on a contiguous memory
layout.
Furthermore, arrays created with this function often contain self
overlapping memory, so that two elements are identical.
Vectorized write operations on such arrays will typically be
unpredictable. They may even give different results for small, large,
or transposed arrays.
Since writing to these arrays has to be tested and done with great
care, you may want to use writeable=False
to avoid accidental write
operations.
For these reasons it is advisable to avoid as_strided
when
possible.
ndindex
-
dipy.core.ndindex.ndindex(shape)
An N-dimensional iterator object to index arrays.
Given the shape of an array, an ndindex instance iterates over
the N-dimensional index of the array. At each iteration a tuple
of indices is returned; the last dimension is iterated over first.
- Parameters
- shapetuple of ints
The dimensions of the array.
Examples
>>> from dipy.core.ndindex import ndindex
>>> shape = (3, 2, 1)
>>> for index in ndindex(shape):
... print(index)
(0, 0, 0)
(0, 1, 0)
(1, 0, 0)
(1, 1, 0)
(2, 0, 0)
(2, 1, 0)
-
class dipy.core.onetime.OneTimeProperty(func)
Bases: object
A descriptor to make special properties that become normal attributes.
This is meant to be used mostly by the auto_attr decorator in this module.
-
__init__(func)
Create a OneTimeProperty instance.
- Parameters
- funcmethod
- The method that will be called the first time to compute a value.
- Afterwards, the method’s name will be a standard attribute holding
- the value of this computation.
-
class dipy.core.onetime.ResetMixin
Bases: object
A Mixin class to add a .reset() method to users of OneTimeProperty.
By default, auto attributes once computed, become static. If they happen
to depend on other parts of an object and those parts change, their values
may now be invalid.
This class offers a .reset() method that users can call explicitly when
they know the state of their objects may have changed and they want to
ensure that all their special attributes should be invalidated. Once
reset() is called, all their auto attributes are reset to their
OneTimeProperty descriptors, and their accessor functions will be triggered
again.
Warning
If a class has a set of attributes that are OneTimeProperty, but that
can be initialized from any one of them, do NOT use this mixin! For
instance, UniformTimeSeries can be initialized with only sampling_rate
and t0, sampling_interval and time are auto-computed. But if you were
to reset() a UniformTimeSeries, it would lose all 4, and there would be
then no way to break the circular dependency chains.
If this becomes a problem in practice (for our analyzer objects it
isn’t, as they don’t have the above pattern), we can extend reset() to
check for a _no_reset set of names in the instance which are meant to be
kept protected. But for now this is NOT done, so caveat emptor.
Examples
>>> class A(ResetMixin):
... def __init__(self,x=1.0):
... self.x = x
...
... @auto_attr
... def y(self):
... print('*** y computation executed ***')
... return self.x / 2.0
...
About to access y twice, the second time no computation is done:
>>> a.y
* y computation executed *
5.0
>>> a.y
5.0
Changing x
>>> a.x = 20
a.y doesn’t change to 10, since it is a static attribute:
>>> a.y
5.0
We now reset a, and this will then force all auto attributes to recompute
the next time we access them:
>>> a.reset()
About to access y twice again after reset():
>>> a.y
* y computation executed *
10.0
>>> a.y
10.0
Methods
reset ()
|
Reset all OneTimeProperty attributes that may have fired already. |
-
__init__(*args, **kwargs)
-
reset()
Reset all OneTimeProperty attributes that may have fired already.
auto_attr
-
dipy.core.onetime.auto_attr(func)
Decorator to create OneTimeProperty attributes.
- Parameters
- funcmethod
The method that will be called the first time to compute a value.
Afterwards, the method’s name will be a standard attribute holding the
value of this computation.
Examples
>>> class MagicProp(object):
... @auto_attr
... def a(self):
... return 99
...
>>> x = MagicProp()
>>> 'a' in x.__dict__
False
>>> x.a
99
>>> 'a' in x.__dict__
True
-
class dipy.core.optimize.NonNegativeLeastSquares(*args, **kwargs)
Bases: dipy.core.optimize.SKLearnLinearSolver
A sklearn-like interface to scipy.optimize.nnls
Methods
fit (X, y)
|
Fit the NonNegativeLeastSquares linear model to data |
predict (X)
|
Predict using the result of the model |
-
__init__(*args, **kwargs)
-
fit(X, y)
Fit the NonNegativeLeastSquares linear model to data
- Parameters
-
class dipy.core.optimize.Optimizer(fun, x0, args=(), method='L-BFGS-B', jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None, evolution=False)
Bases: object
- Attributes
- evolution
- fopt
- message
- nfev
- nit
- xopt
Methods
-
__init__(fun, x0, args=(), method='L-BFGS-B', jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None, evolution=False)
A class for handling minimization of scalar function of one or more
variables.
- Parameters
- funcallable
Objective function.
- x0ndarray
Initial guess.
- argstuple, optional
Extra arguments passed to the objective function and its
derivatives (Jacobian, Hessian).
- methodstr, optional
Type of solver. Should be one of
‘Nelder-Mead’
‘Powell’
‘CG’
‘BFGS’
‘Newton-CG’
‘Anneal’
‘L-BFGS-B’
‘TNC’
‘COBYLA’
‘SLSQP’
‘dogleg’
‘trust-ncg’
- jacbool or callable, optional
Jacobian of objective function. Only for CG, BFGS, Newton-CG,
dogleg, trust-ncg.
If jac is a Boolean and is True, fun is assumed to return the
value of Jacobian along with the objective function. If False, the
Jacobian will be estimated numerically.
jac can also be a callable returning the Jacobian of the
objective. In this case, it must accept the same arguments
as fun.
- hess, hesspcallable, optional
Hessian of objective function or Hessian of objective function
times an arbitrary vector p. Only for Newton-CG,
dogleg, trust-ncg.
Only one of hessp or hess needs to be given. If hess is
provided, then hessp will be ignored. If neither hess nor
hessp is provided, then the hessian product will be approximated
using finite differences on jac. hessp must compute the Hessian
times an arbitrary vector.
- boundssequence, optional
Bounds for variables (only for L-BFGS-B, TNC and SLSQP).
(min, max)
pairs for each element in x
, defining
the bounds on that parameter. Use None for one of min
or
max
when there is no bound in that direction.
- constraintsdict or sequence of dict, optional
Constraints definition (only for COBYLA and SLSQP).
Each constraint is defined in a dictionary with fields:
- typestr
Constraint type: ‘eq’ for equality, ‘ineq’ for inequality.
- funcallable
The function defining the constraint.
- jaccallable, optional
The Jacobian of fun (only for SLSQP).
- argssequence, optional
Extra arguments to be passed to the function and Jacobian.
Equality constraint means that the constraint function result is to
be zero whereas inequality means that it is to be non-negative.
Note that COBYLA only supports inequality constraints.
