`actors`#

Module: `actors.odf`#

sh_odf(centers, coeffs, degree, sh_basis, ...)

Visualize one or many ODFs with different features.

Module: `actors.odf_slicer`#

OdfSlicerActor(odfs, vertices, faces, ...[, ...])

VTK actor for visualizing slices of ODF field.

Module: `actors.peak`#

PeakActor(directions, indices, *[, values, ...])

FURY actor for visualizing DWI peaks.

Module: `actors.tensor`#

`tensor_ellipsoid`(centers, axes, lengths, ...)	Visualize one or many Tensor Ellipsoids with different features.
`double_cone`(centers, axes, angles, colors, ...)	Visualize one or many Double Cones with different features.
`main_dir_uncertainty`(evals, evecs, signal, ...)	Calculate the angle of the cone of uncertainty that represents the perturbation of the main eigenvector of the diffusion tensor matrix.

sh_odf#

fury.actors.odf.sh_odf(centers, coeffs, degree, sh_basis, scales, opacity)[source]#

Visualize one or many ODFs with different features.

Parameters:

centersndarray(N, 3): ODFs positions.
coeffsndarray: 2D ODFs array in SH coefficients.
sh_basis: str, optional: Type of basis (descoteaux, tournier) ‘descoteaux’ for the default descoteaux07 DIPY basis. ‘tournier’ for the default tournier07 DIPY basis.
degree: int, optional: Index of the highest used band of the spherical harmonics basis. Must be even, at least 2 and at most 12.
scalesfloat or ndarray (N, ): ODFs size.
opacityfloat: Takes values from 0 (fully transparent) to 1 (opaque).

Returns:

box_actor: Actor

`OdfSlicerActor`#

class fury.actors.odf_slicer.OdfSlicerActor(odfs, vertices, faces, indices, scale, norm, radial_scale, shape, global_cm, colormap, opacity, *, affine=None, B=None)[source]#

Bases: vtkActor

VTK actor for visualizing slices of ODF field.

Parameters:

odfsndarray: SF or SH coefficients 2-dimensional array.
vertices: ndarray: The sphere vertices used for SH to SF projection.
faces: ndarray: Indices of sphere vertices forming triangles. Should be ordered clockwise (see fury.utils.fix_winding_order).
indices: tuple: Indices given in tuple(x_indices, y_indices, z_indices) format for mapping 2D ODF array to 3D voxel grid.
scalefloat: Multiplicative factor to apply to ODF amplitudes.
normbool: Normalize SF amplitudes so that the maximum ODF amplitude per voxel along a direction is 1.
radial_scalebool: Scale sphere points by ODF values.
global_cmbool: If True the colormap will be applied in all ODFs. If False it will be applied individually at each voxel.
colormapNone or str: The name of the colormap to use. Matplotlib colormaps are supported (e.g., ‘inferno’). If None then a RGB colormap is used.
opacityfloat: Takes values from 0 (fully transparent) to 1 (opaque).
affinearray: optional 4x4 transformation array from native coordinates to world coordinates.
Bndarray (n_coeffs, n_vertices): Optional SH to SF matrix for projecting odfs given in SH coefficients on the sphere. If None, then the input is assumed to be expressed in SF coefficients.

display(*, x=None, y=None, z=None)[source]#: Display a slice along x, y, or z axis.

display_extent(x1, x2, y1, y2, z1, z2)[source]#: Set visible volume from x1 (inclusive) to x2 (inclusive), y1 (inclusive) to y2 (inclusive), z1 (inclusive) to z2 (inclusive).

set_opacity(opacity)[source]#: Set opacity value of ODFs to display.

slice_along_axis(slice_index, *, axis='zaxis')[source]#: Slice ODF field at given slice_index along axis in [‘xaxis’, ‘yaxis’, zaxis’].

update_sphere(vertices, faces, B)[source]#: Dynamically change the sphere used for SH to SF projection.

`PeakActor`#

class fury.actors.peak.PeakActor(directions, indices, *, values=None, affine=None, colors=None, lookup_colormap=None, linewidth=1, symmetric=True)[source]#

Bases: vtkActor

FURY actor for visualizing DWI peaks.

Parameters:

directionsndarray: Peak directions. The shape of the array should be (X, Y, Z, D, 3).
indicestuple: Indices given in tuple(x_indices, y_indices, z_indices) format for mapping 2D ODF array to 3D voxel grid.
valuesndarray, optional: Peak values. The shape of the array should be (X, Y, Z, D).
affinearray, optional: 4x4 transformation array from native coordinates to world coordinates.
colorsNone or string (‘rgb_standard’) or tuple (3D or 4D) or array/ndarray (N, 3 or 4) or array/ndarray (K, 3 or 4) or array/ndarray(N, ) or array/ndarray (K, ): If None a standard orientation colormap is used for every line. If one tuple of color is used. Then all streamlines will have the same color. If an array (N, 3 or 4) is given, where N is equal to the number of points. Then every point is colored with a different RGB(A) color. If an array (K, 3 or 4) is given, where K is equal to the number of lines. Then every line is colored with a different RGB(A) color. If an array (N, ) is given, where N is the number of points then these are considered as the values to be used by the colormap. If an array (K,) is given, where K is the number of lines then these are considered as the values to be used by the colormap.
lookup_colormapvtkLookupTable, optional: Add a default lookup table to the colormap. Default is None which calls fury.actor.colormap_lookup_table().
linewidthfloat, optional: Line thickness. Default is 1.
symmetric: bool, optional: If True, peaks are drawn for both peaks_dirs and -peaks_dirs. Else, peaks are only drawn for directions given by peaks_dirs. Default is True.

property cross_section#

display_cross_section(x, y, z)[source]#

display_extent(x1, x2, y1, y2, z1, z2)[source]#

property global_opacity#

property high_ranges#

property is_range#

property linewidth#

property low_ranges#

property max_centers#

property min_centers#

tensor_ellipsoid#

fury.actors.tensor.tensor_ellipsoid(centers, axes, lengths, colors, scales, opacity)[source]#

Visualize one or many Tensor Ellipsoids with different features.

Parameters:

centersndarray(N, 3): Tensor ellipsoid positions.
axesndarray (3, 3) or (N, 3, 3): Axes of the tensor ellipsoid.
lengthsndarray (3, ) or (N, 3): Axes lengths.
colorsndarray (N,3) or tuple (3,): R, G and B should be in the range [0, 1].
scalesfloat or ndarray (N, ): Tensor ellipsoid size.
opacityfloat: Takes values from 0 (fully transparent) to 1 (opaque).

Returns:

box_actor: Actor

double_cone#

fury.actors.tensor.double_cone(centers, axes, angles, colors, scales, opacity)[source]#

Visualize one or many Double Cones with different features.

Parameters:

centersndarray(N, 3): Cone positions.
axesndarray (3, 3) or (N, 3, 3): Axes of the cone.
anglesfloat or ndarray (N, ): Angles of the cone.
colorsndarray (N, 3) or tuple (3,): R, G and B should be in the range [0, 1].
scalesfloat or ndarray (N, ): Cone size.
opacityfloat: Takes values from 0 (fully transparent) to 1 (opaque).

Returns:

box_actor: Actor

main_dir_uncertainty#

fury.actors.tensor.main_dir_uncertainty(evals, evecs, signal, sigma, b_matrix)[source]#

Calculate the angle of the cone of uncertainty that represents the perturbation of the main eigenvector of the diffusion tensor matrix.

Parameters:

evalsndarray (3, ) or (N, 3): Eigenvalues.
evecsndarray (3, 3) or (N, 3, 3): Eigenvectors.
signal3D or 4D ndarray: Predicted signal.
sigmandarray: Standard deviation of the noise.
b_matrixarray (N, 7): Design matrix for DTI.

Returns:

anglesarray

Notes

The uncertainty calculation is based on first-order matrix perturbation analysis described in [1]. The idea is to estimate the variance of the main eigenvector which corresponds to the main direction of diffusion, directly from estimated D and its estimated covariance matrix $Δ D$ (see [2], equation 4). The angle $Θ$ between the perturbed principal eigenvector of D, $ϵ_{1} + Δ ϵ_{1}$ , and the estimated eigenvector $ϵ_{1}$ , measures the angular deviation of the main fiber direction and can be approximated by:

Θ = t a n^{- 1} (‖ Δ e p s i l o n_{1} ‖)

Giving way to a graphical construct for displaying both the main eigenvector of D and its associated uncertainty, with the so-called “uncertainty cone”.

References

[1]

Basser, P. J. (1997). Quantifying errors in fiber direction and diffusion tensor field maps resulting from MR noise. In 5th Scientific Meeting of the ISMRM (Vol. 1740).

[2]

Chang, L. C., Koay, C. G., Pierpaoli, C., & Basser, P. J. (2007). Variance of estimated DTI-derived parameters via first-order perturbation methods. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 57(1), 141-149.

Table of Contents

`actors`#

Module: `actors.odf`#

Module: `actors.odf_slicer`#

Module: `actors.peak`#

Module: `actors.tensor`#

sh_odf#

`OdfSlicerActor`#

`PeakActor`#

tensor_ellipsoid#

double_cone#

main_dir_uncertainty#

About

Friends

Support

Table of Contents

actors#

Module: actors.odf#

Module: actors.odf_slicer#

Module: actors.peak#

Module: actors.tensor#

sh_odf#

OdfSlicerActor#

PeakActor#

tensor_ellipsoid#

double_cone#

main_dir_uncertainty#

`actors`#

Module: `actors.odf`#

Module: `actors.odf_slicer`#

Module: `actors.peak`#

Module: `actors.tensor`#

`OdfSlicerActor`#

`PeakActor`#