1252 lines
43 KiB
Python
1252 lines
43 KiB
Python
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"""An internal module for wrapping the use of mappers."""
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from __future__ import annotations
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import sys
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from typing import Optional
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from typing import cast
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import numpy as np
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import pyvista
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from pyvista._deprecate_positional_args import _deprecate_positional_args
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from pyvista.core._typing_core import BoundsTuple
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from pyvista.core.utilities.arrays import FieldAssociation
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from pyvista.core.utilities.arrays import convert_array
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from pyvista.core.utilities.arrays import convert_string_array
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from pyvista.core.utilities.arrays import raise_not_matching
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from pyvista.core.utilities.helpers import wrap
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from pyvista.core.utilities.misc import _BoundsSizeMixin
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from pyvista.core.utilities.misc import _NoNewAttrMixin
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from pyvista.core.utilities.misc import abstract_class
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from . import _vtk
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from .colors import Color
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from .colors import get_cmap_safe
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from .lookup_table import LookupTable
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from .tools import normalize
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from .utilities.algorithms import set_algorithm_input
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@abstract_class
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class _BaseMapper(
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_NoNewAttrMixin, _BoundsSizeMixin, _vtk.DisableVtkSnakeCase, _vtk.vtkAbstractMapper
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):
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"""Base Mapper with methods common to other mappers."""
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def __init__(self, theme=None, **kwargs) -> None:
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self._theme = pyvista.themes.Theme()
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if theme is None:
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# copy global theme to ensure local property theme is fixed
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# after creation.
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self._theme.load_theme(pyvista.global_theme)
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else:
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self._theme.load_theme(theme)
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self.lookup_table = LookupTable()
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self.interpolate_before_map = kwargs.get(
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'interpolate_before_map',
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self._theme.interpolate_before_map,
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)
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@property
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def bounds(self) -> BoundsTuple: # numpydoc ignore=RT01
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"""Return the bounds of this mapper.
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Examples
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--------
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>>> import pyvista as pv
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>>> mapper = pv.DataSetMapper(dataset=pv.Cube())
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>>> mapper.bounds
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BoundsTuple(x_min = -0.5,
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x_max = 0.5,
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y_min = -0.5,
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y_max = 0.5,
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z_min = -0.5,
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z_max = 0.5)
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"""
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return BoundsTuple(*self.GetBounds())
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@property
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def center(self) -> tuple[float, float, float]:
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"""Return the center of mapper.
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Returns
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-------
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tuple[float, float, float]
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Center of the active renderer.
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"""
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return self.GetCenter()
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def copy(self) -> _BaseMapper:
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"""Create a copy of this mapper.
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Returns
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-------
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pyvista.DataSetMapper
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A copy of this dataset mapper.
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Examples
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--------
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>>> import pyvista as pv
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>>> mapper = pv.DataSetMapper(dataset=pv.Cube())
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>>> mapper_copy = mapper.copy()
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"""
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new_mapper = type(self)(theme=self._theme)
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# even though this uses ShallowCopy, the new mapper no longer retains
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# any connection with the original
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new_mapper.ShallowCopy(self)
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if hasattr(self, 'dataset'):
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new_mapper.dataset = self.dataset
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return new_mapper
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@property
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def scalar_range(self) -> tuple[float, float]: # numpydoc ignore=RT01
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"""Return or set the scalar range.
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Examples
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--------
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Return the scalar range of a mapper.
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>>> import pyvista as pv
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>>> mesh = pv.Sphere()
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>>> pl = pv.Plotter()
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>>> actor = pl.add_mesh(mesh, scalars=mesh.points[:, 2])
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>>> actor.mapper.scalar_range
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(-0.5, 0.5)
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>>> pl.close()
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Return the scalar range of a composite dataset. In this example it's
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set to its default value of ``(0.0, 1.0)``.
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>>> import pyvista as pv
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>>> dataset = pv.MultiBlock([pv.Cube(), pv.Sphere(center=(0, 0, 1))])
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>>> pl = pv.Plotter()
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>>> actor, mapper = pl.add_composite(dataset)
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>>> mapper.scalar_range
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(0.0, 1.0)
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>>> pl.close()
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"""
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return self.GetScalarRange()
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@scalar_range.setter
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def scalar_range(self, clim) -> None:
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self.SetScalarRange(*clim)
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@property
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def lookup_table(self) -> pyvista.LookupTable: # numpydoc ignore=RT01
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"""Return or set the lookup table.
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Examples
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--------
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Return the lookup table of a dataset mapper.
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>>> import pyvista as pv
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>>> mesh = pv.Sphere()
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>>> pl = pv.Plotter()
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>>> actor = pl.add_mesh(mesh, scalars=mesh.points[:, 2], cmap='bwr')
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>>> actor.mapper.lookup_table
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LookupTable (...)
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Table Range: (-0.5, 0.5)
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N Values: 256
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Above Range Color: None
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Below Range Color: None
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NAN Color: Color(name='darkgray', hex='#a9a9a9ff', opacity=255)
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Log Scale: False
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Color Map: "bwr"
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Return the lookup table of a composite dataset mapper.
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>>> import pyvista as pv
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>>> dataset = pv.MultiBlock([pv.Cube(), pv.Sphere(center=(0, 0, 1))])
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>>> pl = pv.Plotter()
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>>> actor, mapper = pl.add_composite(dataset)
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>>> mapper.lookup_table # doctest:+SKIP
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<vtkmodules.vtkCommonCore.vtkLookupTable(...) at ...>
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"""
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return self.GetLookupTable()
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@lookup_table.setter
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def lookup_table(self, table) -> None:
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self.SetLookupTable(table)
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@property
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def color_mode(self) -> str: # numpydoc ignore=RT01
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"""Return or set the color mode.
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Either ``'direct'``, or ``'map'``.
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* ``'direct'`` - All integer types are treated as colors with values in
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the range 0-255 and floating types are treated as colors with values
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in the range 0.0-1.0
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* ``'map'`` - All scalar data will be mapped through the lookup table.
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"""
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mode = self.GetColorModeAsString().lower()
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if mode == 'mapscalars':
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return 'map'
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return 'direct'
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@color_mode.setter
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def color_mode(self, value: str):
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if value == 'direct':
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self.SetColorModeToDirectScalars()
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elif value == 'map':
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self.SetColorModeToMapScalars()
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else:
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msg = 'Color mode must be either "default", "direct" or "map"'
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raise ValueError(msg)
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@property
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def interpolate_before_map(self) -> bool | None: # numpydoc ignore=RT01
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"""Return or set the interpolation of scalars before mapping.
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Enabling makes for a smoother scalars display. When ``False``,
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OpenGL will interpolate the mapped colors which can result in
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showing colors that are not present in the color map.
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Examples
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--------
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Disable interpolation before mapping.