- tolfloat, optional
Tolerance for termination. For detailed control, use
solver-specific options.
- callbackcallable, optional
Called after each iteration, as callback(xk)
, where xk
is
the current parameter vector. Only available using Scipy >= 0.12.
- optionsdict, optional
A dictionary of solver options. All methods accept the following
generic options:
- maxiterint
Maximum number of iterations to perform.
- dispbool
Set to True to print convergence messages.
For method-specific options, see
show_options(‘minimize’, method).
- evolutionbool, optional
save history of x for each iteration. Only available using Scipy
>= 0.12.
See also
scipy.optimize.minimize
-
property evolution
-
property fopt
-
property message
-
property nfev
-
property nit
-
print_summary()
-
property xopt
-
class dipy.core.optimize.SKLearnLinearSolver(*args, **kwargs)
Bases: object
Provide a sklearn-like uniform interface to algorithms that solve problems
of the form: \(y = Ax\) for \(x\)
Sub-classes of SKLearnLinearSolver should provide a ‘fit’ method that have
the following signature: SKLearnLinearSolver.fit(X, y), which would set
an attribute SKLearnLinearSolver.coef_, with the shape (X.shape[1],),
such that an estimate of y can be calculated as:
y_hat = np.dot(X, SKLearnLinearSolver.coef_.T)
Methods
fit (X, y)
|
Implement for all derived classes |
predict (X)
|
Predict using the result of the model |
-
__init__(*args, **kwargs)
-
abstract fit(X, y)
Implement for all derived classes
-
predict(X)
Predict using the result of the model
- Parameters
- Xarray-like (n_samples, n_features)
Samples.
- Returns
- Carray, shape = (n_samples,)
Predicted values.
minimize
-
dipy.core.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)
Minimization of scalar function of one or more variables.
- Parameters
- funcallable
The objective function to be minimized.
where x
is an 1-D array with shape (n,) and args
is a tuple of the fixed parameters needed to completely
specify the function.
- x0ndarray, shape (n,)
Initial guess. Array of real elements of size (n,),
where ‘n’ is the number of independent variables.
- argstuple, optional
Extra arguments passed to the objective function and its
derivatives (fun, jac and hess functions).
- methodstr or callable, optional
Type of solver. Should be one of
‘Nelder-Mead’ (see here)
‘Powell’ (see here)
‘CG’ (see here)
‘BFGS’ (see here)
‘Newton-CG’ (see here)
‘L-BFGS-B’ (see here)
‘TNC’ (see here)
‘COBYLA’ (see here)
‘SLSQP’ (see here)
‘trust-constr’(see here)
‘dogleg’ (see here)
‘trust-ncg’ (see here)
‘trust-exact’ (see here)
‘trust-krylov’ (see here)
custom - a callable object (added in version 0.14.0),
see below for description.
If not given, chosen to be one of BFGS
, L-BFGS-B
, SLSQP
,
depending if the problem has constraints or bounds.
- jac{callable, ‘2-point’, ‘3-point’, ‘cs’, bool}, optional
Method for computing the gradient vector. Only for CG, BFGS,
Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg, trust-krylov,
trust-exact and trust-constr.
If it is a callable, it should be a function that returns the gradient
vector:
jac(x, *args) -> array_like, shape (n,)
where x
is an array with shape (n,) and args
is a tuple with
the fixed parameters. If jac is a Boolean and is True, fun is
assumed to return a tuple (f, g)
containing the objective
function and the gradient.
Methods ‘Newton-CG’, ‘trust-ncg’, ‘dogleg’, ‘trust-exact’, and
‘trust-krylov’ require that either a callable be supplied, or that
fun return the objective and gradient.
If None or False, the gradient will be estimated using 2-point finite
difference estimation with an absolute step size.
Alternatively, the keywords {‘2-point’, ‘3-point’, ‘cs’} can be used
to select a finite difference scheme for numerical estimation of the
gradient with a relative step size. These finite difference schemes
obey any specified bounds.
- hess{callable, ‘2-point’, ‘3-point’, ‘cs’, HessianUpdateStrategy}, optional
Method for computing the Hessian matrix. Only for Newton-CG, dogleg,
trust-ncg, trust-krylov, trust-exact and trust-constr. If it is
callable, it should return the Hessian matrix:
hess(x, *args) -> {LinearOperator, spmatrix, array}, (n, n)
where x is a (n,) ndarray and args is a tuple with the fixed
parameters. LinearOperator and sparse matrix returns are only allowed
for ‘trust-constr’ method. Alternatively, the keywords
{‘2-point’, ‘3-point’, ‘cs’} select a finite difference scheme
for numerical estimation. Or, objects implementing the
HessianUpdateStrategy interface can be used to approximate
the Hessian. Available quasi-Newton methods implementing
this interface are:
Whenever the gradient is estimated via finite-differences,
the Hessian cannot be estimated with options
{‘2-point’, ‘3-point’, ‘cs’} and needs to be
estimated using one of the quasi-Newton strategies.
‘trust-exact’ cannot use a finite-difference scheme, and must be used
with a callable returning an (n, n) array.
- hesspcallable, optional
Hessian of objective function times an arbitrary vector p. Only for
Newton-CG, trust-ncg, trust-krylov, trust-constr.
Only one of hessp or hess needs to be given. If hess is
provided, then hessp will be ignored. hessp must compute the
Hessian times an arbitrary vector:
hessp(x, p, *args) -> ndarray shape (n,)
where x is a (n,) ndarray, p is an arbitrary vector with
dimension (n,) and args is a tuple with the fixed
parameters.
- boundssequence or Bounds, optional
Bounds on variables for Nelder-Mead, L-BFGS-B, TNC, SLSQP, Powell, and
trust-constr methods. There are two ways to specify the bounds:
Instance of Bounds class.
Sequence of (min, max)
pairs for each element in x. None
is used to specify no bound.
- constraints{Constraint, dict} or List of {Constraint, dict}, optional
Constraints definition (only for COBYLA, SLSQP and trust-constr).
Constraints for ‘trust-constr’ are defined as a single object or a
list of objects specifying constraints to the optimization problem.
Available constraints are:
LinearConstraint
NonlinearConstraint
Constraints for COBYLA, SLSQP are defined as a list of dictionaries.
Each dictionary with fields:
- typestr
Constraint type: ‘eq’ for equality, ‘ineq’ for inequality.
- funcallable
The function defining the constraint.
- jaccallable, optional
The Jacobian of fun (only for SLSQP).
- argssequence, optional
Extra arguments to be passed to the function and Jacobian.
Equality constraint means that the constraint function result is to
be zero whereas inequality means that it is to be non-negative.
Note that COBYLA only supports inequality constraints.
- tolfloat, optional
Tolerance for termination. When tol is specified, the selected
minimization algorithm sets some relevant solver-specific tolerance(s)
equal to tol. For detailed control, use solver-specific
options.
