/
composite.py
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/
composite.py
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"""Filters module with a class to manage filters/algorithms for composite datasets."""
import numpy as np
import pyvista
from pyvista import _vtk, abstract_class, wrap
from pyvista.core.filters import _get_output, _update_alg
from pyvista.core.filters.data_set import DataSetFilters
@abstract_class
class CompositeFilters:
"""An internal class to manage filters/algorithms for composite datasets."""
def extract_geometry(self):
"""Extract the surface the geometry of all blocks.
Place this filter at the end of a pipeline before a polydata
consumer such as a polydata mapper to extract geometry from
all blocks and append them to one polydata object.
Returns
-------
pyvista.PolyData
Surface of the composite dataset.
"""
gf = _vtk.vtkCompositeDataGeometryFilter()
gf.SetInputData(self)
gf.Update()
return wrap(gf.GetOutputDataObject(0))
def combine(self, merge_points=False, tolerance=0.0):
"""Combine all blocks into a single unstructured grid.
Parameters
----------
merge_points : bool, default: False
Merge coincidental points.
tolerance : float, default: 0.0
The absolute tolerance to use to find coincident points when
``merge_points=True``. Note, this was added in VTK v9.
Returns
-------
pyvista.UnstructuredGrid
Combined blocks.
Examples
--------
Combine blocks within a multiblock without merging points.
>>> import pyvista
>>> block = pyvista.MultiBlock([
... pyvista.Cube(clean=False),
... pyvista.Cube(center=(1, 0, 0), clean=False)
... ])
>>> merged = block.combine()
>>> merged.n_points
48
Combine blocks and merge points
>>> merged = block.combine(merge_points=True)
>>> merged.n_points
12
"""
alg = _vtk.vtkAppendFilter()
for block in self:
if isinstance(block, _vtk.vtkMultiBlockDataSet):
block = CompositeFilters.combine(
block, merge_points=merge_points, tolerance=tolerance
)
alg.AddInputData(block)
alg.SetMergePoints(merge_points)
if pyvista.vtk_version_info >= (9,):
alg.SetTolerance(tolerance)
alg.Update()
return wrap(alg.GetOutputDataObject(0))
clip = DataSetFilters.clip
clip_box = DataSetFilters.clip_box
slice = DataSetFilters.slice
slice_orthogonal = DataSetFilters.slice_orthogonal
slice_along_axis = DataSetFilters.slice_along_axis
slice_along_line = DataSetFilters.slice_along_line
extract_all_edges = DataSetFilters.extract_all_edges
elevation = DataSetFilters.elevation
compute_cell_sizes = DataSetFilters.compute_cell_sizes
cell_centers = DataSetFilters.cell_centers
cell_data_to_point_data = DataSetFilters.cell_data_to_point_data
point_data_to_cell_data = DataSetFilters.point_data_to_cell_data
triangulate = DataSetFilters.triangulate
def outline(self, generate_faces=False, nested=False, progress_bar=False):
"""Produce an outline of the full extent for the all blocks in this composite dataset.
Parameters
----------
generate_faces : bool, default: False
Generate solid faces for the box.
nested : bool, default: False
If ``True``, these creates individual outlines for each nested dataset.
progress_bar : bool, default: False
Display a progress bar to indicate progress.
Returns
-------
pyvista.PolyData
Mesh containing the outline.
"""
if nested:
return DataSetFilters.outline(
self, generate_faces=generate_faces, progress_bar=progress_bar
)
box = pyvista.Box(bounds=self.bounds)
return box.outline(generate_faces=generate_faces, progress_bar=progress_bar)
def outline_corners(self, factor=0.2, nested=False, progress_bar=False):
"""Produce an outline of the corners for the all blocks in this composite dataset.
Parameters
----------
factor : float, default: 0.2
Controls the relative size of the corners to the length of
the corresponding bounds.
nested : bool, default: False
If ``True``, these creates individual outlines for each nested dataset.
progress_bar : bool, default: False
Display a progress bar to indicate progress.
Returns
-------
pyvista.PolyData
Mesh containing outlined corners.
"""
if nested:
return DataSetFilters.outline_corners(self, factor=factor, progress_bar=progress_bar)
box = pyvista.Box(bounds=self.bounds)
return box.outline_corners(factor=factor, progress_bar=progress_bar)
def _compute_normals(
self,
cell_normals=True,
point_normals=True,
split_vertices=False,
flip_normals=False,
consistent_normals=True,
auto_orient_normals=False,
non_manifold_traversal=True,
feature_angle=30.0,
track_vertices=False,
progress_bar=False,
):
"""Compute point and/or cell normals for a multi-block dataset."""
if not self.is_all_polydata:
raise RuntimeError(
'This multiblock contains non-PolyData datasets. Convert all the '
'datasets to PolyData with `as_polydata`'
)
# track original point indices
if split_vertices and track_vertices:
for block in self:
ids = np.arange(block.n_points, dtype=pyvista.ID_TYPE)
block.point_data.set_array(ids, 'pyvistaOriginalPointIds')
alg = _vtk.vtkPolyDataNormals()
alg.SetComputeCellNormals(cell_normals)
alg.SetComputePointNormals(point_normals)
alg.SetSplitting(split_vertices)
alg.SetFlipNormals(flip_normals)
alg.SetConsistency(consistent_normals)
alg.SetAutoOrientNormals(auto_orient_normals)
alg.SetNonManifoldTraversal(non_manifold_traversal)
alg.SetFeatureAngle(feature_angle)
alg.SetInputData(self)
_update_alg(alg, progress_bar, 'Computing Normals')
return _get_output(alg)