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>>> import pyvista as pv
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>>> dataset = pv.MultiBlock([pv.Cube(), pv.Sphere(center=(0, 0, 1))])
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>>> dataset[0].point_data['data'] = dataset[0].points[:, 2]
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>>> dataset[1].point_data['data'] = dataset[1].points[:, 2]
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>>> pl = pv.Plotter()
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>>> actor, mapper = pl.add_composite(
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... dataset,
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... show_scalar_bar=False,
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... n_colors=3,
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... cmap='bwr',
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... )
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>>> mapper.interpolate_before_map = False
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>>> pl.show()
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Enable interpolation before mapping.
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>>> pl = pv.Plotter()
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>>> actor, mapper = pl.add_composite(
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... dataset,
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... show_scalar_bar=False,
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... n_colors=3,
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... cmap='bwr',
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... )
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>>> mapper.interpolate_before_map = True
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>>> pl.show()
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See :ref:`interpolate_before_map_example` for additional
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explanation regarding this attribute.
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"""
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return bool(self.GetInterpolateScalarsBeforeMapping())
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@interpolate_before_map.setter
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def interpolate_before_map(self, value: bool) -> None:
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self.SetInterpolateScalarsBeforeMapping(value)
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@property
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def array_name(self) -> str: # numpydoc ignore=RT01
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"""Return or set the array name or number and component to color by.
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Examples
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--------
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Show the name of the active scalars in the mapper.
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>>> import pyvista as pv
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>>> mesh = pv.Sphere()
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>>> mesh['my_scalars'] = mesh.points[:, 2]
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>>> pl = pv.Plotter()
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>>> actor = pl.add_mesh(mesh, scalars='my_scalars')
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>>> actor.mapper.array_name
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'my_scalars'
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>>> pl.close()
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"""
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return self.GetArrayName()
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@array_name.setter
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def array_name(self, name: str) -> None:
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"""Return or set the array name or number and component to color by."""
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self.SetArrayName(name)
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@property
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def scalar_map_mode(self) -> str: # numpydoc ignore=RT01
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"""Return or set the scalar map mode.
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Examples
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--------
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Show that the scalar map mode is set to ``'point'`` when setting the
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active scalars to point data.
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>>> import pyvista as pv
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>>> dataset = pv.MultiBlock([pv.Cube(), pv.Sphere(center=(0, 0, 1))])
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>>> dataset[0].point_data['data'] = dataset[0].points[:, 2]
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>>> dataset[1].point_data['data'] = dataset[1].points[:, 2]
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>>> pl = pv.Plotter()
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>>> actor, mapper = pl.add_composite(
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... dataset, scalars='data', show_scalar_bar=False
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... )
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>>> mapper.scalar_map_mode
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'point'
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>>> pl.close()
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"""
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# map vtk strings to more sensible strings
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vtk_to_pv = {
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'Default': 'default',
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'UsePointData': 'point',
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'UseCellData': 'cell',
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'UsePointFieldData': 'point_field',
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'UseCellFieldData': 'cell_field',
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'UseFieldData': 'field',
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}
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return vtk_to_pv[self.GetScalarModeAsString()]
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@scalar_map_mode.setter
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def scalar_map_mode(self, scalar_mode: str | FieldAssociation):
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if isinstance(scalar_mode, FieldAssociation):
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scalar_mode = scalar_mode.name
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scalar_mode = scalar_mode.lower()
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if scalar_mode == 'default':
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self.SetScalarModeToDefault()
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elif scalar_mode == 'point':
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self.SetScalarModeToUsePointData()
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elif scalar_mode == 'cell':
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self.SetScalarModeToUseCellData()
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elif scalar_mode == 'point_field':
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self.SetScalarModeToUsePointFieldData()
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elif scalar_mode == 'cell_field':
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self.SetScalarModeToUseCellFieldData()
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elif scalar_mode == 'field':
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self.SetScalarModeToUseFieldData()
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else:
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msg = (
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f'Invalid `scalar_map_mode` "{scalar_mode}". Should be either '
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'"default", "point", "cell", "point_field", "cell_field" or "field".'
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)
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raise ValueError(msg)
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@property
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def scalar_visibility(self) -> bool: # numpydoc ignore=RT01
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"""Return or set the scalar visibility.
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Examples
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--------
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Show that scalar visibility is ``False``.
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>>> import pyvista as pv
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>>> mesh = pv.Sphere()
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>>> pl = pv.Plotter()
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>>> actor = pl.add_mesh(mesh)
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>>> actor.mapper.scalar_visibility
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False
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>>> pl.close()
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Show that scalar visibility is ``True``.
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>>> import pyvista as pv
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>>> dataset = pv.MultiBlock([pv.Cube(), pv.Sphere(center=(0, 0, 1))])
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>>> dataset[0].point_data['data'] = dataset[0].points[:, 2]
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>>> dataset[1].point_data['data'] = dataset[1].points[:, 2]
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>>> pl = pv.Plotter()
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>>> actor, mapper = pl.add_composite(dataset, scalars='data')
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>>> mapper.scalar_visibility
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True
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>>> pl.close()
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"""
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return bool(self.GetScalarVisibility())
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@scalar_visibility.setter
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def scalar_visibility(self, value: bool) -> None:
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self.SetScalarVisibility(value)
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def update(self) -> None:
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"""Update this mapper."""
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self.Update()
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class _DataSetMapper(_BaseMapper):
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||
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"""Base wrapper for :vtk:`vtkDataSetMapper`.
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||
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Parameters
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||
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----------
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||
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dataset : pyvista.DataSet, optional
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Dataset to assign to this mapper.
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theme : pyvista.plotting.themes.Theme, optional
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|
|
Plot-specific theme.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
_cmap = None
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
dataset: pyvista.DataSet | None = None,
|
||
|
|
theme: pyvista.themes.Theme | None = None,
|
||
|
|
) -> None:
|
||
|
|
"""Initialize this class."""
|
||
|
|
super().__init__(theme=theme)
|
||
|
|
if dataset is not None:
|
||
|
|
self.dataset = dataset
|
||
|
|
|
||
|
|
@property
|
||
|
|
def dataset(self) -> pyvista.core.dataset.DataSet | None: # numpydoc ignore=RT01
|
||
|
|
"""Return or set the dataset assigned to this mapper."""
|
||
|
|
return cast('Optional[pyvista.DataSet]', wrap(_mapper_get_data_set_input(self)))
|
||
|
|
|
||
|
|
@dataset.setter
|
||
|
|
def dataset(
|
||
|
|
self,
|
||
|
|
obj: pyvista.core.dataset.DataSet | _vtk.vtkAlgorithm | _vtk.vtkAlgorithmOutput,
|
||
|
|
) -> None:
|
||
|
|
set_algorithm_input(self, obj)
|
||
|
|
|
||
|
|
def as_rgba(self) -> None:
|
||
|
|
"""Convert the active scalars to RGBA.