- optionsdict, optional
A dictionary of solver options. All methods accept the following
generic options:
- maxiterint
Maximum number of iterations to perform. Depending on the
method each iteration may use several function evaluations.
- dispbool
Set to True to print convergence messages.
For method-specific options, see show_options()
.
- callbackcallable, optional
Called after each iteration. For ‘trust-constr’ it is a callable with
the signature:
callback(xk, OptimizeResult state) -> bool
where xk
is the current parameter vector. and state
is an OptimizeResult object, with the same fields
as the ones from the return. If callback returns True
the algorithm execution is terminated.
For all the other methods, the signature is:
where xk
is the current parameter vector.
- Returns
- resOptimizeResult
The optimization result represented as a OptimizeResult
object.
Important attributes are: x
the solution array, success
a
Boolean flag indicating if the optimizer exited successfully and
message
which describes the cause of the termination. See
OptimizeResult for a description of other attributes.
See also
minimize_scalar
Interface to minimization algorithms for scalar univariate functions
show_options
Additional options accepted by the solvers
Notes
This section describes the available solvers that can be selected by the
‘method’ parameter. The default method is BFGS.
Unconstrained minimization
Method CG uses a nonlinear conjugate
gradient algorithm by Polak and Ribiere, a variant of the
Fletcher-Reeves method described in [5] pp.120-122. Only the
first derivatives are used.
Method BFGS uses the quasi-Newton
method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS) [5]
pp. 136. It uses the first derivatives only. BFGS has proven good
performance even for non-smooth optimizations. This method also
returns an approximation of the Hessian inverse, stored as
hess_inv in the OptimizeResult object.
Method Newton-CG uses a
Newton-CG algorithm [5] pp. 168 (also known as the truncated
Newton method). It uses a CG method to the compute the search
direction. See also TNC method for a box-constrained
minimization with a similar algorithm. Suitable for large-scale
problems.
Method dogleg uses the dog-leg
trust-region algorithm [5] for unconstrained minimization. This
algorithm requires the gradient and Hessian; furthermore the
Hessian is required to be positive definite.
Method trust-ncg uses the
Newton conjugate gradient trust-region algorithm [5] for
unconstrained minimization. This algorithm requires the gradient
and either the Hessian or a function that computes the product of
the Hessian with a given vector. Suitable for large-scale problems.
Method trust-krylov uses
the Newton GLTR trust-region algorithm [14], [15] for unconstrained
minimization. This algorithm requires the gradient
and either the Hessian or a function that computes the product of
the Hessian with a given vector. Suitable for large-scale problems.
On indefinite problems it requires usually less iterations than the
trust-ncg method and is recommended for medium and large-scale problems.
Method trust-exact
is a trust-region method for unconstrained minimization in which
quadratic subproblems are solved almost exactly [13]. This
algorithm requires the gradient and the Hessian (which is
not required to be positive definite). It is, in many
situations, the Newton method to converge in fewer iteraction
and the most recommended for small and medium-size problems.
Bound-Constrained minimization
Method Nelder-Mead uses the
Simplex algorithm [1], [2]. This algorithm is robust in many
applications. However, if numerical computation of derivative can be
trusted, other algorithms using the first and/or second derivatives
information might be preferred for their better performance in
general.
Method L-BFGS-B uses the L-BFGS-B
algorithm [6], [7] for bound constrained minimization.
Method Powell is a modification
of Powell’s method [3], [4] which is a conjugate direction
method. It performs sequential one-dimensional minimizations along
each vector of the directions set (direc field in options and
info), which is updated at each iteration of the main
minimization loop. The function need not be differentiable, and no
derivatives are taken. If bounds are not provided, then an
unbounded line search will be used. If bounds are provided and
the initial guess is within the bounds, then every function
evaluation throughout the minimization procedure will be within
the bounds. If bounds are provided, the initial guess is outside
the bounds, and direc is full rank (default has full rank), then
some function evaluations during the first iteration may be
outside the bounds, but every function evaluation after the first
iteration will be within the bounds. If direc is not full rank,
then some parameters may not be optimized and the solution is not
guaranteed to be within the bounds.
Method TNC uses a truncated Newton
algorithm [5], [8] to minimize a function with variables subject
to bounds. This algorithm uses gradient information; it is also
called Newton Conjugate-Gradient. It differs from the Newton-CG
method described above as it wraps a C implementation and allows
each variable to be given upper and lower bounds.
Constrained Minimization
Method COBYLA uses the
Constrained Optimization BY Linear Approximation (COBYLA) method
[9], [10], [11]. The algorithm is based on linear
approximations to the objective function and each constraint. The
method wraps a FORTRAN implementation of the algorithm. The
constraints functions ‘fun’ may return either a single number
or an array or list of numbers.
Method SLSQP uses Sequential
Least SQuares Programming to minimize a function of several
variables with any combination of bounds, equality and inequality
constraints. The method wraps the SLSQP Optimization subroutine
originally implemented by Dieter Kraft [12]. Note that the
wrapper handles infinite values in bounds by converting them into
large floating values.
Method trust-constr is a
trust-region algorithm for constrained optimization. It swiches
between two implementations depending on the problem definition.
It is the most versatile constrained minimization algorithm
implemented in SciPy and the most appropriate for large-scale problems.
For equality constrained problems it is an implementation of Byrd-Omojokun
Trust-Region SQP method described in [17] and in [5], p. 549. When
inequality constraints are imposed as well, it swiches to the trust-region
interior point method described in [16]. This interior point algorithm,
in turn, solves inequality constraints by introducing slack variables
and solving a sequence of equality-constrained barrier problems
for progressively smaller values of the barrier parameter.
The previously described equality constrained SQP method is
used to solve the subproblems with increasing levels of accuracy
as the iterate gets closer to a solution.
Finite-Difference Options
For Method trust-constr
the gradient and the Hessian may be approximated using
three finite-difference schemes: {‘2-point’, ‘3-point’, ‘cs’}.
The scheme ‘cs’ is, potentially, the most accurate but it
requires the function to correctly handles complex inputs and to
be differentiable in the complex plane. The scheme ‘3-point’ is more
accurate than ‘2-point’ but requires twice as many operations.
Custom minimizers
It may be useful to pass a custom minimization method, for example
when using a frontend to this method such as scipy.optimize.basinhopping
or a different library. You can simply pass a callable as the method
parameter.
The callable is called as method(fun, x0, args, **kwargs, **options)
where kwargs
corresponds to any other parameters passed to minimize
(such as callback, hess, etc.), except the options dict, which has
its contents also passed as method parameters pair by pair. Also, if
jac has been passed as a bool type, jac and fun are mangled so that
fun returns just the function values and jac is converted to a function
returning the Jacobian. The method shall return an OptimizeResult
object.
