utils¶
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Apply affine matrix aff to points pts. |
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Get vtkActor from a vtkPolyData. |
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Get vtkActor from a vtkPolyDataMapper. |
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Get the bounding box sizes of an actor. |
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Construct a XY-grid based on the cells content shape. |
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Get points color (ndarrays Nx3 int) from a vtk polydata. |
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Convert vtk polydata to a list of lines ndarrays. |
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Get vertices normal (ndarrays Nx3 int) from a vtk polydata. |
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Get triangles (ndarrays Nx3 int) from a vtk polydata. |
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Get vertices (ndarrays Nx3 int) from a vtk polydata. |
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Get vtkPolyDataMapper from a vtkPolyData. |
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Create colors for streamlines to be used in actor.line. |
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Create a vtkPolyData with lines and colors. |
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Map the input array to new coordinates by interpolation. |
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Evaluate the input_array data at the given indices using trilinear interpolation. |
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Convert Numpy color array to a vtk color array. |
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Convert a numpy array to a VTK matrix. |
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Convert Numpy points array to a vtk points array. |
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Rotate actor around axis by angle. |
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Set Generic input function which takes into account VTK 5 or 6. |
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Set polydata colors with a numpy array (ndarrays Nx3 int). |
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Set polydata normals with a numpy array (ndarrays Nx3 int). |
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Set polydata triangles with a numpy array (ndarrays Nx3 int). |
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Set polydata vertices with a numpy array (ndarrays Nx3 int). |
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Create a shallow copy of a given vtkObject object. |
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Generate and update polydata normals. |
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Convert VTK matrix to numpy array. |
apply_affine¶
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fury.utils.apply_affine(aff, pts)[source]¶ Apply affine matrix aff to points pts.
Returns result of application of aff to the right of pts. The coordinate dimension of pts should be the last. For the 3D case, aff will be shape (4,4) and pts will have final axis length 3 - maybe it will just be N by 3. The return value is the transformed points, in this case:: res = np.dot(aff[:3,:3], pts.T) + aff[:3,3:4] transformed_pts = res.T This routine is more general than 3D, in that aff can have any shape (N,N), and pts can have any shape, as long as the last dimension is for the coordinates, and is therefore length N-1.
- Parameters
- aff(N, N) array-like
Homogenous affine, for 3D points, will be 4 by 4. Contrary to first appearance, the affine will be applied on the left of pts.
- pts(…, N-1) array-like
Points, where the last dimension contains the coordinates of each point. For 3D, the last dimension will be length 3.
- Returns
- transformed_pts(…, N-1) array
transformed points
Notes
Copied from nibabel to remove dependency.
Examples
>>> aff = np.array([[0,2,0,10],[3,0,0,11],[0,0,4,12],[0,0,0,1]]) >>> pts = np.array([[1,2,3],[2,3,4],[4,5,6],[6,7,8]]) >>> apply_affine(aff, pts) #doctest: +ELLIPSIS array([[14, 14, 24], [16, 17, 28], [20, 23, 36], [24, 29, 44]]...) Just to show that in the simple 3D case, it is equivalent to: >>> (np.dot(aff[:3,:3], pts.T) + aff[:3,3:4]).T #doctest: +ELLIPSIS array([[14, 14, 24], [16, 17, 28], [20, 23, 36], [24, 29, 44]]...) But `pts` can be a more complicated shape: >>> pts = pts.reshape((2,2,3)) >>> apply_affine(aff, pts) #doctest: +ELLIPSIS array([[[14, 14, 24], [16, 17, 28]], <BLANKLINE> [[20, 23, 36], [24, 29, 44]]]...)
get_actor_from_polydata¶
get_actor_from_polymapper¶
get_bounding_box_sizes¶
get_grid_cells_position¶
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fury.utils.get_grid_cells_position(shapes, aspect_ratio=1.7777777777777777, dim=None)[source]¶ Construct a XY-grid based on the cells content shape.
This function generates the coordinates of every grid cell. The width and height of every cell correspond to the largest width and the largest height respectively. The grid dimensions will automatically be adjusted to respect the given aspect ratio unless they are explicitly specified.
The grid follows a row-major order with the top left corner being at coordinates (0,0,0) and the bottom right corner being at coordinates (nb_cols*cell_width, -nb_rows*cell_height, 0). Note that the X increases while the Y decreases.
- Parameters
- shapeslist of tuple of int
The shape (width, height) of every cell content.
- aspect_ratiofloat (optional)
Aspect ratio of the grid (width/height). Default: 16:9.
- dimtuple of int (optional)
Dimension (nb_rows, nb_cols) of the grid, if provided.
- Returns
- ndarray
3D coordinates of every grid cell.
get_polydata_colors¶
get_polydata_lines¶
get_polydata_normals¶
get_polydata_triangles¶
get_polydata_vertices¶
get_polymapper_from_polydata¶
line_colors¶
lines_to_vtk_polydata¶
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fury.utils.lines_to_vtk_polydata(lines, colors=None)[source]¶ Create a vtkPolyData with lines and colors.