|
||
|
|
|
||
|
|
This method is used to convert the active scalars to a fixed RGBA array
|
||
|
|
and is used for certain mappers that do not support the "map" color
|
||
|
|
mode.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if self.color_mode == 'direct':
|
||
|
|
return
|
||
|
|
|
||
|
|
if self.dataset is not None:
|
||
|
|
self.dataset.point_data.pop('__rgba__', None)
|
||
|
|
self._configure_scalars_mode(
|
||
|
|
scalars=self.lookup_table(self.dataset.active_scalars),
|
||
|
|
scalars_name='__rgba__',
|
||
|
|
preference=self.scalar_map_mode,
|
||
|
|
direct_scalars_color_mode=True,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _configure_scalars_mode(
|
||
|
|
self,
|
||
|
|
*,
|
||
|
|
scalars,
|
||
|
|
scalars_name,
|
||
|
|
preference,
|
||
|
|
direct_scalars_color_mode,
|
||
|
|
) -> None:
|
||
|
|
"""Configure scalar mode.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
scalars : numpy.ndarray
|
||
|
|
Array of scalars to assign to the mapper.
|
||
|
|
|
||
|
|
scalars_name : str
|
||
|
|
If the name of this array exists, scalars is ignored. Otherwise,
|
||
|
|
the scalars will be added to the existing dataset and this
|
||
|
|
parameter is the name to assign the scalars.
|
||
|
|
|
||
|
|
preference : str
|
||
|
|
Either ``'point'`` or ``'cell'``.
|
||
|
|
|
||
|
|
direct_scalars_color_mode : bool
|
||
|
|
When ``True``, scalars are treated as RGB colors. When
|
||
|
|
``False``, scalars are mapped to the color table.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if self.dataset is not None:
|
||
|
|
if (
|
||
|
|
scalars.shape[0] == self.dataset.n_points
|
||
|
|
and scalars.shape[0] == self.dataset.n_cells
|
||
|
|
):
|
||
|
|
use_points = preference == 'point'
|
||
|
|
use_cells = not use_points
|
||
|
|
else:
|
||
|
|
use_points = scalars.shape[0] == self.dataset.n_points
|
||
|
|
use_cells = scalars.shape[0] == self.dataset.n_cells
|
||
|
|
|
||
|
|
# Scalars interpolation approach
|
||
|
|
if use_points:
|
||
|
|
if (
|
||
|
|
scalars_name not in self.dataset.point_data
|
||
|
|
or scalars_name == pyvista.DEFAULT_SCALARS_NAME
|
||
|
|
):
|
||
|
|
self.dataset.point_data.set_array(scalars, scalars_name, deep_copy=False)
|
||
|
|
self.dataset.active_scalars_name = scalars_name
|
||
|
|
self.scalar_map_mode = 'point'
|
||
|
|
elif use_cells:
|
||
|
|
if (
|
||
|
|
scalars_name not in self.dataset.cell_data
|
||
|
|
or scalars_name == pyvista.DEFAULT_SCALARS_NAME
|
||
|
|
):
|
||
|
|
self.dataset.cell_data.set_array(scalars, scalars_name, deep_copy=False)
|
||
|
|
self.dataset.active_scalars_name = scalars_name
|
||
|
|
self.scalar_map_mode = 'cell'
|
||
|
|
else:
|
||
|
|
raise_not_matching(scalars, self.dataset)
|
||
|
|
|
||
|
|
self.color_mode = 'direct' if direct_scalars_color_mode else 'map'
|
||
|
|
|
||
|
|
@_deprecate_positional_args(allowed=['scalars', 'scalars_name'])
|
||
|
|
def set_scalars( # noqa: PLR0917
|
||
|
|
self,
|
||
|
|
scalars,
|
||
|
|
scalars_name,
|
||
|
|
n_colors=256,
|
||
|
|
scalar_bar_args=None,
|
||
|
|
rgb=None,
|
||
|
|
component=None,
|
||
|
|
preference='point',
|
||
|
|
custom_opac: bool = False, # noqa: FBT001, FBT002
|
||
|
|
annotations=None,
|
||
|
|
log_scale: bool = False, # noqa: FBT001, FBT002
|
||
|
|
nan_color=None,
|
||
|
|
above_color=None,
|
||
|
|
below_color=None,
|
||
|
|
cmap=None,
|
||
|
|
flip_scalars: bool = False, # noqa: FBT001, FBT002
|
||
|
|
opacity=None,
|
||
|
|
categories: bool | int = False, # noqa: FBT001, FBT002
|
||
|
|
clim=None,
|
||
|
|
):
|
||
|
|
"""Set the scalars on this mapper.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
scalars : numpy.ndarray
|
||
|
|
Array of scalars to assign to the mapper.
|
||
|
|
|
||
|
|
scalars_name : str
|
||
|
|
If the name of this array exists, scalars is ignored. Otherwise,
|
||
|
|
the scalars will be added to the existing dataset and this
|
||
|
|
parameter is the name to assign the scalars.
|
||
|
|
|
||
|
|
n_colors : int, default: 256
|
||
|
|
Number of colors to use when displaying scalars.
|
||
|
|
|
||
|
|
scalar_bar_args : dict, optional
|
||
|
|
Dictionary of keyword arguments to pass when adding the
|
||
|
|
scalar bar to the scene. For options, see
|
||
|
|
:func:`pyvista.Plotter.add_scalar_bar`.
|
||
|
|
|
||
|
|
rgb : bool, default: False
|
||
|
|
If an 2 dimensional array is passed as the scalars, plot
|
||
|
|
those values as RGB(A) colors. ``rgba`` is also an
|
||
|
|
accepted alias for this. Opacity (the A) is optional. If
|
||
|
|
a scalars array ending with ``"_rgba"`` is passed, the default
|
||
|
|
becomes ``True``. This can be overridden by setting this
|
||
|
|
parameter to ``False``.
|
||
|
|
|
||
|
|
component : int, optional
|
||
|
|
Set component of vector valued scalars to plot. Must be
|
||
|
|
nonnegative, if supplied. If ``None``, the magnitude of
|
||
|
|
the vector is plotted.
|
||
|
|
|
||
|
|
preference : str, default: 'Point'
|
||
|
|
When ``dataset.n_points == dataset.n_cells`` and setting scalars,
|
||
|
|
this parameter sets how the scalars will be mapped to the mesh.
|
||
|
|
Can be either ``'point'`` or ``'cell'``.
|
||
|
|
|
||
|
|
custom_opac : bool, default: False
|
||
|
|
Use custom opacity.