The provided method callable must be able to accept (and possibly ignore)
arbitrary parameters; the set of parameters accepted by minimize may
expand in future versions and then these parameters will be passed to
the method. You can find an example in the scipy.optimize tutorial.
References
- 1
Nelder, J A, and R Mead. 1965. A Simplex Method for Function
Minimization. The Computer Journal 7: 308-13.
- 2
Wright M H. 1996. Direct search methods: Once scorned, now
respectable, in Numerical Analysis 1995: Proceedings of the 1995
Dundee Biennial Conference in Numerical Analysis (Eds. D F
Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.
191-208.
- 3
Powell, M J D. 1964. An efficient method for finding the minimum of
a function of several variables without calculating derivatives. The
Computer Journal 7: 155-162.
- 4
Press W, S A Teukolsky, W T Vetterling and B P Flannery.
Numerical Recipes (any edition), Cambridge University Press.
- 5(1,2,3,4,5,6,7,8)
Nocedal, J, and S J Wright. 2006. Numerical Optimization.
Springer New York.
- 6
Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory
Algorithm for Bound Constrained Optimization. SIAM Journal on
Scientific and Statistical Computing 16 (5): 1190-1208.
- 7
Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm
778: L-BFGS-B, FORTRAN routines for large scale bound constrained
optimization. ACM Transactions on Mathematical Software 23 (4):
550-560.
- 8
Nash, S G. Newton-Type Minimization Via the Lanczos Method.
1984. SIAM Journal of Numerical Analysis 21: 770-778.
- 9
Powell, M J D. A direct search optimization method that models
the objective and constraint functions by linear interpolation.
1994. Advances in Optimization and Numerical Analysis, eds. S. Gomez
and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.
- 10
Powell M J D. Direct search algorithms for optimization
calculations. 1998. Acta Numerica 7: 287-336.
- 11
Powell M J D. A view of algorithms for optimization without
derivatives. 2007.Cambridge University Technical Report DAMTP
2007/NA03
- 12
Kraft, D. A software package for sequential quadratic
programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace
Center – Institute for Flight Mechanics, Koln, Germany.
- 13
Conn, A. R., Gould, N. I., and Toint, P. L.
Trust region methods. 2000. Siam. pp. 169-200.
- 14
F. Lenders, C. Kirches, A. Potschka: “trlib: A vector-free
implementation of the GLTR method for iterative solution of
the trust region problem”, :arxiv:`1611.04718`
- 15
N. Gould, S. Lucidi, M. Roma, P. Toint: “Solving the
Trust-Region Subproblem using the Lanczos Method”,
SIAM J. Optim., 9(2), 504–525, (1999).
- 16
Byrd, Richard H., Mary E. Hribar, and Jorge Nocedal. 1999.
An interior point algorithm for large-scale nonlinear programming.
SIAM Journal on Optimization 9.4: 877-900.
- 17
Lalee, Marucha, Jorge Nocedal, and Todd Plantega. 1998. On the
implementation of an algorithm for large-scale equality constrained
optimization. SIAM Journal on Optimization 8.3: 682-706.
Examples
Let us consider the problem of minimizing the Rosenbrock function. This
function (and its respective derivatives) is implemented in rosen
(resp. rosen_der, rosen_hess) in the scipy.optimize.
>>> from scipy.optimize import minimize, rosen, rosen_der
A simple application of the Nelder-Mead method is:
>>> x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
>>> res = minimize(rosen, x0, method='Nelder-Mead', tol=1e-6)
>>> res.x
array([ 1., 1., 1., 1., 1.])
Now using the BFGS algorithm, using the first derivative and a few
options:
>>> res = minimize(rosen, x0, method='BFGS', jac=rosen_der,
... options={'gtol': 1e-6, 'disp': True})
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 26
Function evaluations: 31
Gradient evaluations: 31
>>> res.x
array([ 1., 1., 1., 1., 1.])
>>> print(res.message)
Optimization terminated successfully.
>>> res.hess_inv
array([[ 0.00749589, 0.01255155, 0.02396251, 0.04750988, 0.09495377], # may vary
[ 0.01255155, 0.02510441, 0.04794055, 0.09502834, 0.18996269],
[ 0.02396251, 0.04794055, 0.09631614, 0.19092151, 0.38165151],
[ 0.04750988, 0.09502834, 0.19092151, 0.38341252, 0.7664427 ],
[ 0.09495377, 0.18996269, 0.38165151, 0.7664427, 1.53713523]])
Next, consider a minimization problem with several constraints (namely
Example 16.4 from [5]). The objective function is:
>>> fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2
There are three constraints defined as:
>>> cons = ({'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
... {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
... {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})
And variables must be positive, hence the following bounds:
>>> bnds = ((0, None), (0, None))
The optimization problem is solved using the SLSQP method as:
>>> res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,
... constraints=cons)
It should converge to the theoretical solution (1.4 ,1.7).
sparse_nnls
-
dipy.core.optimize.sparse_nnls(y, X, momentum=1, step_size=0.01, non_neg=True, check_error_iter=10, max_error_checks=10, converge_on_sse=0.99)
Solve y=Xh for h, using gradient descent, with X a sparse matrix.
- Parameters
- y1-d array of shape (N)
The data. Needs to be dense.
- Xndarray. May be either sparse or dense. Shape (N, M)
The regressors
- momentumfloat, optional (default: 1).
The persistence of the gradient.
- step_sizefloat, optional (default: 0.01).
The increment of parameter update in each iteration
- non_negBoolean, optional (default: True)
Whether to enforce non-negativity of the solution.
- check_error_iterint (default:10)
How many rounds to run between error evaluation for
convergence-checking.
- max_error_checksint (default: 10)
Don’t check errors more than this number of times if no improvement in
r-squared is seen.
- converge_on_ssefloat (default: 0.99)
a percentage improvement in SSE that is required each time to say
that things are still going well.
- Returns
- h_bestThe best estimate of the parameters.
spdot
-
dipy.core.optimize.spdot(A, B)
The same as np.dot(A, B), except it works even if A or B or both
are sparse matrices.
- Parameters
- A, Barrays of shape (m, n), (n, k)
- Returns
- The matrix product AB. If both A and B are sparse, the result will be a
- sparse matrix. Otherwise, a dense result is returned
- See discussion here:
- http://mail.scipy.org/pipermail/scipy-user/2010-November/027700.html
-
class dipy.core.profile.Profiler(call=None, *args)
Bases: object
Profile python/cython files or functions
If you are profiling cython code you need to add
# cython: profile=True on the top of your .pyx file
and for the functions that you do not want to profile you can use
this decorator in your cython files
@cython.profile(False)
- Parameters
- callerfile or function call
- argsfunction arguments
References
http://docs.cython.org/src/tutorial/profiling_tutorial.html
http://docs.python.org/library/profile.html
http://packages.python.org/line_profiler/
Examples
from dipy.core.profile import Profiler
import numpy as np
p=Profiler(np.sum,np.random.rand(1000000,3))
fname=’test.py’
p=Profiler(fname)
p.print_stats(10)
p.print_stats(‘det’)
- Attributes
- statsfunction, stats.print_stats(10) will prin the 10 slower functions
Methods
-
__init__(call=None, *args)
-
print_stats(N=10)
Print stats for profiling
You can use it in all different ways developed in pstats
for example
print_stats(10) will give you the 10 slowest calls
or
print_stats(‘function_name’)
will give you the stats for all the calls with name ‘function_name’
- Parameters
- Nstats.print_stats argument
optional_package
-
dipy.core.profile.optional_package(name, trip_msg=None)
Return package-like thing and module setup for package name
- Parameters
- namestr
package name
- trip_msgNone or str
message to give when someone tries to use the return package, but we
could not import it, and have returned a TripWire object instead.