- Parameters
- lineslist
list of N curves represented as 2D ndarrays
- colorsarray (N, 3), list of arrays, tuple (3,), array (K,), None
If None then a standard orientation colormap is used for every line. If one tuple of color is used. Then all streamlines will have the same colour. If an array (N, 3) is given, where N is equal to the number of lines. Then every line is coloured with a different RGB color. If a list of RGB arrays is given then every point of every line takes a different color. If an array (K, 3) is given, where K is the number of points of all lines then every point is colored with a different RGB color. If an array (K,) is given, where K is the number of points of all lines then these are considered as the values to be used by the colormap. If an array (L,) is given, where L is the number of streamlines then these are considered as the values to be used by the colormap per streamline. If an array (X, Y, Z) or (X, Y, Z, 3) is given then the values for the colormap are interpolated automatically using trilinear interpolation.
- Returns
- poly_datavtkPolyData
- is_colormapbool, true if the input color array was a colormap
map_coordinates¶
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fury.utils.map_coordinates(input, coordinates, output=None, order=3, mode='constant', cval=0.0, prefilter=True)[source]¶ Map the input array to new coordinates by interpolation.
The array of coordinates is used to find, for each point in the output, the corresponding coordinates in the input. The value of the input at those coordinates is determined by spline interpolation of the requested order.
The shape of the output is derived from that of the coordinate array by dropping the first axis. The values of the array along the first axis are the coordinates in the input array at which the output value is found.
- Parameters
- inputarray_like
The input array.
- coordinatesarray_like
The coordinates at which input is evaluated.
- outputarray or dtype, optional
The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created.
- orderint, optional
The order of the spline interpolation, default is 3. The order has to be in the range 0-5.
- mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional
The mode parameter determines how the input array is extended when the filter overlaps a border. Default is ‘reflect’. Behavior for each valid value is as follows:
- ‘reflect’ (d c b a | a b c d | d c b a)
The input is extended by reflecting about the edge of the last pixel.
- ‘constant’ (k k k k | a b c d | k k k k)
The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.
- ‘nearest’ (a a a a | a b c d | d d d d)
The input is extended by replicating the last pixel.
- ‘mirror’ (d c b | a b c d | c b a)
The input is extended by reflecting about the center of the last pixel.
- ‘wrap’ (a b c d | a b c d | a b c d)
The input is extended by wrapping around to the opposite edge.
- cvalscalar, optional
Value to fill past edges of input if mode is ‘constant’. Default is 0.0.
- prefilterbool, optional
Determines if the input array is prefiltered with spline_filter before interpolation. The default is True, which will create a temporary float64 array of filtered values if order > 1. If setting this to False, the output will be slightly blurred if order > 1, unless the input is prefiltered, i.e. it is the result of calling spline_filter on the original input.
- Returns
- map_coordinatesndarray
The result of transforming the input. The shape of the output is derived from that of coordinates by dropping the first axis.
See also
spline_filter,geometric_transform,scipy.interpolateExamples
>>> from scipy import ndimage >>> a = np.arange(12.).reshape((4, 3)) >>> a array([[ 0., 1., 2.], [ 3., 4., 5.], [ 6., 7., 8.], [ 9., 10., 11.]]) >>> ndimage.map_coordinates(a, [[0.5, 2], [0.5, 1]], order=1) array([ 2., 7.])
Above, the interpolated value of a[0.5, 0.5] gives output[0], while a[2, 1] is output[1].
>>> inds = np.array([[0.5, 2], [0.5, 4]]) >>> ndimage.map_coordinates(a, inds, order=1, cval=-33.3) array([ 2. , -33.3]) >>> ndimage.map_coordinates(a, inds, order=1, mode='nearest') array([ 2., 8.]) >>> ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool) array([ True, False], dtype=bool)
map_coordinates_3d_4d¶
numpy_to_vtk_colors¶
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fury.utils.numpy_to_vtk_colors(colors)[source]¶ Convert Numpy color array to a vtk color array.
- Parameters
- colors: ndarray
- Returns
- vtk_colorsvtkDataArray
Notes
If colors are not already in UNSIGNED_CHAR you may need to multiply by 255.
Examples
>>> import numpy as np >>> from fury.utils import numpy_to_vtk_colors >>> rgb_array = np.random.rand(100, 3) >>> vtk_colors = numpy_to_vtk_colors(255 * rgb_array)
numpy_to_vtk_matrix¶
numpy_to_vtk_points¶
rotate¶
set_input¶
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fury.utils.set_input(vtk_object, inp)[source]¶ Set Generic input function which takes into account VTK 5 or 6.
- Parameters
- vtk_object: vtk object
- inp: vtkPolyData or vtkImageData or vtkAlgorithmOutput
- Returns
- vtk_object
Notes
- This can be used in the following way::
from fury.utils import set_input poly_mapper = set_input(vtk.vtkPolyDataMapper(), poly_data)