|
||
|
|
|
||
|
|
annotations : dict, optional
|
||
|
|
Pass a dictionary of annotations. Keys are the float
|
||
|
|
values in the scalars range to annotate on the scalar bar
|
||
|
|
and the values are the string annotations.
|
||
|
|
|
||
|
|
log_scale : bool, default: False
|
||
|
|
Use log scale when mapping data to colors. Scalars less
|
||
|
|
than zero are mapped to the smallest representable
|
||
|
|
positive float.
|
||
|
|
|
||
|
|
nan_color : pyvista.ColorLike, optional
|
||
|
|
The color to use for all ``NaN`` values in the plotted
|
||
|
|
scalar array.
|
||
|
|
|
||
|
|
above_color : pyvista.ColorLike, optional
|
||
|
|
Solid color for values below the scalars range
|
||
|
|
(``clim``). This will automatically set the scalar bar
|
||
|
|
``above_label`` to ``'above'``.
|
||
|
|
|
||
|
|
below_color : pyvista.ColorLike, optional
|
||
|
|
Solid color for values below the scalars range
|
||
|
|
(``clim``). This will automatically set the scalar bar
|
||
|
|
``below_label`` to ``'below'``.
|
||
|
|
|
||
|
|
cmap : str, list, or pyvista.LookupTable
|
||
|
|
Name of the Matplotlib colormap to use when mapping the
|
||
|
|
``scalars``. See available Matplotlib colormaps. Only applicable
|
||
|
|
for when displaying ``scalars``.
|
||
|
|
``colormap`` is also an accepted alias for this. If
|
||
|
|
``colorcet`` or ``cmocean`` are installed, their colormaps can be
|
||
|
|
specified by name.
|
||
|
|
|
||
|
|
You can also specify a list of colors to override an existing
|
||
|
|
colormap with a custom one. For example, to create a three color
|
||
|
|
colormap you might specify ``['green', 'red', 'blue']``.
|
||
|
|
|
||
|
|
This parameter also accepts a :class:`pyvista.LookupTable`. If this
|
||
|
|
is set, all parameters controlling the color map like ``n_colors``
|
||
|
|
will be ignored.
|
||
|
|
|
||
|
|
flip_scalars : bool, default: False
|
||
|
|
Flip direction of cmap. Most colormaps allow ``*_r`` suffix to do
|
||
|
|
this as well.
|
||
|
|
|
||
|
|
opacity : str or numpy.ndarray, optional
|
||
|
|
Opacity mapping for the scalars array.
|
||
|
|
A string can also be specified to map the scalars range to a
|
||
|
|
predefined opacity transfer function (options include: 'linear',
|
||
|
|
'linear_r', 'geom', 'geom_r'). Or you can pass a custom made
|
||
|
|
transfer function that is an array either ``n_colors`` in length or
|
||
|
|
shorter.
|
||
|
|
|
||
|
|
categories : bool, default: False
|
||
|
|
If set to ``True``, then the number of unique values in the scalar
|
||
|
|
array will be used as the ``n_colors`` argument.
|
||
|
|
|
||
|
|
clim : Sequence, optional
|
||
|
|
Color bar range for scalars. Defaults to minimum and
|
||
|
|
maximum of scalars array. Example: ``(-1, 2)``.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if scalar_bar_args is None:
|
||
|
|
scalar_bar_args = {'n_colors': n_colors}
|
||
|
|
|
||
|
|
if not isinstance(scalars, np.ndarray):
|
||
|
|
scalars = np.asarray(scalars)
|
||
|
|
|
||
|
|
# Set the array title for when it is added back to the mesh
|
||
|
|
if custom_opac:
|
||
|
|
scalars_name = '__custom_rgba'
|
||
|
|
|
||
|
|
if not np.issubdtype(scalars.dtype, np.number) and not isinstance(
|
||
|
|
cmap,
|
||
|
|
pyvista.LookupTable,
|
||
|
|
):
|
||
|
|
# we can rapidly handle bools
|
||
|
|
if scalars.dtype == np.bool_:
|
||
|
|
cats = np.array([b'False', b'True'], dtype='|S5')
|
||
|
|
values = np.array([0, 1])
|
||
|
|
clim = [-0.5, 1.5]
|
||
|
|
else:
|
||
|
|
# If str array, digitize and annotate
|
||
|
|
cats, scalars = np.unique(scalars.astype('|S'), return_inverse=True)
|
||
|
|
values = np.unique(scalars)
|
||
|
|
clim = [np.min(values) - 0.5, np.max(values) + 0.5]
|
||
|
|
scalars_name = f'{scalars_name}-digitized'
|
||
|
|
|
||
|
|
n_colors = len(cats)
|
||
|
|
scalar_bar_args.setdefault('n_labels', 0)
|
||
|
|
|
||
|
|
self.lookup_table.SetAnnotations(convert_array(values), convert_string_array(cats))
|
||
|
|
|
||
|
|
# Use only the real component if an array is complex
|
||
|
|
if np.issubdtype(scalars.dtype, np.complexfloating):
|
||
|
|
scalars = scalars.astype(float)
|
||
|
|
scalars_name = f'{scalars_name}-real'
|
||
|
|
|
||
|
|
if scalars.ndim != 1:
|
||
|
|
if rgb:
|
||
|
|
pass
|
||
|
|
elif scalars.ndim == 2 and (
|
||
|
|
scalars.shape[0] == self.dataset.n_points # type: ignore[union-attr]
|
||
|
|
or scalars.shape[0] == self.dataset.n_cells # type: ignore[union-attr]
|
||
|
|
):
|
||
|
|
if not isinstance(component, (int, type(None))):
|
||
|
|
msg = 'component must be either None or an integer'
|
||
|
|
raise TypeError(msg)
|
||
|
|
if component is None:
|
||
|
|
scalars = np.linalg.norm(scalars.copy(), axis=1)
|
||
|
|
scalars_name = f'{scalars_name}-normed'
|
||
|
|
elif component < scalars.shape[1] and component >= 0:
|
||
|
|
scalars = np.array(scalars[:, component]).copy()
|
||
|
|
scalars_name = f'{scalars_name}-{component}'
|
||
|
|
else:
|
||
|
|
msg = (
|
||
|
|
'Component must be nonnegative and less than the '
|
||
|
|
f'dimensionality of the scalars array: {scalars.shape[1]}'
|
||
|
|
)
|
||
|
|
raise ValueError(msg)
|
||
|
|
else:
|
||
|
|
scalars = scalars.ravel()
|
||
|
|
|
||
|
|
if scalars.dtype == np.bool_:
|
||
|
|
scalars = scalars.astype(np.float64)
|
||
|
|
|
||
|
|
# Set scalars range
|
||
|
|
if clim is None:
|
||
|
|
clim = [np.nanmin(scalars), np.nanmax(scalars)]
|
||
|
|
elif isinstance(clim, (int, float)):
|
||
|
|
clim = [-clim, clim]
|
||
|
|
|
||
|
|
if log_scale and clim[0] <= 0:
|
||
|
|
clim = [sys.float_info.min, clim[1]]
|
||
|
|
|
||
|
|
if np.any(clim) and not rgb:
|
||
|
|
self.scalar_range = clim[0], clim[1]
|
||
|
|
|
||
|
|
if isinstance(cmap, pyvista.LookupTable):
|
||
|
|
self.lookup_table = cmap
|
||
|
|
self.scalar_range = self.lookup_table.scalar_range
|
||
|
|
else:
|
||
|
|
self.lookup_table.scalar_range = self.scalar_range
|
||
|
|
# Set default map
|
||
|
|
if cmap is None:
|
||
|
|
cmap = pyvista.global_theme.cmap if self._theme is None else self._theme.cmap
|
||
|
|
|
||
|
|
# have to add the attribute to pass it onward to some classes
|
||
|
|
if isinstance(cmap, str):
|
||
|
|
self._cmap = cmap
|
||
|
|
if categories:
|
||
|
|
if categories is True:
|
||
|
|
n_colors = len(np.unique(scalars))
|
||
|
|
elif isinstance(categories, int):
|
||
|
|
n_colors = categories
|
||
|
|
|
||
|
|
self.lookup_table.apply_cmap(cmap, n_colors)
|
||
|
|
|
||
|
|
# Set opactities
|
||
|
|
if isinstance(opacity, np.ndarray) and not custom_opac:
|
||
|
|
self.lookup_table.apply_opacity(opacity)
|
||
|
|
|
||
|
|
if flip_scalars:
|
||
|
|
self.lookup_table.values[:] = self.lookup_table.values[::-1]
|
||
|
|
|
||
|
|
if custom_opac:
|
||
|
|
# need to round the colors here since we're
|
||
|
|
# directly displaying the colors
|
||
|
|
hue = normalize(scalars, minimum=clim[0], maximum=clim[1])
|
||
|
|
scalars = np.round(hue * n_colors) / n_colors
|
||
|
|
scalars = get_cmap_safe(cmap)(scalars) * 255
|
||
|
|
scalars[:, -1] *= opacity
|
||
|
|
scalars = scalars.astype(np.uint8)
|
||
|
|
|
||
|
|
# configure the lookup table
|
||
|
|
if nan_color:
|
||
|
|
self.lookup_table.nan_color = nan_color
|
||
|
|
if above_color:
|
||
|
|
self.lookup_table.above_range_color = above_color
|
||
|
|
scalar_bar_args.setdefault('above_label', 'above')
|
||
|
|
if below_color:
|
||
|
|
self.lookup_table.below_range_color = below_color
|
||
|
|
scalar_bar_args.setdefault('below_label', 'below')
|
||
|
|
if isinstance(annotations, dict):
|
||
|
|
self.lookup_table.annotations = annotations
|
||
|
|
self.lookup_table.log_scale = log_scale
|
||
|
|
|
||
|
|
self._configure_scalars_mode(
|
||
|
|
scalars=scalars,
|
||
|
|
scalars_name=scalars_name,
|
||
|
|
preference=preference,
|
||
|
|
direct_scalars_color_mode=rgb or custom_opac,
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(self, PointGaussianMapper):
|
||
|
|
self.as_rgba()
|
||
|
|
|
||
|
|
@property
|
||
|
|
def cmap(self) -> str | None: # numpydoc ignore=RT01
|
||
|
|
"""Colormap assigned to this mapper.
|
||
|
|
|
||
|
|
See :ref:`named_colormaps` for supported colormaps.
|
||
|
|
"""
|
||
|
|
return self._cmap
|
||
|
|
|
||
|
|
@property
|
||
|
|
def resolve(self) -> str:
|
||
|
|
"""Set or return the global flag to avoid z-buffer resolution.
|
||
|
|
|
||
|
|
A global flag that controls whether the coincident topology
|
||
|
|
(e.g., a line on top of a polygon) is shifted to avoid
|
||
|
|
z-buffer resolution (and hence rendering problems).
|
||
|
|
|
||
|
|
If not off, there are two methods to choose from.
|
||
|
|
`polygon_offset` uses graphics systems calls to shift polygons,
|
||
|
|
lines, and points from each other.
|
||
|
|
`shift_zbuffer` is a legacy method that is used to remap the z-buffer
|
||
|
|
to distinguish vertices, lines, and polygons,
|
||
|
|
but does not always produce acceptable results.
|
||
|
|
You should only use the polygon_offset method (or none) at this point.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
str
|
||
|
|
Global flag to avoid z-buffer resolution.
|
||
|
|
Must be either `off`, `polygon_offset` or `shift_zbuffer`.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import pyvista as pv
|
||
|
|
>>> from pyvista import examples
|
||
|
|
|
||
|
|
>>> mesh = examples.download_tri_quadratic_hexahedron()
|
||
|
|
>>> surface_sep = mesh.separate_cells().extract_surface(
|
||
|
|
... nonlinear_subdivision=4
|
||
|
|
... )
|
||
|
|
>>> edges = surface_sep.extract_feature_edges()
|
||
|
|
>>> surface = mesh.extract_surface(nonlinear_subdivision=4)
|
||
|
|
|
||
|
|
>>> plotter = pv.Plotter()
|
||
|
|
>>> _ = plotter.add_mesh(surface, smooth_shading=True, split_sharp_edges=True)
|
||
|
|
>>> actor = plotter.add_mesh(edges, color='k', line_width=3)
|
||
|
|
>>> actor.mapper.resolve = 'polygon_offset'
|
||
|
|
>>> plotter.show()
|
||
|
|
|
||
|
|
"""
|
||
|
|
vtk_to_pv = {
|
||
|
|
_vtk.VTK_RESOLVE_OFF: 'off',
|
||
|
|
_vtk.VTK_RESOLVE_POLYGON_OFFSET: 'polygon_offset',
|
||
|
|
_vtk.VTK_RESOLVE_SHIFT_ZBUFFER: 'shift_zbuffer',
|
||
|
|
}
|
||
|
|
return vtk_to_pv[self.GetResolveCoincidentTopology()]
|
||
|
|
|
||
|
|
@resolve.setter
|
||
|
|
def resolve(self, resolve):
|
||
|
|
if resolve == 'off':
|
||
|
|
self.SetResolveCoincidentTopologyToOff()
|
||
|
|
elif resolve == 'polygon_offset':
|
||
|
|
self.SetResolveCoincidentTopologyToPolygonOffset()
|
||
|
|
elif resolve == 'shift_zbuffer':
|
||
|
|
self.SetResolveCoincidentTopologyToShiftZBuffer()
|
||
|
|
else:
|
||
|
|
msg = 'Resolve must be either "off", "polygon_offset" or "shift_zbuffer"'
|
||
|
|
raise ValueError(msg)
|
||
|
|
|
||
|
|
@_deprecate_positional_args(allowed=['opacity'])
|
||
|
|
def set_custom_opacity( # noqa: PLR0917
|
||
|
|
self, opacity, color, n_colors, preference='point'
|
||
|
|
):
|
||
|
|
"""Set custom opacity.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
opacity : numpy.ndarray
|
||
|
|
Opacity array to color the dataset. Array length must match either
|
||
|
|
the number of points or cells.