Default message if None.
- Returns
- pkg_likemodule or
TripWire
instance If we can import the package, return it. Otherwise return an object
raising an error when accessed
- have_pkgbool
True if import for package was successful, false otherwise
- module_setupfunction
callable usually set as setup_module
in calling namespace, to allow
skipping tests.
Examples
Typical use would be something like this at the top of a module using an
optional package:
>>> from dipy.utils.optpkg import optional_package
>>> pkg, have_pkg, setup_module = optional_package('not_a_package')
Of course in this case the package doesn’t exist, and so, in the module:
and
>>> pkg.some_function()
Traceback (most recent call last):
...
TripWireError: We need package not_a_package for these functions, but
``import not_a_package`` raised an ImportError
If the module does exist - we get the module
>>> pkg, _, _ = optional_package('os')
>>> hasattr(pkg, 'path')
True
Or a submodule if that’s what we asked for
>>> subpkg, _, _ = optional_package('os.path')
>>> hasattr(subpkg, 'dirname')
True
LEcuyer
-
dipy.core.rng.LEcuyer(s1=100001, s2=200002)
Return a LEcuyer random number generator.
Generate uniformly distributed random numbers using the 32-bit
generator from figure 3 of:
L’Ecuyer, P. Efficient and portable combined random number
generators, C.A.C.M., vol. 31, 742-749 & 774-?, June 1988.
The cycle length is claimed to be 2.30584E+18
- Parameters
- s1: int
First seed value. Should not be null. (default 100001)
- s2: int
Second seed value. Should not be null. (default 200002)
- Returns
- r_numberfloat
pseudo-random number uniformly distributed between [0-1]
Examples
>>> from dipy.core import rng
>>> N = 1000
>>> a = [rng.LEcuyer() for i in range(N)]
WichmannHill1982
-
dipy.core.rng.WichmannHill1982(ix=100001, iy=200002, iz=300003)
Algorithm AS 183 Appl. Statist. (1982) vol.31, no.2.
Returns a pseudo-random number rectangularly distributed
between 0 and 1. The cycle length is 6.95E+12 (See page 123
of Applied Statistics (1984) vol.33), not as claimed in the
original article.
ix, iy and iz should be set to integer values between 1 and
30000 before the first entry.
Integer arithmetic up to 5212632 is required.
- Parameters
- ix: int
First seed value. Should not be null. (default 100001)
- iy: int
Second seed value. Should not be null. (default 200002)
- iz: int
Third seed value. Should not be null. (default 300003)
- Returns
- r_numberfloat
pseudo-random number uniformly distributed between [0-1]
Examples
>>> from dipy.core import rng
>>> N = 1000
>>> a = [rng.WichmannHill1982() for i in range(N)]
WichmannHill2006
-
dipy.core.rng.WichmannHill2006(ix=100001, iy=200002, iz=300003, it=400004)
Wichmann Hill (2006) random number generator.
B.A. Wichmann, I.D. Hill, Generating good pseudo-random numbers,
Computational Statistics & Data Analysis, Volume 51, Issue 3, 1
December 2006, Pages 1614-1622, ISSN 0167-9473, DOI:
10.1016/j.csda.2006.05.019.
(http://www.sciencedirect.com/science/article/B6V8V-4K7F86W-2/2/a3a33291b8264e4c882a8f21b6e43351)
for advice on generating many sequences for use together, and on
alternative algorithms and codes
- Parameters
- ix: int
First seed value. Should not be null. (default 100001)
- iy: int
Second seed value. Should not be null. (default 200002)
- iz: int
Third seed value. Should not be null. (default 300003)
- it: int
Fourth seed value. Should not be null. (default 400004)
- Returns
- r_numberfloat
pseudo-random number uniformly distributed between [0-1]
Examples
>>> from dipy.core import rng
>>> N = 1000
>>> a = [rng.WichmannHill2006() for i in range(N)]
architecture
-
dipy.core.rng.architecture(executable='/N/u/skoudoro/Carbonate/.conda/envs/dipy-env-39/bin/python', bits='', linkage='')
Queries the given executable (defaults to the Python interpreter
binary) for various architecture information.
Returns a tuple (bits, linkage) which contains information about
the bit architecture and the linkage format used for the
executable. Both values are returned as strings.
Values that cannot be determined are returned as given by the
parameter presets. If bits is given as ‘’, the sizeof(pointer)
(or sizeof(long) on Python version < 1.5.2) is used as
indicator for the supported pointer size.
The function relies on the system’s “file” command to do the
actual work. This is available on most if not all Unix
platforms. On some non-Unix platforms where the “file” command
does not exist and the executable is set to the Python interpreter
binary defaults from _default_architecture are used.
floor
-
dipy.core.rng.floor(x, /)
Return the floor of x as an Integral.
This is the largest integer <= x.
-
class dipy.core.sphere.HemiSphere(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)
Bases: dipy.core.sphere.Sphere
Points on the unit sphere.
A HemiSphere is similar to a Sphere but it takes antipodal symmetry into
account. Antipodal symmetry means that point v on a HemiSphere is the same
as the point -v. Duplicate points are discarded when constructing a
HemiSphere (including antipodal duplicates). edges and faces are
remapped to the remaining points as closely as possible.
The HemiSphere can be constructed using one of three conventions:
HemiSphere(x, y, z)
HemiSphere(xyz=xyz)
HemiSphere(theta=theta, phi=phi)
- Parameters
- x, y, z1-D array_like
Vertices as x-y-z coordinates.
- theta, phi1-D array_like
Vertices as spherical coordinates. Theta and phi are the inclination
and azimuth angles respectively.
- xyz(N, 3) ndarray
Vertices as x-y-z coordinates.
- faces(N, 3) ndarray
Indices into vertices that form triangular faces. If unspecified,
the faces are computed using a Delaunay triangulation.
- edges(N, 2) ndarray
Edges between vertices. If unspecified, the edges are
derived from the faces.
- tolfloat
Angle in degrees. Vertices that are less than tol degrees apart are
treated as duplicates.
- Attributes
- x
- y
- z
Methods
find_closest (xyz)
|
Find the index of the vertex in the Sphere closest to the input vector, taking into account antipodal symmetry |
from_sphere (sphere[, tol])
|
Create instance from a Sphere |
mirror ()
|
Create a full Sphere from a HemiSphere |
subdivide ([n])
|
Create a more subdivided HemiSphere |
-
__init__(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)
Create a HemiSphere from points
-
faces()
-
find_closest(xyz)
Find the index of the vertex in the Sphere closest to the input vector,
taking into account antipodal symmetry
- Parameters
- xyzarray-like, 3 elements
A unit vector
- Returns
- idxint
The index into the Sphere.vertices array that gives the closest
vertex (in angle).
-
classmethod from_sphere(sphere, tol=1e-05)
Create instance from a Sphere
-
mirror()
Create a full Sphere from a HemiSphere
-
subdivide(n=1)
Create a more subdivided HemiSphere
See Sphere.subdivide for full documentation.
-
class dipy.core.sphere.Sphere(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None)
Bases: object
Points on the unit sphere.
The sphere can be constructed using one of three conventions:
Sphere(x, y, z)
Sphere(xyz=xyz)
Sphere(theta=theta, phi=phi)
- Parameters
- x, y, z1-D array_like
Vertices as x-y-z coordinates.
- theta, phi1-D array_like
Vertices as spherical coordinates. Theta and phi are the inclination
and azimuth angles respectively.
- xyz(N, 3) ndarray
Vertices as x-y-z coordinates.
- faces(N, 3) ndarray
Indices into vertices that form triangular faces. If unspecified,
the faces are computed using a Delaunay triangulation.
- edges(N, 2) ndarray
Edges between vertices. If unspecified, the edges are
derived from the faces.
- Attributes
- x
- y
- z
Methods
find_closest (xyz)
|
Find the index of the vertex in the Sphere closest to the input vector |
subdivide ([n])
|
Subdivides each face of the sphere into four new faces. |
-
__init__(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None)
-
edges()
-
faces()
-
find_closest(xyz)
Find the index of the vertex in the Sphere closest to the input vector
- Parameters
- xyzarray-like, 3 elements
A unit vector
- Returns
- idxint
The index into the Sphere.vertices array that gives the closest
vertex (in angle).
-
subdivide(n=1)
Subdivides each face of the sphere into four new faces.
New vertices are created at a, b, and c. Then each face [x, y, z] is
divided into faces [x, a, c], [y, a, b], [z, b, c], and [a, b, c].
y
/\
/ \
a/____\b
/\ /\
/ \ / \
/____\/____\
x c z
- Parameters
- nint, optional
The number of subdivisions to preform.
- Returns
- new_sphereSphere
The subdivided sphere.
-
vertices()
-
property x
-
property y
-
property z
auto_attr
-
dipy.core.sphere.auto_attr(func)
Decorator to create OneTimeProperty attributes.
- Parameters
- funcmethod
The method that will be called the first time to compute a value.
Afterwards, the method’s name will be a standard attribute holding the
value of this computation.
Examples
>>> class MagicProp(object):
... @auto_attr
... def a(self):
... return 99
...
>>> x = MagicProp()
>>> 'a' in x.__dict__
False
>>> x.a
99
>>> 'a' in x.__dict__
True
cart2sphere
-
dipy.core.sphere.cart2sphere(x, y, z)
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
- xarray_like
x coordinate in Cartesian space
- yarray_like
y coordinate in Cartesian space
- zarray_like
z coordinate
- Returns
- rarray
radius
- thetaarray
inclination (polar) angle
- phiarray
azimuth angle
disperse_charges
-
dipy.core.sphere.disperse_charges(hemi, iters, const=0.2)
Models electrostatic repulsion on the unit sphere
Places charges on a sphere and simulates the repulsive forces felt by each
one. Allows the charges to move for some number of iterations and returns
their final location as well as the total potential of the system at each
step.
- Parameters
- hemiHemiSphere
Points on a unit sphere.
- itersint
Number of iterations to run.
- constfloat
Using a smaller const could provide a more accurate result, but will
need more iterations to converge.
- Returns
- hemiHemiSphere
Distributed points on a unit sphere.
- potentialndarray
The electrostatic potential at each iteration. This can be useful to
check if the repulsion converged to a minimum.
Notes
This function is meant to be used with diffusion imaging so antipodal
symmetry is assumed. Therefor each charge must not only be unique, but if
there is a charge at +x, there cannot be a charge at -x. These are treated
as the same location and because the distance between the two charges will
be zero, the result will be unstable.
disperse_charges_alt
-
dipy.core.sphere.disperse_charges_alt(init_pointset, iters, tol=0.001)
Reimplementation of disperse_charges making use of
scipy.optimize.fmin_slsqp.
- Parameters
- init_pointset(N, 3) ndarray
Points on a unit sphere.
- itersint
Number of iterations to run.
- tolfloat
Tolerance for the optimization.
- Returns
- array-like (N, 3)
Distributed points on a unit sphere.
euler_characteristic_check
-
dipy.core.sphere.euler_characteristic_check(sphere, chi=2)
Checks the euler characteristic of a sphere
If \(f\) = number of faces, \(e\) = number_of_edges and \(v\) = number of
vertices, the Euler formula says \(f-e+v = 2\) for a mesh on a sphere. More
generally, whether \(f -e + v == \chi\) where \(\chi\) is the Euler
characteristic of the mesh.
Open chain (track) has \(\chi=1\)
Closed chain (loop) has \(\chi=0\)
Disk has \(\chi=1\)
Sphere has \(\chi=2\)
HemiSphere has \(\chi=1\)
- Parameters
- sphereSphere
A Sphere instance with vertices, edges and faces attributes.
- chiint, optional
The Euler characteristic of the mesh to be checked
- Returns
- checkbool
True if the mesh has Euler characteristic \(\chi\)
Examples
>>> euler_characteristic_check(unit_octahedron)
True
>>> hemisphere = HemiSphere.from_sphere(unit_icosahedron)
>>> euler_characteristic_check(hemisphere, chi=1)
True
faces_from_sphere_vertices
-
dipy.core.sphere.faces_from_sphere_vertices(vertices)
Triangulate a set of vertices on the sphere.
- Parameters
- vertices(M, 3) ndarray
XYZ coordinates of vertices on the sphere.
- Returns
- faces(N, 3) ndarray
Indices into vertices; forms triangular faces.
remove_similar_vertices
-
dipy.core.sphere.remove_similar_vertices(vertices, theta, return_mapping=False, return_index=False)
Remove vertices that are less than theta degrees from any other
Returns vertices that are at least theta degrees from any other vertex.
Vertex v and -v are considered the same so if v and -v are both in
vertices only one is kept. Also if v and w are both in vertices, w must
be separated by theta degrees from both v and -v to be unique.