|
||
|
|
|
||
|
|
color : pyvista.ColorLike
|
||
|
|
The color to use with the opacity array.
|
||
|
|
|
||
|
|
n_colors : int
|
||
|
|
Number of colors to use.
|
||
|
|
|
||
|
|
preference : str, default: 'point'
|
||
|
|
Either ``'point'`` or ``'cell'``. Used when the number of cells
|
||
|
|
matches the number of points.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# Create a custom RGBA array to supply our opacity to
|
||
|
|
if opacity.size == self.dataset.n_points: # type: ignore[union-attr]
|
||
|
|
rgba = np.empty((self.dataset.n_points, 4), np.uint8) # type: ignore[union-attr]
|
||
|
|
elif opacity.size == self.dataset.n_cells: # type: ignore[union-attr]
|
||
|
|
rgba = np.empty((self.dataset.n_cells, 4), np.uint8) # type: ignore[union-attr]
|
||
|
|
else: # pragma: no cover
|
||
|
|
msg = (
|
||
|
|
f'Opacity array size ({opacity.size}) does not equal '
|
||
|
|
f'the number of points ({self.dataset.n_points}) or the ' # type: ignore[union-attr]
|
||
|
|
f'number of cells ({self.dataset.n_cells}).', # type: ignore[union-attr]
|
||
|
|
)
|
||
|
|
raise ValueError(msg)
|
||
|
|
|
||
|
|
default_color = (
|
||
|
|
self._theme.color if self._theme is not None else pyvista.global_theme.color
|
||
|
|
)
|
||
|
|
|
||
|
|
rgba[:, :-1] = Color(color, default_color=default_color).int_rgb
|
||
|
|
rgba[:, -1] = np.around(opacity * 255)
|
||
|
|
|
||
|
|
self.color_mode = 'direct'
|
||
|
|
self.lookup_table.n_values = n_colors
|
||
|
|
self._configure_scalars_mode(
|
||
|
|
scalars=rgba, scalars_name='', preference=preference, direct_scalars_color_mode=True
|
||
|
|
)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
"""Representation of the mapper."""
|
||
|
|
mapper_attr = [
|
||
|
|
f'{type(self).__name__} ({hex(id(self))})',
|
||
|
|
f' Scalar visibility: {self.scalar_visibility}',
|
||
|
|
f' Scalar range: {self.scalar_range}',
|
||
|
|
f' Interpolate before mapping: {self.interpolate_before_map}',
|
||
|
|
f' Scalar map mode: {self.scalar_map_mode}',
|
||
|
|
f' Color mode: {self.color_mode}',
|
||
|
|
'',
|
||
|
|
]
|
||
|
|
|
||
|
|
mapper_attr.append('Attached dataset:')
|
||
|
|
mapper_attr.append(str(self.dataset))
|
||
|
|
|
||
|
|
return '\n'.join(mapper_attr)
|
||
|
|
|
||
|
|
|
||
|
|
class DataSetMapper(_DataSetMapper, _vtk.vtkDataSetMapper):
|
||
|
|
"""Wrap :vtk:`vtkDataSetMapper`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
dataset : pyvista.DataSet, optional
|
||
|
|
Dataset to assign to this mapper.
|
||
|
|
|
||
|
|
theme : pyvista.plotting.themes.Theme, optional
|
||
|
|
Plot-specific theme.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
Create a mapper outside :class:`pyvista.Plotter` and assign it to an
|
||
|
|
actor.
|
||
|
|
|
||
|
|
>>> import pyvista as pv
|
||
|
|
>>> mesh = pv.Cube()
|
||
|
|
>>> mapper = pv.DataSetMapper(dataset=mesh)
|
||
|
|
>>> actor = pv.Actor(mapper=mapper)
|
||
|
|
>>> actor.plot()
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
dataset: pyvista.DataSet | None = None,
|
||
|
|
theme: pyvista.themes.Theme | None = None,
|
||
|
|
) -> None:
|
||
|
|
"""Initialize this class."""
|
||
|
|
super().__init__(dataset=dataset, theme=theme)
|
||
|
|
|
||
|
|
|
||
|
|
class PointGaussianMapper(_DataSetMapper, _vtk.vtkPointGaussianMapper):
|
||
|
|
"""Wrap :vtk:`vtkPointGaussianMapper`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
theme : pyvista.Theme, optional
|
||
|
|
The theme to be used.
|
||
|
|
emissive : bool, optional
|
||
|
|
Whether or not the point should appear emissive. Default is set by the
|
||
|
|
theme's ``lighting_params.emissive``.
|
||
|
|
scale_factor : float, default: 1.0
|
||
|
|
Scale factor applied to the point size.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, theme=None, emissive=None, scale_factor=1.0) -> None:
|
||
|
|
super().__init__(theme=theme)
|
||
|
|
if emissive is None:
|
||
|
|
emissive = self._theme.lighting_params.emissive
|
||
|
|
self.emissive = emissive
|
||
|
|
self.scale_factor = scale_factor
|
||
|
|
|
||
|
|
@property
|
||
|
|
def emissive(self) -> bool: # numpydoc ignore=RT01
|
||
|
|
"""Set or return emissive.
|
||
|
|
|
||
|
|
This treats points as emissive light sources. Two points that overlap
|
||
|
|
will have their brightness combined.
|
||
|
|
"""
|
||
|
|
return bool(self.GetEmissive())
|
||
|
|
|
||
|
|
@emissive.setter
|
||
|
|
def emissive(self, value: bool) -> None:
|
||
|
|
self.SetEmissive(value)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def scale_factor(self) -> float: # numpydoc ignore=RT01
|
||
|
|
"""Set or return the scale factor.
|
||
|
|
|
||
|
|
Ranges from 0 to 1. A value of 0 will cause the splats to be rendered
|
||
|
|
as simple points. Defaults to 1.0.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return self.GetScaleFactor()
|
||
|
|
|
||
|
|
@scale_factor.setter
|
||
|
|
def scale_factor(self, value: float) -> None:
|
||
|
|
self.SetScaleFactor(value)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def scale_array(self) -> str: # numpydoc ignore=RT01
|
||
|
|
"""Set or return the name of the array used to scale the splats.