- Parameters
- vertices(N, 3) ndarray
N unit vectors.
- thetafloat
The minimum separation between vertices in degrees.
- return_mapping{False, True}, optional
If True, return mapping as well as vertices and maybe indices
(see below).
- return_indices{False, True}, optional
If True, return indices as well as vertices and maybe mapping
(see below).
- Returns
- unique_vertices(M, 3) ndarray
Vertices sufficiently separated from one another.
- mapping(N,) ndarray
For each element vertices[i]
(\(i \in 0..N-1\)), the index \(j\) to a
vertex in unique_vertices that is less than theta degrees from
vertices[i]
. Only returned if return_mapping is True.
- indices(N,) ndarray
indices gives the reverse of mapping. For each element
unique_vertices[j]
(\(j \in 0..M-1\)), the index \(i\) to a vertex in
vertices that is less than theta degrees from
unique_vertices[j]
. If there is more than one element of
vertices that is less than theta degrees from unique_vertices[j],
return the first (lowest index) matching value. Only return if
return_indices is True.
sphere2cart
-
dipy.core.sphere.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
- rarray_like
radius
- thetaarray_like
inclination or polar angle
- phiarray_like
azimuth angle
- Returns
- xarray
x coordinate(s) in Cartesion space
- yarray
y coordinate(s) in Cartesian space
- zarray
z coordinate
Notes
See these pages:
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.
unique_edges
-
dipy.core.sphere.unique_edges(faces, return_mapping=False)
Extract all unique edges from given triangular faces.
- Parameters
- faces(N, 3) ndarray
Vertex indices forming triangular faces.
- return_mappingbool
If true, a mapping to the edges of each face is returned.
- Returns
- edges(N, 2) ndarray
Unique edges.
- mapping(N, 3)
For each face, [x, y, z], a mapping to it’s edges [a, b, c].
y
/ / a/
/ / /__________ x c z
unique_sets
-
dipy.core.sphere.unique_sets(sets, return_inverse=False)
Remove duplicate sets.
- Parameters
- setsarray (N, k)
N sets of size k.
- return_inversebool
If True, also returns the indices of unique_sets that can be used
to reconstruct sets (the original ordering of each set may not be
preserved).
- Returns
- unique_setsarray
Unique sets.
- inversearray (N,)
The indices to reconstruct sets from unique_sets.
vector_norm
-
dipy.core.sphere.vector_norm(vec, axis=- 1, keepdims=False)
Return vector Euclidean (L2) norm
See unit vector and Euclidean norm
- Parameters
- vecarray_like
Vectors to norm.
- axisint
Axis over which to norm. By default norm over last axis. If axis is
None, vec is flattened then normed.
- keepdimsbool
If True, the output will have the same number of dimensions as vec,
with shape 1 on axis.
- Returns
- normarray
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.])
-
class dipy.core.sphere_stats.permutations(iterable, r=None)
Bases: object
Return successive r-length permutations of elements in the iterable.
permutations(range(3), 2) –> (0,1), (0,2), (1,0), (1,2), (2,0), (2,1)
-
__init__(*args, **kwargs)
angular_similarity
-
dipy.core.sphere_stats.angular_similarity(S, T)
Computes the cosine distance of the best match between
points of two sets of vectors S and T
- Parameters
- Sarray, shape (m,d)
- Tarray, shape (n,d)
- Returns
- max_cosine_distance:float
Examples
>>> import numpy as np
>>> from dipy.core.sphere_stats import angular_similarity
>>> S=np.array([[1,0,0],[0,1,0],[0,0,1]])
>>> T=np.array([[1,0,0],[0,0,1]])
>>> angular_similarity(S,T)
2.0
>>> T=np.array([[0,1,0],[1,0,0],[0,0,1]])
>>> S=np.array([[1,0,0],[0,0,1]])
>>> angular_similarity(S,T)
2.0
>>> S=np.array([[-1,0,0],[0,1,0],[0,0,1]])
>>> T=np.array([[1,0,0],[0,0,-1]])
>>> angular_similarity(S,T)
2.0
>>> T=np.array([[0,1,0],[1,0,0],[0,0,1]])
>>> S=np.array([[1,0,0],[0,1,0],[0,0,1]])
>>> angular_similarity(S,T)
3.0
>>> S=np.array([[0,1,0],[1,0,0],[0,0,1]])
>>> T=np.array([[1,0,0],[0,np.sqrt(2)/2.,np.sqrt(2)/2.],[0,0,1]])
>>> angular_similarity(S,T)
2.7071067811865475
>>> S=np.array([[0,1,0],[1,0,0],[0,0,1]])
>>> T=np.array([[1,0,0]])
>>> angular_similarity(S,T)
1.0
>>> S=np.array([[0,1,0],[1,0,0]])
>>> T=np.array([[0,0,1]])
>>> angular_similarity(S,T)
0.0
>>> S=np.array([[0,1,0],[1,0,0]])
>>> T=np.array([[0,np.sqrt(2)/2.,np.sqrt(2)/2.]])
Now we use print
to reduce the precision of of the printed output
(so the doctests don’t detect unimportant differences)
>>> print('%.12f' % angular_similarity(S,T))
0.707106781187
>>> S=np.array([[0,1,0]])
>>> T=np.array([[0,np.sqrt(2)/2.,np.sqrt(2)/2.]])
>>> print('%.12f' % angular_similarity(S,T))
0.707106781187
>>> S=np.array([[0,1,0],[0,0,1]])
>>> T=np.array([[0,np.sqrt(2)/2.,np.sqrt(2)/2.]])
>>> print('%.12f' % angular_similarity(S,T))
0.707106781187
compare_orientation_sets
-
dipy.core.sphere_stats.compare_orientation_sets(S, T)
Computes the mean cosine distance of the best match between
points of two sets of vectors S and T (angular similarity)
- Parameters
- Sarray, shape (m,d)
First set of vectors.
- Tarray, shape (n,d)
Second set of vectors.
- Returns
- max_mean_cosinefloat
Maximum mean cosine distance.
Examples
>>> from dipy.core.sphere_stats import compare_orientation_sets
>>> S=np.array([[1,0,0],[0,1,0],[0,0,1]])
>>> T=np.array([[1,0,0],[0,0,1]])
>>> compare_orientation_sets(S,T)
1.0
>>> T=np.array([[0,1,0],[1,0,0],[0,0,1]])
>>> S=np.array([[1,0,0],[0,0,1]])
>>> compare_orientation_sets(S,T)
1.0
>>> from dipy.core.sphere_stats import compare_orientation_sets
>>> S=np.array([[-1,0,0],[0,1,0],[0,0,1]])
>>> T=np.array([[1,0,0],[0,0,-1]])
>>> compare_orientation_sets(S,T)
1.0
eigenstats
-
dipy.core.sphere_stats.eigenstats(points, alpha=0.05)
Principal direction and confidence ellipse
Implements equations in section 6.3.1(ii) of Fisher, Lewis and
Embleton, supplemented by equations in section 3.2.5.