|
||
|
|
|
||
|
|
Scalars used to scale the gaussian points. Accepts a string
|
||
|
|
name of an array that is present on the mesh.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Setting this automatically sets ``scale_factor = 1.0``.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
Plot spheres using `style='points_gaussian'` style and scale them by
|
||
|
|
radius.
|
||
|
|
|
||
|
|
>>> import numpy as np
|
||
|
|
>>> import pyvista as pv
|
||
|
|
>>> n_spheres = 1_000
|
||
|
|
>>> pos = np.random.random((n_spheres, 3))
|
||
|
|
>>> rad = np.random.random(n_spheres) * 0.01
|
||
|
|
>>> pdata = pv.PolyData(pos)
|
||
|
|
>>> pdata['radius'] = rad
|
||
|
|
>>> pl = pv.Plotter()
|
||
|
|
>>> actor = pl.add_mesh(
|
||
|
|
... pdata,
|
||
|
|
... style='points_gaussian',
|
||
|
|
... emissive=False,
|
||
|
|
... render_points_as_spheres=True,
|
||
|
|
... )
|
||
|
|
>>> actor.mapper.scale_array = 'radius'
|
||
|
|
>>> pl.show()
|
||
|
|
|
||
|
|
"""
|
||
|
|
return self.GetScaleArray()
|
||
|
|
|
||
|
|
@scale_array.setter
|
||
|
|
def scale_array(self, name: str):
|
||
|
|
if not self.dataset: # pragma: no cover
|
||
|
|
msg = 'Missing dataset.'
|
||
|
|
raise RuntimeError(msg)
|
||
|
|
if name not in self.dataset.point_data:
|
||
|
|
available_arrays = ', '.join(self.dataset.point_data.keys())
|
||
|
|
msg = (
|
||
|
|
f'Point array "{name}" does not exist. '
|
||
|
|
f'Available point arrays are: {available_arrays}'
|
||
|
|
)
|
||
|
|
raise KeyError(msg)
|
||
|
|
|
||
|
|
self.scale_factor = 1.0
|
||
|
|
self.SetScaleArray(name)
|
||
|
|
|
||
|
|
def use_circular_splat(self, opacity: float = 1.0) -> None:
|
||
|
|
"""Set the fragment shader code to create a circular splat.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
opacity : float, default: 1.0
|
||
|
|
Desired opacity between 0 and 1.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
This very close to ParaView's PointGaussianMapper, but uses opacity to
|
||
|
|
modify the scale as the opacity cannot be set from the actor's property.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.SetSplatShaderCode(
|
||
|
|
'//VTK::Color::Impl\n'
|
||
|
|
'float dist = dot(offsetVCVSOutput.xy,offsetVCVSOutput.xy);\n'
|
||
|
|
'if (dist > 1.0) {\n'
|
||
|
|
' discard;\n'
|
||
|
|
'} else {\n'
|
||
|
|
f' float scale = ({opacity} - dist);\n'
|
||
|
|
' ambientColor *= scale;\n'
|
||
|
|
' diffuseColor *= scale;\n'
|
||
|
|
'}\n',
|
||
|
|
)
|
||
|
|
# maintain consistency with the default style
|
||
|
|
self.scale_factor *= 1.5
|
||
|
|
|
||
|
|
def use_default_splat(self) -> None:
|
||
|
|
"""Clear the fragment shader and use the default splat."""
|
||
|
|
self.SetSplatShaderCode(None) # type: ignore[arg-type]
|
||
|
|
self.scale_factor /= 1.5
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
"""Representation of the Gaussian mapper."""
|
||
|
|
mapper_attr = [
|
||
|
|
f'{type(self).__name__} ({hex(id(self))})',
|
||
|
|
f' Scalar visibility: {self.scalar_visibility}',
|
||
|
|
f' Scalar range: {self.scalar_range}',
|
||
|
|
f' Emissive: {self.emissive}',
|
||
|
|
f' Scale Factor: {self.scale_factor}',
|
||
|
|
f' Using custom splat: {self.GetSplatShaderCode() is None}',
|
||
|
|
'',
|
||
|
|
]
|
||
|
|
|
||
|
|
mapper_attr.append('Attached dataset:')
|
||
|
|
mapper_attr.append(str(self.dataset))
|
||
|
|
|
||
|
|
return '\n'.join(mapper_attr)
|
||
|
|
|
||
|
|
|
||
|
|
@abstract_class
|
||
|
|
class _BaseVolumeMapper(_BaseMapper):
|
||
|
|
"""Volume mapper class to override methods and attributes for to volume mappers."""
|
||
|
|
|
||
|
|
def __init__(self, theme=None) -> None:
|
||
|
|
"""Initialize this class."""
|
||
|
|
super().__init__(theme=theme)
|
||
|
|
self._lut = LookupTable()
|
||
|
|
self._scalar_range = (0.0, 256.0)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def interpolate_before_map(self) -> bool | None: # numpydoc ignore=RT01
|
||
|
|
"""Interpolate before map is not supported with volume mappers."""
|
||
|
|
return None
|
||
|
|
|
||
|
|
@interpolate_before_map.setter
|
||
|
|
def interpolate_before_map(self, *args) -> None:
|
||
|
|
pass
|
||
|
|
|
||
|
|
@property
|
||
|
|
def dataset(self): # numpydoc ignore=RT01
|
||
|
|
"""Return or set the dataset assigned to this mapper."""
|
||
|
|
return wrap(_mapper_get_data_set_input(self))
|
||
|
|
|
||
|
|
@dataset.setter
|
||
|
|
def dataset(
|
||
|
|
self,
|
||
|
|
obj: pyvista.core.dataset.DataSet | _vtk.vtkAlgorithm | _vtk.vtkAlgorithmOutput,
|
||
|
|
) -> None:
|
||
|
|
set_algorithm_input(self, obj)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def lookup_table(self): # numpydoc ignore=RT01
|
||
|
|
return self._lut
|
||
|
|
|
||
|
|
@lookup_table.setter
|
||
|
|
def lookup_table(self, lut) -> None:
|
||
|
|
self._lut = lut
|
||
|
|
|
||
|
|
@property
|
||
|
|
def scalar_range(self) -> tuple[float, float]: # numpydoc ignore=RT01
|
||
|
|
"""Return or set the scalar range."""
|
||
|
|
return self._scalar_range
|
||
|
|
|
||
|
|
@scalar_range.setter
|
||
|
|
def scalar_range(self, clim) -> None:
|
||
|
|
if self.lookup_table is not None:
|
||
|
|
self.lookup_table.SetRange(*clim)
|
||
|
|
self._scalar_range = tuple(clim)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def blend_mode(self) -> str: # numpydoc ignore=RT01
|
||
|
|
"""Return or set the blend mode.
|
||
|
|
|
||
|
|
One of the following:
|
||
|
|
|
||
|
|
* ``"composite"``
|
||
|
|
* ``"maximum"``
|
||
|
|
* ``"minimum"``
|
||
|
|
* ``"average"``
|
||
|
|
* ``"additive"``
|
||
|
|
|
||
|
|
Also accepts integer values corresponding to
|
||
|
|
:vtk:`vtkVolumeMapper.BlendModes`. For example
|
||
|
|
:vtk:`vtkVolumeMapper.COMPOSITE_BLEND`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
value = self.GetBlendMode()
|
||
|
|
mode = {
|
||
|
|
0: 'composite',
|
||
|
|
1: 'maximum',
|
||
|
|
2: 'minimum',
|
||
|
|
3: 'average',
|
||
|
|
4: 'additive',
|
||
|
|
}.get(value)
|
||
|
|
if mode is None: # pragma: no cover
|
||
|
|
msg = f'Unsupported blend mode return value {value}'
|
||
|
|
raise NotImplementedError(msg)
|
||
|
|
return mode
|
||
|
|
|
||
|
|
@blend_mode.setter
|
||
|
|
def blend_mode(self, value: str | int):
|
||
|
|
if isinstance(value, int):
|
||
|
|
self.SetBlendMode(value)
|
||
|
|
elif isinstance(value, str):
|
||
|
|
value = value.lower()
|
||
|
|
if value in ['additive', 'add', 'sum']:
|
||
|
|
self.SetBlendModeToAdditive()
|
||
|
|
elif value in ['average', 'avg', 'average_intensity']:
|
||
|
|
self.SetBlendModeToAverageIntensity()
|
||
|
|
elif value in ['composite', 'comp']:
|
||
|
|
self.SetBlendModeToComposite()
|
||
|
|
elif value in ['maximum', 'max', 'maximum_intensity']:
|
||
|
|
self.SetBlendModeToMaximumIntensity()
|
||
|
|
elif value in ['minimum', 'min', 'minimum_intensity']:
|
||
|
|
self.SetBlendModeToMinimumIntensity()
|
||
|
|
else:
|
||
|
|
msg = (
|
||
|
|
f'Blending mode {value!r} invalid. '
|
||
|
|
'Please choose either "additive", '
|
||
|
|
'"composite", "minimum" or "maximum".'
|
||
|
|
)
|
||
|
|
raise ValueError(msg)
|
||
|
|
else:
|
||
|
|
msg = f'`blend_mode` should be either an int or str, not `{type(value)}`' # type: ignore[unreachable]
|
||
|
|
raise TypeError(msg)
|
||
|
|
|
||
|
|
def __del__(self) -> None:
|
||
|
|
if hasattr(self, '_lut'):
|
||
|
|
del self._lut
|
||
|
|
|
||
|
|
|
||
|
|
class FixedPointVolumeRayCastMapper(_BaseVolumeMapper, _vtk.vtkFixedPointVolumeRayCastMapper):
|
||
|
|
"""Wrap :vtk:`vtkFixedPointVolumeRayCastMapper`."""
|
||
|
|
|
||
|
|
def __init__(self, theme=None) -> None:
|
||
|
|
"""Initialize this class."""
|
||
|
|
super().__init__(theme=theme)
|
||
|
|
self.AutoAdjustSampleDistancesOff()
|
||
|
|
|
||
|
|
|
||
|
|
class GPUVolumeRayCastMapper(_BaseVolumeMapper, _vtk.vtkGPUVolumeRayCastMapper):
|
||
|
|
"""Wrap :vtk:`vtkGPUVolumeRayCastMapper`."""
|
||
|
|
|
||
|
|
def __init__(self, theme=None) -> None:
|
||
|
|
"""Initialize this class."""
|
||
|
|
super().__init__(theme=theme)
|
||
|
|
self.AutoAdjustSampleDistancesOff()
|
||
|
|
|
||
|
|
|
||
|
|
class OpenGLGPUVolumeRayCastMapper(_BaseVolumeMapper, _vtk.vtkOpenGLGPUVolumeRayCastMapper):
|
||
|
|
"""Wrap :vtk:`vtkOpenGLGPUVolumeRayCastMapper`."""
|
||
|
|
|
||
|
|
def __init__(self, theme=None) -> None:
|
||
|
|
"""Initialize this class."""
|
||
|
|
super().__init__(theme=theme)
|
||
|
|
self.AutoAdjustSampleDistancesOff()
|
||
|
|
|
||
|
|
|
||
|
|
class SmartVolumeMapper(_BaseVolumeMapper, _vtk.vtkSmartVolumeMapper):
|
||
|
|
"""Wrap :vtk:`vtkSmartVolumeMapper`."""
|
||
|
|
|
||
|
|
def __init__(self, theme=None) -> None:
|
||
|
|
"""Initialize this class."""
|
||
|
|
super().__init__(theme=theme)
|
||
|
|
self.AutoAdjustSampleDistancesOff()
|
||
|
|
self.InteractiveAdjustSampleDistancesOff()
|
||
|
|
|
||
|
|
|
||
|
|
class UnstructuredGridVolumeRayCastMapper(
|
||
|
|
_BaseVolumeMapper, _vtk.vtkUnstructuredGridVolumeRayCastMapper
|
||
|
|
):
|
||
|
|
"""Wrap :vtk:`vtkUnstructuredGridVolumeMapper`."""
|
||
|
|
|
||
|
|
def __init__(self, theme=None) -> None:
|
||
|
|
"""Initialize this class."""
|
||
|
|
super().__init__(theme=theme)
|
||
|
|
self.AutoAdjustSampleDistancesOff()
|
||
|
|
|
||
|
|
|
||
|
|
def _mapper_has_data_set_input(mapper):
|
||
|
|
"""Check if mapper has a data set input using the appropriate method.
|
||
|
|
|
||
|
|
Some mappers use 'GetDataSetInput', others use 'GetInputAsDataSet'. This has
|
||
|
|
been standardized to 'GetDataSetInput' in VTK >= 9.5.
|
||
|
|
"""
|
||
|
|
return hasattr(mapper, 'GetDataSetInput') or hasattr(mapper, 'GetInputAsDataSet')
|
||
|
|
|
||
|
|
|
||
|
|
def _mapper_get_data_set_input(mapper):
|
||
|
|
"""Get data set input from mapper using the appropriate method.
|
||
|
|
|
||
|
|
Some mappers use 'GetDataSetInput', others use 'GetInputAsDataSet'. This has
|
||
|
|
been standardized to 'GetDataSetInput' in VTK >= 9.5.
|
||
|
|
"""
|
||
|
|
return (
|
||
|
|
mapper.GetDataSetInput()
|
||
|
|
if hasattr(mapper, 'GetDataSetInput')
|
||
|
|
else mapper.GetInputAsDataSet()
|
||
|
|
)
|