- Parameters
- pointsarraey_like (N,3)
array of points on the sphere of radius 1 in \(\mathbb{R}^3\)
- alphareal or None
1 minus the coverage for the confidence ellipsoid, e.g. 0.05 for 95%
coverage.
- Returns
- centrevector (3,)
centre of ellipsoid
- b1vector (2,)
lengths of semi-axes of ellipsoid
-
class dipy.core.subdivide_octahedron.HemiSphere(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)
Bases: dipy.core.sphere.Sphere
Points on the unit sphere.
A HemiSphere is similar to a Sphere but it takes antipodal symmetry into
account. Antipodal symmetry means that point v on a HemiSphere is the same
as the point -v. Duplicate points are discarded when constructing a
HemiSphere (including antipodal duplicates). edges and faces are
remapped to the remaining points as closely as possible.
The HemiSphere can be constructed using one of three conventions:
HemiSphere(x, y, z)
HemiSphere(xyz=xyz)
HemiSphere(theta=theta, phi=phi)
- Parameters
- x, y, z1-D array_like
Vertices as x-y-z coordinates.
- theta, phi1-D array_like
Vertices as spherical coordinates. Theta and phi are the inclination
and azimuth angles respectively.
- xyz(N, 3) ndarray
Vertices as x-y-z coordinates.
- faces(N, 3) ndarray
Indices into vertices that form triangular faces. If unspecified,
the faces are computed using a Delaunay triangulation.
- edges(N, 2) ndarray
Edges between vertices. If unspecified, the edges are
derived from the faces.
- tolfloat
Angle in degrees. Vertices that are less than tol degrees apart are
treated as duplicates.
- Attributes
- x
- y
- z
Methods
find_closest (xyz)
|
Find the index of the vertex in the Sphere closest to the input vector, taking into account antipodal symmetry |
from_sphere (sphere[, tol])
|
Create instance from a Sphere |
mirror ()
|
Create a full Sphere from a HemiSphere |
subdivide ([n])
|
Create a more subdivided HemiSphere |
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__init__(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)
Create a HemiSphere from points
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faces()
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find_closest(xyz)
Find the index of the vertex in the Sphere closest to the input vector,
taking into account antipodal symmetry
- Parameters
- xyzarray-like, 3 elements
A unit vector
- Returns
- idxint
The index into the Sphere.vertices array that gives the closest
vertex (in angle).
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classmethod from_sphere(sphere, tol=1e-05)
Create instance from a Sphere
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mirror()
Create a full Sphere from a HemiSphere
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subdivide(n=1)
Create a more subdivided HemiSphere
See Sphere.subdivide for full documentation.
create_unit_hemisphere
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dipy.core.subdivide_octahedron.create_unit_hemisphere(recursion_level=2)
Creates a unit sphere by subdividing a unit octahedron, returns half
the sphere.
- Parameters
- recursion_levelint
Level of subdivision, recursion_level=1 will return an octahedron,
anything bigger will return a more subdivided sphere. The sphere will
have \((4^recursion_level+2)/2\) vertices.
- Returns
- HemiSphere
Half of a unit sphere.
create_unit_sphere
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dipy.core.subdivide_octahedron.create_unit_sphere(recursion_level=2)
Creates a unit sphere by subdividing a unit octahedron.
Starts with a unit octahedron and subdivides the faces, projecting the
resulting points onto the surface of a unit sphere.
- Parameters
- recursion_levelint
Level of subdivision, recursion_level=1 will return an octahedron,
anything bigger will return a more subdivided sphere. The sphere will
have \(4^recursion_level+2\) vertices.
- Returns
- Sphere
The unit sphere.
afb3D
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dipy.core.wavelet.afb3D(x, af1, af2=None, af3=None)
3D Analysis Filter Bank
- Parameters
- x3D ndarray
N1 by N2 by N3 array matrix, where
1) N1, N2, N3 all even
2) N1 >= 2*len(af1)
3) N2 >= 2*len(af2)
4) N3 >= 2*len(af3)
- afi2D ndarray
analysis filters for dimension i
afi[:, 1] - lowpass filter
afi[:, 2] - highpass filter
- Returns
- lo1D array
lowpass subband
- hi1D array
highpass subbands, h[d]- d = 1..7
afb3D_A
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dipy.core.wavelet.afb3D_A(x, af, d)
- 3D Analysis Filter Bank
(along one dimension only)
- Parameters
- x3D ndarray
- N1xN2xN2 matrix, where min(N1,N2,N3) > 2*length(filter)
(Ni are even)
- af2D ndarray
analysis filter for the columns
af[:, 1] - lowpass filter
af[:, 2] - highpass filter
- dint
dimension of filtering (d = 1, 2 or 3)
- Returns
- lo1D array
lowpass subbands
- hi1D array
highpass subbands
cshift3D
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dipy.core.wavelet.cshift3D(x, m, d)
3D Circular Shift
- Parameters
- x3D ndarray
N1 by N2 by N3 array
- mint
amount of shift
- dint
dimension of shift (d = 1,2,3)
- Returns
- y3D ndarray
array x will be shifed by m samples down
along dimension d
dwt3D
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dipy.core.wavelet.dwt3D(x, J, af)
3-D Discrete Wavelet Transform
- Parameters
- x3D ndarray
N1 x N2 x N3 matrix
1) Ni all even
2) min(Ni) >= 2^(J-1)*length(af)
- Jint
number of stages
- af2D ndarray
analysis filters
- Returns
- wcell array
wavelet coefficients
idwt3D
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dipy.core.wavelet.idwt3D(w, J, sf)
Inverse 3-D Discrete Wavelet Transform
- Parameters
- wcell array
wavelet coefficient
- Jint
number of stages
- sf2D ndarray
synthesis filters
- Returns
- y3D ndarray
output array
permutationinverse
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dipy.core.wavelet.permutationinverse(perm)
Function generating inverse of the permutation
- Parameters
- perm1D array
- Returns
- inverse1D array
permutation inverse of the input
sfb3D
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dipy.core.wavelet.sfb3D(lo, hi, sf1, sf2=None, sf3=None)
3D Synthesis Filter Bank
- Parameters
- lo1D array
lowpass subbands
- hi1D array
highpass subbands
- sfi2D ndarray
synthesis filters for dimension i
- Returns
- y3D ndarray
output array
sfb3D_A
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dipy.core.wavelet.sfb3D_A(lo, hi, sf, d)
- 3D Synthesis Filter Bank
(along single dimension only)
- Parameters
- lo1D array
lowpass subbands
- hi1D array
highpass subbands
- sf2D ndarray
synthesis filters
- dint
dimension of filtering
- Returns
- y3D ndarray
the N1xN2xN3 matrix